Numerical and experimental investigation of the impact of preferential flow and nonequilibrium thermodynamics on meltwater transport through snow

Water stored in terrestrial snowpack is an essential buffer in hydrological systems. Extreme weather events induced by climate change, such as frequent heat waves and prolonged droughts, have made it difficult for current water resource models to accurately predict the timing and volume of snowmelt arrival in groundwater and surface water systems. Improving the mechanistic understanding of snowmelt transport is a key component in building a more robust prediction platform to better manage water resources under climate variability. This project will contribute to the education of undergraduate and graduate students learning state-of-art modeling methods and computer simulation programs and will provide students with the experience of developing novel laboratory experiments. This project will generate fundamental knowledge on how the percolation of meltwater interacts with the porous structure of snowpack under various environmental conditions, and how such interactions influence the volume and timing of melt drainage into the underlying groundwater system. Existing models of snowmelt transport are limited by simplified flow physics and equilibrium thermodynamics, which fail to address important field observations. This project will use a new model to systematically investigate melt transport in heterogeneous environments under dynamic atmospheric conditions. The model resolves important physics missing in previous models that significantly influence the residence time of melt within snowpack during transport. Additionally, observations of melt transport through snow under controlled environmental conditions have only been made in 3D snow samples, rendering quantitative comparison with numerical models challenging. This project will design and perform controlled laboratory experiments to directly visualize real-time dynamics of melt transport and refreezing in quasi-2D flow cells. The combined numerical-experimental effort will provide new insights on meltwater transport and storage within snowpack, which is necessary to improve parameterization of snowmelt processes in large-scale hydrological models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.