Author(s): Fan Li; Pieter Van Gelder; Dave Callaghan; Roshanka Ranasinghe; Ruben Jongejan
Linked Author(s):
Keywords: Extreme analysis; Wave climate; Gaussian copula; Logistic model; Statistics
Abstract: Coastal engineers are required to find effective ways to mitigate the damage due to extreme storm events, especially in light of the unprecedented population and economic development along the world’s coastline. Therefore, this study was undertaken with the main objective of developing a probabilistic model which can provide robust statistical estimates of the wave climate. The wave forces in a storm can be predicted by constructing a joint multivariate distribution for the wave climate parameters using historical field measurements. This study uses the wave climate dataset collected in deep water along the Dutch coast from 1969 to 2009 as a testing ground and compares the four-dimensional extreme wave climate variates (i. e. the maximum significant wave height, peak wave period, peak storm surge and storm duration) simulation results obtained by applying the Gaussian Copula, a physics-combined method and the multivariate logistic model methods to estimate the dependency structure. Copula functions, which are increasing popular in the field of civil engineering, are able to join or couple multivariate distribution functions to their one-dimensional margins. For the physics-based method, a function describing wave steepness as a function of significant wave height and wave period is employed. The logistic model can compute the dependency parameters and the conditional distributions for bivariate extremes. The quality and ability of the three multivariate modelling methods were compared based on the goodness-of-fit test in this study. The Gaussian copula method was proposed as the recommended model for the study site.
Year: 2013