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

Abstract. Glacier surface mass balance (SMB) data are crucial to understand and quantify 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 surface 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 and remote sensing SMB observations, meteorological reanalyses and topographical data from glacier inventories. This data science reconstruction approach is embedded as a SMB component of the open-source ALpine Parameterized Glacier Model (ALPGM). An extensive cross-validation allowed to assess the method’s validity, with an estimated average error (RMSE) of 0.49 m w.e. a−1, an explained variance (r2) of 79 % and an average bias of +0.017 m w.e. a−1. We estimate an average regional area-weighted glacier-wide SMB of −0.72 ± 0.20 m w.e. a−1 for the 1967–2015 period, with moderately negative mass balances in the 1970s (−0.52 m w.e. a−1) and 1980s (−0.12 m w.e. a−1), and an increasing negative trend from the 1990s onwards, up to −1.39 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 this period are the Chablais (−0.90 m w.e. a−1) and Ubaye and Champsaur ranges (−0.91 m w.e. a−1 both), and the ones presenting the lowest mass losses are the Mont-Blanc (−0.74 m w.e. a−1), Oisans and Haute-Tarentaise ranges (−0.78 m w.e. a−1 both). This dataset (available at: https://doi.org/10.5281/zenodo.3663630 ) (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 meltwater contributions in glacierized catchments.