A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967-2015
Bolibar, Jordi ; Rabatel, Antoine ; Gouttevin, Isabelle ; Galiez, Clovis
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±0.21 (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.</p>
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