Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 9
Articles | Volume 18, issue 9
https://doi.org/10.5194/gmd-18-2545-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-2545-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 9
Model description paper
 | 
09 May 2025
Model description paper |  | 09 May 2025

A gradient-boosted tree framework to model the ice thickness of the world's glaciers (IceBoost v1.1)

Niccolò Maffezzoli, Eric Rignot, Carlo Barbante, Troels Petersen, and Sebastiano Vascon

Viewed

Total article views: 930 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
732 176 22 930 15 24 29
  • HTML: 732
  • PDF: 176
  • XML: 22
  • Total: 930
  • Supplement: 15
  • BibTeX: 24
  • EndNote: 29
Views and downloads (calculated since 23 Sep 2024)
Cumulative views and downloads (calculated since 23 Sep 2024)

Viewed (geographical distribution)

Total article views: 930 (including HTML, PDF, and XML) Thereof 899 with geography defined and 31 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 May 2025
Download
Short summary
In this work we introduce IceBoost, a machine learning framework to model the ice thickness distribution of all the world's glaciers with greater accuracy than state-of-the-art methods. The model is trained on 3.7 million measurements globally available and provides skilful estimates across all regions. This advancement will help in better assessing future sea level changes and freshwater resources, with significance for both the scientific community and society at large.
Read more
Share