Preprints
https://doi.org/10.5194/egusphere-2024-2249
https://doi.org/10.5194/egusphere-2024-2249
22 Aug 2024
 | 22 Aug 2024

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 Thomas, Ian Shirley, Sarah Maebius, Colleen M. Iversen, and Katrina E. Bennett

Abstract. 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.

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

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2249', Anonymous Referee #1, 17 Sep 2024
    • AC1: 'Reply on RC1', Katrina Bennett, 03 Nov 2024
  • RC2: 'Comment on egusphere-2024-2249', Anonymous Referee #2, 20 Sep 2024
    • AC2: 'Reply on RC2', Katrina Bennett, 03 Nov 2024
  • RC3: 'Comment on egusphere-2024-2249', Anonymous Referee #3, 23 Sep 2024
    • AC3: 'Reply on RC3', Katrina Bennett, 03 Nov 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2249', Anonymous Referee #1, 17 Sep 2024
    • AC1: 'Reply on RC1', Katrina Bennett, 03 Nov 2024
  • RC2: 'Comment on egusphere-2024-2249', Anonymous Referee #2, 20 Sep 2024
    • AC2: 'Reply on RC2', Katrina Bennett, 03 Nov 2024
  • RC3: 'Comment on egusphere-2024-2249', Anonymous Referee #3, 23 Sep 2024
    • AC3: 'Reply on RC3', Katrina Bennett, 03 Nov 2024
Claire L. Bachand, Chen Wang, Baptiste Dafflon, Lauren Thomas, Ian Shirley, Sarah Maebius, Colleen M. Iversen, and Katrina E. Bennett

Data sets

iButton and Tinytag snow/ground interface temperature measurements at Teller 27 and Kougarok 64 from 2022-2023, Seward Peninsula, Alaska Katrina Bennett, Claire Bachand, Lauren Thomas, Eve Gasarch, Evan Thaler, and Ryan Crumley https://data.ess-dive.lbl.gov/view/doi:10.15485/2319246

iButton snow-ground interface temperature measurements in Los Alamos, New Mexico from 2023-2024 Lauren Thomas, Claire Bachand, and Sarah Maebius https://data.ess-dive.lbl.gov/view/doi:10.15485/2338028

Model code and software

Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico Claire Bachand, Chen Wang, Baptiste Dafflon, Lauren Thomas, Ian Shirley, Sarah Maebius, Colleen Iversen, and Katrina Bennett https://data.ess-dive.lbl.gov/view/doi:10.15485/2371854

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

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Short summary
Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. In this work, we develop 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 was not previously possible.