Articles | Volume 12, issue 3
https://doi.org/10.5194/essd-12-1973-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-1973-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
03 Sep 2020
Data description paper |
| 03 Sep 2020
| 03 Sep 2020
A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015
Jordi Bolibar
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, CNRS, IRD, G-INP, Institut des Géosciences de l'Environnement
(IGE, UMR 5001), Grenoble, France
INRAE, UR RiverLy, Lyon-Villeurbanne, France
Antoine Rabatel
Univ. Grenoble Alpes, CNRS, IRD, G-INP, Institut des Géosciences de l'Environnement
(IGE, UMR 5001), Grenoble, France
Isabelle Gouttevin
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre
d'Études de la Neige, Grenoble, France
Clovis Galiez
Univ. Grenoble Alpes, CNRS, G-INP, LJK, Grenoble, France
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Short summary
We present a dataset of annual glacier mass changes for all the 661 glaciers in the French Alps for the 1967–2015 period, reconstructed using deep learning (i.e. artificial intelligence). We estimate an average annual mass loss of –0.69 ± 0.21 m w.e., the highest being in the Chablais, Ubaye and Champsaur massifs and the lowest in the Mont Blanc, Oisans and Haute Tarentaise ranges. This dataset can be of interest to hydrology and ecology studies on glacierized catchments in the French Alps.
We present a dataset of annual glacier mass changes for all the 661 glaciers in the French Alps...
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