Probabilistic Hurricane Wind-Induced Loss Model for Risk Assessment on a Regional Scale

Abstract

Hurricane hazard is one of the major causes of the loss of life and property. It has recently led to enormous economic losses and social disruption, specifically in coastal regions. Monetary loss and damage to the built infrastructure represent a significant portion of overall hurricane-induced financial losses. A detailed simulation of hurricane loss at a regional scale requires a large amount of specific information, which is usually not available with a sufficient level of certainty. The existing wind-induced loss models often assume a prescribed mathematical structure to describe the dependency between aggregated loss and hazard intensity in an average sense. The effect of uncertainty is introduced by treating model parameters as random variables. In the present study, a new approach to tackle this problem is introduced, which relies on a more rigorous and reliable quantification of the associated uncertainties. In particular, the loss induced by wind is modeled as a nonstationary stochastic process for which a probabilistic representation is constructed using polynomial expansion. As a case study, economic loss data collected by an insurance company are used to calibrate and test the predictive capability of the proposed stochastic hurricane loss model. This representation has the advantage of being based on minimal prior assumptions and constraints, in addition to being computationally less demanding because it generates the vulnerability at a coarser regional level. In order to quantify the regional risk from the proposed loss model, an extension to the evaluation of the storm risk curve or loss-exceedance curve for the region is presented.