Time-varying copula-based compound flood risk assessment of extreme rainfall and high water level under a non-stationary environment

Abstract

Quantifying flood risk depends on accurate probability estimation, which is challenging due to non-stationarity and the combined effects of multiple factors in a changing environment. The threat of compound flood risks may spread from coastal areas to inland basins, which have received less attention. In this study, a framework based on time-varying copulas was introduced for the treatment of compound flood risk and bivariate design in non-stationary environments. Archimedean copulas were developed to diagnose the non-stationary trends of flood risk. Return periods, average annual reliabilities, and bivariate designs were estimated. Model uncertainty was analyzed by comparing the results for stationary and non-stationary conditions. The case study investigated the extreme rainfall and water level series from the Qinhuai River Basin and the Yangtze River in China. The results showed that marginal distributions and correlations are non-stationary in all bivariate combinations. Ignoring composite effects may lead to inappropriate quantification of flood risk. Excluding non-stationarity may lead to risk over or underestimation. It showed the limitations of the 1-day scale and quantified the uncertainty of non-stationary models. This study provided a flood risk assessment framework in a changing environment and a risk-based design technique, which is essential for climate change adaptation and water management.