Nonparametric approach to copula estimation in compounding the joint impact of storm surge and rainfall events in coastal flood analysis

Abstract

Abstract The joint probability modelling of storm surge and rainfall events is the main task in assessing compound flood risk in low-lying coastal areas. These extreme or non-extreme events may not be dangerous if considered individually but can intensify flooding impact if they occur simultaneously or successively. Recently, the copula approach has been widely accepted in compound flooding but is often limited to parametric, or in limited number of cases to semiparametric, distribution settings. However, both parametric and semiparametric approaches assume the prior distribution type for univariate marginals and copula joint density. In that case, there is a high risk of misspecification if the underlying assumption is violated. In addition, both approaches suffer from a lack of flexibility. This study uses bivariate copula density in the nonparametric distribution setting. The joint copula structure is approximated nonparametrically by employing Beta kernel and Bernstein copula estimators, and their performances are also compared. The proposed model is tested with 46 years of rainfall and storm surge observations collected on Canada's west coast. Based on the different model compatibility tests, the Bernstein copula with normal KDE margins defined the joint dependence structure well. The selected nonparametric copula model is further employed to estimate joint and conditional return periods. The derived model is further used to estimate failure probability statistics to assess the variation of bivariate hydrologic risk during the project lifetime due to compounded storm surge and rainfall events.