Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne Lidar data

Abstract. Automated snow station networks provide critical hydrologic data. Whether point observations represent snowpack at larger areas is an enduring question. Leveraging the recent proliferation of airborne Lidar snow depth data, we revisit the question of snow station representativeness at multiple scales surrounding 111 stations in Colorado and California (U.S.A.) from 2021–2023 (n= 476 total samples). In about 50 % of cases, station depths were at least 10 cm higher than areal-mean snow depth (from Lidar) at 0.5 to 4 km scales. The nearest 50 m Lidar pixels had lower bias and were more often representative than coincident stations. The closest 3 m Lidar pixel often agreed (within 10 cm) with station snow depth, suggesting differences between station snow depth and the nearest 50 m Lidar pixel result from highly localized conditions, not the measurement method. Representativeness decreased as scale increased up to 6 km, mainly explained by the elevation of a site relative to the larger area. The bias direction at individual snow stations is temporally consistent, suggesting the relationship between station depth and that of the surrounding area may be predictable. Improving understanding of snow station representativeness could allow for more accurate validation of modelled and remotely sensed data.