气候变化研究进展 ›› 2013, Vol. 9 ›› Issue (1): 29-34.doi: 10.3969/j.issn.1673-1719.2013.01.005

• 气候系统变化 • 上一篇    下一篇

未来情景下南水北调中线工程水源区极端降水分布特征

张利平1,杨艳蓉2,秦琳琳3,段尧彬2,杨珍2   

  1. 1. 武汉大学水资源与水电工程科学国家重点实验室
    2.  浙江省温州市水文站
  • 收稿日期:2012-02-23 修回日期:2012-12-03 出版日期:2013-01-31 发布日期:2013-01-31
  • 通讯作者: 张利平 E-mail:zhanglp@whu.edu.cn

Distribution of Extreme Precipitation Events in Water Source Area for the Middle Route Project of South-to-North Water Transfer Under A2, A1B, B1 Scenarios

  • Received:2012-02-23 Revised:2012-12-03 Online:2013-01-31 Published:2013-01-31

摘要: 利用南水北调中线工程水源区9个气象站点1961—2008年的日降水资料和IPCC第四次评估报告多模式数据结果,抽取逐年的最大日降水量序列样本,运用广义极值分布(GEV)和广义帕累托分布 (GPD)两种极值统计模型对样本进行拟合,遴选出描述流域最大日降水量分布规律的最优概率模型,推算重现期对应的降水量值,并预估该流域极端降水事件在未来气候变化情景下的响应。研究表明:南水北调中线工程水源区降水极值均符合GEV和GPD分布,但GPD模型更适合用于描述该流域降水极值分布;未来气候变化情景下用GPD分布拟合的降水极值优于使用GEV分布;A2情景下极端降水事件的发生将更频繁、更强烈,A1B情景下次之,B1情景下相对较小,表明未来高排放气候情景对极端降水事件的影响比中、低排放情景大。

关键词: 极端降水, 广义极值分布, 广义帕累托分布, 气候变化情景

Abstract: The maximum daily precipitation samples are constructed based on the daily observations of 9 meteorological stations from 1961 to 2008 and the multi-model ensembles of the IPCC Fourth Assessment Report in the water source area for the Middle Route Project of South-to-North Water Transfer. The generalized extreme value distribution (GEV) and the generalized Pareto distribution (GPD) are employed to fit the samples. The results show that during 1961-2008, the maximum daily precipitation conforms to both the GEV and GPD, but the simulated results of GPD model are superior to those of GEV model. Then, the corresponding values of different return periods are evaluated. Finally, the extreme precipitation of different return periods are estimated under the A2, A1B, and B1 scenarios. Extreme precipitation events will be more frequent and intense under A2 scenario than under A1B and B1 scenarios, indicating that impacts of the high emissions scenario on future extreme precipitation will be more intense than those of the medium and low emissions scenarios.

Key words: extreme precipitation, generalized extreme value distribution, generalized Parato distribution, climate change scenario

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