58 citations as recorded by crossref.

1. More than a century of direct glacier mass-balance observations on Claridenfirn, Switzerland M. Huss et al.
2. A two-fold deep-learning strategy to correct and downscale winds over mountains L. Le Toumelin et al.
3. Machine learning improves seasonal mass balance prediction for unmonitored glaciers K. Sjursen et al.
4. Incorporating accumulated temperature and lagged precipitation effects in LSTM modeling improves the streamflow simulation in glacier dominated basins Y. Ao et al.
5. Region-Wide Annual Glacier Surface Mass Balance for the European Alps From 2000 to 2016 L. Davaze et al.
6. Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al.
7. EisNet: Extracting Bedrock and Internal Layers From Radiostratigraphy of Ice Sheets With Machine Learning S. Dong et al.
8. Incorporating empirical orthogonal function analysis into lumped hydrological models with grid data P. Jiang et al.
9. Divergent mountain runoff dynamics but declining per capita freshwater availability across the Third Pole by mid-21st century L. Wang et al.
10. Atrous spatial pyramid pooling enhanced CNN for SAR based glacier facies segmentation S. Sambyal et al.
11. Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al.
12. Regional glacier melt modeling: insights from surface energy balance and positive degree-day comparisons H. Phelps et al.
13. Future projections of glacier mass change in High Mountain Asia using GRACE and climatemodel data J. Dharpure et al.
14. Results from the Ice Thickness Models Intercomparison eXperiment Phase 2 (ITMIX2) D. Farinotti et al.
15. Climatic and Morphometric Explanatory Variables of Glacier Changes in the Andes (8–55°S): New Insights From Machine Learning Approaches A. Caro et al.
16. Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature N. Bochow et al.
17. Snow and Ice Animation Methods in Computer Graphics P. Goswami
18. A minimal machine-learning glacier mass balance model M. van der Meer et al.
19. 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.
20. Glacier Melt as a Source of Mercury: Implications for Ecosystem Recovery and Environmental Trends D. Mattio et al.
21. Glacier retreat in Himachal from 1994 to 2021 using deep learning S. Rajat et al.
22. The West Kunlun Glacier Anomaly and Its Response to Climate Forcing during 2002–2020 J. Luo et al.
23. Deep learning-based framework for monitoring of debris-covered glacier from remotely sensed images A. Khan et al.
24. The Importance of Solving Subglaciar Hydrology in Modeling Glacier Retreat: A Case Study of Hansbreen, Svalbard E. De Andrés et al.
25. Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas V. Sood et al.
26. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen
27. Rapid prediction of lab-grown tissue properties using deep learning A. Andrews et al.
28. Glacial retreat delineation using machine and deep learning: A case of a lower Himalayan region S. Vemuri et al.
29. Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches J. Che et al.
30. Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks M. van der Meer et al.
31. Computationally efficient subglacial drainage modelling using Gaussian process emulators: GlaDS-GP v1.0 T. Hill et al.
32. Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al.
33. Brief communication: Non-linear sensitivity of glacier mass balance to climate attested by temperature-index models C. Vincent & E. Thibert
34. Acceleration of diverging runoff trends on the Third Pole L. Wang et al.
35. Overall negative trends for snow cover extent and duration in global mountain regions over 1982–2020 C. Notarnicola
36. Continuous Karakoram Glacier Anomaly and Its Response to Climate Change during 2000–2021 D. Lhakpa et al.
37. Arctic glacier snowline altitudes rise 150 m over the last 4 decades L. Larocca et al.
38. Depth of Liquid Water Infiltration in Greenland Firn Based on L-Band Radiometry, a Snow Physics Model, and Machine Learning T. Moon et al.
39. Universal differential equations for glacier ice flow modelling J. Bolibar et al.
40. A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015 J. Bolibar et al.
41. 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.
42. FirnLearn: A neural network-based approach to firn density modeling in Antarctica A. Ogunmolasuyi et al.
43. 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.
44. Climate Variability and Glacier Evolution at Selected Sites Across the World: Past Trends and Future Projections A. Al‐Yaari et al.
45. Empirical glacier mass-balance models for South America S. Mutz & J. Aschauer
46. Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić
47. Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India M. Haq et al.
48. Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning J. Bolibar et al.
49. The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021) M. Vernay et al.
50. Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia W. Ren et al.
51. Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research L. Taylor et al.
52. 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
53. Application of Artificial Intelligence in Glacier Studies: A State-of-the-Art Review S. Nurakynov et al.
54. Accelerating Subglacial Hydrology for Ice Sheet Models With Deep Learning Methods V. Verjans & A. Robel
55. Insight into glacio-hydrologicalprocesses using explainable machine-learning (XAI) models H. Hao et al.
56. Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling C. Diaconu & N. Gottschling
57. Ice‐Dynamical Glacier Evolution Modeling—A Review H. Zekollari et al.
58. Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities C. Persello et al.