Journal of Arid Land

, Volume 10, Issue 6, pp 905–920 | Cite as

Simulating hydrological responses to climate change using dynamic and statistical downscaling methods: a case study in the Kaidu River Basin, Xinjiang, China

  • Wulong Ba
  • Pengfei DuEmail author
  • Tie Liu
  • Anming Bao
  • Min Luo
  • Mujtaba Hassan
  • Chengxin Qin


Climate change may affect water resources by altering various processes in natural ecosystems. Dynamic and statistical downscaling methods are commonly used to assess the impacts of climate change on water resources. Objectively, both methods have their own advantages and disadvantages. In the present study, we assessed the impacts of climate change on water resources during the future periods (2020–2029 and 2040–2049) in the upper reaches of the Kaidu River Basin, Xinjiang, China, and discussed the uncertainties in the research processes by integrating dynamic and statistical downscaling methods (regional climate models (RCMs) and general circulation modes (GCMs)) and utilizing these outputs. The reference period for this study is 1990–1999. The climate change trend is represented by three bias-corrected RCMs (i.e., Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA), Regional Climate Model version 4 (RegCM4), and Seoul National University Meso-scale Model version 5 (SUN-MM5)) and an ensemble of GCMs on the basis of delta change method under two future scenarios (RCP4.5 and RCP8.5). We applied the hydrological SWAT (Soil and Water Assessment Tool) model which uses the RCMs/GCMs outputs as input to analyze the impacts of climate change on the stream flow and peak flow of the upper reaches of the Kaidu River Basin. The simulation of climate factors under future scenarios indicates that both temperature and precipitation in the study area will increase in the future compared with the reference period, with the largest increase of annual mean temperature and largest percentage increase of mean annual precipitation being of 2.4°C and 38.4%, respectively. Based on the results from bias correction of climate model outputs, we conclude that the accuracy of RCM (regional climate model) simulation is much better for temperature than for precipitation. The percentage increase in precipitation simulated by the three RCMs is generally higher than that simulated by the ensemble of GCMs. As for the changes in seasonal precipitation, RCMs exhibit a large percentage increase in seasonal precipitation in the wet season, while the ensemble of GCMs shows a large percentage increase in the dry season. Most of the hydrological simulations indicate that the total stream flow will decrease in the future due to the increase of evaporation, and the maximum percentage decrease can reach up to 22.3%. The possibility of peak flow increasing in the future is expected to higher than 99%. These results indicate that less water is likely to be available in the upper reaches of the Kaidu River Basin in the future, and that the temporal distribution of flow may become more concentrated.


RCM GCM climate change downscaling bias correction SWAT Tianshan Mountains 


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This research was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2015211B031). We wish to thank the Xinjiang Tarim River Basin Management Bureau and the Xinjiang Meteorological Administration for providing the necessary data to build the Soil and Water Assessment Tool (SWAT) model. We also grateful for the Coordinated Regional Downscaling Experiment-East Asia (CORDEX-EA) project and the Coupled Model Inter-comparison Project Phase 5 (CMIP5) project for providing dynamic downscaling RCMs and GCMs data free of charge.


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Copyright information

© Xinjiang Institute of Ecology and Geography, the Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Wulong Ba
    • 1
    • 2
  • Pengfei Du
    • 1
    Email author
  • Tie Liu
    • 2
  • Anming Bao
    • 2
  • Min Luo
    • 2
    • 3
  • Mujtaba Hassan
    • 4
  • Chengxin Qin
    • 1
  1. 1.State Key Laboratory of Environmental Simulation and Pollution Control, School of EnvironmentTsinghua UniversityBeijingChina
  2. 2.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqiChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Department of Space SciencesInstitute of Space TechnologyIslamabadPakistan

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