Casson et al. (2026) Propagating meteorological uncertainty in physically based mountain snow modeling

Identification

• Journal: ˜The œcryosphere
• Year: 2026
• Date: 2026-10-15
• Authors: David R. Casson, Guoqiang Tang, Nicolás Vasquéz, Andrew W. Wood, Martyn Clark
• DOI: 10.5194/tc-20-5895-2026

Research Groups

• Civil Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
• State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
• Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, USA
• Colorado School of Mines, Golden, USA

Short Summary

This study generates meteorological forcing ensembles designed for mountainous terrain and applies them to physically based snow modeling. The approach explicitly corrects for precipitation undercatch, generates spatially and temporally coherent high-resolution meteorological ensembles, and propagates these uncertainties through the SUMMA energy-balance snow model.

Objective

• To develop a framework for quantifying and propagating meteorological uncertainty in physically based mountain snow modeling

Study Configuration

• Spatial Scale: Global to regional ( Chena, Bow, and Tuolumne river basins)
• Temporal Scale: Daily to annual (1950-2019)

Methodology and Data

• Models used:
  • Geospatial Probabilistic Estimation (GPE) for ensemble generation
  • SUMMA energy-balance snow model for physically based modeling
• Data sources:
  • Global Historical Climatology Network-Daily (GHCN-D)
  • Global Surface Summary of the Day (GSOD)
  • European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5)
  • Canadian Surface Reanalysis (CaSR)

Main Results

• The study demonstrates a transferable approach for representing and evaluating forcing uncertainty through to physically based snow modeling in complex terrain.
• Ensemble verification yields positive Brier Skill Scores, and ensemble-forced SUMMA simulations reproduce observed snow water equivalent with realistic magnitude, timing, and uncertainty.

Contributions

• This study advances mountain snow estimation by explicitly representing forcing uncertainty in complex terrain, supporting physically based modeling, data assimilation, and water resource management.

Funding

• Not specified

Citation

@article{Casson2026Propagating,
  author = {Casson, David R. and Tang, Guoqiang and Vasquéz, Nicolás and Wood, Andrew W. and Clark, Martyn},
  title = {Propagating meteorological uncertainty in physically based mountain snow modeling},
  journal = {˜The œcryosphere},
  year = {2026},
  doi = {10.5194/tc-20-5895-2026},
  url = {https://doi.org/10.5194/tc-20-5895-2026}
}