A regional and nonstationary model for partial duration series of extreme rainfall

Regional extreme value models for estimation of extreme rainfall intensities are widely applied, but their underlying assumption of stationarity is challenged. Many recent studies show that the rainfall extremes worldwide exhibit a non-stationary behavior. This paper presents a spatio-temporal model of extreme rainfall. The framework is built on a Partial Duration Series approach with a non-stationary, regional threshold value. The model is based on Generalized Linear Regression solved by Generalized Estimation Equations. It allows a spatial correlation between the stations in the network and accounts furthermore for variable observation periods at each station and in each year. Marginal regional and temporal regression models solved by Generalized Least Squares are used to validate and discuss the results of the full spatio-temporal model. The model is applied on data from a large Danish rain gauge network for four durations ranging from 10 minutes to 24 hours. The observation period differs between stations, and the number of stations with more than 10 years of observations has increased over the years. A spatio-temporal model for the threshold is suggested, applying the Mean Annual Precipitation and time as the explanatory variables in the regional and temporal domain, respectively. Further analysis of Partial Duration Series with non-stationary and regional thresholds shows that the mean exceedances also exhibit a significant variation in space and time for some rainfall durations, while the shape parameter is found to be constant. This article is protected by copyright. All rights reserved.