Simulating liquid water distribution at the pore scale in snow: water retention curves and effective transport properties

Abstract. Liquid water flows by gravity and capillarity in snow and modifies drastically its properties. Unlike dry snow, observing wet snow remains a challenge and data from 3D pore-scale imaging are scarce. This limitation hampers our understanding of the water, heat and vapor transport processes in wet snow, as well as their modeling. Here, we explore a simulation-based approach in which 3D images of dry snow were digitally filled and drained with water through imbibition and drainage simulations driven by capillarity. Time series of wet snow images at various water contents were produced. The water retention curves, i.e. the capillary pressure as a function of the water content, are derived for different snow types. New parameters to model the water retention curves of snow are proposed based on the van Genuchten model and compared to existing ones from laboratory experiments. From the 3D images, the hydraulic conductivity, water permeability, effective thermal conductivity, and water vapor diffusivity of wet snow were computed. We analyzed their evolution in relation to water content, density and snow type, compared them with existing regressions and presented new ones when needed. The proposed relationships are a step forward in improving the physical description of wet snow in snowpack scale models.