Modeling the multiple time scale response of hydrological drought to climate change in the data-scarce inland river basin of Northwest China

  • Nina Zhu
  • Jianhua XuEmail author
  • Chong Wang
  • Zhongsheng Chen
  • Yang Luo
Original Paper


It is difficult to quantitatively assess the response of hydrological drought (HD) to climate change in the inland river basins of northwest China because of the complicated geographical environment and scarce data. To address this problem, we conducted a hybrid model by integrating the ensemble empirical mode decomposition (EEMD), the long short-term memory (LSTM) model, and the statistical downscaling method and selected the Aksu River Basin (ARB) as a typical representative of data-scarce inland river basin in northwest China to simulate its hydrological drought in the period of 1980–2015 based on reanalysis climate data and hydrological observation data. The coefficient of determination (R2), the mean absolute error (MAE), the Nash–Sutcliffe efficiency coefficient (NSE), and the index of agreement d (d index) all showed high simulation accuracy of the hybrid model (R2 = 0.712, MAE = 0.304, NSE = 0.706, and d index = 0.901 of the Aksu River Basin), and the simulated effect of the hybrid model is much better than that of a single long short-term memory model. The simulated results in the Aksu River Basin by the model revealed that hydrological drought in the two subbasins (i.e. the Kumarik River Basin (KRB) and the Toshkam River Basin (TRB)) showed similar cycles on the seasonal scale, the interannual scale, and the interdecadal scale, which are mainly controlled and influenced by regional climate change. On the seasonal scale, the effect of precipitation and temperature on hydrological drought is not significant; on the interannual scale, precipitation is the key factor compared to temperature in inducing hydrological drought change; on the interdecadal scales, the correlations between precipitation, temperature, and hydrological drought were the strongest and most significant.


Deep learning Ensemble empirical mode decomposition Hybrid model Long short-term memory model Simulation Statistical downscaling 


Funding information

This work was supported by the National Natural Science Foundation of China (Grant Nos. 41871025, 41630859), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA20100303), 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

© Saudi Society for Geosciences 2019

Authors and Affiliations

  • Nina Zhu
    • 1
    • 2
  • Jianhua Xu
    • 1
    • 2
    Email author
  • Chong Wang
    • 3
  • Zhongsheng Chen
    • 4
  • Yang Luo
    • 5
  1. 1.Key Laboratory of Geographic Information Science (Ministry of Education)East China Normal UniversityShanghaiChina
  2. 2.Research Center for East-West Cooperation in ChinaEast China Normal UniversityShanghaiChina
  3. 3.School of Social SciencesShanghai University of Engineering ScienceShanghaiChina
  4. 4.College of Land and ResourcesChina West Normal UniversityNanchongChina
  5. 5.Jianhu Data Technology (Shanghai) Co., LtdShanghaiChina

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