A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015
[摘要] 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 m w . 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 σ ) m w . 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 m w . e . a - 1 ), Champsaur ( −0.86 m w . e . a - 1 ), and Haute-Maurienne and Ubaye ranges ( −0.84 m w . e . a - 1 each), and the ones presenting the lowest mass losses are the Mont-Blanc ( −0.68 m w . e . a - 1 ), 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. , 2020 a ) – 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.
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[效力级别] [学科分类] 眼科学
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