The Cryosphere
The Cryosphere
TC
Articles | Volume 19, issue 8
Articles | Volume 19, issue 8
https://doi.org/10.5194/tc-19-3123-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-3123-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 8
Research article
 | 
18 Aug 2025
Research article |  | 18 Aug 2025

Leveraging snow probe data, lidar, and machine learning for snow depth estimation in complex-terrain environments

Dane Liljestrand, Ryan Johnson, Bethany Neilson, Patrick Strong, and Elizabeth Cotter

Related authors

High-resolution 4D electrical resistivity tomography and below-ground point sensor monitoring of High Arctic deglaciated sediments capture zero-curtain effects, freeze–thaw transitions, and mid-winter thawing
Mihai O. Cimpoiasu, Oliver Kuras, Harry Harrison, Paul B. Wilkinson, Philip Meldrum, Jonathan E. Chambers, Dane Liljestrand, Carlos Oroza, Steven K. Schmidt, Pacifica Sommers, Lara Vimercati, Trevor P. Irons, Zhou Lyu, Adam Solon, and James A. Bradley
The Cryosphere, 19, 401–421, https://doi.org/10.5194/tc-19-401-2025,https://doi.org/10.5194/tc-19-401-2025, 2025
Short summary

Cited articles

Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A., Malczyk, J., and Jetz, W.: A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, Scientific Data, 5, 1–15, 2018. a
Anderton, S., White, S., and Alvera, B.: Evaluation of spatial variability in snow water equivalent for a high mountain catchment, Hydrol. Process., 18, 435–453, 2004. a
Bales, R. C., Molotch, N. P., Painter, T. H., Dettinger, M. D., Rice, R., and Dozier, J.: Mountain hydrology of the western United States, Water Resour. Res., 42, W08432, https://doi.org/10.1029/2005WR004387, 2006. a
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, 2005. a
Barrett, A.: National Operational Hydrologic Remote Sensing Center SNOw Data Assimiliation System (SNODAS) products at NSIDC, National Snow and Ice Data Center, Boulder, Special Report 11, 19, 2003. a
More articles (47)
Download
Short summary
This work introduces a model specifically designed for high-resolution snow depth estimation, leveraging in situ snow observations and snow-off lidar terrain features to provide an accessible and cost-effective method for snowpack modeling in regions lacking high-quality data products or collection networks. This work demonstrates that reliable basin-scale snow depth estimates can be achieved in difficult environments with very few observations and low institutional costs.
Read more
Share