Probabilistic characterization of extreme storm surges induced by tropical cyclones

Abstract

The overtopping of flood defenses by extreme storm surges during tropical cyclones poses a significant threat to life and property in coastal and estuarine regions. Since little effort has been devoted to investigating the complex interaction of multiple mechanisms causing extreme storm surges, for the first time, we propose a robust data-driven framework to explicitly uncover the complex interaction and thus to improve the characterization of extreme storm surges induced by tropical cyclones. The framework constructs a probabilistic ensemble of dependence structures of multiple climatological forcing factors that potentially cause extreme storm surges based on a multi-structure regular vine copula approach. The uncertainty in the vine-based model structure is explicitly addressed in a Bayesian framework. The climatological forcing factors sensitive to extreme storm surge levels are selected using partial correlations, including 10-m wind, 850-mb temperature, 700-mb geopotential height, precipitation, sea level pressure, and its spatial gradient extracted from the ERA5 reanalysis data. We demonstrate the framework by an in-depth analysis of the extreme storm surge levels observed over two tidal gauging stations in Hong Kong during 1979–2018. Our findings show that the proposed framework substantially improves the characterization of extreme storm surges by taking into account the joint evolution of multiple mechanisms causing extreme storm surges and underlying uncertainties. Furthermore, the framework not only demonstrates higher skill than previous single-structure vine-based models but also can outperform the principal components regression and the random forest regression in terms of characterizing extreme storm surges.