Quantifying mountain glacier mass balance, snow depth, and landscape evolution on a continental scale


The recent widespread availability of high-resolution (~30–50 cm) satellite imagery has revolutionized multiple geoscience and engineering disciplines. Digital elevation models (DEMs) derived from stereo satellite imagery offer on-demand topographic measurements with nearly global coverage and quality approaching that of airborne LiDAR. We developed an automated, open-source workflow to generate 2-m DEMs from this imagery with <0.5 m horizontal and vertical accuracy after co-registration. We are producing 1000s of DEMs in a high-performance computing environment, and assembling dense time series and regional mosaics for Antarctica, Greenland, the contiguous US (CONUS), and High Mountain Asia (HMA). We are integrating these products with historical DEMs, LiDAR data, and Structure from Motion (SfM) data to study the long-term (~50-year), decadal, annual, and seasonal mass balance of mountain glaciers on a regional to continental scale. These efforts provide a systematic assessment of regional climate change, and offer basin-scale assessments of snow water equivalent (SWE) and snow/ice melt runoff contributions for downstream water resource applications. In addition, these observations offer measurements of dynamic landscape evolution (e.g., landslides, sediment redistribution by rivers) that can be used for both hazard assessment and rapid response to natural disasters.

University of Oregon, Department of Earth Sciences Seminar
Eugene, OR