An approach to simulate the climate-driven streamflow in the data-scarce mountain basins of Northwest China

  • Chong Wang
  • Jianhua XuEmail author
  • Yaning Chen
  • Weihong Li


With global warming, the inland river basin in the arid region of Northwest China is facing a serious water supply situation. The headwater basin of the inland river is located in the high-altitude mountainous region, and there are few meteorological observation sites, so it is difficult to apply distributed hydrological models and other models based on the physical mechanism of runoff generation to evaluate climate change and its impact on streamflow. To simulate the climate-driven streamflow in data-scarce mountain basins of Northwest China, we developed an integrated approach by using downscaled reanalysis data, Mann–Kendall test, ensemble empirical mode decomposition and backpropagation artificial neural networks together with the weights connection method. We validated the approach in the Kaidu River basin located in the Tianshan mountains. The results showed that the streamflow increased 12.9% by \(2.5 \times 10^{8}\,\hbox {m}^{3}\) per decade with the warm and wet climate, while the average annual temperature increased 5.2% at a rate of \(0.3{^{\circ }}\hbox {C}\) per decade and the precipitation increased 37.3% at a rate of 16.4 mm per decade during the period from 1980 to 2015. The impact of temperature variability on streamflow was 44.21 ± 2.08% and the impact of precipitation variability on streamflow was 55.79 ± 2.08%.


Climate change impact streamflow data-scarce mountainous basins Northwest China 



This work was supported by the National Natural Science Foundation of China (Grant Nos. 41871025 and 41630859); the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19030204); and the Open Foundation of State Key Laboratory, Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences (Grant No. G2014-02-07).


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Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.School of Social SciencesShanghai University of Engineering ScienceShanghaiPeople’s Republic of China
  2. 2.Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic SciencesEast China Normal UniversityShanghaiPeople’s Republic of China
  3. 3.Research Center for East–West Cooperation in ChinaEast China Normal UniversityShanghaiPeople’s Republic of China
  4. 4.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqiPeople’s Republic of China

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