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Evaluation of the MIKE SHE Model for Application in the Loess Plateau, China 1
Author(s) -
Zhang Zhiqiang,
Wang Shengping,
Sun Ge,
McNulty Steven G.,
Zhang Huayong,
Li Jianlao,
Zhang Manliang,
Klaghofer Eduard,
Strauss Peter
Publication year - 2008
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2008.00244.x
Subject(s) - watershed , environmental science , surface runoff , hydrology (agriculture) , land cover , loess plateau , soil conservation , watershed management , hydrological modelling , streamflow , land use , runoff curve number , drainage basin , distributed element model , soil science , agriculture , geology , geography , ecology , computer science , climatology , physics , geotechnical engineering , archaeology , cartography , quantum mechanics , machine learning , biology
Abstract: Quantifying the hydrologic responses to land use/land cover change and climate variability is essential for integrated sustainable watershed management in water limited regions such as the Loess Plateau in Northwestern China where an adaptive watershed management approach is being implemented. Traditional empirical modeling approach to quantifying the accumulated hydrologic effects of watershed management is limited due to its complex nature of soil and water conservation practices (e.g., biological, structural, and agricultural measures) in the region. Therefore, the objective of this study was to evaluate the ability of the distributed hydrologic model, MIKE SHE to simulate basin runoff. Streamflow data measured from an overland flow‐dominant watershed (12 km 2 ) in northwestern China were used for model evaluation. Model calibration and validation suggested that the model could capture the dominant runoff process of the small watershed. We found that the physically based model required calibration at appropriate scales and estimated model parameters were influenced by both temporal and spatial scales of input data. We concluded that the model was useful for understanding the rainfall‐runoff mechanisms. However, more measured data with higher temporal resolution are needed to further test the model for regional applications.