A fast nonparametric spatio-temporal regression scheme for generalized Pareto distributed heavy precipitation

Abstract

Analyzing the behavior of heavy precipitation, high temperatures, and extremes of other environmental variables has become an important research topic both for hydrologists and climatologists. Extreme value theory provides a well-developed mathematical foundation to statistically model excesses above a high threshold. Practitioners often assume that those excesses approximately follow a generalized Pareto distribution. To infer the two parameters of this distribution, a variety of estimations has been proposed and studied. Among them, maximum likelihood estimation offers an elegant way to include covariates, but imposing an explicit form on the parameters dependence. When analyzing large data sets, this procedure can be too slow and sometimes produce aberrant values due to optimization problems. To overcome these drawbacks, a method based on probability weighted moments and Kernel regression is proposed, tested, and applied to a Swiss daily precipitation data set. The method is implemented as a freely available R package. Key Points A novel nonparametric approach for climate extremes is proposed The method is fast and flexible Simulations and a real application show the potentiality of the method © 2014. American Geophysical Union. All Rights Reserved.