Extending the vadose zone: characterizing the role of snow for liquid water storage and transmission in streamflow generation

对于世界各地的社区来说，宝贵的水资源来源于雪。尽管它很重要，但对融水在进入溪流和河流之前如何在雪中移动和储存的理解仅限于从一些小规模的雪研究中获得的知识。我们迫切需要更好地了解这些动力学是如何影响水流的。季节性积雪是当前气候趋势下变化最快的水资源之一，对径流生产具有重要但不确定的影响。虽然土壤和溪流之间的水文联系的概念已经受到关注，但与径流产生相关的斜坡尺度上的山积雪内的液态水储存和流道的连通性却鲜为人知。这是山间溪流产生知识的一个主要空白。遥感技术、地面观测和基于物理的水文建模的新兴能力为更好地了解水源集水区的雪水资源提供了新的机会。具体来说，提高对积雪中液态水分布的理解将改变科学界。监测和预测融雪期间发生的物理水文过程的能力。该项目利用最先进的科学观测和数值模拟技术来了解季节性积雪中液态水的储存和运输。利用新开发的雪内液态水测绘技术，以及基于航空的雪深测绘技术，我们将在一个受到严格监测的高山流域的倾斜地形上追踪融水的储存和移动。一个灵活的水文模型将被用来评估我们模拟观测过程的能力。结果将提高科学能力：a)概念化和模拟融雪过程，b)了解集水区尺度上雪的水力特性的重要性，以及c)限制气候和土地覆盖变化的潜在水文影响。在更广泛的影响中，这些项目将帮助学生了解水文学的基本概念，并了解水文学研究的各种方法，如建模、野外观测和遥感。该项目将支持一名研究生和两名本科生，他们将参与数值模拟和实地工作。该项目将在科罗拉多大学和山地研究站进行。该项目将招收许多不同群体的学生，包括第一代大学生、有色人种学生、退伍军人和农村学生。该项目的目标是表征和约束控制融雪输送到源头盆地溪流的物理机制。该项目利用新的观测和建模技术来量化和模拟积雪分布、积雪蓄水能力、融雪量和动态雪内流动路径。这是通过最先进的科学技术实现的，包括探地雷达（GPR）、仪表化无人驾驶飞行器（uav）、全球定位系统（GPS）仪器和自动化传感器节点网络，以集中监测土壤湿度和雪深，以及监测水流的堰。最后，将使用统一多种建模方案结构（SUMMA）模型进行水文建模，以评估建模决策的影响和模拟融雪动力学的能力。这个项目的首要研究问题是：积雪液态水的储存和穿过雪的水文流道如何影响山坡-溪流的连通性，以及这些过程如何在融雪季节演变？本工作将观测和模拟时空变化的融雪季节，以完成以下项目目标：1)利用无人机飞行、探地雷达调查、传感器节点网络和人工观测绘制集水区雪水当量（SWE）的季节动态图。2)利用无人机和探地雷达联合调查、GPS自动信号衰减、土壤水分传感器和流域径流响应等方法监测积雪液态水含量和输送的时空变化。3)通过系统分析水文过程和尺度行为的多个模型表征，评估水文模型模拟积雪、土壤和河流响应观测动态的能力。这个研究问题将在科罗拉多州博尔德附近落基山脉的一个积雪覆盖的水源集水区进行调查。我们的工作建立在数十年的当地水文学、生物地球化学和生态过程研究的基础上。科罗拉多大学博尔德分校的北极和高山研究所（INSTAAR）受益于与当地强大的科学界的合作，包括LTER和CZO研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

For communities worldwide, valuable water resources originate from snow. Despite its importance, understanding of how meltwater moves and is stored within snow before it gets to streams and rivers is limited to knowledge gained from a few small-scale snow studies. There are pressing needs to better understand how these dynamics affect streamflow. Seasonal snow-cover is one of the fastest changing water resources under current climate trends with important yet uncertain implications on runoff production. While the concept of hydrologic connection between soils and streams has received attention, the liquid water storage and connectivity of flowpaths within a mountain snowpack at slope scales relevant to runoff generation are less understood. This is a major gap in knowledge of mountain streamflow production. Emerging capabilities in remote sensing technology, ground-based observations, and physically-based hydrologic modeling provide new opportunity to better understand snow water resources in headwater catchments. Specifically, improved understanding of the distribution of liquid water in a snowpack will transform the scientific community?s ability to monitor and predict the physical hydrological processes that occur during snowmelt. This project leverages state-of-the-science observation and numerical modeling techniques to understand the storage and transport of liquid water within a seasonal snowpack. Using newly developed in-snow liquid water mapping techniques, together with airborne-based snow depth mapping, we will track the storage and movement of meltwater across sloping terrain of a heavily monitored alpine watershed. A flexible hydrologic model will be used to assess our ability to simulate the observed processes. Results will improve scientific capabilities to: a) conceptualize and model snowmelt processes, b) understand the importance of the hydraulic properties of snow at the catchment scale, and c) constrain the potential hydrologic impacts of climate and land-cover change. In broader impacts, thos project will help students to understand fundamental concepts of hydrology and understand the various methods of study of hydrology such as modeling, field observations and remote sensing. The project will support one graduate and two undergraduate students who will be involved in numerical modeling and field work. The project will be carried out at the University of Colorado and Mountain research Stations. The project will in many diverse group of students who are first generation college students, students of color, veterans and rural students. The goal of this project is to characterize and constrain the physical mechanisms that control snowmelt delivery to streams in headwater basins. This project leverages new observation and modeling techniques to quantify and simulate the snow distribution, snowpack water holding capacity, snowmelt production, and dynamic in-snow flowpaths. This is achieved through state-of-the-science techniques including ground penetrating radar (GPR), instrumented unmanned aerial vehicles (UAVs), global positioning system (GPS) instrumentation, and a network of automated sensor nodes to intensively monitor soil moisture and snow depth, and a weir to monitor streamflow. Finally, hydrologic modeling will be conducted with the Structure for Unified Multiple Modeling Alternatives (SUMMA) model to assess the impact of modeling decisions and the ability to simulate snowmelt dynamics. The overarching research question of this project is: How do snowpack liquid water storage and through-snow hydrologic flowpaths affect hillslope-stream connectivity, and how do these processes evolve through the snowmelt season? This work will observe and simulate the spatially and temporally variable snowmelt season to complete the following project objectives: 1) Map the seasonal dynamics of catchment snow water equivalent (SWE) using UAV flights, GPR surveys, a network of sensor nodes, and manual observations. 2) Monitor the spatial and temporal progression of snowpack liquid water content and transport using combined UAV and GPR surveys, automated GPS signal attenuation, soil moisture sensors, and catchment streamflow response. 3) Evaluate the skill of hydrologic models to simulate the observed dynamics of the snowpack, soil, and streamflow response by systematically analyzing multiple model representations of hydrologic processes and scaling behavior. This research question will be investigated in a snow-dominated headwater catchment in the Rocky Mountains near Boulder, Colorado. Our work builds upon decades of local research in hydrology, biogeochemistry, and ecological processes. The Institute of Arctic and Alpine Research (INSTAAR) at the University of Colorado Boulder benefits from partnerships with the strong local scientific communities including LTER and CZO research.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.