Sourp et al. (2025) Assessment of Snow Cover Fraction Parameterizations for High Resolution Snowpack Reanalyses

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Hydrological Processes
• Year: 2025
• Date: 2025-10-28
• Authors: Laura Sourp, Vanessa Pedinotti, Esteban Alonso‐González, Lionel Jarlan, Simon Gascoin
• DOI: 10.1002/hyp.70491

Research Groups

The specific research groups, labs, or departments are not explicitly detailed in the provided abstract. However, the study involves the Airborne Snow Observatory (ASO) and focuses on the Tuolumne River Basin, USA.

Short Summary

This study assesses the performance of various snow cover fraction (SCF) parameterizations within a 100 m resolution data assimilation framework in a mountainous region, finding that a simple asymptotic parameterization consistently performs best for improving high-resolution snow depth estimates through remote sensing SCF assimilation.

Objective

• To assess the performance of several snow cover fraction (SCF) parameterizations within a 100 m resolution data assimilation framework in a mountainous region with a Mediterranean climate (Tuolumne River Basin, USA).

Study Configuration

• Spatial Scale: Model resolution of 100 m; MODIS SCF products at 500 m resolution. Study area is the Tuolumne River Basin, USA.
• Temporal Scale: Not explicitly detailed in the abstract, but implies seasonal snowpack dynamics and "pre-melt" conditions.

Methodology and Data

• Models used: Data assimilation framework utilizing a particle batch smoother; snowpack models (not explicitly named, but implied for snow depth/SWE simulation).
• Data sources:
  • Remotely sensed snow-covered area data.
  • Accurate lidar snow depth products from the Airborne Snow Observatory (ASO).
  • Snow cover fraction (SCF) maps derived from ASO observations.
  • 500 m SCF from MODIS products.

Main Results

• An asymptotic parameterization performed best in initial assessments, yielding an SCF root mean square error (RMSE) of 16%.
• When assimilating SCF maps derived from ASO observations, the asymptotic function continued to perform best with a snow depth RMSE of 0.40 m.
• When assimilating 500 m SCF from MODIS products, the asymptotic parameterization and a parameterization accounting for pre-melt subgrid snow water equivalent (SWE) variability yielded similar results, both achieving a posterior snow depth RMSE of 0.33 m.
• Both the asymptotic parameterization and the parameterization accounting for pre-melt subgrid SWE variability are well suited to improve high-resolution SWE or snow depth estimates through the assimilation of remote sensing SCF in mountainous and Mediterranean climatic contexts.

Contributions

• Provides the first assessment of snow cover fraction (SCF) parameterizations within a 100 m resolution data assimilation framework, specifically addressing the scale-dependency issue for high-resolution snowpack models.
• Utilizes highly accurate lidar snow depth products from the Airborne Snow Observatory (ASO) to reduce model forcing uncertainties during the assessment.
• Identifies and validates effective SCF parameterizations (asymptotic and pre-melt subgrid SWE variability) for improving high-resolution snow depth and SWE estimates in complex mountainous terrain using remote sensing data.

Funding

Not explicitly detailed in the abstract.

Citation

@article{Sourp2025Assessment,
  author = {Sourp, Laura and Pedinotti, Vanessa and Alonso‐González, Esteban and Jarlan, Lionel and Gascoin, Simon},
  title = {Assessment of Snow Cover Fraction Parameterizations for High Resolution Snowpack Reanalyses},
  journal = {Hydrological Processes},
  year = {2025},
  doi = {10.1002/hyp.70491},
  url = {https://doi.org/10.1002/hyp.70491}
}