Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar

• Published in: The Cryosphere
• Main Authors: Dharmadasa, Vasana, Kinnard, Christophe, Baraër, Michel
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2023
• Subjects: article; Verlagsveröffentlichung; Canada; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-17-1225-2023; https://noa.gwlb.de/receive/cop_mods_00065433; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00063955/tc-17-1225-2023.pdf; https://tc.copernicus.org/articles/17/1225/2023/tc-17-1225-2023.pdf

Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. In such a context, understanding and assessing the effect of vegetation and topographic conditions on snow depth variability is required. 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 of less than 10 m in the forested area at all three sites, whereas open areas showed comparatively larger scale break distances (i.e., 11–14 m). The effect of vegetation and topographic variables on the spatial variability in snow depths at each site was investigated with random forest models. Results show that 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 physically based models for accurate estimates of snowpack dynamics.