SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Impacts of Global Circulation Model (GCM) bias and WXGEN on modeling hydrologic variables 
Authors:Lee, S., C.W. Wallace, A.m. Sadeghi, G.W. McCarty, H. Zhong, and I.-Y. Yeo 
Year:2018 
Journal:Water 
Volume:10(6) 
Pages: 
Article ID:764 
DOI:10.3390/w10060764 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:hydrologic & pollutant 
Primary Application Category:climate change  
Secondary Application Category:crop/forest/plant growth/yield and/or parameters 
Watershed Description:220.7 km^2 Tuckahoe Creek, a tributary of the Choptank River located on the Delmarva Peninsula in eastern Maryland, U.S. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:A WXGEN weather generator is commonly used to generate daily climate data for Soil and Water Assessment Tool (SWAT) model when input climate data are not fully available. Of all input data for WXGEN, precipitation is critical due to its sensitivity to the number of wet days. Since global climate model (GCM) data tend to have excessive wet days, use of GCM precipitation data for WXGEN may cause errors in the estimation of climate variables and therefore SWAT predictions. To examine such impacts of GCM data, we prepared two climate data for SWAT using WXGEN with both the original GCM data with the excessive number of wet days (EGCM) and the processed GCM data with the reasonable number of wet days (RGCM). We then compared SWAT simulations from EGCM and RGCM. Results show that because of the excessive wet days in EGCM, solar radiation generated by WXGEN was underestimated, subsequently leading to 143 mm lower ET and 0.6–0.8 m3/s greater streamflow compared to the simulations from RGCM. Simulated crop biomass under EGCM was smaller than RGCM due to less solar radiation. Although use of WXGEN is increasing in projecting climate change impacts using SWAT, potential errors from the combination of WXGEN and GCM have not well investigated. Our findings clearly demonstrate that GCM bias (excessive wet days) leads WXGEN to generate inaccurate climate data, resulting in unreasonable SWAT predictions. Thus, GCM data should be carefully processed to use them for WXGEN. 
Language:English 
Keywords:SWAT; WXGEN; GCM bias; excessive wet days