Articles | Volume 20, issue 2
https://doi.org/10.5194/tc-20-1427-2026
https://doi.org/10.5194/tc-20-1427-2026
Research article
 | 
03 Mar 2026
Research article |  | 03 Mar 2026

Improving snow water equivalent modelling: a comparative study of hybrid machine learning techniques

Oriol Pomarol Moya, Madlene Nussbaum, Siamak Mehrkanoon, Philip D. A. Kraaijenbrink, Isabelle Gouttevin, Derek Karssenberg, and Walter W. Immerzeel

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Cited articles

Alonso-González, E., Revuelto, J., Fassnacht, S. R., and Ignacio López-Moreno, J.: Combined influence of maximum accumulation and melt rates on the duration of the seasonal snowpack over temperate mountains, J. Hydrol., 608, 127574, https://doi.org/10.1016/j.jhydrol.2022.127574, 2022. a
Bair, E. H., Abreu Calfa, A., Rittger, K., and Dozier, J.: Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan, The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, 2018. a
Beniston, M., Farinotti, D., Stoffel, M., Andreassen, L. M., Coppola, E., Eckert, N., Fantini, A., Giacona, F., Hauck, C., Huss, M., Huwald, H., Lehning, M., López-Moreno, J.-I., Magnusson, J., Marty, C., Morán-Tejéda, E., Morin, S., Naaim, M., Provenzale, A., Rabatel, A., Six, D., Stötter, J., Strasser, U., Terzago, S., and Vincent, C.: The European mountain cryosphere: a review of its current state, trends, and future challenges, The Cryosphere, 12, 759–794, https://doi.org/10.5194/tc-12-759-2018, 2018. a
Biemans, H., Siderius, C., Lutz, A. F., Nepal, S., Ahmad, B., Hassan, T., von Bloh, W., Wijngaard, R. R., Wester, P., Shrestha, A. B., and Immerzeel, W. W.: Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain, Nature Sustainability, 2, 594–601, https://doi.org/10.1038/s41893-019-0305-3, 2019. a
Brun, E., Martin, E., Simon, V., Gendre, C., and Coleou, C.: An energy and mass model of snow cover suitable for operational avalanche forecasting, J. Glaciol., 35, 333–342, https://doi.org/10.3189/S0022143000009254, 1989. a, b
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Short summary
Two hybrid Machine Learning (ML) approaches predicting daily Snow Water Equivalent (SWE) were evaluated across ten Northern Hemisphere sites. By integrating meteorological data with Crocus snow model simulations, these hybrid models outperformed both standalone Crocus and traditional ML models. Notably, augmenting measured SWE data with Crocus simulations significantly improved performance at unseen locations, offering a promising new approach to long-term SWE prediction.
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