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Optimization vs rule-based simulation in regional water management modeling
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Optimization vs rule-based simulation in regional water management modeling


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  • 1. Optimization vs Rule-based Simulation in Regional Water Management Modeling Tingju Zhu International Food Policy Research Institute Washington, DC Systematic Analysis of Climate Resilient Development Workshop, IFPRI and UNU-WIDER, Washington, DC, October 7-8, 2010
  • 2. Regional Water Management Issues
  • 3. River Basins are Coupled Natural- Human Systems!
  • 4. Engineering-Economic IssuesWater Management Issues
  • 5. Water Management Issues
  • 6. Variants of Optimization and Simulation Models for Regional Water Management  Optimization models  Rule-based simulation models  Optimization-driven simulation models (e.g. priority driven models – WEAP, OASIS, DWRSIM, CALSIM)  ‘Optimized rules’-based models (common in reservoir operation)
  • 7. An Optimization Model Example: Climate Change & California Water Resources
  • 8. California Value Integrated Network  Statewide integrated engineering-optimization model (CALVIN)  Integrates hydrology, infrastructure, operations, economics, and environmental flows  Models adaptations to changed conditions  Highlights importance of North-South flows (Courtesy of Lund and Howitt)
  • 9. Optimization Components Objective: Maximize net economic benefits Decisions: Reservoir releases, storage allocations Constraints: Mass balance, physical capacities, environmental flows, policies
  • 10. Optimized Rules vs Dynamic Optimization for Flood Protection under Climate Change
  • 11. Sacramento Valley, California Yolo Bypass Downtown Sacramento Levee and Dam Safety
  • 12. 0 10 20 30 40 50 60 70 0 200 400 600 800 1000 1200 1400 1600 Existing Levee Setback (ft) ExistingLeveeHeight(ft) Do nothing Raise to optimal height at current setback Rebuild - inward Rebuild - outward Optimal setback First Critical Setback Second Critical Setback X*h0 Xc h0 Alwaysrebuild-inward Xc h0 Levee Re-design Rules based on Cost Minimization (Details: Zhu & Lund, 2009)
  • 13. 95 9099 75 50 25 10 1 0.15 0.52 10 100 1,000 10,000 100,000 Percent Chance Exceedence Three-dayFlow(m3 /s) HCM2000 HCM2025 HCM2065 HCM2090 Sacramento, California Flood control under Urbanization & a Changing Climate … Stochastic Dynamic Programming Model (Details: Zhu et al., 2007)
  • 14. Move Backward? Levees height increases over time, and setback expansion seems desirable in distant future …
  • 15. Setback expansion for increased channel capacity Continuously increasing flood protection standard driven by economic growth
  • 16. Expected annual flood damage Land value loss Levee construction cost
  • 17. Optimization vs rule-based simulation  Optimization can explore many options ‘quickly’ and identify promising solutions for detailed study by simulation models  More simplifications are usually needed in optimization models; simulation models can consider more details  Optimization models can provide useful economic information (e.g. scarcity value); simulation models usually cannot  For distant future: rule-based simulation models face the difficulty of specifying operating rules; similar challenge exists for optimization models, but seems “more doable”
  • 18. Thank you! Tingju Zhu