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- W3021783735 abstract "Climate change, which impacts the spatial and temporal distribution of water resources, has a significant influence on the future hydropower generation. Studying the future evolution pattern of hydropower generation under climate change is of great significance for the medium- and long-term hydropower prediction. The objective of this paper is to predict the future hydropower generation of large-scale reservoir groups under climate change. The innovation of this paper is that the macro-scale distributed hydrological model combined with the optimal operation model of large-scale reservoirs was proposed for hydropower generation prediction. The established model considers the specific operation processes of large-scale reservoir groups, including 62 reservoirs in the case study. First, the Statistical Downscaling Model (SDSM) was built, and the evolution trend of future rainfall and temperature was predicted. Second, the macro-scale distributed Variable Infiltration Capacity (VIC) model was built to predict the future runoff. Finally, the optimal operation generation model of large-scale reservoir groups was established to predict the trends of hydropower generation under climate change. Results demonstrate that under RCP2.6 scenario, there is no significant increase or decrease trend of hydropower generation in the future. But under RCP4.5 and RCP8.5 scenarios, the hydropower generation shows a growing trend, and the increase trend under RCP8.5 scenario is more obvious than that under RCP4.5 scenario. Thus, the development of hydropower generation is sensitive to climate change. This study can provide a reference for the long-term prediction of hydropower generation capacity in the upper Yangtze River Basin." @default.
- W3021783735 created "2020-05-13" @default.
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- W3021783735 date "2020-09-01" @default.
- W3021783735 modified "2023-10-14" @default.
- W3021783735 title "Future hydropower generation prediction of large-scale reservoirs in the upper Yangtze River basin under climate change" @default.
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- W3021783735 doi "https://doi.org/10.1016/j.jhydrol.2020.125013" @default.
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