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Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kgm-3 for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km2 may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval. [ABSTRACT FROM AUTHOR]

Snow is a critical water resource for the western United States and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge. To confront these challenges, NASA initiated the SnowEx program, a multiyear effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team acquired an L-band interferometric synthetic aperture radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide SWE change estimates, allowing the snow community to leverage the upcoming NASA–ISRO (NASA–Indian Space Research Organization) SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez Mountains, NM, between 12 and 26 February 2020. We developed a snow-focused multi-sensor method that uses UAVSAR InSAR data synergistically with optical fractional snow-covered area (fSCA) information. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow-covered pixels within the radar swath. For all InSAR pairs, we converted phase change values to SWE change estimates between the three acquisition dates. We then evaluated InSAR-derived retrievals using a combination of fSCA, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions. [ABSTRACT FROM AUTHOR]

Modeling the multidimensional flow of liquid water through snow has been limited in spatial and temporal scales to date. Here, we present simulations using the inverse TOUGH2 (iTOUGH2) model informed by the model SNOWPACK, referred to as SnowTOUGH. We use SnowTOUGH to simulate snow metamorphism, melt/freeze processes, and liquid water movement in two-dimensional snowpacks at the plot scale (20 m) on a sloping ground surface during multi-day observation periods at three field sites in northern Colorado, USA. Model results compare well with sites below the treeline and above the treeline but not at a site near the treeline. Results show the importance of longitudinal intra-snowpack flow paths (i.e., parallel to ground surface in the downslope direction and sometimes referred to as lateral flow), particularly during times when the snow surface (i.e., snow–atmosphere interface) is not actively melting. At our above-treeline site, simulations show that longitudinal flow can occur at rates orders of magnitude greater than vertically downward percolating water flow at a mean ratio of 75:1 as a result of hydraulic barriers that divert flow. Our near-treeline site simulations resulted in slightly less longitudinal flow than vertically percolating water, and the below-treeline site resulted in negligible longitudinal flow of liquid water. These results show the increasing influence of longitudinal intra-snowpack flow paths with elevation, similar to field observations. Results of this study suggest that intra-snowpack longitudinal flow may be an important process for consideration in hydrologic modeling for higher-elevation headwater catchments. [ABSTRACT FROM AUTHOR]

In many mountainous regions around the world, snow and soil moisture are key components of the hydrologic cycle. Preferential flow paths of snowmelt water through snow have been known to occur for years with few studies observing the effect on soil moisture. In this study, statistical analysis of the topographical and hydrological controls on the spatiotemporal variability of snow water equivalent (SWE) and soil moisture during snowmelt was undertaken at a subalpine forested setting with north, south, and flat aspects as a seasonally persistent snowpack melts. We investigated if evidence of preferential flow paths in snow can be observed and the effect on soil moisture through measurements of snow water equivalent and near-surface soil moisture, observing how SWE and near-surface soil moisture vary on hillslopes relative to the toes of hillslopes and flat areas. We then compared snowmelt infiltration beyond the near-surface soil between flat and sloping terrain during the entire snowmelt season using soil moisture sensor profiles. This study was conducted during varying snowmelt seasons representing above-normal, relatively normal, and below-normal snow seasons in northern Colorado. Evidence is presented of preferential meltwater flow paths at the snow-soil interface on the north-facing slope causing increases in SWE downslope and less infiltration into the soil at 20 cm depth; less association is observed in the near-surface soil moisture (top 7 cm). We present a conceptualization of the meltwater flow paths that develop based on slope aspect and soil properties. The resulting flow paths are shown to divert at least 4% of snowmelt laterally, accumulating along the length of the slope, to increase the snow water equivalent by as much as 170% at the base of a north-facing hillslope. Results from this study show that snow acts as an extension of the vadose zone during spring snowmelt and future hydrologic investigations will benefit from studying the snow and soil together. [ABSTRACT FROM AUTHOR]