Recent years have seen a surge in assessment of potential impacts of climate change. As one of the most important tools for generating synthetic hydrological model inputs, weather generators have played an important r...Recent years have seen a surge in assessment of potential impacts of climate change. As one of the most important tools for generating synthetic hydrological model inputs, weather generators have played an important role in climate change impact analysis of water management. However, most weather generators like statistical downscaling model （SDSM） and long Ashton research station weather generator （LARS-WG） are designed for single site data generation. Considering the significance of spatial correlations of hydro-meteorological data, multi-site weather data generation becomes a necessity. In this study we aim to evaluate the performance of a new multi-site stochastic model, geo-spatial temporal weather generator （GIST）, in simulating precipitation in the Qiantang River Basin, East China. The correlation matrix, precipitation amount and occurrence of observed and GiST-generated data are first compared for the evaluation process. Then we use the GiST model combined with the change factor method （CFM） to investigate future changes of precipitation （2071 2100） in the study area using one global climate model, Hadgem2 ES, and an extreme emission scenario RCP 8.5, The final results show that the simulated precipitation amount and occurrence by GiST matched their historical counterparts reasonably. The correlation coefficients between simulated and his- torical precipitations show good consistence as well. Compared with the baseline period （1961 1990）, precipitation in the future time period （2071-2100） at high elevation stations will probably increase while at other stations decreases will occur. This study implies potential application of the GiST stochastic model in investigating the impact of climate change on hydrology and water resources.展开更多
基金Projcct supportcd by the International Scicncc ＆ Technology Co- operation Program of China （No. 2010DFA24320）, and the National Natural Science Foundation of China （Nos. 51379183 and 50809058） ~ Zhcjiang Univcrsity and Springcr-Vcrlag Bcrlin Hcidclberg 2014
文摘Recent years have seen a surge in assessment of potential impacts of climate change. As one of the most important tools for generating synthetic hydrological model inputs, weather generators have played an important role in climate change impact analysis of water management. However, most weather generators like statistical downscaling model （SDSM） and long Ashton research station weather generator （LARS-WG） are designed for single site data generation. Considering the significance of spatial correlations of hydro-meteorological data, multi-site weather data generation becomes a necessity. In this study we aim to evaluate the performance of a new multi-site stochastic model, geo-spatial temporal weather generator （GIST）, in simulating precipitation in the Qiantang River Basin, East China. The correlation matrix, precipitation amount and occurrence of observed and GiST-generated data are first compared for the evaluation process. Then we use the GiST model combined with the change factor method （CFM） to investigate future changes of precipitation （2071 2100） in the study area using one global climate model, Hadgem2 ES, and an extreme emission scenario RCP 8.5, The final results show that the simulated precipitation amount and occurrence by GiST matched their historical counterparts reasonably. The correlation coefficients between simulated and his- torical precipitations show good consistence as well. Compared with the baseline period （1961 1990）, precipitation in the future time period （2071-2100） at high elevation stations will probably increase while at other stations decreases will occur. This study implies potential application of the GiST stochastic model in investigating the impact of climate change on hydrology and water resources.
