Deep ice layer formation in an alpine snowpack: monitoring and modeling
Ice layers may form deep in the snowpack due to preferential water flow, with impacts on the snowpack mechanical, hydrological and thermodynamical properties. This detailed study at a high-altitude alpine site aims to monitor their formation and evolution thanks to the combined use of a comprehensiv...
Published in: | The Cryosphere |
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2020
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Online Access: | https://doi.org/10.5194/tc-14-3449-2020 https://tc.copernicus.org/articles/14/3449/2020/tc-14-3449-2020.pdf https://doaj.org/article/7e23c1b398d04bba8d4bbf2b6daab112 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:7e23c1b398d04bba8d4bbf2b6daab112 2023-05-15T18:32:20+02:00 Deep ice layer formation in an alpine snowpack: monitoring and modeling L. Quéno C. Fierz A. van Herwijnen D. Longridge N. Wever 2020-10-01 https://doi.org/10.5194/tc-14-3449-2020 https://tc.copernicus.org/articles/14/3449/2020/tc-14-3449-2020.pdf https://doaj.org/article/7e23c1b398d04bba8d4bbf2b6daab112 en eng Copernicus Publications doi:10.5194/tc-14-3449-2020 1994-0416 1994-0424 https://tc.copernicus.org/articles/14/3449/2020/tc-14-3449-2020.pdf https://doaj.org/article/7e23c1b398d04bba8d4bbf2b6daab112 undefined The Cryosphere, Vol 14, Pp 3449-3464 (2020) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2020 fttriple https://doi.org/10.5194/tc-14-3449-2020 2023-01-22T19:12:17Z Ice layers may form deep in the snowpack due to preferential water flow, with impacts on the snowpack mechanical, hydrological and thermodynamical properties. This detailed study at a high-altitude alpine site aims to monitor their formation and evolution thanks to the combined use of a comprehensive observation dataset at a daily frequency and state-of-the-art snow-cover modeling with improved ice formation representation. In particular, daily SnowMicroPen penetration resistance profiles enabled us to better identify ice layer temporal and spatial heterogeneity when associated with traditional snowpack profiles and measurements, while upward-looking ground penetrating radar measurements enabled us to detect the water front and better describe the snowpack wetting when associated with lysimeter runoff measurements. A new ice reservoir was implemented in the one-dimensional SNOWPACK model, which enabled us to successfully represent the formation of some ice layers when using Richards equation and preferential flow domain parameterization during winter 2017. The simulation of unobserved melt-freeze crusts was also reduced. These improved results were confirmed over 17 winters. Detailed snowpack simulations with snow microstructure representation associated with a high-resolution comprehensive observation dataset were shown to be relevant for studying and modeling such complex phenomena despite limitations inherent to one-dimensional modeling. Article in Journal/Newspaper The Cryosphere Unknown The Cryosphere 14 10 3449 3464 |
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English |
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geo envir |
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geo envir L. Quéno C. Fierz A. van Herwijnen D. Longridge N. Wever Deep ice layer formation in an alpine snowpack: monitoring and modeling |
topic_facet |
geo envir |
description |
Ice layers may form deep in the snowpack due to preferential water flow, with impacts on the snowpack mechanical, hydrological and thermodynamical properties. This detailed study at a high-altitude alpine site aims to monitor their formation and evolution thanks to the combined use of a comprehensive observation dataset at a daily frequency and state-of-the-art snow-cover modeling with improved ice formation representation. In particular, daily SnowMicroPen penetration resistance profiles enabled us to better identify ice layer temporal and spatial heterogeneity when associated with traditional snowpack profiles and measurements, while upward-looking ground penetrating radar measurements enabled us to detect the water front and better describe the snowpack wetting when associated with lysimeter runoff measurements. A new ice reservoir was implemented in the one-dimensional SNOWPACK model, which enabled us to successfully represent the formation of some ice layers when using Richards equation and preferential flow domain parameterization during winter 2017. The simulation of unobserved melt-freeze crusts was also reduced. These improved results were confirmed over 17 winters. Detailed snowpack simulations with snow microstructure representation associated with a high-resolution comprehensive observation dataset were shown to be relevant for studying and modeling such complex phenomena despite limitations inherent to one-dimensional modeling. |
format |
Article in Journal/Newspaper |
author |
L. Quéno C. Fierz A. van Herwijnen D. Longridge N. Wever |
author_facet |
L. Quéno C. Fierz A. van Herwijnen D. Longridge N. Wever |
author_sort |
L. Quéno |
title |
Deep ice layer formation in an alpine snowpack: monitoring and modeling |
title_short |
Deep ice layer formation in an alpine snowpack: monitoring and modeling |
title_full |
Deep ice layer formation in an alpine snowpack: monitoring and modeling |
title_fullStr |
Deep ice layer formation in an alpine snowpack: monitoring and modeling |
title_full_unstemmed |
Deep ice layer formation in an alpine snowpack: monitoring and modeling |
title_sort |
deep ice layer formation in an alpine snowpack: monitoring and modeling |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/tc-14-3449-2020 https://tc.copernicus.org/articles/14/3449/2020/tc-14-3449-2020.pdf https://doaj.org/article/7e23c1b398d04bba8d4bbf2b6daab112 |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
The Cryosphere, Vol 14, Pp 3449-3464 (2020) |
op_relation |
doi:10.5194/tc-14-3449-2020 1994-0416 1994-0424 https://tc.copernicus.org/articles/14/3449/2020/tc-14-3449-2020.pdf https://doaj.org/article/7e23c1b398d04bba8d4bbf2b6daab112 |
op_rights |
undefined |
op_doi |
https://doi.org/10.5194/tc-14-3449-2020 |
container_title |
The Cryosphere |
container_volume |
14 |
container_issue |
10 |
container_start_page |
3449 |
op_container_end_page |
3464 |
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