Articles | Volume 17, issue 3
https://doi.org/10.5194/tc-17-1225-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-1225-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Mar 2023
Research article |
| 14 Mar 2023
| 14 Mar 2023
Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar
Vasana Dharmadasa
CORRESPONDING AUTHOR
Department of Environmental Sciences, University of Québec at
Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada
Center for Northern Studies (CEN), Québec City, QC GV1 0A6, Canada
Research Centre for Watershed–Aquatic Ecosystem Interactions (RIVE),
University of Québec at Trois-Rivières, Trois-Rivières, QC G8Z
4M3, Canada
CentrEau, the Québec Water Management Research Centre, Québec
City, QC GV1 0A6, Canada
Christophe Kinnard
Department of Environmental Sciences, University of Québec at
Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada
Center for Northern Studies (CEN), Québec City, QC GV1 0A6, Canada
Research Centre for Watershed–Aquatic Ecosystem Interactions (RIVE),
University of Québec at Trois-Rivières, Trois-Rivières, QC G8Z
4M3, Canada
Michel Baraër
Department of Construction Engineering, École de technologie
supérieure, Montréal, QC H3C 1K3, Canada
CentrEau, the Québec Water Management Research Centre, Québec
City, QC GV1 0A6, Canada
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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 physically based models for accurate snowpack estimates.
This study highlights the successful usage of UAV lidar to monitor small-scale snow depth...
