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Putting people and risk in the same picture via hazard ensemble diagrams Tim Lutz Dept. of Geology & Astronomy West Chester University West Chester, PA.

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Presentation on theme: "Putting people and risk in the same picture via hazard ensemble diagrams Tim Lutz Dept. of Geology & Astronomy West Chester University West Chester, PA."— Presentation transcript:

1 Putting people and risk in the same picture via hazard ensemble diagrams Tim Lutz Dept. of Geology & Astronomy West Chester University West Chester, PA tlutz@wcupa.edu

2 GSA’s position statement on Natural Hazards (2008): “Geoscientists have a professional responsibility to inform the public about natural hazards and the need to build an increasingly natural hazard-resilient society, thereby enabling more responsible actions and decisions.” National Research Council’s report on flood risk reduction (2000): “Identifying sound, credible, and effective risk reduction priorities and solutions depends greatly on a well-informed public. The public should be knowledgeable about risk issues and should be given opportunities to express opinions and become involved in risk assessment and risk management activities.”

3 Tarbuck & Lutgens, 2011

4 National Flood Insurance Program (NFIP) Flood Insurance Rate Map (FIRM)

5 Mean = median = mode = 100 years Default concept: Probability decreases symmetrically around the mean

6 Mean = 100 years Median = 69 years Mode = 0 years Recurrence interval distribution predicted for independent random events (exponential distribution) Standard deviation = Mean (e.g., 100 years ± 100 years)

7 Series 1 Series 2 Series 3 Series 4 Series 5 Five series of random events; average recurrence = 100 years

8 Series 1 Series 2 Series 3 Series 4 Series 5 Five series of random events; average recurrence = 100 years

9 Example: USGS 05331000 Mississippi River @ St Paul, MN (114 years of record) Twin Cities 7 Metro map.png by Davumaya (2008), provided by Wikimedia

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12 The inverse of a magnitude-exceedance probability model can be used to simulate annual peak flows

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15 An ensemble is a set of simulations which together define the distribution of most probable outcomes conditional on exposure.

16 Unwarranted pessimism Unwarranted optimism Weighing of risks

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18 Top of right bank levee in S. St. Paul (29 ft) Flood walls deployed at St. Paul airport (17 ft) Flood stage (14 ft)

19 Annual peak flow history  Magnitude-frequency model Magnitude-frequency model  Flow ensemble

20 Flow ensemble + Rating model = Stage ensemble

21 Generalization History of hazard  Magnitude-frequency model  Ensemble diagrams Straightforward extensions Seismic hazard  Gutenberg-Richter model  Ensemble diagrams Volcanic hazard  VEI 1 -based m-f model  Ensemble diagrams Nuclear hazards  INES 2 -based m-f model  Ensemble diagrams 1 VEI = Volcano Explosivity Index 2 INES = International Nuclear & Radiological Event Scale

22 For more information about hazard ensemble diagrams, check out Lutz, 2011, JGE v. 59, pp. 5-12; and email me (tlutz@wcupa.edu) for an Excel file that can generate ensembles from annual peak flow data.


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