Abstract ID: 3.11597 | Accepted as Talk | Talk/Oral | TBA | TBA

This study evaluates the role of snow accumulation and melt in soil temperature and moisture across varying elevations in the Snake Range in eastern Nevada in the USA. In-situ observations were used from the Nevada Climate-Ecohydrology Assessment Network along with gridded datasets with different spatial resolutions, including Western Land Data Assimilation System (WLDAS, 1 km by 1 km), Weather Research and Forecasting model for Contiguous USA (CONUS404, 4 km by 4 km), and Land Information System (LIS, 25 km by 25 km). The analysis focuses on soil temperature, soil moisture dynamics, freeze-thaw transitions, and snowpack characteristics to assess model performance under diverse climatic and topographic conditions. Key findings reveal that WLDAS consistently demonstrates the highest accuracy in modeling soil temperature and moisture across most elevations, particularly it performs best at mid-elevations (1580–2200 m). It achieves the lowest mean absolute error (MAE), and root mean square error (RMSE) values, accurately capturing freeze-thaw dynamics and snowpack timing. CONUS404 shows improved performance during winter months at high elevations but struggles with seasonal transitions and wet periods with volumetric soil moisture content greater than 0.3 compared to WLDAS. Coarser-resolution models like LIS and CONUS404 exhibit larger biases in snowpack timing and depth compared to WLDAS, which more accurately captures snow accumulation start and snow-free dates across elevations. Observed wet soil conditions occur earlier at lower elevations (2200 m) during April-May due to delayed snowmelt. Cold soil temperature biases are more pronounced in dry years potentially due to an underestimation of snow depth by models, resulting in reduced snow insulation. Higher-resolution models like WLDAS performs best in simulating localized processes such as freeze-thaw transitions and late-season wet soils, particularly at middle to high elevations. The results emphasize the need for improved modeling of shallow snowpacks, particularly during dry years, to enhance predictions of soil temperature and moisture dynamics critical for hydrological and ecological applications. Changes in snowpack dynamics due to climate warming—such as reduced snow cover duration and earlier melt—are expected to exacerbate these challenges by altering the timing and extent of soil freeze-thaw cycles and reducing the snow depth.