Propagating meteorological uncertainty in physically based mountain snow modeling

Casson, David R.; Tang, Guoqiang; Vásquez, Nicolás; Wood, Andrew W.; Clark, Martyn P.

doi:10.5194/tc-20-5895-2026

Articles | Volume 20, issue 10

https://doi.org/10.5194/tc-20-5895-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-20-5895-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 10

Research article

| Highlight paper

|

15 Oct 2026

Research article | Highlight paper |

| 15 Oct 2026

Propagating meteorological uncertainty in physically based mountain snow modeling

David R. Casson, Guoqiang Tang, Nicolás Vásquez, Andrew W. Wood, and Martyn P. Clark

Supplement

https://doi.org/10.5194/tc-20-5895-2026-supplement

Data sets

SUMMA Model and GPEP Input Files for Chena, Bow and Tuolumne David R. Casson https://doi.org/10.5281/zenodo.17740654

Ensemble Meteorological Forcing for Chena, Bow, Tuolumne David R. Casson https://doi.org/10.20383/103.01532

Northern Hemisphere Historical In-Situ Snow Water Equivalent Dataset (NorSWE, 1979-2021) C. Mortimer and V. Vionnet https://doi.org/10.5281/zenodo.15263370

MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 61 D. K. Hall and G. A. Riggs https://doi.org/10.5067/MODIS/MOD10A1.061

ASO L4 Lidar Snow Water Equivalent 50m UTM Grid, Version 1 T. Painter https://doi.org/10.5067/M4TUH28NHL4Z

Model code and software

Gpep_snakemake: Workflow for Generating Probabilistic Ensemble Meteorological Forcings David R. Casson https://doi.org/10.5281/zenodo.17727619

Gpep_to_summa_snakemake: Workflow for Coupling GPEP Ensemble Forcings to the Physically Based Hydrological Modelling David R. Casson https://doi.org/10.5281/zenodo.17727623

Editorial statement

Quantifying the distribution of mountain snow is critical issue to ensure effective water resource management, skillful hydrological prediction, and hazard mitigation. This manuscript presents a highly impactful framework for quantifying meteorological forcing uncertainty in model simulations of mountain snow using novel machine learning applications and large sample emulators. By strictly adhering to FAIR data principles and utilizing open-source, automated workflows, the study provides a compelling demonstration of best practices in hydrological modeling. This paper will serve as an invaluable guide for early-career hydrologists seeking practical examples of rigorous code development and modeling workflows.