34 citations as recorded by crossref.

1. High-resolution snow water equivalent estimation derived from downscaled snow depth and non-constant snow density in Chinese Altai Mountains Z. Li et al. https://doi.org/10.1016/j.jhydrol.2025.133708
2. An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0 J. Aschauer et al. https://doi.org/10.5194/gmd-16-4063-2023
3. Enhancing daily runoff prediction: A hybrid model combining GR6J-CemaNeige with wavelet-based gradient boosting technique B. Mohammadi et al. https://doi.org/10.1016/j.jhydrol.2025.133114
4. Deep learning of seasonal peak snow water content of global boreal forest and arctic using spaceborne L-band radiometry D. Kumawat et al. https://doi.org/10.1016/j.rse.2025.114963
5. Object-based ensemble estimation of snow depth and snow water equivalent over multiple months in Sodankylä, Finland D. Brodylo et al. https://doi.org/10.5194/tc-19-6127-2025
6. Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and process-based model simulations L. Boeykens et al. https://doi.org/10.5194/tc-20-3187-2026
7. Flood changes and generating mechanisms in the Upper Rhine Basin under a warming climate Y. Yi et al. https://doi.org/10.1016/j.jhydrol.2025.134508
8. Insights into the North Hemisphere daily snowpack at high resolution from the new Crocus–ERA5 product S. Ramos Buarque et al. https://doi.org/10.5194/essd-17-7227-2025
9. Hybrid approach for estimating snow water equivalent in Siberian basins using GRACE and climate data H. Mohasseb & S. Yi https://doi.org/10.1016/j.jhydrol.2026.135483
10. Effects of reduced snowpack due to climate warming on abiotic and biotic soil properties in alpine and boreal forest systems A. Kosolapova et al. https://doi.org/10.1371/journal.pclm.0000417
11. Sensitivity of river flow regime to snow water storage variability across mid˗latitude region in Eastern Europe U. Somorowska https://doi.org/10.1016/j.scitotenv.2025.180160
12. Evaluating methods to estimate the water equivalent of new snow from daily snow depth recordings J. Magnusson et al. https://doi.org/10.1016/j.coldregions.2025.104435
13. Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere A. Fontrodona-Bach et al. https://doi.org/10.5194/hess-30-2613-2026
14. Retrieving snow water equivalent from GRACE/GRACE-FO terrestrial water storage anomalies using modified spectral combination theory F. Fatolazadeh et al. https://doi.org/10.1016/j.jhydrol.2025.133754
15. Hyper-resolution large-scale hydrological modelling benefits from improved process representation in mountain regions J. Janzing et al. https://doi.org/10.5194/hess-29-7041-2025
16. Snow Depth Downscaling Retrieval Based on Spatial-Environment XGBoost Model: A Case Study of the Arid Region of Northwest China G. Wang et al. https://doi.org/10.1109/TGRS.2025.3597950
17. Divergent responses of historic rain-on-snow flood extremes to a warmer climate D. Hao et al. https://doi.org/10.1038/s43247-025-02354-6
18. Can streamflow observations constrain snow mass reconstructions? Lessons from two synthetic numerical experiments P. Wiersma et al. https://doi.org/10.5194/hess-30-3331-2026
19. Introduction to a 45-year (1979–2023) global daily snow cover fraction product from multiple AVHRR satellites with accuracy assessment X. Xiao et al. https://doi.org/10.1016/j.rse.2026.115235
20. Improving the accuracy of gridded snow depth estimation through multi-source data and a machine learning fusion model D. Qiao et al. https://doi.org/10.1038/s41598-025-22347-x
21. Projected changes in streamflow seasonality and flood characteristics in the Moskva R. Basin O. Erina et al. https://doi.org/10.1007/s00704-026-06212-z
22. Snow Density Retrieval Based on Sentinel-2 Multispectral Data and Deep Learning S. Yang et al. https://doi.org/10.3390/rs18081200
23. Evidence of human influence on Northern Hemisphere snow loss A. Gottlieb & J. Mankin https://doi.org/10.1038/s41586-023-06794-y
24. Seasonal snow cover indicators in coastal Greenland from in situ observations, a climate model, and reanalysis J. van der Schot et al. https://doi.org/10.5194/tc-18-5803-2024
25. Warming leads to both earlier and later snowmelt floods over the past 70 years Y. Guo et al. https://doi.org/10.1038/s41467-025-58832-0
26. Machine-Learning-Based Ensemble Prediction of the Snow Water Equivalent in the Upper Yalong River Basin J. Zhang et al. https://doi.org/10.3390/su17093779
27. Accelerated propagation from meteorological to hydrological drought under diminishing snow J. Han et al. https://doi.org/10.1088/1748-9326/adccdc
28. Northern Hemisphere in situ snow water equivalent dataset (NorSWE, 1979–2021) C. Mortimer & V. Vionnet https://doi.org/10.5194/essd-17-3619-2025
29. Warmer growing seasons improve cereal yields in Northern Europe only with increasing precipitation F. Tootoonchi et al. https://doi.org/10.5194/bg-23-2583-2026
30. Combining Causal Inference with Machine Learning for Reconstructing Mountain Snow Water Equivalent Data Z. Ouyang et al. https://doi.org/10.3390/w18101243
31. Duration of vegetation green-up response to snowmelt on the Tibetan Plateau J. Ni et al. https://doi.org/10.5194/bg-22-2637-2025
32. UAV LiDAR surveys and machine learning improve snow depth and water equivalent estimates in boreal landscapes M. Ylönen et al. https://doi.org/10.5194/tc-19-4585-2025
33. Structural Similarity-Guided Siamese U-Net Model for Detecting Changes in Snow Water Equivalent K. Malik & C. Robertson https://doi.org/10.3390/rs17091631
34. Fusing daily snow water equivalent from 1980 to 2020 in China using a spatiotemporal XGBoost model L. Sun et al. https://doi.org/10.1016/j.jhydrol.2024.130876