中文摘要：

为探索气候变化影响下水文极值的非平稳性和预测方法,建立了水文极值非平稳广义极值（GEV）分布的统计预测模型。利用1952—2010年淮河上游流域累计面雨量和流量年最大值资料、同期500 h Pa环流特征量资料以及17个CMIP5模式对环流特征量的模拟结果,筛选出对水文极值影响显著的年平均北半球极涡强度指数作为GEV分布参数的预测因子。分析了在RCP2.6、RCP4.5和RCP8.5情景下2006—2050年淮河上游流域水文极值对气候变化的响应。结果表明,10年以下与10年以上重现期的水文极值在非平稳过程中呈现前者下降而后者上升的相反变化趋势;多模型预测的集合平均在未来情景中均呈现上升趋势,情景排放量越大增幅越大,重现期越长增幅也越大。与极值的常态相比,极值的极端态更易受气候变化影响。

英文摘要：

In order to investigate the non-stationarity and prediction method of hydrological extremes under the climate change impact, statistical prediction models for the non-stationary generalized extreme value（GEV）distributions of hydrological extremes were developed. Annual mean North Hemisphere polar vortex intensity was selected as the predictor of distribution parameters. Observational hydrological data from the upper reaches of the Huaihe River basin（HRB） and 500 h Pa circulation monitoring data during 1952-2010 and 17 CMIP5 GCM simulations for historical, RCP2.6, RCP4.5 and RCP8.5 greenhouse gas emission scenarios were used for the model fitting. Responses of hydrological extremes to climate change during 2006-2050 under the 3 future scenarios were analyzed by using the fitted models. The results show that hydrological extremes with less- and longer-than-10-year return periods from non-stationary GEV distributions may have decreasing and increasing trends, respectively.However, ensemble means of multi-model predictions for different return periods under the 3 future emission scenarios all show increasing trends. The higher the emission, or the longer the return period, the faster the increase of the trend. Compared with the averages of extremes, extremes of extremes（tails of GEV distributions） are more liable to change under these scenarios.