Articles | Volume 17, issue 3
https://doi.org/10.5194/tc-17-1225-2023
https://doi.org/10.5194/tc-17-1225-2023
Research article
 | 
14 Mar 2023
Research article |  | 14 Mar 2023

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

Vasana Dharmadasa, Christophe Kinnard, and Michel Baraër

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Revised manuscript accepted for TC
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Cited articles

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Aygün, O., Kinnard, C., Campeau, S., and Krogh, S. A.: Shifting hydrological processes in a Canadian agroforested catchment due to a warmer and wetter climate, Water, 12, 739, https://doi.org/10.3390/w12030739, 2020. 
Bair, E. H., Abreu Calfa, A., Rittger, K., and Dozier, J.: Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan, The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, 2018. 
Baños, I. M., García, A. R., Alavedra, J. M. I., Figueras, P. O. i., Iglesias, J. P., Figueras, P. M. I., and López, J. T.: Assessment of airborne lidar for snowpack depth modeling, B. Soc. Geol. Mex., 63, 95–107, 2011. 
Blue Marble Geographics: Global Mapper, Blue Marble Geographics, Hallowell, ME, USA, 2020. 
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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.