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 (1?)?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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