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- Title
A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015.
- Authors
Bolibar, Jordi; Rabatel, Antoine; Gouttevin, Isabelle; Galiez, Clovis
- 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 ± 0.21 (1 σ) 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 10.5281/zenodo.3925378 – 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.
- Subjects
ALPS; BLANC, Mont (France &; Italy); MASS budget (Geophysics); GLACIERS; DEEP learning; HYDROLOGIC cycle; ARTIFICIAL neural networks
- Publication
Earth System Science Data, 2020, Vol 12, Issue 3, p1973
- ISSN
1866-3508
- Publication type
Article
- DOI
10.5194/essd-12-1973-2020