Climate Change Research ›› 2017, Vol. 13 ›› Issue (4): 346-355.doi: 10.12006/j.issn.1673-1719.2016.186

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Statistical Model and Threshold Value Selection of Gridded Daily Precipitation Extremes in China

Zhang Xinyi1, Fang Guohua1, Wen Xin1, Ye Jian2, Guo Yuxue1   

  1. 1 College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
    2 Water Resources Department of Jiangsu Province, Nanjing 210029, China
  • Received:2016-09-12 Revised:2017-02-22 Online:2017-07-30 Published:2017-07-30


Based on the national daily precipitation 0.5°× 0.5° gridded dataset, annual maximum (AM) samples and peaks over threshold (POT) samples were selected. The generalized extreme value distribution (GEV) and the generalized Pareto distribution (GPD) were employed to establish statistical models of precipitation extremes respectively. The goodness of fit of each model was evaluated by Kolmogorov-Smirnov test. The statistical analysis was performed. Extreme value distribution model of precipitation and threshold value selection criteria applicable to different areas were proposed. The results show that: (1) The simulated results of POT samples are superior to those of AM samples; (2) The method of sample percentile for determining threshold value is better than the others; (3) The geographical distribution pattern of optimization results is similar to the distribution of dry and wet regions in China. The 90?94 percentile is the fittest to determine threshold value in humid regions. The 94?97 percentile is better in semi-arid and sub-humid regions. The 97?99 percentile is the most suitable in arid regions.

Key words: extreme precipitation events, generalized extreme value, generalized Pareto distribution, Kolmogorov-Smirnov test, threshold

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