Preprints
https://doi.org/10.5194/tc-2022-124
https://doi.org/10.5194/tc-2022-124
 
23 Jun 2022
23 Jun 2022
Status: this preprint is currently under review for the journal TC.

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

Vasana Dharmadasa1,2,3,5, Christophe Kinnard1,2,3, and Michel Baraër4,5 Vasana Dharmadasa et al.
  • 1Department of Environmental Sciences, University of Québec at Trois-Rivières, QC G8Z 4M3, Canada
  • 2Center for Northern Studies (CEN), Québec City, QC GV1 0A6, Canada
  • 3Research Centre for Watershed-Aquatic Ecosystem Interactions (RIVE), University of Québec at Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada
  • 4Department of Construction Engineering, École de technologie supérieure, Montréal, QC H3C 1K3, Canada
  • 5CentrEau, the Québec Water Management Research Centre, Québec City, QC GV1 0A6, Canada

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.

Vasana Dharmadasa et al.

Status: open (until 18 Aug 2022)

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Vasana Dharmadasa et al.

Vasana Dharmadasa et al.

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Short summary
This study highlights the successful usage of UAV-lidar to monitor small-scale snow depth distribution. Our results show that underlying topography and 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. This emphasizes the importance of including and better representing these processes in process-based models for accurate snowpack estimates.