Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors
<p>Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. The model performed well on A...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-01-01
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| Series: | The Cryosphere |
| Online Access: | https://tc.copernicus.org/articles/19/393/2025/tc-19-393-2025.pdf |
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| author | C. L. Bachand C. L. Bachand C. Wang B. Dafflon L. N. Thomas L. N. Thomas I. Shirley S. Maebius S. Maebius C. M. Iversen K. E. Bennett |
| author_facet | C. L. Bachand C. L. Bachand C. Wang B. Dafflon L. N. Thomas L. N. Thomas I. Shirley S. Maebius S. Maebius C. M. Iversen K. E. Bennett |
| author_sort | C. L. Bachand |
| collection | DOAJ |
| description | <p>Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We trained 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 Arctic evaluation sites (RMSE <span class="inline-formula">≤</span> 0.15 m). It performed poorly at temperate sites with deeper snowpacks, partially due to training data limitations. Small temperature sensors are cheap and easy to deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring at high latitudes to an extent previously infeasible.</p> |
| format | Article |
| id | doaj-art-f74629250b074837b7c459608df4e1c7 |
| institution | Kabale University |
| issn | 1994-0416 1994-0424 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The Cryosphere |
| spelling | doaj-art-f74629250b074837b7c459608df4e1c72025-01-28T11:51:14ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242025-01-011939340010.5194/tc-19-393-2025Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensorsC. L. Bachand0C. L. Bachand1C. Wang2B. Dafflon3L. N. Thomas4L. N. Thomas5I. Shirley6S. Maebius7S. Maebius8C. M. Iversen9K. E. Bennett10Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USADepartment of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, AK, USA Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USAEarth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USAEarth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USADepartment of Geography, University of Colorado Boulder, Boulder, CO, USAEarth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USAEarth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USADepartment of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USAEnvironmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USAEarth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA<p>Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We trained 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 Arctic evaluation sites (RMSE <span class="inline-formula">≤</span> 0.15 m). It performed poorly at temperate sites with deeper snowpacks, partially due to training data limitations. Small temperature sensors are cheap and easy to deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring at high latitudes to an extent previously infeasible.</p>https://tc.copernicus.org/articles/19/393/2025/tc-19-393-2025.pdf |
| spellingShingle | C. L. Bachand C. L. Bachand C. Wang B. Dafflon L. N. Thomas L. N. Thomas I. Shirley S. Maebius S. Maebius C. M. Iversen K. E. Bennett Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors The Cryosphere |
| title | Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors |
| title_full | Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors |
| title_fullStr | Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors |
| title_full_unstemmed | Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors |
| title_short | Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors |
| title_sort | brief communication monitoring snow depth using small cheap and easy to deploy snow ground interface temperature sensors |
| url | https://tc.copernicus.org/articles/19/393/2025/tc-19-393-2025.pdf |
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