From headwaters to the ocean: hydrological changes and watershed management

FROM HEADWATERS TO THE OCEAN

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON HYDROLOGICAL CHANGES AND MANAGEMENT FROM HEADWATERS TO THE OCEAN – HYDROCHANGE 2008, KYOTO, JAPAN, 1–3 OCTOBER, 2008

From Headwaters to the Ocean Hydrological Changes and Watershed Management Editors Makoto Taniguchi Research Institute for Humanity and Nature (RIHN), Kyoto, Japan

William C. Burnett Department of Oceanography, Florida State University, Tallahassee, Florida, USA

Yoshihiro Fukushima Research Institute for Humanity and Nature (RIHN), Kyoto, Japan

Martin Haigh Department of Anthropology and Geography, Oxford Brookes University, Oxford, UK

Yu Umezawa Faculty of Fisheries, Nagasaki University, Nagasaki, Japan

CRC Press/Balkema is an imprint of the Taylor & Francis Group, an informa business © 2009 Taylor & Francis Group, London, UK Typeset by Charon Tec Ltd (A Macmillan Company), Chennai, India Printed and bound in Great Britain by Antony Rowe (A CPI Group Company), Chippenham, Wiltshire All rights reserved. No part of this publication or the information contained herein may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, by photocopying, recording or otherwise, without written prior permission from the publishers. Although all care is taken to ensure integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers nor the author for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein. Published by:

CRC Press/Balkema P.O. Box 447, 2300 AK Leiden, The Netherlands e-mail: [email protected] www.crcpress.com – www.taylorandfrancis.co.uk – www.balkema.nl

ISBN: 978-0-415-47279-1 (Hbk) eISBN (13): 978-0-203-88284-9

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Table of Contents

Preface

XIII

Reviewers List

XV

1 Land-atmosphere interaction Effects of selective cutting of large trees on transpiration and surface temperature: A predictive study of evergreen broad-leaf forest in central Cambodia K. Tamai, T. Nobuhiro, N. Kabeya, M. Araki, S. Iida, A. Shimizu, T. Shimizu, S. Chann & N. Keth The role of cavities in land-atmosphere interactions N. Weisbrod, U. Nachshon, M. Pillersdorf & M.I. Dragila

3

9

A simulation of sea breeze in Fukuoka facing the Sea of Japan Y. Hisada, N. Matsunaga & Y. Oda

15

Characteristics of water vapor content and its budget over Tarim river basin H. He, G. Lu & F. Zhao

23

Climatological changes in rain and non-rain days over the East Asian region using long term rain gauge observation data M.K. Yamamoto, A. Higuchi & S. Kikuchi

31

Hydrological balance over northern Eurasia from gauge-based high-resolution daily precipitation data H. Takashima, A. Yatagai, H. Kawamoto, O. Arakawa & K. Kamiguchi

37

Investigating changes in rainfall characteristics using the copula approach H. Chowdhary & V.P. Singh

43

2 Headwater environment: Impacts of climate change and human intervention (IAHC topics The 7th International Conference on Headwater Control) Effects of various rainfall-runoff characteristics on streamwater stable isotope variations in forested headwaters M. Katsuyama, K. Fukushima & N. Tokuchi

51

The effect of antecedent moisture condition on storm flow water sources in young forest headwater catchment T. Oda, Y. Asano, N. Ohte & M. Suzuki

57

Combined roles of soil horizons and fractured bedrock in subsurface water concentration in a valley-head T. Furuta & T. Tamura

63

Generation of a saturated zone at the soil–bedrock interface around a tree on a hillslope W.-L. Liang, K. Kosugi, Y. Yamakawa & T. Mizuyama Factors controlling stream water chemistry in ten small forested watersheds with plantation forests of various proportions and ages in central Japan N. Tokuchi, K. Fukushima & M. Katsuyama

V

69

75

Nitrate and phosphate uptake in a temperate forest stream in central Japan Y. Tanio, N. Ohte, M. Fujimoto & R. Sheibley

83

Impact of forestry practices on groundwater quality in the boreal environment E. Kubin & J. Krecek

91

Water yield and nitrogen loss during regrowth of Japanese cedar forests after clearcutting K. Fukushima, N. Tokuchi, R. Tateno & M. Katsuyama

97

Environmental impacts of the acid atmospheric deposition and forest clear-cut in a mountain catchment J. Krecek, Z. Horicka & J. Novakova Climate changes and debris flows in periglacial areas in the Italian Alps L. Marchi, M. Chiarle & G. Mortara Changes in the hydraulic properties of forest soils resulting from litter removal and compaction by human traffic Y. Hayashi, K. Kosugi & T. Mizuyama Effects of land use on soil physical and chemical properties of sandy land in Horqin, China A.M. Hao, T. Watanabe, T. Haraguchi & Y. Nakano

105 111

117 123

3 Strategic planning and environmental assessments of activities in headwater areas (IAHC topics The 7th International Conference on Headwater Control) A comparative policy analysis for headwater management T. Endo The change of water balance including municipal and irrigation waters – A case study of the Koise River basin, Ibaraki Prefecture, Japan M. Motoki

131

137

4 Environmental education for sustainable development: The role of mountain and headwater landscapes (IAHC topics The 7th International Conference on Headwater Control) Teaching sustainable headwater land management through problem based field study M. Haigh Implementation of school catchments network for water resources management of the Upper Negro River region, southern Brazil M. Kobiyama, P.L.B. Chaffe, H.L. Rocha, C.W. Corseuil, S. Malutta, J.N. Giglio A.A. Mota, I. Santos, U. Ribas Junior & R. Langa

145

151

5 Hydrological models in support of integrated water resources management On integrated water resources management V.P. Singh

161

Distributed simulation of basin water cycle under changing environment R. Zhu & C. Liu

167

A continuous rainfall-runoff model as a tool for the critical hydrological scenario assessment in natural channels L. Brocca, F. Melone, T. Moramarco & V.P. Singh Simulating water balance of the small-forested watershed using BROOK90 model E.A. Combalicer, S. Im, S.H. Lee, S. Ahn & D.Y. Kim

VI

175 181

Quantitative effect of land use and land cover changes on green water and blue water in Northern part of China L. Ren, X. Liu, F. Yuan, V.P. Singh, X. Fang, Z. Yu & W. Zhang

187

Development of a distributed water circulation model for assessing human interaction in agricultural water use T. Masumoto, T. Taniguchi, N. Horikawa, T. Yoshida & K. Shimizu

195

Sensitivity analysis to evaluate the effect of land use change on discharge rate F.A. Soria, M. Sawamoto & S. Kazama An integrated hydrological model for the long-term water balance analysis of the Yellow River Basin, China Y. Sato, A. Onishi, Y. Fukushima, X. Ma, J. Xu, M. Matsuoka & H. Zheng Application of geographic information system in hydrological models: A review C.L. Liu & Y.Q. Chen

203

209 217

Relationship between residence time and geographic source of stream flow in small watersheds – Analysis with a distributed rainfall-runoff model and field observation data – T. Sayama & J.J. McDonnell

223

Evaluation of seawater intrusion to a coastal aquifer by developing a three dimensional numerical model E.D.P. Perera, K. Jinno, Y. Hiroshiro & A. Tsutsumi

231

A hidden Markov model for non-stationary runoff modeling conditioned on El Niño information E. Gelati, D. Rosbjerg & H. Madsen Integrated simulation by hydrological, hydraulic and water management modelling techniques in support of water resources management in the Czech Republic O. Novicky, L. Kasparek & P. Vyskoc Multi-model approach to hydrologic impact of climate change P. Coulibaly Adapting to climate change impacts on the water resources systems of the Seyhan River Basin in Turkey Y. Fujihara, T. Watanabe, T. Nagano, K. Tanaka & T. Kojiri Impact of climate change on annual drought severity A.K. Mishra & V.P. Singh

237

243 249

257 265

Conceptual investigation of time of concentration: Case study of the Pequeno River watershed, São José dos Pinhais, PR, Brazil R.V. Da Silva, F. Grison & M. Kobiyama

271

Application of aritificial neural network in rainfall-runoff model Z. Li, P. Deng & J. Dong

277

Research on system dynamics model of water resources harmonious management J. Wang, J. Zhang & Z. Li

281

6 Groundwater-surface water interaction Importance of groundwater discharge in developing urban centers of Southeast Asia W.C. Burnett, R. Peterson, M. Taniguchi, G. Wattayakorn, S. Chanyotha & F. Siringan

289

Comparative study on water quality among Asian megacities based on major ion concentrations T. Hosono, Y. Umezawa, S. Onodera, C-H. Wang, F. Siringan S. Buapeng, R. Delinom, T. Nakano & M. Taniguchi

295

Surface and groundwater interactions in the lower reach of the Yellow River J. Chen, Y. Fukushima & M. Taniguchi

301

VII

Saline groundwater flow in the Yellow River delta, China K. Miyaoka, M. Taniguchi, T. Ishitobi, Y. Fukushima, S. Onodera, J. Chen & G. Liu

307

Long-term changes of water and salinity management in Lower Seyhan Plain, Turkey T. Nagano, T. Onishi, T. Kume, T. Watanabe, K. Hoshikawa & S. Donma

313

Intensive groundwater-surface water interaction in an alluvial fan: Assessment using a numerical model and isotopic tracer T. Yamanaka & H. Wakui The thermal effect of groundwater flow on temperature distribution in the Sendai Plain H.G.L.N. Gunawardhana, S. Kazama & M. Sawamoto Thorium and uranium concentrations in 44 Japanese river waters – Possible uranium addition from agricultural fields to river waters K. Tagami & S. Uchida Roles of deep bedrock groundwater in surface hydrological processes in a headwater catchment K. Kosugi, S. Katsura, T. Mizutani, H. Kato, T. Mizuyama, K. Goto & K. Ishio

321 329

337 341

A GIS-based, hypothetical model of groundwater seepage into a former mining open pit: 1. discrete fractures scenario A. Salama, E.R. Negeed, I. Djamaluddin, T. Esaki, Y. Mitani & H. Ikemi

349

Mechanism for the production of dissolved iron in the Amur River basin – a modeling study of the Naoli River of the Sanjiang Plain T. Onishi, H. Shibata, M. Yoh & S. Nagao

355

7 Remote sensing for measuring water balance, hydrodynamics and hydrological processes The 2006 Australian drought detected by GRACE T. Hasegawa, Y. Fukuda, K. Yamamoto & T. Nakaegawa

363

Improvement of JLG terrestrial water storage model using GRACE satellite gravity data K. Yamamoto, T. Hasegawa, Y. Fukuda, T. Nakaegawa & M. Taniguchi

369

The European SMOS for large-scale water balance and climate modelling studies A.A. Van de Griend, J-P. Wigneron, P. Waldteufel & J. Krecek

375

Remote sensing-based estimates of evapotranspiration for managing scarce water resources in the Gezira scheme, Sudan M.A. Bashir, H. Tanakamaru & A. Tada Investigation of fresh and salt water distribution by resistivity method in Yellow River Delta T. Ishitobi, M. Taniguchi, J. Chen, S-i. Onodera, K. Miyaoka, T. Tokunaga, M. Saito & Y. Fukushima

381 387

8 Interaction between the groundwater resources and ecosystems Riparian vegetation changes from hydrological alteration on the River Murray, Australia – Modelling the surface water-groundwater dependent ecosystem T.M. Doody & I.C. Overton An analysis of groundwater conditions in a saline groundwater area, Thailand P. Mekpruksawong, T. Suwattana, T. Ichikawa, S. Aramaki & S. Chuenchooklin

395 401

The role of monsoon rainfall in desalinization of soil-groundwater system and in vegetation recovery from the 2004 tsunami disaster in Nagapattinam district, India T. Kume, C. Umetsu & K. Palanisami

409

Effect of stream diversion on densities of Aeromonas hydrophila in a mountain stream in a headwater area H. Hirotani & K. Ochi

415

VIII

9 Socio-economic models and monitoring of vulnerable water resource Water resources estimation and allocation in the rapid developing area of China K. Wang, X. Chen & Y.D. Chen

423

Policy evaluation of China’s pollution charge system with a measurement adjusted by water quality M. Morisugi, N. Sawazu & A. Onishi

431

Prediction of water resource carrying capacity of Changchun city, northeast China, based on BP neutral network method W. Su, T. Matsumoto, J.S. Liu & J.X. Dou

439

External dependency of water supply system in Beijing: An application of water mileage J. Aoki, C. Chen, S. Kaneko & J. Chen

445

Urbanization and water use situation in Beijing, China – an evidence from water production and supply sector L. Banchongphanith & S. Kaneko

451

Study on future water supply and demand in the Yellow River basin of China based on scenario analysis A. Onishi, Y. Fukushima, H. Imura, F. Shi, J. Han & W. Fang

459

Study on sustainable agricultural production and agricultural water use efficiency in the Yellow River Basin of China A. Onishi, Y. Sato, T. Watanabe, Y. Fukushima, X. Cao, H. Imura, M. Matsuoka & M. Morisugi

465

Combinatory efficiency of water and power transfer systems in North China F. Shi, H. Imura, A. Onishi, X. Cao & O. Higashi

471

Estimation of groundwater resource demand in the Yellow River Basin, China T. Ichinose, K. Otsubo, I. Harada & M. Ee

477

Long-term urban growth and water demand in Asia K.A.B. Jago-on & S. Kaneko

483

Impact of municipal waste and waste water management change on nutrients flow to surface water and ground water in Asian mega-cities Y. Xue & T. Matsumoto

491

Frequency analysis of extreme hydrologic events and assessment of water stress in a changing climate in the Philippines F.P. Lansigan

497

Why farmers still invest in wells in hard-rock regions when the water-table is fast declining? K. Palanisami, C. Umetsu & C.R. Ranganathan Quantifying vulnerability and impact of climate change on production of major crops in Tamil Nadu, India K. Palanisami, P. Paramasivam, C.R. Ranganathan, P.K. Aggarwal & S. Senthilnathan Modeling the country based land use change and spatial distribution K. Matsumura, K. Sugimoto, W. Wu, R. Shibasaki & A. Onishi

503

509 515

10 Reconstruction of human impacts on the surface and subsurface environments during past 100 years Long-term temperature monitoring in boreholes for studies of the ground surface thermal environment and groundwater flow M. Yamano, H. Hamamoto, S. Goto & A. Miyakoshi

523

Ground surface temperature history reconstruction from borehole temperature data in Awaji Island, southwest Japan for studies of human impacts on climatic change in East Asia S. Goto, M. Yamano, H.C. Kim, Y. Uchida & Y. Okubo

529

IX

Estimation of the past ground surface temperature change from borehole temperature data in the Bangkok area H. Hamamoto, M. Yamano, S. Kamioka, J. Nishijima, V. Monyrath, S. Goto & M. Taniguchi Reconstructions of climate change and surface warming at Jakarta using borehole temperature data R.F. Lubis, A. Miyakoshi, M. Yamano, M. Taniguchi, Y. Sakura & R. Delinom

535 541

Subsurface thermal environment change due to artificial effects in the Tokyo metropolitan area, Japan A. Miyakoshi, T. Hayashi, V. Monyrath, R.F. Lubis & Y. Sakura

547

Land expansion with reclamation and groundwater exploitation in a coastal urban area: A case study from the Tokyo Lowland, Japan T. Hayashi & A. Miyakoshi

553

Shallow groundwater quality and potential for groundwater pollution by nitrogen fertilizer in an agricultural area Y. Iizumi, T. Kinouchi & K. Fukami

559

The restoration of historical hydro-environment from historical materials and topographical maps in Tokyo, Japan T. Taniguchi

565

Urbanization and the change of water use in Osaka City – Spatio-temporal analysis with data maps A. Yamashita

571

Urbanization in Asian Metropolis and the changes of hydrological environment in and around Bangkok Y. Kagawa

577

A comparative study on history of sewage works construction between Bangkok and Tokyo T. Imai, C. Vitoonpanyakij, S. Kessomboon, P. Banjongproo, S. Kaneko, R. Fujikura & T. Matsumoto

583

Estimation of historical/spatial changes in subsurface material stock related to the construction sector of urban areas in Japan H. Tanikawa, R. Inadu, S. Hashimoto & S. Kaneko

591

Ecosystem changes on the River Murray floodplain over the last 100 years and predictions of climate change I.C. Overton & T.M. Doody

599

Degradation of subsurface environment in Asian coastal cities M. Taniguchi, J. Shimada, Y. Fukuda, S. Onodera, M. Yamano, A. Yoshikoshi, S. Kaneko, Y. Umezawa, T. Ishitobi & K.A.B. Jago-on

605

11 Land-ocean interaction Global assessment of submarine groundwater discharge M. Taniguchi, T. Ishitobi & W.C. Burnett Potential effects of terrestrial nutrients in submarine groundwater discharge on macroalgal blooms in a fringing reef ecosystem Y. Umezawa, I. Herzfeld, C. Colgrove & C.M. Smith

613

619

Estimation of groundwater discharge to the sea using a distributed recharge model M. Katsuki, J. Yasumoto, A. Tsutsumi, Y. Hiroshiro & K. Jinno

625

Groundwater discharge into the Caspian Sea from the Iranian Coast and its importance G.A. Kazemi & U. Tsunogai

631

Concentrations and distributions of 9 major ions and 54 elements in major Japanese river waters S. Uchida & K. Tagami

637

X

The geochemistry of heavy metals, yttrium, and rare earth elements in Wakasa Bay, Japan H. Takata, T. Aono, K. Tagami & S. Uchida

641

The interactions between the Yukon River and Bering Sea K.A. Chikita, Y. Kim, I. Kudo, S. Saito, T. Wada & H. Miyazaki

647

Evaluation of denitrification potential in coastal groundwater using simple in situ injection experiment M. Saito, S. Onodera, K. Okada, M. Sawano, K. Miyaoka, J. Chen, M. Taniguchi, G. Liu & Y. Fukushima Bohai Sea coastal transport rates and their influence on coastline nutrient inputs R.N. Peterson, W.C. Burnett, I.R. Santos, M. Taniguchi, T. Ishitobi & J. Chen Water and phosphorus budgets in the Yellow River estuary including the submarine fresh groundwater M. Hayashi & T. Yanagi

653

659

665

Decrease in Yellow River discharge and its impact on the marine environment of the Bohai Sea T. Yanagi

669

Author Index

675

Keyword Index

677

XI

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Preface

The vulnerability of water resources due to climate change and human activities is globally increasing. The phenomenon of hydrological change is complicated because of the combinations and interactions between natural climatic fluctuations, global warming and human activities including changes in land utilization. The impact areas of hydrological changes are also not only within basins, but reach to the ocean through coastal water exchanges. This book aims to integrate such problems from headwaters to the ocean which may occur globally in the future because of climate change and increasing human population. An International conference on “Hydrological changes and management from headwaters to the ocean” was held from October 1 to 3, 2008 at Kyoto, Japan. The conference topics included land-atmosphere interaction, land-ocean interaction, groundwater – surface water interaction, headwater studies under climate changes and human impacts, coastal zone and estuary studies, socio-ecological analyses and monitoring of vulnerable water resources, integrated models and management for sustainable uses of water resources, reconstruction of human impacts on the surface and subsurface environments, vegetation and water resources, and other water vulnerability issues. This book includes selected papers from the conference that provides interdisciplinary knowledge and current awareness on integrated water management under the pressures of climate change and human activities. There were 11 sessions at the conference. Session 1 concerned interactions between land and atmosphere. Human activities have caused significant changes in the land-cover conditions on Earth in recent decades and have likely induced inevitable impacts on the Earth’s climate. Land-surface processes are one of the critical issues for prediction of climate change, maintenance of ecological systems, and management of water resources. Measurement techniques have progressed quickly in recent decades and meteorological databases have been constructed by various research communities. This session focused on land-atmosphere interactions to enhance our understanding of the issue in relation to climate change and human activities. Sessions 2–4 focused on the “headwater environment” which includes impacts of climate change and human intervention, strategic planning and environmental assessments of activities in headwater areas, and environmental education for sustainable development: the role of mountain and headwater landscapes. These sessions were also authorized as the 7th International Conference on Headwater Control (IAHC). Headwater areas are generally characterized by a high potential of recharge of both surface- and groundwater resources, but also by conflicts in the exploitation of natural resources (water, timber, minerals or wildlife), tourism and leisure industries, and nature protection (frequently, these resources remain among the great natural resources of a nation). Many headwater regions are in mountain steep-lands, and are frequently source areas for natural hazards. Headwater environments often consist of fragile ecosystems being impacted by global climate change and human interventions. Care of headwater areas thus demands their full integration into environmental management plans. Effective management of headwaters in the frame of integrated watershed planning also demands assessment of the role of key components and more effective participatory processes. Session 5 dealt with hydrological models in support of integrated water resources management. Integrated water resource management aims at sustainable use of water, land, and related resources. Hydrological models can help resource managers to analyze and quantify effects of spatial and temporal changes in the availability and quality of freshwater resources. The integration of global change aspects into hydrological models and the use of different modeling techniques can also provide decision makers with scenarios of potential anthropogenic interventions and their resulting impacts on fragile freshwater resources. The session focus on hydrological modeling and the integration of global change aspects (including climate change) from both the natural and social sciences using different modeling techniques. Groundwater-surface water interactions were discussed in session 6. Understanding the mechanisms of water movement and transport of dissolved materials between surface waters and groundwater is essential to improve the management of these resources and to protect associated ecosystems from deterioration. Although surface water and groundwater have been considered separately for a long time, we now understand that they are closely associated in the water cycle in terms of water quantity and quality. An understanding of these associations is critical for the maintenance of the ecological systems of both rivers and aquifers. This session brought together scientists to advance integrated analysis of groundwater-surface water systems.

XIII

Session 7 focused on remote sensing methods for measuring water balance, hydrodynamics and hydrological processes. Remote sensing analyses using satellite and aerial photo images collected in a broad range of spatial and temporal scales allow us to have an overview of the hydrodynamics, water balance and environmental changes on the watershed and basin scale. For example, the recently developed technique using GRACE (Gravity Recovery and Climate Experiment) satellite gives terrestrial water storage and their temporal changes even in remote areas with limited data. Session 8 dealt with the interaction between the groundwater resources and ecosystems. Groundwater dependent ecosystems (GDEs) frequently occur in wetlands, terrestrial vegetation, riparian areas in arid regions, coastal zones, coral reefs and cave ecosystems. Critical damages or more gradual changes in composition and/or ecological function of communities are expected in these areas according to climate change and/or human impacts on hydrological settings. On the other hand, the degradation of vegetation can conversely cause a shift of the related hydrological environment including water quality and water mass balance. The approaches for quantifying the physiology of plant communities and hydrodynamics in watersheds are becoming better established individually. Thus, it appears to be time to integrate such interactions between ecosystems and groundwater systems. This session provided contributions to the broad examples collected in a variety of groundwater dependent ecosystems, including field observations and model predictions. Socio-economic models and monitoring of vulnerable water resource was the focus of session 9. Water is a basic necessity and has been the main resource for human activities. Nowadays, intensive socio-economic activities have caused the depletion of many water resources, deterioration of water quality, and damage to the water environment in many areas. In order to promote sustainable development, it is necessary to manage human activities efficiently and effectively while satisfying the condition of water resource and its environment. This session discussed the impacts of human activities on water resource systems and their functions. Session 10 dealt with reconstruction of human impacts on the surface and subsurface environments during the past 100 years. In cities and surrounding areas, overuse of water resources associated with expanded human activities have caused drastic changes in subsurface environments such as water shortage and land subsidence. To understand the causal relationships on these issues, it is necessary to trace the effects of human activities on the environments accurately at each developing stage of the targeted areas. In this session, we focused on the potential approaches and ideas from various fields to complement the data, which is available only with different resolutions in space and time. Methods can systematically integrate these data from different fields, including Geographic Information System (GIS) applications and other innovative approaches. Finally, session 11 focused on Land-ocean Interactions. Both river discharge and submarine groundwater discharge (SGD) are important pathways to carry chemical components from land to ocean. SGD and associated chemical fluxes, especially over substantial areas or time periods, are still uncertain due in part to their heterogeneous nature. Furthermore, intensive human activities along the coast may make these processes complicated. In this session, we discussed how to practically estimate SGD and their associated chemical fluxes. Numerical hydro-dynamical and ecological models for coastal areas, which are improved by these new approaches, are also discussed. Many people helped to organize the sessions of this conference successfully. We wish to thank the organizers from RIHN (Research Institute for Humanity and Nature) in particular Project 2–4 “Human impacts on urban subsurface environment” and Project 1–2 “Recent rapid change of water circulation in the Yellow River and its effects on environment”. In addition, we acknowledge support from the IAHS (International Association of Hydrologic Sciences), GWSP (Global Water System Project), the European Observatory of Mountain Forests, and IAHC (International Association of Headwater Council). We also thank Drs. T. Endo, K. Yamamoto, M. Katsuyama, A. Takahashi, Y. Sato, T. Onishi, M. Matsuoka, A. Onishi, A., Yamashita, and T. Taniguchi for their intensive help for the conference. Finally we would like to thank Mr. Germaine Seijger, Senior Editor Engineering, Water & Earth Sciences, CRC Press/Balkema, Taylor & Francis Group for accepting the idea of this book. October 2008 Editors: Makoto Taniguchi William C. Burnett Yoshihiro Fukushima Martin Haigh Yu Umezawa

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Reviewers List

We would like to express our gratitude to all the reviewers. Without their help, this book will not be realized. Akiyama T. Alcantar A.J. Baskaran M. Borga M. Brocca L. Burnett W. C. Cable J. Chen J. Chikushi K. Corbett R. de Asis A. M. Dulaiova H. Eamus D. Ehara S. Endo T. Fujihara Y. Fujimaki H. Fujinami H. Fukuda Y. Fukushima Y. Gooseff M. N. Goosen M. Goto S. Haga H. Hagiwara H. Hama A. Hamamoto H. Han J. Hao Z. Hara M. Hatano T. Hayashi M. Hayashi T. Higuchi A. Hiyama T. Horicka Z. Horino H. Hosono T. Ichinose T. Imaizumi M. Ioka S. Jago-on K. A. B. Kachi N. Kato M. Katsuyama M. Kawase H. Kihara Y.

Aichi University, Japan University of the Philippines Los Baños, Philippines Wayne State University, USA University of Padova, Italy Institute for Geo-Hydrological Protection, Italy Florida State University, USA Louisiana State University, USA Sun Yat-sen University, China Yamanashi Gakuin University, Japan East Carolina University, USA University of the Philippines Los Baños, Philippines Woods Hole Oceanographic Institute, USA University of Technology, Australia Kyushu University, Japan Research Institute for Humanity and Nature, Japan Japan International Research Center for Agricultural Sciences, Japan University of Tsukuba, Japan Nagoya University, Japan Kyoto University, Japan Research Institute for Humanity and Nature, Japan Pennsylvania State University, USA New York Institute of Technology, Jordan Geological Survey of Japan Tottori University, Japan Shikoku University, Japan University of Innsbruck, Austria The University of Tokyo, Japan Nagoya University, Japan Hohai University, China Rissho University, Japan Nastec,LLC, Japan University of Calgary, Canada Akita University, Japan Chiba University, Japan Nagoya University, Japan Charles University, Czech Republic Osaka Prefecture University, Japan Akita University, Japan Natiomal Institute for Environmental Studies, Japan National Institute for Rural Engineering, Japan Horonobe Research Institute for the Subsurface Environment, Japan Hiroshima University, Japan Nagoya University, Japan Ritsumeikan University, Japan Research Institute for Humanity and Nature, Japan Frontier Research Center for Global Change, Japan Shimane University, Japan

XV

Kim K. Kitano S. Koba K. Kobayashi N. Kosugi K. Krecek J. Kuraz V. Lansigan F. P. Li Z. Lu M. Lubis R. F. Lydia D. G. Ma X. Masumoto T. Matsui K. Matsumoto M. Matsumoto T. Matsuoka M. McDonnell J. J. Melone F. Mishra A.K. Miyakoshi A. Miyaoka K. Monyrath V. Mori K. Morisugi M. Nagai H. Nagano T. Nagasaka S. Nakaegawa T. Nakamura F. Nakamura K. Nicomedes D. B. Nishijima J. Nishimura K. O’Grady A. Ohno E. Ohte N. Onda Y. Onishi A. Onishi T. Onodera S. Parachini M. L. Pat J. -F. Y. Peterson R. Qian J. Ryu D. Saito M. Sakura Y. Santos I. Sato Y. Sayama T. Sekino T. Shibata H. Shibusawa H. Shibuya K. Shinoda T.

Japan Bank for International Cooperation, Japan Kanazawa University, Japan Tokyo University of Agriculture and Technology, Japan Nagoya University, Japan Kyoto University, Japan Czech Technical University, Czech Republic Czech Technical University, Czech Republic University of the Philippines Los Baños, Philippines Hohai University, China Nagaoka University of Technology, Japan Chiba University, Japan Global Water System Project, Germany Japan Agency for Marine-Earth Science and Technology, Japan National Institute for Rural Engineering, Japan Kinki University Japan Kochi University, Japan The University of Kitakyushu, Japan Kochi University, Japan Oregon State University, USA National Research Council, Italy Texas A & M University, USA Geological Survey of Japan Mie University, Japan Chiba University, Japan Geosphere Environmental Technology Corporation, Japan Meijo University, Japan National Institute for Agro-Environmental Sciences, Japan Kobe University, Japan Nihon University, Japan Japan Meteorological Agency, Japan Hokkaido University, Japan Kyoto University, Japan University of the Philippines Los Baños, Philippines Kyushu University, Japan Nihon Fukushi University, Japan University of Tasmania, Australia Meijo University, Japan The University of Tokyo, Japan University of Tsukuba, Japan Nagoya University, Japan Research Institute for Humanity and Nature, Japan Hiroshima University, Japan European Commission Joint Research Centre, Italy The University of Tokyo, Japan Florida State University, USA Hefei University of Technology, China University of Melbourne, Australia Ehime University, Japan Chiba University, Japan Florida State University, USA Kyoto University, Japan Kyoto University, Japan Research Institute for Humanity and Nature, Japan Hokkaido University, Japan Toyohashi University of Technology, Japan National Institute of Polar Research, Japan Nagoya University, Japan

XVI

Singh V.P. Song X. Suzuki K. Swarzenski P. Takagi M. Takahashi A. Tanaka H. Tani M. Taniguchi M. Taniguchi T. Tokuchi N. Tokunaga T. Tsujimura M. Umezawa Y. Van de Griend A. Xinyu G. Yamanaka T. Yamashita A. Yanagi T. Yang H. Yasumoto J. Yoshikoshi A. Zheng H. Zhou X.

Texas A & M University, USA Chinese Academy of Sciences, China Japan International Cooperation Agency, Japan USGS, Florida, USA Kochi Institute of Technology, Japan Nagoya University, Japan Nagoya University, Japan Kyoto University, Japan Research Institute for Humanity and Nature, Japan Rissho University,Japan Kyoto University, Japan The University of Tokyo, Japan University of Tsukuba, Japan Nagasaki University, Japan Free University, The Netherlands Ehime University, Japan University of Tsukuba, Japan Rakuno Gakuen University, Japan Kyushu University, Japan Swiss Federal Institute for Aquatic Science and Technology, Switzerland Research Institute for Humanity and Nature, Japan Ritsumeikan University, Japan Chinese Academy of Sciences, China Institute for Global Environmental Strategies, Japan

XVII

1

Land-atmosphere interaction

Human activities have caused significant changes in the land-cover conditions on Earth in recent several decades and have possibly induced inevitable impacts on the Earth’s climate. The land-surface processes are one of the critical issues for prediction of climate change, maintenance of ecological systems, and management of water resources. Measurement techniques have progressed abruptly in recent decades and meteorological databases have been constructed by various research communities. This session especially focuses on the land-atmosphere interaction to enhance our understanding of the issue in relation to climate change and human activities. Research issues on energy, water, and mass transfers at various land surfaces and meteorological and climatic topics are welcome. Conveners: Helen Cleugh (CSIRO, Atmospheric Research, Australia) Tetsuya Hiyama (Nagoya University, Japan) Atsushi Higuchi (Chiba University, Japan) Takahashi Atsuhiro (RIHN, Japan)

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Effects of selective cutting of large trees on transpiration and surface temperature: A predictive study of evergreen broad-leaf forest in central Cambodia K. Tamai∗, T. Nobuhiro, N. Kabeya, M. Araki & S. Iida Forestry & Forest Products Research Institute, Tsukuba, Japan

A. Shimizu & T. Shimizu Kyushu Research Center, Forestry & Forest Products Research Institute, Kumamoto, Japan

S. Chann & N. Keth Forest Wildlife Science Research Institute, Forestry Administration, Cambodia

ABSTRACT: Most of the evergreen broad-leaved forest in the Indochina peninsula has been replaced by agricultural land or deciduous forest as a consequence of reclamation or timber cutting, and only a small area of evergreen broad-leaved forest now remains in Cambodia. This remaining forest is under development pressure caused by Cambodian reconstruction. The evergreen broad-leaved forest in there is reported to transpire 6 mm day−1 , even in the late dry season, which is almost the same as the rate in the rainy season, despite the surface soil. It is thought that tall trees with extensive root systems use soil moisture from soil layers deeper than 250 cm. When these tall trees are cut, the remaining forest is unable to access the deep soil moisture. Consequently, transpiration is expected to decrease, while the surface temperature will increase. In this report, we used our observations to estimate these effects using the Jarvis–Stewart model. As a result of cutting large trees, it is estimated that the transpiration rate will decrease to around 25% in the late dry season, while the daytime average surface temperature will rise between 1.9◦ C and a maximum of 6.2◦ C. Keywords:

1

Jarvis-Stewart model; Moisture availability; Tall tree cutting

INTRODUCTION

and the regional environment. Similar extreme levels of development taking place in Cambodia are expected to result in problems similar to those experienced in northeastern Thailand between 1970 and 2000. The evergreen broad-leaved forest in central Cambodia is reported to transpire 6 mm day−1 , even in the late dry season, which is as much as in the rainy season, despite the surface soil drought evident from flux monitoring data. It is thought that tall trees that have extensive root systems in the deep soil layer (deeper than 250 cm) use deep soil moisture. When these tall trees are cut, the remaining forest consists of small trees that cannot use deep soil moisture. Consequently, transpiration is expected to decrease and surface temperature to increase. Here, we estimate these effects using the Jarvis-Stewart model.

Evergreen broad-leaved forest once dominated the Indochina peninsula. However, most of the natural evergreen broad-leaved forest has been replaced by agricultural land or deciduous forest as a consequence of reclamation or timber cutting, and only a small area of the original forest now remains in Cambodia. This remaining forest is under development pressure from Cambodian reconstruction, despite the conservation policies of the Cambodian government. In northeastern Thailand, the forest area was developed into agricultural land in the 1970s, and the forested area has decreased to less than 10% in many such areas. The agricultural land was later abandoned because of soil erosion and salt damage. Thus, economic refugees flowed from agricultural areas to urban areas, leading to social problems. The radical decrease in the forested area also worsened water availability ∗

2

SITE DESCRIPTION

Observations were made in a forest located in the O Thom I basin (12◦ 44 N, 105◦ 28 E; 88 m ASL)

Corresponding author ([email protected])

3

Table 1.

Forest composition.

Total Overstory (>20 m) Secondary story (20 m > 10 m) Lower story (>10 m)

Number of stems N ha−1

11.3 27.2

1600 96

14.1 7.3

416 1088

Tokyo, Japan) located at 34 and 38m were used to measure dry and wet bulb temperatures. A net radiometer (Q*7; Campbell) and pyranometer (CMP-3; Campbell) recorded observations at 36 and 60 m, respectively. The soil moisture content ratio and suction were monitored at a depth of 50 cm, 150 cm and 250 cm using a UIZ-ECH device (Uizin; Tokyo) and UIZ-SMT (Uizin), respectively. The soil heat flux was measured using a heat flux plate (MF-180M; Eko) at a depth of 2 cm. The Bowen ratio method yielded estimates of latent heat flux at this site (Nobuhiro et al., 2007). We analyzed data recorded at 30-min intervals from 1 to 10 August 2004 (rainy season) and 11 to 20 March 2005 (late dry season).

Figure 1. Location of the observation tower site.

in Kompong Thom Province, Cambodia (Figure 1). The basin ranges from 46 to 273 m in altitude, covers approximately 137 km2 , and has flat topography. A 60-m study tower stands in the middle of the flat plain. Forest surrounds the tower for dozens of kilometers in all directions. Evergreen trees, including Vatica odorata, Calophyllum inophyllum, and Myristicaceae species, dominate the forest, which consists of overstory (higher than 20 m), secondary (height is 10–20 m), and lower-story (smaller than 10 m) trees. The lower story trees are shorter than 10 m and constitute around 70% of all tree stems (Table 1). The maximum tree height is approximately 45 m, and the average height of the canopy surface is 34 m. The leaf area index (LAI) at the observation site was measured using an LAI-2000 (LI-COR, Lincoln, NE, USA) at 4.37 and 3.85 in August 2004 and March 2005, respectively (Ito et al., 2007). Extensive research has been performed at this site and catchment, including studies of the characteristics of runoff (Shimizu et al., 2007), water discharge and water residence times (Kabeya et al., 2008), seasonal fluctuation in groundwater (Araki et al., 2008), seasonal fluctuation in evapotranspiration (Nobuhiro et al., 2007), stomatal response of trees (Daikoku et al., 2007), soil structure (Ohnuki et al., 2008), and canopy phenology (Ito et al., 2007).

3

Averaged Height m

4

MODEL

4.1 Latent heat calculations using the Penman-Monteith equation and Jarvis-Stewart model Latent heat flux was calculated using the PenmanMonteith equation proposed by Monteith (1965):

where Rn is the net radiation, G is the soil heat flux, lE is the latent heat flux, gc is the surface conductance, ga is the aerodynamic conductance, ρ is the air density, δq is the vapor pressure deficit (VPD; g kg−1 ), Cp is the specific heat of air at constant pressure (J kg−1 ◦ C−1 ), and  is the slope of the saturation vapor pressure at air temperature (mmHg ◦ C−1 ), γ is the psychrometric constant (mmHg◦ C−1 ). The value of ga was obtained from the following formula:

OBSERVATION

The equipment on the study tower included anemometers (03101; Campbell, Logan, UT) located at a height of 36 m. Data from these instruments allowed analysis of wind profile characteristics. Average wind speeds were logged (CR10X; Campbell) every 10 min. Ventilated thermometers (MH-020; Eko Instruments,

where u(z) is the wind speed (m s−1 ); z is the calculated height (36 m); z0 and d are the roughness length (7.5 m)

4

Table 2.

and zero plane displacement height (18.3 m), respectively (Tamai et al., 2007); CH is the bulk transfer coefficient; and κ is the von Karman constant. Stewart (1988) proposed the following equation for surface conductance:

where LAI is the leaf area index (m2 m−2 ), S is the solar radiation (W m−2 ), T is the air temperature (◦ C), δθ is the soil moisture deficit (%), and f represents the functions of each of the environmental variables; α is a constant. The units of gc are mm s−1 . Thus, lE can be calculated using Eqs. (1)–(3). Tamai (2008) verified that the following equations are applicable to Eq. (3) at this site.

Case

1

2

3

d (m) z0 (m) LAI Rainy season (m2 m−2 ) Late dry season f(δθ)

18.3 7.5 4.37 3.85

5.6 0.5 2.93 2.58

5.6 0.5 4.37 3.85

Eq. (9)

Eq. (9)

1.0(Const.)

Case 1: Present forest before cutting. Case 2: Just after selective cutting. Case 3: 1 year after selective cutting.

5

CALCULATION METHODS

The forest canopy consists of three layers: overstory, secondary story, and lower story (Shimizu et al., 2007). We regarded the overstory and secondary story as the selectively cut trees, which would mean that approximately one-third of the stems would be cut.This cutting of potential tall trees changes the values of z0 , d, CH , and LAI. The calculation was performed under three cases (Table 2). Cases 1, 2, and 3 represent “before cutting,” “just after cutting,” and “after one growing season following cutting,” respectively. The z0 and d values in Cases 2 and 3 are defined as 7% and 78%, respectively, of the average tree height of the lower story (7.26 m). Daikoku (2007) obtained the following equation for lower story trees at this site:

Th and Tl are the upper and lower limits of air temperature (◦ C) with values of 0◦ C and 45◦ C, respectively. P1, P2, and P3 are constants with values of 250, 30.2, and −0.064, respectively. 4.2

Parameters in Eqs. (1)–(3) and (8).

Surface temperature calculation using the moisture availability equation where θmin is the minimum θ, which has a value of 17% at this site. In contrast, Tamai (2008) verified that the soil moisture deficit did not affect the decrease in lE even in the late dry season, when values of f(δθ) are 1.0 at this tower flux observation site. Such values of tower lE flux include transpiration from tall trees using deep soil moisture. However, after selective cutting, only the lower story, which uses shallow soil moisture, would be retained. Thus, the lE rate using f(δθ) with θ at 50cm depth represents the transpiration rate after selective cutting. Strictly speaking, the albedo is expected to increase when tall trees are cut. This might affect the calculations. However, the change in lE has a greater effect on surface temperature than does albedo (Jackson, 1977). Thus, changes in albedo were neglected.

Surface temperature was calculated using Eq. (8) and the following:

where Qa is the specific humidity; TE is the surface temperature; and β is the moisture availability. In general, β must be known in order to obtain TE in Eq. (8). Moreover, Eq. (8) cannot be solved for TE because TE is included in the first and second terms in Eq. (8). lE can be determined for any value of β with measured values for Rn , G, and so on, using a successive approximation method such as the Newton-Raphson procedure (Watanabe, 1994). Conversely, because Eq. (8) is a linear function of β, the values of TE and β can be evaluated when lE is known. In practice, the value of lE calculated using Eq. (8) increases as the value of β gradually increases from 0. The calculated value of lE ultimately equals the LE evaluated using Eqs. (1)–(3). The values of TE and β are evaluated when lE from Eqs. (1)–(3) and Eq. (8) are equal.

6 6.1

RESULTS AND DISCUSSION Observation result

Figure 2 shows the seasonal course of the soil water matrix potential at this site. From 1 to 10 August 2004,

5

Latent heat flux (Wm-2) Latent heat flux (Wm-2)

Figure 2. Annual course of soil matrix potential. Small black points 50 cm depth, gray black points 150 cm depth, large black points 250 cm depth. R: rainy season observation period. D: late dry season observation period.

800 600 400 200 0 2004/8/1 0:00

2004/8/3 0:00

2004/8/5 0:00

2004/8/7 0:00

2004/8/9 0:00

2004/8/11 0:00

2005/3/13 0:00

2005/3/15 0:00

2005/3/17 0:00

2005/3/19 0:00

2005/3/21 0:00

800 600 400 200 0 2005/3/11 0:00

Present rate by Bowen ratio

Case1

Case2

Case3

Figure 4. Comparison of predicted latent heat flux in respective cases.

6.2 Decrease rate of latent heat flux The lE was calculated using Eqs. (1)–(3) in Cases 1, 2, and 3. The α in Eq. (3) was set at 5.2 because the total latent heat flux in Case 1 and the Bowen ratio method (Nobuhiro et al., 2007) are equal when α is 5.2. θ at a depth of 50 cm was 36% in the rainy season and 18% in the late dry season. This gave f(δθ) values of 0.819 and 0.086 in the respective seasons. Figure 4 shows the results of the calculation of lE. In the rainy season, a large decrease in lE was not recognized. In contrast, an increase in lE was sometimes found. This may have been caused by the rise in TE from a change in d and z0 , and this worsened exchange efficiency would have more effect than the decrease in LAI. However, in the late dry season, daily lE was calculated to decrease from 10.29 MJ m−2 day−1 in Case 1 to 1.73 MJ m−2 day−1 and 2.40 MJ m−2 day−1 in Cases 2 and 3, respectively. These decreased rates corresponded to 3.51 mm day−1 and 3.23 mm day−1 , respectively.

Figure 3. Diurnal solar radiation, air temperature and vapor pressure deficit. First column: solar radiation in rainy season. Second column: air temperature (black points) and vapor pressure deficit (white points) in rainy season. Third column: solar radiation in late dry season. Fourth column: air temperature (black points) and vapor pressure deficit (white points) in late dry season.

6.3

Increase in surface temperature

The TE was calculated from lE in Figure 4 for the three cases. Figure 5 shows the predicted increase in surface temperature caused by the cutting of tall trees. In the rainy season, TE decreased with an increase in lE. However, a large temperature increase was predicted at around noon in the late dry season. The maximum increase in temperature was predicted to be approximately 6◦ C (Table 3). This large increase in surface temperature and the decrease in lE in the late dry season might have adverse ecological effects on the forest.

the soil water matrix potential at 50 cm depth was several kPa and near saturation even in the shallow soil layer. From 11 to 20 March 2005, however, the soil moisture was near saturation only in the deep soil layer (250 cm depth). The shallow soil layer became very dry, with a soil water matrix potential of about −60 kPa at 150 cm depth. Figure 3(a)–(d) shows the values of observed S, T, and δq. Comparison between the rainy and late dry seasons showed that the diurnal ranges ofT and δq were wider in the late dry season. However, the differences in the diurnal ranges of S between the rainy and late dry seasons were small.

7

CONCLUSION

The evergreen forest in Cambodia is under the under development pressure. When large trees are cut in the

6

REFERENCES Araki M., Shimizu A., Kabeya N., Nobuhiro T., Ito E., Ohnuki Y., Tamai K., Toriyama J., Tith B., Pol S., Lim S. and Khorn S. 2008. Seasonal fluctuation of groundwater in an evergreen forest, central Cambodia: experiments and two-dimensional numerical analysis. Paddy Water Environment, 6:37–46. Daikoku K., Hattori S., Deguchi A., Fujita Y., Araki M., and NobuhiroT. 2007. Stomatal response characteristics of dry evergreen and dry deciduous forests in Kampong Thom, Cambodia. In H. Sawada (eds), Forest Environments in the Mekong River Basin: 97–111. Heidelberg: Springer. Ito E., Khorn S., Lim S., Pol S., Tith B., Pith P., Tani A., KanAraki M. 2007. Comparison of the leaf area index (LAI) of two types of Dipterocarp forest on the west bank of the Mekong river, Cambodia. In H. Sawada (eds), Forest Environments in the Mekong River Basin: 214–221. Heidelberg: Springer. Jackson I. J. 1977. Climate, water and agriculture in the tropics, (Trans. by Uchijima et al., 1991), Maruzen, Tokyo, pp280. Kabeya N., Shimizu A., Nobuhiro T. and Tamai K. 2008. Preliminary study of flow regimes and stream water residence times in multi-scale forested watesheds of central Cambodia. Paddy Water Environment, 6:25–35. Monteith J. L. 1965. Evapotranspiration and environment. In Fogg G. E. (ed), The state and movement of water in living organs, Soc. Exp. Bipl. Symp., 19, Cambridge University Press. Nobuhiro T., Shimizu A., Kabeya N., Tsuboyama Y., Kubota T., Abe T., Araki M., Tamai K., Chann S., and Keth N. 2007. Year-round observation of evapotranspiration in an evergreen broadleaf forest in Cambodia. In H. Sawada (eds), Forest Environments in the Mekong River Basin: 75–86. Heidelberg: Springer. Ohnuki Y., Kimhean C., Shinomiya Y., Sor S., Toriyama J. and Ohta S. 2007. Apparent change in soil depth and soil hardness in forest areas in KampongThom province, Cambodia. In H. Sawada (eds), Forest Environments in the Mekong River Basin: 263–272. Heidelberg: Springer. Shimizu A., Kabeya N., Nobuhiro T., Kubota T., Tsuboyama Yo, Ito E., Sano M., Chann S., and Keth N. 2007 Runoff characteristics and observations on evapotranspiration in forest watersheds, central Cambodia. In H. Sawada (eds), Forest Environments in the Mekong River Basin: 135–146. Heidelberg: Springer. Stewart J. B. 1988. Modelling surface conductance of pine forest. Agric. & Meteor., 43, 19–35. Tamai K., Shimizu A., Nobuhiro T., Kabeya N., Chann S., and Keth N. 2007 Measurements of wind speed, direction and vertical profiles in an evergreen forest in central Cambodia. In H. Sawada (eds), Forest Environments in the Mekong River Basin: 87-96. Heidelberg: Springer. Tamai 2008. Comparison of soil moisture effects on surface conductance between late dry and rainy seasons in an evergreen forest of central Cambodia, Paddy Water Environment, 6:47–53. Watanabe T. 1994. Vegetation and Atmosphere, In: Kondo J. (Ed.), Meteorology based on the water –Heat and water budgets on a land surface:208-239. Tokyo: Asakurashoten.

Figure 5. Predicted increase in surface temperature. Table 3. Averaged results of calculations for latent heat flux and surface temperature. Case lE (MJ m−2 day−1 ) Decrease in lE (MJ m−2 day−1 ) Decrease in lE (mm day−1 ) Increase in surface temp. (◦ C) Max. increase in surface temp. (◦ C)

1

2

3

Rainy season 6.14 5.03 5.84 Late dry season 10.29 1.73 2.40 Rainy season – 1.11 0.30 Late dry season – 8.56 7.89 Rainy season – 0.45 0.12 Late dry season – 3.51 3.23 Rainy season – 0.06 −0.09 Late dry season – 1.92 1.76 Rainy season – 1.18 1.01 Late dry season – 6.19 5.15

Max. increase in surface temp. is the maximum value in each season.

evergreen forest, the remaining small trees are unable to consume the deep soil moisture. Consequently, transpiration is expected to decrease, while the surface temperature will increase. We estimated these effects using the Jarvis–Stewart model. The selective cutting of large trees is expected to cause a decrease in transpiration rate of around 25% in the late dry season, while the daytime average surface temperature may rise by between 1.9◦ C and a maximum of 6.2◦ C. ACKNOWLEDGMENTS This study was the research partly funded by Ministry of the Environment, Japan (Global environment research coordination system). We also thank our Cambodian colleagues, including Mr. Ty Sokun and staff members of the Forestry Administration of Cambodia.

7

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

The role of cavities in land-atmosphere interactions N. Weisbrod∗ , Uri Nachshon & Modi Pillersdorf Department of Environmental Hydrology and Microbiology, The Zuckerberg Institute for Water Research, Blaustein Institutes for Desert Studies, Ben-Gurion University of the Negev, Israel

M.I. Dragila Department of Crop and Soil Sciences, Oregon State University, USA

ABSTRACT: Throughout the past two decades, most studies that explored flow and transport processes through surface-exposed fractures, focused merely on the role of these fractures as fast conduits for water, salts and contaminants during intensive rain events, and flooding or leakage from contamination sources. Conventional approach has assumed that as long as fractures are dry, their role in the hydrological cycle is negligible. This study, however, explores the processes occurring within surface-exposed fractures during the dry season, and shows that their role in hydrological and atmospheric cycles is not negligible. Keywords:

1 1.1

fractures; convection; salinization; earth-atmosphere interaction; day-time, night-time

INTRODUCTION

suggested that air convection could be a potential mechanism responsible for the enhanced accumulation of salts within these fractures. Convection can potentially remove moist-air from the fracture aperture at rapid rates, increase water vapor loss, and subsequently enhance salt precipitation and accumulation on the fracture walls. Weisbrod and Dragila (2006) theoretically demonstrated that during nighttime, when the atmospheric air is cooler and denser than the warmer fracture air, convection can discharge large quantities of moist-air from the fracture. Evaporation and salt accumulation are coupled processes that result in salinization. Salt precipitation near an evaporating-surface will draw additional solution towards the salt, producing an ongoing hydrologic cycle and resulting in land salinization. This salinization mechanism is of great importance, since dissolved salts and other chemical contaminants in the vadose zone travel a relatively short distance to the fractures and can continue to the groundwater via these fractures. Increase in salt concentration reduces the vapor pressure and consequently reduces the evaporation rate; similarly, high solute concentrations in soil solution can reduce soil water evaporation (Nassar and Horton, 1999). Salt precipitation over long periods influences porous media properties, which indirectly influence the evaporation rate. As result of evaporation from the matrix solution, salt can cause pore clogging and reduce the evaporation rate by decreasing the evaporative surface area and decreasing the soils

Fractures

Many rock formations and soils are crossed by fracture networks and cavities. Fractures, cracks and cavities creating a short-cut between land surface and underlying aquifers and in many cases serve as major conduits for water and solutes, enhancing their migration via the vadose zone (Nativ et al., 1995). Many studies explored the role of fracture in flow and transport processes. Nevertheless, in most cases it was assumed that fractures are important for the hydrological cycle only during an active infiltration event. In arid environments, fractures are empty most of the time, excluding rare flood or intensive rain events. How important are these fractures during these dry periods? This question is addressed in this study. 1.2 The role of fractures in groundwater salinization Weisbrod et al. (2000) explored in situ accumulation, dissolution and mobility of salts in a surface-exposed fracture in the chalk formation covering large areas in the Negev desert of Israel. In their study, salt accumulated within the fracture at rates significantly higher than could be expected from diffusion. Weisbrod et al. (2005) and Weisbrod and Dragila (2006) ∗

Corresponding author ([email protected])

9

Recently, Nachshon et al. (2008) presented the results of an experimental investigation of convection in an air-filled Hele-Shaw chamber of dimensions commensurate with vadose-zone fractures that are open to the surface and of hydrological importance. Results from this research support the conceptual mechanism suggested by Weisbrod and colleagues (Weisbrod et al., 2005) that fractures crossing the upper vadose zone vent convectively. Thermal convection was established within the Hele-Shaw chamber under thermal conditions similar to those in natural surfaceexposed fractures, and mass-transfer rates concur with the high deposition rates of salts within vadose-zone fractures reported by Weisbrod et al. (2000). Quantitative relationships were established between the average and maximum velocities of convection cells, the mass-transfer rate due to convection and the Ra and Sherwood (Sh) numbers for the system. The most significant result of this study was establishing the exponential relationship between the overall masstransfer rates and Ra, and similarly between Ra and the Sh value for the system. The advantage of this RaSh relationship is as follows: the Ra value can easily be established in the field by evaluating an average aperture of a field fracture and measuring the thermal gradient. Once Ra is estimated for surface-exposed fractures, the convective mass-transfer rate from the fracture can be determined by calculating the Sh and multiplying it by the diffusion mass-transfer rate.Thus, the relationships established in this experimental work could facilitate up-scaling of local venting data. Here, we present results from field measurements showing that thermal convection indeed occurs on a daily base, and depends on the thermal gradients developed between the fractures and the atmosphere. We also discuss the potential implication of the convective fluxes on the overall earth-atmosphere interactions.

permeability (Shimojima et al., 1996). Modeling has shown that even before the crust is formed, evaporation from the soil is strongly affected by saline concentration and salt chemical composition (Yakirevich et al., 1997). Adams et al. (1969), Selim and Kirkham (1970), and Ritchie and Adams (1974) explored evaporation from cracked soil. Their conclusions were that: (1) the crack existence increases the total evaporation from a bare plot; (2) the water content in the vadose zone adjacent to the crack was lower compared to the average vadose zone water content, meaning that solution was drawn towards the crack; and (3) air movement between the crack surfaces increased as a result of forced convection, and resulted in an increased evaporation rate. 1.3 Theory: Air convection through fractures In theory, the onset of convection occurs in fractures when the Rayleigh number (Ra), which compares buoyant and viscous forces, exceeds a critical value (Nield, 1982):

where ρ is the density (kg/m3 ) difference between the air at the top and bottom of the fracture over a length scale L (m); µ is the dynamic viscosity of the air (kg/m/s), g is the gravitational constant (m/s2 ), κ is the thermal diffusivity (m2 /s), and k is the fracture permeability (m2 ) (k = (2b)2 /12), where 2b is the fracture aperture (Shemin, 1997). Within the range of 0–80◦ C, air density can be assumed to be a linear function of temperature and expressed by the thermal expansion coefficient (α = 0.00367(1/◦ C)). Equation 1 can be recast as a function of temperature:

2 ◦

where T ( C) is the temperature difference between fracture air at the top and bottom of the fracture over the length scale L, and v is the kinematic viscosity (m2 /s). The critical Ra (Rac ) for the onset of convection in fractures is approximately 4π2 (Nield, 1982). There is some disagreement as to the exact value of Rac (Tournier et al., 2000) for various slot geometries, but all reported Rac values are on the order of 40. Very few studies have explored convection cells in natural fractures. Previous work has typically investigated the onset of convection and focused on convection in liquid-filled fractures (Tournier et al., 2000). Most hydrological and geological studies have focused on liquid convection in fractures and faults (Cherkaoui and Wilcock, 1999), exploring tectonic faults that were a hundred times larger than the fractures relevant to the phenomenon discussed in this paper.

MATERIAL AND METHODS

2.1 The field site Following an extensive survey in which potential sites were tested throughout the Negev desert in Israel, a site was selected at the Secher wash, about 12 km south of the city of Beer Sheva. A large-aperture size fracture was selected. A 2 × 2 × 1.5 m hole was dug in the chalk in such a way that the near-vertical fracture was exposed inside the hole from the land surface to a depth of 1.2 m. A series of 10 thermocouples (TCs) were installed within the fracture from the land surface to a depth of 1 m, at 10-cm spacing. Additionally, four relative humidity (RH) probes were installed at depths of 10, 20, 40, 60 cm. Additional series of TC’s, at similar depth to the series of probes installed within the fracture aperture, was installed within the chalk rock, 50 cm away from the fracture. All sensors were

10

occur). Once understood, it was compared to data from the host rock and the air layer above the ground. Ra number was calculated and compared with the critical Ra number (Rac ). The following values and terms were calculated from the data set: (1) fracture air temperature gradient, between top and bottom; (2) rock temperature gradient between top and bottom; (3) temperature difference between rock and fracture air (at 10 cm intervals); (4) vapor density; (5) moist air density; (6) Ra number; (6) air mass flux; and (7) vapor mass flux. Once the diurnal processes were understood, annual patterns were analyzed, based mostly on monthly averages. Only some of the data and values are discussed here. 3

RESULTS AND DISCUSSION

3.1 Atmospheric air annual sequence During the year, air temperature typically reaches its diurnal maximum and minimum values at noon (12:00–14:00) and early morning (06:00–07:00), respectively, and its annual maximum and minimum values in July and February, respectively). During summer, air temperature is more stable than during the winter. However, during the transition period (i.e. fall and spring, November and April respectively), air temperature is unstable and changes dramatically during short periods (days) (Figure 2a). Therefore, during those months average temperatures exhibited large standard deviations (Figure 2b). Furthermore, during daytime, standard deviations reached maximum values. This is due to the fact that, unlike nights with zero incoming solar radiation, during daytime the incoming solar radiation and long-wave radiation can be affected by clouds and storm events and significantly affects the surface temperature and the air temperature.

Figure 1. A schematics of the field site (a); the entrance to the underground access hole (b); and a picture of the cross section of the fracture (c). Sensors were installed within the aperture at different depths and 50 cm away from the aperture within the chalk matrix.

connected to a data logger (CR-10X, Campbell Scientific) and multiplexer. Data were collected for one year at a resolution of 20 min. The TC measurements are continuous throughout the year, excluding relatively short time periods caused by technical problems. The RH probes did not function well under the field conditions and data were obtained only for several weeks before they could not be used any longer. Figure 1 is a schematic of the field setup and the sensors setup. Weather data were collected from a meteorological station nearby. 2.2

3.2

Rock temperature

Rock temperature measurements were taken 0.5 m away from the fracture and therefore it is assumed that there was no influence from the fracture domain. However, near to the fracture, heat-flow patterns become much more complicated due to the fact that natural air-convection could occur. When convection occurs, it removes heat from the fracture walls creating 2-D heat flow patterns. Moreover, evaporation from fracture walls reduces its temperature and water content in the nearby matrix and can results in salt accumulation on fracture walls and inside the nearby matrix pores. These will have a great impact on the thermal diffusivity. However, for the purpose of this paper, the aforementioned potential complications had to be eliminated. The penetration of the heat and cold air into the rock during the summer and winter months,

Data processing

In each full month of data collection about 56,000 temperature values were collected (13 depths × 2 locations (rock and fracture) × 72 measurements per day × 30 day per month). The large data set was analyzed with the aim to determine when and where conditions for natural convection of air inside the fracture occurred. The host rock and ambient air layer near the ground surface act as the physical boundaries of the fracture. It is therefore essential to understand their microclimatic behavior. The first step was to characterize the air behavior within the fracture on a daily basis (i.e. at specific times of the day convection was most likely to

11

Figure 3. Monthly average rock temperatures isopleths in ◦ C, 2004–2005.

Figure 2. 12 months average atmospheric air temperatures (measured 25 cm above land surface) (◦ C) (a); and its standard deviation (◦ C) (b) by hours of the day and by months.

respectively, can be clearly seen (Figures 3 and 4). On the other hand, temperature was very stable with depth during the spring and autumn months. 3.3

Natural convection of the fracture air

Moist air convection in the fracture will occur only when fracture air is less dense then the atmospheric air above it so that the Ra number (Eq. 2) exceeds Rac . Moist air density depends almost solely on its temperature and not on its water vapor concentration. Therefore most of the results will be presented as temperature rather than density. Fracture air temperature depends on the temperature of the host rock and of the atmospheric air above it. Whenever convection occurs, cold air penetrates the fracture, absorbs heat from the fracture walls and transports it outside to the atmospheric air (hence cooling and drying the fracture space).

Figure 4. Penetration of warm and cold waves into the rock during 5 days (◦ C), 23–27 July 2004 (a), and 1–5 January 2005 (b).

Temperature distribution of fracture air is much more complicated than in the rock and it is strongly affected by fracture geometry, onset of convection and thermal diffusion. Generally speaking, whenever convection occurs, air starts to circulate in the fracture

12

Figure 5. Fracture air temperature during 12 month (July 2004–June 2005), masurments were taken every 20 min with a series of thermocouples. Thermocouples are located as depict in Fig. 1c.

and convection cells develop (with an aspect ratio depended on the value of Ra).Temperature distribution of the fracture air is dependent on the 3-D configuration of the air movement. However, in the case study discussed in this paper, only 1-dimensional air temperature was measured inside the fracture. Thus, depending on convection cell position, if the line of TCs measuring the temperature of the air inside the fracture is located where warm air rises from the bottom, they will show a temperature increase and if the TCs are located in the region where cold air is sinking they will show temperature decreases. Unfortunately, this is a very complicated system and no models were found in the literature that fit the complex boundary conditions. Since fracture wall temperature couldn’t be measured, Ra was calculated without taking into consideration the fracture wall’s temperature contribution. Fracture air temperature distribution with depth during 12 months is shown in Figure 5. During summer (May through September) as the temperature of the surface rock and the atmospheric air layer near the ground increased, fracture air temperature decreased with depth (negative numbers in Figure 5). However, the diurnal variability in the thermal gradient was significant; temperature at AT_0 (upper boundary) changed remarkably during the course of the day and decreased below RT_120 at night. During winter (November–March), the temperature distribution mirrors the summer so that the average temperature increases with depth. The spring and autumn transition periods are characterized by unstable periods where temperature changes remarkably. Irregular hot days occur, causing AT_0 to increases due to temperature increases of the surface rock and the atmospheric air layer near the ground. As noted before, the onset of convection occurs when the Ra number exceeds the value of Rac . The Ra value is mostly related to temperature differences between bottom and top boundaries (hence, DT(120−0) = AT_120 − AT_0); Figure 6).

Figure 6. Fracture Monthly average DT(120−0) (◦ C) (a); and its standard deviation (◦ C) (b) by hours of the day and by months (2004–2005).

An approximation of how many convection hours (t  ) occurred each month was obtained. t  was calculated by dividing the hours with convection (Ra ≥ Rac ) by the total hours of measurements (specific for each hour of the day). If t  = 1, there is 100% of convection at this time and if t  = 0, there will never be convection at that time. Figure 7 shows the different values of t  for 12 month of measurements. From August 2004 until June 2005 t  = 1 at 06:00– 07:00 (except for March and April 2004, where t  = 0.9 and 0.7, respectively), meaning that conditions for convection were consistently present in the early dawn throughout the year. During the remainder of the 24 hr day, conditions for convection are seasonally dependent.

3.4 Summary and conclusions The result presented in this paper show that: 1. Fracture air convection (free thermal convection) is related to moist air density gradient, which is influenced mainly by temperature.

13

REFERENCES Adams, J. E., J. T. Ritchie, E. Burnett, and D. W. Fryrear. 1969. Evaporation from simulated soil shrinkage crack, Soil Science Society of America, 33, 609–613. Cherkaoui, S. M. A., and S. D. W. Wilcock. 1999. Characteristics of high Rayleigh number two-dimensional convection in an open-top porous layer heated from below. J. Fluid Mechanics 394: 241–260. Nachshon, U., N. Weisbrod. And Dragila, M.I. 2008. Quantifying air convection through surface-exposed fractures: a laboratory study. Vadose Zone Journal, 7(3): 1–9. Nassar, I. N., and Horton R. 1999. Salinity and compaction effects on soil water evaporation and water and solute distributions, Soil Science Society of America, 63: 752–758. Nativ, R., E. Adar, O. Dahan, and M. Geyh. 1995. Water recharge and solute transport through the vadose zone of fractured chalk under desert conditions. Water Resources Research 31: 253–261. Nield, D. A. 1982. Onset of convection in a porous layer saturated by an ideal gas. International J. of Heat and Mass Transfer 25: 1605–1606. Ritchie, J. T., and J. E. Adams. 1974. Field measurement of evaporation from soil shrinkage cracks. Soil Science Society of America 38: 131–134. Selim, H. M., and D. Kirkha. 1970. Soil temperature and water content changes during drying as influenced by cracks: A laboratory experiment. Soil Science Society of America Proc. 34: 565–569. Shemin, G. 1997. A governing equation for fluid flow in rough fractures. Water Research. 33: 53–61. Shimojima, E., R. Yoshioka, and I. Tamagawa. 1996. Salinization owing to evaporation from bare soil surfaces and its influences on the evaporation, Journal of Hydrology, 178: 109–136. Tournier, C., P. Genthon, and M. Rabinowicz. 2000. The onset of natural convection in vertical fault planes: consequences for the thermal regime in crystalline basements and for heat recovery experiments. Geophysics. J. International. 140: 500–508. Weisbrod, N., and M. Dragila. 2006. Potential impact of convective fracture venting on salt-crust buildup and ground-water salinization in arid environments. Journal of Arid Environments 65: 386–399. Weisbrod, N., R. Nativ, E. M. Adar, and D. Ronen. 2000. Salt Accumulation and Flushing in Unsaturated Fractures in an Arid Environment. Groundwater 38: 452–461. Weisbrod, N., M. Pillersdorf, M. I. Dragila, C. Graham, and J. Cassidy. 2005. Evaporation From Fractures Exposed at the Land Surface: Impact of Gas-Phase Convection on Salt Accumulation. Dynamics of Fluids and Transport in Fractured Rock, American Geophysical Union: 151–164. Yakirevich, A., P. Berliner, and S. Sorek. 1997. A model for numerical simulating of evaporation from bare saline soil, Water Resources Research, 33(5): 1021–1033.

Figure 7. monthly t  (convection hours/hours with valid measurements per month) as a function of time of day (2004–2005).

2. The bottom boundary (120 cm depth) is always in thermal equilibrium with the host rock and does not change during the course of the day but changes slowly at longer time scales (weeks). 3. The upper boundary exhibits strong diurnal variability and is influenced by rock temperature near the ground surface and the ambient atmospheric air temperature. 4. The average rock temperature gradient was negative during summer (i.e. cooler with depth) and positive (i.e. warmer with depth) during winter. This created better conditions for convection during winter. Nevertheless, rock temperature in the shallow vadose zone (0–40 cm depth) changed on an hourly basis so that even during summer, condition for convection occurred (during cold nights). 5. The largest rock temperature gradient was obtained during December 2004. Also the maximum DT (120−0) (12.27◦ C for fracture air) and Ra number (1.54 × 104 ) were obtain in December. 6. During fall and winter convection seems to occur during most hours of the day where Ra number fluctuates between 103 to 1.5 × 104 . 7. The lowest values of t  were obtained at noon time (14:00) where during the summer time days no convection is expected to develop. 8. Natural convection, occurring on a daily basis, may enhance exchange of all gases between land and atmosphere. Predictive models, of the gas cycles such as CO2 , must incorporate this mechanism in their calculations.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

A simulation of sea breeze in Fukuoka facing the Sea of Japan Y. Hisada∗, N. Matsunaga & Y. Oda Dept. of Earth System Science & Technology, Kyushu University, Fukuoka, Japan

ABSTRACT: A numerical simulation was carried out to investigate characteristics of sea breeze intrusion into the Fukuoka Metropolitan area using the Advanced Regional Prediction System (ARPS). The results were compared with sea breeze field data obtained on August 2, 2003. The sea breeze front runs parallel to the coastline at the beginning of the sea breeze intrusion; however, as it moves inland, the two-dimensional front becomes wavy and breaks. The sea breeze consists of a tail of about 600 m thickness and head height of 1000 m or higher. The head is accompanied by a pair of upward and downward currents that reach to about 2000 m altitude. Keywords:

1

sea breeze; numerical simulation; ARPS

INTRODUCTIONS

etc. However, observation results from few observation points for wind directions, wind speed and weather in the upper layers do not suffice to elucidate details of the invasive property of a sea breeze that extends throughout the Fukuoka metropolitan area. Therefore, this study reproduces the characteristics of a sea breeze flowing into the Fukuoka metropolitan area and investigates its invasive properties using a numerical analysis approach with a nonhydrostatic atmospheric-air model: the Advanced Regional Prediction System (ARPS). In addition, those results are compared to those of simultaneous large-area observations of air temperature.

Sea breezes that appear on the coast during the daytime on a sunny day are local winds that have been familiar to communities from ancient times. A local front accompanied by a strong upflow designated as a sea breeze front is known to be formed at its edge line, where cumulus convection develops. In recent years, heat island effect has been observed in many cities. Cooling effects of sea breezes have attracted attention as a countermeasure; studies of them are in progress at various sites (Tonimura et al. 2003, Iwatani et al. 2003). The Fukuoka plain, the target region of this study, is surrounded by Genkainada to the north, the Mt. Sefuri system to the southwest, and the Mt. Sangun system to the southeast. A sea breeze is therefore known to flow therein quite often all year (Fukuda et al. 2001). It is very important to solve the invasive property of sea breezes also from a viewpoint of atmospheric and thermal environment preservation of the Fukuoka metropolitan area, Kyushu Island, Japan. The authors have so far evaluated the relaxation effect of the temperature rise phenomenon of a sea breeze in the Fukuoka metropolitan area (Hisada et al. 2006). Results of those studies show that the invading sea breeze depresses air temperatures in the metropolitan center, and demonstrates such effects even in the backlands of the Fukuoka plain. In addition, Fukuda et al. (2001) performed intensive observations using Doppler sodar in the Fukuoka plain. They clarified air temperature change before and after the invasion of a sea breeze front, the vertical profile of the wind speed, ∗

2

CALCULATION CONDITIONS

Calculations for August 2, 2003 were performed as an example of a day of sea breeze. The model used for this calculation, ARPS, is a nonhydrostatic atmosphere model developed at the Center for Analysis and Prediction of Storms (CAPS), University of Oklahoma, which consists of a system of equations using three components of wind speed, atmospheric pressure, potential temperature, and aqueous matter (cloud water, rain water, cloud ice, snow, and hail) as forecast variables. In fact, ARPS is a model for mesoscale atmospheric phenomena. It is suitable for analyses of local weather. Calculation regions are shown in Figure 1. Region 1 is a region of 100 km × 100 km in northern Kyushu, with a horizontal grid spacing of 2 km. Region 2 is a region of 30 km × 30 km in the Fukuoka metropolitan area, with horizontal grid spacing of 500 m. The 16 km of atmospheric layers of Regions 1 and 2 are divided into 40

Corresponding author ([email protected])

15

Figure 2. Comparison between aerological observation data in the Fukuoka District Meteorological Observatories and the calculation result in Region 2 (August 2, 2003, 0900 JST): (a), (b), horizontal wind velocity; (c), air temperature; (d), mixture ratio. Triangle symbols (
) represent observed values while circle symbols (•) indicate calculation results. Figure 1. Calculation regions.

vapor qv g/kg. A calculation grid at the coordinate nearest to the latitude and longitude of the FDMO was used for comparison. Although some conflict is apparent between calculation results and observed values in horizontal wind speed and the mixture ratio of moisture vapor, it does not degrade the reliability of reproduction. The estimate and observed value of air temperature are mostly in agreement. They are also in agreement in comparable accuracy at 2100 JST, although the figure is not shown. These prove that the vertical distribution of each datum is well reproduced. Also, the observed values and calculation results of air temperature near the ground surface are compared. Their mutual correlation is good. Apparently, the calculation result well reproduces horizontal air temperature distribution at each time. Consequently, it is considered that the spatiotemporal reproducibility of each meteorological element is satisfactory. Next, based on the calculation result of Region 2, the invasive property of a sea breeze in the Fukuoka metropolitan area is discussed.

layers in the vertical direction. The grid spacing is set to coarsen upward toward the sky from 20 m at the bottom layer to 674 m at the top layer. Contour lines are drawn with 200 m spacing from the altitude of 200 m in Region 1, and with 100 m spacing from the altitude of 200 m in Region 2. The solid line X-X’ in Region 2 is a cross-sectional line used for analysis, set up in the invasion direction of a sea breeze. Furthermore, GTOPO30, which was released by the EROS Data Center, U.S. Geological Survey (USGS), was used for generation of topographical data. Also, Japan Profile for Geographic Information Standards (JPGIS) was used for the classification of vegetation. The center of the Fukuoka plain is covered with artificial structures from the coast to the backland, while paddies and fields lie in the western and eastern parts. The integration time of this calculation is 18 hr from 0300 JST to 2100 JST. The Meso Regional ANALysis Data Set (MANAL), issued by the Meteorological Agency of Japan, was used with interpolation for initial and boundary values.

3.2 Wind direction, wind speed, and air temperature change in Region 2

3 ANALYSIS RESULT 3.1

Figure 3 presents the calculation result at 0900 JST: (a) shows horizontal wind vectors at 10 m above the ground. The wind vectors are drawn with intervals of 3 grids. (b) shows the vertical and horizontal wind speed vectors at 300 m above the ground. The vertical wind speed is expressed with isolines of 0.5 m/s increment, the solid lines represent an upflow and dot lines represent a downflow. (c) shows the air temperature distribution obtained by calculation. Gray areas in (a) and (c) show regions with an altitude of more than 200 m. (d) presents the observed values of air

Reliability of calculation result

Figure 2 presents a comparison with the calculation result in Region 2 with aerological observation data in the Fukuoka District Meteorological Observatories (FDMO) at 0900 JST, August 2, 2003. Triangle symbols (
) represent observed values while circle symbols (•) indicate calculation results. (a), (b), (c) and (d) respectively express the east-west component u m/s and north-south component v m/s of wind speed, air temperature T K, and the mixing ratio of moisture

16

Figure 3. Calculation results in the region of 30 km × 30 km in the Fukuoka metropolitan area (Region 2 in Figure 1) at 0900 JST, August 2, 2003.

However, no formation of cloud particles is found. It is in a calm state. Figure 4 shows the calculation result at 1100 JST. Each figure presents the same information as that shown in Figure 3. The isolines in (a) express cloudwater mixture ratio with a spacing of 0.1 g/kg. A sea breeze of about 3 m/s invades from the seashore to the 1 km point from the eastern part to the center of the Fukuoka plain, and a sea breeze front accompanied by an upflow appears clearly. An isotherm parallel to the coastline is formed on the coast area in both calculations and observations; the calculated value is around 30◦ C, which is almost the same as the observed value. However, calculations suggest dense isotherms at the edge of the sea breeze front, where the air temperature is overestimated. Because the wind at 300 m above the ground blows toward the sea overall at this instant, a sea breeze has not fully developed but is

temperature distribution. The contour interval of (c) and (d) is 0.5◦ C. Observation method is explained in Hisada (2006). At 0900 JST, air temperature is 28–30◦ C in (c) and (d). It is almost uniform throughout the metropolitan area. Although calculated values are slightly lower than the observed values, high temperature regions are apparent in the west and south of Fukuoka city. For that reason, the characteristic is reproduced well. In the west, the wind is blowing inland. The wind speeds of the surface wind and the horizontal wind at 300 m above the ground are both small. A land breeze extends from the eastern part to the center: no invasion of a sea breeze is apparent. The predominant wind direction of the horizontal wind at 300 m above the ground is northbound, and the wind speed is higher in the Fukuoka plain backland than on the coast. An upflow of about 0.5–1.0 m/s is apparent at the southwest foothills.

17

Figure 4. Calculation results in the region of 30 km × 30 km in the Fukuoka metropolitan area (Region 2 in Figure 1) at 1100 JST, August 2, 2003.

clearly also in the inland region; a downflow is visible in the rear, as it is at 1100 JST. The upflow that had been generated in the inland region is involved in the upflow by which a sea breeze front is accompanied, and forms a stronger upflow. The isotherms of the sea breeze front parallel to the coastline are now unclear, and a low temperature region spreads out to inland in the center. The sea breeze has fully developed by this time, and the wind direction of surface and upper winds is identical. At the edge of the sea breeze front, where the sea breeze and a land breeze meet, a physiographic factor also applies, and formation of cloud particles is observed. Although the horizontal wind speeds of an upper wind are equal over the sea and ground, the horizontal wind speed of a surface wind is quite small in the city center. This is considered to be the result of surface roughness by artificial structures. However, this study does not take scattered green

instead still of slight thickness. A strong upflow of about 1.5–2.0 m/s is visible along the foothills in the inland region, although the western region has already been covered using a wind of the direction of a sea breeze at this instant. The variation of air temperature distribution obtained using the multipoint simultaneous observation of air temperature by the authors also confirmed the same sea breeze invasion. Wind around the Mt. Sefuri system in the south converges into the mountains. Figure 5 shows the calculation result for 1300 JST. Each figure expresses the same as that in Figures 3 and 4. The whole Fukuoka metropolitan area is mostly covered with a sea breeze at 1300 JST. The sea breeze front maintains a straight shape at the beginning of invasion, and advances inland. However, the shape changes with time and disappeared. The sea breeze front accompanied by an upflow is observed

18

Figure 5. Calculation results in the region of 30 km × 30 km in the Fukuoka metropolitan area (Region 2 in Figure 1) at 1300 JST, August 2, 2003.

All horizontal winds on X-X’ at 0900 JST are in the direction of a land breeze; the wind speed near the ground surface is about 2 m/s. A land breeze is slightly stronger than over the sea and coast and a wind of 4 m/s blows around 100 m above the ground in the inland region. Winds of vertical direction are seldom observed. A sea breeze occurred at X-X’ at 1100 JST. It invaded to about 1 km inland from the coast. It is presumed that a wind in the direction of a sea breeze blows from off Genkainada. The sea breeze thickness is as thin as 100-200 m, with horizontal wind speed of about 3 m/s at the maximum. An upflow, although very weak, is apparent at the sea breeze front; a downflow is observed in the rear. Another upflow is identifiable also at 10 km of inland. This is considered to be a convection current produced by a physiographic factor, as confirmed in Figure 6. The sea breeze front

spaces or water areas into consideration because of the resolution limit. Accordingly, the effect of roughness requires more detailed analysis. 3.3

Cross-sectional analysis of a sea breeze

Figures 6(a)–(f) express the vertical and horizontal wind speed distribution on X-X’ shown in Figure 1 ((a) & (b) 0900 JST; (c) & (d) 1100 JST; (e) & (f) 1300 JST). The upper panel presents vertical wind speed at each time, where solid contours indicate the upward wind speed and dot contours indicate the downward wind. The lower panel expresses horizontal wind speed at each time, where positive and negative values represent the wind speeds of a sea and land breeze, respectively. Black vertical lines drawn in the figures show the location of the coastline. The width of one division of the horizontal axis is 5 km.

19

Figure 6. Vertical and horizontal wind speed distribution X-X’ ((a)&(b) 0900 JST, (c)&(d) 1100 JST, (e)&(f) 1300 JST).

speed are dense in the city center from the ground to about 100 m above the ground.

subsequently involves this upflow produced in the inland region, and rapidly passes to the inland region, although that is not shown the figure. The sea breeze has arrived up to 18 km of inland at 1300 JST. A wind of about 8 m/s blows also near the ground surface at the sea breeze front. The sea breeze has fully developed, and an upflow in the front and a downflow in the rear that were not clear at 1100 JST have appeared distinctly. The sea breeze is of about 400 m thickness behind the front. It appears to be uniform throughout X-X’. The thickness is greater than 1,000 m at the edge, and the upflow reaches to about 2,000 m height. Because observation with Doppler sodar is limited to an altitude of about 400 m, such a phenomenon has not been identified until now. A sea breeze front accompanied using a strong upflow also reaches to about 700 m above the ground, according to a simulation for Hong Kong by Ding et al. (2004): a result similar to this calculation was obtained in that study. The speed of advance of a front is about 8 km/h by this calculation, which is mostly in agreement with the observation result. The isolines of horizontal wind

4

CONCLUSION

August 2, 2003 was chosen as an example of a day of sea breeze. This study investigates the invasive property of a sea breeze flowing into the Fukuoka metropolitan area, with the numerical-analysis approach using the nonhydrostatic atmospheric-air model ARPS. The obtained results are as follows: (1) A sea breeze front maintains a straight shape that is parallel to the seashore at the very beginning of invasion, and advances inland. However, the shape changes with time and collapses as a wave would. Although isotherms are formed at the edge of a sea breeze front, their shape becomes indistinct while passing into the inland region. (2) The thickness of a sea breeze is about 100 m at the beginning of incursion. Then the back of the

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Iwatani, K., Mochida, A., Yoshino, H., Sasaki, K. & Watanabe, H.: 2003. Effect of sea breeze on city thermal environment. Journal of Wind Engineering 95: 70–71. Fukuda, K., Matsunaga, N. & Sakai, S. 2001. Behavior of Sea Breeze in Fukuoka. Annual Journal of Hydraulic Engineering 44: 85–90. Hisada, Y., Matsunaga, N. & Ando, S. 2006. Effect of sea breeze on atmospheric and thermal environment of Fukuoka metropolitan area. Annual Journal of Hydraulic Engineering 50: 487–492. Ding, A., Wang, T., Zhao, M., Wang, T. & Li, Z. 2004. Simulation of sea-land breezes and a discussion of their implications on the transport of air pollution during a multi-day ozone episode in the Pearl River Delta of China. Atmospheric Environment 38: 6737–6750.

front becomes a uniform thickness of about 600 m when it reaches an inland region. The thickness of the front becomes greater than 1,000 m. The upflow accompanying it extends up over 2,000 m in the sea breeze front, where a pair of upflows and a downflow are confirmed to form. As future subjects of study, it is necessary to improve the accuracy of calculation and to solve the effects of roughness in detail. REFERENCES Junimura, Y., Watanabe, H. & Suzuki, H. 2003. Study of mitigation of urban thermal environment by sea breeze. Journal of Wind Engineering 95: 69–70.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Characteristics of water vapor content and its budget over Tarim river basin H. He∗ State Key Laboratory of Hydrology and Water Resources and Hydraulic Engineering, Nanjing, China

G. Lu & F. Zhao Water Problem Institute, Hohai University, Nanjing, China

ABSTRACT: The study, which based on the Tarim river basin in China, focused on the temporal and spatial characteristics of water vapor transportation and its budget.The results show that the annual mean water content of Tarim River Basin was 9.2 km3 , that is less than 1/15 of the annual mean water content of China. In the vertical direction, water vapor is concentrated on the level of 850 hPa–600 hPa, and accounted for 60 percent of the whole air column. From 1950s to the middle of 1980s, water vapor content of Tarim river basin kept the tendency of decrease, and then had a slight increase since 1985. The difference of water vapor content between the two periods (from 1948 to 1985 and from 1985 to 2005) showed that water content increased at the westnorthern and western edge of the basin. The annual mean net inflow of water vapor is 0.18 km3 . The annual mean net inflow of water vapor kept increasing before 1978, and started decreasing after 1978. If the river basin was divided into two sub-areas according to the topography of Tarim river basin, water vapor budget of the two sub-areas showed that sub-area 1 (mountain region) became the source region of water vapor and in the sub-area 2 (desert area) water vapor convergence appeared. The results of water vapor content, transport and budget show that the atmosphere process within hydrological cycle is closely linked with the topography in the Tarim river basin. Keywords: Tarim river basin in China; water balance; water budget; water transportation; water vapor content; water vapor flux. 1

INTRODUCTION

from 1967 to 1976 and from 1977 to 1986 respectively in Tarim river basin. This phenomenon was looked as the signal of climate change from warmed to warmwet in northwest arid region of China (Shi et al, 2002). But Zhang’s work showed that water vapor content increasing in Tarim river basin was due to land-using changes but not climate changes (Zhang, 2003). It was obvious that different understanding of hydrological cycle in Tarim river basin could lead to different water resource development modeling. In order to understanding the characteristics of hydrological cycle of Tarim river basin accurately, some research works were done by our research team. The research work included study on the basic variation facts of precipitation, runoff, air temperature and evaporation of Tarim river basin, study on the characteristics of water vapor content and transport, and study on the precipitation recycling in Tarim river basin. According to these research work the characteristics of hydrological cycle in Tarim river basin were better clarified, especially for the characteristics and trends in moisture

Tarim river is the longest continental river of China, and Tarim river basin is one of the most important inland river basin in the word. Water vapor transportation and its budget over Tarim river basin is one of the most important concerns of the water cycle study in northwest arid area in China because of its critical impact on the regional climate and ecosystem. Following Yu’s work on water vapor content and its variation over northwest arid region of China (Yu et al, 2003), Wang studied regional features and variation of water vapor in the same area (Wang et al, 2006). According to their research conclusion, water vapor content variation was positively increasing in northwest of China. At the same time, observing data showed that the annual mean precipitation was increased 23.2 per cent and 30.1 per cent since 1987 to 1996 compared with the precipitation of two periods ∗

Corresponding author ([email protected])

23

content, transportation, and budget over Tarim river basin. 2 2.1

DATA AND METHODOLOGY Data

In this paper, the National Centers for Environmental Prediction / National Center forAtmospheric Research (NCEP / NCAR) reanalysis data were used to calculate the moisture content, flux convergence and its budget over the Tarim river basin for a 58-year period from 1948 to 2005. When reanalysis data was used to calculate water vapor budget, grid of 2.5◦ × 2.5◦ was interpolated to 1.25◦ × 1.25◦ grid because of bigger grid meaning bigger error (Zhao et al, 2007). In the vertical direction, air column from ground to 300 hPa level was selected to represent total air column. 2.2

Figure 1. Distribution of annual mean water vapor content (mm) from 1948 to 2005.

3 ANALYSIS OF WATER VAPOR CONTENT

Methodology

Water vapor content which is equivalence to the regional average water depth is calculated with Tyson Polygon Method according to the weights of area controlled by different grids. The results show that the annual mean moisture content of Tarim river basin from 1948 to 2005 is 8.8 mm, which accounts for 58 per cent of the annual mean surface moisture content of China (15.1 mm) (Liu, 1997). The content of annual mean water vapor over Tarim river basin is 9.5 km3 , which is less than 1/15 of the annual mean water vapor content over the country which is 144.5 km3 .

Water vapor content was calculated through integral equation as following expression:

Water Vapor Budget is calculated with the following equation:

In equation (1) and (2), q is specific humidity, Ps is the ground pressure, Pz is the reference-level pressure, g is acceleration due to gravity, V is velocity. When the research region was simplified as a square area in order to calculate water vapor budget conveniently, equation (2) could be disintegrated as following:

3.1 Spatial distribution of water vapor content Water vapor content showed the “static” features of the moisture in the atmospheric. Figure 1 illustrated annual mean water vapor content spatial distribution in Tarim river basin. It is seen from Fig. 1 that water vapor content distribution is closely linked with the topography in the Tarim river basin. In the center of Taklimakan desert water vapor content is the largest where the terrain is the lowest. From the center, water vapor content decreases, and the minimum of water vapor content lies at the southwestern and southern region of Tarim river basin. It shows that water vapor content distribution has a close relationship with the distribution of undulating terrain in Tarim river basin. In order to study the vertical distribution of water vapor in research region, the atmospheric structure is divided into three levels which are ground to 850 hPa, 850 hPa to 600 hPa, and 600 hPa to 300 hPa respectively. The moisture distribution of each level shows in Figure 2 . It is seen from Fig. 2 that the near-ground atmospheric (ground to 850 hPa) has the lowest water vapor

Where k = 1, 2, 3, 4 represents four different borders which are respectively, the west, east, south and north; n(k) is the quantities of paragraphs of k border;lki is the length of paragraph i of k border; Fki is the total air column moisture flux of paragraph i, that is:

In the calculation of water vapor budget, moisture inflow is positive, and outflow is negative. The difference of moisture inflow and outflow is the net water vapor inflow of Tarim river basin.

24

Figure 3. Regional water vapor content trends from 1948–2005.

Figure 2. Vertical distribution of water vapor content (mm) in three levels.

content whose moisture accounts for 9.3 per cent of the moisture of whole air column. In the horizontal direction, the water vapor content in center of Tarim river basin is higher than other area, which is 4.0 mm. Because of the high mountains in the southern region, there is no moisture in the layer of ground to 850 hPa. There is 59.6 per cent of the moisture of Tarim river basin concentrated on the layer of 850 hPa to 600 hPa. In the horizontal direction, the moisture content is higher than any other layers everywhere except southern mountain region. Taklimakan desert region has also the highest moisture content, which exceeds 8.0 mm. There is 31.1 per cent of the moisture of whole air column in the layer of 600 hPa to 300 hPa, and has an even distribution in the basin. The moisture content is about 2.0 mm. The result of water vapor vertical distribution is consistent with the Zhang’s study that water vapor mainly concentrated in the center of basins in Xinjiang and moisture content in the center region occupying 68.9 per cent of the whole water vapor content in Tarim river basin (Zhang, 2003).

Figure 4. The difference of water vapor content between two periods (after – before).

It can be seen from Fig. 4, since the year of 1985 water vapor content is lower than before in the region of Taklimakan desert, and higher in the around region which is close to Tian Mountain, Pamir Plateau and Kunlun Mountain. This trends are consistent with the precipitation in Tarim river basin. The monthly mean water vapor content distribution is expressed by Fig. 5. From Fig. 5, it is seen that monthly mean water vapor content distribution is uneven. The maximum monthly moisture content appears in July. The difference of maximum monthly mean moisture content (July) with the minimum average monthly moisture content (January) is 12.0 mm. Detailed analysis of the monthly mean water vapor content illustrated that the largest water vapor content appeared in summer which accounts for 41.1 per cent of the whole year. The mean water vapor content of spring accounts for 23.7 per cent of whole year and autumn does 22.1 per cent. The smallest water vapor content appeared in winter. It was just 13.1 per cent of the whole year. The moisture content trend of seasons between different years is the same as the trend of whole year.

3.2 Temporal trends of water vapor content The temporal trends of annual water vapor content in Tarim river basin can be showed by Fig. 3: It is seen from Fig. 3 that water vapor content keeps decreasing trend from 1948 to 1985, and then has an increasing trend since 1985. It means that in the early time water vapor content was high, and with time going it becomes lower until 1985. Though there is a little increase trend recently but water vapor content is still lower than the value of 1950. The difference of water vapor content between two periods which one is from 1948 to 1985, the other is from 1985 to 2005 is given by Fig. 4.

25

Figure 5. Monthly mean water vapor content distributions (mm).

4 ANALYSIS OF WATER VAPOR BUDGET 4.1 Water vapor flux Water vapor flux shows the "dynamic" feature of the moisture in the atmospheric. Figure 6 (a), (b) describe the annual mean water vapor flux at latitudinal and longitudinal direction. It is seen from Fig. 6 (a) that the latitudinal annual mean water vapor transfer flux is from west to east region, and the moisture flux value reduced from north to south. From Fig. 6 (b), it can be seen that longitudinal water vapor transfer flux can be divided into two parts clearly. Water vapor transport from south to north in the western region of the basin, and there is a high flux centre which is 12 kg · m−1 · s−1 . Water vapor transports from north to south in the eastern region in Tarim river basin. The main water vapor transport way is from west to east region over Tarim river basin. This result is consistent with Yatagai’s study that the main annual moisture route for the Silk Road region is from the west to east (Yatagai, 2003). According to the water vapor transfer above mentioned, a conclusion can be derived that the main water vapor transfer is latitudinal direction transport in Tarim river basin, which is effected by westerly wind circulation. The source of moisture is the warm – wet air current from Indian Ocean in the west of basin and the cold – dry air current from Siberia in the east of basin. The water vapor transfer flux and the wind contour distribution are similar with each other. So, the wind decided not only the direction of water vapor transfer but also the quantity of water vapor flux. Detailed analysis of mean divergence of water vapor flux at July of 850 hPa illustrated that water vapor flux is convergence in the northwest region where the precipitation and water vapor content all increased since 1985. Fig. 8 (a) and (b) is the water vapor flux scene of summer (JJA) and winter (DJF) respectively. It is seen from Fig. 8 (a) that the water vapor flux in northern

Figure 6. Geographic distribution of annual mean water vapor flux (kg · m−1 · s−1 ) in the total air column.

Figure 7. Mean July divergence of water vapor flux (10−8 g · cm−2 ·s−1 · hPa−1 ) of 850 hPa.

region is greater than the southern in summer clearly. It can be concluded that during the water vapor transfer from the Central Asian Continent to Indian Ocean, moisture was transported to Tarim river basin because

26

meanwater vapor net infl ow (km^3/month)

20

Figure 8. Water vapor flux in summer and winter (kg · m−1 · s−1 ).

water vapor net infl ow (mm/year)

10 5 0 1

2

3

4

5

6

7

8

9

10

-5

11

12 month

-10 -15 -20

150

Figure 10. Monthly mean water vapor net inflow.

10-year average

100

15

50

of 1970s. From the results of annual water vapor net inflow, it can be concluded that climate change leads to the increasing of water vapor inflow in Tarim river basin. Fig. 10 is the monthly mean water vapor net inflow in Tarim river basin. It is seen from Fig. 10 that net water vapor is imported in the month of June to November and exported in the month of December to next May. The further study on the seasonal moisture net inflow showed that net water vapor inflow of spring (MAM), summer (JJA), autumn (SON), and winter (DJF) was 38.1 km3 , −32.5 km3 , −15.5 km3 , and 10.1 km3 respectively. It indicated that moisture is outflow in summer (JJA) and autumn (SON), and inflow in winter (DJF) and spring (MAM). Monthly mean net inflow of water vapor in vertical air level was given in Table 1. Because there is almost no moisture in the air level of ground to 925 hPa, it is neglected in the table. It is seen from Tab. 1, the air column of 850 hPa to 600 hPa and 400 hPa to 300 hPa are the water vapor outflow levels and that of 925 hPa to 850 hPa and 600 hPa to 300 hPa are the water vapor inflow levels in the vertical direction. Water vapor net flow in lower air column is negative, and in upper air column is positive. Detailed analysis of seasonal water vapor budget in the vertical direction illustrated that moisture was imported in the air column from ground to 700 hPa, and was exported in the air column of 700 hPa to 300 hPa because of the high air temperature in summer (JJA). It is opposite in winter (DJF). More attention should be paid to the fact that the layer where moisture outflows most is the air level of 700 hPa to 600 hPa, the layer where moisture inflows most is the air level of 600 hPa to 500 hPa, and there is a great water vapor transfer between each other. Fig. 11 is the monthly mean water vapor net inflow of each boundary of Tarim river basin. It shows the condition of water vapor net in flows of west, south, east and north boundaries in Tarim river basin. From Fig. 11, it can be seen that the main import boundary is the west boundary, and the main export boundary is the east boundary, as well as the quantity of the former

0 1948

1953

1958

1963

1968

1973

1978

1983

1988

1993

1998

-50 100

year

-150 -200 -250

Figure 9. Water vapor net inflow trends from 1948 to 2005.

of the westerly wind, and the north of the area is influenced more than the southern region because of the higher mountain in southern and southwestern region of Tarim river basin. Water vapor transfer direction in the southern region showed that water vapor transport is also impacted by South-Asia wet monsoon in summer during the transferring. From Fig. 8 (b), it can be seen that the source region of moisture came from Eurasian Continent which crossing Kun-lun Mountains to Tarim river basin in winter and the water vapor flux in Taklimakan desert is a little bigger than other regions in Tarim river basin. This condition is consistent with that of the longitudinal water vapor flux. 4.2 Water vapor budget With Equation (3) & (4) water vapor budget of Tarim river basin was gotten. The annual mean inflow of water vapor was 1905.13 km3 , the outflow was 1904.95 km3 . The annual mean net inflow of water vapor is 0.18 km3 , that is, 0.15 mm when converted to areal net inflow value. Fig. 9 shows the annual net inflow of water vapor and its trend from 1948 to 2005 in Tarim river basin. It is seen from Fig. 9 that water vapor net inflow was unevenly distributed temporally. The annual net inflow of water vapor has the tendency of gradual increasing, and during 30 years before 1978, the value of water vapor net inflow is negative, and then during 20 years after 1978, the value of water vapor net inflow is positive. It shows that water vapor is export during the period from 1948 to 1978, and inflow during 1978 to 2005. This phenomenon is consistent with the climate change in northwest China which occurred in the late

27

Table 1.

Monthly mean water vapor net inflow in vertical air level (km3 ).

month/ layer

925 ∼ 850 hpa

850 ∼ 700 hpa

700 ∼ 600 hpa

600 ∼ 500 hpa

1 2 3 4 5 6 7 8 9 10 11 12 Total

−0.77 −0.48 0.46 1.27 3.41 4.30 4.28 4.38 1.98 −0.04 −1.08 −1.05 16.66

−8.12 −7.38 −5.79 −3.75 6.91 9.97 8.78 8.71 −0.91 −8.62 −11.77 −10.52 −22.48

−2.32 −1.45 −0.30 1.00 0.73 −3.35 −8.93 −10.70 −7.53 −3.82 −1.77 −1.73 −40.17

6.66 8.88 11.82 9.22 3.02 −4.20 −10.34 −9.26 −0.97 5.10 7.34 8.28 35.54

500 ∼ 400 hpa 4.52 5.60 6.63 3.54 −0.01 −2.95 −2.86 −1.00 0.54 3.47 4.40 5.22 27.10

400 ∼ 300 hpa

Total

1.34 1.55 1.51 0.25 −1.86 −5.38 −7.24 −6.67 −3.70 0.23 1.63 1.85 −16.48

1.31 6.72 14.34 11.53 12.20 −1.62 −16.31 −14.55 −10.59 −3.68 −1.24 2.06 0.18

Figure 12. Grid and sub-region division inTarim river basin.

Figure 11. Monthly mean water vapor net inflow of each boundary.

is less than that of the latter, which illustrates water vapor transport of longitude does not contribute to the water vapor content of Tarim river basin. The south and north boundary are all moisture import boundary, and the former is more helpful for water vapor content increasing even if it is not the main water vapor transfer way of the basin. The moisture source area of south boundary is from the southwest of the Bengali bay which crossing Tibetan Plateau by evaporating way. Water vapor inflow of the north boundary is more complex because of the complex polar region continent air mass transfer and need to be study in the future. When the river basin is divided into two regions based on the terrain of Tarim river basin, water vapor budget analysis is carried out. The result shows that the total water vapor inflow and outflow of the two subregions and water vapor net inflow of whole region can reach balance. Sub-region 1 is the water vapor source area of Tarim river basin. The transportation of water vapor was mainly latitude transportation from the west

Figure 13. Water vapor inflow (km3 ) balances of two sub-regions.

to the east region. The sub-region division and balance of water vapor inflow in each boundary is showed with Fig. 12 and Fig. 13.

5

CONCLUSIONS

(1) Water vapor content distribution is closely linked with the topography in the Tarim river basin. In the center of Taklimakan desert water vapor content is the largest where the terrain is the lowest. Water vapor content keeps decreasing trend from 1948 to

28

appeared. The transportation of water vapor was mainly latitude transportation from the west to the east.

1985, and then has an increasing trend since 1985. After the year of 1985 water vapor content is lower than before in the region of Taklimakan desert, and higher in the around region which is close to Tian Mountain, Pamir Plateau and Kunlun Mountain. Monthly mean water vapor content distribution is uneven. The difference of monthly mean water vapor content between the maximum (July) and minimum (January) is 12.0 mm. In the vertical direction, the near-ground atmospheric (ground to 850 hPa) has the lowest water vapor content whose moisture accounts for 9.3 per cent of the moisture of total air column. There is 59.6 per cent of the moisture concentrated on the air level of 850 hPa to 600 hPa. (2) Affected by westerly wind circulation, the main water vapor transfer is latitudinal direction transport in Tarim river basin. The source of moisture is the warm – wet air current from Indian Ocean in the west of basin and the cold – dry air current from Siberia in the east of basin in summer. Water vapor flux is convergence in the northwest region of the basin. Water vapor transport at longitude direction is smaller than latitude, which can be divided into western part and eastern part. Water vapor transfer of the western part is from south to north region of the basin and the eastern part is from north to south. Water vapor flux in northern region is greater than the southern in summer due to the water vapor transfer from Indian Ocean to Tarim river basin. The source area of moisture came from Eurasian Continent which crossing Kun-lun Mountains to Tarim river basin in winter. (3) The annual mean inflow of water vapor was 1905.13 km3 , and the outflow was 1904.95 km3 . The annual mean net inflow of water vapor is 0.15 mm. The annual net inflow of water vapor kept increasing before 1978, and started decreasing after 1978. It can be concluded that climate change leads to the increasing of water vapor inflow in Tarim river basin. If the river basin was divided into two sub-areas according to its topography, water vapor budget of the two subareas showed that sub-area 1 (mountain region) became the source region of water vapor and in the sub-area 2 (desert area) water vapor convergence

ACKNOWLEDGEMENT This work was supported by National Profession R&D Funds (No. 200701039); and National KeyTechnology R&D Program (No. 2006BAC05B02). REFERENCES CuiY.Q. 1994. Inland over the Northwest and the water vapor transmission source [J]. Water Journal. 9(9): 79–87. Gao X.Q. 1994. Northwest arid regions, the average atmospheric water vapor transmission [J]. Plateau weather. 9(13): 307–313. Li W, Hao J, Zhao H.Y. 2006. Northwest China’s stratospheric water vapor transmission and transmission convergence [J]. Water scientific progress. 17(2): 164–169. Liu G.W. 1997. Hydrological cycle of atmospheric processes [M]. Beijing: Science Press. Shi Y.F, Shen Y.P, Hu R.J. 2002. Preliminary study on signal, impact and foreground of climatic shift from warmdry to warm-humid in Northwest China[J]. Journal of Glaciology and Geocryology. 24(3): 219–226 Shi Y.F, Shen Y.P. 2003. Discussion on the Present Climate Change from Warmed to Warm-wet in Northwest China[J]. Quaternary Science. 23(2): 152–164 Wang B.J, Huang Y.X, Tao J.H. 2006. Regional Features and Variation of Water Vapor in Northwest China, Journal of Glaciology and Geocryolog. 28(1): 15–21. Yatagai, A., 2003. Evaluation of hydrological balance and its variability in arid and semi-arid regions of Eurasia from ECMWF 15 year reanalysis. Hydrological Processes. 17, 2871–2884 Yu Y.X, Wang J.S, Li Q.Y, 2003. Spatial and Temporal Distribution of Water Vapor and Its Variation Trend in Atmosphere over Northwest China, Journal of Glaciology and Geocryolog, 25(2): 149–156. Zhang X.W. 2003. Understanding on climate and ecological change in Xinjiang[J]. Xinjiang meteorology, 26(2): 44–45 Zhang X.W. 2003, Basin meteorology and some of the concept [J]. Xinjiang meteorology, 26(5): 4–5. Zhao F, Wu Z.H, Lu G.H. 2008. Analysis of the water vapor satate in Tarim river basin, on-line science and technology papers of China.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Climatological changes in rain and non-rain days over the East Asian region using long term rain gauge observation data M.K. Yamamoto∗ & A. Higuchi Center for Environmental Remote Sensing, Chiba University, Japan

S. Kikuchi Department of Earth Sciences, Faculty of Science, Chiba University, Japan

ABSTRACT: Recently, the global warming attracts a big attention. One of the great concerns is the change in the global rain distribution and extreme events of rainfall. In order to identify the changes in rainfall characteristics, we investigated the annual change in the top five days at maximal daily rainfall amount and those for the continuous non-rain periods using Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE’s Water Resources) which stores long term (1978–2002) gridded rain gauge observation data around East Asian regions. The maximum daily rainfall amount and non-rain days tend to increase although their inter-annual variation is large. The annual variation in the maximal daily rainfall amounts corresponds to those in non-rain days in Thailand. We also show the relationship between these results and atmospheric fields using reanalysis data.

1

INTRODUCTION

to measure precipitation continuously with a long period although the regional representative is high (Yatagai 2007). The climatological changes in precipitation using long-term rain gauge observation data are mentioned in total precipitation amount, precipitation intensity, and rainy days. For example, Endo et al. (2005) reported that the trends of the rain amount and rainy days in 1961–2000 summer seasons in China show regional difference, while that of rain intensity becomes increase regardless in regions. Fujibe et al. (2005) showed increasing (decreasing) trends in rain intensity (frequency of weak rainfall) using a hundred year of daily and six-hourly rain data in Japan. Such kind of trends in 100 years from the present also shows the results from a coupled oceanatmosphere climate model (Kimoto et al. 2005). Using 40–50 years daily rain gauge data in China, the precipitation revision (Qian & Qin 2007) and regional characteristics of extreme events (Zhai et al. 2005) are investigated. These increasing trends of rain intensity could cause flood, in contrast, a long absence from rainy days induce droughts. Thus, further understandings of rain characteristics including continuous non-rain days could contribute to predict disasters induced by a climate changes. The purpose of this study is to understand the trends in rain characteristics using long-term rain gauge observation data.

Recently, the global warming attracts a big attention. A lot of studies report the global changes about variations in temperature (e.g., Xu et al. 2007), sea level (e.g., Michida et al. 2004; Rohling et al. 2007), and so on. In addition, one of the biggest concerns is the change in the global precipitation distribution and extreme events. There are regional differences of changes in precipitation amount so that the Intergovernmental Panel on Climate Change 2007 (IPCC 2007) supposes that the regions suffered from floods or droughts have been expanding since 1970’s. Thus, better understands for the variation in precipitation characteristics are needed. A key to the settlement of the issue is a long-term precipitation observation. Satellite observation is a powerful tool to monitor the global distributions of precipitation. For example, the Global Precipitation Climatology Project (GPCP) that combines precipitation information observed by several satellite sensors (i.e. microwave imager, infrared radiometer, and sounder) provides global monthly precipitation amount (Adler et al. 2003). However, it is never free from disadvantages of deterioration and sampling limitations due to remote sensing and satellite orbit, respectively. On the other hand, the rain gauge observations are considered ∗

Corresponding author ([email protected])

31

Figure 2. Trend of the annual rain amount from 1978 to 2002. Significant trend is identified by 95% confidence level. The grid with the slope less than 1 mm/10 years is classified into very small trend.

Figure 1. Geophysical distribution of mean daily rainfall for 1978–2002. Dots show the location of the rain gauge stations.

2

and a percentile are then estimated. After the significance test is to be carried out using Mann-Kendall rank statistic (Kendall 1938), the grid boxes with 95% confidence level are called as “significant trend”.

DATA

This study used a 0.5◦ grid precipitation product that named the East Asia Daily Precipitation Analysis V0409 adjusted by the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (Xie et al. 2007). This product was constructed from the combination with some data sources using an interpolating method by Chen et al. (2002). The field and the term of the product is the East Asian region in 5◦ –60◦ N, 65◦ –155◦ E from January 1, 1978 to July 31, 2003. Figure 1 shows the analyzed regions with the location of rain gauge stations (see also Fig. 5 in Xie et al. (2007) for the number of rain gauges in a 0.5◦ grid). Almost all nations over East Asia reasonably cover the rain gauge stations, in particular over the Yellow River Basin with dense sampling except for Cambodia, Myanmar, and Nepal. The further explanations of the products are written in Xie et al. (2007). For the atmospheric fields on a moisture flux, we used the National Centers for Environmental Prediction-Department of Energy Atmospheric Model Intercomparison Project (NCEP-DOP AMIP-II) Reanalysis data, which consists of monthly meteorological variables with 2.5◦ grid boxes and 17 vertical layers (Kanamitsu et al. 2002). In this study, we count up continuous rainy and nonrainy days and daily rain amount for every year at the grid point with rain gauge observation in 1979–2002 (24 years), because Xie et al. (2007) cautions that grid boxes far from the gauge stations and boundaries such as the coast tends to degrade the quality of the interpolated rain amount. Over most of the analyzed regions, warm-season precipitation is dominated. However, the variety of rainfall is not always characterized by warmseason. Particular in non-rainy days, it may be hard to detect the durations with limited period. Thus, this study deals with the trends through the year. The interannual variations and linear trends for an average

3

RESULTS

3.1 Trends in annual rain characteristics Figure 2 shows the spatial distribution of the trends in annual rain amount. The increasing trends widely appear in South China from the right bank of the Yangtze River, Taiwan, the Indochina Peninsula, and Philippines. The similar trends also confirmed over North Japan and Korea. On the contrary, the rain amounts over the basin areas between theYellow River and the Yangtze River (the Huangtu Gaoyuan Plateau and the North China Plain) and over the Amur River Basin tend to decrease. Compared to the 40 years trends in summer total precipitation (Endo 2005), the trends over the middle reaches of the Yellow River are opposite. This would be due to the differences of analyzed period and data sources. Both the trends in annual rain amount (Fig. 2) and in conditional annual rain rate (not shown) show similar distribution patterns. This means that the heavier rainfall frequently occurs as the annual rain amount increase. The spatial distributions of the trends in the rainy days are illustrated in Figure 3. The increasing or decreasing trends of rainy days generally correspond to those of rain amount. Both significant increasing (decreasing) trends of annual rainfall amount and rainy days appears over Philippine, the coastal regions in Vietnam (the upper reaches of the Yangtze River and Yellow River Basin and the Amur River Basin). However, the middle reaches of theYangtze River Basin and the Xi River Basin show opposite trend (i.e. decreasing rainy days trend). These results suggest that a heavier rain could frequently and/or heavily occur.

32

Figure 3. Same as Figure 2 but for the annual rainy days. The grid with the slope less than 2.5 days/10 years is classified into very small trend.

Figure 5. Same as Figure 2 but for the continuous non-rain days of 90% of the cumulative relative frequency. The grid with the slope less than 2 days/10 years is classified into very small trend.

tendency of the dry season distribute widely over the coastal regions in Southeast Asia such as the Malay Peninsula, Philippines, and South China. The inland regions in China where is the upper reaches of the Yangtze River and theYellow River Basin are also seen to be the same trend. As an interesting characteristic, the lower reaches of the Yellow River clearly divides extending/shortening trend. Account for the arid/wet tendency of the surface, the change in the short-term continuous non-rain days are more suitable particularly in the humid climate regions. The trend for the number of continuous nonrain days less equal 2 days (not shown) generally shows the opposite trends against that for the long-term continuous non-rain days (Fig. 5). Thus, the extending/shortening trends of the dry season would be linked with the arid/wet tendency of the surface.

Figure 4. Same as Figure 2 but for the 90th percentile of daily rainfall amount. The grid with the slope less than 2 mm/10 years is classified into very small trend.

Figure 4 demonstrates the trends in 90th percentile of daily rainfall amount that is a synonym for the trends in heavy rainfall. The above mentioned regions are certainly seen to be the increasing trend, particularly significant around Sichuan. The inland regions in China and the Amur River Basin show significant decreasing trend. On the contrary, the trends in weak rainfall (number of daily rainfall less than 24 mm, not shown) generally shows decreasing trends except for Philippine, the Indo-China Peninsula, and the coastal regions of the Bay of Bengal. This may be due to the difference of the rain systems in tropics and mid-latitudes.

4

DISCUSSIONS

4.1 Climatological changes in precipitation characteristics Although the above mentioned trends generally support to the previous studies, every trends show distinct regional differences. Thus, the combination of the trends could help to the better understandings of the climatological changes in precipitation characteristics. Figure 6 illustrates the combination of the 90th percentile of daily rainfall amount (Fig. 4) and the continuous non-rain days of 90% of the cumulative relative frequency (Fig. 5). Over the south China regions, reinforcing trends of heavy rainfall with shortening non-raining periods widely appears so that these regions would be likely to be affected by a flood. The annual rainy days (Fig. 3) also appears to be decreasing, particularly for the middle reaches of the Yangtze River. Thus, this tendency may be harder in the inland

3.2 Trends in continuous non-rainy days Figure 5 depicts the trend in the continuous non-rain days of 90% of the cumulative relative frequency. This trend would represent the change in the dry seasons. An extending trend of the dry season appears over India, Pakistan, Russia, and North China. The significant decreasing trends that suggest a shortening

33

Figure 6. Combination of the 90th percentile of daily rainfall amount and the continuous non-rain days of 90% of the cumu-lative relative frequency.

Figure 7. Geophysical distributions of mean moisture flux (gray image) with those of mean winds vector (wind barb every 2 m s−1 ) at 850 hPa for 1979–2002 in May-September.

regions. The similar tendency also appears in Philippines, Thailand, and Vietnam. However, annual rainy days can be seen as the increasing trend. This means that precipitation systems would change to more frequently and heavily. On the other hand, the Amur River Basin, the middle reaches of the Yellow River Basin, and the Indus River Basin shows trends of weakening heavy rainfall and extending non-rainy periods so that these regions may be afraid of aridity. The surrounding regions do not show the trend of extending non-rain periods, however, the decreasing trend of annual rainy days appears commonly. Thus, the arid tendency may extend over the regions. 4.2

Effects on the Asian Summer Monsoon

Figure 8. Trend of the moisture flux at 850 hPa during May-September from 1978 to 2002. Significant trend is identified by 95% confidence level. The grid with the slope less than 0.2 g kg−1 m s−1 is classified into very small trend.

The daily rainfall over tropical East Asia is characterized by the wind fields of the Asian summer monsoon and the topography. Figure 7 illustrates the distributions of mean moisture flux with those of mean wind vector at 850 hPa for 1979–2002 in May–September that is well known as the Asian summer monsoon seasons although there are regional differences. The moist air created over the warm oceans, then flows in the inland regions by south–west winds. The mountains on the way of the wind force it doing updraft so that the there are often large rainfall mount along the windward side of the mountains (gray image in Fig. 1). The moisture flux at the level represents the source of precipitation. In the mid-latitude regions, atmospheric disturbances with extra-tropical cyclones and front are rather dominant since the surface cyclogenesis frequently occurs over the analyzed regions in mid-latitude (Adachi & Kimura 2007). Based on the climatology in the East Asian regions and our classified trends, possible causes of the regional trends are discussed. Figure 8 shows the trend of the moisture flux at 850 hPa during the Asian summer monsoon seasons. Over the Bay of Bengal and the South China Sea, the

trend of the moisture flux increase. The trend in wind (not shown) affects the increase of moisture flux, while that of mixing ratio (not shown) decreases. For the physical mechanisms of these changes, further studies are needed such as an ocean-atmospheric interaction and an intra-seasonal variation. The windward side of the regions such as Philippines, Thailand, and Vietnam shows the increasing trend of the moisture flux. The increasing trends of the heavy rainfall over the regions are reasonable. Over India and Pakistan, however, the increasing trend of the moisture flux appears in spite of the decreasing trend of the annual rain amount and heavy rainfall (Figs 2, 4). According to the continuous non-rainy days and rainfall days (Figs. 5, 3), these regions tend to be arid and dry seasons extending. Thus, the effect of climate changes in dry seasons may large rather than that of rainfall conditions. Over the southwest regions in China, decreasing trend of the moisture flux appears although there are very small trend around the lower reaches of the Yangtze River

34

REFERENCES

Basin. However, these regions tend to reinforce heavy rainfall. Since the trends of the rainy days (Fig. 3) and wind speed (not shown) decreases significantly, local circulation would become stronger. The other possible cause is a spatially coarseness of the reanalysis data since the meso-scale rainfall system would not analyze specifically. In any cases, more specific studies are needed. The inland areas in East Asia such as Takla Makan Desert, Gobi Desert, and the West Siberian Plain generally show decreasing trends both of moisture flux and annual rainfall amount, that is, the aridity would be accelerated. Even if it rains, the soil does not have an ability to maintain water (Wang 2006), so that it would be hard to grow vegetations. 5

Adachi, S. & Kimura, F. 2007. A 36-year climatology of surface cyclogenesis in East Asia using high-resolution reanalysis data. SOLA, 3, doi:10.2151/sola.2007-029. Adler, R.F. & coauthors 2003. The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeorol., 4, 1147–1167. Chen, M., Xie, P., Janowiak, J.E. & Arkin, P.A. 2002. Global land precipitation: A 50-year monthly analysis based on gauge observations. J. Hydrometeor., 3, 249–266. Endo, N., Ailikun, B. & Yasunari, T. 2005. Trends in precipitation amounts and the number of rainy days and heavy rainfall events during summer in China from 1961 to 2000. J. Meteor. Soc. Japan, 83, 621–631. Fujibe. F., Yamazaki, N. & Katsuyama, M. 2005. Long-term trends in the diurnal cycles of precipitation frequency in Japan. Pap. Meteor. Geophys., 55, 13–19. IPCC 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M. & Miller H.L. (eds.), Cambridge University Press, Cambridge & New York. Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J.J., Fiorino, M. & Potter, G.L. 2002, NCEP-DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteorol. Soc., 83, 1631–1643. Kendall, M.G. 1938. A new measure of rank correlation. Biometrika, 30, 81–93. Kimoto, M., Yasutomi, N., Yokoyama, C. & Emori, S. 2005. Projected changes in precipitation characteristics around Japan under the global warming. SOLA, 1, doi:10.2151/sola.2005-023. Michida, Y., Tateoka, A., Kinoshita, H., Namiki, M. & Odamaki, M. 2004. Long-term and seasonal changes of the mean sea level at Syowa Station, Antarctica, from 1981 to 2000. Polar Meteorol. Glaciol., 19, 19–29. Onogi, K. & coauthors 2007. The JRA-25 Reanalysis. J. Meteor. Soc. Japan, 85, 369–432. Qian, W. H. & Qin,A. 2007. Precipitation division and climate shift in China from 1960 to 2000. Theor. Appl. Climatol., 90, doi:10.1007/s00704-007-0330-4. Rohling, E. J., Grant, K., Hemleben, Ch. Siddall, M. Hoogakker, B. A. A., Bolshaw, M. & Kucera, M. 2008: High rates of sea-level rise during the last interglacial period, Nature Geoscience, 1, 38–42. Wang, B. 2006. The Asian Monsoon, UK, Praxis Publishing. Xie, P., Yatagai, A., Chen, M., Hayasaka, T., Fukushima, Y., Liu, C. & Yang, S. 2007. A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeor., 8, 607–626. Xu, X., Shi, X., Xie, L. & Wang, Y. 2007. Consistency of interdecadal variation in the summer monsoon over eastern China and heterogeneity in springtime surface air temperatures. J. Meteorol. Soc. Japan, 85A, 311–323. Yatagai, A. 2007. Analysis of hydrological cycle – Developments and utilization of gridded data sets–, Tenki, 54, 999–1002 (in Japanese). Zhai, P., Zhang, X., Wan, H. & Pan, X. 2005. Trends in total precipitation and frequency of daily precipitation extremes over China. J. Climate, 18, 1096–1108.

CONCLUSIONS

This study examined the trends of annual rainfall characteristics and continuous non-rainy days for recent 24 years over East Asia using a rain gauge observation grid dataset. The relationships between these trends and moisture flux in the Asian summer monsoon are discussed. The increasing trend of rain amount appears over Philippines, Thailand, and South China. The continuous non-rainy days also become short. These regions correspond to the windward of Asian summer monsoon. On the other hand, the annual rain amount decreasing with longer tendency of non-rainy days are found over the inland regions such as the upper reaches of the Yellow River Basin, the Amur River Basin, and the Takla Makan Desert. These regions are located around the leeward side of summer monsoon winds. This study mentioned the recent trends of the annual rainfall characteristics and the dry seasons derived from rain and non-rain days. The analyzed regions have a large seasonal changes with a >10 days (generally 30–60 days) variations called as “intra-seasonal oscillations”. It may be interesting to investigate the inter-decadal changes of the seasonal variations and the intra-seasonal oscillations. Recently, the finer spatial scale of the reanalysis data has been providing, for example, the Japanese 25-year Reanalysis (Onogi et al. 2007). Such kinds of dataset are expected to improve understandings of the climate changes in precipitation system and atmospheric fields. ACKNOWLEDGEMENTS The East Asia Daily Precipitation Analysis data product was provided by Asian Precipitation –HighlyResolved Observational Data Integration Towards Evaluation of the Water Resources, Research Institute for Humanity and Nature.

35

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Hydrological balance over northern Eurasia from gauge-based high-resolution daily precipitation data H. Takashima∗, A. Yatagai & H. Kawamoto Research Institute for Humanity and Nature (RIHN), Kyoto, Japan

O. Arakawa & K. Kamiguchi Meteorological Research Institute, Tsukuba, Japan

ABSTRACT: A gauge-based high-resolution daily precipitation data set for northern Eurasia, including Russia and former Soviet Union countries, has been developed as a part of the Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE’s Water Resources) project. It is important to validate climate models with high-resolution grid precipitation data, because precipitation is a key parameter used by such models to evaluate atmospheric circulation. However, a daily gridded precipitation data set for Asia and, in particular, northern Eurasia has not yet been fully developed. We developed a grid precipitation data set for 1960–1990 (31 years) by using daily gauge-based precipitation data homogenized with the technique of Groisman and Rancova (Internat. J. Climatol, 21, 2001). Using this new grid precipitation data set, we validated the precipitation simulated by the European Centre for Medium-Range Weather Forecasts 40 years re-analysis and water balance over northern Eurasia; this validation is important for understanding the freshwater resources of both that region and the Arctic. Keywords: 1

daily precipitation; Russia; central Asia

INTRODUCTION

Resources) project. The primary objectives of the APHRODITE project are to (1) release official stateof-the-art daily gridded precipitation data sets based on rain-gauge observations, (2) assess the projections of climate models by observing precipitation in the field, including extreme events, and (3) make suggestions to regional water resources managers in Asian countries. See the APHRODITE’s Water Resources website (http://www.chikyu.ac.jp/precip/) for details. The hydrological cycle over northern Eurasia significantly affects the freshwater supply to the Arctic, thus the oceanic thermohaline circulation. Interseasonal variations of meteorological fields in relation to storm track or transient eddy can influence moisture transport from low latitudes to the polar region (e.g. Fukutomi et al. 2007), especially during the northern summer, when the temperature gradient is largest. During the northern summer, an east-west oscillation with a period of 6–8 years in precipitation is the dominant pattern over northern Eurasia (Fukutomi et al., 2003), but the mechanisms accounting for this oscillation, especially in relation to the interseasonal variation, are still not fully understood. To evaluate interseasonal variation and the hydrological balance over northern Eurasia, high temporal and spatial resolution gridded precipitation data are needed.

Various studies have recently showed the global and regional precipitation changes that are likely to be associated with global warming (IPCC, 2007), thus improving our understanding of the impacts of global warming on regional water resources and on the frequency of extreme events such as heavy rainfalls and drought. To validate the capability of a climate model to simulate not only various aspects of climate change but also regional precipitation, it is necessary to compare the model’s precipitation results against observed precipitation data (e.g. Yatagai et al. 2008a, b). However, no precipitation data set with sufficiently high spatial and temporal resolution is available for evaluating climate models for Asia or, especially, northern Eurasia. In order to validate highresolution climate model, a long-term gauge-based high-resolution daily precipitation data set has been developed for northern Eurasia, including countries of the former Union of Soviet Socialist Republics (USSR), as a part of the Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE’s Water ∗

Corresponding author ([email protected])

37

rain gauge data for northern Eurasia (hereafter, we call this product “APHRO-RU”). The algorithm of Yatagai et al. (2008a, b) is the same as that of Xie et al. (2007) except for the interpolation method used; like Yatagai et al., we apply the Shepard (1968) method to interpolate the daily climatology from the observation data for each target day. In this study, we (1) derived the 31year daily precipitation climatology and interpolated the climatology onto a 0.05◦ grid, (2) computed the analysis field of the daily climatology anomalies for each target day, and finally (3) averaged daily precipitation at 0.05◦ grids over 0.5◦ . See Xie et al. (2007) and Yatagai et al. (2008a) for details. For the regions out of former USSR countries, we used a rain gauge data set obtained via the Global Telecommunication System network for the climatology.

Number of gauges

NCDC (No.9813,Groisman-Rankova) 2000 1500 1000 500 0

60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90

year

Figure 1. Number of daily gauge observations for the former USSR countries collected by NCDC (data set 9813) for 1960–1990.

2.3 Meteorological data We used the meteorological data set of precipitation and vertical integral of divergence of moisture flux from the ECMWF 40-year re-analysis (hereafter ERA40) on a 2.5◦ grid, 4 times daily, for 1960– 1990, corresponding to the period of the APHRO-RU data set.

Figure 2. Precipitation [mm day−1 ] on 1 July 1979 from rain gauge data (NCDC data set 9813). The areas enclosed by boxes are eastern Siberia (the Lena River basin) and western Siberia (the Ob River basin).

In this paper, we describe in detail a new daily gridded data set developed for northern Eurasia as a part of the APHRODITE project, and we use this data set to evaluate the simulation of precipitation by the European Centre for Medium-range Weather Forecasts (ECMWF) forecast model’s 40-year re-analysis as a validation result.

3 VALIDATION RESULT 3.1 Validation of ERA40 with APHRO-RU

Gauge-based daily precipitation data (data set 9813 Daily and Sub-daily Precipitation for the Former USSR, version 1.0) homogenized for wetting bias and rain gauge changes by the technique developed by Groisman & Rankova (2001) are available from the US National Climatic Data Center (NCDC) for 1891– 2001 (NCDC, 2005). In former USSR countries, there are 2188 daily precipitation observation stations, but many fewer observations were recorded before 1960 and after 1990; therefore, we used the data for 1960– 1990 (31 years). During this period observations were continuously conducted at more than 1500 stations (Fig. 1). Figure 2 shows an example of precipitation from data set 9813. The observation sites are dense over the southwestern former USSR and somewhat sparser in the north of central Eurasia.

As a preliminary analysis, we first validated precipitation as simulated by ERA40 with our product. Figure 3 shows the 31-year climatologies of APHRO-RU and ERA40 precipitation for four months. The general precipitation pattern of ERA40 is very similar to that of APHRO-RU and quantitatively reasonable throughout the year, but smaller scale structures are found in APHRO-RU, particularly over mountainous regions, such as to the east of the Black Sea and north of Mongolia. In APHRO-RU, we can also see small structures over the Amur River basin during the northern summer. Precipitation maxima in ERA40 sometimes differ from those of APHRO-RU in some regions. For example, in July we can see a maximum at around 100◦ E, 70◦ N in ERA40 but not in APHRO-RU. These differences may be due in part to moisture convergence of the ERA40 model, or they might be caused by the sparse gauge distribution in some regions in APHRO-RU. In general, ERA40 tends to overestimate the precipitation climatology over northern Eurasia during the northern winter/spring, and underestimate that during summer.

2.2

3.2 Water budget over northern Eurasia

2 2.1

DATA AND METHODOLOGY Input gauge data

Objective technique

We used an algorithm similar to that of Yatagai et al. (2008a, b) to create gridded precipitation data from the

Figure 4 shows the annual cycle of precipitation over western (60–85◦ E, 50–70◦ N; the Ob River basin) and

38

Figure 3. Precipitation climatologies for January, April, July, and October for (a) APHRO-RU on a 0.5◦ ×0.5◦ grid and (b) the ECMWF 40-year re-analysis on a 2.5◦ ×2.5◦ grid. The areas enclosed by boxes are eastern Siberia (the Ob River basin) and western Siberia (the Lena River basin).

eastern (110–135◦ E, 50–70◦ N; the Lena River basin) Siberia for APHRO-RU and ERA40 simulation, where precipitation is largest (Fig. 3), and where Fukutomi et al. (2003) showed an east-west dipole structure of circulation and precipitation anomalies in interannual variation during the northern summer. Over both regions, precipitation maxima are found during the summer, and minima during the winter (Fig. 3). Moisture convergence (ERA40) (Fig. 4) shows an annual cycle over the Ob River basin, with a summer maximum, and a semiannual cycle over the Lena River basin, as described in previous studies (e.g. Fukutomi et al. 2003). If the total of precipitable water is constant despite the seasonal variation, then evaporation (evapotranspiration) can be estimated by subtracting the moisture flux convergence from the precipitation. This is one of the advantages of gridding; a gridded precipitation data set is useful for

estimation of evaporation. During summer over the Ob River, the moisture flux convergence is negative, indicating that evaporation is larger than precipitation. This finding is consistent with those of previous studies (e.g. Fukutomi et al. 2003), though our time period and data set are different. During summer, the precipitation climatology as simulated by ERA40 is underestimated ∼ 0.16–0.17 mm day−1 and the evaporation climatology overestimated. On the other hand, during winter, evaporation is underestimated in ERA40 simulation. This underestimation might be partly because rain gauges cannot accurately capture snow, but the precipitation over the Lena River basin in APHRO-RU shows quantitatively good agreement with the moisture convergence. The precipitation maximum over western Siberia in July-August in APHRO-RU (Fig. 4a) lags slightly behind the maximum reported by Fukutomi et al.

39

(a) Western Siberia [CLM1960-1990] Lon:60-85E, Lat:50-70N 4

(b) Eastern Siberia [CLM1960-1990] Lon:110-135E, Lat:50-70N 4

2

2

0

0

[mm day-1]

[mm day-1]

Precip (APHRO-RU) Evap (Precip-Conv) Conv (ERA40)

Precip (APHRO-RU) Evap (Precip-Conv) Conv (ERA40)

-2

-2 J

F

M

A

M

J J A month

S

O

N

D

J

J

F

M

A

M

J J A month

S

O

N

D

J

Figure 4. Annual cycle of precipitation (diamonds), moisture flux convergence (open circles), and evaporation (pentagons) over (a) western Siberia and (b) eastern Siberia (see Fig. 1 for the domains). Evaporation was estimated by subtracting convergence from precipitation. Dashed and dotted-dashed lines indicate precipitation and convergence based on total precipitation of the ECMWF 40-year re-analysis instead of on APHRO-RU precipitation.

the year, but more small-scale structures are found in the APHRODITE product, particularly over mountainous regions, and ERA40 tends to underestimate the wintertime 31-year precipitation climatology.

(2003), who observed a clear maximum in July. This difference is partly due to the different observation time periods; the precipitation maximum occurred in August during 1961–1966 in APHRO-RU (data not shown). We need to carefully investigate changes in the seasonal cycle, because such changes may occur in response to global warming or global climate change associated with human activities. To understand the hydrological balance over northern Eurasia we also need to investigate the role of interseasonal variation of meteorological fields or transient eddy, and to clarify the possible relation to the seasonal variation. Because APHRO-RU is a high temporal and spatial resolution data set, we can use APHRO-RU high-resolution precipitation data to clarify the mechanisms determining the hydrological balance over northern Eurasia, including the seesaw pattern observed during northern summer.

4

ACKNOWLEDGEMENTS The authors thank Dr. P. Xie of CPC (NOAA) for generous support. We also thank Dr. Y. Fukutomi of FRSGC/JAMSTEC and Dr. K. Oshima of Hokkaido University for their useful comments. This work was supported by the Global Environment Research Found (GERF-FS051, B062). The figures were produced with the GFD-Dennou Library. REFERENCES Fukutomi,Y., H. Igarashi, K. Masuda, and T.Yasunari (2003). Interannual variability of summer water balance components in three major river basins of northern Eurasia, J. Hydrometeorol. 3: 249–266. Fukutomi, Y., Masuda, K. and Yasunari, T. (2007). Cyclone activity associated with the interannual seesaw oscillation of summer precipitation over northern Eurasia, Global and Planetary Change 56: 387–398. Groisman, P. Ya. and E. Ya. Rankova (2001). Precipitation trends over the Russian permafrost-free zone: removing the artifacts of pre-processing, Internat. J. Climatol 21: 657–678. IPCC. (2007), Climate Change 2007 The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 996 pp.

SUMMARY

A gauge-based high-resolution daily precipitation data set (0.5◦ horizontal resolution) for northern Eurasia, including the countries of the former USSR, for 1960– 1990 has been developed as a part of APHRODITE’s Water Resources project, on the basis of data homogenized by using the algorithm of Groisman and Rancova (2001). In this analysis, we used this data set to validate the precipitation in the ECMWF 40-year re-analysis and the water balance over northern Eurasia. ERA40 reliably predicts the precipitation pattern throughout

40

National Climatic Data Center (NCDC), 2005. DATA DOCUMENTATION FOR DATASET 9813 Daily and Sub-daily Precipitation for the Former USSR, Version 1.0, 18 pp. Shepard, D. (1968).A two-dimensional interpolation function for irregularly spaced data. Proc. 23 ACM Nat’l Conf., Princeton, NJ, Brandon/Systems Press: 517–524. Yatagai, A., P. Xie and P. Alpert (2008a). Development of a daily grid precipitation data set: Toward evaluation of global warming effects on water resources in the East Mediterranean, Advance in Geophysics 12: 165–170.

Yatagai, A., H. Kawamoto and P. Xie (2008b). Products and validation of GAME re-analyses and JRA25: Precipitation, Extended abstract for Third WCRP International Conference on Reanalysis, Jan. 28–Feb. 1, 2008, Tokyo, Japan (extended abstract is available at http://wcrp.ipsl.jussieu.fr/Workshops/Reanalysis2008/ abstract.html). Xie, P., A. Yatagai, M. Chen, T. Hayasaka, Y. Fukushima, C. Liu and Y. Song (2007). A Gauge-Based Analysis of Daily Precipitation over East Asia. J. Hydrometeor 8: 607–627.

41

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Investigating changes in rainfall characteristics using the copula approach Hemant Chowdhary∗ Louisiana State University, Louisiana, USA

Vijay P. Singh Texas A and M University, Texas, USA

ABSTRACT: Multivariate characterization of rainfall processes has been an important aspect of many hydrometeorological applications in the past. Frequency analysis of average storm intensity, duration, volume and interarrival time is useful for simulation studies, rainfall simulators and for deriving flood frequency distributions, among others. The correlation structure among these features has a significant effect on surface runoff and may also reflect regional climatic characteristics.A perceptible shift in these inter-relationships, in a longer time frame, may indicate correspondence to possible climatic changes in the region. Bivariate frequency modeling of these rainfall processes has been proposed and done in the past by employing bivariate exponential, bivariate normal, Box-Cox-transformed bivariate normal and bivariate Gumble distributions. Such conventional distributions are restrictive in having to use marginals from the same family of distributions and sometimes in having restrictions on the range of admissible dependence. Some of these conventional multivariate formulations involve Pearson’s linear correlation coefficient that is not invariant to the non-linear monotone transformations and thus are not appropriate for deriving inter-variate dependence characteristics. The concept of copula, which is relatively new, overcomes such limitations by allowing the combination of different and arbitrary types of marginals as well as by offering a wider choice of dependence functions. Copulas are becoming increasingly popular in various fields, including finance, biomedical, and reliability engineering. A number of copula applications have been made in hydrologic engineering in which multivariate interdependence of variables is important. A few copula formulations have been recently proposed for bivariate and trivariate rainfall frequency analysis, involving storm rainfall intensity, duration, and/or depth. This paper presents an application of the copula-based approach for determining long-term changes in rainfall characteristics in terms of multivariate features. Keywords:

1

bivariate; trivariate; multivariate; frequency; distribution; copula

INTRODUCTION

involve consideration of rainfall intensity and duration as well as the length of the intervening dry periods. Bivariate joint distribution of intensity and duration or depth and duration or depth and intensity of rainfall storms, among hydro-meteorological variables, are the commonly performed frequency analyses. There have been several studies investigating the joint statistical characteristics of rainfall variables such as those by Grace and Eagleson (1966), Todorovic and Yevjevich (1969), Eagleson (1972), Carlson and Fox (1976), Cordova and Bras (1981), Diaz-Granados et al. (1984), Cordova and Rodriguez-Iturbe (1985), Nagao and Kadoya (1971), Bacchi et al (1994), Kurothe et al. (1997), and Goel et al. (2000), among others. In most of these studies, rainfall intensity and duration were modeled by considering them either independent or negatively dependent Weibull or exponentially distributed marginals.

Most hydrological plans and designs are based on statistical analysis of stochastic atmospheric-phase of the hydrological cycle. Design precipitation values required for water availability or drainage studies are arrived at using statistical modeling approaches and are based on the frequency analysis of the pertinent variables. Owing to the dominant role of individual variables, many hydrological management plans and designs have traditionally been based on the univariate analysis. Most hydro-meteorological processes, however, exhibit multivariate characteristics and a complete description of their stochastic nature is given by multivariate statistical frequency distributions. For example, an inflow forecast or simulation study may ∗

Corresponding author ([email protected])

43

of hydrometeorology is the Archimedean family. This copula family has the following form:

One of the limitations of the approaches used in the above studies has been in having to consider all the marginals from the same family of distributions. In practice, however, rainfall variables may have different distributions. A bivariate distribution with specified marginals has first been applied by Singh and Singh (1991). Long and Krzysztofowicz (1995) constructed a newer family of density functions with specified marginals. Furthering this work, Kelly and Krzysztofowicz (1997) constructed a meta-Gaussian bivariate density that can belong to any arbitrarily specified marginal and have any possible dependence structure. The copula concept is a new approach that also allows for the selection of specified marginals and a wider choice of dependence structure. Several studies such as Salvadori and De Michele (2004), Favre et al. (2004), Grimaldi et al. (2005), Singh and Zhang (2007), Zhang and Singh (2007a), Kao and Govindaraju (2007) and Zhang and Singh (2007b), among others, have demonstrated the usefulness of copula-based approach for multivariate modeling of various rainfall variables. This study attempts to investigate the changes in rainfall characteristics over a longer time period by employing the copula approach that allows for consideration of desirable marginals for describing the multivariate character of mean rainfall intensity and duration of depth-wise annual largest storm events.

2

where φ is a continuous, strictly decreasing function from I [0,1] → [0,∞] and with φ(1) = 0, is called a generating function. The joint probability function can then be written as

Parameter θ is hidden in the generating function. For example, the Clayton generating function, one that is commonly employed for hydrometeorological variables, involves θ as

The bivariate cumulative probability distribution and density functions are obtained as

CONCEPT OF COPULAS

Although the development and application potential of copulas is a topic of current research, it is rooted in the theorem due to Sklar (1959), stating that the joint distribution function of any randomly distributed pair (X, Y) may be written as

where fX (x) and fY (y) are the marginal densities and f(x,y) and F(x,y) are the joint bivariate density and probability functions respectively. The maximum likelihood function for estimating the copula parameters is given by

where FX (x) and FY (y) are marginal probability distributions and C : [0, 1] × [0, 1] → [0, 1], a mapping function, is the “copula”. In turn it means that a valid model for (X,Y) is obtained whenever the three constituents (C, FX , and FY ) are chosen from given parametric families, viz.

3

where δ and η are the parameters of marginal distributions and θ is the parameter for the dependence structure. u and v are the quantiles of the uniformly distributed variables U = FX (x) and V = FY (y) respectively. Of the several families of copulas that exist, the one that has been frequently applied in the field

Modeling of rainfall processes such as intensity or depth, duration, and storm inter-arrival period is essential for understanding rainfall characteristics for better planning and design strategies as well as for arriving at flood frequency distributions. A copula-based bivariate distribution, involving the Clayton copula, is employed here for assessing the long term changes in the bivariate distributional characteristics of mean

where cθ is copula-based joint bivariate density.

44

BIVARIATE COPULA FOR RAINFALL INTENSITY AND DURATION

1947-1976 1977-2006

(a)

30 10

20

Duration (hr)

40

50

intensity and duration of depth-wise largest annual rain storms. Hourly rainfall data from the Baton Rouge metropolitan airport station in the State of Louisiana, USA for 60 hydrological years from 1947 to 2006 is considered in this illustration. The Louisiana State in the United States of America is spread between latitudes 29◦ and 33◦ N and longitudes 89◦ and 94◦ W and experiences a moist sub-tropical climate. A period of six hours of rainfall hiatus is considered for defining rainfall events in order to enhance mutual independence of various rain events. A minimum of 2.54 mm rainfall qualifies to be counted as a rainfall event. The yearly extreme event is selected on the basis of the storm that has the largest rainfall volume (i.e., rainfall depth). This results in identification of 60 rain storms during the study period. In order to study the changes in rainfall characteristics over time, this entire period is divided in two equal portions of 30 years each, i.e., 1947 to 1976 and 1977 to 2006. Although, the two 30 years portions are considered primarily to ensure sufficient length of data for the purpose of comparison, such division of data also coincides with the significant climatic regime shift that was experienced in the mid 1970s and that may have an effect on rainfall characteristics. Figure 1 shows the bivariate scatter plot of intensity and corresponding duration of storm events in these two periods alongwith the individual histograms. First of all, distributions such as exponential, Gamma, Weibull, lognormal, Gumbel, and general extreme value among others, are attempted in order to adequately fit univariate marginals to the intensity and duration data, pertaining to both the periods. On the basis of Kolmogorov-Smirnov statistic and the overall fit, the three-parameter Gamma (P3) and lognormal (LN) distributions are considered suitable for the intensity (X) and duration (Y) data respectively. While making a selection, it is also attempted to select a common and overall better distribution type for the two periods, for both data types individually. The nature of the fits for the two periods, for both intensity and duration, are shown superimposed in Figure 1(b) and (c), respectively. These concurrent plots for the two periods illustrate the shifts in the univariate distributions. For the purpose of studying these shifts in a bivariate context, a bivariate copula fromArchimedean family along with Clayton generating function is subsequently considered to fit the storm intensity and duration data by employing the maximum likelihood estimation method. The density and probability functions employed for P3 and LN distributions, fX (x), fY (y) and FX (x) and FY (y) are respectively given as

2

4

6

8

10

12

14

0.20

Mean Intensity (mm/hr)

(b)

0.10 0.00

0.05

Density

0.15

1947-1976 1977-2006

0

5

10

15

20

0.05

Mean Intensity (mm/h)

(c)

0.00

0.01

0.02

Density

0.03

0.04

1947-1976 1977-2006

0

20

40

60

80

Duration (h)

Figure 1. Intensity-duration bivariate data – (a) scatter plot, (b) and (c) histograms and distributions.

45

Table 1.

Parameter estimates and standard errors.

Distribution and Variable

Parameter



θ

For Period 1977–2006  P3 for X γY  αY LN for Y Dependence

βY µ ˆY σˆ Y 

θ

2.472 3.072 0.594

0.067 0.073 0.095

−0.567

0.014

0.002 0.000 20 15 0

10 20 5 60 0

1.803 2.332

0.298 0.837

2.214 3.130 0.570

0.850 0.104 0.081

−0.418

0.063

40 ) n (h atio

Dur

80

1.0

Probability



0.155 0.135

) /h m m y(



1.097 2.521

0.004

it ns te In

Dependence

βX µ ˆY σˆ Y

0.006

n ea M

LN for Y

Standard Error Density

For Period 1947–1976  P3 for X γX  αX

Estimate

0.8 0.6 0.4 0.2 0.0 20

ea

M

15

n

where α, β > 0 are the scale and shape parameters and γ ≤ x < ∞ is the location parameter.

20

ity

ns

te

In

0

10

/h

m

(m

5

)

60 0

40 ) n (h atio

Dur

80

Figure 2. Perspective plots of copula-based bivariate density and probability for the period 1977–2006.

1947–1976 and 1977–2006, are investigated for the changes that may, in part, be due to regional changes in the rainfall characteristics. As these shifts are easy to be visualized in 2-dimensional plots, the contours of the bivariate densities and probabilities for the two periods under investigation are shown in Figure 3. It is apparent from Figure 3(a) that there has been a clockwise twisting of the bivariate density plot in the later period as compared to the former. This results in a marked variation in the bivariate probability contours, as shown in Figure 3(b), especially for larger intensities and durations. The differences start mounting, as intensity and duration become greater than 10 mm/hr and 40 hours, respectively. Another way to look at the changes in the rainfall characteristics is to compare the contours of the return periods for different types of exceedances. For this, two types of return periods are considered: those corresponding to the exceedances of (a) intensity or duration, indicated as “rpOR”, and (b) intensity and duration, indicated as “rpAND.” The superimposed contours of these two return periods, for both the periods, are presented in Figure 4(a) and (b), respectively. The nature of deviations in Figure 4(a) is similar to that in Figure 3(b), owing to the functional relationship between bivariate probability and the return period rpOR. The differences in the return period rpAND, indicating the average frequency of

where N (·) and N (·) are the density and probability function of the standard normal distribution and µ and σ > 0 the two parameters. Various parameters of the marginal distributions and the dependence structure are estimated using the maximum likelihood (ML) method. The maximized log-likelihood of −172.6 and −187.5 is obtained while fitting bivariate copula distributions for the two periods. Various parameter estimates and corresponding standard errors for the two periods are given in Table 1. The perspective plots of the resulting copula-based bivariate density and probability functions for the period 1977–2006 are illustrated in Figure 2. 4

INVESTIGATING CHANGES IN RAINFALL CHARACTERISTICS

A bivariate distribution symbolizes the nature of simultaneous disposition of intensity and duration as well as their mutual dependence structure. The copula-based distribution helps preserve the features of marginals closest to their univariate distributions. Any significant shift in the nature of this bivariate disposition would be reflected in the parameters and the shape of the fit. The two periods of rainfall observation, viz.,

46

80

80

1947-1976 1977-2006

60 40

Duration (h)

20

40 20

Duration (h)

60

(a)

(a) 0

0

1947-1976 1977-2006

0

5

10

15

0

20

5

15

20

80

80

(b)

40 20

40

Duration (h)

60

60

1947-1976 1977-2006

20

Duration (h)

10 Mean Intensity (mm/h)

Mean Intensity (mm/h)

(b)

0

5

0

0

1947-1976 1977-2006 10

15

0

20

10

15

20

Figure 4. Comparison of return periods for (a) rpOR and (b) rpAND, for the two periods.

Figure 3. Comparison of contours of bivariate (a) density and (b) probability for the two periods.

exceeding both intensity and duration, also show a clockwise twisted trend, as given in Figure 4(b). It is apparent from this figure that the differences are pronounced for larger intensities that correspond to lower durations. The deviations are reversed for larger durations and smaller intensities. The bivariate account of these differences in the return period rpAND, in absolute and relative terms, is given in Figure 5(a) and (b), respectively. It may be seen from these figures that a difference of about 50 to 100 years results at the mean intensities of about 15 to 20 mm/hr. This amounts to as much as 80 to 100% of change in percentage terms. 5

5

Mean Intensity (mm/h)

Mean Intensity (mm/h)

of copula-based approach, is an important development as these overcome the prevailing restriction of only a few known functional form of bivariate or multivariate distributions. The Clayton type Archimedean copula method is employed in this study for studying the changes in bivariate characteristics of mean intensities and corresponding durations for annual depth-wise largest rain storms. Small to moderate differences are observed in the shape of the density and probability functions as well as in the estimates of various types of return periods. As this approach involves selection of suitable marginals as per the nature of the individual univariate data, it helps in bringing out the changes without being unduly affected by the weaker assumption of similar marginals that is required to be made while using the conventional multivariate distribution fitting methods. Although a portion of these differences in the rainfall characteristics may be attributable to the variability in the process itself, part of it is likely due to the changing regional climatic

CONCLUSIONS

Illustration of the copula-based approach for fitting multi-dimensional variates in the field of hydrometeorology is done by taking the case of storm intensityduration data. Allowance of arbitrary marginals and feasibility of wider dependence space, through the use

47

40 20

Duration (h)

60

80

De Michele, C., Salvadori, G., Canossi, M., Petaccia, A. & Rosso, R. 2005. Bivariate statistical approach to check adequacy of dam spillway. Journal of Hydrologic Engineering Vol. 10(1): 50–57. Diaz-Granados, M. A., Valdes, J. B. & Bras, R. L. 1984. A physically based flood frequency distribution. Water Resources Research 20(7): 995–1002. Eagleson, P. S. 1972. Dynamics of flood frequency. Water Resources Research 8(4): 878–898. Favre, A.-C., El Adlouni, S., Perreault, L., Thiemonge, N. & Bobee, B. 2004. Multivariate hydrological frequency analysis using copulas, Water Resources Research 40(W01101): 1–12. Goel, N. K., Kurothe, R. S., Mathur, B. S. & Vogel, R. M. 2000. A derived flood frequency distribution for correlated rainfall intensity and duration. Journal of Hydrology 228: 56–67. Grace, R. A. & Eagleson, P. S. 1966. The synthesis of short-time-increment rainfall sequences. Hydrodynamics Laboratory, MIT. Grimaldi, S., Serinaldi, F., Napolitano, F., & Ubertini, L. 2005. A 3-Copula function application for design hyetograph analysis, IAHS Publication 293: 1–9. Kao, S. & Govindaraju, R. S. 2007. Probabilistic structure of storm surface runoff considering the dependence between average intensity and storm duration of rainfall events. Water Resources Research 43(W06410): 1–15. Kelly, K. S. & Krzysztofowicz, R. 1997. A bivariate metaGaussian density for use in hydrology. Stochastic Hydrology and Hydraulics 11: 17–31. Kurothe, R. S., Goel, N. K. & Mathur, B. S. 1997. Derived flood frequency distribution for negatively correlated rainfall intensity and duration. Water Resources Research 33(9): 143–148. Long, D. & Krzysztofowicz, R. 1995. A family of bivariate densities constructed from marginals. Journal of the American Statistical Association 90(430): 739–746. Nagao, M. & Kadoya, M. 1971. Two-variate exponential distribution and its numerical table for engineering application. Bulletin of the Disaster Prevention Research Institute, Kyoto University 20(3): 183–215. Salvadori, G. & De Michele, C. 2004. Frequency analysis via copulas: Theoritical aspects and applications to hydrological events. Water Resources Research 40: 1–17. Singh, K. & Singh, V. P. 1991. Derivation of bivariate probability density functions with exponential marginals. Stochastic Hydrology and Hydraulics 5: 55–68. Singh, V. P. & Zhang, L. 2007. IDF curves using the Frank Archimedean copulas. Journal of Hydrologic Engineering 12(6): 651–662. Sklar, A. (1959) Fonctions de Repartition a n Dimensions et Leurs Marges. Publishing Institute of Statistical University of Paris 8: 229–231. Todorovic, P. & Yevjevich, V. 1969. Stochastic process of precipitation. Hydrology Paper, Colorado State University. 35. Zhang, L. & Singh, V. P. 2007a. Bivariate rainfall frequency distributions using Archimedean copulas. Journal of Hydrology 332: 93–109. Zhang, L. & Singh, V. P. 2007b. Gumbel-Hougaard copula for trivariate frequency analysis. Journal of Hydrologic Engineering 12(4): 409–419.

Diff. in rpAND 0

(a) 0

5

10

15

20

15

20

40 20

Duration (h)

60

80

Mean Intensity (mm/h)

% Diff. in rpAND 0

(b) 0

5

10 Mean Intensity (mm/h)

Figure 5. Difference in return period rpAND in (a) absolute and (b) percent terms.

conditions. Furthermore, in this study the rainfall station of Louisiana State in USA was considered as an illustration only. A study involving stations with possible temporal shifts would bring out such differences more pronouncedly. REFERENCES Bacchi, B., Becciu, G. & Kottegoda, N.T. 1994. Bivariate exponential model applied to intensities and durations of extreme rainfall. Journal of Hydrology 155: 225–236. Carlson, R. F. & Fox, P. 1976. A northern snowmeltflood frequency model. Water Resources Research 12(4): 786–794. Cordova, J. R. & Bras, R.L. 1981. Physically based probabilistic models of infiltration, soil moisture, and actual evapotranspiration. Water Resources Research 17(1): 93–106. Cordova, J. R. & Rodriguez-Iturbe, I. 1985. On the probabilistic structure of storm surface runoff. Water Resources Research 21(5): 755–763.

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2

Headwater environment: Impacts of climate change and human intervention (IAHC topics The 7th International Conference on Headwater Control) Headwater areas are generally characterized by a high potential of recharge of both surface- and groundwater resources, but also by conflicts in the exploitation of natural resources (water, timber, minerals or wildlife), tourism and leisure industries, and nature protection (frequently, these resources remain among the great natural reserves of a nation). Many headwater regions are in mountain steep-lands, and are frequently source areas for natural hazards. However, most headwater areas are the dominant features of plains and plateaus. Headwater environment often consists of fragile ecosystems being in a confrontation with the global climate change and human interventions. Conveners: Josef Krecek (Czech technical University, Prague, Czech republic) Lorenzo Marchi (CNR IRPI, Padova, Italy) Lilang Ren (Hohai University, China) Yoshihiro Fukushima (RIHN, Japan) Masanori Katsuyama (RIHN, Japan)

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Effects of various rainfall-runoff characteristics on streamwater stable isotope variations in forested headwaters M. Katsuyama∗ Research Institute for Humanity and Nature, Kyoto, Japan

K. Fukushima Graduate School of Agriculture, Kyoto University, Kyoto, Japan

N. Tokuchi Field Science Education and Research Center, Kyoto University, Kyoto, Japan

ABSTRACT: We discuss differences in rainfall-runoff processes in small catchments based on water isotope tracer and Mean Residence Time (MRT) estimates. Observations conducted at granite and sedimentary rock catchments in Japan revealed large differences in rainfall-runoff characteristics between the two catchment types. Granite catchments had stable flow conditions compared to sedimentary rock catchments. Using δ18 O, streamwater MRT was estimated in each catchment. Generally, granite catchments had longer MRTs than sedimentary rock catchments. The parameter representing residence time distribution functions was similar between the two types; however, the response functions were very different. Our results suggest hydrological controls of MRTs. In granite catchments, the smaller contributions of the shortest flow line and larger contributions of the longer flow line corresponded to the longer storage of groundwater within the soil and bedrock, resulting in substantial contributions to streamwater and stable discharge. In contrast, in the sedimentary rock catchment, larger contributions of the shortest flow line and smaller contributions of the longer flow line indicated the rapid drainage of water that infiltrated the soil and fractured bedrock, resulting in large storm flow and small baseflow. Our results provide important information to consider when comparing hydrological processes between basins with diverse geology. Keywords: tary rock 1

forested headwater; rainfall-runoff characteristics; mean residence time; granitic rock; sedimen-

INTRODUCTION low cost. In hydrological studies, the stable isotope ratios of water δ18 O, δD, or other radioactive tracers can be used to indicate the mean residence time (MRT) of streamwater and groundwater. Because a large fraction of rain is returned to the atmosphere by evapotranspiration, the residual isotopic signature in streamwater, when monitored regularly, conveys information about terrestrial water pathways and transit times (Vitvar et al. 2007). The MRT is a fundamental descriptor of hydrological and biogeochemical processes, revealing information about water storage, flow pathways, and sources in a single integrated measure (McGuire & McDonnell 2006). Thus, MRT is commonly used when considering upscaling effects on hydrological processes (McGlynn et al. 2003; Rodgers et al. 2005a; Soulsby et al. 2006); however, the relationship between basin area and baseflow residence time remains equivocal (Vitvar et al. 2005).

Forested headwater catchments are potential recharge areas for surface water and groundwater, as well as a source area for natural hazards. When considering human–environment interactions, it is important to clarify the rainfall-runoff characteristics of forested headwaters.Various subsurface water dynamics within the soil and bedrock affect hydrological processes. However, it is difficult to directly observe hydrological parameters below the ground surface. River runoff carries an integrated memory of hydrological processes in a watershed. Therefore, the discharge rate or chemical and isotopic information can help us estimate the subsurface water dynamics. Recent advances now allow us to easily and quickly conduct many kinds of isotope analyses at relatively ∗

Corresponding author ([email protected])

51

McDonnell et al. (1999) and McGlynn et al. (2003) indicated that the internal flowpath composition may be a first-order control on stream baseflow age. Based on that concept, Uchida et al. (2006) compared small catchments in Japan and New Zealand and demonstrated that bedrock permeability may control the direction of water aging; they also noted that functional intercomparisons should yield many new insights into the first-order controls on hillslope hydrological processes. Katsuyama et al. (2008) reported that bedrock geology highly controls rainfall-runoff characteristics, showing that granite catchments have stable flow conditions compared to sedimentary rock catchments. They concluded that bedrock geology is of primary importance for categorizing and identifying universal rainfall-runoff characteristics (Katsuyama et al. 2008). While various studies have reported the characteristics of granitic and sedimentary rock catchments (e.g., Holmes et al. 2002; Onda et al. 2006), the effects of these characteristics on isotopic patterns and MRT estimates have not been discussed. Therefore, in this study we examined differences in rainfall-runoff processes between small catchments with different bedrock geology based on water isotope tracers and MRT. 2

SITE DESCRIPTION

Figure 1. Topographic maps of KEW (Top) and GEF (Bottom).

Observations were conducted at the Kiryu Experimental Watershed (KEW: 34◦ 58 N, 136◦ 00 E; 190–255 m above mean sea level [AMSL]), Shiga Prefecture, and at the Mt. Gomadan Experimental Forest (GEF: 34◦ 04 N, 135◦ 35 E; 860–1370 m AMSL), Nara Prefecture, central Japan. Figure 1 shows topographic maps of the sites. KEW is underlain by Cretaceous biotite granite, called the Tanakami granite. From 1972 to 2005, the area had 1631.0 mm of mean annual precipitation; the air temperature averaged 13.5◦ C from 1997 to 2005. The vegetation is mainly 50-yr-old Japanese cypress (Chamaecyparis obtusa). Soil depth ranges from less than 1 m to several meters, and sandy soil is abundant. The underlying bedrock has properties of saprolite and is homogeneously weathered (Katsuyama et al. 2005). We used rainfall and discharge rate data from the entire KEW watershed, which included the K catchment. Moreover, discharge data from four subcatchments (R, M, H, A) and one hillslope plot (AP) within the K catchment were also considered. Table 1 shows the catchment areas, mean gradient, and hillslope aspects. GEF is underlain by Cretaceous sedimentary rock composed of alternating beds of sandstone and shale with some mudstone. From 2004 to 2006, GEF had 2951.2 mm of mean annual precipitation, including snowfall in winter. Soils are shallow, typically ranging from 0.2 to 0.7 m in depth, and composed of

much gravel. From 1912 to 1916, the natural forest was clear-cut and then reforested mainly with Japanese cedar (Cryptomeria japonica) for timber use. The forest has been well managed. Since 1958, timber harvesting by skyline logging and replanting have coincided with subcatchment boundaries, resulting in a mosaic of variously aged subcatchment stands. Therefore, chronosequence data can be obtained from simultaneous observations in multiple subcatchments (Fukushima & Tokuchi 2008). We used data from subcatchments S5, S11, S12, S16, S17, and S20. Table 1 shows the catchment areas, mean gradient, hillslope aspects, and forest age. S17 was a 90-yearold forest, which was clear-cut and then reforested in summer 2005. 3

METHODS

3.1 Hydrological observations and sampling Rainfall and discharge rates were continuously monitored using tipping buckets and weirs, and rainwater and streamwater samples were collected monthly in each catchment. Rainwater samples were collected by a bottle with a funnel. Streamwater samples were collected directly at the weir in each catchment. At KEW, isotopic observation of rainfall and streamwater at

52

Table 1.

Name KEW

GEF

For DM,

Catchment characteristics.

K R M H A AP S5 S11 S12 S16 S17 S20

Area (ha) 5.99 1.75 0.68 0.4 0.086 0.024 3.97 6.52 7.13 23.92 3.15 9.55

Gradient (deg.)

Aspect

9.2 9.6 14.3 17.0 22.0 23.4

⎫ N ⎪ ⎪ ⎪ NE ⎪ ⎪ ⎬ N E ⎪ ⎪ ⎪ N ⎪ ⎪ ⎭ NW

29.4 25.5 29.7 22.3 31.3 28.1

SE SES SES SWS SES ENE

Forest age (yrs in 2007) 48

where Dp is the apparent dispersion parameter (= D/vx, reciprocal of the Peclet number). The parameters τ and η for EPM and Dp for DM were obtained by trial and error to fit the measured output contents. The goodness of fit in the FlowPC program was defined as:

34 31 19 8 2 45

where δ18 Oobs and δ18 Ocal are the observed and calculated isotopic compositions, respectively, and n is the number of samples. To help define the quality of the fit, two additional goodness-of-fit measures, the square root of the mean square error (RMSE) and the mean absolute error (MAE), were calculated as follows:

catchment M began in June 1997, April 2000 at K and A, June 2002 at R and H, and July 2003 at AP. At the GEF, isotopic observation of rainfall and streamwater at all catchments began in December 2003. 3.2

Isotope analyses and mean residence time estimations

The 18 O analysis was conducted using the standard CO2 equilibration method to estimate the MRT of streamwater in each catchment. Samples were sealed in small glass vials and kept at room temperature. The analytical methods followed those of Kabeya et al. (2007). We calculated MRTs using the FlowPC program (version 3.2), which is designed for interpreting environmental tracer data in aquifers using the lumped parameter approach of Maloszewski and Zuber (1996) and input (rainfall) and output (groundwater and streamwater) data sets with long sampling periods. In this approach, the output can be related to the input using the convolution integral as follows:

4

RESULTS

4.1 Hydrographs At KEW, we never observed baseflow discharge from the hillslope plot AP; the hydrograph for AP showed peaky responses to rainstorms. On the other hand, the runoff was perennial at catchments A, M, R, and K, although catchment M had smaller discharge than catchments A, R, and K. Catchment H showed transitive discharge that ceased during the driest condition but began to flow during rainstorms and was then maintained for a relatively long time. At GEF, every catchment had peaky responses to rainstorms. The total rainfall and rainfall intensity were larger at GEF than at KEW, and peak discharge was high. After the rainfall, however, the discharge rate rapidly decreased, and baseflow discharge was small (Katsuyama et al. 2008). In contrast, the KEW catchments showed stable baseflow discharge (Kosugi et al. 2006; Katsuyama et al. 2008).

where δin (t) and δout (t) are the 18 O tracer input and output contents and τ is the turnover time. A response function, g(τ), defines the transit-time distribution. In this study, response functions derived from the exponential piston flow model (EPM) or dispersion model (DM) were applied (e.g., Vitvar & Balderer 1997; McGuire et al. 2005) such that for EPM,

4.2 Isotopic patterns of rainfall and streamwater

where η is the ratio of the total volume to the volume with an exponential residence time distribution, i.e., η = 1.0 indicates the exponential flow model (EM).

The variation of the rainfall δ18 O composition was large at both sites, ranging from −16.0 to −3.0‰ at

53

Table 2.

Solutions of mean residence time. Parameters

Goodness of fit

σ MAE RMSE Catchment Model τ (mo.) η/Dp (‰) (‰) (‰)

Figure 2. Isotopic variations of rainfall and streamwater RF denote Rainfall. Numbers in parenthesis are sample size.

K H R M A AP

EM EM EPM EM EM DM

26.1 15.9 27.1 22.4 28.9 0.1

1.0 1.0 1.25 1.0 1.0 10

0.04 0.09 0.06 0.03 0.04 0.23

0.30 0.48 0.35 0.28 0.33 1.30

0.40 0.70 0.49 0.35 0.39 1.65

S5 S11 S12 S16 S17 S20

EPM EM EPM EPM EPM EPM

15.9 14.9 9.3 18.7 12.1 12.1

1.2 1.0 1.1 1.15 1.15 1.15

0.07 0.06 0.11 0.16 0.09 0.10

0.34 0.23 0.45 0.50 0.35 0.38

0.47 0.32 0.68 0.98 0.54 0.63

Year 97 -6.0 -6.5 -7.0 -7.5 -8.0 -8.5 -9.0 -6.0 -6.5 -7.0 -7.5 -8.0 -8.5 -9.0

98

99

00

01

02

03

04 05

06

07

M

S11

Obs.

Fit.

Figure 3. Measured and simulated δ18 O for M and S11. Figure 4. Residence time distribution functions.

KEW and −13.4 to −4.3‰ at GEF. Compared to the rainfall, the streamwater had smaller variations and an average value of −8‰, similar to that of the rainfall at both sites (Figure 2). However, the outflow from AP had large variations and higher isotopic compositions. 4.3

The η values at K, H, M, and A in KEW and S11 in GEF were 1.0; that is, the shortest flow line had a transit time of zero and the longest flow line had a transit time of infinity (Maloszewski & Zuber 1996). In contrast, the η values for the five GEF catchments (S5, S12, S16, S17, and S20) and for the R catchment in KEW ranged from 1.1 to 1.25, meaning that 9 to 20% of the water was discharged by piston flow in these catchments. For both K and S11, the η value was 1.0; catchments R and S5 had similar values of 1.25 and 1.2, respectively, but very different response functions (Figure 4).

Mean Residence Time estimation

Figure 3 shows the measured and simulated δ18 O for catchments M and S11, while Table 2 lists the MRT in each catchment. Except for extremes, the observed values were well simulated in both catchments, although less so since 2005 in catchment M (Figure 3). Generally, the KEW catchments had longer MRTs than the GEF catchments (Table 2). Within KEW, hillslope plot AP had an especially short residence time (0.1 mo), although the simulation was less fitted. Catchment H had a relatively shorter MRT (15.9 mo) compared to catchments K, R, M, and A (22.4 to 28.9 mo). The estimated MRTs for GEF had some variation, ranging from 9.3 to 18.7 mo, but were relatively short compared to the MRTs for the KEW catchments.

5

DISCUSSION

5.1 Rainfall-runoff characteristics The hydrological characteristics of KEW have been intensively studied (e.g., Katsuyama et al. 2005; Kosugi et al. 2006); the homogeneously weathered bedrock beneath the soil is permeable (Katsura et al. 2005), and the bedrock groundwater plays an

54

headwater catchments, the effects of catchment area on streamwater MRT are minimal.

important role in runoff generation. Katsuyama et al. (2004) used simple water-budget analysis to demonstrate that saturated throughflow at the soil–bedrock interface flowed out only during storms from the bottom of hillslope plot AP. They further showed that half of the annual rainfall infiltrated into the soil–bedrock interface in this hillslope with thin soil. The plentiful bedrock groundwater contributes to stable baseflow discharge. On the other hand, the hydrographs of the GEF catchments were highly responsive to rainstorms. In observations at the GEF catchments, Katsuyama et al. (2008) found that water that infiltrated the soil and fractured bedrock drained quickly, causing large direct runoff over short, rainstorm timescales. As a result, the baseflow discharge was small, despite much rainfall at GEF. 5.2

5.3

Effects of runoff characteristics on isotopic patterns and MRT distributions

Within KEW, hillslope plot AP had a very short MRT (0.1 mo, i.e., 3 days). The runoff water from the hillslope plot was saturated throughflow on the soil– bedrock interface (Katsuyama et al. 2004; Kosugi et al. 2006); the short MRT means that the water was substantially rainfall, although part of the water was the residual of antecedent rainfall which had larger δ18 O by the effect of evapotranspiration (Figure 2). Catchment H had a relatively shorter MRT. The discharge from catchment H ceased during the driest condition, but started to run off during rainstorms; the runoff was then maintained for a relatively long time. These runoff characteristics are reflected in the MRT; during a rainstorm, saturated throughflow with a short MRT began to run off; when the catchment soil became sufficiently wet, the stored water with longer MRTs continuously contributed to the discharge. In catchments A, M, R, and K, on the other hand, streamflows were perennial because of plentiful water stored within the weathered bedrock (Katsuyama et al. 2005), and the MRTs were longer. The differences between these catchments may reflect the contribution of bedrock groundwater with long MRT. The GEF catchments had shorter MRTs, indicating fast water turnover. Although annual rainfall was abundant at GEF compared to KEW, the MRTs were shorter because the rainfall quickly drained by storm flow and baseflow discharge was small (Katsuyama et al. 2008). The η values (Table 2) and response functions (Figure 4) obtained in this study suggest the hydrological controls of the MRTs. In KEW, as mentioned above, four catchments had η values of 1.0; that is, the shortest flow line had a transit time of zero. KEW had gentle hillslope gradients, allowing for the presence of riparian zones. The presence of a riparian zone affects runoff generation and hydrochemical processes in catchments with gentle slopes (McGlynn et al. 1999). Therefore, riparian water, including direct precipitation on the riparian zone and stream channel, will contribute at first during rain events. However, compared to the GEF catchments, the KEW catchments showed relatively small contributions of the shortest flow line and large contributions of the longer flow line (Figure 5). This means that groundwater stored within the soil and bedrock, which had longer residence time, substantially contributed to the streamwater and resulted in stable baseflow discharge at KEW. In contrast, larger contributions of the shortest flow line and smaller contributions of the longer flow line corresponded to large storm flow and small baseflow at GEF.

Effects of catchment area and forest growth on MRT distributions

Some studies (e.g., McDonnell et al. 1999) have implied a positive correlation between catchment area and residence time. However, other recent studies (e.g., McGlynn et al. 2003; McGuire et al. 2005) have reported that catchment area does not appear to be related to residence time. Rodgers et al. (2005b) showed that the effect of increasing scale on estimated streamwater MRT was minimal beyond that for the smallest (∼1 km2 ) headwater catchment scale. Instead, they suggested that the interaction of catchment soil cover and topography might be the dominant controlling influence. Soulsby et al. (2006) showed that catchment soil characteristics appear to act as a firstorder control on flowpath partitioning and residence times in a mesoscale catchment. We studied small headwater catchments (Table 1). Among the GEF catchments, forest ages differed. Forest age can affect the storm flow peak height and water budget, with older catchments generally having smaller peak flow because of soil development (Katsuyama et al. 2008) and smaller annual discharge because of increasing evapotranspiration with forest growth. However, we found no clear relationship between MRT and forest age. For example, S12 (a 19-year-old forest) had the shortest MRT, and S16 (8-year-old forest) had the longest MRT among the GEF catchments. Therefore, at least at first glance, there is no clear relationship between the soil development associated with forest growth and the MRT distributions. We also did not find a consistent relationship between catchment area and MRT in our study. The smallest catchment A had the longest MRT at KEW. At GEF, catchment S16 had the largest area and longest MRT, although the smaller catchments S5 and S17 had longer MRTs compared to other larger catchments. Our results suggest that even in small

55

6

CONCLUSION

and their effects on runoff generation in a small headwater catchment. Water Resour. Res. 42: W02414, doi:10.1029/2005WR004275. Maloszewski, P. & Zuber, A. 1996. Lumped parameter models for the interpretation of environmental tracer data, in Manual on Mathematical Models in Isotope Hydrology. IAEA-TECDOC 910: 9–58. Vienna: IAEA. McDonnell, J., Rowe, L.K. & Stewart, M.K. 1999. A combined tracer-hydrometric approach to assess the effect of catchment scale on water flow path, source and age. In C. Leibundgut, J. McDonnell & G. Schultz (eds), Integrated Methods in Catchment Hydrology—Tracer, Remote Sensing, and New Hydrometric Techniques; Proc. IUGG 99 Symposium HS4. IAHS Publ. 258: 265–273. McGlynn, B.L., McDonnell, J.J., Shanley, J.B. & Kendall, C. 1999. Riparian zone flowpath dynamics during snowmelt in a small headwater catchment. J. Hydrol. 222: 75–92. McGlynn, B., McDonnell, J., Stewart, M. & Seibert, J. 2003. On the relationships between catchment scale and streamwater mean residence time. Hydrol. Process. 17: 175–181. McGuire, K.J., McDonnell, J.J., Weiler, M., Kendall, C., McGlynn, B.L., Welker, J.M. & Seibert, J. 2005. The role of topography on catchment-scale water residence time. Water Resour. Res. 41: W05002, doi:10.1029/ 2004WR003657. McGuire, K.J. & McDonnell, J.J. 2006. A review and evaluation of catchment transit time modeling. J. Hydrol. 330: 543–563. Onda, Y., Tsujimura, M., Fujihara, J. & Ito, J. 2006. Runoff generation mechanisms in high-relief mountainous watersheds with different underlying geology. J. Hydrol. 331: 659–673. Rodgers, P., Soulsby, P. & Waldron, S. 2005a. Stable isotope tracers as diagnostic tools in upscaling flow path understanding and residence time estimates in a mountainous mesoscale catchment. Hydrol Process. 19: 2291–2307. Rodgers, P., Soulsby, C., Waldron, S. & Tetzlaff, D. 2005b. Using stable isotope tracers to assess hydrological flow paths, residence times and landscape influences in a nested mesoscale catchment. Hydrol. Earth Syst. Sci. 9: 139–155. Soulsby, C., Tetzlaff, D., Rodgers, P., Dunn, S. & Waldron, S. 2006. Runoff processes, stream water residence times and controlling landscape characteristics in a mesoscale catchment: An initial evaluation. J. Hydrol. 325: 197–221. Uchida, T., McDonnell, J.J. & Asano, Y. 2006. Functional intercomparison of hillslopes and small catchments by examining water source, flowpath and mean residence time. J. Hydrol. 327: 627–642. Vitvar, T. & Balderer W. 1997. Estimation of mean water residence times and runoff generation by 18 O measurements in a Pre-Alpine catchment (Rietholzbach, Eastern Switzerland). Appl. Geochem. 12: 787–796. Vitvar, T., Aggarwal, P. & McDonnell, J.J. 2005. A review of isotope applications in Catchment Hydrology. In: P.K. Aggarwal, J. Gat & K. Froehlich (eds). Isotopes in the Water Cycle: Past, present and future of a developing science; Chapter 12: 151–170. Dordrecht: Springer. Vitvar, T., Aggarwal, P.K. & Herczeg, A.L. 2007. Global network is launched to monitor isotopes in rivers. Eos Trans. 88(33): 325–326.

Residence time is a fundamental catchment descriptor that reveals information about the water storage, flow pathways, and sources (McGuire & McDonnell 2006). Differences in bedrock geology are of primary importance for identifying rainfall-runoff characteristics (Katsuyama et al. 2008) and result in the streamwater MRT distributions. Our results provide important information for considerations of residence time and hydrological processes in large basins, scaling effects, and comparisons of basins having diverse geology. ACKNOWLEDGEMENT We would like to acknowledge Prof. Nobuhito Ohte, and the members of Laboratories of Silviculture and Forest Hydrology, Kyoto University. We also thank to Prof. Ichiro Tayasu and Prof. Tetsuya Hiyama for isotope sample analysis assistance and discussion. This work was supported by Grant-in-Aid for Scientific Research from JSPS, Japan (No. 18780122 & 18201036), Research Grant from Inamori Foundation, and Research Project No. 5-2 in Research Institute for Humanity and Nature. REFERENCES Fukushima, K. & Tokuchi, N. 2008. Effects of forest clearcut and afforestation on streamwater chemistry in Japanese cedar (Cryptomeria japonica) forests: comparison among watersheds of various stand ages. J. Jap. For. Soc. (in press) (in Japanese with English Summary). Holmes, M.G.R., Young, A.R., Gustard, A. & Grew, R. 2002. A region of influence approach to predicting flow duration curves within ungauged catchments. Hydrol. Earth Syst. Sci. 6: 721–731. Kabeya, N., Katsuyama, M., Kawasaki, M., Ohte, N. & Sugimoto A. 2007. Estimation of mean residence times of subsurface waters using seasonal variation in deuterium excess in a small headwater catchment in Japan. Hydrol. Process. 21: 308–322. Katsura, S., Kosugi, K., Yamamoto, N. & Mizuyama, T. 2005. Saturated and unsaturated hydraulic conductivities and water-retention characteristics of weathered granitic bedrock, Vadose Zone J. 5: 35–47. Katsuyama, M., Ohte, N. & Kosugi, K. 2004. Hydrological control of the streamwater NO− 3 concentrations in a weathered granitic headwater catchment. J. Jpn. For. Soc. 86: 27–36 (in Japanese with English abstract). Katsuyama, M., Ohte, N. & Kabeya, N. 2005. Effects of bedrock permeability on hillslope and riparian groundwater dynamics in a weathered granite catchment. Water Resour. Res. 41: W01010, doi:10.1029/2004WR003275. Katsuyama, M., Fukushima, K. & Tokuchi, N. 2008. Comparison of rainfall-runoff characteristics in forested catchments underlain by granitic and sedimentary rock with various forest age, Hydrol. Res. Lett. (in press). Kosugi, K., Katsura, S., Katsuyama, M. & Mizuyama, T. 2006. Water flow processes in weathered granitic bedrock

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

The effect of antecedent moisture condition on storm flow water sources in young forest headwater catchment T. Oda∗, Y. Asano, N. Ohte & M. Suzuki Graduate school of Agricultural and Life Science, The University of Tokyo, Tokyo, Japan

ABSTRACT: We investigated how antecedent moisture conditions affect the contributions of runoff water sources in a small mountainous headwater catchment in central Japan. Spatial variations of Na+ and Cl− showed that streamwater was a mixture of three components: throughfall, transient groundwater, and perennial groundwater. The contributions of end-members varied by storm event. The contributions of throughfall and transient groundwater increased and the contribution of perennial groundwater decreased when total rainfall increased. The contribution of perennial groundwater decreased when the antecedent moisture condition was drier, particularly when the groundwater level was lower. The contribution of perennial groundwater significantly decreased, and that of throughfall increased, when antecedent moisture conditions were extremely dry. Keywords:

1

storm event; End-members mixing analysis; antecedent moisture condition; overland flow

INTRODUCTION

Most previous studies identifying water sources in temperate regions have been conducted in Europe and North America. In these areas, precipitation inputs show little seasonal variation. Moisture conditions and storm sizes exhibit low variation as well. In contrast, the central part of the Japanese archipelago has great variation in precipitation input from the Asian Monsoon and frequent typhoons. Precipitation patterns, antecedent moisture conditions, and rainfall amount and intensity vary widely; however, only a few studies have been conducted in this region (Katsuyama et al. 2001, Subagyono et al. 2005). We hypothesized that the effects of the antecedent moisture condition and storm size on the storm runoff sources were clearer in the Asia Monsoon region because of the large variation in antecedent moisture conditions and rainfall in the area. The objective of this study was to investigate how storm size and antecedent moisture conditions affected the contributions of runoff water sources in a headwater catchment in central Japan.

In recent decades, much effort has been devoted to determining the contribution of runoff water sources (spatially separated soil components) to streamwater discharge, for the development of realistic hydrological models, the understanding of nutrient transport, and the evaluation of impacts of land use change on streamwater chemistry. Many studies have relied on hydrograph separation to determine runoff components and their contributions successfully (e.g., Hooper et al. 1990, Burns et al. 2001). Such studies have also found that the contribution of runoff water sources can vary with antecedent moisture conditions (Burns et al. 2001, Bernal et al. 2006, Inamdar & Mitchell 2007). Previous studies have suggested that catchment wetness and storm event size have important implications for temporal sequencing and contributions from runoff sources (Inamdar & Mitchell 2007). For example, some studies have reported that a large storm with wet antecedent moisture favors storm flow from the transient groundwater or soil water (Burns et al. 2001, Katsuyama et al. 2001, McGlynn & McDonnell 2003). DeWalle et al. (1988) argued that soil water contributions would be smaller when basin soils were relatively dry because the soils possess greater water retention capacities. ∗

2

METHODS

2.1 Study site The study was conducted in a small headwater catchment, the Fukuroyamasawa Experimental Watershed Catchment B, located in Chiba Prefecture, central ◦ Japan (35◦ 12 N, 140 06 E; Figure 1). The catchment

Corresponding author ([email protected])

57

had an area of 1.087 ha and was underlain by Tertiary formation sedimentary rock. The average slope of the mainstream was 23.5◦ , and the mean soil depth was 2.22 m. A mixed stand of Cryptomeria japonica and Chamaecyparis obtusa planted in April 2000 covered the entire catchment, although the forest canopy was not completely closed in 2007. The annual mean temperature in the area was 14◦ C, and the mean annual precipitation was 2239 mm (1994–2005). The mean annual discharge was 1224 mm/year. Surface water appeared about 10 m upslope from weir B on nonrainy days. A channel was generated only during storm events, and the maximum channel area is less than 2% of the entire catchment area. 2.2

samples were collected regularly by grab sampling at weir B. Groundwater samples were collected from cups embedded in 11 observation wells and from a tube attached to the bottom of each observation well. At point B2, wells of three different depths (50, 100, and 200 cm) were installed. At point B6, wells of four different depths (50, 100, 200, and 270 cm) were installed. At points B7, B8, Y6, Y7, and Y8, the well depths were 150, 150, 300, 350, and 400 cm, respectively. The code names of wells (B2-50, B2-100, B2-200, B6-50, B6-100, B6-200, B6-270) indicate the point and the depth. Two types of groundwater wells were installed. The wells at Y6, Y7, Y8, B6-270, and B7 were made of bore pipes 6 cm in diameter screened with small holes around their peripheries throughout the vertical dimension, whereas wells B2-50, B2-100, B2-200, B6-50, B6-100, B6-200, and B8 had small holes at the bottom of the pipes. Streamwater samples during storm events were collected using an automatic sampler (American Sigma, NewYork, Model 900) with a time interval of 10 to 120 minutes in five events [16 June (storm 1), 5 July (storm 2), 26–27 September (storm 3) in 2006 and 6 September (storm 4) and 12 September (storm 5) in 2007]. Storm 4 was a typhoon event that brought substantial sea-salt-containing precipitation to the catchment. Water samples were brought to the laboratory in bottles and stored in a refrigerator before treatment. Samples were filtered through 0.2-µm Millipore membrane filters, and concentrations of Cl− and Na+ were determined by ion chromatography (Shimadzu, Kyoto, LC-10A).

Observations

Rainfall amount was measured using a tipping bucket rain gauge at a meteorological station (Shinta Weather Station) located 200 m east of Fukuroyamasawa Experimental Watershed catchment B. The discharge was measured continuously at a 90◦ V-notch weir (weir B in Figure 1). The groundwater level on the bedrock was measured by a water level data logger (Trutrack, Christchurch, SE-TR/WT2000) from June 2006 to October 2007 at point B6, where the bedrock was 270 cm below the surface (Figure 1). Precipitation samples for chemical analysis were collected in a polyethylene bottle with a funnel 21 cm in diameter at the Shinta Weather Station. Throughfall samples were collected by the same collection device at three points in catchment B. Streamwater

2.3 Hydrograph separation An end-member mixing analysis (EMMA; Hooper et al. 1990) was used as a tool for hydrograph separation of water sources. The tracer concentrations in streamwater were assumed to be determined by mixing the concentrations of end-members that contribute to streamwater runoff. Cl− and Na+ are useful tracers that have often been used to perform hydrograph separation (Peters & Ratcliffe 1998, Katsuyama et al. 2001). Cl− is highly mobile in most ecosystems because it is not readily absorbed onto surfaces, and it is not readily incorporated into secondary minerals (Peters & Ratcliffe 1998). Na+ is mainly derived from weathering and related to the residence time of water. In our study, the end-members were first selected by measuring the spatial variation of tracer concentrations of Cl− and Na+ . The contributions of end-members to streamwater during storm events were then calculated using the following mass balance equations: Figure 1. Fukuroyamasawa Experimental Watershed, showing the topography and the locations of sampling wells in the catchment. The gray line shows the spread channel during storm events.

58

after rainstorms. The wells at B2, Y6, Y7, and Y8 were in this zone, and the sampled water was called transient groundwater (TGW).

where fa , fb , and fc are the proportions of each endmember, and C1a , C1b , C1c , C2a , C2b , and C2c are the concentrations of each end-member of the two tracers. C1s and C2s are the weighted mean concentrations of tracers in streamwater during storm events. 2.4

3.2 Characteristics of sampled storms The characteristics of storm events are summarized in Table 1. Storms 1–3 and 5 were caused by a seasonal rain front, and storm 4 was caused by a typhoon. The total rainfall of storm events ranged widely from 63 to 178 mm (Table 1). The sampled storm events were in the largest quartile of all storm events from January 2005 to October 2006. Maximum rainfall intensity ranged from 17 mm/h to 62 mm/h, within the highest 10% intensity class for all storm events observed from January 2005 to October 2006. Discharge prior to storm events ranged from 0.005 to 0.023 mm/h, and the antecedent moisture conditions prior to the storms were variable in storms sampled from January 2005 to October 2006.

Indicator of antecedent moisture conditions

The antecedent moisture conditions of storm events were assessed in three ways: [1] by stream discharge prior to the storm event (mm/h); [2] by the antecedent precipitation index (API); and [3] the groundwater level from bedrock measured at point B6 just before each storm event (cm). API is calculated for rainstorms to determine antecedent moisture conditions prior to each storm (McHale et al. 2002):

3.3 Spatial variation of solutes in the catchment where API on any given day i (APIi ) is equal to a recession constant K normally reported in the range of 0.85 to 0.98 ((McHale et al. 2002); 0.9 was used for calculations) multiplied by the API on the previous day (APIi−1 ) plus the daily precipitation on the i-th day, Pi . 3 3.1

Throughfall, TGW, and PGW showed distinctly different tracer concentrations (Figure 2). The mean Cl− concentration in throughfall, TGW, and PGW was 75, 100, and 126 µmol/l, respectively. The variation of Cl− in PGW was smaller than in TGW and throughfall. The increases in Cl− concentration in the TGW and PGW relative to throughfall indicated concentration by evapotranspiration during infiltration through the soil. The effect of evapotranspiration was greater for PGW. The catchment site was close to the sea, and throughfall with high Cl− concentration was supplied during some storm events such as typhoons. The high Cl− concentration in throughfall was different from the concentration in TGW and PGW, making it useful for analysis. The difference in concentration between TGW and PGW, however, was small. The concentration of Na+ also increased in TGW and PGW relative to that in throughfall, indicating

RESULTS Groundwater responses

The groundwater wells were divided into two groups based on groundwater response. One was in the zone that was saturated all year. The wells at B6, B7, and B8 were included in this group, and the sampled water was termed perennial groundwater (PGW). The second group was in the zone that was transiently saturated. This zone was saturated during storm events, and the saturated condition disappeared immediately Table 1.

Characteristics of storm events.

Starting date Storm 1 Storm 2 Storm 3 (First half) Storm 3 (Second half) Storm 4 Storm 5

Antecedent moisture condition Maximum Total Total rainfall Discharge Groundwater rainfall discharge intensity API(7) API(15) API(30) API(45) prior to storm level prior to (mm) (mm) (mm/hr) (mm) (mm) (mm) (mm) (mm/hr) storm (cm)

16-Jun-06 05-Jul-06 26-Sep-06

63 80 96

30 22 29

17 24 17

34 1 0

34 4 12

47 21 17

48 22 19

0.023 0.015 0.005

160 163 152

27-Sep-06

82

65

62

80

91

96

97

0.250

188

06-Sep-07 153 12-Sep-07 119

58 68

19 38

12 108

16 109

16 110

16 110

0.005 0.013

140 163

59

Figure 2. Box plots of Cl− and Na+ concentrations in precipitation, transient groundwater, perennial groundwater, streamwater during baseflow, and streamwater during stormflow.

dissolution by chemical weathering. The mean concentration in precipitation, TGW, and PGW was 68 198, and 286 µmol/l, respectively. The variation was large within each component; nonetheless, differences in concentrations among the three components were clear. The Cl− and Na+ concentrations in streamwater during baseflow were similar to those in PGW, whereas those in storm flow varied by storm (Figure 2). This indicated that during baseflow, PGW mainly contributed to the stream, but TGW and throughfall were potential end-members in addition to PGW during storms.

Figure 3. Variation of precipitation, discharge, and concentrations of Cl− and Na+ in streamwater during storm events.

of precipitation as throughfall when the throughfall data were incomplete because the concentrations of precipitation and throughfall were similar (Figure 2). During three storm events (first half of storm 3 and storms 4 and 5), streamwater concentrations were explained via the mixing of the three end-members, namely throughfall, TGW, and PGW (Figure 4). However, during three different storm events (storm 1, storm 2, and the second half of storm 3), the contributions of TGW to the streamwater concentrations were unclear. Therefore, we separated the hydrograph using three component mixing analysis during three storm events: the first half of storm 3 and storms 4 and 5. Solute concentrations in the stream water were similar to those in PGW at the beginning of storm events, and then evolved toward those of the throughfall and TGW concentrations during the storms. During storm 4, the mixing diagram exhibited clockwise hysteresis. The contributions of the components varied among the storms (Table 2). For three component mixing, TGW was dominant for storm 5, and it was the main source for storm 4. In the first half of storm 3, PGW was dominant. For storm 4, the contribution of PGW was 3%, which was much smaller than in the other two storms when it was more than 40%.

3.4 Temporal variation in solute concentrations during storm events The concentration of Na+ decreased with increasing hydrograph level during every storm event, indicating dilution by throughfall and TGW with low Na+ concentration (Figure 3). The concentration of Cl− also decreased with hydrograph elevation during three events (storms 1, 2, and the second half of 3), but it increased during storm 4 and did not change during storm 5 and the first half of storm 3. Storm 4 was the typhoon event for which the Cl− concentration in precipitation was extremely high (380 µmol/l) and was probably derived from sea salt. 3.5

Hydrograph separation of storm events with mixing analysis for end-members

The concentration of PGW was determined as the mean concentration of PGW collected before and after each storm event. The concentration of TGW was determined as the mean concentration of TGW collected immediately after each event. Similarly, the concentration of throughfall was determined immediately after each event. We used the concentration

4

DISCUSSION

The total rainfall amount was positively correlated with the contribution of throughfall (R2 = 0.93) and

60

Figure 5. Relationship between the contribution of components and total rainfall, maximum rainfall intensity, discharge prior to storm, groundwater level prior to storm for Storm 3 (first half), Storm 4 and Storm 5. 120

API

90 Storm 3 (first half) Storm 4 Storm 5

60 30

Figure 4. Mixing diagram for each storm. 0 0

Table 2. Contributions and total amounts of runoff components during 3 storm events [Storm 3 (first half), Storm 4 and Storm 5].

Storm 3 (First half) Storm 4 Storm 5

TGW % (mm)

PGW % (mm)

12 (4)

27 (8)

61 (18)

35 (20) 15 (10)

62 (36) 43 (29)

3 (2) 42 (28)

20

30

40

50

60

Lag time of API (day)

Figure 6. Variation API with time lag during Storm 3 (first half), Storm 4 and Storm 5.

The contributions of components TF % (mm)

10

dry antecedent conditions. This occurred because the amount of PGW runoff was extremely small, even though storm 4 was the largest storm in the study. The groundwater level prior to storm 4 was the smallest of all storm events observed in 2 years (2006 to 2007), suggesting that storm 4 occurred under extremely dry conditions. These results suggest that during storms with moderate to large total rainfall and very dry antecedent conditions, incoming water discharges through flowpaths at or near the surface without recharging the deep groundwater.This hypothesis is further supported by the clockwise hysteresis observed during storm 4 (Figure 4). The API of storm 5 was the largest of three storm events (first half of storm 3 and storms 4 and 5); the API of storm 4 was the second largest of three events when the time lag was shorter than 25 days, but was the smallest when the time lag was longer than 25 days (Figure 6). Storm 4 had the driest antecedent moisture condition from the groundwater level prior to the

TGW (R2 = 0.99), and negatively correlated with the contribution of PGW (R2 = 0.99). The antecedent moisture condition, particularly the groundwater level, was positively correlated with the contribution of PGW (R2 = 0.44) and negatively correlated with the contribution of throughfall (R2 = 0.65) (Figure 5). The total rainfall amount had a major impact on the water sources during storm events. The antecedent moisture condition also had an impact on the water sources. In particular, the smallest contribution and amount of PGW and the largest throughfall to runoff water during storm 4 (Table 2) were caused by

61

REFERENCES

storm (Table 1). The longer time lag APIs were more closely related to the groundwater level prior to the storm than those with shorter lag times. These results indicated that the contribution of sources was influenced by precipitation occurring up to several weeks previously. These results are in agreement with those of previous studies, suggesting that a larger storm size leads to higher contributions of TGW (or soil water) in storm discharge (Katsuyama et al. 2001, McGlynn & McDonnell 2003, Subagyono et al. 2005). However, we also found the contribution of TGW to be larger when the antecedent moisture condition was drier. This result was not found in the previous studies conducted in North America, suggesting that contributions of soil water were small when the soil was relatively dry (DeWalle et al. 1988). This could be of importance in understanding the hydrological pathways of the Asia Monsoon climate. In this study, it was only possible to separate the storm runoff into three components in only three storm events. Clearly, the number of cases studied must be increased in future work. 5

Bernal, S. Butturini, A. & Sabater, F. 2006. Inferring nitrate sources through end member mixing analysis in an intermittent Mediterranean stream. Biogeochemistry 81: 269–289. Burns, D.A. McDonnell, J.J. Hooper, R.P. Peters, N.E. Freer, J.E. Kendall, C. & Beven, K. 2001. Quantifying contributions to storm runoff through end-member mixing analysis and hydrologic measurements at the Panola Mountain Research Watershed (Georgia, USA). Hydrological Processes 15: 1903–1924. DeWalle, D.R. Swistock, B.R & Sharpe, W.E. 1988. Threecomponent tracer model for stormflow on a small Appalachian forested catchment. Journal of Hydrology 104: 301–310. Hooper, R.P. Christopherson, N. & Peters, N.E. 1990. Modelling streamwater chemistry as a mixture of a oilwater end-members —an application to the Panola Mountain catchment, Georgia, USA. Journal of Hydrology 116: 321–343. Inamdar, S.P. & Mitchell, M.J. 2007. Contributions of riparian and hillslope waters to storm runoff across multiple catchments and storm events in a glaciated forested watershed. Journal of Hydrology 341:116–130. Katsuyama, M. Ohte, N. & Kobashi, S. 2001. A threecomponent end-member analysis of streamwater hydrochemistry in a small Japanese headwater catchment. Hydrological Processes 15: 249–260. McHale, M.R. McDonnell, J.J. Mitchell, M.J. & Cirmo, C.P. 2002. A field-based study of soil water and groundwater nitrate release in an Adirondack forested watershed. Water Resources Research 38, 4, 10.1029/200WR000102. McGlynn, B.L. & McDonnell, J.J. 2003. Quantifying the relative contributions of riparian and hillslope zones to catchment runoff. Water Resources Research 39 (11), 1310. doi:10.1029/2003WR00209. Peters, N.E. & Ratcliffe, E.B. 1998. Tracing hydrologic pathways using chloride at the Panola Mountain Research Watershed, Georgia, USA. Water, Air, and Soil Pollution 105: 263–275. Subagyono, K. Tanaka, T. Hamada, Y. & Tsujimura, M. 2005. Defining hydrochemical evolution of streamflow through flowpath dynamics in Kawakami headwater catchment, Central Japan. Hydrological Processes 19: 1939–1965.

CONCLUSIONS

Solute concentrations, rainfall amounts, and discharge were measured during five storm events of various rainfall intensity, total rainfall, and antecedent moisture conditions in a headwater catchment in central Japan. The spatial variation of Na+ and Cl− showed that the streamwater was a mixture of throughfall, TGW, and PGW. The storm discharge of three storm events was determined by mixing of the three sources. The total rainfall amount and antecedent moisture conditions, which were estimated from groundwater levels prior to each storm, had the largest influence on the dominant water sources contributing to storm hydrographs. A larger storm size led to higher contributions of TGW in storm flow. Furthermore, the contribution of PGW tended to be smaller when the antecedent moisture condition was drier. This may be a unique feature of the storm runoff process under the Asia Monsoon climate, which has larger storm sizes on average and a variable moisture condition in summer. ACKNOWLEDGEMENTS We thank the staff of the University Forest in Chiba, The University of Tokyo for their ongoing support. We also thank Mr T Yamamoto and Mr K Matsuzawa for their great assistance.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Combined roles of soil horizons and fractured bedrock in subsurface water concentration in a valley-head T. Furuta∗ Open Research Center, Graduate School of Geo-environmental Science, Rissho University, Saitama, Japan

T. Tamura Faculty of Geo-environmental Science, Rissho University, Saitama, Japan

ABSTRACT: In the rainfall-runoff response in a watershed, concentration of infiltrated water to produce streamflow is an important process. In order to recognize this process, we carried out separated measurements of runoff from several soil horizons including fractured bedrock at a stream-head in a head-hollow covered by forest in a hilly zone of northeastern Japan. Soil profile observation and handy penetration tests revealed thick and a little permeable soil to be enough to store infiltrated water in the upper head-hollow. B horizon’s thickness decreases in the lower head-hollow and it is replaced by AB horizon. Because of the stored water in a head-hollow, runoff from the fractured bedrock (QR ) was the major runoff except the time around the peak of unusually intensive rainfall when runoff from soil surface(Qs ) or runoff from an outlet on the soil surface(QP3 ) exceeded QR . Even in a dry condition when no runoff occurred, pressure at each horizons and groundwater level changed because of a rainstorm. The above results of observation and structure of the head-hollow suggest some of QR is supplied by direct permeation from AB horizon to bedrock in the lower head-hollow. Keywords: subsurface water concentration; head-hollow; pipe flow; soil horizons; rainfall-runoff response; direct water permeation

1

INTRODUCTION

river system, a branch of the Natori river. Soil in the study area is mostly classified as yellow-brown forest soil with parent material of residual, creeping or colluvial origin (Sato, 1991) which rest on semisolidificated sandstone and silty sandstone of Miocene age (Ogasawara, 1989; Kitamura et al., 1986). The normal annual precipitation in the term between 1971 and 2000 at Sendai District Meteorological Observatory, about 5 km to the northeast from the study site is 1241.8 mm. The precipitation outside the forest is observed at Loc.B shown in Figure 1, about 300 m north of the study head-hollow (Loc.A), which was substituted by the rainfall data measured at Loc.C, about 100 m north of Loc.B in a part on July 2004.

A head-hollow situated at the headmost part of a valley(Tamura, 1969, 1996, 2001) becomes a partialcontributing area in the condition of continuous rain (Betson, 1964; Dunne and Black; 1970a, 1970b). Its contribution is made by subsurface water flow, which is variable according to precipitation. In order to recognize the pathways in which subsurface water concentrates to a channelway, we carried out both separated measurements of runoff from several soil horizons including fractured bedrock at a streamhead and observations of soil and soil-water conditions in and around a head-hollow. 2

STUDY AREA AND METHOD

2.2 Microgeomorphology and soil horizon Figure 2 shows the microlandform of the study headhollow with the depth of the solum. The altitude of the stream-head Loc.1 is about 90 m. The area, length, relief, relief ration, and average inclination of the head-hollow is 1005 m2 , 28 m, 16 m, 0.43, and 22 degree, respectively. The microgeomorphic classification follows fundamentally to Tamura (1996, 2001).

2.1 Study area Figure 1 shows the place of the study area in a headhollow covered by forest in a hilly zone near Sendai, northeastern Japan. The area is situated in the Zaru ∗

Corresponding author ([email protected])

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The head-hollow where water concentrates extends up to 15 m from Loc.1. Soil profile observation and handy penetration tests which were carried out after the runoff observation period in order to keep the stable observation revealed that most part of the head-hollow has thick subsoil less permeable than the surface soil which are enough to store infiltrated water. Because the surface of the fractured but compact bedrock defines the bottom of solum, the bottom was detectable by not only soil profile observation and soil auger observation but also handy penetration tests (Furuta et al. 2007). As shown in Figures 3(a) and (b), soil profiles consist of O horizon, A and AB horizon, B horizon, and fractured bedrock. At Loc.1, the thickness of humic and porous A and AB horizon reaches up to 55 cm and B horizon is almost lacking. It decreases at Loc.2 and Loc.3, where thick B horizon is clayey and less permeable. At many points particularly in the lower head-hollow, the bottom of solum was indicated by the sudden increase of handy penetration value Nc to 10. Nc means the count of dropping of a 5 kg weight made of steel from 50 cm above. In reference to the soil profile at Loc.1 to Loc.3, Nc never shows 10 in this solum but exceeds 10 in the weathered or fractured bedrock. Then the Nc 10 was applied to the estimation of the distribution of solum depth in the head-hollow as shown in Figure 2. Solum is particularly deep in the upper part of the head-hollow and along the talweg. It reaches 2 m at Loc.3. It suggests that water and soil move along the talweg to Loc.3 where weathering may have been promoted. While, thickness of solum particularly B horizon decreases in the lower head-hollow, where AB zone is thick instead. This structure is considered as a result of weathering and local deposition and to contribute much to the concentration of subsurface water. The small cliff about 0.8 m high is formed at the end of the catchment area, and a clear channelway is initiated there. Steep lower sideslopes with 40 degree continue along the downstream channel. There are three water outlets of pipes in the lower head-hollow. The first pipe, named as P1 , is formed along a crack of fractured bedrock at Loc.1, and its diameter is 1 mm. Second pipe, P2 , is formed between AB horizon and fractured bedrock with a squarish configuration of 3 cm across. The third pipe, P3 , is in a shallow position in A horizon located 1.7 m upstream of the channel head and shows a rounded shape. At the channel-head, particular attention is made to runoff from a crack Cr 1 mm wide and 10 cm long, which is formed in bedrock exposed on channel wall.

Figure 1. Study area. Reference 1:25000 Digital map (Issued by Geographical Survey Institute).

3

SOIL MOISTURE AND RUNOFF OBSERVATION

Figure 3(a) shows the location of tensiometers T1 , T2 , and T3 and the location of water level gauge WL1 ,

Figure 2. Micro-landfrom classification of hillslopes with reference to soil depth in the valley-head observed.

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were set at 32 cm deep in the B horizon. A water-level gauge SE-TR/WT500, which is 50 cm long and provided by Co. Senecom Ltd., is contained in a vinyl chloride pipe with inside diameter of 25 mm which was inserted into the ground at the point shown in Figure 3(c). The observation interval of pore pressure and water level is 10 minutes. Runoff was observed at 5 points (Qs , QP3 , QA , QAB , and QR ) near the stream-head formed at the end of the lower head hollow. Qs occurred temporally almost as saturated overland flow in the lower head-hollow. QP3 is runoff from pipe P3 situated 1.7 m upstream of the stream-head. QA , QAB , and QR are runoff from A horizon, AB horizon and fractured bedrock, respectively, at the stream head. Amount of QA and QAB was very limited and almost negligible. As Figure 3(c) shows, Qs is generated near the channel head. QR is almost equal to the sum of QP1 (runoff from P1 ), QP2 (runoff from P2 ), and Qcrack (runoff from the crack Cr). Applied for the observation of QP3 and QAB , hand-made tipping water buckets were used. The volume of the two buckets are 127 ml to 402 ml, respectively. QR was measured by TQX-2000, a tipping water bucket made by Ikeda Keiki, with volume of 2160 ml. For runoff, data loggers Hioki electronics’ 3639 and T&D’s Amenbo were used. Qs was measured by a weir box with a V-notch. Rainfall observation was carried out using a raingauge 34-T, made by Ota Keiki, with a bucket of 15.7 ml, connected to a logger Hobo event made by Onset cooperation. One rainfall event was separated by non-rainfall interval longer than 8 hours. All the observation were continued in the period from July to October in 2004. 4 4.1

RESULTS AND DISCUSSION Hydrologic responses to rainfall

Figure 4 shows rainfall, runoff, pore pressure measured by T2 , and groundwater level in the study period. The observation period contains the rainy season 1 and the rainy season 2, separated by a summer dry season. Rainy season 1 is before summer, and rainy season 2 is after summer. The three seasons were divided on August 4th and September 28th. Figure 4(a) shows the rainfall in the rainy season 1, in the summer season, in the rainy season 2, and total rainfall, which are 156 mm, 120.5 mm, 380.5 mm, and 657 mm respectively. The seasonal amount of runoff has close relation with rainfall in each rainy season as shown in Figure 4(b). Moreover, runoff rates are also high in rainy seasons as around 35% to 40%. In contrast to this, runoff rate is almost 0 in dry summer season. Figure 4(c) shows runoff from fractured bedrock (QR ) is more sensitive than runoff from ground surface and respective soil horizons (Qs + QP3 + QA + QAB ). For example, Figure 4(d) shows many peaks of the pore

Figure 3. The distribution of observation instruments in profiles and a plan of the head-hollow.

which are the observation points of pore pressure and groundwater level. The tips were set at 11 cm and 13 cm depth of T1 and T2 , respectively, which correspond to the bottoms of AB horizon. At T3 , the tips

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Figure 4. The observed precipitation, runoff, water level, and soil moisture in 2004.

Figure 5. Rainfall, pore pressure, water level, and runoff in rainy season 1.

pressure which respond to respective rainfalls including those derived from small ones. Figure 4(e) shows that groundwater rose up near the surface in a few days after most rainfall events in the rainy season 1 and 2, while it rose at most to −13 cm even in the considerable rainfall in the summer dry season. The correlation among rainfall, pore pressure, groundwater, and runoff in each rainstorm was analyzed more in detail as presented in Figures 5 and 6. Runoff amounts from each outlet, that is Qs , QP3 , QAB , and QR , show their total amount in 4 months as 38.7, 32.8, 5.3, and 122.5 mm, respectively. Figure 5 shows runoff, pore pressure, and groundwater level in rainy season 1. In response to an intermittent rainfall of 107 mm in the period from July 10 to July 14 which was preceded by rainfalls on July 8 and July 9 with the maximum of hourly rainfall of 15 mm, runoff occurred from each horizon. The runoff occurred earlier in the lower horizon in the period from 23:00 July 12 to 2:00 July 13. In the event, the groundwater rapidly rose six hours after the pore pressure at T1 and T3 rose. Runoff occurred from the lower horizon just after the groundwater rose. In rainy season 1, pore pressure in the solum at T1 , which is located downstream of Pipe P3 , showed larger value than that at T2 which is located 25 cm upstream of pipe P3 and 45 cm higher than pipe P3 . The pore pressure at T2 rose

Figure 6. Rainfall, pore pressure, water level, and runoff in rainy season 2.

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in response to slight rainfall of only 2.5 mm on July 16. On the rainfall event which started at 21:00 of July 12, the pore pressure at T2 rose earlier than that at T3 , where a tensiometer was set at 19 cm deeper than that at T2 . The sequence suggests that the permeation from AB horizon to B horizon requires longer time than that of permeation from ground surface to AB horizon. Stable and higher value of T3 in the period from July 11 to July 21 seems to indicate almost saturated condition in clayey B horizon. Hydraulic conductivity of AB horizon and B horizon at Loc.1 was 3.4 × 10−5 cm/s and 1.6 × 10−5 cm/s, respectively. Figures 5(b) and (c) show that runoff from the upper horizon increases rapidly when a pore pressure and groundwater level rise. QR occurred first and was followed by QP3 and Qs a few hours later in the low rainfall intensity and wet soil conditions in the beginning of a rainfall event, while QP3 occurred first in conditions of an intense rainfall and dry soil. The increase in Qs indicates the expansion of partial contributing area. Figures 6(a), (b) and (c), (d), and (e) show rainfall, runoff, pore pressure, and groundwater level in rainy season 2, respectively. Figure 6 shows three succeeding but separated rainfall-runoff events immediately after summer dry season. Comparison of figure 6(a) and (b) demonstrates that the runoff ratio, which means the rate of runoff amount to rainfall amount in the corresponding event, increased considerably in the later events. The fact indicates the effect of antecedent rainfall on runoff production from subsurface water. In the recession processes, runoff decreased slowly and ceased earlier in the upper horizon, as in the order of Qs , QP3 , and QR . Pore pressure increased in both events from the upper horizon. In the rainfall event on October 8–11, runoff QR began when the pore pressure reached to 0 cm and groundwater rose, as in rainy season 1. WL1 declined slowly after the rainfall on October 8, and in a few days it declines rapidly. After it decreases −30 or −40 cm, the regression speed became slowly again. As QP3 declined 1 day before the regression of WL1 and stage pore pressure of T1 , and T2 declined rapidly at the same period with WL1 regression, the first slow regression stage and the second rapid regression stage are considered the results of subsurface water supply from upslope, and its recession, respectively. Because AB horizon is porous and B horizon is clayey and less permeable, as discribed before, the third slow regression stage is considered to be due to the difference of hydraulic conductivity between AB horizon and B horizon.

Figure 7. Water level change in the hole at Loc.2 and Loc.3. The 0 cm is the groundwater level on 19:20 April 21, 2006.

Figure 8. Estimated subsurface water pathways QA and QAB are negligible amount and omitted.

than in lower head-hollow. It is considered to be the effect of thick B horizon in upper head-hollow, which has a big storage capacity because of low hydraulic conductivity. Hydraulic conductivity of AB horizon and B horizon at Loc.3 are unknown, but at Loc.1 B horizon has only a little lower hydraulic conductivity thanAB horizon. In fractured bedrock cracks and pipes exist, and a lot of runoff QR is drained from them. The above results of runoff observation in Figures 4, 5, and 6 and three dimensional structure of the head-hollow suggest the following three systems of subsurface water pathways shown in Figure 8, where Qα , Qβ , and Qγ , designate not only pathways but also amount of flowing water. The pathway Qα connects water in AB horizon in the upper head-hollow laterally to AB horizon in the lower head-hollow without vertical percolation to B horizon. While Qβ indicates the

4.2 Estimation of runoff pathways in a head-hollow Figure 7 shows the water level change in the hole at Loc.2 and Loc.3 on April 2006. The groundwater level was maintained in high position in upper head-hollow

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The Sendai City Taihakusan Nature Watching Center, Ono Zoen, Inc., and Yoshida Densetsu, Inc. afforded the use of observation site and some instruments. Thanks are extend to Koki Goto, Toshifumi Imaizumi, Seiichi Okazaki, Nobuto Morishita, Hiroaki Suzuki, Akimichi Sasaki, Tadaki Mizumoto for valuable discussion and cooperation.

lateral flow from B horizon in the upper head-hollow to AB horizon in the lower head-hollow without vertical percolation to fractured bedrock overlain by B horizon. Qα and Qβ join in AB horizon in the lower head-hollow to percolate vertically to fractured bedrock. Qγ denotes lateral flow in fractured bedrock. Water passing the three ways is mixed together in fractured bedrock in the lower head-hollow and generate the pipeflow QR. Although Montgomery et al.(1997) reported the existence of intra-bedrock lateral flow supplied by vertical percolation from colluvium upslope, this paper takes the difference of storage capacity between A or AB horizon and B horizon, as discussed in the preceding section, into the consideration of lateral and vertical subsurface waterflow pathways shown in Figure 8. 5

REFERENCES Betson, R. P. 1964. What is watershed runoff?. Journal of Geophysical Research 69: 1541–1551. Dunne,T. & Black R. D. 1970a.An experimental investigation of runoff production in permeable soils. Water Resources Research 6: 478–490. Dunne, T. & Black R. D. 1970b. Partial area contributions to storm runoff in a small New England watershed. Water Resources Research 6: 1296–1311. Furuta, T., Goto, K., & Tamura, T. 2007. Infiltration-runoff processes through soil horizons in a forest-covered valley head. Quarterly Journal of Geography 59: 123–139 (in Japanese with Engrish abstracts). Kitamura M., Ishi, T., Sangawa, A., & Nakagawa, H. 1986. 1:50000 Geological map “Sendai”, Geological Survey of Japan (in Japanese). Montgomery, D.R., Dietrich, W.E., Torres, R., & Anderson, S.P., Heffner, J.T., & Loagues, K. 1997. Hydrologic response of a steep, unchanneled valley to natural and applied rainfall. Water Resoueces Research 33: 91–109. Ogasawara, K. 1989. Geology. Report on natural environment of Taihakusan nature watching forest. (foundation) The Nature Conversation Society of Japan and the society of Nature education, Sendai: 11–19 (in Japanese). Sato, T. 1991. Soil. Report on natural environment of Taihakusan nature watching forest. The society of Nature education, Sendai: 11–20 (in Japanese). Tamura, T. 1969. A series of micro-landform units composing valley-heads in the hills near Sendai. Science Reports of Tohoku University, 7th Series(Geography) 19: 111–127. Tamura, T. 1996. Micro-landfrom classification and chronology of hillslopes with reference to valley-heads. Onda, Y., Okunishi, K., Iida, T. and Tsujimura, M. eds. Hydrogeomorphology, Kokon Shoin Inc., Tokyo, 177–189 (in Japanese). Tamura, T. 2001. Hill landforms. In: Yonekura, N. et al. eds. Regional geomorphology of the Japanese Islands (Nihon no chikei) vol.1: Introduction to Japanese geomorphology (Yonekura N., Kaizuka S., Nogami M., Chinzei K. editors). Univ. Tokyo Press, 210–222 (in Japanese).

CONCLUSION

Separated observation of runoff from several soil horizons and fractured bedrock at a stream-head in four months with two rainy seasons revealed that 78% of total runoff was supplied from pipes and cracks, among which runoff from fractured bedrock played an important part in any event while shallow seepage responded promptly to particularly intense storms. Three major pathways of subsurface water to fractured bedrock at the stream-head, Qα , Qβ , and Qγ were estimated on the basis of records of rainfall, runoff from several outlets, capillary potential at several soil horizons, and groundwater level in the head-hollow, as well as its micro-geomorphological and pedelogical characteristics. Qα transmits water from thin AB horizon in the upper head-hollow to AB horizon in the lower headhollow. Qβ transfer water from thick B horizon in the upper head-hollow to AB horizon in the lower headhollow. Qγ connects fractured bedrock in the upper and the lower head-hollows. Qα and Qβ join in AB horizon in the lower head-hollow to percolate vertically to fractured bedrock where water supplied via Qγ is also mixed to produce the major pipe flow. It demonstrates the function of lateral percolation supported by water storage in thick B horizon in the wide upper head-hollow and rather fast vertical percolation in permeable AB horizon in the narrow lower headhollow where B horizon is lacking. Further analysis is intended on differential response of the three pathways to rainfall of different intensity. ACKNOWLEDGEMENT JSPS Grant-in-Aid for Scientific Research(B) in FY2004 (Subject number: 19300298, Chief researcher: Toshikazu Tamura) was used for some of this research.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Generation of a saturated zone at the soil–bedrock interface around a tree on a hillslope W.-L. Liang∗, K. Kosugi, Y. Yamakawa & T. Mizuyama Graduate School of Agriculture, Kyoto University, Kyoto, Japan

ABSTRACT: Precipitation in a forest is intercepted by the canopy and partitioned into throughfall and stemflow, leading to heterogeneous water inputs that affect soil water dynamics. To clarify the effects of a tree stand on rainfall infiltration processes on a steep forested hillslope, we conducted detailed and long-term observations of pore water pressure, soil water content, and stemflow around trees of tall stewartia (Stewartia monadelpha). The results indicated that the pore water pressure at the soil–bedrock interface increased rapidly and greatly in the region downslope of the tree stem, especially at the points close to the tree stem. Locally concentrated rainwater inputs attributable to stemflow on the downslope side of the tree trunk probably caused the large and rapid increases in water content and pore water pressure in the region downslope of the tree stem, resulting in the development of an asymmetric saturated zone around the tree. Keywords: stemflow

1

hillslope hydrology; pore water pressure; saturated zone; soil–bedrock interface; soil water content;

INTRODUCTION

this to the volume of rainwater reaching the soil as stemflow. Liang et al. (2007) conducted detailed observations of soil water dynamics at high spatial resolution for many storm events, and indicated that locally concentrated stemflow rapidly flowed into to soil layers along the pathways around roots as bypass flow which caused that maximal soil water storage was more than 100 to 200% of the cumulative openarea rainfall at the points downslope from the tree stem. Additionally, they observed the generation of an asymmetric saturated zone at the soil–bedrock interface around the tree on a hillslope for just a few storm events. The generation of a saturated zone at the soil–bedrock interface is important information for the prediction of the location and timing of shallow landslide occurrences (Anderson & Sitar 1995, Montgomery et al. 2002, Wang & Sassa 2003), and can contribute to runoff generation and deeper bedrock flow maintaining base flow (Montgomery & Dietrich 2002). Therefore, stemflow has major implications for catchment hydrology and is greatly affected by the acceleration of global warming. The purpose of this study was based on the study by Liang et al. (2007) and to clarify the effects of a broad-leaved tree stand on the generation of a saturated zone at the soil–bedrock interface on a steep forested hillslope for a long-term observation through highspatial-resolution observations of pore water pressure at the soil–bedrock interface, soil water content, and

Under the climatic condition like Japan, it is predicted that the global warming may affect strongly the groundwater recharge process depending on forest stand succession from conifer trees to broad-leaved evergreen trees (Tanaka et al. 2008). Iida et al. (2005) observed that, during the period of forest stand succession from Japanese red pine to oak trees, there was a substantial increase in stemflow, essentially no change in throughfall, and a substantial decrease in interception. In forested landscapes, trees have a major impact on water movement in the soil because precipitation is intercepted by the canopy and is partitioned into throughfall and stemflow, as diffuse input and point input, respectively, so that water reaching the forest floor is not uniform. Ford & Deans (1978) and Durocher (1990) observed very rapid water movement underneath trees and indicated that this kind of rapid soil water movement was primarily controlled not by variability in soil physical properties (e.g., macropore distribution), but rather by small-scale spatial variability in water input to the soil surface. For a single storm event on a gentle hillslope inclined at 3 degrees, Durocher (1990) observed that soil water pressure tended to show more rapid and greater changes underneath trees than in areas without trees, and he attributed ∗

Corresponding author ([email protected])

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stemflow. Using observations of 140 storm events, we analyzed the processes and the local generation of a saturated zone around a tree. 2

stewartia (S1 in Fig. 1b; height, 17.47 m; diameter at breast height, 22.3 cm) and delineated a longitudinal observation line from upslope to downslope of this tree (Fig. 1c). There was no understory vegetation or other trees along the observation line, such that only the effects of the tree (S1) would be identified. We installed tensiometers at the soil–bedrock interface at each of ten points: 250 cm (P1), 200 cm (P2), 150 cm (P3), 100 cm (P4), and 50 cm (P5) upslope from the tree stem and 25 cm (P6), 50 cm (P7), 100 cm (P8), 150 cm (P9), and 200 cm (P10) downslope from the tree stem (Fig. 1b, c). The soil depth to bedrock at each point was determined by penetration tests using a knocking-type cone penetrometer. Previous studies have proposed that a penetration resistance of 100 is an indicator of the boundary between soil and bedrock (Okimura & Tanaka. 1980; Yoshimatsu et al. 2002). The soil depth of all points was estimated to be between 104 and 190 cm (Fig. 1c). Furthermore, we installed capacitance meters (Sentek, EasyAG-5p) at the same 10 points to monitor soil water dynamics around a tree. Each capacitance meter consisted of five sensors to measure soil water content at depths of 10, 20, 30, 40, and 50 cm. The equipment was used to monitor the process of rainfall infiltration from the soil surface through the soil–bedrock interface. We selected five tall stewartia trees (S2, S3, S4, S5, and S6 in Fig. 1b) to measure stemflow. To separately collect stemflow data upslope (SF-up) and downslope (SF-down) of the trunks of trees, we used two tubes cut longitudinally and wrapped spirally around the upslope and downslope sides of the trunk. For tree S2, the flow rates of SF-down were measured using a tipping-bucket gauge that tipped at 500 ml (IKEDA, TQX-500). For trees S3, S4, S5, and S6, we used four 16-l containers with siphon drainage equipment

METHODS AND MATERIALS

Observations were conducted on a hillslope (Fig. 1a) at the Kamigamo Experimental Station of Kyoto University, located in southern Kyoto Prefecture, central Japan (35◦ 04’N, 135◦ 46’E). The mean annual air temperature for 1971–2000 was 14.6◦ C, with maximum and minimum monthly averages of 27.8◦ C (August) and 4.6◦ C (January), respectively. The mean annual precipitation was 1582 mm. Rainfall was distributed year-round, with a peak in summer and just a few centimeters of snow in winter. The hillslope has a mean gradient of 28 degrees, with brown forest soil classified as Cambisol underlain by sandstone and slate with some fractures contributing to bedrock flow recharge. It is predominantly covered with tall stewartia (Stewartia monadelpha), planted in 1956. Tall stewartia, which is widespread in natural forests in western and southern parts of Japan, is a deciduous broad-leaved tree with smoothexfoliating bark, and it exhibits leaf fall in November and re-growth in April. Liang et al. (2007) observed the root architecture of tall stewartia that, within the surface soil layer 40 cm thick, several thick roots 2 to 10 cm in diameter elongated in the downslope direction from the stem bottom, which could form the flow pathways. They also found many fine roots from the soil surface down to 50-cm depth, but the number of roots was small below the depth of 50 cm. To monitor the generation of a saturated zone at the soil–bedrock interface around a tree, we selected a tall

Figure 1. (a) Map showing catchment area and a small channel (dashed line). (b) Topography of the observation area showing the locations of tree stems, tree canopy areas, and measurement points of soil water dynamics. (c) Longitudinal section along the observation line for tree S1, showing the measurement points for soil water content (θ) and pore water pressure head (ψ) (P1–P10).

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to determine SF-down by changes of the water level in containers (previously calibrated in the laboratory).All the flow rates of SF-up were measured using tippingbucket gauges that tipped at 4 ml (DAVIS, 7852M). Gross precipitation (open-area rainfall) was measured at an open site 112 m from the observation slope. All measurements were simultaneously and automatically recorded at 5-min intervals by a data logger (Campbell, CR-1000). 3

value increased greatly at every point, especially at P6 and P7, where it showed very spiked responses on the hyetograph. In periods of no precipitation, ψ showed greater decreases at points close to the tree (P5, P6, and P7) than at points away from the tree (P3 and P10). The results suggest that, during both the light and the heavy storm events, obvious increases of ψ occurred at the points closest to the tree stem. Marked increases in ψ and occurrences of positive pore water pressure, however, were limited to points in the downslope region.

RESULTS AND DISCUSSION 3.2 Generation of a saturated zone around the tree

3.1

General trends in pore water pressure at the soil–bedrock interface

For light storms and heavy storms, the response of ψ at the soil–bedrock interface was greatly affected by the point relative to the tree stem. To clarify the relationship between the generation of a saturated zone and points relative to the tree stem, the maximum pore water pressure head (ψmax ) for each storm event was compared with the cumulative amount of open-area rainfall from the start of the event to the time when ψmax was recorded (Fig. 3). The value for ψmax was larger in the region downslope of the tree stem than in the upslope region. In the downslope region, ψmax was frequently positive, indicating the generation of a saturated zone, which rarely occurred in the upslope region. ψmax was largest and frequently exceeded 20 cm at P6, where positive pore water pressure was measured for storm events with more than 2 mm of cumulative open-area rainfall. At P6, positive pore water pressure occurred for 92% of the storm events that caused a response of ψ at P6. This ratio was also

During the observation periods, from 19 June 2006 through 16 December 2006 and from 20 April 2007 through 1 December 2007, 140 storm events were observed (Fig. 2). Individual storm events were defined as being separated by six consecutive hours without rain. The cumulative rainfall for each event ranged between 0.5 to 194 mm. Figure 2 does not show the results for points P1, P2, or P4, which were similar to those for P3, or the results for P8 and P9, which were similar to those for P10. For light precipitation events, values of pore water pressure head (ψ) rose slightly in the upslope region, where a more significant response was measured at P5. In the downslope region, however, the ψ values rose markedly. In particular, ψ at P6 and P7 frequently became positive, indicating the transient generation of a saturated zone. For heavy precipitation events, the ψ

Figure 2. Hyetograph, and change in pore water pressure head (ψ), in the upslope region from the tree, P3 and P5, and in the downslope region from the tree, P6, P7, and P10. The arrow corresponds to the storm event shown in Figure 4.

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ψmax (cm)

50 0 -50 -100 -150 -200 -250 50 0 -50 -100 -150 -200 -250

P5

P4

P3

P2

P1

P10

P9

P8

P7

P6

0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 Cumulative rainfall (mm) Figure 3. Relationship between the maximum pore water pressure head (ψmax ) for each storm event and the cumulative open-area rainfall from the start of the event to the time when ψmax was recorded.

cumulative SF-down: 5 l), θ responses were observed at each depth, and ψ increased rapidly in the positive range (4.9 cm). Positive ψ values were caused by little cumulative rainfall, indicating that occurrence of a saturated zone was most directly attributable to the contribution of stemflow rather than rainfall. At 150 min (cumulative rainfall: 6.5 mm, cumulative SF-down: 23 l), rainfall and SF-down intensities rose greatly, and θ increased dramatically and became greater particularly at depths of 30 and 50 cm. Several studies have pointed out that tree roots can form channels that serve as pathways for rapid water movement (Gaiser 1952, Noguchi et al. 1999), and when a large amount of rainwater was supplied by stemflow, not all of the water infiltrated into the soil matrix, causing infiltration excess and activation of these pathways (Liang et al. 2007). Thus, a large amount of rainwater supplied by stemflow would flow along tree root channels, causing dramatic increases in θ. At 180 min (cumulative rainfall: 14.5 mm, cumulative SF-down: 60 l), rainfall and SF-down intensities reached a maximum (2.5 mm and 9.5 l per 5 min, respectively) for the event, and the maximum ψ (35 cm) was recorded during all observation periods. After the peak of the storm event, rapid and great decreases were measured in ψ and in θ at depths of 30 cm and 50 cm. These results suggest that the great water-content increases at specific depths (i.e., 30 cm and 50 cm) and the generation of a saturated zone at point P6, 25 cm from the tree stem in the downslope direction, were probably the result of stemflow concentrated on the downslope side of the tree. As clearly shown in Figures 2, 3, and 4, different ψ response patterns between the upslope and downslope

high at points P7 through P9 (34–76%), but small at the remaining points (3–31%). P5 had the most frequent positive ψ responses in the upslope region. We found that the large increases in ψ at P6 caused a total head gradient in the upslope direction, expanding the saturated zone at P6 to the upslope region and causing ψ responses at P5. Additionally, we assumed that a pore water pressure head increase of more than 5 cm indicated a response to the storm event. At P6 and P7, ψ responded to 64% and 45% of all storm events, respectively. The percentage was much lower at the other points, ranging from 9% to 38%. These results indicate that a saturated zone occurred frequently in the region downslope of the tree stem. Conversely, a saturated zone was hardly evident in the upslope region, even at P5, which was adjacent to the tree. Although P7 and P5 were both located 50 cm from the tree stem, the maximum pore water pressure head was much larger at P7 than at P5, showing that the amount of water input was very different between the downslope and upslope sides of the tree stem. 3.3

Interactions among soil water content, pore water pressure, and stemflow close to the trees

To clarify the process of a saturated zone at the soil–bedrock interface, the soil water content (θ) at depths of 10 to 50 cm at P6 were compared with the downslope-side stemflow (SF-down) of the tree S2. Figure 4 shows responses to the heavy storm event on 3 July 2007 with an initial ψ value of −17 cm (corresponding to the arrow in Fig. 2). At 40 min (cumulative rainfall: 1.5 mm, cumulative SF-down: 0.5 l), θ began to increase only at a depth of 10 cm. At 95 min (cumulative rainfall: 2.5 mm,

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Figure 4. Open-area rainfall, stemflow on the downslope side of the tree (S2), and the response of soil water content (θ) at depths of 10 cm through 50 cm and pore water pressure head (ψ) at the soil–bedrock interface at point P6 for the storm event on 3 July 2007.

November 2007. All SF-down rates were markedly higher than SF-up rates, which were approximately 1.07%, 16.05%, 0.69%, 5.64%, and 2.58% of the SF-down values at S2, S3, S4, S5, and S6, respectively. Generally, trees on a steep hillslope incline toward the slope and form an “S” shape (Schweingruber 1996, p. 276), unlike trees growing on flat land. Consequently, the stemflow distributions differ between trees on hillslopes and those on flat land. We presume that such large differences in stemflow occurring on the downslope and upslope sides of a stem are attributable to the uneven distribution of canopy structure (Fig. 1b) and the tilt of the whole tree in the downslope direction. Although the quantity of stemflow differed among trees, which depends on complex factors such as size, branch angle, bark roughness, and canopy structure of the tree (Aboal et al. 1999, Levia & Frost 2003), stemflow generally occurred more on the downslope side of a stem. The result was asymmetrical water input between upslope and downslope sides of trees. 4

CONCLUSIONS

Our study demonstrates significantly different responses of pore water pressure at the soil–bedrock interface between the upslope and downslope regions for various storm events, thereby clarifying the effects of a tree on rainfall infiltration processes on a steep broad-leaved forest hillslope. The rapid and great increases in water content and pore water pressure at points close to the downslope side of a tree can be explained by the large amount of stemflow that develops locally on the downslope side of the tree stem. An understanding of stemflow distribution and its effects

Figure 5. Relationship of open-area rainfall to stemflow along the upslope (SF-up) and the downslope (SF-down) for trees S2, S3, S4, S5, and S6.

sides of the tree were probably caused by asymmetrical distributions of stemflow. Figure 5 shows SF-up and SF-down rates for trees S2, S3, S4, S5, and S6 for 72 storm events, from May 2007 through

73

Levia, D.F. & Frost, E.E., 2003. A review and evaluation of stemflow literature in the hydrologic and biogeochemical cycles of forested and agricultural ecosystems. Journal of Hydrology, 274(1–4): 1–29. Liang, W.-L., Kosugi, K. & Mizuyama, T., 2007. Heterogeneous Soil Water Dynamics around a Tree Growing on a Steep Hillslope. Vadose Zone Journal, 6(4): 879–889. Montgomery, D.R. & Dietrich, W.E., 2002. Runoff generation in a steep, soil-mantled landscape. Water Resources Research, 38(9): 1168, doi:10.1029/2001WR000822. Montgomery, D.R., Dietrich, W.E. & Heffner, J.T., 2002. Piezometric response in shallow bedrock at CB1: Implications for runoff generation and landsliding. Water Resources Research, 38(12): 1274, doi:10.1029/ 2002WR001429. Noguchi, S., Tsuboyama, Y., Sidle, R.C. & Hosoda, I., 1999. Morphological characteristics of macropores and the distribution of preferential flow pathways in a forested slope segment. Soil Science Society of America Journal, 63(5): 1413–1423. Okimura, T. & Tanaka., S., 1980. Researches on soil horizon of weathered granite mountain slope and failured surface depth in a test field. (In Japanese, with English abstract.) Journal of the Japan Society of Erosion Control Engineering, 33(1): 7–16. Schweingruber, F.H., 1996. Influence of mass movement. In F.H. Schweingruber (ed.), Tree rings and environment dendroecology: 271–287. Bern, Switzerland: Haupt Publ. Tanaka, T., Iida, S., Kakubari, J. & Hamada, Y., 2008. Effect of forest stand succession from conifer trees to broad-leaved evergreen trees on infiltration and groundwater recharge processes. In C. Abesser et al. (ed.), Groundwater-Surface Water Interaction: Process Understanding, Conceptualization, and Modelling: (in press). IAHS Publ. Wang, G.H. & Sassa, K., 2003. Pore-pressure generation and movement of rainfall-induced landslides: effects of grain size and fine-particle content. Engineering Geology, 69(1–2): 109–125. Yoshimatsu, H., Kawamitsu, K., Seo, K., Hasegawa, S. & Muranaka, S., 2002. Simplified penetrometer for surface structure survey in hillslops. (In Japanese.) Transaction of Japan Society of Erosion Control Engineering, 2002: 392–393.

on soil water dynamics for trees on a steep hillslope is necessary for modeling runoff generation from steep headwater catchments. Additionally, this understanding also indicates that, expected stemflow increase will directly contribute bedrock flow recharge at points close to the downslope side of a tree when forest stand succession from conifer trees to broad-leaved trees is accelerated by global warming. ACKNOWLEDGMENTS The open-area rainfall and study site environmental data in this study were provided by Kamigamo Experimental Station, Kyoto University. This work was supported by the Japan Forest Technology Association and the Association for Disaster Prevention Research. REFERENCES Aboal, J.R., Morales, D., Hernandez, M. & Jimenez, M.S., 1999. The measurement and modelling of the variation of stemflow in a laurel forest in Tenerife, Canary Islands. Journal of Hydrology, 221(3–4): 161–175. Anderson, S.A. & Sitar, N., 1995. Analysis of RainfallInduced Debris Flows. Journal of Geotechnical Engineering-Asce, 121(7): 544–552. Durocher, M.G., 1990. Monitoring Spatial Variability of Forest Interception. Hydrological Processes, 4(3): 215–229. Ford, E.D. & Deans, J.D., 1978. Effects of Canopy Structure on Stemflow,Throughfall and Interception Loss in aYoung Sitka Spruce Plantation. Journal of Applied Ecology, 15(3): 905–917. Gaiser, R.N., 1952. Readily Available Water in Forest Soils. Soil Science Society of America Proceedings, 16(4): 334–338. Iida, S., Tanaka, T. & Sugita, M., 2005. Change of interception process due to the succession from Japanese red pine to evergreen oak. Journal of Hydrology, 315(1–4): 154–166.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Factors controlling stream water chemistry in ten small forested watersheds with plantation forests of various proportions and ages in central Japan N. Tokuchi∗ Field Science Education and Research Center, Kyoto University, Kyoto, Japan

K. Fukushima Graduate School of Agriculture, Kyoto University, Kyoto, Japan

M. Katsuyama Research Institute for Humanity and Nature, Kyoto, Japan

ABSTRACT: Despite the importance of plantation forests, few empirical studies have examined the factors related to plantations that control stream water chemistry in Japan. We examined such factors in ten adjacent watersheds (W1–W10) with similar climate and site histories but significant differences in stream chemistry, ascertained on the basis of extensive sampling in forested watersheds of the Wakayama Forest Research Station, Wakayama Prefecture, Japan. Concentrations of Ca2+ , the dominant component, were high in W3, W4, and W5. High correlations among Ca2+ , Mg2+ , and Si concentrations in streams indicated a geological influence on stream chemistry. Geology was also influenced on NO− 3 concentration, which was negatively correlated with the Ca2+ and Si concentrations. Apart from geological influences, stream NO− 3 was weakly and negatively correlated with the proportion of the watershed that consisted of plantations. The plantation proportion was significantly correlated with net primary production. However, there was no significant correlation between stream NO− 3 concentration and net primary production, indicating that stream NO− 3 concentration was not controlled only by vegetation regrowth through uptake. The plantation proportion was found to be an integrated index of net primary production and of the combined effects of differences in vegetation and soil characteristics. Keywords: forest watershed; geology; stream chemistry; stream Ca2+ concentration; stream NO− 3 concentration; plantation

1

INTRODUCTION

and over the course of forest succession (Bormann and Likens 1979; Vitousek and Reiners 1975; Goodale et al. 2000). The influences of clear-cutting and road construction on stream NO− 3 are well known (Likens et al. 1977; Swank et al. 2001), but there is not enough information after rapid early regrowth. In Japan, plantation forest occupies 41% of total forest area, and 67% of plantations are 35 to 50 years old, because of expansive afforestation in the 1950s and 1960s (Forestry Agency 2006). It is important to clarify the influences of middle-aged plantations on stream NO− 3. We aimed to describe the stream water chemistry of forested watersheds containing middle-aged plantations. To compare forests with different proportions and ages of plantations, we estimated the net primary production in each watershed.

NO− 3

Stream is a sensitive indicator of the biogeochemical status of forest ecosystems. A number of factors control stream water chemistry, including climate, geology, topography, vegetation conditions, and land-use changes (Goodale et al. 2000; Lovett et al. 2000; Christopher et al. 2006). Traditional biogeochemical theories suggest that ecosystem N losses are controlled by varying degrees of biotic N limitation, with N losses increasing as N availability exceeds plant and microbial demands. To examine these theories, many studies of stream NO− 3 in forest ecosystems have been conducted at sites with varying plant demand for N: for example, in disturbed forest, old growth forest, ∗

Corresponding author ([email protected])

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2

SITE DISCRIPTION

japonica) with some cypress (Chamaecyparis obtusa) plantations and the other half is secondary deciduous forest. The original vegetation occurs in watershed 3 (W3; Fig. 1), which has been preserved since 1926 when the WFRS was first established (Table 1). The original vegetation is cool-temperate coniferous and deciduous mixed forest composed of Japanese fir (Abies firma), hemlock (Tsuga sieboldii), beech (Fagus crenata), and carpinus (Carpinus laxiflora). Data from a weather station at 455 m show a mean annual temperature of 12.3◦ C and mean annual precipitation of 2647 mm. The bedrock is sedimentary, of alternating sandstone, mudstone, and shale (Kurimoto et al. 1998).

The study was conducted in the Wakayama Forest Research Station (WFRS) of the Field Science Education and Research Center, Kyoto University (34◦ 04 N, 135◦ 31 E; 860–1370 m asl; Fig. 1). Half of the WFRS is composed of Japanese cedar (Cryptomeria

3

METHODS

3.1 Sampling method and analysis Bulk precipitation was sampled weekly at the WFRS using a polyethylene bucket with a funnel, except when the soil surface was covered with snow (January to March). For stream water sampling, we chose 10 adjacent watersheds within the WFRS (Table 1). Samples were collected weekly from the 10 streams from April 2004 to March 2006 and biweekly from April 2006 to March 2007. Water temperature and pH were measured using a glass electrode (HM-20P, TOA-DKK). Water samples were filtered into 50-mL polyethylene bottles through a cellulose acetate filter (0.45-µm pore size) immediately after collection and stored at 4◦ C until analysis. In the laboratory, the concentrations of Na+ , K+ , 2− Mg2+ , Ca2+ , Cl− , NO− were analyzed 3 , and SO4 using ion chromatography (ICS-90, Dionex). Total nitrogen (TN) was measured using a Shimadzu 5000 carbon analyzer (Shimadzu Scientific Instruments). Dissolved organic nitrogen (DON) was calculated as

Figure 1. Topographic map of the sampled watersheds (W1-10) in WFRS Circles indicate the sampling point, grey areas indicate plantation forest.

Table 1. Watershed characteristics for 10 sampling streams of the Wakayama Forest Research Station, Wakayama Prefecture, Japan.

watershed no.

area (ha)

elevation range (m)

slope

W1 W2 W3 W4 W5 W6 W7 W8 W9 W10

11.9 20.2 7.1 10.3 3.8 11.4 9.9 51.7 3.9 79.6

650–1100 650–1140 730–1020 770–1020 820–960 620–1060 670–1050 700–1210 710–1100 710–1200

0.633 0.564 0.525 0.633 0.484 0.697 0.760 0.510 0.872 0.517

∗

Net primary production (Mgdw ha−1 year−1 )

dominant vegetation

plantation (%)

plantation age (year)

plantation

natural

total

cedar, cypress cedar, cypress fir, hemlock cedar, cypress cedar, cypress cedar, cypress cedar, cypress maple, carpinus maple, carpinus maple, carpinus

95.9 86.1 0.0 82.2 86.4 97.0 92.4 23.1 9.5 11.3

37 35 (>100)∗ 40 53 36 34 68 67 67

10.2 10.1 0.0 5.2 8.6 8.1 8.5 1.2 0.4 0.8

0.0 0.1 0.9 0.2 0.1 0.1 0.1 0.7 0.8 0.9

10.2 10.2 0.9 5.4 8.7 8.2 8.6 1.9 1.3 1.7

W3 is the natural forest.

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+ TN minus inorganic N (NO− 3 -N + NH4 -N), ignor− ing NO2 because of its low concentration. The bulk dissolved Si concentration was analyzed using inductively coupled plasma emission spectrometry (SPS 1500VR, Seiko). Because the stream water was approximately pH 7.0, acid neutralizing capacity (ANC) was evaluated using the following equation for electrical charge balance (Reuss and Johnson 1986; Hemond 1990):

3.3 Statistical analysis The relationships among the means of stream water chemistry, ANC, and watershed characteristics in each watershed were analyzed using Pearson correlations. Watershed characteristics were defined as: total watershed area in hectares; slope, defined as the difference in elevation from top to bottom divided by the longest length of the watershed; elevation difference, calculated as maximum – minimum elevation, in meters; and plantation ratio, defined as the proportion of the total watershed area that was plantation, in percent. Topographical information was obtained using a map of scale of 1:25000 (Geographical Service Institute 2001). Information on forest management was obtained from the records of WFRS. The level of significance for Pearson’s correlations was determined using two-tailed tests. All statistical analyses were conduced using SPSS 11.0J (SPSS 2001).

where [X] is the concentration (mmol L−1 ) of ion − 3− X. Other ions such as NH+ 4 , NO2 and PO4 were negligible (Fukushima, unpublished). The chemistry of bulk deposition was evaluated as volume-weighted means in each observation year. Stream water in each watershed was evaluated as arithmetic means during observation period. 3.2

4

RESULTS AND DISCUSSION

4.1 Net primary production

Estimation of net primary production

Net primary production rates ranged from 0.9 Mg ha−1 year−1 in W3 to 10.2 Mg ha−1 year−1 in W1 and W2 (Table 1), which is similar to that of cool temperate deciduous forest in Japan (Tateno et al. 2004). Net primary production tended to be higher in plantations than in natural forest. Among watersheds, the net primary production varied, but there was a high positive correlation between net primary production and the proportion of the forest that was in plantations (i.e., the plantation proportion; r = 0.951, P < 0.001, Table 4). This finding indicates that there was little difference in net primary production in the 30–70 year plantations in this study site.

Tree diameter (D>5 cm) at 130 cm and height (H) were measured in 1–8 permanent plots (25 × 20 m) established within each watershed in 1994 and 1999 (Field Science Education and Research Center, 2000). Totally 45 plots were used. Aboveground biomass was defined as dry weights of stems. Stem volume (V) was determined from allometric relationships as follows (Shibata & Furuno 1976). Japanese cedar in plantation; V = 10(0.94103 log (A−4.18779)) Japanese cypress in plantation; V = 100.94681 log (A−4.20607) Other species; V = 0.05672A0.9506 (Ohtata 1991) where A = D2 H. Net primary production of stems was calculated between mean annual volume increments of stems in 1994–1999. The volume increment was converted to a dry weight basis assuming a mean bulk density of cedar stems of 0.35 gDW cm−3 , cypress 0.40 gDW cm−3 , and fir 0.40 gDW cm−3 as other species (Kijima et al. 1962).

4.2 Precipitation characteristics and N deposition Precipitation was approximately pH 5.0, indicating anthropogenic influences (Table 2). In contrast, streams were approximately pH 7.0 and the ANC was always >0.5 meq L−1 in all watersheds (Table 3), indicating acid insensitivity (>0.05 meq L−1 ; Driscoll et al. 2001). Thus, we conclude that stream water

Table 2. Annual volume weight mean of the total atmospheric deposition from April 2004 to March 2007 at the Wakayama Experimental Forest Station. Na+

K+

year

rainfall (mm)

pH

(µeq L−1 )

2004 2005 2006

3453 2214 2765

4.84 4.65 4.46

5.7 8.7 20

1.6 2.4 4.3

Mg2+

2.7 4.1 10

Ca2+

8.5 13 52

NH+ 4

Cl−

14 22 15

6.4 9.9 22

77

NO− 3

SO2− 4

9.7 15 14

20 31 51

Si (µmol L−1 )

DON (mg L−1 )

2.0 3.1 3.1

0.03 0.04 0.01

78

W10

W9

W8

W7

W6

W5

W4

W3

W2

W1

Watershed No.

(µeq L−1 )

144.9bc (10.1) 139.3a (16.6) 167.7d (20.2) 141.6ab (16.7) 149.3bc (15.4) 150.4c (18.8) 142.0bc (18.8) 142.4bc (22.7) 158.5e (27.3) 140.2a (16.3)

pH

7.48cde (0.35) 7.51de (0.31) 7.59e (0.39) 7.54de (0.37) 7.44cd (0.40) 7.35bc (0.31) 7.28ab (0.28) 7.29ab (0.34) 7.27ab (0.36) 7.22a (0.40)

Na+

11.7cd (1.6) 10.4b (2.5) 12.8e (2.8) 9.9ab (2.7) 10.3b (2.2) 11.7cde (2.7) 9.1a (2.1) 10.9bc (2.9) 12.5de (4.2) 12.0de (2.4)

K+

72.7bc (7.4) 78.8c (15.8) 154.5e (29.8) 132.0d (21.0) 150.7e (25.2) 74.8bc (13.8) 67.9b (11.2) 72.9bc (13.0) 78.1c (16.6) 57.3a (9.5)

Mg2+

333.3c (49.7) 285.6a (72.5) 676.1e (137.7) 572.7d (125.2) 585.9d (132.8) 318.9bc (76.1) 289.4ab (55.4) 306.8abc (59.9) 302.7abc (60.9) 289.1ab (60.2)

Ca2+

1.6 (2.7) 2.5 (5.4) 1.5 (3.7) 1.6 (3.4) 2.1 (3.5) 1.6 (2.7) 1.5 (2.8) 2.2 (4.4) 3.4 (14.2) 1.9 (5.1)

NH+ 4

61.8ab (4.2) 65.9bc (10.0) 79.1d (8.6) 68.3bc (8.2) 56.4a (6.4) 66.5bc (9.5) 72.4c (9.3) 67.8bc (10.7) 65.6bc (10.9) 65.9bc (6.7)

Cl−

25.2c (6.0) 40.0e (9.4) 9.5a (7.8) 10.5a (3.5) 10.9a (4.0) 17.0b (4.9) 24.9c (6.7) 37.0d (7.7) 40.2de (8.6) 36.7de (7.0)

NO− 3

101.0bc (16.2) 74.2a (17.4) 158.8d (34.6) 102.1bc (20.8) 115.4d (19.6) 106.9c (26.8) 102.7bc (20.8) 93.9b (18.3) 110.0c (25.4) 109.7c (21.5)

SO2− 4

5.11bc (0.42) 4.84ab (0.49) 5.49d (0.48) 5.45d (0.38) 6.06e (0.77) 5.27cd (0.48) 4.81a (0.52) 4.76a (0.41) 5.43d (0.45) 4.96ab (0.45)

Si (µmol L−1 )

0.2 (1.1) 0.5 (2.5) 0.2 (0.8) 0.1 (0.6) 0.2 (0.8) 0.2 (1.3) 0.2 (1.4) 0.3 (1.8) 0.6 (4.3) 0.3 (1.9)

DON (mg L−1 )

0.673c (0.126) 0.601bc (0.179) 1.407e (0.296) 1.258d (0.283) 1.314d (0.315) 0.642c (0.157) 0.543ab (0.137) 0.597bc (0.153) 0.587bc (0.142) 0.507a (0.136)

ANC (meq L−1 )

Table 3. Chemical characteristics of sampled 10 streams of Wakayama Experimental Forest Station from 2004 to 2007. Different characters in the same column indicate the significant difference among watersheds ( p < 0.05).

Table 4.

Pearson’s correlation (r) matrix for stream chemistry and watershed characteristics (∗ p < 0.05). Stream chemistry

NH+ 4 K+ Mg2+ Ca2+ Cl− NO− 3 SO2− 4 Si ANC area slopea eleb %c NPPd

Watershed characteristics

Na+

NH+ 4

0.02 0.67∗ 0.56 0.58 0.43 −0.41 0.88∗ 0.58 0.53 −0.44 0.15 −0.43 −0.51 −0.50

0.18 −0.18 0.03 −0.31 0.08 0.98∗ −0.40 0.12 0.18 0.61 0.09 −0.78∗ −0.35 0.54 0.63∗ 0.10 0.23 0.80∗ −0.27 0.03 0.99∗ −0.01 0.12 −0.49 0.31 0.02 −0.37 0.12 0.16 −0.88∗ −0.29 −0.62 −0.06 −0.23 −0.53 −0.11

K+

Mg2+

Ca2+

Cl−

NO− 3

SO2− 4

Si

ANC

0.27 −0.82∗ 0.71∗ 0.75∗ 0.99∗ −0.41 −0.40 −0.85∗ −0.11 −0.17

−0.15 0.54 −0.26 0.19 0.01 0.07 0.05 −0.48 −0.52

−0.58 −0.67∗ −0.83∗ 0.53 0.20 0.78∗ −0.32 −0.18

0.53 0.64∗ −0.21 −0.13 −0.51 −0.51 −0.52

0.78 −0.53 −0.09 −0.88∗ 0.00 −0.01

−0.45 −0.40 −0.45 −0.87∗ 0.58 0.12 −0.04 −0.44 0.11 −0.10 −0.41 0.04

area

slopea eleb

%c

−0.15 −0.06 0.95

Watershed slope. b (Maximum – minimum) elevation (m). c Proportion of plantation (%). d Net primary production (Mgdw ha−1 year−1 ) a

acidification caused by acid deposition is not serious at the study site. + The total N deposition (NO− 3 + NH4 + DON) was −1 −1 0.8 kmol ha year , similar to the high N deposition in the northeastern US (Driscoll et al. 2003).

1998). They showed that bedrock containing appreciable concentrations of fixed nitrogen contribute a large amount of nitrate to surface waters in certain California watersheds. To exclude geological influences, we reanalyzed the relationship between stream NO− 3 concentration and watershed characteristics for all watersheds except W3, W4, and W5, which had high stream Ca2+ , Mg2+ concentrations (Table 3). The stream NO− 3 concentration was weakly negatively correlated with the plantation proportion in W1, W2, W7, W8, W9, and W10 (r = −0.721, P = 0.07, Fig. 2). Several studies have examined the effects of vegetation regrowth after clear-cutting on stream NO− 3 concentrations (Vitousek & Reiners 1975; Boring et al. 2001). We found a significant correlation between the plantation proportion and net primary production. However, the relationship between net primary production and stream NO− 3 concentration was not significant (r = −0.557, P > 0.1). Christopher et al. (2006) studied two adjacent watersheds in New York that had similar atmospheric N inputs but markedly different stream water solute concentrations; high stream Ca2+ concentrations were associated with high NO− 3 concentrations. They explained the different concentrations of base cations (particularly Ca2+ ) and NO− 3 in stream water by the combined effects of differences in vegetation species and soil characteristics. This explanation suggests that not only vegetation regrowth, but also the combined effects of differences in vegetation and soil characteristics control stream NO− 3 concentration. Lovett et al. (2002) showed NO− 3 export from forested watersheds was strongly influenced by the carbon: nitrogen (C:N) ratio of the watershed soil. They also

4.3 Factors controlling stream NO− 3 concentration in plantation forests Under high N deposition, substantial N tends to leach as NO− 3 from the watershed (Stoddard 1991). It is confirmed that stream NO− 3 concentration in the watersheds in this study was relatively high compared with that previously measured in the US, at 30 µmolN L−1 vs. 10 µmolN L−1 , respectively (Binkley et al. 2004). Nitrogen losses from forested watersheds vary, and the variation has been ascribed to differences in atmospheric N inputs (Stoddard 1991; Dise and Wright 1995), geology (Holloway et al. 1998), hydrology (Creed and Band 1998), and forest history (Vitousek 1977; Goodale et al. 2000). The stream NO− 3 concentrations varied among the watersheds and were low in W3–W5 and high in W2, W9, and W10 (P < 0.05; Table 3). Stream NO− 3 concentration negatively correlated with stream Ca2+ , Mg2+ and Si concentration and positively correlated with elevation difference (P < 0.05; Table 4). At that time the correlation among stream Ca2+ , Mg2+ and Si concentrations were strong positive and those concentrations correlated negative with elevation difference (P < 0.05; Table 4). These elements originate from rocks, suggesting that there might be geological influences on not only stream Ca2+ , Mg2+ and Si concentrations, but also NO− 3 concentrations (Holloway et al.

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No. 5-2 at the Research Institute for Humanity and Nature.

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NO3-(µ eq L-1)

50 40

REFERENCES

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Binkley, D., Ice, G.G., Kaye, J. & Williams, A. 2004. Nitrogen and phosphorus concentration in forest streams of the United States. J. Amer. Water Resour. Assoc. 40, 1277–1291. Boring, L.R., Swank, W.T. & Monk, C.D. 1988. Dynamics of early successional forest structure and processes in the Coweeta Basin. In: Swank, W.T., Crossley, D.A.Jr. (Eds.), Forest Hydrology and Ecology at Coweeta. Ecological Studies, Vo. 66. Springer, New York, pp. 161–180. Bormann, F.H. & Likens, G.E. 1979. Pattern and process in a forested ecosystem. Springer-Verlag, New York. Christopher, S.F., Page, B.D., Cambell, J.L. & Mitchell, M.J. 2+ 2006. Contrasting stream water NO− in two 3 and Ca nearly adjacent catchments: the role of soil Ca and forest vegetation. Global Change Biol. 12, 364–381. Creed, I.F. & Band, L.E. 1998. Export of nitrogen from catchments within a temperate forest: evidence for a unifying mechanism regulated by variable source area dynamics. Water Resour. Res. 34, 3105–20. Dise, N.B. & Wright, R.F. 1995. Nitrogen leaching from European forests in relation to nitrogen deposition. For. Ecol. Manage. 71, 153–61. Driscoll, C.T., Lawrence, G.B., Bulger, A.J., Butler, T.J., Cronan, C.S., Eagar, C., Lambert, K.F., Likens, G.E., Stoddard, J.L. & Weathers, K.C. 2001.Acidic deposition in the northeastern United States: sources and inputs, ecosystems effects, and management strategies. BioScience 51, 180–198. Driscoll, C.T., Whitall, D. & Aber, J. 2003. Nitrogen pollution in the Northeastern US: sources, effects, and management options. Bioscience. 53, 357–374. Forestry Agency. 2006. Annual Report on Trends in Forests and Forestry. Tokyo. (In Japanese with English summary). Geographical Service Institute. 2001. Koyasan. Tokyo. Goodale, C.L., Aber, J.D. & McDowell, W.H. 2000. The long-term effects of disturbance on organic and inorganic nitrogen export in the White Mountains, New Hampshire. Ecosystems 3, 433–50. Hemond, H.F. 1990. Acid neutralizing capacity, alkalinity, and acid-base status of natural of waters containing organic acids. Environ. Sci. Tech. 24, 1486–1489. Holloway, J.M., Dahlgren, R.A., Hansen, B. & Casey, W.H. 1998. Contribution of bedrock nitrogen to high nitrate concentrations in stream water. Nature 395, 785–8. Kijima, T., Sakamoto, S. & Hayashi, S.1962.Atlas wood in color. Hoikusya. Osaka. Kurimoto, C., Makimoto, H. & Yoshida, F. 1998. Geological Map of Japan 1:200,000, Wakayama. Geological Survey of Japan. Tokyo. Likens, G.E., Bormann, F.H., Pierce, R.S., Eaton, J.S. & Johnson, N.M. 1977. Biogeochemistry of a forested ecosystem. Springer-Verlag. New York. Lovett, G.M., Weathers, K.C. & Sobczak, W. 2000. Nitrogen saturation and retention in forested watersheds of the Catskill Mountains, NY. Ecol. Appl. 10, 73–84. Lovett, G.M, Weathers, K.C. & Arthur, M.A. 2002. Control of nitrogen loss from forested watersheds by soil

20 10 0

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plantation (%) Figure 2. Relationship between plantation ratio and the stream NO− 3 concentration. Vertical lines indicate the standard error. Regression line is shown (P = 0.07).

showed that variation in soil C:N was associated with variation in tree species composition. It implies that N retention and release in forested watersheds is regulated at least in part by tree species composition. In contrast, most of the watersheds in this study were planted with the same species, viz. Japanese cedar and cypress. Therefore, it is likely that the combined effects of differences in vegetation and soil characteristics are absent in plantations. Under such conditions, it is considered that the plantation proportion is an appropriate integrated index of vegetation regrowth and of the combined effects of differences in vegetation and soil characteristics with regard to control of stream NO− 3 concentrations. Clarification of the mechanism controlling stream NO− 3 concentrations using quantitative data is needed. 5

CONCLUSION

We found high variability in stream water chemistry among the examined watersheds. The stream water chemistry varied by geological influences. Apart from the geological influence, the proportion of forest that consists of plantations is considered an integrated index of the controlling factors of stream NO− 3 concentrations in the study area. ACKNOWLEDGEMENT We thank Dr. Nobuhito Ohte, Dr. Ryunosuke Tateno, and Dr. Tetsuya Shimamura. We acknowledge the Forest Environment Research Team at the Wakayama Experimental Forest Station, Kyoto University, for their support with field sampling and measurements. This work was supported by a Grant-inAid for Scientific Research from JSPS, Japan (Nos. 15380105 & 19380086) and Research Project

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carbon: nitrogen ratio and tree species composition. Ecosystems 5, 712–718. Ohata, S. 1991. A study of estimate the forest biomass. Forest Research. 63, 23–36. (in Japanese with English abstract) Reuss, J.O. & Johnson, D.W. 1986. Acid deposition and the acidification of soils and waters. Ecol. Stu. 59. Springer, New York, 119pp. Shibata, M. & Furuno, T. 1976. Stem volume table of Japanese cedar (Cryptomeria japonica) and cypress (Chamaecyparis obtuse) of Wakayama Forest Research Station. Report of the Kyoto University Forests. 11, 69–77. (in Japanese) SPSS. 2001. SPSS 11.0J for Windows. SPSS Inc. Chicago. Stoddard, J.L. 1991. Trends in Catskill stream water quality: Evidence from historical data. Water Resour. Res. 27, 2855–2864.

Swank, W.T., Vose, J.M., Elliott, K. J. 2001. Long-term hydrologic and water quality responses following commercial clear cutting of mixed hardwoods on a southern Appalachian catchment. For. Ecol. Manage. 143, 163–178. Tateno, R., Hishi, T. & Takeda, H. 2004. Above- and belowground biomass and net primary production in a cooltemperate deciduous forest in relation to topographical changes in soil nitrogen. For. Ecol. Manag. 193, 297–306. Vitousek, P.M. & Reiners, W.A. 1975. Ecosystem succession and nutrient retention: a hypothesis. BioScience 25, 376–381. Vitousek, P.M. 1977. The regulation of element concentrations in mountain streams in the northeastern United States. Ecol. Monogr. 47, 65–87.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Nitrate and phosphate uptake in a temperate forest stream in central Japan Y. Tanio∗ Graduate School of Agriculture, Kyoto University, Kyoto, Japan

N. Ohte Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan

M. Fujimoto Graduate School of Agriculture, Kyoto University, Kyoto, Japan

R. Sheibley US Geological Survey, Tacoma, Washington, USA

ABSTRACT: In-stream nutrient dynamics of forested catchments in warm temperate regions with a monsoon climate are not yet fully understood. We conducted in situ injection experiments in a mountainous stream in central Japan to determine which processes affect nitrogen and phosphorus dynamics in the stream reach. The experimental results indicated that phosphate was removed from the stream reach throughout the year, whereas little nitrate was removed. However, we detected significant nitrogen removal under low flow conditions, suggesting that lower than usual discharge rates affected the residence time of water flowing into sediments with nutrient-requiring organisms. Our data confirmed that phosphate uptake in this stream is controlled by hydrological characteristics, nutrient concentration, and stream temperature suggesting that both abiotic and biotic removal processes affect phosphate uptake in this mountain stream system. Keywords: streams 1

instream processes; nitrogen; phosphorus; injection experiments; forest catchments; mountain

INTRODUCTION

been investigated by estimating nutrient uptake in the stream channel using methods such as in situ nutrient addition experiments. In Japan, several studies have examined stream nitrogen dynamics; for example, Shibata et al. (2004) investigated nitrate storage in the hyporheic zone and its subsequent supply of ammonium and dissolved organic nitrogen to the stream channel. However, the effects of in-stream processes on nutrient dynamics in warm temperate regions with a monsoon climate have not been studied. Using an in situ nutrient addition experiment technique, we examined the nutrient dynamics of a mountain stream in a monsoon climate by estimating nutrient uptake and influence of transient storage on uptake in the stream reach.

Nutrient transport from forest catchments warrants thorough examination because these nutrients can cause eutrophication in downstream ecosystems such as lakes and seas. Because forest catchments and downstream ecosystems are connected by streams and rivers, research must focus on nutrient cycling processes in these lotic systems. To accurately predict nutrient loading from forest catchments, nutrient production or removal processes in the stream body must be characterized. Bernhardt et al. (2003) reported that nitrate exported from forest catchments was retained in streams. In addition, they predicted that nitrate concentrations in streams would be 80–140 % higher without stream removal of nitrate after forest disturbances. Transient storage zones in streams, such as the hyporheic zone, are particularly important for nutrient removal in lotic systems (Valett et al. 1997). In Europe and the United States, nutrient removal processes have ∗

2

MATERIAL AND METHODS

2.1 Study area The study stream was located within the Fudoji experimental watershed (hereafter, Fudoji; Figure 1). Fudoji

Corresponding author ([email protected])

83

PO4 -P, NO3 -N, and Cl− concentrations. In addition, we monitored the time series response in conductivity from the chloride addition at the downstream end of the reach using a field probe in order to model stream transient storage. PO4 -P and NO3 -N in water samples were measured using an AutoAnalyzer 3 (SEAL Analytical, Burgess Hill, West Sussex, UK). PO4 -P was measured using the molybdate-antimony method, and NO3 -N was measured using the cadmium-copper reduction method. Cl− in water samples was measured using ion chromatography on an ICS-90 (Dionex, Sunnyvale, CA, USA) with an AS4A anion column.

is located in southeastern Shiga Prefecture, central Japan, and covers an area of 427 ha. From 2004 to 2006, the mean annual atmospheric temperature was 10.9◦ C, and the mean annual precipitation was 1587 mm. The catchment is primarily forested by Japanese cypress (Chamaecyparis obtusa) and oaks (Quercus mongolica, Q. acuta, Q. salicina). The study area was located in the lower portion of Fudoji. The experimental reach encompassed approximately 140 m of a fifth-order stream, with an average slope of 2.36 degree. The surface stream sediments are composed of gravel (39%; φ = 2–75 mm), stone (36%; φ > 75 mm), and bedrock (23%), with little attached algae. The abundance of organic debris dams increased with litter input from fall through winter. 2.2

2.3 Data analysis We calculated the uptake length Sw (m) from the slope of background and dilution corrected changes 3− in NO− concentrations versus distance 3 and PO4 downstream. This method is explained in detail in Newbold et al. (1982). The uptake length is the average distance traveled by a nutrient ion before uptake, and uptake velocity Vf (mm s−1 ) was calculated from Sw :

Injection procedures

From September 2006 to November 2007, we conducted eight nutrient addition experiments (using chloride as a conservative tracer) to measure nutrient uptake and transient storage in the study area. This method is commonly used in stream studies and is explained in detail in the Stream Solute Workshop (1990). Nutrients were added as NaNO3 and KH2 PO4 at the same time, along with chloride as NaCl to act as a hydrologic tracer. Nutrients and chloride were dissolved in a carboy using stream water in the field and injected at a constant rate into the study reach using a peristaltic pump. Water samples were collected in the study reach prior to nutrient addition experiments at ten locations along the reach for PO4 -P, NO3 -N, Cl− , and specific conductivity. After the specific conductivity at the downstream end of the study area reached steady state, we sampled the ten stations again for

where Q (m3 s−1 ) is discharge, and w (m) is the stream width. Vf is a mass-transfer coefficient that corrects for the effects of depth and velocity on nutrient uptake. Vf can be considered the velocity at which a nutrient moves through the water column toward the sediments, and thus represents demand for nutrients relative to their concentration in the stream water (Hall et al.

Figure 1. Fudoji experimental watershed in Japan. The detailed map shows the study area.

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2002). For a given stream, small values of Sw and large values of Vf indicate efficient uptake of nutrients. We used the One-dimensional Transport with Inflow and Storage (OTIS) model [details in Runkel (1998)] to estimate the hydrological characteristics of the study reach. The OTIS model is composed of solute transport equations for the main channel, including exchange with the storage zone. Advection and dispersion in these conceptual areas are represented by the following relational equations:

would expect that a stream with high transient storage will be more efficient at processing nutrients due to greater contact time with the sediments and stream biota (Jones and Mulholland. 2000). The values of the four model parameters (A, D, AS , and α) were estimated by simulating the conductivity curves recorded at the downstream location in the study reach with the OTIS-P model. The OTIS-P model pairs the quantitative framework of OTIS with an approach using automated parameter-estimation techniques for A, D, AS , and α. Transient storage characteristics such as AS /A and RH were estimated from these parameters. AS /A (m2 m−2 ) is the area of storage zone normalized to the area of the main channel, and RH (s m−1 ) is mean storage residence time per unit stream length, normalized to the length of channel calculated as AS /Q (Morrice et al. 1997).

where A (M2 ) is the main channel cross-sectional area, AS (M2 ) is the storage zone area, C (ML−3 ) is the main channel a conservative tracer concentration, CS (ML−3 ) is the storage zone a conservative tracer concentration, D (L2T−1 ) is dispersion, Q (L3T−1 ) is discharge, t (T) is time, x (L) is distance, and α (T−1 ) is the storage zone exchange rate. The fundamental units are of Mass [M], Length [L], and Time [T]. These equations are used to estimate the amount of transient storage within a given stream reach. Transient storage refers to areas of the channel (pools, backwaters, eddies) or hyporheic exchange that act to slow down bulk advective flow in the channel. In general, one

3

RESULTS

Phosphate clearly decreased along the stream reach throughout the year (Figure 2b), whereas nitrate exhibited a clear decrease only in October 2007 (Figure 2a). The uptake velocity Vf was calculated from significant results (P < 0.01) of the addition experiments.The uptake velocity and conditions for each experiment are presented in Table 1. The uptake velocities of phosphorus ranged from 0.58 to 2.95 mm min−1 and averaged 1.42 mm min−1 . In the study area, phosphorus removal was high in

Figure 2. Results from addition experiments of (a) nitrate and (b) phosphate. Values of Sw are calculated from −1/slope from the linear regression of Ln(c) versus distance.

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Table 1.

Summary of data from all nutrient addition experiments.

Date

Sw -N (m)

Vf -N (mm min−1 )

Sw -P (m)

Vf -P (mm min−1 )

Q (L s−1 )

*NO3 (µg L−1 )

*PO4 (µg L−1 )

WT (◦ C)

AS /A (m2 m−2 )

RH (s m−1 )

Sept. 2006 Nov. 2006 Jan. 2007 Mar. 2007 May 07 Aug. 2007 Oct. 2007 Nov. 2007

NS NS NS NS NS NS 151.9 NS

NS NS NS NS NS NS 2.26 NS

576.3 480.6 1044.0 1055.6 566.6 728.6 116.5 351.7

1.56 1.89 0.63 0.58 0.92 1.23 2.95 1.51

60.18 51.97 45.22 38.64 33.36 57.51 22 33.52

408.4 110.3 358.6 381.3 226.8 373.8 367.3 43.6

8.9 9.6 6.9 0.6 2.9 0 17.9 0

19.4 9.2 4.5 5.2 14 21.3 12.9 9.6

0.158 0.134 0.195 0.107 0.113 0.188 0.185 0.151

0.865 0.917 1.692 1.332 1.615 1.354 4.751 2.514

WT = water temperature, * = background nutrient concentration in the stream prior to addition experiments, NS = not significant

2.22 mm min−1 . Compared to the results of Lautz and Siegel (2007), very little nitrate was removed from the stream body at our study site. However, when the nitrate removal occurs, the uptake velocity of nitrate became comparable to those in streams of Europe and the United States. Overall, our results indicated that nutrient removal processes occurred and affected nutrient dynamics in this mountain stream in a monsoon climatic region.

summer and fall and low in winter. The uptake velocity of nitrate in October 2007 was 2.26 mm s−1 . Background NO− 3 concentrations were high in summer and winter and low in spring and fall. Background PO3− 4 concentrations did not exhibit clear seasonality and were occasionally undetectable. In terms of hydrological characteristics, the discharge rate during the experiments ranged from 22.0 to 60.8 L s−1 and averaged 41.9 L s−1 . The sampling day in October 2007 was also the day of lowest discharge in 2007, according to discharge monitoring by a flow gauging weir at fifth-order catchment (49.7 ha). The relative size of the storage zone (As/A) ranged from 0.107 to 0.195 and averaged 0.154. The mean storage residence time per unit stream length (RH ) ranged from 0.87 to 4.75 s m−1 and averaged 1.89 s m−1 . The mean storage residence time reached a maximum in October 2007 and was five times higher than the minimum value.

4 4.1

4.2

Effects of hydrological characteristics on nutrient uptake

Nitrate removal rarely occurred in the study area, but it was detected when the discharge was lowest and the mean storage residence time per unit stream length was longest (October 2007; Table 1). In general, water is exchanged between the stream channel and hyporheic zone (Wondzell 2006). Many chemical and biological processes that play a key role in controlling the nutrient dynamics of a stream occur in the hyporheic zone. Moreover, these processes are influenced by hydrologic characteristics. Slow water movement through the hyporheic zone greatly reduces the average distance traveled by a nutrient ion before uptake (Naiman and Bilby. 1998) because the hydrologic exchange with slow water facilitated the delivery of nitrate to stream biota for uptake. The results in Oct. 2007 suggested that the extremely low flow condition accelerated the nitrate uptake by biota in the hyporheic zone in the stream bed. It would be reasonable to expect the nitrate uptake to be inversely proportional to the flow velocity, because the slow flow increased the residence time of nitrate in the hyporheic zone, as described in the above reference. Phosphorus removal occurred year-round in the stream reach (Table 1). The relationships between phosphorus uptake velocity and the area of the storage zone AS /A or the mean storage residence time per unit stream length RH are presented in Figure 3.

DISCUSSION Nutrient uptake

Phosphate removal occurred in the stream body throughout the year, and the uptake velocity ranged from 0.58 to 2.95 mm min−1 and averaged 1.42 mm min−1 (Table 1). Hall et al. (2002) reported that the phosphate uptake velocity of streams in Europe and the United States ranged from 1.9 to 11.5 mm min−1 and averaged 7.3 mm min−1 . Our results indicated that phosphate uptake velocity in the study stream was lower than those of streams in Europe and the United States. Although nitrate removal was rarely detected during our experiments, nitrate clearly decreased in October 2007, and the uptake velocity was 2.26 mm min−1 . Lautz and Siegel (2007) reported that the uptake velocity of nitrate in streams of Europe and the United States ranged from 0.28 to 9.62 mm min−1 and averaged

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Figure 3. Relationship between uptake velocity of phosphate (Vf ; mm min−1 ) and (a) normalized area of storage zone (AS /A; m2 m−2 ) and (b) the mean storage residence time per unit stream length (RH ; s m−1 ). The numbers in each symbol are the month during which each experiment took place.

Figure 4. Relationship between phosphate uptake velocity (Vf -P; mm min−1 ) and (a) water temperature (oC) and (b) background PO43 - concentrations prior to experiments (µg L−1 ). The numbers in the symbols indicate the month during which each experiment took place.

4.3

The uptake velocity increased with increasing area of the storage zone (Figure 3a). This result suggests that the phosphate uptake may occur in the storage zone. In addition, the uptake velocity increased with longer mean storage residence time per unit stream length (Figure 3b). Thus, the mean storage residence time per unit stream length may affect abiotic removal processes (e.g., absorption on stream sediments) as well as biotic removal processes (e.g., uptake by microorganisms). Previous studies have also indicated that the uptake velocity of nutrients is affected by the area of the storage zone and/or current velocity, which correlates with discharge (Valett et al. 1996; Mulholland et al. 1997; Peterson et al. 2001). Our results also demonstrated that the uptake velocity of nutrients is controlled by hydrological characteristics in this mountain stream system.

Biotic removal processes of phosphate

Here, we only consider the biotic removal processes of phosphate, as nitrate uptake rarely occurred in the study area. The relationships between phosphate uptake velocities and water temperature and background PO3− concentrations are presented in 4 Figure 4. The uptake velocity increased with water temperature increased from winter to summer (Figure 4a). This result suggests that water temperature affected the nutrient uptake ability of microorganisms in the storage zone. Moreover, the uptake velocity of phosphate was also related to background PO3− con4 centrations (Figure 4b). In general, phosphate is an essential element for organisms and biomass within stream ecosystems may be limited by phosphate (Klotz 1985). Our results showed that phosphate concentrations in the stream water were much lower than nitrate

87

processes in this region will require additional study sites in order to see if these relationships hold.

concentrations (Table 1) indicating that phosphate may be limiting growth in this stream. Therefore, increases in PO3− 4 concentrations should result in higher uptakes velocities, which is what we observed (Table 1, Figure 4b). Our results indicate that phosphate uptake is affected by the nutrient uptake ability of organisms and their biomass within the stream ecosystem. The uptake velocity of phosphate may be a factor of water temperatures and concentration, as described above. Therefore, high uptake velocities at midrange water temperatures in the fall may be attributable to high nutrient concentrations (Figure 4a, b). During the November 2007 experiment, the nutrient concentrations were much lower than in November 2006; however, the uptake velocities were equal in both experiments. In this case, differences in the mean storage residence time per unit stream length may explain the uptake velocity in November 2007 (Table 1, Figure 3b). The longer mean storage residence time per unit stream length in November 2007 compared to November 2006 suggests that a small biomass may efficiently remove nutrients from the stream with increased contact time between phosphate and microorganisms in the stream sediments. Furthermore, sorption of phosphate to stream sediments may also increase with this increase in the mean storage residence time per unit stream length. Thus, abiotic removal processes may also have affected phosphate retention in November 2007. Abiotic removal processes, such as adsorption on fine soil particles and organic matter, is an important phosphate removal processes in some streams (Meyer 1979; Mulholland et al. 1985; Davis & Minshall 1999); however, our findings suggest that biotic uptake is also important in this mountain stream. In addition, biotic removal processes increased with longer mean storage residence time per unit stream length.

5

ACKNOWLEDGMENTS We thank Makoto TANI (Laboratory of Forest Hydrology) for providing this study sight. We thank the editor and 2 reviewers for constructive comments. Every one of Laboratory of Forest Hydrology, erosion control, and Silviculture supported to conduct nutrient addition experiments. REFERENCES Bernhardt, E.S., Likens, G.E., Buso, D.C., & Driscoll. C.T. 2003. In-stream uptake dampens effects of major forest disturbance on watershed nitrogen export. Proceedings of the National Academy of Sciences USA 100(18): 10304– 10308. Davis, J.C., & Minshall, G.W. 1999. Nitrogen and phosphorus uptake in two Idaho (USA) headwater wilderness streams. Oecologia 119: 247–255. Jones, J.A. and Mulholland, P.J. 2000. Streams and Ground Waters. Academic Press: San Diego. 425p. Naiman R. J. and Bilby R. E. 1998. River ecology and management: lessons from the Pacific Coastal Ecoregion. Springer-Verlag, New York. Pages 347–372. Hall, R.O., Jr., Bernhardt, E.S., & Likens, G.E. 2002. Relating nutrient uptake with transient storage in forested mountain streams. Limnology and Oceanography 47(1): 255–265. Klotz, R.L. 1985. Factors controlling phosphorus limitation in stream sediments. Limnology and Oceanography 30(3): 543–553. Lautz, L.K., & Siegel, D.I. 2007. The effect of transient storage on nitrate uptake lengths in streams: an inter-site comparison Hydrological Processes 21: 3533–3548. Meyer, J.L. 1979. The role of sediments and bryophytes in phosphorus dynamics in a headwater streams. Limnology and Oceanography 24: 365–375. Morrice, J. A., Valett H. M., Dahm C. N., & Campana M.E. 1997. Alluvial characteristics, groundwater-surface water exchange and hydrological retention in headwater streams. Hydrological Processes 11: 253–267. Mulholland, P.J., Newbold, J.D., Elwood, J.W., Ferren, L.A., & Webster, J.R. 1985. Phosphorus spiraling in a woodland stream: seasonal variation. Ecology 66(3): 1012–1023. Newbold, J.D., O’Neill, R.V., Elwood, J.W., & Winkle V.W. 1982. Nutrient spiraling in streams: implications for nutrient limitation and invertebrate activity. American Naturalist 120(5): 628–652. Newbold, J. D., Elwood J. W., O’Neill R. V., & Sheldon A. L. 1983. Phosphorus dynamics in a woodland stream ecosystem: a study of nutrient spiralling. Ecology 64(5): 1249–1265. Peterson B. J. et al. 2001. Control of nitrogen export from watershed by headwater streams. Science 292: 86–90. Runkel, R.L. 1998. One-dimensional transport with inflow and storage (OTIS): a solute transport model for streams and rivers. US Geological Survey Water Resources Investigations Report 98-4018.

CONCLUSIONS

These results demonstrate that nutrient uptake by microorganisms and adsorption occur in this mountain stream within a monsoon climatic region and that these removal processes were controlled by hydrological characteristics such as stream discharge and storage zone area. Based on the relationship between the uptake of phosphate and water temperature or substrate concentrations, our results suggest that both the abiotic and biotic phosphate removal processes are important within this system.Although nitrate removal processes were rarely detected during our experiments, nitrate removal occurs during low discharge and the long mean storage residence time per unit stream length. When we were able to measure nitrate uptake, it was comparable to that of streams in Europe and the United States. Further investigations of nitrate removal

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Shibata, H., Sugawara, O., Toyoshima, H., Wondzell, S.M., Nakamura, F., Kasahara, T., Swanson, F.J., & Sasa, K. 2004. Nitrogen dynamics in the hyporheic zone of a forested stream during a small storm, Hokkaido, Japan. Biogeochemistry 69: 83–104. Stream Solute Workshop. 1990. Concepts and methods for assessing solute dynamics in stream ecosystems. Journal of the North American Benthological Society 9: 95–119. Valett, H.M., Morrice, J.A., Dahm, C.N., & Campana, M.E. 1996. Parent lithology, surface–groundwater exchange,

and nitrate retention in headwater streams. Limnology and Oceanography 41: 333–345. Valett, H.M., Dahm, C.N., Campana, M.E., Morrice, J.A., Baker, M.A., & Fellows, C.S. 1997. Hydrologic influences on groundwater–surface water ecotones: heterogeneity in nutrient composition and retention. Journal of the North American Benthological Society 16: 239–247. Wondzell S. M. 2006. Effect of morphology and discharge on hyporheic exchange flows in two small streams in the Cascade Mountains of Oregon, USA. Hydrological Processes 20: 267–287.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Impact of forestry practices on groundwater quality in the boreal environment E. Kubin∗ Muhos Research Unit, Finnish Forest Research Institute, Muhos, Finland

J. Krecek Department of Hydrology, Czech Technical University, Prague, Czech Republic

ABSTRACT: Since 1985, effects of forestry practices upon leaching of nutrients into surface and ground waters have been studied at the field plots of Pahalouhi and Hautala (64◦ 28 N, 27◦ 33 E) in the boreal forest environment. The special attention has been paid to potential harms by leaching nitrate-nitrogen into the aquatic ecosystems. The main aim of this study is to evaluate impacts of natural forest regeneration in comparison with common clear-cut interventions, waste wood collection, site preparation and tree planting. All the tested treatments caused a rise of NO3 -N concentrations in ground waters. However, in the case of natural regeneration, the maximum observed concentrations were significantly lower than by clear-cut practices. The content of NO3 -N was rising up during 3–6 years after a treatment, and a significant increase was still detected during approximately 20 years. After the clear-cut, maximum concentrations reached 500–800 µg/1, while by the natural regrowth of pine, the observed peak was only 120 µg/1. Keywords:

1

boreal forest environment; forestry practices; ground water quality; nitrogen cycling

INTRODUCTION

(Hänninen et al. 1996). The duration of accelerated nutrient leakage after a forest treatment was found during several next years (Kubin, 1995; Rosen et al., 1996). Kubin (2006) reported nearly a twenty years prolonged effect of a regeneration-cut on acceleration of nitrate leakage in ground waters. Reasonably, in ground water aquifers, the evidence of maximum leakage of nutrients takes few more years in comparison with surface waters (Kubin, 1998). Generally, the accelerated leakage into watercourses might be prevented by two systems controlling overland-flow in a treated watershed: undisturbed or slightly thinned forest buffer zones (Forest and Park Service, 1998), and special infiltration belts (Kubin et al., 2000). An additional protection of ground waters requires a complex system of modified forestry practices (Kubin 1998, 2006).The main aim of this study is to evaluate impacts of natural forest regeneration in comparison with common clear-cut, waste wood collection and tree planting.

Impacts of forests on water phenomena have been discussed for a long time (Anderson, Hoover & Reinhardt, 1976). It is evident that humans can control water resources (quantity and quality) by forestry practices (Hänninen et al., 1996; Krecek, 1987; Kubin et al., 2000). Forest clear-cuts with following site preparations represent a strong human intervention with serious side effects on the environment. In Scandinavia, research oriented on relationship between forestry practices and the leakage of nutrients into watercourses started in Sweden at the beginning of the 1970s (Tamm et al., 1974; Wiklander, 1974). In Finland, particularly, effects of forest clear-cut and site preparation on surface water quality have been monitored since 1974 (Kubin, 1995). Since 1983, an outflow of nutrients from a forest catchment has been studied by Ahtiainen (1988) and Finer et al.(1997). Research oriented on impacts of forest regeneration and waste wood harvest on nutrients leakage in groundwater aquifers was introduced in 1986 (Kubin, 1998). Nowadays, the urgent task of forest practitioners is to minimize potential harms to watercourses ∗

2

MATERIAL AND METHODS

The Muhos Research Unit of the Finnish Forest Research Institute established several experimental fields to study environmental effects of forest regeneration in the mid-part of the Northern

Corresponding author ([email protected])

91

Table 1. Correlations between NO3 -N contents at control (CHA−C and CPA−C ) and treated plots (1 clear-cut, 2 clear-cut with waste removal, 3 clear-cut with ploughing): Spearman coefficient r (rc = 0.4, α = 0.1).

CHA−C CPA−C CHA−C CPA−C

CHA−1

CHA−2

CHA−3

0.26 0.26

0.23 0.73

−0.08 0.63

CPA−1

CPA−2

CPA−3

0.23 0.41

0.41 0.04

0.02 0.49

preparation was partly included. The installed wells consist of plastic pipes 4–6 m long with 1,5 m perforated and a bottom plug. Ground waters have been sampled monthly (May–October) by a low-pressure pump. Chemical analyses were carried out at the Muhos Research Station following standard methods. From regressions found between tested and control plots in the calibration period (1985–1986), NO3 -N contents at the tested plots were predicted (scenario ‘not affected’) and compared with values affected by a treatment.

Figure 1. The experimental sites Pahalouhi and Hautala.

Boreal Zone. The experimental fields of Pahalouhi and Hautala (64◦ 28 N, 27◦ 33 E, Figure 1) represent prevailing conditions in the middle-boreal coniferous zone (Hämet-Ahti, 1981). The climate is continental/micro-thermal (Dfc type of the Köppen classification), the long-term mean annual temperature of the air is 1◦ C, and the sum of precipitation 542 mm/year. The site types are dryish and fresh uplands, soils range from sand to sandy till. In 1986, at Pahalouhi, 141 m3 /ha of timber was removed: 51% – Scots pine (Pinus. silvestris), 46% – Norway spruce (Picea abies), and 3% – birch (Betula pendula, Betula pubescens). Comparatively, at Hautala, 127 m3 /ha was harvested: 11% – pine, 88% – spruce, and 1% – birch. In the spring of 1987, all the harvested plots were replanted with Scots pine using manually made scalps or ploughing. The study on groundwater quality started at both sites in 1985: 24 and 21 wells were installed at Pahalouhi and Hautala, respectively. After two years of calibration (1985–1986), three different clear-cut experiments were introduced there. Additionally, in 2001, 11 wells were installed to study effects of natural regeneration on the leakage of nitrogen. In total, five forest sites have been monitored (Figure 1): clear-cut without removal of cutting waste, clear-cut with collected waste, clear-cut with ploughing, natural regeneration and untreated control plot. By the site preparation by ploughing, more than a half of the soil surface was disturbed. The natural regeneration included a shelter-wood belt (300 spruce stems per ha) without soil preparation, and seed-tree cutting (50 pine stems per ha) where soil

3

RESULTS

3.1 Control plots and calibration Data of two control plots of Pahalouli and Hautala differ significantly in the studied period (Man-Whitney Test, U = 1956.5, U = 15468, two tailed P < 0.0001). Matching of values was not effective because of a high correlation between the data sets (Spearman r = 0.36, rc = 0.16, n = 132, α = 0.1, P < 0.0001). For both control plots, a trend in ground water NO3 -N concentrations with time is not significant (Figure 2): r = −0.137 and -0.009. Two years prior to an expected effect of forest practices (1985–1986, twelve sampling ranks), was considered as a calibration period. Correlations between control and treated fields were tested (Table 1) and linear regressions found. 3.2 Clear-cut treatments Initially, mean concentrations of NO3 -N in ground waters were 12–48 µg/1 (Pahalouhi) and 27–74 µg/1 (Hautala). After the clear-cut interventions, NO3 -N concentrations increased dramatically in the next 4–6 years (Figures 3, 4, 5). The highest concentrations exceeded values of 600 (Pahalouhi) and 800 µg/1 (Hautala). Contrary to NO3 -N, concentrations of NH4 -N did not indicate a corresponding change. The differences between observed (affected) and predicted (‘not affected’) NO3 -N concentrations are plotted with time in Figures 6 and 7. The data sets

92

Control NO3-N Content (ug/l)

Hautala

300

Pahalouhi

250 200 150 100 50 0 0

20

40 60 80 100 120 Sampling rank (1985-2007)

140

Figure 2. NO3 -N concentrations at control plots in 1985–2007.

did not pass the test of normality. Differences between observed and predicted concentrations were tested by the Wilcoxon approach of matched-pairs signed-ranks. For all the studied treatments, differences between medians were found extremely significant (two tailed P < 0.0001), and pairing effective. Individual data were tested against the upper 95% limit of the scenario ‘not affected’. Thus, the first significant increase in NO3 -N concentrations was found 1–3 years after the interventions. The differences culminated with a lag time of 4–7 years after a treatment (520–760 µg/1). In Hautala, a relatively long delay (7 years) after the site preparation by ploughing, was probably affected by initial direct run-off into surface waters. 3.3

Figure 3. Clear-cut without recovery of residues, Pahalouhi (1985–2006) and Hautala (1985–2007).

Natural forest regeneration

In the autumn of 2001, natural regeneration of Norway spruce by shelter-wood cutting and Scots pine by seed tree cutting was applied in Pahalouhi. Concentrations of NO3 -N in ground waters were increasing gradually during five next years (Figure 8). The maximum concentration 405 µg/1 in the fifth year (2006) was 50–80% of the clear-cut levels (Figures 3, 4, 5). The Wilcoxon test of matched-pairs signed-ranks confirmed again extremely significant differences (two tailed P < 0.0001) in studied data sets. After seed-tree cutting in Pahalouhi, both, a site preparation by harrowing, and not treated plot, were accepted there (Figure 1). The differences in NO3 -N concentrations are statistically significant already since the first year after the treatment (Figure 9). In comparison with clear-cut treatments, changes in NO3 -N concentrations produced by the natural regeneration (Figure 9) are significantly lower (10–23%). 3.4 Hydrological consequences After the forestry practices, both reduced canopy interception and transpiration contributed to an accelerated

Figure 4. Clear-cut with recovery of residues, Pahalouhi (1985–2006) and Hautala (1985–2007).

93

NO3-N Content (ug/l)

900

Not collected Collected waste

700 Ploughing 500 300 100 -100

20 40 60 80 100 120 Sampling rank (1985-2007)

Figure 7. Differences between observed and predicted NO3 -N concentrations in ground water at Hautala.

Figure 5. Clear-cut with site preparation, Pahalouhi (1985–2006) and Hautala (1985–2007).

NO3-N Content (ug/l)

900

Not collected Figure 8. NO3 -N concentrations after shelterwood cutting of Norway spruce.

Collected waste 700 Ploughing 500 300 100 -100

20 40 60 80 100 120 Sampling rank (1985-2007)

Figure 6. Differences between observed and predicted NO3 -N concentrations in ground water at Pahalouhi.

recharge of ground water aquifers. At investigated sites, Hamon’s mean annual potential evapotranspiration EP = 297 mm/year. However, in mature coniferous stands, this value might be even exceeded, namely, by a high canopy interception (cca 30% of annual precipitation). Assuming just a change in albedo from 12% (mature coniferous stands) to 20% (clear-cut), an increase of annual water yield might result in 50 mm (Climate Index of Budyko), Krecek (1982). Base-flow characteristics of the experimental plot 2 (clear-cut with harvested waste) were evaluated from the lag-time TL of NO3 -N concentrations observed in

Figure 9. NO3 -N concentrations in the groundwater after seed-tree cutting of Scots pine.

the control well (No 25, Figure 1). The topography of the site is smoothly inclined towards this well with the distance L = 80 m. From the observed shift in NO3 -N concentrations (Figure 10), the average lag-time is approximately TL = 14 months. Thus the mean baseflow velocity vb = L/TL = 2.2 10−6 m/s. By the mean value of soil porosity f = 0.4, the average velocity of filtration vf = 0.88 10−6 m/s.

94

Kubin 1995 or Rosen et al. (1996) reported a lasting effect of few years of increased NO3 -N contents after the clear-cut. Dahlgren (1998) found a significant nutrients immobilization by a rapid growth of seedlings and sprouts. But, in the studied sites Pahalouhi and Hautala, the growth of new seedling stands is relatively slow. However, the exact reasons for long-lasting NO3 -N leaching need still further investigation on the input of nitrogen. In Pahalouhi and Hautala, contrary to NO3 -N, concentrations of NH4 -N did not show any change after the forest treatments. Also Rusanen et al. (2004) did not find increasing ammonium concentrations after a clear-cut in boreal areas. In opposite to clear-cut practices, presented results indicate the natural regeneration effective in control of nitrogen leakage from forest catchments. However, the presented study on natural regeneration is still relatively short (six years) to long-lasting effects (twenty years) supposed. Anyway, it is evident that the natural regeneration can contribute to sustainable forestry in boreal environment. In Finland, rising demand on the forest base energy now leads to extended forestry practices. The variety of interventions is increasing but their environmental impacts not fully understood. Particularly, a special attention still needs the recovery of logging residues and stumps by forestry practices.

Figure 10. NO3 -N concentrations in the base-flow (clear-cut with harvested waste).

4

CONCLUSIONS AND DISCUSSION

Nitrate-nitrogen belongs to the most serious harm emissions produced by forestry practices. Impacts of timber harvest and forest regeneration on surface waters can be prevented quite effectively (Kubin et al., 2000). However, to prevent impacts on ground waters is more complicated. The key process to control the aquatic environment should be considered in biological cycling of nutrients (Borman et al. 1979, Dahlgren 1998). The groundwater investigation has been included in the frame of the long-term ecological research on environmental effects of forest regeneration in Northern Finland, Kubin (1977, 1995, 1998), and Kubin et al. (1991, 1994). All the investigated forestry practices applied at experimental fields of Pahalouhi and Hautala initiated rising of NO3 -N concentrations in ground waters. The observed lag time (from 3 to 6 years) has been described mainly by hydrological consequences in studied sites (soil parameters, groundwater depth, and regime of rainfall and snowmelt). Although, the observed maximum nitrate concentrations after forestry interventions are relatively low (2–5 mg/l) in comparison with the hygienic water standard (50 mg/l), they represent a serious load of nitrogen to the aquatic environment (Krecek & Horicka, 2006). Clear-cut practices also change water cycling in a forest site. The loss of evapotranspiration is decreasing by reduced canopy interception and transpiration of harvested trees. Therefore, water yield (recharge of ground water resources) can rise up and increase the total input of nitrogen into an aquatic system (Krecek, 1987). Anyway, the observed changes of NO3 -N concentrations in ground waters are driven mainly by accelerated decomposition of cutting waste. The total duration of increased NO3 -N contents in ground waters was approximately 20 years, much longer than increased NO3 -N concentrations observed in watercourses. In surface waters, Tamm et al. (1974), Haveraaen (1981), Krecek (1987), Ahtiainen (1988),

ACKNOWLEDGEMENT All the field data were collected by the staff of the Muhos Research Unit. The help provided by the landowner, UPM Kymmene Ltd., was of prime importance to establish and organize the experimental plots. Our special thanks are going to Anna-Liisa Mertaniemi, Timo Mikkonen and Pekka Honkanen for chemical analyses, to Reijo Seppänen and Jorma Pasanen for field activities, to Tuula Aspegren and Jouni Karhu for their help with drawing the figures and data processing. The English translation was checked by Erkki Pekkinen. REFERENCES Ahtiainen, M. 1988. Effects of clear cutting and forestry drainage on water quality in the Nurmes study. Proceedings of The International Symposium on the Hydrology of Wetlands in Temperate and Cold Regions. Joensuu, Finland 6–8 June, 1988. VoI. 1. Publications of the Academy of Finland 4/1988. Anderson, H.W., Hoover, M.D. & K.G. Reinhardt. 1976. Forests and water: effects of forest management on floods, sedimentation and water supply. USFS HW, KG Reinhardt – 1976 – GTR-PSW-18: 115 p., Berkeley, CA. Borman, F.H. & G.E. Likens. 1979. Pattern and process in a forested ecosystem. Springer-Verlag. New York, Heidelberg, Berlin, 253 pp.

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Dahlgren, R.A. 1998. Effects of forest harvest on streamwater quality and nitrogen cycling in the Caspar Creek watershed. In: Ziemer, R.R (editor), Proceedings of the conference on coastal watersheds: the Caspar Creek story. General Tech. Rep. PSW GTR-16, Albany, California: Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture: 45–53. Forest and Park Service. 1998. Environmental Guidelines to Practical Forest Management. Finnish Forest and Park Service, Helsinki (Finland),124 p. Finer, L. & Ahtiainen, M., Mannerkoski, H., Möttönen, V., Piirainen, S., Seuna, P. and Starr, M. 1997. Effects of harvesting and scarification on water and nutrient fluxes. A description of catchments and methods, and results from the pretreatment calibration period. Research Papers of Metla No. 648. Haveraaen, O. 1981. Virkning av hogst på vannmengde og vannkvalitet fra en norsk barskog Summary: The effeet of cutting on water quantity and water quality from EastNorwegian coniferous forest. Reports of the Norwegian Forest Research Institute 36(7), 27 pp. Hämet-Ahti, L. 1981. The boreal zone and its biotic subdivision. Fennia 159, 69–75. Hänninen, E., Kärhä, S. and Salpakivi-Salomaa, P. 1996. Forestry and the Protection of Watercourses. Metsäteho, 23 pp. Krecek, J. 1982. Energy and water balance estimates for a forested basin. In: Hydrological Research Basins and their Use in Water Resources Planning. Landeshydrologie Proceedings, Bern (Switzerland), 321–325. Krecek, J. 1987. Effects of forest practices on nitrate content in stream-waters. In: Proceedings of the 5th CIEC International Symposium on Protection of Water Quality from Harmful Emmissions, Balatonfured (Hungary), 186–191. Krecek, J. and Z. Horicka. Forests, air pollution and water quality: influencing health in the headwaters of Central Europe’s “Black Triangle”. Unasylva, 57: 46–49. Kubin, E. 1977. The effect of clearcutting upon the nutrient status of a spruce forest in Northern Finland. Acta For. Fennica 155, 1–40. Kubin, E. 1995. Site preparation and leaching of nutrients. Proceedings of the Symposium Northern Silviculture and

Management, August 16–22, 1987 in Lapland, Finland. Finnish Forest Research Institute, Research Papers of Metla No. 567, 55–62. Kubin, E. 1998. Leaching of nitrate nitrogen into the groundwater after clear felling and site preparation. Boreal Environmental Research 3:3–8. Kubin, E. 2006. Leaching of nitrogen from upland forestregeneration sites into wetland areas. In: Krecek, J. and Haigd, M. (eds.) Environmental Role of Wetlands in Headwaters. Springer-Verlag. 87–94. Kubin, E. and Kemppainen, L. 1991. Effect of clear cutting of boreal spruce forest on air and soil temperature conditions. Acta For. Fennica 225, 1–42. Kubin, E. and Kemppainen, L. 1994. Effect of soil preparation of boreal spruce forest on air and soil conditions in forest regeneration areas. Acta For. Fennica 244, 1–56. Kubin, E., Ylitolonen, J., Välitalo, J. and Eskelinen, J. 2000. Prevention of nutrient leaching from a forest regeneration area using overland flow fields. In: M. Haigh and J. Krecek (eds.) Environmental Reconstruction in Headwater Areas, Kluwer Academic Publishers, Dordrecht, pp.161–169. Rosen, K., Aronson, J-A. and Eriksson, H.M. 1996. Effects of clearcutting on streamwater quality in forest catchments in cenyral Sweden. For. Ecol. Manage. 83(3), 237–244. Rusanen, K., Finér L., Antikainen, M., Korkka-Niemi, K., Backman, B. and Britschgi, R. 2004. The effect of forest cutting on the quality of groundwater in large aquifers in Finland. Boreal Environmental Research 9(3), 253–261. Tamm, C.O., Holmen, H., Popovic, B. and Wiklander, G. 1974. Leaching of plant nutrients from soils as a consequence of forestry operations. Ambio III(6), 211–221. Wiklander, G. 1974. Hyggesupptagningens inverkan på växtnäringsinnehåll i yt- och grundvatten. Summary: Effect of clear felling on the content of nutrients in surface and ground water. Sveriges Skogsvårdsförbundets Tidskrift 1, 86–90. Wiklander, G. 1983. Kväveutlakning från bördig skogsmark i södra Sverige. Summary: Loss of nitrogen by leaching from fertile forest soils in southern Sweden. Kungliga Skogs- och Lantbruksakademiens Tidskrift 122 No 5, 310–317.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Water yield and nitrogen loss during regrowth of Japanese cedar forests after clearcutting K. Fukushima∗ Graduate School of Agriculture, Kyoto University, Kyoto, Japan

N. Tokuchi Field Science Education and Research Center, Kyoto University, Kyoto, Japan

R. Tateno Faculty of Agriculture, Kagoshima University, Kagoshima, Japan

M. Katsuyama Research Institute for Humanity and Nature, Kyoto, Japan

ABSTRACT: To evaluate whether the generalized, lumped-parameter PnET-CN model for northeastern United States is applicable to Japanese watersheds, we compared observed data of water and nitrogen (N) balances with PnET-CN simulations, and discussed changes in N loss during forest development. The study was conducted in watersheds covered by Japanese cedar plantations located in central Japan. The stand age of cedars is even within the watersheds, and ranges from 0 to > 90 years old among adjacent watersheds. Relationships between observations and PnET-CN simulations indicated that monthly water discharge was underestimated in younger stands. In stands < 5 years old, drastic increases in N loss occurred in the watersheds and were predicted by PnET-CN simulations. Measured N loss was better predicted by simulated water yield and observed N concentrations than by simulated N loss from the PnET-CN model. These results suggest that for accurate prediction in our study watersheds, the model requires parameter modification to better simulate the seasonality in stream N concentration generated by the combination of plant-soil internal N cycling and the hydrologic flowpath. Furthermore, the effects of understory vegetation clearance and canopy opening on rates of evapotranspiration and direct run-off during storm events should be incorporated. Keywords:

1

streamwater chemistry; stand age; PnET-CN model; water balance; nitrogen cycling

INTRODUCTION

observations are necessary. Several hypotheses of forest succession or development have been proposed (Odum 1969; Vitousek & Reiners 1975) and tested using both long-term monitoring after forest clearcutting (Pardo et al. 1995; Swank et al. 2001) and comparative studies among forest stands of various ages or disturbance history (Goodale & Aber 2001; White et al. 2004). Although the recovery of vegetation or growth of planted trees appears to contribute to declines in N loss, it is still unknown; 1) the length of time until N loss declines to pre-cut levels, 2) whether hydrological N loss is related to N uptake by plants, and 3) the nature of changes in the N retention system during forest development. The studies at Hubbard Brook Experimental Forest (HBEF), New Hampshire, USA, have monitored the long-term effects of hydrological N loss after

Biological nitrogen (N) retention in forest ecosystems plays an important role in the prevention of hydrological N loss and resultant aquatic eutrophication (Vitousek & Howarth 1991). Disturbances due to human activities such as forest management alter the N retention system between plants and soils. Several previous studies have shown that forest clearcutting eliminates this N retention system and may lead to high nitrate loss (Bormann & Likens 1979; Vitousek et al. 1979; Gundersen et al. 2006; Fukushima & Tokuchi 2008). However, it is difficult to evaluate recovery processes from forest clearcutting, because long-term ∗

Corresponding author ([email protected])

97

clearcutting, stems are transported to logging yards using a skyline system. The slash and logging debris are gathered and arranged into strips. During the following year, seedlings are replanted. For 5 to 6 years after afforestation, weeds and understory vegetation are removed every summer. Only one selective thinning (a cutting of approximately 30% of the stand) was practiced ca. 30 years after afforestation. Further details are provided in Fukushima & Tokuchi (2008).

forest clearcutting (Bormann & Likens 1979). Aber et al. (1992, 1995, 1997) constructed and developed a generalized, lumped-parameter model of carbon (C), N, and water interactions in forest ecosystems (i.e. the Photosynthesis and EvapoTranspiration [PnET]-CN model), based on findings in HBEF. This simple model includes biotic processes and a disturbance scenario and thus provides estimates of net primary production, N uptake by plants, and water balances. In addition, PnET-CN can evaluate and predict long-term impacts of land disturbances, such as forest clearcutting, and climatic effects on C, N, and water balances (Aber & Driscoll 2001; Ollinger et al. 2002; Ohte 2006; Shibata et al. 2006; Tokuchi et al. 2006). Our study site is located in the Mt. Gomadan Experimental Forest (GEF). This site is ideal for investigations of forest regrowth processes after clearcutting, because the stand age of replanted Japanese cedars is even within watersheds and ranges from 0 (right after clearcutting) to > 90 years old among adjacent watersheds. The objective of this study was to evaluate whether the PnET-CN model is appropriate for simulating the processes of altered water and N cycling and their recovery during Japanese cedar forest regrowth following clear-cutting. In this paper, we compared observed data of water and N balances to PnET-CN simulations and discussed the modifications necessary for developing the PnET model for the GEF. 2

2.2 Hydrological observations and streamwater nitrogen analysis Water discharge was monitored using weirs in watersheds S17, S16, S11, S12, S5, and S20, the stand ages of which were 1, 7, 18, 30, 33 and 44 in 2006, respectively. S17 was clearcut in 2005. Characteristics of each watershed are described by Katsuyama et al. (this volume). Streamwater samples were collected biweekly to monthly near the weir at each catchment from April 2002 to December 2007. Water samples were filtered through a 0.45−µm cellulose acetate filter and stored at 4 C until chemical analy− sis. The NH+ 4 and NO3 concentrations were analyzed using ion chromatography (LC-10A, Shimadzu, and ICS-90, Dionex). Inorganic N loss was calculated by multiplying water discharge (10 min. resolution) by N concentrations, which were linearly interpolated between sampling dates.

MATERIALS AND METHODS 2.3

2.1

Study site description

Model structure and parameterization

The PnET-CN model is a monthly time-step model that combines generalized relationships for ecosystem processes such as photosynthesis, respiration, litter production, decomposition and N mineralization along with climate inputs (monthly average maximum and minimum temperatures, insolation, radiation, and total precipitation) to estimate C, N and water balances (see Aber et al. 1997; Ollinger et al. 2002). Climate variables at the GEF were determined as follows: temperature was calculated using the lapse rate of −0.55◦ C 100 m−1 based on WFRS metrological data (FSERC 2007), monthly average insolation and radiation were determined from public data available from the Osaka Meteorological Observatory (34◦ 41 N, 135◦ 31 E, elevation of 23 m, http://www.jma.go.jp/jma/menu/report.html), and monthly total precipitation was observed at the GEF and WFRS. For 2002–2007, observed data were used as climatic variables, and for 1750–2001, average monthly data from 2002 to 2007 were used. The disturbance scenario was determined by the actual management history in each watershed at the GEF, as described in 2.1 (Table 1). Selective thinning was assumed to have removed 100 and 0% of the thinned biomass during the first and second thinning, respectively. The

The study was conducted in the GEF in Nara Prefecture, Japan (34◦ 04 N, 135◦ 35 E, elevation of 860– 1370 ; Katsuyama et al. this volume). The bedrock geology of GEF is sedimentary rock. Weather station is located in Wakayama Forest Research Station (WFRS), Field Science Education and Research Center (FSERC), Kyoto University (34◦ 04 N, 135 41 E, elevation of 533 m), which is 4 km west of the GEF. All watersheds within the GEF are primarily Japanese cedar (Cryptomeria japonica) plantations. In the few years following 1912, all watersheds covered with fir, hemlock, beech, and other hardwoods were removed, and in 1916, cedar planting began. Nearly every year from 1958 to the present, grown trees have been clearcut in each watershed. In the year following clearcutting, cedar seedlings are planted to establish the next plantation rotation. Therefore, stands > 86 years old were planted during the first plantation period (1912–1916), and stands < 45 years old were planted during the second plantation period (1958present). The stand age ranged from 0 to 90 years old in 2006 and was even within each watershed. We assumed that the first clearcutting for afforestation was conducted in 1915. In the GEF, for a few months after

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Table 1.

Disturbance scenario used to simulate the effects of forest clearcutting. Managed year

Watershed

Age (year in 2006)

Clearcut for afforestation

First thinning

Clearcut for timber harvest

Second thinning

S16 S12 S11 S5 S20 S17 S18§

7 18 30 33 44 90(2005)-1 † 5

1915 1915 1915 1915 1915 1915 1915

1945 1945 1945 1945 1945 1945 1945

1999 1988 1976 1972 1962 2005 2005

2006 2002 1992

Values 0.9 0.9 0.01

0.3 1 0

0.9 0.9 0.01

0.3 0 0

PnET-CN scenario biomass mortality ratio fraction removed soil loss fraction

† S17 was clearcut in 2005. § Because no weir was installed in S18, water discharge data were not available.

uncut watershed (S20), clearcutting in S17 in 2005 led to a 15% increase in water yield the following year. Water yield did not differ between S16 and S20 in 2006, indicating that clearcutting effect was still apparent 6 years later. Namely, vegetative evapotranspiration rates in the PnET-CN simulation took 6 years to completely recover disturbance. This predicted duration of altered water yields in the GEF is consistent with (Bormann & Likens 1979; Swank et al. 2001) or shorter than (Moore & Wondzell 2005) the results of previous studies. Using stable isotope ratios of water, Katsuyama et al. (this volume) demonstrated that the mean residence time of streamwater was not clearly related to forest stand age, but peak flow during storms was larger in the younger stands (Katsuyama et al. 2008). These results suggest that baseflow should be insusceptible to forest clearcutting and subsequent development in the GEF, whereas storm flow should be related to them. To estimate variation in monthly water yield among watersheds, particularly in younger stands (S16 and S12), variation in rates of evapotranspiration and direct runoff during storm events throughout forest development should be incorporated into PnET-CN simulations.

other input parameters to run the PnET-CN model are presented in Table 2. We used a spruce-fir data set as default parameters (Aber et al. 1995) as well as values cited from several studies of Japanese cedar forests (Table 2). 2.4

Statistical Analysis

Correlations between observed data and PnET-CN simulations were analyzed using Pearson’s correlation coefficients. The significance level was determined by a two-tailed test (SPSS 10.0J, 1999). 3 3.1

RESULTS AND DISCUSSION Effects of stand ages on water yields

Relationships between observations and PnET-CN simulations indicated that monthly water discharge was underestimated in younger stands (watersheds S16 and S12, Fig. 1). In contrast, monthly water yield at stands > 30 years old were predicted well by the PnET-CN model. In young stands, the rate of evapotranspiration, including interception evaporation in the open-canopy, may have been overestimated in part because the PnET assumes rapid transpiration recovery due to understory vegetation, and is regardless of episodic effects of canopy opening on the evaporation rate. The observed annual water yield data were limited, because the hydrological data-logging system sometimes malfunctioned due to heavy storms and/or deposition of sediments. The observed annual precipitation and predicted annual water yield in each watershed are presented in Figure 2. Compared to

3.2

Nitrogen loss

We found large differences in streamwater N concentrations between PnET-CN simulations and observed data for all watersheds (Fig. 3). Ohte (2006) noted that this difference was caused by the absence of an algorithm in PnET-CN for groundwater recharge process and hydrological flowpath through the soil to the stream. In high precipitation regions such as GEF, hydrological processes largely contribute to the

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Table 2.

Input parameters and values required to run the PnET-CN model.

Parameter (unit) Site variables lat (degree) WHC (cm) Canopy variables k FolNCon (%) FolReten (year) SLWMax (g m−2 ) SLWdel (g m−2 ) FolRelGrowMax (% year−1 ) GDDFolStart GDDFolEnd GDDWoodStart GDDWoodEnd Photosysnthesis variables AmaxA (µ mol CO2 m−2 leaf s−1 ) AmaxB (µ mol CO2 m−2 leaf s−1 ) BaseFolResFrac HalfSat (µ mol CO2 m−2 s−1 ) AmaxFrac PsnTOpt (◦ C) PsnTMin (◦ C) RespQ10 Water balance variables DVPD1 DVPD2 PrecIntFrac WUEConst FastFlowFrac f Carbon allocation variables CFracBiomass (%) RootAllocA RootAllocB GRespFrac RootMRespFrac WoodMRespA PlantCReserveFrac MinWoodFolRatio Soil respiration variables SoilRespA SoilRespB SoilMoistFract

Description

Value*

latitude soil water holding capacity; plant available water

34.04 12

canopy light attenuation, constant 0.256 foliar N concentration 1.1 foliage retention time 3 specific leaf weigh (SLW) at top of canopy 140 SLW change with increasing foliar mass above 0 max relative growth rate for foliage 0.5 growing-degree-days (GDD) at which foliar 300 production begins GDD foliar production ends 1400 GDD wood production begins 300 GDD wood production ends 1400 relationship between foliar N and Amax (A; intercept, B; slope)

Citation

site specific

Miyaura et al. 1995 site specific (Tateno, unpubl.) Kobayashi & Tashiro 2003 Kobayashi & Tashiro 2003 Kobayashi & Tashiro 2003

5.3 21.5

respiration as a fraction of max photosyn. half saturation light level daily Amax as a fraction of early morning instantaneous rate optimum temp. for photosyn. min. temp. for photosyn. Q10 value for foliar respiration coefficient for vapor pressure deficit (VPD) to DVPD fraction of precipitation intercepted and evaporated constant in equation for WUE as a function of VPD fraction of water inputs lost directly to drainage soil water release parameter C% of foliage mass relationship between foliar and root allocation (A; intercept, B; slope) growth respiration, as a fraction of allocation ratio of fine root maintenance respiration to biomass production wood maintenance respiration as a fraction of gross photosyn. fraction of PlantC held in reserve after allocation to BudC min. ratio of C allocation to wood and foliage relationship between mean monthly temp. and soil respiration (A; intercept, B; slope)

0.1 100 0.75 25 0 2

Miyaura et al. 1996 Matumoto et al. 2000

0.21 1 0.15 10.9 0.1 0.04 0.48 0 1.93 0.25 1

site specific (Tateno, unpubl.) site specific (Tateno, unpubl.) site specific (Tateno, unpubl.)

0.07 0.75 1.25 27.46 0.06844 0 (Continued)

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Table 2.

(Continued)

Parameter (unit)

Description

Value*

Biomass turnover and N concentration variables WoodTurnover fractional mortality of live wood per yr WoodLitTrans fractional transfer from dead wood to SOM per yr WoodLitCLoss fractional loss of mass as CO2 in wood decomposition RootTurnoverA coefficients for fine root turnover RootTurnoverB as a function of annual net N mineralization RootTurnoverC max. N content in PlantN pool MaxNStore (g m−2 ) Kho decomposition constant for SOM pool NImmobA coefficients for fraction of mineralized N remobilized NImmobB as a function of SOM C:N RLPctN (%) min. N conc in root litter FLPctN (%) min. N conc in foliar litter WLPctN (%) min. N conc in wood litter FolNConRange max fractional increase in N conc

Citation

0.025 0.1 0.8 0.789 0.191 0.0211 20 0.07499 151 −35 0.01098 0.0036 0.00234 0.6

site specific (Tateno, unpubl.) site specific (Tateno, unpubl.)

See Aber et al. (1995, 1997) for detailed equations and algorithms. ∗ Boldface fonts indicate values cited from studies conducted in Japanese cedar plantations. Other values refer to parameters of spruce-fir forests, which appear to be similar to Japanese cedar.

Figure 2. Observed precipitation and predicted water yield in each watershed from 2002 to 2007.

Figure 1. Comparison of observed and PnET-CN predicted mean monthly water yield. The dashed line indicates 1:1.

seasonality in streamwater chemistry (Ohte et al. 2001; Ohte 2006; Shibata et al. 2006). The PnET-CN model was also used to simulate annual N loss to streamwater. We compared two methods of simulating N loss: first, PnET-CN simulated N loss without modifications using the sum of monthly predicted water discharges multiplied by − monthly mean dissolved inorganic N (NH+ 4 + NO3 ) concentrations. The second method involved multiplying monthly predicted water yields by observed inorganic N concentrations. Subsequently, we compared

observed data to both methods of simulated and calculated N loss. Predictions using simulated water yield and observed N concentrations (second method) better matched measured N loss than N loss simulated by the PnET-CN model without modification (Fig. 4). Because water yield was relatively well predicted on a monthly basis, these results indicate that the large differences in the seasonality of streamwater N concentration between observed data and PnET-CN predictions will be greatly improved. We predicted a drastic increase of N loss in stands < 5 years old, as many previous studies found (Fig. 5; Bormann & Likens 1979; Vitousek et al. 1979; Gundersen et al. 2006; Fukushima & Tokuchi 2008). The PnET-CN model assumes secondary succession after clearcutting, including the rapid recovery of vegetation. Although the proliferation of early vegetations,

101

Figure 5. Relationship between stand age and observed, calculated, and predicted N loss. See text for methods used to calculate N loss.

clearcutting. Furthermore, N loss between 2- and 5year-old watersheds was not measured, because there is no weir in the S18 watershed (5 years old in 2006; Table 2). However, since water yield predictions may have been under-estimated in younger stands, N loss calculated by multiplying predicted water yields by observed N concentrations may also have been underestimated. Nitrogen deposition in the GEF is 1.15 gN m−2 yr−1 (2002–2007 annual mean), which is lower than any N loss from watersheds of stands younger than 5 years old (Fig. 5). Therefore, PnET-CN simulations suggest that the declines of evapotranspiration rates and increases in stream N concentrations lead to critically high N loss and low N retention for at least 5 years after clearcutting in the GEF. However, further modifications including the effects of early vegetative growth and canopy openness in young stands are needed for more accurate quantitative predictions.

Figure 3. Seasonal trend of monthly stream N concentrations from 2002 to 2007 at S17, S16, and S20. S17 was clearcut in 2005.

4

Figure 4. Relationship between observed and PnET-CN simulated (open circles and dashed-dotted line; slope = 2.10; intercept =−46.4; P < 0.001) and calculated N loss (solid circles and solid line; slope = 1.07; intercept = −2.26; P < 0.001; see text for calculation methods). The dashed line indicates 1:1.

such as pioneer perennial herbs, is critical to recover from N loss (Bormann & Likens 1979; Mou et al. 1993), we did not estimate the effects of continuous weed clearance in GEF for the 5 years following

CONCLUSIONS

We compared observed data of water yields and N loss to predictions of a PnET-CN model, and determined that modified parameterization was necessary for accurate predictions in the GEF. The effects of developmental processes of Japanese cedar plantations on evapotranspiration rates and direct run-off rates during storm events must be took into consideration in the PnET-CN, particularly in younger stands. Furthermore, in the GEF, we did not estimate the effects of understory vegetations, which may have contributed in part to the large differences between observed and predicted data in younger stands. Parameters in the PnET-CN model involving the growth patterns of Japanese cedar will require further modifications.

ACKNOWLEDGMENT We would like to thank Dr. Hideaki Shibata, Dr. Nobuhito Ohte, and Dr. Keisuke Koba for valuable

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comments, and the members of Laboratory of Silviculture and the staffs of WFRS, FSERC, Kyoto University for their helpful support in field surveys. This research was supported by Research Project No. 5–2 in Research Institute for Humanity and Nature. REFERENCES Aber, J.D. & Federer, C.A. 1992. A generalized, lumpedparameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems. Oecologia 92: 463–474. Aber, J.D., Ollinger, S.V., Federer, C.A., Reich, P.B., Goulden, M.L., Kicklighter, D.W., Melillo, J.M. & Lathrop, R.G.Jr. 1995. Predicting the effects of climate change on water yield and forest production in the northeastern United States. Clim. Res. 5: 207–222. Aber, J.D., Ollinger, S.V. & Driscoll, C.T. 1997. Modeling nitrogen saturation in forest ecosystems in response to land use and atmospheric deposition. Ecol. Model. 101: 61–78. Aber, J.D. & Driscoll, C.T. 2001. Effects of land use, climate variation, and N deposition on N cycling and C storage in northern hardwood forests. Glob. Biogeochem. Cycles 11: 639–648. Bormann, F.H. & Likens, G.E. 1979. Pattern and Process in a Forested Ecosystem. New York: Springer. FSERC. 2007. Wakayama Forest Research Station. In Field Science Education and Research Center (ed.),Meteorological observation data in the Kyoto University forests: 25–31. Kyoto University. Fukushima, K. & Tokuchi, N. 2008. Effects of forest clearcut and afforestation on streamwater chemistry in Japanese cedar (Cryptomeria japonica) forests: Comparison among watersheds of various stand ages. J. Jpn. For. Soc. 90: 7–17 (in Japanese with English summary). Goodale, C.L. & Aber, J.D. 2001. The long-term effects of land-use history on nitrogen cycling in northern hardwood forests. Ecol. Appl. 11: 253–267. Gundersen, P., Schmidt, I.K. & Raulund-Rasmussen, K. 2006. Leaching of nitrate from temperate forests- effects of air pollution and forest management. Environ. Rev. 14: 1–57. Katsuyama, M., Fukushima, K. & Tokuchi, N. 2008. Comparison of rainfall-runoff characteristics in forested catchments underlain by granitic and sedimentary rock with various forest age. Hydrol. Res. Let (in press). Katsuyama, M., Fukushima, K. & Tokuchi, N. This volume. Effects of various rainfall-runoff characteristics on streamwater stable isotope variations in forested headwaters (in press). Kobayashi, H. & Tashiro, N. 2003. Temporal variation in leaf nitrogen content in a Cryptomeria japonica canopy. Jpn. J. For. Environ. 45: 99–102 (in Japanese with English summary). Matsumoto, Y., Taoda, H., et al. 2000. Evaluation of artificial forest ecosystems. In Study on evaluation of vulnerability in biosphere caused by global warming; Final Report of Glob. Environ. Res. Fund, Ministry of the Environment: 101–119 (in Japanese only). Miyaura, M., Hagihara, A. & Hozumi, K. 1995. Estimation of gross production in a Cryptomeria japonica plantation on

the basis of Monsi-Saeki’s model of canopy photosynthesis. Bull. Nagoya Univ. For. 14: 49–88 (in Japanese with English summary). Miyaura, M., Hagihara, A. & Hozumi, K. 1996. Effects of changes in photosynthesis and dark respiration rate with aging foliage shoots on the estimation of gross production and canopy respiration consumption in a Cryptomeria japonica plantation. Bull. Nagoya Univ. For. 15: 85–109 (in Japanese with English summary). Moore, R.D. & Wondzell, S.M. 2005. Physical hydrology and the effects of forest harvesting in the Pacific Northwest: A review. J. Am. Water Resour. Assoc. 41: 763–784. Mou, P., Fahey, T.J. & Hughes, J.W. 1993. Effects of soil disturbance on vegetation recovery and nutrient accumulation following whole-tree harvest of a northern hardwood ecosystem. J. Appl. Ecol. 30: 661–675. Odum, E.P. 1969. The strategy of ecosystem development. Science 164: 262–270. Ohte, N., Tokuchi, N., Shibata, H., Tsujimura, M., Tanaka, T. & Mitchell, M.J. 2001. Hydrobiogeochemistry of forest ecosystems in Japan: Major themes and research issues. Hyrdol. Process. 15: 1771–1789. Ohte, N. 2006. Necessity to consider hydrological controls of biogeochemical cycling when developing a catchmentscale ecosystem model. Jap. J. Limnol. 67: 259–266 (in Japanese with English summary). Ollinger, S.V., Aber, J.D., Reich, P.B. & Freuder, R.J. 2002. Interactive effects of nitrogen deposition, tropospheric ozone, elevated CO2 and land use history on the carbon dynamics of northern hardwood forests. Glob. Change Biol. 8: 545–562. Osaka Meteorological Observatory. http://www.jma.go.jp/jma/ menu/report.html (access in 19 Feb. 2008). Pardo, L.H., Driscoll, C.T. & Likens, G.E. 1995. Patterns of nitrate loss from a chronosequence of clear-cut watersheds. Water Air Soil Pollut. 85: 1659–1664. Shibata, H., Ohte, N., Satoh, F. & Yoshioka, T. 2006. Biogeochemical model in forest ecosystem; Application and problem of PnET model. Jap. J. Limnol. 67: 235–244 (in Japanese with English summary). Swank, W.T., Vose, J.M. & Elliot, K.J. 2001. Long-term hydrologic and water quality responses following commercial clearcutting of mixed hardwoods on a southern Appalachian catchment. For. Ecol. Manage. 143: 163–178. Tokuchi, N., Tateno, R. & Fukushima, K. 2006. Influence of forest disturbance and test application of PnET model for determining long-term impact. Jap. J. Limnol. 67: 245–258 (in Japanese with English summary). Vitousek, P.M. & Reiners, W.A. 1975. Ecosystem succession and nutrient retention: a hypothesis. BioScience 25: 376–381. Vitousek, P.M. Nitrate losses from disturbed ecosystems. Science 204: 469–474. Vitousek, P.M. & Howarth, R.W. 1991. Nitrogen limitation on land and in the sea: How can it occur? Biogeochemistry 13: 87–115. White, L.L., Zak, D.R. & Barnes, B.V. 2004. Biomass accumulation and soil nitrogen availability in an 87-yearold Populus grandidentata chronosequence. For. Ecol. Manage. 191: 121–127.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Environmental impacts of the acid atmospheric deposition and forest clear-cut in a mountain catchment J. Krecek∗ Department of Hydrology, Czech Technical University, Prague, Czech Republic

Z. Horicka Department of Ecology, Charles University, Prague, Czech Republic

J. Novakova Laboratory of Landscape Ecology, Czech Agricultural University, Prague, Czech Republic

ABSTRACT: In the 1980s, the acid atmospheric deposition in Central Europe has resulted in the decline and rapid harvest of spruce plantations (Picea abies). In mountain watersheds, fragmented forests and the extensive spread of clearings dominated in 1980s and 1990s. Since 1982, environmental aspects of acid load, forest dieback and clear-cut of spruce stands were studied in the Jizerka experimental basin (area of 1.0 km2 , the Jizera Mts., Czech Republic). After the clear-cut of spruce stands, Junco effusi-Calamagrostietum villosae became a new dominant plant community there. The composition of herb layer has been changing due to increasing soil moisture corresponding to rising values of the Ellenberg’s soil moisture indicator. The long-term annual stream-flow increased by 108 mm (13% of the long-term annual mean). The accelerated loss of soil (from 0.01 to 1.34 mm/year) was affected mainly by not adequate skidding technology. In the surface waters, low pH values (4–5), high content of toxic metals (namely aluminium, 1–2 mg/l), extinction of fish and reduced benthic fauna were observed. It is evident, that forestry practices can significantly influence the acidification of soils and waters. The recent recovery of surface waters in the Jizerka basin results from both the drop in sulphur emissions, and the reduction of spruce stands. Grass ecosystems in clear-cut sites are competitive in the process of reforestation but effective in the control of soil erosion and reclamation of erosion rills. Keywords: mountain watershed; forest clear-cut; herbaceous vegetation; Ellenberg’s indicator F; soil water content; run-off genesis; soil erosion; stream water chemistry.

1

INTRODUCTION

Humans have affected forests for a long time. This has resulted in profound changes in forested environments. In temperate forests of Europe, where human influence has been strong and long-lasting, some important habitat elements, structures and processes have disappeared or weakened (Björk, 2002). Moreover, in the 1980s, the acid atmospheric deposition (mainly by the combustion of lignite in power-stations) has resulted in the decline of spruce stands, leading to fragmented forests and the extensive spread of clearings in Central Europe (Fanta, 1997). Therefore, establishment of protected areas in temperate forests should be supplemented by restoration efforts (Haigh & Krecek, 2006). An effective restoration should be based on the ∗

Corresponding author ( [email protected])

detailed ecological monitoring of the site development (De Vries et al, 2003; FAO, 2007). In the 1980s, the Jizera Mts. Region (350 km2 , North Bohemia, Czech Republic, Figure 1) was heavily affected by consequences of the acid atmospheric deposition. The acid deposition has been affected mainly by sulphur emitted to the air by soft coal combustion in the “Black Triangle” (border area between the Czech Republic, former East Germany, and Poland). Acidification of both terrestrial and aquatic ecosystems, defoliation and the die-back of dominant spruce plantations have lead to commercial forestry practices (extensive clear-cut, heavy mechanisation, skidding timber by wheeled tractors). Particularly in higher elevations, the plantations of Norway spruce (Picea abies) were replaced by communities Junco effusiCalamagrostietum villosae with dominant invasive

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Figure 1. The Region of the Jizera Mountains.

Calamagrostis sp., very competitive in the process of reforestation (Novakova & Krecek, 2006). The aim of this paper is to document impacts of acidification, forestry practices and restoration processes on water phenomena in a small catchment at the upper plain of the Jizera Mts. 2 2.1

Figure 2. The Jizerka experimental catchment: A – outlet gauging station, AB – transect, MCO – microclimate observation system.

MATERIAL AND METHODS Catchment water cycle

Since 1982, the hydrological processes were studied in the Jizerka experimental catchment (area of 1.0 km2 , 50◦ 48 N, 15◦ 21 E, elevation 860–980 m, NE slope). The basin was instrumented by the ordinary climate station with both standard (500 cm2 ) and automatic (200 cm2 ) rain-gauges, three additional storage gauges (200 cm2 ) in forest openings at different elevations, and a sharp-crested V-notch weir with an automatic water level recorder at the stream-outlet. 2.2

Figure 3. Topography of the studied transect (length is registered from the basin outlet).

Forest inventory

The Jizerka basin is a forested catchment with a long record of forestry interventions. Since the 17th century, several signs of forest devastation have been recorded there. Particularly, in 1829–1911, an intensive timber harvest for the nearby glass manufacture has changed the original mixed forests (spruce, fir and beech) into the monocultures of Norway spruce. In studied watershed, a forest inventory has been carried out annually in high summer seasons since 1982. Basic forest parameters (tree species and age, basal area, tree height, timber volume, horizontal canopy density, clear-cut extend, and reforestation progress) were evaluated by standard techniques. (Björk, 2002). In 1984–1988, mature spruce stands were harvested by a clear-cut extended in the whole catchment area (Figure 2). After the clear-cut of spruce plantations, invasive grass communities (particularly Calamagrostis sp.) have spread over the studied watershed Therefore, the evidence of herbaceous

vegetation was included in the system of catchment inventory. 2.3 Soil moisture and herbaceous vegetation The depth of sandy loamy to loamy podzolic forest soils varies between 0.7 and 1.2 m above the weather bedrock (porfyritic granite). The root sytem dominates in the topsoil occuring upto the depth of 15 cm. The topsoil is created by litter (Ol , depth of 0–2 cm), humus layer (Of + Oh , 2–10 cm), and leached horizon (Aeh , 10–15 cm). Mor is the most common humus (2–5 cm) there. Monitoring of soil moisture climatology included twelve spots of the botanical transect (AB), Figure 3. The soil moisture at near surface (volumetric water content upto the depth of 15 cm) was measured in situ by a sensitive electrode (Kelway, HB-2), and tested by

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the standard gravimetric method (Miller and Garnier, 1998). Detailed botanical investigations were carried out in vegetation seasons of 1991, 1998, 2002, and 2005. The phytosociological relevès (4 × 4 m, Braun-Blanquet’s seven-point scale) were taken at each of the twelvepoint transect situated along the main slope of the basin in the step of 100 m. To include the impact of all species abundance, the data were transformed from the Braun-Blanquet’s scale to a nine-point scale according to the approach of Van der Maarel (Novakova & Krecek, 2006). For each relevè, the Ellenberg’s indicator F value for soil moisture (a twelve-point scale) was calculated as the weighted average of all the recognized species (Ellenberg, 1979). 2.4

Micrometeorological observations

The micrometeorological system was established in the vertical profile of the mature spruce stand (mean height of 20.5 m) in 1983 (Krecek, 1990). The sun radiation was registered above the canopy at the height of 23 m. Global radiation was measured by the starpyranometer (8001 Ph. Schenk) and net radiation by the net-radiometer (8110 Ph. Schenk). Both air temperature and humidity were observed by the electrically ventilated psychrometers (3010 Friedrichs) at levels of 23 and 28 m. Soil temperatures were measured at the depth of 5, 10, 20 and 50 cm. The latent heat flux was estimated by the method of Bowen ratio. Additionally, ten storage rain-gauges (area of 200 cm2 ) were exposed under the forest canopy to collect the canopy through-fall. From several earlier studies (Krecek. 1990), the stem-flow in spruce stands was supposed negligible. Five modified storage gauges were installed in the soil to evaluate the through-fall through the herbaceous vegetation.

cross-sections marked at each 10 meters of their length. The sheet erosion was observed at two runoff plots (30 m2 ). Sediment yield from the Jizerka basin was collected at the outlet gauging station. Phytosociological reléves (4 × 4 m, Braun-Blanquet scale, Novakova & Krecek, 2006) were taken to identify vegetation characteristics of erosion rills (plant cover percentage, species composition, life forms and growth types). 3

RESULTS AND DISCUSSION

3.1 Forests and water yield Before the clear-cut (1982–1984), plantations of mature Norway spruce dominated in the Jizerka catchment (1,452 trees per ha, 82% canopy density, 20.5 m mean height). Immediately after the clear-cut, the basin has been reforested. However, in the 1990s, the herbaceous vegetation dominated there, and the progress in forest regeneration was rather low. Nowadays, the variety of tree species is higher than in 1982 but the coniferous stands still dominate (53% Norway spruce, 26% Colorado blue spruce, 16% dwarf pine, and only 3% silver birch and 1% mountain ash). In 1988, the average number of trees recorded is 1237 per ha. After the clear-cut of spruce plantations, the mean annual yield of water increased by 108 mm: the loss of evaporation calculated from the annual catchment balance decreased from 533 mm (1982–1984) to 425 mm (1988–1994). Recently (2002–2007), with forest regeneration, the water yield came back again back to the level 58 mm higher than in the pre-treatment period. From the hydrograph separation by a constant slope line (Hewlett & Hibbert, 1967), the fast flow component (direct run-off) increased in the period of 1988–1994 from 52 to 68% of the mean annual run-off.

2.5 Stream-water quality

3.2 Soil moisture

Stream water at the outlet of the catchment has been sampled weekly, and, additionally during rain-storms or rapid snow-melt events. Both open-field rainfall and canopy through-fall were sampled monthly in bulk collectors (20 gauges, area of 200 cm2 ). In the winter, monthly sampling of snow followed the 100 m interval snow-line at the main valley of the basin. The laboratory analyses included pH, conductivity and alkalinity (ANC) as well as chemical components related to the process of acidification. Chemical analyses were performed by methods developed for soft water studies of mountain lakes (Fott et al, 1994).

After the clear-cut, the Elleberg’s indicator F is increasing (Figure 4) with higher soil moisture. The length of the transect (0–1,100 m) starts from the basin outlet uphill. While the soil water content correlates most significantly with the slope of the investigated transect (Figure 5), the Ellenberg’s indicator F reflects mostly the length of the transect (elevation). The correlation matrix is shown in Table 1. The observed water content of the topsoil (SWC) correlates significantly with the slope gradient (Rs = −0.62, Rsc,0.05 = 0.55) in periods of “moderate” moisture conditions (Figure 5). During both “dry” and “wet” situations, that correlation is not significant.

2.6

Soil erosion and sedimentation

The inventory of erosion rills was carried out at the Jizerka basin in 1982, 1985, 1988, 1992, 1995 and 2002–2003. The depth of rills was monitored in

3.3

Evapotranspiration

With the forest treatment, annual evapotranspiration decreased from 542 to 428 mm (Table 2). It

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Table 2. Mean annual heat balance (RG – global radiation, Rn – net radiation, S – sensible heat flux, L – latent heat flux, ET – evapotranspiration, INT – canopy interception, TR – transpiration ).

RG (MJ m−2 y−1 ) Rn (MJ m−2 y−1 ) S (%Rn ) L (%Rn ) ET (mm) INT (mm) TR (mm)

Figure 4. Ellenberg’s indicator F along the investigated transect (1991–2005).

Spruce stand

Clear-cut

2,077 1,574 76 86 542 318 224

2,077 1,350 20 80 428 198 230

Figure 5. Soil water content SWC and slope gradient S at the Jizerka transect. Figure 6. Erosion rills in the Jizerka catchment: maximum length and the depth.

Table 1. The correlation matrix: F – Ellenberg’s moisture indicator (−), S – gradient of slope (%), E – elevation (m), SWC – soil water content (mm).

F S E SWC

F

S

E

SWC

1 −0.015 −0.83 0.52

−0.015 1 0.15 −0.63

−0.83 0.15 1 −0.58

0.52 −0.63 −0.58 1

corresponds with results of catchment water balance. In the mature spruce stand, the interception storage dominated (55% of the mean annual loss of evaporation). After the harvest, interception dropped to 62% of it’s original value, while transpiration did not change significantly. 3.4

Soil loss

In 1982 – 1984 (before clear-cut), mean annual loss of soil observed in the outlet of the catchment was 0.01 mm/year. In 1985–1990 (timber harvest and skidding), the loss of soil increased to 1.34 mm/year. During the watershed regeneration, soil loss turned back again to 0.6 mm/year (1991–1995) and 0.2 mm/year (2001–2005). The potential annual loss of soil estimated by USLE (Wischmeier & Smith, 1978) varies

from 0.2 mm (mature spruce stands) to 1.2 mm (grass). However, negligible sheet erosion has been observed in run-off plots covered by herbaceous layers (Calamagrostis sp. particularly). An important soil loss occurred only in the channel network and additional erosion rills originated by skidding the timber (Novakova & Krecek, 2006). In the studied basin, 14 significant erosion rills were found in the Jizerka catchment by skidding the timber. The maximum depth of rills varies from 0.2 to 1.2 m related to their length (Figure 6). While the length evidently affects the depth of rills (Spearman correlation coefficient Rs = 0.88, Rsc,0.05 = 0.45), the correlation between the depth and slope was not significant (Rs = 0.24). Thus, the spontaneous succession of herb vegetation seems to be important for stabilisation and reclamation of erosion rills. The most significant parameter affecting the recovery of rills is their depth. Generally, erosion rills deeper than 0.5 m require already a human intervention (stabilisation by check-dams etc.). 3.5 Stream water quality Without air pollution or acid rain, stream waters in the Jizera Mts. would have a pH level near 6.5 (Krecek &

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values of pH increased from 4.0 to 5.6, and concentrations of sulphate decreased from 13.0 to 6.0 mg/l as well as nitrate from 6.0 to 4.0 mg/l (Figure 8). These changes in stream water chemistry can be explained by two main processes: reduction of sulphur contents in the air (Spearman correlation coefficient Rs = 0.72, Rsc,0.05 = 0.38), and reduction of forest canopy by the clear-cut of spruce plantations (Rs = 0.86). Generally, forest canopy supports the atmospheric flux of sulphur into a catchment with an enlargement of surface area and roughness (Krecek & Horicka, 2001). Figure 7. Mean annual content of SO2 in the air: the Jizerka station, elevation of 860 m (20 µg/m3 is supposed a long-term critical value for spruce forests).

Figure 8. Stream water chemistry and clear-cut (% of basin area), the Jizerka basin, 1981–2004.

Horicka, 2006). However, the region has been affected by a significant load of acids since the end of the 19th century (Hruska, Cerny & Krecek, 1997). In the 1980s, the stream water chemistry at the Jizerka basin showed a serious decline: namely very low pH values (4–4.5) and high content of aluminium (1–2 mg/l, with a high level of toxic forms freeAl3+ as well as inorganic complexes of Al). The rapid increase in acidity and aluminium levels has been deadly to aquatic wildlife. The extinction of fish and reduced benthic fauna was documented in large area of the upper plain of the mountains (Krecek & Horicka, 2001). In the Jizerka basin, the acid atmospheric deposition culminated in 1987 with the annual load of sulphur 36 kg/ha (bulk of an open field). In mature spruce stands, the annual through-fall of sulphur exceeded 100 kg/ha. In the 1990s, the open field deposition of sulphur dropped to cca 40% of the 1987 level (Figure 7). This fact corresponds with the drop of SO2 emissions into the air by reduced coal power stations in central Europe (Krecek & Horicka, 2006). In the 1990s, at the outlet of the Jizerka basin, mean annual

4

CONCLUSIONS AND PERSPECTIVES

Forests in the Jizera Mts. belong to the most sensitive ecosystems in Europe: slow weathering bedrock and shallow podzolic soils with a very shallow pool of basic cations have a relatively small buffering capacity in comparison with the actual deposition (Krecek & Horicka, 2001). It is evident that spruce plantations in the Jizera Mts. are not in a phase of steady state because of a relatively high uptake of basic cations, and an acceleration of the acid deposition into the soil. The acid atmospheric load affected hydrochemical processes in the Jizerka basin directly by an open field acid rain, and indirectly by canopy through-fall, defoliation and die-back of spruce stands, forest harvest and skidding the timber, as well as by reforestation practices. It is evident, that forestry can significantly influence the acidification of soils, depletion of basic cations and water quality. Recent recovery of surfaces waters in the Jizerka basin has been affected from both sides: the drop in sulphur emissions (Figure 7) and the reduction in canopy area and roughness by the clear-cut of mature spruce plantations (Figure 8). The rapid harvest of spruce stands was followed by increased water yield (108 mm, 13% of the long-term annual mean). With a relatively low progress in reforestation (only 21% canopy density found in 2007), this effect has been prolonged. Harvest technology (skidding timber by wheeled tractors) accelerated the loss of soil from 0.01 (pre-treatment level) to 1.34 mm/year. This situation might be prevented by environmentally friendly forestry practices (skidding timber by horses or cable-ways, seasonal skidding etc.). The herbaceous vegetation at the clear-cut sites is competitive in the process of reforestation but effective in the control of soil erosion, reclamation of erosion rills (up to the depth of 0.5 m), as well as in stabilization of slopes. A successful revitalisation of headwater catchments should be self-sustainable (Haigh and Krecek, 2006). Unfortunately, the recent reforestation in the Jizerka basin preferred again the coniferous stands. Nowadays, 96% of coniferous trees were registered there with mainly Norway spruce (53%) and Colorado spruce (26%). In a long-term perspective, the water

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quality might be improved by planting forest stands near the native composition (deciduous or mixed forests with lower leaf area and surface roughness in comparison with spruce plantations). Moreover, coniferous forests in higher elevations of the Jizera Mts. are endangered by potential global climate changes (IPCC, 2007). Deep-rooting tree species (beech, fir) can also improve the ecological stability: to include subsoil horizons in the cycling of nutrients, to minimise leaching of nutrients and to support stabilisation of slopes (including wind-cast susceptibility). ACKNOWLEDGEMENTS The research has been supported by the Earthwatch Institute (Project Mountain Waters of Bohemia) and by the Czech Ministry of Education (MSM 6840770002, Revitalization of LandscapeAffected byAnthropogenic Changes). REFERENCES Björk, L. 2002. Sustainable Forestry in Temperate Regions. Reports in Ecology and Environmental Engineering: 2002/1, Lund University, Lund (Sweden), 167 pp. De Vries, W. 2003. Intensive Monitoring of Forest Ecosystems in Europe. Technical Report 2003, EC-UN/ECE, Brussels (Belgium), 162 pp. Ellenberg, H. 1979. Zeigerwerte der Gefasspflanzen Mitteleuropas. 2nd edition, Göttingen. Fanta, J. 1997. Rehabilitating degraded forests in Central Europe into self-sustaining forest ecosystems. EcologicalEngineering, 8: 289–297.

FAO. 2007. State of the world’s forests. FAO, Rome, 144pp. Fott, J., Prazakova, M., Stuchlik, E. & Z. Stuchlikova. 1994. Acidification of lakes in Sumava (Bohemia) and in the High Tatra Mountains (Slovakia). Hydrobiologia, 274: 37–47. Hewlett, J.D. & A.R. Hibbert. 1967. Factors affecting the response of small watersheds to precipitation in humid areas. In: Proceedings of International Symposium on Forest Hydrology. Pergamon Press, New York, 275–290. Hruska, J., Cerny, J. & J. Krecek. 1997. Acidification in the Czech part of the Black Triangle region. In: Proceedings of CRIEPI International Seminar on Transport and Effects of Acidic Substances, Tokyo, November 28–29, 1996. Central Research Institute of Electric Power Industry, Tokyo (Japan), 113–124. IPCC. 2007. Climate Change 2007: the physical science basis. Cambridge University Press, 864 pp. Krecek, J. 1990. Evapotranspiration from a forested basin in the Jizera Mountains. IAHS Publication, 190: 229–237. Krecek, J. & M.J. Haigh. 2006. Environmental role of wetlands in Headwaters. Springer, Dordrecht, 354pp. Krecek, J. & Z. Horicka. 2001. Degradation and recovery of mountain watersheds in the Jizera Mountains, Czech Republic. Unasylva, 37: 247–259. Krecek, J. & Z. Horicka. 2006. Forests, air pollution and water quality: influencing health in the headwaters of Central Europe. Unasylva, 57: 46–49. Miller, R.W. & D.T. Garnier. 1998. Soils in our environment. 8th Edition, Prentice-Hall Publishers, Upper Saddle River (New Jersey), 453 pp. Novakova, J. & J. Krecek. 2006. Soil erosion and plant succession at clear-cut areas in the Jizera Mountains, Czech Republic. Ecology (Bratislava), 25: 259–269. Wischmeier, W.H. & D.D. Smith. 1978. Predicting RainfallErosion Losses – A Guide to Conservation Farming. USDA Handbook, No. 537.

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Climate changes and debris flows in periglacial areas in the Italian Alps L. Marchi∗ National Research Council of Italy, Institute for Geo-hydrological Hazards Prevention (CNR IRPI), Padova, Italy

M. Chiarle & G. Mortara National Research Council of Italy, Institute for Geo-hydrological Hazards Prevention (CNR IRPI), Torino, Italy

ABSTRACT: Debris flows pose major hazards in many mountainous regions. The most apparent influence of climate changes on debris flows is related to variations in the precipitation regime. In glacial and periglacial areas, also glacier retreat and permafrost degradation have a major role in debris flow occurrence, as they can substantially increase the availability of erodible debris and provide the conditions for sudden water release. The paper analyses some recent debris flows in periglacial environments in the Italian Alps. The study is based on field observations and on the comparison of present geomorphic conditions with those that existed before the glacial retreat of the last decades. The results of researches on climate changes in Italy indicate that the studied debris flows occurred in the frame of a general trend of increasing temperature. In this context, debris flows in periglacial areas should be viewed as a particular aspect of geomorphological and hydrological changes driven by climate changes in alpine headwaters. Most of studied debris flows have been caused by rainfall. In these cases, the influence of cryosphere degradation essentially consists in an increased availability of mobilisable material. Other processes, which trigger debris flows in areas affected by glacial shrinkage, are glacial lake outbursts and the melting of ground-buried ice. Keywords:

1

debris flow; glacier; permafrost; climate change; Italian Alps

INTRODUCTION

Debris flows are amongst the most hazardous geomorphic processes in many mountainous regions. Debris-flow occurrence requires high slopes, large water inflows, which can derive from rainfall, lake outburst, and snow or ice melt, and availability of mobilisable debris. The latter two conditions are very sensitive to climate changes, which influence both triggering factors of debris flows (rainfall and temperature) and resistance factors (Zimmermann & Haeberly, 1992; Rebetez et al., 1997). Regarding triggering factors, an important influence of climate changes on debris flows is due to variations in the precipitation regime (e.g. increase or decrease of rainfall intensity). For debris flows in periglacial areas, temperature is particularly important because it influences snow/icemelting runoff, glacial drainage systems and temporal and spatial snowfall occurrence versus rainfalls. Moreover, glacial retreat may result in the formation of ∗

Corresponding author ([email protected])

marginal and epiglacial lakes, whose outburst often causes debris-flow triggering (Clague & Evans, 2000; Huggel et al., 2003). With regard to resistance factors, glacier shrinkage and permafrost degradation can substantially increase the availability of erodible debris. This paper analyses some recent debris flows in periglacial environments in the context of climate changes in the Italian Alps. The aim of the paper is not an exhaustive overview of the topic, but rather to highlight the role of cryosphere dynamics in debris flow occurrence under a warming climate. 2

METHODS

Study methods consist of field observations, analysis of historical documentation and interpretation of aerial photos. Field observations in debris-flow sites make it possible to recognise and describe the processes that cause debris-flow initiation in glacial and periglacial areas, and provide elements for comparing the present geomorphic conditions with those that existed before

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the environmental changes of the last decades. Information about past geomorphic conditions is obtained by analysing historical topographic maps, aerial photos taken since the 1950s, and mountain guidebooks. In particular, the book series ”Guide to the Mountains of Italy”, published by the Italian Alpine Club and the Italian Touring Club since the 1930s, describes the location and extent of glacier and snow, and the presence of ice-filled couloirs. Studies by various authors have been taken into consideration in order to outline the influence of climate changes, with particular regard of the increase of temperature, on glacier-related and periglacial debris flows.

3

Figure 1. Areas studied in the Italian Alps. a: Frébouge Glacier; b: Ormeleura Glacier; c: Belvedere Glacier – Lago effimero; d: Mugoni Pass; e: Mt. Antelao.

STUDY AREAS

The Alps cover a large part of northern Italy. They extend east–west for about 720 km and reach almost 160 km in width. Geologically, the Alps are composed of a heterogeneous Mesozoic–Cenozoic sedimentary and volcanic sequence that overlies the Palaeozoic metamorphic basement. Complex structures have developed as a result of several phases of tectonic deformation over the past 15 million years. Quaternary glacial and fluvioglacial deposits are widespread throughout the Alpine valleys. Little Ice Age (LIA) moraines are common in the upper parts of the valleys. The morphology of the alpine region is very rugged, with high elevation difference from the valley floors to the mountain summits; vertical and subvertical rock cliffs are widespread. Alpine glaciers have experienced an areal shrinkage of approximately 51% from the end of the LIA (Zemp et al., 2008). The area presently covered by glaciers in Italy is about 500 km2 , whereas location and extent of permafrost areas are poorly known. Permafrost is discontinuously present from 2000 to 3000 m of altitude, while it is more continuous at higher elevations (Guglielmin, 2006). Complex orography influences the climate of the Italian Alps, causing high variability in the spatial distribution of precipitation and temperature even at a local scale. Valleys parallel to the Alpine structure are relatively dry, with annual precipitation of about 500–600 mm, whereas transverse-orientated valleys have a higher precipitation (1500–2000 mm). Annual amounts of precipitation exceed 3000 mm in some prealpine areas. Seasonal distribution of precipitation is continental, with a summer maximum in the inner part of the alpine range, where most of the glaciers are located, whereas spring and autumn maxima are observed in the outer belt. As to geomorphological settings, the Italian Alps are similar to other highelevation, rugged-relief regions of the world. The location of the Italian Alps at the contact between

Mediterranean and inland continental regions contributes to the complexity of climatic conditions and makes them particularly sensitive to climate changes. Debris flows in the Italian Alps occur under different geolithological settings (Moscariello et al., 2002; Tiranti et al., 2008); debris-flow risk has urged studies aimed at improving the knowledge of these phenomena, and assessing their principal quantitative variables (e.g. Berti et al., 1989; Marchi et al., 2002; Marchi & D’Agostino, 2004; Turconi & Tropeano, 2008; Berti & Simoni, 2007; Gregoretti & Dalla Fontana, 2007). Debris flows in periglacial areas display particular features, which have been studied by Deline et al. (2004) and Chiarle et al. (2007). The sites considered in this paper are shown in figure 1.

4

RESULTS

In the Dolomites (eastern Italian Alps), where only a few small-sized glaciers are left since the end of the LIA, debris flows influenced by climate changes in periglacial areas are linked to the presence of buried ice masses and to the melting of ice and snow in couloirs and on scree slopes, which makes prone to erosion the underlying debris. Three examples are reported below. Tanesini (1942) describes a “snow-filled couloir downslope of the Mugoni Pass”. This site has northeasterly aspect and its elevation ranges approximately from 2400 to 2700 m. Recent field observations show that bare rocks outcrop in the upper part of the couloir (Fig. 2a), formerly filled with snow, whereas the downslope scree is incised by debris flows, exposing a mass of buried ice (Fig. 2b). Berti (1950) reports the presence of snow-firn in the upper part of a scree slope at the foot of the rock cliffs of Mt. Antelao (southerly aspect, approximate elevation 2550 m). A picture taken in October 2001

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Figure 3. Retreat of a snow-firn field on the Mt. Antelao (Dolomites; 46◦ 26◦ 45 N, 12◦ 15 55 E). The dashed line indicates the approximate extent of the snow from the description by Berti (1950).

Figure 2. Mugoni Pass, Dolomites (46◦ 26 40 N, 11◦ 37 35 E): exposed bedrock in the previously snow-filled couloir (a); buried ice in the scree slope entrenched by debris flows (b). Photo taken in August 2002.

(Fig. 3) shows that snow cover is now limited to a small area just at the outlet of a rocky couloir, whereas the snow-free scree slope is deeply entrenched by debris flows, whose frequency has shown a marked increase in the most recent years. Del Longo et al. (2001) studied the role of buried ice in a scree slope (elevation: 2200–2300 m), which had been affected by a rainfall-triggered debris flow in the Dolomites: the contribution of melting water to the mobilisation of the debris was small, whereas the buried ice acted as slide surface for the overlying water-saturated debris. In the western Alps, where the glacial cover is still important, climate change is affecting debris-flow occurrence in high-altitude areas both by increasing the amounts of erodible debris, and by modifying geometry and drainage systems of glaciers. Three examples are reported below. The steep fluvioglacial cone at the foot of the Ormeleura Glacier (Aosta Valley) was cut for a depth of 20–30 meters during a heavy rainstorm in July 1996. A large debris flow (about 300 · 103 m3 ) was so

Figure 4. Path of the debris flow occurred in the forefield of Ormeleura Glacier in July 1996, started at an elevation of about 2500 m a.s.l.

generated, which flooded the downslope Grand’Alpe alluvial plain (Fig. 4) (Mortara & Chiarle, 2005). In July 2003, during dry and warm weather, some debris flows occurred at the foot of Frébouge Glacier (Mont Blanc). The flows deeply incised the proglacial polygenic fan from an elevation of about 2000 m a.s.l., and deposited about 30 · 103 m3 of coarse sediment. The first debris flow on July 17 was probably triggered by the release of an englacial water pocket. The other debris flows developed in four stages: (i) ice avalanching from the front of the Frébouge Glacier;

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Figure 5. Erosion phenomena at the foot of the left side of the Belvedere Glacier caused by the Lago Effimero outburst in June 2003 (Photo by P. Semino, Regione Piemonte).

(ii) damming of the proglacial gorge by the avalanched ice; (iii) outburst flood due to dam collapse; and (iv) debris flow formation (Deline et al., 2004). A surge-type behaviour of the Belvedere glacier (Monte Rosa Group), caused by recent increase in temperature, resulted in the formation of a large depression, filled since 2001 by a supraglacial lake (Lago Effimero). In June 2003, the Lago Effimero bursted out through en-glacial channels, producing a hyperconcentrated flow with maximum discharge of approximately 15–20 m3 s−1 . The outburst flood lasted three days, draining 2.3 · 106 m3 of lake water (Fig. 5). A faster outburst, e.g. caused by hydraulic dambreak would have produced much larger geomorphic effects and hazards (Kääb et al., 2004).

5

DISCUSSION AND CONCLUDING REMARKS

Studies by various authors, both at broad scales covering the Italian region or the whole alpine area (Brunetti et al., 2004; Auer et al., 2005), and at local scale (Rampanelli et al., 2004) have demonstrated an increase in air temperature in the last decades, both in summer, and in the cold season. In the Italian Alps, the increase in mean annual temperature over the 1865–2003 period was 1 ± 0.1 K/100 years, i.e. higher that the global temperature increase (Brunetti et al., 2006). The rise of temperature influences the occurrence of debris flows in periglacial areas through different mechanisms. The increase of temperature causes melting of glaciers and snow-firn and permafrost degradation, increasing debris volumes prone to mobilization. Moreover, the increase of runoff due to enhanced glacier melting leads to a larger sediment transport capacity of proglacial streams. The formation of new contact and supraglacial lakes increases

the possibility of debris flows caused by lake outburst. Climate changes influence geometry and drainage systems of glaciers, resulting in the sudden release of en-glacial water pockets. Melting of buried ice masses can provide water inputs in the initiation areas of debris flows; moreover, buried ice masses can act as slide surfaces for the overlying debris. Landsliding associated to climate changes enhances the input of solid material to the channel network. Finally, higher temperatures cause increase of intense, debris-flow triggering summer rainstorms in the upper parts of mountainous slopes, which were mostly affected by non-erosive snowfall in the past decades. Indications exist about an increase in number and frequency of debris-flow processes at the margins of glaciers (Zimmermann & Haeberli, 1992; Chiarle et al., 2007). However, the lack of systematic data collection about debris-flow events and meteorological conditions in high altitude mountains makes it difficult to assess, on a statistical basis, the actual impact of climate changes on debris-flow occurrence. Moreover, the geomorphological response of slopes may be strongly influenced by local geological and topographical conditions (Jomelli et al., 2007). Further studies on debris flows in periglacial areas are needed both for better clarifying the relations between these phenomena and climate change, and because of the associated hazards. The initiation zones of debris flows related to glaciers and periglacial areas in the Alps are located in headwaters where generally no human structures are present. However, the fast downstream transfer of large amounts of sediment can result in high risk when debris flows encroach roads or buildings. Moreover, the development of mountain tourism in the upper parts of alpine mountains requires increased attention to natural hazards and urges the implementation of control measures. Because of the limited presence of structures at risk, control measures could be mostly of non-structural type (zoning of hazard, guidelines helping the hikers to deal with mountainous risks). REFERENCES Auer, I., Matulla, C., Böhm, R., Ungersböck, M., Maugeri, M., Nanni, T. & Pastorelli, R. 2005. Sensitivity of frost occurrence to temperature variability in the European Alps. International Journal of Climatology 25: 1749–1766. Berti, C. 1950. Guida dei Monti d’Italia – Le Dolomiti Orientali. Milano: Club Alpino Italiano & Touring Club Italiano (in Italian). Berti, M., Genevois, R., Simoni, A., & Tecca, P. R. 1989. Field observations of a debris flow event in the Dolomites. Geomorphology 29: 265–274. Berti, M. & Simoni, A. 2007. Prediction of debris flow inundation areas using empirical mobility relationships. Geomorphology 90: 144–161.

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Brunetti, M., Buffoni, L., Mangianti, F., Maugeri, M. & Nanni, T. 2004. Temperature, precipitation, and extreme events during the last century in Italy. Global and Planetary Change 40: 141–149. Brunetti, M., Maugeri, M., Monti, F. & Nanni, T. 2006. Temperature and precipitation variability in Italy in the last two centuries from homogenized instrumental time series. International Journal of Climatology 26: 345–381. Chiarle, M., Iannotti, S., Mortara, G. & Deline, P. 2007. Recent debris flow occurrences associated with glaciers in the Alps. Global and Planetary Change 56: 123–136. Clague, J.J. & Evans, S.G. 2000. A review of catastrophic drainage of moraine-dammed lakes in British Columbia. Quaternary Science Reviews 19: 1763–1783. Deline, P., Chiarle, M. & Mortara, G. 2004: The July 2003 Frébouge debris flows (Mont Blanc Massif, Valley of Aosta, Italy): water pocket outburst flood and ice avalanche damming. Geografia Fisica e Dinamica Quaternaria 27: 107–111. Del Longo, M., Finzi, E., Galgaro,A., Godio,A., Luchetta,A., Pellegrini, G.B. & Zambrano, R. 2001. Responses of the Val DArcia small dolomitic glacier (Mount Pelmo, Eastern Alps) to recent climatic changes. Geomorphological and geophysical study. Geografia Fisica e Dinamica Quaternaria 24: 43–55. Gregoretti, C. & Dalla Fontana, G. 2007. The triggering of debris flow due to channel-bed failure in five alpine basins of the Dolomites: analyses of critical runoff. Hydrological Processes DOI: 10.1002/hyp.6821. Guglielmin, M. 2006. Permafrost on the ItalianAlps and slope stability. Terra Glacialis 9: 95–98. Huggel, C., Kääb, A., Haeberli, W. & Krummenacher, B. 2003. Regional-scale GIS-models for assessment of hazards from glacier lake outbursts: evaluation and application in the Swiss Alps. Natural Hazards and Earth System Sciences 3: 647–662. Jomelli, V., Brunstein D., Grancher, D. & Pech, P. 2007. Is the response of hill slope debris flows to recent climate change univocal? A case study in the Massif des Ecrins (French Alps). Climatic Change 85: 119–137. Kääb, A., Huggel, C., Barbero, S., Chiarle, M., Cordola, M., Epifani, F., Haeberli, W., Mortara, G., Semino, P., Tamburini, A. & Viazzo G. 2004. Glacier Hazards at Belvedere Glacier and the Monte Rosa east face, Italian Alps: processes and mitigation. International Symposium Interpraevent 2004, Riva del Garda, 24–27 maggio 2004: I/67–78.

Marchi, L., Arattano, M. & Deganutti, A.M. 2002. Ten years of debris-flow monitoring in the Moscardo Torrent (Italian Alps). Geomorphology, 46: 1–17. Marchi, L. & D’Agostino, V. 2004. Estimation of debrisflow magnitude in the Eastern Italian Alps. Earth Surface Processes and Landforms 29: 207–220. Mortara, G. & Chiarle, M. 2005. Instability of recent moraines in the Italian Alps. Effects of natural processes and human intervention having environmental and hazard implications. Giornale di Geologia Applicata 1: 139–146. Moscariello, A., Marchi, L., Maraga, F. & Mortara, G. 2002. Alluvial fans in the Italian Alps: sedimentary facies and processes. In: P. Martini, V.R. Baker & G. Garzon (eds), Flood & Megaflood Processes and Deposits – Recent and Ancient Examples: 141–166, Oxford: Blackwell Science. Rampanelli, R., Rea, R. & Zardi, D. 2004. Analisi di serie storiche di temperatura. In: A. Bellin, D., Zardi (eds), Analisi climatologia di serie storiche delle precipitazioni e temperature in Trentino. Quaderni di Idronomia Montana 23: 135–214 (in Italian). Rebetez, M., Lugon, R. & Baeriswyl, P-A. 1997. Climatic change and debris flows in high mountain regions: the case study of the RitigrabenTorrent (SwissAlps). Climatic Change 36: 371–389. Tanesini, A., 1942. Guida dei Monti d’Italia – Sassolungo, Catinaccio, Latemar. Milano: Club Alpino Italiano & Touring Club Italiano (in Italian). Tiranti, D. Bonetto, S. & Mandrone, G. 2008. Quantitative basin characterisation to refine debris-flow triggering criteria and processes: an example from the Italian Western Alps. Landslides 5: 45–57. Turconi, L. & Tropeano, D. 2008. Debris flows in the Marderello catchment in summer 2005 (Cenischia Valley, western Italian Alps): a critical rainfall-process analysis. Wildbach- und Lawinenverbau, 72: 42–61. Zimmermann, M. & Haeberli, W. 1992. Climatic change and debris flow activity high mountain areas – A case study in the Swiss Alps. Catena Supplement 22: 59–72. Zemp, M., Paul, F., Hoelzle, M. and Haeberli, W. 2008. Alpine glacier fluctuations 1850–2000: An overview and spatio-temporal analysis of available data and its representativity. In: B. Orlove, E. Wiegandt & B. Luckman (eds). The Darkening Peaks: Glacial Retreat in Scientific and Social Context: 152–167, Berkeley: University of California Press.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Changes in the hydraulic properties of forest soils resulting from litter removal and compaction by human traffic Y. Hayashi∗, K. Kosugi & T. Mizuyama Department of Forest and Biomaterial science, Graduate school of Agriculture, Kyoto University, Kyoto, Japan

ABSTRACT: To examine the effects of litter removal and human traffic on rainwater dynamics in forest ecosystems, we analyzed soil hydraulic properties and conducted numerical simulations of rainwater infiltration at three sites: a forest in which litter has been removed for approximately 200 years (RF), a forest in which litter has not been removed for approximately 50 years (NRF), and a trail (T) that is located between the two forests. Results indicated that litter affected the development of subsurface soil structure, rendering it more similar to the soil surface and increased the number of pores with radii of 15–30 µm. Moreover, human traffic decreased the number of large pores (radius >15 µm) and increased the number of small pores (radius <15 µm) in the top 10 cm of the soil surface. Numerical simulations incorporating these hydrologic properties indicated that both NRF and RF exhibited similar hydrographs that responded less quickly to rainfall and had higher recession rates compared to T. Water storage at NRF and RF was similar and larger than at T. In conclusion: (1) the removal of litter affected soil structure in the surface layer and structurally developed the thickness of subsurface layers, but did not significantly affect water storage, and (2) human traffic on the trail substantially altered soil structure and water storage. Keywords: human traffic; litter removal; rainwater infiltration; soil hydraulic properties; soil pore size distribution

1

INTRODUCTION

To examine the effects of human intervention on rainwater dynamics in a forest ecosystem, we analyzed the soil hydraulic properties at three forest sites. In areas of Nakatomi, Japan, forest litter has been removed for use as fertilizer in agricultural fields for approximately 200 years. Previous studies have demonstrated various roles of litter in forest ecosystems. For example, intra-aggregate soil pores are obstructed by litter and associated microbial products (Caron et al., 1996). Litter also indirectly influences soil structure by serving as food for soil fauna, particularly earthworms, which contribute to aggregate formation (Marinissen et al., 1996). In this way, litter affects the structural development of pores. Kimoto et al. (2002) reported that overland flow occurred on a bare slope from which litter had been removed, although work to revegetate the area had begun. The structural development of the soil was retarded by litter removal (Kimoto et al., 2002), resulting in increased overland flow. However, the effects ∗

Corresponding author ([email protected])

of litter on soil hydraulic properties remain to be examined. Human traffic also has an important anthropogenic effect on hydraulic properties in forests. In Nakatomi, we found several trails throughout the forest. Lei (2004) reported that trampling from human traffic increased soil bulk density and decreased pore space through compaction. However, the hydraulic properties of soil in forest trails have not been characterized in detail. In this study, we investigated the effects of litter removal and human trampling on soil hydraulic properties as well as consequent changes in rainwater dynamics.

2

MATERIAL AND METHODS

2.1 Sampling sites Nakatomi is a flat-terrained region, 25 km west of Tokyo (35◦ 48 N, 139◦ 29 E), with a mean annual temperature of 14.1◦ C and a mean annual precipitation of 1444 mm. In this area, the soils are dominated by Kanto loam volcanic ash and are classified as Humic

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Andosols. Nakatomi is known for its unique, traditional agricultural system in which forest litter, weeds, and shrubs are thoroughly cleared in January of every year and spread onto local fields as fertilizer. We selected three sites in this area: two forests and one trail that passes between the two. In one forest, litter has been removed for the past 200 years (RF), and in the other forest, litter has not been removed for approximately 50 years (NRF). Oaks (Quercus serrata and Q. acutissima) dominate the vegetation in the two forests (Kasahara et al., 2007). In NRF, fermented oak leaves densely cover the forest floor and comprise the top 5 cm of the A0 layer, whereas in RF, few leaves cover the forest floor. Kasahara et al. (2007) reported that the soils in NRF were higher in total phosphorus (TP), carbon: nitrogen ratios (C:N), and exchangeable calcium (Ca) and magnesium (Mg) than those in RF, because the fermented leaves in NRF contained high levels of nutrients (57.6 kg N ha−1 , 1.21 kg P ha−1 , 8.8 kg Ca ha−1 , and 13.2 kg Mg ha−1 ). In the trail (T) between the two forests, the soils were compacted by daily human trampling, and the surface of the trail was unvegetated. We selected three points at each site for sampling.

each sample was oven-dried for measuring bulk density. We transformed water retention curves into pore size distributions, which were represented as the relationship between the frequency of pores (F value) and the pore radius. The F value was calculated as the slope of the water retention curve, which was expressed as the relationship between volumetric water content and the logarithm of the matric pressure head. 2.4 Water retention and hydraulic conductivity models The observed water retention curves were fitted using functional models (or equations). We used the log-normal (LN) model for soil water retention (Kosugi, 1994, 1996), because it includes physical parameters distinct from those of empirical models (e.g., Brooks & Corey, 1964; van Genuchten, 1980).The LN model was developed by applying a lognormal distribution to the pore radius (r) distribution on a volumetric basis:

2.2 Soil samples We collected undisturbed soil samples from each sampling point using thin-walled steel samplers (volume: 100 cm3 , inner diameter: 5 cm, height: 5.1 cm). The sharpened edge of the sampler was inserted vertically into the soil. Impact energy was applied to the cylinder using a hammer-driven device (DIK-1630, Daiki Rika Ko-gyo, Tokyo, Japan). To ensure sampling with minimum disturbance, we followed the method of Grossman & Reinsch (2002) for collecting undisturbed soil samples. During the process of insertion, roots and organic material were carefully severed from the soil layer around the sampler. Sampling depths were 2.5–7.5, 12.5–17.5, 27.5–32.5, and 52.5–57.5 cm in the forests and 2.5–7.5 and 12.5– 17.5 cm on the trail. 2.3

Laboratory experiment

Undisturbed soil core samples were placed on an aluminum tray and were slowly saturated by adding water from the bottom over 24 h. Soil water retention curves were measured using pressure plate methods (Jury et al., 1991) for matric pressure heads (ψ) of −5, −10, −20, −30, −50, −70, −100, −200, −500, −1000, and −15,000 cm. After measuring the water content at ψ = −15,000 cm, the soil samples were resaturated from the bottom over 24 h. The saturated hydraulic conductivity (Ks ) of each sample was then measured using the falling head method (Klute & Dirksen, 1986). After measurement of Ks ,

where θr and θs (m3 m−3 ) are the residual and saturated water contents, respectively, rm is the median pore radius, and σ is the standard deviation of the log-transformed soil pore radius, lnr. Matric pressure ψ can be related to r using the capillary rise function of Young and Laplace (Kutílek & Nielsen, 1994). Assuming this relationship, expressions for the effective saturation, Se were derived from Eq. (1) as follows:

where ψm is related to the median pore radius and represents the matric pressure when Se = 0.5, and Q denotes the complementary normal distribution function (Kosugi, 1996). The relationship between hydraulic conductivity K and ψ (Kosugi, 1996) was derived by substituting Eq. (2) into the pore structure model proposed by Mualem (1976):

Equations (2) and (3) produce adequate descriptions of measured hydraulic properties of various field soils (e.g., Kosugi, 1996, 1997; Tuli et al., 2001).

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10

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Figure 2. Observed pore size distributions of the soils at the deepest sampling depth at each site.

2.5-7.5 cm 12.5-17.5 cm 27.5-32.5 cm 52.5-57.5 cm

0

Ks (cm s-1) 0.001 0.01

10 Depth (cm)

Figure 1. Observed mean pore size distributions for soil samples from a (a) non litter-removed forest (NRF), (b) litterremoved forest (RF), and (c) trail (T).

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RESULTS AND DISCUSSION

20 30 40 50

Pore size distributions

60

Figure 1a–c presents pore size distributions for NRF, RF, and T. From the surface to a depth of 30 cm, the pore size distributions in NRF were similar; however, the F values of the pore size distribution fell sharply at depths of 52.5–57.5 cm (Fig. 1a). In contrast, the F values of pore size distributions in RF decreased gradually with depth, maintaining the same mode pore size (Fig. 1b). These results demonstrate that at NRF, litter contributed to the development of soil structure at subsurface layers as deep as 30 cm, making them more similar in soil structure to the surface. In addition, there were fewer pores with F values in the range of 1.30 > log (−ψ) > 0.69 (75–300 µm radius), which hold non-capillary water, and significantly more pores with F values in the range of 2.00 > log (−ψ) > 1.70 (15–30 µm radius), which hold capillary water, in NRF than in RF, at depths of 5 to 30 cm (Fig. 1a, b). In T, at a depth of 12.5–17.5 cm, the pore size distribution of the soil was similar to that in soils at 52.5–57.5 cm in NRF and RF (Fig. 2). At this depth range (52.5–57.5 cm), the original soil properties of the study site should remain unaffected by human intervention or strong biological activity. At T, the F values in soils at 2.5–7.5 cm were smaller at a range of log (-ψ) < 2.00 (radius > 15 µm) than those at 12.5–17.5 cm, but the opposite was true at ranges of log (−ψ) > 1.99 (radius < 15 µm; Fig. 1c). Therefore,

NRF RF T 100 1000 Ks (mm h-1)

Figure 3. Vertical distributions of geometric mean values of observed saturated hydraulic conductivities (KS ) in soil samples from NRF, RF, and T.

human traffic decreases the number of large pores and increases the number of small pores at depths of less than approximately 10 cm. 3.2 Saturated hydraulic conductivity Figure 3 compares the vertical distributions of Ks among NRF, RF, and T. The value of Ks at RF decreased gradually with depth, whereas values of Ks at NRF were stable, except for a decrease at a depth of 55 cm (Fig. 3). Thus, at 30 cm, Ks was greater at NRF than at RF. The value of Ks at T was smaller at 5 cm than at 15 cm. The value of Ks at T at 15 cm did not significantly differ from that at NRF and RF at 55 cm. 3.3 Hydraulic properties By substituting parameters that were optimized to fit the water retention curves into Eqs. (2) and (3), the water retention and hydraulic conductivity functions were calculated for NRF, RF, and T (Fig. 4).

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(d)

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Soil sampling depths for characterization of hydraulic properties 2.5 - 7.5 cm 12.5 - 17.5 cm 27.5 - 32.5 cm 52.5 - 57.5 cm M

Figure 5. Soil layers assumed for the numerical simulations. M represents the mean soil hydraulic property of soils at a depth of 55 cm in NRF and RF and at a depth of 15 cm in T.

T 1e-5

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Figure 4. (a–c) Water retention and (d–f) hydraulic conductivity functions for soil samples at NRF, RF, and T.

At NRF, the θs of the soil was similar at each depth range (Fig. 4a); however, in the dry region, the rate of change in θ for soil at 52.5–57.5 cm was lower than at the other depth ranges. Consequently, the soil at 52.5–57.5 cm exhibited a larger θ than that at other depths (Fig. 4a). At RF, the θs of the soil was similar at each depth range, except for a large value at 2.5–7.5 cm (Fig. 4b). The rate of change in θ decreased with depth, because the number of pores also decreased with depth (Fig. 1b). Therefore, in the dry region, θ increased with depth. At T, the θs of the soil at a depth range of 2.5–7.5 cm was larger than that at 12.5–17.5 cm. However, the rate of change in θ of soil at 12.5–17.5 cm was larger than that at 2.5–7.5 cm due to the presence of a higher number of larger pores (Fig. 1c). In the dry region, the difference between θ at depth ranges of 2.5–7.5 cm and 12.5–17.5 cm was larger than in the wet region. Figure 4d-f presents the hydraulic conductivity curves for each site. At NRF, the rates of change in K were similar at each depth range. Therefore, differences in log-transformed values of K between each depth range in the dry region were relatively similar to those in the wet region. The value of K of soils at 52.5–57.5 cm was remarkably smaller than that at other depths.

Even though Ks decreased with depth at RF, this relationship did not hold in the dry region, because the rate of change in K for surface soil, which had a higher number of large pores, was higher than for deeper soils. At T, Ks was smaller at depths of 2.5–7.5 cm than at 12.5–17.5 cm. The rate of change in K was lower for soils at 2.5–7.5 cm, which had fewer large pores, than that at 12.5–17.5 cm. Consequently, values of K for soils at both depths were similar in the dry region. 3.4

Simulations of rainwater infiltration and drainage

Numerical simulations of rainwater infiltration were conducted for soil profiles. We assumed three 120-cm soil layers and further divided those into two layers: the upper 60-cm soil layers were characterized by the hydraulic properties of each site, and the lower 60-cm soil layers were characterized by common hydraulic properties. For all sites, we stratified the upper layers as shown in Fig. 5 and characterized each layer by the hydraulic properties shown in Fig. 4. The observed pore size distributions and values of Ks of the soils at the deepest sampling depth were similar at each site; thus, we used the mean of these values to represent the hydraulic properties of the lower 60-cm soil layer. The Richards equation for vertical unsaturated water flow was solved using a fully-implicit finite difference scheme. The time and space increments used were 1 min and 1 cm, respectively, and the modified Picard’s method (Celia, 1990) was adopted to ensure strict mass conservation. We assumed zero-flux initial conditions:

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Rain (mm h-1)

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20 40 60 80 100 120

(a)

0 20 40

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-70 -60

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60 100 -10

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(b)

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6

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18 Time (h)

24

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20 40 0.03 60 80 0.18 100 0.15 0.12 0.09 0.06 120 0.12 0.09 0.06 20 40 0.03 60 80 100 120 0 6 12 18 24 Time (h)

(b)

(c)

(d) NRF RF T

10 0

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-80

(e)

Depth (cm)

80

0.15 0.12 0.09 0.06

30

Figure 7. Simulated increments in water contents from initial condition (a) for NRF, (b) RF, and (c) T.

30

Figure 6. (a) Applied hyetograph, simulated matric potential for (b) NRF, (c) RF, and (d) T, and (e) simulated hydrograph. Broken lines indicate the boundary of layers shown in Fig. 5.

where t and z represent time and vertical distance, respectively, L is the thickness of the soil layer, and z = 0 corresponds to the bottom of the soil layer. A constant water table (i.e., ψ = 0) was imposed at z = 0, and the matric potential ψ, water content θ, and water drainage rate, qout , were calculated. We applied a hyetograph with a rainfall intensity of 15 mm h−1 for 5 h (Fig. 6a). Because the rainfall intensity was lower than values of Ks for all soils (see Fig. 2), neither positive pressure on the soil surface nor surface runoff was computed, and the total amount of infiltrated water was the same for all runs. Thus, we examined the effects of soil hydraulic properties on the propagation of the infiltrated water in the soil profile. Soil surface evaporation and root water uptake were not considered. The computed hydrograph, time series of vertical distributions of ψ, and increments in θ from initial conditions are presented in Figs. 6e, 6b-d, and 7a-c, respectively. Although peak ψ was somewhat larger for RF than for NRF, the time series of the vertical

distribution of ψ were similar at the two sites (Fig. 6b, c); therefore, the hydrographs of NRF and RF did not significantly differ. At T, ψ responded more quickly to rainfall and immediately decreased (Fig. 6d), and the hydrographs of T exhibited larger peak discharge rates and smaller recession discharge rates than those of NRF and RF (Fig. 6e). Thus, we expected that soil layers in NRF and RF would have larger water storages than those of T; actually, NRF and RF exhibited greater increments in θ than T (Fig. 7). Water storage occurred in lower soil layers at NRF (on the layer of 48 cm) than at RF (on the layer of 22 cm), because the potential for water retention decreased more in surface soils at RF than at NRF (see Fig. 5a,b).

4

CONCLUSIONS

To examine changes in forest rainwater dynamics due to litter removal and human traffic, we analyzed the hydraulic properties of the soil in a non litter-removed forest (NRF), a litter-removed forest (RF), and a trail (T) and conducted numerical simulations of rainwater infiltration and drainage at these three sites.

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At depths of 0–30 cm at NRF, the soil layers exhibited similar pore size distributions and values of the saturated hydraulic conductivity Ks , whereas at 55 cm, the number of pores and Ks rapidly decreased. At RF, Ks and F values of the pore size distribution decreased gradually with depth, of which the latter maintained the same mode pore size. These results clearly indicate that leaf litter contributes to the development of soil structure in the subsurface layers (i.e., to approximately 30 cm), rendering them similar to the surface. In addition, there were fewer pores with radii of 75–300 µm, which hold non-capillary water, and significantly more pores with radii of 15–30 µm, which hold capillary water, at NRF than at RF at depths of 5 to 30 cm. Human traffic decreased the number of large pores in the top 10 cm of soil. At 12.5–17.5 cm depth at T, the soil was similar to soils at 52.5–57.5 cm in NRF and RF, a depth range that represents the original soil properties at the study site (i.e., unaffected by human intervention or strong biological activity). Moreover, the soil at T contained fewer pores with radii >15 µm and more pores with radii < 15 µm at 2.5–7.5 cm than at 12.5–17.5 cm. We conducted numerical simulations using these hydrological properties for each site. NRF and RF exhibited similar hydrographs that responded less quickly to rainfall and had larger recession rates than hydrographs of T. Thus, water storage at NRF and RF was similar and larger than at T. In conclusion: (1) litter removal affected the characteristics of soil structure at the surface layer and structurally developed the thickness of subsurface layers, but did not significantly affect water storage, and (2) human traffic on the forest trail considerably affected soil structure and water storage. ACKNOWLEDGMENTS We thank Aya Kasahara for help with soil sampling. This research was supported by the Japan Society for the Promotion of Science. REFERENCES

Celia, M.A., E.T. Bouloutas, & R.L. Zarba. 1990. A General Mass-Conservative Numerical-Solution for the Unsaturated Flow Equation. Water Resour. Res 26:1483–1496. Grossman, R. B. & Reinsch, T. G. 2002. Core method. In: Klute, A. (ed.), Method of Soil Analysis. Part 4-physical Methods, Monograph, vol. 9: 207–209. Madison, WI: ASA and SSSA. Jury, W. A., W. R. Gardner, & W. H. Gardner. 1991. Soil Physics: 328. New York: Wiley. Kasahara, A., H. Toda, & K. Haibara. 2007. Effects of litter removal on chemical characteristics of soils in a flat forest (in Japanese). In, Proceedings of the Annual Meeting of the Japanese Forest Society, Tokyo, 2007: J03. Kimoto, A., T. Uchida, T. Mizuyama, & C. H. Li. 2002. Influences of human activities on sediment discharge from devastated weathered granite hills of southern China: effects of 4-year elimination of human activities. Catena 48: 217–233. Klute, A. & Dirksen, C. 1986. Hydraulic conductivity and diffusivity: Laboratory methods. In: Klute, A. (ed.), Methods of soil Analysis, Part 1: Physical and Mineralogical Methods, Monograph No. 9. Madison, WI: Am. Soc. Agron. Kosugi, K. 1994. Analysis of water retention curves of forest soils with three-parameter lognormal distribution model. J. Jpn. For. Soc. 76(5): 433–444. Kosugi, K. 1996. Lognormal distribution model for unsaturated soil hydraulic properties. Water Resour. Res. 32: 2697–2703. Kosugi, K. 1997. Effect of pore radius distribution of forest soils on vertical water movement in soil profile, J. Jpn. Soc. Hydrol. Water Resour. 10: 226–237. Kutílek, M. & Nielsen, D. R. 1994. Soil Hydrology. Cremlingen, Germany: Catena Verlag. Lei, S. A. 2004. Soil compaction from human trampling, hiking, and off-road motor vehicle activity in a blackbrush (Coleogyne ramosissima) shrubland. West. N. Am. Naturalist 64: 125–130. Marinissen, J.C.Y., E. Nijhuis, & N. vanBreemen. 1996. Clay dispersability in moist earthworm casts of different soils. Appl. Soil Ecol. 4: 83–92. Mualem, Y. 1976. Catalogue of the hydraulic properties of unsaturated soils. Proj. 442: 100, Haifa, Israel: TechnionIsrael Inst. of Technol. Tuli, A., K. Kosugi, & J. W. Hopmans. 2001. Simultaneous scaling of soil water retention and unsaturated hydraulic conductivity function assuming lognormal pore-size distribution. Adv. Water Resour. 24: 677–688. van Genuchten, M. Th. 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44(5): 892–898.

Brooks, R. H. & Corey, A. T. 1964. Hydraulic properties of porous media. Hydrol. Pap. 3: 1–27. Fort Collins, CO. Civil Eng. Dept. Colo. State Univ. Caron, J., C. R. Espindola, & D. A. Angers. 1996. Soil structural stability during rapid wetting: Influence of land use on some aggregate properties. Soil Sci. Soc. Am. J. 60: 901–908.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Effects of land use on soil physical and chemical properties of sandy land in Horqin, China A.M. Hao∗ & T. Watanabe Research Institute for Humanity and Nature, Kyoto, Japan

T. Haraguchi Department of Agricultural Sciences, Faculty of Agriculture, Saga University, Saga, Japan

Y. Nakano Faculty of Agriculture, Kyushu-Kyoritsu University, Fukuoka, Japan

ABSTRACT: Transitional changes were studied in soil physical and chemical properties after implementing plant restoration trials to counter desertification in Naiman Region in the Horqin desert, a district in northeastern China. Soil samples were taken from typical points in areas under different land uses located in an area of about 20 km × 30 km. Land uses were classified as sandy land, grassland, bush, forest, upland field and rice field. Water content of air dried soil, organic content, cation exchange capacity (CEC), pH, electrical conductivity (EC), soil composition particle diameter and soil density were measured in three soil layers, i.e., upper (2.5–7.5 cm), middle (17.5–22.5 cm) and lower (37.5–42.5 cm). In the upper layer, values for air dried soil water content, organic matter content and CEC were in the order rice field > upland field > forest > grassland > bush > sandy land. pH values ranged between 8.04 and 10.29. Maximum EC was 0.59 mS/cm in upland field, indicating that irrigation water and chemical fertilizer caused salt accumulation. Density of soils ranged from 2.55 to 2.68 g/cm3 . Soil particle size distribution did not differ among sandy land, grassland and bush. The percentages of clay and silt were greater in upland and rice fields. Soil moisture characteristic curves were measured on sandy land, forest and bush at six layers, from the surface to 100 cm depth. Holding capacities for plant-available soil moisture in these soils were 3%, 8% and 17%, respectively. Keywords:

1

desertification; land use conditions; soil physical and chemical properties

INTRODUCTION

Before 1960, there was enough water to grow grass and trees on Horqin arid land in northeastern China. Since then, a rapid population increase has resulted in overgrazing, and excessive land reclamation and tree felling. Climate change, especially a trend of decreasing rainfall, has intensified desertification. Moreover, the lack of a development plan has accelerated desert expansion. By 1990, 54% of the land was sandy desert (Jiang et al., 2002). As pointed out by Zhao et al. (1998), to control the desert expansion, soil conditions of different land uses should be analyzed in the desert area where are some different land covers, including bush, grassland, crop fields converted from desert, as the basic ∗

Corresponding author ([email protected])

information for developing measures with vegetation restoration. In this paper, to assess the effect of land use on soil properties, actual transitional changes in soil physical and chemical characteristics in typical land uses observed in the Horqin Sandy Land are analyzed.

2

GENERAL CONDITIONS OF THE STUDY AREA

2.1 Geographical conditions Horqin arid land is located at latitude 41◦ 41−47◦ 39 N and longitude 116◦ 21 −123◦ 43 E. Altitude ranges between 178.5 m (TongLiao) and 631.9 m (Wudan). This area of arid land comprises 12% of the total area of Inner Mongolia (Figure 1). The study area is located in the Naiman Region (total area 8,159 km2 ),

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3

RESULTS

3.1 Soil particle size distribution The International Soil Classification Standard defines particle diameters of clay, soil, fine sand and coarse sand as < 0.002 mm, 0.002 mm–0.02 mm, 0.02 mm– 0.2 mm and 0.2–2.0 mm, respectively. In sandy land soil all particle sizes were larger than 0.1 mm. Grassland and bush soils showed very similar characteristics to sandy land soil. In forest soils about 25% of soil particles were between 0.01 mm and 0.1 mm. Rice field and upland field soils had higher percentages of particle sizes less than 0.1 mm than the other soils (Figure 3).

Figure 1. Geographic location of Horqin arid land and study site.

which is one of the most well known areas suffering from desertification. Annual average air temperature, rainfall and wind speed are 6.4◦ C, 366 mm and 3.5 m/s, respectively. Wind speed exceeds 17 m/s on 22.4 days per year. Annual average evaporation rate measured by a small pan of 20 cm diameter was 1,935 mm. 2.2

Survey methods

Observations were conducted on sandy land, grassland, bush, forest, upland field and rice field (Figure 2). The main crops cultivated on upland field were corn, beans and oats; the main species of grassland, bush and forest were Artmisia ordosica, Agriophyllum squarrosum and poplar, respectively. Soil samples were taken from typical land uses located in an area of about 20 km × 30 km of the Naiman Region in the Horqin arid land. At total of 18 points were sampled. Six different points in each land use were selected for comparison. At each point, soil samples of about 300 g were taken from three layers: upper (2.5–7.5 cm), middle (17.5–22.5 cm) and lower (37.5–42.5 cm). Soil particle size distribution, Soil density, air dried moisture content, pH, electrical conductivity (EC), organic matter content and cation exchange capacity (CEC) were measured on these samples. Soil density and air dried moisture content were measured following JIS-A1202 and JIS-A1203, respectively. pH and EC were measured following JGS-0211 and JGS-0212, respectively (The Japanese Geotechnical Society 2001). CEC was measured following the acetic acid barium method (Kosino 1988). Soils of sandy land, bush and forest were sampled to measure water holding capacity from six layers: 0–5 cm, 7.5–12.5 cm, 17.5–22.5 cm, 27.5–32.5 cm 47.5–52.5 cm and 97.5–102.5 cm.

3.2 Soil chemical and physical properties Soil pH ranged between 8.04 and 10.29 for all land uses, indicating that the soils in Naiman were alkaline. Maximum EC of 0.59 mS/cm was observed in the upper layer in upland field. EC in grassland and rice field were lower than in upland field but greater than in sandy land, bush and forest. Density of soils ranged from 2.55 to 2.68 g/cm3 . Soil water contents of air dried soil were highest in rice field. Organic matter contents and CEC of rice field and upland field were higher than those of the other land uses. Soil water and organic matter contents and CEC showed the same tendency for each land use (Figure 4). 3.3 Soil moisture retention characteristics In sandy land, soils showed almost the same soil water characteristic curves at each depth. In bush and forest soils, curves at 100 cm depth were the same as those at each depth in sandy land. Soil moisture retention of surface soils deviated from that of the original sandy soil.The holding capacity for plant-available soil moisture in sandy land, bush and forest was 3%, 8% and 17%, respectively, indicating that establishing plant cover has improved soil water retention characteristics (Figure 5). 4

DISCUSSION

The soil particles observed in sandy land are considered to be the original soil of sandy desert. Sand particles in the study site are mostly between 0.1 mm and 1.0 mm diameter. These fine sands are easily blown at wind speeds exceeding 4.0 m/s, and in this area wind speeds greater than 17 m/s occur on 22.4 days a year. Strong winds blow mainly during winter, from November to March, when deciduous trees have no leaves, causing movement of sand dunes and desertification.

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Accumulated amount of soil particle (%)

Figure 2. The vegetation of the different land used described in Naiman Region. 100 80 60

Sandy Land Grass land Bush Forest Upland Field Rice Field

40 20 0 0.00001

0.0001

0.001 0.01 Soil particle (mm)

0.1

1

Figure 3. Soil particle characteristics under different land use conditions of Naiman Region.

Soil particle distribution in the different land uses showed that the diversity of particle size increased with increasing agricultural development or plant cover. One of the reasons for this diversity will be the

disintegration of soil particles by chemical and biological processes. Such an increase in diversity will improve the holding capacity of plant-available water. Soil pH in the study area was higher than 8, indicating alkaline soils. The pH of grassland and upland field soils was slightly higher than that of other soils, which might affect accumulation of salts in the root zone as a result of high evaporation. pH of rice field soils was lower than that of upland field soils, as salts were leached by continual application of irrigation water. EC was highest in upland field, mainly as a result of salt accumulation after fertilizer application. EC is the most reliable indicator of salt accumulation in soil. Most crops are damaged when soil EC exceeds 1.0 mS/cm. Although salt concentrations in this area are not seriously high, it is recommended that a crop rotation system including rice cultivation should be introduced to prevent salt accumulation.

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5 4

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(d) Electrical conductivity

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6 4 2

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(c) pH

Figure 4. Soil physical and chemical properties under different land use conditions of Naiman Region.

CEC is a good indicator of soil fertility, and a high CEC indicates high levels of soil calcium, magnesium, potassium and ammonium. High CEC values in rice field and upland fields would be the result of organic matter and chemical fertilizer application.

The lowest soil density was observed in the upper soil layer of sandy land, a result of the lack of clay and silt which are easily blown by strong winds. The results for volumetric water content show that establishing plant cover has improved soil water retention characteristics. Forestation with poplars is one of

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4.5

Sandy Land

4.0 3.5

pF

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the most effective ways of improving soil properties with higher water contents and fertility.

0cm 10cm 20cm 30cm 50cm 100cm

5

2.5

Transitional changes in soil physical and chemical properties after implementing plant restoration trials to counter desertification were studied at Naiman Region in the Horqin desert, a district in northeastern China. Soils of sandy land, grassland, bush, forest, upland field and rice field were studied. The diversity of particle size increased with increasing agricultural development or plant cover. Also, agricultural activities and forestation with poplars improved holding capacity of plant-available water. A rotating system of forestation and agriculture would be an effective way of improving sandy land soil.

2.0 1.5 1.0 0.5 0.0 0

4.5

CONCLUSIONS

10 20 30 40 50 Volumetric water content (%) Bush

4.0 3.5

pF

3.0

REFERENCES

2.5

The Japanese Geotechnical Society 2001 Soil test-Basis and guidance, pp. 61–70. Jiang, D., Liu, Z. Cao, Y. Kou Z. & Wang R. 2002 Desertification and Ecological Restoration of Horqin Sandy Land. China Environmental Science Press (Beijing), pp. 2–94, 488. Kosino, M. 1988 Detailed fertilizer analysis method, Yokendo, Tokyo, pp. 315–318. Zhao, H., Liu, X. Li S. & Zhang T. 1998 The analysis of the basic factor of the fragile ecology environment in Horqin sandy land. The Chinese desert, 18(2): 10–17.

2.0 1.5 1.0 0.5 0.0 0

10 20 30 40 50 Volumetric water content (%) Forest

4.5 4.0 3.5

pF

3.0 2.5 2.0 1.5 1.0 0.5 0.0 0

10 20 30 40 50 Volumetric water content (%)

Figure 5. Soil moisture retention characteristics of soils under three different land use conditions in Naiman Region.

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3

Strategic planning and environmental assessments of activities in headwater areas (IAHC topics The 7th International Conference on Headwater Control) The management of headwater areas demands their full integration in environmental management plans. Towards this end, a new and comprehensive inventory of headwater watersheds is urgently required. The aim of management should be to maximize the benefits of these wetlands to their stakeholders. There remains a need to assess the role of headwaters in land use systems, especially farming, forestry, grazing, water resource management, tourism, and nature conservation. The effective management of headwaters in the frame of integrated watershed planning also demands some assessment of the role of key components and more effective participatory processes. Are the EIA (Environmental Impact Assessment) and SEA (Strategic Environmental Assessment) procedures effective tools in headwater control? Conveners: Einar Beheim (NVE, Oslo, Norway) Bruce Van Haveren (US-EPA, Denver, USA) Pier Carlo Zingari (EOMF, Chambery, France) Takahiro Endo (RIHN, Japan)

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

A comparative policy analysis for headwater management Takahiro Endo∗ Research Institute for Humanity and Nature, Kyoto, Japan

ABSTRACT: Increasing attention is now given to the protection of headwaters. Across Japan, variety of practices such as headwater fostering taxes, voluntary afforestation by fishermen (the so-called “fish-breeding” forest movement), eco-labeling (wood certificate) and local currency have been introduced. Many studies have been done on headwater management reflecting these policies. Most have dealt with each individual policy with little attention given to comparative analyses. By contrast, this paper compares two headwater management policies (headwater fostering tax and local currency) and will show the advantages and shortcomings of each. Policy-makers will find these analyses provide useful information for deciding headwater management policies. Keywords:

1

headwater management; social dilemma; local currency; headwater fostering tax

INTRODUCTION

Forests have many functions. They provide diverse materials for living. Moreover, they, to some extent, contribute to flood protection, drought alleviation and purification of water resources. We thus enjoy many benefits from forests as headwaters. Increasing attention is now been given to the protection of headwaters. Across Japan, a variety of practices have been introduced; such as headwater fostering taxes (also referred as forest environment tax), voluntary afforestation by fishermen (the so-called “fish-breeding” forest movement), eco-labeling (wood certificate) and local currency. However, which is the best way to manage headwaters to promote the many benefits possible from forests? The answer naturally depends on the criteria defining the meaning of “the best” or the circumstances in which the practices are introduced. Therefore, it will be hard to determine “the best” way in each situation. What is more important is to clarify the advantages and disadvantages of each proposed policy. There have been many studies considering these policies. However, they have not always been linked with environmental problems. Most of them deal with each individual policy (for example, Seyfang and Pearson 2000, Kawai and Simazaki 2003, Gulbrandsen 2005, Rametsteiner and Simula 2003, Aoki 2003 and Banba 2004) paying little attention to comparative analyses. Although it is desirable to compare all the policies mentioned above with various criteria, it will ∗

Corresponding author ([email protected])

lead to sterile confusion. So, at present, we will confine the discussion to two headwater management policies (headwater fostering taxes and local currency) and show the advantages and shortcomings of each policy with a criterion of economic efficiency. This analysis will provide policy-makers with useful information for determining headwater management policy.

2 ANALYTICAL FRAMEWORK A common analytical framework is necessary for comparative study. Here we introduce the concept of “social dilemma” as our analytical tool. Social dilemma is defined as involving a conflict between “individual rationality” and “group rationality.” We see such dilemma in a situation where the following conditions are met. 1) A number of individuals share a common interest, 2) However, each individual does not have an incentive to contribute the benefit by themselves (Olson 1965, Dawes 1975). Headwater management has two kinds of benefit. The first is the sale of timber. This kind of benefit can be called a private benefit in that it is owned exclusively by an individual who sells the timber. A well-managed headwater also produces, to some extent, other kinds of benefit such as flood protection, drought alleviation and purification of water resources. Such benefits are called social ones in that these are potentially shared by a number of individuals. It would be fallacious to say that individuals who share a common benefit automatically promote it. The reason is that the benefit is common to the individuals.

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When an individual contributes to the benefit, he/she can get only a small portion of the benefit his/her contribution makes. The rest will spill over to other individuals because the benefit is common. If he/she finds what he/she can get is not enough and the contribution does not pay, he/she will not make any contribution. Rather, he/she has an incentive to be a free-rider who just expects other individuals’ contribution. Where free-riders prevail, the common benefit will not be provided sufficiently. This social dilemma situation is not desirable from a criterion of economic efficiency. If the benefit is realized sufficiently by solving the free-rider problem, an individual’s welfare will be improved without decreasing any other individuals’ welfare. In Japan, when the price of timbers was much higher, forestry was a profitable economic sector. They made voluntary as well as unconscious contributions to the promotion of social benefits while they sought private ones. However, now the price is very low and we can’t expect that anymore. The low price now gives everyone an incentive to be a free-rider just expecting someone else’s contribution. That is, the fact that the benefit is common to individuals has such a reverse effect that we can not expect each individual’s voluntary contribution. Tadaki once noted that a price tag should be put on the social functions provided by forests to help promote forest management (Tadaki 1990:200–202). It seems that this idea would also contribute to resolving the social dilemma situation involved in forest management. There have been prices put on timber, but nothing has been put on the social services provided by forests such as flood prevention and purification for water quality. Indeed, the latter has always been provided for free. The solution proposed in this paper is to establish an institution that puts a de facto price not only on timber but also on the social services of the headwaters themselves. Such an institution would make people pay for the social services and will then contribute to resolving the social dilemma found in headwater management. This is because it will give those who are engaged in forestry financial incentives or a lesser burden. We cannot obviously buy or sell the social benefits of headwater in the way we do clothes or cars. That’s why arrangements are needed to make a framework; this is the reason we use the term “de facto” price or payment. Although studies have been done on policy-tools such as a headwater fostering tax and a local currency, few have attempted to do comparative studies. But both can be regarded as solutions for the posited social dilemma. To put it differently, both can be considered as arrangements that prevent people to be free-riders and make people pay for the social benefits of headwaters. In following chapters, we will compare the two policies with a criterion of economic efficiency,

paying attention to which is better solution to free-rider problem.

3

POLICY TOOLS

3.1 Local currency A local currency (community currency) is one that can be effective in certain areas. It is not an official currency sanctioned by the central government, but at the same time, it does not deny the existence of an official currency. Some local currencies are not attended by interest (or some are attended even by negative interest) to induce people not to save, but to spend (Nishibe, 2002:17). In Japan, several communities have begun introducing local currencies during the past ten years. Local currencies named “Peanut” (Chiba city, Chiba prefecture) or “Oumi”(Oumi city, Shiga prefecture) are famous as pioneering cases. According to Izumi, although about 40% of newly established local currencies stops its functions within a year or two, overall number of local currencies keep growing, because more and more currencies are newly created every year. As a result, 580 communities created local currencies and 268 were still working in December, 2005 (Izumi 2006:97, 119). Because the currencies vary in terms of their purposes and transaction methods, it is impossible to give an overall evaluation of local currencies. Thus, in this paper, we confine discusion to a local currency named Fushino, because it is a typical example of local currency that relates directly to water management issues. Fushino was introduced in 2003 as a tool for conserving environment in general in the Fushino river basin ofYamaguchi prefecture, Japan (Yamaguchi Prefecture 2003). This was designed to work as a tool to induce people to make voluntary contributions for headwater management, in the following way. First, suppose that there are some residents living downstream and they take part in forest management activities such as thinning or tree-planting. They receive some Fushinos as a reward. Fushino is no more than a piece of paper like an official currency that everyone is willing to accept. However, a Fushino is current only in certain shops that declare they will accept it. Such shops are called “support shops.” (According to the official web site of Fushino, the number of those shops amounted to 40 as of 2005.) They usually accept local currency as part payment. For example, when a liquor store is one of the support shops, residents can make payment for a bottle of wine costing 1000 Japanese Yen (JPY) with, say, official currency of 900 JPY and some Fushinos. This means that the residents can enjoy a de facto discount. If the liquor store can use the Fushino as payment for

132

stocking, then these will move into the hands of wholesalers. But not all wholesalers are support shops. If that is the case, the liquor store’s profit will decreases by the amount of the discount (100 JPY). What has to be noticed is that the liquor store effectively donates 100 JPY for forest management. That is, the residents who take part in forest management and the owner of the liquor store make de facto payments for the social benefits given by forest through direct activities or indirect contributions. In this way, the Fushino is a policy tool that makes it possible for volunteers and support shops to share the burden of forest management. 3.2

Headwater fostering tax

Headwater fostering taxes (a forest environment tax) are collected by the government for promoting the public functions of forests. Through this method, the government forces people to pay for the social benefits of forests. Such governmental intervention is effective for resolving the free-rider problem because being a free-rider will be prohibitively costly because of the penalty imposed by government. Although this kind of tax has now been introduced in many prefectures, there was once discussion within central government on whether such a tax was necessary to promote the social benefits of forests. In the middle of the 1980’s, ideas of a “water source tax” or a “tax for emergent maintenance of forests and rivers” were proposed by ministries such as the Forestry Agency, the Ministry of Agriculture, Forestry and Fisheries and the Ministry of Construction. But these proposals were fruitless. (Banba 2004:76–78). During the past ten years, a package of decentralization laws came into effect and so enabled local governments to create new taxes of their own (Usui M. 2001:17–18, Ishizaki, R. 2004:229). The Kochi Prefecture Government (KPG) took advantage of this law and pioneered a forest environment tax system in advance of any other prefecture. KPG established the tax by overcoming the following problems that were attendant on the introduction. Q1. Which is the better way to collect funds? (creating a new tax or raising the water charge?), Q2.If a tax was preferable, what kind of tax would be better (a general tax or an earmarked tax), Q3. If a general tax was chosen, how does the government restrict its use to forest management? KPG’s answers were : A1. Create a new tax, A2. As a general tax, A3. Make a fund to ensure it is used for just forest management.(KPG. 2002: 14–20.) The significance of KPG’s endeavor lies in that it forged a model for a headwater fostering tax that many prefectures then followed. The number of prefectures that have introduced or will soon introduce similar tax system is 28 (approximately 60% of all prefectures), as of February 2008. (For details see the Appendix).

4 4.1

COMPARATIVE EVALUATIONS Local currency

Taking part as a volunteer for thinning or reforestation is a way to contribute to foster the public function of forests. These volunteers pay for the social benefits of forests not with money, but with direct activities. But this method has a major defect in that it can not prevent free-riders. A local currency does, to some extent, make up for this defect. It disperses costs that would otherwise be concentrated on volunteers to one shouldered between volunteers and support shops. This lessens the costs accruing to each volunteer and contributes to giving each one a disincentive to be a free-rider by that much. But we can not say that a local currency is a panacea for headwater management. From observation of Fushino case, we can point at least two deficits. First, there are no fundamental differences between a volunteer method and a local currency method. The latter is essentially based on the former. Practically speaking, although people may be curious about a local currency and have incentives to be a volunteer soon after it is introduced, they may become a free-rider again as they get accustomed to and lose their curiosity about the local currency. Another problem is the cost sharing among support shops. If the local currency is spent intensively in a particular support shop, that shop has to bear much of the burden and the owner will lose the incentive to be a support shop. (When such shops pass the cost on to the customer, narrow range of customers will has to bear the burden and lose incentive to be a volunteer by that much.) If this leads to the reduction of the number of support shops, the local currency comes close to being mere paper and people lose nearly all incentives to be volunteers. 4.2

Headwater fostering tax

We find a merit of a headwater fostering tax in that this method is accompanied with governmental coercion. This is quite effective in preventing people from being free-riders because they will find the cost of being a free-rider prohibitively high because of the administrative sanction or the criminal penalty imposed by government. This method is also helpful in collecting fees from wider sources. In other words, it makes it possible for the total cost of headwater management to be dispersed among far more people than with a local currency method. For these reasons, we believe that a headwater fostering tax is more useful than a local currency for resolving the social dilemma situation and for creating a stable fund to promote headwater management. However, just as a local currency is not a perfect solution, neither is a headwater fostering tax. Just

133

because the tax method is effective, it does not follow that the method can always be justified. Even if it effectively promotes headwater management, it cannot always be introduced immediately because it does not always produce benefits that are sufficient to compensate for the tax burden. A cost-benefit analysis needs to be done before it is introduced. But this is a difficult task in Japan because we still lack ways to precisely calculate the benefits of headwater management. First, the social benefits provided by headwaters accrue to people for free and do not have any price tag, by their very nature. Second, the public functions of headwaters are integrated and we cannot easily evaluate them one by one. Third, we do not have enough data about the functions of forests that would enable us to make a precise evaluation (Nihon Gakujutsu Kaigi, 2001). And moreover, related to these obstacles, another problem will rise especially where the number of tax payers is relatively small compared to the size of forest. In such case, tax burden to each resident may be prohibitively high. (personal communication with Associate Professor MATSUMOTO Mitsuo, Faculty of Humanities & Economics, Kochi University). These reasons show that headwater fostering taxes are effective but that they cannot be introduced unconditionally. Therefore, when the tax method is introduced, what is important is not whether it is justified or not, but how the method is justified considering the uncertain evaluation of the social benefits of forests. Management that is based on uncertainty is often called “adaptive management.” In this style, natural resource management is thought to be experimental and the original plan gradually changed through verification of the results of plan execution (Schreiber et al. 2004). The question is how we implement such management practices. Some prefectures have already made headwater fostering tax systems that incorporate elements of adaptive management. For example, most prefectures limit the life of the tax to 5 years. This shows that they think the tax is not a permanent one, but experimental. Creating a fund is another example because if the use of tax is not restricted to headwater management, there cannot be a cost-benefit analysis of the tax. Moreover, some prefectures have introduced a system with an evaluation committee composed of academic professionals from outside of government organizations. These committees hold periodical meetings to check the effects of the tax. However, these might not be enough. It is because the function of such committee is to evaluate and it is not given any authority to order government to withdraw the tax. So there is a room for government to keep the tax contrary to the committee’s opinion. Creating new schemes for repetitive evaluations will be an on-going task for prefectures that have already introduced a headwater fostering tax.

5

CONCLUSION

As increasing attention is paid to the protection of headwaters, a variety of practices and policies are introduced across Japan. In this paper, a comparative study was undertaken on two policies (headwater fostering taxes and local currency). The conclusion is that neither provides a complete solution. The local currency method is not useful for resolving the social dilemma attendant with the headwater management problem.Although headwater fostering taxes are effective against the problem, they need exact evaluations for benefit-quantification of the social benefit to be justified. Such an evaluation cannot yet be made; therefore, some elements of adaptive management need to be incorporated into headwater fostering tax systems. ACKNOWLEDGEMENTS This research was supported in part by grants from the Humans and Water (Inter-institutional and External Joint Research Program of the National Institutes for the Humanities) and the Asahi Glass Foundation. And I also express my gratitude for the assistance from relevant departments in Kouchi and the other Prefecture Governments, as well as those involved with the Fushino conference. Full responsibility for the text (with any errors) rests entirely with the author. REFERENCES Aoki, M. 2003. “Mizushigen kankyô zei” no riron to genjitsu. Zei (Theory and reality of “tax for water resource environment.” Tax) 58(9):40–50. Banba, T. 2004. “Shinrin kankyô zei” to suigen chiiki no hozen. Jichi Kenkyû (“Forest environment tax” and protection of headwater areas. Studies on Autonomy) 80(6):73–88. Dawes, R.1975. Formal models of dilemmas in social decision making. In M.F. Kaplan and S. Schwartz (eds), Human Judgement and Decision Processes:87–107. New York: Academic Press Inc. Fushino Project (The official website of Fushino, http://www. fushino.jp/money/fushino.html, accessed 2008/5/19). Gulbrandsen, L.H. 2005. Mark of sustainability? challenges for fishery and forestry eco-labeling. Environment 47(5):8–23. Ishizaki, R. 2004. Shinrinseisaku no zaisei shishutsu. in Sakai M (ed.), Shinrin seisaku gaku (Fiscal expenditure on forest management policy. In Sakai M (ed.), Science on Forest Management Policy):223–237. Tokyo: Nihon Ringyô Chôsa Kai. Izumi, R. 2006. The development and future challenges of the community-based currency in Japan. Economic Bulletin of Senshû University 40(3):97–133 (in Japanese). Kawai, M. & Shimazaki, A. 2003. Japan’s community currency systems. Journal of Social Science 54(1):145–169 (in Japanese).

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Kouchi Prefecture Government. 2002. Shinrin kankyô hozen no tame no shin zeisei (shinrinkankyôzei) no kangaekata (On NewTax Scheme for Protection of Forest Environment, Forest Environment Tax). Nihon Gakujutsu Kaigi 2001. Chikyû Kankyô, Ningen Seikatsu ni Kakawaru Nôgyô oyobi Shinrin no Tamenteki na Hyôka ni tsuite, (Science Council of Japan. On Evaluation of Multi-dimensional Functions of Agriculture and Forest That Relate to Global Environment and Human Life). Nishibe, T. 2002. Chiiki Tsûka wo Shirou, (On Community Currency). Tokyo: Iwanami Shoten. Olson, M.1965. The Logic of Collective Action, Public Goods and theTheory of Groups. Cambridge: Harvard University Press. Rametsteiner, E. & Simula, M. 2003. Forest certification – an instrument to promote sustainable forest management? Journal of Environmental Management 67:87–98.

Schreiber, E.S.G.et al. 2004. Adaptive management: a synthesis of current understanding and effective application. Ecological Management and Restoration 5(3):177–182. Seyfang, G and Pearson, R. 2000. Time for change: international experience in community currencies. Development 43(4): 56–60. Tadaki, Y. 1990. Mori to ningen no bunkashi (Culutural History of Forest and Man). Tokyo: Nihon hôsô kyoku shuppan kyôkai. Usui, M. 2001. Hôteigaizei wo meguru shomondai. Jichi Kenkyû (Problems on Tax not Stipulated in Local Tax Law. Studies on Autonomy) 77(1):17–32. Yamaguchi Prefecture Government. 2003. Yamaguchino Yutakana Ryûiki Zukuri Kôsô, Fushinogawa Moderu, (A Plan Toward Sound River Basins in Yamaguchi, The Fushino Model).

Appendix: Headwater fostering taxes in Japan: February 2008. (Expected) Revenue/year Prefecture

Fiscal Year introduced

Iwate Akita Yamagata Fukushima Tochigi Kanagawa Shizuoka Nagano Ishikawa Toyama Shiga Hyogo Nara Wakayama Okayama Hiroshima Tottori Shimane Yamaguchi Ehime Kouchi Fukuoka Saga Nagasaki Oita Kumamoto Miyazaki Kagoshima

2006 2008 2007 2006 2008 2007 2006 2008 2007 2007 2006 2006 2006 2007 2004 2007 2005 2005 2005 2005 2003 2008 2008 2007 2006 2005 2006 2005

Tax per capita/year Individual

Private corporation∗

Conversion into Japanese Yen

U.S.Dollar (1$ = ¥110)

¥1, 000 ¥800 ¥1, 000 ¥1, 000 ¥700 ¥950∗∗ ¥400 ¥500 ¥500 ¥500 ¥800 ¥800 ¥500 ¥500 ¥500 ¥500 ¥300 ¥500 ¥500 ¥500 ¥500 ¥500 ¥500 ¥500 ¥500 ¥500 ¥500 ¥500

fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital 0 fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital ¥500 fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital fixed amount of capital

¥710, 000, 000 ¥480, 000, 000 ¥600, 000, 000 ¥1, 000, 000, 000 ¥800, 000, 000 ¥3, 800, 000, 000 ¥840, 000, 000 ¥680, 000, 000 ¥380, 000, 000 ¥330, 000, 000 ¥600, 000, 000 ¥2, 100, 000, 000 ¥300, 000, 000 ¥260, 000, 000 ¥480, 000, 000 ¥810, 000, 000 ¥100, 000, 000 ¥200, 000, 000 ¥400, 000, 000 ¥320, 000, 000 ¥160, 000, 000 ¥1, 300, 000, 000 ¥230, 000, 000 ¥320, 000, 000 ¥290, 000, 000 ¥420, 000, 000 ¥280, 000, 000 ¥410, 000, 000

US$6,454,545 US$4,363,636 US$5,454,545 US$9,090,909 US$7,272,727 US$34,545,455 US$7,636,364 US$6,181,818 US$3,454,545 US$3,000,000 US$5,454,545 US$19,090,909 US$2,727,273 US$2,363,636 US$4,363,636 US$7,363,636 US$909,091 US$1,818,182 US$3,636,364 US$2,909,091 US$1,454,545 US$11,818,182 US$2,090,909 US$2,909,091 US$2,636,364 US$3,818,182 US$2,545,455 US$3,727,273

∗

Although most prefectures introduce a fixed tax rate for corporations, the rate itself differed among prefectures. In the case of Kanagawa Prefecture, the tax was imposed on the income base. This number shows an average rate. (The data mostly obtained from official websites of prefecture governments.) ∗∗

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

The change of water balance including municipal and irrigation waters – A case study of the Koise River basin, Ibaraki Prefecture, Japan M. Motoki∗ Department of Geo-environmental Science, Rissho University, Kumagaya City, Saitama, Japan

ABSTRACT: In this study, water balance including natural, municipal and irrigation waters has been analyzed quantitatively in the Koise River Basin, Ibaraki Prefecture. Amount of artificially supplied and drained waters in each grid on map was estimated by water balance method. The amount of municipal water supply increased from 1.8 × 106 m3 /y in 1965 to 15.6 × 106 m3 /y in 1995. On the other hand, sewerage system had not been constructed until 1990. Therefore, the great portion of discharge water was untreated. The amount of polluted water into the sewerage was 10.3 × 106 m3 /y in 1995. Since the sewerage has been developed year after year, untreated discharge water has reduced accordingly, and the amount became 5.4 × 106 m3 /y in 1995. The amount of irrigation water supply tends to be influenced by precipitation. Water consumed and drained in the paddies is proportional to irrigation water supply. Comparing the artificial water supply to the natural water in the basin, the artificial water does not exceed the natural water in most of the river basin. The rate of untreated discharge water to natural runoff was examined for each grid. As a whole, the influence of artificial discharge is small at present, but it influences on water balance in some parts of the river basin. Artificial water supply, including the municipal and irrigation waters, in each grid has been increasing every year. Artificial water supply is an important factor in the water balance of this basin. Keywords: instruction; municipal water; irrigation water; water balance; historical change; Koise River Basin

1

INTRODUCTION

This study discussed the flow and balance of waters based on the idea of LCA (Life Cycle Assessment), which has become an important method in environmental protection policy. Water is a vital substance for human life, and artificial water transport makes up for the shortage in natural waters in many regions. Therefore, the environmental meanings of the anthropogenic water transport should be examined. Two main elements in the flow of artificial waters are water supply and drainage. The study area is a small river basin of the Koise River, Ibaraki Prefecture, central Japan. The study area includes four municipalities of Ishioka City, Yasato Town, Chiyoda Town, and Niihari Village (2004). The total area of the basin is 496.7 km2 where 42.6% is forest on the low mountain, 21.2% is farm land mainly paddies, 12.8% is residential and urbanized ∗

Corresponding author ([email protected])

land use. The highest peak (Mt. Tsukuba) is 877 m above sea level. The population of the river basin is about 115,000 and about 60% of it is concentrated in the lower half of the basin. Residents and settlements are fairly scattered except for the center of Ishioka City (Fig. 1). As the area located in the warm humid climate of summer monsoon, rainfall is relatively much in warm seasons of a year. Main product of the basin is rice, which needs much water in summer. To meet the water deficiency in summer, irrigation water and municipal water have been supplied from the other drainage area since 1960s, mainly from Lake Kasu-migaure. The total amount of influx waters into the basin in 1995 was 1442 mm which was composed of 1,390 mm of rainfall, 52 mm of transported irrigation water and municipal water. Irrigation water mainly for paddies and its drainage have great influence on the water circulation in the rural area. As municipal water supply 2 mm is added. Efflux is 743 mm of evapotranspiration and natural runoff of 708 mm.

137

Population in a grid was also estimated from statistics and land use map. By multiplying the unit volume and area of farm land or population in a grid, the maps were completed for each stage. 3 3.1

MUNICIPAL WATER Change of municipal water

The amount of municipal water supply increased since 1960s. Per capita use of municipal water and treated discharge volume in litter/day for each stage are summarized in Table 1. The total amount of municipal water supply in the Koise River Basin increased every year, namely, from 1.8 × 106 m3 /y in 1965, through 7.1 × 106 m3 /y in 1980, and to 15.6 × 106 m3 /y in 1995. Fig. 2 shows the amount of municipal water supply (m3/y) for each grid from 1965 to 1995. By analyzing grid maps, spreads of municipal water service and changes in regional distribution of its volume have become clear. The supply is much in urban area of Ishioka City, although it spreads into the rural area except for mountains in recent years. The reason of this tendency is the change of life style and use of household appliances in rural regions. Figure 1. Study area, A: Ishioka city B: Yasato town, C: Chiyoda town D: Niihari village.

2

METHOD

The study area is divided by a grid of 500 m by 500 m. Water balance including natural, municipal and irrigation waters in the basin was analyzed using a simple raster type GIS for each grid and compiled in maps. Water balance for each grid was calculated, including climatic water balance and anthropogenic water transport. The analysis was made for three stages of 1965, 1970 and 1995 to explain the historical trends in water use and water balance. The results are finally combined in maps for each stage. Basic data on irrigation water was taken from the report of land and water improvement wards, records on municipal water supply was taken from the annual report of the city and town, record on waste water treatment was from the report of sewerage treatment office. For irrigation water, consumption for unit land area was estimated from the report. For municipal waters, per capita consumption was estimate. These unit volumes are used as the basis of the calculation. The total area of farm land (paddies) in a grid was estimated from statistics and land use maps.

3.2 Change of sewerage discharge and the untreated discharge The sewerage system in this basin had not been operated until 1990s. Therefore, a great portion of the drainage domestic water was untreated (Table 1). Since the municipal water supply had begun in 1965, it is clear that a great amount of water has drained without purification in a period from 1965 to 1990s. Even in 1995, some areas were remanded untreated in the basin. The amount of polluted water into the sewerage system was 10.3 × 106 m3 /y in 1995 (Fig. 3). The quantity reached 72% of the total drainage. In this region, simple water purification system for each house has been widely used. The total amount of treated domestic discharge by this system has been estimated at 3.6 × 106 m3 /y. Fig. 4 shows distribution of untreated drainage basin on unit grid. Since the sewerage system has been expanded every year, untreated discharge water has reduced accordingly, and the amount became 5.4 × 106 m3 /y in 1995. It is clear from the figure that pollution is recovered in the urbanized area of Ishioka City, although the untreated areas are found on the margin of the city and rural areas. It is also said that untreated drainage is spreading into the upper reaches of the basin according to the widening of the municipal water service. This will become a new problem in river pollution.

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Table 1.

Per capita municipal water use. 1965Y (× 10 3 m 3 )

1980Y (× 10 3 m 3 )

1995Y (× 10 3 m 3 )

municipal water supply

treated discharge water

untreated discharge water

municipal water supply

treated discharge water

untreated discharge water

municipal water supply

treated discharge water

untreated discharge water

Ishioka city

67

0

67

135

0

135

172

Yasato town Chiyoda town Niihari village

0 0

0 0

0 0

38 41

0 0

38 41

72 115

0

0

0

45

0

45

103

1721) 1722) 693) 293) 1151) 463) 1031) 413)

0 0 103 43 0 69 0 62

Figure 2. Change of municipal water supply in the Koise River basin.

4

IRRIGATION WATER

The natural runoff from valleys and groundwater had been used for irrigation before 1987 by the traditional way. In 1988 Water-for-Irrigation Enterprise of Kasumigaura was established to keep irrigation water during summer cultivation season when the natural water balance is in slight shortage. The source of water in this project is Lake Kasumigaura. Net water consumption in paddies is evapotranspiration, although, it has been a common practice in paddies to intake much water than the net consumption. The rest of the intake water drains from the paddy through small gate and seepage into the soil. Thus the excess water is integrated to a considerable amount in the water course and some of it is used several times. In this way, irrigation water has an important role in the water circulation of the agricultural region.

Elements of water balance under natural condition are precipitation as influx and evapotranspiration, infiltration and runoff as efflux. Main factors affecting water surplus and deficits are precipitation and evaporation, where, variation of precipitation is the dominant one governing the amount of water in the region. Therefore, the volume of irrigation water supply is influenced by the precipitation of each year. It is much in dry year and less in rainy year. The inflow amount of irrigation water for each grid was arranged for each land improvement district basing on statistics. Because actual water management is performed by each farmer, detailed amount is not recorded in the documents. In the report, the consumption of irrigation water in a grid is assumed to be equivalent to the supply as a rough estimate. As the source region of this basin is not wide, introduction of irrigation water has stabilized the water use

139

in paddies. The original unit applied to the irrigation water was calculated by the averages from 1989 to 1994 and from 1995 to 2000. Table 2 expresses the unit water use in paddies in 0.25 km2 .

Introduced of irrigation water increased 1.9 times in the area of Ishioka Plateau Irrigation Water Land Improvement Ward, and 1.4 times in the area of Kasumigaura irrigation water land improvement Ward during 1990s (Fig. 5). Areas of paddies where irrigation water is not supplied are mapped and excluded from the calculation. As shown in Fig. 5, introduced irrigation water shows a substantial increase in the upstream section of the basin and also in the area of the Kasumigaura Irrigation Water Land Improvement Ward .The amount of irrigation water is proportional to the surface area of paddies and it is used repeatedly from the upstream to the downstream, therefore, flux of water is much in the down-stream region.

5

COMBINATION OF MUNICIPAL AND IRRIGATION WATERS

5.1 The change of quantity of artificial inflow water and its influence on water balance

Figure 3. Amount of sewerage discharge in the Koise River Basin.

Balance or ration between the amount of natural water mass may be a basic criterion to evaluate the water environment. Studies made in big cities as Tokyo have revealed that the amount of transported water for exceeds the natural water supply such as small rivers and rainfall (Hosono, 1978; Arai, 1993). Therefore, unsound river condition and water circulation have been duvinant in big cities. On the other hand in the Koise River basin transported waters do not exceed the natural water yield of

Figure 4. Change of estimated amount of untreated discharge in the Koise River Basin.

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the basin. Annual precipitation was 1,390 mm in 1995 as the source of water, and it provided 350 × 103 m3 /y of water for each grid as on average of the basin. This volume exceeds the artificial inflow in all grids. In the case of the Koise River Basin, including wide rural areas, the total volume of municipal and irrigation waters do not exceed the natural water surplus in most grids. However, the transported waters amounted to 50–80% of natural water in some grids in the urban area of Ishioka City. herefore, it is said that the natural condition is dominated in the water circulation in this basin, although some parts of this basin has been urbanized in recent years. The characteristics of the water balance are divided where into two. The first is located the up-stream areas strongly influenced by the irrigation water. The second is the city area of Ishioka City where urban area is dominated.

6

CONCLUSION

5.2 The change of quantity of discharge water and its influence

In this study, water balance including municipal and irrigation waters is analyzed in the Koise River. Basin, Ibaraki Prefecture, Japan. Amount of artificially supplied and drained waters in each grid was estimated by water balance method. Artificial water supply, including the municipal and irrigation water, in each grid has been increasing every year in the Koise River Basin. It can be said that the artificial water is an important factor for domestic life and agriculture in the Koise River Basin, today. (1) Artificial water increased in recent years, and the water balance of the basin has changed. (2) It became possible to detect the change of the amount of waters for every area by simple GIS using grid maps. (3) Historical viewpoint is necessary in the analysis of the present condition of the basin. (4) Anthropogenic transport of water cannot be ignored in the water balance of the present days. This is the important point of the environmental evaluation of the basin. And for the better understanding of the present, the historical

Sewerage system begun in 1995 transfers the polluted water to outside of this basin by aqueducts, therefore, rivers drains natural water of this basin residue of irrigation water and untreated domestic discharge. It is important to trace the historical changes in water load or rivers. For this purpose, natural runoff (708 mm in 1995) and the artificial drained waters were compared (Fig. 6b). As sewarge transports 15.9 × 106 m3 /y of polluted water to outside of the basin,this amount is not taken in figures. Comparing the artificial water supply to the natural runoff amount, the artificial water and runoff, irrigation water does not exceed the effective precipitation in most of the river basin. The rate of untreated discharge water to natural runoff was examined for each grid. As the whole, the influence of artificial discharge is small at present, but it is considered that the discharge water influences on water balance in some parts of the river basin.

Figure 5. Change of the irrigation water supply in the Koise River basin.

Table 2. Per capita irrigation water. 1988 ∼ 1994Y average (× 103 m3 )

Ishioka plateau irrigation water Land improvement Kasumigaura irrigation water Land improvement

1995 ∼ 2000Y average (× 103 m3 )

drained into waters

net water requirements

drainage irrigation waters

22

12

10

84

45

39

141

net water requirements

drainage irrigation waters

41

22

19

120

65

55

drained into waters

changes in waters will give fundamental information on it. REFERENCES Arai, T. 1993. Change of hydrological environment and water balance in Tokyo. Journal of municipal problems. 45(8): 59–69. Hosono, Y. 1978. Unconfirmed groundwater in Musashino Plateau. Ichikawa, M. & Kayane, I (eds), Kokon, 174–188.

Figure 6a. Estimated amount of municipal water supply and irrigation water supply.

Figure 6b. Estimated amount of discharge including un-treated discharge and irrigation drainage.

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4

Environmental education for sustainable development: The role of mountain and headwater landscapes (IAHC topics The 7th International Conference on Headwater Control) Better quality of headwaters starts with environmental education. Conflicts in headwater environments address all the issues highlighted by “United Nations Decade of Education for Sustainable Development” (2005–2014): water, climate change, biodiversity, and disaster prevention. Higher education prepares the future decision-makers. Therefore, the more effective system on higher education is a key to better knowledge of the society. Better communication between researchers, students, communities and policy makers can increase the quality of headwater environment. The interdisciplinary perspectives in the watershed management should be progressed by more effective cooperation between social science, Earth sciences and engineering sciences. Conveners: Martin J. Haigh (Oxford Brookes University, Oxford, UK) Andrej Hocevar (University of Ljubljana, Slovenia) Marie Studer (Earthwatch Organization, Boston, MA – USA)

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Teaching sustainable headwater land management through problem based field study M. Haigh∗ Oxford Brookes University, Oxford, UK

ABSTRACT: Increasingly, environmental agencies employ graduates with a broad-based educational training that allows relatively little time for practical fieldwork, so it is important to maximize the benefits from very limited fieldwork opportunities. This study evaluates a short, intensive, Problem-Based Learning exercise that aims to help learners connect textbook theory with field realities. It tackles the management of soils and drainage on reclaimed coal-lands in the headwaters of South Wales through a self-paced field trail that leads learners to examine land degradation problems, first in detail, then more holistically in terms of landscape sustainability. Findings highlight the problems that the learners encounter, which include difficulty understanding the motivations and intentions of either the land designers or land users and of evaluating hydrological processes in conditions other than those current during their visit. Despite this, learners value this first-hand experience and gain a realisation that real-world answers are not always simple, that prior learning is essential to effective field interpretation, that teamwork is a problem-solving tool, and that developing these skills requires ‘practice’. Learners who score well in fieldwork also score well in class quizzes, spoken presentations, essays, other field projects and formal examinations. Keywords: Problem Based Learning (PBL); fieldwork; soil conservation; drainage engineering; reclaimed land; surface coal mining; sustainability; headwater control

1

INTRODUCTION

Building skills in education for sustainable development in the context of watershed management is made difficult by the failure of most curricula to devote adequate time for field instruction and the problem of helping learners apply technical knowledge from textbooks to complex field situations. Learners need to be able to apply the ‘microscope’ of technical expertise and, simultaneously, see the bigger picture, understand the landscape context, by applying the ‘telescope’ of holistic vision. This study explores the value of an intensive, single day, problem based-learning exercise that attempts to build such skills in an advanced undergraduate “Environmental Management” course at Oxford Brookes University, UK, which enrolls 30–45 learners/year. It involves a self-directed field trail, a programme developed over 20 years field observation on the Blaenant and Cwm Llamarch Land Reclamation projects, Brynmawr, South Wales (Haigh, 1996; Higgitt, 1996). This paper evaluates the experience of learners undertaking this work using ∗

Corresponding author ([email protected])

three types of evidence. These are learner responses: to course evaluation questionnaires, as learner performance in a post experience class test (compared with results from other kinds of assessment); and as expressed in series of 7 videos and one Virtual Field Trail, produced in support of the exercise in different years by student teams.

2

BACKGROUND

The Blaenant Field Trail is an exercise in ProblemBased Learning (PBL) (Pawson et al. 2008). It aims to help learners translate their theoretical knowledge into practical knowledge by solving problems in the field, especially the recognition and diagnosis of land degradation and the implementation of sustainable solutions. The exercise is prefaced by three weeks, 9 hours, of classroom preparation, which includes the showing of two videos: first ‘A Stake in the Soil ’ which was produced by Shell in 1990 and second “Erosion Control: Best Management Practices”, which was produced the International Erosion Control Association in 1994

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(Haigh, 2000). The first preparatory sessions emphasize land degradation processes and technical erosion control solutions but, as the preparations progress, emphasis is shifted to the needs of the land user, to solutions based on land management and the ‘Better Land Husbandry’ approach (Hellin, 2006). The field exercise takes place after a week allocated to preparatory reading. Its subject is an area of reclaimed coal-land that is typical of the Heads of the Valleys region on the northern edge of the South Wales Coalfield. The first target area was created after 1978 by the reclamation of the Blaenant Opencast Coal Mine, which was cut into the upper convexity of the Clydach Gorge, a steep and exposed site. The initial reclamation was only partially successful and its end result was a grass/moss pasture developed on a thin layer of applied topsoil above a highly compacted and largely impermeable layer of opencast spoil (Kilmartin, 2000; Haigh and Sansom, 1999). After an outbreak of gully erosion, in 1982–3 the site was reworked, reseeded in parts, forested in parts and new channel management structures installed. The whole site was managed until 1988, and then returned to pasture, mainly for sheep. Of course, the problems of this reclamation make it an ideal location for the evaluation of land degradation and erosion processes along with the measures taken to control them. The first part of this field exercise consists of a 2 km field trail that leads through a series of sites where questions encourage learners to examine, critically and in detail, the symptoms of environmental change manifest in the soil, water courses, vegetation, engineering structures and land use. The trail examines seepage scars, the evolution of artificial watercourses, where hydrologically flashy channels are affected by sedimentation and bank retreat (Kilmartin, 1994), drainage structures (including contour drains, French drains, drop structures and concrete channels), and problems downstream of the site where hydrological control is passed to different land managers. The trail also examines land management issues connected to forestry and grazing, including overgrazing of the areas managed as Common Land, and problems caused by trafficking and the formation of erosive wheel rut channels. Finally, it considers safety, aesthetics, and the interactions between the site’s qualities and the socioeconomic activities of local land-users. A second part of the trail targets a different but adjacent land reclamation project. This is a 1989–1990 reclamation of deep mine spoils in adjacent Cwm Llamarch. This area suffers similar, but not identical, problems of land degradation. Once again, there are problems due to compacted subsoils and waterlogging of the applied topsoil layer but here these are exacerbated by the seepage of acid water from natural soil pipes forming in the subsoil, which is creating large devegetated surface washes. There is also much

more intense grazing on some relatively steep slopes and a different, arguably more aesthetically sensitive, approach to the creation of a central concrete channel, which has suffered major breakdown and wear since its creation. So, the site is similar but it has some different qualities. This second part of the trail challenges the learners to provide their own assessment of the problems and to suggest sustainable management solutions by transferring learning from both their classroom/textbook and their guided experience on the Blaenant site in the first part of the trail. The aim is for learners to recognize the problems exhibited in Cwm Llamarch and assemble these into a holistic diagnosis for the site. Inevitably, this raises the intellectual stakes for the learners, who find this part of the trail far more difficult and lose twice as many marks as in the first part of the trail. 3

FINDINGS

In 20 years, more than 600 learners have tackled this exercise. However, assessment records remain from 1999 while detailed PBL questionnaire and class quiz responses have been collected since 2005 only. Learner responses to the PBL experience questionnaires concern their recognition of what they thought the exercise was about, what they learnt, what was good/bad about the experience and the approach and whether they would like to do more work of this kind? In 54 questionnaires retrieved (2005–2007), 32 respondents wanted to do more, 2 did not. Thirty-one recognized that the theme was sustainable land management, but 27 mentioned technical soil and erosion control and 20 land reclamation. Only two mentioned the needs of land users and just 10 discussed the need to consider sustainability and propose land management solutions. However, 43 thought the exercise improved their understanding, 38 their ability to apply a solution, while 11 thought they had built skills in analysis and 9 in team-working. The main benefits recognized were gaining firsthand experience (31), connecting theory and practice (19) and thinking holistically about a problem and its solution (15). The main difficulties experienced arose from an absence of prior knowledge (22), the observation that there were rarely simple answers (21), and bad weather (14), although the first two encouraged learners to discuss and work in teams. Analysis found significant positive correlations between ‘learning team-working’ and recognizing the ‘need for prior knowledge’ as well as links between worries about the weather and the ability to find solutions (Table 1). There was also a negative correlation between bad weather and recognition that there may be ‘no simple answers’, which suggest that learners thought harder about solutions in better weather.

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Table 1.

Learner experience: correlation analysis.

Spearman’s rho Sample n = 54 Better understanding Better ability to apply solution Main problem: Need prior learning Main problem: no simple answers

Improved understanding

Improved ability to apply solutions

Learnt teamwork

Learnt land management methods

Main problem: bad weather

Like to do more

0.327 (p = 0.016)

0.409 (p = 0.002) 0.287 (p = 0.035)

0.617 (p < 0.0005) 0.617 (p < 0.0005) 0.415 (p = 0.02)

Positive correlations linked learners who wanted more field work with the belief that it improved their understanding and ability to find solutions. Naturally, examination of mark sheets and the work submitted by learners offer a different perspective. Marks were lost for many reasons but several result from the failure to understand that a reclaimed opencast landscape is entirely artificial. It was designed and so represents the physical manifestation of its designer’s thought processes. In general, learners failed to consider the thought processes that guided the landscape designer. Equally, few tried to understand the landscape from the perspective of its land users and hence found inexplicable some of their treatment of the land and their neglect of some of its erosion control precautions. Relatively few considered how a designed landscape might interact with natural processes; indeed, it proved difficult for many to focus on the ways in which a recently engineered artificial landscape might differ from one created by natural processes through a long time-span. However, even on the most basic level of environmental management, few recognized the reason why the site margins, where land management passes to new controllers and where imbalances in hydrological or sediment regime are most likely to be expressed, were important places for investigation. This was despite this issue being emphasized in the formal classroom teaching and the fact that the trail took them to sites where boundary issues were a particular focus. Of course, the one thing a short field experience cannot provide is the experience needed for a long term perspective on environmental change. In theory, the series of open-access student trails videos and the VFT, which collectively chart changes over 20 years, provide such perspective but for most learners, if the channel was not flowing vigorously on the day of their visit, they could not imagine it in flood spate, and if a part of the land was not grassed, then grass had never grown at that point, even when ample field evidence to the contrary surrounded them. For most, assessments and recommendations were founded purely on the site’s

−0.326 (p = 0.016)

−0.366 (p = 0.006)

appearance on the day of the visit and it may be wondered to what extent this problem affects the judgments of inexperienced young professionals after graduation. Curiously, despite their general education, some learners also displayed the purblind-ness of the specialist. Those with a special affinity for engineering structures, soils or natural ecology tended to dismiss the welfare of the land uses or agricultural practices, while those who espoused a ‘Geographer’s’ interest in landscape considered the aesthetics and land use but overlooked the functionality of the engineering. One common worry from the learners was that they had failed to identify particular situations correctly or that they had produced an answer that was incorrect. Despite the number of Geographers on the trail, many found orienting themselves and working with the simple trail map very hard. Several comment from their group-work that they found it difficult to cope with the variety of views and diagnoses put forward by their peers. So, there was a wish for feedback and although this was available in the form of a VFT (Virtual Field Trail), an array of trail videos (some of which could be reviewed on the coach on the way home) and an in-class review of the trail, several wanted their feedback to be immediate. Finally, some mainly lower performing learners found it difficult to connect memorized textbook learning with what reality in the field. Interestingly, a small number argued that this expectation was unreasonable because the normal task of passing a university course concerned answering a written examination not the analysis of real world situations. Of course, this PBL field study favored those adopting deep learning strategies, involving comprehension and the understanding of principles, ahead of those surface learners who relied on simple memorization (Ramsden, 1982). Analysis of examination papers finds further patterns of errors.The first is error by association, seepage scars, viewed from a distance are often confused with landslide scars or sheep ‘burrows’ – it takes close inspection to confirm that they are created by wash, not by slippage and not by animals. However, more than

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Table 2. Correlations between fieldwork marks and other types of coursework (2005–2007). Spearman’s rho Sample n = 134 Fieldwork Fieldwork

Theory Quiz

Essay

Team Project

0.279 0.270 0.207 (p = 0.003) (p = 0.004) (p = 0.021)

Theory Quiz

0.279 (p = 0.003)

Essay

0.270 0.194 (p = 0.004) (p = 0.012)

Team Project

0.207 0.048 (p = 0.021)

0.194 0.048 (p = 0.012) 0.273 (p = 0.001) 0.273 (p = 0.001)

half the learners were content to admire the feature from a distance and hence the error was made. Lack of preparation was a common problem. Learners who failed to describe the components of a drop structures also failed to recognize other structures because, simply, they had not done their reading. Curiously, more than half of those who correctly identified a contour bund in the field, failed to identify the same feature in an examination photograph, possibly an outcome of team-working in the field. One suspects that someone else called out the answer for the trail report sheet and they simply wrote it down. A significant negative correlation linked the examination marks and marks for identification of the contour drain in the field (rho = −0.431, p = 0.045). This opens questions concerning what are the special qualities of this PBL Fieldwork exercise (Table 2). Analysis of the mark sheets that contain the fieldwork assessments show that they correlate most strongly with marks for the class quizzes, which require immediate knowledge and the essay, which requires reasoning and argument, but least well with the Team project, which involves self-selected project design and problem solving in the laboratory (Haigh and Kilmartin, 1987). In its earlier years, the field trail exercise was an optional part of a more specialized course on Soil Conservation. Spearman correlations between marks for the field trail and other coursework components on the mark sheets for the 301 students enrolled between 1999–2003, show that the field trail marks correlate most closely with those of students undertaking assessment through a spoken presentation (rho:0.611, p < 0.0005, n = 64), results from a formal 2 hour essay-based examination (rho:0.261, p = 0.015, n = 69), and the class quizzes (rho:0.197, p = 0.025, n = 99). There was no correlation between the field trail score and that from, in this case, the

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mainly laboratory based team project. There was no correlation between marks won for the VFT virtual version of the Blaenant Trail and any other assessment style except the class quizzes (rho: 0.0373, p = 0.001, n = 67). Indeed, if you hold the memory-related Quiz scores constant and recalculate the partial correlation between fieldwork and examinations the outcome is very far from significant (r: 0.12, p = 0.35), which confirms that these techniques are assessing different aptitudes (Haigh, 2007). 4

DISCUSSION

PBL (Problem Based Learning) is a common strategy in professional education (Fenwick, 2002). Active learning is thought to be effective way of developing the understanding and problem-solving skills needed by environmental managers (McLaughlan, 2007). Being able to ‘think outside the box’ and understand real problems from the perspective of both the microscope and telescope remain key skills (Teichler, 1998). In support of the UN Decade of Education for Sustainable Development, Fien (2006) stresses holistic vision and the importance of prioritizing the recognition of uncertainty and the necessity for precautionary action. All of these aptitudes are engaged by PBL field exercises and many others that seek to develop the use ‘an expanded classroom’ for experiential education (McEwen et al. 2001; Katula and Threnhauser, 1999). Whether it is better to build this broad vision in each learner or engage multidisciplinary teamwork to develop sustainability understanding in environmental professionals, this remains an open question (Meehan and Thomas, 2006). Of course, the Blaenant results show that individual work encourages voluntary teamworking and knowledge pooling. However, from an educational point of view, there remains a need to find out more about what affects useful learning and what role learners play in this process (cf. Rickinson, 2001). This study shows that different modes of assessment test different learning attributes and suggest that these modes may be used to encourage the development of engaged learners who seek to increase marks (Haigh, 2007). Learners’ responses to this Trail stress that there is a limit to how much can be achieved by a single field experience and that the skills of field interpretation must be reinforced by practice. However, the questionnaire returns suggest that the exercise caused learners to become more aware of what they needed to do to enhance their performance, namely: better preparation, more teamwork and more focus. The study has also highlighted some distinctive qualities of PBL. One inherent weakness, but also strength, is that the solutions that are produced are not always the best and may be seriously misjudged. In

fact, it might be argued that in this type of learning, the solution offered is less important than the processes the learner has to go through in order to reach their solution and that, if their analysis were in error and solution less than optimal, that they learn from their mistakes. However, learners faced with an isolated individual exercise of this type, commonly seem reluctant to carry the exercise to its final conclusion and to learn from their errors. There is even the risk that the learners may fail to realize that they have made an error and the exercise might then embed incorrect understandings. For such reasons, several proponents advocate that PBL should be built into a curriculum at several levels and developed through iteration (Berringer, 2007; Shepherd and Cosgriff, 1998, Pawson et al. 2008). On the other hand, the technique does tend to create an understanding, important for environmental management, that there may be a range of answers to any one issue and that there may be no single perfect solution. In other words, the PBL field experience runs much closer to reality than its VFT equivalent, where answers are provided on demand as immediate feedback that is usually accepted without questioning or deep reflection about alternatives. For many years, this exercise was assessed by means of the trail booklet, which students completed in the field. However, ultimately, it became apparent that some learners were circumventing real field learning by means of shadowing other students and copying down, more or less, what they said in discussion of each site, or better still, if they could manage this, by finding a member of staff and pumping them for the answers. The educational problem was that, for them, the trail booklet had become the goal, not the field interpretation. This problem was solved when the assessment was changed from the field booklet to a class quiz based on the field trail, especially in the case where photographs were used instead of written questions. This meant that the learners had to shift their attention to the field and, those keen to win good grades, to check up their field intuitions by reference to the VFT and video resources created for the exercise. One interesting side effect of this PBL activity was that it encouraged learners to work in teams, an activity that they usually try to avoid. This engagement was linked to their recognition of the fact that, for solving a problem in the field, some prior knowledge is essential. Several had done little preparation and then found themselves to be unable to bluff their way through this PBL scenario. Their solution was to attach themselves to one or more other learners – preferably those that seemed properly, or at least better, prepared. However, several realized that uninformed reliance on the knowledge of their peers was a risky strategy and that not all of those who appeared knowledgeable were truly well informed. Nevertheless, this borrowing process did mean that some student-to-student learning took place,

while many learners resolved not to place themselves in this situation in future. This also created another issue for instructors, which was to resolve inquiries from students seeking ‘to learn how to learn’ in the field (Pawson et al. 2008). The fact remains that a number of, especially the less committed, learners are ambivalent about PBL. Especially where it is an unfamiliar learning strategy, the approach requires a commitment to learning. Learners find it easier to copy notes from a lecture or read a text than to work out a problem in the field. So, while many students recognize the potential of PBL others are unwilling to pay the cost of this commitment and the associated necessity to do more thinking and take more responsibility for their own learning (Pawson et al. 2008). From the teachers point of view, another problem of such PBL exercises is that they require a new style of input with more time and energy invested outside the classroom for preparation and facilitation. This, in turn, requires support from managers to enable this kind of shift from teaching to learning as well as all the associated increases in administration and legal risk that fieldwork involves (Pawson et al., 2008). This can be hard to obtain in modern educational institutions where emphasis has shifted more toward financial efficiency than educational enhancement. 5

CONCLUSION

Increasingly, graduates with broad-based educational qualifications in environmental management or one of the environmental sciences are finding their way into Government planning agencies and commercial environmental consultancies. These new graduates commonly arrive with relatively little real-world experience or training in problem solving skills. This suggests that building such experience should be a major goal for Higher Education. However, instead, the practical fieldwork components of courses are being reduced. This can lead to serious problems when graduates enter employment, have to make decisions relating to environmental security or sustainable development, but remain unaware of the challenges and pitfalls of such policies. To counter this, it is important to use those limited fieldwork opportunities that remain in the curriculum productively and to use them to engage learners in the kind of problem-solving work they will encounter as young professionals. Hence, this paper describes an attempt to provide a short, intensive, Problem Based Learning, field study experience to help learners engage classroom theory in solving the real world issues of sustainable development and land management. The exercise is set in the context of the management of soils and drainage on the fragile reclaimed coal-lands of the South Wales

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headwaters. It employs a self-paced field trail that first encourages learners to adopt a methodology that involves the detailed examination of field evidence for land degradation processes and then encourages them to use this methodology to diagnose land management issues and propose sustainable land management solutions. Information provided by these learners in the form of questionnaire returns and class quiz results highlight the problems that they encounter in their attempt to translate academic learning into field practice. These include an inability to comprehend the character or purposes of the physical, social, engineering and economic processes that affect the land they would assess and insecurity about their ability to find answers. Despite this, most participants valued this kind of first-hand experience and the appreciation that real world problems can be complex and that teamwork is useful method of pooling environmental expertise. Less committed learners, especially, developed the understanding that prior knowledge is necessary prerequisite to effective field interpretation. They also recognized that ‘practice’ is necessary to build skills in this kind of sustainability diagnosis. Analysis of marksheets showed that that learners who scored well in fieldwork also scored well in assessment that required both immediate knowledge recall like quizzes and sustained deductive reasoning such as critical essays. REFERENCES Berringer, J. (2007) Application of Problem Based Learning through research investigation. Journal of Geography in Higher Education 31, 3: 445–458. Fenwick, T. 2002. Problem-based learning, group process and the mid-career professional: implications for graduate education. Higher Education Research and Development 21, 1: 5–21. http://www.ualberta.ca/∼tfenwick/publications/publications.htm (accessed May, 2008). Fien, J. 2006. Reorienting education for a sustainable future. In: Teaching and Learning for A Sustainable Future. Paris, UNESCO, Module 4: 1–18. Available on line at: http://www.unesco.org/education/tlsf/TLSF/theme_a/ mod04/uncom04 (accessed Feb 2008). Haigh, M. 2007. Sustaining learning through assessment: an evaluation of the value of a weekly class quiz. Assessment and Evaluation in Higher Education 32, 4: 457–474. Haigh, M. 2000. Reclaimed Land: Erosion Control, Soils and Ecology. Rotterdam: A.A. Balkema / New Delhi: Oxford and IBH (Land Reconstruction and Management 1). Haigh, M. 1996. Empowerment, ethics, environmental action: a practical exercise. Journal of Geography in Higher Education 20, 3: 399–411. Haigh, M. and Kilmartin, M.P. 1987. Teaching soil conservation in the laboratory using the “Bank Erosion Channel” flume. Journal of Geography in Higher Education 12, 2: 161–167.

Haigh, M. and Sansom, B. 1999. Soil compaction, runoff and erosion on reclaimed coal-lands (UK). International Journal of Surface Mining, Reclamation and Environment 13, 4: 135–146. Hellin, J. 2006. Better Land Husbandry: from Soil Conservation to Holistic Land Management. Enfield, NH: Science Publishers (Land Reconstruction and Management 4). Higgitt, D.L. 1996. The effectiveness of student-authored field trails as a means of enhancing geomorphological interpretation. Journal of Geography in Higher Education, 20, 1: 35–44. Katula, R.A. and Threnhauser, E. 1999. Experiential education in the undergraduate curriculum. Communication Education 48: 238–255. Kilmartin, M.P. 2000. Hydrological management of reclaimed opencast coal sites. Land Reconstruction and Management 1: 137–158. Kilmartin, M.P. 1994. Runoff Generation and Soils on Reclaimed Land, Blaenant, South Wales. Oxford, Oxford Brookes University, unpublished PhD thesis. McEwen, L., Haigh, M., Smith, S., Steele, A. and Miller, A. 2003. Real world’ experiences? An evaluation of regional practitioner inputs to advanced environmental taught programmes from the student perspective. Planet 10: 18–22. Available on line at: http://www.gees.ac.uk (accessed December 2007). McLaughlan, R.G. 2007. Instructional strategies to educate for sustainability in technology assessment. International Journal of Engineering Education, 23, 2: 201–208. Meehan, B. and Thomas, I. 2006. A Project-Based Model for Professional Environmental Experience,Applied Environmental Education and Communication, 5, 2: 127–135. Pawson, E., Fournier, E., Haigh, M., Muniz, O., Trafford, J. and Vajoczki,S. 2008. Assessing the value of ProblemBased Learning in Geography. Academic Exchange Quarterly 12, 1: 201–210. Available on line at: http://www. rapidintellect.com/AEQweb/ab3930.htm (accessed May, 2008). Ramsden, P. 1992. Learning to Teach in Higher Education. London, Routledge. Rickinson, M. 2001. Learners and learning in Environmental Education: a critical review of the evidence. Environmental Education Research 7, 3: 207–321. Shepherd, A. and Cosgriff, B. (1998) Problem-based Learning: a bridge between planning education and planning. Journal of Planning Education and Research 17: 348–357. Teichler, U. (1998) The Requirements of the World of Work, Working Document drafted for Thematic Debate for the World Conference on Higher Education ‘Higher Education in the Twenty-first Century: Vision and Action’, UNESCO, Paris, 5–9 October 1998. Available on line at: http://portal.unesco.org/education/en/files/9685/1042723 7720world-work. pdf/world-work.pdf (accessed August 2007).

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Implementation of school catchments network for water resources management of the Upper Negro River region, southern Brazil M. Kobiyama∗, P.L.B. Chaffe, H.L. Rocha, C.W. Corseuil, S. Malutta, J.N. Giglio & A.A. Mota Dept. of Sanitary & Environmental Eng., Federal Univ. of Santa Catarina, Florianópolis, SC, Brazil

I. Santos Dept. of Geography, Federal Univ. of Paraná, Curitiba, PR, Brazil

U. Ribas Junior & R. Langa Battistella Florestas, Rio Negrinho, SC, Brazil

ABSTRACT: The Upper Negro River (UNR) basin (3552 km2 ), southern Brazil, is one of the headwater basins of the Iguaçu River basin, and is characterized with the Subtropical Ombrophilous Forest (SOF) which formerly covered the southern Brazilian plateau. Local communities have thought that water-sediment related problems have frequently occurred because of transformation of the SOF to pine reforestation and agriculture. To answer the question of what kind of land-use is best for the water resources management, the school catchments network has been implemented in the UNR basin. Till now, seven small experimental catchments (0.1–10 km2 scale) with hydrological monitoring system were constructed. Furthermore, this network contains larger experimental catchments (100–1000 km2 scale), among which four have been operated by the Brazilian government. In all the experimental catchments, scientific researches and extension have been executed, which transforms the experimental catchments to the school catchments. Scientific results and the network are used for environmental education. This network could serve as the instrument to increase an individual’s knowledge on hydrology and enhance individual’s participation in community discussion. Consequently, an enhanced participation of each member elevates the quality and quantity of the community action and makes the community-based management of headwater catchments more efficient. Keywords: school catchment; Upper Negro River; Subtropical Ombrophilous Forest; pine reforestation; environmental education

1

INTRODUCTION 2

The Upper Negro River (UNR) basin (3552 km ) is one of the headwater basins of the Iguaçu River basin (68,410 km2 ) which is located along the border between the Paraná and Santa Catarina States, southern Brazil (Figure 1). In possessing a high value of the specific discharge (2.18 × 10−2 m3 /s · km2 ), the Iguaçu River basin is characterized with a very high potential to generate the hydroelectric energy. There are 5 large hydroelectric-power-dams along the Iguaçu River (from the upper to downstream, Foz do Areia, Segredo, Salto Santiago, Salto Osório and Salto Caxias). The regional socio-environmental reasons will increase the number of small hydropower plants from now on in this basin (ANA, 2001). The ∗

Corresponding author ([email protected])

above mentioned large dams are located in the downstream region from the UNR. According to ANA (2001), it is necessary to comprehend how the operations of these dams change the regional hydrological processes. Some local communities have thought that the frequent floods are caused by the dam construction. This basin has a geological, topographical and climatic heterogeneity. The hydrological processes are very complex and difficult to be understood. The Iguaçu River basin is also characterized with the Subtropical Ombrophilous Forest (SOF). Since the remainders of the SOF which formerly covered the plateau region of the southern Brazil are now only 2% of its original area, this ecosystem must be preserved. Recently the conversion of the pine reforestation areas to the SOF has been strongly requested without the consideration that the regional economy depends mainly on the reforestation activities.

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Headwater Catchments University

Science + Technology

University

Extension

Utility Permission

Reforestation Company

Experimental Catchments Participants

City Office + Communities

School Catchments

Figure 1. Localities of the Upper Negro River basin and some large dams in the Iguaçu River basin. (The black circular and black square points indicate some dams and cities, respectively).

Therefore, the ecological and hydrological researches in the UNR basin are indispensable to reduce the damages caused by the water-related disasters. In these circumstances, Kobiyama et al. (2007) constructed seven small experimental catchments (0.1 to 10 km2 scales) with hydrological monitoring in the UNR basin in order to answer the question about what kind of land-use is best for the water resources management. With the above mentioned small experimental catchments and some preexistent relatively-large experimental catchments, the school catchments network (SCN) has been implemented in the UNR basin. The objective of the present study was to present the concept of the SCN, some results from scientific researches, and extension activities (environmental education) in the UNR basin. The natural disaster management mainly aims (1) to understand the natural phenomena that trigger the natural disasters and (2) to raise society’s resistance to such phenomena (UNDP, 2004, Kobiyama et al., 2006). It is, therefore, very clear that the present SCN implementation can contribute to the natural disaster management in the headwater regions. Since this type of network did not exist in the Iguaçu River basin, the present SCN in the UNR basin will serve as a pilot strategy for reducing the water-related disasters.

2

SCHOOL CATCHMENT

By reporting the Forest Hydrology Project (FHP) which is the cooperative activity between the Federal University of Santa Catarina (UFSC) and the local reforestation company Modo Battistella Reflorestamento S.A. – MOBASA (currently Battistella Florestas), Kobiyama et al. (2007) defined the school catchment as an experimental catchment which serves for scientific researches and environmental education activities. In this project, all the experimental school catchments can be used for the environmental education activities of local communities and also for some

Scientific Researches Environmental Education

Figure 2. Construction of school catchments.

qualification lectures for technicians that work with the water and forest resources. In this sense, Farrell (1995) showed a historical perspective of experimental catchments and their importance in hydrology and water resources management. The FHP has been realized in Rio Negrinho City, which is located at the central part of the UNR basin. In this city, there are not enough researchers or information to recognize the relationship between water and forest. The local communities require universities to provide some scientific and technical support, which implies the importance of the participation of UFSC in the FHP. Since the reforestation companies like the Battistella Florestas possess a lot of headwater catchments, the participation of such companies in any forest hydrology project through offering their headwater catchments is fundamental. It would be almost impossible to construct any experimental catchments without the support of the reforestation companies. In this context, the cooperation between the UFSC and the Battistella Florestas converted the common headwater catchments to the experimental catchments. Furthermore, the execution of environmental education with participation of local communities and city office converts them to the school catchments (Figure 2). In this way, school catchments increase an individual’s knowledge on hydrology, which enhances his (or her) participation in the community in terms of water resources management. Consequently, an enhanced participation of each member elevates the quantity and quality of the community action. According to Hillman & Brierley (2005), the community-based management is essential for the recent stream rehabilitation programs. Such a management with governmental supports must be executed for any program that treats catchments and water resources. Figure 3 shows the relationship between the school catchments and the community-based management. This kind of cooperation between universities and reforestation companies, together with local communities’ participation, might be indispensable in any forest

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Figure 4. Study area (Upper Negro River basin) and school catchments. (The numbers of this figure agree with those of Table 1). Figure 3. Relation between school catchments and community-based management. (Source: Kobiyama et al., 2007).

hydrology project which ensures the integrated management of water resources. It is worth mentioning that school catchments are important not only for local communities but also for the hydrological sciences’ community. These catchments are fundamental fields (objects) for achieving catchment hydrology. According to Uhlenbrook (2006), in catchment hydrology pure scientific interests overlap with practical water management to support sustainable development. As mentioned above, the UNR basin’s communities need to understand hydrological effects of land-uses and of dams’ operations, which requires to have a certain number of school catchments characterized with different land-uses and dams. Furthermore, it is necessary to have school catchments with different scales once the hydrological processes depend on the catchment scale (Pilgrima et al., 1982, Laudon et al., 2007). Therefore, by constructing a set of school catchments with different land-uses and different scales, the SCN has been implemented for the UNR basin’s management. The concept of catchment network is not new. In justifying the catchment studies and the long term monitoring system for the investigation of hydrological effects of forest, Whitehead & Robinson (1993) reported some European examples of the catchment networks. Besides, O’Connell et al. (2007) introduced the Catchment Hydrology and Sustainable Management (CHASM) research program that contains the catchment network in the UK and that adopts a common multiscale experimental design. These networks seem to be established just for the scientific researches. The concept of such networks is, therefore, very different from that of the present study where the SCN contributes not only to the scientific researches but also to the environmental education activities.

3

STUDY AREA

In this study, the outlet of the UNR basin is determined as the point of the Rio Negro gauge station (Code No. 65100000). Then the total area of the UNR basin is 3552 km2 . The northern and southern parts of the Negro River belong to Paraná and Santa Catarina States, respectively. Inside the UNR basin there are more three preexistent gauge stations (Rio Preto do Sul (650950000), Avencal (65094500) and Fragosos (65090000)) (Figure 4). The climate is Cfb (Maritime Temperate climate) in the Köppen classification. The principal geology is Paleozoic sedimentary rocks (sandstone and shale) that demonstrate horizontal stratification. The general relief is moderate and there are a lot of swamps in headwater areas (Kobiyama et al., 2007). Historical analysis of Rio Negrinho city shows the increase of hazard zones’ occupation in the urban areas, which implies the fundamental roles of the city office’s actions to reduce flood disasters (Schoeffel, 2004). This aspect is very common in other cities inside the UNR basin.

4

ESTABLISHMENT OF SCHOOL CATCHMENTS NETWORK

As mentioned above, the present network uses the four preexistent gauge stations and constructs 10 more river gauge stations. Then the network consists of 14 school catchments (Table 1). The outlets of all the school catchments are shown in Figure 4. The smallsize catchments (5 to 11) and the middle-size catchment (12) are all inside the Rio Preto do Sul catchment (3), which will permit discussion of the hydrological effects of small catchment-scale. All the school catchments permit two types of activities: the hydrological researches (monitoring and computational modeling);

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Table 1. Gauge stations and their corresponding school catchments’ characteristics in the Upper Negro River basin.

100%

2%

6%

4%

6%

51%

42%

50%

49%

80%

No. 1 2

Catchment

Area (km2 )

Land-use characteristics

3552 2611

Mixture* Mixture

3 4 5

Rio Negro Rio Preto do Sul Avencal Fragosos P1

1001 800 0.16

6

P2

0.24

7

A

0.20

8 9 10 11 12

M1 M2 N1 N2 R

2.69 8.98 0.15 0.24 201

13

W1

195

14

W2

78

Mixture Mixture 20-years-old pine reforestation 20-years-old pine reforestation (after calibration period, the clear cutting will be done.) Agriculture (corn, soy beans, etc.) Mixture Mixture Native forest Native forest Reservoir for small hydropower plants, mixture Current water-supply catchment (Negrinho River), mixture Future water-supply catchment (Bugres River), mixture

60% 40%

6%

20%

40%

11%

4%

Qd Qb

42%

35%

41%

0% Fragosos

Avencal

Rio Preto

Rio Negro

Figure 5. Water balance obtained from the HYCYMODEL application. (Qb = base flow; Qd = direct runoff; E = real evapotranspiration; and dS = soil water storage).

∗

Mixture consists of agriculture, pine reforestation, native forest

and the extension activities (environmental education courses). At each station on the outlet, the water-level and suspended sediment (SS) are monitored. Though these parameters have been manually measured at the station (3), its monitoring system will be automated in the near future. Then, the monitoring interval at the stations (1 to 4) will be all one hour. And the rest of the stations will have the measurement interval of 10 minutes. At these stations, the short time is necessary because the time of concentration in the corresponding catchments (5 to 14) is relatively short. If the early warning system needs to be introduced to the catchments in the future, this shorter interval will be very useful (Kobiyama & Goerl, 2007).

5

dS E

10%

PRELIMINARY SCIENTIFIC RESEARCHES

Sul and Rio Negro). In order to study the rainfallrunoff processes of these catchments, the HYCYMODEL (Fukushima, 1988) was applied to a daily rainfall-runoff series for the period of 1982 to 2000. The values of the mean annual rainfall for the Fragosos, Avencal, Rio Preto do Sul and Rio Negro school catchments for the period 1982–2000 are 1780 mm/year, 1706 mm/year, 1698 mm/year and 1685 mm/year, respectively. The water balance obtained through the HYCYMODEL application to each school catchment is shown in Figure 5 where the annual rainfall is considered 100%. There seems to be no significant effect of catchment scale on the water balance. Figure 6 demonstrates the relations of the runoff Q ( = Qb + Qd), where Qb is the base flow and Qd is the direct runoff, and the evapotranspiration E to the rainfall P. The increasing rate of Q over P is larger than that of E. The annual rainfall at which P = E is here defined as the critical rainfall index. This can be determined graphically as the intersection. The values of this index for the Fragosos, Avencal, Rio Preto do Sul and Rio Negro school catchments are 1964 mm/year, 1434 mm/year, 1811 mm/year and 1818 mm/year, respectively. The catchment scale’s effect on the critical rainfall index is not encountered, either (Figure 7). It seems that this value for the Avencal catchment is different from those of the other catchments. This might occur due to the relatively-large reservoir’s area in the Avencal catchment. Lino et al. (2007) analyzed the catchment-scale’s effect on sediment transport in the UNR basin and showed the negative correlation between the catchment scale and the specific suspended-sediment yield, which is commonly observed in many studies (for example, Dunne & Leopold, 1978).

Using the Thiessen method and the daily data obtained at 16 rainfall stations (Lino et al., 2007), the present study estimated the mean daily rainfall for the large school catchments (Fragosos, Avencal, Rio Preto do

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6

EXTENSION ACTIVITIES

In 2006, the UFSC initiated the extension project “Learning hydrology for natural disaster prevention”

Critical Rainfall Index (mm/year)

1600

Q and E (mm/year)

Q = 0.740P -486.4 R² = 0.93 1200

800 E = 0.286P+ 405.1 R² = 0.97

400

500

1000

1500

(a)

2000

2500

3000

Q and E (mm/year)

1000 500 0 1000

2000

3000

4000

Rainfall (mm/year)

Figure 7. Effect of catchment scale on the critical rainfall index. Q = 0.819P-513.8 R² = 0.94

1200

in which several qualification courses on hydrology for primary school teachers and technicians that work with the water and forest resources have been already carried out. It is very important to qualify the school teachers, because they can quickly and efficiently spread their knowledge with their pupils. By reporting some parts of the activities, Kobiyama et al. (2007, 2008) concluded that most of the local participants were keen to attend more complementary hydrology courses that relate forest, water resources and natural disasters. It is easily perceived that visiting school catchments allows a person to understand hydrology better. Construction and utilization of school catchments will elevate the quality of individual’s understanding of hydrology.

800 E = 0.232P + 328.0 R² = 0.96

400

0 500

1000

1500

(b)

2000

2500

3000

Rainfall (mm/year) 1600

Q and E (mm/year)

1500

Area (km2)

1600

Q = 0.871P-701.8 R² = 0.94

1200

800

E = 0.184P + 542.6 R² = 0.91

400

7

0 500

1000

(c)

1500

2000

2500

3000

Rainfall (mm/year) 1600

Q and E (mm/year)

2000

0

0

Q = 0.742P-490.6 R² = 0.93

1200

800 Q = 0.310P+ 294.7 R² = 0.96 40

0 500 (d)

2500

1000

1500

2000

2500

3000

Rainfall (mm/year)

Figure 6. Relations of the total discharge Q and the evapotranspiration E to the rainfall P: (a) Fragosos; (b) Avencal; (c) Rio Preto do Sul; and (d) Rio Negro catchment.

FINAL CONSIDERATIONS

The present work described the SCN establishment in the UNR basin that is one of the headwater regions of the Iguaçu River basin. Pasquini & Depetris (2007) analyzed the discharge trends and periodicity of South American rivers, including the Iguaçu River. By using the monthly mean discharge data recorded at the Puerto Iguaçu gauge station, the authors showed that the Iguaçu River has an increasing tendency and quasibidecadal periodicity. Though this study was carried out at the large catchment scale, similar investigations will need to be done in order to find the local hydrological effects. This network will, consequently, contribute to global climate change research. The main aims of the SCN are: (i) scientific activities to investigate the hydrological effects of land-uses, dam operation, and catchment scales; and (ii) environmental education activities in the UNR basin to elevate the individual’s knowledge on hydrology. Any results obtained with this SCN will possess persuasive powers to local communities, more than those with other

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ACKNOWLEDGEMENTS

School Catchment

This study was supported in part by the National Research Council of Brazil (CNPq) and the Study and Project Financer (FINEP) through the Grant no. 553240/2005-6 and the Grant no. 520288/20068, respectively. The authors are also thankful to Elaine Cristina Schoeffel (Rio Negrinho City office) for local support and discussion.

Environmental education + Hydrological researches

Teacher Citizen

+

Pupils (Children)

Intensified participation

Community

REFERENCES

Natural Disaster Management Intensified participation Participatory Natural Disaster Management

Disaster Reduction

Figure 8. Contribution of school catchments network to disaster reduction.

catchments (especially foreign catchments). That is why the communities will place reliance on technical suggestions resulting from this SCN. The final goal of the SCN is disaster reduction (Figure 8). The network associated with scientific researches and environmental education would certainly serve as the instrument to increase an individual’s knowledge on hydrology which enhances his (or her) participation in the community discussion about water-related problems. Consequently, an enhanced participation of each member elevates the quality and quantity of the community action and creates the community-based management of headwater catchments. In other words, the natural disaster management becomes the participatory natural disaster management. The latter has more efficiency for disaster reduction than the former. Since there are few researches on the hydrological processes of the SOF and pine trees in Brazil, the present SCN will serve as a pilot undertaking and contribute constructing the community-based management of water-forest resources reducing natural disasters in this region. ANA (2001) identified various problems in the La Plata River basin management and proposed some programs for solving them, among which there are (i) training courses execution and (ii) establishment and modernization of hydrological monitoring network. These two programs can be efficiently supported by the SCN implemented in the present study.

ANA 2001. Bacias brasileiras do rio da Prata: Avaliações e propostas. Brasília: ANA. Dunne, T. & Leopold, L.B. 1978. Water in Environmental Planning. New York: W.H. Freeman and Company. Farrell, D. 1995. Experimental watersheds: A historical perspective. Journal of Soil and Water Conservation 50 (5): 432–437. Fukushima, Y. 1988. A model of river flow forecasting for small forested mountain catchment. Hydrological Processes 2: 167–185. Hillman, M. & Brierley, G. 2005. A critical review of catchment-scale stream rehabilitation programmes. Progress of Physical Geography 29 (1): 50–70. Kobiyama, M. & Goerl, R.F. 2007. Quantitative method to distinguish flood and flash flood as disasters. Hydrological Research Letters 1: 11–14. Kobiyama, M., Mendonça, M., Moreno, D. A., Marcelino, I. P. V. O., Marcelino, E. V., Gonçalves, E. F., Brazetti, L. L. P., Goerl, R. F., Molleri, G. & Rudorff, F. 2006. Prevenção de desastres naturais: Conceitos básicos. Curitiba: Organic Trading. Kobiyama, M., Checchia, T., Corseuil, C.W., Lino, J.F.L., Lopes, N.H.Y., Grison, F., Chaffe, P.L., Malutta, S., Ribas Junior, U., Langa, R. & Basso, S. 2007. Forest hydrology project (UFSC–MOBASA) for water resources management in Rio Negrinho City, Santa Catarina, Brazil. In: N.V. de Giesen, X. Jun, D. Rosbjerg & Y. Fukushima (eds.) Changes in Water Resources Systems: Methodologies to Maintain Water Security and Ensure Integrated Management, 250–257, Wellington: IAHS. Kobiyama, M., Giglio, J.N. & Mota, A.A. 2008. Popularização da hidrologia para prevenção de desastres naturais como projeto de extensão universitária. In: Proc. Intern. Seminar of Public Health Engineering, Florianópolis, 10–15 September 2008. Brasília: FUNASA. (Submitted). Laudon, H., Sjöblom, V., Buffam, I., Seibert, J. & Mörth, M. 2007. The role of catchment scale and landscape characteristics for runoff generation of boreal streams. Journal of Hydrology 344:198–209. Lino, J.F.L., Malutta, S. & Kobiyama, M. 2007. Relação de sólidos em suspensão com vazão e precipitação na bacia hidrográfica do Alto Rio Negro, região sul do Brasil. In: Proc. 24th Brazilian Congress of Sanitary and Environmental Engineering, Belo Horizonte, 2–7 September 2007. Belo Horizonte: ABES. O’Connell, P.E., Quinn, P.F., Bathurst, J.C., Parkin, G., Kilsby, C., Beven, K.J., Burt, T.P., Kirkby, M.J., Pickering, A., Robinson, M., Soulsby, C., Werritty, A. & Wilcock, D. 2007. Catchment hydrology and sustainable

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management (CHASM): an integrating methodological framework for prediction. In: D. Schertzer, P. Hubert, S. Koide & K. Takeuchi (eds.), Prediction in Ungauged basins: PUB Kick-off, 53–62, Wellington: IAHS. Pasquini, A.I. & Depetris, P.J. 2007. Discharge trends and flow dynamics of South American rivers draining the southern Atlantic seaboard: An overview. Journal of Hydrology 333: 385–399. Pilgrima, D.H., Corderya, I. & Baronb, B.C. 1982. Effects of catchment size on runoff relationships. Journal of Hydrology 58: 205–221. Schoeffel, E.C. 2004. Relação cidade e natureza na evolução urbana da cidade de Rio Negrinho/SC associada à

ocupação de áreas de risco de enchentes. Curitiba: Universidade Federal do Paraná. (Monograph for Specialized Course of City, Environment and Public Policy). Uhlenbrook, S. 2006. Catchment hydrology – a science in which all processes are preferential. Hydrological Processes 20: 3581–3585. UNDP 2004. Reducing disaster risk: A challenge for development. New York: UNDP. Whitehead, P.G. & Robinson, M. 1993. Experimental basin studies-an international and historical perspective of forest impacts. Journal of Hydrology 145: 217–230.

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5

Hydrological models in support of integrated water resources management Integrated water resources management aims at sustainable uses of water, land, and related resources. Hydrological models can help resource managers to analyse and quantify effects of spatial and temporal changes in the availability and quality of freshwater resources. The integration of global change aspects into hydrological models and the use of different modeling techniques can also provide decision makers with scenarios of potential anthropogenic interventions (land-use, reservoir operations, drainage and irrigation) and their impacts on fragile freshwater resources. The aim of this session is to address long-term changes of surface water and groundwater quantity and quality from headwater areas to the ocean. The session will focus on hydrological modeling and the integration of global change aspects (including climate change) from both the natural and social sciences using different modeling techniques. Conveners: Vijay P. Singh (Texas A&M University, USA) Marcel Endejan (former GWSP Deputy Executive Officer) Lydia Dumenil Gates (GWSP Deputy Executive Officer) Xieyao Ma (Frontier Research System for Global Change, Japan) Yoshinobu Sato (RIHN, Japan)

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

On integrated water resources management Vijay P. Singh∗ Department of Biological & Agricultural Engineering, Texas A & M University, Texas, USA

ABSTRACT: Integrated water resources management involves simultaneous management of the quantity and quality of surface waters of rivers, lakes and reservoirs or other water bodies; and of ground water and vadose zone water, without causing undue harm to the environment and in concert with acceptable socio-economic policy. Employing hydraulics, hydrology, and environmental science and engineering, the management may entail multiple sectors, multiple stakeholders, multiple players, and decision makers. Decision making often is subject to multi-criteria, multi-objectives, and multi-constraints; and is governed by socio-economic evaluation and public policy. In order to reach the decision making stage, a lot of groundwork needs to be done, involving data gathering and processing, hypothesis formulation and modeling, calibration and verification, uncertainty and risk analysis, learning from data analysis and observations, and public (stakeholder) participation. The session on integrated water resources management has attracted a significant response, involving a number of interesting contributions. This paper provides an overview of these contributions and attempts to tie them together in the context of integrated water resources management. Keywords: integrated water resources management; hydrologic models; watershed models; climate change; land use impact; surface water; ground water

1

INTRODUCTION

There is growing demand for water almost everywhere, because of increasing population, rising standard of living, growing energy demand and production, and expanding industrial and agricultural activities. Thus, the competition amongst water users is intensifying. Is it possible to meet all water demands? The answer to this question is partly yes and partly no, and that also depends on a particular river basin. Irrespective of what the answer is, the only way to satisfactorily answer the question is integrated water resources management (IWRM). The Global Water Partnership Technical Committee (Background Technical Paper No. 4) defines integrated water resources management as a “process which promotes the coordinated and development and management of water, land and related resources in order to maximize the resultant economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems.” In a similar vein, World Bank calls for an “integrated water resources perspective ensuring that social, economic, environmental, and technical dimensions are taken into account in the management and development of water resources.” ∗

Corresponding author ([email protected])

IWRM stands on three pillars: (1) institutional framework, (2) nurturing environment, and (3) organization and management. In order to develop and manage water resources in a river basin, the development of an institutional framework is vital. Depending on political governance, cultural and social fabric, this framework must be location specific. It can be centralized, local, public-private partnership, or a combination thereof. Then comes the nurturing environment which is comprised of policies and legislation which are formulated and promulgated by the elected governing bodies, such as congress or parliament. It is this environment that sustains the institutional framework. When formulating policies and legislation, the expertise and skills of professionals come into play, thus emphasizing the interaction between the first pillar and the second pillar. The organization and management instruments put IWRM into action. These instruments may entail assessment, information, and allocation. The experience gained from implementation of IWRM is fed back into the development of the first two pillars and finally into itself. Thus, all aspects of IWRM are interactive and bring water, the user of water and the manager of water, and the government in association with each other. This session is timely and appropriate. The objective of this note is to provide an overview of the

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contributions included in this session against the backdrop of IWRM.

2 AN OVERVIEW OF CONTRIBUTIONS INCLUDED IN SESSION 5 There were 35 abstracts that were accepted for inclusion in the session. However, only 18 contributions were finally submitted and two of these contributions are for poster presentations and the remainder for oral presentations. These contributions encompass a broad range of topics and can approximately be classified in 5 groups: (1) catchment water balance modeling, (2) hydrologic modeling, (3) water resources management, and (4) climate change.

2.1

Catchment water balance modeling

There are several contributions devoted to this important topic. A distributed hydrologic model under changing environment, called HydroInformatic Modeling System (HIMS), was developed by Zhu and Liu (2008). The model is an integrated water cycle simulation system, is a distributed hydrological model and can be customized for any actual basin. The runoff model, coupling HIMS with LL-II model, was customized for Wudinghe River basin, the biggest branch located between Hekou Town and Longmen in the middle reach of Yellow River. For integrated water resources management, the customized distributed hydrological model integrated surface water with ground water and continuously simulated hydrological processes. Additionally, it considered anthropogenic impacts on water cycle. A continuous rainfall-runoff model was developed by Brocca et al. (2008) for critically assessing hydrological scenarios in natural channels. The model is an extended formulation of the Tiber River basin semi-distributed hydrological model, including a new component for the soil water balance, which represents a useful tool for runoff prediction in a climatic and land use changing scenario. The model assesses for each single uniform unit (sub-basin) both the average soil moisture condition and the different runoff components at the basin outlet. The model requires an estimate of a few physically based parameters and hydrological quantities routinely measured as input data. The daily discharge assessment during the pluriannual period allows for evaluating the contribution of each river branch and hence the temporal ranges of the channel system for low flows. Combalicer et al. (2008) analyzed the water balance of a 15-ha watershed located in southern Korea for evaluating the fractions of precipitation that became streamflow, evaporation, surface flow and

ground flow. A lumped hydrologic simulation model, BROOK90, was calibrated for the forested watershed. Data revealed that high values of streamflow, evaporation, surface flow and ground flow occurred during summer and low values throughout winter season. Analysis of the water budget showed that about 45 percent of the annual precipitation was measured as streamflow and 55 percent as evaporation. From the streamflow component, annual average surface flow and ground water flow of about 32 percent and 68 percent, respectively, were found. In the ground water flow simulation, strong correlation was found using the BROOK90 model compared to the PART and WHAT programs. Overall, it can be asserted that the partitioned amount of water varied from one component to another as affected by seasonal variation, canopy, and soil characteristics. Ren et al. (2008) quantified the effect of land use and land cover on green water and blue water in Laohahe catchment in the northern part of China, using a distributed hydrological model. Land use and land cover data for representing the vegetative cover over the catchment were obtained by remote sensing. The distributed hydrological model was coupled with a two-source potential evaportranspiration model to simulate daily runoff. Streamflow simulation was conducted for each period under four land cover scenarios. The results showed that the change in land use and land cover had a significant influence on evapotranspiration and runoff. The land cover data showed that forest land and water body had decreased from 1980 through 1999 and farm land and grass land had increased. This change caused the vegetation interception evaporation and vegetation transpiration to decrease, whereas the soil evaporation tended to increase. Thus the green water decreased, and the blue water increased over the Laohahe Catchment. Masumoto et al. (2008) presents the development of a distributed water circulation model for assessing human interaction in agricultural water use in the Mekong River Basin. A high proportion of water is used for agriculture in Monsoon Asia given the various types of paddy irrigation utilized there, and the distinct dry and wet seasons. The proposed model incorporates those characteristics and reproduces the mechanism of the water cycle in that region. The use of agricultural water in rain-fed paddies of the basin is first classified as three types of practices: using only rainfall, temporarily using supplementary water, and using flood water. Irrigated paddies are also classified into six types based on the major types of irrigation and facilities employed. Secondly, the model consists of four sub-models used to calculate potential evapotranspiration, simulate cropping patterns and planting/harvesting areas, estimate the use of agricultural water, and analyze runoff components, respectively. Third, actual evapotranspiration is estimated based on

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the estimated water content in the root zone, one of the three layers modeled. Model simulations compare well with observed data at key points in the basin. Soria et al. (2008) evaluated land use change impacts in total runoff using a distributed rainfallrunoff model applied to a middle size spatially heterogeneous catchment (10 000 km2 ) in central Bolivia. Using a statistical approach, uncertainty was determined about future land use scenarios. Eleven land use types were randomly combined to form 264 scenarios, and model outputs were evaluated by sensitivity indices, estimated with the Monte Carlo based Sobol method. Computational experiments showed that land use units close to the catchment outlet were significant during peak flow and hydrograph recession; cells in geographically significant locations far from the main channel were more important before event occurrence. In the contribution by Sato et al. (2008), an integrated semi-distributed hydrological model is employed for long-term (1960–2000) water balance analysis of the Yellow River basin in China. The objective of this contribution was to assess the influence of climate change and human activities on fresh water resources. To estimate the impact of the major anthropogenic factors, three sub-models, including (1) landuse change, (2) reservoir operation and (3) irrigation water use, were applied. The model quantitatively showed the impact of soil water conservation in the Loess Plateau, large reservoir operation in the upper reach, and the irrigation water use between upper and lower reaches.

2.2 Hydrologic modeling In a review paper, Liu et al. (2008) address applications of geographic information system in hydrologic modeling, general approaches to linking GIS and hydrological models, and some popular integrated hydrological models. Also discussed are current problems, challenges and the prospect of integrating GIS and hydrological models. Sayama and McDonnell (2008) discuss the relationship between residence time and geographic source of stream flow in small watersheds employing a distributed rainfall-runoff model and field observation data. They first demonstrate the ability to simulate new/old water fractions and mean residence time of streamflow. After demonstrating the model capability to capture flow and transport dynamics for the right process reasons, they conduct a series of virtual experiments (numerical experiments driven by collective field intelligence) by switching soil depths and climate conditions between the two catchments to understand the impact of these variables on the interaction between water age and source information. The results indicate that thick soil depths increase mean

residence time of the catchment and concentrate the source of old water more in the near stream zone. The strong correlation between mean residence time, spatial sources and soil depths implied the possibility to estimate the sources and catchment representative soil depths from isotope-based mean residence time estimations. A conceptual investigation of the time of concentration of the Pequeno River watershed, Sao Jose dos Pinharis, Brazil, was undertaken by da Silva et al. (2008). They considered two definitions of the time of concentration: from the beginning of a constant rainfall event to the time of constant discharge on the hydrograph, and from the end of rainfall event to the time of cessation of direct runoff. The time of concentration was computed in three ways: geomorphological empirical relations, observed hydrograph analysis, and simulated hydrograph analysis. The latter two methods gave similar results for medium and high rainfall intensities. There are however uncertainties in the estimated values of the time of concentration that need attention. Li and Peng (2008) applied an artificial neural network to a rainfall-runoff model. Hourly rainfall runoff data for Huangchuan catchment in Huaihe River basin, China, were considered to demonstrate an ANN application. The network frame set input nodes consisting of previous hour’s rainfall, previous hour’s discharge, present hour’s rainfall. After training was completed, the trained network was used for real time forecasting. The level of precision was satisfactory when the prediction time was less than 2 or 3 days. ANNs are expected to serve as a useful tool for the solution of specific problems in watershed hydrology. The contribution by Perera et al. (2008) evaluates seawater intrusion to a coastal aquifer by developing a three dimensional numerical model. The Motooka region in Fukuoka, Japan, is a coastal region where agriculture is dominant. Green houses and wineries cover their water demand from groundwater. With increased water demand, seawater intrusion is identified as an alarming threat to the Motooka coastal aquifer in the near future. In this paper a numerical study of the seawater intrusion of the Motooka is discussed. The numerical model couples the groundwater flow equation with the mass transport equation to simulate the density dependent solute transport in the three dimensional space. The model serves as a management tool by simulating the salinity variation with groundwater pumping. A hidden Markov model for non-stationary runoff modeling conditioned on El Niño information is developed by Gelati et al. (2008). The stochastic runoff modeling uses a Markov-modulated autoregressive model with exogenous input (MARX). The model assumes runoff parameterization to be conditioned on a hidden climatic state following a Markov chain,

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where state transition probabilities are functions of the climatic information. MARX allows a heteroscedastic, pseudo-linear, and conditionally stationary description of the runoff process, as its parameters change over time according to the climatic regime. El Niño – Southern Oscillation (ENSO) information is used to condition runoff parameterization. Various climatic indices are considered as covariates: sea surface temperature anomalies in the eastern equatorial Pacific Ocean perform best as predictors. MARX was used to generate long term scenarios and to perform short term forecasts, in the perspective of both long and short term reservoir optimization. The model yielded reasonable predictions especially at the occurrence of strong El Niño episodes, during both calibration and validation. Predictive ability was found low for periods with negative runoff anomalies. However, this pitfall might be overcome by using a climate index that correlates properly with negative inflow anomalies. 2.3 Water resources management The paper by Novicky et al. (2008) discusses a system, which uses three interlinked models for simulation of hydrological, hydraulic (groundwater) and water management conditions in a basin. The system permits simulation of monthly series of hydrological and water management variables for present or historical conditions as well as for possible future conditions in response to climate change scenarios. In this manner, the system can provide information for integrated water resource management (both surface water and groundwater) as well for strategic water management planning. The paper illustrates application of the system on selected case studies. Wang et al. (2008) discusses a model for harmonious water resources management, including 3 aspects: supply-demand harmoniousness, benefit harmoniousness, and organization harmoniousness. The model emphasizes the harmoniousness between water resources supply and demand, and the relation between water resources, society, economy and ecology. It can also optimally harmonize functions of the management system. The model was demonstrated in a region of Jiangsu province. The results indicated that the model could bring into play the harmonizing functions, and thus realizing the sustainable use of water resources. 2.4

Impact of climate change

The paper by Coulibaly (2008) proposes a multimodel approach for assessing the variability of climate change impact on streamflow. The approach includes three downscaling models, namely a statistical method (SDSM), a stochastic weather generator (LARS-WG)

and a temporal neural network (TLFN) along with four hydrologic models, namely a physically based watershed model WATFLOOD, two lumped-conceptual modeling systems HBV and CEQUEAU. The downscaling models are used in parallel to downscale total daily precipitation, daily maximum and minimum temperature based on climate predictors derived from the Canadian global climate model forced with the Intergovernmental Panel on Climate Change (IPCC) IS92a scenario. The hydrologic models are validated with meteorological data from both the historical records and the downscaled predictor variables. The ensembles of flow simulations generated by different hydrologic models demonstrate the possible range of future flow regime variability in the selected watersheds. The results highlight the uncertainty due to the downscaling methods and the hydrological models, and emphasize the advantage of multi-model approach in estimating the possible hydrological impact of climate change at the watershed scale. Fujihara et al. (2008) develop an approach for simulating flood and drought risks under present and future climate with both present and alternative reservoir operation rules. MRI-CGCM2 and CCSR/ NIES/FRCGS-MIROC were dynamically downscaled to the Seyhan River basin in Turkey. The downscaled data covered two 10-year time slices corresponding to the present (1990s) and the future (2070s). The hydrologic models with a reservoir model were driven using these downscaled data. Relative to the present, the MRI and CCSR models predicted average annual temperature rises of 2.0◦ C and 2.7◦ C, precipitation decreases of 157 mm and 182 mm, and annual runoff decreases of 118 mm and 139 mm, respectively.Analysis of the water resources systems, taking into account changes in water use and examining alternative reservoir operation rules to cope with the projected changes in river flows and water use, indicated that the drought risk would not increase if water use did not increase. However, if water use increased and reservoirs continued to operate under the present rules, the reservoir reliability would decrease. Alternative operation rules would reduce reliability losses in the reservoir system; however, the alternative operation that was the best adaptation in terms of drought risk would considerably raise the flood risk. Therefore, integrated water management is required so that operation rules can be changed to meet hydrological and water use conditions. Mishra and Singh (2008) discuss the impact of climate change on annual drought severity based on future climate scenarios derived from GCM outputs using downscaling techniques. It is observed that the intensity of short-term droughts (based on SPI 1) in the Kansabati basin in India is higher in terms of severity and spatial extent for the period 2051–2100 than for 2001–2050.

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3

CONCLUSIONS

The following conclusions are drawn: 1. There is an accumulated evidence to support the definitive impacts of climate change: rising temperatures, increasing frequency of floods and droughts, reduced rainfall in some places, increased evaporation in other places, reduced runoff, and so on.

2. Man made changes are having a significant impact on the hydrologic cycle. 3. Hydrologic and watershed models coupled with remote sensing, DEM and GIS provide significant capability to quantify the impact of anthropogenic changes on the hydrologic cycle.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Distributed simulation of basin water cycle under changing environment R. Zhu∗ Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China Graduate School of the Chinese Academy of Sciences, Beijing, China

C. Liu Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

ABSTRACT: Nowadays, great efforts have been made on distributed hydrological modeling. It is believed that, distributed hydrological model can describe the hydrological processes in more detail and more physically. On the other hand, the rapid development of computer and information technology has turned distributed hydrological model from scientific concepts to application reality. HydroInformatic Modeling System (HIMS), an integrated water cycle simulation system, mainly consists of distributed hydrological model and can customize and develop one new hydrological model adapted to an actual basin. This study has coupled self-developed runoff model by HIMS with LiLan-II (LL-II) model and then customized one distributed hydrological model suited for Wudinghe River basin, which is the biggest branch located between Hekou Town and Longmen in the middle reach of Yellow River. As for integrated water resources management, the customized distributed hydrological model has integrated surface water with ground water and continuously simulated basin hydrological processes from 1980 to 2000. Additionally, it has considered both natural water cycle and anthropogenic impacts on water cycle. Computation results show that the customized hydrological model can simulate water cycle process for Wudinghe River basin, China. Keywords: HydroInformatic Modeling System; distributed hydrological model; integrated water resources management; hydrological process

1

INTRODUCTION

Water is a vital resource for human survival and economic development; as populations and economies grow, water demand increases while the availability of the resource remains constant. Shortage of water resources engenders water use conflicts, both in terms of quantity and quality. By the 20th century, the issue of water resources has become more prominent and a concerned subject by all countries in the world. Integral to any effective water resources management is the quantification of the natural availability of water within a catchment area. However, integrated water resources management has become increasingly complex (David et al. 2005). Hydrologic processes play a crucial role in water resources management (Hall 1968, Tallaksen 1995). With the development of hydrological simulation techniques, computer-based simulation model can describe the various stages of ∗

Corresponding author ([email protected])

the water cycle has become an important support for water resources management (Hao et al. 2001). What’s more, distributed hydrological model can take into account space-temporal variability of hydrological elements, describe actually hydrological environment and adapt to research on hydrological processes affected by climate change and human activities. As a result, distributed hydrological model will be more applied in basin water resources management in future (Hao et al. 2001). HydroInformatic Modeling System(HIMS)(Liu et al. 2006, in press, Wang et al. 2005), an integrated water cycle simulation system, mainly includes distributed simulation model and can customize and develop one new hydrological model adapted to actual some basin. It can be used in different space-temporal scale and adapted to hydrological simulation under different natural and human environment. HIMS has succeeded in some typical basins of Yellow River, China (Wu et al.2004, Wang et al. 2004) and Australia (Liu et al. in press). Under the frame of HIMS, this

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study has customized one distributed hydrological model suited for Wudinghe River Basin(selected study area in this paper). As for integrated water resources management, this study has coupled surface water with ground water and taken into account both natural water cycle and anthropogenic impacts on water cycle of Wudinghe River Basin. It has simulated hydrological processes from 1980 to 2000 and provided an important support for integrated water resources management of Wudinghe River Basin. 2 2.1

STUDY AREA AND DATA Study area

Wudinghe River basin(WDRB) is the biggest branch located between Hekou Town and Longmen in the middle reach of Yellow River. The area of WDRB is 30261 km2 . The river length is 491.2 km, and the river mean gradient is 1.97‰. In the whole area, average annual rainfall is about 382 mm but in flood season (from June to September) storms are highly intense, which account about 68 percent of average annual precipitation. According to geography and soil erosion feature, the basin is divided into three geography units: sand, headwaters and loess rolling ravine region (Figure 1). The northwestern part of the basin is characterized as sand region and valley region with gentle slopes, covering 54.3% of the total catchment area. There is a large area of desert and a great difference in temperature between day and night in this region. Because of high infiltration rate, this region generates little overland flow compared to the other parts of the basin. Runoff flow is mainly comprised of groundwater runoff supplied with precipitation and condensation water. Water table is higher in valley region than in sand region so that it will rise up during flood seasons so that Dunne runoff maybe take place in valley region. Headwaters region in the southwest covers 3454 km2 . Its elevation is varied between 1100 m to 1600 m. In this region with deep groundwater table, runoff flow is principally resulted from rainfall of excess infiltration. Loess rolling ravine region stands in southeastward of WDRB. Its area is 10361 km2 , 34.3 percent of the whole basin. Land surface slope over 25 degree is about 60 percent of whole basin. In this region, surface runoff is the dominant runoff generation mechanism in the region due to high rainfall intensities and low infiltration rate. At the same time, there is one dry soil layer cutting off hydraulic conversions between groundwater and surface water so that rainfall can’t effectively recharge groundwater. In addition, water quantity has decreased greatly in WDRB since 1970, which is mainly caused by human activities (Wang-hao et al. 2004), such as terracing,

Figure 1. Wudinghe River basin Map.

building check-dams and changing land cover by planting trees and improving pastures. Water usage of agriculture, industry and life has increased, and various water conservation measures have intercepted a lot of water (Yang-xin et al. 2005). 2.2 Data There are more than 90 rainfall stations, half of which were built after 1975, and 11 hydrological stations in WDRB (Figure 1). To well research on hydrological processes in WDRB, 1980 to 2000 was chosen as a study period and 1988, 1982 and 1997 were selected to represent wet, normal and dry conditions respectively for simulation results analysis. The wet, normal and dry year were defined as annual rainfall less than that of 10%, 50% and 90% probability respectively. Hydrometeorological information is from the Yellow River Water Conservancy Committee Hydrological Bureau; DEM, soil, vegetation and land use information are from the resources and environmental science data center, CAS. The basin’s soil types mainly consist of loess soil and sand and hence the soil is classified into three categories in the computation: loess soil, sand and others. The characteristic water content of those three soil types is different, such as the saturated soil moisture content, field capacity, capillary fracture moisture, wilting coefficient. Besides them, thickness of the altered aquifer, infiltration capacity, etc, is decided by the experimental and observed data by predecessors in the same region or similar soil type. 3

HYDROLOGICAL MODEL STRUCTURE

In this study, grid cells are computation units and center points of grids are network nodes for calculation.

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Runoff idea is based on hillsides hydrology and watershed interface theory. As for anthropogenic impacts on water cycle, only agriculture irrigation is considered in the model structure. Irrigation water is computed according to irrigation ration and rule. This model mainly considers interactions among rainfall intensity, agriculture irrigation, interception, infiltration, evaporation and soil moisture content. In the vertical direction, soil is divided into three layers within the calculation unit: the first layer, generating overland flow; the middle layer (it can be further refined), resulting in underflow; the third layer, leading to stable groundwater flow. 3.1

3.2 Flow concentration model 3.2.1 Overland flow concentration model After calculating runoff formation, overland flow concentration will be done by continuous flow hydrodynamic equations and momentum equations. Equations (3)–(5) are solved by numerical difference scheme. The upper infiltration and former flow are their inputs so that vertical and horizontal movement of water flow is coupled together. Therefore, unit runoff discharge qsum flowing into river can be acquired, which equals the sum of overland flow, underflow and groundwater runoff.

Runoff formation model

LiLan-II (LL-II) model (Li-lan 2001a, b, Li-lan & Zhong 2003), one physically-based distributed rainfall-runoff model, has participated in the Distributed Model Intercomparison Project (DMIP) from the Hydrology Laboratory of the NWS in America (Seann Reed et al. 2004). In LL-II model, the infiltration capacity fm is defined as a nonlinear function about shortage of soil water which equals the saturated soil moisture content wm minus the actual soil moisture content w (Equation(1)). That is, as shortage of soil water comes to zero, runoff is generated by Dunne runoff; nevertheless, runoff is resulted from rainfall of excess infiltration. In flood season (from June to September) storms are highly intense in WDRB, which account about 68 percent of annual precipitation. What’s more, it is located at a typical transitional zone from desert area to the loess region. Whether soil water is saturated or not, runoff is bound to be caused by rainfall of excess infiltration as long as rain intensity is greater than infiltration intensity. Basin rainfall is very little and soil is usually dry. Hence, Dunne runoff hardly happens there. As a result, this study has prior considered rainfall of excess infiltration for peak runoff formation and hence used self-developed infiltration model Equation(2) by HIMS, which is developed on storms with high intensity. Equation (2) is semi-experiential formula got by a large number of observed artificial rainfall tests at small basins, and its two critical parameters R and r can be supplied with looking up known table, according to soil water moisture content, vegetation cover and land-use.

Where wm , w: the saturated and actual soil moisture content respectively, mm; fc : the stable infiltration capacity, mm; fm : the infiltration capacity, mm; α, β: coefficient and exponent for infiltration capacity; R, r: experiential parameters.

Where q(x, t), qw (x, t), qg (x, t): overland runoff, underflow runoff, groundwater runoff, m2 /s; v1 , ω1 , ω: wave velocity of overland runoff, underflow runoff and groundwater runoff respectively m/s; r1 , r2 , r3 : rainfall generating overland runoff, underflow runoff and groundwater runoff respectively, mm; k1 , kg : coefficient of underflow runoff, groundwater runoff. 3.2.2 River flow concentration model Stream routing is worked by convection-diffusion equation derived from Saint-Venant equations, as follows:

Where, qsum : unit runoff discharge flowing into river, m2 /s;Q: stream flow, m3 /s; c: wave velocity of river flood wave, m/s; d: diffuse coefficient of river flood wave, m/s. 4

RESULTS AND DISCUSSIONS

With the support of GIS, the basin is discretized into 2115 5 km × 5 km grid cells and 65 subbasins based on 100 m × 100 m DEM and river network is also extracted (Figure 2). Number of 1–65 means distribution of subbasins in Figure 2. The convergence path of water flow is determined by single-flow method (Beven, et al, 2001). The study is carried out at a day scale from 1980 to 2000 and 1980 to 1989 are the calibration period while 1990 to 2000 is verification period. Baijiachuan

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NSE

R2

XW

Calibration

Computation precision index statistics.

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1980–1989

0.426 0.652 0.700 0.479 0.507 0.470 0.572 0.450 0.500 0.442 0.585

0.750 0.820 0.850 0.870 0.780 0.780 0.860 0.640 0.730 0.810 0.810

0.019 −0.005 0.069 0.001 0.051 −0.035 0.016 0.060 −0.029 0.092 −0.027

Verification

Table 1.

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 1990–2000

0.445 0.329 0.574 0.336 0.384 0.437 0.379 0.565 0.236 0.354 0.340 0.470

0.710 0.620 0.780 0.710 0.490 0.590 0.670 0.750 0.480 0.710 0.690 0.700

0.044 −0.035 −0.010 0.150 0.003 −0.051 −0.111 0.235 0.086 0.148 0.189 0.089

Year

Figure 2. Map of defined subbasins and river network.

hydrologic station is situated at the export of WDRB and hence its observed daily runoff process is used to calibrate model parameters. Computation precision is judged by Nash-Sutcliffe efficiency coefficient (NSE), the flood volume of relative error (XW ) and certainty coefficient (R2 ). Computation results are as shown in Tables 1–2, Figures 3–6.

Where qri , qci : measured and computed stream flow respectively, m3 /s; qri , qci : measured and computed stream flow respectively, m3 /s; n: samples number. 4.1

Runoff processes simulation

After overland flow concentration has computed, unit runoff discharge flowing into river can be achieved. Only groundwater flow process can be simulated if surface flow is not included in computing river flow concentration(Figures 3). Similarly, only surface flow process can be done if groundwater flow can be

ignored (Figures 4). If the above two runoff components are all considered, total stream flow can be achieved (Figures 4). In order to compare with simulated results, one parameter digital filter technique (Arnold et al, 1995; Arnold and Allen, 1999) is used to separate groundwater flow from measured stream flow graph (Figures 3). Because study period is very long, three different hydrological years are selected to show computing results (Figures 3–4). There are two flood seasons every year in WDRB: spring and autumn (Figures 4). WDRB is frozen from this November to the following March. Its groundwater runoff in winter and summer is regulated by different runoff mechanism, which results in spring and autumn flood. Figures 3 shows that customized distributed hydrological model by HIMS has well simulated groundwater runoff process both in spring and autumn. One parameter digital filter technique is only based on river recession curve and doesn’t consider its runoff generation mechanism. Separated groundwater runoff usually involves underflow runoff so that it is more than computed one from customized hydrological model by HIMS (Figures 3). Figure 4 shows that calculated stream flow process is well consistent with measured one except from March to May. This maybe result from that the processes of thaw, soil freezing and melting river ice data from March to May. Seen from figures 5–6, the simulated flood peaks is somewhat less than the measured one. From June to September, total runoff accounts almost 90 percent of average annual runoff, flood rises

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Table 2.

Statistics for water budget elements of the WDRB (Unit : mm/a). Water budget

Runoff

components

Year

Precipition

Evaporation

Runoff

Irrigation water

Storage water

Surface water

Ground water

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

250.33 370.4 324.76 312.06 376.86 454.02 300.66 308.97 409.55 296.88 387.08 314.09 376.9 286.33 395.49 328.7 347.89 277.14 354.3 249.05 284.07

230.82 338.49 295.05 282.07 341.89 410.99 276.05 278.92 368.42 263.59 355.42 279.44 340.92 270.56 358.25 305.74 318.56 258.75 333.15 232.57 267.56

33.85 37.01 37.09 31.97 35.5 38.65 33.1 33.95 41.15 34.68 34.01 30.9 34.18 28.75 39.01 35.15 33.35 28.52 30.51 25.48 25.56

8.66 8.33 8.43 8.50 7.65 5.93 7.05 4.65 5.39 5.83 6.41 5.33 6.41 6.61 11.23 6.88 9.74 10.22 9.14 8.39 9.00

−5.68 3.23 1.05 6.52 7.12 10.31 −1.44 0.75 5.37 4.44 4.06 9.08 8.21 −6.37 9.46 −5.31 5.72 0.09 −0.22 −0.61 −0.05

16.02 17.62 17.17 12.28 15.47 18.66 13.34 14.70 18.02 16.24 16.65 14.95 18.33 11.69 24.14 19.44 17.37 13.59 15.41 10.75 11.30

17.83 19.39 19.92 19.69 20.03 19.99 19.76 19.25 23.13 18.44 17.36 15.95 15.85 17.06 14.87 15.71 15.98 14.93 15.10 14.73 14.26

Figure 3. Comparison of computed and separated groundwater flow in three different hydrological years.

rapidly and falls off in very short time. Although June to September is the flood season in WDRB, flood doesn’t respond very well to rainstorm, which may be caused by reservoirs regulation, water fetching and pondage action of basin underlying surface. In some degree, it reflects anthropogenic impacts on hydrological processes of WDRB. From this November to the following April is dry season while it enters into rainy season since May in WDRB. This study has prior calculated rainfall of excess infiltration contribution to

peak runoff formation and hence computation result is better from June to September. In general, simulated results are very good and both stream flow volume and shape is highly approach to measured one. Seen from table 1, from 1980 to 2000, annual R2 is more than 0.48, Maximum R2 is 0.87; absolute of annual XW is less than0.25, minimum XW is 0.001; annual NSE ranges between 0.236 and 0.7. During 21 years, computation result is best in 1982 while worst in 1998 as far as NSE is concerned. Average NSE in

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Figure 4. Comparison of computed and measured stream flow in three different hydrological years.

calibration period is 0.585, more than 19.66 percent of that in verification period. R2 in calibration period is 0.81, more than 13.58 percent of that in verification period. In addition, absolute of annual XW in calibration period is much less than 69.66 percent of that in verification period. As a result, customized hydrological model by HIMS has well simulated water cycle in WDRB, but the simulation results are less in verification period than in calibration period (Table 1).This is because various water conservation measures built in the earlier 1980s, such as dam, reservoirs, have been full of sediment deposition in the late 1980s, gradually lost their roles, while a massive reforestation project of farmland has been carried out in WDRB in the 1990s. Human activities are different in those two periods which has led to diverse effects on hydrological processes. As a result, varied parameter should be used to reflect water cycle under changing environment while this computation has still used the same parameter value in the 1980s and 1990s. According to three computation precision index statistics and runoff process graphs (Table 1, Figures 3–6), it can be concluded that customized distributed hydrological model by HIMS has well simulated water cycle processes from 1980 to 2000 in WDRB. However, real environment has changed from the 1980s to the 1990s while model computation has still used the same parameter values from 1980 to 2000. Hence, calculation validity is not good in verification period. 4.2

Long-term water budget analysis

Water cycle elements in WDRB, computed by above distributed hydrological model, are as shown in table 2. Annual precipitation and runoff flow are 90 percent and 10 percent of precipitation respectively from 1980 to 2000 (Table 2). Water quantity is mostly from sand

area, mainly including ground runoff. Surface runoff is only 10 percent of total runoff in sand region while 50–60 percent in loess rolling ravine region. Amount of annual evaporation is in normal correlation with annual precipitation while runoff is reverse. Storage water reflects the objective law that water should be stored in wet year and consumed in dry year. From 1980 to 2000, ground runoff is about 52.56 percent of the total runoff and the surface runoff is about 47.44 percent. Additionally, irrigation water in the 1990s is more than 21 percent in the 1980s.

5

CONCLUSIONS

Hydrologic processes play a crucial role in basin integrated water resources management. With the development of hydrological simulation techniques, numeric hydrological model has been able to describe the various stages of the water cycle so that hydrological model has become an important support for water resources management. Moreover, appearance of Geographic Information System and remote sense technique have driven hydrological model to a new stage. The distributed hydrological model based on GIS can simulate space-temporal variability of hydrological elements, describe actually hydrological environment and adapt to research about climate change and human activities impacts on hydrological processes. As a result, the distributed hydrological model is concerned by many experts and has been more applied in basin water resources management. Traditional hydrological model calculates surface water and ground water process respectively, which not only makes some important information lost but also neglects their hydraulic connections. Under the frame of HIMS, this study has customized one distributed hydrological model suited for WDRB. As

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for integrated water resources management, this customized distributed hydrological model has coupled surface water with ground water and taken into account both natural water cycle and anthropogenic impacts on water cycle. It has well simulated hydrological processes from 1980 to 2000. However, there are some differences between calculated and measured runoff. On the one hand, those differences may be caused by model structural insufficiency and parameter value. Especially, actual environment has changed from the 1980s to the 1990s while model computation has still used the same parameter values from 1980 to 2000 and hence the calculation validity is not good in verification period. On the other hand, input error will also affect the simulation accuracy. ACKNOWLEDGEMENTS This research was supported by China Natural Science Fund Project (Grant No: 40671031). REFERENCES Arnold, J.G. & Allen, P.M. 1999. Automated methods for estimating base flow and ground water recharge from stream flow records, Journal of the American water resources association. 35(2): 411–424. Arnold, J.G., Mutiah, P.M., Bernhardt G. 1995. Automated baseflow separation and recession analysis techniques. Ground water, 33(6): 1010–1018. Beven, Keith J. 2001.Rainfall-Runoff Modelling. John & Wiley Sons Ltd. 136–145. David J. Sample & Robert A. Bocarro.2005. Integrated water resources management in North Georgia implications of waste water management. Proceedings of the 2005 Georgia Water Resources Conference. Hall, F.R., 1968. “Base flow recessions:a review”. Water Resour. Res., 4(5): 973–983.

Hao, F., et al. 2001.Hydrological model applications in water resources management. Water resources and hydrological engineering, 32(6): 1–3 (in chinese). Li-lan. 2001. A distributed dynamic parameters inverse model for rainfall runoff. Netherlands: LAHS Pub., No. 270. Li-lan. 2001. A physically based rainfall-runoff model and distributed dynamic hybrid control inverse technique. Netherlands: LAHS Pub., No. 270. Li-lan, Zhong, M., 2003. Structure of the LL–II distributed rainfall-runoff model based on GIS. Water resources power. 21(4): 35–38 (in Chinese). Liu C., Zheng, H., Wang Z., 2006. Basin Water Cycle Distributed Simulation. Yellow River Conservancy Press. Seann Reed, Victor Koren, 2004. Overall distributed model intercomparison project results. Journal of Hydrology. 298: 27–60. Tallaksen, L.M., 1995. “A review of baseflow recession analysis”. J. Hydrol., 165: 349–370. Wang-hao, Wang, C., et al. 2004.The Dual Water Evolvement Mode and its application in Wudinghe River basin. Science in China, Ser.E. Technological Sciences. 34: 42–48. Wang, Z., Zheng, H., Liu, C. 2004. Distributed hydrological model and its application in the Yellow River basin. Science in China (Series E). 34: 49–59. Wang, Z., Zheng, H., Liu, C., 2005. A Modular Framework of Distributed Hydrological Modeling System : Hydrolnformatic Modeling System, HIMS. Progress in Geography. 24(6): 109–115. Wu, X., Wang, Z., Liu, C., 2002. Digital Rainfall-Runoff Model Based on DEM: The Application to XiaolangdiHuayuankou Section of the Yellow River Basin. Acta Geographica Sinica. 57(6): 671–678. Wu, X., Liu, C., Hao, F., et al. 2004. Storm-runoff simulation of distributed hydrological model in the Yellow River basin. Advances in water sciences. 15(4): 511–516. Yang-xin, Yan, J, et al.2005. The analysis on the change characteristics and driving forces of Wudinghe River runoff. Advances in Earth Science. 20(6): 637–642.

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A continuous rainfall-runoff model as a tool for the critical hydrological scenario assessment in natural channels L. Brocca∗, F. Melone & T. Moramarco National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy

V.P. Singh Department of Biological and Agricultural Engineering, Texas A & M University, Texas, USA

ABSTRACT: Hydrological models are being increasingly considered as reliable tools in providing insights to decision makers on potential scenarios of catchment management when climatic and anthropogenic factors are changing. In this context, an appropriate identification of the physical processes involved in rainfall-runoff transformation, starting from the soil moisture conditions of the basin, is required. This paper, addressing the soil moisture issue, investigates, for a pluriannual period, the scenarios for part of a natural channel network which is fundamental for water resources management in the upper Tiber River basin, Central Italy. The analysis is based on the extended formulation of the Tiber River basin semi-distributed hydrological model, including a new component for the soil water balance, which represents a useful tool for runoff prediction in a climatic and land use changing scenario. In particular, the model permits, for each single uniform unit (sub-basin), to assess both the average soil moisture condition and the different runoff components at the basin outlet. The model requires an estimate of a few physically based parameters and hydrological quantities routinely measured, as input data. The model performance is found quite accurate in terms of runoff prediction at gauged sections, belonging to the investigated channel network. The daily discharge assessment during the pluriannual period has allowed for evaluating the contribution of each river branch and hence the suffering temporal ranges of the channel system for low flows. Keywords:

rainfall-runoff modeling; water resources; drought; low flows; soil moisture

The water resources and catchment management deeply depends on climate changes. As emphasized in Xu et al. (2005), the effects of these changes can be analyzed through a procedure which can be schematized in three steps. The first provides global scenarios by using General Circulation Model (GCM); the second one allows downscaling the CGM outcomes at regional scale so that they can be suitable for hydrological applications; the third step addresses hydrological models to assess the effects at different scales of climate changes. At local scale, the manager of water resources is involved in facing the direct impact of climate changes on the supply of freshwater. In this context, hydrological models represent a reliable tool for providing insights to the decision maker on potential scenarios of catchment management when climatic and anthropogenic factors are ∗

Corresponding author ([email protected])

changing. However, the contribution of hydrological models is sound, provided that they are able to provide an appropriate identification of the physical processes involved in rainfall-runoff transformation, starting from the soil moisture conditions of the basin. Therefore, the assessment of this quantity is a key feature of the hydrological modelling and due to a limited knowledge on the spatial variability of soil moisture, many uncertainties affect its estimation. Recently, Brocca et al. (2008a) proposed a simple soil water balance model to assess the surface soil moisture for small catchments. This approach has been incorporated as a module into a semi-distributed rainfall-runoff model providing, in principle, a useful tool for runoff prediction in a climatic and land use changing scenario. Indeed, the model requires estimates of a few physically based parameters and hydrological quantities routinely measured as input data. Therefore, the objective of this paper is to analyze, for a pluriannual period, the suffering scenarios for

175

part of the secondary channel network of the upper Tiber River basin, Central Italy, which is fundamental for water resources management of the basin. The analysis is based on an extended formulation of a semidistributed hydrological model by including the soil water balance component, such as that developed by Brocca et al. (2008). 1

MODEL FORMULATION

The continuous hydrological model is based on an extended formulation of a semi-distributed conceptual model developed by Corradini et al. (1995) for rainfall-direct runoff transformation coupled with a soil water balance component, such as that proposed by Brocca et al. (2008a). Considering the drainage network, the basin under investigation is divided into N sub-basins with the outlet along the main channel or draining directly into it. Moreover, the main channel is split up into Nr homogeneous branches. The runoff hydrograph, Qj , at the downstream end section of the j-branch of length, Lj , is computed as:

where the symbol ⊗ represents the convolution; Yij is the discharge of the i-element draining into the main reach at a distance Xij ; g is the diffusive kernel function; and cj , and Dj are the celerity and diffusivity parameters, respectively. For the first branch, j = 1, the upstream contribution Q0 may be zero or furnished by the discharge observed at a hydrometric station defining the upstream limit of the main channel. The discharge at the outlet of each contributing element is expressed by the sum of the surface runoff and the subsurface runoff:

where As is the sub-basin area; re is the effective rainfall; αp(t) characterizes the contribution of both interflow and baseflow to streamflow, with α parameter and p(t) percolation; hs and hg are the Unit Hydrographs (UH) for the surface runoff and subsurface runoff, respectively. Specifically, hs is obtained through the geomorphological formulation and using for the dynamic parameter a lag-area relationship incorporating only parameter ηs ; hg is derived through a linear reservoir approach with a mean residence time ηg . Quantities re and p in eq. (2) are determined through a water balance model of the upper soil layer, where the following equation holds:

where r is the rainfall rate, e(t) is the evapotranspiration rate, p(t) is the drainage or percolation rate, and Wmax is the maximum water capacity of the soil layer. The rainfall excess, re , is estimated by using the SCS-CN method:

where R(t) is the rainfall depth from the beginning of the storm t  :

S is the potential maximum retention of the soil that Brocca et al. (2008b) expressed, for the Upper Tiber basin, through a linear relationship on the basis of soil moisture observations as:

where SCN (III ) and SCN (I ) are the well-known values of S for wet and dry soil moisture conditions, such as proposed in the classical procedure of the SCS-CN method for abstraction (Singh, 1992). For the drainage component the following relation is adopted:

where Ks is the saturated hydraulic conductivity and λ is the pore size distribution index linked to the structure of the soil layer. In this case the flow is assumed to be gravity-driven with drainage consisting of deep percolation. Evapotranspiration, which mainly controls the soil moisture temporal pattern in the periods without rainfall, is represented by a linear relation depending on the potential evapotranspiration, ETp (t), and the soil saturation:

The potential evapotranspiration, assumed that of the reference crop, is computed through the empirical relation of Blaney and Criddle as modified by Doorenbos and Pruitt (1977):

where Ta (t) is the mean air temperature in ◦ C, ξ is percentage of total daytime hours for the period used (daily or monthly) out of total daytime hours of the year (365 × 12), b is a parameter to be calibrated (Brocca et al. 2008a).

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3

A continuous time series of 3 years (1996–1999) of half-hourly hydrological data has been used. The model was calibrated, considering 4 months (01/09/96–31/12/1996) of half-hourly discharge observed at the outlet gauged station P. Felcino. The number of parameters optimized in the calibration process was restricted to the most sensitive model parameters. Moreover, the parameter intervals and parameters of great uncertainty were kept spatially uniform and not distributed. Besides the agreement of both the overall shape of the time series of discharge and the total accumulated volumes between observed and simulated discharge, the Nash & Sutcliffe (1970) efficiency measure, NS, was adopted. In particular, it was computed for the log transformation of discharge in order to give more weight to the low values. NS is then given by:

Figure 1. Main drainage network of the Tiber River at P. Felcino with basin partition used in the model.The locations of the hydro-meteorological stations are also shown.

2

RESULTS

CASE STUDY

The model was applied to the Upper basin of the Tiber River located between the hydrometric sections of S. Lucia and P. Felcino with an area of 1105 km2 (see Fig. 1). This configuration was employed because the basin subtended by the upstream end is poorly monitored in terms of precipitation. The basin is located in Central Italy and its topography varies from 200 m above sea level in the south and 1080 m in the east. The region is characterized by a Mediterranean semi-humid climate (Pandey & Ramasatri, 2001) with precipitation occurring mostly in the autumn-spring period, when floods generally occur. Geologically, the basin is characterized by a flysch formation and 57% of the total watershed area is wood, the remaining area is 37% agricultural crop and 5% pasture. Based on the period 1951–1999, the average annual precipitation is about 890 mm. The maximum mean monthly precipitation occurs in November (130 mm) and the minimum in July (40 mm). In the summer period the minimum and maximum temperature are in average 16.9◦ C and 26.7◦ C, respectively; whereas in the winter period 3.2◦ C and 9.7◦ C, respectively. In the study area, a hydro-meteorological network is operating, as shown in Fig. 1, and the data are recorded with a time interval of 30 minutes. The rainfall recorded at 10 rainfall stations, distributed in and around the basin (see Fig. 1), was used to account for the spatial distribution of rainfall. Specifically, the Thiessen polygon method was adopted to estimate the spatially averaged rainfall distribution in each sub-basin.

where subscripts o and s denote observed and simu¯ o is the mean observed lated discharges, respectively; Q discharge; and M is the number of data. Optimal results for the calibration period were obtained with an NS value equal to 0.79 for the mean daily discharge. The model was tested through the overall set of 3-year data through the same criterion as used for calibration. In particular, the half-hourly results were aggregated to daily and monthly time scale. At daily scale the model kept the same level of accuracy of the simulation phase, with a slightly greater efficiency value, NS = 0.85. This is a satisfactory result in comparison to analogous outcomes obtained for similar applications presented in the scientific literature. Comparison between observed and estimated discharges is shown in Figure 2a and Figure 3a for daily and monthly data, respectively. As it can be seen, the model was able to reproduce with a fair accuracy the discharge time evolution both for daily and monthly discharge at P. Felcino site. In order to define the suffering scenarios for low flows in the natural channel network of the upper Tiber basin, a suffering index can be defined as the percentage of days having a discharge less than a prefixed threshold. Specifically, the threshold value was fixed as 15% of the pluriannual mean discharge computed by the model for each sub-basin (Smakhtin, 2001). Figure 5 reports the spatial variability of the suffering index. The left side of the intermediate basin shows a flow persistence during dry periods similar to that observed at the outlet of the basin, as can also been inferred through the duration curves (Figure 4).

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Figure 2. Comparison of daily discharge predicted by the model against observations for a) Tiber River at P. Felcino, and b) Assino at Serrapartucci.

Figure 4. Comparison of daily flow duration curve for the year 1997 predicted by the model against observations for a) Tiber River at P. Felcino, b) Niccone at Migianella, and c) Assino at Serrapartucci.

Figure 3. Comparison of monthly discharge predicted by the model against observations for a) Tiber River at P. Felcino, b) Niccone at Migianella, and c) Assino at Serrapartucci.

Figure 5. Spatial pattern of suffering index, LF, given by the percentage of days having a discharge less than 15% the pluriannual mean discharge.

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Indeed, the most of left sub-basins have a suffering index not exceeding 25%. On the contrary, for the right tributaries the percentage rises up to 60%, as it can be inferred from the Migianella duration curve where, on average, no flow occurred for 40% of the year. The model was also tested using time series of the discharge monitored at two hydrometric stations located inside the basin. The two stations are Migianella on the Niccone stream and Serrapartucci on the Assino stream, the right and left tributaries of Tiber River, respectively, with an area of about 150 km2 . At the monthly timescale, comparison between observed discharge and estimated one is shown in Figure 3b and 3c for the Niccone and Assino stream, respectively. From these figures, the model can be surmised to be almost accurate in predicting the mean monthly discharge for the two sub-basins. At the daily timescale, the flow duration curve and complete temporal evolution of discharge were used to assess the model performance. Figure 4 shows the comparison between observed and predicted flow duration curves for both the sub-basins and the Tiber River basin at P. Felcino. As can be seen, the duration curves were reproduced with a fair accuracy for the investigated sections, although they are characterized by a very different flow persistence during dry periods. Furthermore, the model accurately predicted the temporal variability of daily discharge for both sub-basins. In particular, Figure 2b shows the model results for the Assino sub-basin for which predictions were slightly less accurate than those obtained at P. Felcino section. 4

CONCLUSIONS

A simple continuous rainfall-runoff model proposed here was found fairly accurate in predicting discharge at different timescales (hourly, daily and monthly) for both the outlet section and the main tributaries of the Upper Tiber basin. The study has allowed us to define the low flow suffering scenarios for the channel network useful to

make decisions on the resource allocation. Moreover, due to the schematization of the basin in homogeneous sub-basins, the model can also provide decision makers with scenarios of potential anthropogenic interventions. Based on the low computational effort and data requirements, the model can be conveniently adopted to investigate long-term changes of surface water and ground-water quantity on medium-large basins. REFERENCES Brocca, L., Melone, F. & Moramarco, T. 2008a. On the estimation of antecedent wetness conditions in rainfall-runoff modelling. Hydrological Processes 22: 629–642. Brocca, L., Melone, F., Moramarco, T. & Singh, V.P. 2008b. Assimilation of observed soil moisture data in storm rainfall-runoff data. J. Hydrologic Engineering. (Submitted) Corradini, C., Melone, F. & Ubertini, L. 1995. A semidistributed model for direct runoff estimate. In M.H. Hamza (ed), Applied Simulation and Modelling: 541–545. Anaheim(CA): IASTED Acta Press. Doorenbos, J. & Pruitt, W.O. 1977. Background and development of methods to predict reference crop evaporatraspiration (ET0). In: CropWater Requirements. FAO Irrigation and Drainage Paper No. 24: 108–119. Rome: FAO. Nash, J.E. & Sutcliffe, J.E. 1970. River flow forecasting through conceptual models 1. A discussion of principles. J. of Hydrology 10: 282–290. Pandey, R.P. & Ramasastri, K.S. 2001. Relationship between the common climatic parameters and average drought frequency. Hydrological Processes 15: 1019–1032. Singh, V.P. 1992. Elementary Hydrology. Englewood Cliffs (NJ): Prentice-Hall. Smakhtin, V.U. 2001. Low flow hydrology: a review. J. of Hydrology 240: 147–186. Xu, C.-Y., Widen, E. & Halldin, S. 2005. Modelling hydrological consequences of climate change – progress and challenges. Advances in Atmospheric Sciences 22(6): 789–797.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Simulating water balance of the small-forested watershed using BROOK90 model E.A. Combalicer Department of Forest Sciences, College of Agriculture and Life Sciences, Seoul National University, Seoul, Korea College of Forestry, Nueva Vizcaya State University, Nueva Vizcaya, Philippines

S. Im∗, S.H. Lee, S. Ahn & D.Y. Kim Department of Forest Sciences, College of Agriculture and Life Sciences, Seoul National University, Seoul, Korea

ABSTRACT: The paper describes a simplistic water balance simulation in support of integrated water resources management. The water balance of the 15-ha watershed located at the southern part of Korea was analyzed to evaluate the model performance and to determine the fractions of precipitation that become streamflow, evaporation, surface flow and ground flow. A lumped hydrologic simulation model, BROOK90, was calibrated for the forested watershed. Results described that the model efficiency using r 2 (0.82 and 0.84) and NashSutcliffe (0.69 and 0.81) coefficients were fitted quite well over the monthly observed and simulated streamflow values. The model could also capture that high values of streamflow occurred during summer and low throughout winter season. The water balance investigation showed that about 46 percent of the annual precipitation remained evaporation, 39 percent as streamflow, and 15 percent for the seepage loss. In the ground water flow simulation, significant correlation was found using BROOK90 model compared to the PART and WHAT programs. Overall, it can be asserted that the partitioned amount of water varied from one component to another as affected by seasonal variations, canopy, soil, and drainage flow characteristics. Keywords:

1

BROOK90 model; integrated water resources management; streamflow; water balance

INTRODUCTION

About 65 percent (6.4 million ha) of the Korean Peninsula is covered by forests (KFS 2007). The high percentage of the forested areas is mostly dominated by Pinus densiflora, Pinus koraienses, Pinus rigida stands, and mixed evergreen coniferous and deciduous broad-leaved forests which have beneficial influence to the water regimes. Stable forest conditions have equal importance to water supply, balancing runoff dynamics and being a habitat for different plant and animal species. Forest soils have great capacity for water storage and infiltration, thus preventing or reducing surface runoff and soil erosion (Chang 2002). The protective functions of forest vegetation and forest soils have even greater magnitude especially in the low-lying areas. In this study, the lumped BROOK90 model was utilized with input based on available meteorological, vegetative, soil and hydrological characteristics from the small and forested watershed, the Bukmoongol ∗

Corresponding author ([email protected])

watershed, in Korea. The water balance of the Bukmoongol watershed is not well known. In the integrated water resources management perspective, knowledge of water balance can be used to help manage water supply and predict where there may be water shortages. For a sustainable forest management system, site specific knowledge of the water balance is a prerequisite (Schwärzel et al. 2007). The study aimed to 1) calibrate BROOK90 model for mixed forested watershed, 2) utilize the adaptability of the BROOK90 model in water balance analyses, and 3) determine the distributions of rainfall being converted to the streamflow, evaporation, surface flow and ground flow.

2

MATERIALS AND METHODS

2.1 Site description The study area is ◦the Bukmoongol small-forested watershed within 35 01 30 –35◦ 03 00 N latitude and ◦ 127◦ 36 00–127 37 30 E longitude located in the

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southern part of Korea. The total area of the watershed is about 15 hectares. It is one of the tributaries of the Choosan watershed. Elevation ranges from 120– 341 m above sea level (asl) with around 850 m stream length lying within the area. The watershed is dentritic in pattern with compactness coefficient and drainage density of 0.86 and 56.8 m/ha, respectively. The Bukmoongol watershed is a temperate mixed forest of pine and deciduous trees consisting mostly of Pinus densiflora, Pinus rigida and Fraxinus rhynchophylla with an average age of about 50 years old. Soils are mostly loam to clay loam in texture. The climate is classified as monsoon. Peaks of monthly temperatures recorded was −15.4◦ C in winter (January) and 34.9◦ C during summer (August). Annual precipitation averages about 1,388 mm and most of the annual rainfall occurs in a warm summer season from June to August. 2.2 Methods Meteorological and hydrological parameters have been monitored in the area since 1991. The meteorological parameters such as solar radiation, temperature, relative humidity, wind speed and rainfall were recorded in the experimental gauging station. The water level from rectangular sharp-crested weir was monitored using a gauge recorder and lately OTT Thalimedes. The meteorological and water level gauging stations are within the experimental forested watershed. Hydrometeorological data, physical characteristics and soil conditions of the watershed were used as input to calibrate the BROOK90 (Federer 2002) model. This is a typical plot model designed for small and uniform watersheds. However, some parameter values taken from published documents and actual surveys were adjusted, which can equally imitate the mixed forest conditions. Basically, the model was used to demonstrate the short-cut water balance computation particularly for a forested watershed. As a result, various hydrological components such as streamflow, evaporation, surface flow, and ground flow were determined on the basis of different algorithms and equations that are already integrated in the model. A detailed procedure of the model simulation and modeling is shown in Figure 1. An additional data set was considered to validate the observed and simulated streamflow of the succeeding four years of observations. Similarly, in the ground water flow validation, results of our previous investigations on the base flow separation using different methods (Combalicer et al. unpubl) were used for a model outcome comparison. PART (Rutledge 2005) and the Web-based Hydrograph Analysis Tool through the Recursive Digital Filter (WHAT-RDF) (Lim et al. 2004, Eckhardt 2005, Lim et al. 2005) methods were

selected as appeared consistent pattern in estimating the base flow in the study site. 2.3 The BROOK90 model The BROOK90 model (Federer 2002) was used to model the water balance. The model has a strong physically-based description, which simulates the above and below liquid phases of the precipitation– evaporation-streamflow-ground water flow part of the hydrological cycle for a point scale stand at a daily time-step. The model calculates evaporation with the Shuttleworth–Wallace approach (Shuttleworth & Wallace 1985), an improvement of the Penman– Monteith equation as well as the temporal and quantitative flow mechanisms within a catchment. The soil–water characteristics are defined using a modified approach of the Brooks & Corey (1964), Saxton et al. (1986), Saxton & Rawls (2006) from 11, 10 and 12 classified textural classes, respectively. The water movement through the soil is simulated using the Darcy–Richards equation. It considers water stored as intercepted rain, intercepted snow, snow on the ground, soil water in from one to many layers, and groundwater. Evaporation is the sum of five components: evaporation of intercepted rain and snow, snow and soil evaporation, and transpiration. Streamflow is generated using the following simplified processes: storm flow by source area flow or subsurface pipe-flow and delayed flow from vertical or downslope soil drainage and first-order groundwater storage. Further details are provided in the BROOK90 documentation manual (Federer 2002, Federer et al. 2003). 2.4

Parameters estimation

Calibration is an iterative procedure of parameter evaluation and refinement through comparing simulated and observed values (Jacomino & Fields 1997). It may be a process to measure values of the model parameter set until the observed and simulated values are in an acceptable efficiency coefficient. The coefficient of determination (r 2 ) and Nash-Sutcliffe coefficient,

Figure 1. The BROOK90 model adaptation for the small-forested watershed.

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which are widely used for hydrological model assessments, were considered. In this study, the calibration phase of the modeling effort was consisted of matching simulated and measured variables such as streamflow equivalent. The applied calibration was accomplished using an ordered approach of manual trial and error after attaining a good agreement between the simulated and observed values. Two data sets were prepared for the calibration (1992–94) and validation (1995–98) using the final parameter values. Calibrated canopy parameter values were selected based on watershed characteristics and values derived from literature. The soil water parameter values were derived from a modification of the Campbell (1974) expressions with the near-saturation interpolation of Clapp & Hornberger (1978). The matrix porosity for forest soil was computed using the Saxton & Rawls (2006) approach. Soil water parameter values were based primarily on the actual textural classes, organic matter, and bulk density. 2.5

Sensitivity analysis

A sensitivity analysis is used to determine how sensitive a model is to changes in the value of the parameters of the model and to changes in the structure of the model. It was performed to identify which parameters would be sensitive during the model predictions for the water balance analyses of the small-forested watershed. The BROOK90 model is composed of complex analytical parameters. An initial set of canopy parameter values based on evergreen forest type conditions documented in BROOK90 were utilized. Each variable was identified by its minimum and maximum range of values to find the numerical input limitations of the model. 3 3.1

RESULTS AND DISCUSSION Calibration and validation of the BROOK90 model

A comparison of the observed and simulated streamflow values during the calibration and validation Table 1.

periods is shown in Table 1. Results of the total streamflow simulation showed good agreement between the observed and simulated values with the final parameter sets using the BROOK90 model. There was a distinct seasonal variation in streamflow that also reflected the differences in precipitation and temperature inputs. Seasonal relative errors between observed and simulated values for the calibration period were approximately −1.5 percent for total streamflow, −51.8 percent for fall flow, 127.7 percent for spring flow, −7.1 percent for summer flow, and −28.3 percent for winter flow. During the validation period, the discrepancies were about −21.1 percent for total streamflow, −77.5 percent for fall flow, 49.9 percent for spring flow, −21.5 percent for summer flow, and −48.1 percent for winter flow. In the spring season, streamflow simulation appeared to be overestimating while throughout the remainder of the seasons it was consistently underestimated. This may imply a weakness in response to the simulation of low flows. Nevertheless, small disparity was distinguished in summer high flow simulations during the two observation periods. Outcomes of the simulation were most likely affected by the increasing rate of surface flow and ground water flow until reaching the saturation point occurred in May (Fig. 2). Thus, a large portion of precipitation became streamflow through surface flow even though a decreasing ground water flow. Stottlemyer and Toczydlowski (1996) also found a declining soil water level in late June and minimal stream response to summer rains, which they attributed to significant evapotranspiration in the forested watershed. In some instances, however, the rate of ground water flow was higher than surface flow as influence by soil moisture stores in winter and spring seasons on hydrological processes. Overall, the monthly average streamflow nearly conformed to both periods. Efficiency criteria are described as statistical measurements of how well a model simulation fits the available observations (Beven 2001). In this study, the computed coefficient of determination (r 2 ) was 0.82 and 0.84 for total streamflow during calibration and validation periods, respectively. Similarly, the simulation showed positive Nash-Sutcliffe coefficients

Summary of the streamflow calibration and validation for the forested watershed. Calibration

Characteristics Total Streamflow Seasonal Flow Variation Fall (SON) Spring (MAM) Summer (JJA) Winter (DJF)

Validation

Observed (mm)

Simulated (mm)

1390.79

1369.96

359.46 198.47 697.3 87.52

173.23 451.92 647.65 97.16

Relative Error (%)

Observed (mm)

Simulated (mm)

Relative Error (%)

−1.5

2967.86

2341.07

−21.1

−51.8 127.7 −7.1 −28.3

461.00 418.77 1887.68 199.65

127.87 627.73 481.97 103.50

−77.5 49.9 −21.5 −48.1

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120

Surface Flow Ground Water Flow

300 300 250

100 200 80 150 60 100

40

Streamflow/Evaporation (mm/mo)

Flow (mm/mo)

140

350

350 Precipitation Streamflow

Precipitation (mm/mo)

160

200 150 100 50 0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (mo)

50

20

Figure 3. Monthly distribution of the precipitation, streamflow and evaporation for the forested watershed.

0

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (mo)

Figure 2. Simulated average monthly streamflow, surface flow, and ground water flow using BROOK90 model for the forested watershed.

(0.69 and 0.81) for both periods. However, the coefficient during validation indicated a better performance between the simulated and observed streamflow than the calibration period. In some instances, this condition probably leads to an overestimation of the model performance during peak flows and under estimation during low flow conditions (Krause et al. 2005). It should be noted that the average total precipitation in the site was lower during the calibration. Results of moderately high Nash-Sutcliffe efficiency indices revealed that the mean value of the observed streamflow would have a better relation to the model.

3.2

Precipitation Streamflow Evaporation

250

Sensitivity analysis

Results of sensitivity analysis show that BROOK 90 model is sensitive to canopy and soil parameters. The model is most sensitive to canopy height, leaf area index, maximum plant conductivity, maximum length of fine roots per unit ground area, maximum leaf conductance, and average leaf width. Decreasing values of the identified sensitive variables had resulted to the increase in simulated streamflow. In contrast, increasing values had decreased its simulated flow. Similarly, changes in soil conditions greatly affected their simulation performance. Simulated values can reasonably increase if the site soil conditions are changed to closer sandy classes. A great disparity was also observed if the site is in more clayish conditions. Canfield & Lopez (2000) found the same observation to albedo, relative distribution of rainfall in the top three layers, flow and location parameters which are insensitive to any change particularly for allowing or disallowing deep drainage and porosity. Overall, an increase and decrease of the streamflow, evaporation, surface flow and ground flow is brought by factors related to canopy and soil characteristics.

Table 2. Summary of the water balance components for the forested watershed in Korea. Water balance variables

Mean annual (mm)

Distribution (%)

Precipitation Evaporation Flow Seepage

1374 629 531 214

100 46 39 16

3.3 Water balance simulation Figure 3 provides an overview of the annual average water balance components for the duration of the measurement period in the forested watershed in Korea. Approximately 46 percent of the precipitation (1374 mm) turned into evaporation, 39 percent became streamflow, and 15 percent into deep seepage loss (Tab. 2). The average total annual streamflow was approximately 531 mm. The peak monthly streamflow occurred in August (143 mm) while the lowest was recorded in November (5.4 mm). Similarly, the mean annual evaporation was approximately 629 mm for the conifer dominated watershed. The same pattern was observed in evaporation, which was highest in August (156 mm) and lowest in January (4 mm). With regard to the below ground liquid component, about 15 percent was recognized as ground water flow (base flow). In the BROOK90 model the ground water flow was equivalent to the seepage loss, assuming a minimal effect on groundwater storage below soil layers. The total annual ground water flow reached 214 mm, which was highest in May (41 mm) and declined for the rest of the year (Fig. 2). The monthly water balance variations demonstrated how evaporative losses affect the seasonality of streamflow components in the site. There is a distinct seasonal variation in water balance components, which reflects the variation in precipitation inputs in the four seasons. Evaporation losses are higher than

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80 Ground Water Flow (mm)

70 60 50

BROOK90 PART WHAT-RDF

40 30 20 10 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (mo)

Figure 4. Average monthly ground water flow simulated using three models.

streamflow during the summer season. Negative water balance around September indicated that evaporative losses can exceed precipitation inputs. Patterns established based on the monthly water balance of the mixed coniferous watershed implied continuous precipitation until attaining its peak period. This condition responded to the increased in streamflow discharge and increased in ground water flow until saturation even though increasing evaporation rate due to rising mean temperature fluctuations. In contrast, the decreasing precipitation trend plus seasonal change resulted in the slight decrease of evaporation losses, quick decreased of streamflow, and constant declined of the ground water flow. Under these conditions, Wu and Johnson (2008) state that forests must therefore rely on soil moisture stores or have access to ground water. The simulated annual evaporation loss rate (629 mm) in the watershed given the coniferous forest type was normally within an acceptable range. Komatsu et al. (2007) reported an evaporation loss range from 465 mm to 614 mm with the rainfall values ranging from 804 mm–1604 mm in Japan’s old coniferous forest. They found and concluded that coniferous forests do not evaporate more water than their broadleaved counterparts. In Korea, Chung (2002) reported about 43 percent of total water was disappeared through evaporation and the rest became available for the surface runoff and ground water flow. One way to validate the result of the simulation is by comparing it with other models (Wainwright and Mulligan, 2004). In this study, the BROOK90 simulated ground water flow was compared to the WHAT-RDF and PART programs. Figure 4 depicts the simulated average monthly ground water flow using three models: approximately 351 mm (PART), 250 mm (WHATRDF) and 218 mm (BROOK90), annual ground water flow for the Bukmoongol watershed. Results revealed that the BROOK90 seasonally varied its estimated mean monthly values weighed against the two methods involved. Simulated values were generally lower

for winter, summer, and fall seasons. During the spring season, simulated ground water flow appeared higher considering the early occurrences of moderate rainfalls and favorable temperatures, rising of surface flow, and slow rate of evaporation. These conditions facilitated the immediate infiltration to deeper soil layers until reaching the saturation point, a possible factor in explaining the discrepancy among models simulations. In the BROOK90 model, months of April and May ground water flow was not overestimated since it was only comparable to the simulated average monthly streamflow (Fig. 2). Unlike the two methods, the separation algorithm relied on the availability of the observed streamflow and filter parameters which could definitely neglect other factors that could contribute to seasonal base flow changes in the watershed. However, the three models are still logically comparable for the entire duration. The BROOK90 model is significantly correlated to the WHAT-RDF (0.539) and PART (0.573) models. On the basis of mean differences, the BROOK90 ground water estimates were relatively higher during the spring season and lower the rest of the year than the PART and WHAT-RDF models. The high mean difference of the methods involved was due to algorithm variations in simulating the ground water flow.

4

CONCLUSIONS

Essentially hydrological models are site specific and spatially changing. In this study, however, the water balance modeling was favorably described under mixed forest conditions. Considering the seasonal changes, vegetation conditions, soil properties, and physical characteristics of the watershed, the BROOK90 model demonstrated its valuable response to simulation processes. Distributions of the precipitation with respect to evaporation, streamflow, and seepage loss estimates were clearly refined according to their current conditions. Based on the results, the BROOK90 featured its simplistic approach for water balance analyses which might be useful for the integrated water resources management. It captured the issues of land cover type, seasonal variation influences and even the effects of days of negligible surface runoff. Findings of an investigation can be used to develop or even predict accurate hydrological characterizations in response to climate and land use changes over time. However, there is still a need to continue collecting relevant hydrological and eco-physiological data for parameter values improvement that determine long-term hydrological dynamics of the mixed forested watershed in Korea. The study recommends the use of appropriate automatic optimization techniques, which still seems to be lacking regarding the model’s applications.

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ACKNOWLEDGMENT This study was conducted with the partial support of Forest Science and Technology Projects (Project No. S210707L1010) provided by Korea Forest Service. REFERENCES Beven, J. K. 2001. Rainfall-Runoff Modelling – The Primer. Chichester: John Wily & Sons Ltd., 319p. Brooks, R.H. & Corey, A.T. 1964. Hydraulic properties of porous media. Hydro. Pap. 3: 1–27. Campbell, G.S. 1974. A simple method for determining unsaturated conductivity from moisture retention data. Soil Sci. 117: 311–314. Canfield, H.E. & Lopes, V.L. 2000. Simulating soil moisture change in a semiarid rangeland watershed with a process-based water-balance model. USDA Forest Service Proceedings RMPS-P-13: 316–319. Chang, M. 2002. Forest hydrology: an introduction to water and forests. CRC press LLC. Chung, S.W. 2005. The use and management of ground water in Korea. In: AOGS 2nd Annual Meeting. 20–24 June, 2005. Sustec Singapore. Clapp, R.B. & Hornberger, G.M. 1978. Empirical equations for some soil hydraulic properties. Water Resour. Res. 14: 601–604. Combalicer, E., Im, S., Lee, S., Ahn, S. & Kim D.Y. 2007. Simulating ground water recharge and base flow in the Bukmoongol small-forested watershed. Unpublished. Eckhardt, K. 2005. How to construct recursive digital filters for baseflow separation. Hydrological Processes 19(2): 507–515. Federer, C.A. 2002. BROOK 90: A simulation model for evaporation, soil water, and streamflow. http:// home.maine.rr.com/stfederer/b90doc.html. Federer, C.A., Vörösmarty, C. & Fekete, B. 2003. Sensitivity of annual evaporation to soil and root properties in two models of contrasting complexity. J. Hydromet. 4: 1276– 1290. Jacomino, V.M.F. & Fields, D.E. 1997. A critical approach to the calibration of a watershed model. J. of the American W. Resour. Asso. 33(1): 143–154.

Krause1, P. Boyle, D. P. & Base, F. 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences 5: 89–97. Komatsu, K., Tanaka, N. & Kume, K. 2007. Do coniferous forests evaporate more water than broad-leaved forests in Japan? Journal of Hydrology 336: 361–375. KFS, 2007. Korea Forest Research Institute. Major tree species distribution map. Seoul, Korea. Lim, K.J, Engel, B.A. Tang, Z. Choi, J., Kim, K.S. Muthukrishnan, S. & Tripathy D. 2004. WHAT: Webbased Hydrograph Analysis Tool. http://cobweb.ecn. purdue.edu/∼what/ (August 08, 2007). Lim, K.J, Engel, B.A., Tang, Z., Choi, J., Kim, K.S., Muthukrishnan, S., Tripathy, D. 2005.Automated web GIS based hydrograph analysis tool, WHAT. Journal of the AmericanWater ResourcesAssociation 41(6): 1407–1416. Rutledge, A.T. 2005. Program user guide for PART. http://water.usgs.gov/ogw/part/UserManualPART.pdf (January 30, 2007). Saxton, K.E., Rawls, W.J., Romberger, J. S., & Papendick. R.I. 1986. Estimating generalized soil water characteristics from texture. Trans. Amer. Soc. Agri. Engr., 50(4): 1031–1035. Saxton, K.E. & Rawls, W.J. 2006. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions. Soil Sci. Soc. Am. J. 70: 1569–1578. Schwärzel, K., Häntzschel, J., Grünwald, T., Köstner, B., Bernhofer, C. & Feger, K.H. 2007. Fundamentals of the spatially distributed simulation of the water balance of forest sites in a low-range mountain area. Adv. Geosci. 11: 43–47. Shuttleworth, W.J. & Wallace, J.S. 1985. Evaporation from sparse crops – an energy combination theory. Quart. J. Roya.l Meteorol. Soc. 111: 839–855. Stottlemyer, R. & Toczydlowski, D. 1996. Precipitation, snowpack, streamwater on chemistry and flux in a Northern Michigan watershed. Canadian Journal of Fisheries and Aquatic Science 53: 2659–2672. Wainwright, J. & Mulligan, M. 2004. Environmental Modeling: Finding Simplicity in Complexity. England: Jonh Wiley & Sons Ltd. p.56. Wu, K. & Johnston, C.A. 2008. Hydrologic comparison between a forested and a Wetland/lake dominated watershed using SWAT. Hydrol. Process. 22: 1431–1442.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Quantitative effect of land use and land cover changes on green water and blue water in Northern part of China L. Ren∗, X. Liu & F. Yuan State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, P.R. China

V.P. Singh Department of Biological & Agricultural Engineering, Texas A & M University, TAMU, Texas, USA

X. Fang, Z. Yu & W. Zhang State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, P.R. China

ABSTRACT: In order to investigate the effect of land use and land cover changes on hydrological processes in northern part of China, a distributed hydrological model was developed and applied in the Laohahe Catchment. Land use and land cover data for representing the vegetative cover over the catchment were obtained by remote sensing. The land cover data were available for 1980, 1989, 1996 and 1999. Daily streamflow measurements were available from 1964 to 2005 and were divided into 4 periods: 1964–1979, 1980–1989, 1990–1999 and 2000–2005, based on the land cover scenarios. The distributed hydrological model was coupled with a twosource potential evaportranspiration model to simulate daily runoff. Streamflow simulation was conducted for each period under these four land cover scenarios. The results showed that the change in land use and land cover had a significant influence on evapotranspiration and runoff. The land cover data showed that forest land and water body had decreased from 1980 through 1999 and farm land and grass land had increased. This change caused the vegetation interception evaporation and vegetation transpiration to decrease, whereas the soil evaporation tended to increase. Thus the green water decreased, and the blue water increased over the Laohahe Catchment. Keywords: distributed hydrological model; land use and land cover; hydrological response; runoff generation; green water; blue water 1

INTRODCTION

Blue water is visible liquid water moving above and below the ground as surface or sub-surface runoff, respectively. Green water is defined as the invisible vapour moving to the atmosphere (Falkenmark & Rockstrom 2004), including productive green water defined as the transpiration from plants and trees, and nonproductive green water consisting of soil evaporation and interception evaporation. Green water thus equates the commonly used term evapotranspiration, which combines productive and non-productive vapour in one term. It is a new concept to divide water into blue water and green water, which may effectively be utilized to study the effect of land use and land cover changes on the catchment water balance. ∗

Corresponding author ([email protected])

Quantification of the effect of land use and land cover changes on the partition of blue water and green water of a river basin has been an area of interest to hydrologists in recent years. However, the conversion of rainfall into runoff is complex and our understanding of the quantitative relationship between the land cover properties and runoff generation mechanisms is less than complete. Nowadays, hydrological models are being used to address the impact of land use and land cover changes (Lorup et al. 1998, Wilk et al. 2001, Chen et al. 2004). In this paper, we established a distributed hydrological model coupled with a two-source potential evapotranspiration model and applied it to investigate the effect of land use and land cover changes on hydrological processes of Laohahe Catchment in northern China. Using the direct evaporation calculated from the intercepted water, potential canopy transpiration and potential soil evaporation as input

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Figure 2. Digital elevation map of the studied area.

2.2 Data preparation

Figure 1. Location of Laohahe Catchment.

to the distributed hydrological model with parameters that were physically estimated, the effect of land cover change on the evapotranspiration process was quantified. The model parameter set was calibrated using the corresponding hydrometeorological data and land cover data during the same period. The effect of land cover changes was then modeled by applying the model to three other land cover scenarios.

2 2.1

STUDY AREA AND DATA PREPARATION Description of the study area

The Laohahe catchment, with a total drainage area of 7720 km2 , is controlled by the Taipingzhuang hydrological station (42◦ 12 N, 119◦ 15 E) and is situated in northern China. The river across the basin is the upstream tributary of the Laohahe River (Fig. 1). The main production approaches are agriculture and stock raising in the catchment, thus grass land and crop land are the dominant vegetations. The major driving force of land cover and land use change is increases in population and direction of national development polices. Since the foundation of China, there were four times large-scale reclamation in the catchment, however conversion of cropland to forest or grassland was also progressing at the same time. Elevation within the catchment ranges from 444 m to 1836 m above mean sea level, with the declining tendency from the southwest towards northeast. There are 19 rainfall gauges, 4 meteorological stations, and 1 hydrological station (Taipingzhuang) in the basin and the recorded data are available from 1964 to 2005. Annual average maximum (minimum) temperature is 14◦ C (2) ranging from −4(−16) in January to 29(18) in July. The average annual precipitation is approximately 451 mm, and the spatial and temporal distribution of precipitation is uneven. About 88% of the annual precipitation occurs during the months from May through September. Thus Laohahe catchment lies in a semi-arid region in northern China.

2.2.1 Topography The digital elevation model (DEM) data at the spatial resolution of 30s by 30s were obtained from the National Geophysical Data Center of the National Oceanic and Atmospheric Administration. The river network and the catchment boundaries (Fig. 1) were automatically generated by the digital elevation drainage network model (Martz & Garbrecht 1992). Figure 2 shows the DEM of the studied area. 2.2.2 Land use and land cover data Four scenarios of land cover maps (Fig. 3) at the spatial scale of the DEM were obtained by remote sensing and were used to represent the vegetative cover over the catchment during the period of 1964–2005. These land cover maps were interpreted from Landsat MSS, TM and ETM+ images with a variety of reference data. Decision tree classification method was used here based on the analyzed spectral characteristics and the spatial patterns of land cover classes. All the user’s and producer’s accuracies of the classification were respectively above 87.7% and 86.9%, and the overall accuracies were above 90.6%, and Kappa statistic were above 90.9%. The vegetation was classified into 3 types: the gross, namely forest land, grass land, and crop land. 2.2.3 NDVI data The NOAA-AVHRR NDVI dataset is available monthly for the globe at an interval of 8 km, covering the period from July of 1981 to September of 2001 except the data-missing period from September to December of 1994. The basin part of NDVI was clipped using the basin boundary and transferred into the Lambert Azimuthal Equal Area projection and the resolution of 30s in Arc/Info software. 2.2.4 Meteorological data The required meteorological data include daily mean, maximum and minimum air temperature, air and water vapor pressure, wind velocity, daylight duration, and precipitation. In this study, daily precipitation data were obtained from 19 rainfall gauging stations spread

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absorbed by canopy and soil (Wm−2 ), respectively; G is the soil heat flux (Wm−2 ); λ is the latent heat of vaporization (MJkg−1 ); ρ is the air density (kgm−3 ); Cp is the air specific heat at constant pressure (KJkg−1 ◦ C−1 ); γ is the psychrometric constant (kPa ◦ C−1 );  is the first-order derivative of saturation vapor pressure with temperature (kPa ◦ C−1 ); Wfr is the wetted fraction of the canopy; rc , rs , rac , and ras are the bulk stomatal resistance of canopy, soil surface resistance, bulk boundary-layer resistance of the canopy and aerodynamic resistance between the soil surface and canopy air space, respectively (sm−1 ); and D0 is water vapor deficit at the source height (kPa). For calculating the potential evapotranspiration consisting of potential canopy transpiration, potential soil evaporation, and direct evaporation from the intercepted water, Yuan (2006) improved the two-source evapotranspiration model:

Figure 3. Four land cover maps in Laohahe Catchment.

across the catchment, and other meteorological data were obtained from 4 meteorological stations around the catchment. Based on DEM, the key meteorological variables were topographically corrected with the empirical relationships extracted by data involved over the Laohahe catchment. All meteorological data were interpolated over the whole study area using the inverse distance square method (Ashraf et al. 1997). 3

MODEL DESCRIPTION

3.1 Two-source potential evapotranspiration model Potential evapotranspiration, PET, is generally considered to be the amount of water which would be lost to the atmosphere from a large land surface where water is enough to satisfy the atmospheric evaporative demand. Mo et al. (2004) developed a two-source evapotranspiration model based on the Penman-Monteith equation for actual evapotranspiration:

where Epc is the potential canopy transpiration, Eps is the potential soil evaporation, rcp is the bulk stomatal resistance of canopy while the soil moisture at field capacity, and rsp is the soil surface resistance while the soil moisture at field capacity. In this study, rsp = 300 sm−1 . The method of calculating evaporation from the intercepted water is the same as Eq. (3). The evaporation from water surface estimated by substituting the aerodynamic resistance of Penman wind speed function and rs = 0 into P-M equation (Shuttleworth 1993) was also considered in this study.

where es and ea are the saturation and actual vapour pressures (kPa), respectively; and u2 is the wind speed (ms−1 ) at a 2 m height. 3.2 Hydrological model

where Ec , Ei , and Es are the canopy transpiration, evaporation from the intercepted water, and soil evaporation, respectively; Rnc and Rns are the net radiations

The potential evapotranspiration was used to drive a grid-based distributed hydrological model. The evapotranspiration component was represented by a model of three soil layers (Zhao 1992). Runoff generation was based on a hybrid runoff model (Hu 1993), and runoff

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Table 1.

Calibrated parameter values and model calibration performance.

parameters∗

1964–1979

WUM (mm) WLM (mm) WDM (mm) b bx f0 (mm/h) fc (mm/h) k kg Ke (hr) Xe DC Bias (%)

20 80 40 0.22 0.25 21 3 0.2 0.88 25 0.26 0.709 0.31

1980–1989 20 80 40 0.17 0.32 20 3 0.39 0.94 26 0.33 0.621 −0.37

1990–1999

2000–2005

20 80 40 0.25 0.2 23 3 0.18 0.94 27 0.15 0.329 3.43

20 80 40 0.16 0.15 30 3 0.1 0.92 27 0.16 0.455 49.18

∗

WUM, WLM and WDM are tension water capacities of upper, lower and deeper layer, respectively; b is the exponential of tension water storage capacity curve; bx is the shape parameter of infiltration quantity cure; f 0 is the maximum infiltration rate; fc the static infiltration constant at a surface point; k is decay coefficient with time; kg is recession constant of groundwater storage; and Ke and Xe are parameters of the Muskingum-Cunge method.

routing was carried out using the Muskingum-Cunge method. Numerous field studies show that runoff within a basin is mainly generated by two mechanisms: infiltration excess runoff and saturation excess runoff. The hybrid runoff model combines the two runoff mechanisms by means of the combination of spatial distribution curve of soil tension water storage capacity and that of infiltration capacity. It was used in Laohahe catchment successfully for daily runoff simulation and flood forecasting. 4

MODEL CALIBRATION

Since the hybrid runoff model is a conceptual model, its 11 parameters had to be estimated through model calibration against observed catchment response. The performance of the model calibration was measured on the daily time step using the Nash-Sutcliffe efficiency (DC) and relative error (Bias). The results of model calibration during the 4 periods using the corresponding hydrometeorological data and land cover data are shown in Table 1. In Table 1, the best performance appears in the period of 1964–1979, while during the period of 2000–2005, the simulated discharge was much larger than the observed one. In the Laohahe catchment, the effect of human activities on streamflow has intensified since 1980. Ren et al. (2002) found that the amount of streamflow in northern China has a decreasing tendency in terms of natural basins and administrative regions. The increase of water use outside the stream channel is the primary reason, besides climate change, for the decrease of streamflow. Thus

Table 2. Land use and land cover in variation in Laohahe catchment, 1964–2005. Percentage (%)

Forest land Grass land Crop land Urban Others

1980

1989

1996

1999

25.9 29.1 42.7 0.6 1.7

16.9 66.8 16.1 0.2 0.0

31.6 16.1 48 1.9 2.4

10.5 46.5 42.9 0.1 0.0

the calibrated performance is acceptable, although not good performances appear during the periods of 1990–1999 and 2000–2005. 5

RESULTS AND DISCUSSIONS

5.1 Land use and land cover changes since 1980 The percentages of the main vegetation types from 4 land cover maps are shown in Table 2 which shows important changes in land use and land cover during the period of interest. The main land cover types are forest land, grass land and crop land, which account for more than 95% of the total area. The variation of land use and land cover in Laohahe catchment is complex from 1980 to 1999 and it is difficult to discern a consistent trend of change. Nevertheless, for the whole, the forest land decreased from 25.9% of the area in 1980 to 10.5% in 1999, despite increased to 31.6% in 1996; grass land and crop land both have an increasing tendency.

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Table 3. Mean annual potential evapotranspiration under 4 land cover scenarios during different periods (Unit: mm). Land cover scenario Period 1964–1979

1980–1989

1990–1999

2000–2005

5.2

Ei Epc EpS PET Ea Ei Epc EpS PET Ea Ei Epc EpS PET Ea Ei Epc EpS PET Ea

1980

1989

1996

1999

40.1 135.5 563.8 739.4 423.2 38.0 133.9 571.9 743.7 392.3 39.5 136.8 564.1 740.3 445.9 35.0 130.0 583.9 748.9 399.6

36.8 118.2 581.5 736.5 423.8 34.5 117.9 590.6 742.9 392.9 35.9 118.6 584.3 738.9 446.5 31.8 114.5 604.9 751.2 400.0

58.4 204.8 490.1 753.3 427.2 55.4 202.0 498.3 755.8 394.6 57.9 205.9 487.9 751.6 449.4 51.5 195.1 507.9 754.8 401.1

39.1 138.2 557.1 734.4 422.5 37.0 135.7 566.8 739.4 392.3 38.0 139.2 558.7 735.9 445.0 33.9 131.1 580.2 745.2 399.6

Figure 4. Spatial distribution of mean annual PET over the period of 1980–1999 under 4 land cover scenarios.

Quantitative effect of land use and land cover changes on green water

Incoming rainfall is partitioned into green water and blue water in the hydrological cycle. Land cover and soil characteristics can influence the rainfall partition. The daily direct evaporation from the intercepted water (Ei ), potential canopy transpiration (Epc ) and potential soil evaporation (Eps ) during 4 periods were computed using the two-source potential evapotranspiration model under 4 land cover scenarios. Table 3 presents the calculated mean annual values. In this table, the shaded portions show the results that were calculated from the corresponding land cover data and hydrometeorological data. Potential evapotranspiration (PET) is equal to the sum of Ei , Epc and Eps . Comparing the shaded results between the period of 1964–1979 and period of 1990–1999, mean annual PET during the latter period is larger than that during the former period by 12.2 mm. Comparing the results between the period of 1964–1979 and period 1990–1999 both under the 1980 land cover scenario, the latter mean annual PET is larger than the former one only by 0.9 mm, which may be regarded as the cause of the climate change. Comparing the results during the period of 1990-1999 under the 1980 and 1996 land cover scenarios, the latter mean annual PET is larger than the former one by 11.3 mm. This difference is caused by land cover changes. Thus, one can conclude that land cover change is the dominant

factor of the PET change between the periods of 1964–1979 and 1990–1999. Comparing the results during the period of 1964–1979 under the 1980 and 1996 land cover scenarios, the latter annual mean Ei is larger than former one by 18.3 mm, and the latter annual mean Epc is larger than the former one by 69.3 mm, the latter annual mean Eps is smaller than the former one by 73.7 mm and the latter actual evapotranspiration (Ea ) is larger than the former one by 4 mm. From 1980 to 1996, the forest land increased from 25.9% to 31.6%, the grass land decreased from 29.1% to 16.1%, and the crop land increased from 42.7% to 48% (Table 2).Thus the change of grass land into forest land and crop land in the Laohahe catchment caused the increment in Ei and Epc , the reduction in Eps , and the increment in PET and Ea . Similarly, comparing the results over the period of 1990–1999 under the 1996 and 1999 land cover scenarios, one can conclude that change of forest land and crop land into grass land in Laohahe Catchment caused the reduction in Ei and Epc , the increment in Eps , and the reduction in PET and Ea . Since Ea depends not only on PET but also on precipitation, the degree of change of Ea attributed to land cover changes is slighter than that of PET in the Laohahe catchment. Comparing Figure 4 with Figure 3, the distribution of mean annual PET is greatly influenced by the land cover data. The area with forest land could have a larger PET, and the area with the grass land could have a smaller PET over the Laohahe catchment. Taking the 1999 land cover scenario as an example, the mean annual PET of forest land, grass land and crop land over the period of 1980–1999 was 772.1 mm, 715.4 mm and 753.2 mm, respectively. 5.3

Quantitative effect of land use and land cover changes on blue water

Daily runoff during the period of 1964–2005 was simulated by the distributed hydrological model under 4 land cover scenarios. Table 4 presents the simulated mean annual surface runoff (Rs), groundwater runoff

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(Rg) and total runoff (R) during each period under those 4 land cover scenarios. The observed annual precipitation and runoff during the 4 periods are shown in Table 5. The shaded portions in Table 4 are the results that were simulated through the corresponding land cover data and hydrometeorological data. The effect of land use and land cover changes on the blue water (runoff) is contrary to the green water (evapotranspiration), because the sum of green water and blue water should be equal to the incoming rainfall in the hydrological cycle from the average viewpoint. As land cover changed from 1980 to 1996, grass land changing into forest land and crop land in the catchment, not only surface runoff also groundwater runoff decreased during the period of 1964–1979. The quantitative changes in land use and land cover from 1980 to 1996 are that the grass land decreased by 1004 km2 , the forest land increased by 440 km2 and the crop land increased by 409 km2 . The runoff ratio decreased from 0.0981 to 0.0896. When land cover changed from 1980 to 1999, forest land changing into grass land in the catchment, surface runoff and groundwater runoff during the period of 1964– 1979 increased. The quantitative changes in land use Table 4. Simulated mean annual runoff during each period under 4 land cover scenarios. Land cover map Period

Item

1964–1979 Rs(mm) Rg(mm) R(mm) Rr∗ 1980–1989 Rs(mm) Rg(mm) R(mm) Rr∗ 1990–1999 Rs(mm) Rg(mm) R(mm) Rr∗ 2000–2005 Rs(mm) Rg(mm) R(mm) Rr∗ ∗

1980

1989

1996

1999

7.23 37.86 45.10 0.0981 3.98 15.66 19.64 0.0473 2.95 46.78 49.75 0.1000 0.78 13.70 14.49 0.0342

7.22 37.22 44.45 0.0967 3.95 15.14 19.10 0.0460 2.95 46.18 49.14 0.0987 0.78 13.34 14.13 0.0334

6.91 34.25 41.17 0.0896 3.69 13.72 17.41 0.0419 2.82 43.30 46.14 0.0927 0.76 12.69 13.45 0.0318

7.30 38.41 45.73 0.0995 4.05 15.54 19.60 0.0472 2.99 47.60 50.61 0.1017 0.79 13.80 14.59 0.0345

Rr = runoff ratio

and land cover from 1980 to 1999 are that grass land increased by 1343 km2 , forest land decreased by 1189 km2 and crop land in creased by 15 km2 . The runoff ratio increased from 0.0981 to 0.0995. From these two results, one could conclude that the effect of land use and land cover changes on runoff is non-linear. The larger difference between simulated runoff ratio and observed runoff ratio during the period of 2000– 2005 (Tables 4 & 5) may be a result of the water use out of the river channel. 6

CONCLUSIONS

The purpose of this study was to assess the quantitative effect of land use and land cover changes on green water and blue water in the Laohahe catchment using a distributed hydrological model coupled with a two-source potential evapotranspiration model. The observed daily hydrometeorological data from 1964 to 2005 was divided into 4 periods: 1964–1979, 1980–1989, 1990–1999 and 2000–2005. The land cover scenarios for those four periods were developed using the land cover data obtained in 1980, 1989, 1996 and 1999, respectively. The annual interception evaporation, potential canopy transpiration, potential soil evaporation and actual evapotranspiration in 4 periods under each land cover scenario show that land cover change is the dominant factor for the change of potential evapotranspiration between the periods of 1964–1979 and 1990– 1999, and change of forest land and crop land into grass land in Laohahe Catchment caused the reduction in annual interception evaporation and potential canopy transpiration, the increment in annual potential soil evaporation, and the reduction in annual potential evapotranspiration and actual evapotranspiration. The distribution map of mean annual PET over the Laohahe catchment showed that the area with forest land could have a larger annual PET, and the area with the grass land could have a smaller annual PET. When land cover data changed from 1989 to 1999, the blue water during the period of 1980–1989 increased by 2.6%, which is inconsistent with the fact that the runoff ratio in the period of 2000–2005 was less than that in the period of 1980–1989, even though precipitations during the both period was comparable (Table 5). The effect of land use and land cover changes on blue water and green water is non-linear.

Table 5. Annual mean precipitation and runoff during those 4 periods.

Precipitation (mm) Observed Runoff (mm) Runoff ratio

1964–1979

1980–1989

1990–1999

2000–2005

459.7 44.96 0.0978

408.3 19.17 0.0470

497.5 44.61 0.0897

423.2 9.78 0.0231

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In general, the change of land use and land cover is not continuous, and small in consecutive years. However, the change is quite large on a decade basis. In this study, according to the availability of land use data, four scenarios of land use and land cover were selected for representing vegetative cover over the studied area during four different periods. This presentation has errors, to some extent. The analyses remarked above imply that four scenarios could represent the situations of land use and land cover in the corresponding periods, respectively. If conditions permit, annual land use and land cover change should be obtained by remote sensing or field investigation, and then the deduced result could be more reliable. ACKNOWLEGEMENT This study was supported by the National Key Basic Research Program of China under Project No. 2006CB400502. Also this research is the result of the 111 Project under Grant B08048, Ministry of Education and State Administration of Foreign Experts Affairs, P. R. China. This work was also supported by the Program for Changjiang Scholars and Innovative Research Team in University under Grant No. IRT0717, Ministry of Education, China and the Grand Sci-Tech Research Project of Ministry of Education under Grant No. 308012. REFERENCES Ashraf, M., Loftis, J., Hubbard, K.G. 1997. Application of geostatistics to evaluate partial weather station networks. Agricultural and Forest Meteorology 84: 255–271.

Chen, J.F., Li, X.B. 2004. Simulation of hydrological response to land-cover changes. Chinese Journal of Applied Ecology 15(5): 833–836. Hu, C.Q. 1993. Computational method of runoff generation in semi-humid and semi-arid regions. Proceedings of the Symposium on Hydrological Information and Forecasting, China Water Power Press, Beijing, China, 57–62 (In Chinese). Falkenmark, M. & Rockstrom, J. 2004. Balancing water for humans and nature: the new approach in ecohydrology. Earthscan, UK and USA. Lorup, J.K., Refsgaard, J.C., Mazvimavi, D. 1998. Assessing the effect of land use change on catchment runoff by combined use of statistical tests and hydrological modeling: case studies from Zimbabwe. Journal of Hydrology, 205: 147–163. Martz, W. & Garbrecht, J. 1992. Numerical definition of drainage network and subcatchment areas from digital elevation models. Computers and Geosciences 18(6): 747–761. Mo, X., Liu, S., Lin, Z., Zhao, W. 2004. Simulating temporal and spatial variation of evapotranspiration over the Lushi basin. Journal of Hydrology 285: 125–142. Ren, L., Wang, M., Li, C. 2002. Impacts of human activity on river runoff in the northern area of China. Journal of Hydrology, 261(1–4): 204–217. Shuttleworth, W.J. 1993. Evaporation. In: Maidment, D.R. (Ed.), Handbook of Hydrology. McGraw-Hill, New York, pp. 4.1–4.53. Wilk, J., Andersson, L., Plermkamon, V. 2001. Hydrological impacts of forest conversion to agriculture in a large river basin in northeast Thailand. Hydrol. Process. 15: 2729–2748. Yuan, F. 2006. Hydrological process modeling considering the effect of vegetation. Ph. D. thesis, Hohai University, China. Zhao, R.J. 1992. The Xianjiang model applied in China. Journal of Hydrology, 135(3): 371–381.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Development of a distributed water circulation model for assessing human interaction in agricultural water use T. Masumoto∗, T. Taniguchi, N. Horikawa & T. Yoshida National Institute for Rural Engineering, NARO, Tsukuba, Japan

K. Shimizu Tottori University, Tottori, Japan

ABSTRACT: This paper describes the development of a distributed water circulation model that characterizes and assesses human interaction in unique water uses for agriculture in the Mekong River Basin. A high proportion of water is used for agriculture in Monsoon Asia given the various types of paddy irrigation utilized there, and the distinct dry and wet seasons. The proposed model incorporates those characteristics and reproduces the mechanism of the water cycle in that region. The use of agricultural water in rain-fed paddies of the basin is first classified as three types of practices: using only rainfall, temporarily using supplementary water, and using floodwater. Irrigated paddies are also classified into six types based on the major types of irrigation and facilities employed. Secondly, the model consists of four sub-models used to calculate potential evapotranspiration, simulate cropping patterns and planting/harvesting areas, estimate the use of agricultural water, and analyze runoff components, respectively. Thirdly, actual evapotranspiration is estimated based on estimated water content in the root zone, one of the three layers modeled. Finally, model simulation was conducted for the years 1999 through 2003 and volumes of calculated discharge were compared with observed data at key points in the basin, showing a high degree of accuracy. Keywords: paddy

1

classification of water use; distributed hydrologic model; irrigation water demand; Monsoon Asia;

INTRODUCTION

The large portion of water used for agriculture, the various patterns of paddy rice irrigation, and the distinct dry and rainy seasons characterize the use of water in Monsoon Asia. The mechanism of the water cycle and the effects of water circulation changes on food production, however, have yet to be fully understood in this region. In addition to the lack of basic hydrologic data being collected, even the accuracy of existing data has not yet been verified. Clearly addressing such issues in view of the emerging food security problems in Monsoon Asia has thus become an urgent task. Hence, the effects of water circulation changes on food production should be investigated in terms of agricultural land use, water use, irrigation patterns, and other related factors. In order to analyze those effects, it is essential to utilize a runoff model for a specific area. There are ∗

Corresponding author ([email protected])

also examples in that hydrologic models are applied to practical aspects. None of the models proposed or used in the previous studies, however, included the components of agricultural water use in a specific area or the mechanism of the water cycle in agricultural land, although the dominant or most significant aspect of agriculture is the usage of water. At the same time, although much research has been conducted on irrigation and agricultural water use, such research is not linked. On the contrary, this body of research relied on hydrologic modeling technology, especially for detailed descriptions of the water cycle, and entailed socioeconomic analyses that largely ignored the hydrologic factors. This paper therefore proposes and develops a distributed hydrologic model integrated with agricultural water use in order to evaluate the effects of agricultural water use on the mechanism of the water cycle as applied to the Mekong River Basin (MRB), which is considered a representative river basin in Monsoon Asia, both in terms of area size and significance for water resources management.

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Table 1.

Classification of agricultural water use. Type

Classification

Area (km2 )

Gravity irrigation (Weir) 11,870 Pump irrigation 3,580 Irrigated Reservoir supply 6,030 Agricultural Colmatage system 890 water use Tidal irrigation (Sluice) 1,830 Tube-well (Ground water) 730 Rainfall 100,140 Rainfed Supplemental water use 42,920 Flooding water 6,830

2.2 Classification of agricultural water use Due to the large share of paddy rice in agricultural areas of the basin, this study mainly focuses on water use for paddy rice. Based on available literature and mainly our own field surveys, rain-fed paddies are classified as three types: fields using only rainfall, fields using supplementary water, and fields using floodwater. Irrigated fields were also classified as six types based on the type of irrigation type and facilities used: gravity irrigation, pump irrigation, reservoir water supply, the ‘colmatage’ system, tidal irrigation, and groundwater use (Shimizu et al. 2006b). Table 1 lists this classification of agricultural water use. The agricultural land of the MRB is mapped on 0.1◦ grids based on these categories (see Fig. 2). However, as noted the Section 2.1, irrigation ratios in the MRB are as low as 9%; therefore, these areas are partially irrigated and the hatching indicates the type of irrigation facilities used in those areas. The attribute table for the GIS-based map in Figure 1 contains such information as the number, size, and capacity of facilities used in irrigated areas.

Figure 1. Outline of the Mekong River Basin.

2 2.1

BACKGROUND AND FEATURES OF AGRICULTURAL WATER USE Study area

The Mekong River is an international river that flows through or along the borders of six countries. The Mekong is the largest river in Southeast Asia and 12th longest (4,200 km) in the world. Figure 1 shows the use of land in the MRB. The figure was derived from USGS 1-km mesh land use data obtained by reclassifying about 250 types of vegetation and land uses into five categories, such as irrigated and rain-fed paddy fields, irrigated and rain-fed upland fields, and others (mainly forest). Based on this data, the MRB covers about 800,000 km2 of land. Agricultural land occupies 43% of the basin, of which rain-fed areas account for 90%. In addition, paddy rice is grown in 90% of the rain-fed areas as shown in the figure. Therefore, agricultural production in the MRB largely depends on rice production in rain-fed areas, and it is assumed that rice production in rain-fed paddies will continue playing an important role in future food supply in the basin (Shimizu et al. 2006a).

3

STRUCTURE OF A DISTRIBUTED HYDROLOGIC MODEL

3.1 Fundamental structure The structure of a runoff model combined with agricultural water use is discussed here. A runoff model is developed based on a 0.1 degree mesh along with the variable source area concept. The model assumes three layers of soil or runoff modeling: root zone, unsaturated zone, and saturated zone. Then the Hortonian flow is assumed and calculated by using the GreenAmpt infiltration method. Secondary runoff caused by saturating soil with unsaturated through flow is counted from the water balance within the unsaturated layer and a local storage deficit in an unsaturated layer (Beven 1995). In the model, all water balance components are calculated in each cell. Accordingly, the Mekong River Basin is divided into 6,926 cells and

196

Figure 4. Calculation algorithm in a cell of the distributed hydrologic model considering agricultural water use.

Figure 2. Classification of irrigated lands by irrigation facilities in the lower Mekong River Basin (in 0.1 degree cells).

the channel width obtained from field observations and/or data acquired from Google Earth, assuming river cross sections and Manning roughness coefficients. The model consists mainly of four sub-models: i) potential evapotranspiration, ii) cropping patterns and planting/harvesting areas, iii) agricultural water use, and iv) runoff components and channel flows. 3.2 Calculation of potential evapotranspiration

Figure 3. Definition and combination of land use categories.

a flow direction of each cell is determined by using a 0.1 by 0.1 degree elevation map. Land use of the cells is expressed by the size ratios of irrigated and rainfed areas, forest, city, and water. Then the information on classified agricultural water use described above is input for irrigated and rain-fed area in the cells, respectively (see Fig. 3). Figure 4 shows the basic calculation algorithm of the distributed hydrologic model combined with agricultural water use. Runoff from a cell is input to its downstream cell in the next time step. The flow in the channels follows a kinematic equation. Parameters for channel and river flows were determined by utilizing

The FAO Penman-Monteith Method (FAO 1998) is used to calculate reference evapotranspiration (ET) with maximum and minimum temperatures, maximum and minimum humidity, wind velocity, and geographical and topographical information on a daily basis. Point data is interpolated to calculate the meteorological data of each cell. Then the actual ET is automatically estimated from the potential ET and soil moisture content in the root zone of the model in the process of running the total model. The weight of ratio of different land uses determines the parameters used for potential ET and runoff calculations. 3.3 Estimation of cropping patterns and planting/harvesting areas Paddies are classified as rain-dependent, rainwaterstoring, floodwater-utilizing, and irrigated paddies. For each category, a planting time, planting and harvested areas are estimated for each cell. 3.3.1 Estimation of planting commencement days for rain-dependent and rainwater-storing paddies Where possible to distinguish between rain-dependent and rainwater-storing paddies through satellite data

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planting commencement days are determined from the estimated results. Calculations are made with the planting delay day length being 30 days and the growing day being 100 days.

Figure 5. Examples of cropping patterns for each type of paddy.

and field surveys, a rainwater-storing paddies area ratio (Fr ) is calculated from the proportional areas to determine the respective paddy areas. A plantingpossible cumulative rainfall (PP) is set so that planting commencement times will match actual planting times. In the MRB, planting usually begins around mid-May. Accordingly, PP is set to equal 400 mm in this study, so that the planting commencement time for rainwater-storing paddies having the highest cumulative rainfall will be mid-May. 3.3.2 Planting patterns of rain-dependent and rainwater-storing paddies In a rain-fed paddy area, planting commences in the paddies most likely to be filled with water, and then gradually proceeds onward from these paddies. In order to represent this progress for a rainy season crop, the number of days that planting is delayed, the number of days for growing, and the number of days that harvesting is delayed are set. Planting patterns differ with different varieties of rice. (i) Non-photosensitive rice: Since the growing period does not vary with the day of planting non– photosensitive rice, the planting delay day, the growing day for each variety, and the reaping delay day are set. The program calculates a certain percentage of reduction in the harvesting area by using an IWMI formula according to the ratio of actual ET to potential ET (IWMI 2006). (ii) Photosensitive rice: Even if there is a delay in planting photosensitive rice, the reaping period is substantially the same. Therefore, the reaping delay day length is zero, and the planting pattern assumes a trapezium-like form (see Fig. 5). 3.3.3 Floodwater-Utilizing Paddies Floodwater depths for cells of floodwater-utilizing paddies are estimated with a developed flooding model in our study team (ie. Pham et al. 2006), and the day when the water depth falls below a plantingpossible depth is the planting commencement day for a floodwater-utilizing paddy. For this study, the planting-possible water depth is set at 150 mm, and

3.3.4 Irrigated paddies As water can be freely managed for irrigated paddies, a planting delay day length, reaping delay day length, and harvest reduction ratio are not set as for rain-fed paddies; only the planting commencement day and number of growing days are set. Various cultivation systems are employed in the irrigated paddies in our target area of the MRB, ranging from one-season to three-season cropping patterns. We assume that a two-crop system (one dry season crop and one rainy season crop) is applied to all irrigated paddies in this model. Where detailed data of planting systems is available, the real cropping patterns are set to match the actual cultivation system. 3.4 Estimation of agricultural water use The amounts of agricultural water used for both irrigated and rain-fed areas are input in the runoff model in addition to rainfall after converting the amount of water used by the cell area. Irrigation supplies the amounts of water consumed by permeation and evapotranspiration during the growing period of a dry season crop in irrigated paddies. Field water needs and net water use amounts in irrigated paddies are calculated. In addition, the gross water use is obtained by considering the irrigation efficiency between water-intake points and the paddies (IE). Should gross water use exceed the waterintake capacity of the irrigation systems, however, the water intake is limited. Actual irrigated water is determined by comparing available water (AW), which is total inflow from upstream cells, and the gross irrigation water requirement (GIWR). The design discharges of main canals in each irrigation system are input, then actual irrigated water is determined by comparing water demand according to the irrigated area with available water in the cell. On the other hand, regarding rain-fed paddies, water use based solely on rainfall is differentiated from that using supplementary water. Paddy areas with floodwater are initially specified by overlaying a map of the maximum inundated area for the year 2000 with the 0.1-degree grid map. As for parameters of the model, especially for agricultural water use, infiltration rate in paddies is assumed as 0.84 mm/d and small pump capacities for groundwater usage 77 mm/d, following our field observation. In addition, IE is assumed according to facility types, such as 1.0, 0.8 and 0.6 for tube- well, pumping and other irrigation, respectively.

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Figure 7. Estimated paddy area during cultivation period (2000) (Unit: %).

Figure 6. Schematic diagram of groundwater and intermediate runoffs.

3.5 Analysis of runoff components and channel flow Soil layers are divided into three layers and soil moisture is calculated from the water balance of each layer. These amounts of water flow are directed to the connecting soil layers in the downstream cell determined by the flow direction judgment, with the water balance being calculated (see Fig. 6). We introduced the storage function of paddies by plot levees with runoff outlets. The shapes of paddies and associated water management practices are virtually the same as those in other countries in Monsoon Asia. Therefore, the parameters of plot outlets are assumed to have height dimensions of 30 mm and a width density of one meter per hectare. The lower Mekong diverts into two rivers (the main channel of the Mekong to the east and the Bassac River to the west). Consequently, the mesh of a divergence location is determined from geographical information, and the flow direction of a downstream portion from the confluence is corrected to match the actual river system. 4 APPLICATION OF THE MODEL TO THE MEKONG RIVER BASIN AND DISCUSSION

in northeast Thailand (where rainfall is high), with planting being completed for almost all meshes by August 1. On October 1, the reaping commenced in some paddies had reduced the paddy area during the cultivation period. 4.2 Seasonal variations in actual ET

The proposed model was applied to water use and water circulation in the MRB. The results applied from 1999 through 2003 were as follows: 4.1

Figure 8. Monthly sequence of actual ET estimated by the model (2000).

Changes of cultivated paddy area

Figure 7 shows rain-fed paddy areas during a cultivation period on June 1, August 1, and October 1, 2000 as obtained from simulation using the model to estimate planting period and planting area. From these statistical results, planting is estimated to began in mid-May

The monthly cumulative actual evapotranspiration is depicted in Figure 8 for even-numbered months in the year 2000, as provided by the water supply and water use model. In June, the actual evapotranspiration increases from meshes with both rain-fed and irrigated paddies where the planting-possible cumulative rainfall has been exceeded. Then when reaping commences in October, it again decreases. This model is used to estimate the water available in each cell on a daily basis, thus allowing us to assess the relation between agricultural water use and basin water circulation.

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discharge for these five years was 30.4%. The final results at Pakse produced a fairly well calculated hydrograph, although there are differences between measured and calculated flows at the beginning of the rainy season. Similar comparisons were also made at Vientiane, Thakhek and other points. The advantage of the proposed model is that it can produce results at any place and any time in the target region. 4.5 Discussions

Figure 9. Storage of 12 large scale reservoirs in Thai-Mekong river basin from 1996 to 2000.

Figure 10. Comparisons of measured discharge with values estimated by the model at Pakse in the Mekong River Basin (1999–2003).

4.3

Irrigation water demand and irrigated areas

Respective amounts for the 230 reservoirs having volumes of 1 million m3 or more in the MRB (Masumoto et al. 2007) were calculated. Then, irrigation withdrawal water and reservoir storages were estimated at all irrigation projects for 20 years by the model. The estimated and observed total storage fluctuations of 12 large-scale reservoirs are carried out (Fig. 9). The observed values agree with the results estimated by the model. The model also estimates the rice planted area of dry season and rainy season paddies and dry upland fields for all irrigation projects. The estimated dry rice cropping areas in Northeast Thailand (19 provinces) are compared with the measured area (Horikawa, 2006). 4.4 Comparison of estimated discharge with observed flows Figure 10 compares the actual discharge with measured flows at Pakse over five years (1999–2003). The relative error between the observed and estimated

The proposed model implicated many new and quite unique features in the field of “water and food.” These features are listed as follows: i) The model is a fully distributed hydrologic model in which each cell is independently connected to each other, thus helping us to determine the possible amount of intake for agriculture, and the return flow into the river from agricultural lands. ii) In order to estimate water requirements, trapezoid cropping patterns were introduced to allow us to calculate the reduction in harvesting area from planting area. iii) Rain-fed paddies were considered as paddy plots that utilize only direct rainfall, but new types of water use, such as a supplementary water supply and paddies that use floodwater were newly introduced. In addition, all irrigation facilities/equipment were categorized and coupled with water circulation. It is possible to evaluate the affects of changes in types of irrigation on food production. iv) Actual evapotranspiration (ET) is estimated as the product of soil moisture determined by the runoff model, and the reduction in planting area extending to harvesting area is also calculated based on actual ET. v) The effects of human activities in agricultural practices on water circulation are evaluated through irrigation efficiency and/or changes to this parameter. 5

CONCLUSIONS

This study developed a prototype distributed hydrologic model that integrates agricultural water use sub-models, and applied it to the Mekong River Basin where agricultural water use, especially for paddy rice cultivation, accounts for the major consumption of water. Three types of agricultural water use are classified for rain-fed paddies and six types for irrigated paddies, with these classifications being modeled and combined with the hydrologic model to estimate the water available for agriculture in each cell. Then the simulation results were verified with data observed at certain points along the main flow. Moreover, flow networks were modified and flood simulations were linked to introduce flood water use in analyzing the lower part of the basin. This model proved quite useful in assessing the effects of agricultural water use on water cycle changes in the basin.

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ACKNOWLEDGEMENTS The authors wish to acknowledge their deep gratitude for the invaluable support provided by the Revolutionary Research Project on “Coexistence of People, Nature and the Earth (RR2002, August 2003 to March 2007)” of Japan’s Ministry of Education, Culture, Sport, Science and Technology (MEXT), and the “Assessment of the Impact of Global-Scale Change in Water Cycles on Food Production and Alternative Policy Scenarios”(April 2003 to March 2008) and “Assessment of the Impact of Climate Change on Agricultural Water Use and Low-lying Paddies”(April 2008 to March 2011) projects sponsored by the Agriculture, Forestry and Fisheries Research Council (AFFRC) of Japan’s Ministry of Agriculture, Forestry and Fisheries (MAFF). REFERENCES Food and Agriculture Organization. 1998. Crop evapotranspiration – Guidelines for computing crop water requirements – Irrigation and Drainage Paper No. 56, FAO: 300p. Horikawa, N. 2006. Estimation of Irritated Area, Return Flow and Reservoir Storage by an Irrigation Water Demand Model in Northeast Thailand. Proceedings of International conference on Mekong Research for the People of the Mekong: 110–114.

International Water Management Institute (IWMI). 2006. Preliminary study on the elaboration of water management and main supply factors of global food production. Report on the Achievement of the Contracted Undertaking:1–11. Masumoto, T., Tanji, H., Ogawa, S., Horikawa, N., Rikimaru, A., Kubo, S., Somura, H. & Laal, R. 2007. Development of prediction model fo the Change of Agricultural Water Use in the Asia Monsoon Region. Bulletin of the National Institute for Rural Engineering, No. 46: 67–90. (in Japanese) Molden, D. 2001. Meeting the water needs for food and environmental security. Proc. of the 8th International Symposium on Water for Sustainable Agriculture in Developing Regions, Japan International Research Center for Agricultural Sciences (JIRCAS): 19–32. Pham, T. Hai, Masumoto, T., & Shimizu, K. 2006. Evaluation of Flood Regulation Role of Paddies in the Lower Mekong River Basin Using A 2D Flood Simulation Model. Annual Journal of Hydraulic Engineering, JSCE, 50: 73–78. Shimizu, K. & Masumoto, T. 2006a. Classification and Mapping of Agricultural Water Use in the Mekong River Basin, Transactions, Japanese Geomorphological Union, Vol. 27, No. 2: 235–244 (in Japanese). Shimizu, K., Masumoto, T. & Pham, T.H. 2006b. Factors Impacting Yields in Rain-fed Paddies of the Lower Mekong River Basin, Paddy and Water Environment. Vol. 4, No. 3: 145–151. Beven, K. et al. 1995. TOPMODEL. In Singh, V.P. (ed.), Computer Models of Watershed Hydrology. Water Resources Publications: 627–668. Littleton: Water Resources Publications.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Sensitivity analysis to evaluate the effect of land use change on discharge rate F.A. Soria∗ & M. Sawamoto Tohoku University, Graduate School of Engineering, Sendai, Japan

S. Kazama Tohoku University, Graduate School of Environmental Sciences, Sendai, Japan

ABSTRACT: Potential effects of land use change on total discharge are assessed by evaluating the spatial trends of sensitivity indices. Given the uncertainty about future land use scenarios, a statistical approach was considered. 11 land use types were randomly combined into 264 scenarios, a distributed rainfall-runoff model was applied in Central Bolivia, and the outputs generated were used to estimate sensitivity indices by applying Monte Carlo based Sobol’ method. Our results showed that variances observed during peak and recession flows are more likely to be influenced by land use units close to the outlet; land use units located far from the main channel were observed to be influential on the variance of time steps previous to event occurrence. Keywords:

1

Monte Carlo experiments; sensitivity indices

INTRODUCTION

To assess the impact of land use change on the environment generates considerable interest among researchers and planners. Evaluation is commonly carried in regions where anthropogenic processes have already changed natural conditions (e.g. Siriwardena 2006), and areas where future land use scenarios can be drawn having a clear notion of upcoming changes (e.g. Niehoff 2002). Alteration of land cover frequently alters hydrological regimes, hence the use of rainfallrunoff models as a tool to quantitatively assess the nature of those changes is common. The framework of the evaluation process is relatively simple, and as in most modeling practices, is subject to uncertainties due to data quality, model structure and model parameters, and in some cases due to legislative and social gaps playing an important role in developing effective planning policies. The latter is of particular interest to identify and define hypothetical land use change scenarios. Being an obstacle difficult to overcome, the process can be reasonable treated using statistical techniques. This paper aims to evaluate the effects of land use change in the variance of observed discharge rate at the outlet of a basin in Central Bolivia. Model outputs were obtained from a particular distributed ∗

Corresponding author ([email protected])

rainfall-runoff model, and discharge variances were estimated using distributed sensitivity indices under Monte Carlo based Sobol’ scheme. It was particularly important to evaluate the effect of the spatial arrangement of land use units, leaving apart uncertainties introduced by other sources.

2

STUDY AREA AND LAND USE SCENARIOS

2.1 Caine River and data Caine River basin is located in the transition from the Andean to the inter-Andean region, covering an area of approximately 10,000 km2 at Molineros stream gauge. The catchment feds one of the most ancient and largest irrigation areas in the country, hence its importance. Geographical heterogeneity characterizes the region. Altitudes vary from 2500 m to 2700 m in the central region, up to 4000 m relative to mean sea level in the northern part of the basin (the Tunari Cordillera). Climate is predominantly semiarid, with mild weather, and annual potential evapotranspiration rate higher than precipitation. Average temperatures vary from 12◦ C in the east and the south, 19◦ C in the central region, and 7◦ C to 15◦ C toward the Cordillera. Mountainous regions are characterized by geological formations product of deflections, meanwhile valleys and

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regions in the transition to the mountains are characterized by medium (grave, sand) to fine granulometry (lime, clay), product of rising blocks forming a depression later filled by fluvio-glacial sediments. Land use is predominantly agricultural, with a protected area in the north (Tunari Cordillera), urban and industrial interferences located in the northern and the eastern part of the basin. Daily records were obtained from 11 meteorological stations controlled by the National Meteorological and Hydrological Service, Bolivia. Potential evapotranspiration ET was calculated using a modified Penmann equation considering data from 4 stations; areal precipitation and ET were estimated using the inverse distance method. Model performance was evaluated considering only observed records at the outlet of the basin. Contour lines at 100 m intervals were used to construct a square gridded digital elevation model (DEM). Due to the high computational effort demanded by Sobol’ method, experiments were carried at a daily resolution, during the period 1975–1975, and considering input DEM at 8 km resolution.

flow routing is described by a kinematic wave equation derived from the continuity equation as

2.2

where Smax = maximum condition of Ss ; Fmax = maximum condition of Fo ; Smin = minimum condition of Ss ; Fmin = minimum condition of Fo ; dayrate = expected rainfall depth to soil saturation; and dr = total rainfall depth for the current calculation time step. Storage depth S is estimated with a storage function, controlled by parameters K (dimensional) and m (dimensionless) as

Land use scenarios

The study area is going through complex sociopolitical changes. Land tenure has been an issue of debate, and proposals referred to its sustainable use do not seem to find consensus. Under such panorama, it can be expected a redistribution of current land use patterns into multiple future arrangements; therefore, is seems reasonable to define a finite number of probable “random” combinations of land use units based on current observed patterns. Definition of land use scenarios requires an appropriate sampling methodology, and Monte Carlo based Sobol’ scheme (Chan et al. 2000) was selected as the most suitable. Sobol’ is designed to fully sample the variation range of each factor, offering improved convergence over Latin hypercube and random sampling methods; moreover, samples in such way generated can be further used for the variance based sensitivity analysis method selected (described in the next section), since they implicitly consider interactions among land use units. As a Monte Carlo based technique, Sobol’ scheme evaluates a deterministic model using randomly sampled sets of model parameters as inputs, and is commonly used for uncertainty assessment.

3 3.1

where A = flow cross-sectional area; t = time; Q = discharge; x = distance along the longitudinal axis of the watercourse; q = lateral inflow or outflow per lineal distance along the watercourse; r = rainfall rate; E = evapotranspiration rate; B = surface flow width; and h = surface flow depth. The infiltration module assumes that soil saturation (Ss ) and initial infiltration (Fo ) vary linearly; based on Eq. 3, the actual value of infiltration Fis estimated as

where qs = runoff depth; and re = effective rainfall. To calculate groundwater storage, the storage function is modified as

where qin and qout are referred to groundwater. The model includes an additional module to account overland flow, which is routed from cell to cell by flow direction vectors toward an explicitly defined river network. Routing in the main channel is described by a dynamic wave model, where St. Venant equations of mass (Eq.1), and momentum conservation are used to calculate water depth and water velocity as

METHODS Distributed model

Kazama et al. (2004) suggested the structure of the distributed model used in this work. There, overland

where g = gravity acceleration; H = water level; v = flow velocity; and n = Manning roughness.

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3.2

4

Sobol’ sensitivity indices

In general, variance based sensitivity methods account the variance contributed by k model factors x to model output Y = f (xi , xj , .., xk ). Contributed variances are commonly quantified using non dimensional sensitivity indices, which depending on the method considered, can theoretically account the influence of the variations on a single factor x, and on the mutual interactions involving x and other factors (i.e. model parameters). In this work sensitivity indices were used as an alternative to identify the relative influence of land use scenarios on total discharge calculated at the outlet of the studied basin. As described by Chan (2000), the technique is based on the decomposition of the total variance V observed in the model output function Y , into summands of decreasing dimensions as

with

where E(Y /xi ) = expectation of Y conditional on xi . Terms of the decomposed total variance are used to estimate sensitivity indices of first order Si = Vi /V , second order Sij = Vij /V ,and so on. Among them, perhaps the most important is the total order sensitivity index STi = 1 − V−i /V , which theoretically accounts the main effect of parameter xi as well as all its mutual interactions, for instance useful when aiming to separate influential from non-influential factors. For the calculation of the indices, we considered the method presented by Chan (2000), who enhanced the scheme by reducing the number of demanded model runs to n(2k + 2), where n = size of the sample considered in the Monte Carlo schemes used to solve the decomposition of Y ; and k = number of model factors to be sampled. Details about the numerical schemes are detailed in the works of Tang (2006), and Lilburn (2006). Sensitivity indices can be estimated at each grid cell since the theoretical framework does not rely on performance measures. The method is very flexible, and widely applicable when enough information is available to assess the spatial patterns of the variable described. Even then, the high computational effort demanded may reduce its applicability.

4.1

RESULTS AND DISCUSSION Computational experiments

Two modules of the distributed model were assumed affected by land use change: overland flow, and infiltration; the analysis was further simplified considering invariable conditions in other modules/parameters. Computational experiments were carried being aware of the high model sensitivity to the groundwater storage function used, and the expected low sensitivity to the infiltration module (Soria 2008). The methodology applied is based on the approach presented by Tang (2007). The study region was covered with 178 cells at 8 km resolution, and currently observed land use patterns were grouped into 11 units not necessarily contiguous. To generate the sample, each land use unit was assumed to vary form currently observed conditions, into others already observed in a different unit; only values in units representing urban and protected areas (the Tunari Cordillera) were considered permanent. Characteristic values of surface roughness, maximum potential retention, and soil storage capacity followed the patterns defined by land use units (see Table 1). Sobol’ sampling was applied under mentioned conditions, 264 land use scenarios were identified, and generated sample sets (i.e. scenarios) were used as inputs to the distributed model. Values of remaining parameters were assumed based on characteristic values. Finally, simulations were run at a daily and a monthly basis, and the values of calculated discharges were used to calculate sensitivity indices at the outlet of the catchment. Estimation of the indices demanded high computational effort, therefore for the experiments carried

Table 1.

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Parameters for land use scenarios. fmax∗∗

Smax∗∗

Unit code/Soil cover/land use

n∗

mm/h

m

La/No cover/no cover Lb/Urban areas/urban areas L1/Sparse brush/natural areas L2/Graminoid/extensive use L3/Sparse brush/intensive use L4/Sparse forest/pasture L5/Sparse brush /extensive use L6/Brush/natural area L7/Graminoid/extensive use L8/Sparse brush/natural areas L9/Graminoid/extensive use L10/Light brush/pasture L11/Sparse brush /extensive use

0.04 0.05 0.05 0.05 0.06 0.05 0.06 0.05 0.05 0.05 0.04 0.04 0.04

244 45 85 104 125 120 45 109 72 177 192 149 64

0.75 0.75 1.25 0.50 1.25 0.75 0.50 0.40 0.40 0.75 0.75 0.50 0.40

∗

Manning roughness. Infiltration parameters in the distributed model

∗∗

Figure 1. Monthly time series of sensitivity indices (lower figure) compared to correspondent average monthly discharge at the outlet of the basin (upper figure). High values of STi would represent high influence of the land use unit.

Figure 2. Daily time series of sensitivity indices (lower figure) compared to correspondent average daily discharge at the outlet of the basin (upper figure). High values of STi would represent higher uncertainty contribution from the land use unit. Only representative units were plotted at this time.

at a daily resolution, only a referential period was considered in the analysis.

number of model runs necessary to accurately estimate sensitivity indices. As a result, represented trends were distorted, affecting units L9, and L3 located upstream and relatively far from the main channel, whose high contributed variance observed before event occurrence was probably overestimated. Units L5, L2 and L9 presented similar problems. Trends with special characteristics were observed in remaining units. Unit L4 was composed by one part located near main channel and the other far from it. The resultant trend presented high influence during peak discharge, and a similar response before event occurrence. A second group that presented special behavior was composed by units L3, L6, L7 and L11, with significant contributed variance during a range of time steps, even without having a location geographically significant to runoff generation, and being particularly small (the three first cover approximately 9% of the total area).

4.1.1 Monthly analysis Modelling results at a monthly resolution averaged process interactions, and simplified the interpretation. The summary presented in Figure 1 suggested that the influence of land use change was variable and dependent on the spatial location of the unit. Cells near the outlet were more significant during high discharge. Land use unit L8 was the closest location to the catchment outlet; it presented total order sensitivity trends that varied together with those drawn by the hydrograph, and despite having a size representing only 5% of the total area, was the unit with highest uncertainty contribution during peak flow. The behavior was demonstrated not to be constant, when trends observed in land use units L5 (20% of the area), L2 (40%), and L9 (8%), located on both sides of the main channel, presented an influence that decreased together with the size of the units. Land use units near the catchment outlet were previously identified as an important uncertainty source during high flows; thereby contiguous cells were also expected to be important contributors. The influence of L1 (a unit that discharges into L8) reached its peak during hydrograph recession, being the highest and the most significative when compared against others. It confirmed the importance of the region containing both land use units. Model performance uncertainty was higher during low flow than during high flow conditions, due to an enhanced sensitivity in the numerical schemes considered in the structure of the rainfall-runoff model. Such aspect in turn enhanced the negative effect of our generalizations and assumptions, and increased the

4.1.2 Daily analysis Analysis of daily time series was expected to be more complicated, and higher uncertainty was expected since interactions at such scale were stronger. Taking as a reference the results obtained at a monthly resolution, we reduced the number of land use units to: L8 (significant for peak discharge); L1 (significant for hydrograph recession); L4 (important before the occurrence of main events); L3, L6, and L11 (in order to confirm whether or not their trends remained uncertain). It was confirmed that changes on grid cells closer to the catchment outlet were the most influential on peak discharge uncertainty. At a daily resolution, land use unit L8 was again recognized as the most important uncertainty contributor to both peaks (Fig. 2),

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following trends similar to those described by the hydrograph that confirmed the importance of such locations on runoff generation. Far from the clear tendency observed at a monthly resolution, it was not observed a significant variance contribution to hydrograph recession from a particular unit: the response of L1 was under our expectations, and the uncertain trends described by units L3 and L11 affected their reliability. Contributions from land use unit L4 were still important during main events, but were no longer the highest during dry conditions. Moreover, its effect was dimmed by the trends of cells L3, L6 and L11, which in turn presented a behavior that remained difficult to be characterized. It was not possible to identify especially significant contributors during low flow conditions. Sensitivity of numerical schemes and error sources mentioned before, produced less noise in the trends observed, apparently implying a lower influence than the one observed at a monthly resolution. 5 5.1

CONCLUSIONS Computational experiments

Land use change effects on watershed discharge were evaluated using a variance based sensitivity technique. Sobol sampling method was employed to generate a “random” sample; 264 combinations (i.e. scenarios) based on 11 land use units were defined; and correspondent characteristics of surface roughness, maximum potential retention, and soil storage capacity were also derived for the watershed studied. Generated samples were used as inputs, computational runs were carried, model outputs (i.e. discharge rate at the outlet of the basin) were qualitatively evaluated, and the values were used to estimate spatially distributed total order sensitivity indices under Sobol’ scheme. A model with low sensitivity to infiltration was applied in a mid-sized catchment, not dominated by surface runoff processes, and characterized by native graminoid species and sparse brushes. Those conditions became the first obstacle, since the conversion from current into assumed land use types did not have notorious effect on catchment response at the outlet. Since our experiments were highly influenced by the tools used and the environment where they were applied, different results should be expected when considering scenarios that will more likely cause drastic changes in land cover (e.g. conversion of forest into crop land), or in environments where watershed response is dominated by runoff processes (e.g. mountainous catchments). 5.2

Sensitivity analysis at different resolutions

A statistical method was selected to cope with all uncertainties introduced by our assumptions and the

methods considered. Sobol’ method is computationally expensive in terms of time consumption, and was used to describe the nature of the changes assumed in land use in a monthly and daily basis. At both resolutions, overestimated indices (i.e. values higher than 1) were frequently found during high flow events, establishing the necessity of a high number of iterations. The results obtained during hydrograph recessions and low flow conditions were uncertain, and affected by various error sources. Monthly time series were certainly easier to be analyzed, since process interactions were averaged. The total uncertainty observed at the outlet of the basin was contributed in proportions variable in time and location by every land use unit. It was observed that cells closer to the outlet were more likely to dominate peak discharge in rates highly dependent on their relative size, meanwhile cells located near the main channel were more likely to dominate hydrograph recession. Even though remaining cells seemed to dominate dry conditions, uncertain model performance during such periods demanded additional information to confirm our observations. At a daily resolution, it was confirmed the dominance of cells near the catchment outlet on high flows.A different behavior was observed on remaining cells, including those located to the sides of the main channel, whose trends were difficult to characterize. It was not possible an individual characterization of land use units highly influential on dry conditions, since they described superimposed trends, although less noisy than those observed at a monthly resolution. Sensitivity indices demonstrated to be an effective technique to statistically describe and evaluate the behavior of a large number of model outputs, despite the fact that there is no way to determine the number of experiments necessary to produce an accurate estimation of the indices. Increasing the number of iterations would improve the results obtained, and by considering additional uncertainty sources not covered in this work, it would be possible to provide enough evidence to determine the spatial variability of dominant locations in watersheds. ACKNOWLEDGMENTS This work was supported by the Global Environment Research Fund (S-4) of the Ministry of Environment, Japan. REFERENCES Bronstert, A. 2004. Rainfall-runoff modeling for assessing impacts of climate and land use changes, Hydrological Processes 18: 567–570. Chan, K., Tarantola, S., Saltelli, A. 2000. Variance based methods. In Saltelli, A., Chan, K., and Scott E.M.(eds)

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Sensitivity Analysis: 190–195, 174–177, 181 Chichester: Wiley. Kazama, S., Hyejin, K., Sawamoto, M. 2004. Uncertainty of morphological data for rainfall-runoff simulation, Proceedings of the International Conference on Sustainable Water Resources Management in the Changing Environment of the Monsoon Region 1: 400–406. Lilburne, L., Gatelli, D., Tarantola, S. 2006. Sensitivity analysis on spatial models: a new approach, 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 329–337. Niehoff, D., Fritscha, U., Bronstert, A. 2002. Land-use impacts on storm-runoff generation: scenarios of land-use change and simulation of hydrological response in a mesoscale catchment in SW Germany, Journal of Hydrology 267: 80–93. Siriwardena, L., Finlayson, B.L., McMahon T.A. 2006. The impact of land use change on catchment hydrology in

large catchments: The Comet River, Central Queensland, Australia, Journal of Hydrology 326: 199–214. Soria, F., Kazama, S., Sawamoto M. 2008. Evaluation of predictive uncertainty in distributed rainfall-runoff models, Annual Journal of Hydraulic Engineering, JSCE 52: 73–78. Tang, Y., Reed, P., Wagener, T., van Werkhoven, K. 2006. Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation, Hydrology and Earth System Sciences Discussions 3: 3333–3395. Tang Y., Reed P., van Werkhoven K., Wagener T. 2007. Advancing the identification and evaluation of distributed rainfall-runoff models using global sensitivity analysis, Water Resources Research 43: W06415.

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An integrated hydrological model for the long-term water balance analysis of the Yellow River Basin, China Y. Sato∗, A. Onishi & Y. Fukushima Research Institute for Humanity and Nature (RIHN), Kyoto, Japan

X. Ma & J. Xu Frontier Research Center for Global Change (FRCGC), Yokohama, Kanagawa, Japan

M. Matsuoka Kochi University, Nankoku, Kochi, Japan

H. Zheng Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences (CAS), Beijing, China

ABSTRACT: To clarify the influences of human activities on fresh water resources, the long-term (1960–2000) water balance of the Yellow River Basin (YRB) was analyzed using a semi-distributed hydrological model. To estimate the impact of the major anthropogenic factors, the following three sub-models (1) land-use change, (2) reservoir operation and (3) irrigation water use were applied. The model showed the impacts of soil water conservation in the Loess Plateau, large reservoir operation in the upper reach, and the deference of irrigation water use between upper and lower reaches quantitatively. The model developed in this study will contribute to the Integrated Water Resources Management (IWRM) in many countries and regions which are suffered by the various human activities. Keywords: long-term water balance; human activity; irrigation; reservoir operation; land-use change; Yellow River Basin 1

INTRODUCTION

In recent years, the deficit of fresh water resources has been a great challenge for sustainable development in many countries and regions (Liu & Zheng 2004). The Yellow River basin (YRB), the second largest river in China, plays a critical role in water supply, food production, and socio-economic development for wide areas in northern and northeastern part of China (Cai & Rosegrant 2004, Liu & Xia 2004). However, the YRB is now confronted with the frequent water shortages and serious water pollution along with associated problems of intensifying floods and the eco-environmental deterioration (Liu & Zheng 2002). Therefore, a wise comprehensive management of the water resources is required in the YRB (Yang et al. 2004). Although, it is believed that integrated water resources management (IWRM) based on the knowledge of hydrological cycle and socioeconomic conditions, is an essential strategy for the ∗

Corresponding author ([email protected])

decision making and sustainable development (Xia & Chen 2001), the integrated management of the YRB is still a complex and difficult task (Wang et al. 2005). Because the variation of long-term water balances in the YRB is sensitive to the human activities. Most of recent investigations have concluded that the frequent water shortage in the lower YRB is mainly induced by the human activities, especially the over-abstraction of irrigation water from river channels (Ren et al. 2002, Chen et al. 2003, Cai & Rosegrant 2004, Liu & Zheng 2002, Liu & Zheng 2004, Yang et al. 2004). The land cover changes may also have significant implications on long-term water balances of theYRB. Furthermore, some large dams constructed in the upper YRB would alter the distribution of monthly runoff during a year (Ren et al. 2002, Yang et al. 2004). However, these influences have not been investigated quantitatively. Thus, in the present study, to clarify the influences of human activities on fresh water resources, we analyzed the long-term water balance of the YRB using a semi-distributed hydrological model.

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Figure 2. Outline of the Yellow River basin.

activities, three sub-models (land-use change, irrigation water use and reservoir operation) were embedded to the main model. The details of the model and parameter settings used in this study are summarized in Sato et al. 2007a, b. Then, the monthly runoff data from six major hydrological stations located in the main stream of YRB were used to validate the model (Fig. 2). 3 Figure 1. The basic structure of the model. 1: SVAT-HYCY model (main model), 2: Sub-model, and 3: Evapotranspiration model.

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DATA AND METHODS

As the input parameters for the distributed hydrological model, we used long-term (1960–2000) daily observation data from 128 metrological stations within and adjacent to the YRB and high resolution satellite remote sensing dataset. The remote sensing dataset includes elevation, land-cover classification (Matsuoka et al. 2007), and vegetation index. Then, all of them were interpolated into 0.1◦ × 0.1◦ grid scales using the distance-weighing method. The basic structure of the model is shown in Figure 1. The main frame of the model is based on the SVAT-HYCY model developed by Ma & Fukushima (2002). The model consists of the heat balance, runoff formation and river routing models. However, the original SVAT-HYCY model was developed for relatively humid climate regions. Therefore, it is difficult to apply the semi-arid YRB directly. Then, in the present study, to clarify the water balance in the dry climate condition, the evapotranspiration model was modified from the Penman-Monteith method to the Bulk method, and then, the following two parameters were considered: the change of leaf area index (LAI) and soil water content. These two parameters enabled to estimate the influence of seasonal and spatial change of vegetation cover and the influence of the restriction of evapotranspiration due to the soil water deficit. To clarify the impact of human

RESULTS AND DISCUSSION

3.1 Performance of model simulation In this study, the YRB was divided into the following six sub-basins: (a) Source area (upstream of Tangnaihai), (b) Upper reach-1 (from Tangnaihai to Lanzhou), (c) Upper reach-2 (from Lanzhou to Toudaoguai), (d) Middle reach-1 (from Toudaoguai to Sanmenxia), (e) Middle reach-2 (from Sanmenxia to Huayuankou), and (f) Lower reach (downstream of Huayuankou) to clarify the water balance more precisely (Fig. 2). Figure 3 indicates that the results of the model simulation, which assumes that the long-term landuse changes had not occurred since 1960 (except for the irrigation area in the lower reach). According to this figure, we can see that the estimated runoffs were agreed well with the observed data except for the Middle reach-1. This result implies that the influence of land-use change on long-term water balance might not be so severe in the Upper and Lower YRB. 3.2 Influence of land-use change In the Middle reach-1, relatively large errors (under estimation) were shown particularly in the period from the 1960s to the 1970s (Fig. 3d). In other words, the model had overestimated the amounts of evapotranspiration in these periods. Then, to reduce evapotranspiration, we modified the parameters of the vegetation cover ratio (VCR) of the Middle reach-1 using a land-use change model (Sato et al., 2007b). The VCR is based on non-irrigated vegetation area in 2000 (VCR = 100%). By reducing the VCR to 30%

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Figure 5. Decadal change of annual runoff (a) and surface runoff (b) in the Loess plateau.

3.3 Impact of the soil and water conservation

Figure 3. Performance of model simulation. (a)Source area, (b)Upper reach-1, (c)Upper reach-2, (d)Middle reach-1, (e)Middle reach-2, (f)Lower reach.

In the Middle reach-1, most of the region is located in the Loess Plateau (LP). With the increase of population, most vegetated area in the LP had been deteriorated and induced sever soil erosions. Thus, to protect soil surface, soil and water conservation (SWC) project had been conducted in the LP since early 1970s (Xu 2005). The SWC includes land terracing, re-vegetation, and silt controlling (check dam construction). It is well recognized that the practice of SWC changes land-use and land-cover, and thereby will change the runoff generation processes (Xu 2005, Sun et al. 2006). However, these hydrological impacts had not been investigated quantitatively. Thus, to clarify the impact of the SWC, we investigated the change of annual runoff and surface runoff using a land-use change sub-model. Figure 5 indicates the results of model simulation assuming the case of with and without land-use changes. According to these results, we found that the SWC decreased the annual runoff about 10–50% and decreased the surface runoff about 14–74% respectively. These results suggested that the SWC measures will decrease not only soil erosions by reducing surface flow, but also may decrease available water resources in the LP by increasing evapotranspiration loss with the vegetation recovery. 3.4 Influence of large reservoir operation

Figure 4. The result of model simulation considering the land-use change in the Middle reach-1.

in the 1960s and 40% in the 1970s, the estimated annual water balances were improved significantly (Fig. 4). In this simulation, the annual total water balance error (TWBE) during the period from the 1960s to the 1970s decreased from 20.8% to 3.1% (Sato et al., 2007b). This result implies that it is necessary to consider the influences of the land-use changes to clarify the long-term water balances in the middle reach of the YRB.

Dam is a major tool for water resources management, which affects the monthly pattern of river runoff (Yang et al. 2004). Both the amount and timing of fresh water inflow to the irrigation area located in the dry region can be quite important to sustainable agricultural production. Therefore, a lot of dams had been constructed in the upstream of the many river basins for the stabilization of river runoff (enhancing flow in dry season and mitigating floods in wet season) as well as hydropower generation. In the YRB, there are two large dams in the Upper reach-1 (between Tangnaihai and Lanzhou) due to steep and narrow

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Figure 6. Influences of reservoir operation in the Upper reach-1 of the YRB (at Lanzhou). The estimated results were obtained from our original hydrological model (without including reservoir operation sub-model).

topological conditions. One of them is the Liujiaxia dam (5.7 billion m3 ) which had started its operation in 1968, located approximately 100 km upstream of Lanzhou.Another one, the Longyangxia dam (24.7 billion m3 ), is the largest dam in the YRB, which had started its operation in 1987, and is located near the Tangnaihai. Thus, the monthly river runoff regime observed at the downstream of these large dams can be altered since they went into operation. Figure 6 shows the variations of monthly runoff observed at Lanzhou and estimated runoff by our original hydrological model (without considering the influence of the reservoir operations). From this figure, we can see that observed runoff at Lanzhou in the dry season increased particularly after 1968 because of the construction of Liujiaxia dam. Furthermore, we can see that the observed peak runoff in wet season decreased obviously in the 1980s and the 1990s. From this result, we can see that the seasonal change of the natural runoff coming from the source area of theYRB have been strongly controlled by these two large dams. And thus, our original hydrological model could not capture the observed monthly runoff after the 1970s. 3.5

Performance of the reservoir operation model

Then, to predict the influence of artificial reservoir operations by these two large dams, a simple reservoir operation model was developed and applied to the Upper reach-1. The model controls discharges from a virtual (dummy) reservoir located adjacent to the outlet of the sub-basin (Lanzhou), using the following three parameters: (1) inflow to the reservoirs, (2) storage within the reservoir, and (3) reservoir operation rules. The details of this reservoir operation model are described in Sato et al. 2007c. Figure 7 shows the performance of the reservoir operation model applied to the Upper reach-1 of the YRB. According to this figure, it was found that the monthly runoffs estimated by the model agreed well with the observed runoffs even though after the large reservoir dams had started their operations. Therefore, this reservoir operation model will contribute as one of the effective tools for the IWRM of the YRB.

Figure 7. Influences of reservoir operation in the Upper reach-1 of the YRB (at Lanzhou). The estimated results were obtained from reservoir operation sub-model developed in this study.

3.6 Influence of irrigation water use Under the arid climate conditions, irrigation is essential for agriculture. Rapid growth in both irrigation area and irrigation water use have contributed significantly to increased agricultural production and the robust growth of the economy in the YRB (Chen et al. 2003). The main crops in the Yellow River basin include winter wheat, spring wheat, and summer corn. In addition, soybean and cotton are also popular crops for planting in the early summer. Planting starts from the lower basin and then shifts to the middle and upper streams over time (Yang et al. 2004). There are two major irrigation areas (Qingtongxia and Hetao) located in the Upper reach-2 (between Lanzhou and Toudaoguai). These two irrigation areas consume about 20–30% of annual river runoff, and causes serious water deficit in its downstream (Chen et al. 2003). On the other hand, there are other major irrigation areas in the both sides along the Lower reach (downstream of Huayuankou). In the Lower reach, due to a lack of sufficient water flows, a large amount of sediments had been deposited and raised the riverbed several meters, of which elevation is higher than the surrounding grounds. Therefore, the residuals of abstracted water cannot return to the river channel and thus, the increase in the amount of water taken is the direct cause of runoff decrease (Ren et al. 2002). Consequently, the annual runoff from the upstream to the downstream region does not increase in proportion to the drainage area in the Upper reach2 and the Lower reach of the YRB. This implies that a huge amount of artificial water use exists in these regions. However, it is difficult to assess that how much water can be used for irrigation actually. Thus, in this section, we attempt to estimate the amount of irrigation water use by the irrigation water use model. In the present study, the evaporation ratio in the irrigation areas during the irrigation period was assumed to be equal to the potential evaporation (Ep ). The evaporation ratio was calculated by the method defined by Xu et al. (2005). The water balance of each irrigation area is estimated as follows:

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Figure 8. Decadal change of irrigation water use in the Upper reach-2 of the YRB (a) and river runoff at Toudaoguai (b). “difference” indicates difference of observed annual runoff between Lanzhou and Toudaoguai.

where Qirr = discharge from irrigation area; Pirr = precipitation supplied to the irrigation area. When the Ep exceeded Pirr in the Equation 1, the deficit was supplied from the adjacent river channel (= irrigation water use) of the river routing model. The irrigation period was decided by the seasonal variation of the Leaf Area Index (LAI). Then, the LAI of nonirrigation period was set to zero ( = same as the bare land). 3.6.1 Irrigation water use in the upper reach Figure 8 shows the decadal change of irrigation water use and river runoff in the Upper reach-2 of the YRB. Due to the arid climate condition, most of the precipitation supplied in this region can not contribute to the runoff. Thus, the difference of observed annual runoff between Lanzhou and Toudaoguai can be almost same as the amount of water use in this region (Fig. 8a). Although, we did not change the parameter of the irrigation area in the model, the estimated water use during past 40 yrs agreed well with the observed data (Fig. 8a). Furthermore, the decadal change of the river runoff observed at Toudaoguai was also reasonably captured by the model (Fig. 8b). These results suggest that the amount of irrigation water use in the Upper reach-2 of the YRB have been almost constant (9–10 billion m3 /yr) during the past 40 yrs. It means that the efficiency for water uses of this region has improved compared with the 1960s–1970s, because it is reported that the irrigation area in the 1960s–1970s was much smaller than that of the 1990s (Liu and Xia 2004). 3.6.2 Irrigation water use in the lower reach The total amount of water use (TWU) in the lower reach of the YRB can be simply obtained using observed data as follows because no flow supplied to the river channel except for the inflow from its upstream:

where Qin = inflow to the basin; P = amount of precipitation supplied within the basin; Qout = outflow from the basin.

Figure 9. Change of annual total water use and irrigation area in the Lower reach of the YRB.

On the other hand, the TWU can be directly estimated by integrating the amount of irrigation water use, evaporation loss from river channel, drinking water use, and water transport to outside of the basin. The irrigation water use can be estimated using the Equation 1. The irrigation area in the 1960s, 1970s, 1980s and 1990s was estimated to be 3.3 × 103 , 10.0 × 103 , 19.3 × 103 , and 23.6 × 103 km2 respectively (Xi 1996, Li 2003, and Liu & Xia 2004). Then, it was interpolated linearly to obtain annual values. The evaporation from river channel was estimated using Ep assuming the average river width is approximately 1 km and the total length of lower reach is 780 km in the 1960s. Then, the amount of evaporation was modified in proportion to the river runoff at Huayuankou. The amount of drinking water was estimated by the population (81.9 millions) in the 1990s and the basic unit of annual domestic water use (approximately 100 m3 per person). Then, it was also modified in proportional to the irrigation area. The amount of annual water transport outside of the basin allocated for Hebei province and Tianjin city since 1987 was estimated to be 2.0 billion m3 and for Qingdao city since 1989 was estimated to be 0.2 billion m3 (Chen, unpubl.). Figure 9 indicates the change of annual TWU and irrigation area in the Lower reach of the YRB. From this figure, we can see that both of the observed and estimated TWU increased rapidly during the 1960s to the 1970s and then gradually increased with the increase of irrigation area. The TWU estimated by the model agreed well with the observed data and almost same as the value reported by Liu and Xia (2004). Therefore, the assumption applied in this study can be reasonable to predict the long-term water balance of the Lower reach of the YRB. And it was found that the change of irrigation area will strongly impact on the long-term water balance of Lower reach of the YRB. Figure 10 summarizes the decadal change of the distribution of water use in the Lower reach of the YRB. From this result, we can see that more than 80% of total surface water abstraction (= TWU) goes to the

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ACKNOWLEDGEMENTS This study was supported by three research projects: (1) the Yellow River Project of the Research Institute for Humanity and Nature (RIHN), (2) a project to examine surface-water changes of the Yellow River domain (RR2002) funded by the Ministry of Education, Culture, Sports, Science and Technology of Japan (the Project Leader for both of these projects is Dr. Yoshihiro Fukushima of RIHN), and (3) the Chinese National Key Program: 973 project (G19990436) directed by Prof. Changming Liu of the Chinese Academy of Sciences (CAS). Figure 10. Distribution of water use in the Lower reach of the YRB.

agricultural irrigation. This result is also well in agreement with the major previous studies (Liu & Zheng 2002, Chen et al. 2003, Cai & Rosegrant 2004). Furthermore, it was found that the ratio of evaporation from river channel had decreased with the decrease of river runoff (see also Fig. 3f), and, on the contrary, the ratio of amount of water transported to outside of the basin had been increasing. These results suggest that the water demand for non-agricultural sections (i.e. industrial water use or water supply for large cities) are increasing, in spite of the available water resources of this region is considerably limited.

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CONCLUSIONS

To clarify the long-term water balance of the YRB, a semi-distributed hydrological model which can consider the influences of human activities as well as climate variations was developed and applied whole of the YRB. The results obtained in this study are summarized as follows: (1) the hydrological impact of land-use change might not so severe in theYRB except for the Loess Plateau, (2) massive land-use change (soil and water conservation) in the Loess Plateau decreased not only soil erosion but also available water resources about 10–50%, (3) large reservoir dams constructed in the upper YRB can alter seasonal river water distribution and can increase available water resources, (4) the amount of water use in the irrigation area located in the upper reaches seems to be constant (9–10 billion m3 /yr) during the past 40 yrs, (5) the amount of total water use (TWU) in the lower reach had increased with the development of the irrigation area along the lower reaches, (6) more than 80% of TWU is occupied with the agriculture irrigation in the Lower reach, and (7) the model introduced in this study can address all the issues described above, thus will contribute to the integrated water resources management (IWRM) in many countries and regions which are suffered by the human activities.

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in the middle-reaches of the Yellow River basin. IAHS publication 315: 85–91. Sato, Y., Ma, X. & Fukushima, Y. 2007c. Application of a reservoir operation model to the upper reaches of the Yellow River basin. YRiS (Yellow River Studies) News Letter 7: 9–12. Sun, G., Zhou, G., Zhang, Z., Wei, X., McNulty, S.G. & Vose, J. M. 2006. Potential water yield reduction due to forestation across China. Journal of Hydrology 328: 548–558. Wang, G., Wu B. & Wang, Z.Y. 2005. Sedimentation problems and management strategies of Sanmenxia Reservoir, Yellow River, China. Water Resources Research 41, W09417, doi:10.1029/2004WR003919. Xi, J. 1996. Water Resources in the Yellow River basin (in Chinese), Zhengzhou: Yellow River Press: 92–93. Xia, J. & Chen, Y. 2001. Water problems and opportunities in the hydrological sciences in China. Hydrological Sciences Journal 46: 907–921.

Xu, J.X. 2005. Temporal variation of river flow renewability in the middle Yellow River and the influencing factors. Hydrological Processes 19: 1871–1882. Xu, J.Q., Haginoya, S., Saito, K. & Motoya, K. 2005. Surface heat balance and pan evaporation trends in Eastern Asia in the period 1971 to 2000. Hydrological Processes 19: 2161–2186. Yang, D., Li, C., Hu, H., Lei, Z., Yang, D., Kusuda, T., Koike, T. & Musiake, K. 2004. Analysis of water resources variability in theYellow River of China during the last half century using historical data. Water Resources Research 40, W06502, doi:10.1029/2003WR002763.

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Application of geographic information system in hydrological models: A review C.L. Liu∗ & Y.Q. Chen Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China

ABSTRACT: The hydrological model is not only the base of flood prediction and design of dam and reservoir engineering, but also the foundation of developing physically-based soil erosion model based on the physical hydrologic processes. It can be divided into different object models according to different research objects. The last 20 years have seen the great progress of spatially distributed hydrological modeling with the application of geographic information system and remote sensing in hydrology. This paper addresses the main functions of the application of geographic information system in hydrologic modeling, the general approaches of linking GIS and hydrological models, some popular integrated hydrological models and some relevant practices also including brief discussion of GIS-based hydrological models for IWRM. In addition, the current problems, challenges and the prospect of integrating GIS and hydrological models are also discussed in the paper, based on relevant practices and researches. Keywords:

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geographic information system; hydrological model; coupling model; IWRM; review

INTRODUCTION

Mathematically hydrological model has been accepted as a useful tool for explaining hydrological cycle in a drainage area. They are the base of flood prediction, design of dam and reservoir engineering and water resource management, as well as the foundation of developing physically-based soil erosion model based on the physical hydrologic processes. There have been many popular hydrological models such as Sacramento model, Tank model, HEC-1 model, SCS model, SHE model, SWAT model and TOPMODEL, among which the last three famous models should be classified as distributed hydrological modes in broad sense. Distributed hydrological models have been hot research topics replacing lumped models since 1990s, especially GIS-based distributed hydrological model. The last 20 years have seen the great development of spatially distributed hydrological models with the development of computing capability and general application of geographic information system, remote sensing in hydrology. There are new opportunities for distributed hydrological modeling with the advent and development of Geographic Information System (GIS), because that GIS has potentiality for storing large amounts of data, ∗

Corresponding author ([email protected])

saving time-consuming workload and achieving rudimentary visual display. Wan and Wang (2000) indicated that application of GIS in hydrological modeling mainly focuses on spatial data mining and management, making automatic parameter extraction possible, facilitating construction of a hydrologic model, computation and visualization of modeling output. Wang and Ying (1998) indicated that GIS plays an important role in spatially distributed modeling by managing spatial data, extracting hydrological characteristic parameters, preparing data for models, visualizing and analyzing the modeling output (Wang et al., 2004). Nevertheless, Josef classifies GIS-support for hydrological models into the following four levels: hydrological assessment; estimation of hydrological parameters; hydrological modeling in GIS; coupling of GIS and hydrological models (Fürst, 2004). A review of GIS applications in hydrological modeling was published in 1993, which delineates and assesses the progress made in the development of GIS applications in hydrology (DeVantier and Feldman, 1993). However, great advances have been observed after 15 years. This paper aims to describe and assess the progress of GIS applications in hydrological models from the following different parts: spatial data extraction and estimation of parameters; hydrological modeling in GIS, Coupling of GIS and hydrological models. In addition, the current problems, challenges

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and the prospect of integrating GIS and hydrological models are to be discussed in the paper, based on relevant practices and researches. 2

FUNCTIONS OF GIS IN HYDROLOGICAL MODEL

The functions of GIS in hydrological modeling focus on data management, spatial analysis and visually displaying outputs. Parameterization of hydrological characteristics of watersheds and manipulation of input/output data for certain models by using GIS tools are currently most frequent and popular application of GIS in hydrological modeling. It’s particularly important to extract parameters of hydrological attributes and topographic features in research basin from DEM. For example, the Hydrological Model Module in ArcGIS 9.0 can calculate flow direction, flow accumulation, flow length, stream network, and stream order based on DEM (Tang and Yang, 2006). 2.1

Data management

Hydrological models, especially distributed ones, need large amounts of basic geographic data for delineating spatial heterogeneity, meteorological data such as precipitation, temperature and hydrological data. Powerful data management helps to improve the efficiency and quality of hydrological modeling. The popular GIS software packages, such as ArcInfo, ArcView, MapInfo and SuperMap, have capacity to powerfully manage very large spatial data and attribute data. In addition, they have the functions of querying, searching, updating, and maintaining and so on (Liu et al., 2006). 2.2

Spatial analysis and parameterization

Spatial analysis of geographic data is the core of Geographic Information System’s functions and most GIS software systems have the function. GIS-based spatial analysis has obtained great concerns in more and more research regions, such as flood assessment and water pollution monitoring. Spatial analysis of vector data can be categorized into proximity, overlay, extraction and networking. Spatial analysis of raster data includes the following approaches: clustering and polymerization analysis, multi-layer overlay analysis, tracking analysis, viewshed analysis, statistical analysis and measurement (Tang and Yang, 2006). A Digital Elevation Model (DEM) is often used in distributed hydrological models for delineation of drainage networks, extraction of hydrological attributes and estimation of hydrological parameters such as mean elevation, slope, aspect and field capacity. Wang et al derive stream networks from DEM

at two scales and compare various drainage parameters common in hydrology and geomorphology, which indicates that superior estimations are produced from the 24K DEMs, better estimates for stream length and frequency parameters can be obtained than for gradient parameters from the 250K DEMs, and the estimation of the mean gradient parameters from the 250K DEMS seems to improve with increasing terrain complexity (Wang and Ying, 1998). Schumann et al. (2000) used GIS to derive lumped catchment characteristics and estimate the regional model parameters by taking catchment characteristics into consideration. Ko and Cheng (2004) used hydrological model in ArcInfo GIS system package to delineate the watersheds and then relate the different physical properties of the watersheds to the statistical results. DEM has been widely accepted as a useful dataset for delineating stream networks, but the problem of spurious sinks in DEM and the limitation of D8 algorithm in hydrological model tool of ArcInfo GIS is particularly acute in relatively flat areas, which is still attracting and challenging (Jenson and Domingue, 1988; Tang and Liu, 2006). Jia et al. (2006) utilize RS data and GIS (ArcGIS) for extracting hydrological attributes and performing temporal-spatial interpolation of water use data. 3

LINKAGE OF GIS AND HYDROLOGICAL MODEL

Linkage of GIS and hydrological model has become increasingly common and significant progress has been made. Numerous examples exist in the lastdecade literature.As for the approaches for integration, it’s not easy to distinguish them to an inch. According to former researches, generally, four different approaches have been widely used to link GIS and hydrological model, which are respectively embedding hydrological models in GIS, embedding GIS into hydrological models, loose coupling of GIS and hydrological models, and tight coupling of GIS and hydrological models. We will briefly discuss the new advances about the above approaches, rather than repeat their former definitions and examples. 3.1 Embedding hydrological functions in GIS As shown above, parameters estimated based on GIS are usually input data into hydrological models which run independently from the GIS. However, there are some new useful extensions embedded in popular GIS software packages, which use the hydrological analysis functions to extract hydrological information, simulate surface flow and create drainage systems from a DEM. For example, both ArcGIS 9.0 and ArcView 3.x have a spatial analyst module with hydrology function. The approach can fully avail itself of

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the functions provided by GIS software, but most of the hydrological modules have great limitation in the capability of modeling complex hydrological system. However, Arc Hydro data model has more powerful hydrological functions. 3.2

Embedding GIS in hydrological models

Besides River Tools and MODFLOE often discussed before, there have been more of the latest versions of hydrological modes with GIS-functionality into the model packages. For example, MWSWAT with the GIS MapWindow system provides a free, open source interface to SWAT; XP-SWMM (Stormwater and Wastewater Management Model) can dynamically display results far more accurately than previous steady state SWMM, which is now very popular in world-wide water conservancy industry and research institutes. In addition, the Watershed Modeling System is a comprehensive modeling environment including all phases of watershed hydrology and hydraulics, which has a GIS module for importing, creating, and manipulating GIS vector and raster data. MWSWAT, XP-SWMM and WMS all can work with the GIS data effectively with or without ArcGIS/ArcView. 3.3

Coupling GIS and hydrological model

Loose coupling and tight coupling are two important approaches for integration of GIS and hydrological model, big difference exists between them. ASCII or binary data files acts as linking programs between GIS and hydrological models for online data transfer of loose coupling. A loose-coupled system for the runoff model RORB is developed, in conjunction with ARC/INFO (Coroza, 1997). Lv et al. (2004) developed a GIS-based distributed Time Variant Gain Model system, which integrate GIS and hydrological model by means of loosing coupling. However, GIS macro or conventional programming acts as a tool for running hydrological models in a GIS system to realize tight coupling. Tight coupling tends to be attracting more attention due to its more effectiveness, compared with loose coupling of the early days.ArcSWAT is designed to tightly couple the SWAT model (Soil and Water Assessment Tool) and the GIS package ARC/INFO, which is a graphic user interface written in AML (Arc Macro Language). Liu (2005) introduced a distributed hydrological modeling system, ArcTOP, which tightly couples a cell-based distributed model TOPKAPI with the Arcview GIS. AVYOP is a realization of full integration of TOPMODEL into GIS with the macro language Avenue of ArcView, which is an example to illustrate the advantages of such a full integration manner for modeling and visualization (Huang and Jiang, 2002). In recent years, Object-Oriented Programming (OOP) provides

a new way for the integration of GIS and hydrological models. Yao et al. (2006) proposed to embed DHSVM with ARCGIS for linkage based on Component Object Model (COM) ArcObejects. The Component Object Model (COM) provides some advantages for linkage of hydrological mode with GIS, which needn’t the fundamental programming of GIS. 4

POPULAR GIS-BASED HYDROLOGICAL MODELS

4.1 Arc Hydro data model The ArcGIS Hydrol data model (i.e. Arc Hydro), developed though collaboration between ESRI and the Center for Research on Water Resources (CRWR) of the University of Texas at Austin, is a hydrographic data model with time series component based on Geodatabase (Maidment, 2002; Zhu and Wu, 2006). Arc Hydro is a data structure that provides the capacity to link hydrologic data to water resources modelling and decision-making methods, whose toolkit is mainly applied for creating watershed delineations and hydrologic flow paths from DEM. Particularly, Arc Hydro data model is extensible and its data structure can be linked directly to hydrological simulation models (Maidment, 2002). Therefore, hydrologic models can be more easily and closely integrated with GIS based on Arc Hydro data model, as well as easily obtain necessary hydrological data and parameters from DEM. The University of Georgia intended to convert Georgia’s water resources database to Arc Hydro data model in 2003, so that a hydrological information system is obtained for eventually construction of a comprehensive, integrated, real-time, environmental modeling in GIS context. Tang et al extract characteristics of Wujiang Watershed in Guizhou province, Southwest China, based on Arc Hydro Tools, which provide parameters for the distributed hydrological models (Tang and Yang, 2006). Xiong et al. (2007) developed a Hydro-network model based on Arc Hydro for better simulation of the spatial and temporal distribution of water flows in a watershed. 4.2 WMS-watershed modeling system The watershed Modeling System (WMS) is an integrated system for developing computer simulations of all phases of watershed hydrology and hydraulics, which can take advantage of all types of GIS data available for hydrologic and hydraulic modeling without any support from ArcGIS/ArcView. Its GIS module provides a toolkit for importing, creating and manipulating GIS vector and raster data. In addition, there are some powerful tools for achieving its direct linkage with ArcGIS (WMS Version 7.0 Software).

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4.3 ArcSWAT and MWSWAT ArcSWAT and MWSWAT provide the same functionality, i.e. to streamline GIS processes for extract spatially explicit input parameters for SWAT, despite their different linkage approaches with GIS. ArcSWAT is an extension of ArcGIS and ArcVIEW, which is a friendly graphic interface for SWAT. The development of ArcSWAT aims to effectively apply spatial data for enhancing hydrological modeling. MWSWAT is the MapWindow-SWAT interface, which provides a free, open source interface to SWAT, together with Mapwidow. ArcVIEW and Arc-GIS are not required component of MWSWAT. 5

PRACTICES OF GIS-BASED HYDROLOGICAL MODELS

As well as the researches and development of the popular GIS-based hydrological models, many relevant practices and case studies are also worthwhile to be concerned. For example, Gosain et al ran SWAT on ArcView GIS for quantifying the climate change impact on water balance, which has been included in the revised version (Gosain, 2006). The watershed model SWIM, aiming to provide a comprehensive GIS-based tool for hydrological and water quality modeling in mesoscale watersheds, adopted the modified interface of SWAT/GRASS for extracting spatially distributed parameters; its coupling to GRASS facilitates its application and its transferability to other regions (Krysanova, 1998). The test and validation of the hydrological module of SWIM were performed in five mesoscale watersheds of the Elbe drainage basin, which indicated general quite satisfactory results, although the model has to be further tested for up-scaling purposes in basins up to several thousand km2 with ‘nested’ sub-basins. Jia Y. et al. successfully developed a distributed hydrological model for large basins, WEP-L, and conducted dynamic assessment of water resources in the Yellow River basin, utilizing RS data and GIS to perform basin subdivision, land cover classification, and spatial and temporal interpolations of water use data (Jia, 2003). The long-term hydrological impact assessment (L-THIA) web application, a Decision Support System (DSS) based on an integration of web-based programs, geographic information system (GIS) capabilities, and databases, was intended to support decision makers, who may have limited hydrological Knowledge and provide information regarding the hydrological impacts of water quantity and quality caused land use change (Bernard, 2003). Wolski et al developed a hybrid reservoir-GIS model for a better spatial resolution of model results, which provided distributed variables reflecting principal drivers of the Okavango Delta ecosystem and also provided a cost-effective tool to improve the

understanding of the functioning of the wetland system that can be used as a first step in screening of management scenarios (Wolski, 2006). In addition, there are many other such examples of GIS-based hydrological models for modeling hydrological response, groundwater vulnerability, flood risk, prediction, inundation area and flood routing, which are not to be included in the paper. All the efforts in researches and practices have shown the potential benefits of GIS to hydrological models and relevant development, despite the need for further study. Moreover, the worldwide concerns for Integrated Water Resources Management (IWRM) have promoted the development of relevant models for achieving the possibility of maximizing the benefits from water resources without any potential adverse impacts on the social and environmental interests (Schulze, 2005). For example, McKinney et al developed an integrated water resources management model for the Syr Darya river basin in Central Asia, which provided an analytical framework to consider both economic and environmental consequences of policy choices and compared alternative solutions based on hydrological, agronomic, economic and institutional conditions within the integrated system (McKinney, 1999). The theories and techniques applied for linking GIS and hydrological modes in the above examples provide some foundation for further research of GIS-based IWRM modeling. However, IWRM on a river basin is so broad and complex that modelers generally are convinced that no single model can be used, to the exclusion of all others (Dent, 2000). With this in mind, efforts for GIS-based IWRM would be confronted with more obstacles than the above single models had, because integrated water resource management must be based on integrated science.

6

RESEARCH PROSPECT AND CHALLENGES

The GIS functions in hydrological modeling, the approaches generally applied for linking hydrological modeling with GIS and some popular GIS-based hydrological models have been discussed in the paper, which has sought to find the research prospect and challenges about integration of hydrological modes and GIS. During last 20 years, some technical progress has been obtained as follows: 1) The latest versions of SuperMap and ArcGIS both provide the development technology based on Component Object Model, namely SuperMap Object and ArcObjects. ArcObjects library is the core set of objects from which the Arc Hydrology Data Model is developed; 2) The development of web GIS and remote sensing techniques helps to implement real-time on-line watershed simulation, especially helps to get an effective real-time

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flood modeling and prediction system. Therefore, a Web-to-hydrological model based on Web GIS will play an increasing important role in future hydrological modeling research (Al-Sabhan et al., 2003); and 3)The availability of large volumes of spatial-temporal data has raised the popularity of dynamic and real-time modeling that requires closer integration of GIS and RS with hydrological models. In addition, there have been lots of important advances in physically-based distributed models, and they still require extensive research efforts. As discussed above, some popular distributed watershed models such as SWAT, WMS and TOPMODEL have gained great progress in their integration with GIS. However, few currently available hydrological modeling software packages are well integrated within geographic information systems and are capable of non-expert implementation. Moreover, the integration of GIS with simulation models is capable of significantly advancing the potential of GIS for spatial analysis. Nevertheless, full and complex integration requires significant programming workload and data management. Perhaps, Objectoriented hydrological model based on Component Object Model in GIS environment is an intending solution to reducing programming efforts. Thirdly, there are still some impediments in current GIS systems such as complexity, interfacing, customization and platform, in spite of some significant advances in recent GIS products. According to the above discussion and some relative research literature, real-time simulation system, an appropriate user interface, and accessibility to non-expert decision-makers and public participators are future research concerns. The development in integration of watershed model with GIS requires more friendly and flexible interfaces for facilitating the creation of watershed model input data sets and the availability of the integrating modeling system. Interfacing is the core part of hydrological model development. Further research efforts are still required to develop integration of GIS and hydrological models and real-time data system. Moreover, based on the above efforts, further research should be conducted on GIS-based hydrological models for IWRM, despite the following difficulties: no single discipline or institution can accomplish integrated water resource management alone; IWRM requires broad participation of stakeholders and intense communication; linkage of GIS with IWRM need more workload for programming.

ACKNOWLEDGEMENTS The work described in this paper was fully supported by a grant from the Research Grants Council of the

Hong Kong Special Administrative Region, China (Project No. CUHK4627/05H), was fully supported by a Direct Grant from the Faculty of Social Science, The Chinese University of Hong Kong (Project No. 4450183), the Outstanding Overseas Chinese Scholars Fund from CAS (The Chinese Academy of Sciences) and by the National Natural Science Foundation of China (Grant No.: 40701015).

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Relationship between residence time and geographic source of stream flow in small watersheds – Analysis with a distributed rainfall-runoff model and field observation data – T. Sayama∗ Department Forest Engineering, Oregon State University, USA On leave from Disaster Prevention Research Institute, Kyoto University, Japan

J.J. McDonnell Department Forest Engineering, Oregon State University, USA

ABSTRACT: The impact of climate change and human activity on hydrologic change can be evaluated only with a hydrologic model that is capable to simulate adequate sources, flowpaths and residence time of water, yet the capability for capturing these measures and their physical interactions with catchment characteristics have been poorly understood. The present study uses a physically based hydrologic model together with field data from the well-studied two catchments. We first demonstrate the ability to simulate new/old water fractions and mean residence time of streamflow. After demonstrating the model capability to capture flow and transport dynamics for the right process reasons, we conducted a series of virtual experiments (numerical experiments driven by a collective field intelligence) by switching soil depths and climate conditions between the two catchments to understand the impact of these variables on the interaction between water age and source information. Results indicated that thick soil depths increase mean residence time of the catchments and concentrate the source of old water more in the near stream zone. The strong correlation between mean residence time, spatial sources and soil depths implied the possibility to estimate the sources and catchment representative soil depths from isotope-based mean residence time estimations. Keywords: hydrograph separation; physically based model; runoff process; time-space accounting scheme; soil depths; virtual experiment

1

INTRODUCTION

Many rainfall-runoff models exist that can reproduce the behavior of storm hydrographs. As these models become the basis for landuse and climate change prediction, it becomes essential that they work for the right process reasons. Much ambiguity still exists in how model structure capture dominant runoff processes and how model parameters relate to field measurements. Recent work has demonstrated that storm hydrograph components and stream water mean residence time (MRT) provide measures of watershed behaviors (ex. Vache & Mcdonnell 2006). However, to date, only conceptual models have been tested in this way. Geographic source of streamflow is also an important measure not only for model validations but also for understanding the impact of a hydrologic change on ∗

Corresponding author ([email protected])

water resources for both quantity and quality. However it is poorly understood mainly due to the limitation in the current estimation techniques such as end member mixing method, which requires significant differences in isotopic and/or geochemical characteristics at different flow pathways. Therefore, the complementary use of a physically based hydrologic model together with the field observed data may be an effective way to understand the characteristics of geographic source more in detail. Furthermore, elucidating the interaction of age and source information as well as the key controlling variables on the interaction is important to predict the geographic source of streamflow based on the relatively easily characterized age information. In this study, we use a physically based hydrologic model together with field data from the well-studied two catchments to demonstrate the ability of the model to simulate new/old water fractions and MRT of streamflow. We apply a spatio-temporal hydrograph separation method (Sayama et al. 2007) for a

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distributed rainfall-runoff model to obtain the age and space information. After demonstrating that the model is capturing flow and transport dynamics for the right process reasons, we conduct a series of virtual experiments to explore how the age and source information are interacting each other and how soil depths and climate conditions control the interaction. 2

STUDY AREA

This study was conducted in the two catchments: Maimai (M8) catchment covering 3.9 ha located in the east of the Paparoa Mountain Range in South Island, New Zealand, and H.J. Andrews (WS10) catchment covering 10.2 ha located in the western Cascade Mountains of Oregon, USA. The two catchments exhibit much similarity in terms of the wet climate conditions with high runoff ratios and the steep slopes covered by forest vegetations. The annual precipitation at Maimai is 2600 mm and 60% of the precipitation becomes runoff, while the annual precipitation at HJA is 2225 mm and 56% of the precipitation becomes runoff. The slopes at Maimai and HJA are both steep with the averages of 34◦ and 29◦ , respectively. Both of the catchments are covered by forest, more specifically with mixed evergreen beech forest at Maimai and by naturally regenerated second growth Douglas-far at HJA. The soils at Maimai are relatively shallow (0.21.8 m with average 0.7 m) and consist of podsolized stony yellow-brown earths, which is underlain by firmly compacted, moderately weathered early Pleistocene conglomerate, known as the Old Man Gravels that are considered to be largely impermeable. At HJA slightly more than 1 m of relatively poorly developed soils are underlain by 2–7 m (average: 3.0 m) of subsoil (saprolite) consisting of highly-weathered coarse volcanic breccias. 3 3.1

METHOD

modeled by Darcy equation with a variable hydraulic conductivity. If θ exceeds the maximum water content in the capillary pore θm , the water flows in the non-capillary pore and capillary pore as saturated subsurface flow, which is modeled by Darcy equation with saturated hydraulic conductivities. If θ exceeds the effective porosity θa , surface flow occurs, which is then modeled by Manning’s equation. These processes for each slope segment are represented with a kinematic wave model using the function of dischargestage relationship (1) (Tachikawa et al. 2006) and the continuity equation (2).

where q is discharge with unit width; h (= Dθ) is water stage; D is soil depth; r is rainfall intensity; i is gradient of slope segment; dm (= Dθm ) is the capacity of water depth in capillary pore; da (= Dθa ) is the capacity of water depth including capillary pore and non-capillary pore; n is surface roughness coefficient; ka is saturated hydraulic conductivity for the non-capillary pore; and km (= ka /β) is saturated hydraulic conductivity for the capillary pore. 3.2 Hydrograph separation method Separating a hydrograph based on the temporal sources and spatial sources of streamflow means to obtain the results illustrated in Figure 1 (a) and (b) from rainfallrunoff simulations. In the example of Figure 1 (a), the hyetograph is divided into the five temporal classes

Distributed rainfall-runoff model

A distributed rainfall-runoff model, named OHDISKWMSS (OHymos-based DIstributed Model – with Kinematic Wave Method for Surface and Subsurface Runoff) (Tachikawa et al. 2006), is used for the analysis. In OHDIS-KWMSS, the catchment topography is represented with a set of slope segments, on which a rainfall-runoff is simulated as unsaturated, saturated subsurface and surface runoff processes. Each slope segment is represented as a slope covered by soil with capillary pore and non-capillary pore on a permeable bed rock. If the volumetric water content θ is smaller than the maximum volumetric water content in the capillary pore θm , the water flows in the capillary pore as unsaturated subsurface flow, which is

Figure 1. Schematic diagram of hydrograph separations based on (a) temporal and (b) spatial sources of streamflow.

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from 0 to 4 based on the periods of rainfall. The 0 class represents rainwater prior to the beginning of the simulation (old water), while the classes 1 to 4 represent rainwater (new water) during the simulation period illustrated in the hyetograph. The colors in the hyetograph correspond to the colors in the hydrograph indicating how rainwater in the each period becomes runoff from the outlet of the catchment. On the other hand, Figure 1 (b) illustrates the example of spatial hydrograph separation. In this example, the catchment is divided into six zones from A to F, and the separated hydrograph shows how rainwater from the different zones becomes runoff at the outlet. In order to separate a hydrograph in terms of time and space sources, a new concept named “spatiotemporal record matrix” is introduced in this method. This matrix denotes the ratios of flow contributed by rainwater originated from certain temporal classes and spatial zones. For example, let the matrix in Figure 2 represent the component of water flowing at time t and at the outlet of a catchment in Figure 1 (b). It shows that 6% of the discharge is originated from rainwater during temporal class 2 and spatial zone C. Adding all the values vertically for each column, we can obtain the ratio of the flow originated from each temporal class. Consequently, the hydrograph at time t can be separated based on the temporal sources of streamflow as shown in Figure 1 (a). On the other hand, adding all the values horizontally for each row, we can separate the hydrograph at time t based on the spatial sources of streamflow. Thus, if we can calculate the temporal change of this matrix in the stream, we can separate the hydrograph as illustrated in Figure 1. The temporal sources and the spatial sources of water differ depending on the location of the slope. In addition, even at the same location they may be different depending on the flowpaths such as surface,

Figure 2. Spatio-temporal record matrix, which denotes the ratio of water from different temporal classes and spatial zones. This matrix is assigned to each flowpaths at each slope segment, so that the values of the all matrices can be updated from upstream to downstream based on the runoff simulation with simple mass balance-type equations.

non-capillary pore and capillary pore. This hydrograph separation method assigns different spatio-temporal record matrices to each flowpaths at each slope segment within the distributed rainfall-runoff model, so that the matrices can be updated from ridges to streams by a simple set of mass balance-type equations. The detailed explanations of the equations are given in a literature (Sayama et al. 2007).

4

RESULTS AND DISCUSSIONS

4.1 Simulated hydrographs The model simulated rainfall-runoff during a rainy season for one month in 1987 for Maimai and in 1999 for HJA. Figure 3 shows the observed and simulated hydrographs (total discharge before separating the hydrographs) at (a) Maimai and (b) HJA. Nash Sutcliffe efficiencies were 0.91 and 0.70, respectively. The model could simulate the overall hydrographs and the different characteristics in the hydrographs

Figure 3. Temporal hydrograph separations at (a)Maimai and (b)HJA. Black colors in the hydrographs represent old water runoff, while the gray colors in the hydrographs show the runoff originated from the corresponding colors in the hyetographs. The dashed lines show the observed hydrographs.

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between the two catchments; namely faster recessions at Maimai and slower recessions at HJA. 4.2 Temporal hydrograph separation Figure 3 presents also the results of temporal hydrograph separations. The colors in the simulated hydrographs correspond to the colors in the hyetographs, except for the black parts which represent old water or stored water runoff before the beginning of the simulations. The ratios of the old water were 53% at Maimai and 71% at HJA. In fact, the ratios can be even higher depending on the definition of old water. For example, during the last rainfall event at Maimai from October 25th to 30th, even though the black part dominates only 32% of the total runoff, the pre-event water (rain water before October 25th) dominates about 73% during the event. Thus, these results suggested that the simulated hydrographs with the simple kinematic wave model considering unsaturated, saturated subsurface and surface runoff processes can capture considerably high old water fractions. These results are consistent with previous isotope-based hydrograph separation results that suggested high old water ratios at Maimai (75– 85% by Sklash et al. 1986) and at HJA (73–90% by McGuire 2004). 4.3

MRT of stream flow and the temporal variation

Based on the calculated spatio-temporal record matrix in Figure 2 of streamwater, we can calculate MRT for each time step t;

where r is the spatio-temporal record matrix after integrating all the values for each column so that the dimension of r becomes (1 × T ). T is the number of temporal classes. According to the previous studies, MRT at Maimai is shorter than MRT at HJA. Reported MRT were about 4 months at Maimai (Pearce et al. 1986) and 1.2 years at HJA (McGuire et al. 2005). Our question was whether the model can express the differences in MRT between the two catchments. Figure 4 shows the simulated hydrographs and MRT time series at Maimai and HJA. Estimated MRT were 46 days at Maimai and 173 days at HJA. Hence, the model could reproduce the relative relationship of MRT between the two catchments. Compared to the previously reported estimated MRT, however, the calculated MRT in the present study were shorter. One of the reasons for this difference can be explained by the temporal variation of MRT. As Figure 4 indicates, MRT during rainfall and immediate after rainfall become shorter than base flow periods, in which most

Figure 4. Calculated MRT time series at (a) Maimai and (b) HJA. The input hyetographs and calculated hydrographs are also presented to show how the MRT change due to rainfall.

of water were sampled for MRT estimations in the previous studies. On the other hand, the above mentioned calculated averages represent all the conditions including low flow and high flow periods. It should also be noted the significant difference in the temporal variations of MRT between the two catchments. So far, not much attention has been paid to temporal variability of MRT mainly because of the limitations of the conventional MRT estimation techniques, which normally need to assume the stationary of residence time distributions. Our simulation results indicated that MRT at Maimai is more sensitive to rainfall than the MRT at HJA. It can be quantified by auto correlations with 1 day time lag, which were 0.64 at Maimai and 0.93 at HJA. On the other hand, the seasonal variations of MRT were more significant at HJA than at Maimai; it can be quantified by the standard deviations, which are 10 days at Maimai and 33 days at HJA. 4.4 Spatial sources of stream water The spatial source components of the stream water were computed using the same hydrograph separation

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Figure 5. Spatial ratio of the contribution from different places for a one year period at (a) Maimai and (b) HJA. If the spatial sources are evenly distributed, the values become 1% and uniform on the bird’s-eye view maps. The lighter color (the higher ratio value in %) in the maps indicates the water originated from the location contributes more compared to other area to the streamflow.

method for a one year period. In Figure 5, the color represents the spatial ratio of the contribution from different places in the watershed at (a) Maimai and (b) HJA. If all the values (colors) in the map are one color (or a value of 1.0 on the percent contribution scale), the spatial source of the runoff is evenly distributed in the entire watershed. On the other hand, if the values are higher than one and are unevenly distributed, this indicates (as in the two model examples) that certain areas of the catchment contribute more than others. Result from Maimai shows higher values at near streams and hollows, indicates that the runoff contributions from the near stream area and hollows are higher than other area. The result at HJA shows more significant uneven spatial distribution, which indicates that water at near ridge areas did not appear from the outlet even for one year time scale. This spatial source is strongly related to the MRT of streamflow. These analyses including the MRT estimations suggest that a catchment with longer MRT tends to have more significant uneven distribution of spatial runoff sources. We analyzed the spatial sources of new water and old water separately to understand the difference between the two catchments. The periods of the simulations are the same as Figure 3. In Figure 6, “Area” bar chart shows the histograms of area distributions

Figure 6. The spatial distributions of new water and old water sources in PDF at different distances from the streams ((a) : Maimai, (b) : HJA). The “Area” bar chart shows the histogram of area distributions. If the filled square or triangle lines are above the “Area” bar chart, the area at certain distance contributes more to the new water or old water runoff. Unfilled lines show the virtual experiment results by switching the soil depths between the two catchments.

located at different distances from the streams. The “New” and “Old” filled marked lines represent the spatial source distributions of new water and old water from different distances at the end of simulation (30 days after the simulation starts) in PDF. In contrast to the new water distributions, which showed similar characteristics between the two catchments (concentrated in near stream zones), the spatial distributions of old water differ between Maimai and HJA significantly. At Maimai, the old water line follows the “Area”, indicating the source distributes almost evenly in the catchment, while at HJA most of the old water is originated from 0–70 m distances from the streams,

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Figure 7. Virtual experiment results on MRT. The bar chart shows the average MRT for a year with the standard deviations at Maimai (left) and HJA (right).

and the old water stored farther than 100 m from the streams did not appear from the outlet at the end of the simulation. 4.5 Virtual experiment on spatial source Spatial runoff sources were re-estimated by switching the soil depths between Maimai and HJA to understand the reason for the difference in the old water sources, whether it is mainly caused by landscape or by soil depths. Figure 6 with unfilled marked lines (Soil) presents the results of the experiments. In this case the soil depths of Maimai and HJA were 3.0 m and 0.7 m, respectively. The figure shows that by making the soil depth thicker at Maimai, the spatial sources of old water become more concentrated to the near stream area. On the other hand, at HJA the source area is more widely spread compared to the original case. Therefore, the result suggests that the difference in the old water sources between the two catchments is mainly caused by the difference in the soil depths between the two catchments. 4.6 Virtual experiment on MRT Another virtual experiment was conducted to identify the control factors creating the difference in MRT at the two catchments. “Control” is the baseline condition used also in the previous simulations. “Uni Soil” assumes uniform soil depths with the average soil depths of the baseline conditions. “Soil” is the condition in which average soil depths are switched between Maimai and HJA with uniform soil depths. “Met” is the condition in which rainfall and evapotranspiration inputs are switched between the two catchments. “Met+Soil” is the condition that both of soil depths and meteorological conditions were switched.

Figure 7 shows the average and standard deviation of MRT at the two catchments under the different conditions. Firstly, the result of “Uni Soil” shows almost no difference between “Control”, which suggest the spatial patterns of soil depths did not influence the MRT significantly at the two catchments. On the other hand, under “Soil” condition that switched the average soil depths, MRT at Maimai is longer than MRT at HJA (118 days and 74 days). This indicates a strong correlation between average soil depths and MRT; MRT increases with the increasing average soil depths. The impact of “MET” condition is smaller than “Soil” condition, but it also controls MRT substantially; Switching the meteorological condition increased the Maimai’s MRT with 34 days and decreased HJA’s MRT with 58 days. The averages and standard deviations of MRT under “Met+Soil” condition were almost reversed from the “Control” one, which imply the difference of MRT between the two catchments were mainly caused by the differences in the average soil depths and meteorological conditions. In summary, MRT and spatial source of streamflow are both influenced by soil depth. As soil depth thinner, MRT decreases and the spatial source of old water is more evenly distributed to the whole catchment. As soil depth thicker, MRT increases and the spatial source of old water is concentrated more to the near stream zone. 5

CONCLUSIONS

We discussed the interaction between MRT and spatial sources of streamflow by a series of virtual experiments with a hydrograph separation method for a distributed rainfall-runoff model. The applications to the well-studied two catchments showed that the model simulating unsaturated, saturated subsurface and surface rainfall-runoff processes were able to capture the important field observed evidences such as high old water fractions during high flow periods and the difference of MRT between the two catchments. Then we used this model as a baseline model for the virtual experiments to elucidate the interaction between MRT and spatial sources of water. Our experiment results showed clear relationship between MRT and spatial sources and the impact of soil depth distributions on the interaction. Our results suggested that the thicker soil depths tend to increase MRT of the catchment and concentrate the spatial sources of old water in the near stream areas.

REFERENCES McGuire, K. 2004. Water Residence Time and Runoff Generation in the Western Cascades of Oregon. Ph D. dissertation, Oregon State University.

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McGuire, K., McDonell, J.J., Weiler, M., Kendall, C., McGlynn, B.L., Welker, J.M., & Seibert J. 2005. The role of topography on catchment-scale water residence time. Water Resour. Res. 41 (W05002). Pearce, A.J., Stewart, M.K., & Sklash, M.G. 1986. Storm runoff generation in humid headwater catchments 1. Where does the water come from? Water Resour. Res. 22: 1263–1272. Sayama, T., Tatsumi, K., Tachikawa, Y., & Takara, K. 2007. Hydrograph separation based on spatiotemporal record of stream flow in a distributed rainfall-runoff model. J. Japan Soc. Hydrol. & Water Resour. 20(3): 214–225 (in Japanese).

Sklash, M.G., Stewart, M.K., & Pearce, A.J. 1986. Storm runoff generation in humid headwater catchments 2. A case study of hillslope and low-order stream response. Water Resour. Res. 22: 1273–1282. Tachikawa,Y., Shrestha, R., & Sayama, T. 2005 Flood prediction in Japan and the need for guidelines for flood runoff modeling, IAHS Publication 301: 78–86. Vache, K. B. & McDonnell, J. J. 2006. A process-based rejectionist framework for evaluating catchment runoff model structure. Water Resour. Res. 42 (W02409).

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Evaluation of seawater intrusion to a coastal aquifer by developing a three dimensional numerical model E.D.P. Perera∗, K. Jinno & Y. Hiroshiro Institute of Environmental Systems, Graduate School of Engineering, Kyushu University, Fukuoka, Japan

A. Tsutsumi S.G. Gijutsu Consultant Co. Ltd, Saga City, Japan

ABSTRACT: The Motooka region in Fukuoka, Japan is a coastal region where agriculture is dominant. The green houses and wineries cover their water demand from groundwater. With the increased water demand, seawater intrusion is identified as an alarming threat to the Motooka coastal aquifer in near future. The measured electric conductivities of the water samples have been analyzed periodically. Until now the measured electric conductivities do not show high values but those values convince the emerging threat of seawater intrusion in this aquifer in future if groundwater will be pumped at current rate further. Therefore it is worth to conduct a numerical study of seawater intrusion and its impact to the Motooka area since the sustainability of the aquifer is inevitable for the future groundwater development and agriculture. In this paper a numerical study of the seawater intrusion of the Motooka is discussed. The numerical model presented in this paper was developed by coupling the groundwater flow equation with the mass transport equation to simulate the density dependent solute transport in the three dimensional space. The usage of this model as a management tool to simulate the salinity variation with groundwater pumping is emphasized. The numerical model was adequately capable of simulating the seawater intrusion and numerical results show a satisfactory agreement with the field observations. Keywords: coastal aquifer; density dependent flow; electric conductivity; groundwater; numerical model; seawater intrusion 1

INTRODUCTION

Rapid urban development, economic growth and intensive population growth in coastal regions of many parts of the world have led to a vast increase in water demand. Inadequacy of surface water supply to fulfill this huge demand tends to increase the groundwater exploitation in coastal regions. Therefore the importance of groundwater has been identified and the usage of groundwater resources has exceeded sustainable limits in most parts of the world. Due to overexploitation and environmental pollution, the available drinking water has been seriously threatened. It has been forecasted that in 2025, two thirds of the world population will face a shortage of drinkable water (Oude Essink, 2001). As a large proportion of the world population lives in coastal zones, the optimal exploitation of groundwater and control of seawater intrusion are the challenges for present and future. Seawater intrusion can be defined as the inflow of seawater into a coastal ∗

Corresponding author ([email protected])

aquifer. The major reason for this phenomenon is abstraction of groundwater in exceeded limits. Motooka is a coastal area located in the western part of Fukuoka prefecture of Japan The water needs of the area are accomplished by river water, irrigation ponds and groundwater. The total groundwater extractions for greenhouses and domestic use are about 700 m3 /day and 400 m3 /day respectively (Tsutsumi et al. 2004). Seawater intrusion is becoming a significant issue in this area due to continuous extraction of groundwater. Even the measured electric conductivities (EC) of pumped groundwater are not too high, the significant fluctuations of EC values have been a critical issue to address since those fluctuations directly affected crops which are grown in greenhouses. Therefore in this study the attention is focused on how to use the numerical model to simulate EC variation of pumped groundwater. The farmers of this area have complained about the EC variation too. Further authors are interested in the simulation of above mentioned fact than usual seawater intrusion modeling which describes the fate and movement of interface towards the freshwater aquifer. Recently

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Figure 2. Detailed information of pumping wells, water table observation well (WL-8) and EC wells (1&2) in the model area.

Figure 1. Location of Motooka – study area.

a new university is constructed in this area and it is going to utilize groundwater as a partial water supply for the daily demand. It will increase the stress on Motooka coastal aquifer in future. Moreover this construction may reduce the groundwater infiltration, lower the groundwater potential and induce saltwater into the aquifer. The selected area for the initial model development of the Motooka is shown in Figure 1. This area was selected considering the future importance of the wells located inside the selected area. As shown in Figure 2, inside the selected area there are three groundwater pumping wells, one water table observation well (WL8) and two EC observation wells (EC well-1 and EC well-2). Due to the distribution of pumping wells and complex geological formations the application of three dimensional model is inevitable. Here the development of the three dimensional numerical model is discussed with its application for the selected area of Motooka coastal aquifer.

2

GEOLOGY OF THE SITE

The Matooka area is located in the western region of Fukuoka City of Kyushu Island in Japan. The elevation

Figure 3. Geology of the area.

of the ground surface ranges from 0.3 m at the lowest point to about 100 m a.m.s.l at the highest point. The lowland area is an alluvial plain used for agriculture such as greenhouse farming and paddy fields. Under this plain, a shallow unconfined aquifer has developed and it is partially affected by saltwater intrusion. The thickness of the unconfined aquifer under the lowland is approximately 50 m. There are two geological units consisting of crystalline schist in the north and cretaceous itoshima granodiorite in the south. In the south east low alluvial plain a confined aquifer has developed (Figure 3). The aquiclude is clayey soil of few meters thick. The hilly areas, which serve as groundwater recharge areas, mainly consist of weathered granite rock at 5–10 m depth and un-weathered granite below 40–50 m depths.

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3 3.1

MODEL DEVELOPMENT Methodology

To simulate the seawater intrusion and consequent electric conductivity fluctuations due to saltwater intrusion, a three-dimensional density dependent numerical model is developed. The complexity of geology, pumping well distribution, special and temporal distribution of hourly recharge rate and topography of the selected area requires a three-dimensional model. In this study, the model is developed for a selected area of the Motooka coastal aquifer, as shown in Figure 1 and Figure 2. In the modeling process basically three different geological regions are considered according to the permeability values. Those are bed rock, aquiclude and flow dominant areas. The model is based on the finite difference method with a non uniform discretized grid system. The method of characteristics is applied to solve the advection part of the mass transport equation. Fine grids are applied to the pumping well locations to have more precise results for the EC fluctuations, and larger grid sizes are applied for rest of the area to reduce the total number of grids which will reduce the calculation time consequently.

3.2

where ρ and ρf are contaminated and fresh water densities, respectively. The advection dispersion solute transport equation is written as:

where θ is the volumetric moisture content, Dxx , Dyy , Dzz , Dxy , Dxz , Dyx , Dyz , Dzx , and Dzy are dispersion coefficients, which are dependent on the velocity as shown below:

Mathematical model

The mathematical model consists of partial differential equations that govern the groundwater flow and transport of solute in a coastal aquifer. The numerical model discussed here uses the groundwater flow continuity equation, Darcy’s law and the mass transport equation. The x and z axes are taken as horizontal, while y axis is considered as vertical. The continuity equation is given by:

where Cw is the specific moisture capacity, S0 is specific storage coefficient, α is a dummy number which takes 0 in an unsaturated condition and 1 in a saturated condition, u,w and v are pore velocities in x, z and y directions respectively. h is hydraulic pressure head. Darcy’s law is given by:

where αL is the longitudinal dispersion length, αT is transverse dispersion length,√DM is molecular diffusion coefficient, and |V | = u2 + w2 + v2 is the magnitude of the velocity vector. The equation of state shows the relationship between fluid density and solute concentration, and is given by:

where C is salt concentration. Equation of state is used to couple the groundwater flow equation and solute transport equation. 3.3 Numerical simulation The model discussed here is based on the finite difference approach to solve the partial differential equation

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Figure 4. Grid distribution.

of flow and transport. The transient groundwater flow equation (1) is solved by an implicit finite difference method using an iterative successive over relaxation (SOR) technique. The solute transport equation (3) is solved in two step processors. Whereas advection term is computed by the method of characteristic (MOC), the dispersion term is calculated by an explicit finite difference method. The method of characteristics is widely used as a high accuracy method to solve the convective-dispersive equation for solute transport in groundwater (Jinno & Ueda 1978, Zheng & Wang 1994). Density differences are taken into account through adding the buoyancy term to the ride side of equation 2.3. The model domain is divided into unequal discretized grid system for the x and z directions. For the Y direction, uniform grid size of 2.0 m is used. Small grid sizes are assigned to pumping area and lager grid sizes are assigned to the rest in the x and z directions. The smallest grid sizes in the x and z directions are 4.0 m and 5.0 m respectively. Figure 4 shows the grid arrangement of the model while 1053.0 m, 417.0 m and 56.0 m are the length, width and height of the numerical model. The total number of grid pointed accompanied in the model is 136,351. For the MOC initially particles have to be distributed. In this model 8 particles are allocated for one cell of three dimensional mesh. The hydro-geological parameters used in the model are obtained from borehole information, field measurements and literature (Appelo & Postma, 2007). The longitudinal and transverse dispersion lengths are set to 3.6 m and 0.36 m, respectively, while molecular diffusion is 1.0 × 10−9 m2 /s. The standard seawater density value of 1025.0 kg/m3 is used. The density of fresh water is set to 1000.0 kg/m3 , and 1.6 × 10−8 m/s, 6.6 × 10−7 m/s, and 4.6 × 10−6 m/s are the hydraulic conductivities of bed rock, aquiclude (confining layer) and flow dominant region, respectively. Time increment is set to four hours. Figure 5 illustrates the major geological regions considered in the model and the boundaries for the numerical model.

Figure 5. Major geological sections of the model & boundaries. Table 1. Boundary conditions of the numerical model according to Figure 5. Boundary

Pressure Head

Concentration

ABCD EFGH BCGF

hABCD (t) = HABCD (t) − y hEFGH (t) = HEFGH (t) − y hBCGF (t) = HBCGF (t) − y

ADHE

hp = (Hs − y) ·

∂C/∂y = 0.0 ∂C/∂y = 0.0 C = 0.0% ∂C u > 0, =0 ∂x u < 0, C = 100%



ABFE CDHG

∂h ∂y  ∂h −ky ∂y −ky

ρ ρf  ρ + =0 ρf  ρ = −Re(t) + ρf

∂C/∂y = 0.0 ∂C/∂y = 0.0

Different boundary conditions are applied to simulate groundwater recharge and seaward boundary of the model. In Figure 5, BCGF is the landside boundary while ADHE is the seaside boundary. For ABCD, EFGH and BCGF boundaries, time dependent pressure head boundary conditions are applied. Hourly recharge rate Re(t) is assigned to the CDHG boundary. The bottom ABFE is kept as impermeable boundary. Time dependent pressure head boundaries and hourly recharge was generated by the groundwater recharge model developed by Tsutsumi et al, (2004) for the Motooka region. Figure 6 shows the initial condition of the model. The interface is assumed to be in a natural state. As shown in Figure 6, there are 3 pumping wells in the model domain. The boring depths of P1, T8 and T9 wells are 40 m, 20 m and 30 m, respectively. The well diameters for P1, T8 and T9 are 100 mm, 100 mm and 120 mm, respectively. Wells P1, T8 and T9 are located at 703 m, 679 m and 647 m from the seaward boundary. Even though these 3 wells have been used for a long time, the pumping rate measurements were

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Figure 6. Initial conditions of the model and pumping wells P1, T8 and T9 locations distribution.

Figure 8. Comparison of measured and calculated electric conductivities of EC observation well 1 for the years 2004 and 2005.

Figure 7. Comparison of measured and calculated water table values for year 2002 to 2006 at water table observation well – WL8.

started only recently; however, electric conductivities have been recorded from year 2000. After initialization the model was run and calibrated.

4

RESULTS & DISCUSSION

The calibrated model was run under the realistic pumping rates to obtain the EC values of at pumping wells. The lack of information of exact pumping rates from year 2000 to 2006 was a problem to overcome during modeling. The proper pumping rate measurements started only 2007. That problem was solved by assuming reasonable pumping rates after collecting information from the field and field surveys. Figure 7, the graph of well WL8 (according to Figure 2) is for the calculation period from 2002 to 2006. The measured water tables and numerical results show a good agreement. Therefore the model can be used as a tool to predict water tables for future water management activities. Figure 8, shows the measured and calculated EC values of EC observation well 1 for the years 2004 and 2005. Numerical results show reasonable agreement with the measured EC profiles. The conversion

between the salt concentration and EC was done assuming a linear relationship in this calculation. Following figures shows the comparison of measured and calculated EC values under the assumed pumping rates for the well T8 and T9. The assumed pumping rates are also shown. Measured electric conductivity values of pumping wells in the selected coastal aquifer show that the present extent of saltwater intrusion is not critical since the electric conductivity values range between 300 µS/cm and 500 µS/cm. However, the fluctuations are more important for agriculture in Motooka. It can be foreseen that, if groundwater exploitation increases in future this area will be influenced by critical seawater intrusion. Non-availability of pumping rate measurements for the years 2001 and 2002 is certainly an issue in obtaining reliable results for the pumping wells. Wells T8 and T9 show good agreements between the measured and the calculated electric conductivity values. For the electric conductivity fluctuations, not only pumping rates but groundwater recharge is influenced. Hydraulic conductivities are also another factor that affects the results. It seems, however, to control the time scale of the simulated processes. The sensitivity of the simulation results to hydraulic properties of the aquifer is not investigated in this study. Applying suitable boundary conditions, as shown in Table 1, numerical simulation is enhanced to reach the reality. One of the significant features of this model is its capability to simulate the relationship between pumping rates and electric conductivity, which is more important to the farmer of Motooka.

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a) Measured and calculated EC variation of well T8 for year 2001

d) Measured and calculated EC variation of well T8 for year 2002 Figure 9. Comparison of numerical results with measured electric conductivities for the years 2001 and 2002 under reasonably assumed pumping rates for the pumping wells T8 and T9.

b) Measured and calculated EC variation of well T8 for year 2002

water tables at WL8 show reasonably good agreement. This model is capable of simulating electric conductivity fluctuations with different pumping rates, groundwater recharge and variable pressure boundaries. Assigning finer grid sizes is recommended to obtain more reasonable results. This is the first attempt to simulate EC fluctuation if Motooka. Therefore in future intense study and numerical simulations are inevitable. REFERENCES

c) Measured and calculated EC variation of well T8 for year 2001

5

CONCLUSION

The numerical simulation results of the developed density dependent transport model show satisfactory compatibility with the measured electric conductivities of pumping wells. The measured and calculated

Appelo, C.A.J. & Postma, D. (2nd Ed.) 2007. Geochemistry groundwater and pollution. Leiden: A. A. Balkema publishers. Jinno, K. & Ueda, T. 1978. On the numerical solutions of convective dispersion equation by shifting particles, Transactions of JSCE,10: 126–129. Oude Essink, G.H.P. 2001, Saltwater intrusion in a threedimensional groundwater system in The Nederland: A Numerical study, Transport in porous media. 43: 137–158. Tsutsumi, A. Jinno, K. Berndtsson, R. 2004, Surface and subsurface water balance estimation by the groundwater recharge model and a 3-D two-phase flow model, Hydrological sciences, 49(2): 205–215. Zheng, C. Wang, P.P.1999, MT3DMS:A Modula three dimensional multi-species transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems; documentation and user’s guide. U.S. Army crops of engineers. Engineer research and development center. 1–76.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

A hidden Markov model for non-stationary runoff modeling conditioned on El Niño information E. Gelati∗ & D. Rosbjerg Department of Environmental Engineering, Technical University of Denmark, Lyngby, Denmark

H. Madsen DHI Water – Environment – Health, Hørsholm, Denmark

ABSTRACT: We approach stochastic runoff modeling with a Markov-modulated autoregressive model with exogenous input (MARX), which belongs to the class of hidden Markov models. The defined model assumes runoff parameterization to be conditional on a hidden climatic state following a Markov chain, where state transition probabilities are functions of the climatic information. MARX allows a heteroscedastic, pseudo-linear, and conditionally stationary description of the runoff process, as its parameters change over time according to the climatic regime. The model is fitted to seasonal inflows to the Daule Peripa reservoir in Ecuador. El Niño – Southern Oscillation (ENSO) information is used to condition the runoff parameterization. Various climatic indices are considered as covariates: sea surface temperature anomalies in the eastern equatorial Pacific Ocean perform best as predictors. MARX is used to generate long term scenarios and to perform short term forecasts, in the perspective of both long and short term reservoir optimization. The model yields reasonable predictions especially at the occurrence of strong El Niño episodes, during both calibration (1950–1989) and validation (1990–1999). A low predictive ability is found for periods with negative runoff anomalies. However, this pitfall might be overcome by using a climate index that correlates properly with negative inflow anomalies. Keywords: modelling

1

climate; El Niño – Southern Oscillation; Ecuador; hidden Markov models; non-stationarity; runoff

INTRODUCTION

Stochastic runoff models are often used to generate scenarios and forecasts for water resources management applications. Including the influence of climatic variability constitutes a challenge for model development, but it is necessary to obtain realistic simulations and derive effective management policies. Several stochastic models have been developed describing the regime-like behaviour of hydrological systems (Fortin et al. 2004, Akintug & Rasmussen 2005). Other approaches used climatic indices, such as Sea Surface Temperatures (SST) and gridded atmospheric variables, to predict runoff anomalies (Uvo & Graham 1998, Kelman et al. 2000, Landman et al. 2001, Grantz et al. 2005). Here we present a method that is able to mimic climate-driven shifts between different runoff regimes by using climatic covariates to predict runoff ∗

Corresponding author ([email protected])

anomalies. We define a Markov-modulated autoregressive model with exogenous input (MARX). It belongs to the class of hidden Markov models and is related to non-homogeneous hidden Markov models (NHMM) that have been applied to downscaling atmospheric patterns to precipitation (Hughes & Guttorp 1994, Hughes et al. 1999, Bellone et al. 2000). MARX assumes that the climatic regime is represented by a hidden state following a Markov chain, where the state transition probabilities are functions of the climatic information. The prevailing climatic state determines the parameters of the linear autoregressive model with exogenous input (ARX) that maps runoff anomalies. Even if the model is globally stationary and linear, the parameterisation changes both over time and in the climatic domain. These features weaken the stationarity assumption and permit a heteroscedastic and pseudo-linear modelling of runoff anomalies. The presented model is applied to inflow time series of the Daule Peripa reservoir located in western Ecuador (Fig. 1). In this region, the occurrence of El Niño manifests itself through anomalously

237

Figure 1. Location of Daule Peripa reservoir and of the areas where SSTA are measured.

heavy rainfall as a consequence of strong positive Sea Surface Temperature Anomalies (SSTA) along the coast (Vuille et al. 1999). Therefore the generation of reliable inflow scenarios and forecasts, to be used to derive optimal reservoir management policies, depends much on the ability to model the impact of El Niño – Southern Oscillation (ENSO) on Inflow Anomalies (IA). 2

transition probabilities depend on the current climatic index ct . Following the parameterization introduced by Hughes & Guttorp (1994) for NHMM, the state transition probabilities are computed as:

DATA

Monthly inflow values to the Daule Peripa reservoir are available for the period 1950–1999. The climatic data were obtained from the NOAA Climate Prediction Center (Camp Springs, Maryland, USA). Data from 1950 to 1989 were used for model calibration, while the period 1990–1999 was reserved for validation. IA were calculated by log-transforming and standardizing the raw values (with respect to seasonal mean and standard deviation), while the climatic time series were standardized by subtracting the seasonal mean values. Figure 1 shows the location of the Daule Peripa reservoir and the portions of the Pacific Ocean where the investigated SSTA are measured. We carried out a preliminary analysis, where the Auto Correlation Function (ACF) of IA and the CrossCorrelation Function (XCF) between IA and lagged SSTA were computed for different temporal discretizations (given in terms of numbers of seasons in a year): Figure 2 shows the results when using NINO 1 + 2 SSTA, which proved to be the best covariate, for 12 and 4 seasons per year. Both ACF and XCF for 4 seasons are slightly higher than for 12 seasons, when considering lags that are multiple of 3 months. 3

Figure 2. Results of the correlation analysis. The numbers of seasons are indicated between the brackets.

MODEL FORMULATION

MARX assumes the IA process to be driven by a hidden climate process. The climate state at time t is represented by the discrete stochastic variable st following a first order Markov chain, where the

where θ is the parameter set; pij is the stationary component of the probability of shifting from state i to j; Vc is a scale factor; and µj can be interpreted as the mean value of ct when st = j given that st−1 = i. The climate state prevailing at t determines the parameters of the ARX spell that models IA at t, i.e. the expected value of at :

where λj , βj and σj are the ARX parameters for state j; and εt ∼ N (0,1) is a white noise process. Hence the conditional probability distribution of at can be defined as:

Model parameters are estimated by maximizing the model likelihood function that is defined as (Akintug & Rasmussen 2005):

where AT1 and C1T are the time series of, respectively, IA and SSTA from time 1 to T ; S is the number of climate states; 1S is a S-dimensional column vector of ones; and the elements of each S 2 -dimensional matrix Qt are defined as:

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A global optimization tool, the Shuffled Complex Evolution (SCE) algorithm (Duan et al. 1993), was used to maximize the model likelihood function (4). 4

Table 1. Maximum likelihood parameter estimates of the fitted MARX model (j indicates a climatic state).

SIMULATION AND FORECAST

The MARX model can be used for simulation and forecast purposes. In both cases, we assumed complete knowledge of C1T at any time step as a simplification for the purpose of model testing. However, the NOAA Climate Prediction Center produces forecasts of ENSO-related SSTA with a lead time up to 9 months, thus giving some degree of realism to our assumption. If the model is used in forecast mode, observed values for both ct and at are used to predict aˆ t+1 in the ARX spell (2). In simulation mode, the observed value for at in (2) is substituted by the simulated value aˆ t resulting from the previous ARX spell. In both cases the model-generated IA consist of mean values calculated from a high number of model runs, which also allow the estimation of confidence intervals by bootstrapping. 5

p1j [–]

p2j [–]

µj [◦ C]

λj [–]

βj [–]

σj [–]

j=1 j=2

0.91 0.09

0.08 0.92

−0.74 1.95

0.56 0.01

0.18 0.40

0.76 0.81

RESULTS

The choice of the most appropriate model setup requires deciding the climatic covariate to be included and the number of states to be defined. NINO 1 + 2 SSTA was chosen as it showed the highest correlation with IA. Initially we wanted to define 3 states to account for respectively La Niña, normal, and El Niño conditions. However, the analyzed climatic indices (ENSO-related SSTA indices, Southern Oscillation Index, Multivariate ENSO Index) correlated poorly with strong negative IA. Therefore we chose a 2-state setup. The number of seasons per year is also a decision variable. In the following we present the results from a 4-season MARX. The choice of the number of seasons depends on the application of the modeling results: a higher number of seasons offers a finer time resolution but a smaller lead time for reliable forecasts. Therefore a monthly-scale model may be suitable for scenario analysis, while a coarser time scale may be appropriate for forecast purposes. 5.1

Unit

Figure 3. Calibration results (1950–1989). (a) Observed IA against model-generated mean value and 90% bootstrap interval. (b) Observed NINO 1 + 2 SSTA.

Parameter estimates

The maximum likelihood estimates of a 4-season MARX model, defining 2 states and including NINO 1 + 2 SSTA (Vc = 0.8), are reported in Table 1. State 1 can be associated to normal or La Niña conditions (µ1 = −0.74◦ C), while state 2 corresponds to well-defined El Niño episodes (µ2 = 1.95◦ C). The autoregressive parameter λj is highest for state 1, while the exogenous input parameter βj is highest for state 2. Thus during El Niño episodes the climatic signal

is more influent than the autocorrelation component, while the opposite is the case during normal or La Niña conditions. 5.2 Simulation results Figures 3–4 compare the observed seasonal IA and the corresponding simulated mean value with the NINO 1 + 2 SSTA time series. The calibration results

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Figure 4. Validation results (1990–1999). (a) Observed IA against model-generated mean value and 90% bootstrap interval. (b) Observed NINO 1 + 2 SSTA.

(Fig. 3a) show a good fit of the model-generated mean value, if positive IA are considered. MARX proved to perform particularly well when intense El Niño episodes occur, as we can see from the SSTA plotted in Figure 3b. In contrast, the model failed to capture strong downward shifts of IA, which is coherent with the apparent low correlation between strong negative IA (Fig. 3a) and the corresponding SSTA (Fig. 3b). The same observations hold for the validation period. The positive IA corresponding to the 19971998 El Niño episode (from season 30 to 38 in Figures 4a-b) are partly under-predicted. However the model reproduces generally well the trend of IA during validation. For both periods the 90% bootstrap confidence intervals include the observed IA, except for the most negative values and for part of the 1997-1998 El Niño episode. 5.3

Forecast results

Figures 5a-b show logarithmic plots of the observed runoff values versus the corresponding 1-season lead

Figure 5. Observed runoff values versus 1-season-ahead forecasts. (a) Calibration period (1950–1989). (b) Validation period (1990–1999).

time forecasts. Calibration and validation results show similar characteristics. As expected, the accuracy of the reproductions decreases as the magnitude of the inflow increases. However, the forecasts are relatively unbiased and consistent with the observed values. 6

CONCLUSIONS

The presented model allows simulating and forecasting non-stationary runoff time series by using a pseudo-linear parameterization, and by modeling directly the correlation between climatic and streamflow anomalies. The definition of Markovian hidden climatic states can mimic the regime-like behavior of large-scale climatic phenomena that have an impact on runoff, such as ENSO, and permits to change parameterization according to climatic conditions. Moreover the model proved to be applicable for both short-term forecasts and log-term simulations, leading in both

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cases to satisfactory predictions. The lack of predictive capability for strong negative inflow anomalies constitutes a pitfall. However, significant improvement could be obtained by including a covariate that correlates well with negative inflow anomalies. This would allow defining a “dry” climatic state and enhance predictive ability. Further research should investigate this possibility. REFERENCES Akintug, B. & Rasmussen, P. F. 2005. A Markov switching model for annual hydrologic time series. Water resources research 41: W09424. Bellone, E., Hughes, J. P. & Guttorp, P. 2000. A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts. Climate research 15: 1–12. Duan, Q. Y., Gupta, V. K. & Sorooshian, S. 1993. Shuffled complex evolution approach for effective and efficient global minimization. Journal of optimization theory and applications 76(3): 501–521. Fortin, V., Perrault, L. & Salas, J. D. 2004. Retrospective analysis and forecasting of streamflows using a shifting level model. Journal of hydrology 296: 135–163.

Grantz, K. & Rajagopalan, B. 2005. A technique for incorporating large-scale climate information in basin-scale ensemble streamflow forecasts. Water resources research 41: W10410. Hughes, J. P. & Guttorp, P. 1994. A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water resources research 30(5): 1535–1546. Hughes, J. P. & Guttorp, P. 1999. A non-homogeneous hidden Markov model for precipitation occurrence. Journal of the royal statistical society 48(1): 15–30. Kelman, J., Vieira, A. M. & Rodriguez-Amaya, J. E. 2000. El Niño influence on streamflow forecasting. Stochastic environmental research and risk assessment 14: 123–138. Landman, W. A., Mason, S. J., Tyson, P. D. & Tennant, W. J. 2001. Statistical downscaling of GCM simulations to streamflow. Journal of hydrology 252: 221–236. Uvo, C. B. & Graham, N. E. 1998. Seasonal runoff forecast for northern South America: a statistical model. Water resources research 34(12): 3515–3524. Vuille, M., Bradley, R. S. & Keimig, F. 1999. Climate variability in the Andes of Ecuador and its relation to tropical Pacific and Atlantic sea surface temperature anomalies. Journal of climate 13: 2520–2535.

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Integrated simulation by hydrological, hydraulic and water management modelling techniques in support of water resources management in the Czech Republic O. Novicky∗, L. Kasparek & P. Vyskoc T.G. Masaryk Water Research Institute, Prague, Czech Republic

ABSTRACT: T.G. Masaryk Water Research Institute in Prague has developed a system, which uses three interlinked models for simulation of hydrological, hydraulic (groundwater) and water management conditions in a basin. The resulting monthly series of hydrological and water management variables can be simulated for current conditions (present or historical) and also for possible future conditions by reflecting climate change scenarios. The system can therefore provide information for integrated water resource management (both surface water and groundwater) as well for strategic water management planning. The paper provides basic description of these tools and illustrates their applicability on selected case studies. Keywords: climate change; water resources; hydrological model; water management model; hydraulic model; water resources management

1

INTRODUCTION

In spite of the fact that stipulations of Water Framework Directive (Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy) do not include almost any requirements for river flow data, the river basin management planning according to this Directive is associated with great requirements for new hydrological inputs in the Czech Republic. This is because of the fact that the country has a long tradition in water management planning and therefore the planning for meeting the requirements of Water Framework Directive involves not only the protection of water as a component of the environment but also flood protection and water supply issues. During preparation of relevant strategic documents, Ministry of Agriculture initiated implementation of hydrological and water management studies, whose purpose is to examine whether the existing water resources would be sufficient for future generations if climate warming continues according to the climate change scenarios. In the studies, the attention is paid to the examination of main water management schemes that were implemented during the last century. For the purposes of these studies, T.G. Masaryk ∗

Corresponding author ([email protected])

Water Research Institute uses interlinked tools, which include mainly Bilan water balance model, Modflow hydraulic model and the water management model.

2

METHODS AND TOOLS

The system that is applied by T.G. Masaryk Water Research Institute for water management studies uses monthly series of basin precipitation, air temperature and relative air humidity together with river flows for simulation of water cycle components by using Bilan water balance model (the model developed by T.G. Masaryk Water Research Institute, described e.g. in Tallaksen and Lanen (2004)). This model (see Figure 1) generates monthly series of basin potential evapotranspiration, actual evaporation, infiltration to zone of aeration, percolation of water towards groundwater aquifer (groundwater recharge), and water storage components in snow cover, soil and groundwater aquifer. The total runoff consists of three components, which are direct runoff, interflow and base flow. The main model parameters influencing the mean flow are: the capacity of soil moisture storage, a parameter for direct runoff and two degree-day factors for snowmelt (for months with important snowmelt and for “winter month”). Time distribution of runoff is mainly affected by parameters controlling distribution of percolation into interflow and groundwater

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Figure 1. Input variables of Bilan model (precipitation, air temperature, relative air humidity) and simulated water cycle components (the remaining variables; river flow is used for calibration of the model parameters).

Figure 3. Input variables of Water management model (storage capacities in reservoirs and their operation rules, water abstractions and waste water discharges, and river flows observed or simulated by Bilan model) and simulated variables (river flows, water storages and water levels in reservoirs, simulated water abstractions and waste water discharges).

water storages and water levels in reservoirs and simulated water abstractions and waste water discharges. The time series can be statistically analysed and the availability of water resources assessed in terms of probability.

3 Figure 2. Input variables of Modflow model (observed groundwater abstractions, groundwater levels in boreholes and recharge simulated by Bilan model) and simulated variables (groundwater levels, direction and velocity of groundwater flow, base flow).

recharge. Those parameters are different under winter conditions, summer conditions and under the conditions of snowmelt. The last parameter is controlling outflow from groundwater storage (base flow). The groundwater recharge and base flow series simulated by Bilan are used together with observed groundwater levels in boreholes and monthly data on groundwater abstractions for simulation by Modflow model (Modular three-dimensional finite-difference groundwater flow model developed by the United States Geological Survey), which simulates direction and velocity of groundwater flow, groundwater levels and resulting base flow series (see Figure 2). The water management is simulated by a water management model (developed by T.G. Masaryk Water Research Institute), which uses flow series (observed and naturalised or simulated by Bilan model), demands for water use (abstractions, waste water discharges, flow regime requirements, such as minimum ecological flows, limits of water levels in reservoirs), technical characteristics (storage capacities of reservoirs, capacities of river channels), and operation rules for flow regulation and water supply of individual users (see Figure 3). The simulated data include time series of flows (affected by the regulation and water use),

CLIMATE CHANGE SCENARIOS

T.G.M. Water Research Institute and other institutions in the Czech Republic use presently the climate change scenarios that have been derived from results of PRUDENCE (Prediction of regional scenarios and uncertainties for defining European climate change risks and effects) project. The scenarios are based on simulations by HIRHAM regional atmospheric climate model (Undén et al. 2002) and RCAO Atmosphere-Ocean model (Döscher et al. 2002) and SRES Emission Scenarios A2 and B2 (Nakicenovic et al. 2000). Scenario A2 (“pessimistic”) assumes higher temperatures than B2 scenario (“optimistic”). The climate change scenarios are applied in Bilan water balance model, whose 8 parameters are calibrated by using observed monthly series of basin precipitation, air temperature, relative air humidity and river flows. The model is subsequently used for simulation of the water cycle components for the unaffected conditions and conditions (the meteorological series) that are modified by using the climate change scenarios (2085 as reference year).

4

CASE STUDIES

4.1 Hydrological study for groundwater management in the Metuje River basin The basin of the Metuje River (a tributary of the Elbe River with basin area of 240 km2 ) is predominantly located in Northern Bohemia (the Czech Republic) with a small part in Poland.

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This basin is highly important for its groundwater resources. Its main aquifers are developed in the permeable Triassic and Cenomanian rocks (deep aquifer) and Middle Turonian formations (shallow aquifer). Groundwater in the deep aquifer shows semi-confined to artesian conditions, whereas in the shallow aquifer unconfined conditions occur. Depths of the deep aquifer exceed 200 m. For the importance of the basin and other reasons (mainly for assessing long-term changes in water resources due to climate variability, for assessing the impact of groundwater abstractions on groundwater resources and ecology, and for modelling the interaction between groundwater and streamflow), Ministry of the Environment of the Czech Republic sponsors groundwater and surface water monitoring in a dense network of the sites in the basin and also hydrological studies that are aimed at providing information for integrated water resource management (both surface water and groundwater) as well as for strategic water management planning. The first studies (Kašpárek et al. 2006 & Novický et al. 2007), which combined the application of Bilan and Modflow model for assessing groundwater conditions in the basin applied climate change scenarios based on simulation by HadCM2 (Johns 1996) and ECHAM4 (Roeckner et al. 1996) Global circulation models (2050 as reference year). The results of these studies were substantiated by conclusions from the most recent study, which already applied the regional scenarios. All of these studies indicate that groundwater resources in the Metuje River basin are highly vulnerable in spite of the fact that its hydrogeological conditions are very good. These results have been illustrated in maps of the changes in groundwater table that show spatial distribution of a decrease in the groundwater level, whose range is around 10 meters and the mean value about 6 meters. In the upper part of the basin (Figure 4), the base flow could drop to levels of existing groundwater abstractions (about 100,l s−1 ). The impact on the groundwater would be also reflected in flow of the Metuje River, which would fully dry in the periods when it is normally almost exclusively fed from the groundwater storage. 4.2 Water management study of the Vltava River basin In 2007 the integrated simulation by hydrological and water management modelling techniques was applied for the Vltava River basin with the aim to evaluate possible impact of climate changes on water resources and water use. The Vltava River basin covers an area of 27 602 km2 with the population of 3 million of inhabitants. Water

Figure 4. Decrease in groundwater levels consequently to climate change according to HIRHAM A2 climate change scenario in the Upper Metuje River basin.

management system in the basin includes 27 reservoirs with a total storage capacity of 1 200 mil. m3 . Diagram in Figure 5 shows location of the reservoirs and water management system in lower part of the basin. Monthly flow series observed in the basin were used together with observed monthly precipitation, air temperature and relative air humidity for simulation of water cycle components in monthly step by using Bilan water balance model. Parameters of the model, which were calibrated by using the observed data from period 1980–2006, were subsequently used for simulation of the water balance components for the meteorological variables that were modified by using regional climate change scenarios. Series of the water cycle components that were simulated for current conditions and conditions modified according to the climate change scenarios were compared and analysed in order to assess possible impacts of climate change on water cycle. The results of this part of the study are illustrated for HIRHAM-A2 and HIRHAM-B2 regional climate change scenarios in Figure 6, which shows decrease in mean river flow in water gauging stations located in the Lower Vltava River basin. The differences between results of climate change scenarios HIRHAM-A2 and HIRHAM-B2 were significant – they ranged from 8% for the Lower Vltava River to 17% for the Sazava. Impacts of climate change on water resources in the basin were subsequently analysed by using the hydrological series together with demands for water use in the water management model. The series resulting from the simulation by the model were statistically analysed (probabilities of exceedance were calculated for managed streamflows, filling of reservoir and

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Figure 5. Diagram showing water gauging stations and control sites in the Lower Vltava River basin.

Figure 6. Decrease in mean river flows in the Lower Vltava River basin consequently to climate change according HIRHAM-A2 regional climate change scenario (pessimistic scenario) and HIRHAM-B2 regional climate change scenario (optimistic scenario).

water abstraction) for derivation of reliability of meeting individual demands for water use (e.g. abstraction, minimum ecological flow, etc.). The reliability was expressed as a probability of meeting demands

within the time period of simulation (1980–2006). The demanded reliability ranges between 95–99.5% depending on the water use (drinking water supply, industrial sector etc.). The results of the statistical

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Figure 7. Degree of reliability of meeting the water demands in the Lower Vltava River basin under conditions of climate change according to HIRHAM-A2 regional climate change scenario (pessimistic scenario) and HIRHAM-B2 regional climate change scenario (optimistic scenario).

analysis showed that water resources in the basin could be highly impacted by climate change. The results of this part of the study are illustrated for HIRHAM-A2 and HIRHAM-B2 regional climate change scenarios in Figure 7, which shows individual river sites that were divided into three categories according to a degree of reliability of meeting the water demands (at risk, uncertain, not at risk). The results for HIRHAM-A2 and HIRHAM-B2 are similar, the most of river sites will be at risk.

5

CONCLUSION

The system that has been briefly described in this paper was already applied in the Czech Republic for examination of current and future availability of water resources in several large basins and their water management systems. These applications substantiated that the system is a powerful tool, whose application provides information that is required mainly by Ministry of Agriculture of the Czech Republic and river basin companies for integrated water resource management and strategic water management planning. Future development of the system and its practical applications will be aimed at incorporating water quality aspects in terms of chemical composition (including specifically dangerous polluting substances) and biological components of the water environment. These developments are supported by Ministry of the Environment and Ministry of Agriculture of the Czech Republic, which initiated implementation of several projects that involve also aspects of possible impacts of climate change on quantity and quality of surface water and groundwater resources in the

Czech Republic and preparation of necessary adaptation measures. These projects include e.g. a project on Improving of current estimates of impacts of climate change in sectors of water management, agriculture and forestry and proposals of adaptation measures that has been initiated by Ministry of the Environment and a project on Research on adaptation measures for elimination of impacts of climate change in regions of the Czech Republic initiated by Ministry of Agriculture. Some of the tools that have been developed by T.G. Masaryk Water Research Institute or are applied by the Institute are also used in international cooperation such as in WATCH (Water and Global Change) project, which is carried out as a component of the 6th Framework Programme of European Union.

REFERENCES Döscher, R. et al. 2002. The development of the coupled regional ocean-atmosphere model RCAO. Boreal Env. Res. 7: 183–192. Johns, T.C. 1996. A description of the Second Hadley Centre Coupled Model (HadCM2). Climate Research Technical Note 71, Hadley Centre, United Kingdom Meteorological Office, Bracknell Berkshire RG12 2SY, United Kingdom. Kašpárek, L., et al. 2006. Water resources in Intra-Sudeten basin. Result of Czech-Polish co-operation in monitoring and modelling (1975–2004). Prague: T.G.M. WRI, Ministry of the Environment of the Czech Republic. Nakicenovic, N. et al. 2000. Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press. Novický, O., Kašpárek, L., Uhlík, J. 2007. Possible impacts of climate change on groundwater resources

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and groundwater flow in well developed water bearing aquifers. Helsinki: In: Proceedings from the Third international conference on climate and water. Roeckner, E., et al. 1996. ENSO variability and atmospheric response in a global coupled atmosphere-ocean GCM. Clim. Dyn., 12, 737–754.

Tallaksen, L. M., van Lanen H. A. J. (Eds) 2004. Hydrological Drought – Processes and Estimation Methods for Streamflow and Groundwater, Amsterdam: Developments in Water Sciences 48, Elsevier B.V. Undén, P. et al. 2002. Hirlam-5 scientific documentation. SMHI Hirlam-5 Project Tech. Rep.

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Multi-model approach to hydrologic impact of climate change P. Coulibaly∗ McMaster University, Hamilton, Ontario, Canada

ABSTRACT: Multi-model approach to both downscaling and hydrologic modeling is proposed to assess the variability of climate change impact on streamflow regime in the Serpent River watershed in northeastern Canada. The proposed method includes three downscaling models, namely a statistical method (SDSM), a stochastic weather generator (LARS-WG) and a temporal neural network (TLFN) along with three hydrologic models, namely a physically based watershed model WATFLOOD, two lumped-conceptual modeling systems HBV and CEQUEAU. The downscaling models are used in parallel to downscale meteorological variables (total daily precipitation, daily maximum and minimum temperature) based on climate predictors derived from the Canadian Global Climate Model (CGCM) forced with the Intergovernmental Panel on Climate Change (IPCC) IS92a scenario.The competitive hydrologic models are validated with meteorological data from both the historical records and the downscaled predictor variables. The ensembles of flow simulations generated by the different hydrologic models demonstrate the possible range of future flow regime variability in the selected watershed. The results highlight the uncertainty due to the downscaling methods and the hydrological models, and emphasize the advantage of multi-model approach in estimating hydrologic impact of climate change at the watershed scale. Keywords:

1

hydro-climatic modeling; climate change; hydrologic model; downscaling; river flow

INTRODUCTION

2

The ultimate objective in assessing hydrologic impact of climate change is to provide reliable local to regional scale information suitable to enhance water resources management. Changes in future streamflow regime are driven mostly by climatic and anthropogenic factors. However, it is of general consensus that the increase in frequency and intensity of extreme climatic events (drought, severe storm, heat waves) due to climate change, appears the main factor of the variability and changes of future streamflow regime, and thus will pose some serious challenges to water resources managers. A key issue in assessing the hydrologic impact of climate change remains the difficulty in estimating the uncertainty due to the various sources of data and methods used to estimate future flows at local or regional scale. Previous studies have shown the uncertainty due to the downscaling methods and the global climate model predictors respectively (Khan et al. 2006a,b), and to the climate change scenarios (Wilby et al. 2006).The objective herein is to investigate multimodel approach to implicitly account for the main sources of uncertainty. The multi-model approach permits to generate ensembles of flow series that depict the range of variability of future flow regime. ∗

Corresponding author ([email protected])

EXPERIMENT DATA

The study area considered in this study is the Serpent River basin which is a sub-basin of the Saguenay watershed located in Northeastern Canada. The basin has an area of 1,760 km2 and is located within a flood prone region where changes in hydrologic extremes are of particular interest. The meteorological station at Chute-des-Passes (located at 49.9◦ N, 71.25◦W) is the closest to the Serpent River basin; therefore, the meteorological data observed at this station is used for the downscaling experiments. Forty years of daily total precipitation as well as daily maximum and minimum temperature records representing the current climate (i.e. 1961 to 2000) were prepared for the downscaling experiments. At the same time, observed daily data of large-scale predictor variables representing the current climate condition of the region is derived from the NCEP reanalysis dataset (Kistler, et al., 2001). Climate variables corresponding to the future climate change scenario for the study area are extracted from the Canadian Global Climate Model (CGCM1). The atmospheric component of the CGCM1 model has a surface grid resolution of roughly 3.7◦ × 3.7◦ (400 km). The CGCM1 output used for this study is the result of the IPCC “IS92a” forcing scenario in which the change in greenhouse gases forcing corresponds to that observed from 1900 to 1990 and increases at a

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rate 1% per year thereafter until year 2100. The direct effect of sulphate aerosols is also included. CGCM1 outputs at the closest grid point to the study area (50◦ N, 71◦W) are used as inputs for the downscaling models. The data is divided into four distinct periods, namely, the current (covering the forty years period between 1961 and 2000), the 2020s (2010–2039), the 2050s (2040–2069) and the 2080s (2070–2099). The NCEPderived predictor data have also been interpolated onto the same grid as that of the CGCM1. All predictors in these datasets have been normalized with respect to the 1961–1990 mean and standard deviation and were made available by the Canadian Climate Impacts Scenarios project. Detailed description of the historical and CGCM datasets used herein is provided by Coulibaly et al. (2005).

3

MULTI-MODEL APPROACH

To implicitly account for uncertainty due to the most widely used downscaling and hydrologic models, a multi-model framework is proposed (Fig. 1). It can include different GCMs, downscaling methods and hydrologic models along with frequency and uncertainty analysis modules. Given that there is no well established standard method for selecting a given GCM over another, it is suggested to select different GCMs but with the same climate scenario (e.g. A2 or B2). The same predictor variables selected from the GCM outputs are fed to each of the downscaling methods, and the output of each downscaling model is the input to each of the hydrologic model. The framework can be extended to include regional climate models (RCMs) with the possibility to use the RCM outputs directly into hydrologic models without downscaling (Fig. 1). For the downscaling, the key components are empirical downscaling methods, namely statistical downscaling model (SDSM), time lagged feedforward neural network (TLFN), and stochastic downscaling method called Ashton Research Station Weather Generator (LARS-WG). Each method is briefly described hereafter followed by the description of the hydrologic models.

3.1

Downscaling methods

SDSM is a multiple linear regression tool proposed by Wilby et al. 2002 to derive predictor-predictand relationships between GCM outputs (large scale predictors) and meteorological station records (local scale variables – the predictands). SDSM is one of the most widely used downscaling methods in climate change impact studies. The selection of relevant predictors is achieved by correlation analysis and scatter plots (between the predictors and the predictand

Figure 1. Multi-model framework for assessing hydrologic impact of climate change.

variables) and by investigating the percentage of variance explained by each predictand-predictor pairs. The influence of individual predictors varies on a month by month basis; therefore, the most appropriate combination of predictors has to be chosen by looking at the analysis output of all the twelve months (Dibike & Coulibaly 2005). Another well known downscaling tool is the LARS-WG model (Semenov and Barrow 1997; 2002). To generate daily precipitation and temperature data corresponding to future scenarios using LARS-WG, site analysis is made using the daily CGCM data for both the baseline (current) and future time periods. To incorporate the change in climate, one needs to calculate the relative change in monthly mean precipitation and monthly mean wet and dry series lengths from the GCM output of the baseline and future time period. Similarly, relative change in mean temperature and its standard deviation for each month is calculated from GCM outputs. All these values are calculated from the parameter files generated during the site analysis of the corresponding climate variables (GCM outputs) for the baseline and future time periods. This information is then used to construct the climate change scenario file which LARS-WG uses to determine how the weather generator parameter values (obtained from the observed precipitation and temperature data) should be perturbed to generate regional climate scenario. That means the relative changes observed in the GCM outputs are the forcing to LARS-WG based on which it generates the daily weather data representing future climate at the particular site. Detailed description of the LARS-WG application is provided by Dibike and Coulibaly 2005. TLFN is a neural network that can be formulated by replacing the neurons in the input layer of multilayer perceptron with a memory structure, which is sometimes called a tap delay line. The size of the memory layer (the tap delay) depends on the number of past samples that are needed to describe the input characteristics in time and it has to be determined on a case-by-case basis (Coulibaly et al. 2005). TLFN

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uses delay-line processing elements which implement memory by simply holding past samples of the input signal. The output (y) of such a network with one hidden layer is given by:

where m is the size of the hidden layer, n is the time step, wj is the weight vector for the connection between the hidden and output layers, wji is the weight matrix for the connection between the input and hidden layers and ϕ1 and ϕ2 are transfer functions at the output and hidden layers respectively. bj and b0 are additional network parameters (often called biases) to be determined during training of the networks with observed input/output data sets. For the case of multiple inputs (of size p), the delay-line with a memory depth k can be represented by:

where X (n) = (x1 (n), x2 (n), . . . , xp (n)) and represents the input pattern at time step n, xj (n) is an individual input at the nth time step and χ(n) is the combined input matrix to the processing elements at time step n. Such delay-line only “remembers” k samples in the past. An interesting feature of the TLFN is that the tap delay line at the input layer does not have any free parameters, therefore the network can still be trained with the classical backpropagation algorithm. In addition, the main advantage of the TLFN downscaling method is its ability to incorporate not only the concurrent, but also several antecedent (or lagged) predictor values as inputs, and its temporal processing ability without any additional computational cost. The TLFN downscaling approach is fully described by Coulibaly et al. (2005). 3.2

Hydrologic models

Similar to the previous components, there is no standard method for selecting a hydrologic model for climate change impact study. In the proposed approach, three well established physically based models and one empirical model are selected. For a detailed description of the selected models, the reader is referred to Dibike & Coulibaly (2005). The selected models include the WatFlood model (Kouwen et al. 1993), the HBV model (Bergström et al. 1973), and the CEQEAU model (Morin et al. 1983). WatFlood is a physically based and distributed parameter modelling system that is designed for long-term hydrologic simulation and flood forecasting using distributed precipitation

data. The processes modeled include interception, infiltration, evapotranspiration, snow accumulation and ablation, interflow, recharge, baseflow, and overland and channel routing (Kouwen et al. 1993). The snowmelt process in WatFlood is modeled by an index method; in this case, a degree-hour based heat input or loss. An accounting of the heat content of the snow pack allows re-freezing. Philip formula (Philip 1954) is used as representing the important physical aspects of infiltration process where the pressure gradient acting on the infiltrating water is used to determine the flow using Darcy’s Law. Hargreaves equation (Hargreaves & Samani, 1982) is used to estimate the potential evapotranspiration from temperature data and actual evapotranspiration is assumed to occur at the potential rate if the soil moisture is at a level of saturation and reduced to a fraction of the potential evapotranspiration for values of soil moisture below the saturation down to zero at the permanent wilting point. WatFlood uses the Grouped Response Unit (GRU) method to model large watersheds where a computational element will have one GRU for each hydrologically significant land cover type. The hydrologic response of each class is computed and its response (flow) is weighted according to its percent coverage in that computational grid. These flows are then added to the channel passing through the grid and the outflows from upstream grids are added as inflow to lower grids, where outflows from other contributing grids are added to the local flow and routed to the next downstream grid. River flows are calculated based on continuity and Manning’s formula but separate roughness parameters are used for channel and floodplain roughness. Base flow is calculated by a two parameter, non-linear storage-discharge function. Because of its distributed nature, a single WatFlood model covers the entire watershed area and computed flows can be compared to measured flows wherever measured flows are available. On the other hand, HBV is an integrated hydrological modeling system developed at the Swedish Meteorological and Hydrological Institute, and has been applied to a wide range of applications including analysis of extreme floods, effects of land-use change and effects of climate change (Brandt 1990; Lidén and Harlin 2000). It is a rainfall-runoff model, which includes conceptual numerical descriptions of hydrological processes at the catchment scale. It has a routine for snow-accumulation and snowmelt based on a degree-day relation with an altitude correction of temperature. The evapotranspiration is calculated based on monthly values of potential evapotranspiration as input and a simplified temperature index method (Lindström et al. 1997). The soil moisture accounting routine accounts for soil field capacity and change in soil moisture storage due to rainfall/snowmelt and evapotranspiration, while the runoff

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generation routine transforms water from the soil moisture zone to runoff. Water not retained in the soil is routed through two stores, an upper one interpreted conceptually as saturated soil and a lower one representing groundwater. Water can percolate from the upper to the lower store, which has a slow linear outflow. The model parameters are determined through a calibration process, where the parameters are adjusted until simulated and observed runoff shows a good agreement. The calibrated model can then be used as a simulation tool in numerous applications. CEQUEAU is also a distributed hydrological model, which takes into account the physical characteristics of the basin by subdividing it in Elementary Representative Areas (ERA), also called whole squares, which are assumed to have homogenous features (Morin et al. 1983). It is a water-balance-type conceptual model with distributed parameters. ERAs are further divided according to sub-basin divides, which allows following the formation and evolution of streamflow in time and for proper routing of runoff. In addition to physiographic data, the model requires meteorological inputs (daily solid and liquid precipitation, as well as maximum and minimum daily air temperature). Calculations are performed to quantify the vertical movement of water through the so-called production function, while downstream routing (or advection) of water is calculated with a transfer function. The production function is modeled by a series of interconnected reservoirs representing the ground. Mass transfers are represented by mathematical equations to simulate the different components of the hydrological water balance (snowpack formation

and melt, evapotranspiration, water in the unsaturated zone, water in the saturated zone and lakes and marshes). In general, the steps required to use these models in the study of the hydrological impact of climate change can be described as follows (Dibike & Coulibaly 2005): •

Setting up and calibration (and verification) of a model with precipitation, temperature, potential evapo-transpiration, stream flow and reservoir inflow data representing the current climate (1961– 2000). • Simulation of stream flow and reservoir inflow corresponding to future climate change scenarios based on downscaled future precipitation data along with estimated future temperature and evapotranspiration input. • Analyses of the simulated stream flow and reservoir inflows corresponding to the different time periods (2020s, 2050s, 2080s) and see if there will be a significant changes in streamflow and reservoir inflow volume as well as magnitude and frequency of high and low flows. 4

RESULTS

Common model performance statistics, the Root Mean Square Error (RMSE) and the Nash and Sutcliffe model efficiency index R2 , are used evaluate the hydrologic model validation performance. Table 1 summarizes the model performance for the validation period (1997–2002). In general, the model validation results

Serpent River 300

RiverDrscharge(cms)

200

100

0 Jan-98 Feb-98 Mar-98 Apr-98 May-98 Jun-98 Jul-98 Aug-98 Sep-98 Oct-98 Nov-98 Dec-98 Date Observed

HBV-96

Figure 2. Comparison of hydrologic models validation results.

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WATFLOOD

CEQUEAU

show that the performance of the HBV and CEQUEAU models is slightly better than the WATFLOOD. Figure 2 also substantiates the model validation statistics shown in Table 1. The ultimate objective is to provide reliable estimate of future changes in flow regime. Therefore, after validating the hydrologic models with the historical records, each model is used to simulate future river flows using the downscaled precipitation and temperature generated by each downscaling method. The simulation results corresponding to each of the downscaling scenario time periods (Current, 2020s, 2050s and 2080s) are analyzed for all months of the year. Monthly mean, high and low flows are calculated for each year and then averaged over the number of years in each scenario period. The simulation results corresponding to each combination of downscaling techniques and hydrological models are summarized in Table 2. The scenario simulation outputs corresponding to each hydrologic model are also presented in Figures 3a,b,c. The figures show simulated changes in monthly mean flows between the current and the 2080s time period corresponding to precipitation and temperature downscaled with each of the three different downscaling methods. Results in Table 2 highlight how the outcome of a hydrologic impact study can be affected by the choice of any one particular downscaling technique and hydrological model over the other. While SDSM and TLFN outputs are used, all three models indicate an increase in the mean river flow, whereas the use of LARS-WG outputs result in a decrease of the

mean river flow whatever the hydrologic model. In general, the results in Table 2 also shows that the overall changes in mean annual flows simulated by the two lumped conceptual hydrologic models are very comparable. However, it appears that the GCM scenario data downscaled with TLFN and SDSM and simulated with HBV model resulted in the highest increase in the river flow in May, and the highest decrease in June (Figure 3a). The same scenario data simulated with WATFLOOD resulted in increasing river flows between December and May and a decrease in flow in June and July. Conversely, the GCM data downscaled with LARS-WG resulted in the largest increase in the river flow in April, with a flow decrease for the rest of the spring and summer months. This is all consistent with the predicted increase in the temperature, particularly the winter temperature (about 5 to 6◦ C), and the associated earlier beginning of snow melting which would increase the runoff effect. Moreover, all the three downscaling methods resulted in an increase in low flow during the winter months once again consistent with the overall increase in winter temperature and its effect in reducing freezing. However, the hydrologic simulation corresponding with the TLFN downscaled data resulted in the highest increase in the spring mean and peak flow in May and the highest decrease in mean flow in the following months also consistent with the highest increase in temperature predicted by the same downscaling technique. Those results are consistent with the recent findings by Dibike & Coulibaly (2007) in the same region.

Table 1. Validation statistics of the hydrologic models. HBV RMSE 17.5

WATFLOOD R2 0.84

RMSE 30.9

R2 0.50

CEQUEAU RMSE 22.5

R2 0.77

Table 2. Simulated changes (average increase or decrease (%)) in mean river flow corresponding to different downscaling and hydrologic models. Models

Periods

SDSM

LARS

TLFN

WATFLOOD

2020s 2050s 2080s

9.0 21.7 30.8

−6.8 −3.4 −6.1

15.1 11.6 14.7

HBV

2020s 2050s 2080s

12.4 28.2 39.1

−4.9 −4.1 −6.4

13.6 12.6 20.5

CEQUEAU

2020s 2050s 2080s

5.3 12.4 16.1

−4.7 −9.4 −15.3

25.0 24.6 44.6

CONCLUSION The preliminary results of the multi-model approach clearly highlight the effects of downscaling and hydrologic models on climate scenario simulation. Overall, it is shown that the downscaling methods play a more important role than the hydrologic model used to simulate the flows. Both WatFlood and HBV hydrologic models demonstrated satisfactory performances in simulating streamflows when driven by historical precipitation and temperature time series. The conceptual models (HBV and CEQUEAU) simulate the flows slightly better than WatFlood. It is shown that the hydrologic models effectively translate the effect of the climate patterns found in the downscaled data. Both hydrologic models mostly underestimate the mean river discharges in the watershed when downscaled meteorological inputs are used. In general, the study demonstrates how both the downscaled meteorological variables and the type of hydrologic models employed for simulation could affect the outcome of a hydrological impact study. The proposed

253

ACKNOWLEDGEMENT

Serpent River flow simulated with HBV-96

SD S M

150 Change in mean flow (cms)

This work was made possible through a grant from the Canadian Climate Change Action Fund, Environment Canada, and a grant from the Natural Sciences and Engineering Research Council of Canada. The author thanks the Aluminum Company of Canada (Alcan) for providing the experiment data. The HBV has kindly been made available by the Swedish Meteorological and Hydrological Institute. Dr. N. Kouwen provided help for WatFlood model calibration. The author acknowledges the contribution of Dr. Y. Dibike and the students’ contribution of S. Khan, X. Shi and B. Sawatzky.

TLFN

200

LARS

100 50 0 J

F

M

A

M

J

J

A

S

O

N

D

-50 -100 -150

(a) Month Serpent River flow simulated with CEQUEAU TLFN

15 0

REFERENCES

Change in mean flow (cms )

SDSM LARS

10 0 50 0 J

F

M

A

M

J

J

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-5 0 -1 0 0

(b)

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Serpent River flow simulated with WATFLOOD TLFN

Change in mean flow (cms)

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SD SM

100

LARS

50 0 -50

JF

M

A

M

J

JA

S

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-100 -150 -200 Month

(c)

Figure 3. Changes in monthly mean flow between the current (1961-2000) and the 2080s time period using (a) HBV; (b) CEQUEAU; (c) WATFLOOD model.

approach provides a range of possible future flows.The ensembles of flow series generated by each combination of models provide a better picture of future flow regime variability, and can be particularly useful for water resources managers. The multi-model approach is still at the development stage, and further extension will include Regional Climate Models (RCMs), Bayesian Neural Networks (BNN), Evolutionary Polynomial Regression (EPR) technique, and probabilistic analysis of the ensembles of flow series.

Bergström, S. & Forsman, A. 1973. Development of a conceptual deterministic rainfall-runoff model. Nordic Hydrology 4: 147–170. Brandt, M. 1990. Simulation of runoff and nitrate transport from mixed basins in Sweden. Nordic Hydrology 21: 13–34. Coulibaly P., Dibike, Y.B., Anctil, F. 2005. Downscaling precipitation and temperature with temporal neural networks. Journal of Hydrometeorology 6(4): 483–496. Dibike Y.B. & Coulibaly P. 2005. Hydrologic impact of climate change in the Saguenay watershed: Comparison of downscaling methods and hydrologic models. Journal of Hydrology 307(1-4): 145–163. Dibike Y.B. & Coulibaly P. 2007. Validation of hydrologic models for climate scenario simulation: the case of the Saguenay watershed in Quebec. Hargraeves, G.H. & Samani, Z.A. 1982. Estimating potential evapotranspiration. ASCE, J. Irrigation and Drainage Division 108(3): 225–230. Khan, M.S., Coulibaly, P. & Dibike, Y.B. 2006. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology 319: 357–382. Khan, M.S., Coulibaly, P., & Dibike, Y.B. 2006. Uncertainty analysis of statistical downscaling methods using CGCM predictors. Hydrological Processes 20(14): 3085–3104. Kistler, et al. 2001. The NCEP/NCAR 50-Year Reanalysis. Bulletin of the American Meteorological Society 82(2): 247–267. Kouwen N., Soulis E.D., Pietroniro A., Donald J. & Harrington R.A. 1993. Grouped response units for distributed hydrologic modeling. Journal of Water Resources Planning and Management 119 (3): 289–305. Liden R. & Harlin J. 2000. Analysis of conceptual rainfallrunoff Modelling performance in different climates. Journal of Hydrology 238: 231–247. Lindstrom, G., Johansson, B., Persson, M., Gardelin, M. & Bergstrom, S. 1997. Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology 201: 272–288. Morin, G., Cluis, D., Couillard, D., Jones, G. & Gauthier, J.M. 1983. Modélisation de la température de l’eau à l’aide du modèle quantité-qualité CEQUEAU. Scientific Report 153, INRS-Eau, Sainte-Foy, Quebec, Canada.

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Philip, J.R. 1954. An infiltration equation with physical significance. Soil Science 77: 153–157. Semenov, M.A. & Barrow, E.M. 1997. Use of stochastic weather generator in the development of climate change scenarios. Climatic Change 35: 397–414. Semenov, M.A. & Barrow, E.M. 2002. LARS-WG: a stochastic weather generator for use in climate impact studies, Version 3.0, user manual. Wilby R.L, Dawson C.W, Barrow E.M. 2002. SDSM – a decision support tool for the assessment of regional climate

change impacts. Environmental Modelling & Software 17: 147–159. Wilby, R.L., Whitehead, P.G., Wade, A.J., Butterfield, D., Davis, R.J., Watts, G. 2006. Integrated modelling of climate change impacts on water resources and quality in lowland catchment: River Kennet, UK. Journal of Hydrology 330: 204–220.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Adapting to climate change impacts on the water resources systems of the Seyhan River Basin in Turkey Y. Fujihara∗ Japan International Research Center for Agricultural Sciences

T. Watanabe & T. Nagano Research Institute for Humanity and Nature

K. Tanaka & T. Kojiri Disaster Prevention Research Institute, Kyoto University

ABSTRACT: We developed an approach to simulate flood and drought risks under present and future climate with both present and alternative reservoir rules. MRI-CGCM2 and CCSR/NIES/FRCGS-MIROC were dynamically downscaled to the Seyhan River Basin in Turkey. The data covered two 10-year time slices corresponding to the present (1990s) and future (2070s). Hydrologic models with a reservoir model were driven using these downscaled data. The hydrologic simulations matched the observed flow, reservoir volume, and dam discharge. Relative to the present, the MRI and CCSR predicted average annual temperature rises of 2.0 and 2.7◦ C, precipitation decreases of 157 and 182 mm, and annual runoff decreases of 118 and 139 mm, respectively. Analysis of the water resources was performed, taking into account changes in water use and examining alternative reservoir rules to cope with the projected changes in runoff and water use. The results indicated that the drought risk will not increase if water use does not increase. However, if water use increases and reservoirs continue to operate under the present rules, reservoir reliability will decrease. Alternative rules would reduce reliability losses in the reservoir system; however, the alternative rule that is the best adaptation in terms of drought risk would considerably raise the flood risk. Therefore, integrated water management is required so that operation rules can be changed to meet hydrological and water use conditions. Keywords:

1

adaptation; climate change; hydrologic model; Turkey; water resources

INTRODUCTION

The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) concluded that the warming of the climate system is unequivocal (IPCC, 2007). The report also described the impacts associated with projected global average surface warming and the impacts for different regions. Other reports and studies have also described the impacts of climate change at global to continental scales. Therefore, the next stage of research is detailed forecasting of the timing and magnitude of impacts at local and basin scales. The usual method for local- or basin-scale predictions has been to statistically or dynamically downscale outputs from General Circulation Models ∗

Corresponding author ([email protected])

(GCMs) to the river basin scale. The local climatic signal is then input into a hydrologic model to assess the direct consequences in the basin. Although many studies have used the above approach, most of these studies examined the impacts of climate change under the present system of water resources management. There have been few studies of the impacts under future or alternative water resource management systems or of adaptations that could reduce the negative effects of climate change on water resources systems. In this study, we developed an approach for simulating flood and drought risks under present and future climate conditions with the present and alternative reservoir operation rules. This approach was applied to the Seyhan River Basin in Turkey. We explored how alternative reservoir operation rules could mitigate the impacts of climate change in the basin and considered adaptations to climate change.

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3 APPROACH 3.1 Dynamical downscaling method

Figure 1. Seyhan River Basin. The white circles indicate the RCM grid points.

2

STUDY BASIN

The Seyhan River Basin (21,700 km2 ) is located in the southern part of Turkey (Figure 1) within the latitude-longitude range of 34.25◦ E–37.0◦ E and 36.5◦ N–39.25◦ N. The main source of water supply to the Seyhan River is from the Taurus Mountains. After the confluence of the Zamantı and Göksu rivers, the Seyhan River drains the Çukurova Plain and discharges into the Mediterranean Sea. The climate in the lower basin is predominantly Mediterranean, while that in the middle and upper parts of the basin is Continental. The annual precipitation is about 700 mm in the coastal areas. It increases up to 1,000 mm at higher elevations and decreases to 400 mm in the northern part of the basin. The minimum and maximum annual inflows in the Seyhan Dam are 3.7 Gm3 (191.1mm/y) and 7.3 Gm3 (377.0 mm/y), respectively, resulting in an average annual inflow of 5.5 Gm3 (284.1 mm/y). The Seyhan and Çatalan Dam reservoirs have storage capacities of 0.8 Gm3 and 1.6 Gm3 , respectively. The stored water is mainly utilized for the Lower Seyhan Irrigation Project (LSIP) that irrigates 133,000 ha of farmland. According to statistical data from 1990 to 2004, the mean annual irrigation water is about 1.6 Gm3 . Since 2003, the Çatalan Dam reservoir has been providing an annual water supply of 0.1 Gm3 for domestic purposes.

We used the Pseudo Global Warming Method (PGWM), which was developed by Sato et al. (2007), as a dynamical downscaling method. For the present climate simulation, 6-hourly reanalysis data were used for the boundary condition of the Regional Climate Model (RCM). In the future climate, a new boundary condition, which is discussed below, was used to simulate the regional climate influenced by global warming. The monthly averages of the mean difference between the present climate and the future climate simulated by the GCM were calculated, and the global warming monthly mean differences in the wind speed, temperature, geopotential height, specific humidity, and sea surface temperature were superimposed on each variable of the 6-hourly reanalysis data independent of time. National Centers for Environmental Prediction/ National Center for Atmospheric Research (NCEP/ NCAR) reanalysis data (Kalnay et al., 1996) from 1993 to 2004 were employed. The GCMs used in this study were MRI-CGCM2 (Yukimoto et al., 2001) and CCSR/NIES/FRCGC-MIROC (K-1 Model Developers, 2004) for the present (1991–2000) and for the future (2071–2080) under the SRES A2 scenario. The Terrestrial Environment Research Center-Regional Atmospheric Modeling System (TERC-RAMS; Sato and Kimura, 2005) was used as the RCM, and was run with a horizontal resolution of approximately 8.3 km, taking into account the scale of the Seyhan River. The downscaled data covered 10-year present (1990s) and 10-year future (2070s) time slices, and hourly meteorological variables (precipitation, downward shortwave radiation, downward longwave radiation, wind speed, air temperature, specific humidity, and air pressure) were used to drive the hydrologic models. After tuning the RCM, the remaining precipitation biases were corrected using monthly ratios so that the downscaled monthly average of the present climate matched the observed monthly average. The upper and lower ratios were set as the quality of the observed precipitation in the basin was poor. The temperature biases were corrected such that the downscaled diurnal range (daily maximum and minimum temperature) matched the range of the observed data and the downscaled monthly data also matched the observed data (Tanaka et al., 2006; Fujihara et al., 2008). 3.2

Hydrologic models

We used the Simple Biosphere including Urban Canopy (SiBUC) model (Tanaka and Ikebuchi, 1994) to simulate the hydrological variables. SiBUC simulates snow accumulation and melting, soil moisture dynamics and evapotranspiration, and surface runoff

258

Figure 2. Observed and simulated monthly flows at Station 1818 for the present.

and base flow, which are subsequently routed through a grid-based flow network to simulate stream flows at selected points within the basin. The simulated surface runoff and base flow in each grid cell were routed using the flow routing network of the Hydrological River Basin Environment Assessment Model (Hydro-BEAM; Kojiri et al., 1998). The flow direction in each grid cell was defined using a Digital Elevation Model (DEM), and flow routing was performed using the kinematic wave method. The simulated region constituted an area of 2.75◦ × 2.75◦ (34.25◦ E–37.0◦ E and 36.5◦ N–39.25◦ N) with a 5-minute latitude-longitude spatial resolution (33 × 33 grids). Gtopo30 (United States Geological Survey Gtopo30 Global Digital Elevation Model) was employed as the DEM, and a land use dataset was produced from LANDSAT satellite images. Several physical parameters of the soil, such as the porosity, field capacity, and root zone depth, were derived from the global dataset ECOCLIMAP (Masson et al., 2003). The hydrologic models were driven by the biascorrected downscaled meteorological data. The calibration generally relied on a visual comparison of the calculated and observed discharges, and two statistical measures (Nash-Sutcliffe efficiency [E] and water balance error [WB]) were used to assess the performance of the hydrologic models. Figure 2 shows the simulated monthly hydrograph for Station 1818. The simulated flows matched the observed flows for all seasons and reproduced the observed monthly flows quite well. Between the discharge simulated by the bias-corrected downscaled data and the observed discharge, E was 0.54 and WB was –4.35%. These values also support the agreement between the results simulated by the bias-corrected downscaled data and the observed flow. 3.3

Reservoir models

We developed the Seyhan Dam (0.8 Gm3 ) and Çatalan Dam (1.6 Gm3 ) reservoir models to simulate the

Figure 3. Present and alternative reservoir operation rules.

storage volume and dam discharge at both dam reservoirs. The Seyhan Dam and hydroelectric power plant, built in 1956, were the first multipurpose structures erected in the Seyhan River Basin for the purpose of providing water for irrigation, generating electricity, and controlling floods. Construction of the Çatalan Dam began in 1982, and the dam began operations in 1997. With the completion of the Çatalan dam, the Çatalan and Seyhan Dam systems provide protection against 500-year floods. The majority of precipitation in the Seyhan River occurs between November and May. The river discharge increases gradually from November and peaks in April. The river discharge then decreases gradually from June, and is the lowest in September. On the other hand, the irrigation period of the LSIP extends mainly from April to October. The demand for irrigation water peaks in July.Therefore, water is stored from the beginning of early spring in the Seyhan and Çatalan Dam reservoirs and is then released for irrigation during the irrigation period (mainly April to October) of the LSIP. Operation rule curves and several water levels were set for both the Seyhan and Çatalan Dam reservoirs. Figure 3 illustrates the operation rule curves, surcharge water level, full reservoir level, and limited water level during the flood season (denoted as “present”) at the Seyhan Dam. During the flood season, the limited water level is lowered below the full reservoir level to create a large capacity for flood control. In actual operations, water is stored in order to maintain the operation rule curve, and the water is released to meet the demand regardless of the rule curve (Fujihara et al., 2008). The river discharge simulated using the flow routing model described in section 3.2 was input into the reservoir models.The operation rule curves were based on the average of the historical operational records, and the water demand was the actual water withdrawn for irrigation, domestic use, and environmental flow. Figure 4 shows the simulated reservoir volume and

259

Table 1.

Figure 4. Observed and simulated reservoir volume and discharge for the present.

dam discharge at the Seyhan Dam reservoir. The simulated volume was almost equal to the observed volume, and the simulated discharge seasonally matched the actual values. Although the results are not shown here, we found that the established reservoir models also reproduced the hydroelectric generation quite well. 3.4 Water use scenarios and alternative reservoir operations The population will certainly grow for at least several decades, and water demand will increase, as a result, unrelated to climate change. It is also anticipated that water use will increase in accordance with the expansion of irrigated farmland. However, it is extremely difficult to forecast future water use. Therefore, in this study, we set two scenarios of water use conditions in the future: the same as the present water use (referred to as “present”), and an increase by 1.525 times compared to that at present (referred to as “increased”) (Fujihara et al., 2008). Several alternative operation rules were examined to evaluate their ability to mitigate the impacts of climate change and water use. The hydrologic simulations (Section 4.1) indicate that the future river flows will differ in magnitude and timing from those at present. Considering these changes, we set four reservoir operation rules (present and alternatives 1, 2, and 3). In alternatives 1 and 2, the operation rules and limited water levels during the flood season were increased by 40% and 60%, respectively, as compared to those of the present (Figure 3). In alternative 3, the operation rule and limited water level during the flood season were increased by 80% as compared with those of the present, and the timing of refill was 2 months and 1 month earlier than those for the present in the Seyhan and Catalan Dam reservoirs, respectively (Figure 3). Table 1 summarizes the combinations of climate, water use, and reservoir operation rules used for the simulations.

Simulations used in this study.

Climate

Water Use

Reservoir Operation

Present Present Present Future (MRI and CCSR) Future (MRI and CCSR) Future (MRI and CCSR)

Present Increased Increased Present

Present Present Alternatives 1,2, and 3 Present

Increased

Present

Increased

Alternatives 1,2, and 3

4

RESULTS

4.1 Temperature, precipitation, and river flow changes Figure 5(a) shows a comparison of the present monthly mean temperature, the future value simulated by MRICGCM2 (MRI-future), and the future value simulated by CCSR/NIES/FRCGC-MIROC (CCSR-future). In MRI-future and CCSR-future, the downscaled data projected an increase in temperature for all months. The difference between the present and future values projected by CCSR was considerably larger than that projected by MRI. The change in the average annual temperature of the Seyhan River Basin was +2.0◦ C by MRI and +2.7◦ C by CCSR. Figure 5(b) shows a comparison of the present, MRI-future, and CCSR-future monthly precipitation. The downscaled data using MRI and CCSR projected a decrease in precipitation for most months. However, unlike the results for temperature, the magnitude of change differed from month to month. Both GCMs projected that precipitation decreases would be greater in January, April, October, November, and December than in the other months. However, the projections were different for February; MRI projected an increase in precipitation, while CCSR projected a decrease. The mean monthly inflow at Station 1818 is illustrated in Figure 6. This figure shows a marked decrease in future inflow as compared to that at present according to both MRI and CCSR. In particular, the decreases in April and May were projected to be greater than those in the other months. In addition, the peak monthly inflow will occur earlier than that at present, primarily due to earlier snow melting. However, the decrease simulated by CCSR was larger than that simulated by MRI because the former projected less precipitation than the latter. The annual precipitation, evapotranspiration, and runoff are summarized as follows. The annual precipitation is 634 mm at present, 476 mm as projected by MRI-future, and 452 mm as projected by CCSRfuture; i.e., the precipitation will decrease by 157 mm

260

Table 2.

Ratio of water use to river flow.

Present Future (MRI) Future (CCSR)

Present Water Use

Increased Water Use

0.31 0.48 0.54

0.48 0.73 0.83

4.2 Water resources systems effects Table 2 shows the ratios of water use to river flow. In many previous studies (e.g., Alcamo et al., 2003; Oki and Kanae, 2006), a region was classified as highly water stressed if this index exceeded 0.4. In the Seyhan River, the ratio is 0.31 for the present climate under present water use, while it ranges from 0.48 to 0.54 for the future climate under present water use. The ratio is 0.48 for the present climate under increased water use, and ranges from 0.73 to 0.83 for the future climate under increased water use. The reliability of the reservoirs is shown in Figure 7. The reliability (R) is defined using the following equation:

Figure 5. Temperature and precipitation changes.

Figure 6. River flow changes at Station 1818.

(25%) and 182 mm (29%) according to MRI and CCSR, respectively. The annual evapotranspiration was projected to decrease by 36 mm (9%) according to MRI and by 39 mm (10%) according to CCSR, primarily due to a decrease in the soil moisture. Annual river flow will decrease by 118 mm (52%) according to MRI and 139 mm (61%) according to CCSR. Therefore, the available water resources in the Seyhan River Basin will decrease markedly in the future.

where Vs is the volume of water supplied and Vd is the volume of water demanded. Under the present water use, this index is always 1 for both the present and future climate. This index is also always 1 for the present climate under conditions of increased water use. These results indicate that the reservoirs can supply all of the water demanded in the above conditions. The 10-year average of reliability for the future under increased water use ranges from 0.905–0.957, and water scarcity will occur 5 to 8 times within those 10 years. Among the alternative operation rules considered in this study, alternative 3 is the best in terms of reservoir reliability. Alternative 3 improves the 10-year average reliability to 0.943–0.980 and reduces the times of water scarcity to 3 to 6 times within those 10 years. The 10-year average of annual maximum river flow at Adana for the present under the present operation rules is 421.3 m3 /s (Figure 8). That for the future will decline to 113.6–156.3 m3 /s, and the flood risk will decrease without any adaptations. In contrast, the annual maximum river flow at Adana for the present under alternative operation 3, which is the best adaptation in terms of drought risk, will be 511.9 m3 /s and 1.22 times as much flow as that of the present operation. The flood risk will be considerably greater than that of the present operation. 5

CONCLUSIONS

In this study, we developed an approach to simulate flood and drought risks under present and future

261

Figure 7. Comparison of reservoir reliabilities.

climate conditions with the present and alternative reservoir operation rules. Two GCMs were dynamically downscaled using the Pseudo Global Warming Method (PGWM) for the present and future. The hydrologic and reservoir models were driven under the present and alternative reservoir operation rules, and the reservoir reliability and annual maximum river flow at Adana City were evaluated. The river flow will decrease considerably (Figure 6) due to climate change, and the ratio of water use to river flow will increase as a result (Table 2). However, if water use does not increase, the drought risk will not increase (Figure 7). These results indicate that controlling water use is extremely important, and is in itself

Figure 8. Comparison of annual maximum flows at Adana.

an adaptation to climate change in the Seyhan River Basin. If water use increases and reservoirs continue to operate under the present rules, the reliability for

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the reservoirs will decrease. The alternative operation rules will reduce the losses in the water resource system and are effective adaptations to mitigate climate change impacts. However, the flood risk will be greater under the alternative operation that is the best in terms of drought risk. Therefore, integrated water management will be required so that operation rules can be changed from year to year to meet hydrological and water use conditions. REFERENCES Alcamo, J., Doll, P., Henrichs, T., Kaspar, F., Lehner, B., Rosch, T., Siebert, S., 2003: Global Estimates of Water Withdrawals and Availability under Current and Future Business-as-usual Conditions, Hydrological Sciences Journal, 48(3): 339–348. Fujihara, Y., Tanaka, K., Watanabe, T., Nagano, T., Kojiri, T., 2008: Assessing the Impacts of Climate Change on the Water Resources of the Seyhan River Basin in Turkey: Use of Dynamically Downscaled Data for Hydrologic Simulations, Journal of Hydrology, 353: 33–48. IPCC, 2007: Climate Change, 2007: Synthesis Report: Summary for Policymakers. K-1 Model Developers, 2004: K-1 Coupled Model (MIROC) Description, K-1 Technical Report 1, Center for Climate System Research, University of Tokyo. Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, B., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Jenne, R., Joseph, D., 1996: The NCEP/NCAR 40-year Reanalysis Project, Bulletin of the American Meteorological Society, 77: 437–472.

Kojiri, T., Tokai, A., Kinai Y., 1998: Assessment of River Basin Environment through Simulation with Water Quality and Quantity, Annuals of Disaster Prevention Research Institute, Kyoto University, 41(B-2): 119–134 (in Japanese). Masson, V., Champeaux, J.-L., Chauvin, F., Meriguet, C., Lacaze, R., 2003: A Global Database of Land Surface Parameters at 1-km Resolution in Meteorological and Climate Models, Journal of Climate, 16, 1261–1282. Oki, T., Kanae, S., 2006: Global Hydrological Cycles and World Water Resources, Science, 313: 1068–1072. Sato, T., Kimura, F., 2005: Diurnal Cycle of Convective Instability around the Central Mountains in Japan during the Warm Season, Journal of Atmospheric Sciences, 62: 1626–1636. Sato, T., Kimura, F., Kitoh, A., 2007: Projection of Global Warming onto Regional Precipitation over Mongolia Using a Regional Climate Model, Journal of Hydrology, 333: 144-154, doi:10.1016/j.jhydrol.2006.07.023. Tanaka, K, Fujihara, Y., Kojiri T., 2006: Bias Correction of the Meteorological Variables from RCM for Hydrological Application, The Advance Report of ICCAP (Impact of Climate Changes on Agricultural Production System), Research Institute for Humanity and Nature: 43–46. Tanaka, K., Ikebuchi, S., 1994: Simple Biosphere Model Including Urban Canopy (SiBUC) for Regional or Basin-scale Land Surface Processes, Proceedings of the International Symposium on GEWEX Asian Monsoon Experiment: 59–62. Yukimoto, S., Noda, A., Kitoh, A., Sugi, M., Kitamura, Y., Hosaka, M., Shibata, K., Maeda, S., Uchiyama, T., 2001: The New Meteorological Research Institute Coupled GCM (MRI-CGCM2): Model Climate and Variability, Papers in Meteorology and Geophysics, 51: 47–88.

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Impact of climate change on annual drought severity A.K. Mishra∗ & V.P. Singh Department of Biological & Agricultural Engineering, Texas A & M University, Texas, USA

ABSTRACT: With increasing water scarcity around the world due to frequent drought incidences along with uncertainties due to climate change, increasing attention is now being focused on a better understanding of different aspects of droughts. This paper discusses the impact of climate change on annual drought severity based on future climate scenarios derived from GCM outputs using downscaling techniques. It is observed that the intensity of short-term droughts (based on SPI 1) in the Kansabati basin is higher in terms of severity and spatial extent for the period 2051–2100 than 2001–2050. Keywords:

1

Drought; SPI; ANN; Downscaling; Drought Severity

INTRODUCTION

Droughts are considered by many to be the most complex but least understood of all natural hazards affecting more people than any other hazard. Droughts are a normal feature of climate and their recurrence is inevitable. However, there remains much confusion within the scientific and policy making community about their characteristics. Research has shown that the lack of a precise and objective definition in specific situations has been an obstacle to understanding droughts which has led to indecision and inaction on the part of managers, policy makers, and others (Wilhite et al., 1986). In a quarter of a century since 1967, droughts have affected 50 percent of the 2.8 billion people who have suffered from all natural kinds of disasters. Because of direct and indirect impacts of droughts, 1.3 million human lives were lost, out of a total number of 3.5 million people killed by disasters (Obasi, 1994). Nearly 50 percent of the world’s most populated areas are highly vulnerable to droughts and more importantly, almost all of the major agricultural lands are located there (USDA, 1994). Droughts produce a complex set of impacts that span many sectors of the economy and they reach well beyond the areas experiencing them. Recent climate changes have had significant impact on the society. Some reports indicate that the mean annual global surface temperature has increased by about 0.3 to 0.6◦ C since the late 19th century and it is anticipated to further increase by 1–3.5◦ C over the next 100 years (IPCC, 1995). The Global Climate Models ∗

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(GCMs) are generally used to simulate the present climate and project future climate with forcing by green house gasses and aerosols. GCMs, which describe the atmospheric cycle by mathematical equations, are the most adapted tools for studying the impact of climate change at regional scales. This study aims to investigate the variability of annual droughts using severity-area-frequency curves. The Standardized Precipitation Index (SPI) (calculated from the probability distribution of precipitation using a two-parameter gamma function) was used. The SPIs were applied at the local scale using monthly rainfall data for the period of 1965–2001 from five raingauge stations in the basin. Using an ANN model monthly rainfall values were simulated for the period 2000 to 2100. The basin is divided into 25 grid-cells of 13 × 13 km with each grid corresponding to approximately 4% of the total area. The Inverse Distance Weighting (IDW) approach was used for the spatial interpolation of projected rainfall at each grid point. Drought severity was then assessed from the estimated gridded SPI values at multiple time scales. The spatial variations of annual drought were used to develop drought severity – area – frequency curves that can assess the severity of localized droughts within a basin. 2 ARTIFICIAL NEURAL NETWORK The Back Propagation Network (BPN), developed by Rumelhart et al., (1986), is the most prevalent of the supervised learning models of artificial neural networks (ANN). BPN uses a gradient steepest descent method to correct the weight of interconnective neurons. BPN easily solves the interaction of the

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processing elements by adding hidden layers. In the learning process of BPN, the interconnection weights are adjusted using an error convergence technique to obtain a desired output for a given input. In general, the error at the output layer in the BPN model propagates backword to the input layer through the hidden layer in the network to obtain the final desired output. The gradient descent method is utilized to calculate the weight of the network and adjusts the weight of interconnections to minimize the output error. In this paper an ANN using back propagation algorithm was used to simulate future monthly rainfall values. 3

ENTROPY APPROACH

The concept of entropy has been popular in the scientific literature for the past several decades and has been applied to a range of problems in hydrology and water resources engineering (Singh, 1997). The entropy theory was developed by Shannon (1948). In the present paper entropy method was used to select predictors for simulating future monthly rainfall values. 4

STANDARDIZED PRECIPITATION INDEX

The Standardized Precipitation Index (SPI) has been developed for the purpose of defining and monitoring droughts (McKee et al., 1993). The U.S. Colorado Climate Center, the U.S. Western Regional Climate Center, and the U.S. National Drought Mitigation Center, among others, use SPI to monitor the current state of droughts in the United States. SPI permits to determine the rarity of a drought or an anomalously wet event at a particular time scale for any location that has a precipitation record. A drought event is considered to occur at a time when the SPI value is continuously negative and ends when SPI becomes positive. Table 1 provides a drought classification based on SPI. SPI is computed as follows (Guttman, 1999): (i) First, a probability density function that describes the long-term time-series of rainfall observations is determined. (ii) The base time of rainfall observation series can be any, depending on the time scale of interest. In the present study, running series of total precipitation Table 1.

corresponding to 1, 3, 6, 9, 12 and 24 months were used and as a result the corresponding SPIs were calculated: SPI 1, SPI 3, SPI 6, SPI 9, SPI 12 and SPI 24 (iii) Once the probability density function is determined, the cumulative probability of an observed precipitation amount is computed. (iv) The inverse normal (Gaussian) function, with mean zero and variance one, is then applied to the cumulative probability distribution function, which results in SPI. 5

STUDY AREA

A portion of the Kansabati River basin, 4265 km2 in area, upstream from the Kangsabati dam, in the extreme western part of West Bengal in eastern India (see Figure 1) was considered in this study. The basin experiences very hot summer, with temperatures reaching up to 45◦ C in May and June. Generally, dry periods are accompanied by high temperatures, which lead to higher evaporation from water bodies and higher evapotranspiration from natural vegetation and agricultural crops. The mean annual precipitation in the basin is about 1268 mm. The Kansabati basin has three main rivers: Kansai, Kumari, and Tongo. At the near confluence of the three rivers in the Purulia district is Kansabati dam. The water from the dam is used primarily for irrigation. Major crops grown in the basin include paddy, maize, pulses and vegetables. The basin has mostly lateritic soils having a low water holding capacity and is drought prone with irregular rainfall. About 50 to 60% of the study area is upland managed by poor farmers. Agricultural lands have limited surface irrigation facilities and are mostly mono-cropped. Increasing water demands due to extensive cultivation has led to over-exploitation of ground water resources, especially in summer and this

Drought classification based on SPI.

SPI values

Class

>2 1.5 to 1.99 1.0 to 1.49 −0.99 to 0.99 −1 to −1.49 −1.5 to −1.99 <−2

Extremely wet Very wet Moderately wet Near normal Moderately dry Severely dry Extremely dry

Figure 1. Location of precipitation stations used in the study.

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has resulted in degradation of water resources. Owing to the lack of water, irrigated crops are not widespread. For this study, five raingauge stations were considered and monthly rainfall data was obtained for the period from 1965 to 2001. The mean annual rainfall varies from 1152.57 mm to 1345.7 mm. Because of the large variation of annual rainfall from a maximum of 2081 mm to a minimum of 674 mm, the standard deviation is quite high at Phulberia station. The SPI values were calculated using monthly rainfall values. 6

DOWNSCALING EXPERIMENT

The ANN model was used to downscale GCM outputs for the Kansabati Basin. Monthly precipitation was used as predictant for the downscaling experiments. In this present study five stations with 37 years of rainfall data were used for downscaling. Climate variables corresponding to the future climate change scenarios were extracted from the second version of the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model (CGCM2) data from an ensemble of three 111-year simulations using the provisional IPCC SRES “B2” GHG and aerosol forcing scenario. 6.1

Selection of variables

The selection of predictors is one of the most important steps in the downscaling experiment. Several attempts have been demonstrated in different parts of the world for identifying predictors. As the predictors vary from one region to another there are no general guidelines for the selection of predictors. In this study, predictor variables were screened using entropy as well as the coefficient of correlation. The predictors sharing the maximum information with the predictant was chosen for the downscaling experiment. The variables chosen seemed physically sensible for downscaling in the study area. 6.2

Development of ANN downscaling model

The available data set was divided into a training set consisting of 70% of data and a testing set consisting of about 30% of data. This model is a non-linear regression type in which a relationship is developed between a few selected large-scale atmospheric predictors and basin scale meteorological predictants. Several training experiments were conducted with different combinations of input time lags and numbers of neurons in the hidden layer till the optimum network was identified. For downscaling of monthly precipitation with ANN, 13 predictors were chosen using entropy. The best model for Kharidwar station was 13-26-1 with the coefficient of correlation for testing data of 0.79. Similarly the coefficient of correlation

for all other stations varied between 0.75 to 0.83 for testing.

7

RESULT AND DISCUSSION

After monthly rainfalls were downscaled to the grid level using GCM outputs, the monthly SPI values for each grid were calculated, based on different time scales of 1-, 3-, 6-, 9-, 12- , and 24- months. The annual drought severity (sum of negative SPI values in dry spells) for each station using run theory were calculated. The annual values of drought severity were used for frequency analysis. For application before fitting to a distribution, the drought magnitude was converted to positive values in order to represent the extreme condition and to analyze the associated risk of droughts using the exceedance probability. These severity values were fitted with normal, log-normal, gamma and extreme value type 1 (EV-1) probability distributions. The EV-1 and gamma distributions passed the tests for all grid points. In this work the EV-1 distribution was selected for frequency analysis, as it passed the two tests for all SPI time scales and at all grid values. It is also a two parameter probability distribution and its parameter values may be estimated with less uncertainty, as the small sample size is used here. It has also been used for numerous extreme drought studies (Mishra and Desai, 2005). The drought severity-area-frequency curves are plotted in Figure 2, where the X axis represents the percentage of area affected by drought and Y axis represents the annual drought severity (sum of negative SPI values in dry spells) for different SPI series with a given frequency interval. The timescale over which precipitation deficit accumulates becomes extremely important and separates the different types of droughts. Based on the average duration of a drought for different SPI series, droughts are classified for the study area. SPI-1 and SPI-3 were used for short-term drought analysis. SPI-6 and SPI-9 were used for medium-term drought analysis, while SPI 12 and SPI 24 were used for long term analysis. The drought severity was tested for different areal extents using different probability distributions to determine the best distribution for frequency analysis. Possible annual drought severities with percentage areas undergoing droughts using different frequency intervals based on CGCM2 (B2 scenarios) for the period 2000 to 2050 are shown in Figure 2 (a-d). The drought severity values are higher when retuen periods are higher covering the less percentage of area under droughts. For example, from Figure 2(d), it can be seen that there is a possible drought based on annual drought severity using SPI 24, with the following probabilities: (a) There is a probability that the

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Figure 2a. SAF curves for different SPI series for a return period of 5 years using 2000 to 2050 CGCM2 outputs.

Figure 2b. SAF curves for different SPI series for a return period of 10 years using 2000 to 2050 CGCM2 outputs.

Figure 2c. SAF curves for different SPI series for a return period of 50 years using 2000 to 2050 CGCM2 outputs.

Figure 2d. SAF curves for different SPI series for a return period of 100 years using 2000 to 2050 CGCM2 outputs.

basin would undergo severe drought (corresponding to SPI 24)covering 20 percent or less area with a severity value of more than 29. (b) When the whole basin was considered the drought severity became less, i.e, the severity value corresponded to 14, indicating that around 90% of the area would be undergoing drought in the basin. This high difference in drought severity values indicates that the basin is undergoing a few local pockets of severe drought. More attentation can be given to these localized areas. It is also observed that the flatness in the SAF curves may be because of smaller catchment size with rainfall pattern mostly homogenous in nature with less heterogenity and with similar rainfall regimes. With these curves, drought severity can be compared with past droughts and the droughts that are anticipated in future. The drought severity corresponding to SPI 1 for different return periods can be

compared for two time periods, say for example, two time periods: (a) 2000 to 2050, (b) 2051–2100. This example is shown in Figure 3(a-c). 8

CONCLUSIONS

The following conclusions can be drawn from this study:

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(i) Since the future projection of monthly rainfall is a key parameter in the present study, it is important to develop a proper downscaling model for assessing future scenarios. ANN seems to be a good approach for doing it, if the model is calibrated carefully as it is highly sensitive to diffeerent inputs and model parameters.

Figure 3a. Comparison of annual drought severity for SPI 1 for two time periods (2000–2050 and 2051–2100) considering a 5 yr return period.

Figure 3c. Comparison of annual drought severity for SPI 1 between two time periods (2000–2050 and 2051–2100) considering 100 yrs return period.

(iv) SAF curves can be used to compare past droughts and projected droughts obtained from future climate scenarios based on the GCM outputs. (v) The intensity of short-term droughts (based on SPI 1) is more in terms of severity and spatial extent for period 2051–2100 than 2001–2050. REFERENCES

Figure 3b. Comparison of annual drought severity for SPI 1 for two time periods (2000–2050 and 2051–2100) considering a 50 yr return period.

(ii) As the choice of predictor variables can significantly affect the predictants in downscaling experiments because of highly spatio-temporal variability in hydrometeorological variables, it is important to identify suitable predictors. The entropy based approach for selecting the predictors seems to be an important development for downscaling experiments. (iii) Using GCM outputs, the projected drought severity-area-frequency curve depicts drought severity and drought area with respect to drought return period and characterizes the spatial and recurrence patterns of droughts. This can be useful for studying annual drought severity anticipated in future and their occurrences covering the percentage of the area in the basin.

Guttman, N. B. 1998. ‘Comparing the Palmer drought index and Standardized precipitation index’, J.of Amer. Water Resources Association., 34(1), 113–121. IPCC, Impacts, Adaptations and Mitigation of Climate Change: Scientific-Technical Analyses, Cambridge University Press, UK, 1995, p. 878. McKee TB, Doesken & NJ, Kliest J. 1993. ‘The relationship of drought frequency and duration to time scales’. In Proceedings of the 8th Conference on Applied Climatology, 17–22 January, Anaheim, CA. American Meteorological Society: Boston, MA, USA, 179–184. Mishra, A. K. & Desai, V. R., 2005, “Spatial and Temporal Drought Analysis in the Kansabati River Basin, India” International Journal of River Basin Management, IAHR, 3(1), 31–41. Obasi, G.O.P. 1994. ‘WMO’s Role in the International Decade for Natural Disaster Reduction, Bulletin of the American Meteorological Society 75: 1, 655–61. Rumelhart, D.E., Hilton, G.E. & Willams, R.J. 1986 ‘Learning representations by back-propagating errors’. Nature 323, 533–536. Singh, V. P. 1997. The use of entropy in hydrology and water resources. Hydrological Processes, 11, 587–626. Shannon, C. E. 1948. A mathematical theory of communication. Bell Syst. Tech. J., 27: 379–423, 623–656. USDA. 1994. ‘Major world crop areas and climatic profiles’, World Agricultural Outlook Board, US department of Agriculture, Agricultural Handbook No. 664, 157–70. Wilhite, D.A., Rosenberg, N.J. & Glantz, M.H. 1986. ‘Improving federal response to drought’, Journal of Climate and Applied Meteorology 25: 332–42.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Conceptual investigation of time of concentration: Case study of the Pequeno River watershed, São José dos Pinhais, PR, Brazil R.V. Da Silva, F. Grison & M. Kobiyama∗ Depto. of Sanitary & Environmental Eng., Federal Univ. of Santa Catarina, Florianópolis, SC, Brazil

ABSTRACT: The time of concentration (Tc) represents the time necessary for the entire area of a watershed to contribute to the discharge peak at its outlet. On hydrographs, Tc can be the time (Ta) from the beginning of the constant rainfall event to the moment when the discharge becomes constant. Tc can also be the time (Tb) from the end of rainfall event to the point at which the direct runoff ceases. In this sense, the present study investigated the Tc values of the Pequeno River watershed (106.3 km2 ), Paraná State, Brazil, with three methods: geomorphologically-based empirical equations, an observed-hydrograph analysis and a simulated-hydrograph analysis with TOPMODEL. The result shows the large variety of the Tc values estimated with 23 empirical equations. The mean value and the standard deviation of Tb estimated with the observed-hydrograph analysis were 80.8 h and 24.1 h, respectively. In the simulated-hydrograph analysis, the relations of Ta with the rainfall intensity and the rainfall duration were observed only for low intensities and short durations. In other cases, Ta becomes equal to Tb (=87 h), and this value can be considered the watershed Tc. Keywords:

1

time of concentration; TOPMODEL; Pequeno River watershed; hydrograph separation

INTRODUCTION

The increasing variability of the spatial and temporal distribution of water resources due to intensified human action without adequate planning has been affecting the natural and social systems. Therefore, investigations of time parameters are of great importance in the water resources management with structural and non-structural measures. According to McCuen et al. (1984), the time of concentration (Tc) is the time parameter more often used in the water resources management. Due to the great importance of Tc, there are a lot of equations to estimate it. Silveira (2005) evaluated the performance of 23 equations for rural and urban watersheds in Brazil and showed that the performance of these equations is better for rural watersheds than urban ones. It indicates a greater difficulty in the estimation of Tc for urban watersheds. The Tc concept is highly questioned, because there is the difficulty to determine it and because the rainfall intensity and the initial moisture conditions of the watershed influence on it. Thus, there is uncertainty as to the best method to calculate Tc and what better define its concept. In hydrographs, Tc can be defined as the time (Ta) from the beginning of the ∗

Corresponding author ([email protected])

event of uniform rainfall until the time when the discharge becomes constant. Tc can be also defined as the time (Tb) since the end of the precipitation to the hydrograph inflexion point. Applying the TOPMODEL (Beven & Kirkby, 1979) already-calibrated and validated for the Pequeno River watershed, Paraná State, Brazil, the present study calculated Ta and Tb by hydrograph separation, and evaluated the influences of rainfall and initial moisture conditions on Ta and Tb. Furthermore, Tc values of this watershed were calculated with 23 geomorphologically-based equations. And 19 observed hydrographs were selected for Tb analysis. By comparing the obtained values of Ta, Tb and Tc, the Tc concept was discussed. To reduce damages due to water-related disasters, the Tc investigation for this watershed is fundamental. 2

STUDY AREA

The Pequeno River watershed (106.3 km2 ) is located in São José dos Pinhais city, Curitiba Metropolitan Region, Paraná State, Brazil (Figure 1). The Pequeno River watershed has been recently undergoing an intensive process of industrialization and consequently a large urban sprawl. Such a process without an adequate planning has caused more occurrences of water-related disasters.

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Table 1.

Figure 1. Watershed location.

The altitudes vary from 881 m to 1260 m with the moderate relief. Its land-uses are classified as urban area (4%), agriculture and exposed land (3%), forest (54%), grassland (35%), wetland (3%) and others (1%). The mean annual precipitation is approximately 1400 mm. There are one gauge station (Fazendinha) and one meteorological station (Chácara Guajubi) in this watershed, and the present study used the hydrological data hourly obtained during the period from December 31st, 1999 to December 31st, 2000.

3 3.1

Name

Equation (Tc =)

Izzard (1) Kerby-Hathaway (2) Kinematic Wave (3) FAA (4) Kirpich (5) SCS Lag (6) Simas-Hawkins (7) Ven te Chow (8) Dooge (9) Johnstone (10) Corps Engineers (11) Giandotti (12) Pasini (13) Ventura (14) Picking (15) DNOS (16) George Ribeiro (17) Schaake et al (18) McCuen et al (19) Carter (20) Eagleson (21) Desbordes (22) Espey-Winslow (23)

85.5(i/36286 + Cr )i−0.667 L0.33 S −0.333 0.619N 0.47 L0.47 S −0.235 7.35n0.6 i−0.4 S −0.3 0.37(1.1 − C)L0.5 S −0.333 0.0663L0.77 S −0.385 0.057(1000/CN − 9)0.7 L0.8 S −0.5 0.313 0.322A0.594 L−0.594 S −0.150 Sscs 0.160L0.64 S −0.32 0.365A0.41 S −0.17 0.462L0.5 S −0.25 0.19L0.76 S −0.19 0.0559(4A0.5 + 1.5L)L−0.5 S −0.5 0.107A0.333 L0.333 S −0.5 0.127A0.5 S −0.5 0.0883L0.667 S −0.333 0.419k −1 A0.3 L0.2 S −0.4 0.222(1.05 − 0.2p)−1 LS−0.04 0.0828L0.24 S −0.16 A−0.26 imp 2.25i−0.7164 L0.5552 S −0.2070 0.0977L0.6 S −0.3 0.274nR−0.67 LS−0.5 0.0869A0.3039 S −0.3832 A−0.4523 imp 0.343ΦL0.29 S −0.145 A−0.6 imp

Table 2. Adopted values of the parameters in the equations.

METHODS Empirical equations for estimating Tc

Twenty three empirical equations used to estimate Tc are showed in Table 1. They can be all considered geomorphologically-based. In the present study, the area (A km2 ) the length of talvegue (L km) the elevation difference in the watershed (H km) and the slope of talvegue (S = H /L) were measured with a digital map (1:50,000 scale) and a GIS software. These equations were all evaluated for Tc analysis by Silveira (2005). The coefficients Cr, N , n, C, CN, k, p, R, Aimp and Φ are the coefficient Izzard; characteristic of the pond surface; roughness of Manning; parameter used in the rational method; SCS curve number; land characteristic of the watershed; fraction of the forest area; hydraulic radius; fraction of impervious area; and conductance factor of Espey, respectively. The coefficient Sscs is given by (25400/CN) − 254. Table 2 shows

Equations for estimating Tc in h.

Equation

Coefficient

Adopted value

Izzard Kinematic Wave/Eagleson FAA SCS Lag/Simas-Hawkins George Ribeiro Eagleson Schaake/Desbordes/Espey Espey-Winslow DNOS Kerby-Hathaway

Cr n C CN p R Aimp Φ k N

0.012 0.016 0.455 80 0.25 0.11 0.04 0.3 4.5 0.4

the used values of these coefficients in the empirical equations. The adopted value of k represents a watershed with vegetation medium and low absorption. The value of Cr represents a surface with concrete pavement. The parameter N means surface covered with grass average. The other parameters in Table 2 were considered for a watershed under accelerated urbanization. Therefore, these parameters represent a mean values between urban and rural watersheds adopted by Silveira (2005). For the Izzard, Kinematic Wave and McCuen equations, the i value was

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Table 3. TOPMODEL parameters description. Parameter

Description

Unit

m

Rate of decline of transmissivity in a soil profile Saturated soil transmissivity. Mean residence time for vertical flow Channel velocity Maximum available root zone storage Initial observed discharge

m

lnT0 TD RV SRMAX Q0

ln(m2 h−1 ) h m−1 m h−1 m m3 h−1

Figure 2. Nineteen observed hydrographs and their inflection points.

adopted 16.8 mm h−1 which corresponds to the maximum intensity observed during the analyzed period. Here, A = 106.3 km2 , L = 41.3 km2 , H = 0.09 km2 , S = 0.0022 and Sscs = 28.2. 3.2 Observed-hydrograph analysis for estimating Tb For estimating the Tb, 19 hydrographs were selected and drawn with a logarithmic scale to facilitate visual analysis. The point of intersection of two segments which trend hydrograph curve determines the end of direct runoff (inflection point). Thus, Tb is the total time measured from the end of rainfall event to this inflection point. Figure 2 shows the analyzed hydrographs and their inflection points. The time of inflection point indicates Tb. 3.3

Simulated-hydrograph analysis with TOPMODEL application

The TOPMODEL calibration was carried out with the hydrological data hourly obtained at the Fazendinha gauge station and the Chácara Guajubi meteorological station during the period from December 31st, 1999 to December 31st, 2000. The evapotranspiration data used in TOPMODEL were calculated with the modified Penman method (Doorenbos & Pruit, 1992). Monte Carlo method was used to find the best set of parameters. The initial range for each parameter was defined as: m (0.003 − 0.1 m); lnT0 (0 − 10 ln(m2 h−1 )); TD (0.05 − 120 h m−1 ); RV (300 − 2000 m h−1 ), and SRMAX (0 − 0.0020 m) (Table 3). The initial observed discharge Q0 was fixed to 16,980.8 m3 h−1 . The TOPMODEL structure and its parameters were detailed explained by Beven et al. (1995) and Beven (2001). Through 5000 simulations, the best parameters set for fitting to the observed data was determined with the Nash coefficient of 0.83: m = 0.0301 m; lnT0 = 0.11 m2 h−1 ; TD = 86.3 h m−1 ; RV = 315.5 m h−1 and SRMAX = 0.00082 m. Figure 3

Figure 3. TOPMODEL calibration for Pequeno River watershed.

shows the observed and calculated hydrographs for the hydrological data. After the model calibration, 1000 rainfall events were simulated with different durations and uniform intensities by changing the initial discharge values.The change of initial discharge was carried out with an initial discharge factor which is a kind of adjustment factor and determines the initial discharge. The rainfall duration time, rainfall uniform intensity and initial discharge factor ranges are 10–500 h, 5–500 mm h−1 and 0.1–10, respectively. Then through the simulated hydrograph analysis, the relations among the peak time (Ta), the time of rainfall duration, the rainfall uniform intensity, the initial discharge factor. TOPMODEL numerically separates hydrographs. Thus, the end of the direct runoff was numerically determined for each simulation. Then, Tb was calculated without subjetivity. In the same way, Ta was computed numerically from simulation result.

4

RESULTS AND DISCUSSION

Table 4 shows the Tc values obtained with the equations of Table 1. The mean value and the standard

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Table 4. Tc values calculated from 23 equations. Equation

Tc (h)

Equation

Tc (h)

Equation

Tc (h)

(1) (2) (3) (4) (5) (6) (7) (8)

4.3 9.8 14.5 11.8 12.3 57.5 5.2 12.3

(9) (10) (11) (12) (13) (14) (15) (16)

7.0 13.7 10.3 19.2 37.4 28.0 8.1 9.2

(17) (18) (19) (20) (21) (22) (23)

10.1 1.2 8.3 5.7 24.4 16.1 11.0

Obs: The numbers of the equations correspond to those in Table 1. Table 5.

Results of Tb for selected events.

Event (date)

Tb Pt imax D im Qm (min) (mm) (mm/h) (h) (mm/h) (m3 /h)

01/01/00 18/01/00 04/02/00 29/02/00 09/03/00 20/06/00 26/06/00 15/07/00 21/07/00 02/08/00 26/08/00 03/09/00 19/09/00 04/10/00 26/10/00 10/11/00 21/11/00 15/12/00 28/12/00

83 64 33 118 113 94 99 65 85 74 79 79 105 82 91 47 80 105 36

9 6 0.6 25.4 2.6 30.8 42.1 13.4 36.4 13.6 33.2 1 5.6 16.2 16.4 1.2 13.6 27.6 7

1.6 5.2 0.4 13.2 0.4 6.2 11.6 3.8 3.2 2.2 4.8 0.4 2.4 2.6 11.8 0.6 3.6 16.8 3.4

37 5 2 7 15 13 26 16 33 19 37 5 5 12 10 4 13 9 4

0.2 1.2 0.3 3.6 0.2 2.5 1.8 0.8 1.1 0.7 0.9 0.2 1.1 1.3 1.6 0.3 1.0 3.1 1.7

15939.3 15408.0 15089.2 34322.7 18064.6 9359.7 10756.5 5904.8 10903.3 8043.7 11030.0 7780.0 24042.3 9759.9 10526.1 10223.0 12626.9 11337.8 15618.3

deviation of Tc were 14.67 h and 12.38 h, respectively. The large variety of Tc values may result from the empirical methodology. Each author of one equation proposed his own method to calculate Tc with a particular watershed. That is why, when these equations were applied to the unique watershed (the Pequeno River watershed), this type of result (large variety) can be normally gained. However, these equations can be the only one option por ungauge watersheds. The Tb values of determined through the 19 observed-hydrographs analysis are shown in Table 5 where Pt is the total rainfall; imax is the maximum rainfall intensity; D is the rainfall event duration; im is the mean rainfall intensity; and Qm is the mean observed discharge. The mean value and the standard deviation of Tb were 80.8 h and 24.1 h, respectively. The different values of Tb can be generated with the uncertainties in determining the end of rainfall

event and the location of the inflection point on the hydrograph. The difficulty for determining the end of rainfall event is inevitable, because a lot of low intensity and short events occur in a non-grouped way, and these events contribute to the hydrograph peak formation. The determination of the inflection point was achieved visually for each event and suffered from certain subjectivity. Furthermore, errors hidden in hydrological data can be another source of uncertainty. Kobiyama et al. (2006) estimatedTc of an urban and small watershed (4 km2 ) with 5 geomorphologicallybased empirical equations (Kirpich, Dooge, Carter, Federal Aviation Agency and McCuen) and the observed-hydrograph analysis. The mean values of Tc obtained with the empirical equations and the hydrograph analysis were very similar, 31.8 minutes and 33.8 minutes, respectively. In the present study, these values are not similar. It implies the possibility that the geomorphologically-based empirical equations perform a better fitting to urban and small watersheds, and consequently that Tc determination is more difficult in rural watersheds than urban ones. It is contrary to the conclusion of Silveira (2005). By using 1000 hydrographs simulated with TOPMODEL, Ta value was calculated. Figures 4 to 6 show the relations ofTa with the rainfall intensity, the rainfall duration and the initial discharge index, respectively. It is observed that in all cases Ta values trend to the same value, i.e., 87 h. When the rainfall intensity is larger than 90 mm h−1 , Ta trends to 87 h. Over the value of 90 mm h−1 , the relation between Ta and the rainfall intensity becomes linear. Below this value, the relation is non-linear. This non-linearity was experimentally demonstrated by Zhang et al. (2007). The relation between Ta and the rainfall duration shows that the rainfall duration reached the equilibrium time defined by Saghafian & Julien (1995) when it is over approximately 100 h. Independent on the initial discharge factor, there is a strong tendency for Ta to concentrate to 87 h. Using the same 1000 simulated hydrographs, the relations of Tb with the rainfall intensity, the rainfall duration and the initial discharge factor were evaluated, and it is observed that the Tb value is constant (87 h), independent on the rainfall intensity, the rainfall duration and the initial discharge factor. Therefore, Tb can be defined as the time necessary for the watershed to drain stored water and it has an exponential relation to the channel velocity parameter RV in the TOPMODEL. It implies that the TOPMODEL simulates the rainfall-runoff processes (total discharge, hydrograph separation) in the Pequeno River watershed very well, and that the manual technique to determine Tb in the observed-hydrograph analysis is well performed. Certain difference between two methods (87 h and 80.8 h)

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observed-hydrographs analysis and (iii) simulatedhydrographs analysis with TOPMODEL application) to estimate the time of concentration in Pequeno River watershed. The obtained results permit to conclude: 1. The large variety of the Tc values estimated with 23 empirical equations might result from an empirical methodology. 2. The mean value and the standard deviation of Tb values estimated with the 19-observedhydrographs analysis were 80.8 h and 24.1 h, respectively. The Tb values variation occurred due to the uncertainties in determining the end of rainfall event and in determining the inflection point in hydrograph curve. 3. The relations of Ta with the rainfall intensity and the rainfall duration were observed only for weak intensities and short durations. After some conditions in TOPMODEL analysis, Ta becomes equal to Tb (=87 h), and then this value may be considered the watershed Tc. Such equality can be explained by aTOPMODEL structure where the same RV value is used for the flow routing during both the ascension and the recession periods. 4. The Tb values estimated through the TOPMODEL application are similar to those with the observedhydrograph analysis.

Figure 4. Relation of Ta with rainfall intensity.

REFERENCES Figure 5. Relation of Ta with rainfall duration.

Figure 6. Relation of Ta with initial discharge factor.

might be due to the subjectivity in determining the inflection point.

5

CONCLUSIONS

The present study applied three methods ((i) geomorphologically-based empirical equations; (ii)

Beven, K.J. 2001. Rainfall-runoff modeling: The primer. New York: John Wiley & Sons. Beven, K.J. & Kirkby, M.J. 1979. A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin 24: 43–69. Beven, K.J., Lamb, R., Quinn, P., Romanowicz, R. & Freer, J. 1995. Topmodel. In: V.P. Singh (ed.) Computer Models of Watershed, 627-668, Highlands Ranch: Water Resources Publication. Doorenbos, J. & Pruitt, W.O. 1992. Guidelines for predicting crop water requirements. 2 ed. Rome: FAO. Kobiyama, M., Grison, F., Lino, J.F.L. & Silva, R.V. 2006 Time of concentration in the UFSC campus catchment, Floriaópolis-SC (Brazil), calculated with morphometric and hydrological methods. In: Proc. Regional Conference on Geomorphology, Goiânia, 6–10 September 2006. Goiânia: IAG/UGB. McCuen, R.H., Wong, S.L. & RAWLS, W.J. 1984. Estimating urban time of concentration. Journal of Hydraulic Engineering 110(7): 887–904. Saghafian, B. & Julien, P.Y. 1995. Time to equilibrium for spatially variable watersheds. Journal of Hydrology 172: 231–245. Silveira, A.L.L. 2005. Performance of time of concentration formulas for urban and rural basins. Brazilian Journal of Water Resources 10: 5–23 (in Portuguese with English abstract). Zhang, S., Liu, C., Yao, Z. & Guo, L. 2007. Experimental study on lag time for a small watershed. Hydrological processes 21: 1045–1054.

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Application of artificial neural network in rainfall-runoff model Z. Li∗, P. Deng & J. Dong College of Hydrology and Water Resources, Hohai University, Nanjing, China

ABSTRACT: Rainfall-runoff models of river basins enable modelers to forecast river discharge. Such forecast should be accurate and reliable in order to be effectively used for warning against hydrological extremes and for water resources management purposes. A neural network is a massively parallel distributed processor that has a natural propensity for storing experimental knowledge and making it available for use. It resembles the human brain in two respects that knowledge is acquired by the network through a learning process and interneuron connection strengths known as synaptic weights are used to store the knowledge. Hourly rainfall runoff data for Huangchuan catchment in Huaihe River basin, China, are considered to demonstrate an ANN application. The network frame set input nodes consisting of previous hour’s rainfall, previous hour’s discharge, present hour’s rainfall. After training was complete, the trained network can be used for real time forecasting. Getting across trial and error, the precision accord with the criterion when the predicting time less than 2 or 3 days. ANN models can play a useful role in dealing with any input-output relationship. Thus, the field of watershed hydrology is no exception. ANN is expected to serve as a useful tool for the solution of specific problems in watershed hydrology. Keywords:

1

neural network; rainfall-runoff model; hydrological forecasting; real-time forecasting

INTRODUCTION

Rainfall-runoff models of river basins enable modelers to forecast river discharge. Such forecast should be accurate and reliable in order to be effectively used for warning against hydrological extremes and for water resources management purposes. The modeling task is complex, due to such issues as nonlinearity, heterogeneity, scale and measurability of rainfall-runoff processes. It is for these reasons that the search for accurate, reliable and physically plausible models still remains one of the most challenging topics in hydrology. According to Haykin (1994), a neural network is a massively parallel distributed processor that has a natural propensity for storing experimental knowledge and making it available for use. It resembles the human brain in two respects: (1) Knowledge is acquired by the network through a learning process. (2) Interneuron connection strengths known as synaptic weights are used to store the knowledge. Artificial Neural Networks (ANN) are now being usefully employed for solution of problems which have been intractable or difficult to solve by traditional methods of computation. Development of ANNs is based on the hope that some of the flexibility of the power of human brain can be reproduced by artificial ∗

Corresponding author ([email protected])

means. Processes which are not fully understood to develop well-defined algorithms can be analyzed by ANNs. An ANN, being capable of generalization and learning, offers distinct advantages over conventional algorithmic approaches. ANN is especially useful for classification of inputs, association of different partners, forecasting, pattern recognition, etc. At present, ANNs are extracting a great deal of attention from a number of disciplines. Neuro-scientists, physicists, mathematicians, engineers from various disciplines and other researchers are finding ANNs a useful tool for analyzing the problems which have kept them perplexed for a very long time.

2 ANN FUNDAMENTALS ANN, computations are performed by a dense mesh of computing nodes and connections. Nodes or neurons are the basic processing elements. Nodes are usually organized in layers and connections both within a layer and towards adjacent layers are allowed. Each connection strength is expressed by a numerical value called weight. A node receives inputs from various other nodes connected to it and it generates one output. For calculation of outputs, each input to node is multiplied by the corresponding weight and summed. This sum is then mapped to output by using a suitable activation function (Zurada, 1992).

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weights such that the error E between the observed output and the output computed using ANN is minimized:

Figure 1. A typical ANN with input and hidden output layers.

Activation functions can be continuous or binary. Continuous activation functions are also termed as sigmoidal functions or soft limiting functions, while binary functions are termed as hard limiting functions. Figure 1 shows a typical ANN with input, and hidden and output layers. Also, the biases θj and θk are shown at hidden nodes and outputs. A path from hidden node j to input node i is assigned a weight wji and a path from output k to the hidden node j is assigned a weight wkj . In terms of the input xi at input node i, the output rj at the hidden node j can be represented as shown by Carpenter & Bythelemy (1994). The connecting weights wji (i indicates input and j hidden node) and biases hbj at hidden node j along with any assumed activation function can be used to compute the output ohj at the hidden node j. The output ohj can be expressed as

Where

It can be seen from Eq. (1) that rj is a sigmoidal output of the inputs netj . A similar sequence of computations leads to the computed output value yck as

In Eq. (3), p = index for the data of training pair, P = the number of training pairs used, and y represents the output. Initially, weights are assumed randomly. Corresponding to these random weights, E will have certain value for given training pairs. This value will change if any of these weights or all weights are changed simultaneously. Thus, Eq. (3) forms an optimization problem where the objective is to optimize E. Ideally, E should tend to zero. During training, weights need continuous adjustment from iteration t to iteration t + 1. In one of the widely used weight adjustment algorithms, also known as Back Propagation Algorithm, the adjustment w (t + 1), which is required in iteration (t + 1), is assumed linearly related to the negative gradient of E with w in iteration t. The constant of proportionality in this linear relationship is also known as the learning rate, η. Mathematically, this linear relationship can be expressed as follows:

Since the inception of the Rumelhart’s back propagation algorithm (Rumelhart et al., 1986), several algorithms have been suggested for updating of weights. These algorithms can be classified as “local search” and “global search”. Gupta et al. (2000) provide details of each of these algorithms. They have also developed a global search algorithm, known as Linear Least Squares SIMplex (LLSSIM), which provides superior results (higher probability of getting a better solution with comparable computational resources) to the conventional algorithms. During training, it may be necessary to scale down inputs and outputs within the limits of 0 to 1, or −1 to +1, depending on the use of sigmoid or hyperbolic tangent activation functions. Salas et al. (2000) use the following relationship to transform the original input data x to transformed input X.

3 TRAINING OF ANN Training forms an important step of any ANN architecture. The objective of training is to obtain unknown

In Eq. (5), Ux and Lx are the upper and lower network ranges for the network input, and Mx and mx are the maximum and minimum of the original input data.

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Table 1.

Train

Validation

Results of rainfall-runoff model.

Flood start time

Total rainfall (mm)

1991-7-30 1992-4-29 1992-9-4 1992-9-29 1993-5-8 1993-6-16 1995-4-16 1995-6-16 1996-6-27 1997-5-4 1998-5-1 1998-6-26 1998-7-27 2003-6-20

178 102 186 215 162 102 108 278 627 208 234 198 244 640

Table 2.

Flood runoff

Flood peak

Observed (104 m3 )

Forecasted (104 m3 )

Relative error (%)

Observed (m3 /s)

Forecasted (m3 /s)

Relative error (%)

Dc

14107 7919 2588 5311 11809 8051 6691 19762 85945 4856 41272 14942 16452 94848

16469 8093 2901 5148 14027 9622 7289 23004 84957 5818 39555 17821 17076 93972

16.74 2.20 12.08 −3.06 18.78 19.50 8.94 16.41 −1.15 19.81 −4.16 19.26 3.79 −0.92

947 824 188 216 645 455 548 993 1930 214 832 1150 397 2150

1022 903 192 181 526 393 598 1029 1856 255 860 1168 412 1934

7.89 9.56 2.13 −16.20 −18.47 −13.53 9.06 3.67 −3.81 19.16 3.41 1.57 3.89 −10.02

0.97 0.93 0.89 0.86 0.83 0.77 0.85 0.86 0.92 0.80 0.94 0.94 0.89 0.94

Results of realtime forecasting.

Forecast start time

Forecast end time

Relative error of flood runoff (%)

Relative error of flood peak (%)

Dc

1991-8-7 8:00 1993-5-21 8:00 2005-7-28 14:00

1991-8-12 8:00 1993-5-25 8:00 2005-7-31 14:00

4.96 9.58 −4.20

2.98 −7.22 5.63

0.95 0.93 0.98

In a similar way, it can be written to present the transformation between the original output data y and the transformed output Y.

4 APPLICATION IN RAINFALL-RUNOFF MODEL Hourly rainfall runoff data for Huangchuan catchment in Huaihe River basin, China, are considered to demonstrate an ANN application. The total area of the catchment is about 1,755 km2 . Rainfall-runoff data from years 1991-96 were used for training the network and subsequently the rainfall data for years 1997-2005 were used for validation. The network frame set eight input nodes consisting of four previous hour’s rainfall, three previous hour’s discharge, present hour’s rainfall. Getting across trial and error, it was found that hidden nodes equal to 5. Output node was predicting runoff. The training and validating results have been depicted in Table 1. After training was complete, the trained network can be used for real time forecasting. Getting across trial and error, the precision accord with the criterion when the predicting time less than 2 or 3 days. The results of

Figure 2. Discharge vs. Time, 1996.6.27 to 7.21 flood in training.

real-time forecasting for Huangchuan catchment were showed in Table 2. Fig. 3 is one of the flood discharge process. 5

CONCLUSION

ANN models can play a useful role in dealing with any input-output relationship. Thus, the field of watershed hydrology is no exception. However, to provide impetus to the use of such models, it is essential that

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REFERENCES

Figure 3. Discharge and Precipitation vs. Time, 2005.7.26 to 7.31 in real-time forecasting.

improvements in the training of these models should continue so that the training can be achieved with minimum computational time. To achieve this, it is not only adequate to rely on the use of certain algorithms, but it is essential that the use of proper scaling of input and output along with proper use of inputs should be also recognized and investigated. ANN is expected to serve as a useful tools for the solution of specific problems in watershed hydrology.

Carpenter, W.C. & Barthelemy, J.F. 1994. Common misconceptions about neural networks as approximators. J. Comput Civil Eng., ASCE 8(3): 345–358. Gupta, H.V., Hsu, K. & Sorooshian, S. 2000. Effective and efficient modeling for streamflow forecasting. In Gaovindaraju, R.S. & Rao, A.R. (eds), Arttificial Neural Networks in Hydrology: 7–22. Kluwer Acadimic Publishers, Dordrecht. Hayken, S.S. 1994. Neural NetworksA-Comprehensive Foundation. MacMillan College Publising Company, New York. Rumelhart, D.E., Hinton, G.E., & Williams, R.J. 1986. Learning internal representations by error propagation. In Rumelhart, D.E., McClelland, J.L., & the PDP Research Group (eds), Paralled Distributed Processing. Volume 1: Foundations: 318–362. The MIT Press, Cambridge, MA. Salas, J.D. Markus, M. & Tokar, A.A. 2000. Streamflow forecasting based on artificial neural networks. In Gaovindraju, R.S. & Rao, A.R. (eds), Artificial Neural Networks in Hydrology: 23–51. Kluwer Academic Publishers, Dordrecht. Zurada, J.M. 1992. Introduction to Artificial Neural Systems. PWS Publishing Company, Boston.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Research on system dynamics model of water resources harmonious management J. Wang∗ & J. Zhang College of International Business, Hohai Univ, Nanjing, China

Z. Li College of Hydrology and Water Resources, Hohai University, Nanjing, China

ABSTRACT: The water resources management is a complicated and big system. The new theory and the connotation of the water resources harmonious management is generalized as three aspects: the supply-demand harmoniousness, the benefit harmoniousness and the organization harmoniousness. On the foundation of the harmonious management’s connotation, the system dynamics model of water resources harmonious management was established by using the system dynamics method. The model emphasized on the harmoniousness between the water resources supply and demand, and on the relation of the water resources and society, economy and zoology, and it also optimized the harmonious function of the management system. Combining some regions of Jiangsu Province, the demonstration was carried out. According to the regions’ facts, different solution schemes were put up to be chosen and corresponding stratagem policies of water resources management were put forward. The results indicated that the model had a good indication function to the water resources management, and could bring into a good harmonious function, thus the sustainable utilization of water resources could be realized. Keywords:

1

water resources, harmonious management, system dynamics

INTRODUCTION

Water resources will be the most restrictive factor of the social sustainable development in the future. Water resources management includes not only the problems of the rational exploitation, development, and protection when water is looked as natural resources, but also the problems of economics benefit management when water is regarded as economic resources. The economic benefits mean that the water resources management should satisfy with many targets such as ensuring society stabilization, promoting economy development, and protecting environment etc. Therefore, in water resources management we should establish the concept of harmonious management in which we synthesize all kinds of influencing factors, and coordinate the investment structure, industry structure, water allocation project, and make reasonable management system, and put forward proper policy advice etc. The multi-targets will be attained on balance. Based on the feedback to control theory, the system dynamics is a quantitative method which mainly

∗

Corresponding author ([email protected])

analyses the complicated social economic system by the computer simulation technique. The water resources management is characterized by complexity, nonlinear and multi-targets.The system dynamics simulates not only the current trend to give warning forecast but also analyses the relationships among the social, economical and ecological development to the future. The article established the water resources harmonious management SD model by the system dynamics, carried out the demonstration of the region of Jiangsu Province, and put up the stratagem of harmonious management. 2 THE ESTABISHMENT OF THE SYSTEM DYNAMICS MODEL 2.1 System Analysis Regarded as a big and complicated system, the water resources management is an organic in whole, which is made up by a series of interactional factors. Some researchers have developed the function of foundational natural resources (Zhang & Wang, 2005). The aim of the system is to carry out the sustainable utilization of water resources, that is to say, the aim is

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not only to satisfy the water request of the contemporary and descendant posterity but also to make the water resources development of society, economy and ecology harmonious and sustainable. Put forward by professor XiYoumin of Xi’an Transportation University and experienced development of more than ten years, the harmonious management theories have gradually evolutes to a modern system which takes the harmonious topics, the He Roles and the Xie Roles as the core concepts (Feng, 2000). The theories embody the system thought and adopt the research philosophy of “total theory” which make management close to the fact and related to real problems. The harmonious management theories provided a solution to the management problems under the complicated environment. We study the harmoniousness of water resources management system by applying the new theories of harmonious management to the water resources management. The harmoniousness analysis is to consider the relationship among the subsystems, the factors, the factors and environments, and the environments at the time of managing, and to use the water resources reasonably, of which the aim is to attain the most whole function of the management system. The topic of the water resources harmonious management is to realize the harmonious allocation, and the harmoniousness between the supply-demands and the benefits, so the benefits of society, economy and zoology can be attained to the most satisfaction in the whole in the long term. The aim of the system dynamics model of the water resources harmonious management is to ensure the rational structure of investment and industry, and the rational management policies, to coordinate the benefits of society, economy and zoology, and to moderate the relationship between the national economy and the water resources, and to use the water resources as economically and rationally as possible. So each target of the region water resources management can be realized harmoniously. On the foundation of the harmonious management theories, the meaning of the water resources management can be showed by mathematics:

2.1.1 H1 (c): Supply-demand harmoniousness The supply-demand harmoniousness means that the water resources allocation is related with different time, space, quantity, quality and use. It embodies the relation of water resources supply-demand balance. In contrary to the water resources management system, the supply-demand gap is smaller, the harmoniousness is better, and the efficiency of management is higher. The constitution harmoniousness means the relationship of system’s factors.

2.1.2 H2 (u): Benefits harmoniousness As to the relation of the system and the exterior environment, the benefits of harmoniousness emphasizes that the water resources management should satisfy the needs of the society, economy and environment to attain the best integration benefit. It also emphasizes that the system should have the strong self-adaptability characteristics. The model and the development direction must be adaptive to the exterior environment. 2.1.3 H3 (o): Organization harmoniousness Organization harmoniousness means the influence of many factors such as advanced technology, reasonable policies and the level of management stuff during the course of the water resources management. It focused on how to rationally exert system function by organizational measure to make the whole organization harmonious. 2.2 Structure Analysis Different regions have different water resources condition and the social economy development level. For example, in the eastern inshore region, the water resources is more abundant, and the economy is more developed, while in the western region, the water resources is more deficient, and the economy condition is more backward. So as to different regions, there will be different system dynamic models. The article carried the study on some regions of Jiangsu Province. In the region, the water resources are not even. In that period, there is more rainfall in summer and autumn than winter and spring. On the space, there is more water resources in east and south than in west and north. The adjustive ability is small. The system dynamics considers that there is the cause and effect relation between the systems and the factors, and the all the relations constitute a feedback track diagram. According to the system analysis mentioned above, on the foundation of means of the water resources harmonious management, the relation of the main cause and effect of the system can be generalized as follows: 2.2.1 Supply-demand sub-model This sub-model can reflect the condition of supplydemand in the water resources management system. The supply-demand harmoniousness can be evaluated according to the value of supply-demand gap. So taking the supply-demand gap as the level variable, and make the supply quantity and the demand quantity as the rate variables. The demand quantity of water resources is divided into three kinds which are the agriculture water use, industry water use and life water use. And in the supply quantity of water, there are two abilities of supply which have been existed and will

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be added. The added ability of supply is an auxiliary variable which is made up with the reuse of water quantity and the added quantity because of the increased investment on water. And the water price will adjust and control the quantity of the reuse of water. 2.2.2 Population sub-model This sub-model can reflect some condition of organization harmoniousness and benefits harmoniousness about the water resources harmonious management system. The level variable is the total population. We mainly study the influence which is the life water use and sewage quantity on the supply-demand sub-model through the population. We can also get the indexes such as the labor population, the technology people to reflect the organizational harmoniousness of the system. 2.2.3 Economic sub-model This sub-model can reflect the condition of benefits harmoniousness. The point lies in the simulation and evaluation of the economic performance. According to the actual condition in region, we chose the value of industry production as the level variable. The influence is made by the industry water use and the industry sewage reuse. Otherwise the increased value of industry production provides the economic support for the development and use of the water resources.The industry sewage quantity is another important influencing factor of the industry production in the whole system, while it is also decided by the sewage rate and the water price. 2.2.4 Sewage sub-model This sub-model can simulate and evaluate the condition of zoology benefit harmoniousness and the corresponding organization harmoniousness. The level variable is the sewage quantity, which can be used to study the mutative trend of the sewage quantity because of water supply quantity, and the mutative trend population because of the sewage quantity. The sewage origin mainly includes industry sewage and life sewage. The factors affecting the sewage treatment rate are the sewage treatment investment, the treatment expense for unit sewage etc. The system dynamics model mainly pays attention to the mutative trend of the whole system and the influence due to the changed policies, so the accurate result doesn’t need and the parameters can be simplified. Making the price, water investment, the strength of sewage treatment etc. as the policy adjusting and controlling parameters in the SD model, the implement results of each policy project will be simulated to attain the aims of the policy analysis, experimenting and assistant decision by changing these policy parameters.

The main variables: TWQ: Total water quantity gap, TPQ: Total population, PWQ: Pollution water quantity, IPV: Industry production value, DWQ: Demand water quantity, SWQ: Supply water quantity, AWQ: Agriculture water use quantity, IPW: Industry water use quantity, LWQ: Life water use quantity, AWP: Agriculture water price, WQPU: Water quantity of per unit of area, FA: Farmland area, BR: Birthrate, DR: Death rate LWP: Life water use, LWUP: Life water use quantity per people, IWP: Industry water price, WQPP: Water quantity per production, IRIP: Investment rate of industry production, PRIP: Producing rate of industry production IUWR: Repetition rate of industry water use, ESWQ: The existed supply water quantity,ASWQ: The added supply water quantity, IW: Investment rate of water, RLW: Rate of life water pollution, RIW: Rate of industry water pollution, PWTR: Rate of pollution water treating RUQ: Reuse water quantity, RUR: Rate of reuse water, IRPW: Investment rate of treating pollution water Figure 1. Flow fig. of system dynamics of water resources harmonious management.

On the base of the internal cause and effect relation, turn the cause and effect chart to the flow chart, such as figure 1 shows:

3 THE SIMULATION AND ANALYSIS OF MODEL 3.1 The Examination and evaluation of model The water resources harmonious management system SD model runs by the computer, and the expression accuracy of the model was examined by the software of VENSIM that was invented in Massachusetts Institute

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Table 1. The simulation examination table for the SD model. Industry water use (ten thousand stere)

Agriculture water use (ten thousand stere)

Total value of industry (hundred million yuan)

Year

History

Simulate

Error

History

Simulate

Error

History

Simulate

Error

1998 1999 2000 2001 2002 2003 2004

5066 5001.9 5179.7 6240.4 7760 9078.6 10235

5002.1 5148.3 5366.7 6593.4 7903.9 9315.5 9983.1

1.3% 3.0% 3.6% 5.6% 1.9% 2.6% 2.5%

57 321 56 990 56 920 51 779 51 000 50 000 47 980

56 963 56 002 55 873 50 910 49 760 48 975 48 295

0.6% 1.7% 2% 1.7% 2.4% 2.1% 0.7%

95.37 99.02 103.87 125.17 155.65 182.10 240.97

89.41 97.04 102.64 126.09 156.53 182.98 228.65

6.2% 2% 1.12% 0.7% 0.7% 0.5% 5.1%

of Technology in 1990. From the result, we can see that the model is rational. Take the stat. data from 1994 to 2004 of some region of Jiangsu province for system forecasting. The terminate time is 2024, and the interval is 1 year. From the 1998–2004 years’ result, we chose the indexes such as industry water use, agriculture water use and the industry production value etc. to be compared with the history data, which is showed as table 1. The error probability is smaller than 5%, and we can draw a conclusion that the model has enough validity. 3.2 The model simulation and policy analysis A series of parameters and initial values in the model is finally decided through analyzing the history stat. data, objective development trend, programming and the present data and through necessity judgment, and through debugging again and again. The adjust and control function of different investment and management policy should be paid more attention to when we simulate the model so that we can get the effective measures to solve the problems of economic development and the environmental protection. The system mainly considers two control factors that are investment proportion of sewage treatment and the water price. The invest proportion is denoted by table function, while the price is denoted by different constant value. According to the structure of the system and the result of the simulation, the best project and control measure will be decided by comparing different projects. The system chose 2 alternative projects during the simulation cause. 3.2.1 Project a: Keeping the water price constant, and enlarging the strength of the pollution treatment, increasing the investment proportion from existing 2% to 6%, let’s study the trend of the water supply and demand quantity. From the figure 2, the water resources are always in the shortage condition, and the total supply-demand

Figure 2. Supply-demand gap change.

gap is very high, and the decreasing range is not very large.The decreasing range of the total sewage is small, such as figure 3. This shows that the supply water quantity can not meet the demand, and the gap will increase gradually with time. The main reason is that the lower water price makes the users can get more consumer surplus which lead more unreasonable water use condition. So the improved supply ability by the investment proportion increasing can not make up the waste influence by the lower water price, at the same time, the investment proportion can not increase limitlessly because of the economic developing level. The aim of harmonious management can hardly be attained by only increasing the investment proportion in water resources management. 3.2.2 Project b: Let’s adjusted the life use water price (including the price of ) from ¥1.9 to ¥3, and industry use water price from ¥2.3 to ¥5, and the investment proportion of pollution treatment from about 2% to about 4%, and

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more attention to the pollution manage and increased the investment proportion, but also made good use of economy lever and adjusted the demand water, reuse water by price, to keep the resources use sustainable and the economy develop continuously and quickly. The water resources management is a complicated big system. The harmonious management request to attain the aims of supply-demand, benefit and organization harmoniousness during the course of water allocation. 4

Figure 3. Total quantity of pollution water change.

at the figure 2, the total supply-demand gap reduced gradually, and it floated up and down at 0 in about 2020 year. The supply and demand of water resources were kept in balance, which can satisfy the need of economic development. The water waste phenomenon reduced and the total sewage decreased obviously, such as figure 3 showed. This project made use of 2 means of price regulation mechanism and increasing investment proportion, and provided the strategy of keeping sustainable water resources using, the stable economy developing, and the water resource harmonious management. One is that the ecosystem environment condition of the region was improved consumedly and the efficiency of water resources use was also raised. On the other hand, because of the market regulate function, the consciousness of saving water was advanced by increasing water price, and the phenomenon of wasting water and using water unreasonably was suppress. Both the 2 ways improved the condition of water resources use and provided a valid path for the harmonious management. According to the above-mentioned analysis, we thought that project b had the better social, economic and ecosystem benefit, and it can be used as the strategic project for the harmonious management in the region. Aim at actual condition of the region, catch hold of the two key factors of manage pollution and giving the reasonable water price. On the condition of water shortage and that the pollution had restricted the economic development, we should not only pay

CONCLUSION

The water resources harmonious management system involves many subsystems such as the supply-demand subsystem, population subsystem, economic subsystem and water pollution subsystem and so on. The relationship is complicated and can not be analyzed by the traditional method. The system dynamics can simulate the feedback relation of each part and can reflect the interaction among each factor comprehensively. It is a good method to analysis the water resources management. The applied analysis on the region of Jiangsu Province showed that the investment of water pollution management and the reasonable water price were 2 keys factors. We only use both two factors coordinately to keep the use of resources sustainable and keep the economy development continuous and quick. REFERENCES Feng, S. 2000. The Guide Talk about Sustainable Utilize and Management of Water Resources, 47–49. Chinese Science Publisher, Beijing, China (in Chinese). Water resources gazette of Pizhou, 1994∼2004, China (in Chinese). Wang, J., Zhang, J. & Dong, Z. 2003. HarmoniousAnalysis on Water Resource Allocation. J. Hohai University (Natural Sciences), 31(6), 702∼705 (in Chinese). Wang, Z. 1988. The Guide of System Dynamics, 35∼49. Shanghai science and technology publishing company. China (in Chinese). Xi, Y. 1989. Harmonious Theory and Stratagem, 55–170. Guizhou People Publisher, GuiZhou, China (in Chinese). Zheng, C. 1996. Study of Coordinative Development between Water Resources and National Economy, 68∼85. Hohai publishing company. China (in Chinese). Zhang, J & Wang, J. 2005. Some Discussions of Water Resources Management System. J. Economics of Water Resources, 6, 56∼58 (in Chinese).

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6

Groundwater-surface water interaction

Understanding the mechanisms of water movement and transport of dissolved materials between surface water and groundwater is essential to improve the management of surface and groundwater resources and to protect the ecosystems from deterioration. Although surface water and groundwater have been considered separately for a long time, we now understand the fact that water cycle in terms of water quantity and quality is critical for the maintenance of the ecological systems of both rivers and aquifers. For example, the combined use of multiple tracers of both radioactive isotopes and stable isotopes, and other geochemical components will facilitate understanding of these hydrological processes. This session will bring together scientists to advance integrated analysis of groundwater-surface water systems. Physical, chemical, and biological processes, together with mathematical approaches focusing on groundwater-surface water interactions, and impacts due to climate changes and human activities/responses (i.e., urbanization, dam construction, water transfer projects, irrigation, and landfill) are welcome. Conveners: Jianyao Chen (Sun Yat-sen University, China) Tsutomu Yamanaka (University of Tsukuba, Japan) Takeo Onishi (RIHN, Japan)

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Importance of groundwater discharge in developing urban centers of Southeast Asia W.C. Burnett∗ & R. Peterson Department of Oceanography, Florida State University, Tallahassee, FL, USA

M. Taniguchi Research Institute for Humanity and Nature, Kyoto, Japan

G. Wattayakorn & S. Chanyotha Departments of Marine Science and Nuclear Technology, Chulalongkorn University, Bangkok, Thailand

F. Siringan Department of Marine Science, University of the Philippines, Quezon City, Philippines

ABSTRACT: We have conducted radioisotope and nutrient surveys of the coastal areas and waterways around two Asian megacities, Bangkok and Manila, in order to evaluate the influx of nutrients from contaminated groundwaters. Around Bangkok, we observed spikes in radon activity in the Chao Phraya River that corresponded to locations where major canals enter the river. We later conducted more detailed surveys and found that groundwater tracers are significantly enriched in some canals indicating seepage of shallow groundwater. Furthermore, nutrient analyses showed that Dissolved Inorganic Nitrogen (DIN) and phosphate correlated significantly with the groundwater tracers. In Manila, multi-detector radon surveys along the shoreline showed the highest activities in Manila Bay occurring in the area where groundwater pieziometric surface contours on land were highest. Time-series experiments in waterways around both Bangkok and Manila were used to evaluate tidal-scale fluctuations and discharge rates. Nutrient flux calculations showed very high rates in the Bangkok canal and moderately high rates in the coastal waters of Manila Bay. Keywords: Asia; groundwater; megacities; nutrients; subterranean environments 1

INTRODUCTION

One of the most dramatic and obvious subsurface environmental problems occurring in Asian coastal cities today is subsidence and related issues due to excessive pumping of groundwater. This has occurred repeatedly in major cities throughout Asia with a time lag depending upon the developmental stage of urbanization in each city (Taniguchi et al., 2008a). Subsidence not only results in dangerous structural problems within cities but has also resulted in a serious danger of flooding in many low-lying coastal cities of Asia. Entire sections of Bangkok, for example, now flood during each spring tide. Government-mandated changes in the reliable use of water resources from groundwater to surface water supplies have typically been initiated to address the over pumping and subsidence problems. However, ∗

Corresponding author ([email protected])

even when land subsidence has ceased due to regulation of groundwater pumping, the resulting increase in groundwater level has caused other types of damage by flooding and exerting buoyant forces on underground infrastructures (e.g., subways) that were constructed during the drawdown period. What is somewhat surprising is that this same scenario (groundwater over pumping → subsidence/flooding → withdrawal restrictions → groundwater level rising) has been repeated several times in different Asian cities over recent decades without any obvious collective wisdom. We continue to make the same mistakes. We address here another, yet related, issue concerning the subsurface environment that has been largely ignored in urban settings: material (contaminant) transport to surface waters. Research over the last few years has shown that Submarine Groundwater Discharge (SGD) to the coastal zone is a significant water and material pathway from land to sea (e.g., Bokuniewicz, 1980; Moore, 1996; Taniguchi

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et al., 2002). This flow may occur through the surficial aquifer or through breaches in deeper semi-confined coastal aquifers. While the overall flow of fresh groundwater into the ocean is likely no more than about 6% of global runoff, it has been estimated that the total dissolved salt contributed by terrestrially-derived SGD may be as much as 50% of that contributed by rivers (Zektser and Loaiciga, 1993). This process will thus affect the biogeochemistry of estuaries and the coastal ocean through the addition of nutrients, metals, and carbon (Moore, 1999). High dissolved N:P ratios in contaminated coastal groundwater relative to surface waters may drive the coastal ocean towards P-limitation within the coming decades, perhaps changing the present N-limited coastal primary production (Slomp and Van Cappellen, 2004; Hwang et al., 2005). In addition to inputs of terrestriallyderived groundwaters, recirculation of seawater through sediments by tidal pumping and other processes can provide significant biogeochemical inputs and is also considered “SGD” (Burnett et al., 2003). While coastal scientists now recognize that groundwater often can be a major contributor to coastal nutrient budgets, most studies to date have been performed in rural, and in many cases, pristine environments. This approach reflects the understandable desire to deal with “natural” systems. While there certainly have been a number of contamination studies in and around urban waterways of the world, few have addressed the issue of interaction between urban groundwaters and surface waters. Exceptions include Charette and Buesseler (2004; urban area of Chesapeake Bay), Rapaglia (2005; Venice Lagoon), Beck et al. (2007; Jamaica Bay, NY), Nakayama et al. (2007; Tokyo Bay), and Swarzenski et al. (2007; Tampa Bay). In light of the trend towards accelerating global urbanization, it now seems prudent to turn our attention to evaluating such impacts in major urban areas. We report here on SGD investigations in the waterways of Bangkok and off Metro Manila, Philippines, both Asian megacities. 2

STUDY SITES AND EXPERIMENTAL METHODS

2.1 Asian Megacities Bangkok, originally Khrung Thep (“City of Angels”), is the capital city and most important port of Thailand. As the economic center of Thailand, Bangkok has an extremely high population density (total population ∼10+ million; population density ∼6500 people/km2 ). Although it is a major port, it is located some 40 km upstream from the Gulf of Thailand on the Chao Phraya River (“The River of Kings,” Fig. 1). Beginning in the mid-19th century, roads were built to facilitate land travel, but the river remained the principal artery of communication and

Figure 1. Index map showing the Chao Phraya River and some of the major canals (“klongs”) that are found in the Bangkok area. The triangle on K. Bangkok Yai marks the location of Wat Intharam.

the man-made canals (“klongs”) served as smaller streets leading into residential districts. The city of Manila, one of the municipalities that comprise Metro Manila, lies at the mouth of the Pasig River on the eastern shores of Manila Bay (Fig. 2). The city sits on top of century’s worth of prehistoric alluvial deposits built by the waters of the Pasig River and on land reclaimed from Manila Bay. The layout of the city was haphazardly planned during the Spanish Era as a set of communities surrounding the original walled city of Intramuros, one of the oldest walled cities in the Far East. Beginning in 1898, the United States occupied and controlled the city (except for the Japanese period during WWII) and the Philippine archipelago until 1946. During World War II, much of the city was destroyed and thus its infrastructure is relatively recent. Manila has one of the highest population densities of any city in the world (10,550 people/km2 ; compare to Tokyo at 4,750 people/km2 or New York at 2,050 people/km2 (http://www.citymayors.com/ statistics/largest-cities-density-125.html)). The very rapid growth and relatively poor economy has resulted in some very unfortunate environmental and social problems. It is estimated, for example, that over 150,000 people make a living by scavenging for resale goods through the ∼6,700 tons of garbage produced each day in Manila. The most famous of several dumps is called “Payatas,” a 30-meter mountain of garbage. This dump collapsed during monsoon rains in 2000 and buried more than 200 squatters in their shacks. The urban collapse of the slums around Manila has clearly led to many environmental disasters.

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Figure 2. Index map of Manila Bay (Philippines) showing the different drainage basins (shown by the numbers) in the area. We were part of a group (Taniguchi et al., 2008b) that performed fieldwork along the southeast coast of the Bataan Peninsula (basin #1) in 2005 and off Metro Manila in 2006. The x’s mark the approximate locations.

2.2

Radon Measurements

Radon serves as an effective groundwater tracer because it is greatly enriched in groundwater relative to ocean water, behaves conservatively in seawater, and is relatively easy to measure. An automated radon system (Burnett et al., 2001) was used to analyze 222 Rn from a constant stream of near-surface water (driven by a submersible pump) passing through an air-water exchanger that distributes radon from this running flow of water to a closed air loop. We first used radon and conductivity in a qualitative manner at both study sites by performing surveys of the areas with a multi-detector radon analysis system (Dulaiova et al., 2005). We ran the radon system together with a YSI temperature-conductivity probe and logging GPS navigation/depth sounding while underway along the klongs of Bangkok and along the shoreline off Metro Manila from small boats at slow speed (∼5–6 km/hr). After the surveys, we selected sites to conduct detailed time-series experiments to evaluate how these tracers vary temporarily. This was done using a small submersible pump connected to an exchanger and a single radon detector. Measurements of radon, as well as temperature, conductivity (salinity), and water depth were made over at least one tidal period. In both locations, we based the study site selections on

the survey results. In Bangkok, we ran a time-series experiment from a floating dock at Wat Intharam (Klong Bangkok Yai), an area of high radon based on our survey. Radon, conductivity, and water level were measured continuously over an approximately 24-hour period from June 23 to June 24, 2006. Nutrient samples were collected at approximately 1-hour intervals over the same period. In Manila, results from the radon survey directed our time-series deployment to an area ∼300 m off Metro Manila. Wind speed data were recovered from local meteorological stations for purposes of calculating radon exchange across the air-water interface. Atmospheric radon measurements over the same time period were made with a separate RAD-7 set up nearby. The main principle of using continuous radon measurements to decipher rates of groundwater seepage is that we can monitor the inventory of 222 Rn over time, making allowances for losses due to atmospheric evasion and mixing with lower activity waters away from the seepage zones. Any changes observed in these inventories can be converted to fluxes required to maintain the observed quantity of radon. Although changing radon inventories in surface waters could be in response to a number of other processes (sediment resuspension, long-shore currents, etc.), we find that the advective transport of groundwater (Rn-rich pore water) through permeable sediments is usually the dominant process. Thus, if one can measure or estimate the radon activity in the advecting fluids, we can convert the 222 Rn fluxes obtained by a mass balance approach to water fluxes by dividing the radon fluxes by the radon activity of the groundwater. Additional details concerning the principles and equations for gas exchange, mixing corrections, and discharge estimates have been presented in Burnett and Dulaiova (2003). 2.3 Nutrient Analyses Surface waters as well as piezometer and monitoring well samples were collected in acid-washed and sample-rinsed polyethylene bottles, and filtered through 0.45 µm cellulose filters immediately upon sample collection. The samples were frozen shortly after collection and then shipped to Japan with dry ice and kept frozen until analysis. We analyzed for nitrate (actually nitrate + nitrite), ammonia, and inorganic phosphate analyses following recommended procedures (Grasshoff et al., 1999). Dissolved inorganic nitrogen (DIN) reported here is the sum of NO3 (and any NO2 ) and NH4 . 3

RESULTS AND DISCUSSION

3.1 Bangkok The time-series results (Fig. 3A) show that the conductivity and radon respond in a nearly identical fashion

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with higher concentrations at lower water level and lowest concentrations at high tide. The water level here responds to the tidal wedge propagating up and down the Chao Phraya River but is truncated at high levels because of flood control procedures, i.e., when the water level reaches a certain point, flood gates are closed and pumps are used to prevent flooding of the low-lying areas surrounding the klongs. The nutrient concentrations showed a similar pattern as the radon with very high DIN (>200 µM, mostly NH4 ) peaking at precisely the same time as the radon (Burnett et al., 2008). The inorganic PO4 has a very similar pattern and reaches concentrations in the klong water of >15 µM. We estimated the groundwater flow into the klong based on the radon time series measurements using the non-steady-state mass balance approach mentioned above. This approach allows one to assess temporal patterns and magnitudes of groundwater discharges. Based on the radon mass balance and the estimated groundwater 222 Rn activity, the mean groundwater advection over a 24-hour period was estimated at 50 ± 47 cm/day (equivalent to 50 cm3 /cm2 day or 0.5 m3 /m2 day). It should be noted that the standard deviation cited here is not an estimate of the error of the calculation (we estimate the uncertainty at approximately 30–40%) but is rather an indication of the actual variation in this non-steady-state system.

3.2 Manila The radon results from the time-series showed a large increase in 222 Rn at low tide together with a distinct freshening of the water, characteristics of groundwater seepage (Fig. 3B). When we model these results using the same mass balance approach as above, we obtain estimated groundwater flow rates that fluctuate from 0 to 12.5 cm/day with an overall average of 2.9 cm/day. These rates overlap but are at the low end of the average values for 3 seepage meters that were also deployed in the area. Those rates were 9, 24, and 5.4 cm/day at 70, 170, and 220 m offshore, respectively (Ishitobi, pers. comm.). Since the radon measurements were collected from the water column, the tracer signal is integrated over a larger area than that covered by a single seepage meter (∼0.25 m2 ). Presumably, the radon results should thus be more characteristic of the entire area. 3.3 Groundwater End-Members Estimates made by the radon model depend critically upon the end-member chosen, i.e., the radon concentration in the waters flowing into the study site. For the Bangkok site, we used an end-member activity for 222 Rn in groundwater of 280 dpm/L based on measurements made from samples collected from a shallow (∼1.5 m deep) “well” at Wat Intharam. The sampled well is actually one of two ceramic water reservoirs that were built into the ground in honor of King Taksin early in the temple’s history. Over time, the ceramic walls have cracked and the reservoir now communicates with the shallow groundwater in the area. We deployed pressure transducers in this well and in the adjacent klong, about 70 m away. The water fluctuations in the well were damped but nearly in phase with the tidal fluctuations in the klong showing that a subterranean connection does exist. In Manila, we had access to several piezometers at the site that allowed us to sample pore waters from 0.5 m below the sea bed. Fortunately, the results from 8 samples collected from 6 different locations fell into a relatively tight grouping. Our selected groundwater 222 Rn end-member of 288 ± 41 dpm/L represents a standard deviation of only about 14%. 3.4 Nutrient Fluxes

Figure 3. (A) A time-series experiment conducted off the pier at Wat Intharam (K. Bangkok Yai) shows the changes in water level, specific conductivity, and radon over an approximately 24-hour period. The water level record is truncated at the higher levels because of flood control measures. (B) Time series experiment performed in surface waters off Metro Manila in May 2006 showing systematic variations in 222 Rn activities, salinity, and water level.

In a previous paper (Burnett et al., 2008), we estimated the inorganic nutrient end-members for the Bangkok study from the regression curves of DIN and PO4 vs 222 Rn from samples collected in the canal during the time-series experiment (Fig 4). This approach resulted in very high groundwater concentrations of 2930 µM for DIN and 200 µM PO4 . The calculated fluxes (DIN = 1500 mmol/m2 day; PO4 = 100 mmol/m2 day) based on these end-members are spectacularly high – several orders of magnitude higher than estimates made from any other area. More recent samplings of

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shallow groundwaters in this area have failed to locate any samples with nutrient concentrations as high as the extrapolated values. We thus revise our estimates based on a sample (point 2C, about 3 km upstream from Wat Intharam, and immediately adjacent to Klong Bangkok Yai, collected Dec. 14, 2007) that had a similar 222 Rn concentration (254 dpm/L) as that from Wat Intharam but with nutrient concentrations of 366 µM DIN and 22 µM PO4 . The results (Table 1), while lower than reported earlier, are still much higher than found in other settings studied thus far. The Metro Manila site showed higher DIN and PO4 fluxes compared to a less impacted site off the Bataan Peninsula that our group investigated in 2005 (Taniguchi et al., 2008b). Both of these Manila Bay data sets are thought to be conservative and are

relatively well-constrained as we were able to confidently assess end-member values for radon and nutrients at the two sites. In addition, the SGD fluxes were also evaluated by automatic seepage meters. The agreement between the radon and seepage meter rates was excellent at Bataan and reasonably good (within a factor of two for the closest seepage meter) at the Metro Manila site. The calculated nutrient fluxes fall within the worldwide range of SGD nutrient fluxes compiled by Slomp and Van Capellen (2004), although these ranges are extremely broad. The fluxes, while much lower than the Bangkok klong, are significantly higher than those reported from other less impacted embayments. For example, the DIN and PO4 fluxes at the Manila Bay sites are 1-2 orders higher than those reported for Waquoit Bay, Massachusetts (Charette et al., 2001), Florida Bay (Corbett et al., 1999), and Kahana Bay, Hawaii (Garrison et al., 2003). We caution, however, that the nutrient flux results for the two sites in Manila Bay and the one site in the Bangkok canal are all based on single experiments. Both of the urban sites were performed in the wet season while the Bataan Peninsula site was occupied during the dry season. In addition, we have not as yet had the opportunity to assess the effects of nitrogen transformations occurring in the groundwater before discharge. Such reactions, especially denitrification, can have dramatic effects on the nitrogen budget (Slomp and Van Cappellen, 2004). More broadbased and seasonal studies should be performed to verify our results. 4

Figure 4. DIN (closed triangles) and PO4 (open squares) versus 222 Rn for the time-series experiment at Wat Intharam.

CONCLUSIONS

Surveys of some of the canals of Bangkok during the wet season (June 2006) showed evidence of elevated

Table 1. Estimated SGD fluxes of inorganic N-species and PO4 into a klong in Bangkok, 2 sites in Manila Bay, and 3 sites from the literature. The Bataan results are from January 2005; the Metro Manila and Bangkok measurements were made in May and June 2006, respectively. Location

NH4

∗

Klong Bangkok Yai Manila Bay: Metro Manila∗∗ Manila Bay: Bataan∗∗∗ Waquoit Bay, MA Florida Bay, FL Kahana Bay, HI

NO3 + NO2

DIN

Nutrient Fluxes (mmol/m2 day) 180 0.2 180 5.3 0.002 5.3 2.4 0.02 2.4 0.44 0.11 0.55 0.28 0.2 0.30 – – 0.16–0.92#

∗

PO4

Reference

11 0.16 0.07 – 0.001 0.001–0.004

This study This study Taniguchi et al., 2008b Charette et al., 2001 Corbett et al., 1999 Garrison et al., 2003

Fluxes based on a Rn-derived average seepage rate of 50 cm/day and the following end-members; 222 Rn = 280 dpm/L; NH4 = 366 µM; NO3 + NO2 = 0.3 µM; PO4 = 21.7 µM. ∗∗ Fluxes based on average advection of 2.9 cm/day based on radon measurements (av. seepage meter rate = 5.4 cm/day); end-members: 222 Rn = 288 dpm/L; NH4 = 177 µM, NO3 = 0.6 µM, PO4 = 5.3 µM ∗∗∗ Fluxes based on radon-derived seepage rate of 4.0 cm/day (av. seepage meter rate = 5.1 cm/day); end-members: 222 Rn = 240 dpm/L; NH4 = 59.3 µM, NO3 = 0.6 µM, PO4 = 1.8 µM. # Total dissolved nitrogen

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radon and conductivities relative to the Chao Phraya River, the major source of the canal water. Furthermore, many areas that were enriched in radon were also enriched in inorganic nitrogen and phosphate. While high conductivities in the canal waters may be related to various domestic and industrial surface discharges, the most reasonable explanation to account for occurrences of both high conductivity and high radon is that these areas represent points of groundwater seepage that also supply additional nutrients. A time-series experiment at Wat Intharam (K. Bangkok Yai) showed systematic changes in radon, conductivity, and nutrient concentrations over a tidal cycle. Higher radon, and assumed higher groundwater seepage, occurs during the lower tidal stages. While the groundwater end-member values for nutrients are not currently well-constrained, it is clear that the unit area fluxes of inorganic N and P are very high, roughly 3 orders of magnitude higher than many other sites reported in the literature. Groundwater seepage into these klongs thus appears to be an important nutrient vector to surface waters of the canals, to the river, and ultimately to the Gulf of Thailand. A time-series experiment off Metro Manila showed clear evidence of groundwater seepage with enhanced flow and freshening during low tide. Well-constrained end-member values for radon and inorganic nutrients were obtained from piezometers installed in the same area. Calculated nutrient fluxes showed that the Metro Manila site was higher by about a factor of two than a less impacted site off the Bataan Peninsula on the opposite side of Manila Bay. The DIN and PO4 fluxes in Manila Bay are about one and two orders of magnitude higher, respectively, than embayments in other areas investigated recently. REFERENCES Beck, A.J., Rapaglia, J.P., Cochran, J.K., & Bokuniewicz, H.J. 2007. Radium mass-balance in Jamaica Bay, NY: Evidence for a substantial flux of submarine groundwater. Mar Chem 106 (3): 419–441. Bokuniewicz, H. 1980. Groundwater seepage into Great South Bay, New York. Estuar Coast Mar Sci 10: 437–444. Burnett, W.C., Kim, G., & Lane-Smith, D. 2001.A continuous radon monitor for assessment of radon in coastal ocean waters. Jour Radioanal Nucl Chem 249: 167–172. Burnett, W.C. & Dulaiova, H. 2003. Estimating the dynamics of groundwater input into the coastal zone via continuous radon-222 measurements. Jour Environ Radioact 69: 21–35. Burnett, W.C., Bokuniewicz, H., Huettel, M., Moore, W.S., & Taniguchi, M. 2003. Groundwater and porewater inputs to the coastal zone. Biogeochem 66: 3–33. Burnett, W.C., Chanyotha, S., Wattayakorn, G.,Taniguchi, M., Umezawa, Y., & Ishitobi, T. 2008. Groundwater as

a pathway of nutrient contamination in Bangkok, Thailand. Sci Total Environ, in press. Charette, M.A. & Buesseler, K.O. 2004. Submarine groundwater discharge of nutrients and copper to an urban subestuary of Chesapeake Bay (Elizabeth River). Limnol Oceanogr 49: 376–385. Charette, M.A., Buesseler, K.O., & Andrews, J.E. 2001. Utility of radium isotopes for evaluating the input and transport of groundwater-derived nitrogen to a Cape Cod estuary. Limnol Oceanogr 46: 465–470. Corbett, D.R., Chanton, J., Burnett, W.C., Dillon, K., Rutkowski, C., & Fourqurean, J. 1999. Patterns of groundwater discharge into Florida Bay. Limnol Oceanogr 44: 1045–1055. Dulaiova, H., Peterson, R., Burnett, W.C., & Lane-Smith, D. 2005. A multi-detector continuous monitor for assessment of 222 Rn in the coastal ocean. Jour Radioanal Nucl Chem 263(2): 361–365. Garrison, G.H., Glenn, C.R., & McMurtry, G.M. 2003. Meas urement of submarine groundwater discharge in Kahana Bay, Oahu, Hawaii. Limnol Oceanogr 48: 920–928. Grasshoff, K., Ehrhardt, M., & Kremling, K. 1999. Methods of seawater analysis, 3rd ed., New York: John Wiley & Sons. Hwang, D.W., Lee, Y.-W., & Kim, G. 2005. Large submarine groundwater discharge and benthic eutrophication in Bangdu Bay on volcanic Jeju Island, Korea. Limnol Oceanogr 50: 1393–1403. Moore, W.S. 1996. Large groundwater inputs to coastal waters revealed by 226 Ra enrichments. Nature 380: 612–614. Moore, W.S. 1999. The subterranean estuary: a reaction zone of ground water and sea water. Mar Chem 65: 111–125. Nakayama, T., Watanabe, M., Tanji, K., & Morioka, T. 2007. Effect of underground urban structures on eutrophic coastal environment. Sci Total Environ, in press, corrected proof available on Science Direct. Rapaglia, J. 2005. Submarine groundwater discharge into Venice Lagoon, Italy. Estuaries 28: 705–713. Slomp, C.P. & Van Cappellen, P. 2004. Nutrient inputs to the coastal ocean through submarine groundwater discharge: controls and potential impact. Jour Hydrol 295: 64–86. Swarzenski, P.W., Reich, C., Kroeger, K.D., & Baskaran, M. 2007. Ra and Rn isotopes as natural tracers of submarine groundwater discharge in Tampa Bay, Florida. Mar Chem 104: 69–84. Taniguchi, M., Burnett, W.C., Cable, J.E., & Turner, J.V. 2002. Investigations of submarine groundwater discharge. Hydro Proc 16: 2115–2129. Taniguchi, M., Burnett, W.C., & Ness, G. 2008a. Integrated research on subsurface environments in Asian urban areas. Sci Total Environ, submitted. Taniguchi, M., Burnett, W.C., Dulaiova, H., Siringan, F., Foronda, J.M., Wattayakorn, G., Rungsupa, S., & Kontar, E.A. 2008b. Groundwater discharge as an important land-sea pathway in Manila Bay, Philippines. Jour Coast Res 24: 15–24. Zektser, I.S. & Loaiciga, H.A. 1993. Groundwater fluxes in the global hydrologic cycle: past, present and future. Jour Hydrol 144: 405–427.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Comparative study on water quality among Asian megacities based on major ion concentrations T. Hosono∗ Department of Earth Science and Technology, Akita University, Akita, Japan

Y. Umezawa Faculty of Fisheries, Nagasaki University, Nagasaki, Japan

S. Onodera Faculty of Integrated Arts and Sciences, Hiroshima University, Hiroshima, Japan

C-H. Wang Institute of Earth Science, Academia Sinica, Taipei, Taiwan

F. Siringan Marine Science Institute, University of the Philippines, Quezon City, Philippines

S. Buapeng Department of Groundwater Resources, Ministry of Natural Resources and Environment, Bangkok, Thailand

R. Delinom Division of Hydrology, Indonesia Institute of Science, Bandung, Indonesia

T. Nakano & M. Taniguchi Research Institute for Humanity and Nature, Kyoto, Japan

ABSTRACT: This paper presents the major ion concentrations of 270 water samples (both surface and subsurface) collected from five megacities in the Southeast Asia (Seoul, Taipei, Bangkok, Jakarta, and MetroManila), in order to define the general feature of water quality and to discuss its deterioration status. Piper diagram plots classify water in up gradient areas and shallow unconfined aquifer as Ca(HCO3 )2 type, while the deeper confined groundwater tends to shift toward NaHCO3 type. In Jakarta and Manila, however, subsurface water in the coastal areas displays Na2 SO4 −NaCl type, which suggests the occurrence of sea water intrusion probably due to the over pumping of groundwater. In addition, in Jakarta, shallow groundwater at dry fields displays CaSO4 − CaCl2 type. Eliminating the possibility of hydrothermal water input in these fields, it appears likely that this type of water were affected by the agricultural activities. This study highlights the general characteristics of water quality of each Southeast Asian megacity through the comparison. These fundamental aspects provide the basic information needed to understand the detailed causes and degree of human impacts to natural water in Southeast Asian countries and to discuss the solution of these problems. Keywords: water quality; Southeast Asia; megacities; human activity 1

INTRODUCTION

Deterioration of water quality due to human activities is becoming increasingly recognized in Asian megacities (Foster, 2003). However, the cause and status of the pollution in surface and subsurface water in these areas ∗

Corresponding author ([email protected])

have not been understood well due to the lack of study with comprehensive data for water quality. In order to understand these problems, our group is investigating the water chemistry in several metropolitan areas in Southeast Asia, based on the major ion concentrations, the occurrence of environmentally hazardous trace elements (such as arsenic, lead, and cadmium), and multiisotope data (http://www.chikyu.ac.jp/USE/). Major

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Table 1.

Size of the cities and geology of the study areas. Seoul

Population (million) Area (km2 ) Geology

Taipei (∗ )

10

2.8 (6.8)

606 ∗ Alluvium

272 ∗ Alluvium

∗

∗

Jurassic granitoid ∗ Precambrian gneiss

Bangkok

Pleistocence volcanic rocks ∗ Oligocene-Miocene sedimentary rocks

6.5 1010 ∗ Holocene-Recent tidal plain ∗ Pleist-Holocence sedimentary deposit

Jakarta (∗ ) 8.7 (23.6) 662 (1360) ∗ Holocene-Recent tidal plain ∗ Pleistocence volcanic rocks ∗ Tertiary sedimentary rocks

Manila (∗ ) 1.6 (9.9) 39 (636) ∗ Holocene-Recent tidal plain ∗ Plio-Pleistogene sedimentary rocks

(∗ ) The population and area including surrounding urbanized area.

ion concentrations provide a general view of water quality, pollution status, and source characteristics (both natural and anthropogenic). However, although major ion data is one of the most basic information and can be determined by simple and easy way, in earlier studies no data of this type has yet been reported for the Southeast Asian countries which can provide the basis of a comparative study. Hosono et al. (2008) discussed the pollution status in the groundwater of the Seoul City, by using multiisotope methods with some major ion data. Umezawa et al. (2008) showed major ion data for waters in Bangkok, Jakarta, and Metro Manila, and explained briefly their characteristics. However, these studies focused on the isotopic and nutrients data. This paper presents the major ion concentrations of water samples collected from Seoul, Taipei, Bangkok, Jakarta, and Metro Manila, which are representative megacities in Southeast Asia. We also attempt to characterize the general water types and status of water quality for these cities, which are situated in the different topographical, geological, and land-use conditions each other. 2

OUTLINE OF THE STUDY AREA AND SAMPLING

The size of the cities and geology of the study areas are summarized briefly in Table 1. The groundwater type and sampling locations are also shown in Fig. 1. Detailed information of the study areas, water characteristics, and sampling is stated in the following part of this section. 2.1

Seoul

Seoul City, the capital of South Korea, is located in the mid-west of the Korean peninsula and has a population of approximately 10 million in an area of 606 km2 . The city is situated in the Seoul Basin surrounded by mountains and hills around 500 m above sea level. Land-use in Seoul City is roughly divided

into five categories (Park et al., 2005): less-developed (37%), residential (38%), agricultural (5%), traffic (10%), and industrial areas (10%) (not shown in Fig. 1 due to the lack of number of samples corresponding to the resolution of this land-use information). The geology of the Seoul area is composed mainly of basement of Precambrian gneisses, Jurassic granitoid intrusions, and an overlying thin alluvium and soils of Quaternary age. Groundwater is situated within either shallow sedimentary deposits or fissure zones of crystalline metamorphic and plutonic rocks. Both types of groundwater are situated under the aerobic condition. We collected four river water samples and 13 groundwater samples from various depths (−12 to −88 m) during the period of 3–5 August 2005. More detailed information is summarized in Hosono et al. (2008).

2.2 Taipei Taipei City is located in the Taipei Basin in northern Taiwan, surrounded by hills and mountains around 300 to 1000 m above sea level. Although the population of the Taipei City (2.6 million) is relatively smaller than the other target cities due to the limited space (272 km2 ) (we categorized the Taipei City as megacity because of its extremely high population density [http://en.wikipedia.org/wiki/Megacity]), it increases up to 6.8 million with including the surrounding urbanized area. Basin area is highly urbanized and dry field is distributed in the mountainous region. The geology of the Taipei area is composed mainly of basement of Oligocene to Miocene sedimentary rocks, Pleistocene volcanic rocks, and an overlying alluvium deposits. There are two types of groundwater in the Taipei Basin, (1) shallow unconfined aquifer (−7 to −13 m) with an aerobic condition and (2) deep confined aquifer with anaerobic condition. We collected 10 river water samples and 22 groundwater samples from shallow unconfined aquifer during the period of 16–28 October 2006.

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Figure 1. Water quality for Seoul, Taipei, Bangkok, Jakarta, and Metro Manila.

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Table 2. Maximum major concentrations (mg/L) of subsurface waters in Asian megacities. Seoul

N+ K+ Ca2+ Mg2+ Cl− SO2− 4 NO− 3

Taipei

Bangkok

Jakarta

Metro Manila

RW n=4

SGW n = 13

RW∗ n = 10

SGW n = 22

RW n = 14

DGW n = 85

RW n = 12

SGW n = 54

DGW n = 24

RW n=4

SGW n = 18

DGW n = 10

37 10 58 6 44 39 17

48 15 88 29 75 43 96

75 6 64 23 64 179 23

724 17 177 99 170 536 36

77 8 43 19 107 77 2

4951 122 2100 836 18937 881 35

86 8 28 6 51 65 8

1435 501 5058 1318 1695 405 47

6552 95 1226 625 4462 1734 9

2761 101 86 347 5187 677 10

848 56 282 159 1338 317 74

278 27 40 19 227 38 8

RW = river water, SGW = shallow groundwater, DGW = deep groundwater ∗ except one sample collected near the estuary

2.3

Bangkok

Metropolitan Bangkok has a population of approximately 6.5 million in an area of 1010 km2 . The city is developed about 25 km to the north of the Bangkok Bay on the floodplain of the Chao Phraya River (Fig. 1). Most of the floodplain behind the city is occupied by paddies maintained by irrigation. Orchard fields are distributed to the west of urbanized area. A large-scale fluvial deposit forms a huge flat plain in the Bangkok area with its total thickness of around 1,800 m, which is composed of alternating layers of fine to coarse sand and impermeable consolidated clay of Pleistocene to Holocene age. Stratigraphically, there are at least eight confined aquifers: Bangkok (−20 to −50 m), Phra Pradaeng (−45 to −120 m), Nakhon Luang (−120 to −190 m), Nonthaburi (−170 to −280 m), Sam Khok (−280 to −360 m), Phya Thai (−360 to −440 m), Thonburi (−440 to −480 m), and Pak Nam (−480 to −600 m) aquifers. We collected 14 river water samples and 85 groundwater samples from various depths (it is categorized in three zones in this paper; −50 m, −100 m, and −150 to −550 m zones) during the period of 18–26 June 2006. More detailed information is summarized in Umezawa et al. (2008). 2.4

Jakarta

Jakarta City, the capital of Indonesia, is located at the northern coastal zone of western Java. Jakarta city has a population of approximately 8.7 million; however, it increases up to 23.6 million with including the surrounding urbanized area (Janotabek) in an area of 1360 km2 . Between the urbanized area at the coastal side and forest zone at the volcanic mountainous region, mixture of residential and agricultural (dry fields) areas are widely distributed (Fig. 1). Geology of this area is composed of the Tertiary basement rocks of sandstone, mudstone, and limestone,

overlying volcanic fan deposits of Pleistocene age, and Holocene to Resent tidal plain. The aquifer system is not defined strictly due to the lack of continuity in geologic layer. We collected 12 river water samples and 78 groundwater samples from two types of wells, (1) shallow unconfined aquifer (−2 to −14 m) with an aerobic condition and (2) deep confined aquifer (∼−270 m) with anaerobic condition, during the period of 3-14 September 2006. More detailed information is summarized in Umezawa et al. (2008). 2.5 Manila Metro Manila, the national capital rejoin of the Philippines, is located between Manila Bay and Laguna de Bay (Fig. 1). The Metro Manila jurisdiction (Manila City + surrounding urbanized area) has a population of approximately 9.9 million in an area of 636 km2 . The city is developed on both the narrow coastal plains with an elevation of 0 to 10 m and inner hilly areas with an elevation of 20 to 70 m. The Metro Manila jurisdiction is highly urbanized: 64.6% is urbanized area (residential, commercial, industrial, and institutional areas), while remaining 18.0, 11.2, 3.6, and 2.7% are agricultural areas (dry field), grassland, forest, and wetland (including water bodies), respectively (MMDA and IAURIF, 1997). Metro Manila is underlain mostly by the Plio-Pleistocene Guadalupe formation, which is composed of alternating beds of tuffaceous sandstone, conglomerate, mudstone, and coarse tuff and alluvium. We collected 4 river water samples and 28 groundwater samples from two types of wells, (1) shallow unconfined aquifer (∼−120 m) and (2) deep confined aquifer (−120 to −270 m), during the period of 26-30 May 2006. More detailed information is summarized in Umezawa et al. (2008). Water samples for major ion analysis were filtered through 0.2 µm cellulose-acetate filters before storing in the sampling bottle. Major ion concentrations were measured by ion chromatography at the Hiroshima

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University and RIHN. The summary of the results for chemical analysis is shown in Table 2, which indicates the concentration range for each type of water.

feature that the increasing of Na+ and Cl− proportions (Fig. 1 and Table 2) is due to the tidal effect as suggested by Umezawa et al. (2008).

3

3.2 Shallow groundwater

MAJOR ION CHARACTERISTICS IN SOUTHEAST ASIAN MEGACITIES

In general, the quality of natural water is classified into five types based on the concentration balance of eight major ions (upper left-hand figure in Fig. 1: A. Ca(HCO3 )2 , B. NaHCO3 , C. CaSO4 − CaCl2 , D. Na2 SO4 − NaCl, and intermediate types). This classification has been commonly used as basic indicator of the source of water. In this section, we discuss the characteristics of water quality and deterioration cause for Southeast Asian megacities, in order of (1) river water, (2) shallow groundwater, and (3) deep groundwater. 3.1

River water

Most of the river water in five target cities is consisted of Ca(HCO3 )2 type (Fig. 1), especially for upstream areas. Therefore, this type of water in up gradient area can be recognized as one with juvenile feature, which is not affected by human impact. However, other types of water can be seen in some areas. First, the majority of river water in Taipei metropolitan area displayed intermediate type (e type), which is characterized by a high SO2− 4 proportion (Fig. 1 and Table 2). Such high SO2− proportion may be caused partly by hot spring 4 water contribution from northeast volcanic province. However, this effect might be insignificant, since this type of water can be found even outside of volcanic zone. Rather, it has been well known that precipitation (both wet and dry) in Taipei Basin is highly contaminated in anthropogenic SO2− 4 (more than NOX ) (i.g. Li et al., 1994). According to our preliminary data, sulfate concentration in Taipei rain water marked over 40 ppm, which is 10 times higher than those from the other cities. Second, downstream water in the Bangkok City also displayed intermediate type (e type) (Fig. 1), indicating that the Cl− and SO2− 4 were preferentially dissolved into water (Table 2). Two reasons are proposed for this chemical change: (1) the partial dissolution of paleo-saline water components, which has been stored in the period of marine transgression (JICA, 1995) and (2) dissolution of man-made materials such as fertilizers and detergents, both of which contains substantial amounts of chlorine and sulfate. However, for judgment of the primary source, more detailed investigation has to be done by using geochemical and isotopic tracers (Robinson and Bottrell, 1997; Böhlke and Horan, 2000; Moncaster et al., 2000; Nakano et al., 2005; Hosono et al., 2007). Finally, downstream water in Jakarta and Metro Manila exhibited Na2 SO4 −NaCl type (Fig. 1). It is easily understood from typographical

Shallow or aerobic groundwater samples were collected from four cities: Seoul, Taipei, Jakarta, and Metro Manila. Of these, most of shallow groundwater from Seoul, Taipei, and Metro Manila (except from Jakarta) exhibited Ca(HCO3 )2 and intermediate (both e and f types) types. It is generally suggested that ‘f’ type water yielded through cation exchange reaction between dissolved Ca2+ in water and Na+ in clay minerals (Appelo and Postma, 2005). On the other hand, the ‘e’ type water suggested to be formed, except at geothermal fields, by two reasons: (1) dissolution of paleo-saline water and/or (2) dissolution of man-made materials. For Seoul City, possibility of paleo-saline water contribution is neglected and previous isotopic (H, N, O, S, and Sr) research has suggested the influence of human impacts (Hosono et al., 2008). However, for Metro Manila, further investigations are needed to be done for elucidating the source of water. For the other important feature, shallow groundwater in coastal zone at Jakarta and Metro Manila displayed Na2 SO4 − NaCl type (Fig. 1), suggesting the occurrence of sea water intrusion. In the widely spread dry fields at volcanic fan area in Jakarta (Fig. 1), shallow groundwater is characterized by CaSO4 − CaCl2 and intermediate type (c type). We strictly excluded the samples from Fig. 1 that show the influence of hot spring water. In addition, there are no evidences to suggest the occurrence of marine transgression up to this area. Moreover, Umezawa et al. (2008) clarified that this area is affected by nitrogen contaminations by the application of fertilizers and household effluents. From these, it is reasonable to assume that high Cl− and SO2− 4 proportions at Jakarta area are due to the large-scale agricultural activities using fertilizers and agrochemicals. 3.3

Deep groundwater

Deep or anaerobic groundwater samples were collected from three cities (Bangkok, Jakarta, and Metro Manila). Many of these displayed NaHCO3 type (Fig. 1), showing the general feature of confined groundwater, which experienced the cation exchange reaction (Na+ ↔ Ca2+ ) (Appelo and Postma, 2005). For the Bangkok City, this type is characteristically found in deepest groundwater zone (−150 to −550 m zone), compared to the other shallower groundwater zones (−50 m and −100 m zones). Looking at the coastal area, Na2 SO4 − NaCl type predominate in Jakarta and Metro Manila in an area from the coastal line to 20 km inland (Fig. 1), suggesting the occurrence of sea water intrusion probably

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due to the excess groundwater pumping at the city. This type of groundwater is also found in the wide area of the Bangkok City (Fig.1). However, sea water intrusion might not be occurred in the area more than 50 km away from coastal line. In contrast, it is suggested that such high Na+ and Cl− contents in Bangkok City (Table 1) is attributed to the contribution of paleo-saline component (JICA, 1995), which is highly concentrated in the Bangkok fluvial deposits. Some groundwater samples in the orchard (Bangkok) and dry fields (Jakarta) displayed CaSO4 − CaCl2 type (Fig. 1). With consideration of nitrate pollution study (Umezawa et al., 2008), it might be assumed that this water quality change is probably due to the agricultural activities using fertilizers and agrochemicals. However, since it seems difficult for a pollutant to reach to such deep groundwater environment, other factors (e.g. long residence time and water rock interaction) might have controlled this chemical change.

4

CONCLUSIONS

The comparison study of major ion compositions of waters (river water and shallow and deep groundwaters) among SoutheastAsian megacities (Seoul,Taipei, Bangkok, Jakarta, and Metro Manila) reveals three important water quality changes; (1) water salinization near the coastal area (∼20 km) at Jakarta and Metro Manila due to excess groundwater pumping, (2) deterioration of water quality at dry fields (Jakarta) due to the agricultural activities using fertilizers and agrochemicals, and (3) watershed contamination by contribution of polluted precipitation in the Taipei metropolitan area.This study suggests that comparison study among different cities can make clear individual environmental problems. The basic information obtained here would be useful for the future detailed research by using geochemical and isotopic methods.

ACKNOWLEDGEMENTS We are thankful for the staff and students at Hiroshima University, Seoul National University, National Taiwan University, Academia Sinica, Water Resources Agency in Taiwan, Ministry of Natural Resources and Environment (MONRE) in Thailand, Indonesian Institute of Science, and University of the Philippines for supporting water sampling and supplying GIS data. We are grateful to J. Shimada (Kumamoto Univ.), T. Yamanaka (Tsukuba Univ.), and T. Ishitobi and K. Jago-on (RIHN) for fruitful discussions. This research was financially supported by the RIHN project “Human Impacts on Urban Subsurface Environment”, Japan Society for the Promotion

of Science (no. 0611749), and Grant-in-Aid for Young Scientists (A) (no. 20681003). REFERENCES Appelo C.A.J., Postma D. (2005) Ion exchange. In: Appelo C.A.J., Postma D., editors. Geochemistry, groundwater and pollution. AA Balkema Publishers, Leiden. pp. 241–309. Böhlke J.K., Horan M. (2000) Strontium isotope geochemistry of groundwaters and streams affected by agriculture, Locust Grove, MD. Applied Geochemistry 15, 599–609. Foster S.S.D., Chlton P.J. (2003) Groundwater: the process and global significance of aquifer degradation. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 358, 1957–1972. Hosono T., Ikawa R., Shimada J., Nakano T., Saito M., Onodera S., Lee K-K., Taniguchi M. (2008) Human impacts on groundwater flow and contamination deduced by multiple isotopes in Seoul City, South Korea. Science of the Total Environment (in press). Hosono T., Nakano T., Igeta A., Tayasu I., Tanaka T., Yachi S. (2007) Impact of fertilizer on a small watershed of the Lake Biwa: use of sulfur and strontium isotopes as an environmental diagnosis. Science of theTotal Environment 384, 342–354. JICA (1995) Report of management plan for land subsidence and groundwater in Metropolitan Bangkok, Thailand. Japan International Cooperation Agency, Tokyo. pp. 116 (in Japanese). Li C-S., Hong Y-T., Jenq F-T. (1994) Acidic and alkaline constituents of air particulates within dwellings. Science of the Total Environment 164, 19–25. MMDA and IAURIF (1997) Updating the land use map of metropolitan Manila through SPOT remote sensing imagery. Metropolitan Manila DevelopmentAuthority and Institute d’Aménagernent et d’Urbanisme de la Région Ile-de-France. pp.72. Moncaster S.J., Bottrell S.H., Tellam J.H., Lloyd J.W., Konhauser K.O. (2000) Migration and attenuation of agrochemical pollutants: insights from isotopic analysis of groundwater sulphate. Journal of Contaminant Hydrology 43, 147–163. Nakano T., Tayasu I., Wada E., Igeta A., Hyodo F., Miura Y. (2005) Sulfur and strontium isotope geochemistry of tributary rivers of Lake Biwa: implications for human on the decadal change of lake water quality. Science of the Total Environment 345, 1–12. Park S.S., Kim S.O., Yun S.T., Chae G.T., Yu S.Y., Kim S., Kim Y. (2005) Effects of land use on the spatial distribution of trace metals and volatile organic compounds in urban groundwater, Seoul Korea. Environmental Geology 48, 1116–1131. Robinson B.W., Bottrell S.H. (1997) Discrimination of sulfur source in pristine and polluted New Zealand river catchments using stable isotopes. Applied Geochemistry 12, 305–319. UmezawaY., Hosono T., Onodera S., Siringan F., Buapeng S., Delinom R., Yoshimizu C., Tayasu I., Nagata T., Taniguchi M. (2008) Tracing the sources of nitrate and ammonium contaminations in groundwater at developing Asian megacities, using GIS data and nitrate δ15 N and δ18 O. Science of the Total Environment (in press).

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Surface and groundwater interactions in the lower reach of the Yellow River J. Chen∗ School of Geography and Planning, Sun Yat-sen University, Guangzhou, China

Y. Fukushima & M. Taniguchi Research Institute for Humanity and Nature, Kyoto, Japan

ABSTRACT: Surface and groundwater interactions were analyzed during the period of 1981–2000 in the lower reach of the Yellow River based on the changes in main water balance components: precipitation, evaporation, water use and storage. Seasonal and yearly patterns were identified with one peak in July-August for precipitation and two peaks in April-May and August for groundwater table and actual evaporation. Temporal patterns were verified with a simple water balance calculation by re-distributing moisture monthly, showing that irrigation is necessary in April-May and Dec, and soil moisture may exceed field capacity in July-August. Water use diverted from the Yellow River is related positively to the deficit, defined as the difference of precipitation and actual evaporation, while water table is not responding to the change of the deficit. On the other hand, water table drops down and the variation increases as the river flow decreases, especially in the extreme drought condition such as in 1997, indicating that the Yellow River is the main water source for the aquifer, at least in the vicinity of the river. Keywords:

1

groundwater; interaction; surface water; water deficit; water use; Yellow River

INTRODUCTION

in precipitation, evaporation, and groundwater table based on water balance analysis.

Water diversion from the Yellow River plays a key role in maintaining the sustainability of agriculture in the lower reach, defined as from Huayuankou to Lijin (Fig. 1), while the aquifer could redistribute moisture temporally and spatially, and provide critical water resource in the dry period when the water from the Yellow River for irrigation is not available (Chen et al. 2007). Although water scarcity associated with water demand and climate change and the environmental issues related have been reported since the 1980s, drainage and salinization problems occurred commonly due to over use of the diverted water and poor construction of drainage systems in the 1960s (Chen et al. 2004a; CDCID, 2002). Understanding surface and groundwater interactions in terms of water balance can help deal with environmental issues, e.g., water shortage, groundwater pollution, drying up, nutrient and sediment in the lower reach and the delta of the Yellow River. The objective of this study is to identify the surface and groundwater interactions and the mechanisms associated with temporal and spatial changes

∗

2

METHODOLOGY

There are twenty meteorological stations in the lower reach of the Yellow River and North China Plain with records from 1951 to 2000. Monthly groundwater table series of 1981–2000 were collected from 284 observation wells in the lower reach (Fig. 1). Monthly discharge data at Lijin and Huayuankou were collected from the documents published by the Yellow River Conservancy Commission. Annual water use based on the flow difference between Lijin and Huayuankou station during the period of 1981–2000 was calculated. Actual evapotranspiration is estimated by multiplying potential evaporation ET with Kc factor, which is calibrated by the lysimeter installed at Yucheng experimental station of Chinese Academy of Sciences, located around 50 km north to Jinan. Monthly potential ET was estimated for the period of 1951–2000 based on the method proposed by Xu et al (2005), referred as ETb, with the impact of ground heat flux ignored. Main water balance components are given as:

Corresponding author ([email protected])

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Figure 3. Monthly Kc factor, defined as the ratio of evapotranspiration measured by lysimeter to that calculated.

Figure 1. Location of observation wells (filled triangle) and contour of groundwater table based on annual average data in 2000. Contour interval is 10 m for water table of less than 100 m a.m.s.l, and 20 m for that of more than 100 m a.m.s.l.

Figure 4. Monthly average precipitation, actual evaporation and change in groundwater table.

Figure 2. Monthly ratio of ETl by lysimeter to that by E601 at Yucheng, E20m2 was used instead of E601 in 1986, 1987 and 1988 since no data available for E601 during these periods.

Where YW is the diverted water from the Yellow River, estimated as the flow difference; AE is the actual evapotranspiration with reference to the measurement by lysimeter; P is precipitation; H is water storage change in the soil and/or aquifer. The equation can be used for either monthly or yearly base, with unit of all these components indicated as mm/m or mm/y. 3 3.1

RESULTS AND DISCUSSIONS Identification of Kc factor

Actual ET, named as ETl, by lysimeter, pan evaporation of 20 (with diameter of 20 cm), E601 and E20m2 (pan with an area of 20 m2 ) atYucheng, were measured during the period of 1987–1996. Since actual evaporation by lysimeter is available only at Yucheng Station,

use of ratio of ETl to E601 or ETb at this station has to be made in order to calculate actual ET at twenty meteorological stations in the lower reach. Averaged monthly ratio of ETl to ETb was used to calculate Kc factor (Kc = ETl/ETb) as parameters for calculation of ETb at these stations are available, while E601 data are limited during the period of 1981–2000. Actually, the ratio of monthly AE, i.e. ETl, to E601 at Yucheng gives a temporal pattern (Fig. 2), with two peaks at April–May and August, corresponding well with the temporal pattern of Kc factor (Fig. 3).

3.2

Interactions from seasonal changes in precipitation, evaporation and water table

Water balance was evaluated based on equation 1 with four basic components, which are well integrated. Averaged monthly precipitation and actual ET for the data series of 1951–2000 were calculated, indicating a water deficit in March, April and May, and the water surplus with precipitation exceeding actual evaporation in July and August, corresponding well to the Monsoon climate (Fig. 4). Annual average of precipitation and potential evaporation is 570 mm and 1400 mm respectively, while actual evaporation by lysimeter at Yucheng Station is 941 mm. One index

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from monthly water table (MGT) change was proposed for the interaction analysis given as: GVAR = Measured MGT − Annual averaged MGT Since three temporal patterns of GVAR were identified in the lower reach (Chen et al. 2007), GVAR was calculated only for the observation wells following the cross section A (Fig 1) in the irrigation area. Monthly average GVAR (MAGVAR) shows two peaks, one occurs in April–May and the other in August. The first one corresponds to the water diversion from the Yellow River, which is generally carried out during the period from March to May (Chen et al. 2005), while the second, indicating the month of the highest water table, corresponds to the rainy season in the summer. One month lag after the peak in the precipitation is principally due to the effect of aquifer storage, i.e., soil moisture is redistributed in the unsaturated and saturated zones within a yearly water cycle. Generally, groundwater table remains relatively stable at 2–3 m depth in the lower reach with saturated water content mainly ranging from 35–45% (Chen et al, 2004b). The root depths of main crops, such as winter wheat and maize are less than 2 m with main root zone at less than 1 m depth (Zhang and Liu, 1995). Field capacity at Yucheng station was measured to be 23–27% (Li and Zhao, 1991). Monthly re-distribution of the diverted water was thus calculated based on a simple surface and groundwater interaction scenario: within a profile of 2 m, irrigation is carried out when soil moisture is less than 20%, i.e., 400 mm, and the maximum storage capacity is assumed to be 700 mm. Field capacity is assumed to be 25% in the lower reach, and initial soil moisture is presumed to be at this level. Monthly average soil moisture and irrigation requirement is thus calculated consequently within one year. Water content is set to be at field capacity level when irrigation is implemented inApril, May and December. Monthly soil moisture distribution in Fig. 5 can explain well the temporal pattern of groundwater table change (Fig. 4). In July and August, water surplus causes soil moisture exceeding field capacity, which may result in the recharge to the beneath aquifer. Surface runoff or overland flow could occur when soil moisture surpasses storage capacity or infiltration capacity decreases to a rate lower than the precipitation rate. Vertical water movement is dominant in the study area, and surface runoff seldom takes place in the last 20 years since soil moisture is basically lower than storage capacity as shown in Fig. 5. It was reported that even in the flooding years in the 1960s, runoff coefficient in the lower reach is less than 15% (Chen, 1997). In December, air temperature reduces and soil moisture has to be kept at a high level to protect winter wheat from been frozen. Though soil moisture is higher than the irrigation level of 400 mm, irrigation was practically carried out in this month by local farmer. On the other hand, that water uses in the upper and middle

Figure 5. A simple interaction scheme to calculate monthly soil moisture and irrigation requirement in the lower reach.

Figure 6. Relationships between annual water deficit, water use and water table change during the period of 1981–2000.

streams diminish in the winter makes it possible to divert water from the Yellow River for this purpose in the lower reach. 3.3 Interaction from annual changes in water balance components As shown in Fig. 4, groundwater table remains relatively stable with variation less than 1 meter due to the diverted water from the Yellow River, i.e., deficit of water balance, DE, defined as the difference of actual ET and precipitation is covered by the Yellow River water YW. Equation 1 can thus be simplified as YW = AE − P assuming water storage change be zero. Since drought occurs frequently in the lower reach and North China Plain during the period of 1980–2000, the decreasing flow rate in the Yellow River can not provide sufficient water for diversion, or DE, especially after 1990. On the other hand, YW exceeded DE in the 1970s and 1980s, when surface water was relatively rich. Imbalance thus arises due to these two aspects: YW = AE − P. Nevertheless, annual actual water use in the lower reach, estimated as the discharge difference between Lijin and Huayuankou, was found related positively with DE (Fig. 6). According to the linear relationship in Fig. 6, 100 mm increase in DE would

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Figure 7. Ratio of water diverted in April and May to water deficit in the lower reach of the Yellow River.

need extra water of approximately 9 × 108 m3 from the Yellow River. Annual Maximum Variation (AMV) of the water table, defined as the difference of the highest and the lowest water table for a specific year, was used to analyze the relationship between water table, water use and DE. Though AMV increases exponentially as annual discharge at Lijin decreases during the period of 1981–2000 (Chen et al. 2007), it does not change much with DE. Nonetheless, AMV is principally larger than 1.8 m after 1992, but ranges from 1.3–1.8 m before 1992. Theoretically, annual water use could be estimated by multiplying DE with irrigation area based on equation 1, and then compared with actual water use. Irrigation area is an important factor associated with the amount of water diversion from the Yellow River, and it could be obtained from the analysis of remote sensing image and field survey, though the result is normally given with much uncertainty. Since only irrigation area in 1979, 1990, and 1997 was known as 9.4 × 105 , 1.93 × 106 , and 2.36 × 106 ha (Xi, 1999; CDCID, 2002), it is not possible to get an annual change of irrigation area in the lower reach. On the other hand, water deficit can not be fully covered by the diverted water, especially in the 1990s, when the Yellow River experienced extreme drought. Nevertheless, estimated water deficit and its related water use can be used to analyze surface and groundwater interaction, i.e., local farmer has to use groundwater for irrigation when the Yellow River water is scarce, yielding a possibly large variation in groundwater table. Since irrigation is primarily carried out in April and May in the lower reach, ratio of water diversion to water deficit in this period was calculated. Fig. 7 shows a ratio of more than 1 before 1992, i.e., extra water was diverted, and salinization and drainage from the irrigation area were expected in this period. Since 1992, when drying up became serious in the lower reach, no sufficient water was guaranteed for irrigation, and almost there was no drainage or drained water was reused for irrigation before it reached the coastal zone. Temporal change of this ratio (Fig. 7) corresponds well with the change in AMV. Sustainability in agricultural production becomes an issue since 1992, when there is no sufficient water from the Yellow River for

Figure 8. Contour of annual maximum variation, AMV of water table in 1997.

irrigation. On the other hand, groundwater could play a more important role to cope with temporary drought to sustain agricultural production. 3.4 Interaction of groundwater and the Yellow River In the lower reach, groundwater flows to the north in the vicinity of theYellow River, with water source from the river, while it flows to the same northeast as that of the Yellow River beyond the vicinity (Fig. 1). Two cross sections, A and B, were selected along these two flow directions for detailed analysis. The zone between cross section A and the Yellow River is called “impact zone” in terms of groundwater flow as proposed by the authors (Chen et al. 2005), and it was identified as an area with a distance of 30–40 km perpendicular from the river. Gradient along the cross section B is estimated to 0.000127, and it is 0.000144 along the cross section A. Though two gradients are in the same order of magnitude, they are uneven spatially with high gradient in the western part of the study area, e.g., gradient from well 258, 256 to 271 (well numbers were enlarged in Fig. 1) in the impact zone is 0.000435. Given a hydraulic conductivity of 10 m day−1 and an aquifer thickness of 50 m (Zhang, 1988; Chen et al. 2002), groundwater flow flux to the lower reach of approximately 780 km length is calculated to be 0.18– 0.62 × 108 m3 year−1 . As mentioned previously, the aquifer near the Yellow River is recharged from the Yellow River, and less change in water table would thus be expected, even in the extreme drought condition. In 1997, there was no river flow in the lower reach for total 226 days; low water table and high variation of AMV occurred. Contour of AMV in 1997 was given in Fig. 8. In the vicinity of theYellow River, AMV is generally less than

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2 m, and it reaches 4 to 5 m in the northern boundary of the lower reach. In the extreme condition as 1997, agriculture production and domestic water use in the country depends exclusively on groundwater, low AMV suggests a possible zone rich in groundwater resource. 4

CONCLUSIONS

As groundwater table is relatively stable, water storage change in the lower reach is not significant in terms of water amount. Imbalance was found between water deficit and water diverted from the Yellow River due to misuse and the drought during the period of 1981–2000. Interaction of surface and groundwater is rather dynamic with seasonal and annual pattern in responding to the change of water balance components. These temporal patterns were well verified by a simple moisture calculation, showing that irrigation is necessary in April and May, and soil moisture may exceed the field capacity in July–August, when precipitation usually surpasses actual evaporation and water surplus occurs. Though a positive relationship was found between water use and water deficit, water table does not show clear tendency when the deficit increases. On the other hand, water table drops down and the range of variation increases when the river flow decreases, especially the extreme drought condition, indicating that the Yellow River is the main water source recharging the aquifer, at least in the vicinity of the river. ACKNOWLEDGEMENTS This study is supported by the National Natural Science Foundation of China (no. 40571027), seed fund of Sun Yat-sen University (2008–2009), “985 Project” of GIS and remote sensing for geosciences from the Ministry of Education of PR China, and research fund of RIHN, Japan. REFERENCES CDCID (China Development Center for Irrigation and Drainage) (2002) Strategic study on water-saving reconstruction for large irrigation projects in the Yellow River basin. Zhengzhou: The Yellow River Conservancy Publishing House (In Chinese).

Chen, J.Y. 1997. On plain runoff by using multiple linear model (MLM) and multiple linear perturbation model (MLPM) model- a case study in Zhaoniu catchment of Shandong Province. Progress in Geography 16(2): 48–54 (In Chinese with English abstract). Chen, J.Y. Fukushima,Y. & Taniguchi M. 2005. Water use and its impact zone in the lower reach of the Yellow River, IN Shang H (ed), Proceedings of the 2nd InternationalYellow River Forum. Zhengzhou: The Yellow River Conservancy Publishing House. Chen, J.Y. Fukushima,Y. &Taniguchi, M. 2007. Groundwater and its association with sustainability in the North China Plain. In van de Giesen N, Xia J, Rosbjerg D, FukushimaY (ed.), Changes in Water Resources Systems: Methodology to Maintain Water Security and Ensure Integrated Management. IAHS 315: 258–265. Chen, J.Y. Fukushima, Y. Tang, CY. & Taniguchi, M. 2004a. Water environmental problems occurred in the lower reach of the Yellow River. J. Japan Soc. Hydrol. & Water Resour. 15(5): 555–564 (in Japanese with English Abstract). Chen, J.Y. Tang, C.Y. Sakura, S. Kondoh, A. & Shen, Y.J. 2002. Groundwater flow and geochemistry in the lower reach of the Yellow River: case study in Shandong Province, China. Hydrogeology Journal 10: 587–599. Chen, J.Y. Tang, C.Y. Sakura, S. Kondoh, A. Shen, Y.J. & Song, X.F. 2004b. Measurement and analysis of redistribution of soil moisture and salute in a maize field in the lower reach of the Yellow River, Hydrological Processes 18:2263–2273. Li, B.Q. & Zhao J.Y. 1991. An overview for water transformation atYucheng Experimental Station of ChineseAcademy of Chinese. In Annual Report (1988–1990) for Yucheng Experimental Station. Meteorological Publication House of China. Xi, J.Z. (eds). 1999. Water resources of the Yellow River. Zhengzhou: The Yellow River Conservancy Publishing House (In Chinese). Xu, J. Haginoya, S. Saito, K. & Motoya, K. 2005. Surface heat balance and pan evaporation trends in Eastern Asia in the period 1971 to 2000. Hydrological Processes 19: 2161–2186. DOI: 10.1002/hyp.5668. Zhang, X.Y. & Liu C.M. 1995. An analysis of field watersaving measures in North China Plain. In ShiYC, Liu CM, Gong YS (ed.), Advances in fundamental research for water saving agriculture. Agricultural Publication House of China (In Chinese). Zhang,Y. Z. 1988. Boundary integral equation for groundwater modeling and determination of parameters. In Liu CM & Ren HZ (ed.), Air, surface soil and groundwater interactions –experiment and calculation analysis: 287–312. Beijing: Science Press (In Chinese).

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Saline groundwater flow in the Yellow River delta, China K. Miyaoka∗ Department of Geography, Mie University, Mie, Japan

M. Taniguchi, T. Ishitobi & Y. Fukushima Research Institute for Humanity and Nature, Kyoto, Japan

S. Onodera Faculty of Integrated Sciences, Hiroshima University, Hiroshima, Japan

J. Chen School of Geography Sciences and Planning, Zhongshan (Sun Yat-san) University, Guangzhou, China

G. Liu College of Environmental Science and Engineering, Ocean University of China, Qingdao, China

ABSTRACT: Saline groundwater occurs at depths shallower than 50 m in the southern Yellow River delta. Recent decreases in water flows in the lower Yellow River will affect the interactions among river water, groundwater, and seawater in the delta. There is concern that this will also affect saline groundwater flow, the relationship between saline groundwater and aquifer water, and groundwater use. We examined deep and shallow saline groundwater, the effects of Yellow River water, and the interaction of shallow groundwater and recharge water. The origins of salinity differ between shallow and deep saline groundwater depending on the area and flow system. Paleo-seawater which is formed in 4000–7500yr before is distributed at 10–20 m in depth at all over area but also distributed at 30 m in depth at inland northern area. At 10–20 m in depth, saline groundwater is affected by the upper shallow fresh groundwater that is recharged by Yellow River water. At 30 m in depth, salinity is strongly affected by present-day seawater. It is suggested that present- day seawater intrude to inland area, especially at the southern area. Keywords: groundwater recharge source; paleo-seawater; saline groundwater; seawater intrusion; Yellow River delta

1

INTRODUCTION

Recent decreases in water flows in the lower Yellow River will affect the interactions among river water, groundwater, and seawater in the Yellow River delta. There is concern that this will also affect the behavior of saline groundwater flow, the relationship between saline groundwater and nearby aquifers, and groundwater use. The spatial distributions of water table in 1991 and 1997 are shown in He et al. (1999). Miyaoka (2008) classified three types of groundwater flow system in the southern Yellow River delta based on the water quality or residence time; the classifications depended on the location or depth and two salinity types in ∗

Corresponding author ([email protected])

the recharge sources of groundwater at approximately 10–50 m in depth. Ishitobi et al. (2007) also indicated that there is high saline groundwater aquifer at 10–50 m in depth by means of electric resistivity survey. The salinity of some saline groundwater is higher than that of present-day seawater. If the recharge rate of shallow groundwater decreases due to environmental change such as a change in land use, precipitation, or river channel course, then saline groundwater might contaminate shallow groundwater (Miyaoka, 2008). Taniguchi et al. (2008) clarified the relation between submarine groundwater discharge to the Bohai Sea and groundwater flow in theYellow River delta. These previous studies are useful for assessing the contribution of saline groundwater to the ecosystems of the Yellow River delta and Bohai Sea.

307

Figure 1. The location of research area and the distribution of water table.

In addition Chen et al. (2007) showed that one of nitrogen source is irrigated water from Yellow River. This suggests that shallow groundwater is also polluted by Yellow River. Saito et al. (2007) indicated that nitrate contamination in shallow groundwater of Yellow River. Our purposes were to elucidate the recharge source of three types groundwater and the interactions of surface/shallow waters, saline groundwater, and seawater in the Yellow River delta. 2

STUDY AREA

We refer to the area downstream from approximately site No. 24 as the Yellow River delta (Fig. 1). This area was formed after the end of the last glaciation. The Yellow River often changes course and has formed many new delta fan by changing flow course since 1855. Some old delta fan areas that formed mainly during from A.D. 11–1048 (Gao et al., 1989) are distributed in the upstream area above 7 m in elevation.

The delta fan is small in size, with the end of the fan located near sites 30 and 31 at approximately 7 m in elevation. Many new delta fans that formed after 1855 are distributed in the lower area. Delta deposits accumulate to depths of 5–7 m near the Yellow River channel. Further from the channel, this depth becomes thinner, and deposits disappear at approximately 15–20 km from the river channel. The distributions of delta deposits are related to the shallow groundwater flow system (Miyaoka, 2008), and it is possible that shallow groundwater is mixed with saline groundwater. Fresh groundwater is distributed deeply at depths >100 m in the southern delta. However, whether such water occurs around the Yellow River and in the northern delta is unknown. The distribution of groundwater-level contours is similar to the distribution of landform contours, indicating that landform conditions control the shallow groundwater flow and the recharge of shallow groundwater by Yellow River water. Comparing with He et al. (1999), remarkable valley shaped water table is formed in Fig. 1. This suggests

308

that the recharge volume to shallow groundwater is decreased by the decreases in water flows in the lower Yellow River in 1990’s. The high electric conductivity is distributed at valleys that have formed between new deltas, but does not distributed at new delta where is accumulated the delta deposits (Miyaoka, 2008).

Present-day sea water 44 MWL at Shijiazhuang N1 N3

3

N6 N4 N8 N10

METHODS

N5 N9 N2

N7

42

Field surveys and water sample collections were carried out at the measurement sites (except sites S3-2, S4-1, and S4-2; Fig. 1) in September 2003 and May 2004. The water level, pH, water temperature, and electrical conductivity were measured at the field sites. Anion and cation analyses were conducted using an ion chromatograph, and the HCO3 ion content was analyzed using the pH 4.8 alkalinity method. Oxygen18 was analyzed using a mass spectrometer (Finnigan DeltaPlus) after preparation by the CO2 equilibration method. In addition, deuterium was analyzed using a mass spectrometer (Finnigan DeltaPlus) after deoxidation by the platinum catalysis method at Nagoya University, Japan (Members of management committee of analytical system for water isotopes at HyARC, 2005). Geological surveys were conducted and observation wells were constructed at sites S3-2, S4-1, and S4-2 in July 2005. Field surveys and sample collections were performed at these sites in November 2005. The field surveys and water quality analyses were the same as those described above. In addition, some samples were carbon-14 (14 C) dated at Ocean University of China. Single or double screen are installed in each borehole; Depth of 3–9 m and 14–20 m in N1-N10 (depth depend on geologic condition in each site.), and 8–10 m in depth in 10 m borehole and 28–30 m in depth in 30 m borehole in S3-2, S4-1 and S4-2. When we have a discussion about fresh water, we have investigated as follows; shallow groundwater is shallower 50 m in depth, deep groundwater is deeper 100 m in depth. 4

RECHARGE SOURCE OF SALINE GROUNDWATER

We plotted a δ18 O–δD diagram for all of the collected water samples (Fig. 2). Rainwater data were obtained from the International Atomic Energy Agency (IAEA) global network of isotopes in precipitation (GNIP) data set. The δ18 O–δD composition of these waters plot on or parallel to the meteoric water line at Shijiazhuang (SMWL), indicating that the origin of river water and groundwater is rainwater at all of the measurement sites. However, the tendencies of the relationship between the SMWL and each groundwater composition indicate different characteristics.

24

Deep groundwater Shallow groundwater Borehole Yellow River S3-1

Figure 2. The relationship between δ18 O and δD for groundwater and rainwater.

Deep groundwater shows the lowest δ18 O–δD ratio in the delta. The regression for this groundwater had a steeper slope than that for SMWL, suggesting that this groundwater is not recharged by the rainwater of this region (i.e., Shijiazhuang rainwater). Wei and Lin (1995) described the distribution of rainwater δ18 O in China and reported that low δ18 O occurs in high latitude northeastern region and high mountains and plateaus distributed western regions. The geographic characteristics and the δ18 O of deep groundwater in the Yellow River delta suggest that the origin of the deep groundwater is rainwater from the western region. The δ18 O–δD ratio for high-salinity groundwater (S3-1) differed from that for the SMWL. However, that of seawater was similar to that for the SMWL. This suggests that saline groundwater (S3-1) is seawater affected by evaporation or is another kind of saline water. Gao et al. (1989) and He et al. (1999) also reported that high-salinity groundwater occurs in the Yellow River delta, but noted that the origin of the salinity is unknown. The δ18 O–δD ratio for shallow groundwater was intermediate to that for Yellow River water and seawater or saline groundwater (S3-1). The part of the shallow groundwater has lower δ18 O ratio than Yellow River water, indicating that this groundwater is mixed with deep groundwater.

5

DATING SALINE GROUNDWATER

To clarify the origin of groundwater with highly concentrated salinity, we analyzed some water samples for 14 C. We then examined the relationship between the chloride (Cl) ion concentration and the date estimated by the 14 C analysis (Fig. 3). Shallower groundwater is young and has a low Cl concentration, whereas groundwater >100 m in depth

309

Postglacialtransgression 1200

Cl (m eq/L)

24(Yellow River)

N2

1000

S4-2 10m S3-2 10m S4-1 10m N2 N1 S4-2 30m S3-2 30m Paleo-seawater S4-1 30m

N1

800

Present-day seawater

N10

600 Present-dayseawater N3

400 200

Deep groundwater

N10

N8

0 0

2

4

6 8 10 12 14 Datefrom14C (1000yr) Shallow ground water Deep ground water

16

Shallow groundwater

18

Borehole

Borehole

Deep groundwater

S3-1

Figure 4. The relationship between the Cl ion content and the depth of the well bottom.

Figure 3. The relationship between the Cl ion content and the 14 C date.

is old, but also has a low Cl concentration. Groundwater at approximately 20 m in depth is of intermediate age and has a high Cl concentration. We thus classified groundwater into three types. High-salinity groundwater was estimated to be 4000–7500 years old at sites N1 and N2. At these sites, the groundwater had higher Cl ion content than present-day seawater. This period corresponded to the postglacial transgression; therefore, this high-salinity water may be paleo-seawater that remained in this area and became concentrated, creating even higher Cl concentrations. The same groundwater age was indicated for sites N3 and N10; however, the Cl concentrations were lower than present-day seawater at these sites. The Cl ion contents and 14 C dates for sites N3 and N10 correspond to those for present-day seawater to deep groundwater. This suggests that this groundwater forms from the mixing of recharge waters from different sources. Thus, although the 14 C dating points to an age of 4000–7500 years, the Cl ion content is not as high as at sites N1 and N2. Sites N1, N2 and N3 are located in relatively close proximity. N1 and N2 have the same geomorphological conditions, but not N3. Delta deposits is accumulated, and located at rim zone of delta in N3. On the other hand, there isn’t delta deposit, and located the valley among delta fans in N1 and N2. Shallow groundwater which is recharged by Yellow River exists in the delta fan (Miyaoka, 2008). Assuming that paleo-seawater occurs at sites N1 and N2, the groundwater at site N3 site would be formed by the mixing of paleo-seawater and shallow groundwater recharged byYellow River water. We constructed double screens in the borehole of site N3; the screen depths of 4–12 m and 14–19 m correspond to the shallow and saline aquifer, respectively. Thus, at this site, these waters mix in the borehole. Further, because site N10 is located close to the Yellow River, a large volume of Yellow River water might recharge the shallow groundwater at that site.

We also constructed double screens at the N10 borehole at depths of 4–9 m and 14–18 m; these suggested that recharge water from the Yellow River flows into this site. Shallow groundwater recharged from Yellow River mainly flows in the delta deposits. According to the location of N1, N2, and N3, shallow groundwater easily flow into shallower screen. This groundwater is mainly a mixture of paleo-seawater and shallow groundwater recharged by the Yellow River at site N3, because site N3 is located at rim of the new delta fan. Paleo-seawater with highly concentrated salinity that was formed 4000–7500 years ago remains at approximately 20 m in depth such as sites N1 and N2. Shallow groundwater recharged from Yellow river doesn’t flow to there, because these sites are located at non delta deposits accumulated area. These results indicate that there are two origins of high-salinity water: present-day water and water that is 4000 to 7500 years old. No 14 C dates are available for the water at site S3-1. Therefore, to estimate the age of this groundwater, we examined the relationship between the Cl ion content and well depth (Fig. 4). This relationship at S3-1 was very similar to those at boreholes N1 and N2. The stable isotope compositions were also similar among these sites. This suggests that the high-salinity groundwater at site S3-1 is also paleo-seawater and that a thick aquifer occurs in this area because the well bottom at S3-1 is deeper than the well bottoms at N1 and N2. The existence of paleo-seawater at S3-1 suggests that such water is widely distributed in the southern Yellow River delta. The deep groundwater with low δ18 O (Fig. 2) might have originated from precipitation that fell in western China (Wei and Lin, 1995). This deep groundwater dates to 11,000–16,000 years old (Fig. 3). The results of both isotope analyses indicate a long residence time for this deep groundwater, which can thus be considered paleo-rainwater recharge that occurred 11,000–16,000 years ago during the last glacial stage.

310

Figure 5. The relationship between δ18 O and the Cl ion content.

6

INTERACTION OF SURFACE WATER AND SALINE GROUNDWATER

To evaluate the saline groundwater flow system and the interaction of surface water and saline groundwater in the southern delta, we excavated new boreholes in the southern area at S3-2, S4-1, and S4-2 in July 2005. A paleo-channel of the Yellow River flowed over sites S4-1 and S4-2 from A.D. 11–1048 (Gao et al., 1989). Site S4-2 is also located at the edge of an old delta fan which was presumably formed during this period. A concern with the comparison of the new borehole water samples and those collected previously was that the seasons of collection differed and seasonal conditions might have affected the water quality. The seasonal variation in groundwater level and the water quality of groundwater and river water were compared with those in September 2003 and May 2004. There was only small seasonal variation in groundwater level and water quality in the delta, suggesting that the sites S3-2, S4-1and S4-2 data collected in November 2005 could be compared with the groundwater and river water data obtained in September 2003 and May 2004. We examined the groundwater flow system and mixing processes creating saline waters in the southern part of the delta, using the relationship between δ18 O and Cl ion content (Fig. 5). The groundwater at sites N1, N2, and S3-1 was paleo-seawater (Figs. 3 and 4). The relationship between groundwater δ18 O and Cl ion content almost boreholes in southern area (at depths of 10 and 30 m at S3-2, 10 m at S4-1, and 10 m at S4-2) were similar to those at sites S3-1, N1, and N2. This indicates that paleo-seawater occurs in the groundwater in the new boreholes. At 10 m depth at sites S3-1 and S4-1, both the Cl ion content and δ18 O were high, similar to those at site N1. These high values correspond with nearly pristine paleo-seawater. A geological cross-section along the historical 1855 coastline shows that marine deposits have accumulated to depths of 10–20 m, and the geological valley shapes the accumulation under the present Yellow River channel (Fig. 6). According to the relationship between the Cl ion content and depth at sites S3-1 and S4-1, a groundwater aquifer with

Figure 6. Geological cross-section along the 1855 coastline and the estimated saline ground-water aquifer corresponding with the geology.

high salinity is located at depths of 10–50 m (Fig. 4). Groundwater with low Cl concentrations also occurs at 20–50 m (Fig. 4), suggesting that the paleo-seawater aquifer is not widely distributed in the delta. However, the paleo-seawater aquifer is clearly distributed at 10–20 m in depth around the Yellow River channel, as indicated by the findings from sites N1, N2, and S3-2. Further, this aquifer reaches 50 m in depth at site S3-1. Although water at site S4-2 has a low δ18 O, the Cl ion content is relatively high at 10 m in depth and plots along the relationship for deep groundwater to paleoseawater (Fig. 5).This suggests that this groundwater is formed by the mixing of deep groundwater and paleoseawater and that deep groundwater is a significant component affecting water quality. At 30 m in depth, the Cl ion concentration is 112.8 meq/l at site S4-1 (Fig. 5). When this value was plotted in Fig. 4, it is located between the mixing line of deep groundwater and present-day seawater and the mixing line of deep groundwater and paleo-seawater. The Cl ion concentration at 30 m in depth at site S4-2 is lower (66.3 meq/l) than that at site S4-1, but it is located on almost the same as 30 m in depth of site S4-1, so suggesting that S4-2 would plot on the same line. This shows that present-day seawater intrude to inland in this area. Table 1 provides a list of water table depths for the new boreholes. Present-day seawater affects the water at 30 m in depth at site S4-2, as at S4-1. On the other hand, Groundwater at 30 m in depth flows upward at site S4-2, supporting the suggestion that this groundwater is affected by deep groundwater.

311

Table 1.

Depth to the water table in the new boreholes.

Borehole No.

Depth to the bottom (m)

Depth to the water table (m)

S3-2

10 30 10 30 10 30

2.18 1.40 0.50 1.80 0.93 0.69

S4-1 S4-2

It is possible that the deep groundwater flow runs against the intruded present-day seawater, upward flow is occurred as a result. Sites S3-1 and S4-1 are located in the coastal area. The paleo-seawater aquifer reaches a relatively deeper layer at site S3-1 than at site S4-1 (Figs. 4 and 5). This indicates that the aquifer and groundwater flow systems differ by area, and the groundwater at 30 m in depth at site S4-1 is affected by seawater intrusion. The groundwater at 30 m in depth at site S3-2 flows upward, as at site S4-2. This tendency supports the suggestion that deep groundwater affects shallower groundwater in inland areas. However, in the coastal area, although deep groundwater appears to affect the water quality at site S4-1, it does not flow upward. It is possible that the downward flow of seawater intrusion is stronger than the upward flow of groundwater. The aquifer at S4-1 corresponds to marine deposits that accumulated at depths of <20 m (Fig. 6). Thus, the saline groundwater aquifer forms in both marine deposits; paleo-sewater aquifer is shallower marine deposits in relatively northern area, present-day seawater intruded aquifer is deeper marine deposits in southern area.

7

CONCLUSIONS

We elucidated the recharge source of three types groundwater and the interactions of surface/shallow water, saline groundwater, and seawater in the Yellow River delta. There are two origins of high-salinity water: present-day water and paleo-seawater is 4000 to 7500 years old. A paleo-seawater aquifer occurs at depths of 10–50 m and corresponds with marine deposits. The distribution of this aquifer differs by area, at site S3-1, thick paleo-seawater aquifer is formed. On the other hand, at southern area, Paleo-seawater aquifer is relatively thin, and the present-day seawater intrudes to inland area. The 30 m in depth groundwater flows upward in the delta such as at site S3-2 and S4-2. It is possible that upward flow is occurred by running against between the pressure of seawater intrusion and the deep groundwater flow.

Yellow River water recharges the shallow delta deposits layer at rim zone of delta fan. So, the impact area of recharge water from Yellow River to shallow groundwater is limited around Yellow River channel and some delta fan. If the volume of recharge water from Yellow river will be decreased in this area, salinity of shallow groundwater will be changed high concentration, because paleo-seawater aquifer is moved shallower layer by the upward flow of under aquifer. At southern area, if the volume of deep groundwater flow will be decreased, present-day seawater intrusion will be reached more inland area. ACKNOWLEDGEMENTS This work was financially supported by the Research Institute for Humanity and Nature (RIHN). REFERENCES Chen J., Taniguchi M., Liu G., Miyaoka K., Onodera S., Tokunaga T., Fukushima Y. (2007), Nitrate pollution of groundwater in the Yellow River delta, China. Hydrogeology J., DOI 10.1007/s10040-007-0196-7. Gao S.M., Li Y.F., An F.T., Wang Y.M., Yan F.H. (1989), Formation and sedimentary environment of Yellow River delta, Scientific Publishing House of China, Beijing, 228p. He Q.C., Duan Y.H., Zhang J.D., Xu J.X., Kang F.X., ZhouY.Q. (1999), Comprehensive management of coastal zone in the Yellow River delta. Ocean Publishing House of China, Beijing, 122p. Ishitobi T., Taniguchi M., Chen J., Onodera S., Miyaoka K., Tokunaga T., Saito M. (2007), Investigation of fresh and salt water distribution by resistivity method in Yellow River Delta. Proceedings of 3rd International Workshop on Yellow River Studies, Feb. 14–15, Kyoto. 24–27. Members of management committee of analytical system for water isotopes at HyARC. (2005), Management for daily analyses of various stable isotope samples of water at HyARC, Nagoya University. J. Japan Soc. Hydrol. & Water Resour, 18,5, 531–538.(in Japanese) Miyaoka K. (2008), Hydrogeology and groundwater flow system in the Yellow River delta. Hydrological environment issues in the Yellow River, Fukushima Y and Taniguchi M ed., Gakuhou-sha, 167–182. (in Japanese) Saito M., Onodera S., Miyaoka K., Chen J., Taniguchi M., Liu G., Fukushima Y. (2007), Nitrate contamination in groundwater of the Yellow River Delta and its effect on the marine environment. Water Quality and Sediment Behaviour of the Future: Predictions for the 21st Century, IAHS Publ. 314, 271–277. Taniguchi M., Ishitobi T., Chen J., Onodera S., Miyaoka K., Burnet W.C., (2008), Submarine groundwater discharge from the Yellow River delta to the Bohai Sea, China. J. Geophysical Research—Oceans (in press). Wei K., Lin R. (1995), Isotope geochemistry of meteoric waters. Tu G and TJ Chow (ed.) Isotope Geochemistry Researches in China. Science Press, 443–464.

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Long-term changes of water and salinity management in Lower Seyhan Plain, Turkey T. Nagano∗ Graduate School of Agricultural Science, Kobe University, Kobe, Japan

T. Onishi, T. Kume & T. Watanabe Research Institute for Humanity and Nature, Kyoto, Japan

K. Hoshikawa Center for South East Asian Studies, Kyoto University, Kyoto, Japan

S. Donma Sixth Regional directorate of State Hydraulic Works (DSI), Adana, Turkey

ABSTRACT: A Long-term change of shallow water table fluctuation patterns was assessed for a large-scale irrigation district which extends over an alluvial plain in Turkey in the Eastern Mediterranean. Over the last 20 years the water table which was previously characterized by large fluctuations and acute peaks, became flat and lost seasonality as a consequences of increases in the amount and duration of irrigation and of improvements in drainage. Salinity level of shallow water table also decreased over this twenty year period. Analysis by the Irrigation Management Performance Assessment Model suggested that leakage from canals was responsible for keeping the groundwater level high. Open drainage and sub-surface drainage were constantly functioning for drainage of excessive water. While many parts of the world suffer from increasing salinity problems associated with over-irrigation, Lower Seyhan Plain seems to be a fortunate exception case because i) introduction of summer irrigation to Mediterranean Climate with winter precipitation kept soil water flux always downward, ii) good drainage networks quickly carried away surplus water and iii) irrigation water supplied from upstream had low salt content and there was no hazard for secondary salinization. Keywords:

1

groundwater; salinity; IMPAM; seepage

INTRODUCTION

Performance assessment is an increasingly relevant concept in present-day irrigation and drainage systems. Many large-scale systems developed in the second half of the twentieth century under the centralized governmental scheme are becoming less efficient because of gradual deterioration. On a long-term perspective, irrigation and drainage system, socioeconomic environment, climatic conditions and water resource availability are all dynamic and their structural changes may be considerable. Therefore, characteristics of the local hydrological environment can be better understood by assessing the history of their interactions, rather than analyzing slight changes over short periods of time. ∗

Corresponding author ([email protected])

For an integrated and holistic approach for the assessment of irrigation systems, simulation models can be powerful tools. However when it comes to composite areas such as regional districts, availability of reliable and accurate data is often the limiting factor and there have been only a limited number of studies carried out for local problems during the last 25 years (Bastiaanssen et al., 2007). Assumptions of the expected spatial variability may result in sufficient uncertainty in the outputs of advanced models thereby reducing their applicability to the irrigation management under consideration (Bastiaanssen et al., 2007). In many irrigation schemes on alluvial plains with unconfined aquifers, leakage of irrigation water diverted from rivers or streams is a common feature. While unsaturated zones in an agricultural area are largely influenced by land and water management

313

in situ, the shallow water table fluctuates as a consequence of land and water management practices executed by groups of farmers over a larger area of the land. When the aquifer is shallow or saline, there is a potential hazard for water-logging and salinization. Shallow water table and salinity are intensively monitored only in such cases and there is a general lack of understanding about the flow processes in the subsoil although they strongly affect the vadose zone water conditions. Because of the lack of data, the majority of previous studies on shallow water tables focused on how to interpolate sparse observation (Desbarats et al., 2002; Lyon et al., 2006). In this study, we obtained an intensive and long-term monitoring record of shallow water table fluctuation in a large irrigation district and carried out a series of analyses to clarify whether shallow water table structure had changed over the years with changes in water management approaches. We also used the distributed quasi-three-dimensional model, the Irrigation Management Performance Assessment Model, to examine if the changes in shallow water table fluctuation could be represented by change of parameters related to the management. 2 2.1

STUDY AREA Location and topography

In the South Central Turkey, three main rivers, the Ceyhan, the Seyhan and the Berdan, rise in the Taurus mountains and flow into the Mediterranean Sea. they form a wide, alluvial plain called the Çukurova. The climate (relatively mild and humid in the winter months) and the alluvial soil make the area highly suitable for agriculture. This plain can be regarded as one of the most important agricultural and industrial areas in Turkey. The Lower Seyhan Irrigation Project (LSIP) uses water supplied from Seyhan and Çatalan reservoirs in the upper stream of the Seyhan river and it has a total planned area of 175,000 ha of which 133,000 ha has already been implemented and principal method of irrigation being implemented is gravity irrigation. Figure 1 shows the topography of the area. The delta is very flat, with the majority of the area being less than 20 m above sea level. The altitude reduces towards the South, approaching 0 m A.S.L.. The slope of the delta ranges between 0.1 to 1%. The soil in the delta is alluvial which developed from deposits of the three main rivers. These are the deepest and most fertile soils, with major soil being calcic fluvisol (young river terrace soil) and chromic vertisol (old river terrace soil) (Cetin and Diker, 2003; Dinc et al., 1995). 2.2

History of irrigation development

In the 1960s prior to the installation of irrigation projects, rainfed agriculture was practiced on 95%

Figure 1. Outline of the study area, Lower Seyhan Irrigation Project in the Eastern Mediterranean coast of Turkey. Different pattern shows different development stages of the project and contour lines show elevations above the sea level.

of the project area, with cotton accounting for about 85% of the cropped area, and wheat the remainder (Scheumann, 1997). In that time the delta had poor drainage and suffered from salinity problems. A soil study carried out in 1959 in the left bank provided data showing that 19,982 ha were slightly saline (EC of saturation extracts: 4–8 mS/cm), 29,053 ha were moderately saline (8–12 mS/cm) and 56,602 ha were highly saline (EC >12 mS/cm) (Dinc et al., 1991). Therefore, implementation of irrigation was coupled with installation of subsurface drainage and construction of drainage canal networks. By 1987, implementation of the project was complete in two thirds of the total surface area (133,000 ha). However more than 50% of previously implemented subsurface drainage systems did not function properly due to lack of maintenance. Therefore, in 1989 a core program for the improvement of drainage systems was initiated in already developed area. Because of lack of finance and the difficulty of implementing drainage, irrigation was not implemented in the low-lying area in the coastal zone which lies below 5 m A. S. L. until 2005. 2.3 Land use The LSIP was originally implemented for the diversification of cropping patterns to generate income for the local farmers relying on rain-fed agriculture (Scheumann, 1997). However until the mid 1980s, cotton remained to be the dominant crop in the area. Towards the 1990s, the variety of crops started to increase and maize became the major crop. These changes were socio-economically driven, mainly by crop prices and labor availability rather than the availability of water. In the 2000s, the area of land under citrus and vegetable cultivation increased while the

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area devoted to maize decreased and more diverse cropping patterns was realized. 2.4 Water management Calculation of principal water demand for the LSIP is carried out using the reference book “DS˙I irrigated crop water consumption and irrigation water requirement” (Özgenç and Erdo˘gan, 1988) published by State Hydraulic Works (DS˙I) which was in charge of planning and operation of the project. Monthly crop water demand multiplied by cultivation area is summed for the command area to calculate water demand at the main canal inlet considering transport loss (presently assumed as 20%) and field application loss (presently assumed as 40%). Irrigation canals in the LSIP are all concrete-lined. Secondary and tertiary canals are often “kanalet” type, which run above ground. Because of aging and lack of maintenance, the amount of leakage from canals is considerably high. Water management was originally carried out by field extension workers of DS˙I under a centralized scheme. In 1994, responsibility was turned over from DS˙I to 18 newly established Water Users Associations (WUAs). The primary reason for this was to ease the financial burden from the national budget and it was also a positive step to ensure farmers’ participation in the operation and maintenance of the irrigation system. A saving in water usage was not achieved however, because water charging continued to be based on a crop-area basis. Irrigation intake has increased with time, mainly due to a shift in the cropping pattern to crops that consume more water, to a substantial increase in released water to help farmers in the lowlying area to irrigate their undeveloped farmland and to less strict water control by WUAs. The authors interviewed WUAs in 2003 and learned that there were increasing conflicts among WUAs during peak irrigation season, in spite of maximum water intake at main canals. If shortages occur, WUAs make claims to DS˙I to have more water released than planned. Therefore, the actual amount released nowadays seems far more than the planned value. Although a consistent of the amount of water actually diverted was not available, some data show that recent actual release is reaching nearly 2 Gm3 . This abundant use of water is enabled by the large capacity of the Seyhan reservoir upstream. Additional construction of the Çatalan reservoir (completed in 1992) further increased water resource capacity.

3 3.1

MATERIALS AND METHODS

Shallow water table data In the 1980s, DSI˙ started monitoring of the shallow water table level (monthly) and salinity (once

a year in July), very intensively over the entire irrigated area. The depths of shallow water table observation wells extended to 4 m below the soil surface. The total number of observation wells was 626 in the 1980s, increasing to 1,134 in the 1990s, covering nearly the entire irrigated area. Each well’s ground surface elevation was surveyed to a resolution of a centimeter. Because records of actual irrigation release or drainage flow are not present, shallow water table fluctuations are only spatially distributed, high resolution and continuous data which enable examination of the change in the water budget of the system over a long-term. Three sets of data (1984–1985, 1992–1993 and 2002–2003) were chosen for analysis.The average precipitation of the two observing stations in Adana and Karata¸s (situated at the North and South end of the delta) from October to the following September were 660 mm, 692 mm and 569 mm for 1984–1985, 1992– 1993 and 2002–2003, respectively. In the past 35 years no clear trend (increase or decrease) in precipitation was detected. To examine whether observation wells were located on representative locations, the Euclidean distances between observation wells and nearest drainage canals were examined. The mean distances were 282 m and 250 m for the wells in 1980s and 1990s onwards, respectively. The distances seemed to be large enough for assuring the representativeness of the data. The wells which became unmeasurable due to damage or destruction even for a month were eliminated from analysis. The number of data points actually used for the analysis was 300 for 1984–85, 774 for 1992–1993 and 759 for 2002–2003. 3.2 Geostatistical analysis of water table fluctuation For comparing spatial structure of shallow water table fluctuation, semivariograms were drawn for monthly water table depths of 1984–1985, 1992–1993 and 2002–2003. Because the quality of data for the right bank was not as good, only data from the left bank were used. Semivarance γ(h) is expressed by following equation:

where z(xi ) = water table depth at xi and N (h) = number of pairs of data points separated by a lag distance equal to h. The lag distance was set for every kilometer to satisfy the rule of thumb that the number of classes multiplied by lag distance should be approximately half the maximum separation distance (30 km for this study case).

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3.3

Table 1. Average water input (mm) to the LSIP in the simulations by the IMPAM.

Irrigation management performance assessment model (IMPAM)

In alluvial plains where water tables are often high, fluctuation of saturated zone is affected by the seasonal water input at the district scale, through cropping pattern and canal configuration and the degree of drainage implementation. The influence of those parameters is in fact larger than that of parameters related to unsaturated flow in the root zone. Therefore, we use the Irrigation Management Performance Assessment Model (Hoshikawa et al., 2005) to analyze the fluctuation. We kept the soil and crop parameter stable and tried to represent difference in fluctuation pattern between decades by changing water management, leakage rate from irrigation canals and drainage rate. The IMPAM is a quasi three-dimensional soil water dynamics model, for which modules for water management and crop growth are assembled. In this model, the basic unit of calculation is a plot or a group of farm plots which can be considered homogeneous in terms of management. For each unit, irrigation schedule, crop management and crop growth are calculated. The irrigation schedule is fed into the model in a two-dimensional table of date and plot number with daily irrigation depth [mm · d−1 ]. Water input to each calculation grid is generated with i) the topological structure of the irrigation channel network and ii) leakage rate of each channel segment, taken into account. Soil water dynamics in the vadose zone and crop growth are calculated with a one-dimensional vertical model based on the simple model of SWAP (Van Dam et al., 1997). Groundwater balance in the saturated zone is calculated separately with a two-dimensional horizontal model based on the dispersion equation:

where, h = head of groundwater [m], T = transmissivity [m2 · d−1 ], S = coefficient of storage [−], qssh = sink/ source flux for the horizontal model [m · d−1 ]. The sink/source term is calculated for each node of the horizontal grid by ;.

where, qbot = bottom flux of one dimensional vertical soil-water dynamics model (upward positive), qdrainsat = drainage from saturated zone, qseepage = seepage from canal sections, qwell : well withdrawal, qin/out = interaction with outside of the modeled area. Drainage from the saturated zone qdrainsat is calculated calculated from the Hooghoudt and Ernst equation as a function of the difference of head of groundwater (φgwl ) and the elevation of drainage

Year

1980s

1990s

2000s

Irrigation Precipitation

930 732

920 727

1110 634

(φdrain ) [m] over the resistance of drainage γdrain ;.

where Khprof = saturated hydraulic conductivity in horizontal direction [m · d−1 ], Ldrain = drainage spacing, Deq is given as a function of distance from the bottom of the drainage to the impervious layer, drainage spacing etc., according to the methodology employed in the SWAP-model (Van Dam et al., 1997). 3.4 Assumptions for analysis with the IMPAM In the LSIP, the amount of irrigation supply is determined by the cropping pattern using the reference book “DS˙I irrigated crop water consumption and irrigation water requirement” (Özgenç and Erdo˘gan, 1988). Regardless of amount of precipitation in winter, irrigation is applied every year under a fixed schedule in recent years. We assumed the area average of irrigation water supply as shown in Table 1. For the whole plain, soil was assumed homogeneous and consisting of a single layer of silty clay. For the crop growth, parameters provided with the SWAP were used without modifications. Simulations were carried out with spin up years with actual weather data and constant cropping pattern. 4 4.1

RESULTS AND DISCUSSION Spatial structure of water table fluctuations

Figure 2 shows the semivariogram of annual average value of shallow water tables of each decade. Whereas the dataset for 2002–2003 satisfied second order stationary conditions in all seasons, the dataset for 1992–1993 did not satisfy second order stationary in the winter precipitation season. The dataset for 1984–1985 only satisfied second order stationary conditions in the irrigation period. For 2002–2003 and 1992–1993 the range was generally less than 5 kilometers. With course of time, water table depths seem have lost trend and started to exhibit stationary structure. This is most probably due to improvement of drainage

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Figure 2. Semivariogram of annual average value of shallow water table depth in 1984–1985, 1992–1992 and 2002–2003.

In 1984–85 and 1992–1993, water table depth fluctuated in a similar pattern to water input. However in 2002–2003, water table fluctuation was very small and was stable around 150 cm below the surface. Unlike the former two, the water table stayed high even with the relatively low amounts of precipitation that occur during winter time. During the irrigation season, the water table became about 10 cm higher but the acute peak was not present. This is probably attributed to i) the improved drainage which prevented the water table from becoming critically high, ii) diversification in cropping pattern inducing longer irrigation periods and iii) abundant irrigation water use caused by less well organized management by the WUAs and increased seepage from canals with deterioration. The depth of 150 cm happens to be the target depth for the subsurface drainage system in the area. Abundant water input keeps the water table level staying high with constant drainage occurring. In the 1980s and the 1990s, the drainage system was not working properly as described in the prior section and water table attained critically high levels when inputs from precipitation or irrigation were at maximum levels. Because water distribution was stricter and leakage was lower under the governmental regime, the water table dropped with a decrease in inputs. 4.3

Figure 3. Average monthly shallow water depth fluctuation (a) and monthly precipitation and irrigation depth (b) in Lower Seyhan Irrigation Project in 1984–1985, 1992–1993 and 2002–2003.

system and the increase in the amount of irrigation. The effect of subsurface drainage, installed to keep the water table depth at a maximum of 1.5 m seems to have turned the plain into more homogeneous condition. 4.2 Temporal structure of water table fluctuation Figure 3 (a) and (b) show changes in average monthly water table depth and water input (monthly precipitation and irrigation) in each decade, respectively.

Salinity distribution in shallow water table

Figure 4 shows changes in Electrical Conductivity (EC) of the shallow water table. In the LSIP, sodium ions were the dominant contributor to the measured EC and thus the measured value represents the degree of salinity. It is quite definite that salinity of the shallow water table has been consistently decreasing over the years. Before the implementation of the project dry summer climate was the major driving force for bringing salt to the soil surface. After the implementation of irrigation, the soil water flux in summer was reversed to a downward direction. Irrigation water supplied from the Seyhan reservoir has very low sodium content and the increase in its use also contributed to the decline in salinity. This general trend of decrease of salinity agrees with observations made in the downstream area where a salinity level of previously rain-fed land decreased with initiation of irrigation (Cetin and Kirda, 2003). Fair drainage networks which were implemented with irrigation mitigated the risk of excessive irrigation and drained salinity from the delta over the long term. 4.4

Simulation of shallow water table fluctuation by the IMPAM

Figure 5 shows fluctuation patterns shallow water table of each decade simulated by the IMPAM. The model is presently not capable of automatic parameter optimization and the result is an example of manual trial to

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Figure 4. Decadal change in salinity distribution in shallow water table in Lower Seyhan Irrigation Project, Turkey. Table 2. Water balance components calculated by the IMPAM as a result of trying to represent water table fluctuation of each decade. For 1985 and 1993 same canal intensity. Year

1985

1993

2003

Feeding from surface water Seepage from irrigation canal Evapotranspiration Drainage

292 29 1133 690

270 29 1125 795

333 300 1122 826

of responsibility of water management from DSI to WUAs. The fair simulation result of decadal trends by the IMPAM confirmed that abundant use of water together with improved drainage canal system in the recent years are responsible for the change in the fluctuation pattern of shallow water table in the LSIP. 5 Figure 5. Simulation of shallow water table fluctuation in each decade by the IMPAM (sample value from a single grid in the center of the plain).

simulate the trend. The data are daily values obtained from a sample point in the center of the plain and the degree of fluctuation is greater than the monthly values shown in Figure 3, being more influenced by irrigation and precipitation events. Absolute values of water table level are not correct because the Digital Elevation Model (Shuttle Radar Topographic Mission, NASA) used was coarse in resolution. Table 2 shows an example water balance components calculated by the IMPAM. The feeding from surface water means the seepage from river beds which feed the water table. The fluctuation pattern of the 1980s and 1990s could be simulated by using the same irrigation and drainage properties with small seepage rate form canals. For simulating the flatter water table fluctuation pattern of the 2000s (in Figure 3 (a)) seepage rate from the canals needed to be increased to 300 mm and drainage resistance had to be decreased. These manipulations reflect less strict gate operation (resulting water flow in non-operational irrigation canals lead to more seepage loss) and better maintenance of drainage canals after the turn over

CONCLUSIONS

The aim of this study was to clarify the structural change of water budget in the large-scale irrigation district on the alluvial plain by comparing datasets at long-term intervals. The effect of the increase in water input and improvement in drainage on spatial structure of shallow water table fluctuation became evident with this approach. To clarify the effect of human interventions on the natural system, this kind of approach proved to be very effective. The general belief that decentralization of water management would raise efficiency of irrigation did not prove to be correct for the case of the Lower Seyhan Irrigation Project so far, because water charge continued to be based of crop area basis and abundant water use continued with deterioration of irrigation canals. Large capacity of the reservoirs upstream also allowed this abundant use. Fortunately implementation of the drainage network from the beginning of project resulted in safeguarding the farmlands from the risk of water logging. For avoiding the risk of salinization, we suggest farmers to keep on using river water instead of shifting to groundwater use which may be saline in some parts. The activity of the WUAs, such as improvement of irrigation efficiency and maintenance of drainage canals

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would become increasingly important for avoiding the risk of high groundwater in this area.

ACKNOWLEDGEMENT This research was supported by the ICCAP Project (Impact of Climate Changes on Agricultural Production System in the Arid Areas), being promoted by the Research Institute for Humanity and Nature (RIHN) and the Scientific and Technical Research Council of Turkey (TÜB˙ITAK) and by the JSPS Grant-in-Aid No.16380164, 18206054 and 19208022. REFERENCES Bastiaanssen, W.G.M., Allen, R.G., Droogers, P., D’Urso, G. and Steduto, P., 2007. Twenty-five years modeling irrigated and drained soils: State of the art. Agricultural Water Management, 92(3): 111–125. Cetin, M. and Kirda, C., 2003. Spatial and temporal changes of soil salinity in a cotton field irrigated with low-quality water. Journal of Hydrology, 272(1–4): 238–249. Cressie, N., 1985. Fitting models by weighted least squares. J. Math. Geology, 17(5):563–586. Desbarats, A.J., Logan, C.E., Hinton, M.J. and Sharpe, D.R., 2002. On the kriging of water table elevations using

collateral information from a digital elevation model. Journal of Hydrology, 255(1–4): 25–38. Dinc, U. et al., 1995. Soils of Cukurova Region, second edition, No. 26. University of Cukurova, 172 pp. Dinc, U. et al., 1991. Formation, distribution and chemical properties of saline and alkaline soils of the Cukurova region, southern Turkey. Catena, 18(2): 173–183. Hoshikawa K, Kume T, Watanabe T, Nagano T. 2005. A model for assessing the performance of irrigation management systems and studying regional water balances in arid zones. Proceedings of the 19th Congress of International Commission on Irrigation and Drainage, Beijing, China. Lyon, S.W., Seibert, J., Lembo, A.J., Walter, M.T. and Steenhuis, T.S., 2006. Geostatistical investigation into the temporal evolution of spatial structure in a shallow water table. Hydrology and Earth System Sciences, 10(1): 113–125. Özgenç, N. and Erdo˘gan, F.C., 1988. DS˙I irrigated crop water consumption and irrigation water requirement. (DS˙I sulamalarinda bitki su tütketimleri ve sulama suyu ihitiyaçları). DS˙I Operation & Maintenance Department, Ankara. Scheumann, W., 1997. Managing salinization: Institutional analysis of public irrigation systems. Springer, 274 pp. Van Dam J.C, Huygen J, Wesseling J.G, Feddes R.A, Kabat P, Van Walsum P.E.V, Groendijk P, Van Diepen C.A. 1997. Theory of SWAP version 2.0, Department of Water Resources, Wageningen Agricultural University.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Intensive groundwater-surface water interaction in an alluvial fan: Assessment using a numerical model and isotopic tracer T. Yamanaka∗ Terrestrial Environment Research Center (TERC), University of Tsukuba, Tsukuba, Japan

H. Wakui Aero Asahi Corporation, Kawagoe, Japan

ABSTRACT: To evaluate quantitatively water fluxes between groundwater body and surface water bodies, a hydrodynamic, Compartmental Mixing-Cell (CMC) model was developed and applied to the Nasu Fan, central Japan. The model simulates horizontal groundwater flow interacted with surface waters, and computes water and isotope mass balance within each cell. Physical parameters in the model have been calibrated with a set of observed hydrometric and isotopic data, and then the model performance was validated with another dataset. It was demonstrated that the hydrodynamic CMC model was capable of reproducing very well spatiotemporal variations of both groundwater level and isotopic composition. The most dominant source for groundwater recharge was estimated to be precipitation fallen onto non-paddy areas, accounting for 39% of total recharge flux. Recharge from paddies was relatively small, even in irrigation period, because of low-permeable soil layer. Contribution from the Sabi River, which is interrupted at the middle zone of the fan, was approximately 31%, and plays an important role driving groundwater flow in the fun apex. Groundwater discharge to the river, which supports its revival, nearly equals to spring discharge in the vicinities, and these fluxes make groundwater level stable in the fan-rim zone. Keywords: groundwater-surface water interaction; groundwater budget; numerical simulation; stable isotope; alluvial fan 1

INTRODUCTION

In the alluvial fan, which is generally composed of highly permeable sand/gravel layer(s), there exists intensive interaction between groundwater and surface water. For instance, we can find interruption of river due to water seepage into the ground and its revival, rapid infiltration of precipitation and paddy water, and many springs maintained by groundwater discharge. For an integrated management of land and water resources, it is crucial to understand quantitatively the groundwater-surface water interaction. Although it is usually difficult to reliably estimate the spatial and temporal variations of groundwater recharge/discharge fluxes, the environmental isotope serves us as one of most promising tools. The Compartmental Mixing-Cell (CMC) model with environmental isotopes is an inverse approach estimating causes (e.g. groundwater flow) from its effects (e.g. tracer concentration), and has been widely used for evaluating groundwater budget and residence time ∗

Corresponding author ([email protected])

(e.g. Przewlocki &Yurtsever, 1974; Allison & Hughes, 1975; Campana & Simpson, 1984; Campana & Mahin, 1985;Adar et al., 1988;Adar & Neuman, 1988;Adar & Sorek, 1989; Kirk & Campana, 1990). Recently, Carroll et al. (2007) developed a robust scheme that automatically optimized the CMC model using a single tracer. The inverse estimation approach is useful under relatively simple situations, though it is less effective under complicated situations such that groundwater recharge flux and tracer input are highly variable in time and space. In contrast, forward simulation using a hydrodynamic model is applicable for more complicated situations. Harrington et al. (1999) proposed a combined use of the CMC model with a hydrodynamic model. They computed groundwater flow field with the hydrodynamic model and then used it as input into forward simulation of tracer concentration variation using the CMC model for calibrating the hydrodynamic model. Dahan et al. (2004) also presented a method to calibrate hydrodynamic model by comparing groundwater flow regime independently estimated with an inverse CMC approach. A three-dimensional, hydrodynamic

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groundwater flow model, MODFLOW (McDonald & Harbaugh, 1988), was applied in these two studies, though there are many components that should be calibrated. In addition, inconsistency in spatial structure between the CMC model and the hydrodynamic model is inconvenient and reduces reliability of both the models more or less. In the present study, the non-steady state, hydrodynamic CMC model is constructed and applied to simulate both groundwater flow and isotope transport. The model is calibrated and validated with observed groundwater level and isotopic composition, and then it is used for estimating groundwater balance and addressing groundwater-surface water interaction in an alluvial fan. 2

STUDY AREA AND FIELD INVESTIGATION

The study area is the Nasu Fan, Tochigi Prefecture, central Japan (Fig. 1), which is a compound fan formed by four rivers (i.e. the Naka River, Kuma River, Sabi River and Houki River). Total area of the fan is approximately 400 km2 , with elevation ranging from 100 to 600 m a.m.s.l. Annual mean temperature is approximately 12◦ C, and annual precipitation is approximately 1350 mm. Water table is situated within sand/gravel layers (called Nasu Alluvial-fan Gravels and Torinome Gravels) overlain by loamy soil layer with a thickness of approximately 2 m, and underlain by pyroclastic flow sediments (called Otawara Pumice-flow), which act as aquitard (Watanabe et al. 1960). The thickness of sand/gravel layers ranges from 20 to 30 m at the middle zone. The Sabi River and Kuma River are fully interrupted at the fun apex because of influent seepage through highly permeable sand/gravel layers, and then revive at around the boundary between the fan middle and rim zones. At the rim zone there exist many springs, maintaining valuable freshwater-ecosystems. Land use within the fan is dominated by pasture and forest in the apex zone, and rice paddies and vegetable fields in the middle and rim zones. Irrigation systems supply water from upper reaches of the Naka River and Sabi River to rice paddies. However, shallow (i.e. unconfined) groundwater is the major source of the irrigation in recent decades. The irrigation usually begins in April and ends in August. Industrial use of shallow groundwater is generally minor. The present study focused on ground water-surface water interaction in a strip along the Sabi River. Three wells were selected in the strip over fan-middle zone. Groundwater level measurement and groundwater sampling were done every month during one full year from February 2004 to February 2005. Samples of the Sabi River water upstream, precipitation (at g3) and paddy water (at five sites) were also collected.

Figure 1. Map of study area. Closed circles denote the location of observation wells (g1, g3, and g7).A series of rectangle represents compartmental mixing-cell grid.

Details of this investigation are given in Wakui & Yamanaka (2006). Additional hydrometric measurement and water sampling were performed four times in 2006. All the collected samples were analyzed for hydrogen and oxygen stable isotopic compositions (δD and δ18 O) by a mass spectrometer (MAT252, Thermo Finnigan). 3

MODEL

3.1 Outline In the present study, an unconfined aquifer is treated as a series of compartmental cells along groundwater flow (Figs. 1, 2). The groundwater balance in a given (the n-th) cell is described as follows:

where An = horizontal area of the cell (m2 ); Syn = specific yield; hnG = groundwater level (i.e. elevation of water table, m a.m.s.l.); t = time (d); Iin = volumetric flow rate (m3 /d) for the i-th inflow component, Ojn = volumetric flow rate (m3 /d) for the j-th outflow component; NI = total number of inflows; and NO = total number of outflows.

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where K n = hydraulic conductivity (m/d); Lx = length of the cell (m), and Ly = width of the cell (m). The lateral groundwater inflow to the first cell is assumed to be none. The groundwater outflow for a given cell equals to groundwater inflow for the downstream neighboring cell (i.e., Gon = Gin+1 ). On the assumption of a constant infiltration capacity for influent seepage of the Sabi River water, IRn is parameterized as:

Figure 2. Schematic illustration of the hydrodynamic CMC model.

On the assumption that different inflows into the cell are completely mix, the mass balance of a conservative tracer in the n-th cell is expressed as:

where hnb = elevation of bottom boundary of the aquifer (m a.m.s.l.); φn = porosity; CGn = tracer concentration in groundwater (kg/m3 ); and Cin = tracer concentration in the i-th inflow component (kg/m3 ). In the present study, δD and δ18 O are used as tracer concentration. This treatment does not violate the law of conservation of mass. Considering distinguishable difference in isotopic signature, one can divide the inflows, Iin , into four components: lateral groundwater inflow from the upstream neighboring cell (IGn ), recharge flux due to influent seepage from the main river (IRn ), recharge flux at paddies (IPn ), and recharge flux at non-paddy areas (INn ). Outflows, Ojn , include four components: lateral groundwater outflow to the downstream neighboring cell (OGn ), discharge flux due to effluent seepage to the main river (ORn ), spring discharge totalized over the cell (OSn ), and groundwater pumping rate for irrigation to paddies (OPn ). We computed variations of groundwater level using Equation 1 with daily time step and isotopic compositions in each cell using Equation 2 with monthly time step, in order to control numeric dispersion appropriately. Parameterization schemes for water fluxes are based on but modified from those of Elhassan et al. (2001), as described below.

3.2

Parameterization of fluxes

The groundwater inflow rate is computed by applying Darcy’s law with the Dupuit assumption, as:

where Ru = rate of the river flow from upriver mountainous areas to the fan (m3 /d); cI = infiltration capacity of the riverbed (m3 /d). The Ru is estimated by a tank model that is calibrated for an adjacent catchment with observed river flow rate. The ORn and OnS are parameterized as follows:

where cR = river discharge factor; and hnR = the lowest elevation of the riverbed within the cell (m a.m.s.l.); cS = spring discharge factor; and hnS = the lowest elevation of the ground within the cell (m a.m.s.l.). Vertical transport of water (and its isotopes) through vadose zone at non-paddy areas is computed with a 5layered tank model. Downward water flux from the k-th tank at the n-th cell (qkn , m/d) and the change in water depth in the tank (dkn , m) are given as follows:

where ck = tank parameter. Only for the 1st tank, equation 8 is replaced by the following.

where P = precipitation (m/d); Ep = potential evapotranspiration estimated by Thornthwaite’s method (m/d); and cE = parameter representing regulation of actual evapotranspiration by soil moisture conditions. Vertical fluxes below paddies are computed with another 5-layered tank model, though it is independent of that for non-paddy areas. During the irrigation period, water balance in the 1st tank is parameterized as follows:

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where d  n1 = water depth in the 1st tank (m); qPn = pumping rate (m/d); cP = ratio of the pumping rate to irrigation rate from canals; q n1 = downward water flux to the lower tank (m/d); qCn = runoff rate through canals (m/d); and cC = tank parameter controlling runoff through canals. Finally, INn , IPn and OPn are given as:

where AP = area of paddies in the cell (m2 ). Tracer concentration corresponding IRn is given by measured δ for the Sabi River, and those corresponding INn and IPn are computed from measured δ for precipitation and paddy water by considering isotope mass balance in the tank model. In this computation, we assumed that isotopic enrichment due to evapotranspiration is negligible at non-paddy areas, while paddy water reflects isotopic effects due to evaporation from water surfaces. Figure 3. Temporal variations of observed and simulated groundwater level at three locations.

3.3 Calibration and validation Parameters of cI , cR , cS , ck , cE , cP and cC are difficult to be determined a priori. In addition, it is also difficult to know spatial distribution of Syn , hnb , φn and K n . Thus, we have set initial values of those parameters considering previous studies (Sasaki et al. 1958; Fujinawa, 1981; Elhassan et al. 2001), and then calibrated by a trial-and-error method using 2004-2005 data. After the calibration, we validated the model using another dataset observed in 2006. 4

RESULTS

4.1 Simulated groundwater level and isotopic composition Figure 3 compares temporal variations of groundwater level observed at three wells with those simulated for the corresponding cells by the model after calibration. Seasonal changes in the simulated groundwater level generally agree very well with observed one for all the locations, in terms of phase and range of the variation. Although unfortunately daily fluctuations cannot be captured by monthly observation, spatial change in

the seasonal variation patterns is very similar between observed and simulated results. Figure 4 compares temporal variations of groundwater isotopic composition, as in Figure 3. Simulation and observation results are generally in very good agreement. However, it should be noted that a spikelike, drastic change in δD at the g1 well in August are not reproduced by the model. This is probably due to spatial difference in spatiotemporal resolution between the model and observation. The hydrodynamic CMC model computes monthly mean isotopic composition of groundwater as values averaged over the cell. In contrast, observed data represents instantaneous values (with one-month intervals) of isotopic composition at each well. In addition, the model cannot represent vertical stratification of tracer concentration if it was captured by observation. In fact, δD of spring water just adjacent to the g1 well is slightly different from that of g1 well water and is sometimes nearer to simulated values. Thus, the partial inconsistency between the model and observation is not crucial in evaluating the model performance.

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of paddy to be more permeable, estimated recharge flux will increase, though isotopic composition will be overestimated. Groundwater discharge to the Sabi River occurs mainly at No. 9-10 cells. In reality, the Sabi River usually revives around there. Spring discharge also occurs at the cells, while it is found in wider region from the middle to rim zones. The difference in spatial distribution patterns between river discharge and spring discharge is originated from riverbed elevation and geomorphology within the cell. Owing to these discharge fluxes, lateral groundwater flow drastically decreases and temporal variation of groundwater level is reduced in downstream cells. Groundwater extraction for agricultural use is a minor component as compared to these discharge fluxes. If we assume larger groundwater extraction, then performance in simulating both groundwater level and isotopic composition will become worse. On average, the most dominant source for groundwater recharge is precipitation fallen onto non-paddy areas, accounting for 39% of total recharge flux. Contribution from the Sabi River is approximately 31% and nearly equals to that from paddy water. Recharge flux at paddies is almost same with recharge at non-paddy areas in irrigation period and is lower in non-irrigation period. Groundwater discharge to the Sabi River was nearly correspondent to spring discharge and is greater than river water infiltration upstream.

Figure 4. Temporal variations of observed and simulated isotopic composition of groundwater. Observed data for spring water adjacent to g3 well are added in the middle panel.

Simulation results in the validation period corresponded to observation results for both groundwater level and isotopic composition as well as in the calibration period (figure not shown).Thus, the parameter values empirically determined for the calibration period seems to be robust, and the model outputs are expected to be highly reliable. 4.2

Quantitative evaluation of groundwater-surface water interaction

Figure 5 shows spatial distribution of inflows and outflows through groundwater body in each cell. River water infiltration at upstream cells is an important component driving groundwater flow system in the fan. Recharge flux at paddies (or non-paddy areas) tends to become higher (lower) in the rim zones, reflecting land use change. Unexpectedly, even in irrigation period, recharge at paddies is not superior to that at non-paddy areas. This is due to low-permeable soil layer. If we assume in the model the 1st tank

5

DISCUSSION AND CONCLUSIONS

The hydrodynamic CMC model could reproduce very well spatiotemporal variations of both groundwater level and isotopic composition, and provided good estimations of groundwater recharge/discharge fluxes associated with intensive groundwater-surface water interaction. The simulation revealed that river water infiltration at the fan apex is important in driving groundwater flow system, though on average the most dominant contributor to groundwater recharge is precipitation at non-paddy areas. Recharge from paddies is relatively small even in irrigation period. Return flow to the river and spring discharge plays a role in stabilizing groundwater level at the rim zone. There may be the other sets of parameter values showing similar performance, whereas it is difficult to specify the best, only one set of the values. Therefore, flux estimations have still some uncertainties more or less. However, simulation of isotopic composition is more sensitive to recharge-sources fraction than in simulation of groundwater level alone, so that the hydrodynamic CMC model with isotopic tracers is useful particularly for source partitioning. Fortunately, difference in isotopic signatures among source

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Figure 5. Spatial distribution of inflows and outflows through groundwater body in each cell (IG = lateral inflow; IR = recharge from the main river; IN = recharge at non-paddy area; IP = recharge at paddies; OG = lateral outflow; OR = discharge to the main river; OP = pumping rate for irrigation to paddies; OS = spring discharge.

waters is distinct in alluvial fans where intensive groundwater-surface water interaction occurs.

REFERENCES Adar, E.M., Neuman, S.P. & Woolhiser, D.A. 1988. Estimation of spatial recharge distribution using environmental isotopes and hydrochemical data, 1. mathematical model and application to synthetic data. Journal of Hydrology 97: 251–277. Adar, E.M. & Neuman, S.P. 1988. Estimation of spatial recharge distribution using environmental isotopes and hydrochemical data, 2. application to Aravaipa Valley in southern Arizona, U.S.A. Journal of Hydrology 97: 279–302. Adar, E.M. & Sorek, S. 1989. Multi-compartmental modeling for aquifer parameter estimation using natural tracers in non-steady flow. Advances in Water Resources 12: 84–89. Allison, G.B. & Hughes, M.W. 1975. The use of environmental tritium to estimate recharge to a South Australian aquifer. Journal of Hydrology 26: 245–254. Campana, M.E. & Simpson, E.S. 1984. Groundwater residence times and recharge rates using a discrete-state compartment model and 14 C data. Journal of Hydrology 72: 171–185. Campana, M.E. & Mahin, D.A. 1985. Model-derived estimations od groundwater mean ages, recharge rates, effective

porosities and storage in a lime stone aquifer. Journal of Hydrology 76: 247–264. Carroll, R.W.H., Pohll, G.M., Earman, S. & Hershey, R.L. 2007. Global optimization of a deuterium calibrated, discrete-state compartmental model (DSCM): application to the eastern Nevada Test Site. Journal of Hydrology 345: 237–253. Dahan, O., McGraw, D., Adar, E., Pohll, G., Bohn, B. & Thomas, J. 2004. Multi-variable mixing cell model as a calibration and validation tool for hydrogeologic groundwater modeling. Journal of Hydrology 293: 115–136. Elhassan, A.M., Goto, A. & Mizutani, M. 2001. Combining a tank model with a groundwater model for simulating regional groundwater flow in an alluvial fan. Transaction of JSIDRE 215: 21–29. Fujinawa, K. 1981.The role of water for agricultural use as the source of groundwater recharge; analysis of groundwater in Nasuno-ga-hara basin by a mathematical model. Bull. Natl. Res. Inst. Agric. Eng. Japan 21: 127–141. Harrington, G.A., Walker, G.R., Love, A.J. & Narayan, K.A. 1999. A compartmental mixing-cell approach for the quantitative assessment of groundwater dynamics in the Otway Basin, South Australia. Journal of Hydrology 214: 49–63. Kirk, S.T. & Campana, M.E. 1990. A deuterium-calibrated groundwater flow model of a regional carbonate-alluvial system. Journal of Hydrology 119: 357–388. McDonald, M.G. & Harbaugh, W.A. 1988. A modular threedimensional finite difference ground-water flow model.

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Techniques of Water Resources Investigations of the U.S. Geological Survey, book 6, chap. A1. Washington D.C.: U.S. Government Printing Office. Przewlocki, K. & Yurtsever, Y. 1974. Some conceptual mathematical models and digital simulation approach in the use of tracers in hydrological systems. In Isotope Techniques in Groundwater Hydrology Vol. 2: 425–450. Vienna: International Atomic Energy Agency. Sasaki, M.,Ajisaka,T. & Okamoto,A. 1958. Hydrogeology of the Nasu plain, Tochigi Prefecture. Journal of Geography 67: 59–73.

Wakui, H. & Yamanaka, T. 2006. Sources of groundwater recharge and their local differences in the central part of Nasu fan as revealed by stable isotopes. Journal of Groundwater Hydrology 48: 263–277. Watanabe, K., Sagehashi, N. & Shindo, S. 1960. Geological structure of the Nasu Plain, Tochigi Prefecture, with special reference to the ancestral river course of the Naka River. The Journal of the Geological Society of Japan 66: 113–122.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

The thermal effect of groundwater flow on temperature distribution in the Sendai Plain H.G.L.N. Gunawardhana∗ & S. Kazama Department of Environmental Engineering, Tohoku University, Japan

M. Sawamoto Department of Civil Engineering, Tohoku University, Japan

ABSTRACT: Understanding the response of aquifer thermal regime on different climatic events is important to predict the effects of climate change in groundwater aquifers. A heat transport model that incorporates the effect of groundwater flow was developed to estimate the seasonal heat flux change in the Sendai Plain. Domenico & Palciauskas’s analytical solution for 2-D heat transport was used to investigate the heat flow during four months time period (From 01-May-2007 to 31-August-2007). Potential distribution of groundwater flow was simulated by means of the MODFLOW numerical code. Simulated results were verified with observed water level and temperature records at three observation wells. 47.5 mW/m2 of maximum vertical heat flux was estimated near to the Nanakita River and it also noticed about 17% heat flux change during two extreme precipitation events of simulation time. Keywords:

1

groundwater; temperature effect; heat flux; MODFLOW; Sendai Plain

INTRODUCTION

Simultaneous transport of heat with groundwater flow has been lead to the many research studies in groundwater hydrology. Aquifer temperature is sensitive to changes in groundwater flow patterns; because heat in the subsurface layer is transported not only by the conduction but also from the convection of groundwater. On the other way, thermal effects cause significant changes in hydraulic conductivity since density and viscosity of water are temperature dependents (Janes 1990, Constantz et al.1994, Constantz and Thomas 1997 and Constantz 1998). Apart from its ecological importance, studying the responses of aquifer thermal regime on different climatic events is therefore might be important to understand the effects of climate change in groundwater aquifers. Considering the fact that simultaneous transport of heat with groundwater flow, Stallman (1963) derived a partial differential equation for conductive and groundwater convective heat transfer. Analytical solutions for Stallman’s equation with appropriately simplified assumptions were subsequently presented by him self and several other researchers. Stallman (1963) proposed a solution for subsurface temperature change ∗

Corresponding author ([email protected])

under the steady state conditions with considering only the vertical conduction and horizontal groundwater flow. Following a quite different approach, Bredehoft and Papadopulos (1965) and Mansure and Reiter (1979) presented a curve matching methods for temperature data in wells to solve the analytical solution for one-dimensional steady state groundwater flow and heat transfer. In those studies groundwater velocity was permitted only in the vertical direction. Considering the importance of both horizontal and vertical groundwater velocities, Lu and Ge (1996) and Reiter (2001) solved the Stallman’s equation under the steady state condition including the horizontal and vertical specific discharge components. Instead of taking the horizontal and vertical flow components separately, Domenico & Palciauskas (1973) considered potential distribution of the two-dimensional flow field and obtained an analytical solution for the two-dimensional temperature distribution. Taniguchi (1995) applied this method to analyze the long term reduction in groundwater temperature due to pumping in the Nara Basin, Japan. As a resource of thermal energy in coastal aquifers, understanding its behavior under different climatic events is important to predict the response of aquifer thermal regime on future climate change. The objective of this study is therefore to estimate the

329

seasonal heat flux change due to seasonal change of groundwater level in the Sendai Plain. 2 THEORY Stallman’s partial differential equation for combined conductive and groundwater convective heat transfer is:

where T = groundwater temperature; t = time; v = velocity of groundwater; ρw = density of water; Cw = specific heat of water; ρws = density of porous medium; Cws = specific heat of porous medium; and kws = thermal conductivity of porous medium. When the density gradient is insignificant and the water movement is forced convective; Darcy’s law can be expressed as

where K = hydraulic conductivity; and h = total hydraulic head of the groundwater. Domenico & Palciauskas simplified the Equation 1 combined with Equation 2 for the steady state incompressible water flow through homogeneous porous medium and for the steady state temperature distribution as:

where α = kws /ρw Cw . When the boundary conditions of the model agree with the Figure 1, potential distribution of the two-dimensional flow field was obtained as:

Figure 1. Boundary conditions.

The customary method of determining the heat flow due to conduction is to multiply observed or simulated temperature gradient by the rock conductivities. In the presence of significant groundwater flow, the total heat flow includes not only the conduction but also the heat energy carried by the mass movement of fluids. For the case of steady water flow, general expression can be derived as follows;

where, E = vertical heat flux; and θ is the effective porosity. When the derivatives of Equation 4 and 5 are obtained and substitute in Equation 6, appropriate value for E can be obtained.

3

When the groundwater head varies in accordance with the Equation 4, first order approximation solution for the temperature distribution in Equation 3 was presented as:

where T1 = constant temperature at the ground surface; T0 = constant temperature gradient across the lower boundary; L = distance between the recharge and the discharge area of the basin; z0 is the water head referring to the bottom boundary; and A and B are constants as shown in Figure 1.

STUDY AREA AND DATA OBSERVATIONS

The Sendai Plain locates in the Tohoku prefecture, Japan, extends about 40 km with a width of 10 km bounded by a mountain region (elevation 10001500 m) in the west. Three main rivers, Nanakita, Natori, and Abukuma emerge from the mountain regions, and flow toward the sea. There are three water level observation stations (5060 m depth) are located in the area (Fig. 2), which are used to measure the land subsidence in the Sendai. Temperature loggers with 0.2◦ C accuracy were used to measure the continuous one hour groundwater temperature at selected depths up to 60 m in wells W1, W2, and W3 (Figs 3a-c). In addition to that, instantaneous temperature measurements were taken in every two months in one meter depth intervals where the well screen exists. Moreover, one hour groundwater levels were also taken in each well throughout the simulation period (Fig. 4a-c).

330

Figure 2. Location of the observation wells.

4 4.1

METHODOLOGY AND DATA ANALYSIS Groundwater flow simulation

Numerical simulation of three-dimensional groundwater flow was performed. MODFLOW, one of the most widely used groundwater model was used. Model boundary was selected to include not only the Sendai Plain, but also the mountain area to represent proper groundwater recharge at the plain. 250m × 250m grid space was selected. The main aquifer of the study area is alluvial and shallow (about 60-80m) compared with other plains such as the Kanto Plain (more than 2000 m) around Tokyo Metropolitan area. The assumption of model boundary specifies that the bottom boundary is impermeable (at z = 0). Uchida and Hayashi (2005) estimate that the hydraulic conductivity of the soil below the main aquifer in the Sendai Plain is significantly less than (approximately 104 times) the hydraulic conductivity of main aquifer. Therefore, it was assumed that main aquifer is unconfined and bounded by an impermeable layer below that. Furthermore, Domenico & Palciauskas solution assumed that porous medium is homogeneous. Though, the MODFLOW has the ability to apply in heterogeneous porous medium, this study considered that the main aquifer is homogeneous to cope with the made assumption. The hydraulic conductivity of the porous medium was determined based on the pumping well test results from city office and past research studies. Evapotranspiration is the primary process of water transfer in the hydrological cycle. Accurate estimation of actual evapotranspiration is therefore great important for reliable groundwater flow simulation. 24-h FAO PM Equation, which is first proposed by Allen et al. (1994) was used to estimate the daily reference evapotranspiration. 24-hour temperature, relative humidity, wind speed, and solar radiation records from the Japan Metrological Agency (JMA) at the Sendai metrological station were obtained.

Figure 3. Temperature change with depth at a) W1, b) W2, & c) W3.

Nature of the land cover strongly affects the actual evapotranspiration and infiltration of rainfall. This study classified the land area for seven categories; 1) Forest area, 2) Agricultural area, 3) Rural area, 4) Low density Housing area, 5) High density Housing and commercial area, 6) Industrial area, and 7) water bodies based on the GIS and land use maps. Daily average precipitation data from JMA was obtained. Two different factors for each kind of land use type were assumed and they were multiplied by reference evapotranspiration and total precipitation to obtain the actual evapotranspiration and recharge values for each kind of land use categories. In addition, other groundwater recharge and discharge stresses such as the Nanakita and the Natori Rivers and small Water bodies as general head boundaries also included. Required parameters such as hydraulic conductivity of the river bed and land use category factors were assumed and adjusted by the model calibration.

331

Continuous and the instantaneous temperature records only within the well screen were taken as the representative groundwater temperature for model fitting. This is because, the temperature records at the depths, which are not directly connect with the natural groundwater flow through the well screen, do not include the heat transfer component from the horizontal convection process. When the appropriate temperature distributions of each well were obtained, Equation 6 was used to estimate the heat flow and heat flux change during two events.

5

Figure 4. Water level fluctuations with rainfall at a) W1, b) W2, & c) W3.

4.2 Temperature distribution and heat flux change Climate events within two time periods (event 1 from 01-May-2007 to 30-June-2007 with average precipitation of 4.4 mm/day and event 2 from 01-July2007 to 31-August-2007 with average precipitation of 7.1 mm/day) were considered. Time periods were selected based on the amount of precipitation and temperature raise in the observation wells. Minimum water levels during first event (0.92 m, 2.54 m and 2.69 m in W1, W2 and W3 respectively) and maximum water levels during second event (1.77 m, 3.71 m and 4.22 m in W1, W2 and W3 respectively) were selected for heat flux calculation. Observed water level data and the simulated results from the groundwater flow simulation were matched with the Domenico & Palciauskas’s solution for groundwater potential (Equation 4) to obtain the values for z0 , L and B during two events. These results were applied in Equation 5 to estimate the steady state temperature distribution in each well.

RESULTS AND DISCUSSION

Coastal aquifers are influenced by upcoming salt water from the sea. The periodic rise and fall of the tide water stage in the ocean produces sinusoidal groundwater level fluctuations in adjacent inland aquifers. To eliminate this effect in the numerical simulation, 24-hour time steps were selected and the daily average water level records were used to verify the numerical results. Temperature variations at W1 and W2 show steady state temperature distribution with the aquifer depth. Short term temperature fluctuations at the surficial zone (within the depth of about 10m from the ground surface) potentially results from the high intensity rainfall occurred before (Figs 3a-b) and it become steady afterwards. Temperature at the geothermal zone (below the depth of around 10m) remains constant (or the variation is less than 0.2◦ C, which is undetectable to the recorders) throughout the observed period. Unlike to the W1 and W2, temperature profile at the deepest point of W3 (Fig. 3c) shows regular fluctuations which possibly caused by the heat generations due to chemical reactions in the mixing zone. Therefore, arithmetic average of maximum and minimum values was taken as the representative temperature results from the heat conduction and groundwater convective heat transfer towards the sea direction. Groundwater flow simulation shows good agreement with the observed water level records in all the well points during two events (Figs 5a-b). Simulated water levels along the lines a-b, c-d and e-f (Fig. 2) towards the sea during the first event are shown in figure 6. These profiles well follow the Domenico and Palciauskas equation of two-dimensional flow field. Physical properties were used in the calculations and the constrained parameters by matching the simulated water levels and Domenico & Palciauskas’s approximation and the estimated heat flux during two events are shown in the Tables 1-2 respectively. Temperature gradient in the Sendai Plain varies systematically from a high of 0.036 ◦ C/m around the Nanakita River area, to a low of 0.009 ◦ C/m near the Natori river. These temperature gradients well match with the Uchida & Hayashi (2005) observations in

332

Figure 5. Comparison of numerical results with observed water level; a) event 1 & b) event 2.

the Sendai Plain. Moreover, the values ofKB/α (11391281) lie in the same range with the Taniguchi (1995) findings in the Nara Basin, Japan (1227). The parameter B alters the mean water table elevation above the z0 when the recharge and discharge activities take place and contributes to the convective heat transport in the aquifer. Dimensionless parameter (=KB/α) in the Equation 5, therefore appears to be analog of the Peclet number, which is the ratio of heat carried by groundwater flow to its transfer by conduction. Taniguchi (1995) used Domenico & Palciauskas’s method to estimate the reduction of groundwater temperature due to pumping in the Nara Basin, Japan. In his study, it was assumed that z0 and A are equal throughout 30 years and decreasing hydraulic head was represented by decrease in the value from B. In our study, it brought the attention that increasing hydraulic head from event 1 to event 2 corresponds to increase the value of B as well as the value of A, which balance the quantity between A to A+B and A to A−B (Fig. 1). Constrained parameters were used in the Equations 5-6 to obtain the corresponding temperature distribution (Fig. 7) and heat flow. Highest heat flux was estimated near to the Nanakita River and about 17%

Figure 6. Comparison of numerical results with the Domenico & Palciauskas’s approximation; a) W1, b) W2 & c) W3. Table 1.

Physical properties used in simulations.

K m/s

k W/m K

ρ kg/m3

C J/kg◦ C

5E-6

1.2

1000

4186

of change was observed during two events. Temperature fluctuation on the top of the groundwater table (due to ground surface temperature change and relatively warm water mixing by the infiltrated water from precipitation) penetrates below the aquifer in various magnitudes at different depths by heat conduction and groundwater advection. The amplitude of temperature fluctuation decays with depth. Taniguchi (1994) observed maximum of 5.3◦ C amplitude of temperature

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Table 2.

Constrained parameters and results. A m

B m

L km

z0 m

T0 ◦ C/m

q mW/m2

Event 1 W1 W2 W3

216.2 219.1 217.1

65.3 68.3 68.6

21.0 19.0 18.5

80 80 80

0.036 0.026 0.009

47.5 35.9 11.9

Event 2 W1 W2 W3

215.9 216.2 216.1

64.2 68.2 66.2

21.0 19.0 18.5

80 80 80

0.030 0.015 0.009

39.5 20.3 11.7

Events

change at 2 m depth due to ground surface temperature change and it becomes insignificant below depth of 15-20 m. In this study, relatively warm precipitated water mixes with the groundwater in the surficial zone which causes some significant temperature fluctuation in relatively shallow aquifer depths. Observations in Fig. 3 show that temperature fluctuations experienced just below the water table do not continue below the depths of 15-20 m. Therefore, during the simulated time, groundwater temperature at the deeper aquifer depths remains constant. However, temperature raise only in the surficial zone reduces the temperature gradient across the lower boundary and therefore, final heat flow during the second event become smaller than the first event.

6

CONCLUSIONS

Groundwater flow in the Sendai Plain was simulated using the MODFLOW numerical code. These results were matched with the Domenico and Palciauskas’s solution for two-dimensional temperature distribution. Simulated results of both water level and temperature depth profiles show good agreement with the observed water level and temperature records. Furthermore, heat flux change was calculated in two events during four months time period. Relatively warm precipitated water infiltrates and mixes with the groundwater which causes some significant temperature change in the surficial zone. This behavior reduced the vertical heat flow during the second event than the first event. However, the groundwater temperature at the grater aquifer depths (depth below 15-20 m in the Sendai Plain) remained constant during the simulation time and it suggest that the temperature fluctuations experienced in shallow aquifer depths do not continue to the grater depths. Heat transport model developed in this study was able to explain the behavior of aquifer thermal regime on different climatic events and will be important

Figure 7. Comparison of simulated temperature profiles with observed temperature values during the second event.

to understand the aquifer response on global climate change in future.

ACKNOWLEDGEMENTS This work was supported by the Global Environment Research Fund (S-4) of the Ministry of Environment, Japan. We are also grateful to Mariko Izumi in Environmental Protection Division, Sendai city office and Seiki Kawagoe in HEST, Tohoku University for support in observations. REFERENCES Allen, R.G. et al. 1994. An update for the definition of reference evaporation. ICID Bul 43: 1–33.

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Bredehoft, J.D. & Papadopulos, I.S. 1965. Rates of vertical groundwater movement estimated from the earth’s thermal profile. Water Resources Research 1(2): 325–328. Constantz, J. 1998. Action between stream temperature, streamflow, and groundwater exchanges in Alpine streams. Water Resources Research 34(7): 1609–1616. Constantz, J. & Thomas, C.L. 1997. Streambed temperatures profiles as indicators of percolation characteristics beneath arroyos in the Middle Rio Grande Basin, USA. Hydrologic Processes 11(12): 1621–1634. Constantz, J. et al. 1994. Influence of diurnal variations in stream temperature on stream flow loss and groundwater recharge.Water Resources Research 30(12): 3253–3264. Domenico, P.A. & Palciauskas, V.V. 1973. Theoretical analysis of forced convective heat transfer in regional groundwater flow. Geological Society of American Bulletin 84: 3803–3814. Janes, D.B. 1990.Temperature variations effects on field measured infiltration. Journal of the Soil Science Society of America 54(2): 305–312. Lu, N. & Ge, S. 1996. Effects of horizontal heat and fluid flow on the vertical temperature distribution of a semiconfining layer. Water Resources Research 32(5): 1449–1453.

Mansure, A.J. & Reiter, M. 1979. A vertical groundwater movement correction for heat flow. Journal of Geophysical Research 84(B7): 3490–3496. Reiter, M. 2001. Using Precision temperature logs to estimate horizontal and vertical groundwater flow components. Water Resources Research 37(3): 663–674. Stallman, R.W. 1963. Computation of ground-water velocity from temperature data. In Bentall R. (ed.), Method of collecting and interpreting Ground-Water Data; Water Supply Paper 1544-H, 36–46. Washington, DC: USGS. Taniguchi, M. 1995. Anlysing the long term reduction in groundwater temperature due to pumping. Journal-des Sciences Hydrologiques 40(3): 407–421. Taniguchi, M. 1994. Estimated recharge rates from groundwater temperatures in the Nara basin, Japan. Hydrogeology Journal 2(4): 7–14. Uchida,Y. & Hayashi, T. 2005. Effects of hydrogeological and climate change on the subsurface thermal regime in the Sendai Plain. Physics of the Earth and Planetary Interiors 152: 292–304.

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Thorium and uranium concentrations in 44 Japanese river waters – Possible uranium addition from agricultural fields to river waters K. Tagami∗ & S. Uchida National Institute of Radiological Sciences, Chiba, Japan

ABSTRACT: Due to the application of phosphatic fertilizers to agricultural fields, river concentrations of uranium (U) may increase since any excess amounts of their irrigation water are returned to rivers. The U and thorium (Th) concentration ratio is of interest to check for possible U addition from agricultural fields. Thus water samples at the upper stream, the middle part (usually surrounded by agricultural fields), and the lower stream from each of 44 major Japanese rivers were collected and their U and Th concentrations were measured by inductively coupled plasma mass spectrometry. The geometric means of U/Th ratios in the upper stream, the middle part, and the lower stream were 1.38, 2.01 and 2.18 respectively. The results suggested possible U addition from the agricultural fields. Keywords: agricultural fields; inductively coupled plasma mass spectrometry; phosphatic fertilizers; river water; thorium; uranium

1

INTRODUCTION

Thorium (Th) and uranium (U) behaviors in geological environments are relatively close to each other compared to their behaviors and those of other elements. High relationships between their concentrations are usually observed in rocks, non-agricultural field soil samples and river sediments, etc. Indeed, in Japan the concentration ratios of U/Th in these types of environmental samples are almost the same, being about 0.20-0.28 (Yamagata & Iwashima 1967, Yoshida et al. 1998, Geological Survey of Japan 2004, Takeda et al. 2004). However, in any environment, Th is very insoluble, and U is often more soluble than Th, thus the U/Th ratio slightly changes with weathering conditions. Moreover, mobilities of U and Th in soil have been measured as soil-soil solution partitioning coefficient, KD , which is defined as the ratio of radionuclides in the solid and liquid phases (Bq per kg soil/Bq per L solution = L kg−1 ). From the compiled data (IAEA 1994), it was clear that KD values for U were lower than those for Th. From the results, it would be possible to explain why U/Th ratio was slightly lower in river sediment (0.20) than in Japanese crust (0.28) as reported by Geological Survey of Japan 2004. In the river water environment, the chemical form of U should be uranyl ion or a complex with carbonate, and those are soluble forms, while Th is very ∗

Corresponding author ([email protected])

particle reactive so that Th concentration in river is expected to be small. Thus, potentially, U/Th ratios in the river water environment would be higher than those found in rock, sediment or soil. In addition, it also is necessary to think about the application of phosphatic fertilizers to agricultural fields. Recently, we reported that half of the U concentration in agricultural fields was from phosphatic fertilizers (Tagami & Uchida, 2006); indeed, the geometric means of U/Th ratios in paddy fields and upland fields were 0.49 and 0.47, respectively, which were higher than those of non-agricultural fields and river sediments. Since any excess amounts of irrigation water from the agricultural fields, i.e. rice paddy fields, are returned to the rivers, the U and Th concentration ratio is of interest to check for possible U addition from agricultural fields. Thus we measured U and Th concentrations in 44 major Japanese rivers and compared U/Th ratios in the upper stream with those in the middle part and the lower stream.

2

EXPERIMENTAL

2.1 Sample collection The selected rivers are shown in Figure 1 and sampling was carried out in 2002–2006. Ten samples per river were collected from the upper stream to the river mouth. Only 2–3 days were spent in collection at any one river, because river conditions can be affected by

337

Figure 1. Rivers selected for sampling.

the weather and the season. At each sampling site, electrical conductivity (EC) and pH were measured. A cleanly washed 100 mL polypropylene bottle was filled with filtered (ø < 0.45 µm) river water and acidified with ultra pure nitric acid (Tama Chemicals, AA-100) to directly use for measurements by inductively coupled plasma mass spectrometry, ICP-MS (Yokogawa, Agilent 7500c). Figure 2 shows the EC distributions for points-1, 5, 9 and 10. It was clear that 4 sampling points of the lowermost sampling point (point-10) contained a seawater effect, so all point-10 samples were excluded. As around the middle part, agricultural fields were larger than fields near point-1, we wanted to include samples from both for a broader analysis. Finally, we used point-1 (the uppermost sampling point), point5 (the middle part), and point-9 (one point above the lowermost sampling point) for this study. 2.2 Measurement The concentrations of dissolvedTh and U in the sample water were directly measured twice by ICP-MS within 1 month after the collection. Standard solutions for ICP-MS were prepared from multi-element standard solutions (SPEX CertiPrep, XSTC-355). Detection limit for Th and U by ICP-MS was around 0.2 ng L−1 . 3 3.1

RESULTS AND DISCUSSION Concentrations of U and Th in river waters

The results for Th and U are shown in Figures 3A and 3B. Each showed a log-normal distribution of

Figure 2. Probability distributions of EC for point-1(the uppermost sampling point) to point–10 (the lowermost sampling point). The circled four points were affected by the seawater.

concentration, thus, geometric means (GMs) were calculated. The GMs of Th and U concentrations in the upper steam water samples (point 1) were 4.5 ng L−1 (range: 1.0 − 26.1 ng L−1 ) and 6.2 ng L−1 (range: 1.0 − 139 ng L−1 ), respectively. For the middle stream sample, the GMs of Th and U were 5.8 ng L−1 (range: 1.1 − 28.0 ng L−1 ) and 11.6 ng L−1 (range: 1.6 − 117 ng L−1 ), respectively. As can be seen from Figures 3 and 4, Th concentration was slightly increased, but U concentration was doubled: the difference was clear from the t-test (p < 0.01) when the concentrations were converted into logarithmical forms (log[U]). At point-9, the GMs of Th and U concentrations were 6.1 ng L−1 (range: 1.7 − 39.7 ng L−1 ) and 13.3 ng L−1 (range: 2.1 − 125 ng L−1 ), respectively. Both concentrations were slightly increased from those of point-5, and especially when log[U] values were compared, a significant increase at point-9 was observed, though log[Th] did not show any difference between point-5 and point-9. From these results, as we expected, possibly due to application of phosphatic fertilizers in agricultural fields, U concentration increased from the upper steam to the lower stream, while no significant increase was found for Th. If evaporation ratio was high, then concentrations of both elements should in-crease, but in this case, only U did. Since Japanese rivers flow fast due to the slope, that is, the distances from their mountainous source areas to the river mouths are usually short, the effect of evaporation would not be significant.

338

stream to the lower stream; the values from point-1 to point-10 were 1.38, 1.67, 1.78, 1.77, 2.01, 2.06, 2.18, 2.18, 2.18 and 2.38, respectively. Major Th and U additions to point-1 would originate from bedrock, but the U/Th concentration ratio was not the same as those of Japanese crust and river sediments (Geological Survey of Japan 2004). The ratio at point-1 indicated high U solubility and low Th mobility in the river environment as mentioned above. For the middle parts of rivers, Th and U additions originated from bedrock would be one of the sources, but also, as we hypothesized above, the U addition from fertilizer would be a major source; indeed, again, the U/Th ratios in agricultural fields were higher than those of crust and sediments as shown in Table 1. Although increase of U concentration in the middle and down streams was found, only the addition of U to the river water would not explain U/Th increases in these streams. In the middle and down streams agricultural activities could produce more sediment erosion, which potentially yielding a low Th in the stream since Th was absorbed by the sediment. In the present study, Th concentrations increased from point-1 to point-5, but then, the concentration did not show any big difference between point-5 and point-9. Possibly due to these effects, U/Th ratios increased from the upper stream to the down stream. The U/Th concentration ratios were then converted into logarithmical forms (log[U/Th]) because the distribution pattern U/Th ratios were a log-normal type. In Figure 4, log[U/Th] of point-1 and point-5 are plotted against log[U/Th] of point-9. We found no significant correlation in the log[U/Th] at point-1 and point-9. However, log[U/Th] at point-5 and point-9 were well correlated (n = 44, r = 0.5, p < 0.01). This fact may indicate that the sources of U (and Th) for the middle part and the lower stream were almost the same, and the major source would be agricultural fields. Although U concentration might increase through U addition from agricultural fields, the WHO guideline value for U in drinking water (WHO 2006) is 0.015 mg L−1 , and the U values we found in river waters at point-5 and -9 were still ca. 1000 times lower than this so that the dose effect due to the excess U is negligible.

4

Figure 3. Probability distributions of Th (A) and U (B) concentrations at point-1, −5 and −9.

3.2

Concentration ratios of U/Th

Concentration ratios of U/Th are summarized in Table 1. The GMs of U/Th ratios increased from the upper

CONCLUSIONS

Water samples at the upper stream (point-1), the middle part (point-5), and the lower stream (point-9) from each of 44 major Japanese rivers were collected and their U and Th concentrations were measured by ICPMS. When Th and U concentrations were compared between point-1 and point-5, Th concentration was slightly high at point-5, but U concentration was doubled (11.6 ng L−1 for U at point-5). These results and

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Table 1.

U/Th concentration ratios in several terrestrial samples. U/Th concentration ratio

Sample

Minimum

Maximum

GM

Note

Japanese crust Paddy fields River sediments Point-1 Point-5 Point-9

– 0.25 0.04 0.11 0.31 0.42

– 1.09 1.65 41.2 29.4 73.4

0.28 0.49 0.2 1.38 2.01 2.18

Geological Survey of Japan 2004 Tagami & Uchida 2006 Geological Survey of Japan 2004 The uppermost sampling point The middle part The lower point

REFERENCES Geological survey of Japan, 2004.Elemental distribution in Japan -Geochemical map of Japan-. Tsukuba: Advanced Industrial Science and Technology. Available at http://riodb02.ibase.aist.go.jp/geochemmap/index.htm. IAEA, 1994. Handbook of parameter values for the prediction of radionuclide transfer I temperate environments. Technical report series No.364. Vienna: IAEA. Tagami, K. & Uchida, S. 2006. Use of a natural U/Th concentration ratio for estimation of anthropogenic uranium concentration in Japanese agricultural soils due to application of phosphatic fertilizers. Radioisotopes 55: 71–78. In Japanese. Takeda, A., Kimura, K. & Yamasaki, S. 2004. Analysis of 57 elements in Japanese soils, with special reference to soil group and agricultural use. Geoderma 119: 291–307. WHO 2006. Guidelines for Drinking-water Quality, Third edition, vol.1: recommendations. Geneva: WHO. Yamagata, N. & Iwashima, K. 1967. Terrestrial background radiation in Japan., Health Physics., 13: 1145–1148. Yoshida, S. Muramatsu, Y., Tagami, K. & Uchida, S. 1998. Concentrations of lanthanide elements, Th and U in 77 Japanese surface soils, Environmental International 24: 275–286.

Figure 4. Log [U/Th] of point-1 and -5 plotted against those of point-9.

the concentration ratios of U/Th suggested possible U addition from the agricultural fields, though the dose effect due to the excess U is negligible. ACKNOWLEDGEMENT This work has been partially supported by the Agency for Natural Resources and Energy, the Ministry of Economy, Trade and Industry (METI), Japan.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Roles of deep bedrock groundwater in surface hydrological processes in a headwater catchment K. Kosugi∗, S. Katsura, T. Mizutani, H. Kato & T. Mizuyama Graduate School of Agriculture, Kyoto University, Kyoto, Japan

K. Goto & K. Ishio Rokko Sabo Office, Kinki Division, Ministry of Land, Infrastructure and Transport, Kobe, Japan

ABSTRACT: This study showed that two different components of groundwater were combined within a single well excavated in a soil mantle. One component (EG) showed an ephemeral-type response, and the other component (SPG) showed a semi-perennial-type response. The generation and cessation of SPG were well explained by the antecedent precipitation index, which represents the long-lasting effects of antecedent rainfall. Hydrogeochemical observations of soil mantle groundwater and bedrock groundwater indicated that the sources of EG were storm rainwater and preevent solum water, whereas the source of SPG was deep bedrock groundwater. SPG led to considerably high antecedent soil moisture conditions between storms, which may affect base flow discharge and biogeochemical processes in the soil mantle, and also facilitate an increased peak runoff and shallow landslides during a later storm event. Thus, this study showed that deep bedrock groundwater plays important roles in surface hydrological processes in headwater catchments. Keywords:

1

bedrock; granite; headwater catchment; discharge; landslide

INTRODUCTION

Previous studies have confirmed that perched groundwater formation in soil mantle greatly affects rainwater discharge and slope instability on steep landscapes (e.g., Anderson & Sitar, 1995). For the purpose of predicting hydrographs and shallow landslides, physical models, based on GIS information such as upslope contributing areas and local slope gradient, have been developed and tested, including the well-known TOPMODEL (Beven et al., 1984) as well as models pioneered by Okimura & Ichikawa (1985). Although such models can calculate topographically driven convergence of rainwater and groundwater table developments in soil mantle, the accuracy of these physical models is still limited mainly because they ignore storm responses in underlying bedrock (Wilson & Dietrich, 1987). Many studies have inferred or implicated that bedrock groundwater plays a significant role in storm runoff generation (Wilson et al., 1993), base flow discharge (Burns et al., 1998), and occurrences of landslides (Montgomery et al., 2002). However, how the bedrock groundwater affects the soil mantle groundwater is not fully understood yet. ∗

Corresponding author ([email protected])

In this study, we discuss a case that two different components of groundwater are combined in a single well excavated in soil mantle, which has been suggested by our previous study (Kosugi et al., 2008). One component shows an ephemeral-type response, and the other component shows a semi-perennialtype response. Sources of these two components are detected by borehole drillings and intensive hydrogeochemical observations, and effects of bedrock groundwater on surface hydrological processes are shown.

2

METHODS

For 5 years from 1995 through 1999, we conducted long-term monitoring of water tables within the soil mantle that developed on a steep topography in Japan’s Rokko mountain range (34◦ 46 N, 135◦ 16 E), which consisted of granite and had a peak of 931.6 m above sea level, a mean annual temperature of 15◦ C, precipitation of 1600 mm, maximum snow depth of 10 cm, and 10 snow cover days. Two wells were installed in the soil mantle at points A and B in Fig. 1, situated along a main hollow that developed on the southwest side of one of main crests. Wells A and B had depths

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of 270 and 200 cm, respectively. Water levels in the wells were automatically measured by using pressure gauges. Precipitation was measured using a tipping bucket rain gauge at an open space near the ridge (Fig. 1a). Because all measurements were closed for winter, we complementarily used precipitation data from weather stations managed by Japan Meteorological Agency (AMeDAS), which are located within 6.9 km from the study site. For the purposed of detecting sources of groundwater, we measured groundwater temperature and SiO2 concentration. Wells were reinstalled near the points A and B in June 2004 because the previous wells had been removed in 1999. The new A and B wells had depths of 227 and 115 cm, respectively. Water samples for the concentration measurement were taken from cups installed in each well at 10-cm intervals, from the ground surface to the bottom of the well. Each cup collected water when the groundwater level was higher than its height. Hence, the water samples express the groundwater SiO2 concentration just before the groundwater level decreased below the cup height (Kosugi et al., 2008). The measurements were conducted in 2006. For getting geological survey, boreholes were drilled at points R1 and R2 in Fig. 1 in August 2005, which had depths of 35 and 38 m, respectively. Rock samples collected from the boreholes were classified based on their degree of weathering, and their saturated hydraulic conductivities were measured by the instantaneous head recovery technique and the water injection technique. Temperature and SiO2 concentration of bedrock groundwater were monitored. Resistivity image profiling along the dotted line in Fig. 1a was conducted on 13 January 2006. 3

RESULTS AND DISCUSSION

3.1 Long-term monitoring In well A, groundwater developed under heavy rainfall conditions, and disappeared just after the rainfall ceased (Figs. 2b and 2g). Such ephemeral groundwater (EG) in soil mantle is commonly observed worldwide in watersheds with various climate and geology. In well B, we observed similar EG in the first half of the observation period (Fig 2c). However, the groundwater table generated at the end of July 1997 was quite unusual; it did not disappear after the rainstorm event and lasted for 58 days, maintaining a level of about 90 to 110 cm (Fig 2h). This semi-perennial groundwater (SPG) suddenly disappeared at the end of September 1997. The generation of SPG was observed twice in both 1998 and 1999. SPG showed gradual changes in level, with values from 85 to 110 cm, even when no precipitation had occurred for a few weeks. Moreover, the peaks in SPG lagged behind heavy rainstorm events by about 2 weeks.

Figure 1. (a) Topography and locations of soil mantle wells A, B, C, and boreholes R1 and R2, and (b) resistivity (in ohm-m) profile along the dotted line in Fig. 1a, where the contour line intervals are 5 m and an open square represents the location of the rain gauge. At boreholes R1 and R2 in Fig. 1b, open, light gray, dark gray, and solid squares represent soil, strongly weathered rock (DH to CL classes), moderately weathered rock (CL to CM classes), and weakly weathered rock (CM class) layers, respectively. Fig. 1b shows the groundwater table in R2 when resistivity image profiling was conducted on 13 January 2006. No groundwater was observed in R1 on the same day.

To evaluate the residual effects of previous precipitation on the generation of EG and SPG, we used the antecedent precipitation index PM (t) [mm] (Descroix et al., 2002) computed as:

where t is time [h], Rh (t) [mm] is total precipitation from t − 1 to t, and M [h] is the half-life

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Figure 2. (a, f) Daily precipitation, groundwater levels in wells (b, g) A and (c, h) B, and antecedent precipitation indices with half-life periods of (d, i) 11 hours and (e, j) 73 days. Solid black lines in Figs. 2d and 2i indicate periods when ephemeral groundwater (EG) was observed in well B. Solid black lines in Figs. 2h and 2j indicate periods when semi-perennial groundwater (SPG) was observed in well B. Broken lines in Figs. 2d and 2i indicate the criti-cal value, PM ,C , for discriminating between the generation and cessation of EG in well B. Broken lines in Figs. 2e and 2j indicate the critical value, PM ,C , for discriminating between the generation and cessation of SPG in well B. Measurements of groundwater levels in wells A and B were started on 27 March and 5 June 1995, respectively.

period which indicates that the effect of precipitation decays to one-half the initial value after M hours from its occurrence. We assumed that groundwater is generated when PM (t) ≥ PM ,C and ceases when PM (t) < PM ,C , where PM ,C [mm] is the critical value for discriminating between the generation and

cessation of groundwater. We changed M between 1 h and 100 days, and optimized PM ,C for each M so that it gave the minimal discrimination error, ER , which was computed as an average of the misdiscrimination ratios for the generation and cessation of the groundwater table.

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Results showed that the generation and cessation of EG in well A was best discriminated (that is, ER became the smallest) when M = 11 h. The same M value produced the smallest ER for EG in well B; that is, the occurrence of EG was mainly governed by the rainfall intensity of the current storm event. In Figs. 2d and 2i, occurrences of EG in well B corresponded well with the period when P11h (t) was greater than PM ,C of 6.6 mm. A contrasting result was obtained for SPG in well B; ER was minimal when M = 73 days. In Figs. 2e and 2j, occurrences of SPG corresponded well with the period when P73d (t) was greater than PM ,C of 705 mm. Thus, the generation and cessation of SPG were controlled by the long-lasting effects of antecedent rainfall. 3.2

Response of bedrock groundwater table

Figure 3d shows that both EG and SPG were observed in the new well B installed in 2004, while only EG was observed in the new well A (Fig. 3c). Again, the generation and cessation of EG and SPG were well described by P11h (t) and P73d (t), respectively (Fig. 3b). In borehole R1, the groundwater was observed from 10 April through 25 August 2006, showing relatively rapid response to rainfall events (Fig. 3e). The groundwater response in borehole R2 was milder than that in borehole R1 (Fig. 3f. It started increasing on 4 April 2006, and had a maximal level on 3 August, 14 days after the peak of the large rainfall event on 15–21 July (Fig. 3a). The waveform of SPG observed in well B (Fig. 3d) was very similar to the waveform of the bedrock groundwater in borehole R2. 3.3

Sources of EG and SPG

Groundwater SiO2 concentration can be a clue for detecting its sources, because deep bedrock groundwater contains more SiO2 than rainwater and shallow solum (Rice & Hornberger, 1998). Figure 3c shows that the SiO2 concentration of EG in well A was always low. In well B, water samples, collected when SPG was present, generally had remarkably higher SiO2 concentrations (Fig. 3d). The SiO2 concentration of EG in well B was as low as that in well A. The SiO2 concentration of bedrock groundwater in borehole R1 was in between those of EG and SPG (Fig. 3e). We observed higher SiO2 concentration in borehole R2 than in borehole R1 (Fig. 3f). When groundwater level in borehole R2 increased rapidly on 25 July, its concentration decreased to a similar value as groundwater in borehole R1 (Fig. 3g). Thus, we found that SiO2 concentration of bedrock groundwater exhibited spatial and temporal variations. The high SiO2 concentration of SPG in well B was very similar to the concentration of bedrock groundwater collected from borehole R2 (Fig. 3g),

which strongly suggests that deep bedrock groundwater contributes to the formation of SPG. On the other hand, the low SiO2 concentration of EG indicates the contribution of storm rainwater and solum water to EG. Water temperature can be another indicator of the origin of groundwater (e.g., Uchida et al., 2003). The temperature measured at the bottom of well A showed a sinusoidal seasonal change (Fig. 4b). This followed the general thermal conduction theory in a soil profile (e.g., Horton, 2002). Although well B had a much smaller depth than well A, the temperature at the bottom of well B was lower than in well A during the summer, when SPG was observed (Fig. 4c). Moreover, the well B temperature showed no seasonal change during the existence of SPG. These trends cannot be explained by the thermal conduction theory. The temperature of SPG, however, was very similar to the temperature at the bottom of boreholes R1 and R2, providing clear evidence for the contribution of deep bedrock groundwater. 3.4

Interaction of bedrock groundwater and surface water

We found that two components of groundwater were combined in a single well excavated in solum at point B. Each component had a particular source; storm rainwater and pre-event solum water were the source of EG, and deep bedrock groundwater was the source of SPG. Rock samples collected from boreholes R1 and R2 indicated the existence of thick, strongly weathered (DH to CL classes) and moderately weathered (CL to CM classes) layers below the soil mantle (Fig. 1b). The saturated hydraulic conductivities, Ks , for the strongly weathered layer were 3.01 × 10−4 (borehole R1, 6–7 m deep), 4.07 × 10−6 (borehole R1, 13–14 m deep), 3.10 × 10−5 (borehole R2, 5–6 m deep), and 3.34 × 10−6 cm s−1 (borehole R2, 13–14 m deep). For the moderately weathered layer, a Ks of 1.17 × 10−6 cm s−1 was observed (borehole R2, 27–33 m deep). In a weakly weathered (CM class) layer in borehole R2, we found some cracks with traces of water flow. Resistivity image profiling (Fig. 1b) detected a thick highresistivity zone in the upstream area from point A, which probably corresponded to an unsaturated zone. In the downstream area from point B, the shallow layer had low resistivity that probably corresponded to a saturated zone because we observed that bottom of a soil mantle, 130 cm thick, was always saturated at point C. The hydrological processes presumed from these geological data are as follows. Storm rainwater first infiltrates into solum, forming EG. Part of the rainwater infiltrates further through the thick unsaturated layer of weathered rock, causing a delayed increase in deep bedrock groundwater over the weakly weathered

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Figure 3. (a) Daily precipitation, (b) antecedent precipitation indices with half-life periods of 11 hours and 73 days, (c, d) groundwater levels in wells A and B, (e, f) groundwater levels in boreholes R1 and R2, and (g) SiO2 concentration. In Figs. 3c through 3f, the groundwater SiO2 concentration is expressed by the size of a circle, and each circle position indicates the height of a water-collection cup and the time when groundwater entered the cup. Gray lines and numbers in parentheses indicate time points for each of which groundwater table was shown in Fig. 5.

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Figure 4. (a) Daily precipitation, and groundwater levels and temperatures measured at the bottoms of wells (b) A and (c) B. Figures 4b and 4c include temperatures measured at the bottoms of boreholes R1 and R2.

longitudinal section of the catchment including the groundwater tables. On 4 April 2006, the bedrock groundwater level in borehole R2 was minimal (Fig. 3f). The bedrock groundwater fed soil mantle groundwater around point C. On 28 May, the bedrock groundwater level in borehole R2 increased to a similar elevation as point B, which triggered the generation of SPG in well B (Fig. 3d). A large gradient in groundwater level between boreholes R1 and R2 probably caused a large amount of groundwater flow within the bedrock. On 3 August, the bedrock groundwater level in borehole R2 was maximal. The increased bedrock groundwater level resulted in the peak of SPG level in well B (Fig. 3d). When the bedrock groundwater level in borehole R2 decreased −20.0 m from the ground surface on 2 October, SPG in well B disappeared (Fig. 3d). This R2 groundwater level was similar to that observed on 28 May when SPG in well B was generated (i.e., −21.5 m from the gourd surface). Thus, the absence and generation of SPG in well B were consistently explained by the bedrock groundwater level in borehole R2. SPG led to considerably high antecedent soil moisture conditions between storms, which may affect base flow discharge and also facilitate an increased peak runoff and shallow landslides during a later storm event. Thus, deep bedrock groundwater is an important factor in surface hydrological processes in the studied catchment. 4

CONCLUSION

This study conducted long-term monitoring of water tables within a soil mantle along with borehole drillings and hydro-geochemical observations. The results reveal the following:

Figure 5. Longitudinal section along the main hollow of the study site showing bedrock groundwater tables. Open, light gray, dark gray, and solid squares at boreholes R1 and R2 are as described in Fig. 1.

rock. Then, the bedrock groundwater flows through cracks and finally discharges into solum around point B to form SPG. In this process, the generation and cessation of SPG reflect the long-lasting effects of antecedent rainfall as shown in Fig. 2j. Using the measured groundwater levels and their corresponding elevation values, Fig. 5 shows the

1. Two different components of groundwater were combined within a single well excavated in the soil mantle. One component showed an ephemeral-type response (EG), and the other component showed a semi-perennial-type response (SPG). 2. The generation and cessation of SPG were not controlled by the magnitude of a current storm event, but by long-lasting effects of anteceding rainfall. 3. Geochemical and geothermal observations indicated that the sources of EG were storm rainwater and pre-event solum water, whereas the source of SPG was deep bedrock groundwater. 4. The absence and generation of SPG were consistently explained by the deep bedrock groundwater level. 5. The generation and cessation of SPG reflects the long-lasting effects of anteceding rainfall because it takes a long time for the water to infiltrate a thick, unsaturated, weathered rock layer in the upstream region along the main hollow.

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Thus, the anomalous responses of SPG demonstrated major effects of bedrock groundwater on surface hydrological processes. ACKNOWLEDGEMENTS This work was partly supported by grants from the Rokko Sabo Office, Kinki Division, Ministry of Land, Infrastructure and Transport, Japan, the Core Research for Evolutionary Science and Technology (CREST) program of the Japan Science and Technology Agency, and the Fund of Monbukagakusho for Scientific Research (18201036, 19380087). REFERENCES Anderson, S.A. & Sitar, N. 1995. Analysis of rainfall-induced debris flows. J. Geotech. Eng. ASCE 121: 544–552. Beven, K.J., Kirkby M.J., Schofield, N. & Tagg, A.F. 1984. Testing a physically-based flood forecasting model (TOPMODEL) for three U.K. catchments. J. Hydrol. 69: 119–143. Burns, D.A., Murdoch, P.S., Lawrence, G.B. & Michel, R.L. 1998. Effect of groundwater springs on NO− 3 concentrations during summer in Catskill Mountain streams. Water Resour. Res. 34: 1987–1996. Descroix, L., Nouvelot, J.-F. & Vauclin, M. 2002. Evaluation of an antecedent precipitation index to model runoff yield in the western Sierra Madre (North-west Mexico). J. Hydrol. 263: 114–130.

Horton, R. 2002. Soil thermal diffusivity. In A. Klute (ed), Methods of soil analysis,Part 4, Physical methods: 1227– 1232. Madison: Soil Sci. Soc. Am. Kosugi, K., Katsura, S., Mizuyama, T., Okunaka, S. & Mizutani, T. 2008. Anomalous behavior of soil mantle groundwater demonstrates the major effects of bedrock groundwater on surface hydrological processes. Water Resour. Res. 44: W01407 doi:10.1029/2006WR005859. Montgomery, D.R., Dietrich, W.E., & Heffner, J.T. 2002. Piezometric response in shallow bedrock at CB1: Implications for runoff generation and landsliding. Water Resour. Res. 38: 1274 doi:10.1029/2002WR001429. Okimura, T., & Ichikawa, R. 1985. A prediction method for surface failures by movements of infiltrated water in a surface soil layer. Natural Disaster Sci. 7: 41–51. Rice, K.C., & Hornberger, G.M. 1998. Comparison of hydrochemical tracers to estimate source contributions to peak flow in a small, forested, headwater catchment. Water Resour. Res. 34: 1755–1766. Uchida, T., Asano, Y., Ohte, N. & Mizuyama, T. 2003. Seepage area and rate of bedrock groundwater discharge at a granitic unchanneled hillslope. Water Resour. Res. 39: 1018 doi:10.1029/2002WR001298. Wilson, C.J., & Dietrich, W.E. 1987. The contribution of bedrock groundwater flow to storm runoff and high pore pressure development in hollows. IAHS Publ. 165: 49–59. Wilson, G.V., Jardine, P.M., O’Dell, J.D. & Collineau, M. 1993. Fieldscale transport from a buried line source in variably saturated soil. J. Hydrol. 145: 83–109.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

A GIS-based, hypothetical model of groundwater seepage into a former mining open pit: 1. discrete fractures scenario A. Salama∗ & E.R. Negeed Nuclear Research Center, Atomic Energy Authority, Egypt

I. Djamaluddin, T. Esaki, Y. Mitani & H. Ikemi Institute of Environmental Systems, Kyushu University, Fukuoka, Japan

ABSTRACT: Groundwater seepage into a former mining site in Egypt is proposed for simulation. This site was used for Basalt extraction. After the mining activities had stopped a large open pit was left over and groundwater seeped into the pit forming a lake. The pit has a dimension of approximately 1200 × 600 × 30 m. Because of the lack of field data, several scenarios may be hypothesized to explain the filling of these open pits with water. In this paper, one of these scenarios is studied. It is suggested that this water comes from an underneath confined aquifer. Through fractures in the host rock, water seeped upwards into the open pit. To estimate the rate at which water seeps into the lake, numerical study based on the finite element method is performed. Firstly, georeferencing of the site was performed using GIS. The boundary of the lake was then digitized and elevation contours was defined. These data was then imported into GridBuilder software to generate a two-dimensional triangular mesh which was then used by HydroGeoSphere software to build the three-dimensional mesh and solve the problem. It was found that the set of discrete fractures was insufficient to fill the lake in the time span that was actually elapsed to fill up the lake which is on the order of two to three years. Keywords: Surface-subsurface water interaction; Groundwater seepage; Mining left-over open pit; Arab Gohaina new lake 1 1.1

INTRODUCTION Background

The mining of metals and minerals-rich rocks creates many environmental problems and hazards both during and after mining activities had ceased. That is to exploit an economic ore body the environment has to be disturbed in a sense such that the state of thermodynamic equilibrium that these materials have enjoyed for quite a long period of time are no longer valid by exposing them to a quite different environment. Surface mining, generally, creates more environmental damage than underground mining due to the exploitation of usually lower grade deposits. Broken ore can be moved at a lower cost which increases environmental impacts. In terms of abandoned sites, one of the many environmental problems may be the fact that large open pits are often left over. The environmental impact of such large open pits depends on many factors that are largely influenced by the natural evolution of the ecosystem surrounding the former mining site. ∗

Corresponding author ([email protected])

An example to this may be the fact that groundwater may seep into these open pits and eventually the pit sites may be filled with water. This phenomenon has been noticed in many sites including, for example, Japan, Canada, USA and Egypt. Simulating such kind of systems is important in the sense that it could provide regulatory authorities with useful information that may help them in the process of decision making. In this regard, groundwater seepage into a former mining site in Egypt is proposed for simulation. The site is located in the sub-tropical dry belt with a mean annual precipitation ranging between 10 to 25 mm/year. Figure 1 shows a satellite image of the site. After the mining activities had stopped a large open pit was left over. The pit has a dimension of approximately 1200 × 600 × 30 m. After the pumping operation had ceased to work, groundwater seeped into the pit forming a lake. If this lake is left unprotected, it can easily get polluted. With any fluctuation in the head of the underneath groundwater reservoir water from the lake may seep back into groundwater. If this water is polluted, groundwater reservoir will eventually get polluted too.

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Figure 2. Schematic diagram of the confined aquifer scenario.

Figure 1. Satellite image of the new lake.

Unfortunately, no data are available that may shed light on the source of this water, i.e., whether it is from a confined or an unconfined aquifers. Several scenarios may be hypothesized to explain the filling of these open pits with water. That is in one scenario, water may be proposed to have seeped into the open pit from an underneath confined aquifer through fractures in the host rock while in another scenario water may be proposed to have seeped from the nearby fresh water canal again trough fractures in the host rock. In either of these scenarios two possibilities may be found, the first of which is based on the assumption that there may exit a set of discrete fractures or that the fractures are so intense that one may assume an equivalent porous media scenario. In both scenarios, the time required to fill up the open pit with water will be the determining factor in supporting one scenario over the other. 1.2

Confined aquifer scenario

In this scenario it is hypothesized that there exists a confined aquifer underneath the open pit from which groundwater seeps upwards through a set of discrete fractures. In this set up, this aquifer could be a layer of highly fractured basalt. Figure 2 below shows a schematic diagram of this scenario. In simulation this was achieved by assigning a very low permeability to the basalt host rock. This implies that the system was actually simulated as an unconfined aquifer with the bottom layer behaving very closely to the confined aquifer. 2 2.1

PROBLEM FORMULATION Governing equations

The transient confined flow (elastic storage) neglecting the compressibility of water is governed by the following equation (Bear, (1972)):

where h is the hydraulic head and Se is the coefficient of elastic storage which is equal to Ss H with Ss [1/L] is the coefficient of specific storage. This equation, however, describes groundwater flow in saturated porous media for which Darcy’s law is valid. In unsaturated porous media, Darcy’s law is modified such that q = −K · kr ∇ (ψ + z) where kr = kr (Sw ) represent the relative permeability of the medium [dimensionless] with respect to the degree of water saturation Sw [dimensionless], ψ is the pressure head [L] and z is the elevation head [L] and thus equation (1) is modified to what is called the Richards’ equation. In HydroGeoSphere (the software used in this study) a modified version of Richards’s equation is used and is given by (Therrien and Sudicky (1996)):

With Sw = θ/θs where θ is the water content [dimensionless]. In equation 2 ex represents the volumetric fluid exchange rate [L3 L−3T−1 ] between the subsurface domain and other domains. Equation 2 is nonlinear with the primary variable of solution is the pressure head and constitutive relations must be established that relate the primary unknown ψ to the secondary variables Sw and kr . The relative permeability may be expressed in terms of either the pressure the pressure head or the water saturation. 2.2 Solution techniques The governing second order, partial differential equation subject to the boundary conditions is solved numerically using HydroGeoSphere software in finite element control volume approach. At first a satellite image (Fig. 1) was used to define the lake boundary and the extent of the domain encompassing the lake (4 km × 4 km). The image was georeferenced then digitized using ArcGIS. The level contours were defined and a field data file for ground surface elevation was generated to be used later with GridBuilder software for the purpose of associating the elevation

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data with the generated 2D finite element triangular mesh. The digitized 2D boundary was used by GridBuilder software to define the domain and to generate the mesh. The 2D finite element mesh was then imported into HydroGeoSphere software to generate the 3D mesh and solve the problem. HydroGeoSphere is a three-dimensional numerical model describing fully-integrated subsurface and surface flow and solute transport. Figure 3 below shows the generated 3D realization of the site with the bottom layer representing the confined aquifer and the light colored irregular lines representing some of the fractures. Figure 4 shows the generated 3D mesh. The domain is composed mainly of two layers, with the lower layer representing the confined aquifer and the upper one representing the Basalt host rock. Figure 3. A 3D section of the lake.

2.3

Initial and boundary conditions

The left and the right hand sides of the confined aquifer were assigned a prescribed head boundary conditions while at the top and bottom sides the head (Fig. 5) was assumed to vary linearly between its value at the left and right sides. On the other hand the boundaries of the Basalt layer were assigned a no flow boundary conditions. On the other hand, since the pit is located in a semi-arid region and hence receives little annual rain fall, precipitation has been ignored in this study. Evaporation has also been ignored in this work. Initially the steady-state variably saturated solution was first introduced assuming almost dry lake conditions. This solution represents the condition of the lake and the surrounding area at the end of the operational phase of the mining site. Then the generated steady state heads were used as an initial condition towards the filling process. During the filling process simulation time step was adopted automatically by HydroGeoSphere and a convergence criterion of 10−13 was chosen. 2.4 The set of discrete fractures It is known that the discontinuity geometry pattern in a rock mass can vary from one statistically homogeneous region to another. Hence, each statistically homogeneous region should be represented by a separate discontinuity geometry model. Therefore, the first step in the procedure of discontinuity geometry modeling in a rock mass should be the identification of statistically homogeneous regions (Miller 1983, Kulatilake et al. 1990, and kulatilake et al. 1996). To model discontinuity geometry in 3-D space, for a statistically homogeneous region, it is necessary to know the number of joint sets, and for each joint set, the intensity, spacing, location, orientation, shape and dimension distributions. These discontinuity geometry parameters are best dealt with in a statistical

Figure 4. A vertical cut showing the generated 3D mesh.

manner. On the other hand, information about fractures is usually available through borehole acoustic televiewer logs. However, these logs cannot provide any information about discontinuity size distributions. As have been said before, there are almost no data about the distribution of fractures in the Basalt host rock.Yet this lack in data should not stop one from proceeding into mathematical modeling based on the fact that sample values of discontinuity geometry parameters obtained by the field methods are usually subject to errors and represent only 1- or 2-D properties. Furthermore, this study focuses on a crude estimation to determine whether or not the set of fractures will be able to conduct enough water to fill up the lake in the actual time span. Thus, in this particular study, the discrete fractures were randomly generated with their number initially set to 8 as shown in Fig. 5. Initially the set of fractures was assumed to only be at

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Figure 6. Vertical cut at 1500 m from the top boundary showing water table at different times of the filling process.

Figure 5. Plan view showing the set of discrete fractures.

the base of the lake, and then it was extended to cover the whole domain to be more realistic. In HydroGeoSphere a fracture is idealized as the space between two-dimensional parallel surfaces, with the assumptions that the total head is uniform across the fracture width, Therrien et al. (2007). 3

RESULTS AND DISCUSSION

The generated output data files are formatted such that they may be read by Tecplot post processing software. We first explain water table evolution in the basalt layer during the filling process then we consider water flow rates into the lake as well as the exchange flux from subsurface into the pit. 3.1 Water table in the confining basalt host rock Figures 6 and 7 show vertical plane cuts of the 3D domain at approximately 1500 m and 1800 m from the top boundary (Fig. 5), respectively. In these figures, successive locations of the zero pressure head surfaces (water table) at different times during the filling process are shown. One may expect that if there were no mining activities in this site, water table in the basalt layer would in general follow the hydraulic head in the underneath confined aquifer. With the mining activities in place, the associated groundwater pumping would result in water table to get lower beneath the newly formed open pit which is clearly shown in Figs 6 and 7. That is, at early times of the filling process (after 1 day), one may notice that the surface of the water table underneath the base of the lake is almost at the surface of the confined aquifer with the upper basalt left unsaturated. There are places, however, where water table rises up to the bottom of the

Figure 7. Vertical cut at 1800 m from the top boundary showing water table at different times of the filling process.

lake. These correspond to the places where fractures exist. With the filling process taking place, water table rises at a rate which decreases with time. That is as the level of the water in the lake rises the head difference along the fractures decreases allowing less water to be conducted along the fractures.

3.2 Groundwater seepage into the pit When pumping activities stopped at the end of the mining operations, groundwater seeped up into the leftover open pit forming a lake. Because of the extremely law hydraulic conductivity assigned to the basalt layer, groundwater effectively seeped up through the set of discrete fractures. Figure 8 below shows the flooded contours of the flux exchange between the subsurface and the pit (m3 /m2 s). It is apparent that the highest groundwater flux is concentrated around the set of fractures. Figure 9, on the other hand, shows the volumetric fluid accumulation rates (m3 /s) from subsurface into the pit with time (on logarithmic scale). It is apparent that as the time increases groundwater seepage into the pit increases too then reaches a peak

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4

CONCLUSIONS

This study shows groundwater seepage into a former mining leftover open pit. In this work it was assumed that there exists an underneath confined aquifer from which groundwater seeped upwards through a set of discrete fractures in the host rock. The main finding is that the assumed set of fractures was unable to fill the open pit in the actual time span it took to fill up the pit which is on the order of three years. That is according to this study a period of more than 40 years is required to fill up the pit to the level shown in the simulation based on the assumed set of fractures. It is thus suggested that larger number of fractures may indeed need to be postulated. Alternatively is the numbers of discrete fractures are so high, an equivalent porous media scenario may be assumed. ACKNOWLEGMENTS Figure 8. Flooded contours of the flux exchange (m3 /m2 s) from the subsurface up into the pit.

The first author acknowledges the financial support provided by Japan Society for the Promotion of Science, JSPS. REFERENCES

Figure 9. Volumetric fluid accumulation rate (m3 /s) from the subsurface into the pit.

after almost 116 days then it decreases again as time proceeds. The decrease in fluid exchange rate may be attributed to the opposing effect to groundwater seepage caused by the increase in water level in the lake (i.e., to the decrease in hydraulic gradient) as explained earlier.

Bear, J., 1972. Dynamics of fluids in porous media, American Elsevier, New York, NY, 764 pp. Kulatilake, P.H.S.W., Wathugala, D.N., Poulton, M. and Stephansson, O., 1990. Analysis of structural homogeneity of rock masses. Int. J. Engng. Geol. 29, pp. 195–211. Kulatilake, P.H.S.W., Chen, J., Teng, J., Shufang, X. and Pan, G., 1996. Discontinuity characterization for the rock mass around a tunnel close to the permanent shiplock area of the Three Gorges Dam site in china. Int. J. Rock Mech. Min. Sci. Geomech. Abstr. 33 3, pp. 255–277. Miller, S.M., 1983. A statistical method to evaluate homogeneity of structural populations. Mathematical Geol. 15 2, pp. 317–328. Therrien, R., and Sudicky, E.A., 1996. Three-dimensional analysis of variably-saturated flow and solute transport in discretely-fractured porous media. J. contam. Hydrol., 23(1–2), 1–44. Therrien, R., McLaren, R.G., Sudicky, E.A., and Panday, S.M. 2007. HydroGeoSphere: A Three-dimensional numerical model describing fully-integrated subsurface and surface flow and solute transport, Draft manual.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Mechanism for the production of dissolved iron in the Amur River basin – a modeling study of the Naoli River of the Sanjiang Plain T. Onishi∗ Research Institute for Humanity and Nature, Kyoto, Japan

H. Shibata Field Science Center for Northern Biosphere, Hokkaido University, Hokkaido, Japan

M. Yoh Department of Environmental and Natural Resource Science, Tokyo University of Agriculture and Technology, Tokyo, Japan

Seiya Nagao Faculty of Environmental Earth Science, Hokkaido University, Hokkaido, Japan

ABSTRACT: The Amur River basin is considered the main contributor of dissolved iron entering the Sea of Okhotsk and the Oyashio current. Recent research shows it is probable that the main source of the dissolved iron is the Amur River basin wetlands. To explore the primary process of dissolved iron production and the effect of land cover conversion from wetland to cultivated land on dissolved iron production, a semi-distributed hydrological model incorporating dissolved iron production was constructed. The model was based on TOPMODEL concept. To explore the mechanism for the production of dissolved iron, the amount of dissolved iron produced by each calculation grid was formulated as a function of the topographic index (a/tanβ). A parameter representing the degree of redox condition was also introduced. To verify the validity of parameterization, a hydrological model of the Naoli river basin was constructed. The best parameter set to simulate runoff was determined by Monte Carlo simulation. The amount of the estimated dissolved iron was much lower than the observed value. The results suggested that there is another dissolved iron production mechanism that has not been incorporated into the present version of our model. It was suggested that possible hydrological processes which must be considered are seepage of shallow groundwater and flooding to the riparian area. Keywords: 1

dissolved iron; redox process; TOPMODEL; Sanjiang Plain

INTRODUCTION

The role of iron in controlling primary production in the world oceans, particularly in high nutrient low chlorophyll (HNLC) regions, is widely recognized (Martin and Fitzwater, 1988; Boyd et al., 2004). The Oyashio current is a HNLC region and iron is the limiting factor for primary production within the current. Recent studies have revealed that the main source of iron in the Oyashio current is wetlands in the Amur River basin. The primal mechanism regulating dissolved iron production in terrestrial systems including wetlands is the reduction of Fe oxide to ferrous iron and the bonding of humic acid and ferrous iron. Though ∗

Corresponding author ([email protected])

these processes are complicated and there are many unknown mechanisms, the necessary condition for triggering dissolved iron production is soil saturation. Thus, through constructing a simple model that incorporates a dissolved iron production mechanism into the hydrological model based on TOPMODEL, we attempted to calculate the amount of dissolved iron for the Naoli River basin of the Sanjiang Plain. Through this modeling study, we explore the main hydrological processes regulating dissolved iron production. 2

SITE DESCRIPTION

2.1 Natural condition The Sanjiang Plain is located in the eastern part of Heilongjiang province, Northeast China (Figure 1).

355

Figure 1. The Amur River basin (upper) and land cover type of the Naoli River basin in 2000 (lower).

It covers an area of 108,829 km2 , mostly dominated by marshes in 1893, and still the second largest marsh in China. Average annual temperature is about 4.0◦ C, with an average minimum of −19.3◦ C in January, and average maximum of 22.4◦ C in July. Average annual precipitation is approximately 536 mm, which mostly occurs between May and September. The watershed area of the Naoli river is 2,2680 km2 , and the annual average discharge (using discharge data from 1956 to 1987) is 58 m3 s−1 (90 mm year−1 ). There is a peat layer formed over most of the plain with an average thickness of several tens of centimeters (Yamagata et al., 2007). Beneath the peat layer, a silt clay layer with low hydraulic conductivity is usually found. A thick gravel layer containing rich groundwater exists below the silt clay layer. The average groundwater level of this confined aquifer is around 10 m below the ground surface. From a comparison of the fluorescent properties of the dissolved humic substances in the river and groundwater collected at the depth of several tens of meters below the ground surface, it is suggested that deep groundwater in the gravel layer has little effect on river discharge. This implies that shallow groundwater and surface water play important roles in forming the runoff regime and various biogeochemical processes. 2.2 Anthropogenic impact Figure 2 shows the distribution pattern of land cover types in the Naoli River basin in 2000. The main

Figure 2. Schematic diagram of the model.

crops were wheat, soybean and maize (Park et al., 2001). Paddy fields were drastically increased after 1990. Hou et al. (2004) reported that the wetland area decreased from 11,499 km2 in 1954 to 2,777 km2 in 2000, and that the area in crops increased from 2,060 km2 in 1954 to 14,402 km2 in 2000. In the Naoli basin, during the period between 1965 and 1975, the area of reclaimed land was relatively small compared to in other periods. Statistical analysis of the discharge regime of the basin during the period between 1964 and 1989 shows that there was rapid change in the hydrological regime around 1975 (Liu et al, 2004). This suggests we can treat the period between 1965 and 1975 as a continuous one in which the statistical properties were stationary.

3

MODEL STRUCTURE AND METHODS

3.1 Model concept The constructed model is based on the TOPMODEL concept (Beven and Kirkby, 1979). While TOPMODEL was originally developed to simulate runoff from a small scale catchment, its concept is also used in global scale Land Surface Models such as MATSIRO (Takata et al., 2003). Figure 2 is a schematic

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diagram of the model, which consists of two modules; one for dealing with the physical process that calculates runoff (TOP-RUNOFF), and the other for dissolved iron production processes (TOP-FE). In the TOP-RUNOFF module, as well as the basic structure that is the same as the original TOPMODEL structure, two processes were taken into account. One is the inflow of surface water runoff from surrounding lands into the wetlands. This process was simply formulated as the equal addition to the wetlands of the amount of surface runoff from grids except for paddy fields. The second process involved water management practices for paddy fields. This management was taken into account as the overflows from paddies when the ponding depth exceeded the prescribed threshold value PDc [m]. Thus, the modeling algorithm assumed that artificial drainage such as mid-summer drainage was not practiced in the basin. Though some exceptions might exist in actual water management, the above mentioned formulation can be justified by information obtained from field observations and inquiries of farmers. In the TOP-FE module, the degree to which dissolved iron is produced is formulated as the function of the duration time for saturation; defined as the length of continuously saturated days. In the model, when both root zone deficit and saturation deficit of each grid reached zero, the grid was considered as saturated. If the saturation duration time of a grid became larger than the threshold value SDc , then dissolved iron is considered to be produced at a prescribed constant rate. The concentration of the dissolved iron produced is formulated as a function of the topographic index a/tanβ. Here, a is defined as drainage area of each calculation grid and β is defined as slope angle of each grid. The validity of our formulation cannot be directly verified; however, according to Shibata et al. (2002), dissolved iron concentration in a forest area in the Hokkaido region increased as the average topographic index of the basin increased. This might support that our formulation is a useful one. Though limiting factors for the productivity of dissolved iron are the degree of saturation of soils and biogeochemical conditions such as organic compound in soil, temperature, soil pH, Fe oxide amount and bacterial species, soil saturation is the necessary condition for triggering the process for the reduction of ferric iron. Thus, to evaluate the productivity potential for dissolved iron, we assumed that the other biogeochemical conditions were not limiting factors for the reduction process. 3.2

Data and parameter setting

NCEP/NCAR reanalysis1 data were utilized as forcing data for the model. Daily precipitation amounts from NCEP/NCAR reanalysis1 data were corrected to match the amounts of monthly precipitation of CRU

Figure 3. Dissolved iron productivity of each grid as a function of topographic index a/tanβ. Table 1.

Summary of parameters.

Parameter

Units

Definition

Fixed or not

szm

m

calibrated

T0

m2 h−1

SRmax td

m hm−1

chv Cw

mh−1 −

scaling parameter for exponential saturated hydraulic conductivity maximum root zone deficit time constant per unit of deficit for recharge to the saturated zone channel routing velocity snow water retention capacity threshold for 100% snow threshold for 100% rain threshold for snowmelt upper limit of ponding depth of paddy fields threshold for starting of dissolved iron production

◦C

Ts Tr Tm PDc

◦C m

SDc

day

◦C

calibrated calibrated calibrated

calibrated calibrated 4.5 0.0 1.0 0.1 10

TS2.1 data (Mitchel and Johns, 2005). The evapotranspiration rate was calculated by the Penman-Monteith equation. Roughness length of vegetation, aerodynamic resistance and surface resistance of each land cover type were calculated using the formulations of Watanabe (1994). LAI and the vegetation height of each land cover type were determined based on measured values. The grid size for the model was set as 1 km2 . The threshold value of SDc was set as 10 days based on experimental data for the iron oxide reduction rate (Roden and Wetzel, 2002). Dissolved iron concentration as a function of the topographic index was generated by extrapolating observed data obtained in 2006 from the Gassi lake watershed located in the Amur River basin in the Russian part (Onishi, 2007). The function is shown in Figure 3. Table 1 lists the parameters needed to operate the model.

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Table 2. Parameter ranges used in the Monte Carlo simulation. Parameter

Upper limit

Lower limit

szm T0 SRmax td chv Cw

0.1 36 0.02 50 1800 1.0

0.001 0.0036 0.001 0.1 360 0.1

3.3

Methods

First, we used Monte Carlo simulation to calibrate the parameters against monthly discharge data at Zaizuizi, which is located near the mouth of the river. As described in 2.2, land use during the period from 1964 and 1975 was relatively stable and the statistical properties of the discharge were assumed as stationary. Thus, the calibration period was set as that during 1964 to 1970. However, the wetland area during this period was much larger than in 2000. Thus, dry land and paddy fields in 2000 were randomly selected and the property of land cover changed from dry land/paddy fields to wetland according to the ratio of the cropland areas during 1964 to 2000. The ranges for the parameters used for the Monte Carlo simulation are shown in Table 2. Total simulations were 10,000. Relative mean square error (RMSE) was used to evaluate the fitness of the parameter sets. Validation of the model was executed against discharge data from 1970 to 1987. During this period, the land cover pattern changed from that period in which the calibration for the model was done. Using the data of Luo (2002), we converted part of the wetland to dry land, and ran the simulations using selected candidate parameter sets. Following these calibrations and validation processes for the model discharge, we calculated dissolved iron concentration using two different land cover conditions. One for the 1960s’, and the other for the 2000s’. The simulation period was from 1964 to 2000. The simulation results were compared with observed iron concentration in 2006 and 2007. 4

RESULTS AND DISCUSSION

The best fitted parameter set was szm = 0.0011[m−1 ], chv = 0.057[ms−1 ], SRmax = 0.12[m], T0 = 8.2 × 10−3 [ms−1 ], td = 2.07[hm−1 ], Cw = 0.425[−]. Hydraulic conductivity of the Sanjiang Plain, as measured in the laboratory, is about the order of 10−3 [cm s−1 ] (Chen et al., 1996). According to the review of Hirabayashi (2004), saturated hydraulic conductivity values was used in TOPMODEL simulation studies are generally

Figure 4. Comparison between observed data (open circle) and calculated value (line) during the period between 1970 and 1987.

Figure 5. Calculated annual dissolved iron amount under the land cover type of 1960s’ (open bar) and 2000s’ (closed bar) and discharge (1960s’:solid line, 2000s’: dotted line).

2 to 3 orders higher than experimentally laboratory obtained values. Thus, calibrated hydraulic conductivity fell into an allowable range. Figure 4 shows the results of validation period using those parameter sets. Though the calculated seasonal discharge regime was generally underestimated, the inter-annual trend of discharge was reproduced fairly well. One possible reason for this discrepancy might be that the wetland water storage process was not considered in the model because of the uncertainty of climate data such as precipitation amounts. Figure 5 shows the dissolved iron concentrations calculated for the period from 1964 to 2000. Total dissolved iron mass that might be produced by the land cover types in 2000s’ was decreased by 30% from that of 1964. If we simply assume that the productivity of dissolved iron has a linear relationship with the wetland area, the total amount of dissolved iron under the land cover type of 2000 would be decreased by 15% from that of 1964. However the simulation results showed a much higher amount of dissolved iron than

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Figure 6. Comparison of monthly mean dissolved iron concentration between calculated value and observed value in 2006 and 2007.

the expected value In the model, the necessary conditions for starting dissolved iron production were that both saturation deficit and root zone deficit were 0. While the dynamics of root zone deficit were uniform in the same land cover type, saturation deficit at each grid was calculated using a deviation of the topographic index of each grid from the average basin value of the topographic index. Thus, it was assumed that difference of the topographic index of each grid caused this discrepancy. Figure 6 shows a comparison between observed dissolved iron data of 2006–2007 and the monthly averaged values of calculated dissolved iron concentrations during the whole simulation period. Because of the lack of input data during the period of 2006–2007, monthly average of calculated value was used for comparison. Daily maximum of calculated value under the condition of 1960s’ land cover type was also shown. Though the most observed data are fallen into within the range of calculated value, in general, the amount of the estimated dissolved iron was relatively lower than the observed value. Though the observed data is sparse, it should be noted that the relatively high concentration in May and July is not well simulated especially. There should be large parameter uncertainties. And this is the important reason for discrepancy between observed and calculated value. In addition to this, it is possible that there is another dissolved iron production mechanism that has not been incorporated into the present simplest version of our model. In our present model, the transport of dissolved iron is formulated as only the surface water runoff from each grid. However, other possible processes of dissolved iron production would be seepage from shallow groundwater and the flooding to riparian area during summer season. Even if the wetland was converted to croplands, there would still be the possibility that

additional dissolved iron production would occur in these areas. This is consistent with the observation that dissolved iron concentration in agricultural drainage in the Sanjiang plain is relatively higher than that of rivers in the same plain. The phenomena related to the spring time dissolved iron production might have a relationship with freezing, thawing and snow melting processes. As well, while dissolved iron was detected during the winter in reality, no dissolved iron was calculated during the same period in the simulation. During the winter season, most discharge comes from shallow groundwater. This suggests that we must consider the contribution that shallow groundwater makes to the amount of dissolved iron. In spite of some such uncertainties several useful insights were obtained from our modeling study that supports further investigation. The main finding of this study is that the possibility of contribution of shallow groundwater and flooding to the total dissolved iron productivity. Especially during spring when groundwater discharge constituted the most significant part of total discharge, the contribution of shallow groundwater might be important. This study was conducted as a part of the AmurOkhotsk Project, being promoted by the Research Institute for Humanity and Nature (RIHN). REFERENCES Beven K.J. and M.J. Kirkby (1979): A physically based variable contributing area model of basin hydrology, Hydrol. Sci. Bull., 24, pp. 43–69 Boyd P.W., Law C.S., Wong C.S., Nojiri Y., et al. (2004): The decline and fate of an iron-induced subarctic plankton bloom, Nature, 428, pp. 549–553 Chen G ed. (1996): Study of wetland in the Sanjiang Plain, Science Press, Beijing, pp. 56 (In Chinese) Hirabayashi Y. (2004): Global analysis on long term variations of extreme river discharge, D. Eng. Thesis, pp. 29 (In Japanese) Hou, W., Zhang, S., Zhang, Y. and Kuang, W. (2004): Analysis on the shrinking process of wetland in Naoli River Basin of Sanjiang Plain since the 1950s and its driving forces, J. Natural Resources, Vol. 19, No. 6, pp. 725–731 (In Chinese) Liu, H., Zhang, S. and Lu, X. (2004): Wetland landscape structure and the spatial-temporal changes in 50 years in the Sanjiang Plain, Acta Geographica Sinica, Vol. 59, No. 3, pp. 391–400 (In Chinese) Luo X. (2002): Study on the wetland water system in the Naoli River watershed in the Sanjiang Plain, Ph.D. Thesis, Chinese Academy of Science, pp. (In Chinese) Martin J.H. and Fitzwater, S.E. (1988): Iron deficiency limits phytoplankton in the northeast Pacific subarctic, Nature, 311, pp. 341–343 Onishi Takeo (2007): Runoff properties of the Amur River and the construction of the hydrological model incorporating dissolved iron transport, Annual report of AmurOkhotsk project No.4, pp. 201–206

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Mitchell, T.D. and Jones, P.D. (2005): An improved method of constructing a database of monthly climate observations and associated high resolution grids. Int. J. Climatol., 25, pp. 693–712 Roden E. E., and Wetzel R.G. (2002): Kinetics of microbial Fe(III) oxide reduction in freshwater wetland sediments, Limnol. Oceanogr, 47, pp. 198–211 Shibata H., Konohira E., Satoh F., and Sasa K. (2002): Export of dissolved iron and the related solutes from terrestrial to stream ecosystems in northern part of Hokkaido, Northern Japan, Annual report of Amur-Okhotsk project No. 2, pp. 87–92

Takata K., S. Emori and T. Watanabe (2003): Development of the minimal advanced treatments of surface interaction and runoff, Global and Planetary Change, pp. 209–222 Watanabe T. (1994): Bulk parameterization for a vegetated surface and its application to a simulation of nocturnal drainage flow, Boundary-Layer Met., 70, pp. 13–35 Yamagata K., Haruyama S., Masuda Y., .and Murooka M. (2007): Geomorphological research in the Sanjiang Plain 2006, Annual report of Amur-Okhotsk project No. 4, pp. 169–172

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7

Remote sensing for measuring water balance, hydrodynamics and hydrological processes

Remote sensing analyses using satellite and aerial photo images collected in a broad range of spatial and temporal scales allow us to have an overview of the hydrodynamics, water balance and environmental changes in watershed and basin scale. For example, recently developed technique using GRACE (Gravity Recovery and Climate Experiment) satellite gives terrestrial water storage and their temporal changes even in the remote areas with less data set. On the other hand, aerial infrared imagery is effectively used to infer submarine groundwater discharge, which occurs with variations in space and time. Intensively collected groundtruth data set related to hydrological processes at local areas would be scaled up to large areas by combining these remote sensing techniques. The multidisciplinary approaches using visible, near and thermal infrared, microwave and other wavebands as well as GRACE like sensors will be welcomed to better understand hydrodynamics and hydrological processes in watershed. Conveners: Pat Yeh (The University of Tokyo, Japan.) Yoichi Fukuda (Kyoto University, Japan) Masayuki Matsuoka (Kohchi University, Japan)

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

The 2006 Australian drought detected by GRACE T. Hasegawa∗, Y. Fukuda & K. Yamamoto Graduate School of Science, Kyoto University, Kyoto, Japan

T. Nakaegawa Meteorological Research Institute, Tsukuba, Ibaraki, Japan

ABSTRACT: We detected the TWS (Terrestrial Water Storage) changes induced by the 2006 Australian drought from the GRACE satellite gravity data. The GRACE data showed significant TWS decrease at southeast Australia in 2006 where historic rainfall deficiency was reported. We compared the GRACE data with those of hydrological models; the GLDAS model and the JLG model. Although the hydrological models indicated TWS decrease in 2006, those amplitudes were much smaller than the GRACE estimation. This suggests that the hydrological models do not properly recover the landwater storage changes caused by the drought. We consider the precipitation data inputted to the hydrological model and/or the land surface model caused the underestimation of the TWS changes induced by the drought. Our results demonstrated that GRACE can provide not only large-scale annual landwater variation but also landwater response to interannual climate changes and it will make great contribution to understand the climate impacts to water resources. Keywords:

1

GRACE; Australian Drought; terrestrial water storage change; climate change; gravity

INTRODUCTION

Australia suffered historic drought attributed to rainfall deficiency in late 2006. The Australian Government Bureau of Meteorology (BoM) reported “in the historical record dating from 1900, it was the driestAugust to November period averaged across south Australia, the second driest averaged over the Murray Darling Basin, the third driest across Australia and the fourth driest for Victoria” [BoM, 2006]. Figure 1 shows the 2006 rainfall deficiency released by the BoM. The drought made severe impacts not only on Australian human society which was resulted in reduction of the overall economic growth, but also on world food shortage. Freshwater stored on ground is one of the most indispensable resources for human activity, and global-scale water shortage is coming to be a priority issue for human society. The IPCC (Intergovernmental Panel for Climate Change) reported many parts of the world will suffer decrease in water resources due to global climate changes and rapid global population growth will enhance the stress on water resources [Kundzewicz et al., 2007]. For sustainable water managements, basin- or continental-scale TWS (Terrestrial Water Storage) ∗

Corresponding Author ([email protected])

Figure 1. 2006 Rainfall Deficiency by BoM (edited by T. Hasegawa).

monitoring is indispensable. However, traditional methods can not measure large-scale TWS variations accurately. Traditional ground and satellite techniques can measure some of the individual components such as soil moisture [Njoku et al., 2003] and surface water [Alsdorf and Lettenmaier, 2003], but they cannot measure integrated TWS changes. Numerical models can be used to evaluate the TWS changes, but their limitations such as insufficient knowledge of surface structure, process description or parameterization as well as errors in model input data (e.g. precipitation data) cause large uncertainty.

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The GRACE (Gravity Recovery and Climate Experiment) satellite, launched in March 2002, solved this problem. GRACE has been providing monthly global gravity field data with an unprecedented accuracy as a series of the Stokes coefficients (a set of coefficients of the spherical harmonic expansion of the static Earth’s gravity field) [Tapley et al., 2004]. GRACE can recover temporal variations of the Earth’s gravity fields due to mass redistribution in and on the Earth, including variations of atmospheric surface pressure, TWS, snow and ice, and ocean bottom pressure [Wahr et al., 1998]. Many previous studies concluded that GRACEinferred annual changes of TWS agree reasonably well with hydrological model estimates in certain large river basins, for instance, the Amazon [Tapley et al., 2004], Mississippi [Chen et al., 2005] and Indochina 4 major combined basin [Yamamoto et al., 2007]. In this paper, we detected the TWS decrease caused by the 2006 Australian drought from GRACE gravity data, and compared it with two different hydrological models. Our results demonstrate that GRACE data provides not only large-scale annual TWS variation but also TWS response to interannual climate changes.

1 represents the position of the Earth’s geocenter. We ignore the change of the degree 0 and 1 components because they may not affect regional scale mass variations. In this study, we also excluded the degree 2 zonal coefficient because large unquantifiable errors are contained in this term. High degree GRACE Stokes coefficients are dominated by noise, especially north-south orientated systematic error called “striping” obscured hydrological signals. We applied the decorrelation filter presented by Swenson and Wahr [2006] to suppress the “striping”. The decorrelation filter can reduce the striping error effectively, but there still remained large measurement errors in high degree and order coefficients. We additionally applied one of the following filters; (1) the Gaussian filter in order to characterize spatial distributions of TWS changes (see Section2.2), or (2) the optimally designed regional filter in order to characterize the time variation of TWS changes (see Section2.3). Gravity changes recovered from GRACE solutions were converted to TWS changes, assuming that mass changes are concentrated in a thin layer at the surface. 2.2 Spatial distributions of the TWS anomaly in 2006

2 TWS DEPRESSION DETECTED BY GRACE 2.1 The GRACE monthly gravity solution The GRACE, which consist of twin spacecrafts, are monitoring changes in Earth’s gravity field by measuring the distance between each craft every few seconds. The influence of no-gravitational forces on the intersatellite range is measured using accelerometer. These data are combined to produce (nearly) monthly Earth’s gravity field solutions by UTCSR (the University of Texas at Austin Center for Space Research), GFZ (the GeoFoschungsZentrum Potsdam) and JPL (the Jet Propulsion Laboratory). These estimates are labeled level-2 solutions or level-2 data. In this study, we employed the latest level-2 GRACE gravity solutions of 47 monthly data sets between 2003 and 2006 released by UTCSR (Release 04). In level-2 data processing, tidal effects, including ocean, solid earth, solid pole tides and ocean pole tide have been removed. Non-tidal variability in the atmosphere and non-tidal short-term ocean mass variability were also removed in the level-2 data process [Bettadpur., 2007]. Therefore, temporal variations of GRACE level-2 data reflect unmodeled atmospheric and oceanic effects as well as instrumental noise, and mainly hydrological phenomena including ice sheet mass variability. The level-2 data are released as the form of Stokes coefficients up to the degree and order 60, but GRACE level-2 data sets do not provide degree 0 and 1 coefficients. In the Earth gravity field, the degree 0 coefficient represents the total mass of the Earth and degree

To characterize the spatial distribution of the TWS anomaly in 2006, we calculated the difference of the gravity solutions between the average of 2006 and the average from 2003 to 2005. After applying a 500 km half-radius Gaussian filter [Wahr et al., 1998], the coefficients up to degree and order 40 were employed to calculate (1◦ × 1◦ ) grid TWS anomalies. Figure 2 shows the result. Apparent negative TWS anomalies are shown in south-east Australia where the serous drought was reported (Figure 1). 2.3 Time variation of TWS anomaly in Murray Darling River basin To characterize the time-varying TWS anomalies at south-east Australia, we evaluated the time variation of TWS anomaly within the Murray Darling River basin (Figure 3) where the significant negative TWS anomalies were detected in Figure 2. We designed an optimal regional filter presented by Swenson et al., [2003]. Because high degree GRACE Stokes coefficients are dominated by GRACE measurement errors, their contributions should be less weighted in the calculations. However, absence of high degree and order Stokes coefficients results in an inaccurate representation of basin shape and it causes contamination of gravity signals from outside the target basin. The contamination from surrounding areas is called “leakage error”. Swenson et al., [2003] represented the method to design a filter which minimizes the summation of measurement errors and leakage errors.

364

Figure 2. The 2006 TWS anomaly reviewed by GRACE. Difference between 2003-2005 meanTWS anomaly and 2006 mean TWS anomaly.

Figure 4. TWS Variation within the Murray Darling River basin estimated by GRACE UTCSR solutions. Note annual and semi-annual component were removed. Table 1. Yearly average of TWS anomaly within the Murray Darling River basin estimated by GRACE UTCSR solutions. [unit: mm/m2 ].

UTCSR

2003

2004

2005

2006

5.8

1.4

−6.7

−68.4

Figure 4 shows the estimated TWS variations. Note that annual and semiannual variations have been subtracted to highlight the interannual TWS changes. Fig. 4 clearly shows large TWS decrease in late 2006. Yearly averages of the TWS anomalies are summarized in Table 1. The average value in 2006 are about 60 [mm/m2 ] lower than those in other years.

Figure 3. The location of Murray Darling River basin.

In order to design the optimal regional filter, we need to determine the degree variances of GRACE measurement errors and to select a covariance function. Following Yamamoto et al., [2007], we employed the GRACE calibrated errors which are released by UTCSR to calculate the degree variances of measurement error, and selected a 600 km half-radius Gaussian averaging function as the covariance function. We also need to evaluate the amplitude attenuation induced by the regional filter [Chen et al., 2007]. The regional filter reduces errors in GRACE solutions, but it also attenuates the real signals. In order to evaluate the attenuation due to the filter, we applied the same filter to two different TWS anomaly models: (1) TWS anomalies calculated from a hydrological model, (2) a 2-D step function (1 for inside the basin and 0 for outside the basin). We restored the amplitude attenuation using the evaluated attenuation factor. The attenuation factors obtained from the two models were not so much different. Therefore we hereafter show the results using the attenuation factor determined by the 2-D step function.

3

COMPARISON BETWEEN GRACE DATA AND HYDROLOGICAL MODEL

3.1 Hydrological model data For the comparison with GRACE TWS estimates, we employed two different landwater models: (1) the GLDAS (the Global Land Data Assimilation System) model developed by NASA: (2) the JGL (JRA-JCDAS LDA and GRiveT) by the Japan Meteorological Agency. The GLDAS model employs the Mosaic land surface model with the observation-based meteorological fields as the atmospheric driving force [Rodell et al. 2004]. We used the summation of soil moisture, accumulated snow and plant canopy surface water for the GLDAS TWS variations in this study. The JLG model employs the Simple Biosphere model [Sellers et al., 1988] and the Global River flow for TRIP (Total Runoff Integration Pathways) model [Nohara et al., 2006] as the land surface model and it

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Table 3. Yearly average precipitation (mm) over the Murray Darling River basin.

Obs.* JRA-25 CMAP

2003

2004

2005

2006

516 222 465

546 221 497

506 225 488

337 187 333

*The average of 112 station data sets within the Murray Darling River basin.

Figure 5. TWS Variation within the Murray Darling River basin estimated by the GLDAS model and the JLG model. Note annual and semi-annual component were removed. Table 2. TWS anomaly within the Murray Darling River basin estimated by the GLDAS model and the JLG model. [unit: mm/m2 ]

GLDAS JLG

2003

2004

2005

2006

−1.1 −1.0

4.5 −4.3

−3.3 5.3

−18.1 −5.0

uses the JRA-25 (Japan Re-analysis 25-year Reanalysis) data as the atmospheric driving force. We used the summation of soil moisture, accumulated snow, and river channel storage for the JLG TWS variations in this study. Although the JLG model includes plant canopy component in the model structure, we did not use canopy water storage for TWS calculation in this study, because the canopy water storage of the JLG model is negligibly small. 3.2

Comparison and Discussion

Figure 5 shows monthly TWS variations within the Murray Darling River basin estimated by the GLDAS model and the JLG model. Annual and semi-annual variations were removed in advance. Yearly averages of the TWS anomalies are summarized in Table 2. Comparing Figure 4 with Figure 5, it is clear that the TWS decrease in 2006 evaluated by the two hydrological models is much smaller than the GRACE observation. This suggests that the hydrological models underestimate the TWS decrease caused by the drought. Many factors affected to the erroneous estimates of the hydrological models. The uncertainties in forcing filed (e.g., precipitation data) will cause an insufficient estimate of TWS changes. In addition, inaccurate modeling of surface structure, process description and parameterization will also make large errors in hydrological model estimates.

To validate the model input of the precipitation data, we employed the ground-based precipitation data (high-quality Australian monthly precipitation dataset [Lavery et al., 1997]). The data sets of 112 ground stations were available within the Murray Darling River basin. We compared the ground-based precipitation data averaged over the Murray Darling River basin with the JRA-25 (Japanese 25-year Reanalysis) precipitation data (input to the JLG model) and the CMAP (Climate Prediction Center Merged Analysis of Precipitation) precipitation data (input to the GLDAS model). Table 3 shows the yearly averages of the precipitation over the Murray Darling River basin. The amplitude of the 2006 rainfall decrease estimated by the JRA-25 is much smaller than the ground observation. We consider it caused the JLG model to underestimate the TWS decrease in 2006. On the other hand, the CMAP precipitation data well agreed with ground observation. It means the uncertainties in surface model, model structure and parameterization might lead to the underestimation of TWS changes. Since the hydrological model estimates the soil moisture only several meters from the surface of the ground, water storage changes in deeper soil layers and groundwater variations may not properly be calculated as the TWS changes. We consider the discrepancy between the GRACE solutions and the GLDAS calculations indicate the uncertainties and missing structure of the GLDAS model.

4

CONCLUSION

We reported the TWS decrease caused by the 2006 Australian Drought from GRACE level-2 data sets. The GRACE estimate of the TWS decrease was much larger than those of the hydrological models. Hydrological models contain many erroneous in TWS estimations, because there remain large uncertainties in forcing fields, model structure, process description, and parameterization. Therefore we consider that the hydrological models underestimate the TWS changes caused by the drought. This also suggests that GRACE data make an important role to constrain unspecified factors in hydrological models.

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Our result demonstrated that GRACE gravity measurements provide not only annual TWS changes but also TWS response to long-term climate changes. It indicates that GRACE gravity measurements will make great contribution to future understanding of the impacts of the climate changes to the water resources and their managements. Appendix – Acronyms Acronym

Name

BoM CMAP

Australian Government Bureau of Meteorology CPC (Climate Prediction Center) Merged Analysis of Precipitation GeoFoschungsZentrum Potsdam Global Land Data Assimilation System Gravity recovery and Climate Experiment Global River flow model with TRIP (Total Runoff Integration Pathways) Intergovernmental Panel for Climate Change Japanese 25-year Reanalysis JMA (Japan Meteorological Agency) Climate Data Assimilation System Jet Propulsion Laboratory Land Data Analysis Terrestrial Water Storage University of Texas at Austin Center for Space Research

GFZ GLDAS GRACE GriveT IPCC JRA-25 JCDAS JPL LDA TWS UTCSR

REFERENCES Alsdorf, D.E. and Lettenmaier, D. P., (2003), Tracking fresh water from space, Science, 301, 1491–1494. Chen, J.L., Rodell, M., Wilson, C. R. and Famiglietti, J. S., (2005), Low degree spherical harmonic influences on Gravity Recovery and Climate Experiment (GRACE) water storage estimates, Geophys. Res. Lett., 32, L14405, doi:10.1029/2005GL022964. Chen J. L., Wilson, C. R., Famiglietti, J. S., and Rodell, M., (2007), Attenuation effect on seasonal basin-scale water storage changes from GRACE time-variable gravity, J. Geod., (2007), 81, 237–245, doi:10.1007/s00190-0060104-2. Bettadpur, S., (2007), UTCSR Level-2 Processing Standards Document For Level-2 Product Release 0004, Available Online: ftp://podaac.jpl.nasa.gov/pub/grace/doc/L2CSR0004_ProcStd_v3.1.pdf.

Bureau of Meteorology, (2006), Drought Statement Issued 4th December., Available Online: http://www.bom.gov.au/ announcements/media_releases/climate/drought/20061204.shtml. Kundzewicz, Z. W., Mata, L. J., Arnell, N. W., Döll, P., Kabat, P., Jiménez, B., Miller, K. A., Oki, T., Sen, Z. and Shiklomanov, I. A., (2007). Freshwater resources and their management, Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Parry, M. L., O. F., Canziani, J. P., Palutikof, P. J., van der Linden and Hanson, C. E., Eds., Cambridge University Press, Cambridge, UK, 173–210. Lavery, B., Joung, G., and Nicholls, N., (1997), An extended high-quality historical rainfall dataset for Australia, Aust. Met. Mag., 46, 27–38. Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K., and Nghiem, S.V., (2003), Soil moisture retrieval fromAMSRE, IEEE Trans. Geosci. Remote Sens., 41, 215–229. Nohara, D., Kitoh, A., Hosaka, M., and Oki, T., (2006), Impact of Climate Change on River Discharge Projected by Multimodel Ensemble, J. Hydromet., 7, 1076–1089, doi:10.1175/JHM531.1. Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D.,Toll, D., (2004),The global land data assimilation system, Bull. Am. Meteor. Soc., 85(3), 381–394. Sellers, P. J., Mintz, Y., Sud, Y. C. and Dalcher, A., (1988), Simple Biosphere model (SiB) for use within general circulation model, J. Atmos. Sci., 43, 505–531. Swenson, S., Wahr, J. and Milly, P. C. D., (2003), Estimated accuracies of regional water storage variations inferred from the Gravity Recovery and Climate Experiment (GRACE), Water Resour. Res., 39(8), 1223–1235, doi:10.1029/2002WR001808. Swenson, S., and Wahr, J., (2006), Post-processing removal of correlated errors in GRACE data, Geophys. Res. Lett., 33, L08402, doi:10.1029/2005GL025285. Tapley, B. D., Bettadpur, S., Watkins, M. M. and Reigber, C., (2004), The gravity recovery and climate experiment: mission overview and early results, Geophys. Res. Lett., 31, L09607, doi:10.1029/2004GL019920. Wahr, J., Molenaar, M. and Bryan, F., (1998), Time-variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE, J. Geophys. Res., 103(B12), 30,205–30,230 Yamamoto, K., Fukuda, Y., Nakaegawa, T., Nishijima, J., (2007), Landwater variation in four major river basins of the Indochina peninsula as revealed by GRACE, Earth Planets Space, 59, 193–200.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Improvement of JLG terrestrial water storage model using GRACE satellite gravity data K. Yamamoto∗, T. Hasegawa & Y. Fukuda Graduate School of Science, Kyoto University, Kyoto, Japan

T. Nakaegawa Meteorological Research Institute, Ibaraki, Japan

M. Taniguchi Research Institute of Humanity and Nature, Kyoto, Japan

ABSTRACT: For the future improvement of JRA-JCDAS LDA and GRiveT Terrestrial Water Storage (JLG) model, the phases and amplitudes of the annual components of mass variations of GRACE (Gravity Recovery and Climate Experiment) and JLG model are compared for 70 major river basins in the world. The phases of the annual components of GRACE and JLG model show good correspondence in most of the river basins, but about 1 to 2 month discrepancies were shown in Lena, Changjiang, Mackenzie, Orinoco, Yukon and Kolyma basins. Because GRACE data represent actual mass variations of terrestrial water storage including groundwater, these discrepancies mean that the current version of JLG model does not well represent the annual components in these basins. Thus the model’s phases can be improved using the GRACE result as constraints. In some basins with large signals, the amplitudes of the annual components of the GRACE mass variations show about 2 to 4 times large values compared with the model’s ones. The discrepancies can be explained by underestimation of GRACE errors, underestimation of the JLG model amplitudes and/or overestimation of the GRACE amplitudes, although we cannot conclude which is the main reason, at this stage. Keywords: 1

GRACE; terrestrial water storage model; landwater

INTRODUCTION

Since the successful launch in 2002, dedicated satellite gravity mission GRACE (Gravity recovery and Climate Experiment, Tapley et al., 2004) has provided monthly gravity field solutions as spherical harmonic coefficients with unprecedented accuracy. The mass variations derived from the gravity field solutions can be interpreted as geophysical signals induced by e.g. landwater movements, ocean circulation, ice sheet mass changes, post glacial rebound, mass changes associated with earthquakes. GRACE data can be utilized for these studies, and among them, one of the most promising applications is the monitoring of landwater movements. GRACE can detect total landwater variations including groundwater variations, because it observes vertical integration of the mass variations of the Earth. This is one of the advantages of GRACE data for estimating total mass of landwater ∗

Corresponding author ([email protected])

movements. Using GRACE data and in situ measurements of soil moisture, Yeh et al. (2006) estimated regional groundwater storage in Illinois. Total landwater variations detected by GRACE are also useful to improve landwater models. Since the initial stage of the mission, comparisons between GRACE data and terrestrial water storage models have been conducted. A result (Wahr et al., 2002) show a good correlation between GRACE data and a global terrestrial water storage model GLDAS (Global Land Data Assimilation Systems, Rodell et al., 2004) in seasonal mass variations, especially in large spatial scale. The same results have been obtained for other global landwater models, e.g. LaD (The Land Dynamics Model, Milly and Shmakin, 2002) or WGHM (WaterGAP Global Hydrology Model, Guntner et al, 2007), and so on. However, in a regional scale, seasonal signals of GRACE and models show some discrepancies in both amplitudes and phases. One of the possible reasons of the discrepancies is inappropriate groundwater modeling due the observation difficulties in most of global landwater models.

369

Figure 1. Geographical location of the 70 river basins compared the mass variations in this study.

Recently, JRA-JCDAS LDA and GRiveTTerrestrial Water Storage Model (JLG) has been developed by Nakaegawa et al. (2007). Yamamoto et al. (2007) compared the mass variations of GRACE and the previous version of JLG model in four major river basins of the Indochina Peninsula. The result shows that the phase of the annual component of the model gains about 1 month compared to the GRACE mass variations. This phase discrepancy in the Indochina Peninsula was dissolved in the new version of JLG model (Fukuda et al., 2008). The new JLG model includes groundwater components explicitly with current speed tuning for each river basins (Nakaegawa et al., 2008). In this study, as an extension of the previous work, we compared the terrestrial water storages of the new version of JLG model and GRACE data for major river basins in the world. We recovered regional mass variations for each of the river basins from GRACE data by a regional filtering method and their phases and amplitudes of the annual components were compared with those of the model.

included the smallest basin because we employed the GRACE data up to degree/order 60 as described later, the degree/order which corresponds to about 330 km in spatial scale.

3

GRACE DATA AND PROCESSING

We employed UTCSR RL04 of GRACE Level 2 monthly gravity field solutions (Bettadpur, 2007) which are provided as spherical harmonic coefficients up to degree/order 60. We used 59 monthly solutions fromApril 2002 to May 2007. C20 values were replaced to the SLR solutions (Cheng and Ries, 2007) because the large error is reported. To obtain the variable components, the average of the 59 data sets were subtracted from each solution. Mass variations of 70 river basins were estimated by a regional filtering method (Swenson et al., 2003). In this method, a regionally optimized filter is represented by the following equation:

2 TEST AREAS We selected 70 major river basins defined in the JLG model as the test areas. Geographical distributions of the basins are depicted in Figure 1. Sizes of these river basins are from 6.2 × 106 km2 (Amazon basin, No. 1 in Figure 1) to 1.1 × 105 km2 (Odra, No. 68). The smallest one corresponds about 350 km × 350 km in spatial scale. It is probably difficult to recover the landwater signal accurately in the smallest areas like Odra basin because of the significant increase of the satellite measurement errors in short wavelength. Nevertheless, we

C S where Wlm and Wlm are the designed filter coefficients, Bl is degree amplitude of the satellite measurement error, σ02 is the local signal variance, Gl is the Legendre coefficients of a covariance function G(γ, d), which is the function of angular distance γ and correlation length d, kl is the load Love number of degree l, and C S ϑlm and ϑlm are the spherical harmonic coefficients of the regional template, i.e., 1 inside and 0 outside of the area. As a function of G(γ, d), following Gaussian

370

function defined by Swenson et al. (2003) was used in this study:

Using the designed filter, the surface mass variation over the region σregion is estimated by the following equation:

where a is the Earth’s equatorial radius, ρE is the average density of the Earth, region is the angular area of the region, and Clm , Slm is variable components of spherical harmonics coefficients of each GRACE monthly solution. In eq. (1), the values of two parameters d and Bl are given in advance. In this study, we allocated 400 km to 1000 km for d value, and the degree amplitudes of calibrated standard deviations for Bl . which are also released by GRACE data centers. In fact, the optimum values of these parameters are unknown. We discuss the issue in Section 5.

For the comparison with GRACE monthly solutions, the model data sets were processed as follows. Firstly, the averages of the model data corresponding to the time period of GRACE monthly solution were calculated, and then the variable components of these monthly data sets were obtained by subtracting the average data over the whole time span. In GRACE monthly solutions, the total mass of the Earth is conserved through the whole time period. One the other hand, the total mass of the JLG and ECCO models is not conserved. Thus we adjusted the ocean mass by uniformly giving an off-set value at each time step so that the total mass of the models is conserved. The model data were converted to spherical harmonic coefficients of gravity field by eq. (12) of Wahr et al. (1998). For the consistency with GRACE data, we used the coefficients up to degree/order 60 and omitted the degree 1 terms, which are not included in GRACE solutions. Regional mass variations of the model were calculated by eq. (3). The Bl value in eq. (1) is set to be zero for the model data. In this case the filter corresponds to a boxcar type. 5

DISCUSSION

5.1 Sensitivity for the parameter “d” 4

JLG TERRESTRIAL WATER STORAGE MODEL

JLG is one of the global terrestrial water storage models developed by Nakaegawa et al. (2007). The total terrestrial storage obtained from JLG consists of soil moisture, snow water equivalent, river water storage and groundwater storage. Soil moisture and snow water equivalent are obtained from JMA-Simple Biosphere model (JMA-SiB). River water storage and groundwater storage are obtained from offline simulation performed with MRI Global River Model forTRIP (GRiveT). The temporal and spatial resolutions are 6 hours and 1◦ , respectively. The schemes of the computation of terrestrial water storage with this model are stated in Fukuda et al. (2008). Although we mainly discuss landwater mass variation, we include an ocean model for the comparison to guarantee the consistency with GRACE data. Estimating the Circulation and Climate of the Ocean (ECCO) model (Kalman filter run, version kf066b, Fukumori et al., 1999) is used as the ocean model. Note that the ocean signals are one order smaller than landwater signals and their errors are not significant. We omitted the landwater data in Antarctica and Greenland, because they were extremely unreliable. This may cause some underestimation in the power spectrum of the global mass variations, but the effects on the regional mass estimations can be negligible.

As mentioned in Section 3, the values of two parameters d and Bl are given in advance for the design of the regional filter of Swenson et al. (2003). These parameters closely related to the obtained amplitudes, and an improper parameter may cause misestimating of the mass variations. Therefore we firstly investigated the parameter d dependency of the obtained annual mass variations from GRACE data. For the following discussions, the phases and amplitudes of the annual components of both GRACE and model data were obtained by means of the least squares fitting to trigonometric function. In this study, we used the Gaussian base function as G(γ, d) (eq. (2)), because The Gaussian function can cut off satellite measurement errors in short wavelength effectively. On the other hand, Swenson et al. (2003) pointed out that the characteristic of the Gaussian function is sensitive to the d value and a large filtering error occurs if the d value is not appropriate. Thus, we tested the d sensitivity by changing the d values from 400 km to 1000 km. The results show that, in most of the river basins, the relative changes of the standard deviations of the amplitudes of the annual components were within 10%, and those of the phases of the annual components were within 0.1 month. Figure 2(a) shows an example. The d sensitivity is relatively large in some river basins, but these areas have the following common features; 1) small Signal/Noise (S/N) ratio of GRACE observation

371

Figure 3. (a) The ratios of estimated amplitudes of the annual components (JLG model/GRACE) when 600 km of correlation length d value is used for the filter designs. (b) The same figure of (a), but the JLG model is replaced to the GLDAS NOAH model.

Figure 2. Estimated GRACE mass variations of (a) Ob (river no. 5), (b) Burdekin (no. 62) and (c) Huanghe (no. 29) river basins obtained by changing the correlation length d value from 400 to 1000 km. The mass variations obtained by JLG model are also shown.

(e.g. Figure 2 (b)), and/or 2) small annual signal (e.g. Figure 2 (c)). In these areas, annual fittings become inaccurate because of the large errors. Thus, the large sensitivities in these areas do not mainly come from improper d value but from the fitting errors of the annual components. Thus we can conclude that the d sensitivity can be neglected at least for 400 to 1000 km. 5.2 Amplitudes of the annual components Figure 3 (a) shows the distribution of the ratios (GRACE/model) of the amplitudes of the annual components. As mentioned above, most of the large or small values of the ratios come from the fitting errors of the annual components. Besides these, a significant feature is that large ratios are found in the basins located at low latitudes. In these areas, the amplitudes of the GRACE’s annual components are about 2 to

4 times larger than the model amplitudes, while they are almost same as the model ones at middle to high latitudes. There are 3 possible reasons of the discrepancies at low latitudes: 1) larger sensitivity of the designed regional filter caused by underestimation of the GRACE error degree variances, 2) underestimation of the JLG model, and/or 3) overestimation of the GRACE solution itself. Regarding the first case, if the Bl in eq. (1) have smaller values, the gain of the filter becomes larger. Thus, in principle, by changing the Bl values to larger values, we can adjust the amplitudes of the GRACE’s annual components to the model amplitudes. Actually, the GRACE error levels may not be so accurate and have some uncertainties. One difficulty of this interpretation is that the discrepancies are found only at low latitudes. Therefore we have to consider a kind of systematic errors or latitude dependency of the errors. To evaluate the second case, we employed another landwater model of the NOAH version of GLDAS (GLDAS NOAH; Rodell et al., 2004). Figure 3 (b) shows the GRACE/model ratio of the amplitudes of the annual components for the GLDAS model. Although there are some differences between Fig. 3 (a) and

372

Figure 4. The phase gains of the annual components of the JLG model with respect to the GRACE’s one when 600 km of correlation length d value is used for the filter designs. The positive value represents that the model’s phase gains compared with GRACE’s one.

(b), the same features described above are recognized in Fig. 3(b). Furthermore, the similar feature is also observed in the comparison between GRACE and the terrestrial water storage obtained from the combined water balance (CWB) method (Rasmusson, 1968), which is calculated mainly from precipitation and evaporation and independent to above models. Thus, in this case, we have to consider that some processes at low latitudes are not commonly taken into account in both JLG and GLDAS models, nor the processes cannot be well estimated even by the CWB method. The third case is the errors in GRACE solutions. So far we used the UTCSR RL04 solutions in this study, because the UTCSR solutions are routinely released and most widely employed in general. There are two other versions of GRACE monthly solutions released, i.e. JPL RL04 (Watkins, 2007) and GFZ RL04 (Flechtner, 2007). It is reported that there are some differences in the amplitudes of the solutions, however the differences are at most 20 to 30%. Therefore, even if GFZ or JPL solutions are employed instead of UTCSR solutions, the basic feature in Figure 3 (a) remains unchanged. Therefore, in this case, we also have to assume unknown common errors in the GRACE solutions.

5.3

Phases of the annual components

As discussed above, it is difficult to assess the amplitudes of the model’s annual components using GRACE data at this stage. On the other hand, the comparisons of the phases of the annual components are more straightforward and reliable. Figure 4 shows the differences of the phases of the annual components with 600 km of the d value. The phase difference between GRACE and the model in most of river basins are within 1 month. It means that the current version of the JLG

model well represents the phase of annual terrestrial water storage including groundwater. In several basins, the differences are relatively large. In Lena (No. 7), Changjiang (No. 11), Mackenzie (No. 12), Orinoco (No. 21), Yukon (No. 22) and Kolyma (No. 34) basis, the phase discrepancies are 1 to 2 months, although the S/N rates in these basins are enough large and the annual signals are prominent. We cannot find any definite reasons for the phase differences and consider that they are due to insufficient tuning of the JLG model. We expect they should be improved through further improvement of the model, and then, GRACE data will give good constraints for the model tuning. 6

CONCLUSION

Amplitudes of the annual components of the JLG landwater model are compared with the ones obtained from the GRACE data. The results show that the GRACE amplitudes are about 2 to 4 times larger than the JLG model at low latitudes. At present, we can not identify the reason of the discrepancy, its further investigation is important not only for the model improvements but also the error assessments of the GRACE data. They are our future works. On the other hand, the differences of phases of the annual components are more reliable. The obtained result gives useful information to constrain the model’s phase of the annual components and they can be employed for the model improvements more directly. REFERENCES Bettadpur, S., 2007. UTCSR Level-2 processing standards document for level-2 product release 0004, GRACE 327742 (CSR-GR-03-03), Center for Space Research, The University of Texas at Austin, Austin. Cheng, M. & Ries, J. 2007. Monthly estimates of C20 from 5 SLR satellites. GRACE Technical Note #05: http://podaac.jpl.nasa.gov/grace/documentation.html. Flechtner, F. 2007. GFZ Level-2 processing standards document for level-2 product release 0004, GRACE 327-743 (GR-GFZ-STD-001). GeoForschungszentrium, Potsdam, Wessling. Fukuda , Y, Yamamoto, K., Hasegawa, T., Nakaegawa, T., Nishijima, J. & Taniguchi, M. 2008. Monitoring Groundwater Variation by Satelllite and Implications for in-situ Gravity Measurements. STOTEN: (in press). Fukumori, I., Raghunath, R., Fu, L. & Chao, Y. 1999. Assimilation of TOPEX/POSEIDON data into a global ocean circulation model: How good are the results?. J. Geophys. Res. 104 (C11): 2647, doi:10.1029/1999JC900193. Guntner, A., Stuck, J., Werth, S., Doll, P., Verzano, K. & Merz, B. 2007. A global analysis of temporal and spatial variations in continental water storage. Water Resour. Res. 43: W05416, doi:10.1029/2006WR005247. Milly, P.C.D., & Shmakin, A.B. 2002. Global modelling of land water and energy balances Part I: The land

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dynamics (LaD) model. J. Hydromet. 3: 283–299, doi:10.1175/1525-7541(2002)003<0283:GMOLWA>2.0. CO;2. Nakaegawa, T., Tokuhiro, T., Itoh, A., & Hosaka, M. 2007. Evaluation of Seasonal Cycles of Hydrological Process in Japan Meteorological Agency Land Data Analysis. Pap. Meteorol. Geophysi. 58: 73–83, doi:10.2467/mripaperes.58.73. Nakaegawa, T. & Hosaka, M. 2008. Effects of calibrated current speeds and groundwater scheme in a global river-flow model on river discharge and terrestrial water storage, Hydrological Research Letters 2: 18–21, doi:10.3178/HRL.2.5. Rasmusson, E.M. 1968. Atmospheric Water Vapor Transport and the Water Balance of North America II. Large-Scale Water Balance Investigations. Monthly Weather Review 96: 720–734. Rodell, M., Houser, P.R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J.K., Walker, J.P., Lohmann, D. & Toll, D. 2004. The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc. 85: 381– 394, doi:10.1175/BAMS-85-3-381. Swenson, S., Wahr, J. & Milly, P.C.D. 2003. Estimated accuracies of regional water storage variations inferred from the Gravity Recovery and Climate Experiment (GRACE). Water Resour. Res. 39: 1223, doi:10.1029/2002WR001808.

Tapley, B.D., Bettadpur, S., Watkins, M. & C. Reigber, C. 2004. The Gravity Recovery and Climate Experiment: Mission overview and early results. Geophys. Res. Lett. 31: L09607, doi:10.1029/2004GL019920. Yamamoto, K., Fukuda, Y., Nakaegawa, T. & Nishijima, J. 2007. Landwater variation of 4 major river basins in the Indochina peninsula revealed by GRACE. Earth Planets Space 59: 193–200. Yeh, P.J.-F., Swenson, S.C., Famiglietti, J.F. & Rodell, M. 2006, Remote sensing of groundwater storage changes in Illinois using the Gravity Recovery and Climate Experiment (GRACE), Water Resour. Res. 42: W12203, doi:10.1029/2006WR005374. Wahr, J., Molenaar, M. & Bryan, F. 1998. Time variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE. J. Geophys. Res. 103 (B12): 30205, doi:10.1029/98JB02844. Wahr, J, Swenson, S., Zlotnicki, V., Velicogna, I. 2004. Timevariable gravity from GRACE: First results. Geophys. Res. Lett. 311: L11501, doi:10.1029/2004GL019779. Watkins, M.M. 2007. JPL Level-2 processing standards document for level-2 product release 04, GRACE 327–744, Jet Propulstion Laboratory. Pasadena.

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The European SMOS for large-scale water balance and climate modelling studies Adriaan A. Van de Griend∗ Guido Guido Gezellelaan, Zeist, The Netherlands Formerly: Department of Hydrology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Jean-Pierre Wigneron INRA – Unité de Bioclimatologie, Villenave d’Ornon CEDEX, France

Philippe Waldteufel Service d’Aéronomie, Vélizy, France

Josef Krecek Department of Hydrology, Czech Technical University, Prague, Czech Republic

ABSTRACT: The European Soil Moisture and Ocean Salinity (SMOS) mission, after some delay now planned for launch late 2008, will carry a two-dimensional L-band (1.4 GHz) microwave interferometric radiometer with a revisit time smaller than 3 days to retrieve soil moisture (and ocean salinity) at global scale with a spatial resolution of ∼30 km. Soil moisture plays a crucial role in the terrestrial hydrological cycle and its timely and repetitive retrieval is essential for medium term meteorological modelling, hydrological modelling and modelling of several other climate related processes such as vegetation biomass production and CO2 assimilation. Repetitive monitoring of soil moisture fields at regional scales, also are important for understanding changes in the hydrological processes at different time scales. This paper gives some insight into the potential capabilities of SMOS and addresses some issues concerning surface heterogeneity and its consequences for the precision of soil moisture retrieval. Keywords:

1

SMOS; soil moisture; hydrological change; heterogeneity; microwaves; passive

INTRODUCTION

The Soil Moisture and Ocean Salinity (SMOS) instrument is planned for launch in 2008. With its capability of observing the globe at 1.4 GHz (L-band), two polarizations (H and V) and at multiple angles simultaneously, SMOS is the most advanced system designed for soil moisture monitoring at global scale. The importance of soil moisture monitoring at global scale follows univocally from two major conclusions drawn from the NASA 2002 Land Surface Hydrology Planning Workshop, formulated as: – “The lack of a global soil moisture observing system is one of the most glaring and pressing deficiencies in satellite remote sensing and climate research” – “Precise in situ measurements of soil moisture are sparse and each value is representative of a ∗

Corresponding Author ([email protected])

small area. Remote sensing ….. would provide truly meaningful wide-area soil wetness for large-scale hydrological and climatological studies” Soil moisture is one of the main factors controlling the water, energy and carbon fluxes, and weather and climate models are strongly driven by regional availability of soil moisture to satisfy the atmospheric demand. In addition, antecedent moisture conditions play a crucial role for the partitioning of infiltration and surface runoff. Long-term changes in hydrological conditions are usually reflected in soil moisture regimes and repetitive monitoring is expected to contribute significantly to further understanding these changes in connection to climate change. Many theoretical and experimental studies have demonstrated the usefulness of passive microwaves for soil moisture retrieval of the top few centimeters, even under a moderate vegetation cover. However, the large footprint of microwave observations from space

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has serious consequences for parameter retrieval from “real-world” inhomogeneous surfaces. At the spatial scale of SMOS (∼30 km for nadir observations), the earth’s surface is inhomogeneous almost by default and this aspect has not yet been fully accounted for. The problem of heterogeneity (effects of forests, water, etc.) play a role in large portions of the globe, especially in extremely heterogeneous areas (build-up, water bodies) and relatively small areas surrounded by water bodies. This issue of course plays a serious role in the case of Japan, i.e. “How heterogeneous is Japan at the footprint scale of SMOS?”. When looking at the fraction of each 30 × 30 km2 pixel with favorable conditions (i.e. agricultural fields, herbs, shrubs) we see that a relatively small number of pixels (19% of Japan) will allow a soil retrieval error smaller than 4% during summer (Pellarin et al, 2003). This result, however, is based on the assumption of ‘no a priori knowledge of surface fraction’ within the pixel. The influence of ‘within-pixel heterogeneity’ of course is evident and incorporation of this knowledge leads to a significant improvement, which will be shown for the case of fractional forest cover. Knowledge of cover fraction of identified surface types is easily available from archives and near-real time data gathered from space using a variety of sensors (e.g. visible/NIR) and active microwave systems. Recently, extensive simulation and retrieval studies for SMOS have shown (Pellerin et al. 2003) that retrieval accuracies of <4% by volume can be achieved for homogeneous surfaces only. For heterogeneous surfaces, however, even small portions of forest (>10%) may have a dramatic effect on the retrieval of pixel-average soil moisture, resulting in retrieval errors that exceed the required precision formulated for the SMOS mission (Van de Griend et al. 2003). However, preliminary theoretical studies (Van de Griend et al. 2004) have shown that substantial improvement can be achieved using a priori knowledge of surface fractions such as forests, (coastal) water bodies and build-up areas. This paper gives an overview of the anticipated capabilities of SMOS in retrieving global soil moisture fields and addresses the increased retrieval precision when using a priori knowledge of surface properties such as forest cover, (coastal) water bodies and build-up areas.

2 THE SMOS INSTRUMENT SMOS differs from other space borne passive microwave instruments by its multi-angle observation capability (Waldteufel et al. 2000). From the receiver measurements, the surface brightness temperature (TB ) field can be reconstructed with a nadir resolution of ∼30 km. The range of view angles depends

on the position of the observed pixel with respect to the sub-satellite flight track (it is maximum, between 0◦ and 55◦ , at nadir). From the independent multi-angular measurements, the land-surface parameters such as surface moisture and vegetation optical depth can be retrieved with improved accuracy. The potential of passive microwave remote sensing for soil moisture monitoring (low frequencies: f < ∼ 6 GHz) and vegetation monitoring (higher frequencies: f > ∼10 GHz) has been demonstrated extensively in the literature (Jackson et al. 1999; Van de Griend and Owe 1994). From the frequencies relevant for surface moisture monitoring ( f < ∼6 GHz), the L-band ( f = 1.4 GHz) is the least affected by vegetation and is therefore most appropriate for moisture monitoring in vegetation covered regions. SMOS is the first L-band instrument specially developed for routine surface moisture monitoring at global scale. At lower frequencies (f < ∼6 GHz) vegetation is partially transparent for microwave radiation and the loss of radiative energy due to both scattering and absorption can be expressed in terms of the (nadir) vegetation optical depth τo . The microwave emissivity of the soil surface and the transmissivity of the vegetation are both incidence angle and polarization dependent. The polarization dependence of canopy transmissivity mainly concerns canopies with a preferential structure (Ulaby et al. 1986). Knowledge of the angular and polarization dependence of canopy radiative transfer offers the possibility to retrieve both soil emissivity, es , (which is strongly related to soil moisture) and τo from dual-polarization (h and v) and multi-angle temperature observations (TB ). It has been shown (Wigneron et al. 2000) that the effective surface temperature, Teff , which is needed to derive the surface emissivity es ∼ = TB /Teff , can be retrieved simultaneously under specific conditions. A multitude of inversion algorithms has been developed and described in the literature (Wigneron et al. 2003) to derive surface moisture from microwave brightness temperatures. All these approaches, however, have been applied under the assumption of surface homogeneity. Although the problem of surface heterogeneity has been addressed in the literature with respect to various surface parameters (see e.g. Njoku et al. 1996; Galantowicz et al. 1998; Drusch et al. 1999), the issue of surface heterogeneity has not been studied with reference to multi-angle observations and will be addressed in the current paper with emphasis on canopy heterogeneity and consequences for multi-parameter retrieval (soil moisture, vegetation optical depth and effective surface temperature). 3 THEORY AND APPROACH The approach followed in the current study is based on forward modeling (first step) and inversion modeling

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(second step) both using the ω − τ modeling approach (Van de Griend & Owe, 1994; Wigneron et al. 1995; Mo et al. 1982), which is based on zero-order radiative transfer. This model ignores the scattering source function and simulates brightness temperatures of vegetation covered surfaces as a function of es , τo and the vegetation single scattering albedo, ω. It is given by (Ulaby et al. 1986):

where, Γ is the transmissivity of the canopy [−], Ts and Tc are the temperatures of the soil and the canopy respectively [K]. Except for Tc and Ts , all parameters in eq. 1 are polarization and incidence-angle dependent. For SMOS, with a planned time of observation around 0600 local time, near surface temperature gradients will be small. For a randomly organized canopy architecture, the angular transmissivity of the canopy (Γµ ) is directly related to the (nadir) vegetation optical depth (τo ) through:

where µ is the incidence-angle. The soil emissivity (ep,µ ) is related to the soil reflectivity (Rp,µ ) according to:

Figure 1. Schematic geometry of the SMOS observations (from: Wigneron et al. 2000).

of surface roughness have been ignored. The number of incidence-angles and their angular range available from SMOS depends on the distance of the footprint to the sub-satellite track. This position can be expressed in terms of the half-swath angle, ηm (see Figure 1) and the range varies approximately between 0◦ and 50◦ for a sub-satellite track (ηm = 0◦ ) and approximately between 38◦ and 44◦ for a half-swath angle of ηm ∼ = 33◦ . We only assumed sub-satellite observations, having the largest variation in simultaneous look-angles and for which a total of 11 observationangles are (assumed to be) available (∼0, 6, 13, 18, 24, 29, 34, 39, 43, 47 and 50◦ ). 3.1 Forward modeling

while the soil reflectivity depends on the dielectric properties of the soil water mixture, expressed in terms of the complex dielectric constant ε, defined as:

The angular dependence of the reflectivity of a smooth surface can be described using the Fresnelequations (Ulaby et al. 1986; Born & Wolf 1964). At 1.4 GHz, the imaginary part of the dielectric constant only marginally effects the reflectivity. Therefore, we ignored the imaginary part of the dielectric constant and approximated the reflection coefficients by:

where, κ = Re{ε}. The heterogeneity of surface roughness is not addressed in the current study and effects

Since forward calculation of angular brightness temperatures requires the complex dielectric constant, ε, as an input parameter, and because the relationship between ε and soil water content is soil-type dependent (Schmugge, 1998), we described all forward calculations and all inversions in terms of the soil dielectric constant, which was approximated by its real part κ = Re{ε}. The range of κ-values used in the current study extends between κ = 4 (eo ∼ = 0.89) and κ = 21 (eo ∼ = 0.59). In the forward modeling study, the brightness temperatures were simulated for the above given range of incidence-angles, all with an imposed Gaussian error of 2 K and for various configurations of eo and τo . We assumed ω to be zero, although values presented in the literature range between 0.05 and 0.12 for crops Jackson & Schmugge, 1991). Higher values of ω were recently obtained over forests (ω ≈ 0.1 − 0.15) by Ferrazzoli et al. (2002). In order to study the problem of inhomogeneity, three different surface types and two wetness conditions were defined, leading to six mixed surface types. For each wetness condition, these surface types were mixed pair-wise in variable proportions to

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generate mixed pixels with pixel-average brightness temperatures for each observation angle. Two different mixel (i.e. mixed pixel) types were created, i.e. mixels of bare surface (τo = 0) and vegetated surface (τo = 0.4) and mixels of bare surface and forested surface (τo = 1.5). The mixels were created for either wet soil conditions (κ = 21; eo = 0.585) or dry soil conditions (κ = 4; eo = 0.888), and soil moisture was assumed to be uniformly distributed within the mixel. It is realized that these assumptions give a best-case scenario compared to those found in nature. The mixel fractions run from 0% of the first surface (100% of the second) to 100% of the first (0% of the second) with a step of 10%. The canopy temperature (Tc ) and the soil temperature (Ts ) were assumed to be the same and were set equal to 300 K in all forward simulations. A Monte Carlo approach was used to generate a large number (100) of independent sets of 11 angular brightness temperatures for each mixel-type, each brightness temperature being affected with a Gaussian error of 2 K. 3.2

Model inversion and parameter retrieval

We used a nonlinear least squares (steepest slope) approximation (Marquardt 1963) for model inversion, by minimizing the differences between the observed, TB(p,µ,obs) , and the simulated, TB(p,µ,sim) , according to:

to determine simultaneously the surface parameters κ, τo and Ts . This resulted in 100 sets of retrieved parameters (κ, τo and Ts ) for each mixel from which we calculated the mean and the Standard Error of Estimate (SEE). 4

RESULTS

The outcome of parameter retrieval resulted in estimated parameter values (eo , τo or Ts ) and corresponding SEEs for each mixel configuration. These were compared with the actual “effective” mixel parameters calculated directly from the mixture fractions by forward modeling taking account non-linearities with respect to up-scaling (for details see (Van de Griend et al. 2003). The retrieval errors in eo are visualized Figure 2. Errors in τo(eff ) and Ts are also discussed. 4.1 Retrieval of eo Figure 2 (a–d) shows the mean absolute errors involved in the retrieved value of eo as a function of the fractional contribution of the vegetated surface (α) with τo = 0.4 or τo = 1.5 and for actual soil emissivities

Figure 2. Absolute errors and SEE in the retrieval of nadir surface emissivity (eo ) as a function vegetation coverage (α), (a) for dry soil and τo = 0.4, (b) for wet soil and τo = 0.4, (c) for dry soil and τo = 1.5 and (d) for wet soil and τo = 1.5. Dashed lines indicate the required retrieval accuracy of 4 Vol.% (for explanation see text).

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of ∼0.9 (dry soil) and ∼0.6 (wet soil). The error bars show the errors, i.e. ± SEE. As expected, the SEE increases with increasing canopy fraction to reach a maximum for α = 100%. For studies over land surfaces within the framework of the SMOS mission, Kerr et al. (2001) considered a threshold error for soil moisture of 0.04 m3 /m3 as a basis for the required accuracy. The corresponding threshold errors in eo (i.e. eo ) have been estimated and indicated in Figure 2 (horizontal dashed lines). It can be seen that the calculated errors in eo fall partially within the thresholds, depending on the fractional vegetation cover (α) and vegetation density, expressed in terms of τo . For mixels of bare soil and agricultural fields with τo ≤ 0.4 and dry soils, the errors due to inhomogeneity are smaller than the accuracy criterion of 4 vol.% (assuming other error sources to be negligible). If the soils are wet, however, the error reaches a maximum of ∼6.1 ± 2.3 vol.% (i.e. for eo = 0.055 ± 0.02) for a vegetation cover of 50%. For mixels of bare soil and dense vegetation (e.g. forest) with τo = 1.5 and dry soils, the errors due to inhomogeneity are <4 vol.% as long as the vegetation cover is smaller than 40%. If the soils are wet, however, any fractional contribution of vegetation larger than 5% leads to errors beyond the required precision. 4.2

Retrieval of τo(eff )

Although the errors in τo are only of secondary importance within the context of soil moisture retrieval, it is indicative of the effect of inhomogeneity on the masking effect by the canopy. The effect of the wetness condition on τo is relatively small if τo is relatively small. For a dry soil the overall error reaches a maximum of τo(eff ) = −0.06 ± 0.03 (for α = 60%). For α = 100% the error in τo (τo = 0.01 ± 0.05) is still relatively small, leading to transmissivities varying between o = 0.63 (τo = 0.4 + 0.06) and o = 0.70 (τo = 0.4 − 0.04). For mixed pixels of bare soil and forest (τo = 1.5) the errors in retrieved optical depth increase substantially with increasing fractional vegetation cover. For high values of α, the masking effect of forest is very large and the sensitivity of the brightness temperature to both soil moisture and optical depth is very small. This leads to a high sensitivity of the retrieved parameters to the noise added to the brightness temperatures. Also here, the effect of the wetness on τo(eff ) was found to be relatively small.

high as ∼30 K for τo = 0.4 and α = 35%. In all cases, the SEE in the retrieved Ts decreases with increasing fractional vegetation cover.

5

CONCLUSIONS

Based on forward calculations of multi-angle brightness temperatures of mixed pixels of bare and vegetated soils, and subsequent inversion to estimate the effective surface parameters eo , τo and Ts simultaneously, the effects of surface heterogeneity was studied. Taking a threshold error of 4 vol.% in the soil moisture, which is the required accuracy for the SMOS mission, it was found that for dry soils (eo ∼ = 0.9) with low vegetation biomass (τo = 0.4), the effect of partial vegetation cover on soil moisture retrieval is relatively small. A τo of 0.4 corresponds to a VWC (vegetation water content) of 3 kg/m2 , which is more or less representative for a mature agricultural crop. For wet soils (eo ∼ = 0.6) and τo = 0.4, the errors reach a maximum of ∼6 ± 2.3 vol.% (eo = 0.055 ± 0.02) for a vegetation cover of 50%. As expected, for dense vegetation, the errors in retrieved soil moisture are larger. However, for mixed pixels partially covered with very dense vegetation of τo = 1.5 (VWC ∼ = 10 kg/m2 , which corresponds to a relatively dense coniferous forest), the error is relatively small for dry soils and a coverage smaller than ∼70%. For higher vegetation fractions, the masking effect starts to dominate which leads to very large errors. Under wet conditions and τo = 1.5, the dominating masking effect of the canopy leads to errors in estimated surface moisture which are much larger than the threshold value of 4 vol.% if the canopy fractional coverage > ∼ 5%. It is shown in the current study that the occurrence of patches with high density vegetation has a significant effect on the retrieval accuracy of surface emissivity. This mean error, which as mentioned above originates from the nonlinear behavior of brightness temperatures in the forward model, could be reduced significantly if a priori knowledge would be available about the fractional coverage of the mixels and/or about the physical temperature of the surface. Parameter retrieval from inhomogeneous surfaces with a priori knowledge of within-pixel partitioning is the subject of ongoing and follow-up studies.

ACKNOWLEDGEMENTS

4.3 Retrieval of Ts The errors in the retrieval of Ts for a dry soil were found to be of the order of 2 K which is equal to the imposed Gaussian error of 2 K. For a wet soil, however, the errors are substantial. The absolute error reaches as

The current study was made possible through a fund from ESA-ESTEC (Contract: 14662/00/NL/ DC; Task 3: Development of retrieval algorithms and model sensitivity analysis) which is gratefully acknowledged.

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REFERENCES Berger, M., Kerr, Y., Font, J., Wigneron, J-P., Calvet, J-C., Saleh, K., Baeza-Lopez, E., Simmonds, L., Ferrazzoli, P., Van de Hurk, B., Waldteufel, Ph., Petitcolin, F., Van de Griend, A.A., Attema, E. & Rast, M. 2003. Measuring Soil Moisture with ESA’s SMOS Mission; Advancing the Science. ESA-Bulletin Vol. 115, August 2003: 40–45. Born, M. & Wolf, E. 1964. Principles of Optics, Electromagnetics Theory of Propagation Interference and Diffraction of Light. Pergamon Press, 1964. Drusch, M., Wood, E.F. & Simmer, C. 1999. Up-scaling effects in passive microwave remote sensing: ESTAR 1.4 GHz measurements during SGP ’97. Geophysical Research Letters, Vol. 26(7): 879–882, 1999. Ferrazzoli, P., Guerriero, L. & Wigneron, J-P. 2002. Simulating L-band emission of forests in view of future satellite applications. IEEE Trans. Geosc. Remote Sens. Vol. 40(2): 2700–2708. Galantowicz, J., Entekhabi, D. & Njoku, E.. 1998. Estimation of soil type heterogeneity effects in the retrieval of soil moisture from radiobrightness. IEEE Trans. on Geosci. and Remote Sensing, 38(1): 312–316. Jackson, T.J. & Schmugge, T.J. 1991. Vegetation effects on the microwave emission of soils. Remote Sensing of Environment, Vol. 36: 203–212. Jackson, T.J., Le Vine, D.M., Hsu, A.Y, Oldark, A., Starks P.J., Swift, C.T., Isham, J.D. & Haken, M. 1999. Soil moisture mapping at regional scales using microwave radiometry: The Southern Great Plains Hydrology Experiment. IEEE Trans. Geosc. Remote Sens., 37: 2136–2151. Kerr, Y.H., Waldteufel, P., Wigneron, J-P., Font J. & and Berger, M. 2001. Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) Mission. IEEE Trans. Geosc. Remote Sens., 39(8):1729–1735. Marquardt, D.W. 1963. An algorithm for least-squares estimation of non-linear parameters. SIAM J.Appl. Math., Vol. 11: 431–441. Mo, T., Choudhury, B.J., Schmugge, T.J. & Jackson, T.J. 1982. A model for microwave emission from vegetationcovered fields. Journal of Geophysical Research, Vol. 87: 11229–1237. Njoku, N., Hook, S.J. & Chehbouni, A. 1996. Effects of surface heterogeneity on thermal remote sensing of land parameters. In J.B. Stewart et al. (eds), Scaling up in hydrology using remote sensing, Institute of Hydrology: 19–37.

Pellarin, T., Wigneron, J.-P., Calvet, J.-C. & Waldteufel, P., 2003. Global soil moisture retrieval from a synthetic L-band brightness temperature data set. J. of Geophys. Res., Vol. 108 (D12): 4364. Schmugge, T.J. 1998. Applications of passive microwave observations of surface soil moisture. J. of Hydrology, Vol. 212–213: 188–197. Ulaby, F.T., Moore, R.K. & Fung, A.K. 1986. Microwave Remote sensing-Active and Passive, Vol. III: From Theory to Applications. Artech House Publ., London. Van de Griend, A.A. & Owe, M. 1994. The Influence of Polarization on Canopy Transmission Properties at 6.6 GHz and Implications for Large Scale Soil Moisture Monitoring in Semi-Arid Environments. IEEE Trans. Geoscience and Remote Sensing, Vol. 32(2): 409–415. Van de Griend, A.A., Wigneron, J-P. & Waldteufel, P. 2003. Consequences of Surface Heterogeneity for Parameter Retrieval from 1.4 GHz Multi-Angle SMOS Observations. IEEE Transactions on Geoscience and Remote Sensing, Vol. 41(4): 803–811. Van de Griend, A.A., Wigneron, J.-P. & Waldteufel, P. 2004. Parameter Retrieval from Heterogeneous Surfaces by 1.4 GHz Multi-Angle SMOS Observations using ‘A Priori Knowledge’ of Surface Cover Fractions. Proceedings of IGARSS 2004, IEEE International, Vol. 7: 4552–4555. Waldteufel, P., Anterrieu, E., Goutoule, J.M. & Kerr, Y. 2000. Field of view characteristics of a microwave 2-D interferometric antenna, as illustrated by the MIRAS concept. In: P. Pampaloni & S. Paloscia (eds.), Microwave Radiometry and Remote Sensing of the Earth’s Surface and Atmosphere:447–483, VSP, Utrecht. Wigneron, J.-P., Chanzy, A., Calvet, J.-C. & Bruguier, N. 1995.A simple algorithm to retrieve soil moisture and vegetation biomass using passive microwave measurements over crop fields. Remote Sensing of Environment, Vol. 51: 331–341. Wigneron, J.-P., Waldteufel, P., Chanzy, A., Calvet, J.-C. & Kerr,Y. 2000. Two-dimensional microwave interferometer retrieval capabilities over land surfaces (SMOS Mission). Remote Sensing of Environment, (73): 270–282. Wigneron, J.-P., Calvet J.-C., Pellarin T., Van de Griend, A.A., Berger, M. & Ferrazzoli, P. 2003. Retrieving near surface soil moisture from microwave radiometric observations: current status and future plans. Remote Sensing of Environment, Vol. 85: 489–506.

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Remote sensing-based estimates of evapotranspiration for managing scarce water resources in the Gezira scheme, Sudan M.A. Bashir∗, H. Tanakamaru & A. Tada Graduate School of Agricultural Science, Kobe University, Kobe, Japan

ABSTRACT: Water is a finite resource and rapid population growth in many arid countries is putting excessive pressure on their limited water resources. Sudan has a large modern irrigated agriculture totaling more than 2 million hectares out of about 84 million that are potentially arable. The River Nile and its tributaries (18.5 km3 /year) are the source of the water for 93% of irrigated agriculture. Gezira scheme is located in the central part of the Sudan. Its cultivated area estimated to be around 0.9 million hectares. The arid climate and the low water use efficiency place more challenges for effective irrigation water management. Thus, spatial information of evapotranspiration (ET) is quite important to water resources managers. In this study remote sensing-based model was used to quantify the values of ET for different crops/vegetation on the surface. Surface Energy Balance Algorithm for Land (SEBAL) and Landsat images were used to identify water consumption on daily, monthly and seasonal basis. On the other hand, daily water required at different minor canals was calculated. For validation purpose, the daily and seasonal data were compared to the actual field data measured by soil water balance method. SEBAL results showed good agreement with the actual measurements. Keywords:

1

evapotranspiration; Gezira scheme; Landsat; SEBAL; soil moisture balance; Sudan

INTRODUCTION

Agriculture by far is the largest water user at the global level accounting for 70% of water withdrawal and 90% of consumption usage (Bastiaanssen et al. 2000). Sudan has a large modern irrigated agriculture sector totaling more than 2 million hectares out of about 84 million hectares that are potentially arable. The River Nile and its tributaries (18.5 km3 /year) are the source of the water for 93% of irrigated agriculture (Sir Elkhatim et al. 2007). The scheduling of irrigation requires the use of crop evapotranspiration (ET) data which can be determined with direct methods based on field measurements or estimated with indirect methods using various approaches such as water balance, energy balance and hydrological models. Because direct methods are impractical for permanent use on a large scale, ET is commonly estimated with indirect methods. Recent advancements in using satellite remote sensing models to determine ET over space and time have made it possible to assess the variation in ET and associated crop coefficient (kc) within different vegetation on the same land area (Bausch 1995, Tasumi et al. 2005, Bashir et al. 2007). These models compute ET

∗

Corresponding author ([email protected])

following a complete energy balance at the land surface so that impacts of water scarcity or poor water management on ET are captured (Tasumi & Allen 2007). In this study, surface energy balance model known as SEBAL (Bastiaanssen et al. 1998, 2005) was used to determine the spatial and temporal distribution of ET on daily, monthly and seasonal basis and it associated crop coefficient for an intensive cropping area in Sudan. The research also quantified the amount of water demand at different minor canals at one administrative unit called Abdelhakam in central group of the Gezira scheme. 2

METHODOLOGY

2.1 Area of interest and data used Gezira scheme (0.9 million ha and 380–430 m altitude) is located in the triangle between the Blue Nile and White Nile rivers south of Khartoum (Fig. 1). The scheme has predominately arid to semi-arid climate. Rainfall in the study area averages only 280 mm year−1 , most of which is falling between July and October. Annual reference evapotranspiration ETo using the Penman-Monteith method amounts to 2520 mm. Data in Figure 2 represent 30-year mean

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satisfying the law of conservation of energy can be expressed as: where λE = latent heat flux; Rn = net radiation at the surface; H = sensible heat flux; and G = soil heat flux. All fluxes are expressed in W m−2 . Net radiation is the algebraic sum of the short wave and long-wave radiative fluxes and is computed as:

Figure 1. Location of the study area.

where α = surface albedo; Rs = solar radiation (Wm−2 ); ε = land surface emissivity; Lin and Lout are incoming and outgoing long wave radiation (Wm−2 ). Surface albedo (α) is determined by integrating band reflectance within the shortwave spectrum using a weighting function (Allen et al. 2007). Lin is calculated using air temperature as approximated from satellitederived surface temperature for a wet pixel, while Lout is computed as functions of surface temperature (Ts ) derived from the images. The ε is computed from Normalized Difference Vegetation Index (NDVI) as explained by Van de Griend & Owe (1993):

Figure 2. Monthly rainfall and daily average reference ET (1971–2000).

The relationship between ε and NDVI is valid for the NDVI values in the range 0.16 to 0.74. Soil heat flux is expressed as a function of net radiation, surface temperature and albedo as shown in Equation 4. The calculation of net radiation and soil heat flux was done after Bastiaanssen (1995):

Table 1. The list of Landsat data used in this study.

Landsat7 Landsat7 Landsat7 Landsat7

Sensor

Path/Row

Date

ETM+ ETM+ ETM+ ETM+

173/50 173/50 173/50 173/50

6/Nov/2001 11/Dec/2001 12/Jan/2002 17/Mar/2002

values. In this area, profitable crop production without reliable irrigation is impossible. Cropping patterns during the winter season (Nov–Apr) consist mostly of wheat and some vegetables, and during summer (Jul–Dec) of cotton, sorghum, groundnut, fodder and summer vegetables. Multi-date remote sensing data of Landsat7 Enhanced Thematic Mapper Plus (ETM+) were used. Landsat7 data has 8 spectral bands in the visible, near-infrared and thermal measurements. Images representing summer and winter cropping periods during January, March, November and December were acquired and used (Table 1). Weather data used in this study were obtained from Gezira Meteorological Station, GMS, (14◦ 23 E, 33◦ 29 E, 407 m elevation). 2.2

Physical approach of SEBAL

In the absence of horizontally advective energy, the surface energy equation of land surface

The instantaneous sensible heat flux (H ) is computed from the vertical difference (δTa ) between the surface temperature (Ts ) and the reference height air temperature (Ta ):

where ρ = the density of air (kg m−3 ); CP = the heat capacity of the air (∼1004 J kg−1 k−1 ); and ra = the aerodynamic resistance (s m−1 ). The latent heat flux (λE) is then obtained as a residual of the energy balance equation shown in Equation 1. The instantaneous ET was computed using Equation 6.

where λ = the energy required to evaporate water. The conversion of instantaneous ET to daily, monthly and seasonal values were done using linear interpolation of ETo fraction (EToF) which is the ratio of actual ET to grass reference ETo. The concept was used by many researchers (Tasumi et al. 2005, Chemin et al. 2004).

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2.3

Field measurement of ET

The actual ET values were measured in the field using water balance method. The method intends to solve the water balance equation on a daily basis according to Equation 7:

where ETa = the actual evapotranspiration; I = the amount of irrigation water applied; P = the effective rainfall; D = the amount of drainage water below the root zone; SR = the surface runoff; and ±S = the change in soil moisture content. Soil moisture content at effective rooting depth was measured gravimetrically during irrigation cycles. Distributed soil samples were collected with a cylindrical probe from the depths of 0–20, 20–40, 40–60, 60–80 and 80–100 cm. The effective rainfall during the summer cropping period (Jul–Dec) was accepted as 80% of the total rainfall. Surface runoff and drainage below the root zone were considered as negligible values because of the special nature of Gezira heavy clay soil. Capillary rise was considered as negligible because of the deep water table level. So the above equation was reduced to: Figure 3. Daily actual ET over the Gezira scheme.

The actual ET was calculated as a daily average according to the soil sampling dates.

Table 2. Comparison of daily and seasonal actual ET over sorghum plot. Methods

3

RESULTS AND DISCUSSION

Figure 3 shows the spatial and temporal daily ET for the Gezira scheme estimated from SEBAL model. The ET was estimated during January, March, November and December. Actual ET ranges from 0–8.3, 0–7.8, 0–7.9 and 0–7.5 mm/day for Jan, Mar, Nov and Dec, respectively. Higher ET was observed on wheat and winter vegetables during Jan and Mar, while higher ET in Nov and Dec was mainly on cotton and late developed sorghum fields. In general actual ET increases as the crop advances to maturity. It is clear from the north part of the image that ET increased from Dec to Jan and then decreased in Mar during the harvesting of winter crops. For the validation of SEBAL actual ET estimates of sorghum were performed by the comparison with actual ET obtained from water balance method. Table 2 shows the comparison between the two methods. The average difference between SEBAL actual ET for sorghum and conventional estimates attained the value of −0.04 mm. Actual ET values from SEBAL were found to underestimate actual measurement for more than 60% of images used.

Julian day

SEBAL

Water balance

Error (mm)

6/Sep/2001 25/sep/2002 20/Oct/2002 28/Nov/2002 28/Jul/2004 29/Aug/2004 16/Oct/2004 17/Nov/2004 Seasonal ET

4.6 4.5 4.7 2.8 4.7 5.5 7.2 2.7 468

5.3 4.6 4.5 3.8 2.6 5.9 7.1 3.0 489

−0.7 −0.1 +0.2 −1.0 +2.1 −0.4 +0.1 −0.3 −21

Seasonal ET calculated from SEBAL for sorghum was 468 mm (excluding the first two irrigation cycles). The comparison shows that SEBAL underestimated water balance method by 21 mm (Table 2). Figure 4 shows the monthly ET of the Gezira scheme during the above mentioned months. Water consumption on monthly basis is a vital issue for farmers. Monthly water consumption ranges from 0–237, 0–218, 0–271, and 0–214 mm for Jan, Mar, Nov and Dec, respectively. The higher water consumption was observed in Nov while summer and winter crops

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Figure 4. Monthly actual ET over the Gezira scheme.

Figure 5. Spatial crop coefficient for different crop/land use.

compete for the scarce water during the recession flow of the Nile from October to March. Figure 5 shows the spatial distribution of crop coefficient estimated from SEBAL. Crop coefficient values were calculated for each image pixel by dividing the ET from SEBAL for the pixel by the ETo calculated for the general area using data from the Gezira weather station. Higher values of kc are due to wetness of surface soil during the early plant growth. Values for kc range from 0–1.26, 0–0.99, 0–1.45 and 0–1.17 for Jan, Mar, Nov and Dec, respectively. Values of kc for winter crops reached more than 1 during the peak of growing period in Jan and then decreased in Mar, similar results were reported by Sir Elkhatim et al. (2007). Moreover, crop coefficient maps in Figure 5 can be used to estimate water requirement for crops that grown in the study area using the generalized crop water requirement method (ETc = kc∗ ETo) of Doorenbos & Kassam (1979). Bashir et al. (2007) found that, kc for sorghum was ranged from 1.15 to 0.62 for mid-season and late season stages, respectively. The published values were compared with experimental values executed by Farbrother (1976). Analysis of water demand was done in Nov as it represents a transition month with areas still having summer crops such as sorghum and cotton and new areas beginning to have winter crops such as wheat

and some vegetables. Water demand was simply estimated as a total sum of SEBAL actual ET for all area planted at each canal. Figure 6 illustrates the amount of water required at minor canal level of Abdelhakam block (small administrative unit in central group of Gezira scheme). The amount of water required at minor canals considered as the key factor to manage scarce water resources in the scheme. SEBAL estimates were compared to the actual values measured at the gate of the each minor canal. In general, the amount of water delivered was less than water required at all minor canals (Fig. 6). However, Terifi, Abdelhakam, Heleiwa andTalha minor canals showed more that 30% discrepancy between delivered and water required estimated by SEBAL. In contrast, water amount delivered to Amin, Beika, Alamelhoda and Abdelhafeez minor canals was sufficient for irrigation. The overestimation of water demand is expected because in many minor canals farmers used to grow their own crops which are not considered in the calculation which is known as “over planted ratio”. Remote sensing can play a very important role in ascertaining the exact crop area. Figure 6 also highlights the values of relative irrigation supply (defined as the ratio of irrigation supply to irrigation demand) for different minor canals of Abdelhakam block. The relative irrigation supply (RIS) varies from 0.37 for Tereifi minor canal to 0.98 for Abdelhafeez minor canal, the average being 0.73.

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water stress which might reflect on high productivity compared to former minor canals. REFERENCES

Figure 6. Daily water demand and relative irrigation supply (RIS) for different minor canals of Abdelhakam block.

The results showed good water supply for Abdelhafeez and water scarcity for Tereifi. The other minor canals were in the range of 0.60 to 0.78. In general relative irrigation supply equal or around one could be taken as an advantage in terms of crop water requirement. Values range from 0.60 to 0.78 could be considered as deficit irrigation practice that contributes a lot for water conservation and irrigation efficiency improvement. In contrast, lower values of relative irrigation supply are good indicator of sever water scarcity. Water estimated at minor canals is very important to compute water demand at all major canals, and the needs of all majors are added up to compute the supply needed into the main canals from the dam.

4

CONCLUSIONS

In this study, surface energy balance model and remotely sensed data were used to estimate water consumption at daily, monthly and seasonal basis. SEBAL estimates showed good comparison with the actual measurements obtained from water balance method. Deviation of SEBAL seasonal evapotranspiration of sorghum from actual measurements was 5%. Spatial crop coefficient maps were also produced for all crop/land use in the study area which are very essential to estimate crop water requirement using crop coefficient-reference evapotranspiration method. On the other hand, water demand at minor canal level was estimated and compared to the amount that measured at the gate of each minor canal. Some minor canals showed a large discrepancy between delivered and required values, however, other minor canals showed that the amount delivered was sufficient for successful irrigation. Therefore, crops grown in latter minor canals were not subjected to any kind of

Allen, R.G., Tasumi, M. & Trezza, R. 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-model. J. Irrig. Drain. Eng. 133(4):380–394. Bashir, M.A., Hata, T., Tanakamaru, H., Abdelhadi, A.W. & Tada,A. 2007. Remote sensing derived crop coefficient for estimating crop water requirements for irrigated sorghum in the Gezira scheme, Sudan. Journal of Environmental Informatics 10(1): 47–54. Bastiaanssen, W.G.M. 1995. Regionalization of surface flux densities and moisture indicators in composite terrain, a remotes sensing approach under clear skies conditions in Mediterranean climate. PhD Thesis, Wageningen Agric. University, Netherlands. Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A. & Holtslag, A.A.M. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol. 212–213: 198–212. Bastiaanssen, W.G.M., Molden, D.J. & Makin, I.W. 2000. Remote sensing for irrigated agriculture: examples from research and possible applications. Agric. Water Managt. 46(2): 137–155. Bastiaanssen, W.G.M., Noordman, E.J.M., Pelgrum, H., Davids, G., Thoreson, B.P. & Allen, R.G. 2005. SEBAL model with remotely sensed data to improve waterresources management under actual field conditions. J. Irrig. Drain. Eng. ASCE 131(1): 85–93. Bausch, W.C. 1995. Remote sensing of crop coefficients for improving the irrigation scheduling of corn. Agric. Water Managt. 27: 55–68. Chemin, Y. Platonov, A. UI-Hassan, M. & Abdullaev, I. 2004. Using remote sensing data for water depletion assessment at administrative and irrigation-system levels: case study of the Feraghana province of Uzbekistan. Agric. Water Managt. 64: 183–196. Doorenbos, J. & Kassam, A.H. 1979.Yield response to water. Irrig. and Drain. Pap. 33, 156 pp., Food and Agric. Organ. of the U.N., Rome, Italy. Farbrother, H.G. 1976. Table of crop water requirements in the Gezira prepared and circulated under FAO project TCP/Sudan, Gezira Research Station Library. Sir Elkhatim, H.A., Abdelhadi, A.W., Elhadi, M.A. & Hussein, S.A. 2007. Water requirements of the main crops in Gezira. Sudan Journal of Agricultural Research 9: 67–89. Tasumi, M. & Allen, R.G. 2007. Satellite-based ET mapping to assess variation in ET with timing of crop development. Agric. Water Managt. 88: 54–62. Tasumi, M., Allen, R.G., Trezza, R. & Wright, J.L. 2005. Satellite-based energy balance to assess within-population variance of crop coefficient curves. J. Irrig. Drain. Eng. 131(1): 94–109. Van de Griend, A.A. & Owe, M. 1993. On the relationship between thermal emissivity and the normalization difference vegetation index for natural surfaces. Int. J. Rem. Sens. 14: 1119–1131.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Investigation of fresh and salt water distribution by resistivity method in Yellow River Delta Tomotoshi Ishitobi∗ & Makoto Taniguchi Research Institute for Humanity and Nature, Kyoto, Japan

Jianyao Chen School of Geography Sciences and Planning, Zhongshan (Sun Yat-sen) University, Guangzhou, China

Shin-ichi Onodera Graduate School of Integrated Arts and Sciences, Hiroshima University, Higashi-Hiroshima, Japan

Kunihide Miyaoka Department of Geography, Mie University, Tsu, Japan

Tomochika Tokunaga Graduate school of Frontier Sciences, University of Tokyo, Chiba, Japan

Mitsuyo Saito Graduate School of Biosphere Sciences, Hiroshima University, Higashi-Hiroshima, Japan

Yoshihiro Fukushima Research Institute for Humanity and Nature, Kyoto, Japan

ABSTRACT: Yellow River is one of the largest rivers in the world and provides many sands to the sea. There is the large delta of Yellow River in the lower reach, and it is continuing to grow up by sediment from the river. However, some environmental issues occur such as the problem which river water can not reach to the river mouth from depletion of river water by overuse since 1970’s. In this region which has experienced some environmental issues, groundwater research include the resistivity survey were conducted for the evaluation of the distribution of freshwater and saltwater under the delta in September 2003, May 2004 and September 2004. According to the results of field research, it is assumed that there is fresh groundwater in the shallow part and south of this delta, and saltwater widely in other region. We also applied the age dating of groundwater using carbon fourteen method. As a result, there are young saltwater near the shore and paleo-seawater in the center of this delta. Keywords:

1

delta of Yellow River; resistivity survey; distribution of fresh and salt water

INTRODUCTION

Yellow river is the second largest river for transfer the sediment to the sea and the delta has been formed by it in lower reach. On the other hands, some environmental issues occur such as the problem which river water can not reach to the river mouth from depletion of the river water by overuse since 1970’s. Therefore, the delta of Yellow River is the region where the change of natural environment is large. ∗

Corresponding author ([email protected])

This delta is expanding at a speed of about 500 m/y by sedimentation (Yu 2002). However, the speed of groundwater flow is very slow in general. It is assumed that there is a possibility which paleo-seawater remains under the delta. In addition to this, it is also assumed that saltwater intrusion into the subsurface under the delta occurs, because Yellow River basin has some problems concerned with water environment such as the cut-off of river water and over-pumping of groundwater. Therefore, the purpose of this study is to clarify of fresh and salt water distribution under the delta where influenced from various environmental issues.

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Figure 2. Spatial distributions of groundwater level. Figure 1. Location of observation wells.

2

METHODS

We conducted the resistivity survey, conductivity measurement and the groundwater level observation. Resistivity survey is the technique for understanding of subsurface structure by applying the electric current to the subsurface. Resistivity and conductivity values have negative correlation. Therefore, if we clarify the relationship between resistivity and conductivity in this delta, we can estimate the distributions of fresh and salt water in the area where there is no observation well. McOHM Profiler-4 (OYO Inc.) was applied as the equipment for resistivity survey in this study.About groundwater sampling, its were taken by groundwater pump (DAIKI Rika Kogyo Inc.). Additionally, the measurements of stable isotope and carbon 14 dating of groundwater were done for the clarifying the origin. The field site and observation wells are also indicated in fig. 1. The water level and the conductivity measurements of groundwater were done at 52 observation wells, the resistivity survey and carbon 14 dating were done at 14 and 23 points. These field measurements were done at Sep 11 to 18, 2003, May 8 to 12, 2004 and Sep 11 to 16. 2004.

3

RESULTS

Fig. 2 shows spatial distributions of groundwater levels in the Yellow River delta (May, 2004). The groundwater levels were higher near the Yellow River than the coastal area. The results of autumn season (Sep 2003

Figure 3. Spatial distributions of conductivity of groundwater.

and 2004) are almost same variations. Therefore, it is thought that there is the groundwater flow to the Bo-hai sea from the Yellow River. Fig. 3 shows spatial distributions of the electric conductivity of groundwater in the Yellow River Delta. High concentrations zones are seen at the central part and the coastal zone of the delta. We applied the resistivity method to evaluate the spatial distributions of fresh and salt water at the point where it is difficult to take the groundwater samples

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Figure 5. Relationship between resistivity under the ground and conductivity of groundwater in the delta.

Figure 4. Results of resistivity survey.

at the deeper part. Fig. 4 shows all results of the resistivity survey. In these figures, the dark color indicates high resistivity values, and the light color indicates low resistivity values. Additionally, the width and the height of figure are 150 m and 100 m respectively. It shows the several patterns of resistivity distributions under the ground. We can separate it to three patterns by distributions of resistivity values, the first is the low resistivity regions at the shallower part and the high regions at the deeper part (N1, N12, N11 and etc), the second is the high resistivity regions at the shallower part and the low regions at the deeper part (N3, N4, N13, YS4-3 and etc), and the last is the little change of resistivity values in the result (N4, YS3-1 and etc). 4

COMPARISONS BETWEEN THE GROUNDWATER CONDUCTIVITY AND THE RESISTIVITY OF THE UNDERGROUND

According to Archie (1942), relationship between the resistivity of underground and groundwater can be described by below equation.

ρ, k and ρW indicate resistivity values under the ground, geological function and resistivity values of groundwater. If function of ρW replace to electric conductivity (σW ), σW is described by the below equation.

Therefore, comparisons of resistivity and conductivity were done to confirm whether it has negative correlation in the delta also. It was done by datasets of the point where both measurements were done. About data points used for this comparison, if groundwater was corrected from the screen of 5 to 20 m depth, average of resistivity values at same depths were used for the comparison. Fig. 5 shows the result of this comparison, and indicated the negative correlation between the resistivity and the conductivity clearly. In general, resistivity value is influenced by the geological factor. However, the geology of this field site is mainly dominated by the sediments from Yellow River. Therefore, it is thought that the influence of geological factors to this result is little and resistivity values in this area are mainly affected by conductivity of groundwater. From the above-mentioned, it is also thought that the low resistivity implies the high conductivity and the saltier water, and the high resistivity indicates the low conductivity and the fresher water. According to fig. 5, the relationship between resistivity under the ground and conductivity of groundwater in this delta could be described as below expression.

Therefore, we can assume the conductivity of groundwater at the point where we can not take water samples by applying the results of resistivity survey into the (3) expression. Spatial distributions of estimated conductivity from the resistivity measurement were indicated in fig. 6. From the result of 10 m depth in fig. 6, the low conductivity values were seen in the southern part (YS4-3 and N13), the eastern part (N9) and the northern part (N5) of the delta. On the other hand, high conductivity values were seen in center part of the delta (N1 and N12). In fact, we can see low conductivity values at YS4-3

389

Figure 6. Horizontal cross sections of estimated groundwater conductivity each 10 m depth.

and N13, and high conductivity values at N1 and N12 from fig. 3. According to the result of 20 m depth in fig. 6, low conductivity values were seen at N9, YS43 and N13 as the result of 10 m depth. About deeper depths than 30 m, it are dominated the groundwater which has higher electric conductivity than 30 mS/cm. Therefore, it is assumed that there are saltwater widely in deeper part than 30 m depth under the ground. Fig. 7 shows the cross sections of conductivity estimated based on fig. 6. From the cross section of the Yellow river to northern part of the delta (N1 to N4, upper side of fig. 7), it was seen the high conductivity region near the Yellow River, and this region expands to the sea side related to the groundwater flow. On the other hand, there is the lower conductivity groundwater recharged from the Yellow River above the high conductivity one in deeper part from the cross section of the Yellow River to eastern part of the delta (YS4-3 to N11, lower side of fig. 7). 5

Figure 7. Vertical cross sections of estimated groundwater conductivity (upper: north line, lower: south line).

RESULTS OF THE STABLE ISOTOPE ANALYSIS AND THE CARBON 14 DATING

It was clarified that saltwater was distributed in broad area by measurements of conductivity and resistivity.

390

Figure 8. Results of analysis of stable isotope.

Figure 10. Changes in position of shoreline in the Yellow River Delta (modified from He et al., 1999).

Fig. 9 shows the results of the age dating by carbon 14 age dating. Ages of groundwater are much differ each area. Coastal zone of the delta (N5, N9 and etc) indicate the younger age about 50 yrBP. On the other hand, center of the delta (N1, N12 and etc) indicate the older age of 4000 yrBP to 12000 yrBP. Additionally, southern part of the delta indicate the middle age about 1000 yrBP.

6 Figure 9. Results of carbon 14 dating.

The stable isotope analysis and the carbon 14 age dating of groundwater were done to clarify the origin of the groundwater. Fig. 8 shows the relationship between results of stable isotope analysis and conductivity of river water from the Yellow River, seawater from the Bo-hai Sea and groundwater in the delta. The result of groundwater in 10 points (N1 to N10, it are same depth) could be separate two types. First one is the groundwater which has about −6‰ values of stable isotope (groundwater in the coastal zone) and about −3‰ values of stable isotope (groundwater in the center of the delta). If saltwater in the delta are the mixture river water and saltwater, values of groundwater should be put on the regression line from the value of river water to sea water. However, groundwater indicating about −3‰ values of stable isotope were not put on this line. Therefore, it is assumed that the origin of groundwater indicating about −3‰ values of stable isotope is not the mixture of river water and seawater and other factor should be consider.

DISCUSSIONS AND CONCLUSION

The delta ofYellow River is expanding by the sedimentation since 1855 (fig. 10). Therefore, it is assumed that there is the groundwater which has younger age and high conductivity made by caught seawater in the ground by the rapid sedimentation in the coastal zone (N5, N9 and etc). About groundwater which has older age and high conductivity in the center of delta (N1, N12 and etc), it is assumed that this groundwater made by concentration the density of paleo-seawater caught by the sedimentation through remaining in this area for longterm and exists in this area. In fact, sedimentation ages are differing around N1–N12 and N5–N9. On the other hand, groundwater which has about 1000 yrBP values exits in the southern part of delta. According to fig. 2 and fig. 6, it is assumed that this groundwater came from the western inland because low conductivity groundwater was confirmed in this area (Chen et al., 2007). From the mentioned, the conditions of groundwater in the delta can be separated three types. The first one is the groundwater which has younger age and high conductivity in the coastal zone, the second one is the

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He Q.C., Duan Y.H., Zhang J.D., Xu J.X., Kang F.X., Zhou Y.Q. (1999): Comprehensive management for the coastal zone in the Delta of the Yellow River (in Chinese), Ocean Publishing, Beijing. Yu L.S. (2002): The Huanghe (Yellow) River: a review of its development, characteristics, and future management issues, Continental Shelf Research, 22, 389–403.

groundwater which has older age and high conductivity, and the last one is the groundwater which has middle age and low conductivity. REFERENCES Archie, G.E. (1942): The electrical resistivity log as an aid in determining some reservoir characteristics. Trans. A.I.M.E., 146, 54–67. Chen J.Y., Taniguchi M., Liu G., Miyaoka K., Onodera S., Tokunaga T. and Fukushima Y. (2007): Nitrate pollution of groundwater in the Yellow River delta, China. Hydrogeology Journal, doi 10.1007/s10040-007-0196-7.

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8

Interaction between the groundwater resources and ecosystems

Groundwater dependent ecosystems (GDEs) frequently occur in wetlands, terrestrial vegetation, riparian area in arid region, coastal zones, coral reefs and cave ecosystem. Critical damages or more gradual changes in composition and/or ecological function of communities are expected in these areas according to climate change and/or human impacts on hydrological settings. On the other hand, the degradation of vegetation can conversely cause a shift of related hydrological environment including water quality and water mass balance. The approaches for quantifying hydrodynamics in watersheds and submarine groundwater discharge at coastal areas are becoming better established, it would be time to integrate the interactions between ecosystems and groundwater system. This session will invite contribution to the broad examples collected at a variety of groundwater dependent ecosystem, including field observation and model prediction. Conveners: Derek Eamus (University of Technology, Sydney, Australia) Nobuhito Ohte (The University of Tokyo, Japan) Yu Umezawa (RIHN, Japan) Tomohiro Akiyama (Aichi Univesity, Japan)

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Riparian vegetation changes from hydrological alteration on the River Murray, Australia – Modelling the surface water-groundwater dependent ecosystem T.M. Doody∗ CSIRO Forest Bioscience, Glen Osmond, South Australia, Australia

I.C. Overton CSIRO Land and Water, Glen Osmond, South Australia, Australia

ABSTRACT: Native riparian vegetation communities on the floodplain of the River Murray in Australia are suffering severe health decline due to changes in the surface water and groundwater systems in this temperate to semi-arid region. It is estimated that over 75% of the riparian tree vegetation is in poor health or dead. The native vegetation, predominantly Eucalyptus open forest and woodlands, are dependent upon a combination of surface flooding and groundwater use. 80 years of river regulation and water extraction and a severe longterm (>4 year) drought, have reduced the available surface water as a result of reducing frequency of flooding. Reduced flooding and low river levels have caused a decrease in the flood and lateral groundwater recharge from fresh river water. In the lower River Murray saline groundwater from the underlying regional aquifer has risen as a result of locking, land clearing and irrigation adjacent to the river. Increased soil salinity, driven by a lack of flooding and rising saline groundwater tables, has caused tree health decline and transitions from large areas of riparian vegetation to rangeland species. In the upper regions of the River Murray from Albury to Mildura, reduced flow regimes have caused tree health to decline and transitions of vegetation communities to align with the new flow regimes. A floodplain inundation model has been used to quantify the hydrological changes in the River Murray and the implications for surface water availability. Soil salinisation in the lower River Murray floodplain has been modelled from surface-groundwater interactions using a spatial and temporal model of salt accumulation from groundwater depth, groundwater salinity, soil type and flooding frequency. The derived soil water availability index (WINDS) is used to infer vegetation health and was calibrated against current extent of vegetation health from a combination of fieldwork and satellite image analysis. The modelling work indicates there is a severe risk to the floodplain vegetation from current unnatural flow regimes and elevated groundwater levels. Management options for the control of surface and groundwater systems have been investigated and indicate that a combination of increased surface flows and saline groundwater lowering is required. This paper highlights the tools for modelling floodplain vegetation health and discusses management options to conserve floodplain health. Keywords: vegetation health; groundwater-surface water; hydrology; River Murray; floodplain ecosystem; climate change

1

INTRODUCTION

Major rivers around the world have been regulated for water supply, flood mitigation, hydropower and navigation. As a consequence of this regulation the river ecosystem and floodplain environments have been degraded from reduced river flows and flood events as well as a change in the variability of the flow regime. These changes have led to altered surface ∗

Corresponding author ([email protected])

water-groundwater interactions which are the major drivers of riparian vegetation health (Overton and Jolly, 2004). This is particularly noticeable in the semiarid south-east ofAustralia.The Murray-Darling Basin catchment occupies one seventh of Australia and contributes 70% of Australia’s irrigated crops and pastures (Figure 1). The largest river in the Basin is the River Murray which extends for over 2,000 kilometres and has a floodplain of over 650,000 hectares. Management of the River Murray has been implemented since the early 1920s to mitigate large floods and to protect

395

the floodplain environment and builds a tree vegetation health model driven by surface water-groundwater interactions. This is achieved though mapping surface inundation, groundwater and interactions between these from recharge and discharge processes. The paper then discusses management options to conserve floodplain health using the developed model.

2 VEGETATION HEALTH AND GROUNDWATER-SURFACE WATER INTERACTIONS

Figure 1. Map of the Murray-Darling Basin within Australia, showing the River Murray.

infrastructure, while maintaining storages for regular water supply. The Lower River Murray Floodplain is a complex environment to manage, with issues of flooding, river regulation, shallow groundwater, declining vegetation health, soil salinisation and biodiversity issues. There are obligations to manage the environment under State, Commonwealth and International policy. The native vegetation, predominantly Eucalyptus tree species, River Red Gum (Eucalyptus camaldulensis) and Black Box (Eucalyptus largiflorens), are dependent on a combination of surface flooding and groundwater use. 80 years of river regulation and water extraction and a severe recent drought, have reduced the available surface water. Reduced flooding and low river levels have caused a decrease in the flood and lateral groundwater recharge from fresh river water. This has led to over 75% of the floodplain tree vegetation exhibiting stress and are in poor condition. In the Lower River Murray, saline groundwater has risen as a result of locking, land clearing and irrigation adjacent to the river. Increased soil salinity, driven by a lack of flooding and rising saline groundwater tables, has caused this decline in tree health. In the upper regions of the River Murray, reduced flow regimes have caused tree health to decline and transitions of vegetation communities to align with the new flow regimes. The reduced resilience of riparian vegetation has increased the threat of invasive species during these changes. Understanding riparian tree health requires understanding water availability to the trees and therefore an understanding of the surface water-groundwater interactions. To design strategies to ameliorate the decline and increase the resilience of the riparian tree species requires modelling that can simulate management options and show future impacts on the floodplain. This paper describes water availability across

Floodplain groundwater and surface water interactions have changed dramatically since river regulation, reducing the frequency of floods and raising the naturally saline regional groundwater system to within metres of the floodplain surface. Salt accumulates on the floodplain as a result of gradients in soil matric potential which create an upward flux of saline groundwater. Evaporation at the soil surface and extraction by plants results in the gradual accumulation of salts in the upper soil layers. Studies have indicated that there is very little leaching of this salt under the current flooding regime (Jolly et al., 1994). Before regulation, this upper part of the soil profile was leached free of salt by regular floods which occurred approximately on a four yearly basis. Regulation of the river, using locks and weirs to control water flows, has led to a reduction in the frequency of floods large enough to carry out this freshening, has been greatly reduced. Medium sized floods (60 to 100 GL per day) have decreased by a factor of between two and three, following river regulation. Secondly, locking, which inhibits groundwater discharge into the river, has led to increases in groundwater levels, which have accelerated the rates of groundwater discharge and hence salt accumulation. The major factor affecting vegetation health of the dominant long lived (about 300 years) riparian vegetation is water availability (Overton et al., 2006). Trees have access to a variety of potential water sources including lateral recharge from creeks, indirect groundwater use at the capillary fringe and freshwater lenses that sit above the saline groundwater and are recharged from flooding. Surface water and groundwater interactions determine the degree to which vegetation use groundwater, creek water and rainfall and are dependent on soil type, recharge rates, aquifer conductivity, groundwater depth, groundwater salinity, flooding frequency and quantity of rainfall. The height difference between the groundwater and the surface water in the creeks is a determinant of whether the creek is a losing (Figure 2) or gaining creek (Figure 3). Modelling riparian health requires an understanding of these processes and the ability to map the drivers

396

Figure 2. A losing creek on the Lower River Murray showing good lateral recharge supporting a dense forest of River Red Gum on the outer bank and recruitment on the inside bend.

Figure 4. The Flood Inundation Model interface in a GIS.

across the floodplain, at a scale that is meaningful to management.

then merged and interpolated using a watershed algorithm and then linked to river height using a series of backwater curves. The backwater curves used within the RiM-FIM were derived from a combination of actual measured heights and predicted heights from existing in-channel flow. The resultant model can predict the extent of flooding associated with any flow and weir configuration. The RiM-FIM was developed using a spatial information system framework as this utilised the best method for integrating non-spatial river flow models with the extent of flood inundation. The integration of the hydrological model with the GIS has enabled the project to go beyond data management and thematic mapping in order to conduct simple overlay analysis and simulation for both scientific research and policy management. The visualisation, quantitative analysis and spatial correlation of environmental and infrastructure data of the GIS has improved the usefulness of the hydrological modelling (Figure 4). The model has been used successfully to analyse flooding in the River Murray (Overton and Doody, 2008(a)).

3

3.2 Mapping groundwater recharge

Figure 3. A gaining creek on the Lower River Murray showing the salt accumulation on the surface and the death of the fringing vegetation.

3.1

MODELLING VEGETATION HEALTH Modelling surface water flows

The first stage of modelling involved the development of an understanding of patterns of flood inundation across the floodplain, linked to the river flow to allow analysis of flooding frequency, duration and seasonality. Hydrographs of the river flow into the Lower River Murray show the pattern of historic floods and the significant dry period in the last five years. A flood model called the River Murray Floodplain Inundation Model (RiM-FIM) was derived from a series of satellite images showing the flood extent (Overton, 2005). The flood extent masks were derived from Landsat TM band 7 images using a density slice to distinguish water from non-water. The masks were

The Lower River Murray floodplain acts as a groundwater sink for the naturally saline regional aquifers of the western Murray Basin. The majority of the floodplain has extremely high groundwater salinity of approximately 55 dS/m (55,000 EC) and is found at shallow depths of less than 5 metres. The floodplain is covered by a layer of alluvial clay known as the Coonambidgal Clay, and in areas where the surface clay layer is absent the groundwater is fresh. Such areas act as recharge zones that are freshened by flooding and rainfall (freshwater lens) and can often be determined by the presence of healthy vegetation. High recharge areas can therefore be detected in areas that display a strong sustained vegetation response to flooding, months after a flood

397

event. Groundwater is also fresher in areas that are close to the river where pressure from high river levels forces recharge into the adjoining aquifer (the flushed zone), providing fresher water for fringing riparian vegetation. When the surface clay layer is present it acts to restrict infiltration of flood water and Jolly et al. (1994) note that the presence and thickness of the clay is an important controlling factor on the hydrology and vegetation of the floodplain. Soils also vary substantially across the floodplain and observed variation in tree health may be accounted for by local soil variation. For example, one live tree may be surrounded by many dead trees. At the Chowilla floodplain, recharge rates were mapped based on different soil types where the recharge areas were predominantly based on previously mapped soil types (Hollingsworth, 1990). Landsat satellite imagery was used to map the vegetation vigour of the floodplain before and after a flood event using the Normalised Vegetation Index (NDVI). The NDVI is a ratio of the light reflected from the ground in the near infra-red wavelength and the light reflected in the red wavelength. NDVI highlights the areas that have a higher near infra-red than red reflectance, indicating large amounts of chlorophyll and turgid leaf structure and therefore healthier vegetation. By comparing before and after flood event images, it is possible to identify the vegetation areas that have responded positively based on an increased vegetation growth compared to other areas. Areas flooded and not flooded will show a distinct difference. Within the areas flooded, those areas that show the best growth response, sustained after 6 months, indicate an area of local recharge. Electromagnetic imagery has also provided an excellent method for determining groundwater salinity, recharge potential and the presence of lateral recharge and freshwater lenses. Airborne Electromagnetic Imagery (AEM) (Figure 5) has been used to map these aspects of the floodplain using upper soil profile slices for lateral recharge and deeper slices for groundwater salinity and freshwater lenses (Munday et al., 2008). The AEM data measures electrical conductivity, with high readings indicating high salinity, high clay content or high moisture. 3.3

Modelling groundwater

Two methods of modelling groundwater depth and salinity have been employed. Firstly, the MODFLOW groundwater model outputs of groundwater surfaces have been used for the Chowilla and the Pike floodplains. Secondly groundwater depth models interpolated from bore records have been used at the Murtho and the Bookpurnong floodplains. The MODFLOW model has been used to predict groundwater depth surfaces for management scenarios. The recharge rates

Figure 5. A slice of electrical conductivity from Airborne Electromagnetic Imagery at a depth of 2 to 4 metres below the surface.

discussed in section 3.2 were used as an input layer into the MODFLOW groundwater (Yan et al., 2005). Groundwater salinity modelling relies on interpolation from bore records or the use of AEM. A MODFLOW extension called MT3D has been used at Chowilla but it requires better calibration and has not been fully tested. 3.4 Modelling vegetation health Slavich et al. (1996) developed the concept of a Moving Salt Front model, to depict the movement of a salt front upwards during drought and salinisation and downwards during leaching by floods, by combining limiting vegetation characteristics, soil hydraulic properties and flood history to develop a salinity index which is indicative of vegetation health. Overton et al. (2006) applied this quasi-steady state Moving Salt Front within a GIS of the floodplain (WINDS model). The WINDS model requires information on vegetation type, groundwater depth, groundwater salinity, soil type, flooding frequency, rainfall and the presence of alternate water sources (Figure 6). Flooding frequency was modelled by defining flood recharge and discharge periods and assigning a weighting factor to these periods based on their duration and time since the present. This weighting factor was combined with a salinisation index that relates the height of a salt front above the watertable. The WINDS model is run in a raster GIS using 30 by 30 metre grid cells over the whole floodplain. WINDS is firstly calculated by determining the groundwater discharge rate from the depth of the water table and the soil hydraulic properties. Soil salinity is then calculated for each 5 year time period using the discharge rate, the number of days dry and flooded and the soil parameters. The third step is to calculate the soil water availability for each of the 5 year time periods with reference to the specific vegetation’s salinity tolerance

398

Figure 6. The WINDS model input layers and layer predictions.

Figure 8. WINDS model output showing predicted tree vegetation health and understorey condition. Table 1. Area and percentage of tree health under different management options.

Current condition (2003) Do-Nothing Last 15 years flow (2033) Do-Nothing Last 5 years flow Flow enhancement 750 GL/day 2 m Groundwater lowering Flow and GW lowering Chowilla Creek Regulator

Figure 7. WINDS model output showing predicted soil salinity for the Chowilla floodplain in 2007. Dark colours represent high salinities of up 100 dS/m.

to then calculate the cumulative soil water availability of the last 15 years as an indication of the plant growth conditions affecting the health status of the trees. Black Box and Red Gum have a limit to the salinity of the water they can tolerate in this environment, of 55 dS/m and 30 dS/m respectively (Overton and Jolly, 2004). A WINDS index between −2 (dead) and 1 (good health) is produced for each grid cell. Comparison of WINDS modelled vegetation health has been tested against field assessed data, producing a 76% spatial match between modelled data and actual field data (Overton and Doody, 2008(b)) providing confidence in the model results. The WINDS model produces a soil salinity map (Figure 7) and a vegetation health map for the tree species (Figure 8). The results of the WINDS model have been used for predicting future vegetation health under: • •

Do-nothing future scenarios; Groundwater lowering strategies. These include the pumping of saline groundwater out of the shallow

• • • •

Dead (ha) % of Trees

Poor (ha) % of Trees

Good (ha) % of Trees

1680 21% 2240 28%

2000 25% 1760 22%

4320 54% 4000 50%

2480 31% 2160 27% 1760 22% 1760 22% 1280 16%

4640 58% 1520 19% 1680 21% 1520 19% 1680 21%

960 12% 4320 54% 4560 57% 4720 59% 4960 62%

aquifer and piped to disposal basins for evaporation and salt harvesting. These schemes operate in many parts of the River Murray; Increased flooding options through environmental flows; Wetland watering by pumping; Surface water infrastructure such as a new creek regulators; and Combinations of the above (Table 1).

Modelling results from Chowilla (Overton and Jolly, 2005) and the Murtho, Pike and Bookpurnong floodplains (Overton and Jolly, 2008) have shown that the best management option for vegetation health is a combination of groundwater lowering by approximately 2 metres and increased flooding from a 1,500 GL/yr enhanced flow regime that has been modelled by the Murray-Darling Basin Commission.

399

Understorey species can be modelled for condition by considering the flooding frequency and the soil salinity. The soil salinity map can be used to predict the occurrence of different understorey species given their salt tolerances. The vegetation distribution against modelled soil salinity was used to provide this modelling capability. 4

DISCUSSION

Spatial analysis and modelling has provided tools for assessing the current environment, prioritising areas for management, modelling the impact of management options on environmental processes and vegetation health, and assessing the cost versus benefit of various management strategies. Flood inundation extent has been modelled using remote sensing linked to a river flow model, as well as modelling the impact of new engineering works by using a creek height model, scaled to the floodplain. A number of management options for any saline floodplain can be modelled using the WINDS model including groundwater and surface water manipulation. A combination of flow and groundwater management options are required to maintain the health of much of the floodplain. The best management option has been identified as a combination of groundwater lowering to stop salt accumulation and to provide needed unsaturated zone space for freshwater accumulation, and increased flooding to provide freshwater to riparian vegetation and to leach existing salt from the soil profile. The costs of this management intervention needs to be weighed against the benefits, considering not simply market values of reduced salt impacts to the river but also non-market values that give credit to ecosystem services and social benefits. Current work is integrating this biophysical modelling with social and economic analysis of management options. The WINDS model has proven to be a useful tool to assist decision making in the management of floodplains of the Lower River Murray by testing the impact of management scenarios on soil salinity and vegetation health.

REFERENCES Hollingsworth, I.D. 1990. A Reconnaissance Soil Survey of the Chowilla Anabranch System of the River Murray in South Australia and New South Wales. Prepared for Murray-Darling Basin Commission by the Loxton Research Centre, South Australian Department of Agriculture. Loxton: SA Department of Agriculture. Jolly, I.D., Walker, G.R. and Narayan, K.A. 1994. Floodwater recharge processes in the Chowilla Anabranch system. Australian Journal of Soil Research 32, 417–35. Overton, I.C. 2005. Modelling Floodplain Inundation on a Regulated River, River Murray South Australia. River Research and Applications 21, 991–1001. Overton, I.C. and Jolly, I.D. 2005. Integrated Studies of Floodplain Vegetation Health, Saline Groundwater and Flooding on the Chowilla Floodplain. CSIRO Division of Land and Water Technical Report No. 20/04. Adelaide: CSIRO. Overton, I.C. and Jolly, I.D. 2008. Vegetation Health Changes under Management Options on the Murtho, Pike, Gurra Gurra and Bookpurnong Floodplains, South Australia. Report prepared for the South Australian Department of Water, Land and Biodiversity Conservation. Adelaide: CSIRO. Overton, I.C., Jolly, I.D., Slavich, P.G., Lewis, M.M. and Walker, G.R. 2006. Modelling Vegetation Health from the Interaction of Saline Groundwater and Flooding on the Chowilla Floodplain, South Australia. Australian Journal of Botany. 54, No.2: 207–220. Overton, I.C. and Doody, T.M. 2008(a). Ecosystem Changes on the River Murray Floodplain over the Last 100 Years and Predictions of Climate Change. Proceedings of the HydroChange’08 Conference, Kyoto. Overton, I.C. and Doody, T.M. 2008(b). Groundwater, salinity and vegetation responses to a proposed regulator on Chowilla Creek. Report for the SAMBBNRMB Slavich, P.G., Walker, G.R. and Jolly, I.D. 1996. Vegetation response to modified flooding regimes and groundwater depth on a saline floodplain. In: Proceedings of Hydrology and Water Resources Symposium, Hobart. The Institution of Engineers, Australia National Conference No. 96/05, pp. 505–10. Yan, W., Howles, S.R. and Marsden, Z. 2005. Chowilla Floodplain Numerical Groundwater Model. DWLBC Report 2004/65. Adelaide: DWLBC.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

An analysis of groundwater conditions in a saline groundwater area, Thailand P. Mekpruksawong∗ & T. Suwattana Royal Irrigation Department, Bangkok, Thailand

T. Ichikawa & S. Aramaki School of Industrial Engineering, Tokai University, Kumamoto, Japan

S. Chuenchooklin Faculty of Engineering, Naresuan University, Phitsanulok, Thailand

ABSTRACT: The study area is part of the Lower Nam Kam River Basin in the northeastern region of Thailand. The class sedimentary rocks of Maha Sarakham Formation contain salt, found in groundwater and surface soil. Farmers in this area often suffer from insufficient surface water and polluted groundwater for agricultural and consumption purposes. With its plan to implement water resource development projects in this area, the government should be made aware of the contamination and distribution of salt water. Drawing on geological, climate, groundwater level, and water quality testing data, this paper presents an analysis of the groundwater flow system, which results in distribution of salt water and thus contamination in drinking water. The analysis results show that in the dry season, the level of shallow groundwater goes down under the piezometric surface of confined aquifers because of reduced recharge and extraction from shallow wells. Thus, saline deep groundwater leaks upward to near the ground surface while the water level of shallow aquifers becomes lower. This then indicates an inverse relation between electric conductivity and shallow groundwater levels. The findings will be used to establish numerical groundwater flow models in the future. Keywords: salt water

1

contamination; electric conductivity; Maha Sarakham; Nam Kam River; piezometric surface,

INTRODUCTION

Soil salinization in Northeast Thailand is not a new problem and is not entirely induced by human activities. The accumulation of salts in surface soils and groundwater in a geological setting characterized by evaporate deposits is a natural phenomenon. Because of the widespread salinity problem in the Korat Plateau, many studies had been undertaken to explain the causes of the problem, estimate its extent and suggest measures to be taken. The main controversy on salinization in Northeast Thailand lies in the explanation of the source and how salt reached the surface (Imaizumi et al., 2002). The human activities included deforestation, the construction of reservoirs, salt making and irrigation also cause the spread of salinization in Northeast Thailand (Ghassemi et al., 1995). This region has the largest paddy area of country but the ∗

Corresponding author ([email protected])

lowest rice yield (2.1–3 ton/ha) and cropping intensity (1.02) (OAE, 2006). The soil salinity influences to the decreasing of rice yield 10–50% when electric conductivity (EC) of soil water value is 4–10 dS/m (Rice Department). Therefore, more thorough and precise study of the behavior of saline water distribution is needed for a successful water resources development project. The continuous recording of data on climate, salinity and water level is also needed to monitor their seasonal change and relationship. The authors, thus, collected data on the climate, river water levels and geology; and installed 8 sets of automatic groundwater level meters and 4 sets of automatic electric conductivity meters with data loggers in the observation wells to obtain continual observation data. In addition, the groundwater (GW) was sampled for quality evaluation at 3-month intervals. The authors use this data for an analysis of flood conditions, flow conditions of the GW, and change of the GW quality. In this paper, the

401

authors only report land use, geological conditions, river water levels, and groundwater levels (GWL) and quality.

2

STUDY AREA

The area of the Lower Nam Kam Basin is located in the northeastern region of Thailand (See Figure 1). This area has a tropical monsoon climate with a mean annual rainfall of 1,500 mm. Eighty percent of rain falls during the months of April through September; however, the amount of rainfall is not always predictable and reliable, and prolonged drought spells are frequent. The average temperature is 31.1◦ C, with a maximum of 33.4◦ C inApril and a minimum of 14.4◦ C in December. The total annual potential evaporation is about 1,740 mm, which exceeds the total average rainfall by 16 percent. The majority of the water available in the Lower Nam Kam basin is provided by surface runoff from the Nam Kam River and its main tributary Lam Nam Bang. The maximum monthly runoff occurs in September at the rate of 393.69 million cubic meters (MCM) and the minimum in February at 9.93 MCM. Surface water resource in this area is limited during the dry season. Water suitable for drinking is confined to relatively shallow aquifers (RID, 1982). According to the previous study, the area has salt rock slopes of 5–10 degrees to the north and the formation exists at the 90–200 m depth from the ground surface.As for the water quality, the salinity of water retained in sand, silt, clay and sandstone varies according to their location and depth. The contact surface of fresh, brackish and saline water is at 30–40 m below the ground surface of the Lower Nam Kam Basin (Khon Kaen University, 2001). However, in comparison with most areas in the northeastern region, the areas show a relatively good potential for irrigation development because of the suitability for irrigation of its soil as well as high runoff in the wet season. The Lower Nam Kam barrage is under construction at the downstream of flood area that occurs in the wet season by The Royal Irrigation Department (RID), Thailand. The purpose of this project is to keep river runoff of wet season for the cultivation in both wet and dry season. The reservoir area covers about 29.9 km2 of flood area when the gate is operated at normal level. This barrage is on the Nam Kam River just 1.7 km upstream from its confluence with Mekong River (RID, 1995). 3

GEOLOGIC SETING

The geologic map of the area is shown in Figure 2 and the geological cross sections are shown in Figure 3, 4 and 5. The area of the Lower Nam Kam Basin is characterized by floodplains and undulating

Figure 1. Location of Lower Nam Kam River Basin.

N

5000

0

5000 Meters

S Ground water well # Geologic cross section line

Mekong River Geologic explanation KTms: Mahasakham formation KkkKhok Kruat formation Kpp: Phu Phan formation Qff Flood Plain deposits

T Salt making areas $ Main River Proposed reservoir Qfv Valley Plain deposits Qth High Terrace deposits Qtl Lower Terrace deposits Qtm Middle Terrace deposits

Figure 2. Geological map of the Lowe Nam Kam River.

terrain. According to the Department of Mineral and Resources (2007), the floodplain consists of the Quaternary unconsolidated sediments. These sediments can be divided into 5 sub-units based on geomorphology, type, and occurrence of sediments, namely high terrace (Qth), middle terrace (Qtm), lower terrace (Qtl), valley plain (Qfv), and flood plain (Qff) deposits. The undulating terrain consists of consolidated sediments of the Phu Pan, Kok Kruat, Maha Sarakham Formation, and Phu Tok Formation. The Phu Pan Formation occurred in the Early Cretaceous and consists of highly resistant sandstone and conglomerate; conglomeratic sandstone beds with cross-bedding structure are common. Rocks of this formation cover most of the hilly areas and some parts of

402

northern part where floodplain deposits do not accumulate. Maha Sarakham Formation was formed in the upper part Cretaceous and is composed of thick to very thick mudstone, shale, siltstone interbedded with rock salt layers. The formation is exposed at salt making areas and along the Nam Kam River from Ban Dong Khun Kram to Ban Kang Pho. The Phu Tok Formation was formed in the latter Cretaceous–Tertiary and consists of fine to medium grained sandstone interbedded with siltstone, with a cross-bedding structure. Hence, the Quaternary unconsolidated sediments are underlain by the Maha sarakam Formation with rock salt. Based on the existing seismic data re-interpreted by Chiang Mai University (RID, 2006) and the boring log data, it is possible that the rock salt layer is distributed at the depth between 115–290 m with the thickness of 50 to 250 m in the area.

Figure 3. Geological Cross section 1-1’.

4

Figure 4. Geological Cross section 2-2’.

Figure 5. Geological Cross section 3-3’.

the undulating terrains over the southern part. The formation presents an anticline axis in the west-northwest to east southeast direction and forms a hill of about 200 meters above sea level (msl). Kok Kruat Formation was formed in the Middle Cretaceous and consists of thick bedded sandstone interbedded with minor siltstone, shale, and lime conglomerate; conglomeratic sandstone is present locally. The formation is distributed over wing parts of the anticline axis of Phu Pan Formation and forms hilly country of 170 m from about 200 msl. Khok Kruat Formation does not appear in the ground surface, but forms hilly country in the

FLOOD CONDITIONS AND INFILTRATION

The assumption of phreatic levels to be discussed here was based mainly on the data indicating high infiltration by flood over natural ground surface in this area and lateral side ground water flow from upstream. Therefore, flood water can become seepage water since subsurface and some parts become shallow GW. The observations of flood and field infiltration testing were set for the study of flood and infiltration in this area. The records of daily river stages since 2005 in the Mekong River, the Nam Kam River, and the Nam Bang River were collected from the three gauging stations, which belonged to the RID. Therefore, the analysis of daily flood heights into flood maps is based on water surface profiles of the three rivers in this area and the Lower Nam Kam Basin. The maximum floods in this area during 2006 and 2007 with average flood levels in the middle part of 140.30 and 141.10 msl were shown in Figure 6 a) and b), respectively. The period of inundations was 7 and 34 days in 2006 and 2007, respectively. The authors conducted a field infiltration testing with 50 points of the wet soil surface in paddy fields. The infiltration testing results based on Horton’s equation (Chow et al., 1988) were 0.2–46.2 mm/d with the mean of 7.3 mm/d. The wide flood area, long period of inundation and high infiltration rate causes much recharged water by flood over natural ground surface in this area. Therefore, the phreatic surface level in an unconfined aquifer could be raised by these recharges during flood seasons. 5

GROUNDWATER CHANGE

Figure 7 shows the GWL change at observation sites in Figure 2. The GWLs at points DM1, DM2, DM4

403

NK2 DM6

DM8

NK8

DM2 NK9 DM4 DM1

DM6

10/1/07

7/1/07

4/1/07

1/1/07

10/1/06

4/1/06

7/1/06 DM4 NK9

80

DM8

70

Figure 7. GWL changes in shallow and deep aquifer.

and DM8 represent the piezometric surface of confined aquifers (depth over 80 m), and GWLs at another 4 points (points DM6, NK2, NK8 and NK9) represent shallow GWLs (depth less than 80 m). In shallow aquifers, GWL changes drastically, nearly reaching the bottom in the end of May and rising in the beginning of October. This behavior means that there are high infiltration periods in the wet season (June to September) and pumping of GW just after the wet season. Thus, if the piezometric surface of GW in confined aquifer becomes higher than the shallow GWL, GW that may have high salt concentration will rise up to near ground surface.

60 50 40 30 20 10 0

Nov-05 Dec-05 Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07

DM2 NK8

1/1/06

10/1/05

Figure 8. Comparison between shallow and deep GWL.

% of the Reversal Area

DM1 NK2

7/1/05

date

GWL change in the lower Nam Kam area

4/1/05

147 145 143 141 139 137 135 133 131 129

1/1/05

GWL from m.s.l.

Figure 6. Maximum flood map in Lower Nam Kam Basin a) in 2006 and b) in 2007.

month-year

Figure 9. Percentage of the reversal area.

The authors overlay GWL of shallow and confined aquifers as shown in Figure 8. The recorded data of shallow aquifers come from over 100 production wells, while confined aquifers were recorded

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Table 1. Guidelines for interpretation of water quality for irrigation. Degree of restriction on use Salinity measurement ECw (dS/m) Salt (g/l) Concentration

None

Slight to moderate

Severe

<0.7 <0.45

0.7–3.0 0.45–2.0

>3.0 >2.0

only from 7 observation wells near salt making areas. The compared area is thus limited as shown within the rectangular boundary of Figure 8. This compared area is 27 km in the east-west direction and 20 km in the north–south direction. If these piezometric heads are higher than shallow GWL, black areas will show in this Figure. In short, this black area is a reversal GWL area. The authors made a calculation of this phenomenon during November 2005 to December 2007. The change in percentage of the reversal area is shown in Figure 9. This indicates that there is a wide area in compared area that GWL of confined aquifers higher than shallow aquifers. This phenomenon causes the leakage of saline water and contaminates shallow aquifers. However, our data is limited near the salt making areas only.

6

SALINITY CONDITION

In this chapter, the authors will describe salinity conditions of groundwater from production wells base on FAO guideline in Table 1 (Ayers et al., 1994). Figure 10 shows the distribution of salt concentration from September 2006 to July 2007 with intervals of 3 months. After the wet season of 2006, salinity conditions became more severe because infiltration through ground surface ended, resulting in the downfall of shallow GW. GW with salinity water then leaked up from deeper underground. The high concentration of salt existed in the reversal area of shallow and confined GWL. The highest concentration of salt water was nearly 1.5 g/L that moderate restriction for the agriculture use. According to the distribution of salt concentration and narrow thickness of first soil layer (Figure 3), we can understand that salinization may occur around salt-making areas where the stratum thickens. The authors observe both shallow GWL and EC at point DM6, located near the Nam Kam River mouth (see Figure 2) by automatic recording equipment. The well’s depth at DM6 is about 60 m and the sensor position of the EC meter is at 50 m from top of the observation well. The result of an observation on GWL and EC values at DM6 from August 2006 to July 2007 is shown in Figure 11. It could be induced that when shallow GWL decreases, EC value increases. This

Figure 10. Change of salt concentration distribution.

graph supports our assumption that salinity water with high concentration comes up from confined aquifers and mixes with shallow GW. Figure 12 is one of the Piper diagrams of GW in the lower Nam Kam River area. Piper diagrams do not display water quality by the concentration but with ingredient percentage of cation and anion for each total chemical equivalent. Percentage of the ion is displayed in the lozenge graph (called Key diagram). By the plotting position method, we can classify water quality into four domain categories (C1, C2, C3 and C4 as shown in Figure 12). The GW qualities belong to category 1 and 2 can be used for agricultural and

405

144.5

8000 EC

7800

143.5

7600

7200 7000

142

6800

141.5

6600

27-Jul

27-Jun

28-May

28-Apr

29-Mar

27-Feb

28-Jan

29-Dec

29-Nov

6000

30-Oct

6200

140

30-Sep

6400

140.5 31-Aug

141

date

EC(mS/m)

7400

143 142.5

1-Aug

GWL from m.s.l.

GWL 144

Figure 11. Observation result of GWL and EC value at DM6.

Figure 13. Category of GW quality in wet and dry season.

Figure 12. Piper Diagrams.

domestic purposes while category 3 and 4 is not suitable for agricultural and domestic purposes (Nakajima et al., 2000). Usually, salinity GW belongs to category 3 and 4. From categories distribution in Figure 13, it is found that Category 3 in the study area increases in the wet season from where it is in the dry season. This means that infiltration of polluted water through ground surface may influence groundwater in the wet season. The area near the salt making has unsuitable GW for agricultural and domestic use as it belongs to category 3.

7

CONCLUSION

Based on the observation of the GW quality of the Nam Kam River area based on geological feature conditions and GWLs, the following conclusions have been reached. 1. The stratum of anhydrite, gypsum and rock salt, exists in the Maha Sarakham strata, and the thick sedimentation conditions of this strata are seen in

the northeast of the central part in the cross sectional geological map. It is concluded that GW with a high concentration of salt is limited to the area where the stratum thickens. 2. The shallow GWL rises due to the flooding of the Nam Kam River, and afterwards drops by 5–6 m due to extraction of GW and less precipitation during dry season. When shallow GWL becomes lower than the piezometric surface of confined aquifers, the saline water in deeper aquifers will flow upward and contaminate shallow GW. This assumption has been proven by the occurrence of high salt concentration of GW in the reversal state of the GWL area. 3. The GW quality testing in production wells indicates that infiltration of polluted water through the ground surface may influence the shallow GW of the Lower Nam Kam area in the wet season. High pollution of water is found in particular around salt making area and near Mekong River. This local GW is unsuitable for drinking and agriculture. REFERENCES Ayers, R.S. & Westcot, D.W. 1994. Water quality for agriculture. FAO irrigation and drainage paper no. 29 Rev. 1. Chow, V.T., Maidment, D.R. & Mays, L.W. 1988. Applied Hydrology. McGraw-Hill International Editions.

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Ghassemi, F., Jakeman A.J. & Nix, H.A. 1995. Salinization of land and water resources. UNSW Press Co. ltd.: 132. Khon Kaen University. 2001. Environmental Impact Study of the Lower Nam Kam Irrigation Project Report. Imaizumi, M., Sukchan, S., Wichaidit, P., Srisuk, K. & Kaneko, F. 2002. Hydrological and geochemical behavior of saline groundwater in Phra Yun, Northeast Thailand. JIRCAS Working Repot, Vol. No. 30: 7–14. Nakajima, S., Kanou M., Kojima Y. & Kaneko Y. 2000.The basics of water environmental engineering. Morikita Pressb Co. ltd.: 132. Office of Agricultural Economics, Thailand. Agricultural Statistics of Thailand year book 2006.

www.ricethailand.go.th/rkb/data_004/rice_xx2-04_manage_ 002-2.himl. Rice Department, Thailand. Royal Irrigation Department, Thailand. 1982. Lower Nam Kam Basin, report on proposed development strategy, by GITEC Consult GMBH. Royal Irrigation Department, Thailand. 1995. The feasibility and environmental impact study of Lower Nam Kam irrigation project, by Asdecon coperation Com.: 1. Royal Irrigation Department, Thailand. 2006. The environment mitigation and rehabilitation for salt rock geology and hydrogeology of Lower Nam Kam irrigation project, by Chiang Mai University.

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The role of monsoon rainfall in desalinization of soil-groundwater system and in vegetation recovery from the 2004 tsunami disaster in Nagapattinam district, India T. Kume∗, C. Umetsu Research Institute for Humanity and Nature, Kyoto, Japan

K. Palanisami Center for Agriculture and Rural Development Studies, Tamil Nadu Agricultural University, India

ABSTRACT: A quite large tsunami struck Nagapattinam district, Tamil Nadu state, India. Coastal belt of the district was salinized due to inundation by sea water and deposits of marine sediment, and agricultural crops damaged destructively. The objective of this study was to assess the effect of Monsoon rainfall on desalinzation of soil-groundwater system and crop growth. The soil EC steeply increased after tsunami. However, it decreased to the level of before tsunami in 2006. Contaminated groundwater by inundation of sea water was also drained to the sea due to rapid permeability, and infiltrate Monsoon rainfall also promoted desalinization process in the aquifer. But, the groundwater EC was still high in the most observation wells compared with non-contaminated water. From the result of phenology analysis using MODIS NDVI data, it was confirmed that rainfall of Monsoon was essential resource not only for desalinization but also for agricultural production system. Keywords: desalinization; groundwater; monsoon; Nagapattinam; salinity; tsunami

1

INTRODUCTION

A quite huge earthquake, of magnitude between 9.1 and 9.3, hit northern Sumatra, Indonesia on 26 Dec. 2004, and the associated tsunami damaged the agricultural production system of surrounding countries. The tsunami caused by the earthquake hit Nagapptinum district, Tamil Nadu, India, and the district was one of the worst affected by the tsunami. This natural disaster caused a huge loss of human and animal lives and damaged residences and agricultural fields. Before the tsunami, the district was suitable for agriculture due to a healthy soil-groundwater system and abundant rainfall. The main crops of the district are rice, groundnuts, coconuts, and vegetables. After the tsunami, soil and groundwater were salinized by sea water and clay sediment that was deposited on the soil surface in coastal area of the district. Agricultural crops were heavily damaged in the tsunami. However, in 2006, agricultural production recovered rapidly because of the removal of clay sediment by farmers, regional government and NGOs. Fortunately, salt from sea water ∗

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leached out from the root zone and saline groundwater was drained by the heavy monsoon rains and high soil permeability. Monsoon rainfall is an indispensable resource for irrigation in the district. FAO (FAO web site) stated that salinity is no longer a threat to the majority of tsunami-affected fields on their web site, and it was reported that desalinization of soil and groundwater was probably facilitated by heavy precipitation (Chaudhary et al. 2006). Rengalakshmi et al (2007) also stated that the soil EC levels reduced to the normal levels that are observed in coastal soils before the tsunami and most of the lands were found to be suitable for normal crop cultivation. These studies were mainly focused on soil salinization and its rehabilitation. Chaudhary et al (2006) reported the presence of high groundwater salinity, however, these measurements were not conducted continuously. The effect of tsunami on crop growth was mentioned in few of these studies. In this study, we assessed the effects of monsoon rainfall on the desalinization of soil and groundwater and vegetation recovery from the tsunami. To reveal the effects, we analyzed changes in soil and groundwater salinity before and after the tsunami and examined

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changes in large scale vegetation cover using MODIS NDVI data.

2

STUDY AREA

Nagapattinam District (Fig. 1) is a coastal area of Tamil Nadu. It lies between 10.1 to 11.2 degrees North latitude and 79.2 to 79.5 degrees East longitude. It is bounded by the Bay of Bengal on the east, the Palk Straits on the south, Thanjavur District on the West and Cuddalore District on the North. Maximum temperature of the district is 33.8C◦ , minimum temperature is 23.4C◦ , and mean temperature is 28.7C◦ . Average annual precipitation is approximately 1,400 mm.There are four seasons in the district; winter from Jan. to Feb., summer from Mar. to May, South-West Monsoon (S.W. Monsoon) from June to Sep., and North-East Monsoon (N.E. Monsoon) from Oct. to Dec., and each season receives 3%, 4%, 17% and 76% of annual rainfall respectively. In Nagapattinam district 0.241 million ha or 89% of total area is used for agricultural production. Of this area, 0.118 million ha or 49% of agricultural land is irrigated by canal irrigation system (Statistical Hand Book 2006, Government of Tamil Nadu, Department of Economics and Statistics). Groundwater from openwells and tank water are used for irrigation in the district. Rice is the dominant agricultural type and the total area of paddy field is approximately 0.16 million

Figure 1. Situation of Nagapattinam district, Tamil Nadu state, INDIA.

ha or 66% of agricultural land. 74% of paddy field is irrigated by canal irrigation system. The installation of the canal irrigation system began from the west part of the district, so the coverage of the canal irrigation system in the east part of the district, most affected by the tsunami, is relatively low. Our study fields are situated in coastal area with less irrigation facilities. A major part of water requirement of rice cropping is supplied by the monsoon rainfall and deficiency is irrigated by groundwater. The predominant soil type in the district is sandy in texture, and the soil belongs to the Valudalakudi series which is taxonomically classified as Typic Udipsamment series; dark brown to brown, deep, sandy and possessing characteristics, of mild to moderate alkalinity levels (Soil Survey & Land Use Organization Department of Agriculture Tamil Nadu, 2005). Permeability of Valudalakudi is classified as rapid (Soil Survey & Land Use Organization Department of Agriculture Tamil Nadu, 1998). 3

MATERIALS AND METHOD

Soil sampling and laboratory analysis were conducted by the Tamil Nadu Rice Research Institute, Aduturai, India, three times: 1) before the tsunami in March 2004, 2) after the tsunami in May 2005 and 3) March 2006. Soil sampling in 2005 was conducted after the removal of marine sediments by government and NGO activities. More than 90% of the marine clay sediments deposited by the tsunami were removed from fields by May 2005 (Rengalakshmi et al. 2007), so that sampled soils were original material represented by the Valudalakudi series. Soil electrical conductivity (EC) (Richards, 1954) was measured in the laboratory to evaluate soil salinity effects of the tsunami. Soil sampling was conducted at 24 villages in Nagapattinam district as shown in Figure 2a. To evaluate the effect of the tsunami on groundwater, monthly monitoring of groundwater depth and groundwater EC were conducted from May and June 2006, respectively. In this paper, groundwater depth means a distance from ground surface to groundwater table. Ten monitoring wells were installed as shown Figure 2b. The effect of rainfall on groundwater depth was analyzed for all ten wells. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the NASA EOS Terra satellite has 36 spectral bands, and vegetation indices were computed from the raw spectral data. Overall, each crop type had unique, multi-temporal NDVI profiles, and clear spectral-temporal differences appeared. The MODIS Normalized difference vegetation index (NDVI) was found to be sensitive to multi-temporal vegetation variation (Huet et al. 2002), and vegetation phenology was successfully monitored using MODIS Vegetation Index data

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(Zhang et al. 2003). The NDVI data is provided from EOS Data Gateway, LP DAAC, NASA. We used 16-day composites of NDVI data at 250 m spatial resolution (MODQ13 V004) observed from Jan. 2004 to Dec. 2006 for evaluation of the effect of salinity on crop growth and its relationship with the monsoon rainfalls. The raw values of NDVI were extracted at the point of the soil surveys, and high salinity fields were determined from the results of the soil EC analysis of May 2005. The NDVI value of rice cropping field shows peak around late Dec. or early Jan. and the tsunami hit at that timing, so we focused on comparison of peak NDVI of 2004, 2005 and 2006.

4

RESULTS

4.1 Changes in soil salinity Figure 3 shows differences in soil EC a) between before tsunami (2004) and after tsunami (2005), and b) between 2005 and 2006. Before the tsunami the average soil EC was 0.98 dS m−1 and it ranged from 0.3 to 4.9 dS m−1 , whereas the EC after the tsunami showed a steep increase between 2004 and 2005 in most sampling points. The increment of soil EC ranged from 0.4 dS m−1 to 13.3 dS m−1 . The soil EC of the fields with marine sediment was up to 23.7 dS m−1 (Soil Survey & Land use Organisation, 2005). The increment mainly depended on the distance from shoreline. In 2006, the average EC was 0.69 dS m−1 and it ranged from 0.1 to 3.2 dS m−1 , which was almost the same level as before the tsunami. 4.2 Groundwater fluctuation and salinity

Figure 2. a) Soil sampling points and b) groundwater monitoring points.

The effect of rainfall on groundwater depth was examined for all ten wells, and the average groundwater fluctuation showed a clear trend. One peak occurred in the fluctuations of the groundwater depths from Sep. to Nov. (Fig. 4a). The increase began in Sep. and remained high until Dec. with approximately 1,000 mm rainfall derived from the N.E. Monsoon, and decreased from Jan. to May in the absence of monsoon. The groundwater fluctuations of most wells seemed to follow this trend. However, the fluctuations of GW5 and GW8 were significantly different with this trend. Figure 4b shows changes in the groundwater EC. The average groundwater EC during the observation period was 2.1 dS m−1 . During the observation

Figure 3. Changes in soil EC a) between 2004 and 2005, and b) between 2005 and 2006. Numbers shown in Fig. 3 correspond with those of Figure 2a.

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Figure 4. a) Fluctuation of groundwater depth and rainfall from May to December in 2006, and b) seasonal changes in groundwater EC from June to December in 2006.

period, the EC of groundwater was not affected by the Monsoon, however, the groundwater EC of GW1 and GW9 increased in Nov. and Dec. It is said that the EC of high quality irrigation water is less than 1.5 dS m−1 (Chhabra, 1996).The water quality in some of the observation wells passed the standard level of irrigation water. However, non-contaminate water from open-wells without sea water inundation was 0.5 dS m−1 (Chaudhary et al. 2006) so that the groundwater EC of the wells was still higher than pre-tsunami period. 4.3

Relationship between NDVI and rainfall

Figure 5 shows a time-series data of average NDVI at 24 soil sampling points and a severely salinized field, respectively. Rainfall data is also drawn in the figure. The phenology of Samba rice was clearly detected, and

this pattern corresponded with the crop calendar. The increase of NDVI began in Aug. and one peak occurred in late December or early January and decreased from January to April. Another small peak observed in May was due to Kuruvai rice cropping. NDVI decreased at the severely salininized field during the period from Nov. 17 to Dec. 19 of 2004 which was pretsunami period. This was due to data characteristics of 16-day composites of NDVI data. NDVI of Dec. 19 of 2004 covers that of Dec. 19–31, so the period from Nov. 17 to Dec. 19 of 2004 as shown in Figure 5 corresponded to pre-post tsunami period. Therefore, NDVI of Dec. 19 of 2004 reflects the effect of the tsunami on rice cropping. The difference of the peak value of the average NDVI between pre-tsunami Jan. 2004 and post-tsunami Jan. 2005 was about 0.1, and the peak value of the average NDVI recovered to pre-tsunami levels in Jan. 2006 as shown in Figure 5. Severely

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Figure 5. Multi-temporal NDVI profiles of average value at 24 soil sampling sites and of severely salinized field, and the Monsoon rainfall in the Nagapattatinam district.

salinized fields exhibited decreasing peak NDVI values just after the tsunami event. The difference in the peak value of NDVI in the field between Jan. 2004 and Jan. 2005 was 0.18, and it increased from 0.58 to 0.78 between Jan. 2005 and Jan. 2006. The seasonal change in NDVI at 24 soil measurement points followed a similar trend as shown in the figure. NDVI increased at the severely salinized field during the period from Dec. 19 of 2004 to Jan. 17 of 2005.

5

DISCUSSIONS

The soil EC rapidly increased after the tsunami event. However, it was confirmed that the soil EC decreased to the level in the periods before the tsunami event by 2006. This result was most probably due to the S.W. Monsoon and the N.E. Monsoon. Because of high permeability of the Valudalakudi dominant soil series of the district, root zone salinity was leached to the groundwater quickly. Prior research found that accumulated salts will leach down during the rainy season due to the sandy nature of the soil, and sea water inundation receded within a few hours in some areas and five to seven days in some other parts (Chaudhary et al. 2006). Other report has stated that sea water intrusion receded from the field within 3 hours to one week (Rengalakshmi et al. 2007). From these results, we can find that the Monsoon rainfall played a role in leaching water in salinized soils and promoted contaminated groundwater drainage in the aquifer. However, the groundwater EC was still high in most observation wells compared with non-contaminated water.The groundwater EC of GW1 in Nov. and Dec. and GW9 in Dec. 2006 rapidly increased. The most likely reason of this phenomenon was sea water intrusion due to tidal

level, because these wells are situated near seashore compared with other wells. The groundwater depth fluctuation of GW5 and GW8 significantly showed a different trend from others. GW5 is surrounded by paddy fields which have sufficient irrigation facilities, so the groundwater began to increase in July with abundant irrigation water. On the other hand, the groundwater of GW8 did not increase between July and Oct. This might be due to groundwater pumping for irrigation of peanuts and soy beans. From the viewpoint of sustainable agricultural production systems, long-term groundwater monitoring should be continued for resolving groundwater salinity issues completely. In the district, the rice cropping season called Samba starts on August associated with the N.E. Monsoon, and peak greenness is observed in late Dec. to early Jan (Soil Survey & Land Use Organization Department of Agriculture Tamil Nadu, 1998). The estimated water requirement for rice production in the Samba season is approximately 1,500 mm, and most parts of the paddy fields near seashore do not have a sufficient irrigation system, so that the rainfall derived from the N.E. monsoon, which provides 900 mm rainfall, is a vital water resource (Dept. of Agriculture, Government of Tamil Nadu and Tamil Nadu Agricultural University, 2005).Also, the multi-temporal NDVI showed its peak after the N.E. Monsoon. Farmers in the coastal area transplant rice seedling regarding the N.E. Monsoon, because they have less irrigation facilities without groundwater wells. This monsoon rainfall plays a role of irrigation water in the early stage of rice cropping of Samba. The increasing of NDVI during the period from Dec. 19 of 2004 to Jan. 17 of 2005 would be due to recession of sea water inundation and removal of clay sediment from the fields.

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From these results, it was confirmed that the monsoon rainfall played a large role not only in desalinization process but also in crop growth. The large amount of the N.E. Monsoon rainfall sometimes induces flood disasters in the district due to poor drainage and flat topography, but it was shown that the monsoon rainfall was an indispensable water resource for the agricultural production system. In addition, this research showed the capability of time series MODIS 250 m NDVI data for the detection of phenology of the Samba paddy cropping period. Also this study showed the applicability of the NDVI data for the evaluation of the effect of natural disasters like tsunamis on agricultural production systems. ACKNOWLEDGEMENT This research was partially supported by the Resilience Project (Vulnerability and Resilience of SocialEcological Systems), Research Institute for Humanity and Nature (RIHN). Also, this research was partially supported by “Distribution and Sharing of Resources in Symbolic and Ecological Systems: Integrative Model-building in Anthropology”, Grant-in-Aid for Scientific Research of Priority Areas, Ministry of Education, Culture, Sports, Science, and Technology, Program No. 14083208. We would like to thank the Rice Research Institute, Adutulai, Tamil Nadu, India for providing soil analysis data, and to thank the Dept. of Meteorology, Tamil Nadu Agricultural University for providing meteorological data.

Chhabra, R. 1996. Soil salinity and water quality, A.A. Balkema Publishers, USA, 156–158 Dept. of Agriculture, Government of Tamil Nadu and Tamil Nadu Agricultural University. 2005. Crop Production Guide 2005, 20–22 FAO http://www.fao.org/ag/tsunami/assessment/salinity.html Government of Tamil Nadu, Department of Economics and Statistics 2006. Statistical Hand Book -2006-, available from web site: http://www.tn.gov.in/deptst/index.htm Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., & Ferreira, L.G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, 83: 195–213 Rengalakshmi, R., Senthilkumar, R., Selvarasu, T. & Thamizoli, P. 2007. Reclamation and status of tsunami damaged soil in Nagappattinam District, Tamil Nadu, Current Science, 92(9): 1221–1223 Richards, L. A. (ed.) 1954. Diagnosis and Improvement of Saline Alkali Soils, USDA Handbook No. 60, Washington DC, USA Soil Survey & Land Use Organization, Department of Agriculture Tamil Nadu (ed) 1998. Soil Atlas -Nagappattinam District-, Coimbatore, Bharathi Pathippagam & Traders Soil Survey & Land Use Organization, Department of Agriculture Tamil Nadu (ed) 2005. Soil survey report of tsunami affected area in the coastal belt of Nagapattinam district, Tamil Nadu, Coimbatore Zhang, X., Friedl, M. A., Schaaf, C.B., Strahler, A.H. Hodges, J.C.F., Feng, G., Reed, B.C., & Huete, A. 2003. Monitoring vegetation phenology using MODIS, Remote Sensing of Environment, 84: 471–475

REFERENCES Chaudhary, D.R., Ghosh, A. & Patolia, J.S. 2006. Characterizaion of soil in the tsunami-affected coastal areas of Tamil Nadu for agronomic rehabilitation, Current Science, 91(1): 99–104.

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Effect of stream diversion on densities of Aeromonas hydrophila in a mountain stream in a headwater area H. Hirotani∗ Osaka Kyoiku University, Kashiwara, Osaka, Japan

K. Ochi Ehime University, Matsuyama, Ehime, Japan

ABSTRACT: In the Ishite River, Japan, the stream water is completely diverted at head works except under flood conditions. The occurrence of Aeromonas hydrophila in channel above and below the head works in a mountain stream was studied monthly for twenty months. A. hydrophila and fecal coliforms were measured by the membrane filter methods. A. hydrophila was present throughout our study period, and was more abundant than fecal coliforms. The analysis of environmental factors in multiple regression revealed that the bacterial densities were positively affected by water temperature, flow velocity, and stream diversion. The impact of stream diversion was much greater than that of temperature rise, and therefore it is suggested that withdrawal of stream diversion can balance the water temperature rise caused by global warming, and thus can keep the risk of waterborne infection of A. hydrophila. Keywords: Aeromonas hydrophila; flow control; stream diversion; water quality; weir

1

INTRODUCTION

Aeromonas hydrophila is a Gram-negative bacterium ubiquitous in aquatic environments (Hazen et al. 1978). The bacterium is occasionally virulent to man (Abeyta & Wekell 1988, Burke et al. 1984, Semel & Trenholme 1990). The virulence factors such as enterotoxins, hemolysins, and proteases are known to be produced by the pathogenic strains (Gibotti et al. 2000, Trower et al. 2000). A. hydrophila strain capable of producing cytotoxins and enterotoxins has been found in chlorinated municipal water (Fernandez et al. 2000). Although some authors relate the abundance of A. hydrophila with the trophic state of the water (Schubert 1975, Rippey & Cabelli 1980, 1989, Araujo et al. 1989), distribution of Aeromonas spp. are likely to have seasonal cycles, with lower densities in the colder months (Kaper et al. 1981, Monfort & Baleux 1991, Pathak et al. 1988, Rippey & Cabelli 1980, Gavriel et al. 1998). This seasonality has been suggested as a contribution to the increased risk of infection during the summer months (Araujo et al. 1989, Davis et al. 1978). Hirotani et al. (1999) demonstrated that the ∗

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factors controlling the bacterial density in the mountain stream is water temperature and precipitation in a prior 60-day period. An association between the pattern of Aeromonas isolation from the drinking water supply and that of rainfall and seasonality has also been demonstrated (Gavriel et al. 1998). There is a concern that the global warming and climate change increase the risk of the waterborne infection of A. hydrophila. In the Ishite River in Ehime Prefecture, Japan, the river water is diverted completely about 3 km above the Ishitegawa Reservoir, except under flood conditions since 1903, and transported to a hydroelectric power plant located ca. 6 km downstream. The stream flow regenerates about 0.2 km below the weir by gaining groundwater and flows from small tributaries. The effects of stream diversion on chemistry of the stream water and on phytoplanktons in the reservoir receiving the stream water have been documented (Kagawa 1992, Kagawa & Hirotani 1995). Using the principal component analysis, the leaching of the dominant ions from the catchment soil contributed to the chemistry of the stream water below the weir. The mean concentration of NO3 -N and PO4 -P was 0.92 mg/l and 5.7 µg/l, respectively. In the present paper, we evaluated the effect of stream diversion on density of A. hydrophila, and the extent of this effect was quantitatively compared

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2.2

Bacterial analyses

The samples transported on ice were immediately subjected to bacterial analyses. A. hydrophila was enumerated by the membrane filter method followed by two in situ tests for identification mentioned elsewhere (Rippey & Cabelli 1979). The identification method was based on the ability to ferment mannitol and the presence of oxidase. Fecal coliforms (FC) were counted by the membrane filter method using mFC medium incubated at 44.5 ± 0.2◦ C (AHPA-AWWAWPCF, 1995). Usually, the coefficient of variation (standard error/mean value) for these bacterial analyses was less than 3%. 3

Figure 1. Map of the Ishite River watershed including the two sampling stations and head works. The river is completely diverted by the head works to supply the water to the power plant located ca. 6km downstream. Sampling stations were located above and below the weir.

with the effect of well-reported factor, i.e., water temperature.

2 2.1

MATERIALS AND METHODS Study site

The study was undertaken in the upper reaches of the Ishite River (Fig. 1), which originates from low peaked mountains ca. 900 to 1200 m above sea level. Coniferous plantations mainly covered the mountains. Head works consisting of a weir and other intake facilities located in Matsuyama, Ehime Prefecture, diverted the stream water at the maximum rate of 2.5 m3 s−1 . Two sampling stations were located in the third order section of the stream, just above the head works (station 1) and about 2.4 km below (station 2). Although a few settlements existed along the stream, these sampling stations and head works were apart from large villages. There were some paddy fields just above station 1. The streamwater serves as 50% of source water for Matsuyama City, which has a population of more than 500,000 at present. Stream water samples were collected monthly from May 22, 1998, to December 15, 1999, at both stations between 10 and 11 AM. Water temperature and flow velocity were measured on site using calibrated alcohol thermometer and an electromagnetic current meter (Toho Keisoku Institute, TK-105X). Discharge was calculated by multiplying the current velocity and the cross sectional area of the stream.

RESULTS AND DISCUSSION

Among 20 monthly samplings, there were four occasions when the weir was open due to maintenances, and two when stream water overflowed the weir. These conditions were defined as “flood”, while all in the other period was defined as “base flow” (Table 1). The mean, the lowest and the highest water temperatures were 14.4◦ C, 4.0◦ C and 21.9◦ C at station 1 and 15.8◦ C, 4.9◦ C and 25.2◦ C at station 2, respectively. A. hydrophila was present throughout the study period at both sampling stations, and was abundant than FC (Fig.2). Usually the bacterial densities above and below the weir were at similar densities, even though the stream water was diverted. In colder months the abundances of both A. hydrophila and FC were lower at station 2, a downstream location. In most 14 out 20 cases the river was completely divided and the streamwater at station 2 was mostly that regenerated from the ground water in the short stretch in the mountain. The density of FC at station 2 can be considered the background level in the groundwater. The difference of FC between station 1 and 2 can be considered the actual contamination the stream has received above station 1. A. hydrophila seemed to indicate seasonal fluctuation with lower densities in colder month and in summer, vice versa. According to Hirotani et al. (1999) Aeromonas density in clear mountain stream is affected by water temperature and precipitation index. In this study, there were significant correlations between A. hydrophila and water temperature as follows:

where, A1 and A2 are A. hydrophila densities (log cfu/100 ml) and T1 and T2 are water temperatures (◦ C)

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Table 1.

Stream condition at the weir and current velocity and discharge at sampling stations Current velocity (m/s)

Discharge (m3 /s)

Date

Condition (weir)

Station 1

Station 2

Station 1

Station 2

22-May-98 16-Jun-98 14-Jul-98 7-Aug-98 16-Sept-98 14-Oct-98 11-Nov-98 15-Dec-98 19-Jan-99 16-Feb-99 16-Mar-99 8-Apr-99 19-May-99 10-Jun-99 15-Jul-99 18-Aug-99 16-Sept-99 18-Oct-99 15-Nov-99 15-Dec-99

base flow base flow base flow base flow flood (overflow) base flow base flow flood (gate open) flood (gate open) base flow base flow base flow base flow base flow base flow flood (overflow) flood (gate open) base flow base flow flood (gate open)

0.37 0.38 0.42 0.36 0.40 0.39 0.81 0.82 0.70 0.64 0.78 0.78 0.74 0.86 0.91 0.82 1.20 0.74 0.53 0.59

0.24 0.26 0.26 0.21 0.41 0.12 0.06 0.49 0.46 0.03 0.04 0.02 0.04 0.11 0.34 0.74 1.23 0.20 0.46 0.74

1.55 1.67 1.53 1.32 1.22 1.23 1.05 0.97 0.69 0.67 0.92 0.73 0.78 1.60 2.48 5.05 7.34 1.61 1.87 1.11

0.11 0.17 0.15 0.14 0.28 0.07 0.04 0.56 0.87 0.02 0.02 0.02 0.03 0.12 0.14 2.93 6.78 0.13 0.29 1.28

Figure 2. Monthly fluctuation of Aeromonas hydrophila and fecal coliforms (FC) densities at station 1 and 2.

at station 1 and station 2, respectively. Temperature may reflect the Aeromonas growth in its source, rather than in the water body (Hirotani et al. 1992). However, the difference of regression coefficient between these two equations seems to be too large to be explained by the experimental error. Because the water qualities and the locations of the both sampling stations were similar, the effect of temperature on the biomass of the bacteria cannot be much different, and thus the difference in the coefficients is considered to derive from the difference in the hydrological conditions. Since the hydrological conditions of streamwater at station 2 differs from that of station 1 in the sense that

it is affected by the operation of the weir located above the station, densities of A. hydrophila were further analyzed to give the following equation:

where A2 is A. hydrophila density (log cfu/100 ml), T2 is water temperature (◦ C), v is current velocity (m/s), and F is a parameter indicating the presence of flow regulation (1 = flood, 0 = base flow). In this equation the regression coefficient of the water temperature became closer to that in the equation (1) than

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Figure 3. Aeromonas hydrophila densities observed in the field study and predicted using equation (3).

equation (2). Absence of the term with current velocity in the equation (1) is balanced by the greater value of the constant compared to the equation (3). Also it was always under “flood” condition at station 1, because there was no weir upstream controlling the flow. This is considered to be included also in the constant in the equation (1). The observed and predicted values from the equation (3) are plotted in Fig. 3. Temperature term in equation (3) reflects the growth in its source as in equation (2). We suppose that its source is the biofilm formed on the streambed. It is shown that biofilms formed on streambed pebbles contain significant amount of bacteria and serve as a source for the bacteria in the water column (Hirotani et al. 2008). The stream current velocity is considered to indicate the force to scrape off the biofilm from the stone surface, thus supplying the microflora in the streamwater. In the previous model reported by Hirotani (1999), precipitation had a positive effect on A. hydrophila densities in the stream. An increased runoff caused by heavy precipitation may have produced a greater flow velocity to scrape away the greater amount of the biofilms rather than diluting the bacteria present in the water column. According to equation (3), the presence of the flow regulation affects the bacterial density downstream. The flood events had the negative effect on A. hydrophila densities. This can be explained by the dilution of the bacteria with streamwater arriving to the station located below the weir. In spite of the image brought about by the wording, “flood” is closer to the natural stream condition without an artificial flow regulation. If the stream flow regulation was ceased to cause a “flood”, A. hydrophila densities at Station 2 is expected to decrease by log 1.4, i.e. to 4%, from the regulated “base flow” condition.

Here, the effect of change in water temperature can be compared with artificial impact given to the mountain stream environment in the sense of bacterial densities. A huge impact was expected from the stream diversion, compared to the temperature change. Climate model summarized by IPCC indicates that global surface temperature will likely to rise 1.1 to 6.4◦ C during 21st century (http://ipcc-wg1. ucar.edu/wg1/Report/AR4WG1_Print_SPM.pdf).The density rise of A. hydrophila caused by the warming is expected to be log 0.42 or 2.6-times at maximum. Compared to the increase of bacterial density caused by the temperature rise, the impact of stream diversion was much greater. Provided the risk of infection is increased by density rise deriving from change in water temperature, it might be able to overcome the risk increase expected in this century by ceasing the stream diversion. Needless to say, the bacterial density as well as the risk of waterborne infection is affected by many environmental factors. Alteration made in one factor may result in change of other factors at the same time. There may be no one simple solution for the climate change. But it may be worth considering abandoning the stream diversion as one solution for the control of bacterial density in the mountain stream.

4

CONCLUSION

A. hydrophila was always detectable in the upper reaches of the Ishite River serving as source water for Matsuyama City with increase occurring in warmer months. The analysis of environmental factors in multiple regression revealed that the bacterial densities were positively affected by water temperature, flow velocity, and stream diversion. The impact of stream diversion was much greater than that of temperature rise, and therefore it is suggested that withdrawal of stream diversion can balance the water temperature rise caused by global warming.

ACKNOWLEDGMENTS The authors thank students of Laboratory of Inland Water Management, Ehime University for the field and laboratory work aids. REFERENCES Abeyta, C. & Wekell, M.M. 1988. Potential source of Aeromonas spp. J. Food Safety 9:11–22. AHPA-AWWA-WPCF (1995) Standard methods for the examination of water and wastewater, 19th ed. Araujo, R.M., Arribas, A.M., Lucena, F. & Pares, R. 1989. Relation between Aeromonas and faecal coliforms in fresh waters. J. Appl. Bacteriol. 67: 213–217.

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Burke, V., Robinson, J., Gracey, M., Peterson, D., Meyer, N. & Haley, V. 1984. Isolation of Aeromonas spp. from an unchlorinated domestic water supply. Appl. Environ. Microbiol. 48: 361–366. Davis, W.A.II, Kane, J.G. & Garagusi, V.F. 1978. Human Aeromonas infections: a review of the literature and a case report of endocarditis. Medicine 57: 267–277. Fernandez, M.C., Giampaolo, B.N., Ibanez S.B. & Guagliardo, M.M. 2000. Aeromonas hydrophila and its relation with drinking water indicators of microbiological quality in Argentine. Genetica 108:35–40. Gavriel, A.A., Landre, J.P.B. & Lamb, A.J. 1998. Incidence of mesophilic Aeromonas within a public drinking water supply in north-east Scotland. J. Appl. Bacteriol. 84: 383–392. Gibotti, A., Saridakis, H.O, Pelayo, J.S., Tagliari, K.C. & Falcao, D.P. 2000. Prevalence and virulence properties of Vibrio cholera non-O1, Aeromonas spp. And Plesiomonas shigelloides isolated from Cambe Stream (State of Panama, Brazil). J. Appl. Microbiol. 89: 70–75. Hazen, T.C., Fliermans, C.B., Hirsch, R.P. & Esch, G.W. 1978. Prevalence and distribution of Aeromonas spp. in the United States. Appl. Environ. Microbiol. 36: 731–738. Hirotani, H., Matsui, Y., Sese, C. & Kagawa, H. 1992. Positive correlations between catchment areas and densities of bacteria in the upper reaches of a river. Wat. Sci. Tech. 26: 1965–1972. Hirotani, H., Sese, C. & Kagawa, H. 1999. Correlations of Aeromonas hydrophila with indicator bacteria of water quality and environmental factors in a mountain stream. Wat. Environ. Res. 71: 132–138. Hirotani, H. & Yoshino, M. 2008. Biofilm formed on streambed pebbles as a source of coliform bacteria and

Escherichia coli. IWA Biofilm Technologies Conference, Singapore, January, Abstract Handbook 396–397. Kagawa, H. 1992. Effects of diversion on the chemistry of a stream in Japan. Regul. Riv. 7: 291–302. Kagawa, H & Hirotani, H. 1995. Predicting the summer chlorophyll a concentration in a reservoir based on the environmental conditions of the preceding spring. Hydrobiologia 310: 59–70. Kaper, J.B., Lockman, H. & Colwell, R.R. 1981. Aeromonas spp.: ecology and toxigenicity of isolates from an estuary. J. Appl. Bacteriol. 50: 359–377. Monfort, P. & Baleux, B. 1991. Distribution and survival of Motile Aeromonas spp. in brackish water receiving sewage treatment effluent. Appl. Environ. Microbiol. 57: 2459–2467. Rippey, S.R. & Cabelli, V.J. 1980. Occurrence of Aeromonas hydrophila in limnetic environments: Relationship of the organism to trophic state. Microb. Ecol. 6: 45–54. Rippey, S. R. & Cabelli, V. J. 1989. Use of the thermotolerant Aeromonas group for the trophic state classification of freshwaters. Wat. Res. 23: 1107–1114. Schubert, R.H.W. 1975. The relation of aerogenic to anaerogenic aeromonads of the “hydrophila-punctata group” in river water depending on the load of waste. Zbl. Bakt. Hyg., I. Abt. Orig. B. 160: 237–245. Semel, J.D. & Trenholme, G. 1990. Aeromonas hydrophila water associated traumatic wound infections: A review. J. Trauma 30: 324–327. Trower, C.J., Abo, S., Majeed, K.N. & von Itzstein, M. 2000. Production of an enterotoxins by a gastro-enteritisassociated Aeromonas strain.J. Med. Microbiol. 49: 121–126.

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9

Socio-economic models and monitoring of vulnerable water resource

Water is the basic necessity of people and has been the main resource for human activities. Nowadays, intensive socio-economic activities have caused the depletion of water resource, deterioration of water quality, and damaged to water environment in many areas. In order to promote sustainable development, it is necessary to manage human activities efficiently and effectively while satisfying the condition of water resource and its environment. This session will discuss the impacts of human activities on water resource systems and their functions. We welcome wide aspects of interdisciplinary studies in sociology, economics, political science, and ecology, focusing on human activities-water resource interactions. Conveners: Felino Lansigan (University of the Philippines, the Republic of the Philippines) Masafumi Morisugi (Meijo University, Japan) Akio Onishi (RIHN, Japan) Karen Jago-on (RIHN, Japan)

From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Water resources estimation and allocation in the rapid developing area of China K. Wang∗ Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong

X. Chen State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, P.R. China

Y.D. Chen Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong

ABSTRACT: The study area Suzhou is located inYangtze River delta of China, which is a rapid developing city with high GDP and serious water pollution. With the rapid development of economy and society, it is undergoing great society change and experiencing great challenges of water shortage caused by water quality. Therefore, how to protect water resources and achieve the sustainable development has become a hot issue. In this paper, a water demand-supply balance analysis method in four typical years of wet, normal, drought and extremely drought was proposed as a basis for developing a water resources allocation model in Suzhou. The spatial and temporal variations of water resources and water quality standards for different water users were characterized in the model. The amount of domestic, productive and eco-environmental water demands were predicted based on the planning of social and economic development and environmental protection in Suzhou. A useful model integrated with water demand prediction and water allocation was developed and four representative allocation schemes were simulated by this model and a reasonable scheme was selected in terms of meeting the requirements of water users, water saving and environmental protection. In practice, the model proved to be a useful tool for getting a reasonable water allocation scheme that can keep the sustainable development of Suzhou. Keywords: 1

water resources allocation; supply and demand balance; water quality

INTRODUCTION

As a developing country, China makes great progress in economic and has become the fastest growing country in the world. The World Bank projects that the nation’s industrial water demand will increase by 62% in the next 10 years from 127 billion cubic meters to 206 billion. Water resource shortage and water pollution are widely believed the most challenging issues in the future development of China (World Bank 1997, ESCAP 1997, MWR 2001). Uncontrolled water usage has led to a series of ecological problems in China such as rivers drying up, loss of wet land sand lakes, and the rapid decline of the groundwater table. Limited water resources, increasing demand, low use efficiency, and serious pollution result in serious water stress and make the situation of ∗

Corresponding author ([email protected])

water security difficult (Ouyang et al. 2004, Xia et al. 2001). Estimation and allocation of water resources has become an important issue in China since 1980s. In the 1980–1985 Five-Year Plan, Hua (1988) used system analysis method to make some research on the exploitation and utilization of water resources in Beijing, which is a rudiment attempt of water resources allocation in China. In the 1990–1995 Five-Year Plan, Xu (1997) represented macro-economics-based water resources optimal allocation theory, which represented some new research on water demand and supply balance, water demand management, water supply management, water quality management, economy system and decision-making system. In the 1995–2000 FiveYear Plan, on the basis of the former achievements, considering the characteristics of inland drought oasis, Wang (2003) combined with water resources system, socio-economic system and ecological system and represented theory & method of rational water resources

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allocation based on dualistic water cycle model. With 16 million people constantly lacking adequate drinking water, the Ministry of Water Resources (MWR) started the national comprehensive water resources planning project in 2002 (MWR 2002), which was extended all over the country. The main tasks were to investigate (1) national and regional water resources under the tremendous changes of land uses and climate conditions in the recent 20 years; (2) future water demand on the basis of population growth and economic development; (3) schedule for reasonable water utilization among agricultural, industrial and domestic and among different regions. However, most of analysis methods and models for water resources estimation and allocation were developed in the water limited regions of north China (Hua 1988). In the humid south with relatively plentiful rainfall, however, water resources problems mainly arise from water pollution (Zhao 2004). In the river network area of the Yangtze River delta, water allocation should be based on river network water quality and water exchanges among different areas by using hydrodynamic model (Wu 2005, Ruan 2003). Our study focused on water estimation and allocation in the river network area of Suzhou, one of the fastest developing areas in China. Present situation of water exploitation and utilization was firstly estimated. Then, water supply-demand balance simulation for different water utilization schemes in the typical years was executed. Finally, a reasonable allocation scheme of water resources allocation was determined for meeting water supply-demand balance in the next 30 years. 2

STUDY AREA AND WATER RESOURCES

The study area is located in the middle-lower part of the Yangtze River Delta (Fig. 1), with the east boundary of Taihu Lake and the north boundary of Yangtze River, Suzhou is composed of six counties (viz. Changshu, Zhangjiagang, Kunshan, Taicang, Wujiang, and Suzhou). The temporal distribution of precipitation in Suzhou is extremely uneven. The areal mean annual precipitation is 1086 mm, with the maximum and minimum of 1519 mm and 598 mm, respectively. 2.1

Current water resources estimation

Situated in the plain region of the Taihu Lake basin, Suzhou is also famous for the complex river network system. There are more than 20 thousand rivers and canals, 323 lakes and 41 main rivers flowing into the Yangtze River. The total local water resource in the study area is 2.656 billion m3 , of which 2.443 billion m3 is surface water resource and 0.937 billion m3 is groundwater resource. The computation repetition between surface water and groundwater is

Figure 1. Location and Map of Suzhou.

0.724 billion m3 . The Taihu Lake drainage area is one of most densely populated areas in China and even of the world. The mean individual water resources in Taihu Lake basin only account 18% of that of the country, and water resources for every unit of area only account 13% of that of the country. It is still under the national average level, even though now it has water diversion from the Yangtze River. In the year 2000, the total water supply of the study area, including transferred water from the Yangtze River and the Taihu Lake, is 5.16 billion m3 , of which 4.99 billion m3 is from surface water and 1.67 billion m3 from groundwater, respectively contributing 97% and 3% to the total water supply. As one of the fast developing areas in China, Suzhou is famous for “world factory”. During the Five-Year Plan from 2000 to 2005, GDP increased from US$19.3 billion to US$50.3 billion (Statistic Yearbook of Suzhou 2000, 2005). More and more people move to this region to seek work opportunities and thus population in Suzhou increased from 6.79 to 8.26 million in the five years. With the rapidly social and economic development, water usage of Suzhou is undergoing profound changes (Table 1): firstly, the water demand grew rapidly in general; secondly, in terms of the structure of water usage (Table 1), the agricultural use decreased year by year, thirdly, the industry water demand increase year by year. In a word, Suzhou is undergoing transformation from agricultural to municipal and industrial sectors. Of the total water use in the study area, agricultural, industrial and domestic water use is 2.485 billion m3 ,

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Table 1.

Structure of water usage in Suzhou (unit: 108 m3 ).

Year

electricity common Agriculture industry industry Domestic Total

1997 1998 1999 2000 2001 2002 2003 2004

27.9 27.23 23.58 26.34 23.89 22.77 20.62 19.61

15.07 15.74 14.56 18.06 18.49 19.87 22.4 25.58

4.73 4.77 5.52 6.52 7.03 7.33 8.98 11.08

4.62 4.27 4.39 4.75 4.79 4.88 5.04 5.19

unit; secondly, the river ,lake, waterworks water-lifting devices, pumping stations, municipal sewage plants were described as nodes, and directional lines that stand for river channels and diversion canals were used to link the nodes to constitute water supply system. Thirdly, based on the topological relations, mathematic models can be established and the water balance and water supply-demand balance can be simulated in terms of different schemes in different target years. In a word, the basic principle of the model is to simulate the real water resources system and analyze the dynamic water supply and demand balance based on the topological relations of water resources system. (Pei et al. 2005, Wang et al. 2004).

52.32 52.01 48.05 55.67 54.2 54.85 57.04 61.46

2.2 billion m3 and 0.476 billion m3 , contributing 48.2%, 42.6% and 9.2% to the total water use. In detail, of the 0.476 billion m3 of domestic water use, urban and rural domestic water supply respectively reaches 0.331 billion m3 and 0.145 billion m3 , contributing 69.5% and 30.5% to the total domestic water supply. Problems in the exploitation and utilization of water resources in Suzhou are mainly as follows: (1) Water pollution has played a negative part in the security of water supply. In the year 2000, only 61.2% polluted water was treated and the centralized treatment rate was merely 10.1%. Since a large quantity of industrial wastewater and urban polluted water is discharged directly into surface water, rivers have been severely polluted. Consequently, less clean water resources are available and more polluted river network makes it difficulty in water transfer from theYangzte River and the Taihu Lake to the inner parts of Suzhou, e.g. Kunshan county. (2) Water loss is tremendous in the municipal water supply system The leakage loss of the municipal water supply pipeline network contributes 13.7% to the total water supply and 15.9% to the effective water supply. The reusing ratio of industrial water use in the study area is low, which is only 58.2%. Wasting water use has intensified the conflict between supply and demand of water resources. (3) Serious groundwater overdraft. The groundwater overdraft in Suzhou is severe, which has resulted in groundwater depression. The depression in the center has exceeded 1 m. 3 WATER ALLOCATION MODEL 3.1

Model principle

The water system in Suzhou is a large-scale complex system with multi-sources, multi-projects and multi-users, including water demand subsystem, water supply subsystem, water transmit subsystem and water drainage subsystem, so we should describe the water resources system in brief. Firstly, the agricultural, industrial, domestic and eco-environmental water demand were described as one water utilization

3.2 Model formulation The model is concentrated on water balance and water supply-demand balance of the water system with multi-sources and multi-users. The main equations are water balance equation and water supply-demand balance equation. The basic unit balance equations will be described in the following parts. 3.2.1 Water balance equation The water balance equation can be described as follows:

t = the amount of j waterworks supply where WSGDjik t = the to k unit in the i period of the t target year; WHjik amount of j treated water reuse device supply to k unit t in the i period of the t target year; WYGjik = the amount of j diversion works supply to k unit in the i period of t the t target year; WZBjik = the amount of j waterlifting device supply to k unit in the i period of the t target year; MDZkt = the group of waterworks supply to the k unit in the t target year; MHKt = the group of treated water supply to the k unit in the t target year; MYGkt = the group of storage works and diversion water devices supply to the k unit in the t target year; MZBkt = the group of water-lifting devices supply to the k unit in the t target year and WXSkit , WXGkit , WXNkit , WXSHkit are the domestic, industrial, agricultural and eco-environmental water demand in the i period, k unit of the t target year; m = the total number of unit; n = the total number of calculation time; r = the total number of target years (Xie et al. 2005).

3.2.2 Other equations and constraints There are some other water balance equations and constraints such as the balance equations of waterworks,

425

water-lifting devices and the constraints of waterworks rated capacity, pumping station rated capacity, channel conveying capacity, municipal sewage plant rated capacity, etc. Due to page limitation, for more details please see the reference (Wang et al. 2006). 3.3

Flow chart of model calculation

According to the theories and methods of water resources system simulation models(You 2005, Yen 2001), based on the model principle, the procedure of the model calculation can be designed as Figure 2. Compared with other optimation water allocation models, this dynamic water supply-demand simulation model is mainly based on rules. All the rules and modules mentioned in the flow chart will be discussed in the following parts. 3.3.1 Water supply according to water quality Water supply of the model must keep to the following steps: (1) Firstly we assessed the quantity of surface water resources in each computation unit in different typical years; (2) Secondly, on the basis of water quality monitoring data from monitor cross-sections, we ascertained water quality classification of each monitor cross-section, according to the river length and area ratio of units, figure out the quantity of dual water supply for each computing unit. (3) As mentioned in the Water Resources Ministry law (MWR 2002) that different users have different water quality requirements: The water quality of domestic use can only be taken from the water with the quality better than grade-III. The quality of water supply for industry should be better than grade-IV, the quality of agriculture irrigation water should be better than grade-V and the quality of eco-environmental water users should be no worse than grade-V according to its specific use. In a word, the water supply should consider the quality, so do the water use, good water for good use (Wang et al. 2003). 3.3.2 The priority of water use and water sources The priority order of different water sources and different water users are as follows: Firstly, the priority order of water sources is that local water and treated water are firstly used, and the water diverted from theYangtze River and Taihu Lake are used secondly; Secondly, the priority order of water users is domestic, industrial and agricultural, and eco-environmental water use in turn; Thirdly, due to the rapid decline of the groundwater table, the utilization of ground water is forbidden by law, which can only be used for emergency. 3.3.3 Mixture model of water quantity and quality When water transfers from one unit to another (or from the Yangtze River and the Taihu Lake), or the treated water in the unit is used for special water sources, the intake unit water quality will be changed when

Figure 2. Calculating steps of water allocation model.

the transferred water come into this unit, the mixture model is as follows:

Where, C0 is the original water pollution consistency, Cq is the water consistency of other transferred units (or polluted water consistency), V0 is the river volume, Vq is input river volume. 3.3.4 Allocation among units As illustrated in Figure 2, the model firstly carries out the water demand-supply balance calculation for each unit. If the water supply can not meet the demand in each unit, the allocation among nearby units should be considered because water can be easily diverted from one unit to another in such a connected river network area. Hydraulic connection may exist through either natural or artificial channels. Since it is difficult to estimate the water exchange (Wang et al. 1999), the estimation can only be made through a simplification defined by the levels of hydraulic connection among units. The levels of hydraulic connection can be determined and classified by natural and artificial river channel capacity. The water allocation among units with higher level of hydraulic connection has the priority. 4

RESULTS AND DISCUSSIONS

4.1 Schemes setting According to the rules that the demand, supply and water projects distribution of the schemes should be

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Table 2.

Results of water resources allocation (unit: million m3 ). Water supply

Scheme

Target year

Demand

supply

Deficit

Rd*(%)

Local

Yangtze

Taihu

Treated

Transferred

A

2010 2020 2030 2010 2020 2030 2010 2020 2030 2010 2020 2030

6093 9213 14083 4592 5611 6650 4592 5611 6650 4592 5611 6650

5118 5988 6619 4500 4938 5054 4592 5611 5985 4592 5611 6650

975 3225 7464 92 673 1596 0 0 665 0 0 0

16 35 53 2 12 24 0 0 10 0 0 0

2098 1976 1920 1800 1679 1516 1837 1683 1496 1837 1683 1463

1791 2276 2581 1575 1876 1971 1561 1908 1975 1561 1908 1995

1228 1737 2118 1125 1383 1567 1102 1403 1556 1102 1403 1596

0 0 0 0 0 0 92 617 958 92 617 931

0 0 0 0 0 0 0 0 0 0 0 665

B

C

D

* Ratio of water deficiency

representative, considering the characteristics of water resources system in Suzhou, four schemes were set as follows: (1) Scheme A keeps the existing water projects, without any water-saving measures, treated water reuse or proposed water projects. (2) Scheme B takes water-saving measures but with no treated water reuse or proposed water projects. (3) Scheme C takes water saving measures and considers treated water reuse but with no proposed water projects. (4) Scheme D considers taking water-saving measures, make use of treated water and propose new water projects. 4.2

Results of different schemes in different years

Taking the average year (probability of rainfall P = 50%) as an example, the balance results of model are shown in Table 2. Based on the analysis of the balance results, we can come to the conclusions as follows: (1) In terms of scheme A, the water demand cannot be met in the target year, and the water shortage is mainly caused by water quality and limited capacity of water engineering. The spatial distribution of water shortages can be described as follows: the units around the Yangtze River are very light, the units around the Taihu Lake are much heavier, and the units in the middle are the heaviest. The reason is that the water supply sources of all the units mainly depend on non-local-water, the Yangtze River and the Taihu Lake.As a result, the units near theYangtze River can access the most plentiful water and the units near the Taihu Lake can access more water than the middle units. (2) In terms of scheme B, the water-saving potentials are higher, so the ratios of water shortage become obviously lower. (3) In terms of scheme C, both water-saving and treated water reuse measures are taken, the water shortage is improved to a certain

extend, the water deficit is zero in 2010 and 2020, but the amount of water shortage in 2030 reaches to 0.665 billion m3 and the water shortage ratio is 10%. (4) Scheme D is based on scheme C, adding proposed water supply projects, all the water demands can be meet in this scheme. From the discussions above, we can draw conclusions as follows: In the near future, water saving and reuse of treated water are the most important solutions for the water problems of Suzhou. In the far future, based on water saving and reuse of treated water, more water supply projects should be built to meet the requirements of water demand. 4.3 Analysis of different water allocation schemes Taking the average year (probability of rainfall P = 50%) as an example, the evaluation indexes of different schemes in different years are shown in Table 3. In the target years 2010 and 2020, if scheme C was selected as a recommended scheme, the water supply Per capita is 459 m3 and 510 m3 , in this case, it has achieved the target indexes of water-saving society of Jiangsu province, China. The ratio of water deficiency is zero, so scheme C is a rational scheme for 2010 and 2020. In the target year 2030, if scheme D was selected as a recommended scheme, the water supply Per capita is 550 m3 , which also achieved the target of water-saving society of Jiangsu province. The ratio of water deficiency is 0, furthermore, the ratio of local water resources to the water from Yangtze River, the water from the Taihu Lake, treated water and transferred water is 11 : 15 : 12 : 7 : 5 that is also reasonable. In conclusion, scheme D is a rational scheme for the future year 2030.

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Table 3. The evaluation indexes of different schemes in different target years.

Target year

Scheme

Water supply per area (m3 )

2010

A B C D A B C D A B C D

547 481 491 491 640 528 599 491 707 540 639 710

2020

2030

5

Ratio of water sources (%) Local

Yangtze

Taihu

Treated

Transferred

Ratio of water deficiency (%)

41 40 40 40 33 34 30 30 29 30 25 22

35 35 34 34 38 38 34 34 39 39 33 30

24 25 24 24 29 28 25 25 32 31 26 24

0 0 2 2 0 0 11 11 0 0 16 14

0 0 0 0 0 0 0 0 0 0 0 10

16 2 0 0 35 12 0 0 53 24 10 0

CONCLUSIONS AND RECOMMENDATIONS

In this paper, the water resources in Suzhou were estimated, a water allocation model based on supplydemand balance simulation was established and the analysis of water supply-demand balance was made in terms of different schemes in different target years. The methods applied here for estimating and allocating water resources and assessing the severity of water shortage proved practical and useful. From the above results and analysis, we can draw the conclusions as follows: (1) Water-saving and treated water reuse play an important role in the demand and supply balance of Suzhou. (2) In the future 30 years, Suzhou would be confronted with serious water resources crisis, the problem in water quantity does not exist, but the water quality-induced water shortage and water projects shortage are very serious. There are three ways to solve water shortage problem. Firstly, we should increase the potential of water saving to promote utilization factor of water resources continually. Secondly, we should continue to promote the ratio of treated water reuse and improve the water quality of inland rivers. Thirdly, in order to solve the problem of water projects shortage, we should protect the water quality of Yangtze River and Taihu Lake, and build more diversion works and water-lifting devices. ACKNOWLEGEMENT This paper is supported by the Key Project of Chinese Ministry of Education (NO. 306012). REFERENCES Fang, H.Y. & Chen Z.C. 2007. Methodology for water resources supply-demand and rational allocation in

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Policy evaluation of China’s pollution charge system with a measurement adjusted by water quality M. Morisugi∗ Faculty of Urban Sciences, Meijo University, Japan

N. Sawazu Japan-China Economic Association, Japan

A. Onishi Research Institute for Humanity and Nature, Japan

ABSTRACT: We analyze China’s Pollution Charge System (PCS) to quantitatively evaluate the regional impacts on pollution control incentives of industrial wastewater. Firstly, we specified the cost function of industrial wastewater treatment in China from 1992 to 2000. Secondary, the water quality-adjusted marginal abatement cost (QAMAC) curves are estimated in 2000 for 29 provinces. Finally, Stochastic Frontier Analysis (SFA) is adopted to identify factors affected the cost efficiency. Keywords: PCS; QAMAC, SFA; wastewater treatment; cost function; water quality; effectiveness of environmental policy

1

INTRODUCTION

This study demonstrates an empirical analysis of Pollution Charge System (PCS) has been enforced in China. Due to recent rapid economic progress, as typical example of Yellow River Basin, water resource shortage and pollution problem have become so serious matter in the country. Such an economic instrument against the water environmental problem is rather progressive and remarkable approach especially for the developing countries, therefore, one of the most notable policies in the world. The findings from previous studies about PCS are controversial mainly in two contexts mentioned below (see Florig et al. (1995), Sinkule and Ortolano (1995), Yun (1997)). One indicates much poor governmental abilities to monitor over all the performance of target industries and sewage treatment plants. The other one argues that the charge rate is quite low. These two points of criticisms are the same about claiming that PCS is not so effective policy. But, it is also true that both of them have not enough objective foundation to prove it. Their results are not ones of comprehensive and quantitatively detected. ∗

Corresponding Author ([email protected])

On the other hand, there are some precedent researches that have recognized the effectiveness of PCS (Jiang and Mckibbin (2002), Wang and Wheeler (2003)). As the same manner, our main contribution in this paper is also to afford PCS a positive evaluation with some econometric models and tests. However, it is noteworthy that the effectiveness of PCS could be acknowledged in only short-time meaning rather than long-run, as mentioned in later part of this paper. In the following chapter, we propose a method to evaluate the effectiveness of PCS in short-term meaning. In particular, impacts of PCS on water quality control incentives of these wastewater treatment principals are examined. To do so, at first, the short-term cost function of wastewater treatment, that is, each provincial representative agent (29) in China is specified, during the period from 1992 to 2000 (See Figure 1 of spatial distributions of provinces). After the most general panel data analysis was applied, water qualityadjusted marginal abatement cost (QAMAC) curves are also derived. These are based on the assumptions that each representative principal minimized their cost at each tax rate and discharged wastewater quantity confronted, and that there was technological substitutability between water quantity and quality. Because PCS is levied on criteria about pollution density of treated water, this assumption assures that the principal

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Figure 1. Spatial distributions of provinces in China.

make more effort to purify it than ever. By comparing the QAMAC curves and the actual levels of collected discharge fee, we found that however policy implications varied by each region, but also PCS is seemed to provide sufficient incentives over all. Furthermore in the third chapter, Stochastic Frontier Analysis (SFA) is applied to the estimated cost equation mentioned above. One of the main alternative explanatory variables is investment for wastewater treatment, which may be key point to evaluate the dynamic effect of PCS. However, because of insufficient volume of these data, we gave up to estimate the cost function above with an explanatory variable of investment directly. Instead of that, with the method of SFA, several social causing factors of inefficiency are extracted here. However one may find that PCS promotes the incentives to invest, but contribution to the cost efficiency is hardly recognized. In the last chapter, main results of this paper and brief comments are attached.

2

ESTIMATION OF COST FUNCTION

2.1 Theoretical foundation There are two types of PCS have been levied actually, so-called, Wastewater PCS and Excess Criteria PCS. Especially, the latter one occupied so much share of PCS payment (more than ten times of the former about accumulated payment of the past 20 years, furthermore, actual levying rate and payment obligation is much heavier), and it has been levied for the excess value of criteria about pollution density of water treated. Our research also focuses on this one. As for the system’s criteria bases on pollution density, the matter of water quantity is considerable enough, but the variable of water quality is rather important here. By right, wastewater treatment principals may have alternatives of technology, for example,

they can choose a way to treat the wastewater much cleaner with higher cost per unit volume. Now we propose our theoretical model in that each principal is assumed to behave rationally when he confronted the tax rate levied given by the government, another to say, the price of water quality. Let suppose, variable x is composite good of all other inputs and numeraire (the price is 1). The principal also can select the quality of water treated, q, the term is defined here as a parameter of the excess degree to the criteria determined legally. He should treat the amount of wastewater given, and dispose the amount of water treated, y. Treatment technology is described as a function of f , on the isoquant curve of which, substitutability between x and q is assumed. Also, each facility level k is predetermined variable. The cost that we now concern is just short-term meaning or annual running cost, therefore, what the principal has to pay is how much he bought the composite good as inputs and how much water treated is disposed at quality q on the price of t (the fixed tax rate of Ad Valorem Duty is assumed here). Then, the principal solves such a cost minimizing problem shown as below;

The direct solutions of the problem above is described with an asterisk *, as x*(t, y, k). Now from q*(t, y, k), we derive the implicit function ω of tax rate explained by y, k, and the solution q*. Rewriting the cost function;

The cost of this form is explained by the quality level itself that each principal has selected (unusual manner of the general cost function), the amount of water treated, and facility level. Also, the structure of the regression model to be estimated (in the next section) also obeys to this form. (Note; Estimation also can be done with the actual tax rate instead of water quality, but the type of equation (2) is much better to fit and the estimates are more significant for the data set available currently.) Furthermore, g is differentiated with respect to q*, with Shepard’s lemma as below;

After some simple calculations, we have;

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The left hand of the equation (4) shows degree of the principal’s willingness to pay in total when one decreases a marginal unit of the quality of water treated. In this model, higher as the variable q becomes, easier the principal can treat wastewater and lower the cost is. But besides, he should have more tax payment. From now on, this term is referred as Quality-Adjusted Marginal Abatement Cost (QAMAC). The term in the bracket on the right hand of the equation (4) is inverse and absolute number of the water quality elasticity with respect to the tax rate, and yω is tax payment per q. We will look back again on this argument. 2.2

Table 1. Variable sets. VCr : TREATr : EXCEEDr : CAPACITYr : ε:

variable cost of wastewater treatment principal in region r per year (×104 yuan) the amount of wastewater treated in region r per year (×104 ton) (the amount of wastewater treated exceeds the criteria)/(the amount of wastewater treated) in region r per year ×100 (%) average capacity of plant (×104 ton) error term

λ from −1 to 1 (grid search method, see Box and Cox (1964)).

Empirical analysis

As mentioned before, the aim of this study is to examine effectiveness of PCS through behavior of each hypothetical representative principal of wastewater treatment in 29 provinces of China. Their reaction to PCS may be acquired from the national macro data book titled “Annual Statistics of Environment in China” during the periods from 1992 to 2000. By pooling the data mentioned above, we now begin to specify and estimate the cost function of each principal. To be consistent with our theoretical model in the previous section, we set the explained variable as annual running cost (or, short-term variable cost) for each principal, and explanatory variables as water quality, the amount of water treated, and a parameter of each facility’s scale. For the value of water quality, the excess degree of water treated to the criteria about COD determined by PCS is used. For the value of facility’s scale, the total amount of industrial wastewater treated divided by the number of the treatment plants, average capacity of plant in each region is substituted. To specify the function form of the regression model, Box-Cox transformation methodology is taken here, the primitive variables Zs are redefined as X s and Y s;

The regression model is rewritten also, then;

Now, the error term ui is assumed to be mutualindependently identically (iid) normally distributed, mean is 0 and variance is δ2 . The parameter λ is intended to maximize the concentrated likelihood function of equation shown below (that is the form of the log-likelihood function which was assigned value of each maximum likelihood estimates of β and δ conditioned λ as given arbitrarily) for every 0.1 steps of

Where, n is the number of samples, and e is residual. In this processing, nevertheless the best matched value of λ was around 0 or 0.1 (there is unique solution because the function is monotonic with λ), to prefer calculation tractability, set λ to equal 0 here. Therefore the regression model has a characteristic of Cobb-Douglas form (log-linear), like as,

As the usual manner on handling such a panel data set, 3 type models are considered to examine the individual effects, plain OLS, fixed effect, and random effect. The results are shown in Table 2. At Table 2, one can see that priority of fixed effect model compared to plain OLS with F statistics, and also that priority of random effect model compared to fixed effect model with Hausman test. In fact, these estimates of random effect model are all significant at least 5% critical level, theoretically consistent that the sign of α1 is plus otherwise are minus (see equation (2)), and from the value of Adj-R2 , it seems to be fitted well. Another interpretation of these estimates of coefficient is the elasticity of VC with respect to each explanatory variable, because of log-linear structure of this model. Hence, first partial differentiation of the estimated cost function estimated above with respect to the variable of water quality, EXCEED, is just that we say, QAMAC (see equation (4)).

As shown in Figure 2, QAMAC is depicted for each region of year 2000 as a function of EXCEED. The

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Table 2.

Results of estimation.

Coefficients

Fixed Effect

α0 α1 α2 α3 Adj-R2 Hausman Test (p-value) Fixed model versus Plain OLS

1.257** (9.395) −0.120 (−1.429) −0.695** (−4.171) 0.914

Random Effect −0.436 (−0.515) 1.196** (16.365) −0.158* (−2.095) −0.619** (−4.749) 0.817 0.597

F(28,229)= 11.147

Notes: (1) Values in parentheses are t values. (2) Asterisks (*, **) represent 5%, and 1% significance, respectively. (3) The total number of observations is 261.

height of the curves is willingness to pay of principals when he could increase marginal unit of EXCEED according to his treatment cost diminishing. Unit of vertical axis is 10 thousands yuan. The value of EXCEED is plotted on horizontal axis, as percentages from 0 to 100. Now we pick up on the previous argument of equation (4). The effectiveness of PCS may be supported only when the principals are recognized to treat wastewater much cleaner than ever. And this matter should be obvious automatically if principal’s cost minimizing behavior such that we suggest in section 2.1 has been confirmed. Looking over QAMACs in the Figure 2, one can see not so robust, but rather strong evidence as a trace of such behavior. Because the cost function is Cobb-Douglas form, a similar structure of the original production function is also established. Then, equation (1) is rewritten as;

After simple calculation, compared with the equation (7) of the regression model, we have such a relationship.

Hence, the tax rate elasticity of water quality at equation (4) is derived.

Figure 2. QAMAC curves for each provinces of 2000 year.

In Figure 2, horizontal broken line is also drawn for each province, that is, multiplied inverse of the elasticity σ with sum of tax payment divided by the actual EXCEED (it is to say, real tax rate on water quality base) of 2000 year, corresponding with right hand of equation (4). And also vertical broken line is there, the actual EXCEED of 2000 year. Unlike the usual cost function, the proposed one of equation (7) has no direct information of actual tax rate or revenue as explanatory variables. Therefore, EXCEED value on the cross point of horizontal broken line and QAMAC is so–called predictive value on assumption of each principal’s rational behavior. Over all of the provinces, these two points are nearby and “adjust”, so our proof might be done successfully. In addition to that, the description of “right” or “left” on the figure means where the cross point of the two broken lines locates compared with QAMAC, therefore, the former is underestimated and the latter is overestimated. 3 3.1

STOCHASTIC FRONTELIER ANALYSIS Further question

The results in the last chapter show that each representative regional principal seems to react rationally to the

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actual tax rate of PCS. Nevertheless, there are also not small differences of performance between provinces. As mentioned above, the predicted value or potentially attainable level of EXCEED and actual level of EXCEED were diverged for some regions as Xinjiang and Liaoning, and effectiveness of PCS is recognized hardly so far. Therefore, one may ask for reasons other than these factors which have been picked up at Table 1 to explain such regional difference between empirical results and theory. To solve this problem, in this chapter, the cost efficiency of wastewater treatment for each province is examined with the SFA (Aiger et al., 1977; Meeusen and Julian, 1977); then several factors that affect the efficiency of cost are identified. In this approach, a cost function of stochastically uncertain is assumed, and divergence of each data from the frontier f is sorted into two categories, error terms and inefficiencies, expressed as:

Where, VC it is variable cost function in province i and time period t, X it are explanatory variables same as shown in Table 2, β are coefficient parameters, Vit is random error term that is mutual-independently identically (iid) and normally distributed ∼N(0, θ 2v ), Uit is non-negative random error term reflecting technical inefficiency and assumed to be half-normally distributed (iid, ∼N + (0, θ 2u )). Then, the equation (7) is rewritten as;

Table 3.

Results of β estimation (1st Step of SFA). ML plain OLS Coefficient

Variable Treat Exceed Capacity Constant Term

1.158*** (30.182) −0.286*** (−3.606) −0.529*** (−5.745) 0.093 (0.166)

θ2 Adj-R2 Log-likelihood

Coefficient 1.194*** (36.799) −0.388*** (−5.449) −0.539*** (−6.518) −0.536 (−1.123) 0.755*** (7.634)

0.820

0.032 0.071 0.083 0.477 0.099

−212.766

Notes: (1) Values in parentheses are t values. (2) Asterisks (*, **, ***) represent 10%, 5%, and 1% significance, respectively. (3) The total number of observations is 261.

Table 4.

Results of exp(U it ) regression (2st Step of SFA).

Variable

Coefficient

1. GRP of the secondary manufacturing

1.111 (1.413) −1.617*** (−3.676) −0.602 (−0.766) 2.059** (2.392) −0.139 (−0.299) −1.001* (−1.719) 5.365** (2.084) 0.569

2. amount of recycled water for industrial use 3. total tax payment of both PCS 4. repayment of PCS

By adopting the maximum likelihood method to the equation above, we have the result of estimation for the 1st step of SFA. Then for 2nd step procedure of SFA (it is to say, extracting explanatory factors of exp(U it ) = TE it ), the residuals for each sample are calculated, OLS method is applied again with the candidate explanatory variables introduced from several published data sources. About specification problem, Cobb–Douglas and Translog forms are also often used for SFA. The later form is the most flexible one, but because the multi-collinearity is likely to occur with the candidate explanatory variable, Cobb–Douglas form is also assumed here. The result is in Table 4. Unfortunately, as some data sources (ex. amount of annual facility investment) are available for only 2000 year, so the number of observation is 29 at most. The final sets of factors those may affect largely on cost efficiency have been chosen by the criteria about statistically significance or the degree of mutual correlations. As shown on Table 4, 6 variables have been selected finally.

Standard error

5. amount of investment for wastewater treatment projects 6. total number of petition Constant Term Adj-R2

Notes: (1) Values in parentheses are t values. (2) Asterisks (*, **, ***) represent 10%, 5%, and 1% significance, respectively. (3) The total number of observations is 29.

In this case, the smaller value of the coefficient is, the better it contributes for the cost efficiency. Therefore, only the variables of 2 and 6 seem to be significant and desirable factors (especially, the former one is rather robust and stable comparing with the other models that have been tried ever). These variables may exhibit the degree of scarcity of the water resource in the subject region also. Referring to the

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variables 1, social and economic circumstance doesn’t seem so influential. The variables of 3 and 4 are the matter related directly with PCS. As mentioned in the section of 2.1, there are two types of PCS to be levied on wastewater treatment. The variable of 3 is actually sum of payment for both types in each province, so far as the model of equation (12) has already considered most of PCS’s political effects, it is not so embarrassment that the estimate is insignificant. However about the variable of 4, there is also repayment or subsidy system in PCS that locate tax revenue as a fund to invest against several environmental problems, and the result shows that the coefficient is + and significant. The reason of the fact may largely depend on the high correlation between the variables of 3, 4, and 5 (over 0.8 of correlation coefficients) and on lack of the data, so the exact value allocation of these coefficients is undetermined at the moment. From these findings, one may recognized that PCS prompts the investment to the wastewater treatment facilities, but the improvement contribution for the cost efficiency, or a long-run effect of PCS, can not be discerned yet. Now, let VC F is the frontier of cost function which can be obtained by using the estimates of coefficients obtained at Table 3 to the equation (12) and ignore the terms of Vit and Uit . Also, Yˆ it is the estimate of VC it itself obtained by using the estimates of coefficients obtained at Table 3 and 4 to the equation (12). Then technical measurement of the cost efficiency (TE, ≥ 1) is defined here, as;

Figure 3. Technical efficiency of the variable cost with SFA.

The value of TE can be calculated straightly with the data of each province of 2000 year. But on this time, by using the coefficients of Table 4, we have derived the value of TE for other time t than 2000 year too as for helpful information, and then taken average. The results are collected up to Figure 3. The description “high” on the Figure means that PCS tax payment per unit water treated is large enough compared with average, and vice versa. On examining these results, the provinces of Liaoning, Jilin, Heilongjiang, Guangdong, Gansu, Qinghai, and Xinjiang show the low efficiency, and the ones of Beijing, Tianjin, Hebei, Inner Mongolia, Jiangxi, Shandong, Henan, Hubei, Guangxi, Hainan, Sichuan, and Ningxia are high. Looking back on Figure 2, for many of the member of the first group, the cross point of two broken lines locates on the right side of QAMAC. This means, PCS is not so effective as much as the average one in China for these regions. And in fact, actual tax rate or payment is rather lower level in some of the group (however, the viewpoint is not appropriate for all of them). In contrary, for most of the member of the second group, the cross point of

two broken lines locates just on or on the left side of QAMAC, means that PCS is effective greater or equal to the average. To be summarized simply, as the contents of Table 4 show, the regional heterogeneity about PCS effectiveness depends heavily on the water resource environment of each region.

4

CONCLUSION

This study has examined the political effectiveness of PCS enforced recently in China with a few empirical methodologies. In chapter 2, because the criteria for taxation of PCS is density of pollution about wastewater treated, the cost function that is the type of water quality-conscious has been specified and estimated. Then QAMACs were derived for each provincial representative principal of wastewater treatment. Comparing with the actual tax rate and the level of water quality attained,

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our proposed model and the hypothesis of principal’s rationality seemed to fit well to the observed behavior, so PCS’s effectiveness to reduce water environmental load was inferable sufficiently at least in short-term meaning. Furthermore in chapter 3, with a method of SFA and the similar model mentioned above, the factors that had affected the cost efficiency were extracted. Due to highly correlation between data sets, only two reliable explanatory variables were found, and these ones indicate the scarcity degree of water resource in that province, neither shortages in investment for facilities nor lowliness of tax rate were persuasive. While especially, the later part of analytical results in this paper depends on current availability of data, as the term of investment related. This point is left as unsolved matter yet and more energetic investigation for dynamical effectiveness of PCS is expected.

Sinkule, B. & Ortolano, L. 1995. Implementing Environmental Policy in China. 226. Yun, P. 1997. The Polution Charge System in China: An Economic Incentive?. Economy and Environment program for Southeast Asia, Research Reports (www.eepsea.org). Jiang, T. & Mckibbin, W. J. 2002. Assessment of China’s Pollution Levy System: An Equilibrium Pollution Approach. Environment and Development Economics, 7(1): 75–105. Wang, H., & Wheeler, D. 2003. Equilibrium pollution and economic development in China. Environment and Development Economics, 8(3): 451–466. Box, G.E.P. and Cox, D.R. 1964, An Analysis of Transformation, Journal of the Royal Statistical Society, Series B, 26, 211–243. Aiger C. A., Knox L. & Schmidt T. 1977. Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics, 6: 21–37. Meeusen W. & Julian B. 1977. Efficiency Estimation from Cobb–Douglas Production Functions with Composed Error. International Economic Review, l. 18: 435–444.

REFERENCES Florig, K., Spofford, W., Ma, X., & Ma, Z. 1995. China Strives to Make the Polluter Pay:Are China’s Market-based Incentives for Improved Environmental Compliance Working? Environmental Science and Technology, 29(6): 268–273.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Prediction of water resource carrying capacity of Changchun city, northeast China, based on BP neutral network method W. Su∗ & T. Matsumoto Faculty of Environmental Engineering, the University of Kitakyushu, Kitakyushu, Japan

J.S. Liu & J.X. Dou Northeast Institute of Geography and Agricultural Ecology, CAS, Changchun, China

ABSTRACT: The prediction model for water resource demand of Changchun was established by the improved BP neural network algorithm of MATLAB based on the data from 1995 to 2004. According to the social and economic development goal of Changchun which was set in the period of the ‘eleventh five-year plan’ (2006–2010), the water resource demand of Changchun from 2007 to 2016 was predicted by the model. The results showed that the water resource carrying capacity will not be able to meet the demand for social and economic development in Changchun in the next ten years by comparing present water resource supply with water resource demand. In order to realize the harmonious development between resource, society and economy, some suggestions were proposed in terms of source developing and consumption reducing. In view of the deficiency of BP neural network we introduced the ‘trial and error’ method to determine the number of connotative neural cell. Keywords: 1

BP neural network; water resource; demand; carrying capacity; prediction; Changchun

INTRODUCTION

Water resource is a kind of fundamental natural resource which affects the environment for human survival. However, it is also a strategic economic resource with fundamental influences on the development of social economy of a region or a country. Due to the development of social economy, population increasing and demand in industrial and agricultural production, etc., water resource demand has been increasing gradually, which seriously challenged the balance of water resource demand and supply. Water resource carrying capacity has become a fundamental issue in the research of water resource security strategy (Daily & English 1996, Kuylenstierna et al. 1997). Water resource carrying capacity is determined by the systematic structure of water resource. It can reflect the relationship between water resource and human activities. Research on water resource carrying capacity involves a complex feedback system with numerous factors including social, economic, environmental, ecological factors and resource. The research on carrying capacity of water resource is to solve the problems such as what amount of available water resource can be obtained under certain economic and ∗

Corresponding author([email protected])

technical conditions, so that the stable economic development would be maintained, and what amount of population could be fed under certain water demand based on different living standards, which is closely related to the research on the balance between the demand and supply of water resource. The research on water resource carrying capacity in China started in 1985. There are fuzzy synthetic evaluation method (Xu, 1993), principal component analysis method (Fu, 1999), systems dynamics method (Li et al. 2000), regular trend method (Wang & Liang, 2001), multiobjective analysis and evaluation core model (Xu, 2003), etc. However, the research on the prediction of water resource carrying capacity by neural network is scarce. In this research, BP neural network is adopted in the establishing of the water resource demand prediction model. Socio-economy system is a complex system which is composed of many subsystems. It is not a simple superposition but a complex coupling of these subsystems. So it is not scientific to use the simple way of thinking which was used to study traditional system before to study complex systems .Under the situation of not knowing the relations of all relevant factors in complex system , BP neural network provides the possibility of modeling. Through study and training known samples, BP neural network can simulate the interaction of the factors in complex system

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and establish the prediction model of complex system to get optimal results. Water resource carrying capacity is reflected by the balance between the demand and supply of water resource in the paper. The prediction of the water resource carrying capacity of Changchun in the next ten years was carried out in order to provide a reference for the harmonious development between regional social economy and water resource.

2

METHODS

2.1 The Principle of BP Neural Network Neural network is a network formed by extensive inter-connection of numerous processing units (neural cell). It grows from subjects of neural science, mathematics, statistics, physics, and computer science and engineering, etc., which is fundamentals of massive information parallel processing and massively parallel calculation (Martin et al. 2002). In recent years, as the prompt development of neural network technology, significant success and progress has been realized in intellectual control, pattern recognition, computer vision, non-linear optimization, signal processing, etc., which has currently become one of the critical fields of research on artificial intelligence (Simon, 2004). Under the condition of fuzzy correlation of various correlated factors in complex system, neural network has provided possibilities in complex system modeling (Shi et al. 2002). Through learning and training of known samples, the neural network can simulate these rules, and then the prediction model of complex system is built, so as to obtain the result which is nearly optimum. Currently, BP (Back-Propagation Network) model proposed by Rumelhart in 1985 is one of the most general neural network models. The multi-layer BP network model has afferent nodes and efferent nodes, and it additionally includes one layer or multiple layers of connotative nodes. Kitahara’s research has proved that, in the BP network model with three-layer network structure, arbitrary continuous mapping can be realized (Kitahara et al. 1992). As is shown in Figure 1,

complete connection has been realized between various neural cells of different layers, namely, each neural cell in the down layer is completely connected with every neural nerve in the up layer, however, the neural cells within the same layer does not connect with each other (Chen & Zhou, 2000). In Figure 1, Aj is the afferent vector of afferent layer; j = 1, 2, . . . m, m indicates the number of afferent layer; Oi is efferent vector of connotative layer; i = 1, 2, . . . s, s indicates number of connotative layer; Cp is efferent vector of efferent layer; p = 1, 2, . . . n, n indicates number of efferent layer. Oi and Cp are respectively expressed by Formula (1) and Formula (2):

Where, Wij is the connection weight between afferent layer and connotative layer; Vpi is the connection weight between connotative layer and efferent layer. In Formula (1) and Formula (2), f is Sigmoid function.

When BP neural network is operating, error backpropagation method is adopted. E is the error between efferent Cp and expected efferent Cp∗ of the efferent layer. If E does not reach the required precision, it studies from back-propagation of afferent layer, and weight values Wij and Vpi are introduced and upgraded to drive the error function into negative gradient until the error between Cp and Cp∗ satisfies requirement. The parameters obtained through studying are used to establish mathematical model to predict unknown samples. It is assumed that error of efferent layer is Gp ; error of connotative layer is Fi ; η1 and η2 are studying rate; α1 and α1 are momentum factors. The functions are following:

Figure 1. Three layers BP Neural Network structure.

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In BP neural network algorithm, minimum deviation between the sample afferent and expected afferent is learned as a target, so as to drive the weight error into negative gradient of error function. The essence of studying is to keep modifying weight value, so as to make error approach zero. 2.2

Model establishment

The prediction model of water resource demand in Changchun was established by BP neural network. In this research water resource demand was taken as the prediction factor which was composed of domestic water, agricultural water and industrial water. Many correlated economic and social indices can influence the water resource demand. We considered that there were six indices which were closely related to the water resource demand including of GDP, gross industrial production (GIP), gross agricultural production (GAP), population (PP), population natural growth rate (PNGR) and effective irrigation area (EIA). In the paper we selected the six indices to establish the prediction model to calculate the water resource demand. By considering the different dimensions of original data and orders of magnitude index value, the original data should be normalized. The following method was adopted.

factors During the network training process, the data from 1995 to 2003 were taken as training samples, and that of 2004 was taken as test value. Table 1 has shown the normalized correlated index values influencing water resource demand of Changchun. There were totally 6 of them, namely, the number of neural cells of afferent layer was 6. As mentioned above we selected six indices to establish the predictable model. So the number of afferent neural cells was six and which of efferent neural cell was one. It should be noted that in the process of training, the number of neural cells in the connotative layer was auto-adjusted. In this research, ‘trial and error’ (Raman & Sunilkumar, 1995) method was adopted, namely, the minimum value of the number of neural cells was given first. If the network falls into local minimum, it will add 1 automatically, until the network approximates convergence. By adopting this method, the number of neural cells of connotative layer was ensured minimum, so as to improve the stability of predicted results. Figure 2 was the network structural diagram of this model, in which there were 6 afferent Table 1. The normalized correlated index values related to the water resource demand of changchun. Year

GDP

GAP

GIP

PP

EIA

PNGR

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

−0.54 −0.38 −0.31 −0.22 −0.10 0.10 0.28 0.46 0.71 0.96

−0.29 −0.13 −0.09 −0.01 0.01 −0.02 0.09 0.18 0.26 0.35

−0.55 −0.41 −0.34 −0.26 −0.16 0.07 0.29 0.52 0.83 0.94

−0.04 −0.02 −0.01 −0.01 0.00 0.01 0.02 0.03 0.04 0.04

−0.04 −0.02 −0.01 −0.01 0.00 0.01 0.02 0.03 0.04 0.04

0.75 0.89 0.50 −0.42 −0.53 0.11 −0.40 −0.37 −0.51 −0.04

In the function Xi is the initial value of any kind of index in certain year. X is the average value of this kind of index during the research years. Xi∗ is the normalized value of any kind of index in certain year. By adopting the above method, each sample factor was limited in the range of [0, 1], so as to ensure the nonlinear shift effect of Sigmoid type transform function, and the sufficient afferent sensitivity and fine fitting capacity for samples of the network. 3

RESULTS

3.1 Model output Based on correlated indices affecting the utilization amount of water resource and the utilization amount of water resource in Changchun from 1995 to 2004, the neural network model for water resource demand was built by adopting the improved fast learning algorithm train lm (Levenberg-Marquardt optimization method) provided by MATLAB neural network tool box. In the model, the utilization amount of water resource was taken as prediction factor with 6 indices as influencing

Figure 2. The structure of the prediction model of the water resource demand.

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Table 2. The weight value between afferent neural cells and connotative neural cells.

1 2 3 4 5 6 7 8

1

2

3

4

5

6

−9.81 −0.18 −1.48 −0.70 −2.31 −0.25 3.03 1.05

−6.53 −0.34 −1.30 4.62 2.62 7.12 −7.11 −3.86

−9.29 −0.26 −2.16 −1.43 −1.08 0.65 0.07 −0.45

−4.37 31.20 31.88 −38.04 −6.96 −31.24 10.21 −20.28

21.20 −20.83 −11.01 2.47 −30.98 −4.45 −30.73 −12.21

4.65 −0.30 −2.52 −0.82 1.88 3.75 −1.10 −1.61

Table 3. The weight value between connotative neural cells and efferent neural cells. 1

2

3

4

5

6

7

8

−2.83 4.01 0.46 −0.21 2.88 −3.09 −5.58 −2.48

Table 4. The weight of connotative neural cells. 1

2

3

4

5

6

7

8

should be maintained at 13%; the growth rate of agricultural output should be maintained above 5%, and effective irrigation area should be ensured above 0.05. The water resource demand of Changchun in the next ten years was predicted as shown in Table 5. Table 5 showed the predicted results of water resource demand from 2007 to 2016. It can be seen from the table that the water resource demand increased fluctuating. The fluctuant reason was the fluctuate of the water resource demand from 1995 to 2004. According to the statistic data ,the average water supply capacity of Changchun was around 21.24 × 106 m3 annually from 1995 to 2004. Compared with the water resource demand from 2007 to 2016, it can be seen that the water resource carrying capacity of Changchun was far from meeting the requirement of social and economic development. As for single social or economic system, the goal of ‘eleventh five-year plan’ was perfect; however, considering the carrying capacity of water resource the obvious contradictory occurs which results in inharmonious development of society, economy and resource. Therefore, in order to ensure the sustainable development of social economy with limited water resource, appropriate measures should be taken to improve the carrying capacity of water resource.

−2.24 4.35 4.48 −3.93 −3.24 −2.83 −0.64 −5.04

4

neural cells, 8 connotative neural cells and 1 efferent neural cell. Through more than 100 times of iteration, the prediction value of utilization amount of water resource of Changchun in 2004 was obtained to be 23.7156. Compared with the actual measuring value 23.71, the relative error was only 0.2%, which indicated that it was practically applicable to establish the prediction model for the utilization amount of water resource of Changchun by adopting neural network method. Tables 2–4 showed the connection weight value between neural cells of afferent layer and connotative layer in the model built, the connection weight value between neural cells of connotative layer and efferent layer and the threshold value of neural cells of connotative layer. The threshold value of neural cells of efferent layer was 9.7130.

CONCLUSIONS

By adopting improved algorithm of BP neural network, the prediction model for water resource demand of Changchun was built. The following conclusions were reached:

3.2 The water resource carrying capacity According to the social economic development goal proposed in the ‘eleventh five-year plan’ of Changchun, GDP growth rate should be maintained at 12%; population natural growth rate should be controlled below 2.3‰; the growth rate industrial output

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(I) The BP neural network has very strong non-linear mapping ability and flexible network structure. Compared with traditional statistical modeling method, it has relatively high precision; therefore, the variation rule of each factor in complex system can be well reflected. By adopting BP neural network in modeling, the complex non-linear functional relationship between the utilization amount of water resource and its influence factors were well represented. It has high prediction precision. By employing the prediction model built in this paper, the variation trend of water resource demand of Changchun from 2007 to 2016 was accurately predicted. (II) The number of neural cells of connotative layer concerns directly to the performance of network. If the number is too small, network tends to fall into local minimum; if the number is too large, the predicted result of network is not stable. There is not existing formula for the determination of the number. It is principally determined empirically. Therefore, during the network training process, determination of the number of neural cells of

Table 5. The water resource demand during the eleventh five years plan of Changchun 106 m3 . Year

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Water resource demand

25.38

25.47

25.45

25.35

25.44

25.51

25.49

25.53

25.54

25.56

connotative layer is generally realized by adopting ‘trial and error’ method. Accordingly, appropriate minimum value of the number of neural cells of connotative layer is obtained, so that the stability of network is ensured. (III) The prediction results showed that according to the present water supply capacity and the social economic development goals of Changchun during the ‘eleventh five-year plan’ period, the water resource carrying capacity of Changchun could not satisfied the requirements of social economic development from 2007 to 2016. In order to realize sustainable development of social economy, we put forward some suggestions which can be realized from the aspects of source developing and consumption reducing: (i) improve water supply facilities to reduce wastage of water resource and step up publicity and education to enhance people’s awareness of water-saving; (ii) implement water reuse project; encourage large-scale industrial enterprises and new-built residence communities to use self-construct waste water treatment and new type of water saving facilities; promote the utilization of reclaimed water and reuse of collected rain water and waste water resource; (iii) Promote the development of water-saving agriculture, water-saving industry and water-saving service industry, prohibit the projects with heavy pollution, and strictly control the construction of high water consumption project in drought areas. ACKNOWLEDGEMENT This research was supported by the project of The formation mechanism and ecological remediation of environmental pollution in northeast industrial base in China (2004CB418507).

REFERENCES Daily, G.C. & English, P.R.1996. Socioeconomic equity, sustainability and earth capacity. Ecological Application 6(4):991–1001. Kuylenstierna, J.L., Bjorklund, G. & Najlis, P. 1997. Sustainable water future with global implications: everyone’s responsibility. Natural Resource Forum 21:181–190. Xu, Y. P. 1993. A study of comprehensive evaluation of the water resource carrying capacity in the arid area. Journal of Natural Resource 8(3):229–237. Fu, X. & Ji, C.M.1999.A comprehensive evaluation of the regional water resource carrying capacity. Resource and Environment in the Yangtze Basin 8(2):168–173. Li, L.J., Guo, H.C. & Chen, B. 2000. Water resource supporting capacity of chaidamu Basin. Environmental Science 21(2):19–23. Wang, Z.G. & Liang, H. 2001. Characteristics of water and land resource and remendy measures of ecological environment in karst mountain ares of south china. Carsologica Sinica 20(2):143–148. Xu, L.S. 2003. Water resource carrying capacity and economy coordinated development in Tianjin. Journal of Tianjin Normal University 23(1):68–72. Simon, H. 2004. Neural Network Principle. Beijing: China Machine Press. Martin, T.H., Howard, B.D. & Mark, H. B. 2002. Neural Network Design. Beijing:China Machine Press. Shi, C., Guo, Z.Y. & Xu, S.Y. 2002. Application of artificial neural network in the sustainable development predication model in coastal area. Environmental pollution and control 24(5):300–301,308. Kitahara, M., Achenbach, J.D. & Guo, Q.C. 1992.Neural network for crack-depth determination from ultrasonic back-scattering data. Review of Progress in Quantitative Nondestructive Evaluation 11,701–708. Chen, S.Y. & Zhou, M.C. 2002. Simulation and Application of Artificial Neural Networks. Wuhan: China university of geosciences Press. Raman, H. & Sunilkumar, N. 1995.Multivariate modeling of water resource time series using artificial neural networks. Hydrological Sciences Journal 40(2):145–164.

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External dependency of water supply system in Beijing: An application of water mileage Junko Aoki, Chunxiao Chen & Shinji Kaneko∗ Graduate School for International Development and Cooperation, Hiroshima University, Higashi-Hiroshima, Japan

Jin Chen College of Resource Science and Technology, Beijing Normal University, Beijing, China

ABSTRACT: Due to the significance and scarcity of water resource, a lot of water reservoirs and water transfer projects were constructed to ensure water availability of the city for its further development. However, the construction of these water supply facilities increased the city’s external dependency of water supply systems to a large extent on the other hand. In order to quantitatively measure the external dependency, the concept of water mileage was put forward in the study. And its feasibility and applicability was examined by the case study of Beijing. The results showed that it was reasonable to measure the external dependency of water supply system by water mileage. And more case studies are required for further research. Keywords:

1

water mileage; water transfer; surface water; external dependency; Beijing

INTRODUCTION

Water resource, one of the most important and unsubstitutable resources in the world, plays an important role in urban development. It is basically required for the production of clean drinking water, agricultural and industrial products, and so on, which is fundamental for sustaining a high quality of citizen’s life and for a rapid economic and social development of the city. On one hand, accompanied by the process of rising affluence, rapid urbanization and growth of population, water demand for a city is becoming larger and larger (Postel et al, 1996). River runoff which is most widely distributed over the land provides a major part of water use in the world. On the other hand, the spatial and temporal distribution of water over the world varies tremendously. In most of the cases, water resources may not coincide with population spread and economic development (Shiklomanov, 2000). As a result, the fresh water resources have become scarcer and scarcer during the past decades for some major cities in the world. One realistic and efficient measure to mitigate this scarcity is to redistribute water sources across territories. The construction of reservoirs can change the temporal-spatial distribution of river runoff ∗

Corresponding author ([email protected])

fundamentally and can enhance water availability for cities suffering water scarcity. Besides the different scale of reservoirs, water transfer projects have also been constructed and served as an effective measure to relocate the water resource in the past years, such as Colorado-Big Thompson Project and California North-to-South Project in United States, Snowy Mountains Project in Australia, South-to-North Water Transfer Project which is now under construction in China, and so on.And the city has to rely on these external water resources located outside the city by miles of pipelines drawing water to waterworks in the city, which in consequence increases the external dependency of water supply system for the city. And high external dependency means that the city is supporting rapid urbanization and economic growth far beyond its own level of self-sustaining capacity. The water supply system of the city is easily affected by many external factors, especially in the long-distance water transfer process and thus becomes more vulnerable. Because of the significance and scarcity of water resources for urban development, external dependency of its water supply system should be taken as an important index to assess its development status comprehensively. However, there was little relevant research work done in the past. In the study, a new concept of water mileage was put forward to measure

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the external dependency of water supply system for a city. And its feasibility and applicability was tested by a case study of Beijing. 2

METHODOLOGY

In former relevant research, an index of water self sufficiency (WSS) was defined and calculated based on the concept of water footprint in order to quantitatively measure the water supply external dependency (Jing, et al, 2005). The concept of water footprint of a country or a region is defined as the total volume of freshwater that is used to produce the goods and services consumed by the local people (Hoekstra & Hung, 2002).The water footprint of a country consists of two parts: the use of internal water resources which means use of domestic water resources, and the use of external water resources, which means use of water resources outside the borders of the country. Either internal or external water resources refer to both virtual and real water in the concept of water footprint. The concept of virtual water was put forward in the 1990s, which is used to describe the ‘virtual’ water flows as a result of export or import of water-intensive commodities (Allan, 1993). And WSS was defined as the proportion of external water resources in the whole water footprint, including both external real water and external virtual water. However, the definition of WSS has two major disadvantages. In the first place, the major one disadvantage is that only the amount of external water is taken into consideration, which means no matter how long the transfer distance is, the contribution of external water to WSS is the same. While in fact, the longer the transfer distance is, more risk may be prone to happen during the transfer process which results in a higher external dependency. Therefore, to measure the water supply external dependency more exactly and comprehensively, not only the amount of the external water transfer, but also the distance of external water transfer need be considered. Besides, the climatic difference between the city and water resources also affects a city’s external dependency. If the city draws water from a more arid area, it will raise its external dependency as a result. In the second place, WSS reflects both virtual and real water external dependency as a whole. It is difficult to tell the real reason of high external dependency only based on WSS. And so far, there is no separate measurement only focuses on external dependency caused by real water transfer, which sometimes is more costly and time-consuming due to the construction of massive water transfer projects. Therefore, it is necessary to develop a new index to quantitatively measure the external dependency caused by real water supply in this study. To overcome

the two mentioned deficiencies of WSS, the concept of water mileage was put forward, considering both the amount and distance of external real water transfer. And annual average precipitation data is used to reflect the climatic difference. Groundwater and surface water are two major sources of water supply for a city. Groundwater is usually exploited not far from the water works for transfer convenience and its transfer distance can be neglected in the research. The utilization of surface water is determined by the distribution of water resource. And long-distance transfer from the water source to the water works generally happens for surface water. Thus we mainly focus on the transfer of surface water in the study, assuming that the transfer distance is zero for groundwater. It is assumed that there are m surface water sources and n groundwater sources for the water supply of the city. For each surface water sources, the amount of water transferred to the water works and the distance from water sources to the water works are QSW i and DSW i (where i = 1,2, … m), respectively. And for each groundwater sources, the amount of water transferred to the water works and the distance from water sources to the water works are QGW j and DGW j (where j = 1,2, … n), respectively. Since we assume that the transfer distance is zero for each groundwater resource, that is, DGW j = 0 (where j = 1, 2, … n). And annual average precipitation for the city is P0 , while annual average precipitation for the ith surface water source is Pi (where i = 1,2, … m). Considering the amount and distance of external water transfer and the climatic difference, water mileage (WM) is defined as Equation 1 below:

In the definition, transfer distance for each water source and the climatic difference between water source and the city itself are considered as the weight of transfer amount. Water mileage is measured by distance, generally in km. 3 A CASE STUDY OF BEIJING Beijing ,the capital city of China, located within the Haihe River Basin with a total area of 16410.54 km2 and the total population of 15.38 million in 2005 (Beijing Statistic Bureau. 2006). Since 1990s, Beijing

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has experienced rapid urbanization process with robust economic growth. The increase in urban population and expanded business activities required additional water demand. On the other hand, due to its climatic and geographical features, the natural water resource for Beijing is limited and cannot catch up with everincreasing water demand. As a result water shortage became a big challenge for its further development. The water supply in Beijing consists of three parts, that is, surface water supply, groundwater supply and reusing of treated sewage water.And the first two kinds of water supply are the major sources. For surface water supply, most of the surface water is supplied by the main two water supply systems of Guanting Reservoir and Miyun Reservoir. The distances from Guanting Reservoir and Miyun Reservoir to Beijing are approximately 80 km and 76.2 km, respectively. However, due to the serious water pollution problems, Guanting Reservoir has not been used for the drinking water gradually since 1997. Facing water pollution problems in major reservoir, rapid depletion of groundwater resources at the central Beijing and longterm drought since 1999, national government decided to transfer water from nearby Hebei province and distant Yangtze River to guarantee the water supply of Beijing. This water transfer project is one part of the well-known ‘South-to-North Water Transfer Project’, which started from 2002 and will last until 2050. The South-to-North Water Transfer Project envisions three main lines – Eastern, Central and Western – linking the well-endowed Yangtze River Basin with water-short North China. And Central Line is designed to guarantee the water supply of both Beijing and Tianjin (Qian et al, 2002). Based on the utilization of different surface water resources, the surface water supply of Beijing can be divided into five phases. Considering data availability, one year in each phase was selected to represent the corresponding phase for analysis. In Phase I, both Guanting Reservoir and Miyun Reservoir were used for surface water supply. In Phase II starting from 1997, Guanting Reservoir and Miyun Reservoir were still the two major sources of surface water supply, but the surface water supplied by Guanting Reservior was decreased to some extent due to the severe water pollution problem. In Phase III, the supply of surface water decreased to a large extent including both Guanting Reservoir and Miyun Reservoir, and the total water supply relied mostly on the groundwater. In Phase IV, to ensure the water supply of 2008 Beijing Olympic Games and to mitigate the rapid depletion of groundwater, 3*108 m3 water in total will be transferred from nearby four reservoirs near Shijiazhuang in Hebei Province, which is part of Central Line of South-to-North Water Transfer Project. And the water transfer distance from these four reservoirs in Hebei Province to Beijing is about 225km. Besides,

Guanting Reservoir and Miyun Reservoir will still provide surface water for Beijing with a relative smaller amount. In Phase V, after the general completion of the Central Line of South-to-North Water Transfer Project, one billion m3 water will be transferred from Yangtze River to Beijing in 2010. The total transfer distance is 1277 km from Yangtze River to Beijing. And Guanting Reservoir and Miyun Reservoir will continue providing surface water sources for Beijing. Since both Guanting Reservoir and Miyun Reservoir are not far from Beijing, the climatic condition of these water resources and Beijing can be considered as the same. The annual average precipitations of Beijing and Shijiazhuang where the four reservoirs in Hebei Province locate in 2006 are used for approximate Phase IV and V’s calculation, which are 318.8 mm and 407.8 mm, respectively (China Statistic Bureau. 2007). And the long-term annual average precipitation of Danjiangkou Reservoir from which the Central Line of South-to-North Water Transfer Project starts is around 800 mm. According to Equation 1, water mileage (WM) can be calculated for each different water supply phase (Table 1) using relevant data. As can be seen from Table 1, the external dependency measured by water mileage for Beijing varies for each different phase and can be further categorized into three periods. The first period includes both Phase I and Phase II. During this period, the total water supply increased due to the rapid urbanization. Although the usage of surface water from Guanting Reservior began to decrease due to pollution, the transfer of surface water from Miyun Reservoir increased a lot as compensation. As a result, the external dependency increased due to the increment in total amount of surface water supply, which was also correctly reflected by the increment of water mileage to some extent. The second period is Phase III, during which the annual precipitation has kept decreasing for five years from 1999, which as a result led to a depression of water level in major reservoirs and a large consumption of groundwater. Besides, the total water supply decreased a lot due to some water saving policies. And the total water supply turned to rely mostly on groundwater, which decreased external dependency of Beijing’s water supply proven by the decrement in water mileage. The third period is made up of both Phase IV and Phase V. During this period, due to the depletion of groundwater and the construction of South-to-North Water Transfer Project, large amount of surface water will be transferred by thousands of miles to Beijing. As a result, the water supply of Beijing could be affected by many external factors, such as the water level of Yangtze River, the long-distance transfer process and so on. Obviously, external dependency will increase to a large extent, which is reflected by the sharp increment in water mileage.

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Table 1. Water supply and water mileage for five different water supply phases of Beijing. Surface water supply

Phase

Year

Total water supply 108 m3

I II III IV V

1991a 2000b 2006c 2008c 2010c

38.09 40.47 31.00 31.00 36.00

a b c

Guanting Reservoir

Miyun Reservoir

Others

108 m3

108 m3

108 m3

3.31 2.96 0.90 0.90 1.00

5.19 7.11 2.60 2.60 2.60

– – –

17.33 19.24 8.71 25.73 149.08

data from Xie, C. 1991 data from Tsinghua University, unpubl. data from Development and reform commission of Beijing municipality. 2006.

Through the comparison among these three periods, it is shown that the construction of large-scale water transfer project increased the external dependency to a large extent. The water supply of Beijing became much more reliable on the external factors. Besides, it is proven that the variation of water mileage can reflect corresponding external dependency difference caused either by change in the amount of surface water supply or the distance of surface water transfer. Thus it can be concluded that the concept of water mileage is feasible and applicable in the case study of Beijing.

water supply system was examined by the case study of Beijing, China. Water mileage was calculated for five different water supply phases based on Equation 1. And the result showed that the variation of water mileage could reflect corresponding external dependency difference either caused by change in the amount of surface water supply or the distance of surface water transfer. Thus it is reasonable to apply this concept to measure the external dependency of the water supply systems in the case study of Beijing.

5 4

3.00 10.00

Water mileage km

DISCUSSION

CONCLUSION

Rising affluence, rapid urbanization and population growth has led to a substantial increase in water demand during last century. However, due to the uneven temporal and spatial distributions of water resources around the world, water resource has become the bottleneck of some cities’ development. Thus, these cities managed to redistribute the water resource through the constructions of reservoirs or large scale water transfer projects. On one hand, these projects can provide more water for the city and mitigate the water scarcity to some extent. On the other hand, the construction of these projects increased the external dependency of city water supply. In order to quantitatively measure external dependency of the water supply, a new concept of water mileage was introduced in analogy to the former WSS concept in the study. Unlike WSS, the water mileage concept focused on only real water transfer. And the definition of water mileage took the amount of external water supply, the distance of external water transfer and the climatic difference between city and its water sources into consideration. Considering data availability, the applicability and feasibility of the concept of water mileage for the

However, water mileage is just one simple and intuitive index of external dependency of the water supply system for the city. Other factors except the amount and distance of transferred water and the precipitation difference could also affect external dependency. Besides, the feasibility and applicability of water mileage was validated only by one case study of Beijing, which was not convincing enough. For future works, water supply data for some other major cities in the world will be collected first. Then based on these data its feasibility and applicability need to be examined. Besides, it is also meaningful to make comparison among different cities on water mileage. REFERENCES Allan, J.A. 1993. Fortunately there are substitutes for water otherwise our hydro-political futures would be impossible. In Priorities for water resources allocation and management, 13–26. London: Overseas Development Administration Beijing Statistic Bureau. 2006. Beijing Statistical Yearbook 2006. Beijing: China Statistics Press China Statistic Bureau. 2007. China Statistical Yearbook 2007. Beijing: China Statistics Press

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Development and reform commission of Beijing municipality.2006. The conservation and utilization planning of water resource in Beijing during the 11th Fiveyears. Retrieved Feb. 2008 from http://www.bjpc.gov. cn/fzgh/guihua/11_5_zx/11_5_zd/200609/t129933.htm Hoekstra, A.Y. & Hung, P.Q. 2002. Virtual water trade: A quantification of virtual water flows between nations in relation to international crop trade. Value of Water Research Report Series No. 11, UNESCO-IHE Institute for Water Education, Delft, The Netherlands, Retrieved Feb, 2008 from http://www.waterfootprint.org/Reports/ Report11.pdf Ma, J., Hoekstra, A.Y., Wang, H., Chapagain, A.K. & Wang, D. 2005. Virtual versus real water transfers within China. Philosophical Transactions of the Royal Society B 361, 835–842

Postel, S.L., Daily, G.C.& Ehrlich, P.R. 1996. Human appropriation of renewable fresh water. Science 271, 785–788 Qian, Z., Lin, B., Zhang, W.& Sun, X. 2002 Comprehensive report of strategy on water resources for China’s sustainable development (1st). Beijing: China Water and Hydropower Press Shiklomanov,I.A. 2000. Appraisal and assessment of world water resources. Water International 25(1), 11–32 Tsinghua University. Analysis on the balance of water supply and use in Beijing. Unpublished report Xie, C. 1991. Comprehensive report of general condition and regionalization of water resource in Beijng. Beijing: Beijing Municipal Water Conservancy Bureau

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Urbanization and water use situation in Beijing, China – an evidence from water production and supply sector L. Banchongphanith∗ & S. Kaneko Graduate School of International Development and Cooperation, Hiroshima University, Hiroshima, Japan

ABSTRACT: Beijing has a rapidly growing economy, but like other big cities in the world, Beijing also receives adverse effects from the economic developments. One among those negative effects is serious water shortage problem. Increasing demand for water along with growth of population, rapid urbanization and changing towards a western lifestyle poses great challenges to the water supply capacity of the city. Water production and supply sector plays a vital roll in urban development of Beijing. However, given the fact that current capacity of water production still can not meet the demand, it is expected that government’s investment policy in improving water services and treatment facilities and promoting less water use industries are urgently needed. Through the water resources economic input-output analysis framework, this paper suggests some industrial sectors consuming high water input from the water production and supply sector such as non-metal mineral production and construction sectors. On contrary, it also unveils that high tech industries namely electronic and computer production sectors consume relatively less water input. In order to overcome the current water stress, changes within economic structure itself is another alternative to save the capital city from drying out of water in the long run. Keywords: Beijing; input-output analysis; management challenges; urbanization; water production and supply; water resources

1

INTRODUCTION

Beijing, the capital city of China, in the recent years enjoys rapid economic growth and its change has become the most dazzling spot in Asia and the global communities. Accompanied with the rapid growth of economy and urbanization, Beijing has become not only one of the most developed cities, but it also has become one of the most crowded cities in China. Being accelerated by the economic booming, the urbanization has been increasing very fast during the recent years. As a result, the demand for natural resources such as water resources has been rising dramatically. It is criticized that the water supply in this capital city does not meet the rising of demand for water (Hou & Hunter, 1998). It is also estimated that the demand for water already exceeds nature’s supply, and a growing number of towns and counties are expected to face water shortages in the near future (“Beijing faces up to water crisis”, 2004). Varis and Vakkilainen (2001) also claimed that in the northern part of china, groundwater is used at a rate much higher than that ∗

Corresponding author ([email protected])

with which the aquifers are filled (Varis &Vakkilainen, 2001). Regarding the rapid urbanization caused water stress, Weiberg (1999) also pointed out that the water supply and distribution system in Beijing cannot keep pace with exploding urbanization and its high water demand (Weiberg, 1999). In studying of the water resource allocation in an economy, Xie (1991) conducted a study by using input-output model to Beijing urban water use system (Xie, Nie, & Jin, 1991). Later on, Chen (2000) suggested the water resources inputoccupancy-output table and applied this model in Shanxi province of China (Chen, 2002). This paper aims to give an overlook at the water resource and water use situation and current water related problems in Beijing in the recent years from the aspect of water production and supply sector side. To have a better understanding on the water allocation situation in the economy driven by urbanization, especially the water products from the water production and supply sector, which is considered one of the most important elements of urban development in the city, a water resources economic input-output modeling is also applied in this paper. The data used in this paper are mainly acquired from the China Water

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Resources Bulletin which published by the Ministry of Water Resources, and Beijing Environmental Statistical Year Book that was published by China Statistics Press and other sources.

of this capital city has a tendency of decline since 1999 which contribute to further deficient of water resources in the city. 3

INCREASING URBANIZATION

2 WATER RESOURCES IN BEIJING

3.1 Exploding of population

Beijing is situated in the Hai River Basin, which has been reported that water exploitation reaches 90% and its groundwater utilization is the highest in comparison to Yellow River Basin and Huai River Basin (Zhu, 2006). There are 5 major rivers flow through Beijing city: Chaobai River, Jiyun River, Beiyun River, Yongding River, and Daqing River. With these rivers and other two major reservoirs, Guanting reservoir and Miyun reservoirs, they provide the major surface water resources in Beijing. After years of heavily exhaustion of surface water in Beijing till it shows its red light to the community, groundwater has become another major water resource. Table 1 displays the amount of water resources in the end of each year. The groundwater resources is relatively much more than that of surface water. However, years of depletion of groundwater has caused a number of problems, among all is land subsiding that have been observed to be around 800 km2 in the city area (Yang, 2007). The surface water resources have a trend of declining in the recent years. This is considered to be closely related to the amount of precipitation in Beijing. The precipitation provides major fresh water source in Beijing. The average rainfall during 20 years (19862005) is 544.7 mm (calculated based on the data available on Beijing Statistics Yearbook in various years). The distribution of rainfall in Beijing varies greatly within the year with most of it concentrating in summer. Figure 1 depicts the rainfall over the period of 20 years (1986-2005). The most recent trend of rainfall

The growth of urbanization in Beijing is observed by expanding of its urban area and population. In Beijing, the rate of urbanization or the urban population has been increasing dramatically in the recent decades. Attracted by its fast economic growth, Beijing has been attracting more population from other provinces to work and live in. Figure 2 displays the trend of overall population growth in Beijing from 1991 to 2005. The total population increased from around 10 millions in 1991 to 15 million in 2005. However, the size of rural population does not have too many changes until 2005, the rural population decreased approximately 540,000 people from rural areas for the city areas expanded. 3.2 Urban area expansion The rapid expansion of city areas places an additional pressure to the scarce of water resources in

Figure 1. Precipitation in Beijing (1986–2005). Source: Beijing Statistics Yearbook (various years).

Table 1. Water resource in Beijing (100 million m3 ).

Year

Surface Water

Ground Water

Double Counting

Total

1997 1998 1999 2000 2001 2002 2003 2004 2005

10.60 19.00 5.16 6.43 7.78 5.25 6.06 8.15 7.58

16.40 29.21 12.81 15.18 15.70 14.69 15.20 16.54 18.46

(4.80) (9.30) (3.75) (4.66) (4.28) (3.83) (2.86) (3.35) (2.86)

22.30 38.90 14.22 16.66 19.20 16.11 18.40 21.34 23.18

Source: China Water Resources Bulletin and Beijing Water Resources Bulletin (various years).

Figure 2. Population in Beijing from 1991–2005. Source: Beijing Statistics Yearbook (various years).

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Beijing. Along with the expansion of urban areas, residential areas, parks, and other modern amenities as well as great number of industries and service businesses also increase. Consequently, even though both water supply and distribution system have been greatly improved and expanded, they cannot keep pace with exploding urbanization and its high water demand (Weiberg, 1999). The statistics from Beijing Statistical Bureau in 1990s shows that the urban areas has expanded by 20 percent, or increased 94.7 Km2 , which is expanded from 395.4 Km2 in 1990 to 490.1 km2 in 1999 (urban and suburb areas including 8 districts). In 2005, the area has increased further to 1366.3 Km2 , which is almost 3 folds as much than that in 1999 (Zhu, 2006). 3.3

4.1

Table 3 shows how the supplied water amount stated in Table 2 is distributed among the economy. The proportion of domestic-use tends to get higher than water consumption of other sectors. One explanation to that is the growth in size of population, changes of lifestyles, and rising of income while the prices of water do not change much. As for industrial sectors, improvement of water recycling technology results in higher proportion of recycled water use in industrial sectors. Based on an economic census conducted in 2004, many heavy water-use industries namely chemical product manufacturing sector has almost 98% of water recycling rate (Beijing economic census, 2004). 4.3 Tap water supply could not feed the demands

Urban life style changes

Along with the rising of income and living standards, housing condition in the cities has great improvement in the recent years. It is getting more and more common that new built apartment nowadays has a numbers of modern facilities namely washing machines, flush toilets, kitchen sinks, shower head or even bath sink, and other basic facilities. Furthermore, it is observed that as income increases, Chinese people tend to change lifestyles towards western standards in order to achieve a comfortable, healthy and hygienic livelihood. Many household appliances such as washing machines, dishwashers, refrigerators, and water heaters that were novelties in the beginning of the early 1980’s are now popular among urban households. (Guan & Hubacek, 2004). 4

4.2 Increasing domestic water consumption

Table 4 shows the fresh water and other water resource dependent rate to see how much water each industry has to mobilize the water resource from self provided water resources namely surface water, ground water and recycled water. It is obvious that tap water from the water production and supply sector can far away to satisfy the water demand from the economy as it can provide only ranging from 8% to 53% of total water Table 2. Water supply in Beijing (100 million m3 ).

1999 2000 2001 2002 2003 2004 2005 2006

IMPACTS OF URBANIZATION ON WATER SOURCES IN BEIJING Beijing is facing water shortage

The increasing in demand for water consumption in agriculture, industry, and municipalities has created a huge burden to the natural water resources of the city. Zhang (2000) mentioned that by around 2030, when the size of Chinese population increases up to 1.6 billion, China will be facing severe water shortage problems (Zhang, 2000). To cope with the problems, the city simply started depleting more groundwater resources. Table 2 shows the volume of water supply in Beijing in the recent years in 100 million m3 . Comparing with water resources (Table 1), the supply of water to economy is much more than the water resource available. It is observed that along with the decreasing of surface water resources, exploitation of groundwater becomes higher until the collection of precipitation, reused water, and seawater desalinization become the solution for water shortage in the later years.

Surface water

Ground water

14.95 13.25 11.70 9.65 8.30 5.70 7.00 6.40

26.76 27.15 27.23 24.24 25.40 26.80 24.90 24.30

Other resources

Total

0.73 1.30 2.00 2.60 3.60

41.71 40.40 38.93 34.62 35.00 34.50 34.50 34.30

Source: China Water Resources Bulletin and Beijing Water Resources Bulletin. Table 3. Water use in Beijing (100 million m3 ).

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Domestic Industry Agriculture Environment Total 1999 2000 2001 2002 2003 2004 2005 2006

12.70 13.39 12.05 10.83 13.48 12.80 13.90 14.40

10.56 10.52 9.18 7.54 7.70 7.70 6.80 6.20

18.45 16.49 17.40 15.45 12.90 13.00 12.70 12.00

0.80 0.95 1.00 1.10 1.60

41.71 40.40 38.93 34.62 35.00 34.55 34.50 34.50

Source: China Water Resources Bulletin and Beijing Water Resources Bulletin.

Table 4. rate.

Fresh water and other water resource dependent

Year

a

b

c

a*

b*

c*

1999 2000 2001 2002 2003 2004 2005 2006

19% 19% 18% 23% 20% 24% 21% 22%

47% 44% 47% 53% 43% 53% 43% 43%

13% 12% 14% 29% 17% 19% 17% 20%

81% 81% 82% 77% 80% 76% 79% 78%

53% 56% 53% 57% 57% 47% 57% 57%

87% 88% 86% 71% 83% 81% 83% 80%

The direct input from sector i per unit of production sector j and the direct input coefficient for the water production and supply sector is denoted by

The equation (1) can then be rewritten in following matrix form:

Note: a, the percentage of tap water sales to total water use; b, the percentage of domestic tap water use in total domestic water use; c, the percentage of industrial tap water use in total industrial water use; a∗, total fresh water and other water resource dependent rate; b∗, domestic fresh water and other water resource dependent rate; c∗, industrial fresh water and other water resource dependent rate. Source: Calculated by data collecting from Beijing 50 years.

The total water output from the water production and supply sector can be expressed by

used. In another words, fresh water and other water resource dependency is generally over 50%.

5 WATER RESOURCES ECONOMIC INPUT-OUTPUT ANALYSIS This paper also shed a light on seeing how the water from water production and supply such as tap water are consumed by the industries. This could provide evidences for the policy makers to consider how to adjust the economic structure from a water resource perspective. At this point, this paper applies the water resources economic input-output analysis model to study the water consumption by each industry in the economy as shown in Table 5. In Table 5, the water use means the water from the water production and supply sector as this sector provides water production to all industries in the economy. It is converted from the monetary term in the original input-output table of Beijing into the physical terms. The data used here is the original input-output data of Beijing in 2002 for it is the most updated input-output table for Beijing at this moment. From the model, the input-output balance among the industries is expressed by

where the total output from the water production and supply sector W is comprised of water consumption by the industrial sector F, water consumption by water production and supply sector itself G, and final demand water consumption K. Based on equation (5), the direct water input coefficient for the production sector and the direct water input coefficient for the water sector is denoted by

f

where bj represents the direct water input coefficient for the production sector; and bg represents the water input for the water production and supply sector. According to equation (6) and (7), equation (5) can be rewritten into the matrix form as:

By grouping equation (4) and (8), the overall inputoutput balance is expressed in the following matrix form:

And from matrix equation (9), the relationship among output from all industrial sectors, X , total water use (provided by the water production and supply sector), W , in line with the given final demands are presented as: where the total output of production sector i consists of sum of all inter-industry sales of i sector, its sales to the water production and supply sector, and its sales to final demands.

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Table 5. Water resources economic input-output analysis framework. Intermediate Demand

Input

Production Sectors (i) (1, 2, . . . , n) Water Production and Supply (n + 1) Intermediate Input Value Added Total Input

Production sectors ( j) 1,2,. . . , n

Water Production and Supply n+1

Final Demand

Total Output and Total water

zij

Ti

Yi

Xi

Fj

G

K

W

Ij Vj Xj

Iw Vw Wj

Note: zij , Value of input from production sector ito sectorj; Ti , Value of input from production sector i to water production and supply sector; Yi , Final demand for production sector i; Xi , Total output of production sector i; Fi , Consumption of water by production sector j; G, Consumption of water by water production and supply sector; K, Final demands for water production and supply sector; W , Total output of water production and supply sector.

Finally, the total water input coefficient, which is the sum of all direct and indirect water input coefficient is computerized by the following equation (Wang et al. 2005):

Apart from direct and total water input coefficient, water use multipliers are calculated in order to see the water use balance between the direct and indirect water input of each production sectors in the economy. The water use multipliers are presented by

Table 6. Yuan).

Direct and total water input coefficient (m3 /1000

Sector

bf

%

Tf

%

mf

Agriculture Industry Construction Transportation and post Commerce Services and others

1.87 9.60 0.35 0.31

14% 69% 3% 2%

3.29 34.24 2.02 0.96

7% 78% 5% 2%

0.76 2.57 4.72 2.06

0.21 1.53

2% 11%

0.73 2.89

2% 7%

2.38 0.89

Note: bf ,direct water input coefficient T f , , total water input coefficient; and mf , water multipliers. f

where mj presents the proportion of indirect water input coefficient to direct water input coefficient. 5.1 Water use results for Beijing 2002 To study the water use, we only concern the water produced and supplied by the water production and supply sector. However water that the industries extracted from the nature or the use of recycled water is not included for limitation of data availability matters. In general speaking, the results of water resource input-output analysis indicates that most sectors, including agricultural sector use direct water input from the water production and supply sector less than 2 m3 per 1000 Yuan output. In terms of direct water input, Table 7 shows that the industrial sectors are the largest water consumers, which account for 69% of total direct water input. Agriculture which is commonly considered as the largest water consuming sector consume only 1.87 m3 per 1000 Yuan output.

That means it relies heavily on use of fresh water from the nature rather than water provided from the water production and supply sector. Among all the industrial sectors, manufacturing of non-metallic mineral products, electricity power and heat power production consume relatively more direct water input. On the subject of total water input coefficient, apart from agriculture, some industrial sectors namely manufacturing of non-metallic mineral products and manufacture of textile, and construction have relatively high total water input coefficient. On the other hand, machinery, communication equipment production sector, petroleum refiner commerce, etc. . . have relatively low water input coefficient. Regarding the water use multipliers, Table 6 shows that construction sector has the most imbalanced indirect and direct water input coefficient. On the contrary, agricultural sector and services sectors appear to use more indirect water input rather than indirect water input from the water production and supply sector.

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6 WATER MANGEMENT CHALLENGES In spite of many efforts have been paid and progresses are made to a certain extent, there are still a number of challenges that the city management must overcome. First of all, fast growing population and rapid increasing rate of urbanization is one of the major factors contribute to water stress in the city. The water service facilities have been greatly improved, but it is still hard to meet the rising water demand of a crowding mega city like Beijing. Based on a report from the Beijing Water Authority, the demand of water will increase to 4.2 billion m3 /year, which exceeds the water availability of the city (Pan, 2006). Pollution control has been improve to a certain level, but the most recent evidence shows that surface water pollution by various pollutants and nutrients is still severe and puts certain threat to water security in this city. Pan (2006) mentioned that by 2005, 70% of wastewater in Beijing was treated and 30% of that was discharged into rivers without any treatment (Pan, 2006). Under-charging for water is a factor that increases the demand for water which must be met by drawing more on available water resources and investing in higher capacity infrastructure (“North China Water Quality Management Study”, 2005). Another big challenge to Beijing city is to increase water infrastructure investment. In the case of Beijing, it needs more investment to improve tap water services and treatment facilities. Use of reclaimed water could be a choice for the city to save its scarce water resources.

7

CONCLUSIONS

Beijing as the capital city of China, has a rapidly growing economy on one hand, it also suffers from many adverse effects, among many, one is severe water shortage problem on the other hand. Apart from the natural factors that cause Beijing to face water problems, rapid urbanization combining with increasing population and lifestyle changes also play a vital rule in generating unmatched supply and demand for water. They pose great challenge to the city management of the city in the aspects of facing water shortage, solving water pollution and adjusting water pricing policies. With the most recent data, this study shows that the tap water supply could not feed the water demand in the mega city of Beijing. Thus, more financial efforts are needed to invest in water service systems in terms of expansion of water supply network and treatment facilities in order to meet sustainable social-economic development needs. Given the fact that the water production and supply sector could not be fully developed in a short time

to support the increasing demand from the economy, changing industrial structure can be an alternative to release the water supply burden of the city. Through application of water resources economic input-output model, the analysis results provide some evidences that some industries such as mining sectors, electricity power and heat generation sector, construction and agricultural sector have rather high water input coefficient. On the contrary, production sectors like machinery, communication equipment production sector, petroleum refiner commerce, etc . . . have relatively low water input coefficient. From a water saving perspective, besides of promoting water saving policies and technologies, the water stress can be released to a certain extent if the industrial policy carefully shifts the industrial structure from heavy tap water use industries to promoting those less water-consuming industries. This study only concerns the water use from the water production and supply sector. However, as discussed in the paper, apart from the tap water, industries use a large amount of water from natural water resources and recycling activities. For this reason, this study could account those water resources into the analysis once water related data corresponding to each production sector are available.

REFERENCES Beijing 50 Years (1999). Beijing: Beijing Statistics Press. Beijing Economic Census (2004) Beijing: China Statistic Press. Beijing faces up to water crisis (2004). Retrieved January 17, 2008, from http://english.peopledaily.com.cn. Beijing Statistics Yearbook (1987-2006). Beijing: China Statistics Press. Beijing Water Resources Bulletin (2005). Retrieved December 2, 2008. from http://www.bjwater.gov.cn. Chen, X. K. (2000). Shanxi water resources input-occupancyoutput table and its application in Shanxi Province of China Paper presented at the 13th International Conference on Input-Output Techniques on 21–25 August, 2000, Macerata, Italy. China Environment Yearbook (2004–2005). Beijing: China Environment Yearbook Press. Guan, D., & Hubacek, K. (2004). Lifestyle Changes and its Influences on Energy and Water Consumption in China. Paper presented at the International Workshop on Driving Forces of and Barriers to Sustainable Consumption, University of Leeds, Leed, UK. Hou, E., & Hunter, G. (1998). Beijing water: causes, effects, solutions. Retrieved December 29, 2008, from http://www.chsubc.ca:16080/China North China Water Quality Management Study. (2005). Retrieved February 12, 2008, from http://www.worldbank. org.cn. Pan, A. (2006). Water Management in Beijing. Paper presented at the Capacity Building Workshop on Partnerships for Improving the Performance of Water Utilities in the

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Asia and Pacific Region, United Nations Department of Economic and Social Affairs, Bangkok, Thailand. Varis, O., & Vakkilainen, P. (2001). China’s 8 challenges to water resources management in the first quarter of the 21st centrury Geomorphology 41, 93–104. Wang, L., MacLean, H. L., & Adams, B. J. (2005). Water resources management in Beijing using economic inputoutput modeling. Canadian Journal of Civil Engineering Retrieved March 12, 2008, from http://pubs.nrc-cnrc. gc.ca Weiberg, D. (1999). Planning and managing China’s water resources. Retrieved February 2, 2008, from hppt:// www.iiasa.ac.at/Admin/INF/OPT/Summer99. Xie, M., Nie, G., & Jin, X. (1991). Application of inputoutput model to the Beijing urban water-use system. In

Polenske, K.R & Chen, X. K. (Eds.), Chinese economic planing and input-output analysis (pp. 239–253). Oxford, U.K: Oxford University Press. Yang, D. W. (2007). Analysis on the balance of water supply and use in Beijing: Qinghua University. Zhang, S. Q. (2000). Countermeasures and suggestions on capital water issues in the 21 century [Electronic Version]. Policy Research. Retrieved December 16, 2008 from http://www.cqvip.com. Zhu, R. X. (2006). China’s South-North Water Transfer Project and Its Impacts on Economic and Social Development [Electronic Version]. Retrieved February 13, 2008.

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From Headwaters to the Ocean – Taniguchi et al. (eds) © 2009 Taylor & Francis Group, London, ISBN 978-0-415-47279-1

Study on future water supply and demand in the Yellow River basin of China based on scenario analysis A. Onishi∗ & Y. Fukushima Research Institute for Humanity and Nature (RIHN), Kyoto, Japan

H. Imura, F. Shi & J. Han Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan

W. Fang Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs and Ministry of Education of China, Beijing Normal University, China

ABSTRACT: Increases in water demand associated with rapid socio-economic development may lead to severer water shortage in the Yellow River Basin. Therefore it is necessary to apply proper water resource management in the basin towards sustainable use of water resources. Applying data of county or city level, this study presents an estimation of water supply and demand up to 2050 based on different scenarios. Furthermore, the scale of the water shortage is assessed in order to indicate possible extent of reduction in water consumption in the context of water shortage. Keywords: Yellow River Basin; water resource management; future scenarios; water supply and demand balance

1

INTRODUCTION

The Yellow River Basin suffers from a severe water shortage. In the basin, the annual available water resources amount to about 580 m3 per capita, about 6% of the global average, and about 24% of the China’s average level (Japan Bank for International Cooperation 2004, Ministry of Water Resources 1997–2000, Onishi et al. 2007). Meanwhile, the total water use in the basin has been increasing as a result of population growth and the expansion of agricultural irrigation area, as well as industrialization and urbanization. In recent years, the total water use has exceeded 30 billion m3 per year (Sun et al. 2001, Xi 1996, Yellow River Conservancy Commission 1997–2000). Dry-up phenomena occurred from 1972 to the late 1990s. In addition, with the increase in water demand due to changes of industrial structure and lifestyle, the gap between water supply and demand in the basin has been sharpened. In order to achieve sustainable use of water resources in the Yellow River Basin, reasonable water resource management has become increasingly important. Therefore, questions such as how to achieve water

∗

Corresponding author ([email protected])

supply and demand balance, how to allocate water efficiently to sub-regions and industrial sectors, etc. while taking equity into consideration have to be solved. Contributing to the solutions of these questions, this study presents a method for the estimation of water supply and demand in the entire basin up to 2050 based on different scenarios designed by using data of country or city level. Firstly, the water demand is estimated under different social-economic scenarios according to spatial differences in development. Secondly, water resource is estimated under different scenarios according to the historical data in the last 30 years. Finally, by utilizing water resource cascade, we constructed a simulation model for water flows from the upstream to the downstream, including intake, consumption, discharge and recovery and for one kind of use to another. By integrating the above components together, it is possible to estimate the balance between water supply and demand in the future, and to investigate the impact on water shortage. 2 ANALYTICAL FRAMEWORK FOR FUTURE SCENARIOS Figure 1 shows the analytical framework for future scenarios of water supply and demand designed in this

459

The equal growth scenario assumes that disparities within a province will be reduced. Therefore, a county or a city i in province m is assumed to grow at the same rate of province m (Eq. 1). t denotes time in terms of month

Population growth and economic development Population

GDP Industrialization rate

Water demand caltculation module Agricultural Sector

Industrial sector Industrial production

Effective irrigation area

Crop irrigation constants

Agricultural water demand Water use per unit of industrial production

Income Gap Urbanization

Domestic sector

Population; Serviced Not serviced

The central city growth scenarios assume that economic growth in each province is led by some central cities of comparatively large scale, which affect the growth of their surrounding counties or cities. The GDP growth rate of a county or a city i surrounding the central city j is jointly determined by the GDP growth rate of the central city j and the distance dij between i and j (Eq. 2).

Water use per capita

Domestic waterdemand

Industrial water demand Water resources and river flow calculaton module Precipitation

Water resource

Water consumptionrate

Water supply and demand balance

Spatial distributions ofwater resource supply and demand

Figure 1. Framework for scenario analysis.

dij is measured by the distance between the centre points of i and j. For the central city growth scenarios, we set capitals of provinces and main cities as large cities and medium-sized cities, respectively, for the large city growth scenario and the medium-sized city growth scenario.

study. The balance of water supply and demand under different future scenarios are calculated accordingly for each county or city. 2.1

Population growth and economic development

2.1.1 Projection of population growth The population of counties and cities up until 2050 is estimated based on a report of Japan Bank for International Cooperation (2004), which summarized future demographic changes in China. 2.1.2 Projections of economic development For Gross Domestic Product (GDP), we refer the values from a report of Chinese Academy of Engineering (2001) which summarized GDP growth rate of China and Yellow River Basin for different scenarios. Based on their results, we estimate GDP for each county and city in the following sections. Here, in order to keep consistency across different scenarios, GDP in each province in each year under different scenarios is adjusted equally. 2.1.3 Economic growth scenarios This study estimates water demand under different scenarios representing various spatial patterns of economic growth. Scenarios were designed based on the current situation of economic growth in China, which is characterized by distinct regional disparities. Two kinds of scenarios were considered. One is equal growth scenario and the other is central city growth scenario, which is further developed into large city growth scenario and medium-sized city growth scenario according to different scale of cities.

2.1.4 Economic structure To estimate the amount of water for industrial use and domestic use, it is necessary to know the information on industrial production and income. Thus, we developed a formula (Eq. 3) based on provincial time series data from 1952 to 2000 (Onishi et al. 2007). Using data on population and GDP provided in previous sections, we estimated the economic structure for each county or city.

where yi is per capita GDP (in yuan RMB) of county or city i; γ 1,i , γ 2,i , and γ 3,i , are the shares in GDP of county or city i by the primary sector, the secondary sector and the tertiary sector, respectively. Coefficients of a1,i , a2,i , b1,i , and b2,i were estimated based on provincial data. 2.2 Water demand module 2.2.1 Agricultural water use This study assumes that future agricultural water demand will keep the same level as in 2000 because agricultural water use had no significant changes during the past decades. The agricultural water demand

460

is estimated by the following equation (Onishi et al., 2007).

Table 1.

Si Si

where IW i is the irrigation water used for each county or city i; IWU i,l is the irrigation water used for per unit irrigation area for crop l in county or city i; and IAi,l is the irrigation area for crop l in county or city i. The crop type l is included eight different crop types. It should be noted that part of water used for irrigation in theYellow River Basin is supplied from sources other than the Yellow River and its tributaries. Thus, in this study we assume that water used for the irrigation districts in the Yellow River Basin is supplied from counties or cities with water intakes. 2.2.2 Industrial water use The amount of water for industrial use is calculated by multiplying the water usage per unit industrial production (in m3 /103 yuan RMB) by the gross industrial production. The estimation of gross industrial production for each country or city i is shown in Eq. 5 for cities and Eq. 6 for counties using the share of GDP by the secondary sector obtained from Eq. 3. For cities,

For counties,

where Yi is the gross industrial production of county or city i; and GDP i is the gross domestic product of each county or city i.

Diffusion rate of water supply systems.

2000

2010

2020

2030

2040

2050

97% 8%

100% 26%

100% 45%

100% 63%

100% 82%

100% 100%

is determined by the urban non-agricultural population POP i , the diffusion rate of urban water supply systems Si , and per capita domestic water use dwi _piped (in litres per capita). DW i _Non−piped is calculated applying the same method by using POP’i , which equals to urban agricultural population plus rural agricultural population, the diffusion rate of urban water supply systems Si , and per capita domestic water use dwi _Non−piped . The domestic water use is estimated by following equations.

The values for POP i and POP’i are estimated according to the income gap and the urbanization rate provided by the Japan Bank for International Cooperation (2004). The values of Si and Si ’ are assumed as in Table 1. The values for Piped_dwi and NonPiped_dwi are refered from Chinese Academy of Engineering (2001). 2.3 Water resource module

where WI i is the amount of industrial water demand in county or city i; Yi,k is the gross industrial production of industrial sector k in county or city i; wi,k is water usage per unit production of sector k in county or city i; fi,k is the water recovery rate of sector k in county or city i; and γ’i,k is the share of sector k in the gross industrial production of county or city i. Twenty industrial sectors are considered in this calculation. We applied methodologies provided by the Japan Bank for International Cooperation (2004) for the calculation of wi,k and fi,k . 2.2.3 Domestic water use The projection of domestic water use DW i in county or city i consists of two parts. One is urban domestic water use DW i _piped , provided by municipal water supply system, and the other is DW i _Non−piped . DW i _piped

2.3.1 Water supply scenarios Information regarding the amount of water resources at provincial level or at the overall river basin level can be obtained from the Ministry of Water Resources (1997–2000) and theYellow River Conservation Commission (1997–2000). Documentation provided by China uses the term “water resources” to indicate the amount of water available for human beings, such as surface water and ground water. However, it is difficult to estimate future water resource amount due to data limitation. Thus we estimated the amount of water resources for the past three decades using precipitation data. Then we set three scenarios for the rainy year (1983), the average year (2000) and the dry year (1997), depending on the amount of precipitation. Finally,