Deep ice layer formation in an alpine snowpack: monitoring and modeling

Quéno, Louis; Fierz, Charles; Herwijnen, Alec van; Longridge, Dylan; Wever, Nander

doi:10.5194/tc-14-3449-2020

Cited by 10 publications

(5 citation statements)

References 52 publications

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“…Stratigraphy of the firn core points to a shortcoming of the subsurface model: meltwater distributed in cold firn along the parametrized vertical profile usually refreezes in place, producing a diluted density increase in the simulation. Instead, distinct ice layers are known to form at depth after preferential percolation, affecting the mechanical, hydrological, and thermodynamical properties of the snowpack (Quéno et al, 2020). In the present EBFM formulation, such ice layers would not appear even with a very fine model grid.…”

Section: Meltwater Infiltrationmentioning

confidence: 94%

Firn changes at Colle Gnifetti revealed with a high-resolution process-based physical model approach

et al. 2021

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Abstract. Our changing climate is expected to affect ice core records as cold firn progressively transitions to a temperate state. Thus, there is a need to improve our understanding and to further develop quantitative process modeling, to better predict cold firn evolution under a range of climate scenarios. Here we present the application of a distributed, fully coupled energy balance model, to simulate cold firn at the high-alpine glaciated saddle of Colle Gnifetti (Swiss–Italian Alps) over the period 2003–2018. We force the model with high-resolution, long-term, and extensively quality-checked meteorological data measured in the closest vicinity of the firn site, at the highest automatic weather station in Europe (Capanna Margherita, 4560 m a.s.l.). The model incorporates the spatial variability of snow accumulation rates and is calibrated using several partly unpublished high-altitude measurements from the Monte Rosa area. The simulation reveals a very good overall agreement in the comparison with a large archive of firn temperature profiles. Our results show that surface melt over the glaciated saddle is increasing by 3–4 mm w.e. yr−2 depending on the location (29 %–36 % in 16 years), although with large inter-annual variability. Analysis of modeled melt indicates the frequent occurrence of small melt events (<4 mm w.e.), which collectively represent a significant fraction of the melt totals. Modeled firn warming rates at 20 m depth are relatively uniform above 4450 m a.s.l. (0.4–0.5 ∘C per decade). They become highly variable at lower elevations, with a marked dependence on surface aspect and absolute values up to 2.5 times the local rate of atmospheric warming. Our distributed simulation contributes to the understanding of the thermal regime and evolution of a prominent site for alpine ice cores and may support the planning of future core drilling efforts. Moreover, thanks to an extensive archive of measurements available for comparison, we also highlight the possibilities of model improvement most relevant to the investigation of future scenarios, such as the fixed-depth parametrized routine of deep preferential percolation.

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Section: Meltwater Infiltrationmentioning

confidence: 94%

Firn changes at Colle Gnifetti revealed with a high-resolution process-based physical model approach

et al. 2021

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“…Phase change is only possible in the matrix flow domain. An ice reservoir parameterization was further developed by Quéno et al (2020) to better capture the formation of continuous ice layers from discontinuous and growing ice lenses. This development improved the formation of ice and melt-freeze layers and reduced the number of simulated ice layers that were not observed.…”

Section: Water Transport Schemementioning

confidence: 99%

Impact of rain-on-snow events on snowpack structure and runoff under a boreal canopy

Bouchard,

Nadeau,

Domine

et al. 2024

Preprint

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Abstract. Rain-on-snow events can cause severe flooding in snow–dominated regions. These are expected to become more frequent in the future as climate change shifts the precipitation from snowfall to rainfall. However, little is known about how winter rainfall interacts with an evergreen canopy and affects the underlying snowpack. In this study, we document 5 years of rain-on-snow events and snowpack observations at two boreal forested sites of eastern Canada. Our observations show that rain-on-snow events over a boreal canopy leads to the formation of melt–freeze layers as rainwater refreezes at the surface of the sub–canopy snowpack. They also generate frozen percolation channels, suggesting that preferential flow is favored in the sub–canopy snowpack during rain-on-snow events. We then used the multi–layer snow model SNOWPACK to simulate the sub–canopy snowpack at both sites. Although SNOWPACK performs reasonably well in reproducing snow height (RMSE = 17.3 cm), snow surface temperature (RMSE = 1.0 °C), and density profiles (agreement score = 0.79), its performance declines when it comes to simulating snowpack stratigraphy, as it fails to reproduce many of the observed melt–freeze layers. To correct for this, we implemented a densification function of the intercepted snow in the canopy module of SNOWPACK. This new feature allows 27 of the 32 observed melt–freeze layers induced by rain-on-snow events to be formed by the model, instead of only 18 with the original canopy module. This new model development also delays and reduces the snowpack runoff. Indeed, it triggers the unloading of dense unloaded snow layers with small rounded grains, which in turn produces fine–over–coarse transitions that limit percolation and favor refreezing. Our results show that the boreal vegetation modulates the sub–canopy snowpack structure and runoff from rain-on-snow events. Overall, this study highlights the need for canopy snow properties measurements to improve hydrological models in forested snow–covered regions.

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“…Once the upper layers of the snowpack reach saturation, liquid water gradually infiltrates through the snowpack during the ripening phase. This drainage process is influenced by various factors, including refreezing, the presence of preferential flow paths, and capillary barriers (Wever et al, 2014;Würzer et al, 2017), as well as the occurrence of ice layers (Quéno et al, 2020). Ultimately, snowmelt runoff commences when the liquid water exits the snowpack at its base.…”

Section: Introductionmentioning

confidence: 99%

Using Sentinel-1 wet snow maps to inform fully-distributed physically-based snowpack models

Cluzet,

Magnusson,

Quéno

et al. 2024

Preprint

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Abstract. Distributed energy and mass-balance snowpack models at sub-kilometric scale have emerged as a tool for snow-hydrological forecasting over large areas. However, their development and evaluation often rely on a handful of well observed sites on flat terrain with limited topographic representativeness. Validation of such models over large scales in rugged terrain is therefore necessary. Remote sensing of wet snow has always been motivated by its potential utility in snow hydrology. However, its concrete potential to enhance physically based operational snowpack models in real time remains unproven. Wet snow maps could potentially help refining the temporal accuracy of simulated snowmelt onset, while the information content of remotely sensed snow cover fraction pertains predominantly to the ablation season. In this work, wet snow maps, derived from Sentinel-1 and snow cover fraction (SCF) retrieval from Sentinel-2 are compared against model results from a fully distributed energy-balance snow model (FSM2oshd). The comparative analysis spans the winter seasons from 2017 to 2021, focusing on the geographic region of Switzerland. We use the concept of wet snow line (WSL) to compare Sentinel-1 wet snow maps with simulations. We show that while the match of the model with flat-field snow depth observation is excellent, the WSL reveals insufficient snow melt in the southern aspects. Amending the albedo parametrization within FSM2oshd allowed achieving earlier melt in such aspects preferentially, thereby reducing WSL biases. Biases with respect to Sentinel-2 snow line (SL) observations were also substantially reduced. These results suggest that wet snow maps contain valuable real-time information for snowpack models, nicely complementing flat-field snow depth observations, particularly in complex terrain and at higher elevations. The persisting correlation between wet snow line and snow line biases provides insights into refined development, tuning and data assimilation methodologies for operational snow-hydrological modelling.

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Deep ice layer formation in an alpine snowpack: monitoring and modeling

Cited by 10 publications

References 52 publications

Firn changes at Colle Gnifetti revealed with a high-resolution process-based physical model approach

Firn changes at Colle Gnifetti revealed with a high-resolution process-based physical model approach

Impact of rain-on-snow events on snowpack structure and runoff under a boreal canopy

Using Sentinel-1 wet snow maps to inform fully-distributed physically-based snowpack models

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