Scenario-based runoff prediction for the Kaidu River basin of the Tianshan Mountains, Northwest China

  • Changchun Xu
  • Jie Zhao
  • Haijun Deng
  • Gonghuan Fang
  • Jing Tan
  • Dandan He
  • Yapeng Chen
  • Yaning Chen
  • Aihong Fu
Thematic Issue
First Online:
Received:
Accepted:
Part of the following topical collections:
  1. Water in Central Asia

Abstract

Based on the hydro-meteorological data over the past 50 years (1961–2010), the runoff change of the Kaidu River was predicted for the future 30 years (2011–2040). Two statistical downscaling models, the Statistical DownScaling Model (SDSM) and the Statistical Analog Resampling Scheme (STARS), were used to downscale the HadCM3 outputs for projecting the future climate scenarios of the basin. The Soil and Water Assessment Tool (SWAT) hydrological model was driven by the projected climate scenarios to generate the future runoff. Modeling results suggested that the SWAT model can well duplicate the recorded runoff changes in the basin and thus can be applied to simulation of future runoff changes. Both the SDSM and the STARS models performed well in simulating the temperature but relatively poorly in simulating the precipitation. Under the A2 and B2 scenarios the basin will experience a significant increasing trend in temperature and an indistinctive change trend in precipitation during the entire forecast period. Under the S1–S3 scenarios, both temperature and precipitation do not exhibit distinctive changes. In terms of river runoff, the predicted average annual runoff will be relatively abundant during the period from 2010s to 2020s but obviously short after 2020s under A2 scenario and will be kept relatively steady under B2 scenario. The predicted runoff will fluctuate drastically with no any significant trend under S1–S3 scenarios. The relatively high runoffs under S2–S3 scenarios seem to indicate the importance of temperature increasing in generating runoff. The scenario-based predictions suggest that moderate emission (e.g., B2) or moderate warming (e.g., S2) is beneficial to maintaining the expected level of runoff in the future.

Keywords

SWAT Statistical downscaling Runoff prediction Kaidu River 
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Notes

Acknowledgments

This study is jointly supported by the National Natural Science Foundation (Nos. 41561023, 41305125, 41271052) and the Open Fund of State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences (G2013-02-02). Thanks to the data sharing of the International Scientific Data Service Platform, Chinese Academy of Sciences (http://www.cnic.Cn/zcfw/sjfw/gjkxsjjx), the Environmental and Ecological Science Data Center in the West of China (http://westdc.westgis.ac.cn) and the China Meteorological Data Sharing Service System (http://data.cma.cn). Special thanks are owed to the editors and anonymous reviewers for their detailed and constructive comments.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Changchun Xu
    • 1
  • Jie Zhao
    • 1
  • Haijun Deng
    • 2
  • Gonghuan Fang
    • 2
  • Jing Tan
    • 3
  • Dandan He
    • 1
  • Yapeng Chen
    • 2
  • Yaning Chen
    • 2
  • Aihong Fu
    • 2
  1. 1.Key Laboratory of Oasis Ecology, School of Resource and Environment SciencesXinjiang UniversityUrumqiChina
  2. 2.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqiChina
  3. 3.Xinjiang Tarim River Basin Bayingolin Management BureauKorlaChina

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