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        <title>A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015</title>
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    <name type="personal">
        <namePart type="given">Jordi</namePart>
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    <name type="personal">
        <namePart type="given">Antoine</namePart>
        <namePart type="family">Rabatel</namePart>
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    <name type="personal">
        <namePart type="given">Isabelle</namePart>
        <namePart type="family">Gouttevin</namePart>
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    <name type="personal">
        <namePart type="given">Clovis</namePart>
        <namePart type="family">Galiez</namePart>
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            <title>Earth System Science Data, Volume 12, Issue 3</title>
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    <abstract>Abstract. Glacier mass balance (MB) data are crucial to understanding and quantifying 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 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 MB observations and remote-sensing annual estimates, meteorological reanalyses and topographical data from glacier inventories. The method’s validity was assessed previously through an extensive cross-validation against a dataset of 32 glaciers, with an estimated average error (RMSE) of 0.55 mw.e.a-1, an explained variance (r2) of 75 % and an average bias of −0.021 mw.e.a-1. We estimate an average regional area-weighted glacier-wide MB of −0.69\pm0.21 (1σ) mw.e.a-1 for the 1967–2015 period with negative mass balances in the 1970s (−0.44 mw.e.a-1), moderately negative in the 1980s (−0.16 mw.e.a-1) and an increasing negative trend from the 1990s onwards, up to −1.26 mw.e.a-1 in the 2010s. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for the 1967–2015 period are the Chablais (−0.93 mw.e.a-1), Champsaur (−0.86 mw.e.a-1), and Haute-Maurienne and Ubaye ranges (−0.84 mw.e.a-1 each), and the ones presenting the lowest mass losses are the Mont-Blanc (−0.68 mw.e.a-1), Oisans and Haute-Tarentaise ranges (−0.75 mw.e.a-1 each). This dataset – available at https://doi.org/10.5281/zenodo.3925378 (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 annual net glacier mass changes in glacierized catchments.</abstract>
    <identifier type="citekey">Bolibar-2020-A</identifier>
    <identifier type="doi">10.5194/essd-12-1973-2020</identifier>
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        <url>https://gwf-uwaterloo.github.io/gwf-publications/G20-92001</url>
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        <date>2020</date>
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            <start>1973</start>
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%0 Journal Article
%T A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015
%A Bolíbar, Jordi
%A Rabatel, Antoine
%A Gouttevin, Isabelle
%A Galiez, Clovis
%J Earth System Science Data, Volume 12, Issue 3
%D 2020
%V 12
%N 3
%I Copernicus GmbH
%F Bolibar-2020-A
%X Abstract. Glacier mass balance (MB) data are crucial to understanding and quantifying 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 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 MB observations and remote-sensing annual estimates, meteorological reanalyses and topographical data from glacier inventories. The method’s validity was assessed previously through an extensive cross-validation against a dataset of 32 glaciers, with an estimated average error (RMSE) of 0.55 mw.e.a-1, an explained variance (r2) of 75 % and an average bias of −0.021 mw.e.a-1. We estimate an average regional area-weighted glacier-wide MB of −0.69\pm0.21 (1σ) mw.e.a-1 for the 1967–2015 period with negative mass balances in the 1970s (−0.44 mw.e.a-1), moderately negative in the 1980s (−0.16 mw.e.a-1) and an increasing negative trend from the 1990s onwards, up to −1.26 mw.e.a-1 in the 2010s. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for the 1967–2015 period are the Chablais (−0.93 mw.e.a-1), Champsaur (−0.86 mw.e.a-1), and Haute-Maurienne and Ubaye ranges (−0.84 mw.e.a-1 each), and the ones presenting the lowest mass losses are the Mont-Blanc (−0.68 mw.e.a-1), Oisans and Haute-Tarentaise ranges (−0.75 mw.e.a-1 each). This dataset – available at https://doi.org/10.5281/zenodo.3925378 (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 annual net glacier mass changes in glacierized catchments.
%R 10.5194/essd-12-1973-2020
%U https://gwf-uwaterloo.github.io/gwf-publications/G20-92001
%U https://doi.org/10.5194/essd-12-1973-2020
%P 1973-1983

Markdown (Informal)

[A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015](https://gwf-uwaterloo.github.io/gwf-publications/G20-92001) (Bolíbar et al., GWF 2020)

• A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015 (Bolíbar et al., GWF 2020)

ACL

• Jordi Bolíbar, Antoine Rabatel, Isabelle Gouttevin, and Clovis Galiez. 2020. A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015. Earth System Science Data, Volume 12, Issue 3, 12(3):1973–1983.