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

Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We train a random forest machine learning model to predict snow depth from variability in snow-ground interface temperature. The model performed well on Alaska&r...

Full description

Bibliographic Details
Main Authors: Bachand, Claire L., Wang, Chen, Dafflon, Baptiste, Thomas, Lauren, Shirley, Ian, Maebius, Sarah, Iversen, Colleen M., Bennett, Katrina E.
Format: Text
Language:English
Published: 2024
Subjects:
permafrost
Seward Peninsula
Alaska
Online Access:https://doi.org/10.5194/egusphere-2024-2249
https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2249/
Description
Summary:Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We train a random forest machine learning model to predict snow depth from variability in snow-ground interface temperature. The model performed well on Alaska’s Seward Peninsula where it was trained, and at pan-Arctic evaluation sites (RMSE 0.15 m). Small temperature sensors are cheap and easy-to-deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring to an extent previously infeasible.

Similar Items