Abstract. Advancements in remote sensing of snow (e.g., lidar) have allowed for the characterization of mountain snowpacks at higher spatial resolutions (< 10 m) and higher vertical accuracy (< 20 cm) than previously available, which can cover entire catchments repeatedly throughout the snow season. Here, we use distributed snow water equivalent (SWE) over the Tuolumne River Basin in California, USA, from lidar snow depths combined with energy/mass balance modeling of the Airborne Snow Observatory (ASO) program for the period 2013–2017 (48 flight-dates, 50-m resolution) to characterize the spatial and temporal variations in SWE distribution in this headwater catchment. Peak basin snow volume storage ranged from 142 M m3 (i.e., 106 m3) in 2015 to 1467 M m3 in 2017, covering one of the widest ranges in recorded history. Basin SWE empirical distributions vary between unimodal and bimodal distributions earlier in the season to decaying distributions later in the ablation season. Snow storage peaks at mid-elevations between 2750–3250 m, which is a consequence of increases in SWE with elevation and basin hypsometry. The date of peak SWE varies by several weeks across the watershed and between years, according to the combination of accumulation and melt patterns partially explained by elevation and aspect. This variation in peak SWE timing leads to underestimations if a single date is used to uniformly characterize the basin's peak SWE. These results illustrate how understanding SWE spatiotemporal dynamics can improve the understanding of where the snow is and when it melts, support satellite mission planning, and enhance ground survey design and planning.
Trujillo et al. (Mon,) studied this question.