Snow depth survey in North Slope, Alaska, 2022 ...

This is the field measured snow depth data using an automatic snow depth probe (magnaprobe, Snow-Hydro LCC) in April 19 - May 7, 2022 in North Slope, Alaska. The data are in csv format (comma delimited text format with geographical coordinate, WGS 84, and UTM zone 5). The goal of this research proje...

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Bibliographic Details
Main Author: Ohara, Noriaki
Format: Dataset
Language:English
Published: NSF Arctic Data Center 2023
Subjects:
Online Access:https://dx.doi.org/10.18739/a24746t0k
https://arcticdata.io/catalog/view/doi:10.18739/A24746T0K
id ftdatacite:10.18739/a24746t0k
record_format openpolar
spelling ftdatacite:10.18739/a24746t0k 2023-06-11T04:09:24+02:00 Snow depth survey in North Slope, Alaska, 2022 ... Ohara, Noriaki 2023 text/xml https://dx.doi.org/10.18739/a24746t0k https://arcticdata.io/catalog/view/doi:10.18739/A24746T0K en eng NSF Arctic Data Center snow depth magnaprobe Dataset dataset 2023 ftdatacite https://doi.org/10.18739/a24746t0k 2023-06-01T11:27:49Z This is the field measured snow depth data using an automatic snow depth probe (magnaprobe, Snow-Hydro LCC) in April 19 - May 7, 2022 in North Slope, Alaska. The data are in csv format (comma delimited text format with geographical coordinate, WGS 84, and UTM zone 5). The goal of this research project is to quantify the role of thermokarst lake drainage and drained thermokarst lake basin (DTLB) evolution in the arctic system. The joint research team (University of Alaska, Fairbanks, University of Wyoming, and Michigan Technological University) spent several days based in Utqiagvik (Western Coastal Plain) and rest of days in Teshekpuk Lake (Central Coastal Plain). During the travel, manual snow survey was conducted using the mangaprobe to quantify the snowdrift around thermokarst lakes and other land features as complementary to the geophysical and remote sensed snowpack characterizations. ... Dataset Arctic north slope Thermokarst Alaska DataCite Metadata Store (German National Library of Science and Technology) Arctic Fairbanks
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language English
topic snow depth
magnaprobe
spellingShingle snow depth
magnaprobe
Ohara, Noriaki
Snow depth survey in North Slope, Alaska, 2022 ...
topic_facet snow depth
magnaprobe
description This is the field measured snow depth data using an automatic snow depth probe (magnaprobe, Snow-Hydro LCC) in April 19 - May 7, 2022 in North Slope, Alaska. The data are in csv format (comma delimited text format with geographical coordinate, WGS 84, and UTM zone 5). The goal of this research project is to quantify the role of thermokarst lake drainage and drained thermokarst lake basin (DTLB) evolution in the arctic system. The joint research team (University of Alaska, Fairbanks, University of Wyoming, and Michigan Technological University) spent several days based in Utqiagvik (Western Coastal Plain) and rest of days in Teshekpuk Lake (Central Coastal Plain). During the travel, manual snow survey was conducted using the mangaprobe to quantify the snowdrift around thermokarst lakes and other land features as complementary to the geophysical and remote sensed snowpack characterizations. ...
format Dataset
author Ohara, Noriaki
author_facet Ohara, Noriaki
author_sort Ohara, Noriaki
title Snow depth survey in North Slope, Alaska, 2022 ...
title_short Snow depth survey in North Slope, Alaska, 2022 ...
title_full Snow depth survey in North Slope, Alaska, 2022 ...
title_fullStr Snow depth survey in North Slope, Alaska, 2022 ...
title_full_unstemmed Snow depth survey in North Slope, Alaska, 2022 ...
title_sort snow depth survey in north slope, alaska, 2022 ...
publisher NSF Arctic Data Center
publishDate 2023
url https://dx.doi.org/10.18739/a24746t0k
https://arcticdata.io/catalog/view/doi:10.18739/A24746T0K
geographic Arctic
Fairbanks
geographic_facet Arctic
Fairbanks
genre Arctic
north slope
Thermokarst
Alaska
genre_facet Arctic
north slope
Thermokarst
Alaska
op_doi https://doi.org/10.18739/a24746t0k
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