Improving hydrological simulation in the Upper Mississippi River Basin through enhanced freeze-thaw cycle representation

Abstract

Freeze-thaw cycles are important processes relevant to terrestrial hydrological cycling. However, the representation of freeze-thaw cycles has been often simplified in large scale watershed models. The Soil and Water Assessment Tool (SWAT), which has been widely used to understand and assess hydrologic budgets and water resources management, employs a simplified empirical approach to estimate soil temperature and determine the freezing and thawing status of soils. Here, we compared the performance of a physically-based soil temperature module and the built-in empirical approach in SWAT against field measurements at surface and 5, 10, 20, 50, and 100 cm depths at six stations of the U.S. Climate Reference Network (USCRN) within the Upper Mississippi River Basin (UMRB). In general, SWAT consistently underestimated winter soil temperatures and overestimated frozen days at all soil depths, while the modified version of SWAT (equipped with the physically-based soil temperature model; referred to as TSWAT) pronouncedly reduced the bias in estimated winter soil temperatures and frozen days compared with SWAT. Model performance assessment is conducted with three statistical coefficients, i.e., Bias (°C), the coefficient of determination (R2), and Nash-Sutcliffe coefficient (NS). Statistical analyses show that TSWAT accurately simulated surface and soil temperatures at the five depths with R2 and NS values greater than 0.82 at most sites, and Bias values were generally within the range of −1 to 1 °C during winter and ranged between −2.09 and 2.58 °C in non-winter seasons. The differences in freeze-thaw cycle representation between SWAT and TSWAT translate into noticeable discrepancies in simulated key hydrologic variables, such as surface runoff, percolation, and baseflow. Compared against long-term observed streamflow (1980–2015), TSWAT outperformed SWAT in capturing variations in monthly streamflow in both winter and non-winter seasons. These results and analyses highlight the value of improving freeze-thaw cycle representation for enhanced hydrologic modeling in large watersheds that are subject to freeze-thaw cycles.