Non-stationary extreme value analysis of sea states based on linear trends. Analysis of annual maxima series of significant wave height and peak period in the Mediterranean Sea

Abstract

Non-stationary Extreme Value Analysis (NEVA) allows to determine the probability of exceedance of extreme sea states taking into account trends in the time series of data at hand. In this work, we analyse the reliability of NEVA of significant wave height (Hs) and peak period (Tp) under the assumption of linear trend for time series of annual maxima (AM) Hs in the Mediterranean Sea. A methodology to assess the significance of the results of the non-stationary model employed is proposed. Both the univariate long-term extreme value distribution of Hs and the bivariate distribution of Hs and Tp are considered. For the former, a non-stationary Generalized Extreme Value (GEV) probability is used, and a methodology to compute the parameters of the distribution based on the use of a penalty function is explored. Then, non-stationary GEV is taken as a reference to compute the Environmental Countours of Hs and Tp, assuming a conditional model for the latter parameter. Several methods to compute linear trends are analysed and cross-validated on the series of AM Hs at more than 20,000 hindcast nodes. Results show that the non-stationary analysis provides advantages over the stationary analysis only when all the considered metrics are consistent in indicating the presence of a trend. Moreover, both the univariate return levels of Hs and bivariate return levels of Hs and Tp show a marked dependence to the time window considered in the GEV distribution formulation. Therefore, when applying NEVA for coastal and marine applications, the hypothesis of linear trend and the length of the reference data used for the non-stationary distribution should be carefully considered.