关键词: 降水极值, 城市洪涝, 不确定性分析, 马尔科夫链蒙特卡洛方法, 广义极值分布, 广义帕累托分布

Abstract: Analysis of the uncertainty of extreme precipitations is essential to urban flood control. In this study, we select the records of daily precipitations in Beijing, Shenzhen and Jinan in the period of 1952 to 2012, and use them to simulate the annual maximum and peak-over-threshold precipitation sequences for each of these cities with the generalized extreme distribution (GEV) and generalized Pareto distribution (GPD). And the parameters are estimated using Markov chain Monte Carlo (MCMC) method. The simulation results show the MCMC method is well applicable to uncertainty analysis of extreme precipitation simulations. The best models it identified are featured with correlation coefficients and determination coefficients both generally as high as 0.95, root-mean-square errors and mean absolute errors of extreme precipitations generally lower than 0.6 mm and 2.5 mm, respectively, and well-behaved confidence intervals of the simulated values. The confidence intervals simulated using GPD perform better; the design rainstorms with the same recurrence interval calculated using GEV are stronger. Considering the difference of GEV and GPD in applicability to different cities, we recommend the method of combining different distributions be used in simulation of extreme precipitations.

Key words: extreme precipitation, urban flooding, uncertainty analysis, Markov chain Monte Carlo, generalized extreme distribution, generalized Pareto distribution