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      氣候變化研究進展 ?? 2007, Vol. 03 ?? Issue (04): 208-213.

      ? 極端事件專欄 ? 上一篇    下一篇

      1960-2005年長江流域降水極值概率分布特征

      蘇布達 Marco Gemmer 姜彤   

      1. 中國氣象局國家氣候中心 中國科學(xué)院 南京地理與湖泊研究所
      • 收稿日期:2007-01-29 修回日期:2007-05-01 出版日期:2007-07-30 發(fā)布日期:2007-07-30
      • 通訊作者: 蘇布達

      Probability Distribution of Ptecipitation Extremes over the Yangtze River Basin During 1960-2005

        

      • Received:2007-01-29 Revised:2007-05-01 Online:2007-07-30 Published:2007-07-30

      摘要: 摘 要:根據(jù)1960-2005年長江流域147個氣象站逐日降水觀測資料和ECHAM5/ MPI-OM氣候模式20世紀(jì)試驗期(1941-2000年)79個格點逐日降水模擬資料,建立年最大強降水AM(annual maximum)序列及汛期日降水量<1.27 mm的最長干旱持續(xù)天數(shù)MI(Munger index)序列,分析了長江流域降水極值序列的時空分布特征和概率分布模式。結(jié)果表明:1) 長江流域強降水事件的強度和概率最大的地區(qū)位于岷沱江流域中游、洞庭湖湖區(qū)、長江中下游干流區(qū)與鄱陽湖東南部支流等地區(qū),干旱事件強度和概率最大的地區(qū)位于金沙江流域中下游與嘉陵江流域;2) 氣候模式模擬的長江流域AM事件的多年平均值普遍高于觀測值,但離差系數(shù)普遍低于觀測值; 3) 氣候模式模擬結(jié)果與觀測的降水極值空間分布有一定的差異,但對氣候模式和實際觀測的降水極值概率分布的擬合,均證明Wakeby分布函數(shù)能夠較好地擬合降水極值的概率分布。

      關(guān)鍵詞: 降水極值, 概率分布, ECHAM5模式, 長江流域

      Abstract: Abstract: Based on the daily observational precipitation data of 147 stations in the Yangtze River Basin during 1960-2005 and the simulated daily data of 79 grids from ECHAM5/ MPI-OM in the 20th century, time series of precipitation extremes which contain AM (annual maximum) and MI (Munger index) were constructed. The distributive feature of precipitation extremes was analyzed based on the two index series. Research results show that 1) the intensity and probability of extremely heavy precipitation are higher in the mid-Mintuo River Basin, Dongting Lake area, mid-lower main stream section of the Yangtze River, southeastern Poyang Lake Basin; whereas, intensity and probability of drought events are higher in the mid-lower Jinsha River Basin and the Jialing River Basin; 2) compared with observational data, averaged AM of modeled precipitation is higher but the discrete coefficient of the AM is lower; 3) in spite of certain differences of the spatial distributions between observed and simulated precipitation extremes by applying general extreme value (GEV) and Wakeby (WAK) functions with the method of L-moment estimator (LME) to the precipitation extremes, WAK can fit the probability distribution of precipitation extremes calculated both from observed and simulated data quite well. The WAK could be an important function for estimating the precipitation extreme events under future climatic scenarios.

      Key words: precipitation extremes, probability distribution, ECHAM5 model, the Yangtze River Basin

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