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

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The seasonal snowmelt period is a critical component of the hydrologic cycle for many mountainous areas. Changes in the timing and rate of snowmelt as a result of physical hydrologic flow paths, such as longitudinal intra‐snowpack flow paths, can have strong implications on the partitioning of meltwater amongst streamflow, groundwater recharge, and soil moisture storage. However, intra‐snowpack flow paths are highly spatially and temporally variable and thus difficult to observe. This study utilizes new methods to non‐destructively observe spatio‐temporal changes in the liquid water content of snow in combination with plot experiments to address the research question: What is the scale of influence that intra‐snowpack flow paths have on the downslope movement of liquid water during snowmelt across an elevational gradient? This research took place in northern Colorado with study plots spanning from the rain‐snow transition zone up to the high alpine. Results indicate an increasing scale of influence from intra‐snowpack flow paths with elevation, showing higher hillslope connectivity producing larger intra‐snowpack contributing areas for meltwater accumulation, quantified as the upslope contributing area required to produce observed changes in liquid water content from melt rate estimates. The total effective intra‐snowpack contributing area of accumulating liquid water was found to be 17, 6, and 0 m2 for the above tree line, near tree line, and below tree line plots, respectively. Dye tracer experiments show capillary and permeability barriers result in increased number and thickness of intra‐snowpack flow paths at higher elevations. We additionally utilized aerial photogrammetry in combination with ground penetrating radar surveys to investigate the role of this hydrologic process at the small watershed scale. Results here indicate that intra‐snowpack flow paths have influence beyond the plot scale, impacting the storage and transmission of liquid water within the snowpack at the small watershed scale.

Section: Resultsmentioning

confidence: 53%

Section: Resultsmentioning

confidence: 99%

v = \frac{d_{normals}}{(\frac{true}{TWT})} .

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hydrologic connectivity at the hillslope scale through intra‐snowpack flow paths during snowmelt

Webb

Wigmore

Jennings

et al. 2020

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Quantifying snow water equivalent using terrestrial ground penetrating radar and unmanned aerial vehicle photogrammetry

Yildiz

Akyürek

Binley

2021

This study demonstrates the potential value of a combined unmanned aerial vehicle (UAV) Photogrammetry and ground penetrating radar (GPR) approach to map snow water equivalent (SWE) over large scales. SWE estimation requires two different physical parameters (snow depth and density), which are currently difficult to measure with the spatial and temporal resolution desired for basin-wide studies. UAV photogrammetry can provide very high-resolution spatially continuous snow depths (SD) at the basin scale, but does not measure snow densities. GPR allows nondestructive quantitative snow investigation if the radar velocity is known. Using photogrammetric snow depths and GPR two-way travel times (TWT) of reflections at the snowground interface, radar velocities in snowpack can be determined. Snow density (RSN) is then estimated from the radar propagation velocity (which is related to electrical permittivity of snow) via empirical formulas. A Phantom-4 Pro UAV and a MALA GX450 HDR model GPR mounted on a ski mobile were used to determine snow parameters. A snow-free digital surface model (DSM) was obtained from the photogrammetric survey conducted in September 2017. Then, another survey in synchronization with a GPR survey was conducted in February 2019 whilst the snowpack was approximately at its maximum thickness. Spatially continuous snow depths were calculated by subtracting the snow-free DSM from the snow-covered DSM.Radar velocities in the snowpack along GPR survey lines were computed by using UAV-based snow depths and GPR reflections to obtain snow densities and SWEs.The root mean square error of the obtained SWEs (384 mm average) is 63 mm, indicating good agreement with independent SWE observations and the error lies within acceptable uncertainty limits.digital surface model, digital terrain model, ground penetrating radar, photogrammetry, snow density, snow tube, snow water equivalent, unmanned aerial vehicle | INTRODUCTIONSnow water equivalent (SWE) is the product of snow depth and bulk density (relative to water) and is commonly reported in units of mm. It is a key variable that characterizes the hydrological significance of the snow cover. SWE information is needed to calibrate hydrological models, estimate snowmelt runoff in drainage basins, and improve decision making concerning water supply, hydroelectric power and flood forecasting. SWE is also useful to studies related to snow climatology, ecological function, and avalanche forecasting. SWE can

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

Webb

Musselman

Ciafone

et al. 2022

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Streamflow response in headwater catchments is highly sensitive to the hydrologic connectivity of hillslopes to streams during spring snowmelt. Despite strong evidence at point‐ to plot‐scales of flow paths creating lateral connectivity within an alpine snowpack, meltwater is commonly assumed to infiltrate vertically through the snowpack. Hydrologic models only treat the horizontal (downstream) routing of water once released from the snowpack and/or soil column. This assumption limits our ability to represent the full dynamic nature of hydrologic connections in snow‐dominated mountainous headwaters. Thus, the goal of this study is to assess the mechanisms that control the spatiotemporal distribution of liquid water in an alpine snowpack during the spring snowmelt season. We utilize terrestrial laser scanning (TLS), ground penetrating radar (GPR), and manual observations to map the seasonal dynamics of snow depth, snow water equivalent (SWE), and within‐snow liquid water content (LWC). We compare these observations to point‐scale parameter sensitivity analyses with a modular snow model (SUMMA). The results show high spatial variability of LWC in an alpine snowpack during snowmelt. Statistical analyses show LWC is most highly correlated to snow depth (r2 = 0.62). However, including the distance to bare soil and topographical slope slightly improved the coefficient of determination (r2 = 0.67). While hydrologic models have the flexibility to simulate many of the observed dynamics in snowpack liquid water storage, model simulations using previously published parameter ranges always underestimated the high liquid water storage at one of the three sites. This is likely a result of current model structures that lack capabilities for surface ponding of water within a snowpack or surface runoff laterally through a snowpack. Our slope‐scale characterization of the spatiotemporal distribution of in‐snow LWC, together with a model‐based sensitivity assessment, will inform future efforts in hydrologic model development and catchment observations.