Simulating ice layer formation under the presence of preferential flow in layered snowpacks

Wever, Nander; Würzer, Sebastian; Fierz, Charles; Lehning, Michael

doi:10.5194/tc-10-2731-2016

Cited by 76 publications

(124 citation statements)

References 48 publications

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“…Between January and April 2016, the snowpack increased up to ∼150 cm, but both melt-freeze and rain-on-snow events occurred over the study area, especially in early April. Both processes, together with water retention at layer transitions due to capillary barriers [63], favor the development of ice layers [64]. While no pit was excavated during this first field survey due to time constraints, ice layers were observed close to the two streams crossing the study area, where the snow cover was patchy.…”

Section: Uas Vs Manual Probingmentioning

confidence: 97%

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

et al. 2018

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Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20-0.30 m, but decreases to 0.06-0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.

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Section: Uas Vs Manual Probingmentioning

confidence: 97%

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

et al. 2018

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“…With the aggressive layer merging switched on, the computational time is comparable to that required by the IMAU-FDM. Furthermore, SNOWPACK also models microstructural snow properties and includes a recently implemented preferential flow algorithm (Wever et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…In contrast to the bucket scheme, Richards equation solves explicitly for the balance between capillary suction and gravity (Wever et al, 2014). Recently, a scheme for simulating preferential flow was implemented (Wever et al, 2016), but this version has not yet been applied to Greenland. For this study, the bucket scheme is chosen to enable a direct comparison with the IMAU-FDM and because this scheme is computationally less demanding.…”

Section: Snowpackmentioning

confidence: 99%

“…In the current SNOWPACK configuration, the model is relatively insensitive to thin, highdensity layers as the vertical water transport is simulated by the bucket scheme. Using more complex schemes such as the recently implemented preferential flow scheme (Wever et al, 2016), which allows downward water percolation in sub-freezing snow and is sensitive to marked layer transitions, would require a more detailed study of this feature. A GrIS-wide evaluation of this phenomenon is currently difficult due to the limited availability of firn cores with high vertical resolution.…”

Section: Formation Of Ice Layersmentioning

confidence: 99%

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Firn Meltwater Retention on the Greenland Ice Sheet: A Model Comparison

et al. 2017

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Runoff has recently become the main source of mass loss from the Greenland Ice Sheet and is an important contributor to global sea level rise. Linking runoff to surface meltwater production is complex, as meltwater can be retained within the firn by refreezing or perennial liquid water storage. To constrain these uncertainties, the outputs of two offline snow/firn models of different complexity (IMAU-FDM and SNOWPACK) are compared to assess the sensitivity of meltwater retention to the model formulation (e.g., densification, irreducible water content, vertical resolution). Results indicate that model differences are largest in areas where firn aquifers form, i.e., particularly along the south-eastern margin of the ice sheet. The IMAU-FDM simulates higher densification rates for such climatic conditions and prescribes a lower irreducible water content than SNOWPACK. As a result, the model predicts substantially lower amounts of refreezing and liquid water storage. SNOWPACK performs better for this area, confirmed both by density profiles from firn cores and radar-inferred observations. Refreezing integrated over the entire ice sheet and averaged for the period 1960-2014 amounts to 216 Gt a −1 (IMAU-FDM) and 242 Gt a −1 (SNOWPACK), which is 41 and 46% of the total liquid water input (snowmelt and rainfall). The mean areal extents of perennial firn aquifers for 2010-2014 simulated by the models are 55,700 km 2 (IMAU-FDM) and 90,200 km 2 (SNOWPACK). Discrepancies between modeled firn profiles and observations emphasize the importance of processes currently not accounted for in most snow/firn models, such as vertical heterogeneous percolation, ponding of water on impermeable layers, lateral (sub-)surface water flow, and the issue of ill-constrained refreezing conditions at the base of firn aquifers.

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“…Although having important consequences, few models are capable of adequately simulating the formation of ice layers or lenses and their hydrological impact. A dual-domain Richards-based model has begun explaining preferential flow paths, which can reproduce some of the ice layers present in the snowpack (Wever et al, 2016b). The Richards equation, when applied to snow, describes water percolation through a porous ice matrix considering the water retention curve and the hydraulic conductivity of snow layers.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a physically based water percolation routine in the Crocus/SURFEX (V7.3) snowpack model

et al. 2017

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Abstract. We present a new water percolation routine added to the one-dimensional snowpack model Crocus as an alternative to the empirical bucket routine. This routine solves the Richards equation, which describes flow of water through unsaturated porous snow governed by capillary suction, gravity and hydraulic conductivity of the snow layers. We tested the Richards routine on two data sets, one recorded from an automatic weather station over the winter of 2013-2014 at Filefjell, Norway, and the other an idealized synthetic data set. Model results using the Richards routine generally lead to higher water contents in the snow layers. Snow layers often reached a point at which the ice crystals' surface area is completely covered by a thin film of water (the transition between pendular and funicular regimes), at which feedback from the snow metamorphism and compaction routines are expected to be nonlinear. With the synthetic simulation 18 % of snow layers obtained a saturation of > 10 % and 0.57 % of layers reached saturation of > 15 %. The Richards routine had a maximum liquid water content of 173.6 kg m −3 whereas the bucket routine had a maximum of 42.1 kg m −3 . We found that wet-snow processes, such as wet-snow metamorphism and wet-snow compaction rates, are not accurately represented at higher water contents. These routines feed back on the Richards routines, which rely heavily on grain size and snow density. The parameter sets for the water retention curve and hydraulic conductivity of snow layers, which are used in the Richards routine, do not represent all the snow types that can be found in a natural snowpack. We show that the new routine has been implemented in the Crocus model, but due to feedback amplification and parameter uncertainties, meaningful applicability is limited. Updating or adapting other routines in Crocus, specifically the snow compaction routine and the grain metamorphism routine, is needed before Crocus can accurately simulate the snowpack using the Richards routine.

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Simulating ice layer formation under the presence of preferential flow in layered snowpacks

Cited by 76 publications

References 48 publications

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

Firn Meltwater Retention on the Greenland Ice Sheet: A Model Comparison

Implementation of a physically based water percolation routine in the Crocus/SURFEX (V7.3) snowpack model

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