Copula-based multivariate flood probability construction: a review

Basin perspective hydrology and hydraulic water-related queries often demanding an accurate estimation of flood exceedance probabilities or return periods for assessing hydrologic risk. The research on the advancements of flood probability modelling contributed to reduction of flood risk, damage property and human life losses associated with the occurrence of flood events. Higher degree of uncertainty and complex flood dependence structure did not facilitate for their accurate prediction through deterministic approaches, which often demand a probability distribution framework. Unreliability of univariate frequency analysis under parametric or non-parametric framework would be an attribute for underestimation or overestimation of flood risk. Multivariate distribution framework facilitating a comprehensive understanding of flood structure for various possible occurrence combinations among the flood-related random vectors (i.e. flood peak flow, volume and duration). In this literature, copula function is recognized as a highly flexible tool for establishing multivariate joint dependency and their associated return periods in comparison with traditional multivariate functions. The incorporation of vine or pair-copula constructions (or PCC) further exaggerated the efforts of higher dimension copula construction, in terms of precision level in their estimated quantiles, under the minimum information concept. This review explored the efficacy of copula-based methodology for tackling multivariate design problems and can be used as a guideline for water practioner and hydrologist.