Assessing the global performance of a parsimonious soil temperature model for frozen ground prediction

• A parsimonious soil temperature model using minimal inputs shows strong performance in predicting frozen ground conditions. • The model demonstrates high accuracy with a true frozen rate of 0.90 and false frozen rate of 0.06. • The model provides a computationally efficient solution for representing frozen ground effects in hydrological models. Seasonal soil freezing and thawing processes significantly influence runoff generation dynamics during cold periods, affecting various hydrological and agricultural systems, including flood generation, soil erosion, and plant health. Representing frozen soil conditions in land surface or hydrological models is therefore crucial. While fully distributed models implement the process by solving energy-mass balance equations to obtain soil temperature profiles, parsimonious models using “snow tanks” or frozen ground states can provide suitable modeling solutions with reduced computational demands. However, even these parsimonious approaches to representing frozen ground typically require some additional complexity through additional inputs or surface energy balance calculations. This study evaluates the applicability of a simplified soil temperature prediction model that determines frozen/unfrozen ground states using only air temperature and snow cover data, reducing model complexity. We first validate the model performance using AmeriFlux network in-situ measurements across the United States and Canada. Furthermore, we provide a comprehensive assessment at the global scale with ERA5-LAND reanalysis data (1980–2020). The model demonstrates robust performance globally, achieving an average true frozen rate of 0.90 and false frozen rate of 0.06. We also investigate the model performance by month, and, while monthly analyses show drops in model performance for certain months, these lower scores are primarily due to the limited number of freeze–thaw events during these periods, which makes the model appear less accurate than it actually is. In terms of spatial performance, the model shows reduced accuracy in mountainous regions, including the Tibetan Plateau, Rocky Mountains, and Andes, suggesting the need for region-specific parameter calibration in orographic settings. Nevertheless, this parsimonious soil temperature model demonstrates significant potential as a computationally efficient solution for incorporating frozen ground effects in distributed hydrological models with simple conceptual runoff generation schemes; the implications of this work are toward improved flood prediction in cold regions, such as the Yukon-Kuskokwim Delta region during its record 2025 flooding, where the hydrologic response is largely controlled by frozen soil