Technical Report TR2005-4. CSC-University of Siena.

Development of an information technology tool for the management of Southern European lagoons under the

influence of river-basin runoff

(DITTY Project) EVK3-CT-2002-00084

WP8 Draft Report

October 20, 2005

Proposal of a Decision Support System for the management of Southern European lagoons

A. Agnetis, R. Basosi, K. Caballero, M. Casini, G. Chesi, G. Ciaschetti, P. Detti, M. Federici, S. Focardi, E. Franchi, A. Garulli, C. Mocenni,

S. Paoletti, M. Pranzo, A. Tiribocchi, L. Torsello, A. Vercelli, D. Verdesca, A. Vicino

Center for Complex Systems Studies

University of Siena, Siena, Italy

Abstract

This report presents the general structure of the Decision Support System (DSS) for the management of Southern European lagoons that has been developed in the framework of the EU funded DITTY project (EVK3-CT-2002-00084). The correct management of coastal lagoon areas with anthropic exploitation is a difficult task for local authorities. Socio-economic interests and environment preservation are typically contrasting objectives between which a suitable trade-off must be achieved. The solution to this problem requires the development of interdisciplinary and multicriteria approaches. The proposed decision support system is conceived as a model-driven DSS, which integrates the different kinds of mathematical and analytical models (e.g., biogeochemical, hydrodynamic, ecological, socio-economic models, etc.) developed in the previous work-packages of the project. Data and information obtained from the models are used to accomplish the decision task through resort to the Analytic Hierarchy Process (Saaty, 1980), which is suggested as a powerful and flexible tool to make decisions in situations where multiple and conflicting objectives/criteria are present, and both qualitative and quantitative aspects of a decision need to be considered.

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Contents

1 Introduction ............................................................................................................ 4

2 Short project description ....................................................................................... 4 2.1 Objectives and organization of the project ....................................................... 5 2.2 Test sites of the project..................................................................................... 8

2.2.1 Ria Formosa (Portugal) ........................................................................... 8 2.2.2 Mar Menor (Spain)................................................................................... 8 2.2.3 Etang de Thau (France) ........................................................................... 9 2.2.4 Sacca di Goro (Italy) ................................................................................ 9 2.2.5 Gulf of Gera (Greece) ............................................................................ 10

3 The decision support system................................................................................ 10 3.1 Basics on decision support systems................................................................ 10 3.2 Structure of the DSS for Southern European lagoons .................................... 11 3.3 The Analytic Hierarchy Process ..................................................................... 14

3.3.1 How the AHP works ............................................................................... 14 3.3.2 Features of the AHP ............................................................................... 14 3.3.3 Implementation of the AHP .................................................................... 15 3.3.4 An illustrative example ........................................................................... 18

3.4 Implementation of the DSS ............................................................................ 19

4 Case studies ........................................................................................................... 19

Bibliography.................................................................................................................. 20

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1 Introduction The development of management tools for coastal lagoons is a complex task requiring interdisciplinary research and active interaction with the end-users. Mathematical models of the biological, physical and chemical processes are fundamental tools for analyzing disruptions in the ecosystem due to abnormal conditions. However, the successful management of such complex systems requires the integration of the information provided by mathematical models with other kinds of analysis. Socio-economic analysis assumes for instance great importance in coastal lagoons, especially where various kinds of anthropic pressures (aquaculture, fishery, tourism, etc.) are sources of conflicts among different users. Decision support systems, i.e. information systems that help decision making, represent a suitable framework for the implementation of multicriteria approaches, and have been successfully applied to the management of coastal basins. See, e.g., Pastres et al. (2001) and Bromley et al. (2005).

One of the objectives of the EU funded DITTY project (EVK3-CT-2002-00084) is the development of a prototypal Decision Support System (DSS) for the management of Southern European lagoons. In view of the application of the DSS to five different sites (Ria Formosa, Portugal; Mar Menor, Spain; Etang de Thau, France; Sacca di Goro, Italy; and Gera, Greece), this report proposes the general structure of a model-driven DSS into which all site-specific decision problems can be cast. The core of the DSS is represented by the mathematical and analytical models developed for each site in the previous work-packages of the project. These are exploited for simulation and evaluation of different management options. The Analytic Hierarchy Process (AHP), introduced by Thomas Saaty (1980), is used to rank the options. The AHP is a suitable tool for decision making in situations where multiple and conflicting objectives/criteria are present, and both qualitative and quantitative aspects of a decision need to be considered.

This report is structured as follows. In Section 2 a short description of the DITTY project (motivations, objectives, organization, test sites) is presented. Section 3 decribes in detail the general structure of the proposed decision support system. Section 4 is dedicated to the five case study applications considered in the project.

2 Short project description As a consequence of their location between land and open sea, coastal lagoons are characterized by large fluctuations in physical and chemical conditions. Their equilibrium is strongly influenced by the quality of inland waters flowing into them, rich in organic and mineral nutrients derived from urban, agricultural and/or industrial effluents and domestic sewages. Moreover, Southern European lagoons are subject to strong anthropogenic pressures due to tourism and/or intensive shellfish/fish farming. They have been also suffering from multiple and uncoordinated land-use modifications undertaken with only limited sectorial objectives in mind, e.g., structural changes in lagoon topography, artificial increase of the number of sea connections, changes in bathymetry, etc. All these factors are responsible for important ecosystem alterations characterised by eutrophic/dystrophic conditions, algal blooms, oxygen depletion and hydrogen sulphide production. Additional problems arise from cost erosion, subsidence and effects related to extreme meteorological events.

The objective of the EU policy for the environment is to contribute to preservation, protection and improvement of the quality of the environment through a prudent and

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rational utilisation of the natural resources, based on the principle that preventive action should be taken, where necessary, to ensure “good water status” (as described in the Water Framework Directive). Since there are different conditions and needs in the Community, which require specific solutions, this diversity should be taken into account in the planning and execution of measures for ensuring protection and sustainable use of water in the framework of coastal basins. Decisions should be taken as close as possible to the locations where water is affected or used. Priority should be given to actions within the responsibility of Member States through the drawing up of measures adjusted to regional and local conditions.

The DITTY project aims at developing the scientific and operational bases for a sustainable and rational utilisation of the available resources in Southern European lagoons by taking into account all the relevant impacts from agricultural, urban and economic activities affecting the aquatic environment, and by developing information technology tools tailored to these types of ecosystems. Reliability of the developed information tools is pursued by putting particular emphasis on model validation and benchmarking, as well as on a detailed socio-economic assessment of different management options through the close involvement of economists and stakeholders.

One of the expected outputs of the project is the prototype of a decision support system for the management of coastal lagoons. By integrating different kinds of mathematical and analytical models, this tool should be able to simulate and analyze the effects on the ecosystem of lagoon fluid dynamics, river runoff influence, nutrients cycles, shellfish farming, macro-algal blooms, as well as the economical implications of different management options. It should provide the local or national authorities with suitable techniques for choosing the appropriate time and space scale needed to monitor the relevant processes, and assessing the effectiveness of actions designed to achieve a good environment quality status, as described, e.g., in the Water Framework Directive.

Implementations and tests of the decision support system are programmed in five lagoon sites: Ria Formosa (Portugal), Mar Menor (Spain), Etang de Thau (France), Sacca di Goro (Italy), and Gera (Greece). The choice of these sites is mainly dictated by the fact that long-term data sets are accessible, and that each partner of the project has developed good working contacts with local and regional authorities ensuring the exchange of data and information.

The DITTY project builds on, extends and integrates the efforts from the European Commission funded research initiatives to investigate the coastal zones and the interactions between land and ocean, being part of the ELOISE (European Land-Ocean Interaction Studies) cluster of research studies.

2.1 Objectives and organization of the project The objectives of the DITTY project are multiple. Specifically, it intends to:

• Synthesize the knowledge about coastal lagoon ecosystems (also using prior investigations from ELOISE), and gather data in the form of long-term spatio-temporal time series that support this knowledge.

• Develop integrated modelling techniques for the watershed basin, the coastal lagoon, and the coastal zone, and design and validate benchmarking exercises.

• Develop the basis of common information technology tools: Database Management Systems, Geographic Information Systems, and Decision Support Systems.

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• Develop scenario analysis for each site taking into account the end-user priorities.

• Use the above tools for :

1) Assessing the influence of watershed basin runoff.

2) Assessing the influence of shellfish farming on ecosystem equilibrium.

3) Study the spreading factors of bacteria of sanitary concern.

4) Developing early warning detection systems for anoxic crises.

5) Deciding a monitoring strategy that may deal with the time scales involved (short term events, seasonal variability, long term behaviour).

• Assess methodologies for detecting the impact of climate changes on coastal lagoon ecosystems through long-term simulations.

• Apply global bio-indicators (e.g., exergy1 and others) for addressing the issue of a rigorous definition of “good ecological status”, which has been introduced in the Water Framework Directive.

• Apply the LOICZ standard methodology for assessing its efficiency in the case of coastal lagoons and contributing to the flux assessment of the EU Mediterranean basin area.

The project is organized in eight work-packages, that are briefly described in the following. The links between the work-packages are shown in Figure 1.

Figure 1. Links between the work-packages of the DITTY project. The arrows indicate the main flow directions of information, data, results, and products.

WP1. Data compilation and analysis. The first activity of the project is to perform data inventory, compilation and validation at each test site according to the end-user selected priorities. The objective of this WP is to collect all the relevant data concerning climate and meteorological information, topography, bathymetry, hydrology, oceanography, 1 The thermodynamic definition of exergy is the amount of work that a system can perform by being brought into equilibrium with its environment.

Database and GIS development

WP1 WP2 WP3

WP4 WP5

WP6 WP7

WP8

Integrated modelling Comparative analysis

Scenario analysis Socio-economic analysis

DSS development

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soil and geology, living resources, land use, marine activities, etc. Specific data on physicochemical measurements in soil, surface water, ground water and seawater, as well as data concerning fishery, shellfish farming, and economic aspects, are also gathered.

WP2. Database development. Data from WP1 are used to define the appropriate format for data management in the form of a general database (DB). Although the data population of the DB is specific of each test site, the objective is also to implement a common physical model (based on a hierarchic organization) and a coherent dictionary of the data.

WP3. Geographical Information System (GIS) development. The data from WP2 and the relevant information coming from WP1 are used to define the appropriate format for the Geographical Information System, that is expected to act as an integrative tool for spatial statistical analysis, as well as an interface for visualisation of data and results. Along with database structuring (WP2), a common GIS system is to be implemented through a geo-referenced database creation for each specific test site and thematic application development.

WP4. Integrated model development and validation. After the general modelling framework has been setup in WP1, several modelling modules able to describe the different system components (namely, the watershed basin and the lagoon), as well as the ecosystem functioning and the hydrodynamics, are developed and implemented at each test site according to their specific characteristics, data availability and defined end-user scenario analysis and case studies. The approach should consist of using already developed modules for each specific test site, when available, and developing and implementing a common modelling approach for all test sites.

WP5. Comparative analysis at several test sites. A comparative analysis is carried out to assess similarities, differences, range of validity of the different models and tools developed. This should serve as a preliminary screening mechanism to select which techniques and tools are going to be implemented in the decision support system. Furthermore, such an analysis should reinforce confidence in predictions and scenario analysis outputs in support of policy decision making.

WP6. Scenario analysis on selected case studies. One of the most important challenges regarding coastal ecosystems is to develop predictive tools able to anticipate the ecological changes, and to propose management solutions for avoiding the extension of environmental crises both in intensity and frequency. In this WP several scenarios concerning, e.g., the effects of nutrient loads, changes in resource exploitation, adoption of early warning detection systems for anoxic crises, effects of global climate variability, etc., are tested and evaluated.

WP7. Socio-economic analysis of management options. In order to develop a complete DSS, it is necessary not only to take into account ecosystem functioning, but also the different actors involved. In parallel to the development of an integrated understanding of the ecosystem dynamics, a socio-economic study on the different stakeholders, and the costs and benefits of the different possible options is carried out with the aim of developing economic criteria to be included in the final decision system. The results from this analysis should be fed into the DSS and inform policy choices to manage the coastal lagoons.

WP8. Development of a decision support system for Southern European lagoons. The final result of the project is a tested prototype of a DSS which integrates the different

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tools and approaches developed in the previous work-packages. This WP should produce the basis of an integrated, interdisciplinary DSS relating the existing coastal zone use to its ecological consequences, and also providing a framework in which innovative management options can be analyzed as regards their social acceptability, economic evaluation and ecological outcomes.

2.2 Test sites of the project The five lagoon sites involved in the DITTY project range from Ria Formosa (Portugal), at the entry of the Mediterranean Sea, to Gera (Greece), in the Aegean archipelago. Between these, the other three sites are located in Spain (Mar Menor), France (Etang de Thau), and Italy (Sacca di Goro). The sites, with their associated catchment areas and coastal zones, are all economically very important (fishery and aquaculture), and exhibit a variety of anthropogenic pressures (agriculture, industry and tourism). The common denominator is to ensure a sustainable development considering all the involved factors. In some cases restoration measures are urgently needed.

The five sites, and the related problems, are described in the following sections.

2.2.1 Ria Formosa (Portugal)

Ria Formosa is a shallow mesotidal lagoon located at the south of Portugal (Algarve coast), with a wet area of 105 Km2. The lagoon has several channels and an extensive intertidal area (around 50% of the total area), mostly constituted by sand, muddy sand-flats and salt marshes. Due to the slope, the intertidal area is exposed to the atmosphere for several hours, over each semi-diurnal tidal period. The sources of treated and untreated domestic waste, as well as agricultural runoff, may contribute to episodic eutrophication and changes in the N/P ratios. The natural biogeochemical cycles of the Ria Formosa are essentially regulated by tidal exchanges with the seawater and by exchanges with the sediment interface. The tidal amplitude varies from 1 to 3.5 meters, and the mean water depth is 3.5 m. There is a rather intense exchange of water mass (50 – 75%) during each tide. The influence of benthic processes on water chemistry is significant, and this characteristic, in association with high temperatures in summer and local anthropogenic effects, leads to occasional environmental stress and contributes to a large variability of physical, chemical and biological processes.

This ecosystem is considered a nursery for a large number of coastal species, and its extensive intertidal areas are used as growth bank for clams and aquaculture ponds. The main economic activities are tourism, fishery and aquaculture (mainly clams and oysters). The economical annual revenue is the main source of income for approximately 5000 families.

2.2.2 Mar Menor (Spain)

The Mar Menor is a coastal lagoon in the south-east of the Iberian Peninsula. It has a surface area of approximately 135 Km2 and a mean depth of 4 m. The lagoon is semicircular in shape, and is separated from the Mediterranean Sea by a 24 Km long sand strip with width varying between 100 and 1.200 m. There are several small channels that connect the lagoon with the sea. Inside the lagoon there are five islands of volcanic origin. There is no permanent watercourse due to the low annual precipitation (around 300 mm), rather several riverbeds (dry for long periods) that channel water from the Campo de Cartagena. The oxygen content is high and the nutrient concentrations are low.

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Since historical times, the Mar Menor has been one of the main economic areas in the coastal zone of the Murcia province, and past and current human activities have had an impact on the lagoon. These include mining, agricultural, fishing, industrial, tourist, and recreational uses. From the 1960s the lagoon has suffered from a continuous increase of the tourist population (with a consequent increased urban waste water discharge) during the summer. Furthermore, there has been a considerable increase of the irrigated land, which has affected the nutrient load of the lagoon. These two factors have produced significant changes in the ecosystem with the abnormal proliferation of jellyfish during the last five years, and consequent considerable economical repercussions to the tourism and the local fishery.

2.2.3 Etang de Thau (France)

The lagoon of Thau is located on the French Mediterranean coast, with approximately 75 Km2 of surface area and an average depth of 4.5 m. It is under strong marine influence. The lagoon is connected to the sea by the canal of Sète (90% of exchanges). The climate imposes a wide range of water temperatures and salinities. Precipitation also shows large interannual variation (from 200 to 1000 mm per year). Wind is often strong, particularly when it is blowing from north-west (the so called “Tramontane”). The catchment area is about 250 Km2.

In this lagoon there is an intensively developed shellfish farming activity (oysters and mussels), that covers about 20% of the whole lagoon area and provides work to approximately 2000 people. The considerable shellfish production depends to a large extent on nutrient inputs into the ecosystem, mainly supplied by fresh water. Because of the weak tidal range, the residence time of water masses in the Thau lagoon mainly depends on wind and barometric effects, and it has been estimated that the water renewal time is about three months. Anthropogenic pressure on the catchment area is due to agriculture (mainly vineyards), agro-food and fertilizer industries, and domestic sewage. Approximately 80.000 people live around the lagoon, half of them at Sète. However, the population doubles during the summer.

Due to the low water exchange, to the intensive shellfish farming activities, and to the urban and agricultural pollution, the Thau lagoon has experienced, during several summers, acute eutrophication problems with anoxic crises.

2.2.4 Sacca di Goro (Italy)

The lagoon of Sacca di Goro is located in the southern part of the Po River delta, in the province of Ferrara. It is approximately triangular in shape with a surface area of 26 Km2 and an average depth of 1.5 m, and is connected to the sea by a mouth about 1.5 Km wide. The tidal amplitude is approximately 80 cm. The watershed of Burana-Volano is a lowland, flat basin, which is in some parts below the sea level. The watershed on the northern and eastern side is bordered by a branch of the Po river entering the Adriatic sea, and covers an area of about 3000 Km2.

About 80% of the watershed is dedicated to agriculture, whilst the lagoon is one of the most important aquacultural systems in Italy. About 10 Km2 of the aquatic surface (40% of the total) are exploited for farming of the Manila clam (Tapes philippinarum). Fishery and shellfish farming provide work, directly or indirectly, to 5000 people.

Due to the large supply of nutrients, organic matter and sediments coming from the agricultural catchment, the limited water circulation characterised by little water exchange with the sea, and the intensive shellfish production, the water quality is a

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major problem in the Sacca di Goro. Indeed, from 1987 the Sacca di Goro has experienced an abnormal proliferation of macroalgae (Ulva rigida). The decomposition of Ulva in summer determines oxygen depletion and anoxic crises.

2.2.5 Gulf of Gera (Greece)

The gulf of Gera is a semi-enclosed water body located in the island of Lesvos, in the Aegean archipelago. The surface area of the gulf is approximately 43 Km2, and the mean depth about 10 m. It is connected to the open sea through a channel, having width of 200-800 m, length of 6.5 Km and depth ranging from 10 to 30 m. The water circulation in the gulf is tidally driven, the exchange of water between the gulf and the open sea showing a fluctuation during the year due the morphological characteristics of the gulf. The surrounding area extends for approximately 200 Km2.

Olive tree cultivation is the dominant culture in the area. Fishery and aquaculture are also important. However, during the last decade, the development of tourism has become the top priority for the local population, and many tourist resorts have been constructed, especially on the western part of the coastal zone of the gulf.

The flux of nutrients from non-point sources (agricultural runoff) is considerable, especially during the winter period, when the contribution to the total inorganic nitrogen stock (the limiting nutrient in the area) varies between 40 and 60%. The most important point discharges are untreated domestic wastewater and effluents from the local industrial activities, especially olive oil processing by-products. The input of nutrients and organic matter from the surrounding watershed and the low water renewal rate determine eutrophication crises during the year. In addition to this, the environmental impact of tourism and its sustainability for the ecosystem are of main concern for the local authorities (Municipalities of Gera, Evergetoulas and Mytilene), responsible for the management of the area.

3 The decision support system In this section the general structure of the Decision Support System (DSS) for Southern European lagoons is presented. It integrates dynamic and mathematical models for system simulation, analytical models for the computation of performance indicators, and a tool (the Analytic Hierarchy Process, AHP) for multicriteria analysis. The emphasis in this section is primarily on the AHP, since the models used and their interconnections are specific of each site application.

3.1 Basics on decision support systems Decision support systems cover a wide variety of systems, tools and technologies for informing and supporting decision makers. The concept of a decision support system is in fact extremely broad and there exists no unique definition of it. Keen (1980) claims that it is even impossible to give a precise definition including all the facets of decision support systems (“there can be no definition of decision support systems, only of decision support”). Nevertheless, several authors in the literature provide definitions of decision support systems on both a conceptual and a concrete, technical level. Finlay (1994) and others define a DSS broadly as “a computer-based system that aids the process of decision making.” In a more precise way, Turban (1995) defines it as “an interactive, flexible, and adaptable computer-based information system, especially developed for supporting the solution of a non-structured management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows

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for the decision maker's own insights.” For Sprague and Carlson (1982), decision support systems are “interactive computer-based systems that help decision makers utilize data and models to solve unstructured problems.” Operation researchers often focus on optimization and simulation models as the “real” DSS.

Following Power (1997), a DSS is an interactive information system intended to assist decision making activities by helping managers to retrieve, summarize and analyze decision relevant data, thus improving and speeding-up the processes by which people make and communicate decisions. Power also proposes a classification of decision support systems by differentiating communication-driven, data-driven, document-driven, knowledge-driven, and model-driven decision support systems.

The category of model-driven decision support systems includes systems integrating different kinds of mathematical and analytical models. A model-driven DSS emphasizes access to and manipulation of, e.g., statistical, financial, optimization, and simulation models. In model-driven decision support systems the values of key variables or parameters are changed, often repeatedly, to reflect potential changes in management options, and/or environmental and internal factors. Information from the models is then analyzed and evaluated by the decision maker. Key issues when designing a model-driven DSS are the choice of appropriate models and software, and the definition of data formats. Model-driven decision support systems are well suited for decision analysis.

For a more complete taxonomy of decision support systems, see Power (2002).

3.2 Structure of the DSS for Southern European lagoons Being the final product of the DITTY project (WP8), the structure of the DSS has been thought so as to reap the benefits of the different mathematical and analytical models developed in the previous work-packages. Hence, it is naturally conceived as a model-driven DSS, where information from the models under different operating conditions is analyzed, evaluated and compared in order to make the final decision. A first version of the proposed DSS structure, particularly targeted to coastal lagoon areas characterized by shellfish farming and agriculture, was presented in Casini et al. (2005).

The first step of the development of the DSS concerns the definition of what kind of decisions have to be made. Such an analysis (which requires strict and active interaction with end-users) makes it possible to identify the key variables, i.e. all the parameters that can be modified to reflect the different decision options.

Example. If local authorities are asked to grant new farming concessions for aquaculture, and have to decide the amount of such concessions, the allocated farming area is the key variable.

A DSS-scenario is defined by value assignments of all the key variables that are involved in decision making. In other words, a DSS-scenario represents a particular decision.

The second step of the development of the DSS concerns the definition of the criteria on which the decisions have to be made. These criteria should reflect the objectives and/or the constraints of the decision problem. In general, socio-economic interests and environment preservation are typically contrasting objectives in coastal lagoon areas. The aim of the DSS is to help the user in making his or her decision by suitably trading-off between the above contrasting objectives.

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Example. It is well known that shellfish farming activities are responsible for important ecosystem disruptions, such as eutrophic conditions, algal blooms, oxygen depletion and nutrients production. It is expected that increasing the farming area will result into a worsening of the ecosystem health. Hence, in the above example, the decision criteria should include the contrasting objectives of preserving the lagoon ecosystem, still guaranteeing the economic growth of the area.

The performance of a DSS-scenario with respect to a given criterion is assumed to be summarized by the value of a suitable indicator, which can be of general type (qualitative, quantitative, or logical). In the previous work-packages of the project several environmental, social and economic indicators have been developed and tested. Note that the logical indicators may correspond to thresholds defined on the basis of regulations and/or experience.

The third step of the development of the DSS concerns the definition of its structure. The general structure of the DSS for Southern European lagoons proposed in this report is shown in Figure 2. The decision problems specific of all the test sites involved in the DITTY project can be cast into this framework.

Figure 2. Schematic architecture of the DSS.

Each block shown in Figure 2 will be now described in detail.

DSS-scenario generator. The DSS-scenario generator assigns values to all the key variables that are involved in decision making. Assuming that p key variables are considered and n different scenarios are generated, the p-dimensional vector V(i)

contains the values assigned to the key variables in the ith DSS-scenario, i=1,...,n.

Exogenous input generator. The exogenous input generator provides the uncontrollable inputs, i.e. the inputs that cannot be controlled/decided by the decision maker and are needed to run the mathematical and analytical models (e.g., weather conditions, water inflow, etc., for the site models; prices and market data for the economic models).

Mathematical and analytical models. This block represents the interconnection of the models used to produce the data and information needed for accomplishing the decision task. The use of the models is twofold:

• To make simulations and predictions, e.g., of the physical, chemical and biological variables of the ecosystem (in the previous work-packages of the project, watershed and/or lagoon models have been developed for each test site), as well as of the economic and social variables.

• To compute the indicators characterizing the performance of the simulated DSS-scenario with respect to the considered decision criteria.

Mathematical and analytical

models

Multicriteria Analysis (AHP)

Decision

Exogenous input

generator

DSS-scenario generator Storage

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When the ith DSS-scenario, i=1,...,n, is to be simulated and evaluated, the Model block receives as inputs the key variables characterizing that particular DSS-scenario, namely the vector V(i), and the exogenous inputs. The output of the Model block for the ith DSS-scenario is the vector I(i), an m-dimensional vector containing the corresponding values of the performance indicators, where m is the number of decision criteria.

Remark. For a given DSS-scenario, the values of the performance indicators clearly depend also on the exogenous inputs. Hence, in order to perform a fair comparison of the different DSS-scenarios, the same exogenous inputs must be used in all simulations. In addition, for clarity of presentation in this report it is assumed that a DSS-scenario is simulated for only one set of exogenous inputs. However, in order to make the DSS more robust with respect to varying exogenous conditions, it is possible to simulate each DSS-scenario for different sets of exogenous inputs, and then to consider the mean values and the standard deviations of the performance indicators.

The internal structure of the Model block is site-specific, being primarily dependent on the type of models developed for each site. Figure 3 shows a possible first-stage disaggregation of the Model block.

Exogenous inputs

Site Model (watershed+lagoon)

Figure 3. Example of a first-stage disaggregation of the Model block in Figure 2.

Storage. The values of the performance indicators for each simulated DSS-scenario are stored in the n×m matrix I shown in Figure 4, which is formed as follows:

( 3.1)

Indicator 1 ... Indicator m

DSS-Scenario 1

...

DSS-Scenario n

Figure 4. Matrix I of the performance indicators.

Multicriteria analysis. If only the jth decision criterion is considered (j=1,...,m), the best DSS-scenario can be simply selected by taking the optimum over the jth column of the matrix I. However, when all the (possibly contrasting) m decision criteria are considered, it may likely happen that the optimum over each column is not achieved by the same DSS-scenario. In this case the selection of the best DSS-scenario may be neither direct nor intuitive. This justifies the need for multicriteria analysis tools.

Ecological, Social and Economic

Analysis

Indicators I(i)Key variables

V(i)

.][ T(n)(1) III K=

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In the following section the Analytic Hierarchy Process (AHP) is suggested as a powerful and flexible tool to make decisions in situations where multiple and conflicting objectives/criteria are present, and both qualitative and quantitative aspects of a decision need to be considered. The AHP provides a ranking of the simulated DSS-scenarios by means of simple matrix calculations and pairwise comparisons.

3.3 The Analytic Hierarchy Process The Analytic Hierarchy Process (AHP), introduced by Thomas Saaty (1980), is an effective tool for dealing with complex decision making, and may aid the Decision Maker (DM) to set priorities and make the best decision. By reducing complex decisions to a series of pairwise comparisons, and then synthesizing the results, the AHP helps to capture both subjective and objective aspects of a decision. In addition, the AHP incorporates a useful technique for checking the consistency of the DM’s evaluations, thus reducing the bias in the decision making process.

3.3.1 How the AHP works

The AHP considers a set of evaluation criteria (in the DSS, the decision criteria), and a set of alternative options (the DSS-scenarios) among which the best decision is to be made. It is important to note that, since some of the criteria could correspond to contrasting objectives (e.g., socio-economic interests and environment preservation), it is not true in general that the best option is the one optimizing each single criterion, rather the one which achieves the most suitable trade-off among the different criteria.

The AHP generates a weight for each evaluation criterion according to the information provided by the DM. The higher the weight, the more important the objective. Next, the AHP evaluates all the scenarios on each criterion. Again these evaluations are made using the information provided by the DM. Finally, the AHP combines the objective weights and the scenario evaluations, thus determining a global score for each scenario and a consequent ranking. The global score for a given scenario is a weighted sum of the scores it obtained on the single criteria.

3.3.2 Features of the AHP

The AHP is a very flexible and powerful tool because the scores, and therefore the final ranking, are obtained on the basis of the pairwise relative evaluations of both the criteria and the scenarios provided by the DM. The computations made by the AHP are always guided by the DM’s experience, and the AHP can thus be considered as a tool that is able to translate the evaluations (both qualitative and quantitative) made by the DM into a multicriteria ranking. In addition, the AHP is simple because there is no need of building a complex expert system with the DM’s knowledge embedded in it.

On the other hand, the AHP may require a large number of evaluations by the DM, especially for problems with many objectives and scenarios. Although every single evaluation is very simple, since it only requires the DM to express how two scenarios or criteria compare to each other, the load of the evaluation task may become unreasonable for the DM. In fact the number of pairwise comparisons grows quadratically with the number of criteria and scenarios. For instance, when comparing 10 scenarios on 4 objectives, 4·3/2=6 comparisons are requested to build the weight vector, and 4·(10·9/2)=180 pairwise comparisons are needed to build the score matrix.

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However, in order to reduce the DM’s workload the AHP may be completely or partially automated by specifying suitable thresholds for automatically deciding some pairwise comparisons.

3.3.3 Implementation of the AHP

The AHP may be implemented in three simple consecutive steps:

1. Computing the vector of objective weights.

2. Computing the matrix of scenario scores.

3. Ranking the scenarios.

Each step will be described in detail in the following. It is assumed that m evaluation criteria are considered, and n scenarios are to be evaluated. A useful technique for checking the reliability of the results will be also introduced. See, e.g., Mustajoki and Hämäläinen (2000), <http://www.hipre.hut.fi>, for web implementations of the AHP.

Computing the vector of objective weights. In order to compute the weights for the different objectives, the AHP starts creating a pairwise comparison matrix A. The matrix A is a m×m real matrix, where m is the number of evaluation criteria considered. Each entry ajk of the matrix A represents2 the importance of the jth criterion relative to the kth criterion. If ajk > 1, then the jth criterion is more important than the kth criterion, while if ajk < 1, then the jth criterion is less important than the kth criterion. If two criteria have the same importance, then the entry ajk is 1. The entries ajk and akj satisfy the following constraint:

( 3.2) .1=⋅ kjjk aa

Obviously, ajj = 1 for all j. The relative importance between two criteria is measured according to a numerical scale from 1 to 9, as shown in Table 1, where it is assumed that the jth criterion is equally or more important than the kth criterion.

Value of ajk Interpretation

1 j and k are equally important

3 j is slightly more important than k

5 j is strongly more important than k

7 j is very strongly more important than k

9 j is absolutely more important than k

Table 1. Table of relative scores.

The phrases in the Interpretation column of Table 1 are only suggestive, and may be used to translate the DM’s qualitative evaluations of the relative importance between two criteria into numbers. It is also possible to assign intermediate values which do not correspond to a precise interpretation. The values in the matrix A are by construction pairwise consistent, see ( 3.2). On the other hand, the ratings may in general show slight inconsistencies. However these do not cause serious difficulties for AHP.

2 Conventionally, for a matrix A, aij denotes the entry in the i-th row and the j-th column of A. For a vector v, vi denotes the i-th element of v.

15

Once the matrix A is built, it is possible to derive from A the normalized pairwise comparison matrix Anorm by making equal to 1 the sum of the entries on each column, i.e. each entry jka of the matrix Anorm is computed as

.

1∑=

= m

llk

jkjk

a

aa ( 3.3)

Finally, the objective weight vector w (that is a m-dimensional column vector) is built by averaging the entries on each row of Anorm, i.e.

.1

m

m

ljl

j

∑==

aw ( 3.4)

Computing the matrix of scenario scores. The matrix of scenario scores is a n×m real matrix S. Each entry sij of S represents the score of the ith scenario with respect to the jth criterion. In order to derive such scores, a pairwise comparison matrix is first built for each of the m criteria, j=1,...,m. Each matrix is a n×n real matrix, where n is the number of scenarios evaluated. Each entry b of the matrix B represents the evaluation of the ith scenario compared to the hth scenario with respect to the jth criterion. If , then the ith scenario is better than the hth scenario, while if

, then the ith scenario is worse than the hth scenario. If two scenarios are evaluated as equivalent with respect to the jth criterion, then the entry is 1. The entries b and b satisfy the following constraint:

)( j

)( j

)( j )( j

)( j

)( j

)( j

)( j )( j

BB

ih

1>ihb1<ihb

ihb

ih hi

( 3.5) ,1)()( =⋅ jhi

jih bb

and for all i. An evaluation scale similar to the one introduced in Table 1 may be used to translate the DM’s pairwise evaluations into numbers.

1)( =jiib

Second, the AHP applies to each matrix the same two-step procedure described for the pairwise comparison matrix A, i.e. it divides each entry by the sum of the entries in the same column, and then it averages the entries on each row, thus obtaining the score vectors , j=1,...,m. The vector s contains the scores of the evaluated scenarios with respect to the jth criterion.

)( jB

)( js )( j

Finally, the score matrix S is obtained as

( 3.6) ,][ (m)(1) ssS K=

i.e. the jth column of S corresponds to s . )( j

Remark. In the proposed DSS, the pairwise DSS-scenario evaluations are performed by comparing the values of the performance indicators corresponding to the decision criteria (see Section 3.2). Hence, this step of the AHP can be considered as a transformation of the indicator matrix I into the score matrix S.

Ranking the scenarios. Once the weight vector w and the score matrix S have been computed, the AHP obtains a vector v of global scores by multiplying S and w, i.e.

( 3.7) ,wSv ⋅=

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The ith entry vi of v represents the global score assigned by the AHP to the ith scenario. As the final step, the scenario ranking is accomplished by ordering the global scores in decreasing order.

Checking the consistency. When many pairwise comparisons are performed, some inconsistencies may typically arise.

One example is the following. Assume that three criteria are considered, and the DM evaluates that the criterion 1 is slightly more important than the criterion 2, while the criterion 2 is slightly more important than the criterion 3. An evident inconsistency arises if the DM evaluates by mistake that the criterion 3 is equally or more important than the criterion 1. On the other hand, a slight inconsistency arises if the DM evaluates that the criterion 1 is also slightly more important than the criterion 3. A consistent evaluation would be, for instance, that the criterion 1 is strongly more important than the criterion 3.

The AHP incorporates an effective technique for checking the consistency of the evaluations made by the DM when building each of the pairwise comparison matrices involved in the process, namely the matrix A and the matrices . The technique relies on the computation of a suitable consistency index, and will be described only for the matrix A. It is straightforward to adapt it to the case of the matrices by replacing A with , w with , and m with n. The Consistency Index (CI) is obtained by first computing the scalar x as the average of the elements of the vector whose jth element is the ratio of the jth element of the vector A·w to the corresponding element of the vector w. Then,

)( jB

)( jB)( jB )( js

.1−

−=

mmxCI ( 3.8)

A perfectly consistent DM should always obtain CI=0, but small values of inconsistency may be tolerated. In particular, if

,1.0<RICI ( 3.9)

the inconsistencies are tolerable, and a reliable result may be expected from the AHP. In ( 3.9) RI is the Random Index, i.e. the consistency index when the entries of A are completely random. The values of RI for small problems (m ≤ 10) are shown in Table 2.

m 2 3 4 5 6 7 8 9 10

RI 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.51

Table 2. Values of the Random Index for small problems.

The matrices A corresponding to the cases considered in the above example are shown below, together with their consistency evaluation based on the computation of the consistency index. Note that the conclusions are as expected.

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

13/13313/13/131

A ⇒ CI/RI = 1.150 ⇒ inconsistent

17

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

13/13/1313/1331

A ⇒ CI/RI = 0.118 ⇒ slightly inconsistent

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

13/15/1313/1531

A ⇒ CI/RI = 0.033 ⇒ consistent

3.3.4 An illustrative example An example will be here described in order to clarify the mechanism of the AHP. m=3 evaluation criteria are considered, and n=3 scenarios are evaluated. Each criterion is summarized by an indicator. The larger the value of the indicator, the better the performance of the scenario with respect to the corresponding criterion. The DM first builds the following pairwise comparison matrix for the three criteria:

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

13/15/1313/1531

A ,

to which corresponds the weight vector w = [ 0.633 0.261 0.106 ]T. Then, based on the values assumed by the indicators for the three scenarios (see Figure 5), the DM builds the following pairwise comparison matrices:

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

15/17/1513/1731

)1(B , , , ⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

15/1151515/11

)2(B⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

13/19/1315/1951

)3(B

to which correspond the score vectors = [ 0.643 0.283 0.074 ])1(s T, = [ 0.143 0.714 0.143 ]

)2(sT, and = [ 0.748 0.180 0.072 ])3(s T.

Figure 5. Values of the indicators corresponding to the three criteria for each scenario (the scale on each axis is not relevant).

Hence, the score matrix S turns out to be:

,072.0143.0074.0180.0714.0283.0748.0143.0643.0

][ )3()2()1(

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡== sssS

Criterion 1

0 S3 S2 S1

Criterion 2

0 S3 S2 S1

Criterion 3

0 S3 S2 S1

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and the global score vector is v = S·w = [ 0.523 0.385 0.092 ]T. Note that the first scenario results to be the most preferable, though it is the worst of the three with respect to the second criterion.

3.4 Implementation of the DSS The University of Siena is currently developing a software implementation of the DSS user-interface using the GUI Design Environment tool of Matlab™ 7.0. The interface allows the user to select values for the key variables and to run the models under different exogenous conditions. The values of the performance indicators are also evaluated, and time plots of all the simulated variables may be displayed. Output data for each DSS-scenario are saved as text files for future retrieval. A multicriteria analysis tool based on the AHP is also implemented.

4 Case studies The application of the DSS to the five test sites of the DITTY project is currently under development. In the last Steering Committee (which was held in Siena, 22-24 September 2005) the decision problems of interest have been defined for each site, and several aspects related to the DSS functioning have been clarified. In particular, the role of models and tools developed in the previous work-packages of the project has been stressed. Under the coordination of UNISI, all partners are currently developing the specific site applications according to the DSS general structure described in Section 3.

In the final version of the present report, this section will contain a detailed description of the five case studies, including testing and benchmarking.

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