Extreme rainfall is one of the most devastating natural events. The frequency and intensity of these events has increased. This trend will likely continue as the effects of climate change become more pronounced. As a consequence, it is necessary to evaluate the different statistical methods that assess the occurrence of the extreme rainfalls. This research evaluates some of the most important statistical methods that are used for the analysis of the extreme precipitation events. Extreme Value Theory is applied on ten station data located in the Mediterranean region. Furthermore, its two main fundamental approaches (Block-Maxima and POT) and three commonly used methods for the calculation of the extreme distributions parameters (Maximum Likelihood, L-Moments, and Bayesian) are analyzed and compared. The results showed that the Generalized Pareto Distribution provides better theoretical justification to predict extreme precipitation compared to Generalized Extreme Value (GEV) distribution while in the majority of stations the most accurate parameters for the highest precipitation levels are estimated with the Bayesian method. Extreme precipitation for return period of 50, 150 and 300 years were finally obtained which indicated that Generalized Extreme Value Distribution with Bayesian estimator presents the highest return levels for western stations, while for the eastern Mediterranean stations the Generalized Pareto Distribution with Bayesian estimator presents the highest ones.
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An overview of statistical methods for studying the extreme rainfalls in Mediterranean.
Published: 17 July 2017 by MDPI in The 2nd International Electronic Conference on Atmospheric Sciences session Climate and Interannual Variability
Keywords: GEV, Pareto, Mediterranean, Bayesian