文章摘要
张丽娟,陈晓宏,叶长青,张家鸣.考虑历史洪水的武江超定量洪水频率分析[J].水利学报,2013,44(3):
考虑历史洪水的武江超定量洪水频率分析
POT flood frequency analysis with historical floods in Wujiang River
  
DOI:
中文关键词: 超定量  广义Pareto分布  门限值  历史洪水  线性矩法
英文关键词: peak-over-threshold  generalized Pareto distribution  threshold  historical flood  L-moment method
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作者单位
张丽娟 1. 中山大学水资源与环境研究中心广东广州5102752. 华南地区水循环与水安全广东省普通高校重点实验室广东广州510275 
陈晓宏 1. 中山大学水资源与环境研究中心广东广州5102752. 华南地区水循环与水安全广东省普通高校重点实验室广东广州510275 
叶长青 1. 中山大学水资源与环境研究中心广东广州5102752. 华南地区水循环与水安全广东省普通高校重点实验室广东广州510275 
张家鸣 1. 中山大学水资源与环境研究中心广东广州5102752. 华南地区水循环与水安全广东省普通高校重点实验室广东广州510275 
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中文摘要:
      考虑历史洪水的超定量(Peak-over-Threshold,POT)洪水频率分析方法能使洪水信息的利用最大化,并有效提高洪水频率分析的合理性。本文以武江流域犁市(二)站为例,以泊松分布为超定量年发生次数分布,用广义Pareto(GP)分布拟合POT样本,线性矩法(L-M)估计不连续POT样本的分布参数,探讨了历史洪水在POT洪水频率分析中的应用。结果表明,武江选取门限值为1 079m3/s能兼顾分布稳定性和样本独立性;对连续POT 样本和不连续POT 样本的洪水频率分析对比得出,对历史洪水的考虑有效改善
英文摘要:
      Flood frequency analysis of peak-over-threshold (POT) series with historical floods can maximize the use of flood information, and effectively improve the accuracy of flood frequency analysis. In this paper,taking Lishi (2) station in the Wujiang River Basin as an example,use of historical information in POT flood frequency analysis is considered. The POT samples are fitted by the generalized Pareto(GP) distribution with a Poisson model for arrival, using L-moment (L-M) method to estimate distribution parameters. The result shows that choosing 1 079m3/s as the threshold can maximize the stability of the estimation for POT distribution parameters and meet the hypothesis of independence. Comparison between POT method with and without historical floods shows that the application of historical floods does improve the fitting of large flood in POT method. Return period of“2006·07”flood estimated by POT method with historical floods is 501 years, while the estimate result of POT method without historical floods is 330 years, which seems that it’s necessary to consider historical floods in POT modeling. The goodness of fit of POT method is a little better than flood frequency analysis of annual maximum series (AMS), while both using historical information.
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