Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors

Bachand, Claire L.; Wang, Chen; Dafflon, Baptiste; Thomas, Lauren N.; Shirley, Ian; Maebius, Sarah; Iversen, Colleen M.; Bennett, Katrina E.

doi:https://doi.org/10.5194/tc-19-393-2025

Articles | Volume 19, issue 1

https://doi.org/10.5194/tc-19-393-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-19-393-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 1

Brief communication

|

28 Jan 2025

Brief communication |

| 28 Jan 2025

Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors

Claire L. Bachand, Chen Wang, Baptiste Dafflon, Lauren N. Thomas, Ian Shirley, Sarah Maebius, Colleen M. Iversen, and Katrina E. Bennett

Supplement

https://doi.org/10.5194/tc-19-393-2025-supplement

Data sets

iButton and Tinytag snow/ground interface temperature measurements at Teller 27 and Kougarok 64 from 2022-2023, Seward Peninsula, Alaska Katrina Bennett et al. https://doi.org/10.15485/2319246

iButton snow-ground interface temperature measurements in Los Alamos, New Mexico from 2023-2024 Lauren Thomas et al. https://doi.org/10.15485/2338028

Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico Claire Bachand et al. https://doi.org/10.15485/2371854

Continuous snow depth, ground interface temperature and shallow soil temperature measurements from 2021-10-1 to 2022-6-14, Seward Peninsula, Alaska C. Wang et al. https://doi.org/10.15485/2475020

Model code and software

Accompanying code for Brief Communication: Monitoring snow depth using small, cheap, and easy-to-deploy ground surface temperature sensors Claire L. Bachand and Katrina E. Bennett https://github.com/cbachand-LANL/iButton-SnowDepth-ML

Short summary

Temporally continuous snow depth estimates are important for understanding changing snow patterns and impacts on frozen ground in the Arctic. In this work, we developed an approach to predict snow depth from variability in snow–ground interface temperature using small temperature sensors that are cheap and easy to deploy. This new technique enables spatially distributed and temporally continuous snowpack monitoring that has not previously been possible.