The hydrological process in the dry-warm valley of the mountainous area of southwest China has unique characteristics and has attracted scientific attention worldwide.Given that this is an area with fragile ecosystems and intensive water resource conflicts in the upper reaches of the Yangtze River,a systematic identification of its hydrological responses to climate and land use variations needs to be performed.In this study,MIKE SHE was employed and calibrated for the Anning River Basin in the dry-warm valley.Subsequently,a deep learning neural network model of the long short-term memory(LSTM)and a tradi-tional multi-model ensemble mean(MMEM)method were used for an ensemble of 31 global climate models(GCMs)for climate projection.The cellular automata-Markov model was implemented to project the spatial pattern of land use considering climatic,social,and economic conditions.Four sets of climate projections and three sets of land use projections were generated and fed into the MIKE SHE to project hydrologic responses from 2021 to 2050.For the calibration and first validation periods of the daily simu-lation,the coefficients of determination(R)were 0.85 and 0.87 and the Nash-Sutcliffe efficiency values were 0.72 and 0.73,respectively.The advanced LSTM performed better than the traditional MMEM method for daily temperature and monthly precipitation.The average monthly temperature projection under representative concentration pathway 8.5(RCP8.5)was expected to be slightly higher than that under RCP4.5;this is contrary to the average monthly precipitation from June to October.The variations in streamflow and actual evapotranspiration(ET)were both more sensitive to climate change than to land use change.There was no significant relationship between the variations in streamflow and the ET in the study area.This work could provide general variation conditions and a range of hydrologic responses to complex and changing environments,thereby assisting with stochastic uncertainty and optimizing water resource management in critical regions.

Congcong Li;Yanpeng Cai;Zhong Li;Qianqian Zhang;Lian Sun;Xinyi Li;Pengxiao Zhou

State Key Laboratory of Water Environment Simulation,School of Environment,Beijing Normal University,Beijing 100875,China;Department of Civil Engineering,McMaster University,Hamilton,ON L8S 4L7,Canada;Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds,Institute of Environmental and Ecological Engineering,Guangdong University of Technology,Guangzhou 510006,China;Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou),Guangzhou 511458,China;Chengdu University of Information Technology,Chengdu 610225,China

工程（英文）

[1]Congcong Li;Yanpeng Cai;Zhong Li;Qianqian Zhang;Lian Sun;Xinyi Li;Pengxiao Zhou-.Hydrological Response to Climate and Land Use Changes in the Dry-Warm Valley of the Upper Yangtze River)[J].工程（英文）,2022(12):24-39

Anning

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