Climate, Spatial Dependence and Flood Risk: A U.S. Case Study


Flood represents one of the costliest and most disruptive natural disasters in
the United States, and the economic losses from flooding are trending upward. While this trend is known to be driven primarily by an increasing population and wealth exposure, climate change is also affecting flood risk in more subtle ways. We merge data on economic flood losses, historical climate, census population, and geological
characteristics to explore drivers of flood losses and climate trends. Our data cover 292 watersheds spanning the continental United States, over the period 1979–2018. We fit a Bayesian spatial mixed-effects model for flood loss frequency and a Bayesian mixed-effects model for flood severity loss per person. Both models control for measured covariates, contain random effects to capture variation from unmeasured covariates, and quantify climate drivers of flood risk. We show empirically that flood losses exhibit spatial dependence that requires spatial statistical models; climate variables are partial drivers of increased frequency and severity; and measures of spatial dependence have been changing over time. And, through a simulation study, we lay a groundwork to disentangle climate and nonclimate drivers of these changing measures of spatial dependence.

climate model, global climate change, extreme events, flood risk, spatial dependencies
Extreme Event Modeling
Predictive Modeling
Statistical Models and Methods
Robert J. Erhardt
Mathieu Boudreault
David A. Carozza
Kejia Yu
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