Advancing the Understanding of Snow Accumulation, Melting, and Associated Thermal Insulation Using Spatially Dense Snow Depth and Temperature Time Series

Abstract

Snow thermal insulation is a critical factor influencing ground thermal dynamics and associated biogeochemical processes. We analyzed the spatiotemporal variability of snow accumulation, melting, and thermal insulation dynamics using spatially dense, collocated snow depth and ground interface temperature time series over two consecutive years. We demonstrated that considering late-winter snow depth alone was insufficient to fully capture the complexity in snow and insulation dynamics. The influence of vegetation and topography on snow depth distribution varied over the season, across sites and years. We found that deep snow with a long melting period had a substantial impact on thawing n-factors. To better predict snow insulation effects, we proposed a new weighted snow depth metric that integrates mean daily snow depth and air temperature throughout the cold season. Our results provide insights for developing space-time remote sensing products and evaluating the representation of snow and permafrost processes in Earth system models.