Topics on System Analysis and Integrated Water Resources Management

Topics on System Analysis and Integrated Water Resource Management

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Topics on System Analysis and Integrated Water Resource Management

Edited by Andrea Castelletti and Rodolfo Soncini Sessa

A MSTERDAM • B OSTON • H EIDELBERG • L ONDON • N EW YORK • OXFORD PARIS • S AN D IEGO • S AN F RANCISCO • S INGAPORE • S YDNEY • T OKYO

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA First edition 2007 Copyright © 2007, Elsevier Ltd. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloguing in Publication Data A catalogue record for this book is available from the Library of Congress ISBN–13: 978-0-0080-44967-8 ISBN–10: 0-080-44967-0 For information on all Elsevier publications visit our web site at http://books.elsevier.com Printed and bound in Great Britain 07 08 09 10

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Contents

Preface

vii

I.

Introduction

1

1.

A Participatory and Integrated Planning Procedure for Decision Making in Water Resource Systems A. Castelletti and R. Soncini-Sessa

3

II.

Modelling

25

2.

The Data-Based Mechanistic Approach in Hydrological Modelling P.C. Young, A. Castelletti and F. Pianosi

27

Bayesian Networks as a Participatory Modelling Tool for Groundwater Protection H.J. Henriksen, P. Rasmussen, G. Brandt, D. von Bulow and F.V. Jensen

49

Exploring Water Conservation Behaviour through Participatory Agent-Based Modelling A. Rixon, M. Moglia and S. Burn

73

Managing and MODSS

97

Decision Support Systems for Integrated Water Resources Management with an Application to the Nile Basin A.P. Georgakakos

99

3.

4.

III. 5.

v

vi 6.

Water Reservoirs Management under Uncertainty by Approximating Networks and Learning from Data M. Baglietto, C. Cervellera, M. Sanguineti and R. Zoppoli 117

7.

Optimising Irrigation Management at the Plot Scale to Participate at the Regional Scale Water Resource Management J.-E. Bergez, F. Garcia, D. Leenhardt and L. Maton 141

8.

Multi-Objective Optimization of Water Distribution System Design under Uncertain Demand and Pipe Roughness A.V. Babayan, D.A. Savic and G.A. Walters 161

IV.

Planning and MODSS

173

9.

Sustainable Floodplain Management and Participatory Planning in the Red River Basin, Canada S.P. Simonovic 175

10.

Negotiation Support System for Resolution of Disputes over International Water Resources L. Kronaveter and U. Shamir 189

11.

Workflow Oriented Participatory Decision Support for Integrated River Basin Planning J. Dietrich, A.H. Schumann and A.V. Lotov 207

12.

Comprehensive Testing and Application of the PIP Procedure: the Verbano Project Case Study A. Castelletti, F. Cellina, R. Soncini-Sessa and E. Weber 223

13.

Social Science Contributions to the Participatory Planning of Water Systems – Results from Swiss Case Studies B. Junker and M. Buchecker 243

14.

Multi-Criterion Decision Making Approach to Assess the Performance of Reconstructed Watersheds A. Elshorbagy, S.L. Barbour and C. Qualizza 257

V. Future Directions 15.

271

Outstanding Research Issues in Integration and Participation for Water Resource Planning and Management A.J. Jakeman, R.A. Letcher, J.P. Norton et al. 273 Subject Index

291

Preface

The Integrated Water Resources Management (IWRM) paradigm has entered the lexicon of water managers and stakeholders as the mainstream approach to the planning and management of water resource systems. It is the central pillar of the EU Water Framework Directive, which is widely accepted as the most significant piece of water legislation produced in the past 20 years, as well as the inspiring principle of all the water related activities sponsored by UNESCO in developing countries. However, despite huge inputs of financial resources, implementation of full IWRM remains elusive in most of the cases, we argue, owing to the lack of a systematic approach and the inadequacy of tools and techniques to address the intrinsically complex nature of water resource systems. With the purpose of dealing with the recent progress made in overcoming the operational difficulties posed by the IWRM paradigm, a Workshop on Modelling and Control for Planning and Managing Water Systems sponsored by the IFAC Technical Committee 8.3 on the Modelling and Control of Environmental Systems, was held at the Istituto Veneto di Scienze Lettere ed Arti, Venice, Italy from September 29th – October 1st, 2004. The aim of the Workshop was twofold: first, to present the state of the art of modelling and control techniques applied to water resource systems; and, second, to foster an integrated and participatory approach. In order to emphasize the role of System Analysis and Control Theory methodologies as different stages of an integrated, multi-objective approach to planning and management, within a participatory decision-making prospective, the themes of the three days were: 1st day: Modelling: participatory modelling techniques. 2nd day: Managing: Decision Support Systems and algorithms for policy design. 3rd day: Planning: Decision Support Systems and tools for participatory decision-making and negotiations. The unifying idea of the Workshop has been clarified since the call for papers in a position paper made available on the Workshop web-site. Each day/theme was introduced by an invited paper, presented over approximately 90 minutes, and was organized into two vii

viii sessions, involving two papers each, with a total duration of approximately 120 minutes. The other accepted papers were presented through an afternoon poster session followed by a 90-minute discussion, chaired by the invited speaker, which concluded the day. The unusual scheduling of the Workshop was conceived with the aim of reconciling the need, recognized in all conferences, to have a greater time dedicated to each paper, in order to understand and discuss it effectively, with the equally important objective of being able to take advantage of many contributions in order to form a meaningful picture of the variety of research in progress. This volume is an outgrowth of the Workshop and contains selected papers among those orally presented. All the contributions to the Workshop were carefully reviewed by a board of international experts and those collected in this volume have been further revised and improved on the basis of the fruitful discussion that followed the presentation. We believe that the contributors have done invaluable work which have resulted in something more than a mere collection of papers: a monograph with many authors that will facilitate the increased appropriate application of the IWRM template by practitioners, decisionmakers and scientists. Reflecting the framework of the Workshop, the book is composed of three parts, Modelling, Managing and MODSS and Planning and MODSS, plus an opening and a closing chapter. The introductory chapter is a post-Workshop revision of the position paper that enunciates the principles which drove the Workshop. It formalizes the IWRM paradigm in a nine-step decision-making procedure that provides the reader with a conceptual map of the book and shows the value of its subject matter. Each part is opened by an invited contribution from an outstanding scientist in the field: Prof. Peter Young from Lancaster University, UK, Prof. Aris Georgakakos from GeorgiaTech, USA, and Prof. Slobodan Simonovic from Western Ontario University, Canada. Present and future research directions on the border between System Analysis and IWRM are surveyed in the chapter that closes the volume, where a pool of scientists and experts, coordinated by Prof. Anthony Jakeman from the Australian National University, suggests a research agenda to achieve sustainable (environmental, economic and social) outcomes in the implementation of the IWRM paradigm. We acknowledge with much gratitude the support of the International Federation of Automatic Control and the Istituto Veneto di Scienze Lettere ed Arti, of all the contributors to this volume as well as of all the conference participants, and last but not least, of our colleagues Daniele de Rigo and Enrico Weber for their essential role in the organization of the Workshop. Milano, April 30th 2006 Andrea Castelletti & Rodolfo Soncini-Sessa

Part I

Introduction

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CHAPTER 1

A Participatory and Integrated Planning Procedure for Decision Making in Water Resource Systems Andrea Castelletti and Rodolfo Soncini-Sessa Dipartimento di Elettronica e Informazione Politecnico di Milano, Milano, Italy

1.1 Introduction In many parts of the world, water demand is increasing, while at the same time availability and quality of water resources are decreasing, mainly due to human activities, in connection with the growing world population, ongoing urbanization, industrialization and the intensification of agriculture. This development is often associated with general reductions in environmental quality and endangers sustainable development. An integrated approach is required to identify and analyse such unfavourable and undesired developments and to allow sustainable systems to be designed that integrate human society with its natural environment for the benefit of both. It is generally agreed (Bonell and Askew, 2000) that Integrated Water Resources Management (IWRM, see GWP, 2000 and 2003) plays a crucial role in this context and that a participatory approach would help to better control and accelerate the integration, to make the decision-making process more transparent and comparable across various river basins and scales, and to increase confidence in an integrated model-based planning process. Notwithstanding the popularity raised by the IWRM concept in the last decade, the applications to real world cases of the paradigm mentioned in the literature are still a few (see for instance Tortajada et al., 2004), thus feeding a growing scepticism (Biswas, 2004) towards the real potential of this attractive approach. The development of proper legislation and policy is a key-issue to disseminate integration and participation into the water management practice (Wolf, 2002). At the European level the IWRM paradigm has been adopted by the Water Framework Directive (WFD – Directive/2000/60/EC (European Commission, 2000)) that came into force in December 2000. The directive introduces a set of requirements to be fulfilled in order to reach inland and coastal water ‘good status’ by 2015, and sets out a detailed framework for the 3

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improved planning and management of water, including the development of River Basin Management Plans (RBMP) within 2009 (Art. 13). The Guidance Document for the Planning Process (GD11, issued by the European Commission, 2003a) explains (pag. 5) how the term ‘integration’ should be intended in the implementation of such plans. Integration must concern many aspects, among which are particularly relevant: - Integration of environmental objectives. - Integration of all water uses, functions and values into a common policy framework. - Integration of all significant management and ecological aspects [. . .] including those which are beyond the scope of WFD, such as flood protection and prevention. - Integration of stakeholders in decision making, by promoting transparency and information to the public and by [. . .] involving stakeholders in the development of RBMP. The Guidance Document recognizes that planning is a process and requires to provide procedural guidance on the production and development of River Basin Management Plans supported by appropriate toolboxes, that should help to identify the possible tradeoffs among quantifiable objectives so that further debates and analysis can be more informed. Moreover the toolboxes must support planning as a systematic, integrative and iterative process. According to these requirements a general procedure for the Participatory and Integrated Planning (PIP) has been developed (Soncini-Sessa et al., 2007a) and will be here presented. The procedure has been conceived and implemented by formalizing the methodologies developed and experienced in the Verbano Project1 (Soncini-Sessa et al., 2007b and Ch. 12 of this book) and farther tested, completed and validated on other projects (see for instance Castelletti and Soncini-Sessa, 2006b). A Multi Objective Decision Support System (MODSS) has been developed to support each phase of the procedure (Soncini-Sessa et al., 1999). The IWRM paradigm was selected as the conceptual basis for the development of the PIP procedure, but the procedure complies also the rules of the DPSIR scheme adopted by the European Environment Agency (EEA, 1999, see also OECD, 1994 and UNCSD, 1996). Therefore this scheme will be briefly introduced in the following paragraph. Then Section 3 will be devoted to the description of the PIP procedure phase by phase. For each phase, the methodology to be followed in that phase is presented. Finally, some considerations are devoted to the development of ICT tools for supporting the implementation of the procedure. The decision-making process at the management level is considered in the last section. 1.2 Framing the decision-making problem The evolution of a natural system subject to anthropic pressure is well described by the framework DPSIR (Driving forces – Pressures – State – Impact – Responses), which is depicted in Fig. 1.1: the Drivers generate a Pressure upon the system, thus altering its 1 UE - INTERREG II.

A Participatory and Integrated Planning Procedure

5

1 Responses

Drivers

2

3 Pressures

4

State Impacts

Fig. 1.1. The DPSIR framework.

State. This variation produces Impacts on society, which reacts by devising and implementing Responses, which can be directed at the Drivers, as well as the Pressures, the State or the Impacts themselves. An example can be useful to clarify the framework: consider an enchanting lake, surrounded by fields, forests, a fishing village and a few small hotels. Among the Drivers are the agricultural, industrial and domestic practices that produce a flow (Pressure) of nitrogenous substances that reach the lake, after scouring the agricultural land, or through direct or indirect discharge from the sewerage system. It follows that there is an increase in the trophic level of the lake, which induces algal blooms, anoxic conditions and fish pestilence, and so, a variation in the State of the lake. In this way two Impacts are produced: a reduction in fishing activity and a loss of the lake’s appeal to tourists. In order to respond to the fishermen’s and hotel-keeper’s discontent, the Environmental Agency (EA) must design an intervention (Response). It can choose among different forms: issue a regulation for the use of nitrogenous fertilizers in agriculture (arrow 1 in Fig. 1.1), create a stage for the removal of phosphorous in the treatment plant that purifies the sewage prior to discharge (2), collect the algae when necessary or inject oxygen at a certain depth to prevent lake waters from becoming anoxic (3), or simply introduce a monetary compensation (4) for the damages. Generally, the EA is not limited to choosing only one of these interventions, each of which can be realized in different forms and degrees: it can also select a combination of them, in an integrated and coordinated package, that we will call alternative2 . 2 In the literature (see for example, Raiffa et al., 2002) there is a distinction between the alternatives that a party (stakeholder(s) and/or decision-maker(s)) can pursue alone (i.e. without reaching a negotiated agreement), and those that are subject to negotiations, because they contain actions that can be carried out only after an

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The choice of an alternative requires a decision-making procedure, which should be integrated (i.e., it should consider the system as a whole) and participatory (i.e., stakeholders take part in the decision-making process). 1.2.1 Participation The top down approach of the standard planning approach needs to be reversed, proposing and launching a participatory process (Renn et al., 1993; Renn, 1995; Budge, 1996; Delli Priscoli, 2004), that is ‘bottom up’ and based on the management of participation: a process of participation that begins from the proposal of the planning project and continues with the choice of the alternative to be implemented, right through to the monitoring of the effects after the implementation. This process should not be limited to providing the stakeholders with information (Informative Participation), nor to just asking them for information (Consultation), it should also involve the Stakeholders in the design and evaluation of the alternatives (Co-designing) and, ideally, even in the final choice (Co-deciding)3 (Mostert, 2003 and Hare et al., 2003). Such a kind of involvement is of crucial importance, not only when there is more than one decision-maker (DM) – as would be the case if the lake from the example and its inlet were to define the border between two countries – but also in presence of a single DM that is due to make the final decision. Even in that case, involving stakeholders in the negotiations of the Response might result in more public acceptance of the decisions, less litigation, fewer delays and generally better implementation. Indeed, in this way, a process of social learning is created, in which the stakeholders become aware of the problem, of the alternatives, and of the viewpoints of others; they take responsibility and together they develop the alternative to be carried out (Renn, 1995). The goal of the decision-making process is to reach an agreement that is acceptable to all the stakeholders, to which they remain committed, and which is actually implemented. Only the last, decisive phase of the procedure, the choice of the alternative that is to be implemented, is in most cases the reserved responsibility of the DM (or DMs) that has (have) the institutional power to make the choice. 1.3 The PIP procedure In Fig. 1.2, the flow diagram of the PIP procedure4 that we are going to describe is shown. However, before detailing the phases that compose it, a couple of comments is mandatory. agreement has been reached. The term ‘alternative’ is reserved for the first, while the term ‘option’ is used for the second. However in this chapter and in Ch. 12 we will use the term ‘alternative’ to designate both of them. In some contexts instead of ‘alternative’ the term ‘program of measures’ is used. 3 With surprising speed legislation has already adapted to this need: the Århus Convention, signed in 1998 and in force since 2001, recognises citizens’ rights to “have access to information and be enabled to participate in the decision-making process with regard to the environment”, based on the principle that only participation can make sustainable development possible. In escort to that convention, the Directive 2003/35/EC (European Commission, 2003b) was issued by the European Parliament which establishes that “the public concerned shall be given early and effective opportunities to participate in the environmental decision making procedures” right from the initial phases, so that they have an effective possibility to influence the choices. The WFD anticipated this position. 4 Even though it was devised autonomously, it can be interpreted as a variation of the PROACT scheme proposed by Hammond et al. (1999), that has been suitably modified to take into account that in the case we

A Participatory and Integrated Planning Procedure

0. Reconnaissance

1. Defining Actions

2. Defining Criteria and Indicators

3. Identifying the Model CONCEPTUALIZATION

4. Designing Alternatives

8. Mitigation and Compensation

5. Estimating Effects

6. Evaluation

7. Comparison NO

Consent? YES

9. Final decision

Best compromise alternative

Fig. 1.2. The phases of the PIP procedure.

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We are talking about a procedure, not an algorithm. This means the human beings are deeply involved in it and decisions are taken at each phase on the basis of subjective judgments and compromises among the possible, often mutually exclusive, course of actions (e.g. what data are trustable, which model to adopt, what are the relevant interests to consider in designing the alternatives). The PIP procedure is a recursive procedure, in the sense that often in the development of a phase the improved understanding of the system makes apparent that something in a previous phase has to be modified. Recursions may happen in many points, however only one case will be explicitly described in this paper (see Phase 8). The PIP procedure is a methodological approach, and as such it must mainly establish and explain what has to be done in each phase, not how to do it. Many methods and techniques are often available to fulfil the goal of each phase, and a choice has to be done among them on the basis of the specific features of the problem at hand. 1.3.1 Phase 0 – Reconnaissance A conscious and participatory choice ought to begin from the acknowledgement of the problem to be tackled. For this reason the activities within this phase concentrate on defining the Goal of the planning project, the (spatial and temporal) boundaries of the system being considered5 , the normative and planning context in which the procedure operates, the data available, and the information that needs to be collected. One must start off from the identification of the stakeholders involved and their needs, expectations, fears and perceptions, in a word, their interests6 . Then the PIP procedure has to be explained to and accepted by, or, if necessary, negotiated among all the parties (stakeholders and DM(s)). At this point it becomes possible to define the Goal that the planning project must pursue. It is derived from the DM’s strategic goals, from the stakeholders’ interests and from the regulatory and planning context. It must always include the environmental objectives, e.g. good water status and sustainable uses, as required by the WFD (art. 4, see European Commission, 2000). Sometimes, it could be useful to translate the goal for each stakeholder into a vision that visualizes, with words, or better still, with a picture, the condition that the project aims for. The choice of a good vision is important when the stakeholders are not very motivated to take part in the decision-making process or resistant, or unable to express their own goals. An example of vision for a river water quality restoration project is shown in Fig. 1.3. An extensive knowledge of the system is the fundamental support to all these activities and it is acquired by: analysing the regulatory and planning context of the planning project; collecting information and data available; identifying the missing information; and finally, filling the information gaps by conducting hydrological, economic and social surveys. The actors should share all the available information, agree upon its validity (this is crucial!) and the potential need for further investigations. In other words, when necessary, even the validity and availability of the information must be negotiated. examine here the decision-making process is targeted at consensus building and actions include management, i.e. recursive decisions. 5 These two points are often referred to as scoping. 6 “It is crucial for the legitimacy of a planning process to start dialogue as early as possible in the phase of problem definition” (European Commission, 2003a).

A Participatory and Integrated Planning Procedure

9

Fig. 1.3. The vision for a river water quality restoration project (from Nardini, 2005).

1.3.2 Phase 1 – Defining Actions In this phase, the options for intervention that are supposed to achieve the project Goal must be identified, bearing in mind the interests of the different stakeholders. This is no simple operation because the opinions can be very discordant. For example, for some the obvious solution for the well-known flooding (‘high water’) problem in Venice would be the construction of the MOSE7 at the mouths of the lagoon, for others it is the construction of gate-ships8 , which are less complicated to construct and which would be more adaptable to the bradyseism of the lagoon bottom, while still others say that the only sensible option would be the reduction in the green house gas emissions that are responsible for the eustatic sea in front of the lagoon that are held to be the cause of the continuing worsening of the phenomenon. From this initial, decidedly disorganized, collection of ideas, which are in part silly, in part gifted with incredible wisdom, good ones always emerge. It may seem strange to begin with brain storming, however it is essential to promote creative decision-making that considers more than just a set of interventions given a priori, and is able to open new perspectives and discover unexpected alternatives. If an intervention is really useful it will emerge in the following phases, and if all the stakeholders’ suggestions are considered and processed they are more prone to collaborate since they feel they are being “taken 7 A system of submerged hollow steel gates, hinged at the bottom of the lagoon and installed at each of the lagoon’s three openings to the Adriatic sea. When ‘high water’ is foreseen, they can be raised by pumping compressed air into them and creating a sea barrier. 8 Two ships, whose length is about half the width of the mouth of the lagoon, which are hinged to the two offshore piers that mark the mouth’s boundaries. The free end of each ship is equipped with a propeller that allows them to position themselves across the mouth opening. When the ships are in this position they flood their hulls that make up their bottom so that they sink, creating an insurmountable barrier for the sea. When the high water event is over, the mouth of the lagoon is reopened by reversing the operation. The major advantage with respect to the MOSE is that the construction and the maintenance are done on dry land and the mouth of the lagoon would need to be modified only slightly.

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seriously” (WFD, Annex VII point A9, see European Commission, 2000). Moreover, how can there be a participatory process if one does not stop to listen carefully to the ideas and proposals of the parties? This first, creative phase must necessarily be followed by a phase of ‘decanting’, in which the distinction is made between what can be decided and what cannot, and thus, the effective decision space becomes apparent. It is in fact useless to debate passionately about aspects that cannot be decided in the Project. But note carefully that this is not the time to discriminate the useful interventions from the useless ones, or the sensible ones from the less sensible. This will be a task for the next phases. In this way, one obtains a list of the interventions ‘that can be decided upon’ in the context of the planning project. Each intervention is finally broken down into one or more (meta)-actions, i.e. into elementary interventions that can be fully and easily defined by specifying the values of their attributes, that is by specifying who is doing what, how and when. In this way a meta-action is transformed in an instantiated action. Technically, this transformation is carried out by assigning values to the parameters and/or the functions that describe the attributes of the meta-action. The specification of these values is a matter for a future phase (Phase 4: Designing Alternative), but their feasibility sets have to be defined in this phase, thus fixing the meta-actions to be considered. The instantiated actions are the ‘building blocks’ from which the alternatives will be constructed later. In the following, as we have done so far, we will use the term ‘action’ to denote both the meta-actions and the instantiated actions. The actual meaning will be clear from the context.

1.3.3 Phase 2 – Defining Criteria and Indicators To evaluate and compare the effects of the alternatives upon the system, it is necessary to define, by interacting with the stakeholders, a set of evaluation criteria that reflect the features of the problem and the values underlaying the judgments expressed by the stakeholders. The criteria do not have to pertain only to the project Goal, but to all the positive and negative effects the stakeholders hope for or fear: in other words they must express their interests9 . In particular, the criteria for sustainable development will be proposed by the Agencies and by the Environmental Associations which must always be included in the stakeholder group. Not every evaluation criterion is necessarily expressed in an operative way, i.e., it may not spontaneously define a procedure that allows ascertaining how much a given alternative satisfies that criterion. This is why an index is associated to each criterion, that is a procedure with which to associate the criteria with a value that expresses the degree to which it is satisfied (see Soncini-Sessa et al., 2003). This is done through the identification of relationships between the evaluation criterion and the variables (e.g. lake level, etc.) that describe the system condition. In practice, one proceeds by first splitting the evaluation criterion into lower level criteria and in turn, splitting those into even lower level criteria, until it is possible to associate each one of the criteria at the 9 Objectives are the same as interest: unfortunately the negotiation theorists and the decision-making theorists have not agreed on a common term. The first talk about ‘interests’ and the second about ‘criteria and objectives’; but they mean the same thing. We will adopt the jargon of the second group.

A Participatory and Integrated Planning Procedure

11

lowest level (leaf criteria) with an indicator, i.e. a function of the trajectories of the variables describing the system condition. In this way a hierarchy of criteria (evaluation hierarchy) is obtained for each evaluation criterion. The definition of a criterion and of its hierarchy ought to encompass either thresholds or a stakeholder’s wish (leitbild, see Egger et al., 2003), which is often related to the performance level (s)he demands. It is necessary to dedicate a great deal of time and attention to interactions with the stakeholders and to studying their points of view, because it is essential that each stakeholder sees that his/her interests are expressed in at least one of the indicators. If this does not happen, negotiations in Phase 7 will inevitably fail. In the next phase we will see that very often the system is affected by random inputs (either stochastic or uncertain). It follows that also the values assumed by the indicators are not generally deterministic. When this occurs, it is necessary to take account of the riskaversion that the DMs and stakeholders may have. This can be expressed through the classical approach of utility functions, proposed by Keeney and Raiffa (1976), but more often it is translated through criteria10 from which the most frequently adopted are the Laplace criteria (expected value) and the Wald criteria (worst case) (French, 1988). Like all the phases of the PIP procedure this one must also be participatory: the evaluation criteria should be forthcoming from the stakeholders and the definition of the indicators must have their contribution and approval. This last phase is however often very technical and so, as for the technical phases in the following phases, the stakeholders can be supported by experts.

1.3.4 Phase 3 – Identifying the Model In order to quantify the effects that the different alternatives would produce on the different indicators if they were to be implemented, it is necessary to provide a model that describes the cause–effect relationships within the system. The choice of the type of model and of the degree of detail with which to describe the phenomena, is tightly dependents upon both the indicators defined in Phase 2 and the actions considered. In our example, the regulation could be devised by describing the system through a set of algebraic equations, while the project of the artificial ventilation might require a description of the lake through partial differential equations. However, it is not mandatory that the model must be mathematical, it could be for instance an expert, who is able to describe the effects that will be induced by a given alternative on the basis of his experience (see for instance the MÖLL Project (Muhar et al., 2000 and Jungwirth et al., 2000). It may also occur that the physical and/or socio-economical relations that underly some of the components of the considered system are poorly known (i.e., the knowledge on the system is unstructured or limited) or it is comparatively expensive to obtain raw data to characterize them better. In such cases a Bayesian Network (Castelletti and Soncini-Sessa, 2006a, and references therein), i.e. a probabilistic description of the cause–effect relationship linking the variables within the system can be a well-suited solution (Castelletti 10 Take care not to confuse this meaning of the term criteria (stakeholders’ attitude towards risk), with the one previously introduced (judgement category).

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and Soncini-Sessa, 2006b). However in the majority of cases the model is more or less mathematically formalized and therefore it has to be identified, i.e. its structure has to be fixed (conceptualisation) and the values of the parameters that appear in it estimated (calibration). Sometimes more than one model may be adopted, due to the constraints posed by the power of the computer at hand, i.e., a more simple model for designing the alternatives (screening model, see Phase 4) and one for estimating the effects (evaluation model, see Phase 5). When this is the case a good practice would be that the first is a parsimonious version of the second (for an example see Chappell et al., 2001). Alternatively, complexity reduction techniques can be adopted to derive a easily computable version of the evaluation model (Hooijmeijer et al., 1998). The model’s input variables must include the parameters that quantify the attributes of the actions (e.g. the maximum nitrogen supply allowed in agricultural practices and the nitrogen removal efficiency of the treatment plant) as well as all the variables that allow the future conditions of the system to be described (e.g. the precipitation in the catchment area and the users’ water demand). The choice of values to attribute to the former constitutes the matter of the planning, while the values assigned to the latter describe the context within which the alternatives are evaluated and so collectively they are called a scenario11 . Both alternatives and scenarios have to be quantitatively specified before the model can be run. Note that there can be more than one scenario: in the lake example we might be interested in evaluating what would occur in a ‘high’ or in a ‘low’ rainfall scenario. Moreover, the scenario is not compulsory deterministic: very often it can be random. The scenario(s) may be chosen by experts or it (they) can be obtained by running models, if they are available, that describe the processes that produce the driving forces. In the lake example the future scenario of rainfall can be generated by a climate change model, while the future scenario of agricultural practices can be suggested by an expert. When all the processes are stationary, the historic scenario, i.e. the trajectories registered in the past, is often adopted for the reasons that will become apparent in Phase 5. In any case the time horizon of the scenario should be sufficiently long to capture all the types of the significant events the system may face. It is a common practice to adopt different scenarios for designing the alternatives (design scenario) and for estimating the effects (evaluation scenario, sometimes also called baseline scenario), which are generally fixed in the corresponding phases. To facilitate a social learning process it is important that the stakeholders ideally go through the same thinking process and be exposed to the same information and arguments as the analyst even during the modelling phase. For this reason, the implicit assumptions of the models should be made explicit and the modelling activity supported by a MODSS that must be flexible enough to identify models through a participatory process (Loucks, 1989). Only in that way can the stakeholders share a common interpretation of the system behaviour (model), which is necessary for them to be able to trust the effects that are estimated with the model. Agreeing on the same model does not prevent them from 11 Dictionaries give the term scenario the following definition: ‘a possible set of future events’. Brought into our context the term lends itself to three different possible meanings: 1. synonymous to alternative (e.g. businessas-usual scenario means Zero Alternative); 2. the set of effects that an alternative produces; 3. the time series of input variables that are not controlled by the DM. We will strictly adhere to this last meaning.

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having different perceptions (indicators) of these effects. The set of phases 1–3 constitutes the conceptualization of the planning problem.

1.3.5 Phase 4 – Designing the Alternatives It is very often the case that the alternatives considered during a real design process are only those suggested by the stakeholders and the manager’s experience. It is suitable to start off from those, but we think that limiting to them could be a mistake. More correctly, remembering that an alternative is an integrated package of actions, all the alternatives that can be obtained by combining the actions identified in Phase 1 in all possible ways should be considered. Often the number of alternatives that follows is so high that it would be impossible to examine them all in the following phases and so it is necessary to select only the ‘most interesting’ ones. However, these must be chosen still following the stakeholders’ criteria, that were identified in Phase 2, rather than the analyst’s preferences. In more complicated projects, where a higher level of mathematical formalization is required, the identification of the ‘most interesting’ alternatives has to be made according to the principle of Pareto optimality: all the dominated alternatives are removed. These latter are those alternatives that improve the satisfaction of a (group of) stakeholder(s), without worsening the satisfaction of the others; these alternatives would never be chosen by any stakeholder, indeed. The remaining alternatives are the efficient alternatives constituting the Pareto frontier. Figure 1.4 gives a qualitative description of it in the event that, as in our example, the indicators considered are the average annual catch and the costs for a new stage in the wastewater treatment plant. The identification of the Pareto frontier is carried out by defining and solving a Design Problem, which can either be regarded as a Mathematical Programming Problem cost

performances of dominated alternatives

average annual catch Fig. 1.4. A qualitative representation of the Pareto frontier in the event that, as in our example, the indicators considered are the average annual catch and the wastewater treatment plant cost.

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(for an example see Whittington and Guariso, 1983) or an Optimal Control Problem (see for instance Georgakakos and Yao, 1993; Kelman et al., 1990 and Soncini-Sessa et al., 2001) depending on whether the actions making up the alternative are taken una tantum (like the decision of building the wastewater treatment plant) or are recursive (like the algae removal or the air insufflation). The alternatives obtained by solving the Design Problem are efficient with respect to objectives, accordingly called design objectives, that are defined on a design horizon, given the design scenario and taking into account only a subset of the evaluation indicators: the design indicators. This simplification is introduced when considering all the evaluation indicators would make the Design Problem unsolvable in acceptable computation times; it does not excessively polarize the result if the design indicators were carefully chosen, since in Phases 5 and 6 the alternatives will be evaluated with respect to the complete set of indicators. The characteristics of the system appear in the Design Problem as constraints, while other elements that define the design scenario (e.g. user demands, foodstuff prices) contribute, along with the structural and normative actions, to determine the value of parameters that appear in the constraints and in the objectives of the Problem. Solving the Problem through an appropriate algorithm provides the set of alternatives that will be examined in successive phases. To these the Zero Alternative (A0) is always added, that is, the alternative that assumes that nothing is done and everything remains the way it is (business-asusual).

1.3.6 Phase 5 – Estimating Effects Once the alternatives are identified, the effects that each of them produces must be evaluated: in other words, it is necessary to compute the values that the indicators takes on as a result of each of the alternatives being implemented. When the system is not dynamical, the evaluation is straightforward. When the system is dynamical this estimation requires that each alternative be simulated over a time horizon (evaluation horizon) long enough to make extreme events (droughts or floods) likely to occur, in order to avoid the risk of estimating the effects in ‘average conditions’ only. In both cases it is necessary to feed the model with an appropriate input: the actions of the alternative considered and one or more evaluation scenarios. The alternatives will be compared to single out the ‘best’ one on the basis of the effects estimated in correspondence to one of these scenarios, the most probable for example; the effects estimated with the others will be useful for evaluating what would happen if the scenario that was considered didn’t occur and develop a precautionary viewpoint. The choice of the scenario(s) to adopt can be critical, and the parties must agree, otherwise, the following phases would fail. The adoption of a historic scenario (i.e. of a situation that was historically recorded) has an advantage in that it allows the comparison between what happened and what would have happened if the alternative had been implemented at the beginning of the historic horizon being considered. This information has a heightened significance for the parties because it provides a more immediate perception of the effects when they have, as they often do, a direct memory of those events. If the historic horizon is too short, artificially generated scenarios can be used, provided that they are as probable as the historic one.

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By doing so the estimate of the effects is statistically more reliable, but the psychological significance is lost. Both of these ways to proceed are meaningful only when one can reasonably assume that the processes that generate the scenario (e.g. the meteorological system and land use) remain unchanged into the future. If not, the scenarios have to be generated with models that describe the expected changes. At the end of this phase the values that have been obtained for the indicators are organized in a matrix – called Matrix of the effects – whose columns correspond to the alternatives and the rows to the indicators. This matrix is the base for the cost effectiveness analysis required by WFD.

1.3.7 Phase 6 – Evaluation An indicator measures the effect produced by an alternative on a particular leaf criterion in physical units. Nevertheless, the ‘value’ that the stakeholders attribute to an alternative, in other words the satisfaction that they get from it, is not always directly proportional to the value assumed by the indicator. In the lake example, the ‘value’ that the fishermen attribute to the catch grows very rapidly for low catch yield, but very slowly at high catch yield, i.e. it ‘saturates’ when the fishermen feel satisfied. To account for this effect, it is necessary to translate each indicator (sometimes group of indicators) into the ‘value’ assigned by the stakeholders. This can be done by means of a partial value function that has to be identified through interviews to the stakeholders. Once all the indicators are transformed into ‘values’, a stakeholder (or a DM) can express the overall satisfaction that (s)he assigns to an alternative through a dimensionless index, whose value can be computed from the attained ‘values’. Therefore, it is possible to sort the alternatives for decreasing values of the index, thus identifying the alternatives that the stakeholder (or the DM) prefers (the first alternative in the ranking). This methodology is known under the acronym MAVT (Multi-Attribute Value Theory), it is due to Keeney and Raiffa (1976) and is the one most often employed. Nevertheless, it is not the only approach that can be applied: others can be also effectively adopted, like the Analytic Hierarchy Process (Saaty, 1980) or the ELECTRE (Roy, 1993) methods; however, all of them share the following severe disadvantage: in the event that one wish to evaluate a new alternative, it would be necessary to carry out new interviews with all stakeholders, while this almost never happens when the MAVT is adopted. If there is only one stakeholder (or DM), the optimal alternative is found and the decision-making process is concluded. When instead, as it is almost always the case, there is more than one stakeholder (or DM ), working in the aforesaid manner a different ranking is obtained for each one of them. The choice of an alternative then requires the expression of a judgement about the relative importance between the involved parties (stakeholders or DMs), i.e. requires that the parties negotiate between themselves or that a DM (or a Super-DM) expresses her preferences among the parties (stakeholders or DMs). Since, however, preferences and negotiations concern subjective aspects, dealing with them is postponed in the successive phases to maintain the distinction between facts and value judgements.

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1.3.8 Phase 7 – Comparison The aim of this phase is the identification of an alternative that is judged to be an acceptable compromise by all the parties and so does not encounter opposition from anyone. Obviously a win–win alternative, i.e. an alternative that improves all the parties’ indices with respect to the Zero Alternative, would be the ideal solution for the decision-making process. Unfortunately, such an alternative does not always exist. In the case of irresolvable conflict between the interests of different parties, the phase concludes with the identification of the alternatives that gathers a large consent from them and listing the supporting and opposing parties for each of them. We call these alternatives reasonable alternatives12 (or compromise alternatives13 as they are also called). Thus, with this term we refer to the alternatives that are supported by at least one party, are admissible (because they fit physical, technical and legal constraints), are economically feasible, and Paretian, i.e., they are such that it is impossible to improve the satisfaction of one party without worsening that of another. To achieve this result, first of all a series of activities are promoted, which help each of the parties to know and understand the others’ points of view, and, if they exist, the negative effects that the alternative (s)he prefers produce for the others. Once this information has been shared, the heart of the phase is the search for a compromise through negotiations among the parties. Negotiations can be carried out with different methods that can be gathered in two main groups. The methods in the first group are based on negotiations of the weights that each stakeholder assigns to different evaluation criteria; the ones of the second operate in a dual manner upon the thresholds. Leader of this latter family is the method named Pareto-Race (Korhonen and Laakso, 1986 and Soncini-Sessa et al., 2007a). Sometimes it is necessary to suspend negotiations and move back to Phase 4 to design other alternatives, in view of what has been understood of the needs, aspirations, and requests of the Parties; the effects of the new alternatives should be then estimated (Phase 5), evaluated (6) and brought to negotiations (7). In this way and iteration between the Phases 4-5-6-7 is established. 1.3.9 Phase 8 – Mitigation and Compensation If an alternative enjoys the consent of the majority of the parties, but not all them, it is important to explore whether or not it is possible to increase the consent and satisfy some of the unsatisfied parties through measures (meta-actions) of mitigation or compensation. To do this it is necessary to identify new (meta-)actions to include in the alternative, that act specifically on the criteria of unsatisfied parties. Once these (meta-)actions have been identified, they must be instantiated into actions (Phase 4), their effects estimated (5), then they must be evaluated (6) and compared (7) with the reasonable alternatives previously identified, in order to see if they actually produce an enlargement of the consent. In this 12 This term is taken from art. 5 of the Directive 2001/42/ EC (European Commission, 2001) about strategic Environmental Evaluation (VAS). 13 They are given this name because they emerge from a negotiation process in which an attempt is made to find a compromise among different points of view. However, it is not necessarily possible to achieve this, so the term seems equivocal to us and we prefer the first.

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way one obtains a new set of reasonable alternatives, that could be examined in their turn to find new mitigation measures, until no more mitigation measures broadening the consent can be identified. If opponents still exist at this point the possibility of compensating the disadvantage they perceive must be explored, in our example the economic losses of the fisherman can be reimbursed. Here a recursion is established between the Phases 4-5-6-7-8 (Fig. 1.2), which sometimes also includes Phases 1-2-3, during which the whole set of alternatives is ‘sifted’ in order to single out the reasonable alternatives. Sifting ends when a reasonable alternative is identified, which is accepted by all parties; or when it is no longer possible to identify mitigation measures or new measures that make it possible to enlarge the consent; or simply when the time available for the decision-making process has run out. By construction, each alternative obtained in this way has the support of at least one of the parties. The whole of them are presented in the summary document of the study (see for example Ch. 16 in Soncini-Sessa et al., 2007b), that sums up the entire development of the planning project and its results. This document is the material needed to begin the next and last phase. 1.3.10 Phase 9 – Final decision This phase is put into practice only when there are one or more DMs, who are at a higher level than the parties who sifted the alternatives, and are responsible for the final decision about which alternative will be implemented. It is therefore up to these DMs to choose the best compromise alternative from the reasonable alternatives, where ‘best compromise’ means the alternative that best reconciles the different interests, or simply the one upon which they manage to agree. In many cases, this phase is simply a comparison (if there is only one DM) or negotiations (if there is more than one) of the reasonable alternatives, which is often carried out with less formalized methods than those used in Phase 7, being mindful of the customs and local culture. Sometimes however the DM(s) feels the need to explore new alternatives or introduce new criteria. In that case the phase transforms into a new cycle of the Phases 1-8. 1.3.11 Comments and remarks Often the importance of phases that have an engineering or modelling character (in particular Identifying the Model (Phase 3) or Designing Alternatives (Phase 4)) is emphasized, at the expense of more ‘sociological’ phases, like Defining Criteria and Indicators, Evaluation and Comparison. This is a mistake, since a correct decision can be taken only when the expectations, desires, images, knowledge, problems and fears of the stakeholders are as well described and understood as the physical, technical, and economic aspects of the system. Therefore, not only are Phases 3 and 4 of equal importance to the others, and must be considered as such, but the participation of stakeholders should be full and continuous in all the phases, because only in this way will negotiations in Phase 7 be successful. We will never tire of repeating that if the stakeholders do not believe in the index values that are shown to them in that phase, they will never be willing to negotiate. Actually, they will probably not decline to participate in negotiations, but it will develop

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painstakingly, with stakeholders that listen passively or react aggressively and the result, even if formally it can be achieved, will not really change anything in the existing conflict. Not all the phases are always necessary. If, for example, in Phase 2 only one criterion is identified the decision-making process concludes with Phase 4 (or at most with Phase 5). If instead there is only one DM and she does not intend to activate a participatory decisionmaking procedure, the process concludes with Phase 6. If there is no DM above the parties that participated in negotiations, it makes no sense to go through Phase 9. It is important to underline that the real development of the decision-making process is not serial as Fig. 1.2 might lead one to think. Besides the recursion between the Phases 4-5-6-7-8, which is explicitly highlighted in the figure, many others can appear. For example, the criteria cannot actually be correctly identified if one does not take note of the actions being considered, since these latter produce the effects that the stakeholders endure. On the other hand, it is not possible to identify the actions without knowing the interests at stake, and therefore the criteria. The presence of recursions is essentially due to the fact that in carrying out the decision-making process new information is produced, because it is a process of social learning. In view of the new information that are acquired, it is then necessary to re-examine the conclusions of the phases that were considered to be already finished and, when necessary, modify them. In one sense the aim of the decisionmaking process is to increase the parties’ understanding about the planning project, so that they can formulate more and more precise requests and motivated opinions. 1.3.12 The FOTE paradigm Negotiations and relations among the parties are easy or difficult in relation to the degree to which they share and exchange information about the problem, the system and their own personal interests; that is the degree to which they adopt a paradigm of FOTE (Full Open Truthful Exchange (Raiffa et al., 2002). This is why the PIP procedure does not begin with negotiating the alternatives, but instead with an information exchange (Phase 0). It goes on with a participatory definition of the actions (Phase 1), the enunciation of the interests (Phase 2), the identification of a shared model of the system (Phase 3). 1.3.13 The data Do not underestimate the essential role that the data, and the methodologies for their acquisition play in the decision-making process. The data are used, in a qualitative way, in the phases of Recognition and Defining Actions, and in a quantitative form in Phases 2, 3, 4, 5 and 8. Their quantity, availability and accuracy are essential to the success of the decision-making process, but even more important is that all the parties believe that the data is valid and meaningful. The credibility of the model, which is the basis for the credibility of the evaluation of the alternatives, is founded upon this belief, without which the negotiation process is a fruitless exercise. In simple terms: the entire decision-making process depends on the social acceptability of the data. 1.3.14 The uncertainty Ignorance is being unaware that our awareness is imperfect. An imperfect awareness implies uncertainty and uncertainty generates apprehension. For this reason, DMs often

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have the tendency to remove the problem of uncertainty: they want scenarios to be deterministic and models to provide exact estimates, so that their evaluations will be perfect. However, hiding uncertainty is none other than a form of ignorance. Thus, in many phases of the PIP procedure the problem of treating uncertainty arises. Uncertainty, is produced by corrupt, insufficient or scarce information, and by the errors that are committed unknowingly. We will see in the following that all these causes are represented as the effects of disturbances, which can assume different forms: disturbances are stochastic when we know or we can estimate their probability distributions; they are uncertain when we know only the set of values that we guess they might assume. The form of a disturbance depends upon the source that generates it, for example data collection generates the most common uncertainty: measurement errors, which are always described as stochastic and that afflict all the phases in which the data are used. In Phase 1 uncertainty appears also in the description of the actions, since the way in which they will actually be implemented is not always certain (implementation uncertainty); this uncertainty is not only due to implementation aspects, but also to institutional inertia. In Phases 3 and 6 one must account for the disturbances that can make the design and evaluation of scenarios uncertain: it is when confronted with this type of uncertainty about the future that DMs and stakeholders reveal their aversion to risk, which we discussed in the description of Phase 2. In Phase 3 it is necessary to take into account the so-called process errors, i.e. the eventuality that the model does not perfectly describe reality. The effects produced by all these disturbances combine to generate the uncertainty that afflicts the indicator values that make up the Matrix of effects.

1.4 ICT Tools The PIP procedure has to be supported: all the phases, except for the phase of Recognition, must be handled by an appropriate set (toolbox) of ICT (Information and Communication Technologies) tools, which its users must perceive as parts of a unique and coherent system, i.e. a Multi Objective Decision Support System (MODSS). Since these systems were first applied to water related problems (SFWMG, 1987; Simonovic and Savic, 1989; Loucks, 1990 and Soncini-Sessa et al., 1990) considerable progress was made (Jolma, 1999; Soncini-Sessa et al., 1999; Nandalal and Simonovic, 2002 and Liu and Stewart, 2004). However, relatively few of them have been actually and regularly applied in real world decision-making processes. In the authors’ opinion this is essentially due to a general lack of communication and information exchange between analysts, DM(s) and stakeholders. Models and algorithms almost always need an expert to be run and the practitioners (DMs and stakeholders) can only evaluate and believe to the assumptions made by the analyst without being able to effectively handle the tools. To reduce the gap between the academy and real world methods and algorithms developed by the scientist should be usable and accessible even by the non-scientists and shared between stakeholders and policy-makers. A promising step-head in this direction is enabled by the Internet technologies, through which WEB-based MODSS might be developed (some prototypes have been already devised, see Salewicz and Nakayama, 2004). There are three main further reasons for a MODSS to operate on the WEB:

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A prototype version of a MODSS covering Phases 3-6 of the PIP procedure is already running at DEI-Politecnico di Milano (Soncini-Sessa et al., 1999) and has been tested on the Verbano Project and MERIT-FP5 Project as well as on other national projects. 1.5 The decision-making process at the management level Once the best compromise alternative has been selected (Phase 9 of the PIP procedure), it has to be implemented: this is achieved by implementing the structural and normative actions it includes, and applying the regulation policy – if this has been designed – at the scheduled time instants. Since this policy may leave some degree of freedom to the Regulator (see Aufiero et al., 2001 and 2002), a decision-making problem must be formulated at the management level as well, though the degree of freedom that the policy allows is much less than the degree that the DM had when the problem was formulated at the planning level, i.e. at the level that we have been considering until now. The best compromise alternative is therefore at the same time the conclusion of the decision-making process at the planning level and the starting point for the decision-making process that is renewed periodically, often daily, at the management level, on the basis of the new information which are obtained as the time goes on. Acknowledgements The preparation of the chapter was carried on within the Project COFIN 2004 Sistemi di supporto alle decisioni per la pianificazione e gestione di serbatoi e laghi regolati [prot. 2004132971_004]. Bibliography Aufiero, A., R. Soncini-Sessa and E. Weber (2001). Set-valued control laws in minmax control problem. In: Proceedings of IFAC Workshop Modelling and Control in Environmental Issues, August 22–23. Elsevier. Yokohama, J. Aufiero, A., R. Soncini-Sessa and E. Weber (2002). Set-valued control laws in TEV-DC control problem. In: Proceedings of 15th IFAC World Congress on Automatic Control, July 21–26. Elsevier. Barcelona, E. 14 The Beowulf Cluster Site, http://www.beowulf.org; Apple ACG Xgrid, http://www.apple.com/acg/xgrid/; DataGrid Project, http://web.datagrid.cnr.it, visited 01/2006.

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Soncini-Sessa, R., A. Castelletti and E. Weber (2007a). Integrated and participatory water resources management. Theory. Elsevier. Amsterdam, NL. to appear. Soncini-Sessa, R., A. Nardini, C. Gandolfi and A. Kraszewski (1990). Computer aided water reservoir management: a prototype two level DSS. In: Proceedings of the NATO ARW on Computer Aided Support Systems in Water Resources Research and Management, September 23–28. Ericeira, P. Soncini-Sessa, R., A.E. Rizzoli, L. Villa and E. Weber (1999). TwoLe: a software tool for planning and management of water reservoir networks. Hydrolog. Sci. J. 44(4), 619–631. Soncini-Sessa, R., F. Cellina, F. Pianosi and E. Weber (2007b). Integrated and participatory water resources management. Practice. Elsevier. Amsterdam, NL. to appear. Tortajada, C., Varis, O. and Biswas, A.K., Eds.) (2004). Integrated Water Resources Management in South and Southest Asia. Oxford University Press. Oxford, UK. UNCSD – United Nations Commission on Sustainable Development (1996). Indicators of sustainable development framework and methodologies. Technical report. UNCSD. New York, NY. Whittington, D. and G. Guariso (1983). Water Management Models in Practice: A Case Study of the Aswan High Dam. Elsevier. San Francisco, CA. Wolf, A.T., Ed.) (2002). Conflict Prevention and Resolution in Water Systems. Edward Elgar. Cheltenham, UK.

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Part II

Modelling

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CHAPTER 2

The Data-Based Mechanistic Approach in Hydrological Modelling Peter C. Young1 , Andrea Castelletti2 and Francesca Pianosi2 1 Centre for Research on Environmental Systems and Statistics

Lancaster University, Lancaster, UK; and Centre for Resource and Environmental Studies Australian National University 2 Dipartimento di Elettronica e Informazione

Politecnico di Milano, Milano, Italy

2.1 Introduction Throughout the 20th century, the hypothetico-deductive approach to scientific research, as extolled so lucidly by the philosopher Karl Popper (1959), reigned supreme. The inductive approach, that had provided the cornerstone for scientific research in earlier centuries, became less attractive to the physicists and chemists who dominated science during the 20th century and whose well planned experimentation provided some of the stimulus for Popper’s views. Moreover, the rise of the computer, with its ability to construct and solve large mathematical simulation models, provided a magnificent engine for the implementation of the hypothetico-deductive approach. And today, such computer-based simulation modelling has become straightforward, almost simple, with the availability of iconographic software, such as Matlab/Simulink™, where a complex model can be assembled quickly from a built-in and comprehensive library of simulation objects. All this would appear to be good news for environmental scientists involved in the management and planning of environmental systems. But is it? While acknowledging the virtues of simulation modelling, particularly when it is carried out in stochastic terms, this chapter will discuss the limitations of this approach when it is used in the context of environmental systems, where planned experimentation is difficult, if not impossible, and where uncertainty about the nature of the processes involved sits uncomfortably with the deterministic models that appear to dominate simulation modelling practice. The chapter will outline a Data-Based Mechanistic (DBM) approach to modelling, forecasting and control that often starts with the construction and evaluation of a simu27

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lation model which reflects the scientists’ perception of the physical, chemical and biological mechanisms that characterise the environmental system. However, these DBM studies are not simple exercises in simulation modelling: rather they constitute a critical evaluation of the model in both stochastic and response terms; an evaluation that marks only the beginning, not the end, of the modelling process. Exploiting some of the tools of DBM modelling that are later applied to real data, this critical evaluation considers the simulation model as a natural extension of the thought processes and scientific speculation that resides in the mind of the model builder. And, by providing insight into the strengths and limitations of the simulation model, it provides a prelude to the exercises in DBM modelling from real data that becomes possible when data are available on the response of the environmental system to natural or anthropogenicallyinduced perturbations. Of course, in this environmental context, the real data required for DBM modelling is most often the result of monitoring studies, rather than planned experimentation. As a result, such data may not be available or, as is often the case, they may provide an insufficient basis for DBM modelling. Even in this data-deficient situation, however, the DBM modelling methodology can provide valuable insight into the strengths and limitations of the simulation model; insight that can radically effect the way in which the model is used as a tool in planning and management. Whatever model emerges from the DBM modelling process, it should be a model that is well suited to the objectives of the study team. These may range from ‘what-if’ simulation, where the complexity of the simulation model is a clear advantage, to exercises in forecasting and operational control, where the over-parametrization that normally characterizes the large simulation model is a definite disadvantage. This chapter will argue, therefore, that the construction of a single model that suits all purposes is normally impossible and, in any case, undesirable. Rather the objectives of the study should be clearly defined and a well integrated suite of models should be constructed, each designed to satisfy the requirements of these objectives. It is suggested that, wherever possible, the parametrically efficient (or ‘parsimonious’) DBM model should provide a description of the core mechanisms that dominate the observed behaviour of the environmental system under study. And it should also provide the basis for the final construction of a stochastic model that reflects this core behaviour but may involve other, more speculative elements that are required for ‘what-if’ simulation and planning exercises. The advantage of this DBM ‘moderation’ of the simulation model is that the relative confidence in the historically validated DBM core (when sufficient data are available) can be balanced with the reduced confidence in the more poorly validated speculative elements. Thus the results of any analysis can be better evaluated with these relative uncertainties in mind. For instance, if the model contains nonlinearities that have not been well-validated during DBM modelling over the historical period, but are thought to be of potential importance in the future, then the large level of uncertainty in this regard must be reflected clearly in any predictive application of the model. Given its generic nature, the Data-Based Mechanistic approach has wide application potential in many areas of study ranging from ecological (Young, 2000) and biological (Jarvis et al., 1999; Price et al., 2001), through engineering (Young, 1998a; Price et al., 1998) and environmental (Price et al., 2002), to business and macro-economic systems (Young and Pedregal, 1999; Pedregal and Young, 2002). Within a hydrological context, it

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was applied first in the early 1970s to the modelling of water quality in rivers (Beck and Young, 1975) and, in the subsequent years, to the modelling of rainfall–flow processes in river basins (e.g. see Young, 2002 and the prior references therein). However, only recently has its potential been explored to model river flow in snow-affected basins, in which the snow-melt makes a significant, and sometime dominant, contribution to the flow. This chapter will illustrate this novel application through a case study in Switzerland. 2.2 The Fundamentals of Data-Based Mechanistic modelling The term ‘data-based mechanistic modelling’ was first used in Young and Lees (1993) but the basic concepts of this approach to modelling dynamic systems have developed over many years. The evolution of the DBM philosophy and its detailed methodological underpinning are described in previous publications (e.g., Young, 2001a, 2003 and prior references therein), and so it will suffice here to merely outline the main aspects of the approach. The six major stages in the DBM modelling strategy are as follows: 1. The important first step is to define the objectives of the modelling exercise and to consider the type of model that is most appropriate to meeting these objectives. Since DBM modelling requires adequate data if it is to be completely successful, this stage also includes considerations of scale and the data availability at this scale, particularly as they relate to the defined modelling objectives. However, the prior assumptions about the form and structure of this model are kept at a minimum in order to avoid the prejudicial imposition of untested perceptions about the nature and complexity of the model needed to meet the defined objectives. 2. In the initial phases of modelling, it may well be that observational data will be scarce, so that any major modelling effort will have to be centred on simulation modelling, normally based on largely deterministic concepts, such as dynamic mass and energy conservation. In the DBM modelling approach, which is basically Bayesian in concept, these deterministic simulation equations are converted to a stochastic form by assuming that the associated parameters and inputs are inherently uncertain and can only be characterised in some suitable stochastic form, such as a probability distribution function (pdf) for the parameters and a time-series model for the inputs. The subsequent stochastic analysis uses Monte Carlo Simulation (MCS), to explore the propagation of uncertainty in the resulting stochastic model, and sensitivity analysis of the MCS results to identify the most important parameters which lead to a specified model behaviour: e.g. Parkinson and Young (1998). 3. The initial exploration of the simulation model in stochastic terms is aimed at revealing the relative importance of different parts of the model in explaining the dominant behavioural mechanisms. This understanding of the model is further enhanced by employing a novel method of model order reduction that we term Dominant Mode Analysis (DMA). This is applied to time-series data obtained from planned experimentation, not on the system itself, but on the simulation model

30

P.C. Young et al. that, in effect, becomes a surrogate for the real system. This rather unusual analysis is exploited in order to develop low-order, dominant mode approximations of the simulation model (e.g. Young et al., 1996; Young, 1999a) approximations that are often able to explain its dynamic response characteristics to a remarkably accurate degree (e.g. coefficients of determination RT2 , based on the simulated model response error, of greater than 0.999: i.e. greater than 99.9% of the large model output variance explained by the reduced order model output1 ). 4. Conveniently, the statistical methods used for DMA exercises in 3., above, are the same as those used for the DBM modelling from real time-series data that follows as the next stage in the modelling process. Here, appropriate model structures are identified by a process of objective statistical inference applied directly to the timeseries data and based initially on a given generic class of linear Transfer Function (TF) models (i.e. differential equation models or their discrete-time equivalents) whose parameters are allowed to vary over time, if this seems necessary to satisfactorily explain the data. In particular: • If the model is identified as predominantly linear, linear with slowly varying parameters or piece-wise linear, then the parameters that characterize the identified model structure in step 1 are estimated using advanced methods of statistical estimation for dynamic systems. The methods used in the present chapter are based on optimal, recursive Refined Instrumental Variable (RIV) estimation algorithms (Young, 1984) that provide a robust approach to model identification and estimation and have been well tested in practical applications over many years. Here the important identification stage means that objective statistical methods are used to determine the dynamic model order and structure, rather than assuming these a priori. In this manner, the identifiability of the model from the data is assured and the possibility of overparametrization is minimized. Full details of these time series methods are provided in the above mentioned references. • If significant parameter variation is detected over the observation interval, then the model parameters are estimated by the application of an approach to time dependent parameter estimation based on the application of recursive Fixed Interval Smoothing (FIS) algorithms (e.g. Bryson and Ho, 1969; Norton, 1986; Young, 1984, 1999). Such parameter variation will tend to reflect statistically significant nonstationary and nonlinear aspects of the observed system behaviour. In the case of nonlinear systems, the FIS algorithm provides a method of nonparametric (graphical) estimation that can often be interpreted in State-Dependent Parameter (SDP) terms (Young, 1993; Young and Beven, 1994; Young, 2000, 2001b). • If nonlinear phenomena have been detected and identified in step 2, the nonparametric, state dependent relationships are normally parameterized in a finite form and the resulting nonlinear model is estimated using some form of

1 Note that this is a much more discerning measure of the model’s adequacy than the conventional coefficient of determination R 2 based on the one-step-ahead prediction errors.

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numerical optimization, such as nonlinear least squares or Maximum Likelihood (ML) optimization. 5. Regardless of whether the model is identified and estimated in linear or nonlinear form, it is only accepted as a credible representation of the system if, in addition to explaining the data well, it also provides a description that has direct relevance to the physical reality of the system under study. This is a most important aspect of DBM modelling and differentiates it from more classical ‘black-box’ and ‘grey-box’ modelling methodologies, such as those associated with standard TF, nonlinear autoregressive moving average-exogenous variables (NARMAX), neural network and neuro-fuzzy models. 6. The final stage of model synthesis should always be an attempt at model validation: see e.g. Young (2001a). The word ‘attempt’ is important since validation is a complex process and even its definition is controversial. Some academics (e.g. Konikow and Bredehoeft, 1992 within a ground-water context; and Oreskes et al., 1994 in relation to the whole of the earth sciences) question even the possibility of validating models. However, statistical evaluation of the model by confirming that statistical diagnostics are satisfactory (e.g. no significant autocorrelation in the residuals or cross correlation between the residuals and input variables; no evidence of un-modelled nonlinearity, etc.) is always possible and can engender greater confidence in the efficacy of the model. Also, one specific, quantitative aspect of validation is widely accepted; namely ‘predictive validation’, in which the predictive potential of the model is evaluated on data other than that used in the identification and estimation stages of the analysis. When validated in this narrow sense, it can be assumed that the ‘conditionally valid’ model represents the best theory of behaviour currently available that has not yet been falsified in a Popperian sense. Of course, in any specific application, not all of these stages in the DBM modelling procedure will be required. Indeed, in the Case Study discussed in the next section, suitable time series data are available and these are entirely adequate for data-based modelling. Consequently, the simulation modelling, MCS and DMA analyses outlined in stages 2 and 3 are not required. The reader should refer to the cited publications for further information on these aspects of DBM modelling. Whatever stages in the DBM modelling procedure are utilized, however, they are not the end of the modelling process. If the model is to be applied in practice (and for what other reason should it be constructed?) then, as additional data are received, they should be used to evaluate further the model’s ability to meet its objectives. Then, if possible, both the model parameters and structure can be modified if they are inadequate in any way. This process, sometimes referred to as ‘data assimilation’, can be achieved in a variety of ways. Since most data assimilation methods attempt to mimic the Kalman Filter (KF), however, it is likely to involve recursive updating of the model parameter and state estimates in some manner, as well as the use of the model in a predictive (forecasting) sense. This process of data assimilation is made simpler in the DBM case because the favoured estimation methods used in DBM modelling are all inherently recursive in form and so can be used directly for on-line, Bayesian data assimilation (Young, 1984). Recursive estimation is, of course, the embodiment of Bayesian estimation and the KF has long been interpreted in these terms. In the case of linear stochastic systems with

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Gaussian disturbances (or linear systems having only input nonlinearities: see Young, 2002), the KF provides the ideal Bayesian data assimilation and on-line forecasting algorithm. For nonlinear systems, however, other approaches become necessary, some of which can be related directly to the KF: e.g. the Extended Kalman Filter (EKF) (Beck, 1979), the SDP version of the KF (Young, 2000) or numerical Bayesian methods (e.g. Beven and Binley, 1992; Thieman et al., 2001; Beven and Young, 2003). Finally, since most of the computational procedures used in DBM modelling have been described elsewhere in the cited references, they will not be discussed in the present chapter. Most of them are available in the CAPTAIN Toolbox2 , a suite of algorithms developed at Lancaster for use within the Matlab/Simulink™ software environment.

2.3 Case Study: the upper Ticino River basin The upper Ticino River basin (Fig. 2.1) extends for 1515 km2 from the alpine continental divide to the northern shores of Lake Maggiore. It is characterized by significant variations of the climatic and hydrologic conditions and by pronounced orographic heterogeneity. The altitudinal variation in the basin ranges from 220 to 3402 m a.s.l., with more than 80% of the area located higher than 1000 m a.s.l. and a mean slope exceeding the 27%. The dominance of a sub-alpine climate regime results in extremely variable weather conditions, which cause notable flood and drought periods. The meteorological observational data3 used in this study consist of time series covering a 19 yr period (1979–1997). These data are shown in Fig. 2.2: the flow data in the upper panel (a) are measured daily at the Bellinzona stream gauge (225 m a.s.l.); the precipitation values in the middle panel (b) are obtained through interpolation (Thiessen Polygon) of daily sum readings taken at five different rain gauges; finally, the mean daily temperature values in the lower panel (c) are recorded at the Guetsch station (2287 m a.s.l.). It is worth noting that, due to the high elevation of this last station, the snow-melt might be active even if the temperature at Guetsch is negative, because the temperature at lower altitudes might be positive. The flow series plotted in Fig. 2.2a clearly shows the distinctive profile of the subalpine flow scenario, with two seasonal peaks: the first in the late spring, when the smoothing effect of the snow-melt contribution is prevailing; and the second in autumn, when the rainfall is the dominant flow producing process, resulting in sharp and extremely intensive floods. Even in the spring, however, sharp floods are driven by storm events. The strong orography of the basin and the high frequency of precipitation has led, since the 1960’s, to the heavy exploitation of the water resources available in the basin, mainly for hydropower generation purposes. The presence of many alpine hydropower reservoirs, operated on a weekly basis, is easily interpretable from low amplitude, periodic fluctuations in the river flow during the winter periods, which are apparent in Fig. 2.2a. 2 See http://www.es.lancs.ac.uk/cres/captain/ 3 Provided by the Swiss Federal Office for Meteorology (MeteoSwiss) and by the Swiss Federal Office for Water and Geology (BWG).

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Fig. 2.1. The upper Ticino River basin (Switzerland) showing the measurement stations used in the model.

600

3

flow [m /s]

(a)

400

precipitation [mm]

200 (b) 60 40 20

A

B C

0

temperature [°C]

(c) 10 0 −10 −20 J F M A M J J A S O N D J F M A M J J A S O N D

Fig. 2.2. A sample of daily flow yk , precipitation uk and temperature Tk data over a period of 2 years from January 1979 to December 1980 for the upper River Ticino. The capital letters A, B and C are referred to in the text.

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Fig. 2.3. Upper River Ticino: plots of (a) Precipitation and (b) Temperature against Daily flow; (c) cross-correlation diagram for precipitation–flow and (d) temperature–flow.

2.3.1 Choice of the model structure The first task in DBM modelling is the definition of the objectives. In the present example, this is the first time that DBM modelling has been applied to rainfall–flow modelling in a situation where the effects of snow-melt are important. Consequently, the primary objective is that the model should serve as a vehicle for better understanding the nature of the nonlinear precipitation–flow dynamics at the catchment scale and the relative importance of the rainfall and snow melt contributions to the river flow. A secondary objective is to consider the potential of the model in adaptive flow forecasting and flood warning. As the data are plentiful in this example, there is no need for initial simulation modelling, since a dominant mode DBM model, based on the analysis of these data, should be adequate. Moreover, as we shall see, the DBM model obtained here cannot be used directly for simulation modelling purposes. With these objectives in mind, some initial ideas about the choice of a suitable model structure may be obtained by exploring the nature of the nonlinear relationships between the rainfall, flow and temperature data. One simple yet useful, initial exercise is to map the daily flow values against the rainfall and temperature data, as shown in the scatter plots of Figs. 2.3a and 2.3b. These two graphs illustrate how both precipitation and tem-

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perature appear to be highly correlated with flow. This is also illustrated in the two cross correlation plots below in Figs. 2.3(c) and 2.3(d). Within a SDP modelling context, the scatter plots suggests that the flow itself could be used to explain the nonlinear effects of both the precipitation and temperature. Initial estimates of such relationships are given by the full lines in these figures, which are obtained by nonparametric SDP estimation. As far as the precipitation input is concerned, these observations follow rather naturally from previous research on simple rainfall–flow models [Young, 2003 and previous references therein]. Of course, the flow is not actually influencing the effect of the rainfall and temperature inputs, it is simply acting as a surrogate for the catchment storage (soil moisture) which naturally has a considerable influence on how flow is affected by changes in precipitation. As for the temperature input, an interpretation of the temperature–flow relation is discussed later. In the first stage of quantitative modelling, the most parsimonious model structure is initially identified using constant parameter RIV estimation applied to a general linear model structure of the form: xk =

B1 (z−1 ) B2 (z−1 ) u Tk−δT , + k−δ u A1 (z−1 ) A2 (z−1 )

(2.1)

yk = xk + ξk , where yk , uk and Tk are the daily flow, rainfall and temperature measurements, respectively; xk is the unobserved deterministic output of the model; and ξk represents the noise in the model, which will arise from factors such as measurement noise, the effects of unmeasured stochastic inputs and model approximation. Allowance is made for the presence of pure time delays by the introduction of δu and δT . The transfer function polynomials are expressed in terms of the backward shift operator, i.e. z−r yk = yk−r , and are defined as follows: A1 (z−1 ) = 1 + a1,1 z−1 + a1,2 z−2 + · · · + a1,n1 z−n1 , B1 (z−1 ) = 1 + b1,1 z−1 + b1,2 z−2 + · · · + b1,m1 z−m1 , A2 (z−1 ) = 1 + a2,1 z−1 + a2,2 z−2 + · · · + a2,n2 z−n2 , B2 (z−1 ) = 1 + b2,1 z−1 + b2,2 z−2 + · · · + b2,m2 z−m2 . In the present case, however, initial linear identification suggests that the simpler ‘common denominator’ model, where A1 (z−1 ) = A2 (z−1 ) = A(z−1 ), provides as good an explanation of the data as the unconstrained model (2.1). Table 2.1 shows the order of the three best identified models, together with the corresponding identification criteria: namely, the YIC (Young et al., 1996), higher values of which suggest possible over-parameterization; and the coefficient of determination RT2 , which defines the fraction of the measured output variance that is explained by the simulated deterministic output of the model xˆk , where, xˆk =

Bˆ 1 (z−1 ) uk−δu + ˆ −1 ) A(z

Bˆ 2 (z−1 ) Tk−δT ˆ −1 ) A(z

(2.2)

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P.C. Young et al. Table 2.1. Linear model identification. denominator

numerator

time delay

YIC

RT2

2

2 2 2 2 2 2

0 1 0 0 0 2

2.917

0.595

3.096

0.595

3.072

0.592

2 2

and the ‘hats’ denote the estimated values of the associated polynomials. Note that xˆk here and in the later nonlinear models, is the ‘simulated’ output, driven entirely by the measured inputs uk−δu and Tk−δT , without any reference to the measured flow series yk . It is not, in other words, the one-step-ahead predicted output often mis-used in assessing the quality of ‘black-box’ models, such as those based on neural network methods. These results suggest that the best initial identification of the model structure is that presented in the first row (normally denoted by [2 2 2 0 1]). Of course, these model structures are based on linear modelling, so they only provide a preliminary indication of the likely model orders and time delays: they will not necessarily remain exactly the same in the wider nonlinear context discussed below. On the basis of this preliminary identification exercise, and in conformity with previous DBM rainfall–flow modelling exercises (see previous references), initial nonparametric SDP estimation suggests that the major nonlinearities occur at the input to each transfer function, so that the model can be written in the form: yk =

B1 (z−1 ) e B2 (z−1 ) e u + T + ξk A(z−1 ) k A(z−1 ) k

(2.3)

where uek and Tke are the ‘effective’ precipitation and temperature inputs arising from the estimated state-dependent nonlinearities, where: uek = α(yk )uk−δu , Tke

= β(yk )Tk−δk

(2.4a) (2.4b)

in which it will be seen that both SDPs are dependent on the flow variable yk , as discussed further in the next sub-section. The simulated output xˆk of this model is given by, xˆk =

Bˆ 1 (z−1 ) e u + ˆ −1 ) k A(z

Bˆ 2 (z−1 ) e T ˆ −1 ) k A(z

(2.5)

and this is used for computing the coefficient of determination RT2 of the model in subsequent model evaluation. 2.3.2 State-Dependent Parameter modelling of the nonlinearities The nonparametric estimates of the SDPs α(yk ) and β(yk ) in Eq. (2.4) are obtained using the SDP estimation algorithm in the CAPTAIN Toolbox. They are plotted against the flow

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Fig. 2.4. Nonparametric estimate of the state-dependent parameters α and β (full lines), with the estimated standard error bounds shown as dotted lines. The dash-dot lines are the optimized parametric functions (both power law). The marks on these graphs are discussed in the text.

yk in Fig. 2.4 (the dash-dot lines are discussed later). These two curves can be interpreted in physically meaningful terms. Considering first the precipitation SDP α(yk ) in Fig. 2.4a, the flow state dependency has the well-known saturating shape that has been interpreted in previous research as a catchment storage effect (see Young, 2003 and previous references therein). In this previous research, the flow is assumed to be a surrogate or proxy indicator of the catchment storage. However, in the present context where the whole precipitation (rainfall plus snowfall) is important, this prior interpretation needs to be revised to some extent. Depending on the season, the estimate of the SDP α(yk ) at the low flow values in Fig. 2.4a can be explained in two ways: either as the normal effect of the dry summer soil, as considered previously in rainfall–flow modelling; or as the effect of the prevailing snowy nature of the winter precipitation, which simply adds depth to the snowpack and does not generate any immediate flow increase. This latter effect is acting as a ‘seasonal reservoir’, releasing in the summer the water stored as snow during the winter. As an example of the first behaviour, consider the two precipitation events marked as A and B in Fig. 2.2b. Although they have similar intensities, the flow values they produce are significantly different: the A precipitation event occurs in August, on completely dry soil, and so the rainfall acts only to wet the soil, without accompanying run-off. However,

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the precipitation peak in B occurs after a very wet period, during which the soil has been saturated and run-off occurs, resulting in a higher flow. Finally, the event marked as C is a sample of the winter behaviour, when most of the precipitation is trapped in the snowpack. Note how the SDP estimate has a higher standard error for small values of the flow. This is probably due to the intense activity of alpine hydropower reservoirs modulating the flow of many tributaries of the River Ticino (see later discussion on this effect). The estimate of the SDP β(yk ) in Fig. 2.4b modulates the effect of the temperature on the flow. The graph clearly shows that the temperature-related contribution to the flow increases as the flow ranges between 60 to 200 m3 /s. This can be explained as follows. For the very steep Ticino River basin, temperature cannot be assumed uniform over the basin and should be a decreasing function of altitude. The altitude–temperature relationship can be assumed to be approximately linear. The snow-melt process starts when the daily average temperature is higher than a threshold value4 . As temperature is not uniform over the basin, the temperature in the early spring exceeds the threshold only at the lower altitudes, so that the surface Sk involved in the snow-melt process (accordingly called the active surface) is limited to the lower band of the basin and the snow-melt contribution is rather limited. As time progresses, however, the temperature rises all over in the basin, the active surface Sk becomes increasingly larger and the snow-melt intensifies. In order to account for this snow-melt phenomenon, the dynamic behaviour of Sk is generally introduced into conceptual models and the snow-melt volume vk is assumed to be proportional to both temperature and active surface area, i.e. vk = kSk Tk .

(2.6)

This can be interpreted to mean that the proportionality coefficient β(yk ), between the flow arising from snow-melt and the temperature, is an increasing function of Sk . However, provided that yk is increasing with Sk , β(yk ) must also be an increasing function of yk . This is exactly what Fig. 2.4b shows (see trend 1) and suggests that the flow yk is a proxy indicator of Sk . When the temperature exceeds the threshold even at the highest altitude, the whole basin surface is involved in the snow-melt process. Consequently, the coefficient β(yk ) saturates. In this condition, the snow-melt volume vk does not depend upon Sk and, as a result, Eq. (2.6) turns into the form of the classical degree-day formulation, which assumes that the snow-melt is linearly proportional to the difference between the air temperature and a threshold temperature, below which no melting occurs (see, for example, Martinec et al., 1983). As the season proceeds, the snow-pack disappears at the lower altitude, so that the active surface Sk decreases and the contribution to the flow decreases too. The dynamic behaviour of vk is, therefore, once again expressed by Eq. (2.6) and it can be explained by the parameter β(yk ) varying with yk as in Fig. 2.4b (see trend 2). This hypothesis of the snow-melt behaviour suggests that the β(yk ) curve provides a single path approximation to underlying hysteretic behaviour in the system, with different 4 Indeed the snow-melt process starts a few days after the temperature reaches the threshold value. The reason is that the snow-melt occurs along the snowpack base surface; but since air temperature influences snow-melt through the snow-pack top surface, it takes some time for the heat flux to reach the bottom and start the melting process.

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16 14 12 10 (1) 8 6 4

(2)

2 0 -2

80

100

120

140 160 y (active surface)

180

200

220

Fig. 2.5. Comparison of the nonparametric estimate of β computed on the original time series (full line), with that relevant to the increasing phase (dashed line) and that to the decreasing phase (dash-dot line). Only the range of flow values generated by snow-melting is shown.

behavioural mechanisms for increasing and decreasing Sk , and its proxy indicator yk , corresponding to different phases of the snow-melt process. As a consequence, the values it provides must be considered as an average of the actual values in these two phases. This argument can be justified by plotting the nonparametric SDP estimates of β for two time series obtained by considering separately increasing (1) and decreasing (2) phases, as shown in Fig. 2.5, where the hysteresis effect is very pronounced. For simplicity, in the present example, we have not included this hysteretic effect in the model, but it is likely that the model’s explanatory ability could be improved by its introduction. 2.3.3 Parameterization and optimization The final stage of DBM modelling is the parameterization of the effective input nonlinearities, whose form and location has been identified by the nonparametric SDP estimation. Many different parameterizations could be used, such as a power law, polynomial and radial basis functions. However, reasonable results have been obtained using a simple power law in flow: i.e., α(yk ) = ykc1 ,

(2.7a)

β(yk ) = ykc2 .

(2.7b)

Initial estimates of the parameters in these nonlinear functions are obtained using the lscurvefit function in Matlab, applied to the nonparametric estimation results in Fig. 2.4.

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This allows for the identification of the best model order for the transfer function in Eq. (2.3) using RIV estimation applied to the input-output data, with the original inputs replaced by the effective inputs generated using the optimized parameterizations in (2.7). The best identified model structure using these effective inputs is [2 2 2 0 1]. It is now possible to further improve the model through a final iterative optimization procedure that has been used in previous rainfall–flow modelling examples (see previous references). Here, at each iteration, estimates of all the model parameters are obtained using the leastsq optimization function in Matlab, starting with the ci , i = 1, 2, parameters optimized on the basis of the nonparametric SDP estimation results and exploiting the RIV algorithm to estimate the associated transfer function model parameters: Iterate for j = 1 to convergence: 1. the effective inputs uek , Tke are calculated, based on the previous (j − 1)th estimate of the ci parameters; 2. the linear transfer function in Eq. (2.3) is estimated using the RIV algorithm, and the corresponding simulated deterministic model output xˆk is used to evaluate a quadratic cost function based on the model error ek = yk − xˆk ; 3. the new ci estimates provide the basis for the next (j + 1)th iteration. end iteration The final estimated model, obtained in this manner, takes the form yk =

bˆ10 + bˆ11 z−1 bˆ21 + bˆ22 z−1 e u + T e + ξk 1 + aˆ 1 z−1 + aˆ 2 z−2 k 1 + aˆ 1 z−1 + aˆ 2 z−2 k−1

(2.8a)

with   uek = ykcˆ1 uk ,  cˆ2  e Tk−1 , Tk−1 = yk−1

(2.8b) (2.8c)

where the noise ξk is identified by reference to the AIC criterion (Akaike, 1974) as the following autoregressive AR(7) model: ξk =

1 + γˆ1

z−1

1 ek , + · · · + γˆ7 z−7

  ξk = N 0, σξ2 ,

  ek = N 0, σ 2 .

(2.9)

As shown here, in theory ek should be a zero mean, normally distributed, white noise sequence with variance σ 2 . If this were the case, then model estimation could have been made more statistically efficient if this noise model had been incorporated in the optimization and estimated simultaneously, with the ek used as the basis for maximum likelihood estimation. However the estimated noise ξk in this case has two characteristics that affect ek and mean that it does not conform to these assumptions. These are the presence of the periodic component in the flow, arising from the hydropower induced fluctuations; and the ‘heteroscedastic’ nature of the noise, where its variance fluctuates considerably over the observation interval. As a result of these complications, the simpler algorithm

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described above was used for estimation. In these circumstances, this algorithm has the advantage that the optimal instrumental variable nature of the associated RIV estimation algorithm makes it robust to residuals that do not comply fully with the theoretical assumptions (see Young, 1984). The parameter estimates associated with the above model Eqs (2.8a)–(2.8c) and (2.9) are: aˆ 1 = −1.3188(0.0001), aˆ 2 = 0.3285(0.0001),     bˆ10 = 0.1796 0.0059 × 10−3 , bˆ11 = −0.1700 0.0055 × 10−3 ,     bˆ21 = 0.0378 0.0019 × 10−3 , bˆ22 = −0.0382 0.0019 × 10−3 , cˆ1 = 0.6298(0.0002), cˆ2 = 0.7613(0.0013),     γˆ1 = −0.4677 0.4276 × 10−3 , γˆ2 = 0.0956 0.5189 × 10−3 ,     γˆ3 = −0.1066 0.5190 × 10−3 , γˆ4 = −0.0222 0.5239 × 10−3 ,     γˆ5 = −0.0949 0.5194 × 10−3 , γˆ6 = −0.0899 0.5194 × 10−3 ,   γˆ7 = −0.1234 0.4280 × 10−3 , σˆ 2 = 373, σˆ ξ2 = 618, where the figures in parentheses are the estimated standard errors based on the assumption that ξk is a white noise process with estimated variance 618 (see above). Since this assumption is not correct in this case, as pointed out above, these standard errors, as well as the associated parametric covariance matrix used later for uncertainty analysis, can only be considered as approximations. Nevertheless, previous experience and analysis using MCS has suggested that the approximations are acceptable and normally provide a reasonable basis for uncertainty assessment. The estimated parametric SDP functions, with these estimates of the ci parameters defining the power law relationships, are shown as the dash-dot lines in Fig. 2.4. This model explains the data reasonably well, as we see in Fig. 2.6a, which compares xˆk with the measured flow: here the coefficient of determination RT2 = 0.847, based on deterministic output of the model xˆk (see earlier equation (2.5)). The performance of the model is confirmed on a validation data set different from the one used for model estimation, as shown in Fig. 2.6b, where RT2 = 0.836, only marginally less than that obtained in the model estimation. These values of RT2 should be compared with the linear model in Table 2.1, where RT2 is only 0.595. Finally, in line with our objectives, Fig. 2.7 shows the constituent output flows from the two parts of the model that are added together to produce the total flow output. The upper panel is the flow from the first transfer function in the model (2.8a), namely the flow arising from the effective rainfall input uek ; while the lower panel shows the component associated with the effective temperature input Tke . These are plotted on the same scale so that their relative effects are apparent: clearly most of the model output is explained by the effective rainfall transfer function, with the effective temperature transfer function acting to ‘adjust’ this model output, upwards or downwards, to allow for the temperature induced effects.

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Fig. 2.6. Comparison of model output (dots) and measured flow (continuous line) over: (a) a sample of the estimation data set; and (b) a sample of the validation data set.

(a)

flow [m3/s]

600 400 200

(b)

flow [m3/s]

600 400 200 0 J F M A M J J A S O N D J F M A M J J A S O N D

Fig. 2.7. The nonlinear effects of (a) the rainfall, and (b) the temperature on the flow variable, plotted to the same scale to show their relative contributions.

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2.3.4 Physical interpretation of the model The final stage in DBM modelling is the physical interpretation of the model. We have already considered the physical interpretation of the SDP nonlinearities associated with the precipitation and temperature inputs, but we have not yet considered the linear, transfer function part of the model. Using partial fraction decomposition, as in all previous applications of DBM modelling to rainfall–flow data (see earlier references and Young, 2006), each second order transfer function is characterized by real eigenvalues and so it can be decomposed into a parallel connection of first order processes. For illustration, let us consider the transfer function associated with the precipitation– flow process (a similar analysis can be applied to the effective temperature–flow transfer function). The deterministic output xˆ1,k of this transfer function, in response to the effective precipitation input uek , is given by  xˆ1,k =

 βq βs ue , + 1 − αq z−1 1 − αs z−1 k

(2.10)

where βq , βs and αq , αs are the parameters of the first order components revealed by the decomposition. Following the earlier references, the residence times (time constants) of the two parallel processes have very different values, suggesting that they are characterising different flow pathways associated with the quick and slow dynamics of the flow propagation in the catchment. In particular, the quick pathway, with a residence time of Tq = 1/ loge (α1 ) = 0.91 days, appears to reflect the more immediate effects of the various surface and near surface processes; while the slow pathway, with a very large residence time of Ts = 1/ loge (αs ) = 68.62 days, can only be connected with the snow-melt, subsurface and groundwater processes. The ‘partition percentages’ (i.e. the percentages of the flow associated with the two pathways), computed on the basis of the steady state gains of the sub-processes, are Pq = 25.4% for the quick pathway and Ps = 74.6% for the slow pathway. The upper panel (a) of Fig. 2.8 shows the full deterministic output xˆk of the model, compared with the observed flow; while the estimated flow from the quick and slow pathways are shown in the lower two panels, (b) and (c), respectively. In a similar manner, the full line in Fig. 2.9 shows the normalised response of the estimated transfer function to a unit impulsive input of effective precipitation. This represents the unit hydrograph of the effective precipitation–flow component. The decomposed quick and slow components of this hydrograph are shown as dash-dot and dashed lines, respectively. It is clear from both of these Figures that the quick pathway effects dominate the initial response of the catchment to precipitation; while the slow pathway dominates the subsequent response and elevates the tail of the hydrograph. In conventional hydrological terms, therefore, the quick pathway would appear to represent the outcome of the surface and near-surface flow processes; while the slow pathway is a consequence of the deeper groundwater dynamics and the displacement of ‘old water’ into the river channel when effective rainfall events occur (base flow).

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flow [m3/s]

(a) 600 400 200 0 (c) flow [m3/s]

600 400 200 0 (b) flow [m3/s]

600 400 200 0

J F M A M J J A S O N D J F M A M J J A S O N D

Fig. 2.8. Decomposition of the flow: (a) full simulated deterministic model output xˆk compared with the measured flow yk ; (b) the decomposed ‘quick flow’ response; and (c) the decomposed ‘slow flow’ response.

0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0

0

20

40

60

80

100

120

Fig. 2.9. Comparison of the full normalised model impulse response (full line) and the impulse responses of the two decomposed pathways: quick (dash-dot line) and slow (dashed line).

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2.3.5 Uncertainty analysis One aspect of the DBM approach to modelling which differentiates it from alternative deterministic top-down approaches is its inherently stochastic nature. This means that the uncertainty in the estimated model is always quantified and this information can then be utilized for uncertainty and sensitivity analysis based, for example, on the application of MCS methods (see e.g. Young, 1999a). Figure 2.10 shows typical MCS results obtained in the present example: these are the normalized histograms of the derived residence time parameters, as obtained using 5000 Monte Carlo realizations based on the covariance matrix of the estimated parameters (i.e. both the linear TF parameters and the nonlinear SDP s). It is clear that the uncertainty in the quick residence time is relatively small and has an approximately normal distribution; whereas the slow residence time is very uncertain and shows considerable skew towards higher values. The uncertainty on any derived, physically meaningful parameters can be evaluated in this manner, often providing useful insight into the strengths and weaknesses of the model. In addition, MCS can be used to investigate the propagation of uncertainty in the model and the way in which this affects model variables, such as the output flow or the flows in the quick and slow pathways. It can also help in evaluating how well the model parameters, either directly estimated or derived, are identified statistically by plotting the RT2 values for each member of the

Fig. 2.10. MCS analysis: normalized histograms of the residence times associated with the ‘quick’ (a) and ‘slow’ (b) pathways, based on 5000 realizations.

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Monte Carlo ensemble against the associated parameter values (see above reference). In the present example this confirms the poor identification of the slow residence time. 2.3.6 Model developments The present DBM model could be used as a basis for a flow forecasting and flood warning system but, for such an application, it would need to be provided with one-day-ahead rainfall forecasts because, in contrast to the effective temperature–flow transfer function, the effective rainfall–flow transfer function does not incorporate a one day pure time delay (refer to Eq. (2.8a) and the associated definitions). Another possibility would be to re-estimate the model with a false one day time delay in the effective rainfall–flow transfer function (see e.g. Young, 2002) but this has not been investigated so far. The present model does not explain the flow data quite as well as previous DBM rainfall–flow models that have not had to account for snow-melt effects, where the coefficient of determination RT2 is normally in excess of 0.9 (> than 90% of the output flow variance explained by the model), compared with RT2 = 0.847 here. However, it is well known that the modelling of rainfall–flow in rivers where snow-melt effects are important is difficult. Nevertheless, we believe the present model should be considered as a preliminary one that could be enhanced in various ways. For instance, the hysteretic effect associated with the snow-melt has been identified but not included in the present model: there seems no doubt that its inclusion would improve the explanatory ability of the model. Also, the noise component ξk could be improved by allowing for heteroscedasticity and including an explanation of the periodic component in the flow, arising from the hydropower induced fluctuations. In fact, DBM models can include periodic components when considered within a wider, ‘Unobserved Component’ context (e.g., Young et al., 1999). Finally, the present model could provide the basis for the development of a DBM simulation model, as shown in Young (2003), where the SDP based directly on the flow measurement (acting as a surrogate measure of the catchment storage) is replaced by an SDP based of a simple conceptual model for the catchment storage. More recent research has shown that a suitable conceptual model for this purpose is the Matlab/Simulink version of TOPMODEL (Romanowicz, 1997) and this would allow for information obtained from digital terrain data to be included in the overall DBM simulation model. However, such simulation model developments will require further research since the task will be made more difficult by the complex nature of the Ticino Basin and the added complication of the snow-melt effects. 2.4 Conclusions This chapter has outlined the DBM approach to the modelling of environmental systems and applied it, for the first time, to the modelling of flow in the Ticino River basin, where snow-melt effects are important. In this sense, the modelling described in the chapter represents an exploratory exercise, since the inclusion of the snow-melt dynamics considerably complicates the model identification and estimation. As such, there is no doubt that the model could be improved in various ways to enhance its performance and allow for its use in forecasting and simulation modelling applications, as discussed in Sec. 2.3.6

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of the chapter. Nevertheless, the synthesis of the DBM model obtained here does not require any measurements other than flow, temperature and raw precipitation; and it does not depend to any large extent on any a priori assumptions. It is a simple, parametrically efficient (parsimonious) model that is straightforward to identify and estimate using algorithms available in the CAPTAIN toolbox for Matlab. The model is computationally efficient, in contrast to recent rainfall–flow models that rely on numerical Bayesian methods (Thiemann et al., 2001); it explains the data reasonably well on both the estimation and validation sets; and it can also be interpreted in physically meaningful terms, taking into consideration both the rainfall and the snow-melt contributions to the flow. Consequently, even in this initial, preliminary form, the model provides a useful, reasonably validated tool for evaluating the relationship between rainfall–flow and snow-melt on the Ticino River flow. And it provides the basis for rainfall–flow modelling at the catchment scale in other river basins that are similar to the Ticino, where snow-melt is an important part of the flow generation process. Bibliography Akaike, H. (1974). A new look at statistical model identification. IEEE T. Automat. Contr. 19, 716– 723. Beck, M.B. and P.C. Young (1975). A dynamic model for BOD-DO relationships in a nontidal stream. Water Res. 9, 769–776. Beck, M.B. (1979). Model structure identification from experimental data. In: Theoretical Systems Ecology (E. Halfon, Ed.). Academic Press. New York, NY. pp. 259–289. Beven, K.J. and A. Binley (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrol. Process. 6, 279–298. Beven, K.J. and P.C. Young (2003). Comment on ‘Bayesian recursive parameter estimation for hydrologic models’ by M. Thiemann et al. Water Resour. Res. 39(5), 1116. Bryson, A.E. and Y.-C. Ho (1969). Applied Optimal Control. Blaisdell Publishing Co.. Waltham, MA. Jarvis, A.J., P.C. Young, C.J. Taylor and W.J. Davies (1999). An analysis of the dynamic response of stomatal conductance to a reduction in humidity over leaves of Cedrella odorata. Plant Cell Environ. 22, 913–924. Konikow, L.F. and J.D. Bredehoeft (1992). Ground water models cannot be validated. Adv. Water Resour. 15, 75–83. Martinec, J., A. Rango and E. Major (1983). The snowmelt runoff model users manual. Tech. Report 1100. NASA. Washington DC. Norton, J.P. (1986). An introduction to identification. Academic Press. New York, NY. Oreskes, N., K. Shrader-Frechette and K. Belitz (1994). Verification, validation, and confirmation of numerical models in the earth sciences. Science 263, 641–646. Parkinson, S. and P. C. Young (1998). Uncertainty and sensitivity in global carbon cycle modelling. Climate Res. 9, 157–174. Pedregal, D.J. and P.C. Young (2002). Statistical approaches to modelling and forecasting time series. In: A Companion to Economic Forecasting (M.P. Clements and D.F. Hendry, Eds.). Blackwell. Oxford, UK. pp. 69–104. Popper, K. (1959). The Logic of Scientific Discovery. Hutchinson. London, UK. Price, L., M.A. Bacon, P.C. Young and W.J. Davies (2001). High-resolution analysis of tomato leaf elongation: the application of novel time-series analysis techniques. J. Exp. Bot. 52, 1925–1932.

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Price, L., P. Goodwill, P.C. Young and J.S. Rowan (2000). A data-based mechanistic (DBM) approach to understanding dynamic sediment transmission through Wyresdale Park Reservoir, Lancashire, U.K. Hydrol. Process. 14, 63–78. Price, L., P.C. Young, D. Berckmans, K. Janssens and J. Taylor (1999). Data-based mechanistic modelling and control of mass and energy transfer in agricultural buildings. Annu. Rev. Control 23, 71–82. Romanowicz, R.J. (1997). A MATLAB implementation of TOPMODEL. Hydrol. Process. 11, 1115–1129. Thiemann, M., M. Trosset, H. Gupta and S. Sarooshian (2001). Bayesian recursive parameter estimation for hydrological models. Water Resour. Res. 37, 2521–2536. Young, P.C. (1984). Recursive Estimation and Time-Series Analysis. Springer-Verlag. Berlin, D. Young, P.C. (1993). Time variable and state dependent modelling of nonstationary and nonlinear time series. In: Developments in Time Series Analysis (T. Subba Rao, Ed.), Chapman and Hall. London. pp. 374–413. Young, P.C. (1998). Data-based mechanistic modelling of engineering systems. J. Vib. Control 4, 5– 28. Young, P.C. (1999a). Data-based mechanistic modelling, generalised sensitivity and dominant mode analysis. Comput. Phys. Commun. 117, 113–129. Young, P.C. (1999b). Nonstationary time series analysis and forecasting. Progress in Environmental Science 1, 3–48. Young, P.C. (2000). Stochastic, dynamic modelling and signal processing: time variable and state dependent parameter estimation. In: Nonlinear and Nonstationary Signal Processing (W.J. Fitzgerald, A. Walden, R. Smith and P.C. Young, Eds.). Cambridge University Press. Cambridge, UK. pp. 74–114. Young, P.C. (2001a). Data-based mechanistic modelling and validation of rainfall–flow processes. In: Model Validation: Perspectives in Hydrological Science (M.G. Anderson and P.D. Bates, Eds.). John Wiley. Chichester, UK. pp. 117–161. Young, P.C. (2001b). The identification and estimation of nonlinear stochastic systems. In: Nonlinear Dynamics and Statistics (A.I. Mees, Ed.). Birkhauser. Boston, MA. pp. 127–166. Young, P.C. (2002). Advances in real-time flood forecasting. Philos. T. Roy. Soc. A 360(9), 1433– 1450. Young, P.C. (2003). Top-down and data-based mechanistic modelling of rainfall–flow dynamics at the catchment scale. Hydrol. Process. 17, 2195–2217. Young, P.C. (2006). Transfer function models. In: Encyclopedia of Hydrological Sciences (M.G. Anderson, Ed.). Vol. 3. Part II, 128. John Wiley. Chichester, UK. pp. 1985–2000. Young, P.C. and D.J. Pedregal (1999). Macro-economic relativity: government spending, private capital investment and unemployment in the USA 1948-1998. Struct. Change Econ. Dynam. 10, 359–380. Young, P.C. and K.J. Beven (1994). Data-based mechanistic modelling and the rainfall–flow nonlinearity. Environmetrics 5, 335–363. Young, P.C. and M.J. Lees (1993). The active mixing volume: a new concept in modelling environmental systems. In: Statistics for the Environment (V. Barnett and K.F. Turkman, Eds.). John Wiley. Chichester, UK. pp. 3–43. Young, P.C., D. Pedregal and W. Tych (1999). Dynamic harmonic regression. J. Forecasting 18, 369–394. Young, P.C., S.D. Parkinson and M. Lees (1996). Simplicity out of complexity in environmental systems: Occam’s razor revisited. J. Appl. Stat. 23, 165–210.

CHAPTER 3

Bayesian Networks as a Participatory Modelling Tool for Groundwater Protection Hans J. Henriksen1 , Per Rasmusssen1 , Gyrite Brandt2 , Dorthe von Bulow2 and Finn V. Jensen3 1 Geological Survey of Denmark and Greenland (GEUS)

Copenhagen K, Denmark 2 Copenhagen Energy (CE)

Copenhagen K, Denmark 3 Aalborg University, Department of Computer Science

Aalborg, Denmark

3.1 Introduction Participatory approaches help to better control and accelerate the integration, to make the decision making process more transparent and comparable across transboundary river basins and scales, and to increase confidence in an integrated model-based planning process. Integration and participation can be significantly enhanced by using Decision Support Systems (DSS) to assist the planning process, as they provide tools and platforms for collecting data from many sources, integrating models of different nature (physical, socioeconomical and decisional), evaluating the effects of different planning alternatives and, in some cases, negotiating them. Models are an essential component of DSSs since they provide the system representation on the basis of which the planning process is carried on. It is key for the succeeding of this process that stakeholders and water managers share the same representation of the system, i.e. agree on the same model. There are many types of models that can be used to inform and support decision, each of which has its strength and weakness. In this chapter we will describe the use of Bayesian Networks (BNs) – a relatively recent modelling techniques (Pearl, 1988) which is encountering wide diffusion in the environmental modelling community (see for instance Batchelor and Cain, 1991; Varis and Kuikka, 1997; Borsuk et al., 2001, 2004) – in groundwater protection problems. More precisely, we will demonstrate through a case study, the Havelse well field 49

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catchment in Denmark, how BNs can be employed in modelling the ecological, social and economic effects of different management actions aimed at reducing the negative impact of agricultural practices on groundwater quality. As part of an integrated water management strategy the use of pesticides in agriculture is one of the problems to be dealt with. In order to secure good groundwater quality, farming contracts could be one way of decreasing pesticide use. In order to achieve this goal active participation of local farmers, farmers’ associations as well as the local population is necessary in order to convince and carry through the groundwater protection strategy. Farming contracts will influence a number of issues. Therefore, an inter-disciplinary analysis of the possibilities and problems connected with five or ten year farming contracts, with totally stop of the use of pesticides in return for compensational payment is required. The aim of the case study is to describe in what way the introduction in some areas of farming contracts with no pesticide applications may influence farming economics, groundwater quality, biodiversity and the aquatic environment. The classical modelling methods used in groundwater studies (comprehensive models) are not broad enough and cannot encompass other ‘non-technical’ data. This means that these types of classical models can be used for information and consultation but are not really applicable for active involvement. The advantages of the BNs are that they are graphical, focus dialogue, integrate different types of data and are interactive, trans- and interdisciplinary and can quantify difficult cases. The graphical nature of BNs facilitates formal discussion of the structure of proposed model. Also the ability of a BN to describe the uncertain relationships amongst variables is ideal to describe the relationship between events which may not be well understood and intrinsically uncertain. The construction process of Bayesian Networks with stakeholder involvement followed a seven-step procedure. There is no single best way to involve stakeholders, but that does not mean that there is no need for guidelines for how to construct Bayesian Networks with stakeholder involvement. In a recent EU research project (Bromley, 2006) one main deliverable was to establish such a proposal for guidelines for BNs, and the seven step procedure roughly follows this proposal. This chapter is structured in the following way. After the present introduction, Section 3.2 two describes the theoretical basis of Bayesian Networks. Section 3.3 three describes the case study and Section 3.4 four the case study BN model. Section 3.5 is about the pros and cons of BNs for participatory modelling and finally discussion and conclusions. 3.2 Bayesian Networks A Bayesian Network (BN), also called a belief network, is a type of graphical models used for areas where uncertainty is an integral part of the reasoning (Pearl, 1988; Cowell et al., 1999; Jensen, 2001a; Korb and Nicholson, 2004). The structural part is a graph consisting of nodes and directed edges (see Fig. 3.1). A node represents a variable with states, and a directed edge from a node A to a node B represents a cause–effect relation; it indicates that the state of A is likely to have an impact on the state of B. The cause–effect relation needs not be deterministic. For example, the arrow from Water Price to Domestic Abstraction (Fig. 3.1) indicates only, that the price of water is likely

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Fig. 3.1. Example of a simple Bayesian Network representing a river catchment.

to have an impact on domestic consumption, and given a certain price, you will only be able to provide a probability distribution over the domestic abstraction. Therefore, the strength of a cause–effect relation is represented by conditional probabilities. If A is a node with parents B and C, then the model shall include P (A|B, C), the probability distribution for A given any configuration over B and C. For the node Domestic Abstraction for example, you shall specify the probability of each value of domestic abstraction for each combination of values of price and weather (net precipitation). BN s have become a highly successful technique in medical diagnostic systems, analysis, artificial intelligence, and decision-making in real-world domains. They have been applied for many years in practice in a variety of fields, including engineering, science, and medicine (Andreasen et al., 1989; Abramson and Finizza, 1991; Jensen et al., 2001b; Marcot et al., 2001; Borsuk et al., 2002; Gomez, 2004). BN s have gained a reputation of being powerful techniques for modelling complex problems involving uncertain knowledge and uncertain impacts of causes. This is due to the following two characteristics: • BNs as a modelling language has a straightforward semantics, namely that of cause– effect. A domain expert without particular skills in probability theory can easily use them. The transparent semantics, supports communication between humans and a BN model can form a solid basis for discussion. • A BN model over a universe U is a compact representation of the joint probability distribution, P (U ). That is, a BN model holds all sufficient information for reasoning over P (U ). Furthermore, computer systems are available, which efficiently perform the required probability calculations. For example in Fig. 3.1, if water price and land use is known (evidence), these computer systems will very fast calculate (belief propagation) the consequences for the other variables in the model.

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Thanks to their characteristics BNs are a powerful technique to assist decision-making, especially when there is scarcity and uncertainty in the data used in taking the decision and the factors are highly interlinked, all of which makes the problem highly complex. BN s can help formulating environmental management strategies by (Henriksen et al., 2006): • Allowing users to build their own Environmental Decision Support System (EDSS). By building it themselves, users can ensure that the decision support system meets their needs. • Helping users to understand the nature of their decisions better. An EDSS should help users make a better decision, not an easier one. It should not make the decision for the user. Instead, it should encourage the user to identify all relevant information and to analyse it more in depth. • Encouraging users to deal with uncertainty, when appropriate. It must be recognized that is impossible to be certain about the consequences of any environmental management decision, and the uncertainty has to be explicit in the responses from the EDSS . • Encouraging consultation with stakeholders. Without stakeholder consultation, it is unlikely that an environmental management decision can be implemented. The graphical nature of BNs facilitates formal discussion of the structure of a proposed model and the ability of a BN to describe the uncertain relationships amongst variables is ideal to describe the relationship between events, which may not be well understood. When used in integrated water resource management for participatory processes e.g. negotiation and active involvement of stakeholders, BNs are used as a tool that enable and support decision makers to make rational and informed choices between different alternatives. BNs are excellent tools for quantification of the whole picture, e.g. economics, hydrology, hydraulics, environmental and sociological impacts of various instruments and offers a transparent, inclusive, coherent and equitable methodology for dealing with groundwater management that incorporates both formal and informal institutional arrangements (including social norms) of different stakeholder groups (Henriksen et al., 2006). 3.3 The Havelse case study Groundwater is the ‘backbone’ for drinking water supply, industrial supply and supply for aquatic environment in the greater Copenhagen area. In Denmark, 99% of the water supply is groundwater. Furthermore, most Danes agree that clean groundwater and drinking water has the highest priority of all environmental issues. Chemical treatment of groundwater is rarely accepted before it is supplied to the consumers. Agriculture currently accounts for two-thirds of land use in Denmark, and the farmed area covers some 2.7 million ha. More than half of the agricultural area is used to grow cereals, mainly winter cereals. These crops have a high leaching potential, because they need additional fertilisers and pesticides. Pork accounts for about a third of agricultural production value, and diary products have a share of 20% of total production value.

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Denmark is one of the leading countries in the world exporting pork. Agriculture is a rather marginal activity, if measured in terms of the share of agriculture of the Gross Domestic Product. From a preventive groundwater protection point of view, the goal should be no pesticides in the groundwater above the maximum limit value. Perhaps the vulnerability of the area with respect to the deeper aquifers is less than for other Danish aquifers, but we do not know in detail which parameters or factors we should base such an assessment on. In fact, we know very little about the vulnerability of the deep groundwater in the Havelse area at the depths from which groundwater is abstracted. We do know that pesticides have a very low degradation rate (nearly none) once they have reached the anaerobic parts of the aquifer, which in this area is located a few meters below the surface. In the Danish case study the water company responsible for decision making and actions toward well field protection is Copenhagen Energy representing the Municipality of Copenhagen. Copenhagen Energy (CE), is the largest water supply company in Denmark. It supplies roughly one million inhabitants in the greater Copenhagen area with drinking water each day. CE operates Havelse well field together with 55 other large well fields located in northern and eastern Zealand (Fig. 3.2). The aim of the case study was to identify a groundwater protection strategy for CE against pesticide application to agricultural areas, which could be a threat to good groundwater quality. We know a great deal about the quality of the deep groundwater in Denmark from work on a national scale (Jorgensen, 2003). However, we have little knowledge about the quality of the young groundwater and we do not know exactly to which extend pesticide contaminated shallow groundwater, in the long run affects deep groundwater. The application of pesticides to agricultural fields according to regulative guidelines, accidents, point sources, past mistakes, and the spread of pesticides (e.g. BAM) all contribute to a high frequency of findings both in shallow and deep groundwater, as well as in surface water (Henriksen et al., 2004). The most common pesticide found in existing monitoring data is BAM – 2,6-dichlorbenzamide, which is a metabolite of the herbicides dichlobenil or chlorthiamide. BAM is the greatest threat to groundwater quality at the moment (Jorgensen, 2003; Brusch et al., 2004). The use of the BAM herbicide is now prohibited. In the case study area pesticides were found in wells in a village, and it is probably more point source related pollution than diffuse pollution. Similar vulnerable or very vulnerable areas are also found inside the catchment area and in the present well field zone. The vulnerability is here an expression of the thickness of the clay layer above the primary reservoir and whether the aquifers are unconfined or confined. However new pesticide analysis from five drilled wells, four dug wells and Havelse Creek showed various herbicides (BAM and Mechlorprop) in two dug wells and one drilled well, but also AMPA (a degradation product of Glyphosate) in Havelse creek, all with findings above the MAC (maximum admissible concentration for drinking water (0.1 μg/l)). Active groundwater protection from pesticides requires innovative solutions (Brandt and Henriksen, 2003), coordination of actions by various authorities and commitment to implementation from the different stakeholder groups. Lack of knowledge about different sources of pesticide pollution, vulnerability and spreading of pesticides to drinking

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Fig. 3.2. Havelse well field case study area (35 km2 ).

water may hamper and threaten existing protection initiatives and undermine the overall efficiency. In order to identify actions that can be implemented in practice, discussion and dialogue with stakeholders and general public about the factors and consequences are necessary. The traditional approaches taken by CE for groundwater protection are afforestation, establishment of monitoring wells and establishment of local waterworks cooperation forums. Until now farming contracts have only been considered as a possible (novel) future action, not a measure that is a part of CE’s current strategy for groundwater protection. Farming contracts will influence a number of issues. Therefore, an inter-disciplinary analysis of the possibilities and problems connected with five or ten-year farming contracts, with total stop of the use of pesticides in return for compensational payment is required. The aim of the case study is to describe in what way the introduction in some areas of farming contracts with no pesticide application may influence farming economics, groundwater quality, biodiversity and the aquatic environment.

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The involvement of farmers and other stakeholders is vital for developing novel actions for CE’s groundwater protection strategy, because the response and behaviour of these groups are critical for the implementation of such novel initiatives. This situation introduces the need for a participatory modelling and decision-making support tool. The intricate properties of the BNs (interdisciplinary/transdisciplinary, etc.) justified the idea of using BNs for the Havelse case. Section 3.4 describes the way the BNs were applied in the case study, i.e. how the BN was constructed, populated and used. 3.4 The Havelse BN model 3.4.1 BN construction The BN construction process is a complex and tricky process that often require to interactively repeat many steps. For this reason a procedural approach is essential to drive the modeller and the stakeholders through it. Such a procedure makes it easier for all parties to participate in framing the problem and to focus on the dialogue toward areas, which are associated with ambiguity. Problem-framing includes existing views on the problem, how the problem is interwoven with other problems, possibly relevant aspects of the problem that are not dealt with in the management, the role the BN construction is expected to play in the policy process, and the way the BN construction is related to previous studies on the subject. Ambiguity refers to a lack of clarity or consistency in reality, causality, or intentionality. Ambiguous situations are situations that cannot be coded precisely into mutually exhaustive and exclusive categories, which means that the assumptions necessary for rational decision making are not met. The problem in ambiguity is not that the real world is imperfectly understood and that more information will remedy that. The problem of ambiguity is that information may not resolve misunderstandings (Weick, 1995). Although the word ambiguity also means the presence of two or more interpretations, it can also mean something quite different, namely, a lack of clarity, which makes it quite similar to uncertainty. The shock attendant to uncertainty is one of ignorance. So uncertainty comes from imprecision in estimates of future consequences conditional on present actions. To reduce uncertainty and remove ignorance, more information is required. But to remove confusion or ambiguity (multiple meaning), a different kind of information is needed, namely, the information that is constructed in face-to-face interaction that provides multiples cues. To reduce multiple meanings, people need access to more cues and more varied cues, and this is what happens when rich personal media such as meetings (with BN construction and involvement of stakeholders in focused dialogues with managers and researchers as the tool) and direct contact take precedence over less rich impersonal median such as formal information systems and special reports. To resolve confusion, people need mechanisms that enable debate, clarification, and enactment more than simply provide large amounts of data (Daft and Lengel, 1986). The problem with ambiguity is that people are unsure what questions to ask and whether there even exists a problem they have to solve (Weick, 1995). In conclusion, BNs can be characterised as a rather powerful tool for ‘sense-making’, which comes before and is required if subsequent decision making and implementation

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Define the context (Step 1)

Identify factors actions and indicators (Step 2)

Build pilot Network (Step 3)

Collect data (Step 4)

Define states (Step 5)

Construct CPTs (Step 6)

Collect feedbacks from stakeholders (Step 7)

Fig. 3.3. The general procedure for the construction of a tables).

BN

model (CPT: conditional probability

of decisions shall turn vision and innovative instruments into reality. Especially where there are confusion and multiple meanings about management goals and outcomes, BNs are a possible tool, which can enable debate, clarification, and enactment. The activities of relating beliefs and actions are the sense-making process (Weick, 1995). The outcome of such a process is a ‘unit of meaning’ (= the constructed BN), where beliefs and actions are tied together by socially and scientifically acceptable implications. The BN construction in the Havelse case study was undertaken in a stepwise fashion with the scheme of Fig. 3.3. It was considered from the start of the project, if necessary subsequently to repeat Step 3-7 several times based on the character of the feedback. The activities implemented within each step are the following:

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Step 1 Physical and socio-economic boundaries, area of interest, the input to the network (factor, actions and scenarios). A scenario is the background over which the effects of the alternative are evaluated, i.e. the input to the network that cannot be decided, and the outputs (indicators) are tentatively defined. The degree of stakeholder involvement (information, consultation, active involvement) is also determined. Afterwards, at meetings with stakeholders and general public, working groups are set up, stakeholder interests analysed and responsibilities clarified. Step 2 A list of stakeholder and general public concerns is drawn up, and the tentative list of actions and indicators provided at Step 1 is properly modified in accounts of the stakeholder suggestions. A synopsis of data sources, reports, stakeholders and models is described and agreed upon. Step 3 The important variables are identified, and the topology of their direct interconnection (cause–effect relationships) is defined in collaboration with the stakeholders. A tentative network structure is obtained. On the basis of the stakeholder technical expertise and of their willingness to participate the rules for participation are set up and the platform for information decided. Step 4 The data required to fill in the network are collected from different sources (including stakeholders and general public). Data are analysed, processed, and initially the tentative network obtained in Step 3 is presented as an illustration of the type of analysis that will be carried out in the following steps. Step 5 The stakeholders and the public provide the first feedback on the tentative BN, on the basis of which this is adjusted and redefined with additional variables and links. For each variable of the network the range of values (states) which it can assume are specified and agreed upon. Step 6 The BN is populated, i.e. the CPTs (conditional probability tables) of each variable are filled in using the quantitative information obtained from domain models and experts. This includes a review of the networks at individual stakeholder meetings. The BN should also be carefully checked for internal consistency at this stage. Parameter learning is encouraged as a method of bridging data and CPTs. At the end of this Step a populated and ready-to-work BN is obtained. Step 7 Stakeholder and general public opinions on the network obtained at Step 6 are collected. If all the stakeholders and the public agree on the network the final BN is documented and implemented. Otherwise, if additional adjustments are required Steps 3 to 7 are iteratively repeated. Once the procedure ends, the network obtained is run (i.e. the belief is forward propagated from the roots to the outputs) by varying the evidence on the action nodes, so that the values of the indicators are computed for different combinations of actions. In this way a ‘what if’ analysis is conducted through which the stakeholders and the public may look at the effects that the divergent proposed actions have on their interests. Information, consultation and active involvement of professional stakeholders and general public is a necessary part of the BN construction and can be carried out using announcements and facilitated public meeting (Step 1-2), working group meetings (Step 1-3), newsletters, a Web site, individual meetings (Step 4-6) and a joint working group (Step 7).

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In the Havelse case study the facilitator of the public meeting and workshops was the Agenda 21 Centre in Frederikssund. Various subcontractors have participated in the project that, e.g. farm economics (Rasmussen, 2003) and value of biodiversity, land use, etc. (Schou, 2003). By applying the above procedure to the Havelse modelling, the network in Fig. 3.4 was obtained. We will illustrate it in the following section. 3.4.2 The BN model The general idea with the BN for farming contracts was to analyse the effects of compensation payments to farmers for not using pesticides on agricultural fields. The higher the compensation level, the more farmers will join such a voluntary farming contract. However, farmers signing a contract will also try to optimise land use by growing crops more suitable for farming without pesticides, and this means that contracts will also affect land use. Farming contract restrictions and land use affect the farmers’ bottom line (Fig. 3.4), so to speak, and this, together with the compensation payment, has an impact on farm economics as a whole. All the relationships in this part of the farming contract BN were initially provided by subcontractor Svend Rasmussen from the Royal Veterinarian and Agricultural University (KVL), which also collected the data for pesticide application for different crop rotations (Rasmussen, 2003). The other part of the BN shows variables concerning environmental impacts of pesticide application. If farming contracts are established then the pesticide load is reduced, which again has a knock-on effect on diffuse load (the non-point discharge to groundwater from the root zone) and the surface water quality, to an extent dependable of soil and non-point application (whether it is herbicides, fungicides or insecticides we have chosen to analyse). Diffuse load influence shallow groundwater quality, but this variable is also influenced by point sources. Finally, shallow groundwater quality influences deep groundwater quality. These variables were based on information from monitoring programmes at GEUS and CE (Jorgensen, 2003; Brusch et al., 2004). Furthermore, research had shown that high concentrations of herbicides in surface water impacts the reproductive capability of leopard frogs (expressed by the variable biological a BN normality). The types of variables in the BN in Fig. 3.4 can be grouped into five categories: 1. Objectives. Things that are affected: Shallow groundwater quality, Biological abnormality, Biodiversity, Surface water quality, Recreative value and Deep groundwater quality. Overall objective: Safe supply. 2. Actions. Things, which must be implemented or included in CE policy: Compensation, Non-point application and Remove point sources. 3. Intermediate factors. Variables, which link objective variables with action variables: Farming contracts, Land use, Farm economics, Pesticide load, Diffuse, Surface water quality, Point sources and Hunting/fishing. 4. Controlling factors. Factors that control the environmental system: Sand/clay, Animal/vegetable production, SFL area (vulnerable farming areas). Uncertain controlling factors: Perception of vulnerability (stakeholders do not agree).

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Fig. 3.4. Final BN for voluntary farming contracts.

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H.J. Henriksen et al. 5. Decision and Utility variables. Variables that are included in order to calculate and visualise a certain utility: All-farm economy and Utility node.

The decision variable All farm economy is simply added to the BN in order to make the total utility for farmers fully transparent (production exclusive fixed costs for machinery, etc. plus compensation payment). One variable is aimed at directly focusing on overall outcome of BN: Safe supply of drinking water, a boolean variable which can be true or false: true means that clean groundwater will have a content of pesticides below the maximum allowed concentration, for at least 50 years. 3.4.3 Population of BN The next step is to complete the conditional probability tables (CPT) that lie behind each of the variables. This is a task that requires care and patience because if the CPT are wrong, the network output will be equally wrong. As well as being one of the most important, it is also the step that causes most difficulty for the network designer and the stakeholders who provide information for the table. The procedure used to complete CPTs in the Havelse case were based on (1) monitoring data and model data sets and (2) stakeholder opinion/knowledge and expert opinion. Data for variables such as land use, pesticide load and farm economics often have to be simulated using a model. This means that for different scenarios there are model results available providing input for CPT for these variables. Variables like diffuse, point sources, shallow groundwater, deep groundwater quality and surface water quality was available from monitoring programmes covering more than a decade of measurement. In cases where data linking two variables was scarce, or none existent, particularly when the link is difficult to quantify, a turn to local stakeholder knowledge or expert knowledge was chosen. Among the variables difficult to quantify are those dealing with social or cultural issues, but economic factors can also be problematic. The variables compensation and farming contracts are examples of such variables, where it was important to carefully elicit proper values for the case study ‘context’ (which is partly social constructed). Since we are relying on stakeholder input and expert opinion in the Havelse case study, the data (for the CPTs) were carefully elicited and input into the tables manually. It was also possible to handle data manually from the monitoring and model data which was used, because the BN has a reasonable (not too many) number of variables and states. At the workshop meetings the input data were discussed and adjusted based on feedback from stakeholders and experts. A number of things were learned throughout the process using this approach, in order to allow data to be entered manually. It is important to minimise the size of the tables, using expert inputs to input probabilities means that values can be entered directly into the table since he or she is likely to be familiar and comfortable doing so, and some stakeholders may be experts in their own right. A good example is offered by the variable farm economics which has 3 parent nodes: farming contracts, animal/vegetable and land use. A combination of three parameters and a combined number of 21 states meant that the CPT for farm economics required 384 values. However, despite the size and complexity, the CPT was successfully completed in conjunction with an expert familiar with the economics of agriculture in the area, and drawing on farm economic modelling results.

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Parameter learning calculating the conditional probabilities for variables, given the link structures and data is an efficient method available for BN constructors if sufficient extensive data sets to generate enough cases (experiences) for the resulting probability distributions to be robust are available. One of the most common used methods for parameter learning is the expectation-maximisation (EM) algorithm (Jensen, 2001a). However, this method was not applied in the present case study, because the number of cases was too limited to relay on the automated approach. Instead, data were manually entered and adjusted, and by simulation with values in CPTs for different combination of parent variable and a range of actions, the resulting probability distributions for various variables were examined and discussed with experts and stakeholders. Different approaches were subsequently used to check the network for consistency (structure, values placed in CPTs and checks in diagnostic mode) before the completed network and different combination of actions or scenarios on the objective variables were presented to the stakeholders. A first step here is to check whether the network behaves in a logical way, which was done by noting the impact of each implementation and controlling variable one by one, and by setting all implementation and controlling nodes to represent the current situation. Next by changing one of these to another state to represent a different action or scenario (instantiating the node) the impact and response on the variables linked to it was examined to see if it responded in a logical way. A check of the network in diagnostic mode is a test where a child node is fixed, which then propagates back to describe the states of the parents needed to generate that condition. In the Havelse case we can give an example of such a diagnostic test. If the variable land use is in the state 22% ‘set aside’ and 78% ‘grass’, then the hunting and fishing income from the land will be 200 DKK per ha. Now, fixing the hunting and fishing income in diagnostic mode at 200 DKK per ha, this then propagated back to indicate that the best combination of land use to achieve this income is 22% ‘set aside’ and 78% ‘grass’, which confirms the original prediction. The diagnostic mode can thus be used to determine the land cover required to generate any selected level of income. 3.5 Use of the BN As already anticipated, the populated BN was used to perform a ‘what if’ analysis by putting the evidence on different values of the input variables representing actions and, via belief propagation, computing the effects on different indicators (output). Two class of alternatives were analysed (Henriksen et al., 2004): 1. Farming contracts: voluntary farming contracts (different compensation level), in the following denoted as A. 2. Both actions: voluntary farming contracts plus removal of point sources (it is assumed that all point sources are simply removed), in the following denoted as B. Figure 3.5 illustrates that the compensation payment must be corresponding to MVJ high level compensation agreement. The Danish acronym MVJ stands for ‘Environmental friendly agricultural agreements’ that beside no pesticide application includes other restrictions, e.g. use of fertilizers, choice of vegetation, etc. The level of compensation for MVJ is DKK 4400 per ha/year, of this up to 60% financed by the EU .

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Fig. 3.5. Comparison of overall indicator (safe supply) for the two alternatives: farming contracts and removal of point sources (two actions) (a) and water quality indicators (probability of water quality exceeding 0.1 μg/l (b) for different levels of compensation.

The only way to achieve the ‘preventive goal’ (minimum a 95% probability for the state ‘true’ of the safe supply) is by the high level compensation of the standardized MVJ agreement. Even if both actions are taken, with an additional cost for removing all the point sources, the result is the same. We have not considered the move of the wellfield away from the Havelse creek in the present analysis. This could both increase the probability of the safe supply being in the state of ‘true’, but other effects could also adversely decrease this probability (intrusion of salt water closer to the Roskilde Fjord or unknown point sources close to the new wellfield location). For a compensation of DKK 500 per ha/year, few farmers (4%) would join voluntary farming agreements prescribing no pesticide application. For DKK 1000 per ha/year,

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a slightly larger fraction would join (11%). At DKK 2500 per ha/year, nearly 50% would join, but their willingness to sign voluntary preventive farming contracts (no pesticides) is much less than the input from the expert indicated (Rasmussen, 2003), which indicated a break-even point of below DKK 1500 per ha/year. The main problem is related to a lack of commitment to realistic levels of compensation. Farmers in the area have suggested compensation payment levels of about DKK 5000 per ha/year. Farmers’ organizations (NOLA and Sjaellands Familielandbrug) have indicated that such agreements should either offer a very high compensation (up to DKK 7000 per ha/year) or not be part of groundwater protection at all (expropriation may be necessary and a more feasible method). The value that alternative A produce for the indicator surface water quality show that the probability of polluted deep groundwater above MAC drops to below 5% at a compensation level of DKK 2500 per ha/year. This probability varies for the different compensation level from 8.3% (none) to 7.3% (500), 7.5% (1000), 6.6% (1500), 5.8% (2000), 4.3% (2500) to 1.3% (DKK 44 per ha/year). Alternative B reaches the 5% level at DKK 1000 per ha/year, signifying that action directed at point sources (removal) may be a necessary element of groundwater protection policy. With alternative A, shallow groundwater has a probability of pesticide content of between 41.7% (none) and 33.2% (DKK 1500 per ha/year). Not until DKK 4400 per ha/year does the probability drop below 10% for clean groundwater (6.6% probability). Alternative B results in an achieved goal of a 5% level at DKK 4400 per ha/year including removal of point sources. Similar results were found for surface water. Furthermore, the exercise demonstrated that cost/benefit issues and especially the implementation of management action plans are associated with many more issues than expert knowledge normally takes into consideration. In our case, a barrier for voluntary farming contracts is not data or information on economic conditions in farming, but, to a much greater extent, perception among stakeholders of the soundness of the action. Attitudes, beliefs and group behaviour (Robbins, 2003) among farmers and their organizations, and the uncertainty and lack of data, play a more important role than a possible financial benefit in the short run. Since farming contracts also are rather difficult to manage (Brouwer, 2003), at least as voluntary agreements negotiated within a wellfield catchment area as part of a groundwater protection plan, the entire approach is both costly and difficult to implement. 3.5.1 Stakeholder involvement The starting point for identifying stakeholders was trying to list categories of water users, potential groundwater pollution sources, and authorities in the area: local waterworks, other water consumers, farmers, industry, anglers, the county and the municipalities. All professional stakeholder organisations that were found to have a potential or even marginal interest in groundwater protection in the specific area were invited to a one-day workshop in October 2002. Many ‘green’ NGOs did not show up and the industrial sector preferred to use their political contacts on groundwater issues. The workshop resulted in the formation of a professional stakeholder working group with ten institutions, including the project end user (Copenhagen Energy, the local

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Agenda 21 Centre (was also used as facilitator in relation to citizens’ group), and the Geological Survey Denmark and Greenland (GEUS). In November 2002, a public meeting was held in the local community hall. Invitations were distributed to more 1100 local households and the meeting was announced in the local newspaper. About 100 people and the local TV station showed up for the meeting. The meeting resulted in a local citizen-working group. At both meetings, stakeholders were asked to present issues and problems they found important in relation to groundwater protection. The idea behind splitting up the stakeholders into two groups, was the perception that the professional stakeholders are already deeply involved in groundwater management and protection, whereas local citizens might have another starting point for their involvement in groundwater management and protection (Henriksen et al., 2004). At meetings and workshops with citizens’ group, we used a facilitator from the local joint municipality Agenda 21 Centre. Facilitation in relation to the group of professional stakeholders was not systematic throughout the project, but the first meeting was facilitated by Agenda 21 Centre. The citizen group met five times in the first half of 2003. The facilitator guided the meetings. GEUS and CE only participated in two of the five meetings to answer specific questions and to introduce and discuss the development of the BNs. The citizen group published two newsletters in the first half-year of 2003. A third newsletter was published in July 2004 after finishing the final MERIT report. They were distributed to 1000 households in the local area. The newsletters included mainly articles related to groundwater protection, water supply and water quality, and introduced the members of the citizen group. The production and distribution of the newsletter was financed by the MERIT project. At the final joint meeting in March 2004, the stakeholder groups were asked to comment on the involvement process on the basis of four questions (Henriksen et al., 2004): 1. Is there a need for further initiatives for the protection of groundwater and the stream/bay? 2. How have you experienced the MERIT project progress (BNs, citizens’ meeting, workshops, citizen groups, newsletter, individual meetings, etc.)? 3. How should stakeholders be involved in the future in, for example, active groundwater protection and the establishment of wetlands? 4. Other comments to the process? Furthermore, comments and suggestions from the stakeholders and citizens’ group to the draft report were collected by consultation (from questionnaires and hearing in May 2004) and consequences for the final reporting addressed. The overall idea was to begin the process as openly as possible, to get an idea of how stakeholders could contribute to an improved protection of the groundwater resources in the area. Problems encountered in BN development were: • Stakeholder input to networks and probability tables (numbers) required individual meetings. • It was not that easy to understand the networks (a way of thinking that requires a little practice, if not properly explained.

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Table 3.1. Strengths, weaknesses, opportunities and threats (SWOT) analysis of stakeholder engagement process. Strengths Public participation can. . .

Weaknesses Public participation can be weakened by. . .

Opportunities Public participation offers the opportunity to. . . Build trust and capacity

Make use of local and citizen knowledge not known by authorities

A lack of resources (time, money, staff)

Encourage diverse perspectives (and thus encourage issues not thought of)

A lack of rules for participation

Empower people by starting a dialogue and improving openness

Enable a better evaluation of the issues

A lack of in-depth involvement of authorities

Expand the limits of understanding (working together to solve problems)

A lack of hands-on BN for stakeholders

Improve the accountability of stakeholders

Threats Public participation can be threatened if. . . The public thinks that the process is a formality (that minds are already made up)

A vocal minority dominates public meetings

A lack of professional supervision of the stakeholder involvement process

• Difficult to motivate stakeholders to become involved in BN development. • Problems with conditional probability tables in different stakeholder groups. • We did not receive as much input to the BNs as expected from stakeholders. BN s can help formulate environmental management strategies by allowing users to build their own decision support system that meets their needs, helping users to better understand the nature of their decisions, and encouraging users to deal with uncertainty and to consult stakeholders and members of the general public. Table 3.1 and Table 3.2 contains an interpretation of BNs with respect to strengths, weaknesses, opportunities and threats based on our experience. An important characteristic of BNs is that the tool can be used ‘interactively’ for uncertainty assessment and communication with the stakeholders involved. However, it is important to apply a kind of ‘protocol’ for BN construction in order to be able to explain to stakeholders, experts, users, etc. what input is required at different stages of the BN development.

3.6 Discussion The results of the case study showed ‘a paradox’. On the one hand, BNs can create space for an open and non-deterministic dialogue with stakeholders due to the flexibility of the decision support tool. This allowed factors (nodes), associations (directed links) and probabilities of the graphical model to be adjusted, reconstructed and validated throughout the

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Table 3.2. Strengths, weaknesses, opportunities and threats (SWOT) analysis of tory modelling tool for water resources management. Strengths Excellent for structural learning, elegant statistical approach and data mining for analysis of complex systems

Weaknesses Difficult to understand for non-experts

Easy to develop a BN with nodes and directed links and update it with input from stakeholders

Feedback nodes are not allowed; some problems in the real world cause strong feedback

Social and ecological issues can be incorporated and coupled with hydrology

The interdisciplinary approach raises problems with organising knowledge input and data input (academic territories, illusion of techniques)

BN s

as participa-

Opportunities Possible to understand for representatives of general public if explained properly by the specialists

Threats Not easily understood if not properly explained

The opportunity to work with more disciplines

Over-expectations with respect to modelling capabilities

Excellent for strategic considerations (indicators, actions and additional data requirements) Expert knowledge and data can be combined and modified/balanced through stakeholder involvement

Requires panel of expert input for all domains ⇒ resources and time (equivalent to numerical models)

Improves conversation/ dialogue with stakeholders

Too much ‘hot air’/soft discussion (difficult to govern the process; ‘focused stakeholders’)

Very useful for complex systems

There is a danger that use of BNs causes ignoring of real data and knowledge

Excellent for integration and breakdown of barriers between different domains, e.g. economy, hydrology, ecology, social (different time and spatial aggregation)

Not useful for implementation of specific protection zones (physical-based model required)

New problems can be structured and analysed quickly

Political manipulation

Holistic approach

BN s describe an understanding of the system and processes that are not physically based (more information flow than mass flow)

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Table 3.2. Continued. Strengths Non-linearity not incorporated

Weaknesses Can be used even if data sets are incomplete

Opportunities The BN hides the complexity of the system from the users (underlying conditional probability tables are rather complex!)

Threats

Ability to predict future state of systems based on simplified assumptions

Data manipulation is possible (it is easy to get carried away)

A possible tool when dealing with water managers sceptical about comprehensive models and questioning the need for catchment modelling

Experts do not want to provide input for the conditional probability tables (numbers): defending academic territories!

It is possible to ‘validate’ the importance of value and belief when consulting stakeholders

BN s work on ‘aggregated data’ (probabilities)

Not possible to utilise all collected data (temporal and spatial variation/information in data)

process and based on inputs from all involved stakeholders and experts. This means that BN s were powerful for integrating data and knowledge from different domain experts and capable of handling uncertain information in a practical and easily understandable manner. Constructing the qualitative part of a belief network (nodes and links), although elaborate, seemed relatively straightforward and experts seemed comfortable doing so. This part of the net was relatively easily communicated to stakeholders. On the other hand, getting stakeholders, general public and even experts to understand and accept the idea behind the BNs used for decision making was a demanding task (Varis and Kuikka, 1999). Especially the required probability assessments for the BNs were not easy to understand or accept by stakeholders. Even domain experts had problems to express all these probabilities numerically, something they were reluctant to do. So the quantitative part, with the probabilities over the variables, was more problematic. Probabilities offer an alternative approach to communicating data and model results which takes advantage of the ability of people to understand outcomes presented in probabilistic language. Instead of a pesticide concentration of 0.12 μg/l simulated by a model, which is likely to be received with scepticism by stakeholders because it does not account for natural variability in the system, the information should be presented categorically in the following form: ‘There is a 37% probability that the concentration will be below 0.01 μg/l, a 51.5% probability that it will be between 0.01 and 0.1 μg/l, and a 11.5% probability that it will be greater than 0.1 μg/l.’ One reason why probabilistic information of this form is useful is that it lends itself to evaluating the risk associated with different alternatives. Probabilities can be multiplied by the potential cost or benefit of different outcomes in order to elicit the ‘expected value’ of a decision. Expected values of different management alternatives can then be compared

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to aid decision-makers in selecting an option. Additionally, the risk of an outcome can be interpreted as a margin of safety associated with the option. In a BN, this is done by introducing ‘utility nodes’ that perform risk assessment by multiplying and adding total costs or benefits (in monetary value or other type of utility). When applying the precautionary approach in groundwater protection, the role of uncertainty is vital: the higher it is, the lower the pressures allowed from various non-point and point pollution sources should be. In addition, recent paradigms for risk-informed decision-making call for a participatory procedure in which the various stakeholders become involved early on in the risk assessment process to ‘characterize’ risks even before a formal assessment of them is made. This does not diminish the role of modelling and quantification, but is aimed at eliciting the ‘values’ and the perspectives of the community involved so that the multiple dimensions of risk can be taken into account early in the assessment. Decision-makers need to be informed not only of the available scientific knowledge but also of policy-relevant uncertainties and lacunae in the knowledge base (Levin et al., 2004). For this to be possible, uncertainties must be transparently discussed and communicated. A key problem with the probabilistic approach is that most people feel more at ease with verbal probability expressions than with numbers. When people communicate probabilities, they frequently do so in words rather than in numbers. So when it comes to reasoning and to communicating the results of BNs to users, the mode in which people normally represent probability must be taken into account as well (Renooij et al., 1999). This has often been considered a major obstacle, one of the reasons being that experts are reluctant to provide numerical probabilities. However, recent research activities have made some progress in this field by developing a probability scale that contains words as well as numbers (Renooij et al., 1999; Witteman and Renooij, 2003 and Gaag et al., 2002). This leads us to the following conclusions regarding BNs as a participatory modelling tool. BNs demonstrated several advantages compared to traditional approaches. BNs enabled locally based solutions (more than before). They provided local acceptance of decisions and solutions and improved the dialogue between the water company, local stakeholders and authorities. They encouraged diverse perspectives (and thus identified issues not thought of). They enabled a better evaluation of the issues. They made use of local and citizen knowledge not known by the authorities (Henriksen et al., 2004), see Fig. 3.6. The weaknesses of BNs as a participatory modelling tool were the time-consuming and long-lasting process in construction and evaluation of BNs, which had to be properly organised and conducted. This means that, if there is a lack of resources (time, money, staff), rules for participation, in-depth involvement of authorities, hands-on BN for the stakeholders and professional supervision of the stakeholder involvement process, then the credibility of the BNs are likely to be questioned by stakeholders and general public. Regarding involvement of stakeholders and general public (Mostert, 2003a), in BN development the following observations can be made: Stakeholders have an impact on the BN development (on variables, links and CPTs, e.g. ‘perception of vulnerability’ variable and behaviour of farmers in relation to compensation payment).

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Benefits of BNs in stakeholder involvement process

Low Type of stakeholder involvement Type 1 information

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Fig. 3.6. The Negotiation Support System.

Evaluation of BNs with input from stakeholders and general public is a necessary step. In the early, more qualitative stages, broader groups of stakeholders and the general public may provide relevant input to BN development. In the later, more quantitative stages, it is better to consult stakeholders and members of the general public at individual meetings or maybe in stakeholder and citizens’ groups that each focused on a ‘domain of interest’ because they were reluctant to give quantitative inputs in larger groups. Experts are necessary for quantitative input to BNs. In the case study, we had two experts in farming economics and socioeconomics in addition to the geological and hydrological experts from CE and GEUS. It would have been an advantage in BN development if the panel of experts had included an expert on biodiversity and/or pesticide-exposed aquatic environment as well. The results of BNs must be followed by a detailed description of the parameters used and why, plus a description of the results and their consequences in order to make BNs as transparent as possible. It is much too risky to allow politicians or civil servants to make decisions based on the ‘naked’ data. So one should rather allow stakeholders and citizens to become more involved. It is important to provide users with more easily understandable explanations of the results, for which numbers not necessarily was the best option, and verbal communication is a necessary alternative. Furthermore, an expert’s assessments may reflect various biases, where an expert consistently gives probability assessments that are higher or lower than they should be.

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In order to provide a credible BN it is necessary to engage stakeholder groups in the validation of BNs. This is a crucial and demanding task. 3.7 Conclusion In conclusion, the use of BNs went beyond information and consultation and required the full involvement of stakeholders in their construction and validation. The advantage was a reduced risk of lack of support by stakeholders during the implementation phase of a given action plan. The benefits of BNs were greatest when there was a high degree of interaction between researchers, users, water managers, stakeholders and general public. BN s are most powerful when integrating different domains, e.g. physical, social, economic and ecological, in the early stages of preparing a management plan with the involvement of stakeholder groups and general public (Mostert, 2003a, 2003b). A panel of experts is valuable in covering each domain included, for providing proper input and/or for reviewing the results from the BNs developed. Clear rules of the game are important: it is necessary to prepare a stakeholder involvement plan describing how to involve stakeholders and general public which is balanced with respect to problem framing and the type of decision support system used for planning and implementation. It is better to involve stakeholders and public in ‘temporary’ groups or at individual meetings when direct input is required, especially when collecting data for BNs; feedback on states, links and CPTs than it is to run the process with broad, permanent groups of stakeholders and citizens. Do not be afraid of actively involving citizens: but be careful to inform and explain properly about the tasks and goals and do always allow feedback and comments on the BN development process by presenting the graphical model and easily understood descriptions and results. If representatives of the general public are involved, be sure there is enough time and money to run the process according to the stakeholder involvement plan. Acknowledgements The present work was carried out within the Project ‘Management of the Environment and Resources using Integrated Techniques’ (MERIT), which is partly funded by the EC Energy, Environment and Sustainable Development programme (Contract EVK 1- CT 2000-00085). Bibliography Abramson, B. and A. Finizza (1991). Using belief networks to forecast oil prices. Int. J. Forecasting 7(3), 299–315. Andreassen, S., F.V. Jensen, S.K. Andersen, B. Falck, U. Kjærulff, M. Woldbye, A.R. Sørensen, A. Rosenfalck and F. Jensen (1989). Computer-Aided Electromyography and Expert Systems. Chap. MUNIN – an expert EMG assistant. Elsevier. Amsterdam, NL, pp. 255–277. Batchelor, C. and J. Cain (1991). Application of belief networks to water management studies. Agr. Water Manage. 4(1), 51–57.

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Borsuk, M.E., C.A. Stow and K.H. Reckhow (2004). A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecol. Modell. 173, 219–239. Borsuk, M.E., C.A. Stow, D. Higdon and K.H. Reckhow (2001). A Bayesian hierarchical model to predict benthic oxygen demand from organic matter loading in estuaries and coastal zones. Ecol. Modell. 143, 165–181. Borsuk, M.E., P. Reichert and P. Burkhardt-Holm (2002). Integrative environmental prediction using Bayesian networks. In: Integrated Assessment and Decision Support, Proceedings of 1st Biennial Meeting of IEMSS, June 24–27 (A.E. Rizzoli and A.J. Jakeman, Eds.). Lugano, CH, pp. 102–107. Brandt, G. and H.J. Henriksen (2003). Protection of drinking water sources for quality and quantity. Groundwater protection in the Greater Copenhagen area. In: Future Scenarios for Water Management in Europe. FIRMA Conference, 19–20 February. Barcelona, SP. Bromley, J. (2006). Guidelines for the use of Bayesian networks as a participatory tool for Water Resource Management. Management of the Environment and Resources using Integrated Techniques (EVK1-CT-2000-0085 MERIT). Centre for Ecology and Hydrology. Wallingford, UK. Brouwer, F. (2003). Co-operative agreements in agriculture. National report: Denmark. Technical report. Agricultural Economics Research Institute (LEI). Brusch, W., J. Stockmarr, Fv. Platen-Hallermund and P. Rosenberg (2004). Pesticide pollutent water from small waterworks. Technical report. Geological Survey of Denmark and Greenland. Copenhagen, DK. in Danish. Cowell, R.G., A.P. Dawid, S.L. Lauritzen and D.J. Spiegelhalter (1999). Probabilistic Networks and Expert Systems. Springer Verlag. Berlin–Heidelberg–New York. Daft, R.L. and R.H. Lengel (1986). Organizational information requirements, media richness, and structural design. Manage. Sci. 32, 554–571. Gaag, L.Cv., S. Renooij and C.L.M. Witteman (2002). Probabilities for a probabilistic network: a case study in oesophageal cancer. Artif. Intell. Med. 25, 123–148. Gómez, M. (2004). Advances in Bayesian Networks. Chap. Real-world applications of influence diagrams. Springer-Verlag. New York, NY, pp. 162–180. Henriksen, H.J., P. Rasmussen, G. Brandt, D. v. Bülow and F.V. Jensen (2006). Public participation modelling using Bayesian networks in management of groundwater contamination. Environ. Modell. Softw. in press. Henriksen, H.J., P. Rasmussen, G. Brandt, Dv. Bülow, L.F. Jørgensen and P. Nyegaard (2004). Test of Bayesian Belief Network and stakeholder involvement. Groundwater management and protection at Havelse well field in Northern Zealand. Evk1-2000-00085. Geological Survey of Denmark and Greenland and Copenhagen Energy. Copenhagen, DK. Jensen, F.V. (2001). Bayesian Networks and Decision Graphs. Springer Verlag. New York, NY. Jensen, F.V., U. Kjærullf, B. Kristiansen, H. Langseth, C. Skaanning, J. Vomlel and M. Vomlelova (2001). The SACSO methodology for troubleshooting complex systems. Artificial Intelligence for Engineering, Design, Analysis and Manufacturing 15, 321–333. Jorgensen, L.F. (2003). Groundwater monitoring 2003. Technical report. Geological Survey of Denmark and Greenland. Copenhagen, DK. Korb, K.B. and A. Nicholson (2004). Bayesian Artificial Intelligence. Chapmann and Hall. Boca Raton, FL. Levin, R., S.O. Hansson and C. Rudén (2004). Indicators of uncertainty in chemical risk assessment. Regulatory and Pharmacology 39, 33–43. Marcot, B.G., R.S., Holthausen, M.G. Raphael, M. Rowland and M. Wosdom (2001). Using Bayesian Belief Networks to evaluate fish and wildlife population viability. Forest Ecol. Manag. 153(1–3), 29–43. Mostert, E. (2003a). The challenge of public participation. Water Policy 5(2), 81–97.

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Mostert, E. (2003b). Public participation and the European Water Framework Directive. A framework for analysis. Inception report of the HarmoniCOP project – Harmonising Collaborative Planning. EU-FP5. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann Publishers. San Francisco, CA. Rasmussen, S. (2003). Economic losses by pesticide free agriculture in Havelse catchment. Technical report. Royal Danish Veterinary and Agricultural University. Copenhagen, DK. Renooij, S. and C. Witteman (1999). Talking probabilities: communicating probabilistic information with words and numbers. Int. J. Approx. Reas. 22, 169–194. Robbins, S.P. (2003). Organizational Behaviour. Pearson Education International. San Diego, CA. Schou, J.S. (2003). Economic analysis of pumping strategies for groundwater abstraction in the Havelse stream catchment. Technical report. National Environmental Research Institute. Roskilde, DK. Varis, O. and S. Kuikka (1997). Bayesian approach to expert judgment elicitation with case studies on climatic change impact assessment on surface waters. Climatic Change 37(3), 539–563. Varis, O. and S. Kuikka (1999). Learning Bayesian decision analysis by doing: lessons from environmental and natural resources management. Ecol. Modell. 119, 177–195. Weick, K.E. (1995). Sensemaking in Organizations. Foundations for Organizational Science. Sage Publications. London, UK. Witteman, C and S. Renooij (2003). Evaluation of a verbal-numerical probability scale. Int. J. Approx. Reas. 33, 117–131.

CHAPTER 4

Exploring Water Conservation Behaviour through Participatory Agent-Based Modelling

Andrew Rixon, Magnus Moglia and Stewart Burn CSIRO Manufacturing and Infrastructure Technology Highett, Victoria, Australia

4.1 Introduction The pre-industrial period found water utilities developing ‘big pipe networks’, with centralised structures aimed at controlling disease outbreaks (Livingston et al., 2004). Since then population growth, technological development, trends in urban and rural development, and human-induced climate change are have been driving future water use (Kuylenstierna et al., 1997). This has meant that urban water utilities have continued to evolve as socio-economic and environmental conditions have changed. With large pipe networks having been established within cities, a strong supplyoriented logic of development prevailed. Water utilities were driven by the basic assumption that economic growth generates new demands for services. However, during the last decade, corporatized water utilities, guided by regulatory frameworks, have had to address environmental concerns, decaying infrastructure, water shortages and the inability to continue the supply-side expansion (Marvin et al., 1999). What has emerged is a demand management paradigm. Under this paradigm, the water utility attempts to influence and reshape demand by mechanisms such as metering, where variable tariff schemes are used to impact on peak demand periods, and initiatives such as water recycling, and the promotion of water-saving technologies and behaviours. With such programs, the water utility can find itself embedded in a complex sociopolitical landscape. How willing are people to accept these water-demand management strategies, and how fair are tariff structures in lower socio-economic areas (Turton, 1999)? It has been suggested that policy debates could start to recognise a much wider role for water utilities within their regions, and the potential for strengthening their contribution to social cohesion (Marvin et al., 1999). 73

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Although such demand-side strategies employed by the water utility focus directly on human behaviour, studies involving human beliefs, behavioural factors on water conservation and adoption rates of water-smart technologies are in their infancy. Although there are a few exceptions (see Clark et al., 2000; Corral-Verdugo et al., 2003; Syme et al., 2004). The growing importance of public participation and engagement within sustainability science has led to a reformulation of tools and methodologies which can be meaningful to both the researcher and the participants (Kasemir et al., 2003). One key strength of tools such as the agent-based modelling approach over more traditional equation-based approaches is that mathematical equations can be represented by soft sentences which make more sense to those not involved in model-building on a daily basis (Tillman et al., 2001). The problem of how to obtain relevant data and rules to incorporate into agent-based models, as well as seeking model validation by participants has been clearly recognised within the literature (Tillman et al., 2001; D’Aquino et al., 2002; Pahl-Wostl, 2002; Barreteau, 2003). The methodology of participatory agent-based modelling (companion modelling) has provided a framework for addressing such issues (Barreteau, 2003). Here, public participation and engagement is a vital ingredient in a three-stage process of field study, modelling and simulation (or game playing). Such a methodology provides access to knowledge elicitation and social learning, developing a dual mechanism with implicit added value for both the participants and the modeller (Pahl-Wostl, 2002). This chapter explores the effects of social networks and imitation on water conservation, and lay some groundwork for further exploration of water conservation behaviour using the participatory agent-based modelling approach. Two agent-based models are discussed and developed. The first is a simple model to explore the effects of social networks and tariff structures on a small population of agents, where the utility who collects the tariffs has constraints on the level of tariffs it may set. The second model, evolved from the first, seeks to explore the effect of imitation of water use behaviour within a population of agents and builds a case for the need for further studies into the potential for imitation being a key driver in water use behaviour and uptake of new technology. In both cases, social interaction forms the basis for the agent behaviour and the agents are characterised with degrees of belief for water saving. 4.2 Water conservation 4.2.1 Urban water use In Australia, the average household residence consumes 250 000 litres of water per year. On average, 34% of domestic water is used in the garden, 20% is used in both the toilet and shower, 12% in the washing machine, and the last 14% is used in the remaining water devices in the house (WSAA, 2001). Studies have shown that water use per capita declined in most large urban centres during the 1990s due to increases in water pricing, consumer education, use of watersaving appliances and higher residential densities being linked to lower outdoor water use (Senate, 2002). Australia’s population is estimated to grow to between 24 and 28 million by the year 2051 (ABS, 2001) and such population growth leads to total water

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consumption levels increasing, placing pressures on resources. It has been estimated that water-efficient devices could potentially reduce consumption by 50% in both the toilet and shower, 35% for the washing machine and 20% for the dishwasher (WSAA, 2001). The issue of technological adoption has been addressed by mainly focusing on the rate of diffusion of the innovation. Clark et al. (2000) has demonstrated an individual-based approach to the study of innovation within the water industry. In particular, it was shown that understanding the diversity of motivations and perceptions which characterise individual organizations’ operational experience is central to managing the implementation of new technology. 4.2.2 The social dilemma of water conservation Water conservation can be thought of as a social dilemma, a conflict between private and public interest. For example, in a hot summer period, the common consensus approach to conserve water may be ignored by a select few who decide to use water without regard for the consensus. If everyone used this strategy of ‘defection from the norm’, then the water resource would deplete rapidly. Water metering, pricing and the effect of tariff structures on water conservation have received a lot of attention in the literature (Saleth et al., 2000; Arbues et al., 2003; Chambouleyron, 2003). Few studies, however, have explored the effects of social cohesion on water conservation. An exception is Vugt (2001), who, treating water conservation as a natural social dilemma, identified two key approaches: the structural and the socialpsychological. The structural approach contains strategies that intervene directly in the outcome structure of the dilemma. For example, installation of domestic water meters makes it possible to charge based on usage, giving a financial incentive to consume less water. The social-psychological approach consists of interventions altering the way people value and think about the resource. Such an example can be found with public education campaigns. Vugt (2001) demonstrated that a community’s social cohesion moderates the effects of tariff structures on resource use. In particular, tariff systems in which overuse is penalised are particularly effective when a community’s social cohesion is low. Greater community social cohesion is suggested as a way to help promote restraint by increasing within the community thus increasing peoples willingness to exercise restraint when needed. 4.2.3 Water use: beliefs, attitudes and memetics On average, a person in Australia uses 350 litres of water a day, with this usage being split across activities in the kitchen, bathroom, toilet, laundry and garden. The garden represents the highest use area, consuming approximately 34% of total daily water use. Garden watering is strongly correlated with lifestyle beliefs and conservation attitudes (Syme et al., 2004). Water usage is also affected by the type of device and the associated behaviour with that device. For example, brushing teeth using a glass instead of leaving a tap running will save 9100 litres per person per year. Similarly, using a AAA-rated shower head instead of a normal shower head will save 28 000 litres per person per year1 . 1 Information drawn from http://www.southeastwater.com.au/sewl/index.asp?link_id=27.521 last visit 05/2006.

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Water user beliefs can be classified into two main classes (Corral-Verdugo et al., 2003). The first class is the water utilitarians - those who believe water is an unlimited resource to be used in an arbitrary way. The second class are the water ecologists (water savers) - those who believe water is a limited resource to be conserved. Corral-Verdugo et al. (2003) showed that water utilitarian beliefs are related with increased water consumption. Similarly, water ecologists’ beliefs on water consumption – believing that water is a resource to save leads to a decrease in consumption. Motives for saving water are significant predictors of water use. That is, the more motives, the greater the chance for water conservation behaviour (Corral-Verdugo et al., 2003). Although attitudes and beliefs of residents have been shown to have a direct effect on external water use (Corral-Verdugo et al., 2003; Syme et al., 2004), no studies on imitation of water-use behaviours have been found. Imitation is considered to be a founding block for learning in humans (Blackmore, 1999), and has been acknowledged to provide a mechanism beyond the rational actor paradigm, where individuals have perfect knowledge and try to optimise their outcomes (Jager et al., 2000). Although a controversial theory, the meme has been described as anything passed on by imitation (Blackmore, 1999) and is considered analogous to the gene and is embedded within an evolutionary process where selection, variation and inheritance operate. For the purposes of this chapter, the adoption of both water-use behaviours and smart water devices is considered memetic. Memetics has been criticised for being merely a conceptual framework (Edmonds, 2002), however it is considered to provide a strong software engineering framework by which technology adoption and beliefs can be implemented within the software models described in this chapter. Using a memetic representation for the description of the behaviours and devices allows a decoupling of the water-use behaviour from the resident’s behaviour in the implementation. This decoupling at the software development level provides flexibility, allowing for scenarios such as investigating the impact of advertising campaigns on residents. Under the memetic framework, the effect of an advertising campaign is captured by introducing a specific meme into the population of residents. Whilst this chapter explores the application of memetics within the community the validity of the approach is still to be shown. 4.3 The models 4.3.1 The agent-based modelling framework Agent-based models differ from equation-based mathematical models in that they are computational models focusing on algorithms to implement the behaviour within the model, rather then evaluating sets of system variables and equations (Parunak et al., 1998). For a full treatment and review on building agent-based models see Rixon et al. (2005). Whether a modeller chooses to use a agent-based modelling platform or build from scratch there are three key phases to the development of an agent-based model (Rixon et al., 2005): Phase 1 requirements; Phase 2 model storming;

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Phase 3 implementation. In Phase 1 Designs for multi-agent systems generally require mechanisms for (Gilbert and Terna, 1999) 1. receiving input from the environment; 2. storing a history of previous inputs and actions (audit trails); 3. carrying out actions and distributing outputs (scenarios). In Phase 2 it is common to work through higher level issues that are not well addressed with code. This might be an inclusive modelling session with stakeholder(s) to understand their requirements, or it could be a quick design session with other developers to determine how to build something. The technique of class responsibility collaborator or CRC cards has been shown to be an effective tool for facilitating group model storming processes (Biddle et al., 2002). Finally, when building agent-based simulations from scratch, there are two methods for dealing with time and events those being the discrete event simulation; and time stepped simulation. With a time stepped simulation, there is an internal ‘clock’ which ticks the model over to where the next group of events takes place. A discrete event simulation, however, does not use the ‘tick’ within the model; instead it utilises a stack of events which are queued and then scheduled and released once conditions are met (Tyszer, 1999). Table 4.1 details the key elements of the two agent-based models described in this paper based on the taxonomy provided by Hare et al. (2004) . By using an object-oriented approach for software development of agent-based models, it becomes possible to create a flexible framework of classes (and objects) that can be Table 4.1. Key characteristics of the explored agent-based models. Criterion Coupling of social and environmental models

Simple model Spatially non-explicit Collection of agents with friend networks not geo-referenced

Memes models Spatially explicit – Collection of geo-referenced households each containing residents with friend networks

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extended and evolved as the modelling process proceeds and the questions under investigation become clarified. 4.3.2 Model validation: understanding robustness and prediction In this paper, two models are described which aim at exploring some aggregated properties relating to water use behaviour that emerge from the interactions between individuals in a society. This is done for two key reasons. The first reason is to provide understanding and the second goal is to provide prediction. The focus however is put on developing an understanding because prediction is not a good indicator of the validity of a complex system model, in particular for a micro-simulation model. This is because of: • Non-linear features: the object of study is a non-linear system where only one ‘true’ model exists, but there is often an infinite number of models that all provide perfectly accurate prediction (Richardson, 2003). • Emergent features: even a perfect understanding of the micro-behaviour of all components (i.e. individuals) is insufficient to predict group behaviour (Gilbert et al., 1999). In addition to the non-linear and emergent features, the studied system also has second order emergence which is typical of human social systems. Second-order emergence refers to when institutions result from behaviour which takes into account emergent features, for example governments, churches and business organisations (Gilbert et al., 1999). There is hence second order emergence within our studied system because institutions and organisations adjust, at least on the long time scale, reflexively according to the expected emergent behaviour of the collection of individuals. This provides a selfreferential property (i.e. feedback) which again makes prediction very difficult. Therefore, while a solid understanding of micro-behaviour, and a reasonable predictive capacity are both indicators of a reasonable model, absolute validation of a model is virtually impossible, and a common path towards ensuring legitimacy of social simulation models is validation through stakeholders (Tillman et al., 2001; D’Aquino et al., 2002; Pahl-Wostl, 2002; Barreteau, 2003), which is typically attempted within Companion Modelling and Participatory Agent-Based Modelling (Barreteau et al., 2003). In line with the Companion Modelling approach, D’Aquino et al. (2001) distinguish between three types of validation processes: • Confrontation to the present reality: in essence a reality check. • Reconstruction by the model of a real past dynamics: can the model be used to reproduce and understand past dynamics (assuming data is available)? • Model acceptance by concerned people: can the model be understood and does it make sense? This requires transparency in terms of being able to understand the components of the model In the Participatory Agent-Based Modelling framework, a pragmatic constructivism is applied in the sense that validation of the model is not strictly necessary, but legitimacy is critical because models are used in a framework to support discussion and social learning.

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Within this process, the model and methodology is used for exploratory purposes and to generate theories in order to ask new questions on the real systems (Barreteau et al., 2004). A model is then legitimate, rather than valid, when it provides participants with new theories about the real system and is an acceptable representation of the real system. However, it should also be remembered that agent-based simulations can sometimes provide a false sense of realism, which makes stakeholder validation difficult. Other key methods to improve the validity and to increase understanding is to apply • Simplicity: make the model as basic or simple as possible so that assumptions or potential errors can easily be evaluated. • Sensitivity analysis: by varying the model’s parameter values, it is possible to explore the solution space of qualitatively different results, and to evaluate robustness, or in other words, evaluate how sensitive the model is to individual parameter values in terms of what is needed to achieve a qualitatively or quantitatively different result. Further work remains on the models explored in this paper around the areas of sensitivity analysis and stakeholder validation. 4.3.3 A simple model for exploring tariff structures The first simple agent-based model uses social networks to embed resident agents within a water-use environment where a water utility agent controls the tariff structure. The tariff structures explored are fixed and variable. The fixed tariff structure refers to a flat daily cost for water (independent of actual usage) determined by the water utility. The variable tariff structure sees a charge dependent on the amount of water used by the resident. All resident agents belong to a social network. The social network is either randomly generated or reconstructed from a ‘real’ data set of friendship links2 . Resident agents unable to get the amount of water that they require, due to the price placed on water usage by the water utility and their income levels, become water stressed. This water stress leads residents to seek out friends who are using more water and place peer pressure on them to reduce water use. Figure 4.1 details the properties and behaviours of the resident and utility classes. The integer variables of the resident agent, such as IncomeLevel and BlockSizeLevel, are chosen to represent high, medium and low scaling. Extrapolating from Syme et al. (2004) water use becomes a function of the resident agent’s IncomeLevel, BlockSizeLevel, Occupancy, WaterTechnology Level, LifestyleLevel, GardenRecreationLevel and GardenState. This water usage figure is then multiplied by a compensating factor based on whether the resident is a water saver or water utilitarian. The utility agent’s ‘willingness to pay’ algorithm is constrained by the acceptable degree of water stress within the resident population, and whether or not the water levels have reached critical levels. The algorithm for increasing or decreasing the tariff is described in Fig. 4.2. 2 Data sourced from http://www.sfu.ca/∼insna/INSNA/data_inf.html last visit 05/2006.

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Fig. 4.1. UML diagram of agent classes within the simple model.

Current water levels

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Fig. 4.2. Utility agent’s ‘willingness to pay’ algorithm.

4.3.4 Model calibration The simple model is initialised first with either a real social network loaded from file or by creating a random friend network as discussed earlier. Acceptable water stress is determined as a percentage proportion of the chosen agent population level. Resident agents demographic variables are assigned random values, leading to a distribution of income levels, block sizes, occupancies etc. Since the water utility agent maximises its profits there is no need to calibrate the initial level of cost for each tariff.

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4.3.5 Results Figures 4.3 and 4.4 show 100 iterations with 21 resident agents and random social networks (3 friends per resident). In these simulations, the utility agent is allowing a 40% water stress within the population and the tariff is fixed; residents pay per day independent of volume used. 3000

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Fig. 4.5. Variable tariff total water use for a population of 21 agents and 100 iterations (daily).

The periodic nature of Fig. 4.3 reflects the decision-making behaviour of the residents who can no longer afford water based on the cost per day imposed by the utility. The residents who can’t afford the daily tariff for water have no option but to cease water use. This creates water stress amongst the resident agents and leads to the water utility temporarily reducing its daily tariff, resulting in the periodic equilibrium of the fixed tariff price shown in Fig. 4.4. Figures 4.5 and 4.6 demonstrate the effects on resident water usage of a utility operating under the variable pricing tariff with the same initial conditions as the fixed tariff simulation. Figure 4.6 shows the fluctuation in cost per litre as the utility agent attempts to maximise its profit. Unlike the fixed tariff regime, the variable tariff regime produces more complex dynamics regarding total water use (see Fig. 4.5). To further understand the dynamics of this simulation model, 1000 simulations each of 200 time steps were run. Tables 4.2 and 4.3 describe the key results obtained from fixed and variable tariff regimes. Tables 4.2 and 4.3 demonstrate the increase in the utility’s profit resulting from the variable tariff regime. Table 4.3 clearly illustrates the effect of increasing the number of friends in the friend network per resident agent on the mean water use. Interestingly, the real social network data appears to fit between the random four and eight friend networks. 4.3.6 The water memes model In the water memes model, residents are considered to have a selection of water memes, some which are water saving memes and some which are not. These memes have a direct mapping between the type of device, the frequency of use and the amount of water used. In this model, ten types of water meme will be used (see Table 4.4). A view of a resident’s set of memes is: M1M2M3M4M5M6M7M8M9M10, where M1 could represent the garden meme, M2 a shower meme, and so on. Each meme, Mi,

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Mean water use Mean utility profit

No social network 124.13 1466.49

Random 4 friends 124.42 1483.13

Random 8 friends 124.87 1476.03

Real social network 124.69 1490.65

Table 4.3. Effect of variable tariff and social networks on mean water use and mean utility profit.

Mean water use Mean utility profit

No social network 163.12 2153.76

Random 4 friends 128.56 2008.27

Random 8 friends 110.83 1925.02

Real social network 117.10 1942.66

has a cost associated with imitation. This cost is implemented as a required belief in water saving. For example, a resident who comes into contact with a rainwater tank meme would have to have a strong belief in water saving to copy and implement this meme. The memes that are inactive within a resident agent are determined not to be enabled. Resident agents are born with a degree of belief in water saving. This water saving belief is dynamic and is calculated by the percentage of water saving memes in their current meme set. A resident agent’s water worry is an inverse function of the current dam level’s capacity – the closer to full capacity, the less worried the agent. Water worry is scaled by the resident agent’s degree of belief in water saving. The stronger the belief in water saving, the more the resident will care about the state of the water reserves. Water memes are copied based on social interaction. Residents within a household tend to become more like each other, sharing a common set of beliefs. Resident agents who are water worried look to their friendship networks to seek out water savers. In the

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Table 4.4. Names and descriptions of the ten water memes used in the water memes model. Name Garden Shower Toilet Brush teeth Prepare food Wash clothes Leaking tap Leaky toilet Dishwasher Rainwater tank

Description Five types of garden watering technology available, each with differing water use. These are bucket, hose, hose sprinkler, fixed sprinkler and drip system AAA-rated shower head is 45% more efficient than normal head Dual-flush toilet saves water over the single flush Brushing the teeth using a glass saves over having a tap running Preparing food in the kitchen sink with the plug in is more efficient than with a running tap Ensuring that the washing machine is full each wash The knowledge of how much water is saved by stopping leaking taps The knowledge of how much water is saved by diagnosing a leaky toilet Whether dishes are washed in the kitchen sink or in a dishwasher Installing a rainwater tank can reduce external water demands

case of no water worry, resident agents generally seek to become more like their friends, independent of whether their friends are water savers or water utilitarians. The key algorithms for the propagation of memes belong to the resident agent and are the SeekSimilarBeliefs and SeekWaterSavers functions. Resident agents who employ the SeekSimilarBeliefs algorithm iterate through their friend networks seeking a friend who shares a similar degree of belief in water saving. Once found, the resident agent employs the BecomeMoreLike(Friend) function, which randomly picks a meme out of the friends set of memes and copies it into their meme set. Similarly, resident agents who employ the SeekWaterSavers algorithm iterate through their friend networks to find a friend who has greater water saving beliefs then themselves, once found the resident agent then employs the BecomeMoreLike(Friend) function ensuring the transfer of a water saving meme. A simple water balance equation is used for the garden (Cook et al., 2003). This water balance incorporates rainfall, evapotranspiration and block size data to determine the level of water use required for a green garden. Figure 4.7 illustrates the core classes with attributes and behaviours for the water memes model. Finally, this agent-based simulation uses fixed increment time steps with conditional events. The core algorithm ticks the simulation through its daily routine. Here, each household (containing residents) is made to do the ‘activities’ which consist of using each of the core water usage areas: toilet, bathroom, kitchen, laundry and garden. Using an object-oriented approach enables events to be used which can fall outside the ticked simulation. For example, the garden object fires a ‘Needs water’ event whenever its stored water level is below what is required to keep it green. This event initiates the gardener of the household to go and water the garden. 4.3.7 Model calibration The model first loads real climate data from a file which includes rainfall and evapotranspiration data. All households were artificially created for the simulation, this includes selecting the number of residents per household and the type of residence (apartment or house) which were both randomised. Each resident agents created has a random friend

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Fig. 4.7. UML diagram containing classes within water memes model.

networks assigned. Resident agents are randomly assigned income levels, lifestyle levels and their water saving beliefs. Each resident agent is assigned a set of 10 random memes which form the basis for the water use behaviours. Finally the rainwater tank objects are initialised with a maximum volume of 1500 litres and the garden objects belonging to each household object are initialised with crop factors, garden areas, soil storage level, maximum soil storage levels, and green garden storage levels realistic for urban environments.

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4.3.8 Results No water worry. The following results were obtained for a simulation run with 30 households over 730 days (2 years). Real rainfall and evapotranspiration data were used. The residents had no water worries (the dam levels were always close to capacity), and each resident had 3 friends in their friend network. The simulation begins in January which is summer time in the southern hemisphere. Figure 4.8 clearly shows seasonal fluctuation in water use, with the highest usage occurring during the summer months (with gardens requiring most watering), and then dropping off as the winter months arrive (when gardens require least watering). Moreover, the mean total water use per household is around 800 litres per day, remarkably close to actual reported figures of 750 litres per household per day. Runoff water from garden watering or water lost through leaks is defined as water waste. Figure 4.9 shows an increase in mean water waste at the very beginning of the simulation. In this scenario, with no water worry, the water utilitarians outnumber the water savers (see Fig. 4.10). Water worry. The next results were obtained using a simulation scenario identical to the previous set. However, in this case the residents face a depleting water resource (dam levels are below 50% capacity). As with Fig. 4.8, Fig. 4.11 shows the seasonal fluctuations in water use, however in the case of Fig. 4.11, it is clear that the peaks and troughs of the seasonal fluctuations are decreasing. Interestingly, Fig. 4.12 shows an initial increase in mean water waste, followed by a reduction in water waste, which corresponds to an increase in the number of water savers in the simulation. 1000 900 800 700

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Fig. 4.10. Distribution of water savers and water utilitarians for no utility campaigning and no water worry.

Finally, in contrast to the previous simulation where the water utilitarian was the predominant belief, Fig. 4.13 demonstrates the flip to the water saver being the major belief within the population. Effects of friend networks on water saving beliefs. To explore the apparent alternate states of water saving beliefs detailed in Figs. 4.10 and 4.13 (the water saver belief is dominant

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Fig. 4.12. Simulation showing effect of targeting campaign on water waste running for 730 days.

in water worried scenarios, and the water utilitarian belief is predominant in non-waterworried scenarios), a Monte Carlo simulation was carried out with 250 scenario runs. Each scenario had 30 households with 730 days of simulations being run. Random friendship networks of 0, 3, 6 and 9 friends per resident were simulated. Figures 4.14 and 4.15 demonstrate a general declining trend for water saver beliefs as the number of friends per resident increases, independent of water worry. In particular,

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80 70 60

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Fig. 4.13. Distribution of water savers and water utilitarians under water worry.

Figs. 4.14 and 4.15 demonstrate that the water-utilitarian belief is a dominant attractor within the simulations. That is, in general, resident agents within the simulation scenario will tend to become more oriented towards non-water-saving beliefs as the number of friends in their friend network increases. Utility campaigning and water worry. Introducing the ability for a water utility to campaign to its population of residents when the water resources reach critical levels is simple with the memetic approach. In this case, the utility agent simply monitors the current water levels and, when they reach critical levels (in this case, capacity below 50%), the utility sends out a random water-saving meme with the bill (analogous to sending specific water-saving information kits in the mail). Residents who are water worried are able to copy and implement this meme. Figure 4.16 demonstrates the marked decline in mean water waste. Curiously, the wintertime water usage is seen to increase during this scenario. Using this memetic approach, the water usage is dependent on which water memes are dominant within the population of residents. As discussed earlier, memes are embedded within an evolutionary process of variation, inheritance and selection. Figure 4.17 illustrates the initial and final distributions for the various memes (described in Tab. 4.4) in this simulation. Memes which are water saving are listed as, for example, GARDEN1, GARDEN2, GARDEN3. The frequency of use – low, medium or high – is then represented as F1, F2 and F3 respectively. In the case of the GARDEN label: 1 = water by bucket, 2 = water by hose, 3 = water by hose sprinkler, 4 = water by fixed sprinkler and 5 = water by drip system. Interestingly, several memes have become extinct within this scenario. Specifically, washing clothes with many loads (CLOTHES2F2), medium and high hose sprinkler usage (GARDEN3F2,3), and low usage drip systems (GARDEN5F1).

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Profile of the water saver and water utilitarian. Table 4.5 profiles the set of water memes of the strongest water utilitarian belief resident and the strongest water saver belief resident for the previous simulation where residents are facing depleting water resources and the utility is actively campaigning water saving ideas. Each of these memes were initially assigned randomly to the residents.

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Fig. 4.17. Change in distribution of water memes throughout the population of residents from the initial situation (dark grey) to the final one (light gray).

4.4 Discussion This chapter describes an approach to implementing a simple agent-based model to explore the effects of social networks and tariff structures on water use behaviour. Social networks were demonstrated to have no effect on simulated water usage under the fixed tariff structure, as residents are unable to place pressure on their friends to cease their water use, only reduce it. Compared to the fixed tariff regime, the variable tariff regime

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80% Water Utilitarian Born 5% Water Saver Shower Normal Shower Medium

90% Water Saver Born 47% Water Saver Shower AAA Shower Low

Key Name

Toilet Dual Flush

Toilet Dual Flush

Key Name

PrepareFood Running Tap

PrepareFood Kitchen Sink

Key Enable

CheckLeakyToilet False

CheckLeakyToilet True

Key Name Frequency

DishWasher Kitchen Sink High

DishWasher Kitchen Sink High

Key Enable

RainwaterTank False

RainwaterTank True

Key Name Enable

BrushTeeth Tap Low

BrushTeeth Glass Medium

Key Enable

CheckLeakingTap False

CheckLeakingTap False

Key Name Frequency

Garden Fixed Sprinkler Low

Garden Bucket High

Key Name Frequency

WashClothes Many Loads Medium

WashClothes Ensure Full Load High

provides the most equitable and fair allocation of water to residents, with residents always being able to afford daily water usage under the variable tariff regime. This is due to the fact that under the fixed tariff regime, there are residents who are unable to afford the daily cost of water and so completely cease their usage for that day (see Fig. 4.3). Most interesting in this simple agent-based model, however, is that under the variable tariff regime, social networks result in a significant reduction in simulated water use. This result suggests that within small communities where social cohesion is strong, there is an ability for non-tariff-based strategies to successfully impact on water use. Further, the result provides a mechanism for decentralisation of water management, with residents empowered to seek sustainable usage of their precious resource. The first criticism of the simple model, however, is that the mean water use predicted per day is inaccurate. Estimations show that actual usage is on average around 350 litres per person per day, or 730 litres per household per day, this simple model shows mean water use of approximately 120 litres per day (see Tables 4.2 and 4.3). Secondly, although the simple model provided a framework for exploring the effects of social networks on water usage, the reality of this functionality is limited to, at best, small communities

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where hardships in the supply of water are common. Thirdly, the simple model provides no flexibility in how beliefs and actions are actually manifested. As discussed earlier, there is strong evidence for beliefs affecting water use and conservation behaviour. Finally, although water utilities today are interested in assessing customers willingness to pay, their roles also now require a water conservation component using advertising campaigns and more complex social involvement. The simple agent-based model contained no such behaviour for the utility agent. The agent-based water memes model was formulated to extend on these limitations of the simple model. In particular, it sought to explore the effects and transfer of watersaving beliefs on water conservation using a memetic framework. The water memes model demonstrated realistic seasonally fluctuating water use behaviour, with households using approximately 800 litres per day (see Fig. 4.8). Furthermore, with water worry, residents are found to reduce their water usage significantly, explained primarily by the uptake of water-smart garden technologies such as using a bucket or medium/high usage drip systems to water the garden. It was found that with increasing numbers of friends in residents’ friendship networks, there was a greater trend towards water utilitarian beliefs irrespective of water worry (see Figs 4.14 and 4.15). Underlying this trend towards water utilitarian beliefs is the implicit cost associated with copying water saver memes. For example, if a resident finds a water saver resident who uses a bucket to water the garden, then this resident would have to believe strongly in water saving to change from hose watering, to this method of watering the garden. Hence, even with water worry, where residents are concerned about the capacity of the dams and seek out water savers to copy, the extent of their friendship networks can have a large impact on the uptake of water saving behaviours. In this case, the water saving memes are lost from the population based on cliquey friendship networks. This selection on the basis of water memes was demonstrated in Figs 4.16 and 4.17, where the water utility can seek to influence residents’ behaviour when the capacity of the dam becomes critical. In this case, the water utility engages in a water saving campaign, where water saving ideas (water saver memes) are distributed to residents with the water bills. Ironically, even though the summertime water usage is seen to decline marginally with the uptake of rainwater tanks and water saving garden technologies, the winter water usage is seen to increase. Those water memes lost from the system were unsuitable for the current water-using environment. For example, the low frequency usage drip system did not satisfy residents gardens’ water requirements. Further study is required into the actual imitation behaviours of people to water use and technology uptake. In particular, further observation and empirical studies of the effects of friends on imitation behaviour is needed. The basis of the water memes model assumes that friends and family have a large effect on the degree of imitating behaviour. Such studies will allow for the rates and degree of infection to be determined more accurately. Studies to determine the actual distribution of memes within communities and the impact this has on spread and infection rates will aid in calibration/validation issues relating to the water memes model. Finally, there is a tendency to use agent-based models in a predictive exploratory capacity. Such uses are fraught with problems such as calibration, validation and sensitivity which all serve as inputs into the variability of the output of an agent-based model. As

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observed in Richardson (2002), a strong focus on the model rather than the modelling process dominates the field of bottom-up computer simulation. It is the authors view that the use of a participatory modelling process is a powerful inclusion into the method of agent-based modelling helping to take the focus away from the model itself and to address issues of validation. Future residential demand models are expected to analyse alternative demand programs, such as the promotion of low-consumption technologies (Arbues et al., 2003). This chapter has demonstrated the utility of an agent-based approach for incorporating aspects such as social networks and imitation into the study of water conservation behaviour. In particular, the water memes approach has been shown to be a productive framework for the exploration of different scenarios for the adoption of different water use technologies, producing realistic water usage based on residential water use behaviours and beliefs. Furthermore, it is concluded that utilising a memetic framework in the software development of an agent-based model, where the specifics of the behaviours are encapsulated within the meme, provides an extremely flexible object-oriented implementation. With further research into the role and importance of imitation in the uptake of water use beliefs, behaviours and devices, it would be possible to use this approach to study and forecast the potential impact of campaigns targeted for specific water user profiles. Embedding such a study within the participatory agent-based methodology can start a dialogue with participants and stakeholders, engaging and increasing their awareness surrounding the issues of water use and water conservation as well as providing a valuable validation process for the modeller. Acknowledgements All authors would like to thank Andrea Castelletti. Without his generosity of spirit, patience and willingness to share his time and expertise with LaTex and Matlab this book chapter would be only a possibility. Thank you so much Andrea. We are in your debt. Bibliography ABS (2001). Australia’s environment: Issues and trends. Australian Bureau of Statistics Catalogue. Arbues, F., M.A. Garcia-Valinas and R. Matrinez-Espineira (2003). Estimation of residential water demand: A state-of-the-art review. J. Socio-Econ. 32, 81–102. Barreteau, O. (2003). Our companion modelling approach. JASSS. Barreteau, O., F. Bousquet, C. Millier and J. Weber (2004). Suitability of multi-agent simulations to study irrigated system viability: application to case studies in the Senegal river valley. J. Appl. Stat. 80, 255–275. Biddle, R., J. Noble and E. Tempero (2002). Reflections on crc cards and oo design. In: Proceedings 40th International Conference on Technology of Object-Oriented Languages and Systems (TOOLS Pacific 2002). Vol. 10. Sydney, AUS. Blackmore, S. (1999). The Meme Machine. Oxford University Press. Oxford, UK. Chambouleyron, A. (2003). An incentive mechanism for decentralized water metering decisions. Water Resour. Manag. 17, 89–111. Clark, T., P. Jeffrey and T. Stephenson (2000). Complex agendas for new technology adoption in the UK water industry. Technovation 20, 247–256.

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Cook, F.J., C.E. Pankhurst, H.P. Bohl, C. D’Amato, D. Fanning, G.E. Rayment and G.D. Carlin (2003). Fate of split sugars from cane harvest: Investigation and modelling of the phenomena. Technical Report 48/03. CSIRO. Canberra, AUS. Corral-Verdugo, V., R.B. Bechtel and B. Fraijo-Sing (2003). Environmental beliefs and water conservation: An empirical study. J. Environ. Psychol. 23, 247–257. D’Aquino, P. and F. Bousquet (2001). Accompanying governing processes in land use management with linking role playing games, gis and mas: The selfcormas experiment in the Senegal river valley. In: Special Workshop on Agent-based Models of Land Use / Land Cover Change. Irvine, CA. D’Aquino, P., O. Barreteau, M. Etienne, S. Boissau, S. Aubert, F. Bousquet, C. Le Page and W. Dare (2002). The role playing games in an abm participatory modelling process: Outcomes from five different experiments carried out in the last five years. In: Integrated Assessment and Decision Support, Proceedings of 1st Biennial Meeting of IEMSS, June 24–27 (A.E. Rizzoli and A.J. Jakeman, Eds.). Lugano, SW. Edmonds, B. (2002). Three challenges for the survival of memetics. Journal of Memetics – Evolutionary Models of Information Transmission 6, 1–6. Gilbert, N. and K.G. Troitzsch (1999). Simulation for the Social Scientist. Open University Press. Philadelphia, PA. Gilbert, N. and P. Terna (1999). How to build and use agent-based models in social science. http://web.econ.unito.it/terna/deposito/gil_ter.pdf. Hare, M. and P. Deadman (2004). Further towards a taxonomy of agent-based simulation models in environmental management. Math. Comput. Simulat. 64, 25–40. Jager, W., M.A. Janssen, H.J.M. De Vries, J. De Greef and C.A.J. Vlek (2000). The human actor in ecological-economic models: Behaviour in commons dilemmas: Homo economicus and homo psychologicus in an ecological-economic model. Ecolog. Econ. 35, 357–379. Kasemir, B., J. Jager, C.C. Jaeger and M.T. Gardner (2003). Public Participation in Sustainability Science: A Handbook. Cambridge University Press. Cambridge, UK. Kuylenstierna, J.L., G. Bjorklund and P. Najlis (1997). Sustainable water future with global implications: Everyone’s responsibility. Nat. Resour. Forum 21(3), 181–190. Livingston, D.J., N.J. Ashbolt and H.K. Colebatch (2004). Urban water management as a changing socio-technical system: Participation, decentralisation and sustainability. In: First Int. Conf. on onsite Wastewater Treatment and Recycling/6th Specialist Conf. on Small Water Systems and Wastewater Systems, Freemantle, Australia 11–13 February. Marvin, S., S. Graham and S. Guy (1999). Cities, regions and privatised utilities. Prog. Plann. 51, 91–169. Pahl-Wostl, C. (2002). Agent based simulation in integrated assessment and resources management. Integrated Assessment and Decision Support, Proceedings of the First Biennial Meeting of the International Environmental Modelling and Software Society 2, 239–244. Richardson, K. (2002). Methodological implications of complex systems approaches to sociality: Some further remarks. JASSS 5(2), 1–11. Richardson, K. (2003). On the limits of bottom-up computer simulation: Towards a nonlinear modelling culture. In: 36th Hawaii International Conference on System Science, Hawaii. Rixon, A., M. Moglia and S. Burn (2005). Bottom-up approaches to building agent-based models discussing the need for a platform. In: Proceedings CABM-HEMA-SMAGET Joint Conference on Multi-Agent Modelling for Environmental Management Bourg St Maurice Les Arcs, France 21–25 March/mars 2005 5(2), 1–11. Saleth, R.M. and A. Dinar (2000). Satisfying urban thirst: Water supply augmentation and pricing policy in Hyderabad city, India. Technical Report 395. The World Bank. Washington, DC.

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Senate (2002). The value of water: Inquiry into Australia’s urban water management – report of the senate environment, communications, information technology and the arts reference committee. Technical report. Parliament of the Commonwealth of Australia. Canberra, AUS. Syme, G.J., Q. Shao, M. Po and E. Campbell (2004). Predicting and understanding home garden water use. Landscape Urban Plan. 68, 121–128. Tillman, T., T.A. Larsen, C. Pahl-Wostl and W. Gujer (2001). Interaction analysis of stakeholds in water supply systems. Water Sci. Technol. 43(5), 319–326. Turton, A.R. (1999). Water scarcity and social adaptive capacity: Towards an understanding of the social dynamics of water demand management in developing countries. MEWREW Occasional Paper No. 9. Tyszer, J. (1999). Object Oriented Computer Simulation of Discrete Event Simulations. Kluwer Academic Publishers. Dordrecht, NL. Van Dyke Parunak, H., R. Savit and R.L. Riolo (1998). Agent-based modelling vs. equation-based modelling: A case study and user’s guide. Proceedings of Multi-agent Systems and Agent-based Simulation (MABS’98) 1534, 10–26. Van Vugt, M. (2001). Community identification in a natural social dilemma. Pers. Soc. Psychol. Bull. 27, 1440–1449. WSAA (2001). The Australian urban water industry wsaafacts 2001. Technical report. Water Services Association of Australia. Melbourne, AUS.

Part III

Managing and MODSS

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CHAPTER 5

Decision Support Systems for Integrated Water Resources Management with an Application to the Nile Basin

Aris P. Georgakakos Georgia Water Resources Institute Georgia Institute of Technology, Atlanta, USA

5.1 Integrated water resources management: science in support of public policy Integrated Water Resources Management (IWRM) is the process of formulating and implementing shared vision planning and management strategies for sustainable water resources development and utilization with due consideration of all spatial and temporal interdependencies among natural processes and human and ecological water uses. The IWRM process is conceptualized in Fig. 5.1. The knowledge to support planning and management decisions resides in various disciplines including climatology, meteorology, hydrology, ecology, environmental science, agro-science, water resources engineering, systems analysis, remote sensing, socioeconomics, law, and public policy. Public policy actors (such as politicians, judges, government agencies, financial institutions, Non-Governmental Organizations, citizen groups, industries, and the general public) are often in a position to make critical decisions that reflect society’s shared vision for water resources utilization. Public policy actors develop consensus and decide on shared vision strategies based on information generated and communicated by Decision Support Systems (DSSs) and associated processes. Thus, the role of DSS is to leverage current scientific and technological advances in developing and evaluating specific policy options for possible adoption by the IWRM process. DSSs are developed and used by research institutions, government agencies, consultants, and the information technology industry. By its nature, IWRM is a process where information, technology, natural processes, water uses, societal preferences, institutions, and policy actors are subject to gradual or rapid change. To keep current, IWRM should include a self-assessment and improvement mechanism. This mechanism is indicated by dashed arrows in Fig. 5.1 and starts with monitoring and evaluating the impacts of decisions made. These evaluations identify the need for 99

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Fig. 5.1.

IWRM:

science in support of public policy.

improvements pertaining to the effectiveness of the institutional set-up, the quality and completeness of the information generated by decision support systems and processes, and the validity and sufficiency of the current scientific knowledge base. IWRM processes can lead to great successes just as they can cause costly failures. In a world where water disputes are on the rise and the delay between science and technology advances and their consideration by management practices widens, IWRM faces important challenges: • lack of integrative tools to support planning and management decisions; • segmentation of institutions responsible for water resources planning and management; • limited participation of stakeholders in decision making processes; • lack of disinterested self-assessment and improvement mechanisms; • continuing specialization of science and engineering education at the expense of interdisciplinary training. Specifically on decision support tools, lack of integration is common with respect to disciplines (water resources, agriculture, environmental science, ecology, energy, public health, socio-economics, etc.) as well as with respect to decision levels (assessment, planning, and operational management). Current DSS tools are narrowly focused, developed by overspecialized professionals, working for institutions operating in silo mode. Thus, lack of integration undermines the effectiveness of tools, people, and institutions and is a major challenge of IWRM processes.

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However, challenges create opportunities, and the design, development, and use of is an excellent opportunity that can thread together a broad range of disciplines, people, and institutions to address the complex IWRM challenges. This is the chapter’s overarching message. DSS

5.2 Decision support systems for IWRM 5.2.1 DSS elements Decision Support Systems (DSS) are technical tools intended to provide valid and sufficient information to IWRM decision makers. A typical DSS for IWRM includes five main components (Fig. 5.2): data acquisition system, user–data–model interface, database, data analysis tools, and a set of interlinked models. The data acquisition system consists of all means by which generic data are collected and made available to IWRM through the DSS. Data may be collected by conventional sensors (rain-gages, stream-gages, etc.), remote sensors (satellite, radar), as well as by manual compilation efforts (e.g., surveys, interviews, and literature reviews). The purpose of the user–data–model interface is to (1) transfer the data to the database, and (2) provide easy and meaningful access to data, data analysis tools, and models. The database is the depository of all data acquired by the data acquisition system and generated by the data analysis tools and application programs. The data analysis tools provide user-friendly means to visualize and analyse various data sets. Geographic Information Systems (GIS) packages are especially important for the visualization and analysis of georeferenced (spatial) data. Lastly and most importantly, the purpose of the DSS models is to quantify the holistic response of the water resources system to alternative scenarios of basin development, hydrology, water use levels, and management policies. In some form, the above-described DSS elements exist in most useful information and modelling tools for water resources planning and management. However, beyond these

Fig. 5.2. Typical DSS elements.

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elements, the effectiveness of a DSS depends on its ability to provide valid, sufficient, and consistent information at all levels of the IWRM process. A generic DSS structure that has the potential to achieve this goal is described next. 5.2.2 DSS conceptual structure for systems with multiple objectives, temporal and spatial scales, and decision makers The key DSS challenge is to support decisions that are made by several decision makers, pertain to various temporal and spatial scales, and concern multiple stakeholder groups and water uses. Experience with the development and application of such DSSs in various regions and institutional settings suggests that the multilayer structure illustrated in Fig. 5.3 emerges naturally and can meet this challenge. This DSS structure includes multiple interconnected layers each of which models the system at a particular temporal and spatial scale, addresses a certain subset of objectives, and involves an appropriate subset of decision makers and stakeholder groups. The linkages among and within the layers ensure that (1) system data, models, and outputs provide an integrative understanding of the system response and (2) decision maker choices are prioritized and implemented consistently as the IWRM process evolves. While this structure is generic, the layers appropriate for each application are system specific. In the example of Fig. 5.3, the three modelling layers shown include (1) near real time models (with an hourly time resolution over a horizon of one day), (2) short/mid range models (with a daily, sub-daily, or hourly resolution and a horizon of one month),

Fig. 5.3. A generic DSS structure.

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and (3) long range models (with a 10-day or monthly resolution and a horizon of one to two years). The DSS also includes an assessment model which evaluates the system response under various inflow scenarios, system configurations, demand scenarios, and policy options. This DSS is designed to operate sequentially. In a typical application, the long range models are activated first to consider long range planning issues such as appropriate water conservation strategies for the upcoming one to two years. In carrying out these evaluations, the long range models utilize climate-hydrologic and demand forecasts with a 10 day or monthly resolution. A central part of this analysis is to quantify all tradeoffs in which the planning authorities and system stakeholders may be interested. The tradeoffs quantify the benefit-cost (or impact) relationships among (1) water users and (2) water uses, and delineate the capacity of the system to meet the various demands placed upon it. Interesting tradeoffs pertain to benefits and costs that would accrue to upstream and downstream users or to specific water uses (e.g., power and agriculture, or agriculture and ecosystem health) if relative water shares were to change. The tradeoffs are provided to planning authorities and stakeholders (water, power, environmental protection, etc.) to use in their decision process. After a consensus is reached, key decisions are made on relative water shares, monthly releases, energy generation, lake levels, and reservoir coordination strategies. The short/mid range models are activated next to consider system operation at finer time scales. The objectives addressed here are more operational rather than planning and may include flood control, power plant scheduling, irrigation management, and environmental flow regulation. This model uses hydrologic and demand (water and power) forecasts with a daily, 6-hour, or hourly resolution and can also quantify the relative importance of upstream versus downstream flooding risks, power generation versus flood control, water supply versus fishery management, and other applicable tradeoffs. Such information is provided to the forum of management authorities and stakeholders to use in their effort to reach consensus on the most preferable operational policy. Such policies are revised as new information on reservoir levels, flow forecasts, and demands becomes available. The model is constrained by the long range planning decisions, unless current conditions indicate that a departure is warranted. The near real time models are activated last to determine the hour to hour operations (e.g., turbine dispatching and flow regulation). All decisions made by the upper levels of the IWRM process are realized at this stage. In developing the above-described DSS, particular attention must be placed on ensuring consistency across modelling layers, both with respect to physical system representations as well as with respect to decisions made. Consistency with respect to decisions is achieved by constraining lower layers to stay within the limits established by the upper layers. Thus, the purpose of the lower layers is to distribute the bulk upper layer decisions (e.g., monthly volumes or energy amounts) at finer temporal scales (e.g., daily, 6-hour, or hourly releases and energy generation) such that system objectives of these finer resolutions are best met. Consistency with respect to system representation is achieved by (1) utilizing models of increasing resolution (temporally and process-wise) and (2) using lower level models to derive (off or on line) aggregate performance functions associated with potential upper layer (bulk) decisions. An example of such an aggregate function is the relationship of power versus plant discharge that provides optimal plant generation

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as a function of reservoir level and total plant discharge. Such functions can be derived by the real time (turbine dispatching) models by determining the optimal turbine loads corresponding to particular reservoir level and plant discharge combinations. These functions are derived for each system plant and are provided to the short/mid range models to ensure that they ‘know’ the power that will actually be generated from a particular level of hourly plant release. Similar aggregate performance functions are derived by each modelling layer for each system use and are communicated to the upper DSS layers. In this manner, each layer has an accurate and consistent representation of the benefits and implications of its decisions. The three modelling layers in this particular example address planning and management decisions for a given system configuration. The scenario/policy assessment model addresses longer term planning issues such as increasing demands, infrastructure change (basin development options), water sharing compacts and policies, potential hydro-climatic changes, and mitigation measures. The approach taken in this DSS layer is to simulate and inter-compare the system response under various inflow, demand, development, and management conditions. Altogether, the DSS provides a comprehensive modelling framework responsive to the information needs of the IWRM process at all relevant time scales. 5.3 Nile DSS The Nile River Basin (Fig. 5.4) covers about 10% of the African continent and is spread over ten countries (Burundi, Congo, Egypt, Eritrea, Ethiopia, Kenya, Sudan, Tanzania, Uganda, and Rwanda). Almost all Nile water is generated on an area covering 20% of the basin, while the remainder is in arid or semi-arid regions. Egypt and Sudan are almost totally dependent on the Nile for their water uses. Most other Nile countries are close to water stress, if not already below the water scarcity threshold of 1000 m3 of water per inhabitant per year. Water stress is compounded by rapid population growth, occurring at nearly twice the average global rate. Hence severe water scarcity conditions are looming over most Nile countries. Nile Basin economies are heavily dependent on agriculture which accounts for more than half of the gross domestic product and employs more than 80% of the workforce. However, lack of water supply infrastructure, climate variability, and poor cultivation practices have seriously restrained, if not completely halted, economic growth. These complex challenges are at the forefront of an unfolding initiative by the Nile Basin nations to set forth equitable and lasting water development and utilization agreements. The goal of the Nile Basin Initiative (NBI) is poverty alleviation and sustainable economic growth. Thus, water sharing is intended to facilitate the creation of efficient markets for food and energy and stimulate environmentally-sound industrial and economic growth. However, effective policy dialogue requires that the countries assess and weigh the benefits and impacts of various water development and management strategies accrued to themselves and other Nile partners. Prerequisite elements in this process are the existence of an institutional cooperative framework, information and modelling systems, and the technical expertise to use them. The Nile Decision Support System (Nile DSS) is the outgrowth of several projects implemented in the course of the last 10 years. These were collaborative efforts of the Geor-

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Fig. 5.4. The Nile River Basin; shaded areas represent wetlands.

gia Water Resources Institute at Georgia Tech, the Nile Governments and their agencies, and various international organizations including the Food and Agriculture Organization of the United Nations (FAO) and the World Bank. The Nile DSS includes planning and operational components developed for and used by individual countries as well as basin planners. Operational management systems have been developed and used in Egypt (High Aswan Dam) and Uganda (Lake Victoria), while a planning DSS was recently completed

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for all Nile countries. In what follows, we describe the elements of the latter. The operational components are being published separately. The following general principles governed the development of the Nile DSS: • The data of the Nile DSS should be shared and agreed upon by the Nile Basin nations. • The Nile DSS should be based on sound and current scientific and engineering approaches able to handle the Nile Basin size, complexity, and range of development and management options. it should also include functionalities useful for users of varying technical backgrounds and experience from novice to advanced. • The Nile DSS should be a neutral decision support tool. Thus, its overriding purpose should be to objectively assess the benefits and tradeoffs associated with various water development and sharing strategies that may interest the Nile Basin partners individually or as an interdependent community of nations. • The Nile DSS should be sustainable and adaptable as future needs arise. The implications of this are twofold: first, the Nile DSS should be based on widely supported computational technology and should be expandable to incorporate new data and effective applications; second, technology and know-how building mechanisms should be implemented during the Nile DSS development as well as in the long term. The Nile DSS follows the concepts illustrated in Figs 5.2 and 5.3 and includes a database, a set of models (for river simulation and management, agricultural planning, hydrologic modelling, and remote sensing), and a user-data-model interface. 5.3.1 Database and interface The Nile DST database is an object-oriented, databasing structure developed to (1) house all types of data (existing as well as future) required by a comprehensive water resources decision support system and (2) to optimize data entry, access, visualization, and analysis. To support the process of water resources planning and management, the data base is designed with the ability to grow, namely, to accept new data, regardless of its type and size. Further, the database tools are capable of visualizing and analysing the data in efficient and meaningful ways. Database contents: The Nile DST database is comprised of several national databases and is of considerable size. Each Nile country has painstakingly compiled station data with measurements of more than 30 hydro-climatic parameters. In addition, the project has compiled 10 years of remotely sensed data that covers the entire basin. The temporal resolution of the remotely sensed data is 30 minutes, and the special resolution is approximately 5 km × 5 km. All together, this data represents nearly 37 GB of information. Data visualization: The data visualization tool in the Nile DSS provides a seamless system to look at all databases. This is a tree-style exploring tool (data tree) that shows the entire contents of the Nile DST database and allows the user to navigate to greater and greater levels of detail. Due to the database size, the data tree is useful in providing a better understanding of the database. Each database has a georeferenced component and a time series component. The geo-referenced data is viewed in the mapping tool,

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which holds a GIS. The mapping tool incorporates the ESRI Incorporated Map Objects to ensure compatibility of the system with industry standard GIS files. The time series data is viewed in the charting tool, which features a powerful chart and aggregation and statistics calculators. Together, the charting tool, mapping tool, and the data tree work seamlessly to provide the user with an ability to view any piece of information in the system quickly and meaningfully. Data analysis: The data analysis tool provides extensive data manipulation capabilities for current and future applications. The user can instruct the Nile DSS to take information from its databases in user specified forms, operate on the data, and construct maps of the output. One example of this is the generation of mean areal precipitation (MAP) and evapotranspiration estimates over user-specified areas. The tool allows the user to save a graphical map of the analysis and revisit/continue the work at a later time. 5.3.2 River and reservoir simulation and management The Nile DSS River Simulation and Management system aims at simulating the Nile response under different hydrologic, development, and management scenarios. Thus, its overriding purpose is to objectively assess the benefits and tradeoffs associated with various water development, sharing, and management strategies that may interest the Nile Basin partners individually or as an interdependent community of nations. Tradeoffs exist among water uses in the same country and across the Nile countries. The river basin planning and management Nile DST component has several unique features: This module includes extensive data in five major categories: (a) river network configuration, (b) river hydrology, (c) existing and planned hydro facilities, (d) water use, and (e) reservoir/lake regulation rules. Data can be viewed, added, or modified as necessary through a userfriendly interface. The actual river system is represented by a network of river nodes, reaches, and reservoirs, each with its own attributes. River nodes represent locations of local inflow and/or water withdrawals and returns. River reaches represent physical river segments and their water transport characteristics. Reservoirs represent man-made or natural lakes that may support various water uses including water supply, flood control, drought management, hydropower, and wetland protection, among others. Models: This module integrates streamflow forecasting, river and reservoir simulation, and reservoir management. Ten-day streamflow forecasts are generated at key basin nodes including Lakes Victoria, Kyoga, and Albert, Torrents, Bahr el Ghazal, Sobat, the Blue Nile, Dinder, Rahad, and Atbara. The forecasts have the form of equally likely realizations reflecting historical streamflow characteristics such as seasonal and long-term variability. Streamflow forecasts are generated by the hydrologic watershed models, or via statistical procedures where hydrologic models are unavailable. The river and reservoir routing models simulate the movement of water through the river reaches and quantify transmission losses and time lags. The routing models are based on statistical or physically-based relationships (depending on available information) and incorporate model error characterizations. Reservoir and lake outflow through hydropower facilities and spillways is modelled with sufficient detail for use in operational applications. The purpose of reservoir management is to determine release sequences from each system reservoir such that sub-basin and basin-wide objectives are met as best as pos-

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sible. System objectives include meeting water supply targets and avoiding water shortages, minimizing losses, maintaining land use patterns (Sudd), regulating river flows, avoiding spillage, and generating as much firm and average energy as possible. The task of the reservoir control module is complicated by the system size, non-linear response, and intrinsic uncertainties. The optimization process is based on the Extended Linear Quadratic Gaussian (ELQG) control method (developed by Georgakakos and associates (1987) through present), a trajectory iteration optimization algorithm suitable for multidimensional, dynamic, and uncertain systems. Applications: The Nile-DST river simulation and management model can be used to provide answers to various important questions. Typical applications are listed below: • Value of various regulation, hydro-power, and irrigation projects along the White, Blue, and Main Nile branches. Such assessments could quantify the incremental benefits from individual development projects as well as the combined benefits from various project configurations. • Implications of reservoir regulation policies for local, upstream, and downstream riparians. • Marginal value (gain or loss) of irrigation with respect to hydropower at various basin locations. • Irrigation versus hydropower tradeoffs for each nation, region, and the entire basin. • Impacts of flow regulation on wetlands. The Nile-DST utilizes several assessment criteria of interest to the Nile Basin nations. These criteria include: 1. severity and frequency of shortages with respect to user-specified water supply targets; 2. water withdrawals and losses over user selected regions and times of the year; 3. reservoir and lake level drawdown and spillage statistics; 4. in-stream flow availability at user-selected river nodes and reaches; 5. flood and drought severity and frequency; 6. annual and firm energy generation statistics; 7. seasonal and permanent extent of wetlands. Figure 5.5 presents an assessment of the flow and hydropower impacts of two development scenarios in the Blue Nile Basin. The first scenario (dark gray) is the baseline of current conditions. The second scenario (light gray) assumes that 4 large hydropower projects have been built in Ethiopia and are operated using dynamic inflow forecasts and multi-reservoir control methods. The results show that flow changes can be significant with benefits both at the high and the low ends (reduction of excessive floods as well as low flow augmentation). Furthermore, energy generation in Ethiopia would increase very substantially with the potential to benefit not only Ethiopia, but also Sudan and Egypt. Other benefits (not shown) include drastic reductions of the drought risk for Sudan and Egypt.

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Fig. 5.5. The graph on the left depicts the flow frequency curves of the Blue Nile at Khartoum under the baseline (dark gray) and the Ethiopian hydropower development scenarios (light gray). The graph on the right compares annual average energy generation under the same two scenarios.

5.3.3 Agricultural planning Agriculture is a major water consumer in the Nile Basin. It is the main source of income for a large part of the population, and many people rely predominantly on crops they grow themselves. Agricultural products are also an important source of foreign currency, as commodities like coffee, cotton, and sugarcane are exported in large quantities and sold on the world market. The Nile Basin contains many regions of high agricultural potential, some of which are already fully exploited, while others await development. Given the importance of agriculture in the basin, decision makers would benefit from reliable assessments regarding potential crop yield in undeveloped lands, irrigation needs, drought vulnerability, and the potential tradeoffs between agriculture and other water uses. The purpose of the agricultural planning model is to address these issues. Models: The Georgia Water Resources Institute at Georgia Tech has developed a comprehensive Agricultural Planning Model (GT -AgroPlan), which combines state-of-thescience irrigation scheduling and crop yield prediction tools with a user-friendly graphical interface. Physiologically-based crop models form the agronomic simulation core of GT -AgroPlan. These models, adapted from the Decision Support System for Agrotechnology Transfer (DSSAT; Tsuji et al., 1994), simulate the daily life-processes of crops using input data for soils, meteorology, and genetics. Currently, GT -AgroPlan includes crop models for eleven crops (maize, cassava, groundnuts, wheat, rice, sorghum, millet, barley, potatoes, soybeans, and dry beans), but five additional crops (including sugarcane)

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have recently been added to the DSSAT and will be incorporated into GT -AgroPlan. The models produce outputs of yield and total irrigation needs as well as daily timeseries of soil moisture and biophysical parameters. GT -AgroPlan requires data on soils, terrain, hydro-meteorology, crop characteristics, and agricultural and irrigation practices. All of these data are integrated within a GIS and graphical user interface (GUI). The GUI allows program users to easily select input data scenarios for simulation by the crop models and to view results in on-screen tables and graphs. Applications: GT -AgroPlan has been used for a wide variety of analyses, such as the assessments of gains from irrigation, spatial distribution of irrigation needs, regional drought vulnerability, and regional calorie supply. Other potential GT -AgroPlan applications include investigating the consequences of deficit irrigation relative to water demand and crop production, assessing various irrigation scheduling scenarios, climate variability impacts on agriculture, and determining water-efficient and high-calorie crop combinations. Figure 5.6 shows a regional assessment carried out in the Lake Victoria Basin (Georgakakos et al., 2000). The purpose was to assess the vulnerability of the agricultural sector to climate variability. The figure includes results for maize assuming only rain-fed conditions. Results are presented for both growing seasons (long rains – March to May; and short rains – October to November) and for typical wet and dry years. The results show that during long rains, Kenya is immune to climate variability while Tanzania, Rwanda,

Fig. 5.6. Agricultural assessments in the Lake Victoria Basin.

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Burundi can experience serious shortages. During the short rains, Kenya becomes most vulnerable with the rest of the basin exhibiting mild potential deficits. Such information is useful in formulating equitable water and benefit sharing strategies. 5.3.4 Hydrologic modelling Hydrologic models are necessary to translate climatic forcing (rainfall and temperature) to lake rainfall, evaporation, and watershed inflow. The temporal scales of interest are ten days for water resources planning and one day for operational applications; the applicable spatial scale varies from a few hundred to several thousand square kilometres. Models and applications: The Nile DSS and its derivatives include Sacramento type watershed models (Peck, 1976) that simulate the hydrologic processes of surface and subsurface runoff through physically-based conceptual elements. Such models are very useful for applications that require flow predictions over daily to sub-daily time steps. Another hydrologic model type that is also part of the Nile DSS is especially suited for medium size (1000 to 10 000 km2 ) to large watersheds (larger than 10 000 km2 ), as well as for weekly, ten-day, and monthly time steps. The model has been developed by Georgakakos and Yao (2000) and has successfully been tested in the Southeastern US and elsewhere. Figure 5.7 shows a comparison of observed and simulated flows for the Nzoia basin in Kenya and indicates that predicted and observed streamflows exhibit good agreement. Some hydrologic model features are summarized below: • Input data include watershed rainfall (weekly, ten-day, or monthly) and temperature or evapotranspiration. Model calibration additionally requires observed stream0.04 0.035

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• Model output includes streamflow at the watershed outlet and spatially-averaged soil moisture. The latter is also important for agricultural planning applications. • The model (a) simulates the watershed water balance processes (rainfall, evapotranspiration, streamflow, and soil moisture storage) on weekly, ten-day, or monthly time steps, and (b) uses a historical analog approach to determine streamflow from current conditions of rainfall, evapotranspiration, and soil moisture. • A particular basin can be sub-divided into smaller sub-basins leading to a semidistributed model implementation. However, this would be appropriate for sub-basins with streamflow measurements. • This model is also used to assess the sufficiency of hydrologic data and develop data monitoring plans. An