In remote Canada, sparse meteorological stations hinder reliable snow data collection, especially for assessing historical records. Yet, these data are crucial to evaluate the effect of climate change on ecosystems and species. Changes in snow cover influence wildlife ecology and can impact body condition, survival, or reproduction. This study validated the SNOWPACK model, forced with North American Regional Reanalysis (NARR) data, to estimate snow cover data for Ram Mountain, Alberta. We used snow depth data from five stations covering the 2022–2023 and 2023–2024 snow seasons for validation. We adjusted NARR radiation and precipitation data and SNOWPACK phase change temperature to improve model fit with snow depth and end date observations. The simulation, after bias calibration, showed an average error of less than 6 cm for snow depth and plus or minus sixteen days for the snow cover end date over the two winters. This parameterized model was then used to reconstruct snow cover data from 1979 to 2024. Though no clear trend in snow cover duration or start/end dates emerged, snow depth increased by an average of 5 mm/year. Our findings suggest that SNOWPACK can estimate snow cover characteristics in remote areas, making them valuable for reconstructing long-term snow cover trends in alpine environments.
Crémel et al. (2026) studied this question.