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K.Fedra ‘051
Water Resources Simulation Water Resources Simulation and Optimization: a web and Optimization: a web
based approachbased approach
MSO August 2005, ArubaMSO August 2005, Aruba
Water Resources Simulation Water Resources Simulation and Optimization: a web and Optimization: a web
based approachbased approach
MSO August 2005, ArubaMSO August 2005, Aruba
DDr. Kurt Fedra ESS GmbH, [email protected] http://www.ess.co.at DDr. Kurt Fedra ESS GmbH, [email protected] http://www.ess.co.at
K.Fedra ‘052
Water resources management:Water resources management:Water resources management: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 ‘053
Water resources management:Water resources management:Water resources management: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 ‘054
OPTIMA: OPTIMA: INCO-MPCINCO-MPC
Ongoing applications, EU supported:
• INCO-MED SMART (2002-2005)– Turkey, Lebanon, Jordan, Egypt, Tunisia;
Italy, France, Portugal, Austria
• INCO-MPC OPTIMA (2004-2007)– Turkey, Lebanon, Jordan, Palestine,
Tunisia, Morocco, Cyprus; Italy, Greece, Malta, Austria
Ongoing applications, EU supported:
• INCO-MED SMART (2002-2005)– Turkey, Lebanon, Jordan, Egypt, Tunisia;
Italy, France, Portugal, Austria
• INCO-MPC OPTIMA (2004-2007)– Turkey, Lebanon, Jordan, Palestine,
Tunisia, Morocco, Cyprus; Italy, Greece, Malta, Austria
K.Fedra ‘055
OPTIMA:OPTIMA:OPTIMA:OPTIMA:
Project Project started July 2004 (3 years):started July 2004 (3 years):The various data sets and scenarios form the The various data sets and scenarios form the
basis for the optimization/basin master plans:basis for the optimization/basin master plans:METHODMETHOD::Monte-Carlo, genetic programming, discrete Monte-Carlo, genetic programming, discrete
multi-criteria (reference point) optimization;multi-criteria (reference point) optimization;OBJECTIVESOBJECTIVES include: include:
maximize demand satisfied;maximize demand satisfied;maximize reliability;maximize reliability;maximize water based net revenues;maximize water based net revenues;minimize environmental impacts (env. water minimize environmental impacts (env. water
demand or minimal flow, WQ standarddemand or minimal flow, WQ standard violations) violations)
Project Project started July 2004 (3 years):started July 2004 (3 years):The various data sets and scenarios form the The various data sets and scenarios form the
basis for the optimization/basin master plans:basis for the optimization/basin master plans:METHODMETHOD::Monte-Carlo, genetic programming, discrete Monte-Carlo, genetic programming, discrete
multi-criteria (reference point) optimization;multi-criteria (reference point) optimization;OBJECTIVESOBJECTIVES include: include:
maximize demand satisfied;maximize demand satisfied;maximize reliability;maximize reliability;maximize water based net revenues;maximize water based net revenues;minimize environmental impacts (env. water minimize environmental impacts (env. water
demand or minimal flow, WQ standarddemand or minimal flow, WQ standard violations) violations)
K.Fedra ‘056
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 ‘057
Project web site:Project web site:Project web site:Project web site:
• http://www.ess.co.at/SMART/
• http://www.ess.co.at/OPTIMA/
• http://www.ess.co.at/WATERWARE/
Including on-line GIS, data bases and interactive modeling tools
• http://www.ess.co.at/SMART/
• http://www.ess.co.at/OPTIMA/
• http://www.ess.co.at/WATERWARE/
Including on-line GIS, data bases and interactive modeling tools
K.Fedra ‘058
Components and toolsComponents and toolsComponents and toolsComponents and tools
Related on-line tools:Related on-line tools:•Stakeholders data base: Stakeholders data base:
register your institution!register your institution!•Water Issues questionnaire Water Issues questionnaire
(benchmarking for river basins)(benchmarking for river basins)
describe your basin !describe your basin !
Related on-line tools:Related on-line tools:•Stakeholders data base: Stakeholders data base:
register your institution!register your institution!•Water Issues questionnaire Water Issues questionnaire
(benchmarking for river basins)(benchmarking for river basins)
describe your basin !describe your basin !
K.Fedra ‘059
Purpose and objectives:Purpose and objectives:Purpose and objectives: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 ‘0510
Optimality and Sustainability :Optimality and Sustainability :Optimality and Sustainability :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 CONSTRAINTSMEET 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 CONSTRAINTSMEET THE CONSTRAINTS
K.Fedra ‘0511
Mediterranean region:Mediterranean region:Mediterranean region: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 ‘0512
Mediterranean region:Mediterranean region:Mediterranean region: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 ‘0513
Development Scenarios:Development Scenarios:Development Scenarios: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 ‘0514
Scenario analysis:Scenario analysis:Scenario analysis: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 ‘0515
Scenarios:Scenarios: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 ‘0516
From scenarios to optimizationFrom scenarios to optimizationFrom scenarios to optimizationFrom scenarios to optimization1. 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 ‘0517
System performance:System performance:System performance: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 ‘0518
WATERWARE WATERWARE (EUREKA 486)(EUREKA 486)WATERWARE WATERWARE (EUREKA 486)(EUREKA 486)
Water resources management Water resources management information system:information system:
• River basin orientedRiver basin oriented• Integrated data managementIntegrated data management• Cascading models for supply-demand Cascading models for supply-demand
pattern simulation incl. qualitypattern simulation incl. quality• Management oriented (allocation, Management oriented (allocation,
efficiency)efficiency)• Use of economic criteriaUse of economic criteriahttp://www.ess.co.at/WATERWARE/http://www.ess.co.at/WATERWARE/
Water resources management Water resources management information system:information system:
• River basin orientedRiver basin oriented• Integrated data managementIntegrated data management• Cascading models for supply-demand Cascading models for supply-demand
pattern simulation incl. qualitypattern simulation incl. quality• Management oriented (allocation, Management oriented (allocation,
efficiency)efficiency)• Use of economic criteriaUse of economic criteriahttp://www.ess.co.at/WATERWARE/http://www.ess.co.at/WATERWARE/
K.Fedra ‘0519
Simulation models: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 ‘0520
Object types:Object types:
Monitoring stationMonitoring station
K.Fedra ‘0521
Object types:Object types:
Monitoring stationMonitoring station
K.Fedra ‘0522
Object types:Object types:
K.Fedra ‘0523
Object type:Object type:
ReservoirReservoir
K.Fedra ‘0524
Simulation models:Simulation models:
K.Fedra ‘0525
Crop data base:Crop data base:Crop data base:Crop data base:
K.Fedra ‘0526
Crop data base:Crop data base:Crop data base:Crop data base:
K.Fedra ‘0527
Simulation models:Simulation models:
K.Fedra ‘0528
Simulation models:Simulation models:
K.Fedra ‘0529
Simulation models:Simulation models:
K.Fedra ‘0530
Simulation models:Simulation models:
K.Fedra ‘0531
Simulation models:Simulation models:
K.Fedra ‘0532
K.Fedra ‘0533
Simulation models:Simulation models:
K.Fedra ‘0534
Simulation models:Simulation models:
K.Fedra ‘0535
Simulation models:Simulation models:
K.Fedra ‘0536
Simulation models:Simulation models:
K.Fedra ‘0537
Simulation models:Simulation models:
K.Fedra ‘0538
Simulation models:Simulation models:
K.Fedra ‘0539
Simulation models:Simulation models:
K.Fedra ‘0540
Simulation models:Simulation models:
K.Fedra ‘0541
Simulation models:Simulation models:
K.Fedra ‘0542
Evaluation:Evaluation: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 ‘0543
Scenario Evaluation:Scenario Evaluation:Scenario Evaluation: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 ‘0544
Evaluation:Evaluation:Evaluation:Evaluation:
Aggregated into Sustainability Index with RULES:
IF EEF == high (medium, low)
AND ENC == high (medium, low)
AND SEQ == high (medium, low)
THEN SUSTAINABILITY = HIGH
Aggregated into Sustainability Index with RULES:
IF EEF == high (medium, low)
AND ENC == high (medium, low)
AND SEQ == high (medium, low)
THEN SUSTAINABILITY = HIGH
K.Fedra ‘0545
Evaluation:Evaluation: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 ‘0546
Decision Support:Decision Support:Decision Support: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 Decision Support (multi-attribute)(multi-attribute)Decision Support Decision Support (multi-attribute)(multi-attribute)
Reference point approach:Reference point approach:
nadirnadirnadirnadir
utopiautopiautopiautopia
A1A1
A2A2
A3A3
A4A4
betterbetter
efficient efficient pointpoint
criterion 1criterion 1
crite
rion
2cr
iterio
n 2 A5A5
dominateddominated
A6A6
K.Fedra ‘0548
In summary:In summary:In summary: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.