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Discussion papers
https://doi.org/10.5194/essd-2020-35
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-35
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 20 Mar 2020

Submitted as: data description paper | 20 Mar 2020

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This preprint is currently under review for the journal ESSD.

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

Jordi Bolibar1,2, Antoine Rabatel1, Isabelle Gouttevin3, and Clovis Galiez4 Jordi Bolibar et al.
  • 1Univ. Grenoble Alpes, CNRS, IRD, G-INP, Institut des Géosciences de l’Environnement (IGE, UMR 5001), Grenoble, France
  • 2INRAE, UR RiverLy, Lyon-Villeurbanne, France
  • 3Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, Grenoble, France
  • 4Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France

Abstract. Glacier surface mass balance (SMB) data are crucial to understand and quantify the regional effects of climate on glaciers and the high-mountain water cycle, yet observations cover only a small fraction of glaciers in the world. We present a dataset of annual glacier-wide surface mass balance of all the glaciers in the French Alps for the 1967–2015 period. This dataset has been reconstructed using deep learning (i.e. a deep artificial neural network), based on direct and remote sensing SMB observations, meteorological reanalyses and topographical data from glacier inventories. This data science reconstruction approach is embedded as a SMB component of the open-source ALpine Parameterized Glacier Model (ALPGM). An extensive cross-validation allowed to assess the method’s validity, with an estimated average error (RMSE) of 0.49 m w.e. a−1, an explained variance (r2) of 79 % and an average bias of +0.017 m w.e. a−1. We estimate an average regional area-weighted glacier-wide SMB of −0.72 ± 0.20 m w.e. a−1 for the 1967–2015 period, with moderately negative mass balances in the 1970s (−0.52 m w.e. a−1) and 1980s (−0.12 m w.e. a−1), and an increasing negative trend from the 1990s onwards, up to −1.39 m w.e. a−1 in the 2010s. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for this period are the Chablais (−0.90 m w.e. a−1) and Ubaye and Champsaur ranges (−0.91 m w.e. a−1 both), and the ones presenting the lowest mass losses are the Mont-Blanc (−0.74 m w.e. a−1), Oisans and Haute-Tarentaise ranges (−0.78 m w.e. a−1 both). This dataset (available at: https://doi.org/10.5281/zenodo.3663630) (Bolibar et al., 2020a) – provides relevant and timely data for studies in the fields of glaciology, hydrology and ecology in the French Alps, in need of regional or glacier-specific meltwater contributions in glacierized catchments.

Jordi Bolibar et al.

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Status: open (until 15 May 2020)
Status: open (until 15 May 2020)
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Jordi Bolibar et al.

Data sets

A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967-2015 J. Bolibar, A. Rabatel, I. Gouttevin, and C. Galiez https://doi.org/10.5281/zenodo.3663630

Model code and software

ALPGM (ALpine Parameterized Glacier Model) v1.1 J. Bolibar https://doi.org/10.5281/zenodo.3609136

Jordi Bolibar et al.

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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 −0.72 ± 0.20 m w.e, being the highest in the Chablais, Ubaye and Champsaur massifs, and the lowest in the Mont-Blanc, Oisans and Haute-Tarentaise ranges. This dataset could 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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