中文摘要：

利用淮河流域110个气象站点1959年1月1日至2008年12月31日的日尺度降水数据，建立了AM及POT极端降水序列，通过4大类33种概率分布函数对其进行了拟合，以建立最优概率分布模型，并利用其参数分析淮河流域极端降水的空间分布及概率特征。研究发现：（1）淮河流域1959—2008年日极端降水的空间分布为东西两端高并逐渐向流域中心降低，且有淮河上游地区及沂沭泗流域东部两处强降水中心；（2）经K-S法检验，Wakeby函数是AM及POT序列的最优概率分布函数。50a一遇的日极端降水预估值与1959—2008年实际最大日极端降水值的误差率随着实际降水量的增加而增大，且大多数站点的误差率在20％以下；（3）通过对最优概率分布模型参数的分析，得出河南省驻马店地区、安徽省阜南和淮南地区以及皖鲁苏交界地区的极端降水发生概率较大，淮河中上游干流周边地区及下游地区的极端降水变化不稳定；（4）以最优概率分布模型的形状、尺度参数为指标，绘制了淮河流域极端降水风险图，为极端降水风险管理与预警工作提供参考。

英文摘要：

Based on the precipitation data during Jan. 1, 1959 and Dec. 31, 2008 from 110 stations around Huaihe River basin, AM and POT extreme precipitation sequences were established, 33 distribution functions of 4 categories were applied to fit the extreme precipitation sequences. Then the optimal probability distribution model was estab- lished. The results show that, daily spatial distribution of average precipitation of 1959 -2008 of the basin reduces gradually from both boundaries to the center of the basin, and there are two strong precipitation eenters, namely Hua- ihe River upstream region and eastern Yishusi valley, exist in the region. Also, K -S method checking showed that, Wakeby function is the optimal probabilistic distribution function for the two series AM. The error rate of the esti- mated values from the observed values increases with the increase of the actual precipitation, and the errors of most stations are less than 20%. According to analysis of the shape and scale parameters of the optimal probability distri- bution model, the probabilities of extreme precipitation are larger in zhumadian, Funan,Huainan regions and the in- tersection region of Anhui, Shandong and Jiangsu Provinces ; the changes of extreme precipitation are not stable in the surroundings of the Huaihe River main stream and downstream areas. Based on the shape and scale parameters of op- timal probability distribution model, the extreme precipitation hazard map was drawn. The map may provide a then- retieal reference for the work of disaster prevention and mitigation of extreme precipitation events.