Snow depth can be mapped from airborne platforms and measured in situ rapidly, but manual snow density and snow water equivalent (SWE) measurements are time consuming to obtain using traditional survey methods. As a result, the limited number of point observations are likely insufficient to capture the true spatial complexity of snow density and SWE in many settings, highlighting the value of distributed observations. Here, we combine measured two-way travel time from repeat ground-penetrating radar (GPR) surveys along a ∼150 m transect with snow depth estimates from UAV-based Structure from Motion Multi-View Stereo (SfM-MVS) surveys to estimate snow density and SWE. These estimates were successfully calculated on eleven dates between January and May during the NASA SnowEx21 campaign at Cameron Pass, CO. GPR measurements were made with a surface-coupled Sensors and Software PulseEkko Pro 1 GHz system, while UAV flights were completed using a DJI Mavic 2 Pro platform and consisted of two orthogonal flights at ∼60 m elevation above ground level. SfM-MVS derived dense point clouds (DPCs) were georeferenced using eight ground control points and evaluated using three checkpoints, which were distributed across the ∼3.5 ha study plot containing the GPR transect. The DPCs were classified to identify the snow surface and then rasterized to produce snow-on digital surface models (DSMs) at 1 m resolution. Snow depths on each survey date were calculated by differencing these snow-on DSMs from a nearly snow-off DSM collected near the end of the melt season. SfM-derived snow depths were evaluated with independent snow depth measurements from manual probing (mean r2 = 0.67, NMAD = 0.11 m and RMSE = 0.12 m). The GPR-SfM derived snow densities were compared to snow density measurements made in snowpits (r2 = 0.42, NMAD = 39 kg m−3 and RMSE = 68 kg m−3). The integration of SfM and GPR observations provides an accurate, efficient, and a relatively non-destructive approach for measuring snow density and SWE at intermediate spatial scales and over seasonal timescales. Ongoing developments in snow depth retrieval technologies could be leveraged in the future to extend the spatial extent of this method.

We present spatially distributed seasonal and annual surface mass balances of Wolverine Glacier, Alaska, from 2016 to 2020. Our approach accounts for the effects of ice emergence and firn compaction on surface elevation changes to resolve the spatial patterns in mass balance at 10 m scale. We present and compare three methods for estimating emergence velocities. Firn compaction was constrained by optimizing a firn model to fit three firn cores. Distributed mass balances showed good agreement with mass-balance stakes (RMSE = 0.67 m w.e., r = 0.99, n = 41) and ground-penetrating radar surveys (RMSE = 0.36 m w.e., r = 0.85, n = 9024). Fundamental differences in the distributions of seasonal balances highlight the importance of disparate physical processes, with anomalously high ablation rates observed in icefalls. Winter balances were found to be positively skewed when controlling for elevation, while summer and annual balances were negatively skewed. We show that only a small percent of the glacier surface represents ideal locations for mass-balance stake placement. Importantly, no suitable areas are found near the terminus or in elevation bands dominated by icefalls. These findings offer explanations for the often-needed geodetic calibrations of glaciological time series.

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Wildfire activity increased markedly in the western United States beginning in the 1980s (Iglesias et al., 2022;Westerling, 2016) as a result of increased fuel loads due to a history of fire suppression, natural climate variability, and anthropogenic-driven climate change (Abatzoglou & Williams, 2016). Elevations exceeding 2,500 m saw the largest increase in burned area between 1984and 2017(Alizadeh et al., 2021, calling attention to the pronounced shift of wildfires from the intermittent snow zone and into the seasonal snow zone (Kampf et al., 2022). Additionally, there have been significant climate-driven declines in various snowpack and associated streamflow metrics in the region. For example, 90% of snow station records show declining 1 April snow water equivalent (SWE) relative to 1955, with the changes on the order of 15%-30% (Mote et al., 2018). This decrease in SWE has coincided with earlier streamflow timing (Clow, 2010;Stewart et al., 2005), which has correlated with increased wildfire activity the following summer (Westerling, 2016;Westerling et al., 2006). These dramatic changes are modest relative to the projected changes over the next 30 years. SWE is projected to decline by up to 60% (Fyfe et al., 2017;Siirila-Woodburn et al., 2021) and forest fire models, which account for declining fuel

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