predicting loss implications of changes in rainfall for flood insurance
DESCRIPTION
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 reliefTRANSCRIPT
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Predicting LossIMPLICATIONS OF CHANGES IN RAINFALL FOR FLOOD INSURANCE
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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
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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
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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?
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Floods are 'acts of God,' but flood losses are largely acts of man.
-Gilbert White
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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
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Physical Data Sources Three day annual maximum precipitation from NOAA COOP stations
◦ Data courtesy of Cameron Bracken◦ Dataset goes to 2013
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Flood Insurance Losses
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Data Structure
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Total Payments Max PrecipitationPrecip Anomaly
Visualizing Losses 2012 - WWA
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Visualizing Losses 2012 - CA Max PrecipTotal Payments Precip Anomaly
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Steps 1. Model likelihood of a loss
2. Model the size of loss
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Methods Determining likelihood of loss
◦ Logistic regression◦ CART◦ Multinomial◦ Cluster analysisDistribution fitting for loss◦ GLM◦ GPP
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Logistic Regression-2012
Model Brier Score Climo
Logistic 2012 .07 .11
Logistic 2013 .25 .298
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Logistic Predictions
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Loss 2012Residuals 2012Payments 2012
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Loss 2013
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Modeling Loss-Gamma
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Modeling Loss-GPD
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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