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Application and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in Poorly Gauged Regions in Western China

1
College of Resources and Environmental Science, China Agricultural University (CAU), Beijing 100094, China
2
Department of Civil Engineering, The University of Hong Kong (HKU), Pokfulam 999077, Hong Kong, China
3
Joint Global Change Research Institute, Pacific Northwest National Laboratory and University of Maryland, College Park, MD 20740, USA
4
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing 100038, China & China Institute of Water Resources and Hydropower Research, Beijing 100038, China
5
Department of Earth and Environmental Science, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
*
Authors to whom correspondence should be addressed.
Water 2019, 11(10), 2171; https://doi.org/10.3390/w11102171
Received: 9 September 2019 / Revised: 4 October 2019 / Accepted: 13 October 2019 / Published: 18 October 2019
(This article belongs to the Section Water Resources Management, Policy and Governance)
The temporal and spatial differentiation of the underlying surface in East Asia is complex. Due to a lack of meteorological observation data, human cognition and understanding of the surface processes (runoff, snowmelt, soil moisture, water production, etc.) in the area have been greatly limited. With the Heihe River Basin, a poorly gauged region in the cold region of Western China, selected as the study area, three meteorological datasets are evaluated for their suitability to drive the Soil and Water Assessment Tool (SWAT): China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), Climate Forecast System Reanalysis (CFSR), and Traditional Weather Station (TWS). Resultingly, (1) the runoff output of CMADS + SWAT mode is generally better than that of the other two modes (CFSR + SWAT and TWS + SWAT) and the monthly and daily Nash–Sutcliffe efficiency ranges of the CMADS + SWAT mode are 0.75–0.95 and 0.58–0.77, respectively; (2) the CMADS + SWAT and TWS + SWAT results were fairly similar to the actual data (especially for precipitation and evaporation), with the results produced by CMADS + SWAT lower than those produced by TWS + SWAT; (3) the CMADS + SWAT mode has a greater ability to reproduce water balance than the other two modes. Overestimation of CFSR precipitation results in greater error impact on the uncertainty output of the model, whereas the performances of CMADS and TWS are more similar. This study addresses the gap in the study of surface processes by CMADS users in Western China and provides an important scientific basis for analyzing poorly gauged regions in East Asia. View Full-Text
Keywords: CMADS; SWAT; poorly gauged regions; comparative analysis CMADS; SWAT; poorly gauged regions; comparative analysis
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MDPI and ACS Style

Meng, X.; Zhang, X.; Yang, M.; Wang, H.; Chen, J.; Pan, Z.; Wu, Y. Application and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in Poorly Gauged Regions in Western China. Water 2019, 11, 2171. https://doi.org/10.3390/w11102171

AMA Style

Meng X, Zhang X, Yang M, Wang H, Chen J, Pan Z, Wu Y. Application and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in Poorly Gauged Regions in Western China. Water. 2019; 11(10):2171. https://doi.org/10.3390/w11102171

Chicago/Turabian Style

Meng, Xianyong; Zhang, Xuesong; Yang, Mingxiang; Wang, Hao; Chen, Ji; Pan, Zhihua; Wu, Yiping. 2019. "Application and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in Poorly Gauged Regions in Western China" Water 11, no. 10: 2171. https://doi.org/10.3390/w11102171

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