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Quantifying Uncertainty in Flood Inundation Mapping

Quantifying Uncertainty in Flood Inundation Mapping. Younghun Jung and Venkatesh Merwade School of Civil Engineering, Purdue University. AWRA Spring Specialty Conference, March 31, 2010. Uncertainty in Flood Inundation Modeling. Input Data (Flow and topography) Modeling type (1D vs 2D)

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Quantifying Uncertainty in Flood Inundation Mapping

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  1. Quantifying Uncertainty in Flood Inundation Mapping Younghun Jung and Venkatesh Merwade School of Civil Engineering, Purdue University AWRA Spring Specialty Conference, March 31, 2010.

  2. Uncertainty in Flood Inundation Modeling • Input Data (Flow and topography) • Modeling type (1D vs 2D) • Model set-up (Geometry) • Model parameters • Lack of model calibration data • Mapping approach.

  3. Objective Probabilistic Flood Inundation Maps

  4. Approach • HEC-RAS Modeling • Monte Carlo Simulations to study the effect of: • Flow • Topography and • Channel Roughness (Manning’n) • Generalized Likelihood Uncertainty Estimate (GLUE)

  5. GLUE O1 O2 ... … … O1000 l1 l2 ... … … l1000 R1 R2 ... … … R1000 Acceptable HEC-RAS 1000 p Unacceptable Manning’s n Input Model Output Likelihood (l) Ranked l R1 R2 ... … … R750 0.00 0.01 0.04 … … 1.00 50% gives the deterministic model output 5% and 95% give the associated uncertainty 95% 50% P 5% Output Behavioral Models Rescaled Output CDF

  6. Likelihood functions W1 W2 E1 E2 F

  7. Approach • HEC-RAS Modeling • Monte Carlo Simulations to study the effect of: • Flow • Topography and • Channel Roughness (Manning’n) • Generalized Likelihood Uncertainty Estimate (GLUE)

  8. Study Area East Fork of White River near Seymour in Jackson county, IN (100 km south of Indianapolis).

  9. Probability Distribution for Streamflow • Regression equation is developed • Observed flow rate is 2730 m3/s with a 95% confidence interval of 2257 m3/s and 3301 m3/s • Flow rate is randomly selected from a t-distribution within 2257 – 3301 m3/s range

  10. Probability Distribution for Elevation • A vertical error of ± 0.25 is assumed for topography • A uniform distribution is assumed and a random number between ± 0.25 is added to each cross-section

  11. Probability Distribution for Roughness • Uniform distribution is used by getting minimum and maximum values from literature

  12. Flow

  13. Topography

  14. Manning’s n

  15. Combined

  16. Summary and Future Work • Uncertainty from each variable in floodplain mapping can be quantifiable • Initial results show that the uncertainty from each variable propagates through the entire process • Uncertainty bound is affected by the technique used and its parameters • How does the uncertainty bound change with topography, geography and other environmental settings?

  17. New Developments: Floodplain Mapping Using Soil Data

  18. Some historical perspective • Eve: lets go for vacation • Adam: where do you want to go? • Eve: How about Orange County? • Adam: According to the soil map, orange county is in a flood zone. Lets go to Apple Town • Eve: Apple town sounds great! .. and then you know what happened…

  19. Present Scenario • Jane: lets go for vacation • Joe: where do you want to go? • Jane: How about Orange County • Joe: I just booked beach home by going to waterhomes.com. • Jane: Excellent! Did you buy flood insurance? • Joe: Oh, I forgot, let me go to FEMA.gov

  20. Flood Map for Tippecanoe County, IN FEMA SSURGO

  21. Thank you! Contacts: Venkatesh Merwade – vmerwade@purdue.edu http://web.ics.purdue.edu/~vmerwade

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