Probability Modeling of Precipitation Extremes over Two River Basins in Northwest of China

Wei Zhao,Yuehua Wang,Zhanling Li,Zhanjie Li

doi:10.1155/2015/374127

Abstract

This paper is focused on the probability modeling with a range of distribution models over two inland river basins in China, together with the estimations of return levels on various return periods. Both annual and seasonal maximum precipitations (MP) are investigated based on daily precipitation data at 13 stations from 1960 to 2010 in Heihe River and Shiyang River basins. Results show that GEV, Burr, and Weibull distributions provide the best fit to both annual and seasonal MP. Exponential and Pareto 2 distributions show the worst fit. The estimated return levels for spring MP show decreasing trends from the upper to the middle and then to the lower reaches totally speaking. Summer MP approximates to annual MP both in the quantity and in the spatial distributions. Autumn MP shows a little higher value in the estimated return levels than Spring MP, while keeping consistent with spring MP in the spatial distribution. It is also found that the estimated return levels for annual MP derived from various distributions differ by 22%, 36%, and 53% on average at 20-year, 50-year, and 100-year return periods, respectively.

Highlights

Precipitation extremes, due to the great damage they caused in both social and economic losses, have been caught lots of attention from both governments and the public [1,2,3,4]
We focus on the precipitation extremes over the two inland river basins, while, what extremes can be considered as “precipitation extremes”? From meteorological point of view, the most commonly used way to indicate the so-called precipitation extremes is those that occurred with the amount exceeding a certain fixed level
For better understanding the characteristics of seasonal precipitation extremes, the seasonal maximum precipitation is investigated in the following analysis

Summary

Introduction

Precipitation extremes, due to the great damage they caused in both social and economic losses, have been caught lots of attention from both governments and the public [1,2,3,4]. Probability distribution models are the useful tools for the frequency analysis of precipitation extremes [8,9,10]. Rahmani et al, [12] used Weibull distribution to calculate the extreme precipitation frequency at stations in Kansas and the adjacent states. Benyahya et al, [14] compared five probability distributions (GEV, Generalized Logistic, Weibull, Gamma, and Lognormal) to identify the appropriate modes providing the most accurate seasonal maximum precipitation in southern Quebec of Canada. Li et al [10] discussed the six probability distributions performances (Exponential, Gamma, Weibull, skewed Normal, mixed Exponential, and hybrid Exponential/Generalized Pareto distributions) in precipitation extremes fitting on the Loess Plateau of China based on 47 stations. Rahman et al [15] investigated the suitability of as many as fifteen different probability distributions based on large Australian annual maximum flood datasets

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Conclusion