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Impacts of Global Circulation Model (GCM) bias and WXGEN on Modeling Hydrologic Variables

1
Department of Environmental Science and Technology, University of Maryland, College Park, MD 20740, USA
2
Hydrology and Remote Sensing Laboratory, USDA-ARS, Beltsville, MD 20705, USA
3
Interstate Commission on the Potomac River Basin, Rockville, MD 20850, USA
4
Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA
5
School of Engineering, The University of Newcastle, Callaghan, NSW 2308, Australia
*
Author to whom correspondence should be addressed.
Water 2018, 10(6), 764; https://doi.org/10.3390/w10060764
Received: 30 April 2018 / Revised: 7 June 2018 / Accepted: 8 June 2018 / Published: 12 June 2018
(This article belongs to the Section Hydrology)
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. View Full-Text
Keywords: SWAT; WXGEN; GCM bias; excessive wet days SWAT; WXGEN; GCM bias; excessive wet days
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Lee, S.; Wallace, C.W.; Sadeghi, A.M.; McCarty, G.W.; Zhong, H.; Yeo, I.-Y. Impacts of Global Circulation Model (GCM) bias and WXGEN on Modeling Hydrologic Variables. Water 2018, 10, 764.

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