Inferring Distributed Snow Depth by Leveraging Snow Pattern Repeatability: Investigation Using 47 Lidar Observations in the Tuolumne Watershed, Sierra Nevada, California

• Published in: Water resources research 2020-09, Vol.56 (9), p.n/a
• Main Authors: Pflug, J. M., Lundquist, J. D.
• Format: Article
• Language: English
• Subjects: Depth; Distribution; Distribution patterns; Lidar; Meteorology; Reproducibility; Snow; Snow accumulation; Snow depth; Snowmelt; Snowpack; Surveys; Water depth; Water management; Watersheds
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1029/2020WR027243

Snow distribution is controlled by the interaction between local meteorology and static features like topography and vegetation. The resulting spatial pattern of snow in mountainous terrain is often repeatable and can be used to infer snowpack distribution at periods when observations are limited. This study uses a library of airborne lidar surveys (ALS) in California's Tuolumne watershed to analyze snow patterns at extents (1,650 km2), resolutions (25 m), and temporal scales (47 ALS observations over 7 years) unmatched by previous research. Distributed snow depth was inferred from snow depth observations covering a portion of the domain (< 4%) at a period near peak‐snowpack timing and snow distribution patterns from different years. Snow patterns from different years differed as a function of snow extents, variability, and interannual noise (r = 0.30 to 0.90). However, matching criteria like seasonal timing and snow extents were able to identify pairs of snow patterns with increased spatial agreement (median r > 0.84). Distributed snow depth inferred using a strip of observations (