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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| Main Authors: | , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2020
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| Subjects: | |
| 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 |
| Summary: | 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. |
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