Abstract
With global warming, it is having a significant impact on the variation of precipitation and temperature. Both of them bring extreme discomfort to human’s life and social development. The city of Xingtai in China, have suffered from many high temperatures and floods, which are resulted from climate change. Therefore, the purpose of this paper is to study the influence of climate change (precipitation and temperature) on water resources in Xingtai city by using the Statistical Downscaling Model (SDSM). SDSM is a coupled downscaling method based on multivariate regression and weather generator, which can effectively solve the spatial scale mismatch problem of small-scale hydrological response and large-scale climate information. Compared with other statistical methods, SDSM can be operated more simply and easily, and its results would be much better. The results show that: (1) the performances of calibration SDSM model are basically acceptable; (2) SDSM can better simulate the trend of precipitation and temperature; (3) The determination coefficients (R2) of measured and simulated values about minimum temperature, maximum temperature, average temperature and precipitation in the verification period can be above 95%, 94%, 93% and 64% respectively.
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