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

陆地积雪中储存的水分是水文系统中必不可少的缓冲。气候变化引起的极端天气事件，如频繁的热浪和长期干旱，使目前的水资源模型难以准确预测融雪到达地下水和地表水系统的时间和数量。提高对融雪输送机制的理解是建立一个更强大的预测平台以更好地管理气候变化下的水资源的关键组成部分。该项目将有助于本科生和研究生学习最先进的建模方法和计算机模拟程序的教育，并将为学生提供开发新颖的实验室实验的经验。该项目将产生关于在各种环境条件下融水的渗透如何与积雪的多孔结构相互作用的基本知识，以及这种相互作用如何影响融水排入地下水系统的量和时间。现有的融雪输送模型受到简化的流动物理学和平衡热力学的限制，无法解决重要的现场观测。本项目将使用一种新的模式系统地研究在动态大气条件下异质环境中的熔体输运。该模型解决了以前模型中缺失的重要物理问题，这些物理问题会显著影响积雪在运输过程中的停留时间。此外，在受控环境条件下通过雪的熔体输送的观测仅在3D雪样中进行，这使得与数值模型的定量比较具有挑战性。该项目将设计和执行受控实验室实验，以直接可视化准二维流动池中熔体传输和再冻结的实时动态。结合数值实验的努力将提供新的见解融水运输和存储在积雪，这是必要的，以提高参数化的融雪过程中的大规模水文models.This奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。

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.