Snow Depth Distribution Patterns and Consistency from Airborne Lidar Time Series

Abstract

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.