Articles | Volume 12, issue 3
Earth Syst. Sci. Data, 12, 1973–1983, 2020
https://doi.org/10.5194/essd-12-1973-2020
Earth Syst. Sci. Data, 12, 1973–1983, 2020
https://doi.org/10.5194/essd-12-1973-2020

Data description paper 03 Sep 2020

Data description paper | 03 Sep 2020

A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015

Jordi Bolibar et al.

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jordi Bolibar on behalf of the Authors (18 May 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (12 Jun 2020) by Reinhard Drews
RR by Matthias Huss (24 Jun 2020)
ED: Publish subject to minor revisions (review by editor) (26 Jun 2020) by Reinhard Drews
AR by Jordi Bolibar on behalf of the Authors (06 Jul 2020)  Author's response    Manuscript
ED: Publish subject to technical corrections (14 Jul 2020) by Reinhard Drews
AR by Jordi Bolibar on behalf of the Authors (15 Jul 2020)  Author's response    Manuscript
Download
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.