Snow provides fresh meltwater to over a billion people worldwide. Snow dominated watersheds
drive western US water supply and are increasingly important as demand depletes reservoir and
groundwater recharge capabilities. This motivates our inter- and intra-annual investigation of
snow distribution patterns, leveraging the most comprehensive airborne lidar survey (ALS)
dataset for snow. Validation results for ALS from both the NASA SnowEx 2017 campaign in
Grand Mesa, Colorado and the time series dataset from the Tuolumne River Basin in the Sierra
Nevada, in California, are presented. We then assess the consistency in the snow depth patterns
for the entire basin (at 20-m resolution) and for subbasin regions (at 3-m resolution) from
a collection of 51 ALS that span a six-year period (2013-2018) in the Tuolumne Basin. Strong
correlations between ALS from different years near peak SWE confirm that spatial patterns
exist between snow seasons. Year-to-year snow depth differs in absolute magnitude, but relative
differences are consistent spatially, such that deep and shallow zones occur in the same
location. We further show that elevation is the terrain parameter with the largest correlation
to snow depth at the basin scale, and we map the expected pattern distribution for periods
with similar snow-covered extents. Lastly, we show at a subbasin scale that distribution
patterns are more consistent in vegetation-limited areas (bedrock dominated terrain and
open meadows) compared to vegetation-rich zones (valley hillslopes and dense canopy cover).
The maps of snow patterns and their consistency can be used to determine optimal locations
of new long-term monitoring sites, design sampling strategies for future snow surveys, and
to improve high resolution snow models.