Author(s): Chong Ma; Xichao Gao; Suli Pan; Ye Tian; Yueping Xu
Linked Author(s):
Keywords: Climate change; Multiple site downscaling; Change factor; Hadgem2-ES
Abstract: Assessment of potential impacts of climate change on water resources is becoming more important in recent decades. Weather generators, which can create synthetic daily weather data of long periods of time, are very common tools to deal with climate change issues. However, most weather generators like SDSM, normally ignore the spatial correlations of hydro-meteorological data. In this study, a stochastic model, GiST is used to generate daily rainfall and temperature for multiple sites in Qiantang River Basin, China. The main difference between GiST and other weather generators is the consideration of spatial correlations among sites. The historical climate data (1961-1990) is used to calibrate the correlation matrix, and change factors are calculated based on the baseline (1961-1990) climate and future (2011-2040) climate. Future daily weather data (temperature and precipitation) are then downscaled based on Scenarios RCP2. 6, RCP4. 5, RCP6. 0 and RCP8. 5 using Hadgem2-ES. The results show that historical climate closely matched its simulated counterpart. For both rainfall amounts and temperature, the correlation coefficients calculated from the observed pair-wise climate data and the ones from GiST-simulated synthetic data show good consistence. The outputs from the future period are compared with the historical climate data. In general, the following conclusions are drawn. First, the relative changes of mean monthly minimum temperature are higher than those of mean monthly maximum temperature. Mean temperature changes slightly except Tianmushan. Second, the future annual precipitation decreases slightly under the four scenarios at Hangzhou, Huangshan and Jinhua while a contrary trend can be observed at Tianmushan and Tunxi. At Shengxian, different patterns of change can be found under the four scenarios. Monthly precipitations at Hangzhou, Jinhua decrease under most conditions while it increases in summer and decreases in winter at Tianmushan and Tunxi.
Year: 2013