Investigating the impacts of clustering of floods on insurance practices; a spatiotemporal analysis in the USA

Recent research has revealed the significance of Hurst-Kolmogorov dynamics and inherent uncertainties in flood inundation and flood mapping. However, classic risk estimation for flood insurance practices is formulated under the assumption of independence between the frequency and the severity of extreme flood events, which is unlikely to be tenable in real-world hydrometeorological processes exhibiting long range dependence. Furthermore, insurable flood losses are considered as ideally independent and non-catastrophic due to the widely spread perception of limited risk regarding catastrophically large flood losses. As the accurate risk assessment is a fundamental process on flood insurance and reinsurance practices, this study investigates the effects of lack of fulfillment of these assumptions, paving the way for a deeper understanding of the underlying clustering mechanisms of stream flow extremes. For this purpose, we present a spatiotemporal analysis of the daily stream flow series from the US-CAMELS dataset, comprising the impacts of clustering mechanisms on return intervals, duration and severity of the over-threshold events which are treated as proxies for collective risk. Moreover, an exploratory analysis is introduced regarding the stochastic aspects of the correlation between  the properties of the extreme events and the actual claim records of the FEMA National Flood Insurance Program which are recently published.