Comment on tc-2022-124

Abstract

Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. Understanding and assessing the effect of vegetation and topographic conditions on the snow depth variability is useful for the accurate prediction of snow depths. In this study, the spatial distribution of snow depth in two agro-forested sites and one coniferous site in eastern Canada was analyzed for topographic and vegetation effects on snow accumulation. Spatially distributed snow depths were derived by Unmanned Aerial Vehicle Light Detection and Ranging (UAV-lidar) surveys conducted in 2019 and 2020. Distinct patterns of snow accumulation and erosion in open areas (fields) versus adjacent forested areas were observed in lidar-derived snow depth maps at all sites. Omnidirectional semi-variogram analysis of snow depths showed the existence of a scale break distance less than 10 m in the forested area at all three sites, whereas open areas showed scale invariance or comparatively large scale break distances (i.e., 18 m). The effect of vegetation and topographic variables on the spatial variability of snow depths at each site was investigated with random forest models. Results show that including wind-related forest edge proximity effects improved the model accuracy by more than 50 % in agro-forested sites, whereas incorporating canopy characteristics improved the model accuracy by more than 60 % in the coniferous site. Hence the underlying topography and the wind-redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. These results highlight the importance of including and better representing these processes in process-based models for accurate estimates of snowpack dynamics. This study also demonstrates the usefulness of UAV-lidar to resolve and understand high-resolution snow depth heterogeneity in agro-forested environments and boreal forests.