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1. Combining Ground‐Penetrating Radar With Terrestrial LiDAR Scanning to Estimate the Spatial Distribution of Liquid Water Content in Seasonal Snowpacks

   https://doi.org/10.1029/2018WR022680  

   Webb, R. W.; Jennings, K. S.; Fend, M.; Molotch, N. P. (December 2018, Water Resources Research)

   Abstract Many communities and ecosystems around the world rely on mountain snowpacks to provide valuable water resources. 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 study is to present a novel method that combines light detection and ranging with ground‐penetrating radar to nondestructively estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring snowmelt. We develop these methods in a manner to be applicable within a short time window, making it possible to spatially observe rapid changes that occur to this property at subdaily timescales. We applied these methods at two experimental plots in Colorado, showing the high variability of liquid water content in snow. Volumetric liquid water contents ranged from near zero to 19%vol within the scale of meters. 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 future studies to estimate the complex spatio‐temporal dynamics of liquid water in snow. 

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2. Characterizing the role of snow for liquid water storage and transmission - Niwot Ridge TLS U-072 PS01 SV01

   https://doi.org/10.7283/cbjg-g696  

   Webb, Ryan (January 2019, UNAVCO, Inc.)

   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. 

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3. Observations and simulations of the seasonal evolution of snowpack cold content and its relation to snowmelt and the snowpack energy budget

   https://doi.org/10.5194/tc-12-1595-2018  

   Jennings, Keith S.; Kittel, Timothy G.; Molotch, Noah P. (January 2018, The Cryosphere)

   Abstract. Cold content is a measure of a snowpack's energy deficit and is a linear function of snowpack mass and temperature. Positive energy fluxes into a snowpack must first satisfy the remaining energy deficit before snowmelt runoff begins, making cold content a key component of the snowpack energy budget. Nevertheless, uncertainty surrounds cold content development and its relationship to snowmelt, likely because of a lack of direct observations. This work clarifies the controls exerted by air temperature, precipitation, and negative energy fluxes on cold content development and quantifies the relationship between cold content and snowmelt timing and rate at daily to seasonal timescales. The analysis presented herein leverages a unique long-term snow pit record along with validated output from the SNOWPACK model forced with 23 water years (1991–2013) of quality controlled, infilled hourly meteorological data from an alpine and subalpine site in the Colorado Rocky Mountains. The results indicated that precipitation exerted the primary control on cold content development at our two sites with snowfall responsible for 84.4 and 73.0% of simulated daily gains in the alpine and subalpine, respectively. A negative surface energy balance – primarily driven by sublimation and longwave radiation emission from the snowpack – during days without snowfall provided a secondary pathway for cold content development, and was responsible for the remaining 15.6 and 27.0% of cold content additions. Non-zero cold content values were associated with reduced snowmelt rates and delayed snowmelt onset at daily to sub-seasonal timescales, while peak cold content magnitude had no significant relationship to seasonal snowmelt timing. These results suggest that the information provided by cold content observations and/or simulations is most relevant to snowmelt processes at shorter timescales, and may help water resource managers to better predict melt onset and rate. 

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4. Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone

   https://doi.org/10.5194/hess-26-2779-2022  

   Kiewiet, Leonie; Trujillo, Ernesto; Hedrick, Andrew; Havens, Scott; Hale, Katherine; Seyfried, Mark; Kampf, Stephanie; Godsey, Sarah E. (January 2022, Hydrology and Earth System Sciences)

   Abstract. Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how the spatial and temporal distribution of rainfall and snowmelt affects discharge in rain–snow transition zones. These zones experience large year-to-year variations in precipitation phase, cover a significant area of mountain catchments globally, and might extend to higher elevations under future climate change. We used observations from 11 weather stations and snow depths measured from one aerial lidar survey to force a spatially distributed snowpack model (iSnobal/Automated Water Supply Model) in a semiarid, 1.8 km2 headwater catchment. We focused on surface water input (SWI; the summation of rainfall and snowmelt on the soil) for 4 years with contrasting climatological conditions (wet, dry, rainy, and snowy) and compared simulated SWI to measured discharge. A strong spatial agreement between snow depth from the lidar survey and model (r2 = 0.88) was observed, with a median Nash–Sutcliffe efficiency (NSE) of 0.65 for simulated and measured snow depths at snow depth stations for all modeled years (0.75 for normalized snow depths). The spatial pattern of SWI was consistent between the 4 years, with north-facing slopes producing 1.09–1.25 times more SWI than south-facing slopes, and snowdrifts producing up to 6 times more SWI than the catchment average. Annual discharge in the catchment was not significantly correlated with the fraction of precipitation falling as snow; instead, it was correlated with the magnitude of precipitation and spring snow and rain. Stream cessation depended on total and spring precipitation, as well as on the melt-out date of the snowdrifts. These results highlight the importance of the heterogeneity of SWI at the rain–snow transition zone for streamflow generation and cessation, and emphasize the need for spatially distributed modeling or monitoring of both snowpack and rainfall dynamics. 

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5. Antecedent Snowpack Cold Content Alters the Hydrologic Response to Extreme Rain-on-Snow Events

   https://doi.org/10.1175/JHM-D-22-0090.1  

   Katz, Lisa; Lewis, Gabriel; Krogh, Sebastian; Drake, Stephen; Hanan, Erin; Hatchett, Benjamin; Harpold, Adrian (October 2023, Journal of Hydrometeorology)

   Abstract Predicting winter flooding is critical to protecting people and securing water resources in California’s Sierra Nevada. Rain-on-snow (ROS) events are a common cause of widespread flooding and are expected to increase in both frequency and magnitude with anthropogenic climate change in this region. ROS flood severity depends on terrestrial water input (TWI), the sum of rain and snowmelt that reaches the land surface. However, an incomplete understanding of the processes that control the flow and refreezing of liquid water in the snowpack limits flood prediction by operational and research models. We examine how antecedent snowpack conditions alter TWI during 71 ROS events between water years 1981 and 2019. Observations across a 500-m elevation gradient from the Independence Creek catchment were input into SNOWPACK, a one-dimensional, physically based snow model, initiated with the Richards equation and calibrated with collocated snow pillow observations. We compare observed “historical” and “scenario” ROS events, where we hold meteorologic conditions constant but vary snowpack conditions. Snowpack variables include cold content, snow density, liquid water content, and snow water equivalent. Results indicate that historical events with TWI > rain are associated with the largest observed streamflows. A multiple linear regression analysis of scenario events suggests that TWI is sensitive to interactions between snow density and cold content, with denser (>0.30 g cm⁻³) and colder (<−0.3 MJ of cold content) snowpacks retaining >50 mm of TWI. These results highlight the importance of hydraulic limitations in dense snowpacks and energy limitations in warm snowpacks for retaining liquid water that would otherwise be available as TWI for flooding. Significance StatementThe purpose of this study is to understand how the snowpack modulates quantities of water that reach the land surface during rain-on-snow (ROS) events. While the amount of near-term storm rainfall is reasonably predicted by meteorologists, major floods associated with ROS are more difficult to predict and are expected to increase in frequency. Our key findings are that liquid water inputs to the land surface vary with snowpack characteristics, and although many hydrologic models incorporate snowpack cold content and density to some degree, the complexity of ROS events justifies the need for additional observations to improve operational forecasting model results. Our findings suggest additional comparisons between existing forecasting models and those that physically represent the snowpack, as well as field-based observations of cold content and density and liquid water content, would be useful follow-up investigations. 

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