Deep ice layer formation in an alpine snowpack: monitoring and modeling
<p>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 com...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-10-01
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| Series: | The Cryosphere |
| Online Access: | https://tc.copernicus.org/articles/14/3449/2020/tc-14-3449-2020.pdf |
| Summary: | <p>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.</p> |
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| ISSN: | 1994-0416 1994-0424 |