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Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L. 

An important consideration for water resources planning is runoff timing, which can be strongly influenced by the physical process of water storage within and release from seasonal snowpacks. The aim of this presentation is to introduce a novel method that combines light detection and ranging (LiDAR) with ground-penetrating radar (GPR) to nondestructively estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring melt. This method was developed at multiple plots in Colorado in 2017 and applied at the small catchment scale in 2019. We developed this method in a manner to observe rapid changes that occur at subdaily timescales. Observed volumetric liquid water contents ranged from near zero to 19%vol within the scale of meters during method development. We also show rapid changes in bulk liquid water content of up to 5%vol that occur over subdaily timescales. The presented methods have an average uncertainty in bulk liquid water content of 1.5%vol, making them applicable for studies to estimate the complex spatio-temporal dynamics of liquid water in snow. During the spring snowmelt season of 2019, we applied this method to a small headwater catchment in the Colorado Front Range. A total of 9 GPR surveys of approximately 3 km in length were conducted over a six-week period. Additionally, five LiDAR scans occurred over the same area. Using this technique, we identify locations that melting snow accumulates and is stored as liquid water within the snowpack. This work shows that the vadose zone may be conceptualized, during snowmelt, as extending above the soil-snow interface to include variably saturated flow processes within the snowpack. 

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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, water holding capacity, snowmelt production, and dynamic flowpaths. This is achieved through state-of-the-science observation techniques including ground penetrating radar (GPR), Terrestrial LiDAR Scanning (TLS), global positioning system (GPS) instrumentation, a network of sensor nodes continuously measuring 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 throughout the snowmelt season? This research question will be investigated in a snow-dominated headwater catchment. This work will observe and simulate the spatially and temporally variable snowmelt season to complete the following project objectives: O1) Map the dynamics of catchment snow water equivalent (SWE) using TLS surveys, GPR surveys, a network of sensor nodes, and manual observations. O2) Monitor the spatial and temporal progression of snowpack liquid water content and transport using combined TLS and GPR surveys, automated GPS signal attenuation, soil moisture sensors, and catchment streamflow response. O3) 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. The work builds upon decades of local research in hydrology, biogeochemistry, and ecological processes.