60 citations as recorded by crossref.

1. More than a century of direct glacier mass-balance observations on Claridenfirn, Switzerland M. Huss et al. https://doi.org/10.1017/jog.2021.22
2. A two-fold deep-learning strategy to correct and downscale winds over mountains L. Le Toumelin et al. https://doi.org/10.5194/npg-31-75-2024
3. Machine learning improves seasonal mass balance prediction for unmonitored glaciers K. Sjursen et al. https://doi.org/10.5194/tc-19-5801-2025
4. Incorporating accumulated temperature and lagged precipitation effects in LSTM modeling improves the streamflow simulation in glacier dominated basins Y. Ao et al. https://doi.org/10.1016/j.ejrh.2025.102872
5. Region-Wide Annual Glacier Surface Mass Balance for the European Alps From 2000 to 2016 L. Davaze et al. https://doi.org/10.3389/feart.2020.00149
6. Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. https://doi.org/10.1017/jog.2021.120
7. EisNet: Extracting Bedrock and Internal Layers From Radiostratigraphy of Ice Sheets With Machine Learning S. Dong et al. https://doi.org/10.1109/TGRS.2021.3136648
8. Incorporating empirical orthogonal function analysis into lumped hydrological models with grid data P. Jiang et al. https://doi.org/10.1016/j.ejrh.2026.103452
9. Divergent mountain runoff dynamics but declining per capita freshwater availability across the Third Pole by mid-21st century L. Wang et al. https://doi.org/10.1038/s41612-025-01313-4
10. Atrous spatial pyramid pooling enhanced CNN for SAR based glacier facies segmentation S. Sambyal et al. https://doi.org/10.1016/j.geoai.2025.100037
11. Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al. https://doi.org/10.5194/tc-17-2811-2023
12. Regional glacier melt modeling: insights from surface energy balance and positive degree-day comparisons H. Phelps et al. https://doi.org/10.1017/S0022143025100531
13. Future projections of glacier mass change in High Mountain Asia using GRACE and climatemodel data J. Dharpure et al. https://doi.org/10.1038/s41598-026-39404-8
14. Learning to melt: Emulating Greenland surface melt from a polar RCM with machine learning E. Schlager et al. https://doi.org/10.5194/tc-20-3313-2026
15. Results from the Ice Thickness Models Intercomparison eXperiment Phase 2 (ITMIX2) D. Farinotti et al. https://doi.org/10.3389/feart.2020.571923
16. Climatic and Morphometric Explanatory Variables of Glacier Changes in the Andes (8–55°S): New Insights From Machine Learning Approaches A. Caro et al. https://doi.org/10.3389/feart.2021.713011
17. Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature N. Bochow et al. https://doi.org/10.5194/tc-20-1841-2026
18. Snow and Ice Animation Methods in Computer Graphics P. Goswami https://doi.org/10.1111/cgf.15059
19. A minimal machine-learning glacier mass balance model M. van der Meer et al. https://doi.org/10.5194/tc-19-805-2025
20. Evaluating the affecting factors of glacier mass balance in Tanggula Mountains using explainable machine learning and the open global glacier model Q. Xu et al. https://doi.org/10.1007/s11629-024-9047-4
21. Glacier Melt as a Source of Mercury: Implications for Ecosystem Recovery and Environmental Trends D. Mattio et al. https://doi.org/10.1021/acs.est.5c16308
22. Glacier retreat in Himachal from 1994 to 2021 using deep learning S. Rajat et al. https://doi.org/10.1016/j.rsase.2022.100870
23. The West Kunlun Glacier Anomaly and Its Response to Climate Forcing during 2002–2020 J. Luo et al. https://doi.org/10.3390/rs14143465
24. Deep learning-based framework for monitoring of debris-covered glacier from remotely sensed images A. Khan et al. https://doi.org/10.1016/j.asr.2022.05.060
25. The Importance of Solving Subglaciar Hydrology in Modeling Glacier Retreat: A Case Study of Hansbreen, Svalbard E. De Andrés et al. https://doi.org/10.3390/hydrology11110193
26. Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas V. Sood et al. https://doi.org/10.3390/su142013485
27. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen https://doi.org/10.5194/essd-16-2741-2024
28. Rapid prediction of lab-grown tissue properties using deep learning A. Andrews et al. https://doi.org/10.1088/1478-3975/ad0019
29. Glacial retreat delineation using machine and deep learning: A case of a lower Himalayan region S. Vemuri et al. https://doi.org/10.1007/s12040-024-02285-4
30. Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches J. Che et al. https://doi.org/10.3390/rs14225775
31. Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks M. van der Meer et al. https://doi.org/10.1029/2022MS003593
32. Computationally efficient subglacial drainage modelling using Gaussian process emulators: GlaDS-GP v1.0 T. Hill et al. https://doi.org/10.5194/gmd-18-4045-2025
33. Evaluating the Performance of Multiple Machine Learning and Deep Learning Models on Glacier Mass Balance Estimation Y. Liao et al. https://doi.org/10.3390/sym18050873
34. Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al. https://doi.org/10.5194/tc-19-645-2025
35. Brief communication: Non-linear sensitivity of glacier mass balance to climate attested by temperature-index models C. Vincent & E. Thibert https://doi.org/10.5194/tc-17-1989-2023
36. Acceleration of diverging runoff trends on the Third Pole L. Wang et al. https://doi.org/10.1038/s43247-025-02854-5
37. Overall negative trends for snow cover extent and duration in global mountain regions over 1982–2020 C. Notarnicola https://doi.org/10.1038/s41598-022-16743-w
38. Continuous Karakoram Glacier Anomaly and Its Response to Climate Change during 2000–2021 D. Lhakpa et al. https://doi.org/10.3390/rs14246281
39. Arctic glacier snowline altitudes rise 150 m over the last 4 decades L. Larocca et al. https://doi.org/10.5194/tc-18-3591-2024
40. Depth of Liquid Water Infiltration in Greenland Firn Based on L-Band Radiometry, a Snow Physics Model, and Machine Learning T. Moon et al. https://doi.org/10.1109/TGRS.2026.3669024
41. Universal differential equations for glacier ice flow modelling J. Bolibar et al. https://doi.org/10.5194/gmd-16-6671-2023
42. A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015 J. Bolibar et al. https://doi.org/10.5194/essd-12-1973-2020
43. A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning S. de Roda Husman et al. https://doi.org/10.1016/j.rse.2023.113950
44. FirnLearn: A neural network-based approach to firn density modeling in Antarctica A. Ogunmolasuyi et al. https://doi.org/10.1017/jog.2025.26
45. Impact assessment of Glacial Lake Outburst Floods (GLOFs) in the Himalaya using satellite imageries over the last 25 years (2000–2024): A comprehensive review A. Mondal et al. https://doi.org/10.1016/j.rsase.2025.101845
46. Climate Variability and Glacier Evolution at Selected Sites Across the World: Past Trends and Future Projections A. Al‐Yaari et al. https://doi.org/10.1029/2023EF003618
47. Empirical glacier mass-balance models for South America S. Mutz & J. Aschauer https://doi.org/10.1017/jog.2022.6
48. Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić https://doi.org/10.3389/frwa.2022.934709
49. Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India M. Haq et al. https://doi.org/10.1017/jog.2021.19
50. Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning J. Bolibar et al. https://doi.org/10.1038/s41467-022-28033-0
51. The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021) M. Vernay et al. https://doi.org/10.5194/essd-14-1707-2022
52. Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia W. Ren et al. https://doi.org/10.3390/rs16060956
53. Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research L. Taylor et al. https://doi.org/10.1177/03091333211023690
54. Estimation of area and volume change in the glaciers of the Columbia Icefield, Canada using machine learning algorithms and Landsat images S. Ambinakudige & A. Intsiful https://doi.org/10.1016/j.rsase.2022.100732
55. Application of Artificial Intelligence in Glacier Studies: A State-of-the-Art Review S. Nurakynov et al. https://doi.org/10.3390/w16162272
56. Accelerating Subglacial Hydrology for Ice Sheet Models With Deep Learning Methods V. Verjans & A. Robel https://doi.org/10.1029/2023GL105281
57. Insight into glacio-hydrologicalprocesses using explainable machine-learning (XAI) models H. Hao et al. https://doi.org/10.1016/j.jhydrol.2024.131047
58. Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling C. Diaconu & N. Gottschling https://doi.org/10.1109/LGRS.2024.3356160
59. Ice‐Dynamical Glacier Evolution Modeling—A Review H. Zekollari et al. https://doi.org/10.1029/2021RG000754
60. Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities C. Persello et al. https://doi.org/10.1109/MGRS.2021.3136100