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分别使用SDSM、SVM、LARS-WG等3种统计降尺度模型对淮河流域蚌埠以上流域在HadCM3模型A2情景下2070-2099年的日降水特征进行了预估,并应用Tebaldi法集成3种模型的结果,量化分析了各月平均日降水与最大日降水变化特征的不确定性.多模型集成的结果显示,平均日降水在总体上呈现出干旱季节增大,而湿润季节减小的趋势,且在干旱季节,平均日降水变化的不确定性更为显著;最大日降水的变化更为一致,除11月与12月外,最大日降水均呈现出较为显著的减小趋势.淮河流域的实例分析表明,Tebaldi法为分析比较降尺度模型的特点、概率预估降水变化以及量化评估因应用不同降尺度模型所带来的不确定性提供了一种灵活有效的途径.
The daily precipitation characteristics of the basin above Bengbu in the Huaihe River basin under the HadCM3 model A2 scenario from 2070 to 2099 were estimated using three statistical downscaling models, including SDSM, SVM and LARS-WG, respectively. The Tebaldi method was used to integrate the three models As a result, the uncertainty of the average daily precipitation and the maximum daily precipitation in each month was quantified and analyzed.The results of multi-model integration showed that the average daily precipitation showed a trend of increasing in dry season and decreasing in wet season In the dry season, the uncertainty of average daily precipitation is more significant; the variation of maximum daily precipitation is more consistent, and the maximum daily precipitation shows a significant decreasing trend except in November and December. The analysis shows that the Tebaldi method provides a flexible and effective way to analyze the characteristics of the downscaling model, predict the precipitation changes by probability, and quantitatively evaluate the uncertainty caused by the application of different downscaling models.