The Aneto Glacier (Central Pyrenees) evolution from 1981 to 2022: ice loss observed from historic aerial image photogrammetry and recent remote sensing techniques
Abstract. The Aneto Glacier, although it may be considered very small (<0.5 km2), is the largest glacier in the Pyrenees. Its shrinkage and wastage have been continuous in recent decades, and there are signs of accelerated melting in recent years. In this study, changes in the area and volume of the Aneto Glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne LiDAR point clouds, and UAV imagery. A 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 glaciated area has shrunk by 64.7 % and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thicknesses of 32.9 m, 19.2 m and 15.0 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.
Ixeia Vidaller et al.
Status: open (until 04 Apr 2023)
- RC1: 'Comment on tc-2022-261', Pierre Pitte, 20 Mar 2023 reply
Ixeia Vidaller et al.
Ixeia Vidaller et al.
Viewed (geographical distribution)
I find this paper to be a convincing contribution to the current state and recent evolution of the Aneto glacier in the Pyrenees. This is a well-studied glacier, with many previous studies, but the current paper puts together an impressive and updated series of datasets. The work is nicely and extensively illustrated as well.
The methods are consistently explained and documented.
On the hind side, I think the results read with some difficulties due to the emphasis in including lots of data for each statement. The text could be made simpler by making better use and focusing on the trends shown by the figures. If needed, the detailed data of area, thickness and depth changes can be included in tables in the supplementary material.
From a methodological point of view, this work integrates data from gridded data sets (DEM), point clouds (UAV DEM) and transects (GPR) with variable spatial footprints. Maybe consider a resampling of the different datasets to a common gridded base, which would make the integration easier, both in terms of the representation of the results in the figures and maps, and in the explanations in the results and discussion sections.
I consider the paper would benefit from minor correction prior to publication.
Line 47. Here and elsewhere glacier areas should be expressed in km2 (WGMS, 2009). While it is not incorrect to use ha, km2 communicates the relative size of the studied glacier
Figure 1. I suggest including political boundaries in A) and keep the same line type in B). In B) increase the hillshadding so that the relief is highlighted. Also I would expect glacier outlines rather than point indicating glacier position here (handle line thickness to make glacier visible at this scale). I think C) is a nice and very clear way to indicate the main geographic features of the study area.
Line 108. I suppose this is the mean annual isotherm, please clarify. Consider including a climograph with the station data. This kind of graph clearly shows the magnitude and seasonality of the main climatic variables. Finally plot the stations location in Figure 1 B).
Line 162. Do you mean mountain areas? Or heterogenous in terms of ground cover? Please clarify.
Line 204-208. While a glacier “true” area is better approximated by a 3D approach, most glaciers inventories report projected areas and this is the standard approach (WGMS, 2009) so, by making this choice you make your data harder to integrate with most other regional and global datasets.
Line 219-221. Again, not the most standard approach. Because mountain glaciers usually shrink in area as they thin, it is customary to use the mean area of the period to convert the volume loss into mean thickness change (e.g.: Berthier and others, 2004; Falaschi and others, 2022).
Table 1. I would place this table in supplementary material and include an area, or even better a cumulative area change plot in Figure 2.
Figure 2. B) I would invert the position or the year labels on top so that they follow the glacier recession as represented in the map (2022 2011 1981). There appears to be a moraine in front of the glacier, probably a LIA feature marking the glacier extent. With a slight offset to the west of the represented area, you could include the entire area encompassed by this moraine and put in even larger perspective the recent area loss.
Line 265. I made a general comment regarding the different footprints of your data. If you use uniform grid base for your data this sentence could simply read: “Between 1981 and 2022 the mean ice thickness loss was 30.51 m”. Also, if you use mean area (see comment of Line 219-221), you can remove the line between brackets.
Line 275-277. This line could be removed if you include a mass balance time series graph in Fig. 3.
Figure 3. Your color scale is not very good here. I think you should use a single color ramp, with linear intervals, from white to dark (0 to -80 m). B) I would recommend a longitudinal profile from the headwall to the glacier front, rather than a frequency distribution.
Figure 4. A) Again colorscale. You have a linear variable from 0 to 45 m. The most appropriate approach is a single color, linear, color ramp. By using several colors, you make the interpretation harder. Two colors would be useful if you had positive and negative values, which is not the case here. B) Again consider using a longitudinal profile here. Note you would share a distance axis and could even combine both plots in a single graph.
Figure 5. Consider longitudinal profile here too. A stack of the three thickness profiles is the simplest way of showing the absolute and relative thinning. See comment in Fig. 4 regarding colorscale.
Line 318. Remove “as is well known” and consider including a reference for this statement.
Line 324-328. Note how your first sentence is part of methods and the rest discussion or methdos. This paragraph of the results should start in line 328.
Line 336. “The rate of area loss was uniform over time”. Maybe use an average here? Also note the number of figures between lines 336 and 343. A multiple line plot could do the job here.
Lines 348-537. Consider a synthesis plot here (i.e.: Fig. 3 in Dussaillant and others, 2019), which allows the comparison of several datasets with different observational periods.
Berthier E, Arnaud Y, Baratoux D, Vincent C and Rémy F (2004) Recent rapid thinning of the “Mer de Glace” glacier derived from satellite optical images. Geophysical Research Letters 31(17). doi:10.1029/2004GL020706.
Dussaillant I and others (2019) Two decades of glacier mass loss along the Andes. Nature Geoscience 12, 802–808. doi:10.1038/s41561-019-0432-5.
Falaschi D and others (2022) Increased mass loss of glaciers in Volcán Domuyo (Argentinian Andes) between 1962 and 2020, revealed by aerial photos and satellite stereo imagery. Journal of Glaciology, 1–17. doi:10.1017/jog.2022.43.
WGMS (2009) Attribute description. World Glacier Monitoring Service, Zurich, Switzerland.