Decision Support for Mainstreaming Climate ChangeAdaptation in Water Resources Management

Carlo Giupponi

Received: 30 July 2013 /Accepted: 18 August 2014 /Published online: 28 August 2014# Springer Science+Business Media Dordrecht 2014

Abstract Climate change adaptation (CCA) has recently emerged as a new fundamentaldimension to be considered in the planning and management of water resources. Because ofthe need to consider the already perceived changes in climate trends, variability and extremes,and their interactions with evolving social and ecological systems, water management is nowfacing new challenges. The research community is expected to contribute with innovativemethods and tools to support to decision- and policy-makers. Decision Support Systems(DSSs), have a relatively long history in the water management sector. They are usuallydeveloped upon pre-existing hydrologic simulation models, providing interfaces for facilitateduse beyond the limited group of model developers, and specific routines for decision making(e.g. optimization methods). In recent years, the traditional focus of DSS research has shiftedaway from the software component, towards the process of structuring problems and aidingdecisions, thus including in particular robust methods for stakeholders’ participation. Thepaper analyses the scientific literature, identifies the main open issues, and proposes aninnovative operational approach for the implementation of participatory planning anddecision-making processes for CCA in the water domain.

Keywords Climatechangeadaptation .Watermanagement .Policy-decision-making .Decisionsupport

1 Introduction

Water resource management (WRM) attracts the attention of policy makers and the generalpublic for the role that water plays for life and its multiple – environmental, economic, cultural,religious – dimensions. Water is in fact a finite, non-substitutable resource, unevenly

Water Resour Manage (2014) 28:4795–4808DOI 10.1007/s11269-014-0776-y

Highlights • Climate change adaptation (CCA) and water management should be integrated• Decision support tools are necessary to deal with the complexity of the problems• Guidelines for the identification of strategies exist but they need operational solutions• Decision support (methods and tools) can provide those solutions and facilitate implementation• The paper presents an operational procedure to integrate CCA and water management

C. Giupponi (*)Venice Centre for Climate Studies at Dipartimento di Economia, Università Ca’ Foscari, and CentroEuro-Mediterraneo sui Cambiamenti Climatici (CMCC), Canneregio 873, 30121 Venezia, Italiae-mail: cgiupponi@unive.it

distributed across the globe. It has a biogeochemical cycle absorbing a substantial proportionof solar energy, which is released in various forms, ranging from hurricanes to hydroelectricpower. Moreover, water flows across the globe, following gravitational paths and climaticforcing variables, which cross political boundaries and bring around opportunities for socialexchanges, but also conflicts and threats such as floods, or pollution.

As water resource issues and conflicts arose over the last decades as a consequence ofincreasing anthropogenic pressures (demographic growth, agricultural and industrial exploita-tion, pollution, etc.), the need emerged for new paradigms and methodological frameworkssupporting more accurate and comprehensive policy instruments and management practices.Amongst them is the well-known Integrated Water Resources Management (IWRM) ap-proach. According to the Global Water Partnership (Global Water Pertnership 2000) IWRMcan be considered as a procedural approach assisting countries “in their endeavour to deal withwater issues in a cost-effective and sustainable way”. Many different definitions of IWRMexist, but one broadly accepted can be extracted from the GWP report cited above: “IWRM isa process which promotes the co-ordinated development and management of water, land andrelated resources, in order to maximize the resultant economic and social welfare in anequitable manner without compromising the sustainability of vital ecosystems”.

It is clear from the vast IWRM literature that the implementation of such concept in a givenregion demands for the integration of multiple disciplinary perspectives, including hydrology,ecology, economics, social sciences, and institutional and legal skills. It is also clear that theintegration of those different disciplines requires a comprehensive methodological frameworkand the capabilities for managing a process, which usually need substantial resources and time.IWRM is well grounded in the broader context of sustainability science and it shares theemphasis on procedural more than substantial issues, the balancing of environmental andsocio-economic objectives and the move from top-down governmental approaches, to bottom-up participative ones (Pahl-Wostl and Borowski 2007; Wilson 2004). On the other hand, andagain similarly to those of sustainable development, IWRM principles are debated by manyauthors, who question the effective impact of those principles and even the practical feasibilityof their implementation in the real world (see for example Biswas 2008; McDonnell 2008;Garcia 2008).

Notwithstanding the methodological debates, competent organisations are compelled tofind solutions now to old, but also new challenges in water management, first of all climatechange. Consideration of the change dimension imposes revising consolidated paradigms andprocedures, forcing them to adopt new approaches to consider multiple uncertainties abouthow the future may unfold. Therefore, the research community has a clear task in offeringwater managers operational solutions to integrate the consideration climate change in watermanagement. In other words, solutions are needed to mainstream climate change adaptation(CCA) into water management policies and strategies.

The European Commission (EC) has recently released the Communication “An EUStrategy on adaptation to climate change” (European Commission 2013a), in which the watersector is at the core of the attempts to introduce climate-proofing in policy actions in keyvulnerable sectors. Moreover, the EC has released a series of documents related to the Strategy,including the Guidelines on developing adaptation strategies (European Commission 2013b).That document and in particular its companion web site presenting the Adaptation SupportTool1 drive our attention to the process of identifying and implementing CCA strategies andmeasures, through robust methodological approaches, and also specific decision support tools.

1 See: http://climate-adapt.eea.europa.eu/en/web/guest/adaptation-support-tool/.

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Decision support may have various goals, which, according to Moser (2009), mainly referto contribute to the process, the outputs, or the wider or longer term outcomes of decisions. Inall those cases an important role can be played by Information and CommunicationTechnology (ICT), which in this case they usually substantiate as specific computer toolsbelonging to the broad category of Decision Information and Support Tools (DIST) orDecision Support Systems (DSS).

This paper focuses on the role of DSS tools for the new challenges imposed by theintegration – mainstreaming – of climate change adaptation within the water domain. Itexplores how such tools should be developed and implemented in policy- and decision-making processes, characterised by multiple – social, economic, and environmental – dimen-sions and the involvement of multiple actors or stakeholders. The next section provides anoverview of the current state of the art of DSS tools in the water domain and their actual orpotential role for contributing to the new challenges of climate change adaptation. The thirdsection introduces to a methodological framework providing the ground for an effectiveadoption of DSS in the CCA/WRM field. Section four provides some concluding remarks.

2 Methods and Tools for Decision Support

2.1 The Tools

Natural resources management, as perhaps no other field, has to rely on a huge amount ofinformation (e.g. maps and remote sensing data, time series of precipitations, etc.), which goclearly beyond cognitive capability of humans, who search then the support of ICT. It is withinthis context that decision support systems have been evolving, since the 1970s when they wereproposed as interactive computer based systems, helping decision makers utilise data andmodels to solve complex and unstructured problems (Gorry and Scott-Morton 1971). Kennand Scott Morton (1978) proposed a definition of DSS focused in particular on their ability tocouple the individuals’ intellectual resources with computer capabilities to improve the qualityof decisions. Later on, Power (1997) was much more generalist in defining DSS as any systemsupporting decision-making, including executive information systems, executive supportsystems, geographic information systems, online analytical processing and software agents.In general, most of the following definitions refer to DSS as a computer-based tool, a higherform of information system (Keenan 1998), consisting of a combination of (i) simulationmodels, (ii) user interfaces, and (iii) techniques for decision analysis. Modelling enginesprovide the analytical basis for simulation and scenario analyses required to simulate waterresources in complex social-ecological systems under the effects of changing climate and othersocio-economic drivers. The techniques for decision analysis support the implementation oftransparent and unbiased judgements and informed rational decisions in particular in situationscharacterised by trade-offs and conflicting interests. User interfaces facilitate access to thevarious data processing routines by non-experienced users. Power and Sharda (2007), pub-lished a general review of model-driven DSS, identifying two peculiar characteristics: (i) themodels made accessible to non-technical specialists through ad hoc interfaces, and (ii) the aimfor repeated use in multiple cases. They provided also a schema to identify the role of DSStools in a bi-dimensional space of decision situations with two axes: structured vs. unstructuredand routine vs. non-routine procedures. According to the authors, the niche for most effectiveexploitations of DSS tools can be found in particular in dealing with semi-structured problems,possibly resulting from evolutions of routine cases, but with significant novelty contents, andwith reasonable perspectives of future re-use. This fits perfectly with the needs emerging from

Decision Support, Adaptation and Water Management 4797

mainstreaming CCA into IWRM, where DSS can provide a combination of data processingtools (models, GIS, etc.), coupled with evaluation routines (e.g. multi-criteria or cost-benefitanalyses), within a customized user interface to manage the decision making process withadequate consideration of uncertainty sources and scenario drivers (Xu and Tung 2008) .

During the last four decades there was a proliferation of more or less sophisticatedmodelling and decision support tools in the field of water management, and given that theywere usually somehow related to the ambition of supporting the IWRM process, they will beidentified here as IWRM-DSS’s for brevity. The interest for IWRM-DSS is manifested by anactive literature, which has grown significantly over the last two decades, form less than 50papers in 1990 to 300 or more per year after 2009.2 Notwithstanding the numerous applica-tions, the DSS literature is rather poor of operational implementation and it is instead mainlypopulated by prototype tests and this raised the question about the practical usefulness of thosetools.3 Indeed, the popularity of DSS tools, in particular in the water domain has showed acyclic behaviour. Triggering factors for increasing interest for DSS tools can be found ininnovative policy documents and regulations, such as the European Water FrameworkDirective (WFD; European Commission EC 2000), and more recently the EU Strategy onCCA (European Commission 2013a, b), which push managers to abandon business-as-usualmethods and tools, and to adopt new approaches.

Given the diversity of IWRM-DSS tools (see Giupponi et al. 2011 for a detailed review),before moving to the presentation of the methodological framework presented in the followingsection, it is worth to attempt their categorisation, to identify those that may be more suitablefor use in a CCA context. Figure 1 presents the four categories of decisions of interest for watermanagement. CCA deals specifically with decisions which have a long term perspective(bottom of Fig. 1), and in particular those with consideration of multiple scenarios. In suchcases, the decision typically develops upon the identification of promising, feasible solutionsto the given problem in face of multiple future scenarios, with a decision process aimed at theidentification of the preferred solution to the given problem, in which the main elements to beconsidered are:

& a set of Alternative Options (strategies or options to integrate CCA and IWRM), which canbe discrete and pre-existing, or generated on demand;

& a set of Criteria and Indicators describing each of the options, the main endogenous andexogenous drivers and in general all the dimensions of the problem at hand, thus definingall the data needs;

& multiple Sources of Knowledge, typically ranging from modelling outcomes to variousforms of qualitative information hold by experts and stakeholders;

& multiple Sources of Uncertainty, deriving from multiple sources (input data, conceptual-isation, future projections, etc.) and of different nature (epistemic vs. stochastic);

& Constraints describing acceptable lower or upper bounds on any one of the criteria; only asolution thatmeets all constraints is deemed a feasible alternative and subsequently considered;

& Objectives or Objective Function(s), expressed in terms of the criteria that should beminimized or maximized by the selection;

2 Scopus search for TITLE+ABS+KEY(“decision support system” AND water).3 Worth to mention at this regard is the fact that successful implementations of existing tools are usually oflimited interest for scientific journals, which search instead for innovative contributions. This is indeed one of themain problems of any effort aimed at producing a state-of-the-art report on DSS implementations and theirimpacts, since it requires to complement literature review with search of grey literature and internet sources anddirect contacts with experts in the field.

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& a set of Plausible Future Scenario upon which the expected performances of the proposedOptions have to be assessed;

& a Preference Structure that defines the relative importance of different criteria in contrib-uting to the objective function, and the different importance of different objectives in anoverall evaluation, expressed by the stakeholders involved through a participatory process.

3 The Methods

In Europe, the most recent reference for approaching the process of CCA can be found in theabovementioned Guidelines released by the EC to facilitate the implementation of betterinformed decision-making. A cyclic flow chart made of six steps is foreseen in theGuidelines and its Adaptation Support Tool: (i) preparing the ground for adaptation; (ii)analysing risks and vulnerabilities; (iii) identifying adaptation options; (iv) assessing theoptions; (v) implementation; and (vi) monitoring and evaluation of the outcomes.

The Guidelines provide potential end users (e.g. river basin authorities facing the develop-ment of an IWRM plan with consideration of CCA) with abundant information, such as flowcharts, check lists, references, etc., but instructions about how to put them into operation islacking. This paper aims at filling that gap, by presenting an innovative methodologicalframework and operational solutions for a decision support package (methods, tools andprocedures) to bring CCA into operation in the water domain. Peculiar features of the proposedapproach are the identification of decision making as a participatory process and the focus onDSS tools. Participation is here intended in a broad sense, which includes all cases in which thedecision process requires contributions from multiple actors. For example, the involvement ofdisciplinary experts in a decision regarding an environmental problem requiring diversifiedfields of expertise, or the involvement of stakeholders in a process aimed at the elicitation oftheir views and preferences with regard to the evaluation of the problem at hand.

Near real �me decisionsHour → Day

Long term decisionsYears → Decades

Mid term decisionsMonth/Season → Year(s)

Short term decisionsDay → Month

Actual system condi�ons

Flow regula�onEarly warning

Hydrologic and demand forecast

Flood managementWater supplyWater alloca�on

Climate-Hydrologic andDemand forecasts

Water conserva�on strategiesConflict resolu�onsDrought management

Clima�c and socio-economic scenarios

Water PoliciesWRM PlansAdapta�on strategies

Fig. 1 A categorisation of Decision Support Systems for IWRM, across typical time scales with the main inputsand outputs (adapted and revised from Georgakakos 2007)

Decision Support, Adaptation and Water Management 4799

With the EU CCA Guidelines as a reference, the NetSyMoD methodological framework(Network Analysis – Creative System Modelling – Decision Support; Giupponi et al. 2008b),appeared as a good basis for the development of the novel operational approach for CCAdecision support. The NetSyMoD approach has been applied in several cases to facilitateparticipatory decision-making processes in the environmental field, including climate changeadaptation (see for example Balbi et al. 2011; Ceccato et al. 2011; Giupponi et al. 2008a). Itdoes not provide a single standard procedure for every possible decision case, nor a singlepiece of DSS software. It provides instead a structured approach, flexible enough to facilitatethe application of tools that may be already in use and the tuning of the workflow to the formalprocedures adopted at the competent decision making body. A new version of the frameworkwas thus developed and made compatible with the EU approach for the identification andimplementation of CCA strategies.

The results of the integration of the European Guidelines for CCA and the NetSyMoDframework are reported in Fig. 2. An initial step with the triggering factors of the wholeprocess is added to identify the – case specific – institutional and normative factors determin-ing the need to implement the process aimed at identifying preferred adaptation options, forexample the prescriptions of a National Adaptation Plan. Step one in the proposed approachfocuses on the set up of the process and the exploration of the decision problem at hand,including the strategy for stakeholders’ involvement since the earlier stages. Step two isspecifically focused on the identification of the main actors to be involved (experts, policymakers, end users and stakeholders in general) and the design of methodologically sound

1. Problem explora�on and process set up

- needs, poten�als, constraints, efforts, etc.

6. Ac�on taking and Monitoring

- Implementa�on plans, investments, etc.

5. Analysis of response op�ons- par�cipatory mul�-criteria or cost-

benefit/effec�veness analyses

3. Analysis of risks, vulnerabili�es and iden�fica�on of response op�ons

- cogni�ve frameworks, plausible solu�ons, etc.

2. Design and launch of par�cipatory ac�vi�es

- SHs’ iden�fica�on, Social Network Analysis, etc.

4. Scenario analysis, modelling and evalua�on

- scenario simula�ons, models, etc.

0. Poli�cal and norma�vetriggering factors

- e.g. IWRM/CCA Strategies & Plans

Par�cipatory Planning

ModellingDecision Analysis

Fig. 2 The sequence of steps for the implementation of climate change adaptation strategies proposed by theNetSyMoD approach, in accordance with the EU CCA Guidelines

4800 C. Giupponi

participatory activities. Steps 2 and 3 of the guidelines are integrated into the third one of theproposed approach, with focus on problem analysis, i.e. the assessment of risks and vulner-abilities to climate change and the identification of the IWRM and adaptation options to beconsidered. Once plausible solutions have been identified with a participatory approach, theymust be assessed (Step 4), with the support of various activities such as scenario developmentand modelling. This brings to Step 5, with the application of methods synthesise the analyticalwork and bring to the identification of preferable solutions. Such methods could be Cost-Benefit, Cost-Effectiveness, or Multi-Criteria Analysis. Eventually, Step 6 brings to actiontaking (implementation) and monitoring of the effects of the solutions adopted. Internal loopsare envisaged whenever the need emerged to revise intermediate results, and also subsequentiteration cycles could bring to the implementation of adaptive management. In the next sectionthe proposed approach presented in detail.

4 The Proposed Approach for Adaptive Management of Water Resources

4.1 Problem Exploration and Actors’ Involvement

As depicted in Fig. 2, right after the launch of the process, the initial step of any decisionmaking process – would it be supported by a DSS tool or not – is dedicated to the explorationof the problem at hand and the organisation of the decision process. Here organisational andinstitutional issues are of greatest relevance. In some cases the decision context is clearlydefined by a specific requirement of legislation, for example the development of a River BasinManagement Plan, or of a CCA Plan. In other cases the normative requirements and thus theobjectives of the competent administration may be not entirely clear in the initial stages. Asstated in a related volume by UNESCO (Loucks and van Beek 2005; p.45) “Objectives asstated at the beginning of a study are rarely the objectives as understood at its end”. Therefore,the management of the process emerges as one of the most important factors of success inplanning and decision making processes. The same volume (p. 44) proposes a detailed list ofissues to be considered in the initial phases of the process: (i) existing rules and regulations; (ii)the history of previous decisions; (iii) the expressed preferences of the important actors andinterest groups, and the probable reactions of stakeholders affected by the decision(s) to betaken; (iv) the relative importance of various issues being addressed; and, last but not least (v)what is the available and applicable disciplinary science, to be exploited in the process andpossibly implemented in an IWRM-DSS. Giupponi et al. (2011) add other important items tobe considered in a sort of check list for the design of the process: (a) identify possibleconstraints; (b) raise users’ interest and initial commitment; (c) identify and clearly commu-nicate reasonable expectations; and, very importantly for the topic of this work, (d) decidewhether or not a DSS may be utilised for the purposes of the case, since suitable tools are notalways available, nor are resources always sufficient for the development of a new tool.

The data issue (i.e. the availability of data for informing the decision process) is offundamental relevance in the initial phases of the process, in order to identify data gaps andtheir perspectives to be solved. This is also the basis for identifying the most suitable DSS, ordefining the design of a new tool to be developed. Unfortunately, and in particular indeveloping countries, quite often large gaps exist between theoretical data requirements andthe data available to users. Moreover, gaps are usually found also in human resources, i.e.trained people available within the management administration. In some case those gaps maybe solved with the involvement of external consultants, who can play a crucial role indisseminating scientific acquisitions to final users, by providing the administration with the

Decision Support, Adaptation and Water Management 4801

specific skills required and by filling – temporary – gaps of the internal staff. Clearly thiscomes at a cost and, very importantly, it raises concerns about the sustainability of the systemin the medium term, if no adequate effort is placed into capacity building activities, improvinginternal competences, and raising users’ motivation and ownership.

Early involvement of the intended end users (i.e. those who are expected to use the tools)and the relevant stakeholders (those that are involved in the decision to be taken) is one of themost important success factors. Decision support is a process of mutual learning and it requiresconsideration of many elements like norms, consolidated habits, informal institutions, andpower relationships. The selection of participants is one of the most critical steps in partici-patory processes, as it will affect its legitimacy and quality. I propose to start such process byestablishing a Task Force Group (TFG), i.e. a team formed by few key stakeholders – usuallythose responsible for the decision to be made – supported by the technical staff in charge of themanagement of the decision process. The TFG implements a sequence of steps in an iterativemanner: at the beginning, the boundaries of the system and problem are defined, allowing theidentification of all actors with a legitimate claim. The actors’ group can be defined through the“snowball” sampling technique (Bryman 2001): the TFG selects a small number ofreadily identifiable actors, the “seeds”. These are then asked to name all otherimportant individuals/organisations that have, or could have, considerable influencein, or that can be affected by, the decision process and the consequent courses ofactions. To minimise strategic incentives to manipulate membership of the interestgroup, a team approach to seeds identification is preferred, as it provides a moreobjective perspective. The proposed general criteria for the selection of the seeds are:(i) vertical comprehensiveness, that is, consider all the actors directly or indirectlyinvolved in the decision-making process at all the different levels of governance (national,regional, local); (ii) horizontal comprehensiveness, i.e. consider all the actors (public institu-tions, NGOs, etc.) which are directly or indirectly affected by the decision; and (iii) cross-sectoral comprehensives, i.e. include representatives of all major social groups.

It is often not practical to involve a large group of individuals in participatory planning, as itbecomes more and more difficult to manage their interactions. Therefore, the selection of acore sub-set of representative stakeholders is needed and it can be done with the support ofSocial Network Analysis (SNA) methodologies and ICT tools, enabling the TFG to translatecore concepts of social and behavioural theories into a formal language, based on relationalterms (Wasserman and Faust 1994). In the proposed approach, SNA is articulated into threemain steps: (i) data collection by means of questionnaires delivered to the selected stake-holders; (ii) data analysis calculating indicators describing the social network and its actors interms of, for example, centrality, betweeness (Freeman 1979), value drivers and divides, etc.;and (iii) validation of results (see Fig. 3).

4.2 Problem Analysis and Modelling

The key actors involved since the initial phase usually hold different perceptions and beliefsabout what the causes of the problem are and how they should be addressed. Those multipleviews can be considered in the decision support process with a sequence of activities identifiedas step 3 and 4 in Fig. 2. The first step refers to the Analysis of risks and vulnerabilities and theidentification of response options in which individual perspectives are explored with thesupport of dedicated workshops. These workshops facilitate collective learning and thebuilding of a shared conceptual model to frame and orient the design of subsequent activities:data processing, modelling, and evaluation procedures provided by the DSS tool. All the mainelements of the decision listed at the end of Section 2.1 must be defined at this stage.

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The degree of, and the approach for actors’ involvement should be adapted case by case.Participatory modelling workshops, informed by the outcomes of the questionnaires used forthe SNA, contribute to the identification of the main elements and to the analysis of thedynamics of the socio-ecosystem considered in the specific case of adaptation and watermanagement. Very importantly, participatory activities offer the opportunity to acquire impor-tant qualitative information held by individuals and not accessible from other sources.Cognitive mapping techniques (Axelrod 1976) are valuable tools to develop shared conceptualmodels of the problem and of the socio-ecosystem under consideration with the contribution ofthe involved actors. The maps produced at the workshop are used for the identification of theelaboration procedures (e.g. simulation models), the design of the evaluation exercise and DSStool, and for easier communication with the general public (see Fig. 4).

A revised version of the DPSIR framework (Driving Forces, Pressures, State, Impacts,Responses; EEA 1999) is proposed as a preferred reference for building a causal model of theproblem at hand. It is named DPSIRS, because in CCA-IWRM we need to include theconsideration of multiple future Scenarios. The adoption of such framework allows to identifyand formalise the most relevant cause-effects chains originated by Driving forces, which exertPressures on the State of the socio-ecosystem, which in turn may generate Impacts to be solvedby adequate Responses. Moreover, it allows to characterise and quantify the most importantelements of the socio-ecosystem by identifying indicators to quantify the main features of eachDPSIRS node. Scenarios are defined by exogenous drivers, which represent the forcingvariables acting from outside the system boundaries (climate change phenomena, higher levelpolicies, international agreements, etc.).

Task Force Group

First Meeting

Define questionnaire for

Collecting network data

Set bounding criteria for

Actors’ identification

Identify and profile actors

Test the questionnaireFinalisation and validation

of the questionnaire

Data collection:

Interview with actors

Data analysis:

Positional/cluster, centrality,

power relations, etc.

Validation of the results

Outputs: List of actors, Social relevance, Power analysis, Value drivers and divides

SN

A

Fig. 3 Diagram of the main components of the phase of design and launch of participatory activities, with theTFG activities on the left hand side and actors’ involvement on the right. Box with dashed lines identify theSocial Network Analysis procedures

Decision Support, Adaptation and Water Management 4803

At the workshop, facilitators guide participants through the identification of the main elementsof the system and their connections, producing one or more cognitive maps, in which the proposedelements are clustered around the six DPSIRS nodes. Thesemaps represent the initial formalisationof a shared cognitive model of the socio-ecosystem, where the elements identify variables andindicators to be considered (data needs), while the links identify the input–output structure andelaboration requirements (e.g. models to simulate cause-effect cascades and system behaviour).

The analysis of risks and vulnerabilities brings to the identification of the possibleResponses to the problem at hand, to be later assessed to support the final decision. AnAnalysis Matrix (AM) is thus built, in which one dimension is given by the set of Responseoptions considered (water management and adaptation strategies or measures), while the otheris defined by the evaluation criteria and/or indicators.

Depending on the resources available, the relevance of the problem to be analysed, theavailability of information, etc., two main application contexts are forseeen for filling thecontents of the matrix: (i) qualitative analyses based only on expert judgement, or (ii)quantitative analyses carried out making use of simulation routines to simulate sysstembehaviour and estimate the expected perfomences of the proposed options, for each one ofthe selected criterion/indicator in the various scenarios considered. Case (ii) should obviouslybe preferred and should follow (i), so that the Scenario analysis, Modelling and Elaborationphase (Step 4), develops upon the shared model of the problem and qualitative analyses andprovides a coherent quantitative analysis of scenarios, adaptation options and indicators. DSStools here provide the support of ICT for facilitating the management and integration of –quantitative and qualitative – data, models, and other elaboration procedures within a commoncommunication interface. DSS tools also facilitate the integration of experts’ knowledge andstakeholders’ preferences elicited at ad hoc workshops or by means of questionnaires, to beconsidered in the following phase of Analysis of Response Options.

Structuring the brainstorm

materials through clustering

Building of causal

network

Framing the problem within the

DPSIRS framework

Brainstorming

(open questions)

Enrolment phase

Analysis of causal chains and

loops

Identification and evaluation

criteria and indicators

Design of the Analysis

Matrix

Preparing materials for expert

knowledge elicitation

Elicitation of actors’ knowledge

and preferences

Outputs: Shared problem model, lists of criteria, values and preferences

Inputs: List of key actors, SNA results

Fig 4 The diagram of the participatory modelling workshops with group activities on the right, with the TFGactivities on the left hand side

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4.3 Analysis of Alternative Response Options

As stated above, the NetSyMoD approach proposes that quantitative assessment follows thepreliminary qualitative analyses within the same conceptual model developed with stake-holders’ participation, to provide an assessment of the expected performances of the alternativeIWRM-CCA options. Three main phases take to the final outcome, i.e. the identification of apreferable solution to the given management problem, according to Simon (1977): (i) intelli-gence phase in which the structure and information basis are defined; (ii) design phase, inwhich data processing and scenario simulations are conducted to fill the AM with quantitativeinformation about the performances of options; and (iii) choice phase, providing the final stepsof decision analysis, including sensitivity and uncertainty analyses (Fig. 5).

As stated above, decision problems in this field are multidimensional in nature and thus theexpected performances of response options are measured according to multiple indicators, e.g.costs, contribution to water saving, effects on water quality, and stored in the AM. Theambition to rank the various possible options and to identify the preferred one poses severalmethodological problems: in particular normalisation of multiple units, weighting and aggre-gation. When the various dimensions of the problem can be converted into monetary units bymeans of adequate valuation techniques, Cost-Benefit Analysis (CBA) provides the solution tothe problem. In other cases the estimation of costs is not accompanied by full monetisation ofbenefits and thus Cost-Effectiveness Analysis (CEA) or Multi-Criteria Analysis (MCA)methods provide methodological solutions for decision analysis. MCA methods, in particular,provide a wide set of techniques aiming at the elicitation and aggregation of decisionpreferences (Figueira et al. 2005), amongst which the most suitable method can be identifiedand adopted (Roozbahani et al. 2012). All MCA decision rules aggregate partial preferencesdescribing individual criteria into a global preference and rank the alternatives. CBA and CEA

Formalising the DPSIRS

model and DSS design

Calculate the expected

outcomes of Responses

Consolidate

scenario analysis

Identify data sources

and simulation models

Apply evaluation methods

(MCA, CBA, CEA)

Rank options for

each actor separately

Input: Consolidated cognitive model and evaluation framework

Uncertainty and

sensitivity analyses

Inte

llig

ence

phase

Desig

n p

hase

Choic

e p

hase

Design data processing

and simulations

Output: Collective choice

Fig. 5 The diagram of the main steps for the identification of preferable CCA-IWRM options. Boxes withdashed lines identify the three main phases of decision analysis

Decision Support, Adaptation and Water Management 4805

assess the alternatives and identify the option which obtains the best performance in terms ofcosts versus benefits or effectiveness.

Uncertainty and the sensitivity of the assessment to sources of errors and unknowns areindeed crucial issues, that pervade all aspects of environmental policy making. Climaticprojections are affected by limited confidence in particular when downscaled to the localscale, but also the behaviour of socio-economic drivers in the coming years is quite uncertain.In some cases scientists may prefer to conceal uncertainty for fear of diminishing theirprofessional credibility and encouraging indecision (Bradshaw and Borchers 2000). In others,policy makers could prefer not to be asked to deal with it, to avoid challenges regardingacceptable levels of knowledge and risk, and how it should be managed by those who have themandate to decide. But, given the specific feature of CCA consisting in the ambition to planand act now with consideration of future scenarios, uncertainty analysis has to be considered asa fundamental component of the process. Guidance references for uncertainty analysis andcommunication are available (see Mastrandrea et al. 2011; Refsgaard et al. 2013; Refsgaardet al. 2007) and methodological proposals specifically focused on the consideration ofuncertainty in the decision making process can be considered, for example the so calledRobust Decision Making approach (RDM; Lempert and Kalra 2013), proposed to explore howcandidate options may perform in a multitude of possible future scenarios and whether theycan reach the goals and be resilient, in face of uncertain future.

As a final result of the decision support process, one or more options emerge as preferred bythe community of actors involved. The outcomes of decision support should be adequatelydocumented, and assumptions, subjective choices and uncertainties of various kinds should betransparently communicated with charts, tables, and statistical annexes. The dossier producedby the process described above represents the knowledge base for decision makers to supportthe choice to be made in front of the general public and other authorities at various.

5 Concluding Remarks

Every decision faces specific knowledge and information needs. Science has thus an obviousand fundamental role to play in supporting decision/policy making. The more our decisionsface complex and dynamic contexts and are affected by uncertainty, such as in the case ofclimate change adaptation in river basins and related socio-ecosystems, the greater is the needfor scientifically sound decision support.

Legislation and regulations are important drivers of planning and decision making process-es. They define objectives and constrains, identify the roles of social actors, trigger theimplementation of new procedures, thus asking competent administrations to revisebusiness-as-usual approaches. The case of new policies and norms for mainstreaming climatechange adaptation and climate proofing of water management plans in Europe and elsewhereis a recent and relevant example.

Decision support systems can provide an ICT environment facilitating the implementationof scientific knowledge in decision making within robust participatory processes (moreinformed, inclusive and transparent decision making) and enhancing the quality of decisionoutcomes (more effective decisions and efficient implementation). However, DSS toolsintended merely as pieces of software can do very little and are also exposed to high risksof misuse if not embedded in a methodologically sound framework. Developing upon whatstated by McCartney (2007), modern DSSs should be seen as the tools assisting in particularthe structuring of problems and of the decision-making processes, with a specific role insupporting the analysis of the system, and facilitating the exploration of the consequences

4806 C. Giupponi

(including trade-offs) of possible choices, with efficient communication means. According tothis, the NetSyMoD approach has been proposed several year ago as a methodologicalframework to support the management of natural resources and it has been revised to providean innovative solution to accomplish the requirements of recent regulations in the fields ofCCA and IWRM. In parallel, also the mDSS software developed through a series of recentresearch projects (Giupponi 2007) has been recoded to provide the means for practicalimplementation. This combination of methods and tools thus provides an operational solutionto the emerging needs of decision makers involved in water management.4

Provided that what proposed here and similar approaches proposed by other authors couldmeet those needs, future efforts of international institutions and donors should be focused onthe support of innovative and methodologically sound decision processes, more than on thedevelopment of new ICT solutions, and in particular they should:

- target the institutional and governance dimensions, to facilitate the adoption of innovativeapproaches, by means of training and capacity building activities and thus developing trust andorganisational ownership;

– facilitate networking, cooperation and exchanges of experiences, including tools, models,data;

– invest on harmonised transnational data infrastructures; and,– learn from past successes and failures.

Within the relatively new application context of climate change adaptation, the waterdomain appears as one of the most challenging and thus it may also represent a preferredambit for testing approaches like the one proposed in this paper and for developing new ones.

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