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WaterWare description

WaterWare is a comprehensive data management and decision support system for integrated water management. It offers monitoring of hydro-meteorological data, forecasts, and rainfall-runoff modeling for flood management. It also provides tools for managing water demand, assessing water quality, and conducting multi-criteria optimization. With user support and system maintenance, WaterWare helps address water management problems by optimizing water allocation and use based on socio-economic and environmental criteria.

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WaterWare description

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  1. WaterWare 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

  2. 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 ?

  3. 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

  4. 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

  5. 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 !

  6. Paradigm change: Replace OPTIMAL with Good enough, but robust New concepts: Robustness, reliability, resilience, sustainability

  7. 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.

  8. 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

  9. 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 (non-dominated), criteria selection, reference point (UTOPIA) Select efficient “best” solution

  10. Decision Support (multi-attribute) Reference point approach: utopia A4 efficient point A5 A2 criterion 2 A6 A1 dominated A3 better nadir criterion 1

  11. Summary: • Define “clearly” what we want (measurable criteria) • Identify possible instruments, policies • Generate large numbers of feasible solutions, different scenarios of change • Find solutions that are FEASIBLE for all scenariosof (climate) change = robust and flexible Strategies: control,mitigate, adapt

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