Probability distribution of extreme precipitation in Beijing based on extreme value theory

Abstract

In this study, the statistical characteristics of the probability distribution in precipitation extremes in Beijing were explored and discussed based on the extreme value theory. The extreme precipitation series were filtered from the daily precipitation data (45 rain gauge stations) in the warm season during 1960–2012 based on the annual maximum (AM) and peak-over-threshold (POT) method. The generalized extreme distribution (GEV) and generalized Pareto distribution (GPD) were used to fit the two precipitation series and to investigate their statistic characteristics. The parameters of GEV and GPD distribution were estimated by the L-moment method. The best distribution was determined by the Kolmogorov-Smirnov test and then the best distribution was used to estimate the precipitation extremes in different return periods. The results showed that the mean values of AM series for all the rain gauges apart from Banbidian and Yanhecheng were larger than that of POT series, whereas the minimum values of AM series were lower than that of POT series. Furthermore, the optimal distribution of 40 sites for the AM series were GEV distribution, and the best distribution 38 sites for the POT series were GPD distribution, based on the results of the Kolmogorov-Smirnov test. Finally, the distributions of precipitation extremes in different return periods for this two series were similar, with the high value in the south and east and the low value in the north and west. Overall, the estimated precipitation amounts by the AM series were larger than that of POT series in the different return periods, which means the precipitation extremes based on the AM series will be relatively safer when it was used in the engineering design.