K.Fedra ‘05 1 Water Resources Simulation and Optimization: a web based approach MSO August 2005, Aruba DDr. Kurt Fedra ESS GmbH, Austria DDr. Kurt Fedra ESS GmbH, Austria

K.Fedra ‘05 2 Water resources management: From principles to procedures and tools How to define optimality ? How to explore, compare options ? How to agree on solutions ? Optimality is not an operational principle easily implemented From principles to procedures and tools How to define optimality ? How to explore, compare options ? How to agree on solutions ? Optimality is not an operational principle easily implemented

K.Fedra ‘05 3 Water resources management: Definition of optimality: Acceptability, satisficing Requires a participatory approach: –Identification and involvement of major actors, stakeholders –Shared information basis –Easy access, intuitive understanding – Web based, local workshops Definition of optimality: Acceptability, satisficing Requires a participatory approach: –Identification and involvement of major actors, stakeholders –Shared information basis –Easy access, intuitive understanding – Web based, local workshops

K.Fedra ‘05 4 OPTIMA: INCO-MPC Ongoing applications, EU supported: INCO-MED SMART ( ) –Turkey, Lebanon, Jordan, Egypt, Tunisia; Italy, France, Portugal, Austria INCO-MPC OPTIMA ( ) –Turkey, Lebanon, Jordan, Palestine, Tunisia, Morocco, Cyprus; Italy, Greece, Malta, Austria Ongoing applications, EU supported: INCO-MED SMART ( ) –Turkey, Lebanon, Jordan, Egypt, Tunisia; Italy, France, Portugal, Austria INCO-MPC OPTIMA ( ) –Turkey, Lebanon, Jordan, Palestine, Tunisia, Morocco, Cyprus; Italy, Greece, Malta, Austria

K.Fedra ‘05 5 OPTIMA:OPTIMA: Project started July 2004 (3 years): The various data sets and scenarios form the basis for the optimization/basin master plans: METHOD: Monte-Carlo, genetic programming, discrete multi- criteria (reference point) optimization; OBJECTIVES include: maximize demand satisfied; maximize reliability; maximize water based net revenues; minimize environmental impacts (env. water demand or minimal flow, WQ standard violations) Project started July 2004 (3 years): The various data sets and scenarios form the basis for the optimization/basin master plans: METHOD: Monte-Carlo, genetic programming, discrete multi- criteria (reference point) optimization; OBJECTIVES include: maximize demand satisfied; maximize reliability; maximize water based net revenues; minimize environmental impacts (env. water demand or minimal flow, WQ standard violations)

K.Fedra ‘05 6 Methodology:Methodology: Analyze socio-economic and regulatory framework, multiple objectives ( issues questionnaire) WaterWare (river basin) model including economic assessment 7 parallel case studies, end user involvement for optimization objectives and criteria (reference point analysis) Comparative analysis, best practice Analyze socio-economic and regulatory framework, multiple objectives ( issues questionnaire) WaterWare (river basin) model including economic assessment 7 parallel case studies, end user involvement for optimization objectives and criteria (reference point analysis) Comparative analysis, best practice

K.Fedra ‘05 7 Project web site: Including on-line GIS, data bases and interactive modeling tools Including on-line GIS, data bases and interactive modeling tools

K.Fedra ‘05 8 Components and tools Related on-line tools: Stakeholders data base:Stakeholders data base: register your institution! Water Issues questionnaire (benchmarking for river basins)Water Issues questionnaire (benchmarking for river basins) describe your basin ! Related on-line tools: Stakeholders data base:Stakeholders data base: register your institution! Water Issues questionnaire (benchmarking for river basins)Water Issues questionnaire (benchmarking for river basins) describe your basin !

K.Fedra ‘05 9 Purpose and objectives: Scientifically based contributions to Water Resources Management through improved efficiency and performance the policy and decision making processes (participatory, empowerment of stakeholders) Scientifically based contributions to Water Resources Management through improved efficiency and performance the policy and decision making processes (participatory, empowerment of stakeholders)

K.Fedra ‘05 10 Optimality and Sustainability : 1.Economic efficiency (“ true cost”, maximize economic benefits, minimize costs ) 2.Environmental compatibility (meeting standards, protect wetlands, sensitive areas, minimize env. costs) 3.Equity (intra- and intergenerational) MEET THE CONSTRAINTS 1.Economic efficiency (“ true cost”, maximize economic benefits, minimize costs ) 2.Environmental compatibility (meeting standards, protect wetlands, sensitive areas, minimize env. costs) 3.Equity (intra- and intergenerational) MEET THE CONSTRAINTS

K.Fedra ‘05 11 Mediterranean region: The projections of water available per person are dropping steeply for most countries: Average values (Wagner, 2001) are moving to 1,000 m3/person and year or below (Southern and Eastern Mediterranean) -based on demographic projections -assumptions on per capita use The projections of water available per person are dropping steeply for most countries: Average values (Wagner, 2001) are moving to 1,000 m3/person and year or below (Southern and Eastern Mediterranean) -based on demographic projections -assumptions on per capita use

K.Fedra ‘05 12 Mediterranean region: Coastal zone development and urbanization increase demand for high-quality drinking water; Tourism with very high per capita demands generates unfavorable demand patterns (summer peak) But agriculture is still the major consumer of water (largely due to inefficient irrigation technologies) Coastal zone development and urbanization increase demand for high-quality drinking water; Tourism with very high per capita demands generates unfavorable demand patterns (summer peak) But agriculture is still the major consumer of water (largely due to inefficient irrigation technologies)

K.Fedra ‘05 13 Development Scenarios: 1.Baseline (status quo for calibration) 2.Business as usual (naïve trend extrapolation) 3.Pessimistic (everything “bad” will happen) 4.Optimistic (all the good things …) 5.Specific existing plans of structural change, legislation, etc. 1.Baseline (status quo for calibration) 2.Business as usual (naïve trend extrapolation) 3.Pessimistic (everything “bad” will happen) 4.Optimistic (all the good things …) 5.Specific existing plans of structural change, legislation, etc.

K.Fedra ‘05 14 Scenario analysis: Objective is NOT to forecast a most likely future, but to explore the range of possibilities (bound solutions, define nadir and utopia to normalize results as % achievements, relative change) Objective is NOT to forecast a most likely future, but to explore the range of possibilities (bound solutions, define nadir and utopia to normalize results as % achievements, relative change)

K.Fedra ‘05 15 Scenarios:Scenarios: 1.Demographic development (population growth, migration, urbanization <== land use change) 2.Economic development (sectoral growth, tourism) 3.Technological development (specific water use efficiencies) 4.Institutional change (regulations, enforcement) 5.Climate change (decreased means, increased variability of precipitation, temperature increase) 1.Demographic development (population growth, migration, urbanization <== land use change) 2.Economic development (sectoral growth, tourism) 3.Technological development (specific water use efficiencies) 4.Institutional change (regulations, enforcement) 5.Climate change (decreased means, increased variability of precipitation, temperature increase)

K.Fedra ‘05 16 From scenarios to optimization 1.Define a most likely scenario 2.Define a set of alternative options: Structurally (reservoirs) Supply management (alternative sources) Demand management (pricing) Water technologies (efficiencies) with their investment operating costs, 3. Find efficient combinations (heuristics, genetic algorithms) 4.Calculate system performance: find feasible solutions 1.Define a most likely scenario 2.Define a set of alternative options: Structurally (reservoirs) Supply management (alternative sources) Demand management (pricing) Water technologies (efficiencies) with their investment operating costs, 3. Find efficient combinations (heuristics, genetic algorithms) 4.Calculate system performance: find feasible solutions

K.Fedra ‘05 17 System performance: Derived from the model results: Demand/Supply balance (by sector incl. environmental water use) Reliability of Supply (% mass, time) Efficiency (benefits/unit water used) Cost/benefit ratios (NPV), penalties Water quality (in stream) Derived from the model results: Demand/Supply balance (by sector incl. environmental water use) Reliability of Supply (% mass, time) Efficiency (benefits/unit water used) Cost/benefit ratios (NPV), penalties Water quality (in stream)

K.Fedra ‘05 18 WATERWARE (EUREKA 486) Water resources management information system: River basin orientedRiver basin oriented Integrated data managementIntegrated data management Cascading models for supply-demand pattern simulation incl. qualityCascading models for supply-demand pattern simulation incl. quality Management oriented (allocation, efficiency)Management oriented (allocation, efficiency) Use of economic criteriaUse of economic criteriahttp:// Water resources management information system: River basin orientedRiver basin oriented Integrated data managementIntegrated data management Cascading models for supply-demand pattern simulation incl. qualityCascading models for supply-demand pattern simulation incl. quality Management oriented (allocation, efficiency)Management oriented (allocation, efficiency) Use of economic criteriaUse of economic criteriahttp://

K.Fedra ‘05 19 Simulation models: Linked set of models: Rainfall-runoff for ungaged catchments Irrigation water demand Water resources (daily water budgets) Water quality (basin wide) Water quality (local, near field) Groundwater flow and transport (2D) Linked set of models: Rainfall-runoff for ungaged catchments Irrigation water demand Water resources (daily water budgets) Water quality (basin wide) Water quality (local, near field) Groundwater flow and transport (2D)

K.Fedra ‘05 20 Object types: Monitoring station

K.Fedra ‘05 21 Object types: Monitoring station

K.Fedra ‘05 22 Object types:

K.Fedra ‘05 23 Object type: Reservoir

K.Fedra ‘05 24 Simulation models:

K.Fedra ‘05 25 Crop data base:

K.Fedra ‘05 26 Crop data base:

K.Fedra ‘05 27 Simulation models:

K.Fedra ‘05 28 Simulation models:

K.Fedra ‘05 29 Simulation models:

K.Fedra ‘05 30 Simulation models:

K.Fedra ‘05 31 Simulation models:

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K.Fedra ‘05 33 Simulation models:

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K.Fedra ‘05 35 Simulation models:

K.Fedra ‘05 36 Simulation models:

K.Fedra ‘05 37 Simulation models:

K.Fedra ‘05 38 Simulation models:

K.Fedra ‘05 39 Simulation models:

K.Fedra ‘05 40 Simulation models:

K.Fedra ‘05 41 Simulation models:

K.Fedra ‘05 42 Evaluation:Evaluation: Aggregated into Sustainability Indicators 1.Economic efficiency 2.Environmental compatibility 3.Equity (intra- and intergenerational) Aggregated into Sustainability Indicators 1.Economic efficiency 2.Environmental compatibility 3.Equity (intra- and intergenerational)

K.Fedra ‘05 43 Scenario Evaluation: Aggregated into Aggregate Sustainability Indicators with RULES: IF Sup/Dem >= 0.99 AND Reliability>> 85% AND …….. == high/medium/low THEN EEF = HIGH Aggregated into Aggregate Sustainability Indicators with RULES: IF Sup/Dem >= 0.99 AND Reliability>> 85% AND …….. == high/medium/low THEN EEF = HIGH

K.Fedra ‘05 44 Evaluation:Evaluation: Aggregated into Sustainability Index with RULES: IF EEF == high (medium, low) AND ENC == high (medium, low) ANDSEQ == high (medium, low) THEN SUSTAINABILITY = HIGH Aggregated into Sustainability Index with RULES: IF EEF == high (medium, low) AND ENC == high (medium, low) ANDSEQ == high (medium, low) THEN SUSTAINABILITY = HIGH

K.Fedra ‘05 45 Evaluation:Evaluation: Evaluation process is open for inspection and participation: easy to understand and change RULES OBJECTIVE: not to offer the ultimate assessment for SUSTAINABILITY, but a framework for structured discourse and user participation Evaluation process is open for inspection and participation: easy to understand and change RULES OBJECTIVE: not to offer the ultimate assessment for SUSTAINABILITY, but a framework for structured discourse and user participation

K.Fedra ‘05 46 Decision Support: Comparative analysis of feasible, non- dominated solutions in terms of the performance indicators; Participatory approach: Stake holders define criteria, objectives, constraints, and expectations – DSS tool finds the nearest feasible solution in the set of alternatives. Comparative analysis of feasible, non- dominated solutions in terms of the performance indicators; Participatory approach: Stake holders define criteria, objectives, constraints, and expectations – DSS tool finds the nearest feasible solution in the set of alternatives.

K.Fedra ‘05 Decision Support (multi-attribute) Reference point approach: nadirnadir utopiautopia A1 A2 A3 A4 better efficientpoint criterion 1 criterion 2 A5 dominated A6

K.Fedra ‘05 48 In summary: Problems are largely man made Solutions involve valuation, trade off: subjective – political – choices; NOT optimal, but acceptable to a majority  Democratic decision making processes No single method, solutions need a well balanced combination of strategies and tools, based on preferences, believes, fears, and a little science. Problems are largely man made Solutions involve valuation, trade off: subjective – political – choices; NOT optimal, but acceptable to a majority  Democratic decision making processes No single method, solutions need a well balanced combination of strategies and tools, based on preferences, believes, fears, and a little science.