Estimating Pluvial Depth–Damage Functions for Areas within the United States Using Historical Claims Data

Flooding has been the most costly natural disaster over last 2 decades within US. Therefore, recent research focused on more accurately predicting economic losses from flooding to aid decision makers and mitigate exposure. For this, depth–damage functions have commonly employed predict relative or absolute damage buildings caused by different magnitudes of flooding. Although functions, such as those adopted US Army Corps Engineers, are widely available for fluvial coastal flooding, less work done develop pluvial-induced Here, we use a database containing 13.5 million claims pluvial functions. recently released flood hazard data utilized identify that likely related We two types regression models fit Secondarily, developed an automated valuation model (AVM) estimate building values across state New Jersey. These were then combined with layers in order apply compute aggregate annualized loss The results indicated moderate agreement between observed Jersey computed applying study-developed curves using layers. It is anticipated this will future quantifying risks associated