The Aneto glacier's (Central Pyrenees) evolution from 1981 to 2022: ice loss observed from historic aerial image photogrammetry and remote sensing techniques

15Citations
Citations of this article
24Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The Aneto glacier, although it may be considered a very small glacier (<0.5ĝ€¯km2), is the largest glacier in the Pyrenees. Its surface and thickness loss have been continuous in recent decades, and there have been signs of accelerated melting in recent years. In this study, thickness and surface losses of the Aneto glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne lidar point clouds and unoccupied aerial vehicle (UAV) imagery. A ground-penetrating radar (GPR) survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glacierised area has decreased by 64.7ĝ€¯%, and the ice thickness has decreased, on average, by 30.5ĝ€¯m. The mean remaining ice thickness in autumn 2022 was 11.9ĝ€¯m, as against the mean thickness of 32.9, 19.2 and 15.0ĝ€¯m reconstructed for 1981 and 2011 and observed in 2020, respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021-2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time.

Cite

CITATION STYLE

APA

Vidaller, I., Izagirre, E., Del Rio, L. M., Alonso-González, E., Rojas-Heredia, F., Serrano, E., … Revuelto, J. (2023). The Aneto glacier’s (Central Pyrenees) evolution from 1981 to 2022: ice loss observed from historic aerial image photogrammetry and remote sensing techniques. Cryosphere, 17(8), 3177–3192. https://doi.org/10.5194/tc-17-3177-2023

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free