Regression quantile models for estimating trends in extreme significant wave heights

• Regression quantile models are proposed to describe the historical trends in long term data sets.
• Regression quantile models are applied to a data set of 44 years of hindcast data.
• The regression generalised Pareto quantile model is found to be the best model following Q–Q plots.

Regression quantile models are proposed to model extreme significant wave height distributions in two Portuguese locations of Figueira da Foz and Azores in the North Atlantic Ocean. A data set from a 44 years hindcast produced in the HIPOCAS project was used in this study. In order to identify trends with time, the data set was divided in 11 samples of 4 year data. Three-parameter Weibull, generalised extreme value and generalised Pareto quantile functions are fitted to data and extrapolated to 50 and 100 year significant wave height return periods. The algorithm of distributional least absolutes is used to estimate the model parameters. The regression quantile models showed the ability to model the historical trends that may subsist in long term data sets, a feature that the traditional fitting of extreme distributions does not account for.