Contribution of meteorological input in calibrating a distributed hydrologic model in a watershed in the Tianshan Mountains, China

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• 作者：Gonghuan Fang ; Jing Yang ; Yaning Chen ; Changchun Xu…
• 关键词：Meteorological input ; Hydrologic modeling ; Hydrologic process ; Sensitivity analysis ; Model calibration
• 刊名：Environmental Earth Sciences
• 出版年：2015
• 出版时间：August 2015
• 年：2015
• 卷：74
• 期：3
• 页码：2413-2424
• 全文大小：1,953 KB
• 参考文献：Agricultural bureau and soil survey office of Xinjiang (1996) Xinjiang soil. Science Press, Beijing
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• 作者单位：Gonghuan Fang (1) (2) (4)
  Jing Yang (1)
  Yaning Chen (1)
  Changchun Xu (3)
  Philippe De Maeyer (4)
  
  1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, 818 South Beijing Road, Urumqi, 830011, Xinjiang, China
  2. University of Chinese Academy of Sciences, Beijing, 100049, China
  4. Department of Geography, Ghent University, Gent, 9000, Belgium
  3. Key Laboratory of Oasis Ecology, School of Resources and Environmental Science, Xinjiang University, Xinjiang, 830046, China
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：None Assigned
• 出版者：Springer Berlin Heidelberg
• ISSN：1866-6299

文摘

Water resources are essential to the ecosystem and social economy worldwide, especially in the desert and oasis of the Tarim River Basin, whose water originates largely from the Tianshan Mountains characterized by complicated hydrologic processes and scarce meteorological observations. In this study, distributed hydrologic model of SWAT (Soil and Water Assessment Tool) was applied to the Kaidu River Basin, a watershed in the Tianshan Mountains and one of the headwaters of the Tarim River. To quantify the contribution of meteorological input to model output, a sensitivity analysis approach (SDP method, State-Dependent Parameter method) was applied before and after the model was calibrated. The sensitivity analysis shows that meteorological input contributes up to 64?% of model uncertainty due to scarcity of observed meteorological data especially in the alpine region, and the groundwater flow is the most important hydrologic process in this watershed. Model calibration is robust with Nash–Sutcliffe coefficients (“NS”s) and -em class="EmphasisTypeItalic">R 2”s over 0.80 for both the calibration period and the validation period where the length of the validation period is five times longer than the calibration period. The significance is obvious when compared to the simulation without considering the effect of spatial variation in meteorological input (NS?=?0.80 and NS?=?0.47 for “with lapse rates-and “without lapse rates- respectively). Accurate meteorological input is of great importance to the distributed hydrological model, especially in the mountainous regions.