Ying Liang

and 4 more

The temporal and spatial distribution of water resources over China has changed and may continue changing in the future under ongoing global warming. Scientific water resources management requires reliable forecasting of the change. Meanwhile, the performance of deep learning in achieving it has not been comprehensively explored. To fill this gap, deep learning, i.e., multilayer perceptron (MLP) in this study, is used to study the change of streamflow over China under climate changes. MLP is compared with other machine learning methods for investigating its strengths, and three river basins (i.e., Xiangxi, Jinghe and Zhongzhou) in central, northwestern and southeastern China, respectively are selected to represent hydrologic regimes over China. Four regional climate models are used to drive MLP for forecasting streamflow from 2021 to 2050 under two greenhouse-gas emission scenarios (i.e., RCPs 4.5 and 8.5). Modeling results show that MLP is more accurate than the other methods, especially in terms of peak streamflow volumes. Annual average temperature in the three basins will increase, while precipitation shows different changing trends. The simulation accuracies among the regional climate models (RCMs) are slightly different. Correspondingly, streamflow will increase, and the increments decrease from Jinghe, through Xiangxi, to Zhongzhou River Basins. Due to climate changes, flooding will become more frequent in Jinghe and Xiangxi River Basins, Jinghe River Basin will experience no runoff in winter, and the timing of peak runoffs in Zhongzhou River Basin will move forward. Compared with the RCP 4.5 scenario, the above trends are more obvious under the RCP 8.5 scenario.