Snow water equivalent (SWE) distribution at fine spatial scales (≤ 10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope’s constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5-year period (2019 – 2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave-based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring snow water equivalent (SWE). Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine-scale and high-frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack.

Montane snowpack is a vital source of water supply in the Western United States. However, the future of snow in these regions in a changing climate is uncertain. Here, we use a large-ensemble approach to evaluate the consistency across 124 statistically downscaled snow water equivilent (SWE) projections between end-of-century (2076 – 2095) and early 21st century (2106 – 2035) periods. Comparisons were performed on dates corresponding with the end of winter (15 April) and spring snowmelt (15 May) in five western US montane domains. By benchmarking SWE climate change signals using the disparity between snow projections, we identified relationships between SWE projections that were repeatable across each domain, but shifted in elevation. In low to mid-elevations, 15 April average projected decreases to SWE of 48% or larger were greater than the disparity between models. Despite this, a significant portion of 15 April SWE volume (39 – 93%) existed in higher elevation regions where the disparities between snow projections exceeded the projected changes to SWE. Results also found that 15 April and 15 May projections were strongly correlated (r 0.82), suggesting that improvements to the spread and certainty of 15 April SWE projections would translate to improvements in later dates. The results of this study show that large-ensemble approaches can be used to measure coherence between snow projections and identify both 1) the highest-confidence changes to future snow water resources, and 2) the locations and periods where and when improvements to snow projections would most benefit future snow projections.