Water Ware description Data management Objects Monitoring time
Water. Ware description • Data management, Objects • Monitoring, time series • • • Hydro-meteorological data, forecasts Rainfall-runoff: RRM, floods Irrigation water demand Water budget modelling Water quality: STREAM, SPILL • Multi-criteria optimization, DSS • User support, system maintenance 1
IWRM, optimization Water Management Problems: • Too much, not enough, • Wrong time and place • Insufficient quality • Poor efficiency, economics • Growing uncertainty Information requirements: • How to make sure we get all the water we want when we want it, cheap, reliable, sustainable ? 2
Decision Support Systems • Manage preferences: criteria, objectives, constraints • Design/manage alternatives simulate alternative strategies • Select best (compromise) solution BUT: Preferences vary; multiple criteria, conflicting objectives; Uncertain effects, uncertain driving forces: future demographics, economy, technology, CLIMATE 3
IWRM optimization We do NOT want water as such, we want water based products and services: Increase the efficiency, reduce specific water needs, costs ? MAXIMIZE net benefit from water allocation/use considering socio-economic and environemntl criteria 4
Paradigm change: NOT about “supply, access, ecological status” Shared “benefits”: • Increasing overall net benefit while meeting individual user constraints as the basis for win- win solutions: everybody is better off ! 5
Paradigm change: Replace OPTIMAL with Good enough, but robust New concepts: Robustness, reliability, resilience, sustainability 6
Paradigm extension: robust solutions, reliable benefits: • Increasing overall net benefit while meeting individual users constraints as the basis for win- win solutions under growing uncertainty: Include reliability, sustainability, as explicit criteria of optimization. 7
Design alternatives: Assign alternative technologies (emission control, water savings from the data base) to emission sources, water users, structures: calculate emission reductions, increase of efficiency, costs and benefits for thousands of combinations optimization 8
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Optimization strategy: Vary the assigned technologies, Monte Carlo, then heuristic, machine learning, genetic algorithms, …. to convergence Separate feasible and infeasible solutions (constraints) Extract pareto-optimal subset (nondominated), criteria selection, reference point (UTOPIA) Select efficient “best” solution 15
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Decision Support (multi-attribute) Reference point approach: criterion 2 utopia A 4 A 5 A 2 A 6 dominated nadir efficient point A 1 A 3 criterion 1 better
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Summary: 1. Define “clearly” what we want (measurable criteria) 2. Identify possible instruments, policies 3. Generate large numbers of feasible solutions, different scenarios of change 4. Find solutions that are FEASIBLE for all scenarios of (climate) change = robust and flexible Strategies: control, mitigate, adapt 22
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