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

Abstract

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.

Seasonal monitoring of melt and accumulation within the deep percolation zone of the Greenland Ice Sheet and comparison with simulations of regional climate modeling

“…2a). In firn, the vertical resolution of the 600 MHz antennas is roughly 17.7 cm and for the 1600 MHz antennas 6.6 cm (Daniels, 2004). Destructive interferences diminish reflections appearing within shorter distance than the respective wavelength.…”

Testing Model Representations of Snowpack Liquid Water Percolation Across Multiple Climates

“…This retention not only affects the resulting snow saturation but also the frequency of liquid fluxes, since fluxes only occur when θ tran > θ resid therefore ensuring a positive numerator in equation . While the residual liquid water content is typically hardcoded in popular models (Bartlet & Lehning, ; D'Amboise et al, ), we tested the model sensitivity to a range of realistic θ resid parameters (supporting information Figure S3).…”

A multilayer physically based snowpack model simulating direct and indirect radiative impacts of light-absorbing impurities in snow

“…The parameterization of liquid water content in Crocus follows a simple conceptual bucket approach which does not accurately represent the evolution of liquid water content in the snowpack, as pointed out in Lafaysse et al (2017). Work is in progress to include a physically based liquid water parameterization in Crocus (D'Amboise et al, 2017). Changing the liquid water content parameterization is expected to improve the modeling of water percolation and impact the scavenging of LAIs in the snowpack.…”

“…Mineral dust depositions are also frequently observed across the European mountain ranges, giving some snow layers a reddish or yellowish color. This is a well-known phenomenon suspected to play an important role in snow-covered surface radiative forcing (De Angelis and Gaudichet, 1991;Di Mauro et al, 2015). Saharan dust depositions, hereafter referred to as dust outbreaks, are very sporadic events mostly occurring from April to August (Varga et al, 2014).…”