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

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 0.55mw:e: A1, an explained variance (r2) of 75% and an average bias of 0:021mw:e: A1. We estimate an average regional area-weighted glacier-wide MB of 0:690.21 (1)mw:e: A1 for the 1967 2015 period with negative mass balances in the 1970s (0:44mw:e: A1), moderately negative in the 1980s (0:16mw:e: A1) and an increasing negative trend from the 1990s onwards, up to 1:26mw:e: A1 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 (0:93mw:e: A1), Champsaur (0:86mw:e: A1), and Haute-Maurienne and Ubaye ranges (0:84mw:e: A1 each), and the ones presenting the lowest mass losses are the Mont-Blanc (0:68mw:e: A1), Oisans and Haute-Tarentaise ranges (0:75mw:e: A1 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.