An Energy and Mass Model of Snow Cover Suitable for Operational Avalanche Forecasting

Brun, E.; Martin, Éric; Simon, Vitor Hugo; Gendre, C.; Coléou, Cécile

doi:10.1017/s0022143000009254

Cited by 416 publications

(242 citation statements)

References 6 publications

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“…Despite the fact that estimating r b using Eq. More recently, estimates of r b have been produced using snow models that solve the surface energy balance and evolve a snowpack layer by layer (Brun et al 1989;Liston and Elder 2006;Lehning et al 2006), but these models require extensive meteorological input as well as the calculation of settlement of individual snow layers. A handbook for the NRCS field surveyors (Davis et al 1970) provides qualitative rules for adjusting the local mean snow density upward or downward depending on wind, exposure, thaws, and the day of the year.…”

Section: Introductionmentioning

confidence: 99%

Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes

Sturm

Taras

Liston

et al. 2010

Journal of Hydrometeorology

418

460

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In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes ;20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure. A statistical model based on a Bayesian analysis of a set of 25 688 depth-density-SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against two continental-scale datasets, 90% of the computed SWE values fell within 68 cm of the measured values, with most estimates falling much closer.

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Section: Introductionmentioning

confidence: 99%

Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes

Sturm

Taras

Liston

et al. 2010

Journal of Hydrometeorology

418

460

View full text Add to dashboard Cite

show abstract

“…For instance, the firn model initially suggested by Greuell and Oerlemans (1986) and developed further by several studies (e.g., Greuell and Konzelmann, 1994;Reijmer and Hock, 2008;Van Pelt et al, 2012) was successfully used in energy/mass balance studies on glaciers and ice sheets (e.g., Bassford et al, 2006;Reijmer et al, 2012;Van Pelt and Kohler, 2015). The CROCUS (Brun et al, 1989) and SNOWPACK (Bartelt and Lehning, 2002) models were originally designed for seasonal snow studies but were also successfully utilized for thicker snow, firn and ice packs (Obleitner and Lehning, 2004;Fettweis, 2007;Gascon et al, 2014;Lang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Parameterizing Deep Water Percolation Improves Subsurface Temperature Simulations by a Multilayer Firn Model

et al. 2017

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Deep preferential percolation of melt water in snow and firn brings water lower along the vertical profile than a laterally homogeneous wetting front. This widely recognized process is an important source of uncertainty in simulations of subsurface temperature, density, and water content in seasonal snow and in firn packs on glaciers and ice sheets. However, observation and quantification of preferential flow is challenging and therefore it is not accounted for by most of the contemporary snow/firn models. Here we use temperature measurements in the accumulation zone of Lomonosovfonna, Svalbard, done in April 2012-2015 using multiple thermistor strings to describe the process of water percolation in snow and firn. Effects of water flow through the snow and firn profile are further explored using a coupled surface energy balance -firn model forced by the output of the regional climate model WRF. In situ air temperature, radiation, and surface height change measurements are used to constrain the surface energy and mass fluxes. To account for the effects of preferential water flow in snow and firn we test a set of depth-dependent functions allocating a certain fraction of the melt water available at the surface to each snow/firn layer. Experiments are performed for a range of characteristic percolation depths and results indicate a reduction in root mean square difference between the modeled and measured temperature by up to a factor of two compared to the results from the default water infiltration scheme. This illustrates the significance of accounting for preferential water percolation to simulate subsurface conditions. The suggested approach to parameterization of the preferential water flow requires low additional computational cost and can be implemented in layered snow/firn models applied both at local and regional scales, for distributed domains with multiple mesh points.

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“…SURFEX is an atmosphere to surface coupling model, with ISBA (Interactions between Soil, Biosphere, and Atmosphere) being the land surface scheme (Noilhan and Mahfouf, 1996). The Crocus model (Brun et al, 1989) is the most detailed of three snowpack models embedded in the ISBA routine. It was classified in the group of "most complex snow models" by the Snow Models intercomparison project (Etchevers et al, 2004).…”

Section: Surfex Isba and Crocus Descriptionmentioning

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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An Energy and Mass Model of Snow Cover Suitable for Operational Avalanche Forecasting

Cited by 416 publications

References 6 publications

Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes

Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes

Parameterizing Deep Water Percolation Improves Subsurface Temperature Simulations by a Multilayer Firn Model

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

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