Propagating meteorological uncertainty in physically based mountain snow modeling
- 1University of Calgary (Calgary, Canada)
Abstract
Snow estimation in mountainous regions is uncertain because meteorology varies with topography, observations are sparse and biased, and snow models are affected by structural and scale limitations. Probabilistic methods are increasingly recognized as essential for quantifying and propagating these uncertainties in hydrological assessment. This study generates meteorological forcing ensembles designed for mountainous terrain and applies them for physically based snow modeling. Precipitation from global station datasets is corrected for wind-induced gauge undercatch using standardized transfer functions. Using geospatial probabilistic estimation, station observations are combined with static topographic predictors and dynamic atmospheric predictors from reanalyses. Deterministic fields are estimated using locally weighted regression and random forests, while spatially correlated random fields sample residuals to produce skillful, reliable ensembles. The resulting ensembles drive the SUMMA energy-balance snow and hydrological modeling framework, calibrated using a Large Sample Emulator approach and applied in three contrasting basins: the Chena (Interior Alaska), Bow (Canadian Rockies), and Tuolumne (Sierra Nevada). Undercatch correction improves cold-season precipitation, dynamic predictors enhance regression skill, and random forests outperform locally weighted regression. Ensemble verification yields positive Brier Skill Scores, and ensemble-forced SUMMA simulations reproduce observed snow water equivalent with realistic magnitude, timing, and uncertainty. This approach advances mountain snow estimation by explicitly representing forcing uncertainty in complex terrain, supporting physically based modeling, data assimilation, and water resource management.
Versions
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BibTeX
@article{casson2026propagating,
title = {{Propagating meteorological uncertainty in physically based mountain snow modeling}},
author = {David R. Casson and Guoqiang Tang and Nicolás Vásquez and Andrew W. Wood and Martyn P. Clark},
journal = {The Cryosphere},
year = {2026},
month = oct,
doi = {10.5194/tc-20-5895-2026},
url = {https://tc.copernicus.org/articles/20/5895/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Propagating meteorological uncertainty in physically based mountain snow modeling
AU - David R. Casson
AU - Guoqiang Tang
AU - Nicolás Vásquez
AU - Andrew W. Wood
AU - Martyn P. Clark
PY - 2026
DA - 2026/10/15
JO - The Cryosphere
DO - 10.5194/tc-20-5895-2026
UR - https://tc.copernicus.org/articles/20/5895/2026/
AB - Snow estimation in mountainous regions is uncertain because meteorology varies with topography, observations are sparse and biased, and snow models are affected by structural and scale limitations. Probabilistic methods are increasingly recognized as essential for quantifying and propagating these uncertainties in hydrological assessment. This study generates meteorological forcing ensembles designed for mountainous terrain and applies them for physically based snow modeling. Precipitation from global station datasets is corrected for wind-induced gauge undercatch using standardized transfer functions. Using geospatial probabilistic estimation, station observations are combined with static topographic predictors and dynamic atmospheric predictors from reanalyses. Deterministic fields are estimated using locally weighted regression and random forests, while spatially correlated random fields sample residuals to produce skillful, reliable ensembles. The resulting ensembles drive the SUMMA energy-balance snow and hydrological modeling framework, calibrated using a Large Sample Emulator approach and applied in three contrasting basins: the Chena (Interior Alaska), Bow (Canadian Rockies), and Tuolumne (Sierra Nevada). Undercatch correction improves cold-season precipitation, dynamic predictors enhance regression skill, and random forests outperform locally weighted regression. Ensemble verification yields positive Brier Skill Scores, and ensemble-forced SUMMA simulations reproduce observed snow water equivalent with realistic magnitude, timing, and uncertainty. This approach advances mountain snow estimation by explicitly representing forcing uncertainty in complex terrain, supporting physically based modeling, data assimilation, and water resource management.
SN - 1994-0424
ER -