Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico ...
Temporally continuous snow depth estimates are vital for understanding changing snow patterns in the Arctic and impacts on permafrost. We trained random forest machine learning models to predict snow depth from temperature data recorded at or just below the ground surface. Training data was collecte...
| Main Authors: | , , , , , , , |
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| Format: | Dataset |
| Language: | English |
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Environmental System Science Data Infrastructure for a Virtual Ecosystem; Next-Generation Ecosystem Experiments (NGEE) Arctic
2024
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.15485/2371854 https://www.osti.gov/servlets/purl/2371854/ |
| Summary: | Temporally continuous snow depth estimates are vital for understanding changing snow patterns in the Arctic and impacts on permafrost. We trained random forest machine learning models to predict snow depth from temperature data recorded at or just below the ground surface. Training data was collected at the Teller 27 Watershed and Kougarok 64 Hillslope during the 2021 - 2022 water year on the Seward Peninsula, Alaska using distributed temperature profiling (DTP) systems. We then applied this model to other sites where ground surface or shallow soil temperature data was available for at least one water year (see Related Datasets). Many of these temperature measurements were collocated with snow depth observations. Ground surface temperature (i.e. snow-ground interface temperature) is easy to measure using small, cheap and easy-to-deploy temperature sensors such as iButtons and TinyTags, and such measurements have previously been used to calculate a variety of snow metrics (e.g. snow onset date). However, this ... |
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