Articles | Volume 18, issue 8
https://doi.org/10.5194/tc-18-3495-2024
https://doi.org/10.5194/tc-18-3495-2024
Research article
 | 
08 Aug 2024
Research article |  | 08 Aug 2024

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

Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small

Data sets

Snow-Telemetry daily snow depth dataset USDA NRCS https://wcc.sc.egov.usda.gov/reportGenerator/

Historical snow sensor data California department of water resources https://cdec.water.ca.gov/dynamicapp/selectSnow

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Short summary
Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.