A New Approach for Environmental Contour and Multivariate De-Clustering

Abstract

Abstract When the long term behaviour of a floating unit is assessed, the environmental contour concept is often applied together with IFORM (Inverse First Order Reliability Method). This approach avoids direct computation on all sea-states, which is computationally very demanding, and most often simply not feasible. Instead, only a few conditions (the contour) are assessed and results in an accurate estimate of the long term extreme. However, most of available methods to derive the contour require the knowledge of the joint distribution of the different random variables (waves, wind, current...), which is often difficult to derive accurately. In fact, some complex dependences exist and are attempted to be simplified in too few coefficients. Another limitation of current environmental contour is its difficulty to deal with the dependence issue. Indeed, extreme sea-states arise by groups (storms, hurricanes...) and are not independent. While de-clustering techniques exist and are quite straightforward in univariate problems, this becomes difficult when the number of dimension increases. In an attempt to tackle those challenges, this paper presents a novel approach to derive IFORM contours. The method does not require any joint distribution and makes use of much more degrees of freedom to capture the dependence between variables. It also allows for an easy de-clustering. The approach is illustrated on two locations, using actual hindcast data of significant wave height and period; the resulting contours are compared to the ones obtained with more traditional methods.