Analyzing Extreme Sea State Conditions by Time-Series Simulation Accounting for Seasonality

Abstract

Abstract This article presents an extreme value analysis on data of significant wave height based on time-series simulation. A method to simulate time series with given marginal distribution and preserving the autocorrelation structure in the data is applied to significant wave height data. Then, extreme value analysis is performed by simulating from the fitted time-series model that preserves both the marginal probability distribution and the autocorrelation. In this way, the effect of serial correlation on the extreme values can be taken into account, without subsampling and de-clustering of the data. The effect of serial correlation on estimating extreme wave conditions have previously been highlighted, and failure to account for this effect will typically lead to an overestimation of extreme conditions. This is demonstrated by this study, which compares extreme value estimates from the simulated times-series model with estimates obtained directly from the marginal distribution assuming that 3-h significant wave heights are independent and identically distributed. A dataset of significant wave height provided as part of a second benchmark exercise on environmental extremes that was presented at OMAE 2021 has been analyzed. This article is an extension of a study presented at OMAE 2022 (OMAE2022-78795) and includes additional preprocessing of the data to account for seasonality and new results.