Compounding joint impact of rainfall, storm surge and river discharge on coastal flood risk: an approach based on 3D fully nested Archimedean copulas

Compound flooding is a multidimensional consequence of the joint impact multiple intercorrelated drivers, such as oceanographic, hydrologic, and meteorological. These individual drivers exhibit interdependence due to common forcing mechanisms. If they occur simultaneously or successively, probability their occurrence will be higher than expected if considered separately. The copula-based multivariate analysis can effectively measure hydrologic risk associated with compound events. Because involvement it necessary switch from bivariate (2D) trivariate (3D) analyses. This study presents an original probabilistic framework by incorporating hierarchal models called asymmetric fully nested Archimedean (or FNA) copula in flood risk. efficacy derived FNA copulas model, together symmetric Elliptical class copulas, are tested compounding rainfall, storm surge, river discharge observations through case at west coast Canada. obtained employed risks primary conditional return periods. estimated periods further estimating failure statistics for assessing (and bivariate) statistical tests found Frank outperforms 3D copulas. Our work confirms that practical univariate periods, essential account assess strength influence different variables successively. (also univariate) events produce lower OR-joint cases. Thus, ignoring impacts via significantly underestimate period.