Temporal clustering of streamflow extremes and relevance to flood insurance claims: a stochastic investigation for the contiguous USA

Abstract

Recent research highlights the importance of Hurst-Kolmogorov dynamics (else known as long-range dependence), characterized by strong correlation and high uncertainty in large scales, in flood risk assessment, particularly in the dynamics of flood occurrence and duration. While several catastrophe modeling professionals nowadays incorporate scenarios that account for previous historical extreme events, traditional flood risk estimation assumes temporal independence of such events, overlooking the role of long-range dependence that has been observed in hydrometeorological processes. This study delves into the validity implications of these assumptions, investigating both the empirical properties of streamflow extremes from the US-CAMELS dataset and the ones of flood insurance claims from the recently published FEMA National Flood Insurance Program database. Analyzing the US-CAMELS dataset, we explore the impact of streamflow’s clustering dynamics on return periods, event duration, and severity of the over-threshold events and corroborate empirical findings with stochastic simulations reproducing the observed dynamics. The association between the observed flood event properties, considered as proxies of collective risk, and the FEMA aggregate flood insurance claims is then investigated. New insights are derived with respect to the strength of their linkage and its spatial variability, which are essential to accurate flood insurance and reinsurance practices.