Field Evaluation of a Novel 2D Preferential Flow Snowpack Hydrology Model

Abstract:

Accurate estimation of snowmelt flux is of primary importance for runoff hydrograph prediction, which is used for water management and flood forecasting. Lateral flows and preferential flow pathways in porous media flow have proven critical for improving soil and groundwater flow models, but though many physically-based layered snowmelt models have been developed, only 1D matrix flow is accounted for in these models. Therefore, there is a need for snowmelt models that include these processes so as to examine the potential to improve snowmelt hydrological modelling. A 2D model is proposed that enables an improved understanding of energy and water flows within deep heterogeneous snowpacks, including those on slopes. A dual pathway theory is presented that simulates the formation of preferential flow paths, vertical and lateral water flows through the snow matrix and flow fingers, internal energy fluxes, melt, wet snow metamorphism, and internal refreezing. The dual pathway model utilizes an explicit finite volume method to solve for the energy and water flux equations over a non-orthogonal grid. It was run and evaluated using in-situ data collected from snowpit - accessed gravimetric, thermometric, photographic, and dielectric observations and novel non-invasive acoustic observations of layering, temperature, flowpath geometry, density and wetness at the Fortress Mountain Snow Laboratory, Alberta, Canada. The melt of a natural snowpack was artificially generated after detailed observation of snowpack initial conditions such as snow layer properties, temperature, and liquid water content. Snowpack ablation and liquid water content distribution over time were then measured and used for model parameterization and validation. Energy available at the snow surface and soil slope angle were set as mondel inputs. Model verification was based on snowpack property evolution. The heterogeneous flow model can be an important tool to help understand snowmelt flow processes, how snowmelt-derived streamflow forecasting might be improved and shows great promise from this initial field evaluation.