Location: Hydrology and Remote Sensing LaboratoryTitle: Modeling soil temperature in a temperate region: A comparison between empirical and physically based methods
|QI, J. - University Of Maryland|
|ZHANG, X. - University Of Maryland|
Submitted to: Ecological Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/14/2019
Publication Date: 2/28/2019
Citation: Qi, J., Zhang, X., Cosh, M.H. 2019. Modeling soil temperature in a temperate region: A comparison between empirical and physically based methods. Ecological Engineering. 129(4):134-143. https://doi.org/10.1016/j.ecoleng.2019.01.017.
Interpretive Summary: Soil temperature modeling is a novel component of the Soil and Water Assessment Tool (SWAT) model and requires evaluation. To perform this evaluation, a four-year period of soil temperature data are used within the model to determine the accuracy of the physically based module, both calibrated and uncalibrated. Overall, the physically based module underestimated the soil temperature, thus calibration was required. This study indicated that for further inclusion of soil temperature into hydrologic modeling, calibration is necessary. Additionally, for agricultural regions, a thorough understanding of the management practices is also necessary as they have a significant impact on soil temperature.
Technical Abstract: Although the Soil and Water Assessment Tool (SWAT) model has been widely used in temperate regions, the performance of its soil temperature module has not been extensively assessed. The aim of the present study was to evaluate the performance of the SWAT model’s built-in empirical soil temperature module and a physically-based soil temperature algorithm using four years of daily soil temperature measurements at three depths (i.e., 5, 10, and 50 cm) across 10 monitoring stations in and around the Choptank River watershed, Maryland, USA. Two versions of physically-based soil temperature module were considered: one was calibrated using parameter values adopted from previous studies, another one remained uncalibrated. Results show that the empirical soil temperature module and both versions of physically-based soil temperature module reproduce well variations of measured soil temperatures at 5 and 10 cm depths for all stations in non-winter seasons. However, the empirical and uncalibrated physically-based soil temperature modules tended to underestimate soil temperatures in non-winter seasons at the deeper soil layer (50 cm depth); they also severely underestimated soil temperatures in winter for soil surface layers (5 and 10 cm depths). Results indicate that the calibrated physically-based soil temperature module improved soil temperature simulation, especially for winter due to its accounting for insulation effect of crop residue on soil surface. This study suggested that when equipping a physically-based soil temperature module in a hydrological model, a better representation of surface residue was needed for applications in agricultural watersheds and assessing best management practices.