Parameter estimation and uncertainty analysis of SWAT model in upper reaches of the Heihe river basin

2009 ◽  
Vol 23 (19) ◽  
pp. 2744-2753 ◽  
Author(s):  
Zhanling Li ◽  
Zongxue Xu ◽  
Quanxi Shao ◽  
Jing Yang
Keyword(s):  
Parameter Estimation ◽  
Uncertainty Analysis ◽  
River Basin ◽  
Swat Model ◽  
Heihe River Basin ◽  
Heihe River ◽  
The Heihe River Basin

scholarly journals The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and it's Application in the Heihe River Basin in China

2016 ◽  
Author(s):  
Hao Wang ◽  
Xian-yong Meng ◽  
Si-yu Cai ◽  
Xue-song Zhang ◽  
Xiao-hui Lei ◽  
...  
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Hydrologic Modeling ◽  
Hydrological Modeling ◽  
Swat Model ◽  
Heihe River Basin ◽  
Heihe River ◽  
Climate Data ◽  
Weather Stations ◽  
The Heihe River Basin

Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data. Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluated their applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 – 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.

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