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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #349646

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: Integration of a physically-based soil moisture module into SWAT to improve hydrology model structure

Author
item Qi, J - University Of Maryland
item Zhang, X. - University Of Maryland
item Mccarty, Gregory
item Sadeghi, Ali
item Cosh, Michael
item Zeng, X. - University Of Arizona
item Gao, Feng
item Daughtry, Craig
item Haung, C. - University Of Maryland
item Lang, M.w. - Fisheries & Wildlife
item Arnold, Jeffrey

Submitted to: Environmental Modelling & Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/23/2018
Publication Date: 8/27/2018
Citation: Qi, J., Zhang, X., Mccarty, G.W., Sadeghi, A.M., Cosh, M.H., Zeng, X., Gao, F.N., Daughtry, C.S., Haung, C., Lang, M., Arnold, J.G. 2018. Integration of a physically-based soil moisture module into SWAT to improve hydrology model structure. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2018.08.024.
DOI: https://doi.org/10.1016/j.envsoft.2018.08.024

Interpretive Summary: Soil moisture is an important state variable in land surface process research. Information on the spatial-temporal variations of soil moisture is critical in climate change studies, since soil moisture governs the processes of energy and water transfer between the land surface and atmosphere. Thus, there is a need to improve performance of watershed models to estimate the spatial-temporal variations in soil moisture. As a watershed scale model, the Soil and Water Assessment Tool (or SWAT model) is designed to simulate hydrological processes and predict water quantity and quality as affected by land use and management practices. The first objective of this study was to develop a physically-based soil moisture module and integrate it with SWAT and test the new soil moisture module against in-situ soil moisture measurements at the U.S. Department of Agriculture (USDA) Long-Term Agricultural Research (LTAR) sites within or near the Choptank River watershed in Maryland. The performances between the new and original soil moisture modules in SWAT model were compared. Results showed that the new module noticeably outperformed the original algorithm with respect to reproducing dynamics of surface soil moisture for both wet and dry time periods. The resulting enhanced SWAT model with its physically-based representation of soil moisture routing increased the capability of SWAT to use detailed in-situ surface soil moisture measurements and remote sensing products to better support soil water content/irrigation management and the overall ecosystem sustainability assessment.

Technical Abstract: This study developed a physically-based soil moisture module to improve hydrology structure of Soil and Water Assessment Tool (SWAT). Water content based Richards equation was numerically solved with no extra input data required. The new module developed was tested using four years of daily soil moisture measurements from 10 monitoring stations at three depths (i.e., 5, 10, and 50 cm) in the Choptank River watershed, Maryland. We compared simulation performances by the original SWAT soil moisture algorithm and the new module. Results showed that the new module pronouncedly outperformed the original algorithm with respect to reproducing dynamics of surface soil moisture for both wet and dry time periods. Additionally, the soil moisture coupling strength between different soil layers was also substantially improved. The resulting enhanced SWAT model with physically-based representation of soil moisture routing is expected to increase the capability of SWAT to leverage field measurements and remote sensing products to better support soil water content/irrigation management and the overall ecosystem sustainability assessment.