Comparing interannual snow pattern repeatability between snowpack reanalyses and airborne lidar observations in the California Sierra Nevadas.

Abstract:

The distribution of snow in mountainous terrain is often repeatable at interannual timescales as deposition, redistribution, and melt processes are driven by the interaction between persistent weather patterns and static features like terrain and vegetation. Here, we discuss how repeatable snow patterns emerged from a library of airborne lidar snow depth observations in the California Tuolumne River watershed, and review how these patterns were used to 1) infer distributed snow depth from observations over only a portion ( < 4%) of the domain, and 2) overcome model shortcomings. We then use this library of lidar observations as a baseline to test how modeled snowpack reanalyses, assimilated with satellite observations of snow-covered area, were able to reproduce lidar-observed snow patterns. Results found that snowpack reanalyses were able to identify periods from different years with similar snow distribution patterns and agreed closely with snow patterns observed by lidar (r > 0.90). As opposed to lidar observations which were collected at finite periods of time, snowpack reanalyses demonstrated the flexibility to identify repeatable snow patterns from various periods of the snow season, including seasons with abnormal snowfall, using a large historical dataset (1985 – 2016). We finish by discussing how distributed snow depth observations could be used to improve snowpack reanalyses, and how water management may be served by using modeled and observed interannually-repeatable snow patterns.