3 years ago

Modelling capillary hysteresis effects on preferential flow through melting and cold layered snowpacks

Accurate estimation of the amount and timing of water flux through melting snowpacks is important for runoff prediction in cold regions. Most existing snowmelt models only account for one-dimensional matrix flow and neglect to simulate the formation of preferential flow paths. Consideration of lateral and preferential flows has proven critical to improve the performance of soil and groundwater porous media flow models. A two-dimensional physically-based snowpack model that simulates snowmelt, refreezing of meltwater, heat and water flows, and preferential flow paths is presented. The model assumes thermal equilibrium between solid and liquid phases and uses recent snow physics advances to estimate snowpack hydraulic and thermal properties. For the first time, capillary hysteresis is accounted in a snowmelt model. A finite volume method is applied to solve for the 2D coupled heat and mass transfer equations. The model with capillary hysteresis provided better simulations of water suction at the wet to dry snow interface in a wetting snow sample than did a model that only accounted for the boundary drying curve. Capillary hysteresis also improved simulations of preferential flow path dynamics and the snowpack discharge hydrograph. Simulating preferential flow in a subfreezing snowpack allowed the model to generate ice layers, and increased the vertical exchange of energy, thus modelling a faster warming of the snowpack than would be possible without preferential flow. The model is thus capable of simulating many attributes of heterogeneous natural melting snowpacks. These features not only qualitatively improve water flow simulations, but improve the understanding of snowmelt flow processes for both level and sloping terrain, and illuminate how uncertainty in snowmelt-derived runoff calculations might be reduced through the inclusion of more realistic preferential flow through snowpacks.

Publisher URL: www.sciencedirect.com/science

DOI: S0309170817300040

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