Catastrophe Economics: Modeling the Losses Due to Tropical Cyclone Related Inland Flooding during Hurricane Ivan in 2004

Transcription

1 Catastrophe Economics: Modeling the Losses Due to Tropical Cyclone Related Inland Flooding during Hurricane Ivan in 2004 Jeffrey Czajkowski 1, Gabriele Villarini 2, Erwann Michel-Kerjan 1, James A. Smith 3 1. The Wharton Risk Management and Decision Processes Center, University of Pennsylvania 2. Department of Civil and Environmental Engineering, University of Iowa 3. Department of Civil and Environmental Engineering, Princeton University Association of State Floodplain Managers 2013 Conference Harford, CT June 12, 2013

2 Tropical Cyclone Inland Flooding Inland riverine flooding from tropical cyclones claims a large number of fatalities and is responsible for significant economic losses in the United States (Rappaport 2000; Pielke et al. 2008; Jonkman et al. 2009). Hurricane Irene in 2011 provides a recent and poignant example of this phenomenon - most of the damage and fatalities were due to heavy rainfall and associated riverine flooding inland (Avila and Cangialosi, 2011). Unfortunately, little is known about the relationship between tropical cyclone related inland flooding and economic losses as most efforts in hurricane risk assessment are focused in coastal areas (Elsberry, 2002; Zandbergen, 2009). 2

3 Study Objectives An Integrated Approach To fill this knowledge gap we combine two critical elements that allow for a unique and detailed characterization of flood claims at a given location: 1) We develop a new quantification method of the spatial structure of tropical cyclone-related flood magnitudes at the regional scale 2) We benefit from an unique access to the entire portfolio of the federally run national flood insurance program (NFIP) that sells the vast majority of flood insurance policies across the U.S.

4 Traditional Modeling of Catastrophic Risks Risk Assessment Hazard Vulnerability Loss Exposure We focus this initial study on Hurricane Ivan (2004), the third most costly NFIP flood event since 1978 (prior to 2012) with a total of $1.58 billion in claims paid (King, 2011). Quantified flood ratio as relevant hazard proxy? Source: Grossi and Kunreuther

5 Methods 1) Quantify the spatial extent and magnitude of flooding related to Hurricane Ivan 2) Combine the quantified Ivan flood magnitudes with associated inland flood claim information from the NFIP database 3) Empirically demonstrate that our approach to quantify flood magnitude is a key driver of the insured economic losses experienced

6 Quantification of the spatial extent and magnitude of Hurricane Ivan inland flooding (Princeton & Iowa) Hurricane Ivan Rainfall Data Hurricane Ivan Flood Ratio Data 6

7 1,959 census tracts with a flood ratio > 1.0, the 10 year flood peak 7

8 Translation of quantified flood magnitudes into economic losses: non-surge NFIP claims A total of 19,273 claims with $800.9 million in flood damages related to inland flooding losses or two-thirds of the total NFIP flood residential insurance claims and more than half (54 percent) of the total residential flood damage 1,241 census tracts highlighted in pink had at least 1 NFIP claim: inland areas match relatively well to the regions with a large flood ratio, in particular along the Appalachian Mountains and in Pennsylvania 8

9 Census tracts with largest number of claims 184 census tracts highlighted in pink had 13 or more claims, i.e., the mean or higher 9

10 Census Tracts with no NFIP policies in-force 6,940 census tracts highlighted in pink had no 2004 NFIP policies in-force 498 (7%) of these tracts had a flood ratio greater than the 10-year flood peak value majority in PA, TN, and NC (85% of the 498) 10

11 Quantification of Flood Ratio to Loss average number of claims incurred Average # of claims per census tract > 2.0 Range of Flood Ratio Value (up to and including listed amount) The raw claims data illustrates an upward trend in the average number of claims per census tract for higher flood ratio values 11

12 Quantification of Flood Ratio to Loss empirical estimation Negative binomial model for the count of claims Estimated coefficient p-value Flood ratio (0.10; 0.25] (0.434) Flood ratio (0.25; 0.50] (0.433) Flood ratio (0.5; 0.75] (0.428) Flood ratio (0.75; 1.00] 1.578(0.439) <0.001 Flood ratio (1.00; 1.50] (0.427) <0.001 Flood ratio (1.50; 2.00] (0.425) <0.001 Flood ratio (>2.00) (0.435) < _census tract population per square mile ( ) <0.001 Number of 2004 NFIP active residential policies-in-force (0.0005) <0.001 Low NFIP market penetration (0.129) <0.001 Intercept (0.411) Logit model for probability of zero claims (inflate) The empirical results indicate flood ratio is a statistically significant and positive driver of not only the probability of a claim occurring, but also the number of claims an average tract incurs Flood ratio (non-binned) (0.217) < _census tract population per square mile ( ) Number of 2004 NFIP active residential policies-in-force (0.0001) Low NFIP market penetration (0.125) <0.001 Intercept (0.125) < (0.243) - 12

13 Predicted Number of Claims Using our coefficient results we see an uptick in the predicted number of claims around the 10 year flood peak and above

14 Conclusion While storm surge and wind damage are important contributors to the overall insured damage, inland riverine flooding indeed plays a very significant but overlooked role - along the path of the landfalling tropical cyclones as well as further away from the center of circulation. Notably, we are able to begin to quantify the relationship between inland flooding magnitudes and insured economic losses There are large areas without insurance in-force, even though some are highly susceptible to flooding from landfalling tropical cyclones While these results are strictly valid for Hurricane Ivan, an extension of these results to a larger set of storms would provide valuable indications about the areas that are prone to tropical cyclone flooding. In July 2012, President Obama signed the 2012 national flood insurance program reform act, which calls for better assessment of flood hazard. The proposed methodology could represent an important way of doing just that

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