The transport of meltwater through porous snow is a fundamental process in hydrology that remains poorly understood but essential for more robust prediction of how the cryosphere will respond under climate change. Here we propose a continuum model that resolves the nonlinear coupling of preferential melt flow and the nonequilibrium thermodynamics of ice-melt phase change at the Darcy scale. We assume that the commonly observed unstable melt infiltration is due to the gravity fingering instabililty, and capture it using the modified Richards equation that is extended with a higher-order term in saturation. Our model accounts for changes in porosity and the thermal budget of the snowpack caused by melt refreezing at the continuum scale, based on a mechanistic estimate of the ice-water phase change kinetics formulated at the pore scale. We validate the model in 1D against field data and laboratory experiments of infiltration in snow and find generally good agreement. Compared to existing theory of stable melt infiltration, our 2D simulation results show that preferential infiltration delivers melt faster to deeper depths, and as a result, changes in porosity and temperature can occur at deeper parts of the snow. The simulations also capture the formation of vertical low porosity annulus known as ice pipes, which have been observed in the field but lack mechanistic understanding to date. Our results demonstrate how melt refreezing and unstable infiltration reshape the porosity structure of snow and impacts thermal and mass transport in highly nonlinear ways, which are not captured by simpler models.
Water stored in snow and ice counts for almost 70% of Earth's freshwater volume (Gleick, 1996). Reliable predictions of the hydrological cycle in cold environments, such as terrestrial snowpack and glaciers, remain challenging but are necessary to improve both water resources and geohazards management under climate variability. A fundamental process that remains poorly understood is how surface-generated melt-water released from its frozen state due to heating of the snow-transports and distributes within the snowpack before entering the groundwater or surface water systems. A robust model for meltwater flow through snow is crucial to formulate reliable predictions in larger-scale models of snow cryohydrology and glaciology.One key challenge of modeling snowmelt hydrology is the ability to robustly capture the infiltration rate and storage location of meltwater within the snowpack. While the generation of melt at snow surface can be relatively uniform in space, meltwater infiltration through the underlying snowpack is known to be highly heterogeneous in nature, forming (a) vertical preferential flow pathways that channelize meltwater (e.g., ice pipes) and (b) lateral flow pathways guided by horizontal low permeability zones (e.g., capillary barriers or ice lenses). Both types of preferential pathways have been observed in the field directly or indirectly (
Articles you may be interested in
Biological filtration systems offer a sustainable alternative to existing engineered solutions. In this computational work, we seek to optimize the surface coverage by an array of hairs to capture particles in channels. A variety of aquatic organisms rely on arrays of hairs to interact with their fluidic environments. The hair functionality can vary from sensing to smelling, filtration to flow control depending on the species considered. Among those organisms are filter-feeders that rely on suspension-feeding, one of the most widespread feeding mechanisms and one of the oldest. Baleen whales are filter-feeders that catch their prey by using the baleen, a complex structure composed of plates and bristles in their mouth. The hairs are hollow cylindrical structures with a diameter of a few hundred micrometers that can extend over tens of centimeters. The baleen filters out the prey while letting the seawater through. The baleen is composed of flexible and elongated structures whose properties fit the feeding habits of the whale. The porosity of the structure depends on the flow feature. Effectively, the flow can tune the filter properties, which sets biological filters apart from their engineered counterpart. Previous mechanical studies have shown that an array of hairs can either act as a sieve, allowing all the fluid to flow through it, or as a rake, forcing the fluid to flow around it instead. As the speed increases, the behavior shifts from rake to sieving for a given hair spacing. From a filtration perspective, the rake regime is not favorable as particles do not enter the array. For a fixed fluid velocity, the flow transitions from rake to sieve as the spacing between the hairs in the array increases. Our recent work has also demonstrated that the confinement of the channel influences the sieve to rake transition. The filtration mechanisms that filter-feeder organisms use to capture food particles exhibit complex fluid-structure interactions that have yet to be leveraged in engineered systems. To guide the development of hair-covered surfaces capable of trapping particles in channel flows, we investigate how different geometric factors affect the fluid transport and capture of particles by the array. In previous work, a small number of hairs, typically 25, were considered. Here, we vary the array geometry, the Reynolds number of the flow, and the surface coverage to study the transport through this confined porous structure. We compare arrays based on their optimal efficiency and the (sub-optimal) operating conditions which make the filter versatile.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.