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...
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Environmental System Science Data Infrastructure for a Virtual Ecosystem; Next-Generation Ecosystem Experiments (NGEE) Arctic
2024
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ftdatacite:10.15485/2371854 2024-09-09T19:25:43+00:00 Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico ... Bachand, Claire Wang, Chen Dafflon, Baptiste Thomas, Lauren Shirley, Ian Maebius, Sarah Iversen, Colleen Bennett, Katrina 2024 https://dx.doi.org/10.15485/2371854 https://www.osti.gov/servlets/purl/2371854/ en eng Environmental System Science Data Infrastructure for a Virtual Ecosystem; Next-Generation Ecosystem Experiments (NGEE) Arctic 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 Dataset dataset Specialized Mix 2024 ftdatacite https://doi.org/10.15485/2371854 2024-07-03T10:15:36Z 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 ... Dataset Arctic Ice permafrost Seward Peninsula Alaska Siberia DataCite Arctic Norway |
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English |
topic |
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 |
spellingShingle |
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 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 ... |
topic_facet |
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 |
description |
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 ... |
format |
Dataset |
author |
Bachand, Claire Wang, Chen Dafflon, Baptiste Thomas, Lauren Shirley, Ian Maebius, Sarah Iversen, Colleen Bennett, Katrina |
author_facet |
Bachand, Claire Wang, Chen Dafflon, Baptiste Thomas, Lauren Shirley, Ian Maebius, Sarah Iversen, Colleen Bennett, Katrina |
author_sort |
Bachand, Claire |
title |
Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico ... |
title_short |
Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico ... |
title_full |
Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico ... |
title_fullStr |
Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico ... |
title_full_unstemmed |
Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico ... |
title_sort |
machine learning snow depth predictions at sites in alaska, norway, siberia, colorado and new mexico ... |
publisher |
Environmental System Science Data Infrastructure for a Virtual Ecosystem; Next-Generation Ecosystem Experiments (NGEE) Arctic |
publishDate |
2024 |
url |
https://dx.doi.org/10.15485/2371854 https://www.osti.gov/servlets/purl/2371854/ |
geographic |
Arctic Norway |
geographic_facet |
Arctic Norway |
genre |
Arctic Ice permafrost Seward Peninsula Alaska Siberia |
genre_facet |
Arctic Ice permafrost Seward Peninsula Alaska Siberia |
op_doi |
https://doi.org/10.15485/2371854 |
_version_ |
1809895468925190144 |