Forest Snow Patterns Derived Using ClustSnow Are Temporally Persistent Under Variable Environmental Conditions Article Swipe
Joschka Geissler
,
Giulia Mazzotti
,
Lars Rathmann
,
Clare Webster
,
Markus Weiler
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1029/2024wr038442
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1029/2024wr038442
Snow distribution affects water availability locally and downstream, especially in forests where snow amounts vary across small spatial scales (<3 m). Uncrewed Aerial Vehicle (UAV)‐based Light Detection and Ranging (LiDAR) measurements revealed that the snow distribution follows persistent patterns which, once identified, can be used to extrapolate point observations across space. Existing methods have derived such patterns from individual snow depth surveys, producing extrapolation bases that are limited to the specific timing of the underlying data. The new ClustSnow workflow addresses this limitation by applying clustering algorithms to multi‐temporal UAV‐based LiDAR snow depth maps, producing patterns that serve as an extrapolation basis for snow measurements across entire snow seasons. Here, we evaluate ClustSnow's transferability among sites and patterns' temporal persistence across three snow seasons with different snow dynamics. To support this assessment, we present a novel data set consisting of 19 UAV‐based LiDAR surveys, a network of snow depth sensors, and manual snow measurements, collected in Alptal (Swiss Prealps) and at the Schauinsland summit (German Black Forest). The derived clusters show similarities with forest structure classes at Alptal (up to 91%), but less at Schauinsland (up to 71%). At both sites, snow depth and snow water equivalent (SWE) maps obtained by ClustSnow are consistent with manual measurements and physics‐based snow model simulations, with mean root mean square errors of 8 cm for snow depth and 30 mm for SWE maps. ClustSnow therefore provides a robust framework for determining temporally persistent snow patterns and generating spatiotemporally continuous snow data sets from observations.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1029/2024wr038442
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2024WR038442
- OA Status
- gold
- Cited By
- 1
- References
- 78
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413987374
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https://doi.org/10.1029/2024wr038442Digital Object Identifier
- Title
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Forest Snow Patterns Derived Using ClustSnow Are Temporally Persistent Under Variable Environmental ConditionsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-09-01Full publication date if available
- Authors
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Joschka Geissler, Giulia Mazzotti, Lars Rathmann, Clare Webster, Markus WeilerList of authors in order
- Landing page
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https://doi.org/10.1029/2024wr038442Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2024WR038442Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2024WR038442Direct OA link when available
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Snow, Environmental science, Variable (mathematics), Physical geography, Hydrology (agriculture), Geography, Meteorology, Geology, Mathematics, Geotechnical engineering, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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