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Water Resources Simulation and Optimization: a web based approach MSO August 2005, Aruba

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MSO August 2005, Aruba. DDr. Kurt Fedra ESS GmbH, Austria. kurt_at_ess.co.at http://www.ess.co.at ... Identification and involvement of major actors, stakeholders ... – PowerPoint PPT presentation

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Title: Water Resources Simulation and Optimization: a web based approach MSO August 2005, Aruba


1
Water Resources Simulation and Optimization a
web based approachMSO August 2005, Aruba

DDr. Kurt Fedra ESS GmbH, Austria kurt_at_ess.co
.at http//www.ess.co.at
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

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

4
OPTIMA INCO-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

5
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)

6
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

7
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

8
Components and tools
  • Related on-line tools
  • Stakeholders data base
  • register your institution!
  • Water Issues questionnaire (benchmarking for
    river basins)
  • describe your basin !

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)

10
Optimality and Sustainability
  • Economic efficiency (true cost, maximize
    economic benefits, minimize costs)
  • Environmental compatibility (meeting standards,
    protect wetlands, sensitive areas, minimize env.
    costs)
  • Equity (intra- and intergenerational)
  • MEET THE CONSTRAINTS

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

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)

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.

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)

15
Scenarios
  1. Demographic development (population growth,
    migration, urbanization lt 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)

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

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)

18
WATERWARE (EUREKA 486)
  • Water resources management information system
  • River basin oriented
  • Integrated data management
  • Cascading models for supply-demand pattern
    simulation incl. quality
  • Management oriented (allocation, efficiency)
  • Use of economic criteria
  • http//www.ess.co.at/WATERWARE/

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)

20
Object types
  • Monitoring station

21
Object types
  • Monitoring station

22
Object types
23
Object type
  • Reservoir

24
Simulation models
25
Crop data base
26
Crop data base
27
Simulation models
28
Simulation models
29
Simulation models
30
Simulation models
31
Simulation models
32
(No Transcript)
33
Simulation models
34
Simulation models
35
Simulation models
36
Simulation models
37
Simulation models
38
Simulation models
39
Simulation models
40
Simulation models
41
Simulation models
42
Evaluation
  • Aggregated into Sustainability Indicators
  • Economic efficiency
  • Environmental compatibility
  • Equity (intra- and intergenerational)

43
Scenario Evaluation
  • Aggregated into Aggregate Sustainability
    Indicators with RULES
  • IF Sup/Dem gt 0.99
  • AND Reliability gtgt 85
  • AND .. high/medium/low
  • THEN EEF HIGH

44
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

45
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

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.

47
Decision Support (multi-attribute)
  • Reference point approach

utopia
A4
efficient point
A5
A2
criterion 2
A6
A1
dominated
A3
better
nadir
criterion 1
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.
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