12 citations as recorded by crossref.

1. Snow cover prediction in the Italian central Apennines using weather forecast and land surface numerical models E. Raparelli et al. https://doi.org/10.5194/tc-17-519-2023
2. Snow Depth Estimation with Combined Terrain and Remote Sensing Information over High-Latitude Asia F. Shi et al. https://doi.org/10.3390/app16010427
3. Bayesian networks and intelligence technology applied to climate change: An application of fuzzy logic based simulation in avalanche simulation risk assessment using GIS in a Western Himalayan region T. Arumugam et al. https://doi.org/10.1016/j.uclim.2022.101272
4. A quantitative module of avalanche hazard – comparing forecaster assessments of storm and persistent slab avalanche problems with information derived from distributed snowpack simulations F. Herla et al. https://doi.org/10.5194/nhess-25-625-2025
5. Impact of climate change on spatiotemporal patterns of snow hydrology: Conceptual frameworks, machine learning versus nested model M. Besharatifar & M. Nasseri https://doi.org/10.1016/j.pce.2024.103691
6. A large-scale validation of snowpack simulations in support of avalanche forecasting focusing on critical layers F. Herla et al. https://doi.org/10.5194/nhess-24-2727-2024
7. Analyzing the sensitivity of a blowing snow model (SnowPappus) to precipitation forcing, blowing snow, and spatial resolution A. Haddjeri et al. https://doi.org/10.5194/tc-18-3081-2024
8. Assessing the key concerns in snow storage: a case study for China X. Wang et al. https://doi.org/10.5194/tc-18-3017-2024
9. Spatial heterogeneity and temporal tendency of channeled snow avalanche activity retrieved from Landsat images in the maritime snow climate of the Parlung Tsangpo catchment, southeastern Tibet H. Wen et al. https://doi.org/10.1016/j.coldregions.2024.104206
10. Clustering simulated snow profiles to form avalanche forecast regions S. Horton et al. https://doi.org/10.5194/gmd-18-193-2025
11. Snow avalanche susceptibility mapping from tree-based machine learning approaches in ungauged or poorly-gauged regions Y. Liu et al. https://doi.org/10.1016/j.catena.2023.106997
12. A random forest approach to quality-checking automatic snow-depth sensor measurements G. Blandini et al. https://doi.org/10.5194/tc-17-5317-2023