Harmoni-CA is a research project supported by the European Commission under the Fifth Framework Programme and contributing to the implementation of the Key Action "Sustainable Management and Quality of Water" within the Energy, Environment and Sustainable Development. EVK1-CT-2002-20003.
www.harmoni-ca.infoDisclaimer:
This report is the sole responsibility of the author(s) and does not represent the opinion of the European Commission, nor is the European Commission responsible for any use that might be made of the information appearing herein.
Dionysis Assimacopoulos, Andrea Castelletti, Kurt Fedra, Alex Hagen, Bernhard Hahn, Hans Jørgen Henriksen, Sebastian Kofalk, Herman van der Most, Lachlan Newham, Martin O’ Connor, Rodolfo Soncini Sessa, Markus Venohr, Edith Zagona.
Doriana Bellani, Fulvio Bollini, Steve Bowser, Norbert Cremers, Mariele Evers, Alfonso Domínguez, Terry Fulp, Chang Fuxuan, Ioannis Glekas, Harry van Huet, Yoshi Kobayashi, Rodrigo Maia, Vibeke Ottung, Nicola Patocchi, Antonio Perdigao, Ambrogio Piatti, Hella Pomarius, April Sanders, Ulrike Scherer, Bettina Schwartz, Bruce Small, Marc S. Sidlow, Marinela Simota, Mike Sully, John Waddingham, Matthias Zessner.
Thierry Davy, Ângela Guimarães Pereira, Bob Harris, Thomas Horlitz, Marta Martinkova, Brian McIntosh, Pasky Pascual, Ioana Popescu, Ken Reckhow, Sophie Rotter
Work financed by Harmoni-Ca, a research project supported by the European Commission, under the Fifth Framework Programme and contributing to the implementation of the Key Action “Sustainable Management and Quality of Water” within the Energy, Environment and Sustainable Development. Contract no: EVK1-2001-00192.
| About this report | 5 |
| 1. Executive summary | 7 |
| 2. Introduction | 10 |
| 3. Methodological background and literature review | 14 |
| 4. Summary of the water management DSS tools considered in the survey | 18 |
| 5. Review of potential end users needs that could be met by DSS tools | 22 |
| 6. Drivers of the implementation success or failure | 26 |
| 7. Guidelines for the development, implementation and application of DSS tools | 27 |
| Phase A: before the development or acquisition of a DSS tool | 28 |
| Phase B: the development of the DSS tool, or acquisition/adaptation of existing ones | 31 |
| Phase C: DSS implementation and application in the decision case | 33 |
| 8. Outlook | 36 |
| References | 38 |
| Appendixes | 42 |
Figure 1: The role of the Harmoni-CA concerted action in Support of the WFD implementation for different categories of users and readers of this Report. | This report reviews and summarises the most recent achievements and the current issues in the domain of Decision Support Systems (DSS) with specific reference to the field of water resources management. The focus is on the role of DSS tools for river basin management, having the EU Water Framework Directive (WFD) as the most important policy reference. The report investigates the reasons for the limited operational implementations of the DSS tools in the domain of interest, in order to learn from previous experiences and thus be able to propose a list of best practices in form of Guidelines for future developments and applications. The targeted audience is broad and includes policy makers and scientists. |
To approach such a great variety of readers, the format of this report is subdivided in sections, with an executive summary presenting the contents of each section, allowing the reader to select the parts of interest. Table 1 provides a key for the different categories of readers, identified with the four categories of actors usually targeted by the activities of the Harmoni-CA concerted actions.
This report may be useful for various reasons. First of all to assess the actual return of research efforts in the development of DSS tools, since the scientific projects designed to develop DSS are not normally required to monitor the implementation and application of the DSS beyond the end of the research grant. Another reason is to trigger a wider discussion about best practices in scientific policy support in general. Section 7 provides specific guidelines for future developments of DSS tools that could be of interest for all the four main categories of actors (in particular for the technology providers) and also for the funding agency (e.g. the E.C.) that may find useful suggestion for targeting future researches.
The contributions of end users to the survey, and their involvement in writing and revising the document represents the main novelty of this report, as compared to several other reviews and assessment efforts on the same topic recently released by research projects or published in the scientific literature.
The evidences collected during the survey and literature review were merged with th experiences of the authors in various projects, including Mulino, Dss-Guide, Transcat, Harmoni-Ca, Newater, Nostrum-Dss, Brahmatwinn.
There are many different understandings of what Decision Support Systems are, what they are composed by, what they do (or should do) and how. They include a vast variety of methods and tools developed for diversified purposes and contexts and for that reason providing a unique definition is practically impossible. A computer-based tool is surely one important component, but more and more DSS is intended as a broader combination of the tool(s) and the process of structuring problems and aiding decisions (Section 2 provides an introduction to DSS concepts and terminology).
DSS in the water management sector usually consist of simulation models, and/or of techniques and methods for decision analysis, recently extended to include the support to participatory processes. Therefore, a DSS typically integrates multi-source geographically referenced data and data management systems, a variety of models and elaboration procedures within a customised user interface. Emphasis is given to hydrologic models accompanied by environmental assessment and/or socio-economic evaluation. The models include both those aimed at reconstructing and simulating the physical reality, and those constructed to manage divergent objectives and to find a compromise among the expectations of different actors in a participatory process. In an idealised view DSS should act as mediators between science and policy/decision making and as catalysts of trans-disciplinary research.
Methodological proposals and tools have been developed since the 1970’s. Unfortunately, DSS have found very limited implementations in the real world, thus demonstrating that most of the DSS tools developed so far have failed to meet the objective of being used in the real world (in Section 3 a brief methodological overview and a literature review are provided).
The question arises then, whether realism could allow us to make proper and effective use of – improved – DSS, or whether abandonment should be preferred. We will not provide a definitive answer, but we will identify a set of best practices to be implemented with realism, to provide the ground for more effective future developments and applications. (see Box A below and Section 7 for details).
As the conflicts for water have exacerbated and the policies have become more articulated and complex, more scientifically robust methods are needed by managers and policy makers. The challenges imposed by the implementation process of the Water Framework Directive (WFD) and the putting in practice of the principles of Integrated Water Resources Management (IWRM) are emblematic in this regard. Those challenges may represent an important triggering factor for the development of improved DSS tools and, indeed, the 5th and 6th Framework Programmes of European research mobilised significant financial resources, targeting that process.
Unfortunately, in the attempt to cope with issues of increasing complexity, methods and computer tools (simulation models in particular) have shown a tendency to become more and more sophisticated and complicated, and there is a growing gap between the specialised knowledge of the DSS developers and the application of this knowledge in decision making.
In order to investigate the reasons for the frequent failures of DSS tools in being adopted by the intended end users (i.e. water managers and policy makers), the report includes substantial contributions from outside the academia and from representatives of the community of DSS end users who were involved in the writing and compiling of an ad hoc questionnaire.
The scope of the questionnaire was to acquire insights into actual and potential users’ needs, expectations and satisfaction, during the tools’ development and application.
As a prerequisite for consideration in this report the responses to the questionnaire given by DSS users had to be distinct from those given by DSS developers. This dramatically limited the choice of tools, since it became clear very soon that DSS applications are seldom used outside/after the original development process and/or by users different from the software developers. The objective of examining the ten tools and collecting at least two end-users’ questionnaires for each tool was very challenging and was met only after great efforts and after several selected DSS were discarded (details about the results are provided in Section 4).
Neither the selection of DSS nor the information collected through the questionnaires are intended as representative samples of the universe of tools and application cases, but at least this work is probably the first attempt to involve potential end users in assessing the outcomes and the impacts of recent research efforts in developing water management DSS.
Most of the respondents reached were specialised in the field of water planning and management; just a few were experts in modelling. In general they were advisors or policy makers, although a significant portion consisted of researchers involved in DSS’ applications in the context of case studies.
Most of the users reached were at their first experience with a DSS. The main motivations for that new experience were acknowledged to be a proposed partnership from research institutions and the emerging needs or inability of the previous management system to approach increasingly complex decisions. Most frequently, the main aim for developing and acquiring the DSS was to encourage and simplify stakeholders’ involvement. Other frequent motivations mentioned by the end users were the need for an enhanced identification of alternative policy options and for providing transparency to previous policy decisions. The satisfaction of specific regulatory obligations and requirements, i.e. the implementation of the WFD, was also pointed out as a target of the tool application. Section 5 goes deeper in the systematic analysis of the needs and the potential role of science.
In the users’ opinion, the DSS resulted useful in a broader context, not just informing the choice of policy, but facilitating the planning process and learning among the actors, contributing to finding a compromise among different expectations and interests.
The adoption of DSS to simplify stakeholders’ involvement did not necessarily make the decision making process easier. In some cases this was due to the inadequate technical features of the systems. In other cases the use of the DSS evidenced the poor quality of the input data, or failed to provide an effective user interface or to communicate hidden uncertainties. Moreover, the lack of cooperation between the partners beyond the development project was pointed out as one reason for limited use and, in some cases, even the failure of the project to deliver the tool.
The results of the survey evidenced the role played by research in DSS implementation in the real world. One can easily say that the adoption of a DSS tool by a potential end user, who is not involved in a research activity, is, to say the least, occasional. Therefore, even assuming that more effective dialogues between the science and policy spheres provide the ground for more effective DSS tools in the future, one of the main issues for future efforts in the fields of training and capacity building is the crucial role of mediators to encourage intended end users to adopt research outputs.
Indeed, science has been increasingly called to inform environmental policy making. Appeals to provide “useful” knowledge, i.e. one with direct policy implications, is a fundamental ethical principle for scientists. At the same time however, the dominant authority of science as the most privileged source of knowledge is being increasingly challenged. Moreover, it is crucial to realise that science and policy making, despite their interdependency, are rooted in different cultures and embodied in distinct frameworks of values, incentives and concerns. These differences have frequently led to frustrating experiences at the interface between science and policy.
Section 6 analyses the known factors of DSS success or failure based on the evidence of the international literature. Despite their crucial importance, the identification of DSS success factors and their measurement is a difficult task, since the development and application of DSS entail multiple potential benefits. The unambiguous detection of DSS failure is at least as difficult as the measurement of its success. From the experiences gathered so far it appears clear in general that the process of policy making is at least as important as its outcomes. Therefore, one important criterion is the degree of change to the usual management introduced by the implementation of the DSS. This could have a dual meaning, since an acknowledged limiting factor is represented by the resistance of managers to changes in conventional practices.
Details about the results of the DSS and questionnaire surveys are reported in the Annexes.
There are many different understandings of what a DSS is, what it is composed by, what it does (or should do) and how. Many different definitions exist and we prefer to avoid proposing a new one, letting the readers develop or keep their own ideas. The concept is indeed usually related to a computer-based tool, which is surely one important component, but more and more DSS is intended as a broader combination of the tool(s) and the process of structuring problems and aiding decisions.
Decision Support Systems (DSS) pervade all domains of water management (e.g. water allocation, flood prevention and forecasting, reservoir management) for a variety of reasons. In general, we may say that researchers are attracted by the capacity of DSS to convey scientific, transdisciplinary knowledge in an accessible format to policy and decision makers.
In an idealised view DSSs act as mediators between science and policy/decision making and as catalysts of trans-disciplinary research. Again, in an ideal situation, DSS tools facilitate dialogue between policy makers and scientists and both groups benefit equally from mutual learning. In the light of increasingly complex and interconnected environmental problems, the science sphere is increasingly called to inform (and improve) actual management and planning practices and scientists are requested to produce “useful” (i.e. applicable context-sensitive) knowledge.
DSS have been developed for over three decades with the purpose of replying to those calls. Unfortunately, DSS are seldom used for the intended purpose due to the misunderstanding of the motivations and incentives leading to DSS development. Even the term DSS itself is a catchword, having different meanings for different people, and covering different interests.
Different situations can be observed in the various application fields, but as far as DSS in the water management sector is concerned, one may easily say that we have already passed the typical initial phase of euphoria and we are now quite often in the disappointment phase, which may be followed by either abandonment or realism . The question arises then, whether realism could allow us to make proper and effective use of – improved – DSS, or whether abandonment should be preferred. We will not provide a definitive answer, but we will identify a set of best practices to be implemented with realism, to provide the ground for more effective future developments and applications. Moreover, in order to gain more insights on that question, a questionnaire has been specifically designed and disseminated among DSS end users. The survey conducted focused on a selection of 10 recently released DSS tools and was mainly targeted to the products of European funded research. Neither the selection of DSS nor the information collected through the questionnaires are intended as representative samples of the universe of tools and application cases. The wide range of issues addressed, the diversified typologies of tools, the varied application contexts, are some of the reasons why a comprehensive and representative assessment would have required a whole multi-annual research project.
Right from the beginning of this report it is worth mentioning the difficulties encountered in identifying DSS tools used outside the development context and/or by users distinct from the software developers.
We will examine in detail the theoretical and terminological issues (see Box 1 for some details). Throughout the report the term DSS is used as a synonym for a wide range of tools (e.g. planning support systems, expert systems, etc.) which may be differentiated elsewhere, sometimes for good reasons. The issues addressed in this report are to a large extent similar for all the tools and the variety of meanings associated with the term DSS, which in this case may be seen as an advantage rather than a source of frustration.
In Box 1 we refer to several definitions of DSS, each of them is defensible in a specific context. The most common definitions refer to DSS as a computer-based tool, a higher form of information system . When asked to explain with their own words what they refer to as a DSS, many developers point to a set of rules, tips or methods. As stated above, currently DSSs are quite often intended as the combination of the tool(s) and the process of structuring problems and aiding decisions.
Many DSS consist of simulation model(s), and/or of techniques and methods for decision analysis. Models’ roles are almost as diverse as the uses and modelling paradigms employed. The variety is comprehensible, given the critical importance of models as instruments for scientific investigation and policy making . In general, models can be used to (i) measure and represent; (ii) describe structure, behaviour and pattern; (iii) reconstruct past or predict future developments; (iv) generate and test theories and hypotheses; (v) surface, encode, transfer, evaluate and interpret knowledge; (vi) guide development and assessment of policies; and (vii) facilitate collective learning and settlement of disputes . Decision analysis (DA) helps to avoid biases in judgement and to make decisions more compatible with normative axioms of rationality for situations involving multiple, conflicting interests and beliefs. The trade-offs or preferences are value judgements, which are frequently not observable and must be revealed or approximated. Such uncovered preferences are context-specific and depend on the description and framing of a problem, and how the questions are formulated. For example, to assess the environmental costs of irrigation, one must consider the value of wetlands and riverine ecosystems deprived by water abstraction.
There is a broad agreement about what DSS are expected to do: in general terms, DSS are set to aid decisions and structure problems. DSS are expected to explore multiple perspectives of the problem at hand; enhance decision makers’ insight into the problem drivers and policy outcomes; and facilitate communication and knowledge transfer between the actors involved in or affected by the decision. Therefore, the DSS role should be intended as tools for supporting the process of decision making much more than tools to provide answers to decision makers’ questions. This is particularly true also because DA methods act as “lenses” through which the policy problem is viewed, lenses may have different curvatures and thus different DMs may (frequently do) lead to different conclusions.
The fact that alternative models or, for instance, different decision rules in multi-criteria analysis, may lead to different solutions of the problem at hand is a crucial issue for DSS tools. Adequate quality assessment procedures guarantee only to some extent that the methods and tools adopted are theoretically and methodologically robust. In some cases different approaches, all suitable for the issue in question, still provide contrasting results. This problem should not be neglected and should support the advisability of testing – as far as possible – alternative approaches in the same case.
The unstructured nature of many problems related to natural resources management may be caused by: (i) different ways in which the involved persons see the problem; (ii) complexity of environmental systems, their dynamics and interconnections; (iii) identification and assessment of actions; (iv) modelling the consequences of a different course of action; or (v) ambiguous and conflicting objectives. The solution of one aspect of an unstructured problem may reveal another, more complex problem. In such situations, the process of solving a problem is the same as the process of understanding its nature; they are mutually concomitant. Only concrete solutions which have been formulated and analysed provide a deeper insight and understanding of what the problem actually is.
Many authors consider DSS as a technology. Environmental management, as perhaps no other field, relies on a huge amount of information (e.g. GIS and remote sensing data, time series of precipitations). To store, visualise and analyse these data, the pure cognitive capability of humans is of limited use. Besides, DSS help to avoid biases in judgement and make decisions more compatible with normative axioms of rationality for situations involving multiple, conflicting interests and beliefs. To qualify for DSS, the tools are expected to turn information into knowledge. As computerised tools, DSS are surrounded by a number of issues related to technology, its diffusion and acceptance. Seeing DSS’ limited success as a failure of technology tips into a number of issues including personal aspects (e.g. prior expectation, education, value and belief of intended users and impact on their job); technical aspects (e.g. user interface, performance, reliability); institutional aspects; management processes that DSS are designed to aid; or any combination of the Above. It is this viewpoint which prompts increasing frustration, negative attitudes towards DSIT and abandonment.
However, hardly any DSS research project aimed at technology in the first place. DSS were supposed to connect disciplinary knowledge and solve methodological differences, in other words, the motivation to develop DSS was to facilitate integration and transdisciplinary research. For DSS to fulfil their task, i.e. aid policy making, a number of quite distinct issues come into play. Superficially these issues may be seen as problems of data collection (different data are connected for different spatial or temporal units), the nature of data (e.g. qualitative versus quantitative), or as a lack of common terminology (e.g. a wider range of different interpretations of uncertainty). The most difficult issues to overcome, however, are problems of epistemological nature, i.e. what is recognised as knowledge, for what purpose, embedded in wider disciplinary differences. Methodological differences between social and natural sciences are notoriously known but significant differences exist even within a single discipline (e.g. ecological economics and environmental economics). The perspective of DSS as a catalyst of trans- or interdisciplinary research provides different views on the success and failure.
Water resource management has attracted a particularly vast level of attention in the recent decades. This is partly because of the fundamental value water exhibits for sustaining life and development. Water is a finite, depletable, non-substitutable and unevenly distributed resource with a unidirectional flow pattern, which incites conflicts and prompts a number of threats such as floods, landslides and erosion. Most of the problems handled by water resource management have the characteristics of unstructured problems. To ensure an efficient allocation and protection of water, a holistic (integrated/comprehensive) management based on the principles of the ecosystem approach was endorsed by a broad scientific and policy community. Such a management favours pro-active, non-structural and demand-side interventions. Yet, the integrated ways to resource management require a trade-off to be made between different, often mutually exclusive interests and values, which aggravate conflicts . As the water resource conflicts become more complex, more accurate and comprehensive evaluation of management activities is required. The process of policy making has increasingly favoured interdisciplinary, pluralistic, and inclusive methodologies, with scientists participating alongside other stakeholders in deliberative decision making, participatory assessment or group model building.
To support the implementation of the new water policy introducing the principles of an integrated, river basin management, the European Commission has dedicated a key action under the 5th Framework Programme (FP5, 1999-2001) to issues related to sustainable management and water quality. The WFD principles like transboundary management of water issues; holistic, cross-disciplinary management regimes; public involvement and application of economic instruments such as water pricing and cost recovery required further research before becoming operable in different conditions across the EU countries. The EU work programme highlighted the “social, economic and cultural pressures” on water resources, causing threats (to the environment) and increasing conflicts between water uses. It emphasised the need to improve the knowledge and technologies supporting a “rational and sustainable” management of water resources which requires policies, tools and methods able to govern interwoven environmental, social (institutional) and economic systems in their full interaction at the river basin scale.
The DSS developed in the context of EU funded research (e.g. MULINO, TRANSCAT, MERIT, GOUVERNE) facilitate WFD implementation by (i) making for effective data management, (ii) providing a suitable environment for the implementation and integration of environmental models, and (iii) aiding social learning processes among stakeholders involved or affected by the measures to achieve the WFD’s objectives. Within the 5th Research Framework Programme (5FP) altogether 150 projects were funded under the water key action with the total budget amounting to 244 mil Euro. 43 projects or 68.6 mil Euro were dedicated to integrated management methodologies and tools at catchment/river basin scale. 7 projects with a budget amounting to 11.2 mil Euro were funded under the priority 1.1.3. “Operational management schemes and decision support”. Although pursuing distinctive objectives, all the projects paid attention to integrated catchment modelling, risk management, quality assessment, optimisation and responses of river basins to policy changes (EC, 2001).
Figure 2. The Water Framework Directive Implementation Plan.
A review of existing DSS was carried out in a number of projects (e.g. NEWATER, AQUASTRESS, FLOODSITE, NOSTRUM-DSS, WATERSTRATEGYMAN, CODES, TRANSCAT and MULINO). The reviewed tools related to each project’s specific objectives and application conditions. Altogether the review encompasses more than 150 tools and DSS application reports (see also Annex I). Also, what was considered as a tool varied considerably, including conceptual tools and process based tools facilitating learning. Most tools addressed in detail, however, were computer-based. Different evaluation criteria have been used in different projects to assess system applicability (see the Box 3). Only a few reviews addressed the lack of implementation success in more detail. The variety of evaluation criteria employed suggests that the evaluation is context-dependent and experiences from one case may be only poor indicators of performance for other cases .
The DSS developed to assist water resource management are mostly model-driven DSS. DSS help to understand feedback among processes operating at different spatial and temporal scales . Emphasis is placed on hydrologic models accompanied by environmental assessment and/or socio-economic evaluation. The application of models is referred to both the environmental models aimed at reconstructing the reality, and decision models constructed to compromise conflicting objectives and to mitigate the expectation of different actors. A DSS for water resource management typically integrates multi-source geographically referenced data and data management systems, a variety of models and optimisation procedures within a customised user interface. Additionally, pre- and post-processor utilities may facilitate data exchange between the implemented models. A set of other functions may assist in data preparation and management tasks. An educational component may assist the decision maker to understand the data and implemented models. The systems may support graphical (network-based) ways to define water resource systems or are based on (or extendable by) a commercial or public domain Geographic Information System. The multi-source data bases may include environmental (hydrological, climate, soil, and meteorological) and socio-economic data (demographics, economic development etc.).
The variety of models includes hydrologic models (run-off, sediment, flood routing, flood inundation, reservoir, point and non-point pollution source models, etc.), physically-based simulation models (e.g. soil erosion, agricultural-chemical transport, crop-soil-water interaction, nutrient and pesticide transport models), socio-economic and environmental impact assessment models (e.g. crop assessment model), management and optimisation models (linear programming, dynamic programming, multiple criteria decision making, genetic algorithm, risk assessment, etc.). The goal of the executive interface is to provide a concise and clear presentation of the data and models and should bridge the gaps in model understanding.
DSS have been developed for a variety of situation contexts which differ significantly in pursued objectives, required functionality, modelled processes and phenomena, spatial and time scale, and supported users. Generic DSS, i.e. systems built upon methodologies and models which are applicable to a range of different management situations (e.g. AQUATOOL, RIVERWARE, WATERWARE, ELBE DSS or RAISON. AQUATOOL, TWOLE and RIVERWARE), support planning and operative decision making in complex basins, including multiple reservoirs, aquifers and demand centres. WATERWARE on the other hand, addresses a range of issues like environmental impact assessment and evaluation of measures against pollution. RAISON represents a kind of generic software tool for rapid prototyping and practical implementation of environmental DSS. The DSS like that described in aim at problems related to the water quantity and help to improve water use efficiency. The problems addressed are typically irrigation planning problems and allocation of water resources between headwater and downstream users. Water quality issues are addressed by a range of DSS: WATERSHEDSS, MOIRA, EFDSS, BASIN, DEMDESS, DESERT, ELBE-DSS and EFDSS. The problems they deal with encompass the identification of water quality problems, integrated assessment of point and non point source pollution, selection of appropriate management practices etc. Assessment of impacts of changed land use or industrial, forestry, urban and agricultural projects on water quality and/or other environmental impacts is the goal of systems like WAMADSS, CMSS and GIBSI. Some DSS aim at more specific problems like optimal remedial strategies to restore radionuclide contaminated fresh water environments (MOIRA) or integration of a range of models of riverine ecology (EFDSS). Real-time river basin flood control and reservoir management is supported by the DSS like SDSS and . FLOODSS aims at forecasting catastrophic events and at preventing and mitigating their effects on the environment. Recently, the Internet prompted development of a new typology of distributed DSS such as AQUAVOICE or Transcat . The development of DSS is facilitated by modelling frameworks, supporting modular model developments and re-use of existing model components such as ICSM and SME described by Argent .
The benefits from the DSS development are not restricted to the final system and its suitability for the supported management processes. Research challenges associated with (or intermingled with) DSS encompass (i) investigation of how innovation makes it way to practical management processes, and how institutional arrangements can help to overcome the initial resistance and maintain commitment to pursued changes; (ii) analysis of human capabilities to comprehend processes and cause-effect relations in complex worlds; and (iii) study of how technological progress and advanced computing algorithm are able to overcome the cognitive limitations. In the water resource management, DSS field is linked to integrated assessment and holistic regime to water and related land and soil resources. Consequently, the assessment of DSS success cannot be reduced to a single measure. The conceptual diversity inherent to the DSS term and the range of benefits from the DSS development process has to be taken into account.
The computer modelling aimed at assisting water resource management is becoming more sophisticated and the gap between specialised knowledge of the DSS developers and application of this knowledge in decision making is more apparent. Water DSS development domain is challenged by the need to manage, if not to hide, the models’ complexity for developing effective interfaces with the users and to make model appropriateness and decision procedures transparent to the decision maker. This is further explained in the Section 6 dedicated to DSS success and failure.
This section aims at analysing some concrete cases of DSS tools developed in support of the Water Framework Directive, by EU funded projects or National projects. A comprehensive review has been carried out to end up with approximately 10 DSS to focus on. After a preliminary categorisation of the tools aimed at selecting samples belonging to different typologies, the main criterion adopted for the final selection was their actual present or past use by potential end users. DSS developers of the selected tools were contacted to know whether end users would have been available to fill in the questionnaires (Annex III). The review scope was to acquire insights into actual and potential users’ needs, expectations and satisfaction, during the tool development and application. The analysis allowed a specific investigation on successes and failures of tool application from a user’s point of view. In case of tools still under development the expectation on the tool’s usefulness and required functionality were assessed.
A preliminary list of 60 tools (see Annex I) was developed from several sources, such as reports of EU funded projects (i.e. , the technical report on DSS , CORDIS online database (http://cordis.europa.eu/search/index.cfm). A list of 15 tools likely to be considered in the review have been identified and included in a draft list to be able to end up with a sample of about 10 tools. The preliminary contacts with DSS tools’ developers allowed to identify their willingness and interest to be involved and, as previously mentioned, the probability of establishing direct contacts with end users, which was considered a pre-requisite for the inclusion in the survey.
Some tools had to be excluded from the review as they were still under development, and/or never transferred into the public domain and/or never applied. DSS tools for which the developers provided their availability and identified a list of at least two users are in the final short list.
The search for the availability of end users of the tools and the interactions with DSS developers suggested a series of revisions in the original list. TWOLE was added as a distinct product related to the MERIT DSS, while the KerDST “Deliberation Support Tool” (DST) was selected as a representative of a group of tools first developed in the GOUVERNe project. Finally, the WFD-EXPLORER was suggested as a suitable example for the products developed at Delft Hydraulics.
End users suggested by developers were contacted via e-mail for the compilation of the questionnaire reported in Annex III, designed to collect users’ perspectives on the tool’s development, acquisition and application.
To contemplate the variety of DSS typologies, a wide range of tools’ types and functions were considered. These included software libraries and tools specifically tailored for developing a DSS for IWRM (i.e. RIVERWARE), single models (i.e. MONERIS), or tools provided of several integrated models, a database and a user interface (i.e. ELBE-DSS, WATERWARE). The tools considered are presented in Box 5 and described through various features such as the domains and sectors included, the kind of support provided, the type of intended users, and other technical characteristics.
An extended description of the selected tools is presented in Annex II. The tools’ characterisations have been prepared through the available documents and investigate the primary scope and domain of the various tools development and application, some features of the technical design, the kind of support provided. Other information included concern the distribution, maintenance, users and applications, documentation and development. The descriptions have been edited by the DSS’ developers.
The questionnaire is divided in four different sections (see Annex III). In the first section the questions focused on the motivations and the scopes of the development, acquisition, and application of the DSS. The next section requires the specification of the role assumed by the user during the system’s development and of the conditions of the usage. Gathering information on the concrete experience, use, implementation and maintenance of the system was the target of the third part of the questionnaire. This part investigates in particular the impacts on the policy making process after the tool’s application. The final part of the questionnaire has been dedicated to the elicitation of benefits and satisfactions resulting from the use of the system and its different components.
A first version of the questionnaire was distributed and tested at «EUROPE-INBO 2006» 4th European Conference held in Megève (France) the 20th-22nd of September 2006. The comments received from the participants on the structure and contents of the questionnaire were taken into account for its dissemination to the intended users. Thanks to the indications of the selected DSS developers, in total 40 questionnaires have been distributed to end users and hardly 26 replies have been collected. We ascertained a willingness to cooperate in just a few of the target persons. Receiving their feedbacks was thus a demanding task. The following paragraphs are the result of the filled questionnaires’ elaboration. The details of the answers given by the 26 respondents are presented in Annex IV. The answers to the free questions are faithfully reported, meaning that the language check was not carried out on this part of the text.Most of the users reached were at their first experience with a DSS and the main motivations a for the development and acquisition of the system were acknowledged to be a proposed partnership with research institutions, but also emerging needs or inability of the previous management system to approach increasingly complex decisions. The main objectives targeted with the development and acquisition of the DSS were the opportunity to facilitate stakeholders’ involvement in the decision making process, to identify alternative policy options and to gain a better understanding of the problem at hand.. The fulfilment of specific regulatory obligations and requirements, i.e. the implementation of the WFD, was also pointed out as a target of the tool application.
The majority of the users contacted couldn’t say whether the implementation of the DSS imposed changes to the traditional decision making process in their institution (i.e. the answer to the specific question was “Neither agree nor disagree”). This result can be explained considering that an important part of respondents was involved in project’s case studies for tool testing. In fact, the questionnaire was rarely filled in with reference to applications for concrete decision making processes. Nevertheless, a remarkable portion of the respondents affirmed that a change occurred in the process due to the tool’s adoption.
Generally, the DSS developed have been integrated with other software previously used and the integration itself was judged important for the decision to develop and acquire the system. Most of the DSS considered incorporate a GIS and seldom a database management system or a model.
Most of the respondents reached are specialised in the field of water planning and management; just a few are experts in modelling. In general they are advisors or policy makers, although a significant part of the respondents are researchers or in close relation with the academic word.
The end-users’ involvement concerned mainly the identification of their current and potential needs and, secondly, the test and evaluation of prototypes during various phases of the development. It emerges how end users’ involvement occurres usually at the very initial phase related to the specification of the problematic and at the final phase of tool testing. Adaptation and adjustments to the user’s needs through the whole process are often missing.
MONERIS and RIVERWARE are systems developed and used for at least 10 years and are generally applied on a daily basis by end-users. Systems like WATERSTRATEGYMAN have been developed more recently, i.e. 2002/2003, and are used on a continuous basis. Other DSS are used but only occasionally. ELBE-DSS and WFD-EXPLORER are still under development, but they have been already applied in the context of pilot case studies.
Most of the users reached interact with the system directly from their PC. The respondents’ opinions on the difficulty in becoming familiar with the system were discordant. Indeed, the perception of the system’s complexity varied more according to the personal background and the application context, than on the type of tool. Moreover, most of the respondents don’t perceive their role in the decision making approach changing because of the adoption of the DSS. This can be explained, again, by considering that most of the tools included in the survey were developed and applied by research projects.
In the users’ opinions, the adoption of the DSS was useful in a broader context, not only to provide information on the choice of a policy, but to facilitate the planning and learning processes among the actors, contributing to finding a compromise among different expectations and interests. It has been pointed out how some tools are designed to meet regulatory obligations, giving insights for the solution of the problems encountered, especially during the implementation of the WFD. Apparently, the majority of the selected tools are still maintained and further developed.
Re-use of the system did not impose great difficulties to the users, but training and assistance were considered as a fundamental prerequisite for the successful application. Similarly, another important condition was the presence of adequately trained individuals within the institution.
Section D provided insights in the perceived benefits and satisfaction level of DSS users. The vast majority of respondents either agreed or strongly agreed that the implementation of DSS improved the pre-existing approaches (D1). The user interface and functionality of the databases were both perceived as sources of high satisfaction for the DSS used (D2) and as the most critical components (in the open questions) for an effective use and application of the system.
An incomplete database and the lack of available data, the complexity of the various modules and the perception of the model as a black box are examples of the system’s limitations cited in response to D3 and D4. The adoption of too short-term time scales, in comparison with the regulatory requirements, and the non-availability of the system as open source software were also valued as a relevant limitation for the system’s adoption.
When the components of the system were questioned by the stakeholders, the disputed points mainly regarded the quality of the input data, the complexity of the user interface and hidden data’s uncertainties.
The analysis of responses to D4 (future recommendations) is very interesting and the development of more friendly user interfaces appears again as the hot issue for the future. The recommendations for the improvement of similar systems in the future are summarised in Box 6.
In conclusion, having remembered the difficulties encountered in identifying a sufficient number of cases of tools that were utilised by users distinct from the developers, and that most of the cases available were still to some extent academic exercises, a general optimism emerged from the end users’ experiences, related to the potentialities DSS tools have to support and improve the decision making process in water resources management. Prerequisites for more effective future developments seem to be an improved user-friendliness, and, related to that, the involvement of users in all phases of the tools’ development and by providing adequate training support during and beyond the end of the development phase.
DSS are built to meet users’ needs which should be, by definition, related to a specific decision issue. In a narrow view the users’ needs stem from the institutional role and the responsibility assigned to the users, or better, to their institutions. For example, the role of river basin authorities may be to develop river basin plans and manage water resources accordingly, monitor water quality and quantity, release warnings in cases of expected floods or droughts etc. For example, after the devastating tsunami in 2004, the competent authorities decided to develop a tsunami forecasting system to allow early warning and the implementation of preventive measures.
In some cases developing DSS to match institutional tasks and functions can be straightforward. In other cases the available knowledge may not be sufficient. The required information may not be available at all or not in the required form (e.g. for specific spatial or temporal unit/scale). For example in the absence of the groundwater quality data the river basin authority may enforce decisions regarding the groundwater protection or management, but these decisions may not be optimal and can be contested. In general, users’ needs can be related to all the impediments preventing users from making decisions based on sound knowledge and management techniques.
Sometimes the required knowledge is not available because of the lack of scientific understanding or the variability of the factors which influence the specific situations. In these cases the investment, e.g. in the monitoring networks, does not improve the knowledge on the basis of which the decision needs to be made. What is required is an improved scientific understanding of the underlying phenomena or an introduction of governance regimes which can cope with the intrinsic uncertainties and which are able to obtain a wider consent among the affected actors. Therefore user needs can also refer to scientific progress (e.g. interdisciplinary research about the consequences of climate change) or to the effectiveness of institutional arrangements (e.g. ability to cope with conflicts).
Figure 3. The IWRM process and the role DSS could play in supporting the implementation of the cycle. Figure modified
Science has been increasingly called to inform environmental policy making. Appeals to provide “useful” knowledge, i.e. one with direct policy implications, is a fundamental ethical principle for scientists. At the same time however, the dominant authority of science as the most privileged source of knowledge is being increasingly challenged, and a greater openness and dialogue between all knowledgeable parties is called for. It is crucial to realise that science and policy making, despite their interdependency, are rooted in different cultures and embodied in distinct frameworks of values, incentives and concerns. Frequently, these differences have led to frustrating experiences at the interface between science and policy.
To overcome the science – policy gap, many have argued that policy (or action) related research differs from mainstream science in several aspects: it is action-oriented (in the sense that the implementation concerns are a part of the research), integrated, value-committed (as opposed to ‘value-free’), situation-specific, operating on the long term and sensitive to the lack of commitment of involved actors . In other words the way science informs policy-making needs to be re-thought. The re-definition of relations between science and society includes release from disciplinary and institutional rigidity; methodological pluralism (embracing ambiguity); surfacing one’s own normative assumptions, values, motives, potentials and limits; and an engagement in ongoing dialogue . This has several important implications: first, scientists should be prepared to facilitate policy processes rather than to determine them; and secondly, the process of policy making is at least as important as its outcomes.
A fundamental challenge of policy-related research is the quality assurance of the whole decision process and its results. Many policies are afflicted with complex conflicts and quality assurance in this context has to take into account intangible and incommensurable aspects such as subtle changes in behaviour, level of trust, and changes in relations. Their weights and how they are translated into decision outcomes (more effective and efficient decisions) and in the decision process (more informed, inclusive and transparent decision making) are to a large extent specific to a given context, and thus hardly transferable. Therefore, the peer review which is the traditional means for assessing research quality alone is not sufficient. Other criteria have been proposed, including extended peer-community review or commitment to the accountability of researchers. For example, the hurricane forecasts of the National Hurricane Center in Florida include the names of researchers who developed them. However, the lack of generally applicable quality assessment criteria poses an important challenge to the transferability of the insights collected in policy related research.
Due to the central role of policy-making models for water resource management, the quality assessment of the simulation modelling process attracted a vast attention. HarmoniQua Guidance and Modelling Support Tool MoST guides model development, monitors the progress done, and advises the modelling team on the basis of model journals, collected during the previous projects. To this end the modelling process has been broken down into five generic steps and forty-four detailed tasks, each further specified in terms of activities to be performed. The steps, tasks and activities have been completed with practical recommendations, further differentiated according to modelling domain, users’ level of expertise, and job complexity.
Drivers of the implementation success or failure
Despite their crucial importance, the identification of DSS success factors and their measurement is a difficult task. The development and application of DSS entail multiple benefits which make measurement of DSS success difficult. Many factors are likely to influence DSS success, but their importance may vary significantly according to situational variables. Very frequently studied factors of DSS success include (i) actual use and re-use of DSS by intended users, and (ii) user (perceived) satisfaction. The former indicates that the benefits from DSS usage were considered to be greater than the costs, especially in case of re-use. The latter expresses the level of the satisfaction perceived by the users as a consequence of having implemented the system. Obviously, these factors are not independent of one another, as a higher satisfaction can lead to its repetitive use and vice versa, see in . Neither of the two factors analyses the changes in decision maker’s performance achieved by using DSS, very difficult to assess on its own. The change itself should not be considered a priori as a positive indicator, first of all because change does not necessarily mean improvement and, secondly, because one recognised characteristic for the successful implementation of DSS tools is their ability to adapt to existing management approaches. Efforts required for changing usual management practices by the end users have been often identified as one of the main barriers to the uptake of the available DSS.
The DSS usage as expression of DSS success, being a twofold variable (is used, is not used) is criticised for its lack to further differentiate the systems used .
The pure subjective assessment of the DSS acceptance is also not unambiguous. Frequent usage of a system, familiarity with its structure and involvement in DSS building have been proven to increase the decision maker’s illusion of control
.The unambiguous detection of DSS failure is at least as difficult as the measurement of its success. In many cases, even when DSS development was stopped or the system was not used by its intended users, essential learning processes took place. Newman refers to a case when the DSS has become obsolete because the end user has learned (by DSS using) its logic and was able to apply it on his own. Bell et al. refer to a similar situation when the decision makers, by using the DSS, were able to get detailed insight into the decision problem and understood their own preferences better, although the adopted solution differed from the solution proposed by the DSS. The examples show that if a DSS is not practically used or the solutions proposed by the systems are not adopted, it cannot be generally considered as a non-ambiguous criterion of the systems’ success or failure.
There is a large body of literature dedicated to the processes behind the acceptance of and resistance to DSS and technology in general, a selection of relevant evidences and references are presented below.
A number of typologies of critical success factors have been developed in management and computer sciences . These typologies distinguish between personal factors (such as prior expectation, education, value and belief, impact on user’s job); technical aspects (user interface, performance, reliability) and the interaction of both (social content gain and loss before and after the DSS implementation), differing in the importance attached to these categories. Other categories frequently applied to study the DSS success include the external environment, the organisational aspects (e.g. changes in interpersonal relations) and the management processes that DSS are designed to aid. The role of some other actors has been reported as crucial for the DSS success : (i) DSS champions - managers who actively promote the DSS concept among the potential users; (ii) supporting political actors - people whose influence is needed to secure support for the DSS; and (iii) direct supervisor of the user - persons to whom the user is directly accountable. It is also practical to distinguish factors which raise commitment to a change (e.g. availability of technical and human resources, presence of a powerful actor inclined to support innovation) from those which maintain the commitment (e.g. early user’s involvement, DSS prototyping).
Another class of approaches analyses cognitive aspects (cognitive styles of decision-makers) and their influence in accepting/resisting formal decision making and employed technology ; , , . Cognitive types are chronic motivations determining the initialisation, course and cessation of information seeking and processing . Cognition may influence perceived facts and behaviour tendencies . Consequently, different people with the same belief may develop different attitudes and different people with the same attitude may develop different behavioural intentions. Different cognitive styles have been described to explain these phenomena. employed three cognitive style measures – the need for cognition; the personal need for structure; and the personal fear of invalidity. analysed cognitive styles determined along information gathering (perception styles) and information evaluation (judgement styles), resulting in sensing/intuition and thinking/feeling cognitive styles. The emphasis on the cognitive modelling characterises a new trend in DSS development. The cognitive DSS focus explicitly on decision maker’s mental models, facilitating their enrichment, validation and supporting backward and forward thinking . Other phenomena analysed in the context of DSS/technology acceptance include illusion of control , user resistance and reliance effect .
Many attempts have been made to explain/predict users’ attitude towards technology . The Technology Acceptance Model TAM , based on the theory of reasoned action TRA tried to explained the impacts of external variables on internal beliefs, attitudes and intentions. Two factors were found to determine system use: perceived ease of use (PEOU) and perceived usefulness (PU). External factors act through these beliefs. Subsequently, the TRA and TAM evolved in a number of different models such as Theory of planned behaviour TPB , decomposed TPB . A different approach in behavioural research was taken by diffusion theory , considering technology success as a social process related to the spread and adaptation of innovation, see also .
In this section we translate the outcomes of this study into concrete recommendations for the development, implementation and application of DSS tools. Both the literature review and the survey of DSS tools and end-users opinions were framed within the context of water resources management. Nevertheless we believe that the evidences of this study may be of interest for the broader field of DSS in general.
The development and application of DSS require passing through a series of different choices. We preferred to organise these Guidelines around the idea of a generic temporal sequence, by identifying three main phases:
Phase A: prior to the actual development or acquisition of the DSS tool;
Phase B: of development (in case of new tools to be implemented) or acquisition/adaptation of existing ones;
Phase C: of implementation and application in the decision case.
The sequence is not necessarily universally valid, in particular for what concerns the organisation of the work within the three main phases, because some aspects could be dealt with in parallel, for instance, or be approached at different times in the variety of real world implementations. Subsequent steps with internal iterations are possible, with “control phases” for assessing the quality of the process and its fitting to the application case.
In general, just like a check list, or a collection of recommendations, we believe that professionals and managers involved in cases in which the use of DSS tools is considered, should go through the whole list, in order to, at least, avoid common problems and mistakes that contributed to the –unfortunately very long– list of failures when approaching the development of new tools, or the practical implementations of one of the numerous tools developed in the WRM field over the last 30 years.
It is important to note that the development of a DSS is always a process involving different groups – of people, playing different roles. A task force could be identified as that group of few people initiating and managing the process. It usually includes the manager(s) and/or policy/decision makers, the DSS developers, the technical staff at the competent institution, external consultants, experts of different disciplines: e.g. a sociologist for managing participatory approaches, a modeller for integrating simulation capabilities in the DSS package, etc. Other groups may include various categories of actors (stakeholders) involved in various ways and possibly at different stages in the decision process.
Figure 4. A general cycle for the implementation of a decision making process.
The nature of problems to be solved should be investigated at the beginning of any decision process. In the specific context of examining the adoption of a DSS tool, this should be done with a specific emphasis on clarifying whether or not sufficient human and financial resources could be available. Financial resources could also be needed, for example to consolidate the available data sets. Indeed, the data issue (i.e. the availability of data for informing the decision process) should be approached in order to acquire preliminary information of fundamental importance for orienting the choice about the DSS already available or the requirements for a new DSS tool to be developed. Given the complexity of the problems at hand and their specificity to local situations, expert knowledge may provide fundamental support for compensating the unavailability of quantitative data. In that case adequate techniques are needed for scientifically robust expert elicitation and integration in the decision process.
The outcomes of the survey with DSS users evidenced that quite often a large gap exists between DSS data requirements and data available to users. Moreover human resources (i.e. trained people available within the management administration) are also often limited. When internal human resources are not available, mediators of DSS development and implementation (e.g. external consultants) can play a crucial role at the interface between DSS developers and managers (final users), by providing the administration with the specific skills required and by filling – temporary – gaps of the internal staff.
To derive a robust understanding of the problem within its social context the users together with the DSS developer(s) need first to find out whose perspectives/viewpoints are relevant and to apply methods and techniques to make the perspectives explicit. For example:
The HarmoniCop HandBook may provide more details .
The participatory approach is becoming a prerequisite of every legislation dealing with environmental management. DSS should be developed taking this need into account: they should be designed to allow multiple users, facilitate the integration of different disciplines and viewpoints, facilitate the transparent processing and inclusion in the DM process of data coming from PP activities.
The institutional mandate of the user (what decisions users can and have to make, and on the basis of which information), the internal rules and practices within the organisation, and the relation to other institutions should be carefully investigated and clarified.
DSS may impose some degree of change to the decision-making process. It does so with the aim to improve the quality of this process, and subsequently, the decision outcomes. However, any change, be it positive or not, usually encounters a resistance of at least a part of those who are affected, thus increasing the risk of implementation failure. These risks may be reduced by actions targeted to provide timely and effective communication, including the anticipation of possible changes to be expected, from the DSS implementation.
DSS impose some degree of change to the end users’ practices: some sort of resistance should therefore be expected. Many known failures of DSS tools resulted from the excessive demand of change imposed to the users. Different is the case of changes imposed by new regulations; in that case DSS may be perceived to play a role in alleviating new administrative burdens. The WFD could be an important triggering factor for the adoption of new technologies, such as DSS.
The development of a DSS tool specific for the application in a decision case should adequately consider what the constraints could be to its practical implementation. Two aspects are worth mentioning: the technical limitations to the use by the intended users. For instance, in case of web based tools, for participative approaches, requiring efficient Internet connections. the limitations coming from the DSS interface and documentation, that should be carefully targeted to the intended users.
DSS interfaces are often developed for experienced users. One common problem is also the language: most DSS are developed in English, but not all the users can understand it.
Initial users’ commitment requires a clear and comprehensible specification of the system that includes what the system should provide and how. The rising of initial commitment depends upon the extent to which the users perceive the practical utility of the system for the tasks they have to carry out. For the success of DSS the support of a sponsor from within the organisation is most beneficial, preferably among the senior managers, who realise the benefits from the DSS and help to explain it to others.
Frequently quoted indicators for assessing the usefulness are:The perception of ownership of the tool and its implementation by the staff of the user administration is very important for effective application and uptake in the ordinary practices.
Figure 5. The guidelines’ flowchart.
DSS can assist the users in a number of ways:
The extent to which these contributions to a specific decision process could be provided depends upon many factors, not last the availability of information. A preliminary assessment should make reasonable expectations clear to the end users.
The DSS do not stop users from making judgements, nor should they attempt to do that. Decision makers willing to delegate decisions (and power) to ICT tools are, to say the least, rare.
Phase A should lead the task force to understand clearly whether or not a DSS could be useful for the decision case. Suitable tools are not always necessarily available nor are resources always sufficient to be adapted to the specific case or even for the development of a new tool from scratch (see Box 8 for more insight about this choice).
Early involvement of the intended users is one of the most critical success factors, but only if both the DSS development and the decision process are ready to adapt to their needs.
As specified later, the development of a new DSS or the adaptation of an existing one is a process of mutual learning between the developers and the users. The users should not be asked to specify their needs only at the beginning of the development, but they should be consulted and involved in the system testing throughout the development process. This has several advantages: the user gains better understanding of the components constituting the system and can consequently better assess the suitability of the system for specific tasks or contexts;
And disadvantages:
DSS end users are often busy managers: their involvement effort should be carefully managed
There are a number of development strategies or techniques approximating the way systems evolve towards more complex techniques and functionality:
Quality assurance techniques may significantly contribute towards enhancing the credibility of ICT tools developed at the interfaces between science and policy. The HarmoniQuA web site (http://harmoniqua.wau.nl/) on quality assurance for catchment and river basin modelling can provide practical materials which can be useful also for DSS development in water management.
Building lasting partnerships with research teams who developed the system makes it easier to adapt the system to changing conditions. This must be an iterative process. Adaptation is easier if you are supported by expert knowledge (e.g. gained though your active involvement in the system’s development). Quite often pre-existing tools have been proposed to potential end users without an adequate adaptation to the specific needs.
DSS tools should facilitate the work of end users: the effectiveness may increase if they are adapted to build upon the existing data bases and provide outputs specifically designed to meet the formal requirements of the administration.
As the development advances, needs, constraints, etc. can be specified in more depth. These specifications include:
Since it is often difficult for various reasons to specify the requirements on DSS in depth at the beginning, it is recommended to design the development process with enough flexibility (see next paragraph) to accommodate required reasonable changes. For an example of requirement analysis refer to the Harmon-IT report .
Users’ requirements may include the translation of the interface in the local language (see also recommendation 4 & 16).
As the DSS development process progresses, the changes to system specification become increasingly costly and require more time. Flexibility can be enhanced e.g. by taking into account up-to-date technical standards for data description (metadata), data exchange (e.g. exchange of spatial data – OPENGIS standards), system description (UML) or component technology. Designing open source systems or providing the systems at no or low costs increases the number of developers and users.
For one of these examples the reader may refer to the Harmoni-IT report on OpenMI . DSS tools should be adapted to the availability of data (see also recommendation 1), but also to cope with unexpected lacks of information. In those cases the ability to implement expert knowledge in the process is of fundamental importance.Uncertainty pervades all aspects of environmental policy making. One reason is that policy outcomes are only partly predictable and their associated uncertainties are large enough to sustain persistent conflicts and indecision. Related to this is the tendency for scientists to conceal uncertainty for fear of diminishing their professional credibility and encouraging indecision . It is also because:
The HarmoniCA guidance on Uncertainty Analysis can provide practical materials for further reading The adoption of the DSS should encourage the competent administration to deal with the various sources of uncertainty and include their treatment in the communication of results.
The tools per se do not guarantee the quality of the process. DSS should be framed within methodological frameworks in which all the phases and components of the policy/decision making process are considered. The recent research projects, in particular those supported within the EU Research Frameworks have delivered a variety of methodological proposals in this field.
Various attempts have been made with the aim of facilitating the dissemination of tools (see for instance the WISE-RTD portal at http://www.wise-rtd.info/) but also of the methodological frameworks: the NetSyMoD portal is an example (http://www.netsymod.eu/), the Guidance for Integrated Deliberative Decision Processes developed by the Project Advisor (http://ecoman.dcea.fct.unl.pt/projects/advisor/iddp/).
(15) Provide effective documentation of the limitations of the results providedDSS, like simulation models, encode knowledge, but they do so selectively, leaving out what is not perceived as relevant and what cannot be formalised. Preserving the relevant features, while discarding the unnecessary complexity can be the main strength of models and DSS, but it should be carefully managed. The choice of what is relevant and how this choice is made is crucial.
The communication of the process and its outcomes is always a very challenging issue in which the subjectivity and bias of the users and developers may play a relevant role.
The quality of the DSS results is determined by the quality of the decision processes and the usefulness of the outcomes depends on the quality of the communication (and training) strategy.The results of DSS applications should be adequately documented, and assumptions, subjective choices and uncertainties of various kinds should be transparently communicated with charts, tables, statistical annexes. Such documentation should allow interested people to go deeper step-by-step in the understanding of all the details of the decision process.
The final delivery package should include handbook, methodological guide, tutorial and other additional features to increase the success prospects of the system and reduce unintended misuse of the system. The inclusion of application examples can be very helpful in this regard. It is also recommended to provide the opportunities for collecting feedback and assessing the satisfaction with the tool.
An online discussion forum where the users can post their questions and report about problems/issues they experienced can be a valuable support for both the developers and users. (several examples could be mentioned in particular in cases of fora/lists connecting the user’s communities, for example in the case of Riverware)
Through training, the familiarity with the system increases as does the perceived usefulness of the system. Training should not be restricted to explaining how to use the software, explanations of the theoretical background and methodological strengths and weaknesses are vital for correct usage of the system. Clarifications of what the DSS cannot provide can also be helpful.
Training of users may become a crucial aspect to allow the reuse of tools outside and after the initial development environment
The most valuable result of DSS is the understanding of the problem at hand in its complexity. Even if the DSS yields practical recommendations such as ranking of various policies, these should not be adopted uncritically.
The success of the DSS implementation and application does not depend only upon the adoption of the results. The improvement of the quality of the decision process is the main indicator of success.
Even the case of a DSS becoming obsolete because the end user has learned (by using) its logic and is able to apply it on his own (without the tool), could be considered a successful case.
DSS is hardly ever finished. In order to remain useful, it has to be continuously updated. Maintenance means the commitment of the developer to assist users beyond the development project and after the final version of DSS has been released. This requirement can hardly be satisfied with the usual structure of research grants which are limited in time to 3-4 years and do not provide specific opportunities for follow up activities, such as the maintenance and dissemination of the products, training activities, etc.
Many DSS developed in academic environment lack long-term commitment and consequently, their usability decreases in time. Development of lasting partnerships is beneficial for both developers and users.
Various strategies are possible, at the EU level, in order to provide opportunities for longer time development and maintenance:
It is clear the current system of research grants is not designed for supporting the transfer of technology from the academic environment to the policy sphere. The end of the research grant often coincides with the release of the prototype. A follow-up phase would be necessary for implementing actions targeted to the release of the operational tool and its dissemination to potential users.
DSS end users identified the need for proposals for technology transfer and for supporting the development from the prototypes to the operational tools. Moreover specific dissemination efforts are required (e.g. “a travelling team of DSS experts who can visit staff on the field and work out case-studies together.
This work contains many suggestions and practical advice that can be useful for future DSS developments, but it clearly does not provide a detailed recipe for success. Although many ingredients of such a recipe are known (e.g. early end-users’ involvement or system flexibility), their exact dosage (quantity and quality) is to a large extent context-dependent. What has worked out in one context is no definite guarantee for a success in a different context.
Nevertheless, we believe that the present work may contribute to fill a specific gap of knowledge on recent experiences in DSS developments and applications in the water management field, leading to the definition of a set of guidelines that can be considered valid in general, and helping to avoid past mistakes, knowing very well that they should be significantly tailored and adapted case by case. Therefore, the recommendations reported in Section 7 should be considered a checklist or to-bear-in-mind caution which is appropriate for any effort to employ formal models and computerised tools in the context of policy/decision-making processes.
We should always remember that policy and decision makers are already solving their relevant decision problems. Analysing how the decision process is currently carried out is always a prerequisite for DSS development. When the current practices are unstructured and/or non-transparent, the DSS potential increase, given that there is a sufficient willingness to change. Moreover, it is known that different decision makers may lead to different conclusions for the same case. Similarly it should be expected that different results can come from the use of alternative models and DSS tools, but also from the use of the same tools by different users.
This document has benefited from the revisions of numerous colleagues in Europe and elsewhere. During the revision process many suggestions have been collected. Most of them have been implemented as far as possible in this final version, others went well beyond the limits the work and they contributed to the list of activities to be left to future – research – work that we list below.
| Tools | Developed by | Project |
| 1 WSM DSS | National Technical University di Athens, Greece; Ruhr-University, Germany; ProGEA S.r.l., Italy | WaterStrategyMan (EU) |
| 2 WATERWARE | Environmental Software and Services (ESS), Austria | Integrates results of the EUREKA project EU487 and related projects |
| 3 ELBE-DSS | Research Institute for Knowledge Systems (RIKS), Maastricht NL; Dept. of Water Engineering & Management, University of Twente NL; Inst. f. Umweltsystemforschung, Univ. Osnabrück ; Infram, Marknesse NL, Federal Institute of Hydrology (BfG) | Elbe-DSS |
| 4 MDSS | Fondazione Eni Enrico Mattei (FEEM), Italy | MULINO (EU) |
| 5 MONERIS | Department of Shallow Lakes and Lowland Rivers; Leibniz-Institute of Freshwater Ecology and Inland Fisheries part of the Forschungsverbund Berlin e.V., Germany | |
| 6 MERIT-DSS | The partners of the MERIT project (The guidelines for participatory use of BNs for integrated water resource management (see www.merit-eu.net). The code developer is the Machine Intelligence Group at Aalborg University, Denmark | MERIT (EU) |
| 7 RIVERWARE | Centre for Advanced Decision Support for Water and Environmental Systems, University of Colorado, USA | |
| 8 DELFT-TOOLS | Delft Hydraulics, The Netherlands | |
| 9 CATCHMODS | Integrated Catchment assessment and management centre (iCAM), Canberra, Australia | |
| 10 RAMCO | National Institute for Coastal and Marine Management and the Research Institute for Knowledge Systems (RIKS), The Netherlands | LWI-project |
| 11 MODULUS | Research Institute for Knowledge Systems (RIKS), Maastricht, The Netherlands in collaboration with the MODULUS Consortium | MODULUS (EU) |
| 12 MEDACTION | Department of Civil Engineering, University of Newcastle Upon Tyne, United Kingdom; Research Institute for Knowledge Systems (RIKS) | MedAction (EU) |
| 13 ECOVISIE | Research Institute for Nature and Forest (INBO), a scientific institute of the Flemish Government in Belgium; Ministry of the Flemish Community (AMINAL), Department of Nature Brussels, Belgium; Research Institute for Knowledge Systems (RIKS) | EcoVisie |
| 14 DADSS | Finnish Environment Institute (SYKE), Finland | VHIMA |
| 15 DANUBIA | Department of Earth and Environmental Sciences Chair for Geography and Geographical Remote Sensing, University of Munich, Germany | GLOWA-Danube |
| 16 TIDDD & DTS | Gelleshaft fur angewandte Informatik mbh (FUTURE-tec Gmbh) in collaboration with Centre d'Economie et d'Ethique pour l'Environnement et le Développement (C3ED) Université de Versailles, France; the European Commission - Joint research Centre, Institute for the Protection and Security of the Citizen. | GOUVERNe (EU) |
| 17 MIKE | Danish Hydraulic Institute (DHI), Danemark | |
| 18 BAYES NET | Duke University, Durham, NC, USA | Neuse River, Estuary Modeling and Monitoring (ModMon) |
| 19 GREATER | Procter & Gamble ETC, Belgium; ECETOC, Brussels, Belgium | |
| 20 AQUATOOL | Istitute for Water and Environmental Engineering (IIAMA), Universidad Politecnica de Valencia, Spain | |
| 21 HDSS (Hula Decision Support Sysrtem) | Faculty of Civil Engineering, Technion-Israel Institute of Technology, Israel | Hula Project |
| 22 TRANSCAT-DSS | Instituto de Soldadura e Qualidade, Portugal (Project coordinator) | TRANSCAT (EU) |
| 23 WMSS (Water Managemnet Support System) | (Bureau de recherches géologiques et minières) BRGM, France | MEDITATE (EU) |
| 24 WFD-EXPLORER | Delft Hydraulics, The Netherlands | |
| 25 MOIRA DSS | ENEA Divisione Protezione dell’Uomo e degli Ecosistemi Centro Ricerche Casaccia Roma, Italy | MOIRA (EU) |
| 26 EIADSS (The Evaluation/Prediction of Environmental Impact Assessment for Irrigation Project) | Center for Environment and Development for the Arab Region and Europe (CEDARE), Cairo, Egypt | |
| 27 REAL-LIFE Scale Integrated Catchment Models for Supporting Water- And Environmental Management Decisions | Water Resources Research Centre (VITUKI), Hungary | The Tisza River (EU) |
| 28 DECIS Decision Making / Management | Consorzio Venezia Ricerche (CVR), Venice, Italy | MODELKEY (EU) |
| 29 TWOLE | Sistemi Ambientali e Territoriali (SAT) group at Politecnico di Milano, Dipartimento di Elettronica e Informazione (DEI), Italy | TwoLe (EU) |
| 30 Dss for the Assessment of Flood Risk | HR Wallingford Ltd, Wallingford UK (Co-ordinator) | EUROTAS (EU) |
| 31 STREAMPLAN | International Institute for Applied Systems Analysis (IIASA) | |
| 32 DESERT | International Institute for Applied Systems Analysis (IIASA) | |
| 33 DSS-DROUGHT | University of Catania, Institute of Hydraulics, Hydrology and Water Resource Management (project coordinator) | DSS-DROUGHT (EU) |
| 34 Dss for fte Management of Integrated Water Resources Systems Focused to Drought Prevention and Mitigation | University of Catania, Catania, Italy (project coordinator) | WAM-ME (EU) |
| 35 Dss for Sustainable Ecosystem Management in Atlantic Rain Forest Rural Areas | Universidade Atlântica and Universität Hamburg (project coordinators) | ECOMAN |
| 36 RAMFLOOD-DSS (for Risk Assessment and Management of Floods) | Centre Internacional De Metodes Numerics en l’Enginyeria (CIMNE) Barcelona, Spain (project coordinator) | RAMFLOOD (EU) |
| 37 INFRAPLAN | Cranfield University, United Kingdom | TiGrESS |
| 38 SCAPT (Strategyc Catchement Analysis Planning Tool) | Cranfield University, United Kingdom | AQUADAPT |
| 39 Decision Support System for the Effective Management of Freshwaters | ULIV - University of Liverpool, United Kingdom | EURO-Limpacs |
| 40 DSS FOR URBAN SYSTEM ASSESSMENT | AGK - Applied Geology Karlsruhe, University of Karlsruhe, Germany | AISUWRS (EU project) |
| 41 ADSS (Adaptive Decision Support System) | CEREVE, Centre d'Enseignement et de Recherche sur l'Eau, la Ville et l'Environnement; Common research centre UPVM, ENPC, ENGREF Ecole Nationale des Ponts et Chaussées (ENPC), France | DayWater (EU) |
| 42 Decision Support Tools for Sustainable Water Network Management | CARE-W consortium | CARE-W (EU) |
| 43 DSS for Rehabilitation of Sewer Networks | CARE-S consortium | CARE-S (EU) |
| 44 DSS for Flood Prevention And Protection | Council for The Central Laboratory of The Research Councils, Department of Information Technology Department, Rutherford Appleton Laboratory, United Kingdom | ANFAS (EU) |
| 45 GIBSI (Gestion Intégrée D des Bassins Versants à l'aide D'un Système Informatisé) | Institut national de la recherche scientifique (INRS), Centre Eau, Terre & Environnement, Québec, Canada | |
| 46 NELUP-DSS | University of Newcastle. Dep. Of Civil Engineering, | NERC-ESRC Land Use Programme (NELUP) |
| 47 RAISON | NWRI Software National Water Research Institute, Canada Centre for Inland Waters, Burlington, Ontario, Canada | |
| 48 FLOODWORKS | Wallingford Software Ltd, Oxfordshire, United Kingdom | |
| 49 WAMADSS (Watershed Management Decision Support System) | Centre for Agricultural, Resources and Environmental systems (CARES) University of Missouri, Columbia, USA | |
| 50 WATERSHEDSS (Water, Soil, And Hydro- Environmental Decision Support System) | NCSU Water Quality Group at North Carolina State Universityand the Agricultural and Biological Engineering Department at The Pennsylvania State University, USA | |
| 51 ILRDSS (Illinois River Decision Support System) | Illinois State Water Survey; Illinois Natural History Survey; Illinois State Geologic Survey, and Waste Management and Research Centre; Illinois Department of Natural Resources, USA | |
| 52 MODSIM-DSS | Colorado State University, Civil Engineering Department, USA | |
| 53 FLOODRELIEF-DSS | Danish Hydraulic Institute (DHI) Water & Environment, Denmark | FLOODRELIEF (EU) |
| 54 REALM (RESOURCE ALLOCATION MODEL) | Department of Sustainability and Environment, Victoria, Australia | |
| 55 BASINS (BETTER ASSESSMENT SCIENCE INTEGRATING POINT AND NONPOINT SOURCES ) | US Environmental Protection Agency (EPA), USA | |
| 56 IQQM (INTEGRATED QUANTITY AND QUALITY MODEL) | Department of Infrastructure, Planning and Natural Resources (DIPNR), Australia | |
| 57 WEAP (WATER EVALUATION AND PLANNING SYSTEM) | The Stockholm Environment Institute, Hydrologic Engineering Center of the US Army Corps of Engineers funded significant enhancements. | |
| 58 OASIS (STAKEHOLDER TOOL FOR WATER RESOURCES) | HydroLogics, Inc., USA | |
| 59 DELFT-FEWS (FLOOD EARLY WARNING SYSTEM) | Delft Hydraulics, The Netherlands | |
| 60 MADWICA DSS | Aquatest S.A., Geologická, Czech Republic | EUREKA E! 2721 MADWICA (EU) |
Primary scope/domain: The Catchment-scale Management Of Diffuse Sources (CatchMODS) model aims to assist users to identify appropriate forms of management intervention to reduce nutrient inputs to the Ben Chifley Dam Catchment (BCDC). CatchMODS also provides a vehicle for communication and interaction with managers and the catchment community.
Technical design: CatchMOD is a scenario-based modelling tool. Several of the processes represented in the SedNet model are integrated (Prosser et al., 2001) and the IHACRES model (Jakeman et al., 1990) is used to estimate surface and sub-surface discharge in the modelling network. CatchMODS includes two nutrients submodels that simulate total nitrogen (TN) and total phosphorus (TP) export and an in-reservoir plankton-response model (specific to the Ban Chifley Dam catchment). A number of socio-economic factors are considered in the model, mostly the costs associated with the available preventative and remediation options.
The model operates with two modelling platforms. Spatial data pre-processing is undertaken using GIS-based queries for each subcatchment and river reach. The other modelling platform used is the Interactive Component Modelling System (ICMS). ICMS is used to perform the modelling and deliver an integrated Decision Support System. ICMS is an object-oriented modelling environment incorporating various models and data used in catchment management modelling. CatchMODS is based on a series of linked river reaches and associated subcatchment areas. A user interface has been developed for the BCDC application of CatchMODS and can be readily modified for new model applications.
Support provided: CatchMODS is designed to support stakeholders focus remediation on specific subcatchments and encourage sustainable management practices more broadly in the catchment. It enables development and testing of realistic land management change scenarios, and has provided valuable information on likely adoption rates for recommended management practices.
Distribution and maintenance: CatchMODS and the associated ICMS software are freely available under licence. To encourage appropriate use of the DSS its distribution is limited to those who have participated in specific CatchMODS training workshops. In the interests of transparency, the underlying code of the DSS can be accessed by all model users but modification is discouraged. Further development of CatchMODS DSS is in progress with the objective of expanding the capabilities of the modelling and increasing confidence in model outputs.
Users and applications: CatchMODS has been used to identify subcatchments that have high pollutant source and transport strengths relative to the remainder of the BCDC. Prototypes of the model have also been developed for several other catchments including the Moruya River and Colia Lake catchments, NSW and the Cox Creek catchment, SA.
Documentation: CatchMODS is distributed with a user guide including a description of the underlying model and tutorials on how to construct scenarios and examine results.
Main scientific papers:
L.T.H. Newham, A.J. Jakeman and R.A. Letcher (submitted) End-User Participation in Modelling for Integrated Catchment Assessment and Management: An Australian Case Study in Participation, International Journal of River Basin Management;
Newham, L.T.H., A.J. Jakeman and R.A. Letcher (2006) Stakeholder participation in modelling for integrated catchment assessment and management: An Australian case study. Intl. J. River Basin Management Vol. 4, No. 3, pp. 1–13.
L.T.H. Newham, R.A. Letcher, A.J. Jakeman, T. Kobayashi (2004). A framework for integrated hydrologic, sediment and nutrient export modelling for catchment-scale management, Environmental Modelling & Software, 19: 1029–1038;
Development: The Ben Chifley Dam catchment study was assisted by the NSW Government through a grant from their Environmental Trust. The Department of Environment and Conservation (DEC) a NSW Government agency established a water quality monitoring program to support the development of the CatchMODS model. The development of CatchMODS was supported by close user participation during the case study.
Primary scope/domain: The tool’s main purpose is the implementation of an IRBM tool in the German part of the Elbe river basin. The system addresses water quantity, chemical quality, highwater and navigation related aspects and ecological status of waters and the floodplain. The interaction of management objectives, external scenarios of climate, agro-economic and demographic change, and selected measures to achieve the desired state of good water quantity and quality, flooding safety and the ecological status of floodplains is investigated.
Technical design: The Elbe-DSS contains a user-oriented interface (in German), which allows handling of models and access to GIS-based information. A first, partly translated version in English is integrated. A modular, scale-related system diagram was drawn up that includes the modules "catchment", "river network", "main channel" (river plus flood-plain), and a 10-km "river section" including the floodplain) in the present version. These modules work in different spatial and temporal resolutions. The users of the DSS may select and enter planned "measures" by which they want to achieve their "management objectives". The previously defined "indicators" describe the degree of attainment of an objective. With these indicators, the user can have the consequences of his/her activity policy displayed and assessed. Additionally, the user may select previously computed "external scenarios", such as climate-change or land-use scenarios. Evaluation tools have been provided for various kinds of decision-making, e.g. risk-based for hazardous pollutant concentrations, monetary-based for engineering measures or ecological services for floodplain restoration. Different models are integrated, for example a model for the calculation of the long-term nutrient discharges from non-point sources (MONERIS) and a simulation model for wastewater pathways (point sources) and aquatic fate assessment (GREAT-ER). Further different models for hydrological dynamics were integrated. To set up all tools and models within one system the DSS-generator software GEONAMICA, developed by RIKS, and additional software was used. The Elbe-DSS is a PC application for Windows NT, 2000, XP. Under Windows NT at least 256, better 512 MB, are recommendable; under Windows XP at least 512, better 1 GB. Microsoft Excel (>2000) and Internet Explorer need to be installed at the users’ PC.
The Elbe-DSS is available in a pilot-version. It will be further developed in future.
Support provided: The DSS has the purpose to facilitate the assessment of the impacts of management options. It highlights cause-effect relations and the sensitivity of different actions. To this end the DSS structures complex problems. Sectoral knowledge and available models are pooled within a basic framework. By applying a customised user interface they are made applicable in the management context. It supports the evaluation of the policy impact and scenario development. It is also intended to support participative decision making.
Distribution and maintenance: The Elbe-DSS is freely downloadable at http://elise.bafg.de/?3283. The pilot-DSS offers an operable system with an interface that is tailored to the specific requirements of users. It will be appropriate to speak of a genuine DSS for real-life decision-making when up-to-date data of the users are provided and functionalities are finally adapted. This implementation process is ongoing.
Users and applications: Potential end-users of the DSS include Internationale Kommission zum Schutz der Elbe (IKSE), Arbeitsgemeinschaft zur Reinhaltung der Elbe (ARGE-Elbe), Ministries of the “Länder” in the catchment area, Biosphere Reservations, Water management associations and authorities, Federal Waterways and Shipping Directorates (WSV), Federal Institute of Hydrology (BfG), Federal Waterways Engineering and Research Institute (BAW), Federal Ministries of Agriculture and Environment (BML, BMU), Federal Environmental Agency (UBA)
Documentation: A context-sensitive library function is generated by means of an on-line help format (WinHelp, HTMLHelp). It is structured through hypertext and internet links and allows rapid access to the desired information. For all integrated models, the scientific approaches are described, and instructions how to handle the DSS features are given. Additionally a number of documents describing different phases of the development are available on the Elbe-Informations-SystEm (ELISE) web site (http://elise.bafg.de/?3283).
Main scientific papers:
Kok, J.L. de and Kofalk S., 2006. Towards a user-oriented design of DSS for integrated river-basin management: the Elbe DSS prototype, In: Voinov, A., Jakeman, A., Rizzoli, A. (eds). Proceedings of the iEMSs Third Biennial Meeting: "Summit on Environmental Modelling and Software". In-ternational Environmental Modelling and Software Society, Burlington, USA, July 2006. CD ROM. Internet: http://www.iemss.org/iemss2006/sessions/all.html
Matthies, M., Berlekamp, J., Lautenbach, S., Graf, N. and Reimer, S., 2006. System analysis of water quality management for the Elbe river basin. Environmental Modelling & Software, 21, (9): 1309-1318.
Berlekamp, J., Lautenbach, S., Graf, N., Reimer, S. and Matthies, M. 2005. Integration of MONERIS and GREAT-ER in the decision support system for the German Elbe river basin. Environmental Modelling & Software, In Press, Corrected Proof;
Kok, J.L. de and Huang, Y., 2005. Assessment of Flood Risk at Various Scales The Elbe prototype DSS. In: Van Alphen, Van Beek, and Taal (Eds.), Floods, from Defence to Management, Symposium Papers of the 3rd International Symposium on Flood Defence, Nijmegen, The Netherlands, 25-27 May 2005, Taylor & Francis Group, London.
Development: The Federal Institute of Hydrology (BfG) initiated the design and development of the Elbe-DSS. The consortium was comprised of Bundesanstalt für Gewässerkunde, INFRAM, RIKS, Universität Osnabrück, Universität Twente. A feasibility study was completed, before the development process started. User needs were identified and refined by repeated consultation of water managers. A list of management objectives, measures, and external scenarios emerged, which was taken as the basis for the DSS development.
Primary scope/domain: The KerDST (Deliberation Support Tool) is designed to function as a complement to scenario modelling and spatial (e.g. GIS based) representations and integrates the KerBabel Deliberation Matrix with its associated KerBabel Indicator Kiosk (KIK). The Deliberation Matrix permits a comparative multi-criteria multi stakeholder evaluation of scenarios for regional futures or for farm practices, and the KerBabel Indicator Kiosk helps make feasible an accessible and comprehensive documentation of information sets used in modelling and maps. The Deliberation Matrix presents, in a synthetic way, the array of judgements offered by stakeholders concerning alternative perspectives on management of the environmental resource. KerDST is applied in projects that deal with agriculture activities and choices at different spatial scales (farm scale, district or sub-catchment, and finally regional scale) and also contrasting timescales (long term planning or on-going monitoring).
Technical design: In the Deliberation Matrix, the scenarios of distinct possible futures are evaluated from distinct stakeholder perspectives. A small number of major stakeholder categories are specified, based on institutional and interview analyses. The procedure of evaluation allows for the existence of diversity within each major stakeholder category. The stakeholders’ may make their evaluations of each scenario in terms of a variety of different criteria The criteria are grouped into a small number of ‘baskets’ corresponding to distinct governance issues. The KIK is, in itself, a generic deliberation support tool whose meta-information structure addresses the contexts of indicator use and pertinence as well as the more traditional domains of information sources. The Deliberation Matrix and the KIK are seen as naturally coupled, and the KerDST system establishes this coupling for use on-line. System’ requirements: Firefox browser Macromedia Flash Player 7 and up.
Support provided: The KerBabel Deliberation Matrix provides the framework for a qualitative multi-stakeholders multi-criteria comparison of scenarios (or sites or options, etc.) by a single person or a community of users. The integrated KerDST Deliberation Matrix with associated Kiosk (KIK) allows multi-criteria evaluation to be supported by indicators and, in the “participatory option” allows judgements by each category of stakeholders to be produced as a composite outcome of multiple participants. Ideally, these tools help to develop and then maintain involvement of members of the business and wider communities around the planning or business choice questions (such as taking environmental quality into account in farming).
Distribution and maintenance: The KerDST is an on-line tool, developed with ‘Open Source’ conventions. Since 2006, the KerBabel Deliberation Matrix has been made available (http://kerdst.c3ed.uvsq.fr/). The integrated KerDST system of the Deliberation Matrix with associated Kiosk (KIK), is available on http://iacaprod.c3ed.uvsq.fr/kerdst2/.
Users and applications: The prototype kerDST system is in use in the SRDTOOLS EU project during 2005, and saw its prospects for the stakeholder based regional futures studies and sustainable agriculture evaluation work that AgResearch, New Zealand (www.agresearch.cri.nz/) is engaged in. The use of kerDST is now incorporated in several projects including “Choosing Regional Futures” (led by the Environment Waikato, the regional council concerned), and a set of industry sponsored projects intended to define methods for incorporating environmental and social performance considerations into assessment of agriculture strategies at farm, district and regional scales, e.g., river water quality and chemical runoff from intensive dairy farming.
Documentation: The system is distributed with a manual: “How to do it. A user’s Manual for KerDST Deliberation Matrix. Non Participative KerDST using the Indicator Dialog Box.” prepared by Martin O’ Connor (Université de Versailles-St Quentin en yvelines - UVSQ), Victoria Reichel and Pierre Bureau (C3ED). Detailed documentation of KerDST system can be found in a series of internal C3ED documents C3ED (e.g., Bureau, Legrand, O'Connor & Reichel, 2007).
Development: During the GOUVERNe (“Guidelines for the Organisation, Use and Validation of Information Systems for Evaluating Aquifer Resources and Needs-Funded by DG RTD, 5th Framework Programme” - Contract. No. EVK1-1999-00032) and then VIRTUALIS (“Social Learning on EnVIRonmental Issues with the InTeractive Information and CommUnicAtion TechnoLogIeS” - Funded by the EC, 5th Framework Programme, 2001-2004 -Contract IST-2000-28121), the C3ED (Centre d'Economie et d'Ethique pour l'Environnement et le Développement) and partners have developed and experimented with various aspects of on-line "deliberation support tools". KerDST has been developed and tested in the context of the SRDTOOLS project (“Methods and Tools for evaluating the Impact of cohesion policies on Sustainable Regional Development”, Funded by the EC 6th Framework Programme, 2005-2006, Contract No.502485).
Primary scope/domain: MERIT DSS is a generic integrated management tool based on the concept of Bayesian belief networks (BBNs) developed to provide an integrated and participatory water resource management methodology suitable for use throughout Europe at the river basin scale. This practical and effective methodology and guidelines for construction of BBNs with stakeholder involvement enables managers to make multi-objective decisions, ensuring that stakeholders are engaged in the participatory decision-making process
Technical design: The approach essentially consists of a decision support tool based on probability theory and Bayesian belief networks (BBNs), with guidelines for the use of BBNs as a participatory tool for Water Resource Management (Bromley, 2005). BBNs are policy development and dialogue tools able to represent the environmental system as a network of nodes linked so as to represent cause and effect within the system. Each node represents a variable and can be of any type (environmental, social, economic, etc.), and the “cause and effect” links between them can be simulated using a range of analytical techniques based on whatever data are available. The impact of a decision can be evaluated by entering the action into the relevant node (variable). This change will then have a knock on effect on all those nodes linked to it, thus the impact on the entire system can be evaluated. Results are presented as bar graphs showing the state of each variable in terms of probability distributions, thus explicitly representing the degree of uncertainty in the system. The explicit recognition of uncertainty helps decision-makers to identify more clearly the risks associated with different management strategies. A Bayesian network package called HUGIN (www.hugin.com) was used to develop the networks. The results were obtained using a procedure known as propagation. The HUGIN decision engine (HDE) performs reasoning on a knowledge base represented as a Bayesian network or an influence diagram. The HDE performs all data processing and storage maintenance associated with the reasoning process and allows application program interface or C, C++, and Java, and an Active X server. The language used in the guidelines and the documentation of the HUGIN interface is in English, but it is possible to construct the BNs using any language for the names of variables, states and documentations within the BBNs, so that the participatory use can be in any selected language.
Support provided: There is no single appropriate method of engagement replicable in all circumstances. Only the principles of engagement are replicable. Strength of BBNs are:
Distribution and maintenance: The MERIT guidelines for the use of BBNs as a participatory tool for Water Resource Management are freely available including a free HUGIN viewer for the four case studies from MERIT. HUGIN is commercial software and maintained and used for a variety of different purposes (see www.hugin.com). The MERIT guidelines and the use of HUGIN as a participatory tool is further enhanced as a fast track existing tool in the EU research project NEWATER (2005-2008).
Users and applications: The approach was applied during the project implementation in the context of four case studies: the Havelse wellfield catchment in North Zealand (Denmark) for wich Geological Survey of Denmark and Greenland (GEUS) was the project leader and Copenhagen Energy (CE) the decision-maker and end-user in relation to the BBN case study; by the Junta Central de Regantes de La Mancha Oriental (Spain) for the Jucar catchment, by the Sperimentale Italiano Giacinto Motta S.p.a - CESI (Italy) for the Vomano catchment and by Centre for Ecology and Hydrology – CEH (UK) for the Loddon catchment. The system has been used beyond the MERIT project e.g. in Denmark for the purpose of integrating hydrological and economic models.
Documentation:
Main scientific papers:
Henriksen, Hans Jørgen, Rasmussen, Per, Brandt, Gyrite, Bülow, Dorthe von and Jensen, Finn Verner, 2007. Bayesian networks as a participatory modelling tool for groundwater protection.
Henriksen, Hans Jørgen, Rasmussen, Per, Brandt, Gyrite, Bülow, Dorthe von and Jensen, Finn Verner (in prep.) Public participation modelling using Bayesian networks in management of groundwater contamination. Environmental Modelling & Software, In Press, Corrected Proof, Available online 3 April 2006.
Bromley, John, 2005. Guidelines for the use of Bayesian networks as a participatory tool for Water Resource Management. Based on the results of the MERIT project. CEH, Wallingford, UK.
Hans Jørgen Henriksen ed., 2004. Test of Bayesian belief network and stakeholder involvement, Ministry of Environment Geological Survey of Denmark and Greenland (GEUS) pub. (www.geus.dk)
Castelletti, Andrea and Rodolfo Soncini-Sessa: Topics on system analysis and integrated water resources management. Elsevier book. Chapter 3.
Development: the system developed by five MERIT’s partners: Centre for Ecology and Hydrology (CEH), the Department of Computer Science, Institute for Electronic Systems, Decision Support Systems Group, Aalborg University, Denmark; the Ministry of Environment, Geological Survey of Denmark and Greenland (GEUS); the “Universidad de Castilla la Mancha” (UCLM), Spain; the “Sperimentale Italiano Giacinto Motta S.p.a”, Milano, Italy (CESI). Copenhagen Energy (CE) was involved in the context of the Danish case study as the decision-maker and end-user. MERIT is a research project supported by the European Commission under the Fifth Framework Programme (Contract no.EVK1-CT-2000-00085: June 2001-May 2004).
Primary scope/domain: The MONERIS model (Modelling Nutrient Emission in River Systems) was developed in order to estimate nutrient emissions into river systems from point sources (waste water treatment plants and direct industrial discharger) and six diffuse pathways (atmospheric deposition, erosion, surface runoff, tile drainage, groundwater and paved urban areas).
Technical design: The model is conceptual, not dynamic, and designed for large river basins. It calculates total nitrogen and total phosphorus emissions from point and diffuse sources, riverine retention and resulting loads for single years or as mean for periods. The model uses information on river flow (from gauging stations), water quality (nutrient concentrations from monitoring stations), statistical data (inhabitants, sewer systems, etc.), GIS-related geographical data (stored and analysed in a GIS). It will estimate equilibrium conditions for a hydrological period. The model runs under MS-Excel 2000 or later.
Support provided: MONERIS can help managers identify proportion of pathways on the total nutrient emissions and helps to identify those to be targeted for management practices aimed at nutrient emission reduction. Combined with geographic information, it can help identify hot spots within the catchment - particular areas that, due to a combination of high potential emission and a susceptibility to efficient transport, contribute nutrients significantly more than other areas. MONERIS provides several management scenarios to reduce nutrient emissions. For example, a manager can ask by how much nutrient emissions into the river would be reduced under a scenario of erosion control.
Distribution and maintenance: MONERIS runs under EXCEL. The files are freely available by contacting Horst Behrendt at the IGB. The IGB tries to stay in close contact with people applying the model, giving explanations on the model and discussing the results
Users and applications: was applied in the Po River Basin (Artioli Y. et Al. (2005), Palmeri L. et Al. (2005)). MONERIS is used by the International Commission for the Protection of the Danube River (ICPDR) and Danube governments to assess nutrient emissions into 388 sub-basins in the Danube River Basin. It is integrated in the Elbe-DSS. MONERIS has been applied to catchments all over Europe. Within the European projects EUROHARP, BUFFER, STREAMS, DANUBS, EUROCATS and Lake Promo or by NATO-CCMS as well as national projects the model has been applied, tested and compared against different input data and other models calculating nutrient fluxes in river systems.
Documentation:
Main scientific papers:
Artioli, Y., Bendoricchio, G. and Palmeri, L., 2005. Defining and modelling the coastal zone affected by the Po river (Italy), Ecological Modelling, 184(1): 55-68;
Behrendt, H. and Dannowski, R.., 2005. Nutrients and Heavy Metal in the Odra River System – Emissions from Point and diffuse Sources, their Loads, and Scenario Calculation on possible Changes. Weißensee Verlag, Berlin, 353 pp.
Behrendt, H., van Gils, J., Schreiber, H. and Zessner, M., 2005. Point and diffuse emissions and loads in the transboundary Danube River Basin. – II Long-term changes. Large rivers, 16,(1-2): 221-247;
Palmeri, L., Bendoricchio, G. and Artioli, Y., 2005. Modelling nutrient emissions from river systems and loads to the coastal zone: Po River case study, Italy, Ecological Modelling, 184, (1): 37-53;
Schreiber, H., Behrendt, H., Constanttinescu, L. T., Cvitanic, I., Drumea, D., Jabucar, D., Juran, S., Pataki, B., Snishko, S. and Zessner, M., 2005. Point and diffuse emissions and loads in the transboundary Danube River Basin. – I A modelling approach;
Behrendt, H., Huber, P., Kornmilch, M., Opitz, D., Schmoll, O., Scholz, G. and Uebe, R., 2000. Nutrient emissions into river basins of Germany. UBA-Texte 23/00: 1-288;
Behrendt, H. and Bachor, A., 1998. Point and diffuse load of nutrients to the Baltic Sea by river basins of North East Germany (Mecklenburg-Vorpommern). Water, Science and Technology (28), 10;
Development: The Model was developed at the Leibniz Institute for Freshwater Biology and inland Fisherie Berlin in the Department of Shallow Lakes and Lowland Rivers.
(http://www.igb-berlin.de/weborg/frames_abtlg/igbabtframeset_e.htmlwas). For the development users have not been involved directly.
Primary scope/domain: Mulino DSS (also mDSS) is a generic decision support system developed to assist the implementation of the Water Framework Directive and the development of the River Basin Plans. It is applicable to a range of decisions - choices from predefined policy options. The main focus is on selection of measures and development of programmes of measures to meet the WFD objectives. Gaps addressed: lack of operational applicability of the IWRM concept, integration of different aspects of the problem at hand and impacts of proposed policies.
Technical design: The DSS consists mainly of methods and techniques to elicit, scrutinise and aggregate decision preferences. Various multiple criteria decision techniques (such as TOPSIS, OWA, ELECTRE, group decision techniques) are implemented and extended with techniques for sensitivity analysis and sustainability impact assessment. The DSS has interfaces to environmental models so that their predicted impacts can be incorporated in the evaluation and preference elicitation. Interface of the mDSS is built upon the DPSIR framework which guides the problem formulation and exploration. The newly released version mDSS4 is integrated within a framework (NetSyMod, see www.netsymod.eu) which contains various analyses (not encoded in the software) on the results on which the mDSS builds. The integration is facilitated in the policy assessment, taking into account various aspects of the problem at hand.
The current system is distributed in the English language only, an earlier version includes a contextual help in various languages. It requires Windows operational system, starting form Win 2000; ca. 30 MB disk space and 128MB RAM. The version 4 requires MS .NET framework to be installed. The system reads GIS data in SHAPE and ASCI formats. Implementation of OpenMI standard is envisaged.
Support provided: mDSS supports mainly the identification and evaluation of policies, and it builds on insights gained through stakeholders’ involvement. The NetSyMod framework on the other hand facilitates collective learning and relationship building, and by doing so increases prospects for compromising different expectations/interests.
Distribution and maintenance: The DSS is freely available; a copy can be obtained form http://www.feem.it/mulino (under the general public license). The system is a stand-alone software working under the Windows (2000, Me, XP) operational system. The system is maintained and further developed by FEEM (www.feem.it).
Users and applications: The system was developed for water authorities dealing with water policies but the use of the software is not restricted to this user group. It was applied in a range of case studies 5 (see http://siti.feem.it/mulino/exesum/exesumm.pdf). Later the system was incorporated also in the Transcat DSS and applied in additional case studies. The software is being used for educational purposes in The Netherlands.
Documentation: The system is distributed with a tutorial (a case study developed with the help of the system), user guide and a description of the methodologies applied (decision guide).
Main scientific papers:
Giupponi, C., 2006. Decision Support Systems for implementing the European Water Framework Directive: The MULINO approach. Environmental Modelling & Software, In Press, Corrected Proof.
Mysiak, J., Giupponi, C. and Rosato, P., 2005. Towards the development of a decision support system for water resource management. Environmental Modelling & Software, 20(2): 203-214;
Giupponi, C., Mysiak, J., Fassio, A. and Cogan, V., 2004. MULINO-DSS: a computer tool for sustainable use of water resources at the catchment scale. Mathematics and Computers in Simulation, 64(1): 13-24;
Development: The system was developed by an international research consortium (9 institutions from 5 European countries, see http://siti.feem.it/mulino/home/consort.pdf). The development was funded by the EC under the FP5 (Energy, Environment and Sustainability Development programme, key action water). The development was driven by a close involvement of 13 policy makers/stakeholders form 7 various water authorities who helped to specify the requirements and, later, applied the system.
Primary scope/domain: RiverWare provides a modelling environment to meet the modelling needs of managers and operators of river and reservoir systems. It allows the management of multiple system objectives as water supply, flood control, navigation recreation, fish and wildlife habitat and hydropower production. RiverWare simulates the hydrologic processes of the river and reservoir system. In simulation the solution is driven by logical operating rules.
Technical design: RiverWare is a general river basin modelling tool framework with a user-friendly point-and-click GUI. The basic building blocks of a RiverWare river basin model are objects which represent the features of the river basin such as reservoirs, river reaches, consumptive users, canals, shallow groundwater aquifers, etc. The objects (icons on the workspace) contain their own data and physical process algorithms. The selection of solution algorithms are all provided through a user-friendly interface (currently in English only). In simulation, the solution is driven by input data and/or prioritised logical operating rules. In optimisation, prioritised objectives/constraints are solved with a linear goal programming solution using CPLEX (must be licensed separately). RiverWare automatically generates the physical constraints and linearisations. Operating rules and objectives are formulated in the RiverWare Policy Lanaguage (RPL), an interpreted language, in a syntax-directed editor. RiverWare also models water ownership and accounting and can allocate flows to satisfy prioritised water rights. RiverWare has numerous features for ease of use such as multiple run management for generation of probabilistic results, linking with any external data source, spreadsheet-like views of data, and sophisticated analysis facilities.
RiverWare is a c++ application which runs on Sun Solaris (Unix) workstation with Solaris 2.7 or higher operating system, or Windows NT / 2000 / XP.
Support provided: The RiverWare software can be applied to river basins for operational scheduling, forecasting and planning applications, for evaluation of new projects, yield studies, and policy analysis and evaluation. Participating agencies and stakeholders can share models, modelling scenarios and model results, facilitating prediction of proposed (or alternative) policy outcomes and communication among agencies and stakeholders. RiverWare is used in interactive meetings with stakeholders to explore possible outcomes of proposed alternatives. Uncertainty analysis is achieved by probabilistic representation of the outputs of multiple (stochastic) hydrologic sequences enabled by RiverWare's Multiple Run Manager and the Graphical Policy Analysis Tool (GPAT). Policy options are identified by use of the interactive policy language that controls simulation, or via the Goal Programming module which considers hard and soft policy constraints and objectives to determine best operations.
Distribution and maintenance: By agreement with the sponsoring agencies, RiverWare is licensed through the University of Colorado Technology Transfer Office. License revenues contribute to software maintenance. RiverWare is currently licensed to more than 60 water management agencies, consultants, universities, research institutes and foreign entities. User and installation support are provided and a web-based bug-tracking facility allows users to log problems directly CADSWES offers “Introduction to Simulation Modelling” and “Rulebased Simulation Modeling”, both 3-day training classes. An annual Users meeting is organised. One or two major releases are made each year. RiverWare is under constant enhancement and new development to meet the evolving needs of users.
Users and applications: The tool has been applied to numerous basins by the sponsoring agencies (the U.S. Bureau of Reclamation (BOR), the Tennessee Valley Authority (TVA) and the U.S. Army Corps of Engineers) and by the many water management agencies and consultants who license the software. RiverWare is used on a daily and annual basis for operations and planning on the Colorado River, the Rio Grande, the Tennessee Valley and many other river systems. New applications are frequently initiated.
Documentation: RiverWare is provided with an online Help. Additional documentation is provided with the training manuals.
Main scientific papers:
Zagona, E., T. Magee, D. Frevert, T. Fulp, M. Goranflo and J. Cotter, 2005. RiverWare. V. Singh & D. Frevert (Eds.), Watershed Models, Taylor & Francis/CRC Press: Boca Raton, FL, 680pp.;
Magee, Timothy M., H. Morgan Goranflo and Suzanne H. Biddle, 2002. Optimizing Daily Reservoir Scheduling at TVA with RiverWare, Proceedings of the Second Federal Interagency Hydrologic Modeling Conference, Las Vegas, NV;
Eschenbach, Elizabeth A., Timothy Magee, Edith Zagona, Morgan Goranflo, and Richard Shane, 2001. Goal Programming Decision Support System for Multiobjective Operation of Reservoir Systems, Journal of Water Resources Planning and Management, ASCE 127(2): 71-141;
Magee, Timothy M., Edith A. Zagona, Donald Frevert, 2001. Operational Policy Expression and Analysis in the RiverWare Modeling Tool, Proceedings of the ASCE World Water & Environmental Resources Congress, Orlando, FL.
Zagona, Edith A., Terrance J. Fulp, Richard Shane, Timothy Magee, H. Morgan Goranflo, 2001. RiverWare: A Generalized Tool for Complex Reservoir Systems Modeling, Journal of the American Water Resources Association, AWRA 37(4):913-929.
Zagona, Edith A., Terrance J. Fulp, H. Morgan Goranflo and Richard M. Shane 1998. RiverWare: A General River and Reservoir Modeling Environment, Proceedings of the First Federal Interagency Hydrologic Modeling Conference, Las Vegas, NV.
Development: RiverWare is under continued development at CADSWES, funded by the R&D sponsors (TVA, Reclamation and Corps of Engineers). It is the intention of CADSWES and the sponsors to maintain the software in the future and provide enhancements as requested. The RiverWare Viewer is a cost-free version that permits viewing and post-processing of model results.
Primary scope/Domain: TwoLe supports the realisation of River Basin Management Plans as foreseen by the Water Frame Directive (2000/60/EC), in accordance with the IWRM (Integrated Water Resources Management) paradigm, the framing and exploration of Multi-Objective problems, with multiple Decision Makers and with uncertainty; the integration of long-term planning and short-mid-term management within the decision-making process.
Technical design: TwoLe supports multi-objective decision-making processes (MODSS, Multi Objective Decision Support System) and is structured on two integrated levels, which emulate the structure of a Water Agency: the planning and the management levels. It follows the phases of the Participatory and Integrated Planning (PIP) procedure (see References below) and provides methods and tools to support the activities foreseen within each phase. It is based on a multi-channel architecture which takes the form of a series of modules interacting in a single environment and makes available various services through different media to respond to the user’s requirements.
The planning level is constituted of a stand-alone application which offers the users the support tools for the various phases of planning. In particular, the conceptualisation phase allows the user to integrate different models into the system: those present within the software environment can be supplemented by others possibly remotely available, and these can interact with the system through ad hoc XML data exchange protocols. The GUI, implemented in JAVA, controls dedicated computation engines (of identification of PARMAX class models, of optimization of policies through Dynamic Programming, and of deterministic, Markovian or Monte Carlo simulation) implemented in C++ and allows the use of a library of functions, expandable by the user, for the calculation of evaluation indicators. This library is written in MASTRAVE, a restriction of the MATLAB® syntax specifically created to be compatible with its open-source clone OCTAVE. To explicitly represent the possible conflicts between the indicators and to make clear and transparent the subjective elements introduced by stakeholders, TwoLe provides the Multi Attribute Value Theory (MAVT), the Analytic Hierarchy Process (AHP) and the ELECTRE family of methods. The negotiation process is then supported by two approaches: the step-by-step approach, in particular negotiations on weights, and negotiations on ranking, based on the Pareto Race, a technique which drives the stakeholders in search of possible compromises in the exploration of efficient solutions.
The management level receives the reference framework (i.e., decisions, data and models) from the planning level (a priori hypothesis) via the sharing of the data structure. This part of the application thus appears as a web-service. Lastly, the results of the experiments can be compared in appropriate synoptic reports and made available. The experimentation environment is strictly linked to that of remote negotiations, which take the form of video chat. The components of TwoLe exposed to external access are protected by implementing appropriate ACL (Access Control Lists) which are extremely granular: they in fact allow the attribution of rights of writing and/or reading at the level of individual service and individual user. The creation of a TwoLe interface to GIS (in particular GRASS) is still in the design phase.
Support provided: TwoLe assists the planning procedure in all its phases. In fact it permits the user to: construct evaluation hierarchies, identify stochastic inflow models, and construct networks of interconnected models to represent water systems, design of alternatives, normative and structural actions, evaluate performance for different scenarios, compare them from different points of view, and assist negotiations for the search for compromise alternatives. TwoLe implements the planning policy in the short-medium term management, assisting the DM in the choice of the decision to implement and allowing remote negotiations among different users.
Distribution and maintenance: The software does not have a public release yet. It is being engineered and it is expected to be available in a beta version in spring 2007. Its development can be monitored on www.twole.info.
HW requirements: Dedicated server (ca. 1GB ram), unlimited clients
OS requirements: OpenSource Linux operating system (kernel >=2.4), Unix (SUN Solaris)
SW requirements: Apache >= 2.2, PHP >= 5, Postgresql >= 7.3, Mediawiki >= 1.7
For possible applications, you are asked to contact the development team (mailto:feedback@twole.info)
Users and applications: A preliminary version of the planning module was used in the international project Verbano Interreg II-UE. It was successively applied to Lake Iseo and to the Vomano water system. A new version of TwoLe is currently being developed and tested on Lake Como. The management module is being developed and, at the same time, applied once again to Verbano. TwoLe has supported the planning negotiation process between government bodies, Regulation Consortia, Draining and Irrigation Consortia, hydropower companies, associations of various kinds and citizens.
Documentation: The documentation utilizes different supports, ranging from canonical pdf manuals to hypertexts, and reflects the modular structure of the support system. The easiest level is devoted to stakeholders and DMs and is based on examples and case studies periodically updated. The more complex level, tailored for experts and analysts, allows them to use the support system and to upgrade it with new models, indicators, etc. The third level (for advanced analysts and programmers) is the very code of TwoLe which has been formulated in such a way as to allow its internal structure to know information about itself and to provide it to the user. The web site of TwoLe constitutes the natural interface among these levels and permits the systematic upgrading of the documentation.
Main scientific papers:
Soncini-Sessa, R., A. Castelletti and E. Weber, 2007. Integrated Water Resource Management. Theory. Elsevier, Amsterdam, NL. (available from May 2007);
Soncini-Sessa, R., F. Cellina, F. Pianosi and E. Weber, 2007. Integrated Water Resource Management. Practice. Elsevier, Amsterdam, NL. (available from May 2007);
Castelletti, A. and R. Soncini-Sessa, 2006. A procedural approach to strengthening integration and participation in water resource planning, Environmental Modelling & Software, 21(10), 1455-1470.
Soncini-Sessa, R., A. Castelletti and E. Weber, 2003. A DSS for planning and managing water reservoir systems, Environmental Modelling & Software, 18(5), 395-404.
Soncini-Sessa, R., A.E. Rizzoli, L. Villa and E. Weber, 1999. TwoLe: a software tool for planning and management of water reservoir networks. Hydrological Sciences Journal, 44(4), 619–631.
Soncini-Sessa, R., A. Nardini, C. Gandol? 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.
Development: The MODSS TwoLe prototype was realised in the course of the Verbano Interreg II-UE project. The improvement of the first version of the MODSS continued in the Sebino project, which was financed by Fondazione Cariplo and within the MERIT (Fp5-UE) project. TwoLe is the System supporting the PIP procedure adopted within the WP3 of the Harmoni-CA project. The second version of the MODSS is being realized within the project Twole-A/B, a system for the Planning and Management of Water Resources; Pilot Application to the Ticino and Adda Basins, financially supported by Fondazione Cariplo.
Primary scope/domain: WaterWare is a modular, interactive water resources management information system, designed to support the implementation of the WFD and similar national legislation. Its purpose is to assist government agencies, river-basin commissions, etc., in decision-making for the efficient management of water resources in terms of both quantity and quality.
Technical design: The system combines geographical information systems, database technology, modelling techniques, optimisation procedures and expert systems. It includes a multimedia user interface with Internet access, a hybrid GIS with hierarchical map layers, object data bases, time series analysis, reporting functions, and embedded expert systems for estimation, classification and impact assessment tasks, and a hypermedia help- and explain system. Real-time data management, simulation and optimisation modelling, with data assimilation, forecasting, and reporting, and support for operational management can be provided with a real-time rule-based expert system.
WaterWare is available for servers running any kind of OpenSource Linux operating system, as well as various UNIX implementation (e.g., SUN Solaris). Other UNIX platforms can be supported on request. Any networked client (standard PC) supporting a standard web browser (MS Internet Explorer, Mozilla, Firefox) with XVGA resolution or better is supported. Auxiliary tools manage user requirements and stakeholder preference structures.
Support provided: The system supports monitoring, water allocation, pollution control and environmental impact assessment tasks.
Distribution and maintenance: A single-server, unlimited-clients version (open-ended, non-transferable binary license), include an installation, training, and first-year support package.
Users and applications: The DSS was developed through a series of applications (River Thames in England, Lerma-Chapala basin in Mexico, West-Bank and Gaza in Palestine, Kelantan River in Malaysia and the Yangtze River in China). Applications around the Mediterranean in the EU-sponsored projects SMART and OPTIMA include river basins in Cyprus, Turkey, Lebanon, Jordan, Palestine, Egypt, Tunisia and Morocco.
Documentation: An On-line Reference Manual is available at the web address
http://www.ess.co.at/MANUALS/WATERWARE/R51reference.html.
Main scientific papers:
Fedra, K. and Jamieson, D. G., 1996. The "WaterWare" decision-support system for river-basin planning. 2. Planning capability. Journal of Hydrology. 177, (3-4): 177-198;
Jamieson D. G. and K. Fedra, 1996. The “WaterWare” decision-support system for river-basin planning. 3. Example applications. Journal of Hydrology. 177, (3-4): 199-211;
Jamieson, D. G. and Fedra, K., 1996. The "WaterWare" decision-support system for river-basin planning. 1. Conceptual design. Journal of Hydrology. 177, (3-4): 163-175.
Development: The DSS is being developed on the basis of concepts and results of the EUREKA project EU487. The initial case study in this project addressed the Thames basin in England, and was sponsored by Thames Water International Ltd.
Primary scope/domain: The primary aim of the WaterStrategyMan DSS (WSM DSS) was the formulation and evaluation of alternative (improved) water management strategies for water deficient regions in the context of implementing the European Water Framework Directive 2000/60/EC. The system simulates and analyses the behaviour of water resource systems under different scenarios of water availability and demand, as well as alternative allocation schemes. The domains addressed by the WSM DSS include: (a) hydrology modelling, (b) water allocation under scarcity conditions, (c) simplified water quality modelling, (d) assessment of financial, environmental and resource costs, and (e) evaluation of alternative allocation/management schemes, on the basis of indicators and multi-criteria analysis.
Technical design: The WSM DSS was developed as a stand-alone GIS-based package with the objective to emphasise the conceptual links between the different components and aspects of water resource system management, instead of merely focusing on a detailed description of the physical systems and related phenomena. Its main functions have been framed under the Driving Forces-Pressures-State-Impacts-Responses (DPSIR) structure of indicators. The core components of the tool are: (a) the database; (b) the object model linked to mathematical models for water allocation, quality and economic estimations, (c) a logical coordination unit, responsible for the communication with external models, and (d) the user interface that allows for the definition of data and simulation parameters, and the presentation of results through customisable charts, tables and maps. The operational framework of the DSS is based on the concept of a water management scheme (WMS), defined as a set of scenarios for variables that cannot be directly influenced by the decision-maker and the application of one or more water management interventions. User interaction with the DSS falls under three functional groups, accessed via a hierarchical navigation tree: (1) base case editing, allowing for the editing and introduction of new data for the reference year; (2) creation of WMSs, providing the capabilities for defining scenarios on water availability and demand, definition of strategies and visualisation of results, and for conducting a parametric economic analysis; and (3) evaluation of alternative schemes, based on a multi-criteria analysis, which takes into account the entire simulation horizon, based on the computation of statistical criteria for reliability, resilience and vulnerability for regionally applicable indicators.
The DSS has been developed in Visual Basic .NET using the Arc Objects COM technology by ESRI. The current version is distributed in the English language only. The system requires Windows operating system (2000, XP or higher), approx. 50 MB of free disk space and 256 MB of RAM. Prerequisites for the installation are the MS .NET framework 1.1 and Arc Info v. 8.x.
Support provided: 1) Assessment of the state of a water resource system in terms of sources, usage, water cycles (pathways) and environmental quality. 2) Evaluation of the impacts of different water management options, on the basis of alternative scenarios and policies.
Distribution and maintenance: A demo of the WSM DSS is available at: http://www.geomin.unibo.it/hydro/WSM/DSS_demo.htm. The DSS can be made freely available upon contact with the National Technical University of Athens, and is subject to a user license agreement. The system is being further maintained and developed by the Environmental and Energy Management Research Unit of the School of Chemical Engineering of the NTUA (http://environ.chemeng.ntua.gr/).
Users and applications: The system has been developed for addressing the needs of decision makers in arid or semi-arid regions and river basins, characterised by current or future water stress problems. Seven case studies have been developed thus far, for the arid or semi-arid regions of the island of Paros, Greece, the region of Ribeiras do Algarve, Portugal, the region of Limassol, Cyprus, the island of Tenerife, the Canary Islands, Spain, the areas of Tel Aviv and Arava, Israel, and the area of the Belice River Basin, Sicily, Italy.
Documentation: A manual can be downloaded from the WaterStrategyMan web site, at http://environ.chemeng.ntua.gr/wsm/Default.aspx?t=60.
WaterStrategyMan Project (2004). The WaterStrategyMan DSS. A Comprehensive Decision Support System for the Development of Sustainable Water Management Strategies. Bologna, Publishable report of the project "Developing strategies for regulating and managing Water Resources and Demand in Water Deficient Regions", EU DG Research, Contract n° EVK1-CT-2001-00098.
Main scientific papers:
Todini E., Shumann A., and Assimacopoulos D., 2006. The WaterStrategyMan Decision Support System. In: Water Management in Arid and Semi-arid Regions: Interdisciplinary Perspectives, Edward-Elgar Publishing Ltd.
Assimacopoulos D., Barraqué B., Berland J.M., Feinerman E., Katsiardi P., and Manoli E., 2005. Estimation of the level of cost recovery of different scenarios of water allocation in arid areas / a proposition for an easy-to-implement approach. Second International Workshop: Implementing economic analysis in the Water Framework Directive. Paris, France. February 2005.
for strategic planning. 9th International Conference on Environmental Science and Technology. Rhodes, Greece. September 2005. pp. 913-920.
Manoli E., Katsiardi P., Arampatzis G., Assimacopoulos D., 2005. Comprehensive water management scenarios
Assimacopoulos, D., 2004. An Integrated Decision Support System for the evaluation of water management strategies. IDS-Water Europe web conference, May 2004.
Manoli, E., Arampatzis, G., Pissias, E, Xenos, D., and Assimacopoulos, D., 2001. Water demand and supply analysis using a spatial decision support system. Global NEST: The International Journal, 3 (3): 199-209.
Development: The WaterStrategyMan DSS was one of the main outputs of the WaterStrategyMan Project, “Developing Strategies fir Regulating and Managing Water Resources and Demand in Water Deficient Regions”, financed by the EC through the Fifth Framework Programme, in the framework of the Key Action Sustainable Management and Quality of Water within the Energy, Environment and Sustainable Development Specific Programme (Contract No: EVK1-CT-2001-00098). All WSM partners were actively involved in the DSS formulation, while the technical development was undertaken by the National Technical University of Athens (Greece), the Ruhr-University Bochum (Germany) and ProGEA S.r.l. (Italy).
Primary scope/domain: The WFD-Explorer performs a number of functions related to the implementation process of the WFD. First of all it provides a platform to integrate and disclose ecological information on water bodies, including a common knowledge base on the effectiveness of measures. It offers to water managers and stakeholders the opportunity to learn about the relationships between the objectives pursued, the measures that might be taken and the impacts of such measures (including costs). It also contributes to the harmonisation of the way in which ecological objectives are determined in the various river basins. As such it promotes the coordination of the national and regional WFD-processes. The WFD-Explorer aims to bridge the gap between the information needs of the (interactive) policy process and the results from detailed studies on water systems and processes.
Technical design: The base version of the WFD-Explorer has a modular set-up and consists of a multilingual user-interface and a computational core which are coupled through code as well as an MS Access Database. The computational core includes a balance of water and substances for the water bodies in the river basin. Most data of the WFD-Explorer are inserted in databases. Knowledge rules for ecological quality describe how the ‘scores’ for the four different biological quality elements (phytoplankton, macro phytes, macro fauna and fishes) depend on the values of the steering variables (hydromorphological and water quality characteristics). Knowledge rules have also been derived for a number of different types of measures. These describe how characteristics of the water system or steering variables will change because of the execution of particular measures.
The current system is stand-alone software and requires Windows XP or NT operational system; ca. 100 Mb hard disk and at least 512 Mb RAM.
Support provided: the WFD-Explorer supports water managers with the development of river basin management plans, in particular with respect to the determination of cost-effective strategies for the realisation of both ecological and chemical objectives. The WFD-Explorer aims to contribute to the discussion and communication of strategies being developed.
Distribution and maintenance: Recently (September 2006) the base version of the WFD-Explorer has been made available to water management authorities and consultancy firms in The Netherlands. The WFD-Explorer is freely available and can be downloaded from the website www.krw-verkenner.nl. The knowledge base with the knowledge rules is protected to promote consistency among the applications for the different water bodies. If new knowledge rules are developed in particular projects, then these new knowledge rules (after peer review) are incorporated in the knowledge base and become available to all users. A maintenance organisation is being set-up for the coming years. This organisation is financed by both the Ministry of Public Works and Water Management and the Association of Water Boards
Users and applications: A number of water boards and river basin districts are currently in the process of developing applications of the WFD-Explorer for their area. Other water boards are planning to do the same. Also applications outside the Netherlands are in progress for Belgium and Romania.
Documentation: The system is distributed with a tutorial, a user manual and a description of the methodology adopted. These include fact sheets for the different types of measures as well as fact sheets for the ecological knowledge rules of different water types. The documentation is currently in the Dutch language. However an English version of the user manual will become available in the coming months.
Most, H., van der, Jeuken, A. and Schijndel, S., van. WFD-Explorer: a planning kit to support the implementation of the water framework directive. Paper presented at 7th International Conference on Hydroinformatics HIC, Nice, FRANCE (2006)
Development: The WFD-Explorer has been developed in an incremental way. The prototype has been further developed into a base version which can be easily applied elsewhere in The Netherlands and abroad. This further development was carried out in conjunction with a number of ongoing planning studies in The Netherlands.
The WFD-Explorer has been developed by a consortium with representatives of different Water Boards, the STOWA (Foundation for Applied Water Research), the RIZA (Institute for Inland Water Management and Waste Water Treatment) and a number of knowledge institutes and consultancy firms. The project is part of the government-sponsored research program ‘Living with water’.
| A1) Is [DSS] the first decision support system you have used? | |
| It is the first DSS I used | I used another DSS before What was the system(s) you used before? ______________ |
| A2) What was the main motivation/reason for development/acquisition of [DSS]? | |
| Curiosity, interest in exploring how to rationalise decisions | |
| Proposed partnership from research institution (e.g. involvement in an exploratory research project) | |
| Emerging needs, inability of the previous system to support increasingly complex decisions | |
| Technological innovation (e.g. updating the system to meet new software/hardware standards) | |
| Formal enforcement in your institution beyond your influence | |
| Other, please specify____________________________ ____________________________ ____________________________ | |
| A3) What outcomes did you aim at as you applied the [DSS]? | |
| Better understanding - explaining the causes of the problem at hand | |
| Satisfying specific regulatory obligations and requirements | |
| Facilitating stakeholders’ involvement | |
| Eliciting perspectives form various actors about the problem at hand | |
| Identifying policy/project alternative options | |
| Evaluating previously adopted policy/design options | |
| Making a previous policy decision transparent, or demonstrating that the decision was correct | |
| Innovating, including testing of new governance styles | |
| Operational management | |
| Other, please specify: ___________________________________________________________ | |
A4) The implementation of the DSS imposed changes to the process of decision-making in your organisation
| |||||||||||
| A5) Does [DSS] integrate other software instruments (such as GIS, database management system, models) previously used in your institution? If yes, please specify: __________________ ________________________________________________________________ ________________________________________________________________ |
The integration with other systems was important for the decision to develop/acquire the system
| |||||||||||
|
B1) What is your area of expertise and the role in your institution? Please specify:
Area of expertise: _______________________________________________________________ Your role in the institution: ________________________________________________________ |
| B2) Your involvement in the development/acquisition included: | |
| Specification of the requirements, identification of the current and potential needs | |
| Adaptation of the system to fit the data flow and systems/databases in your institution | |
| Testing and evaluation of the prototypes at various development stages | |
| Application of the system in practical situations before the system/acquisition was completed | |
| Consultation with other potential users, or parties involved or affected by future application of the system | |
| Training of technicians | |
| None of the above | |
| Other, please specify ____________________________________________________________ ____________________________________________________________ ____________________________________________________________ | |
| B3) Since when have you been using the [DSS] system and how frequently? ___________________ _______________________________________________________________________________ |
B4) Are you a direct user of the system (i.e. you interact with the system installed on your PC)?
| |||||
B5) It was difficult to get familiar with the system……………….
| |||||||||||
B6) Your role in the process of decision making changed because of the adoption of [DSS]
| |||||||||||
| C1) What was the importance of the [DSS]’ results for the policy decision to be taken? | |
| [DSS]’ results informed the decision (choices of the policy) but the choice has been taken independently of the results | |
| [DSS]’ results have been (more or less) directly taken up by the decision/policy choice | |
| [DSS] was useful in a broader context, facilitating the planning process and learning among the actors, contributing to finding a compromise among different expectations and interests. | |
| Other, please specify____________________________________________________ _________________________________________________________ | |
C2) Is the system described above still maintained and further developed within your institution or in collaboration with the developer? ……………………………………………...
| |||||
C3) Have you applied the [DSS] in more than one decision context?
| |||||||||||
| If yes, how many times? __________________________________________________________ | |||||||||||
The re-use of the system was simple
| |||||||||||
D1) The [DSS]’ implementation in policy-making improved the usual/traditional approach of your Institution/Firm
| |||||||||||
| D2) You are satisfied with the various components of the system (Leave blank if not applicable) | |||||||||||
Database management system
| |||||||||||
Simulation model(s) (please specify below if different models are used)
| |||||||||||
User interface
| |||||||||||
Module facilitating the evaluation/ranking of policies
| |||||||||||
Module responsible for documentation and presentation of the results
| |||||||||||
Interactive exploration of the model’s suitability
| |||||||||||
Role attributed to various interests groups (stakeholders)
| |||||||||||
| What were the most effective components and for what reason/s? _____________________ ___________________________________________________________________________ ___________________________________________________________________________ ___________________________________________________________________________ | |||||||||||
| and what were the main limitations and for what reason/s? ___________________________________________________________________________ ___________________________________________________________________________ ___________________________________________________________________________ | |||||||||||
| Additional comments ___________________________________________________________________________ ___________________________________________________________________________ ___________________________________________________________________________ | |||||||||||
D3) The use of the system or the quality of its components were contested by those who were affected by or involved in the decisions made with the help of the DSS
| |||||||||||
| If yes, what were the main contestations? _________________________________________________________________ _________________________________________________________________ __________________________ | |||||||||||
| D4) Do you have any recommendation for the improvement of the development of similar systems in the future? ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ | |
Results of the questionnaire for DSS end users
A) DEVELOPMENT, ACQUISITION AND APPLICATION CONTEXT| A1) Is [DSS] the first decision support system you have used? | |
| It is the first DSS I used 19 | I used another DSS before 5 What was the system(s) you used before?
R1: SWAT, MFA; R6: No WFD system; R14: Spreadsheets/Experience; R15: Site-specific; Fortran-Based; R16: HEC-5; R20: Mike SHE /mudflow; R26: Environment Waikato and Ag research make extensive use of modelling and decision support, e.g., choice of farming regimes. However, there is less experience with DST for territorial futures as in the current “Choosing Regional Futures” project. |
| A2) What was the main motivation/reason for development/acquisition of [DSS]? | |
| Curiosity, interest in exploring how to rationalise decisions | 5 |
| Proposed partnership from research institution (e.g. involvement in an exploratory research project) | 14 |
| Emerging needs, inability of the previous system to support increasingly complex decisions | 9 |
| Technological innovation (e.g. updating the system to meet new software/hardware standards) | 2 |
| Formal enforcement in your institution beyond your influence | 2 |
| Other, please specify R3: Implementation of the Water Framework Directive: Emission register for diffuse sources; R4: Having a tool to predict the effects in change of water quality and its effects on ecology and because of the knowledge database of dosis-effect relations (there is nothing simular in the Netherlands). | |
| A3) What outcomes did you aim at as you applied the [DSS]? | |
| Better understanding - explaining the causes of the problem at hand | 16 |
| Satisfying specific regulatory obligations and requirements | 11 |
| Facilitating stakeholders’ involvement | 18 |
| Eliciting perspectives form various actors about the problem at hand | 5 |
| Identifying policy/project alternative options | 17 |
| Evaluating previously adopted policy/design options | 7 |
| Making a previous policy decision transparent, or demonstrating that the decision was correct | 11 |
| Innovating, including testing of new governance styles | 7 |
| Operational management | 9 |
| Other, please specify:
R2: MONERIS is used in our institute (Department of Water and River Basin Management, University Karlsruhe) in the framework of a research project with the aim of modelling heavy metal emissions into river systems. This project is funded by the Umweltbundesamt (Federal Environment Agency of Germany);
R3: Status quo-investigation on share of different diffuse nutrient input pathways into surface waters | |
A4) The implementation of the DSS imposed changes to the process of decision-making in your organisation
| ||||||||||
|
A5) Does [DSS] integrate other software instruments (such as GIS, database management system, models) previously used in your institution? If yes, please specify:
R1: no
R2: GIS (ESRI ArcView, ArcGIS), database management (UDIS) R3: GIS: data on nutrient balances (administrative units), river catchments, DEM, Corine Landcover, River network, data on water quality, flow etc R4: Yes, the WFD explorer will be expended with a communicationtool that make it possible to make integrate choices in combination with water quantity goals and ecologigal goals for nature and in the future we want to integrate the WFD-Explorer with a model for surfacewaterquantity and –quality (Sobek) R5: GIS information (waterbodies), databases on environmental information (STONE, ERC), hydrological model information (MOZART, NAGROM) R8: YES; GIS: river cadaster/model: VIDRA/SWAT R9: GIS For Caia Water Board District R10: Gis and ground flow model R13: The WSM-DSS tool uses GIS capabilities, to handle both vector and raster information and also includes the ProWam module, devote to the generation of water availability scenarios R14: RiverWare integrates with our hydrologic database (HDB) (an Oracle database), and Modflow integration is underway – GIS integration is planned. R15: yes we use an Oracle based DB and other models including HEC-RAS; we also have a real time data colllection network, all integrated through the DB with RW models R16: HEC-DSS (data-storage system) R18: GIS, Hydrological Models R19: Yes, obviously in order to get the tool we had to use a great quantity of information from a lot of sources R21*: No R22*: Database R23*: Will acquire data from our database R24*: yes GIS R25: In this case study, CatchMODS integrates specifically a reservoir-plankton-model primarily developed by our institution to understand the likely effects of catchment management on the reservoir phytoplankton blooms. R26: The kerDST system is designed to function as a complement to scenario modelling and spatial (e.g., GIS based) representations. For example the Deliberation Matrix permits a comparative multi-criteria multi stakeholder evaluation of scenarios for regional futures or for farm practices, and the KerBabel Indicator Kiosk helps make feasible an accessible and comprehensive documentation of information sets used in modelling and maps. |
The integration with other systems was important for the decision to develop/acquire the system
| ||||||||||
|
B1) What is your area of expertise and the role in your institution? Please specify:
Area of expertise:
R1: River basin management
R2: modelling heavy metal emissions within river systems R3: Nutriente balances in agriculture R4: Water quallity and ecology R5: Decision making, analysing waterpolicies R6: Policy development/ Management/ Water R8: river flood forecasting and modelling R9:GIS and Remote Sensing R10: Hydrology R11: Forestry Sciences R12: Consultancy Services in Environemental Management R13: Fluid Mechanics; Hydraulics; Fluvial Hydraulics; Water Resources (Transboundary Rivers) and Environment R14: Water resource planning, engineering and management R16: Hydraulic Engineer/Reservoire Operations R17: Water resources R18: IRBM, environmental planning R19: Water management, mainly in agricultural systems R20: Modeller, hydrology, organisational psychology R21*: Engineer R22*: Engineer R24*: Biologist R25: Freshwater ecology, eutrophication R26: Sustainable agriculture, science and community interfaces Your role in the institution: R1: Associate Professor
R2: Research Assisatant R3: scientific expert in the field of agriculture and environment (nutrient balances, biodiversity and landscape impacts, organic farming R4: Internal adviser R5: Sr. Project manager Water management R6: Project/Project leader R8: Scientific Director R9: Head of Cartographical Unit R10: Staff scientist R11: Director R12: Director R13: Professor R14: Team leader R15: Hydrology, water policy R16: Reservoir Operation modeller R17: Researcher R18: lecturer, researcher R19: Researcher (my Phd was the MERIT model for La Mancha Oriental Aquifer) R20: Senior Advisor R21*: Director of the technical/management sector R22*: Member of the Administration Council R23*: Director R24*: Scientific Director R25: environmental scientist R26:Science Leader, AgResearch (New Zealand’s largest Crown Research Institute, with research responsibilities across all fields of knowledge and value added for agriculture). |
| B2) Your involvement in the development/acquisition included: | |
| Specification of the requirements, identification of the current and potential needs | 20 |
| Adaptation of the system to fit the data flow and systems/databases in your institution | 12 |
| Testing and evaluation of the prototypes at various development stages | 16 |
| Application of the system in practical situations before the system/acquisition was completed | 11 |
| Consultation with other potential users, or parties involved or affected by future application of the system | 10 |
| Training of technicians | 7 |
| None of the above | 3 |
| Other, please specify | |
|
B3) Since when have you been using the [DSS] system and how frequently? R1: 2001, ongoing R2: Since 1999, all the time R3: 2004: once for the implementation of the WFD, 2005/2006: within the European wide project EUROHARP: MONERIS was applied by the German modellers to the Austrian river catchment “Gurk” R4: We haven’t used it until now R5: Summer 2005, weekly R6: still in progress R10: Not since the project was stopped R12: since 2003 on a continuous base R13: Since 2003, on a a weekly basis R14: Since approximately 1999 - we have developed four model applications (planning, forecast, accounting and daily) that are used occasionally to daily R15: I was originally responsible for choosing and developing a new DSS in the early 1990's; throughout that decade a sure transition was made ; I'm now the manager and this responsibilities are within my (?) Since 1996; used every day by my water operations department R16: Since 1996-at least weekly R17: 8.2005, 5hours/week R18: regularly since 5 months R20*: 2002, since a year R21*: Since it has been acquired whenever necessary R22*: Since 2001, 2 application R25: Since 2004, several times R26: We have seen the prototype kerDST system in use in the SRDTOOLS project in Europe during 2005, and saw its prospects for the stakeholder based regional futures studies and sustainable agriculture evaluation work that AgResearch is engaged in. We have by now incorporated use of the kerDST tools in several projects including “Choosing Regional Futures” (led by the Environment Waikato, the regional council concerned), and a set of industry sponsored projects intended to define methods for incorporating environmental and social performance considerations into assessment of agriculture strategies at farm, district and regional scales, e.g., river water quality and chemical runoff from intensive dairy farming. |
B4) Are you a direct user of the system (i.e. you interact with the system installed on your PC)?
| |||||
B5) It was difficult to get familiar with the system
| ||||||||||
B6) Your role in the process of decision making changed because of the adoption of [DSS]
| ||||||||||
| C1) What was the importance of the [DSS]’ results for the policy decision to be taken? | |
| [DSS]’ results informed the decision (choices of the policy) but the choice has been taken independently of the results | 6 |
| [DSS]’ results have been (more or less) directly taken up by the decision/policy choice | 9 |
| [DSS] was useful in a broader context, facilitating the planning process and learning among the actors, contributing to finding a compromise among different expectations and interests. | 14 |
| Other, please specify R3: MONERIS was useful for a first status quo-survey on diffuse nutrient inputs into river systems
R4: 1. We expected that the WFD-Explorer will give insight in the relation of important measures and the ecological impact. This will help ‘us’inmaking policy decisions; 2. The WFD-Explorer will fauhtate the decisions with other stakeholders. R5: (note: answers are based on expectations!) R9: It hasn't been used by users and Stackeholders R14: We are using RiverWare for multiple decisions over multiple time scales – it was not a single-use DSS R15: RW is also usefull in a specific context, being used each day for water scheduling, but also used to analyze specific impacts from different decisions fro our entire hierechy of decisions R20*: It has also facilitated cross-cutting work with experts from other territories R26: Note: kerDST is called a “Deliberation Support Tool” and it is meant to promote what we in New Zealand call collaborative learning. The regional planning legislation requires extensive community consultation for setting high level goals as well as for many individual project assessment or policy assessments. This means it is intended to help improve decision processes, not take them over. | |
C2) Is the system described above still maintained and further developed within your institution or in collaboration with the developer?
| |||||
C3) Have you applied the [DSS] in more than one decision context?
| ||||||||||
| If yes, how many times?
R5:20 different situations are expected
R8:2 R12: Three times R14: We use it on a daily basis, as well as regularly and occasionally R15: For 4 different EIS processes in the past 4 years R16: two R17: 2 times R21: 3 R26: At present, we are using the kerDST tools in four distinct projects that deal with agriculture activities and choices at different spatial scales (farm scale, district or sub-catchment, and finally regional scale) and also contrasting timescales (long term planning or on-going monitoring). We are also considerijng the use of kerDST for another project on biosecurity in New Zealand. | ||||||||||
The re-use of the system was simple
| ||||||||||
D1) The [DSS]’ implementation in policy-making improved the usual/traditional approach of your Institution/Firm
| ||||||||||
| D2) You are satisfied with the various components of the system (Leave blank if not applicable) | ||||||||||
Database management system
| ||||||||||
Simulation model(s) (please specify below if different models are used)
| ||||||||||
User interface
| ||||||||||
Module facilitating the evaluation/ranking of policies
| ||||||||||
Module responsible for documentation and presentation of the results
| ||||||||||
Interactive exploration of the model’s suitability
| ||||||||||
Role attributed to various interests groups (stakeholders)
| ||||||||||
|
What were the most effective components and for what reason/s? R1: retention approach in groundwater and surface water because it is essential for the results and is highly innovative
R2: the operational approach for calculating the input of substances to surface waters; the organisation or the model on the level of subcatchments ; the aggregation along the flow path of the river systems. R3: Nutrient balance component on political districts, because this was renewed with Austrian nutrient coefficients and first time done on regional level (political districts) R4: 1. the knowledge database with dosis-effect relations for translation of measures to effects on ecology; 2. a fast way to calculate changes in water quality R5: Coupling detailed models and databases in simplified way, so the results (of several policy alternatives) are calculated instandly R6: Visual presentation of policy decision also meant to communicate with stakeholders R8: Module facilitating the evaluation of indicators (value function) for the involvement of stakeholder options in a participative way R9: No Opinion R12: The capability to perform a number of assessments of the water system of the study area in terms of sources, usage, environmental quality, evaluating the effects of actions on the basis of different scenarios, alternatives and policies R13: The most effective components for the work undertaken were the allocation and the economic modules as they were adequate to the needs of our case study. The allocation module allowed the user to analyse on a monthly basis different aspects of the water allocation as the water demand and water deficit. An important aspect is the fact that the user can obtain the characteristics of the water conveyance and supply at each step of the water network. Moreover, the economic module performs a simple economic analysis, which is very helpful for options and strategies comparison. Moreover, the calculation of the rate of cost recovery is beneficial to cope with the WFD requirements. R14: The ability to accurately capture policy and legal constraints in rule statements R15: the ability to enter new policies and the comparison of those policies has allowed us to find innovative solutions to complex problems R18: Simulation of measures and its interaction in different system fields, visulisation with maps and graphs R19: Module to compare options in order to assure sustainability R21*: The database allows for a rapid synthesis and elaboration of a huge amount of data; the module for evaluation and classification of the policy options allows for an interdisciplinary approach to a complex problem R22*: Definition and role of Stakeholders R25: User interface and module facilitating the evaluation/ranking of policies, because of their user-friendliness R26: The main focus of kerDST is to build dialogues between stakeholders and with scientists and technical staff of the institutions involved and to make comparative assessment of policy options. We know that the Deliberation Matrix can help a lot with these two tasks, e.g., highlight for decisionmakers and stakeholders what are the trade-offs in farm business or regional planning decisions. The kerDST system can also help with aspects of data base management, documentation and model suitability assessment, partly through being embedded in interactive multimedia on line. However these functions are more intensive and we have not yet progressed far enough in the projects to give in-depth feedback. | ||||||||||
| and what were the main limitations and for what reasons?
R1: Erosion and particulate transport of P in river systems- the knowledge is generally rather limited an these processes R2: limitations for our work belong mainly to input data, which is not available and data uncertainties and not to the modelling approach R3: Soil map, soil erosion (P): no detailed data. Drainage areas: no data. Snow melting processes: not modelled within MONERIS, estimation on surface runoff. R4: The knowledge database was not complete for all artifical and heavily modified waterbodies (types/kinds) R5: Although it is simplified it takes time for policy makers to work with the system! R6: Scale (winter versus summer), no more detailes information R7: R8: including the indicators in DPSIR chain due to some ambiguities related to including an indicator in P or S R9: Stackeholders and uswes opinions have not been taken into account R12: Data was not always available R13: The main limitations are related with the the fact that it is not an open source software. R14: Technically complex with a significant learning curve for complicated implementations R15: no (?) limitationa R18: Evaluation of measures is too complicate R19: The models runs just for 1 year no more. R20: Structural learning R21*: Concerning the users interface, it is necessary to improve the videoconferencing part R22*: The system is not that practical for use. (No friendly) R24*: The interactive exploration and classification of the various options: these allow to point out the various roles and the background knowledge of the various Stakeholders R25: Nothing particularly R26: Like all tools, people have to be motivated to take the time to learn their use. Some of our partners are very busy in industry, farming, administration or community life, and so they do not invest as much as would be ideal. This is not really a limitation of the tools but it is a real limit on effective use. | ||||||||||
| Additional comments
R14: If your system and / or needs are complicated and policy driven and /or legally constrained, this DSS will likely satisfy you
R18: systems integrates really the most important physical and economic models/issues if IRBM R19:The importance of transparence and PP to allow the effectiveness of the application and of the results R20: Takes time to get familiar with probabilities and what BNs mean R21*: Obstacles and interferences from Stakeholders not relevant to the decision to be taken R24*: The non-applicability of the results at a political level: even if transparency in the method was assured; its authoritativeness wasn’t not recognized by everybody R25: For our institution, CatchMODS integrated with a reservoir-plankton-model was necessary to demonstrate an environmentally friendly way of managing a receiving-water environment as part of pollution reduction program so the whole modelling framework itself was very important to show councils and stakeholders to find alternative decision choices and their likely benefits in concrete terms (e.g. dollar values), all of which CatchMODS could deliver. R26: People often want community consultation and public participation to be done fast and at low cost. They have the idea that these things should take just a few days. Although the Deliberation Matrix can be used to make a surface assessment of options in just a few hours, full use of the full system can engage a team for many weeks. Then we run up against the perception that everything should happen straight away, which is not expected for technical research and modelling, for example. | ||||||||||
D3) The use of the system or the quality of its components were contested by those who were affected by or involved in the decisions made with the help of the DSS
| ||||||||||
|
If yes, what were the main contestations? R1: Quality of input data. R4: 1. Some people in the institution think that the user interface is to full and complicated for stakeholders; 2. the rest we don’t know yet. R5: Clear and simple user interface, presenting the results in a way it can be understood by people not familiar with the context (e.g. WFD). R9: The Portuguese partners didn’t took into account our points of view R12: For the DSS end user (government policy makers) the system was a BLACK BOX, so they did not trust the results and conclusions immediately R13: Some inadequacy of the DSS tool requirements to the available data (mostly for the quality module). R15: We formed a stakeholder group and informed them throughout our process of (?) to RW R20: Gave a full picture and flexible tool. R21: Reducing the number of actors involved in the tools' development phase. Improve the testing during the development phase. R24: The role of the various stakeholders wasn't recognized. R26: Not applicable – everyone with whom we have discussed has reacted positively to the tools and their goals for helping to open up evaluation processes. | ||||||||||
|
D4) Do you have any recommendation for the improvement of the development of similar systems in the future?
R1: Improvement in the software implementation, Implementation of GIS interface. R2: Improvements should mainly be done on the European wide and consistent supply of input data R3: more user-friendly surface to improve handling of model inputs and outputs R4: 1. Complete the knowledge database of doses-effect relations for al kinds of artificial and heavily modified water bodies; 2. Make the user interface more clearer for communication with stakeholders; 3. Connect the knowledge database of dosis-effect relations directly to water quality models (for example: SOBEK) R5: See D3. Developing the DSS together with the end users R9: Technical follow-up of the Project, has to be carried in a more careful way R10: I hope to able to use the mDSS in a future project R13: The use of the WSM DSS tool for the WSM Project purposes and further investigation allowed us to understand the difficulty of such a model to suit everyone’s needs. In fact, it seems that each decision support system tool should, ideally, be adaptable to different scales of analysis and databases. Open access to source codes should help. R14: RiverWare is continuously under development through feedback and requests from users for additional features. The software was designed to be extended and has successfully been enhanced – integration with other models is now underway. Regular training on new features, if useful, should be planned R15: This technology is expensive to develop and to maintain. Partnership between larger Institutions will be critical to fund the next generation of RW-type tools. The "model used to develop RW (collaboration/partnership between Univ. of Colorado Bureau of Reclamation and Tennessee Valley Authority) worked well, including licensing the product to others to help offset maintenance costs. R18: better interface, more flexible data base interface R19: As a dev he did a great job R20: There could be a data base with all used data and assumptions so that it is easier to update the BNs R22*: no R24*: The method is a powerful tool. It could be eventually improved in its interface to allow a better discussion on the alternative options R25: Good communication is a key to integrate our needs in the system to be developed. I believe we maintained good communication throughout the course of the project, resulting in successful development of CatchMODS and its presentation to policy makers R26:We chose this DST system because it fills a gap between production of technical knowledge and building dialogues with stakeholders. For example, in the Waikato “Choosing Regional Furtures” project, we have partners with good system modelling and spatial analysis skills, but we needed to have a way of interfacing the modelling with the stakeholders’ perceptions of issues for the region’s future. The Deliberation Matrix gives us a solution for this. In the case of work with farmers about integrating social and environmental considerations into assessment of farm management systems, the Deliberation Matrix framework helps to present the diversity of stakeholders and their differing concerns in a non confrontational way. The kerDST system is quite new and some of its on-line interfaces can still be improved. But overall we are satisfied with its potential and we expect to keep using it for the next few years. | |
NOTE: (*) the answers given by the respondent (R) have been translated form Italian into English
