Articles | Volume 30, issue 9
https://doi.org/10.5194/hess-30-2613-2026
https://doi.org/10.5194/hess-30-2613-2026
Research article
 | 
04 May 2026
Research article |  | 04 May 2026

Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere

Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, and Joshua R. Larsen

Related authors

DCG-MIP: the Debris-Covered Glacier melt Model Intercomparison exPeriment
Francesca Pellicciotti, Adrià Fontrodona-Bach, David R. Rounce, Catriona L. Fyffe, Leif S. Anderson, Álvaro Ayala, Ben W. Brock, Pascal Buri, Stefan Fugger, Koji Fujita, Prateek Gantayat, Alexander R. Groos, Walter Immerzeel, Marin Kneib, Christoph Mayer, Shelley MacDonell, Michael McCarthy, James McPhee, Evan Miles, Heather Purdie, Ekaterina Rets, Akiko Sakai, Thomas E. Shaw, Jakob Steiner, Patrick Wagnon, and Alex Winter-Billington
The Cryosphere, 20, 1895–1928, https://doi.org/10.5194/tc-20-1895-2026,https://doi.org/10.5194/tc-20-1895-2026, 2026
Short summary
DebDaB: a database of supraglacial debris thickness and physical properties
Adrià Fontrodona-Bach, Lars Groeneveld, Evan Miles, Michael McCarthy, Thomas Shaw, Vicente Melo Velasco, and Francesca Pellicciotti
Earth Syst. Sci. Data, 17, 4213–4234, https://doi.org/10.5194/essd-17-4213-2025,https://doi.org/10.5194/essd-17-4213-2025, 2025
Short summary
NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in situ snow depth time series
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen
Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023,https://doi.org/10.5194/essd-15-2577-2023, 2023
Short summary

Cited articles

Adam, J. C. and Lettenmaier, D. P.: Adjustment of global gridded precipitation for systematic bias, J. Geophys. Res.-Atmos., 108, https://doi.org/10.1029/2002JD002499, 2003. a
Asaoka, Y. and Kominami, Y.: Incorporation of satellite-derived snow-cover area in spatial snowmelt modeling for a large area: determination of a gridded degree-day factor, Ann. Glaciol., 54, 205–213, https://doi.org/10.3189/2013AoG62A218, 2013. a, b, c
Avanzi, F., De Michele, C., Ghezzi, A., Jommi, C., and Pepe, M.: A processing–modeling routine to use SNOTEL hourly data in snowpack dynamic models, Adv. Water Resour., 73, 16–29, 2014. a
Avanzi, F., Gabellani, S., Delogu, F., Silvestro, F., Cremonese, E., Morra di Cella, U., Ratto, S., and Stevenin, H.: Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt, Geosci. Model Dev., 15, 4853–4879, https://doi.org/10.5194/gmd-15-4853-2022, 2022. a, b
Avanzi, F., Gabellani, S., Delogu, F., Silvestro, F., Pignone, F., Bruno, G., Pulvirenti, L., Squicciarino, G., Fiori, E., Rossi, L., Puca, S., Toniazzo, A., Giordano, P., Falzacappa, M., Ratto, S., Stevenin, H., Cardillo, A., Fioletti, M., Cazzuli, O., Cremonese, E., Morra di Cella, U., and Ferraris, L.: IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021), Earth Syst. Sci. Data, 15, 639–660, https://doi.org/10.5194/essd-15-639-2023, 2023. a
Download
Short summary
Investigating changing snow in response to global warming can be done with a simple model and only temperature and precipitation data, simplifying snow dynamics with assumptions and parameters. We provide a large-scale and long-term evaluation of this approach and its performance across diverse climates. Temperature thresholds are more robust over cold climates but melt parameters are more robust over warmer climates with deep snow. The model performs well across climates despite its simplicity.
Share