1. Predicting Loss IMPLICATIONS OF CHANGES IN RAINFALL FOR FLOOD INSURANCE

2. Floods Are Bad… Floods are consistently the costliest disaster in the US each year In the past 5 years all 50 states have experienced some level of flooding Between 2011 and 2013 FEMA spent $55 billion on flood relief and recovery Homes in special flood hazard areas (SFHA) are more likely to be damaged by a flood than fire

3. National Flood Insurance Program (NFIP) Created to fill the gap left by private insurers leaving the market Intended to reduce the burden of floods on taxpayers by creating an insurance system rather than strictly disaster relief

4. Questions and Goals What is the relationship between flood damage depend on rainfall amounts? Can predicted changes in rainfall be used to predict changes in flood insurance?

5. Floods are 'acts of God,' but flood losses are largely acts of man. -Gilbert White

6. Human Data Sources and Challenges FEMA policy and loss statistics ◦FEMA releases data on a monthly basis ◦Loss statistic are for the whole program ◦Policy data is for the month ◦Only one month is available at a time NCAR NWS Flood Reanalysis ◦State level data, low spatial resolution ◦Yearly statistics, low temporal resolution

7. Physical Data Sources Three day annual maximum precipitation from NOAA COOP stations ◦Data courtesy of Cameron Bracken ◦Dataset goes to 2013

8. Flood Insurance Losses

9. Data Structure

10. Total PaymentsMax PrecipitationPrecip Anomaly Visualizing Losses 2012 - WWA

11. Visualizing Losses 2012 - CA Max Precip Total Payments Precip Anomaly

12. Steps 1. Model likelihood of a loss 2. Model the size of loss

13. Methods Determining likelihood of loss ◦Logistic regression ◦CART ◦Multinomial ◦Cluster analysis Distribution fitting for loss ◦GLM ◦GPP

14. Logistic Regression-2012 ModelBrier ScoreClimo Logistic 2012.07.11 Logistic 2013.25.298

15. Logistic Predictions

16. Loss 2012 Residuals 2012Payments 2012

17. Loss 2013

18. Modeling Loss-Gamma

19. Modeling Loss-GPD

20. Future Work Expand analysis further both temporally and spatially Include mitigation measures Decrease spatial scale Development of a non-stationary GPD to incorporate changes in precipitation patterns