U.S. Department of EnergyOffice of Scientific and Technical Information
Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico
Abstract
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 is the first study to estimate snow depth directly from ground surface temperature data. The present dataset contains one *.csv file which includes machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado, and New Mexico and one *.kml file including the locations of sitesmore »
- Authors:
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- Los Alamos National Laboratory; Los Alamos National Laboratory
- Lawrence Berkeley National Laboratory
- Los Alamos National Laboratory
- Oak Ridge National Laboratory
- Publication Date:
- Other Number(s):
- NGA530; LA-UR-24-23694
- Research Org.:
- Environmental System Science Data Infrastructure for a Virtual Ecosystem; Next-Generation Ecosystem Experiments (NGEE) Arctic
- Sponsoring Org.:
- U.S. DOE > Office of Science > Biological and Environmental Research (BER)
- Subject:
- 54 ENVIRONMENTAL SCIENCES; EARTH SCIENCE > CRYOSPHERE > SNOW/ICE; EARTH SCIENCE > CRYOSPHERE > SNOW/ICE > SNOW DEPTH; ESS-DIVE CSV File Formatting Guidelines Reporting Format; ESS-DIVE File Level Metadata Reporting Format; Machine Learning
- OSTI Identifier:
- 2371854
- DOI:
- https://doi.org/10.15485/2371854
Citation Formats
Bachand, Claire, Wang, Chen, Dafflon, Baptiste, Thomas, Lauren, Shirley, Ian, Maebius, Sarah, Iversen, Colleen, and Bennett, Katrina. Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico. United States: N. p., 2024.
Web. doi:10.15485/2371854.
Bachand, Claire, Wang, Chen, Dafflon, Baptiste, Thomas, Lauren, Shirley, Ian, Maebius, Sarah, Iversen, Colleen, & Bennett, Katrina. Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico. United States. doi:https://doi.org/10.15485/2371854
Bachand, Claire, Wang, Chen, Dafflon, Baptiste, Thomas, Lauren, Shirley, Ian, Maebius, Sarah, Iversen, Colleen, and Bennett, Katrina. 2024.
"Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico". United States. doi:https://doi.org/10.15485/2371854. https://www.osti.gov/servlets/purl/2371854. Pub date:Mon Jan 01 04:00:00 UTC 2024
@article{osti_2371854,
title = {Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico},
author = {Bachand, Claire and Wang, Chen and Dafflon, Baptiste and Thomas, Lauren and Shirley, Ian and Maebius, Sarah and Iversen, Colleen and Bennett, Katrina},
abstractNote = {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 is the first study to estimate snow depth directly from ground surface temperature data. The present dataset contains one *.csv file which includes machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado, and New Mexico and one *.kml file including the locations of sites with snow depth predictions. No training data predictions are included in the *.csv file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).},
doi = {10.15485/2371854},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 01 04:00:00 UTC 2024},
month = {Mon Jan 01 04:00:00 UTC 2024}
}