Past and Future Trends in Swiss Snow Cover: Multi-decades Analysis Using the Snow Observation from Space Algorithm

Poussin Charlotte ^* Pablo Timoner Pascal Peduzzi Gregory Giuliani

• Dpt. F.-A. Forel for Environment and Water Sciences, University of Geneva, Geneva, Switzerland

Despite the large availability of satellite and in-situ data on snow cover in the Northern Hemisphere, long-term assessments at an adequate resolution to capture the complexities of mountainous terrains remain limited, particularly for countries like Switzerland. This study addresses this gap by employing two productsthe monthly NDSI (Normalized Difference Snow Index) and snow cover products -derived from the Snow Observation from Space (SOfS) algorithm to monitor snow cover dynamics across Switzerland over the past 37 years. The pixel-wise analysis reveals significant negative trends in the monthly NDSI across all seasons, with the most pronounced decreases at low to mid-elevations, particularly in winter and spring (e.g., a 50% reduction in NDSI for pixels showing positive significative trends in winter below 1000 m, and a 43% reduction in spring between 1000 and 2000 m). Similarly, snow cover area has declined significantly, with reductions of -13% to -15% in spring for the transitional zones between 1000-1500 m and 1500-2000 m. Furthermore, the monthly NDSI values are more strongly influenced by temperature than precipitation, especially at lower altitudes. To estimate trends in snow cover for the 21st century, we modelled the relationship between snow presence and two climatic variables (temperature and precipitation) using a binomial generalized linear mixed model (GLMM). In the context of climate change, projections under various greenhouse gas emission scenarios suggest further declines in snow cover by the end of the century. Even with moderate climate action (RCP 2.6), snow-free areas could expand by 22% at lower elevations by 2100. Under the more extreme scenario (RCP 8.5), snow-free regions could increase by over 43%, with significant impacts during the transitional months of April and May. The SOfS algorithm, developed within the Swiss Data Cube, provides valuable insights into snow cover dynamics across Switzerland. Complementing in-situ observations, this innovative approach is essential for assessing snow cover changes and guiding adaptation strategies in a country where snow is not only an environmental indicator but also a cultural and economic asset.