| 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 (Issue): | 10(6) |
| Pages: | |
| Article ID: | 764 |
| DOI: | 10.3390/w10060764 |
| URL (non-DOI journals): | |
| Model: | SWAT |
| Broad Application Category: | hydrologic & pollutant |
| Primary Application Category: | weather generator effects/processes |
| Secondary Application Category: | climate change |
| 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 |