SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Calibration of a distributed hydrological model in a data-scarce basin based on GLEAM datasets 
Authors:Jin, X. and Y. Jin 
Year:2020 
Journal:Water 
Volume (Issue):12 
Pages: 
Article ID:897 
DOI:10.3390/w12030897 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:hydrologic only 
Primary Application Category:evapotranspiration assessment 
Secondary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Watershed Description:Bayinhe River, located in northwest China. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:The calibration of hydrological models is often complex in regions with scarce data, and generally only uses site-based streamflow data. However, this approach will yield highly generalised values for all model parameters and hydrological processes. It is therefore necessary to obtain more spatially heterogeneous observation data (e.g., satellite-based evapotranspiration (ET)) to calibrate such hydrological models. Here, soil and water assessment tool (SWAT) models were built to evaluate the advantages of using ET data derived from the Global Land surface Evaporation Amsterdam Methodology (GLEAM) to calibrate the models for the Bayinhe River basin in northwest China, which is a typical data-scarce basin. The result revealed the following: (1) A great effort was required to calibrate the SWAT models for the study area to obtain an improved model performance. (2) The SWAT model performance for simulating the streamflow and water balance was reliable when calibrated with streamflow only, but this method of calibration grouped the hydrological processes together and caused an equifinality issue. (3) The combination of the streamflow and GLEAM-based ET data for calibrating the SWAT model improved the model performance for simulating the streamflow and water balance. However, the equifinality issue remained at the hydrologic response unit (HRU) level. 
Language:English 
Keywords:SWAT; GLEAM; ET; streamflow