Suedel Sess5 102309[1]
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The document discusses risk-informed decision making for sustainable management of flood risks. It describes using a risk analysis and decision analysis framework to evaluate alternatives and select recommended plans. Scenario analysis is used to consider future uncertainties. The document provides an example of how these techniques were applied to post-Katrina planning for coastal Louisiana, including specifying objectives, evaluating alternatives, and conducting multi-criteria decision analysis to rank plans. Adaptive management is discussed as a way to incorporate new information over time.
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Suedel Sess5 102309[1]
- 1. Risk-Informed Decision-Making: Sustainable Management of Flood Risks SAME 2009 California Water Conference 26-28 October, Sacramento, CA Burton C. Suedel, Ph.D. Research Biologist USACE ERDC, Vicksburg, MS Igor Linkov, Ph.D. Research Physical Scientist USACE ERDC, Vicksburg, MS Brian Harper Economist USACE Galveston District Todd S. Bridges, Ph.D. Senior Research Scientist, Environmental Sciences USACE ERDC, Vicksburg, MS
- 13. Specify Problems & Opportunities Inventory & Forecast Conditions Formulate Alternative Plans Evaluate Effects of Alternative Plans Compare Alternative Plans Select Recommended Plan Corps Planning Problems Alternatives Criteria Evaluation Decision Matrix Weights Risk and Decision Analysis Framework Synthesis Decision Decision Analysis Tools MAUT Criterium Decision Plus Expert Choice Logical Decisions Decision Lab Risk Analysis Models Wave/Storm Surge Infrastructure Models Ecosystem Models Economic Models RIDM Scenario Analysis
- 15. LACPR Scenarios k = 4 k = 3 BAU employment / compact population k = 2 k = 1 High employment / dispersed population Pattern of Develop- ment (Higher) (Lower) Relative Sea Level Rise
- 18. Distribution of Swing Weights Across Planning Units
- 19. Plan Rankings
- 21. Example LACPR Rankings 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 3 5 4 2 18 7 17 6 19 8 20 21 1 9 10 23 22 12 11 24 13 26 15 25 14 27 16 Alternative Plan Utility score 1 2 3 4 5 6 7 8 9 10 Scenario 1 Metric:
- 22. Example Optimal Decisions by Scenario PU1-NS-400 PU1-NS-400 BAU/Compact PU1-NS-100 PU1-NS-100 High/Dispersed Higher Lower Pattern of Development Relative Sea-level Rise PU-1
- 23. Risk Years 10 20 30 40 50 60 Risk Reduction Comparison
- 25. Risk Time Risk Reduction Trajectories Risk reduction goal New information incorporated A B
Editor's Notes
- The LACPR Technical Team initially selected three uncertain input variables: relative sea level rise, the employment growth rate, and regional land-use policy. The exact rates of relative sea level rise used in modeling plan performance vary by planning unit to reflect differences in observed rates along the Louisiana coast. In general RSLR may take one of two values: it may be “lower” or “higher.” In Planning Units 1 and 4, “lower” means a relative sea level rise of 1.3 feet for 2060. “Higher” means a relative sea level rise of 2.6 feet for 2060. In Planning Units 2, 3a and 3b, “lower” and “higher” relative sea level rise are 1.9 and 3.2 feet, respectively for 2060. LACPR’s original intent was to use these three variables to develop twenty-seven scenarios for simulating the performance of each plan and assess the sensitivity of performance metrics to these planning assumptions. The number of scenarios was reduced to four by collapsing the employment growth rate and the land-use allocation policy into a single variable and dropping the hybrid land-use policy because the scenarios produced limited variation in the modeled performance outcomes.
- Tables Showing the Plan that Maximizes the MAU Score : This table illustrates the sensitivity of the decision to the planning assumptions for each preference pattern. This table make it is easier to assess sensitivity.