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| Main Authors: | , , , , , , , , |
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| Format: | Dataset Open Access |
| Language: | en |
| Published: |
PANGAEA
2022
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| Subjects: | |
| Online Access: | https://doi.org/10.1594/PANGAEA.942279 |
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| _version_ | 1867172225074855936 |
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| author | Laska, Michał Luks, Bartłomiej Kępski, Daniel Gądek, Bogdan Głowacki, Piotr Puczko, Dariusz Migała, Krzysztof Nawrot, Adam Pętlicki, Michał |
| author_facet | Laska, Michał Luks, Bartłomiej Kępski, Daniel Gądek, Bogdan Głowacki, Piotr Puczko, Dariusz Migała, Krzysztof Nawrot, Adam Pętlicki, Michał |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | Analyses of the seasonal snow cover provide basic information on the condition of glaciers, the water cycle and the energy budget in the natural environment. Therefore, they are a valuable source of information for more accurate glacier mass balance estimations, validation of remote sensing products, snowpack evolution and hydrological models. Hansbreen Snowpit Dataset is the largest and longest collection of the snowpit measurements on Svalbard and very few across the entire Arctic. It includes all archived snow profiles performed on Hansbreen (Hans Glacier), Svalbard since 1989, when the glaciological monitoring on this glacier has begun, until 2021. Snowpits were performed at 1 to 3 reference sites, most often once a season, during the maximum snow cover accumulation (April-May), but several were conducted in March, June and September. Physical properties of the snow cover include snow depth, grain shape and size, snow hardness, wetness, temperature and density, or the snow water equivalent at the reference study sites. The entire dataset has been revised and unified to the current protocols and the applicable classification by The International Association of Cryospheric Sciences, making it comparable to different worldwide locations – past, now, and future. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_942279 |
| institution | PANGAEA |
| language | en |
| publishDate | 2022 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Hansbreen Snowpit Dataset: a long-term snow monitoring (since 1989) in the unique field laboratory (SW Spitsbergen, Svalbard) Laska, Michał Luks, Bartłomiej Kępski, Daniel Gądek, Bogdan Głowacki, Piotr Puczko, Dariusz Migała, Krzysztof Nawrot, Adam Pętlicki, Michał Arctic; Cryosphere; Hansbreen; Hornsund; seasonal snow cover; snowpit; snow stratigraphy; Spitsbergen; Svalbard Analyses of the seasonal snow cover provide basic information on the condition of glaciers, the water cycle and the energy budget in the natural environment. Therefore, they are a valuable source of information for more accurate glacier mass balance estimations, validation of remote sensing products, snowpack evolution and hydrological models. Hansbreen Snowpit Dataset is the largest and longest collection of the snowpit measurements on Svalbard and very few across the entire Arctic. It includes all archived snow profiles performed on Hansbreen (Hans Glacier), Svalbard since 1989, when the glaciological monitoring on this glacier has begun, until 2021. Snowpits were performed at 1 to 3 reference sites, most often once a season, during the maximum snow cover accumulation (April-May), but several were conducted in March, June and September. Physical properties of the snow cover include snow depth, grain shape and size, snow hardness, wetness, temperature and density, or the snow water equivalent at the reference study sites. The entire dataset has been revised and unified to the current protocols and the applicable classification by The International Association of Cryospheric Sciences, making it comparable to different worldwide locations – past, now, and future. |
| title | Hansbreen Snowpit Dataset: a long-term snow monitoring (since 1989) in the unique field laboratory (SW Spitsbergen, Svalbard) |
| topic | Arctic; Cryosphere; Hansbreen; Hornsund; seasonal snow cover; snowpit; snow stratigraphy; Spitsbergen; Svalbard |
| url | https://doi.org/10.1594/PANGAEA.942279 |