- This event has passed.
Megan Mason Thesis Defense
June 26 @ 3:00 pm - 4:30 pm MDT
Title: Snow Depth Distribution Patterns and Consistency from Airborne Lidar Time Series
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(s) (ALS) dataset to date. We begin by presenting 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. We then assess the snow distribution spatial location and consistency at the grid cell level for the entire basin (at 20-m resolution) and pattern repeatability for sub basin regions (at 3-m resolution) from a collection of 51 ALS that spans a six-year period (2013-2018) in the Tuolumne Basin. Strong correlations between ALS confirm that spatial patterns exist between snow seasons. Year-to-year snow depth differs in absolute, but relative differences appear controlled, such that deep and shallow zones occur in the same location. We further show that elevation is the greatest terrain parameter driving snow distribution at the basin scale, as well as map the expected pattern distribution for periods with similar snow-covered extents. Lastly, we show at a sub basin scale that spatial alignment of distribution patterns is more pronounced in vegetation limited areas (bedrock dominated terrain and open meadows) compared to vegetation-rich zones (valley hillslopes and dense canopy cover). This effort moves towards high resolution hydrologic model improvements for regions where high-resolution snow depth observations are limited or non-existent for a particular time period.
Advisor: HP Marshall
Co-Advisor, Committee Members: Nancy Glenn and Ernesto Trujillo
Where: Zoom Meeting ID: 950 5239 4642 Password: Defense
When: Friday, June 26th