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

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 (r(2)) of 75 % and an average bias of -0.021 m w.e. a(-1.) We estimate an average regional area-weighted glacier-wide MB of -0.69 +/- 0.21 (1 sigma) mw.e. a(-1) for the 1967-2015 period with negative mass balances in the 1970s (-0.44 m w.e. a-1), moderately negative in the 1980s (-0.16 m w.e. a(-1)) and an increasing negative trend from the 1990s onwards, up to -1.26 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 the 1967-2015 period are the Chablais (-0.93 mw.e. a(-1)), Champsaur (-0.86 m w.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. a1), Oisans and Haute-Tarentaise ranges (-0.75 m w.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.