20 Nov 2023
 | 20 Nov 2023

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

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2543', Hannah Besso, 26 Jan 2024
    • AC2: 'Reply on RC1', Jordan Herbert, 11 Mar 2024
  • RC2: 'Comment on egusphere-2023-2543', Wyatt Reis, 27 Jan 2024
    • AC1: 'Reply on RC2', Jordan Herbert, 11 Mar 2024
  • RC3: 'Comment on egusphere-2023-2543', Hannah Besso, 14 Mar 2024
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small


Total article views: 460 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
324 110 26 460 19 15 15
  • HTML: 324
  • PDF: 110
  • XML: 26
  • Total: 460
  • Supplement: 19
  • BibTeX: 15
  • EndNote: 15
Views and downloads (calculated since 20 Nov 2023)
Cumulative views and downloads (calculated since 20 Nov 2023)

Viewed (geographical distribution)

Total article views: 452 (including HTML, PDF, and XML) Thereof 452 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 23 May 2024
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.