Numerical modelling for improved prediction of ground temperature in seasonal snow-cover regions

Predicting hourly soil temperature variation in seasonal snow cover regions is essential in many scientific and engineering applications. However, the existing snow and soil surface (SS) heat interaction models are predominantly based on empirical correlations, resulting in forecasts restricted to daily average values. Herein, a novel approach is presented to improve hourly spatial soil temperature prediction by incorporating a logarithmic snow depth term into the SS energy balance equation during the snow-covered periods. The proposed approach has been validated using year-long experimental soil temperature measurements from similar climatic condition. Further, the effect of homogenous, isotropic, and spatially heterogeneous soil media on soil temperature distribution has also been explored. The spatial and temporal soil temperature predictions are simulated using a finite-volume numerical solution of the transient heat conduction equation with energy balance at SS. The proposed approach, combined with consideration of spatially variable soil thermal diffusivity, exhibited excellent concordance with the experimental measurements, with coefficient of determination value of 0.958, 0.983, 0.989, 0.994, 0.964, and 0.888 at soil depths of 0.1, 0.2, 0.5, 1, 2, and 5 m, respectively. Additionally, the depth of daily and seasonal soil temperature variations and the Kusuda and Achenbach correlation constants for the study location have been determined.