12 citations as recorded by crossref.

1. Ensemble-based data assimilation improves hyperresolution snowpack simulations in forests E. Alonso-González et al. https://doi.org/10.5194/tc-20-209-2026
2. A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks M. Guidicelli et al. https://doi.org/10.1016/j.hydroa.2024.100190
3. Soil and Water Assessment Tool (SWAT)-Informed Deep Learning for Streamflow Forecasting with Remote Sensing and In Situ Precipitation and Discharge Observations C. Huang et al. https://doi.org/10.3390/rs16213999
4. Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al. https://doi.org/10.1007/s40641-025-00207-0
5. Evaluating precipitation corrections to enhance high-alpine hydrological modeling T. Pulka et al. https://doi.org/10.1016/j.jhydrol.2024.132202
6. Assimilation of synthetic observations of radar backscatters at Ku-band improves SWE estimates N. Leroux et al. https://doi.org/10.5194/tc-20-2773-2026
7. Exploring how Sentinel-1 wet-snow maps can inform fully distributed physically based snowpack models B. Cluzet et al. https://doi.org/10.5194/tc-18-5753-2024
8. High-resolution snow water equivalent estimation: a data-driven method for localized downscaling of climate data F. Zakeri et al. https://doi.org/10.5194/hess-29-6935-2025
9. Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter M. Mazzolini et al. https://doi.org/10.5194/tc-19-3831-2025
10. Review article: using spaceborne lidar for snow depth retrievals: recent findings and utility for hydrologic applications Z. Fair et al. https://doi.org/10.5194/tc-19-5671-2025
11. Does meter-scale snow data matter for modeling alpine plant distribution? A comparison of four data sources at two resolutions A. Kollert et al. https://doi.org/10.1016/j.ecolmodel.2025.111366
12. A high-resolution snow dataset for Switzerland (2016–2025) combining physics-based simulations and in situ observations M. Oberrauch et al. https://doi.org/10.5194/tc-20-3387-2026