Fassnacht et al. (2025) Ski Resort Snow Surface Roughness

Identification

• Journal: Preprints.org
• Year: 2025
• Date: 2025-12-02
• Authors: Steven R. Fassnacht, Javier Herrero, Jessica E. Sanow
• DOI: 10.20944/preprints202512.0081.v1

Research Groups

• University of Granada-CETURSA Sierra Nevada S.A.
• Fulbright Global Scholar program (supporting Steven R. Fassnacht)

Short Summary

This study investigates the spatial variability of snow surface roughness at a Spanish ski resort using 2-D roughness boards and 3-D iPad scans, finding an order of magnitude difference in aerodynamic roughness length (z0) across natural and groomed surfaces, which significantly impacts modelled sublimation.

Objective

• How does the snow surface roughness vary at a ski area?
• What are the implications of the spatial variation on snowpack sublimation modeling?

Study Configuration

• Spatial Scale: Sierra Nevada ski resort, Spain, specifically the Borreguiles area at 2,700 metres elevation. Measurements covered areas of 0.6 metres (2-D roughness boards) and 0.8 metres x 1.6 metres (3-D iPad scans), with resolutions of 0.2 millimetres and 1 centimetre, respectively.
• Temporal Scale: Data collected on 19 April 2024. Meteorological data collected for four hours (10:19 to 14:32).

Methodology and Data

• Models used: Bulk-transfer approach for sublimation estimation. Lettau method for geometric-based aerodynamic roughness length (z0). Kriging interpolation (using Golden SURFER program) for iPad scan point clouds.
• Data sources:
  • Field measurements of snow surface forms (natural, dust-covered, sun-cupped, groomed - tracked and between tracks) using:
    • 2-D snow roughness boards (photographed with a Samsung Galaxy A53, 16 MP, 4624x3468 pixels).
    • 3-D iPad surface scanning (iPad Pro with 3-D Scanner App).
  • One-minute meteorological data collected at the field site.

Main Results

• The snow surface roughness varied significantly across different forms at the ski resort, with groomed snow being much smoother than ungroomed snow, especially in the presence of sun cups.
• Both 2-D roughness boards (0.2 mm resolution) and 3-D iPad scans (1 cm resolution) provided similar estimates for Random Roughness (RR), Scale Break (SB), and Fractal Dimension (D) for distinct surfaces, though the magnitude of roughness differed between methods.
• Sun-cupped surfaces exhibited the largest roughness (largest RR values and lowest D for longer scales), while the groomed surface between tracks was the smoothest and most random (largest D for longer scales).
• Geometric-based aerodynamic roughness length (z0) showed an order of magnitude variability: groomed surfaces were approximately 0.35 mm, while sun-cupped surfaces reached about 9.5 mm (compared to a default modeling value of 0.24 mm).
• These variations in z0 produced substantial differences in modelled sublimation estimates.
• iPad scanning was less effective at resolving features on very smooth snow surfaces compared to roughness boards.

Contributions

• Quantifies the significant spatial variability of snow surface roughness at a ski resort, demonstrating its dynamic nature beyond static assumptions in models.
• Establishes a direct link between observed snow surface roughness variations and substantial differences in modelled snowpack sublimation, highlighting the critical need for dynamic aerodynamic roughness length (z0) in hydrological and energy balance models.
• Compares multi-resolution (millimetre to centimetre) 2-D and 3-D measurement techniques for snow surface roughness, providing insights into their consistency and limitations for different surface types.

Funding

• Project SUBLIMA (UGR OTRI-6429) University of Granada-CETURSA Sierra Nevada S.A.
• Fulbright Global Scholar program

Citation

@article{Fassnacht2025Ski,
  author = {Fassnacht, Steven R. and Herrero, Javier and Sanow, Jessica E.},
  title = {Ski Resort Snow Surface Roughness},
  journal = {Preprints.org},
  year = {2025},
  doi = {10.20944/preprints202512.0081.v1},
  url = {https://doi.org/10.20944/preprints202512.0081.v1}
}

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