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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Research Project #441146

Research Project: Enhancing the SWAT Model for Evapotranspiration Simulation

Location: Hydrology and Remote Sensing Laboratory

Project Number: 8042-13610-030-049-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 1, 2022
End Date: Aug 31, 2024

The Soil and Water Assessment Tool (SWAT) is widely used for assessing water availability in agricultural landscapes. The evapotranspiration (ET) algorithms used in the model do not explicitly differentiate energy budget components (e.g., sensible heat flux, latent heat flux/ET, and ground flux) at the land surface. Given that ET could remove >60% of the precipitation falling on land, there is a critical need to further improve the SWAT model to better describe the energy balance along the soil-plant-air column, thereby providing more reliable prediction of water availability as influenced by diverse crop management practices. This project aims to incorporate process-based algorithms into the SWAT model to represent energy balance along the soil-plant-air column and assess the implications of such an improvement for watershed hydrology modeling.

We plan to examine several new surface energy balance parameterization schemes and introduce them into SWAT. The proposed efforts aim to address some known limitations in the SWAT model in the following aspects. First, we will incorporate the two-stream radiative transfer model that explicitly characterizes downstream and upstream solar fluxes from canopies and soils into the SWAT model. The new scheme will permit distinguishing between foliage and non-photosynthetic components, accounting for canopy architecture (e.g., foliage orientations), and applying plant-specific spectral parameters. Second, we will examine the calculation of ET at sub-daily levels (e.g., hourly) using the Penman-Monteith equation as compared with the daily time steps currently used by SWAT. This improvement is expected to reduce errors arising from ET estimation due to the coarse temporal resolution. Third, we will test a one-layer vegetation scheme that explicitly depicts the energy balance for the canopy layer, coupled with the soil below and the air above. We anticipate these improvements will enable the SWAT model to better represent the energy balance of agroecosystems and more reliably simulate ET. The improved SWAT model will be tested against field and remote sensing data in the Choptank River Watershed in the eastern shore of Chesapeake Bay.