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

Abstract

The Aneto glacier, although it may be considered a very small glacier (<0.5 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 64.7 %, 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 thickness of 32.9, 19.2 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.

Full text

The Cryosphere, 17, 3177–3192, 2023
https://doi.org/10.5194/tc-17-3177-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Aneto glacier’s (Central Pyrenees) evolution from 1981 to 2022:
ice loss observed from historic aerial image photogrammetry and
remote sensing techniques
Ixeia Vidaller1, Eñaut Izagirre2, Luis Mariano del Rio3, Esteban Alonso-González4, Francisco Rojas-Heredia1,
Enrique Serrano5, Ana Moreno1, Juan Ignacio López-Moreno1, and Jesús Revuelto1
1Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE-CSIC), Saragossa, Spain
2Department of Geography, Prehistory and Archaeology, University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain
3Department of Applied Physics, Escuela Politécnica Superior de Cáceres, University of Extremadura, Cáceres, Spain
4Centre d’Etudes Spatiales de la Biosphère, Université de Toulouse, CNRS/CNES/IRD/INRA/UPS, Toulouse, France
5Department of Geography, GIR PANGEA, University of Valladolid, Valladolid, Spain
Correspondence: Ixeia Vidaller (ixeia@ipe.csic.es)
Received: 22 December 2022 – Discussion started: 7 February 2023
Revised: 21 June 2023 – Accepted: 27 June 2023 – Published: 8 August 2023
Abstract. The Aneto glacier, although it may be considered
a very small glacier ( <0.5 km2), is the largest glacier in the
Pyrenees. Its surface and thickness loss have been continu-
ous in recent decades, and there have been signs of accel-
erated 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 exist-
ing ice distribution in 1981 and 2011. Over the last 41 years,
the total glacierised area has decreased by 64.7 %, and the ice
thickness has decreased, on average, by 30.5 m. The mean re-
maining ice thickness in autumn 2022 was 11.9 m, as against
the mean thickness of 32.9, 19.2 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 Pyre-
nean glaciers in a relatively short time.
1 Introduction
Glaciers are excellent indicators of climate variability and
change because their evolution depends on the balance be-
tween snow accumulation during the cold period and ice
and snow ablation during the warmest season (Braithwaite
and Hughes, 2020). The Little Ice Age (LIA) represents the
last cold pulse in almost all mountain ranges of the world
(Solomina et al., 2016; García-Ruiz et al., 2020). As Grove
(2004) and Oliva et al. (2018) point out, the LIA in the
Pyrenees occurred during the period between the 14th and
19th centuries, in line with the rest of the Northern Hemi-
sphere. Since ∼1850, the LIA maximum, the climate has
been warming and glaciers have been receding, albeit with
brief periods of stabilisation or even small advances (Zemp
et al., 2015; Oliva et al., 2018). However, the nearly con-
tinuous surface and thickness losses have accelerated in re-
cent decades (Vidaller et al., 2021), similar to what has been
observed in the majority of mountain ranges in the world
(Hugonnet et al., 2021). The rapid surface and thickness
losses are mainly due to a warming of more than 1.2 ◦C be-
tween 1949 and 2010 (Cuadrat et al., 2018), which could be
even higher in high-elevation areas, affecting snow accumu-
lation and its duration above ground (López-Moreno et al.,
2019; López-Moreno, 2005). Due to the small size of Pyre-
nean glaciers, their evolution has strongly been influenced
by the topographic characteristics of the surrounding area
Published by Copernicus Publications on behalf of the European Geosciences Union.

3178 I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022
(size and height of cirques, aspect, slope, snow avalanche
corridors, etc.), and as well as having an interannual climatic
control, they now also have a topoclimatic control (López-
Moreno et al., 2006; Vidaller et al., 2021).
Consequently, the glacier surface loss in the Pyrenees is
remarkable: there were 52 glaciers in 1850, 39 in 1984 and
21 in 2020, corresponding to an area of 2060 ha (20.6km2) in
1850, 810 ha (8.1 km2) in 1984 and 232 ha (2.3km2) in 2020,
representing a loss of 88.8 % of the glaciated area (Arenillas-
Parra et al., 2008; Rico et al., 2017; Vidaller et al., 2021).
In terms of ice thickness loss, unlike surface loss, there is
generally a lack of information over a long period of time
and a lack of sufficient resolution for small alpine glaciers
(or very small glaciers). Recent studies have identified an
ice thickness loss of 6.3 m for the period 2011–2020 as the
mean for all the glaciers in the Pyrenean massif (Vidaller et
al., 2021). Specifically, at the Monte Perdido glacier, López-
Moreno et al. (2019) reported ice thickness loss of 6.1 m
for the period 2011–2017. In the case of the Ossoue glacier,
the ice thickness loss was 36.8 m for the period 1983–2013
and 20.4 m for the period 2001–2013 (Marti et al., 2015). In
the grid cell corresponding to the Pyrenean glaciers (1◦×1◦
grids; 42◦N, 0◦E and 42◦N, 1◦W), Hugonnet et al. (2021)
indicated a mean ice thinning rate of −0.96 m yr−1for the
period 2000–2019, which is very accurate considering the
dataset characteristics, but it is much higher than the mean
annual ice thickness loss found by Vidaller et al. (2021) of
−0.70 m yr−1for a more recent study period (higher ice loss
could be expected in the later period). This difference be-
tween both studies clearly shows the need for local studies
such as the present study or Vidaller et al. (2021) to validate
large-scale observations and also to reach more accurate es-
timations over shorter time periods.
The Aneto glacier is one of the southernmost glaciers
in Europe (Grunewald and Scheithauer, 2010) and is the
largest in the Pyrenees, although it is a very small glacier
(<0.5 km2) (Huss and Fischer, 2016). It is one of the most
iconic glaciers of the Pyrenees, as it is located below the
highest peak of the mountain range (Aneto peak, 3404 m
above sea level (ma.s.l.)), and it forms part of the natural
and cultural landscape of the Posets–Maladeta Natural Park,
attracting mountaineers and tourists to this park (Carvache-
Franco et al., 2022; Carrascosa-López et al., 2021). Addi-
tionally, this glacier is part of the Natural Monument of
the Pyrenean Glaciers (Lampre-Vitaller, 2003), adding ad-
ditional societal value to this natural landscape heritage. Un-
like other alpine glaciers that are important water sources in
other mountain areas (Fountain and Tangborn, 1985; Braith-
waite and Raper, 2002; Meier et al., 2007; Huss et al., 2017;
Drenkhan et al., 2023), the Aneto glacier, as all Pyrenean
glaciers, has a minor (and nearly negligible) contribution to
river discharge in this region (López-Moreno et al., 2020).
However, the ice surface loss of Pyrenean glaciers has a clear
impact on local erosion rates (Riihimaki et al., 2005), nu-
trient fluxes, biochemistry and macroinvertebrate communi-
ties (Snook and Milner, 2001; Brown et al., 2007) or the mi-
crobiology of these emblematic landscapes and surrounding
downstream areas. The knowledge gap of these processes in
the southernmost glaciers of Europe encourages and justifies
the analysis of their recent evolution.
Despite the fact that the Aneto glacier has not been sub-
jected to mass balance annual monitoring, two recent studies
(Campos et al., 2021; Vidaller et al., 2021) have analysed ice
thickness loss for different time periods. Campos et al. (2021)
presented a reconstruction of the area, volume, ice thickness
and equilibrium line altitude (ELA) of the Aneto glacier for
different time periods from the LIA to 2017 using photo in-
terpretations and satellite imagery to calculate surface and
ice thickness losses in the Aneto glacier. Ice thickness loss in
that work was derived from a steady-state model assuming a
plastic ice rheology, combined with some ground-penetrating
radar (GPR) profiles from 2008 (Campos et al., 2021). On
the other hand, Vidaller et al. (2021) determined changes in
glacier area and thickness for the period 2011–2020 with
high spatial resolution in the 24 Pyrenean glaciers (includ-
ing the Aneto glacier). Surface loss was determined based on
satellite data and drone imagery, and the ice thickness loss
was calculated by comparing 2011 and 2020 digital elevation
models (DEMs) (from laser imaging detection and ranging
(lidar) and unoccupied aerial vehicles (UAVs), respectively).
The results of this work for the Aneto glacier reported a sur-
face loss of 24.9 % (69.3 ha (0.7 km2) in 2011 and 50.0 ha
(0.5 km2) in 2020) and an average ice thickness loss of 8.5m.
This study aims at analysing the recent evolution of the
highest and largest glacier of the Pyrenees, the Aneto glacier,
by using the longest temporal dataset of glacier thickness
loss in the Pyrenees. In addition, this study permits us to
assess the impact of a single extremely warm ablation sea-
son (2022) on glacier evolution. Due to the very last stage
in which the Aneto glacier is, we report thickness and ice
surface losses of this glacier from 1981 to 2022 to discern if
the speed of changes accelerates (because of the existence of
feedback processes) or slows down (because the remaining
ice is progressively restricted to the most favourable areas),
which has an inherent scientific interest and may be extrap-
olated to other mountain areas that will face a similar situ-
ation in the coming decades. The evidence for the demise
of Pyrenean glaciers in the coming decades using the Aneto
glacier as an iconic example is also used to highlight the dra-
matic consequences of rapid climate change in mountain ar-
eas. We use high-resolution 3D point clouds from 1981 (from
structure-from-motion (SfM) methods exploiting historical
aerial photographs), 2011 (from the Spanish National Geo-
graphic Institute (IGN) lidar survey), 2020, 2021, and 2022
(from SfM methods using UAV flights). In addition, 2020 ice
thickness was estimated from an intensive GPR survey con-
ducted in July of this year. The combination of the three tech-
niques allows for the accurate reconstruction of the glacier
ice thickness in 1981 and its evolution until today. Moreover,
the current ice thickness and basal topography of the glacier
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I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022 3179
could be determined. This information is critical for predict-
ing the next changes in the glacier, and the basal topography
reveals sectors where lake formation is likely after the ice
disappears. The combination of these techniques provides an
increase in knowledge over previous work because (1) we
present data with high accuracy and lower uncertainty com-
pared to previous studies, and (2) we determine the evolution
of the Aneto glacier for the longest period observed by quan-
tifying current ice thickness and basal topography, as well as
the annual decrease in ice thickness from 1981 to 2022.
Study area
The Aneto glacier is the largest glacier in the Pyrenees
(48.1 ha (0.48 km2) in 2022), a mountain range where only
four glaciers are larger than 10 ha. It is located in the
Maladeta massif (Fig. 1), on the northeast (NE) side, be-
tween the Maldito (3354 m a.s.l.) and Aneto (3404 m a.s.l.)
peaks. The high elevation of this massif, with more than 40
peaks above 3000ma.s.l., has allowed the preservation of
other smaller glaciers (Eastern Maladeta and Tempestades)
and ice patches (Western Maladeta, Coronas and Barrancs)
in the area. In 2022, the Aneto glacier consisted of two bod-
ies whose glacier front was at 3026 m a.s.l. in the case of the
main body and at 3170 m a.s.l. in the case of the secondary
body.
In this area, the 0 ◦C mean annual isotherm ranges from
2700 to 3000 m a.s.l. (Jomelli et al., 2020), and the mean an-
nual precipitation is about 2000 mm, with winter and spring
being the wettest seasons (Buisan et al., 2015). The mean
annual temperature for the period 2007–2022 was 4.6 ◦C at
the weather station of the Renclusa hut (2140 ma.s.l.); mean-
while the mean temperature for the same period in the abla-
tion season (June–September) was 11.6 ◦C. The year 2022
was an especially warm year, in which the annual mean tem-
perature was 5.2 ◦C and the summer mean temperature was
12.1 ◦C (data from the AEMET database).
2 Data and methods
2.1 Imagery processing and DEM generation
2.1.1 Historical aerial imagery
The earliest imagery dataset exploited here (1981 DEM)
dates from September 1981. Aerial images were ac-
quired by the Spanish National Geographic Institute (IGN)
using analogue photogrammetric cameras (IGN: http:
//centrodedescargas.cnig.es/CentroDescargas/index.jsp, last
access: August 2022) aboard aircraft for national mapping
surveys. The objective was to collect aerial photographs suit-
able to produce topographic maps of Spain at a scale of 1 :
50000 and with contour intervals of 20m (named MTN50).
The overlap was 60% at the front and 30% on the side. The
camera, Wild lens cone RC 10, had a sensor of 230×230 mm,
a lens of 15 UAG II and a focal length of 152.12 mm; thus,
an average image scale of 1 :30 000 was obtained, with a
ground sampling distance (GSD) between 0.35 and 0.18 m
per pixel. For this study, the historical aerial imagery was res-
canned at a resolution of 15 µm. A total of 18 aerial images
of the Aneto massif were used, taken from the same flight in
late summer 1981.
Historical survey imagery was processed using structure-
from-motion (SfM) (Snavely et al., 2006) with Ag-
isoft Metashape Professional v1.6.3 software (https://www.
agisoft.com/, last access: June 2022), which has shown re-
liable results when used for processing historical images
(Llena et al., 2018). Processing parameters were set accord-
ing to official Agisoft guidelines (denser point clouds, bundle
block adjustment (BBA), internal and external camera pa-
rameter calibration; Agisoft Metashape version 1.5, 2019).
The SfM routines enabled the generation of a dense point
cloud (2.4 pts m−3), from which an orthomosaic with a res-
olution of 0.41 m (used to calculate the glacier area) and a
geoid-corrected digital terrain model (DTM) with a grid cell
size of 1.58 m were derived.
The historical survey imagery processing included the fol-
lowing workflow: (1) the alignment of each flight line’s cam-
eras (three lines in total); (2) the assignment of ground con-
trol points (GCPs) based on clearly visible features such
as individual large boulders and trail crossings or moun-
tain summits; (3) the derivation of accurate geographic co-
ordinates and elevation information of these later GCPs us-
ing high-resolution satellite imagery (DigitalGlobe/GeoEye-
1 imagery with 1 m resolution available through the QGIS
service QuickMapServices) and a 2020 UAV flight as a refer-
ence DTM (Vidaller et al., 2021); and, (4) taking advantage
of GCPs, the realignment of camera positions and merging
of all images in one chunk using Agisoft Metashape Pro-
fessional. The georeferencing accuracy of DigitalGlobe’s lat-
est very-high-resolution (VHR) satellites (i.e. GeoEye-1 and
WorldView-1/2/3/4) is between 1.0 and 5.0m, which may be
insufficient for many precise geodetic applications. To im-
prove this, we aligned the 1981 point cloud with that of 2020
using the iterative closest point (ICP) algorithm (Rajendra et
al., 2014).
2.1.2 Lidar survey
The 2011 high-resolution digital elevation model (DEM) was
derived from airborne lidar. The data were acquired in a flight
of 9 November 2011 by the IGN (http://centrodedescargas.
cnig.es/CentroDescargas/index.jsp, last access: May 2022).
The lidar device was the Leica ALS60 with a diode-pumped
transmitter and a low-inertia/high-speed scanning mirror
with a large aperture operating at a wavelength of 1064nm.
The final georeferenced point cloud had an average density
of 0.35 pts m−3. This information was processed and accu-
rately geolocated by the IGN, which provides free access to
the final 3D point cloud.
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3180 I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022
Figure 1. Location of the Aneto glacier. (a) Map of Europe, with the pink rectangle delimiting the central part of the Pyrenees (© Google
Maps). (b) Topographic map of the central Pyrenees; the glaciers in this area are marked with grey dots, and the location of the Aneto glacier
is marked with a pink star. (c) An aerial photo of the Aneto glacier in summer 2021. The main reliefs surrounding the glacier are indicated.
2.1.3 Unoccupied aerial vehicle (UAV) imagery
The 2020, 2021 and 2022 glacier surface DEMs were ob-
tained using a fixed-wing UAV (SenseFly eBee X) on
12 September 2020, 1 October 2021 and 10 September 2022,
respectively. The UAV was equipped with a SenseFly 3D
S.O.D.A. digital camera (20 Mp resolution) and GPS re-
ceivers enabling post-processed kinematic (PPK) position-
ing systems (positioning accuracy <0.05 m after post-
processing). As in previous studies (e.g. Vidaller et al., 2021),
the UAV images had an overlap of 70 % at the front and
50 % on the side (note that the 3D S.O.D.A. camera obtains
images with a tilt of 30◦) with a final ground sampling dis-
tance (GSD) of 2.8 cm per pixel. The UAV images were pro-
cessed using Pix4Dmapper (Pix4D) SfM software, in which
the calculation of BBA and internal and external camera pa-
rameter calibration were enabled (more details on data pro-
cessing can be found in Vidaller et al., 2021). Although Ag-
isoft Metashape could be used for this SfM processing, we
preferred to use the same protocol described in previous
works with UAV at this site. Nonetheless comparison of point
clouds from the SfM software (both Pix4Dmapper and Ag-
isoft Metashape) shows equivalent accuracies to work in this
area (Mölg and Bolch, 2017; Llena et al., 2020). Due to the
three UAV acquisitions having the same acquisition proto-
col, and the GPS PPK geolocation (image geolocation with
deviations below 4cm), the comparison of these three point
clouds yielded negligible deviations (0.06 m) (Revuelto et al.,
2021).
2.2 In situ ground-penetrating radar (GPR),
processing and data interpolation
GPR uses the transmission and rebound of electromagnetic
pulses at different frequencies to determine ice thickness and
glacier interfaces (rocks, bedrock basin, snow, etc.) (del Rio
et al., 2014). Different works have studied the variation in ice
thickness, surface area or volume on glaciers using different
techniques, which highlight the importance of the methodol-
ogy to be applied in each case, considering its scope and lim-
itations (Procházková, 2019; Bohleber et al., 2017; Marcer et
al., 2017; Fischer, 2009).
GPR fieldwork was conducted on 25–26 July 2020, using
a Malå Geoscience radar system consisting of a ProEx con-
trol unit and a 100 MHz rough terrain antenna (RTA). Oc-
casionally several transects were also carried out with the
100 MHz shielded antenna (see Supplement). Georeferenced
radargrams were created using the AtlasLink GNSS smart
GPS antenna connected to the GPR, which were obtained
in “time” tuning. A total of 32 georeferenced radargrams
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I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022 3181
were recorded in the main glacier body in a common off-
set mode, corresponding to a length of 6.8 km and covering
almost the entire glacier surface (more detailed information
can be found in Fig. S1 in the Supplement). The campaign
was conducted during a period when the glacier surface was
covered with snow, in order to allow safe displacement of the
instrument and operators, thus hampering the observation of
deeper ice layers. This required differentiation of the snow
layer in post-processing to accurately quantify glacier thick-
ness.
Radargrams were processed using Reflexw version 9.1.3
(Sandmeier scientific software), with the following work-
flow: (1) the adjustment of the time origin (t=0) to coin-
cide with the arrival of the first surface signal on the glacier;
(2) the homogenisation of the trace increment, since the ac-
quisition of the radargrams with the RTA antenna was done
in time mode and varied in each radargram depending on
the speed of the movement of the antenna on the ground
(0.1 m ns−1was fixed, since this was the smallest value ob-
tained in the radargrams); (3) the removal of the background;
(4) the correction of the energy loss of the signal when pene-
trating the terrain by applying a gain factor of 0.2 (energy de-
cay); and (5) the application of a frequency bandpass filter so
that only signals with frequencies between 50 and 200 MHz
remain (the nominal frequency of the antenna is 100 MHz).
As a first approximation, 0.17 m ns−1was set as the prop-
agation velocity of the waves in the glacier to get a first idea
of the thickness of the snow and ice layers in the radargram
representation. Snow and ice layers must be defined from
the radargrams to create a thickness model of both. To do
this, the wave propagation velocities (RWVs) in both me-
dia must be available beforehand. In a similar study on the
Monte Perdido glacier, RWVs of 0.200 ±0.005 m ns−1for
snow and 0.163±0.007 m ns−1for ice were obtained for the
500 and 200 MHz antennas, respectively (López Moreno et
al., 2019). The coherence of these velocities was checked in
the 1054 radargram at the points where diffraction hyper-
bolas occurred (plot of diffraction hyperbolas is shown in
Fig. S2).
The distribution of the GPR data does not follow a ho-
mogeneous pattern; the GPR record tracks were distributed
along parallel and perpendicular lines, forming an irregular
grid (Fig. S1). Therefore, to determine the thickness of the
glacier over its entire extent, an interpolation method is re-
quired. For this type of data, the interpolation method used
was the radial basis function (RBF), as Otero-García (2008)
recommended. Given the poor distribution of the data, after
several tests, the best method is to work with 16 neighbours,
two per octant (the closest points in each direction), in a cir-
cular area with a radius of 457.62 m, in the same way, again,
as Otero-García (2008). The thickness for glacier limits in
2020 was established as 0 m. To validate this interpolation
method, the data were divided into two groups: training with
70 % of the sample and test with the other 30 %.
2.3 Glacier area outline, point cloud geolocation and
glacier thickness loss computation
The delineation of the Aneto glacier surface was done man-
ually (Table S4 in the Supplement) in a GIS software (Ar-
cGIS), considering: (1) the orthomosaic of the historical
aerial imagery from 1981; (2) a RapidEye satellite image
from 2011 and improved outlines from RGI (RGI Consor-
tium, 2017); and (3) the orthomosaics derived from UAV
flights in 2020, 2021, and 2022. Due to the small extent
of these very small glaciers, the slope was considered in
the calculation of glacier surface to obtain the true glacier
area (3D surface) rather than the 2D projection of glacier
extent. This calculation is justified because the glaciers are
strongly bound to wall cirques, and these had a steepness of
24.3◦in 2020. When the slope is not taken into account, the
glacier surface is underestimated (Vidaller et al., 2021). Oth-
erwise the 2D area computation would also be affected by
the changes in slope during the study period.
Data from DEMs available for this work varied in accu-
racy. The most accurate geolocation is that of the UAV, which
was used as a reference for the point cloud due to the post-
processed kinematic (PPK) GPS geolocation technique (ge-
olocation RMSE <0.05 m). This geolocation error is equiv-
alent for the 2020, 2021 and 2022 point clouds (0.019 for
2020, 0.025 for 2021 and 0.021 for 2022; the differences
were due to weather conditions). Based on the low magni-
tude of these geolocation errors, we assume that the error in-
troduced in ice thickness differences is nearly negligible. 3D
point cloud differences in ice-free areas had RMSEs below
0.02 m, (error computed following Vidaller et al.’s (2021) ac-
curacy method). To coregister the lidar point cloud (2011)
and the point cloud from the historical aerial imagery (1981),
several areas of stable terrain such as ridges, peaks, polished
surfaces, etc. were selected in these later point clouds and in
the 2020 UAV-derived point cloud. These areas were evenly
distributed around the glacier. A rotation and translation ma-
trix was calculated for these areas to align (separately) the
1981 and 2011 point clouds with that of 2020 using an ICP
algorithm (Rajendra et al., 2014), from CloudCompare soft-
ware (Girardeau-Montaut, 2016), in the same way as Vidaller
et al. (2021). Subsequently, these matrices were applied to
the entire point clouds to derive point clouds that were fi-
nally coregistered. Glacier thickness loss (normal surface dif-
ferences; see the Supplement for more information) between
these point clouds were computed using the CloudCompare
tool M3C2 (James, 2017) to determine the differences (sur-
face perpendicular) between the glacier surfaces observed in
different years. Glacier change statistics were derived from
this later comparison, calculated over the most recent (and
smallest) glacier surface.
Glacier thickness loss was determined by considering only
data within the smallest (or more recent) surface of the
glacier. When considering the oldest surface, there are zones
of the glacier that are not present in the most recent acqui-
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3182 I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022
sitions, so the ice thickness loss would be underestimated
(Vidaller et al., 2021). The mass balance was calculated as-
suming a density conversion factor of 850±60 kg m−3(Huss,
2013). Thus, the specific mass balance presented in this study
was determined considering the recent surface of the glacier.
With the aim of determining areas of future glacier lake
formation, the mountain basal topography was derived from
the GPR interpolation and the 2020 UAV acquisition (sub-
traction of the 2020 glacier surface from the ice thickness
interpolation from the GPR). The topographic position in-
dex (TPI) is capable of identifying terrain depressions at var-
ious search distances (Weiss et al., 2001). From this basal
topography, the TPI (de Reu et al., 2013) was derived for
70, 100, 150 and 200 m search distances to describe depres-
sion areas that potentially favour future lake formation. This
index has previously been used in studies of debris-covered
glaciers (Westoby et al., 2020) to determine areas of poten-
tial debris accumulation, but as far as the authors are aware,
this is the first time this index has been used to determine ar-
eas of potential lake formation following the retreat of moun-
tain glaciers. In addition, overdeepenings detected by the TPI
were corroborated using the longitudinal GPR radargrams.
2.4 Correction and accuracy assessment
GPR ice thickness measurements with a 100 MHz RTA an-
tenna are subject to intrinsic error. Assuming a RWV velocity
for ice of 0.163 m ns−1, the λvalue is 1.63 m, so the mini-
mum spatial resolution is λ/2=0.815 m. Summing this un-
certainty for snow and ice gives a thickness resolution of 1m
for this delineation. Thus, the uncertainty in the determina-
tion of the ice layer thickness is 1.8 m.
To check the coherence of the determined thicknesses, a
test was performed at all intersections between transects to
detect any inconsistencies in the values. At these 28 intersec-
tions, the average difference is 1.6±1.6 (σ)m, with some
outliers of 3–5 m (Table S2). This value is consistent with
the uncertainty associated with RWV velocity and ice layers’
delineation (1.8 m). The lengths of the radargrams were de-
termined using Reflexw from the GPS coordinates coupled to
the GPR (see Supplement for more details). General GPR un-
certainty in ice thickness was determined considering differ-
ent velocities for temperate ice in the transects (1043, 1062
and 1073). Based on existing literature (Jiménez-Vaquero,
2016; López-Moreno et al., 2019), we assumed 0.2 m ns−1
in the snow and between 0.157 and 0.186 m ns−1in the ice.
With these velocities, mean and maximum ice thickness was
determined for each transect (Table S3). As a result, mean
ice thickness variation that could be derived from different
velocities into the temperate ice would fit in the range of
the estimated margin of error band (<1.6 m) and would be
smaller than the uncertainties obtained from the differences
in thickness at transect crossings (<1.8 m).
To validate the interpolation of glacier thicknesses, 30 %
of the GPR data were randomly selected, and the remain-
ing 70 % of the GPR dataset was used for the interpolation
(Otero-García, 2008). The mean error between the interpo-
lated thickness and the thickness observed with the GPR was
0.0018 m, and the RMSE was 0.3021 m.
The delineation of glacier boundaries also has some un-
certainty due to pixel size, geometric correction, visual iden-
tification, and the presence of residual snow or debris cover
at the glacier boundaries. The surface uncertainty is 0.048 ha
(0.00048 km2) for the Aneto glacier (Vidaller et al., 2021) in
the case of the glacier surface of 2011, 2020, 2021 and 2022;
the uncertainty error of the 1981 glacier outline is 0.58 ha
(0.0058 km2).
The coregistration of point clouds from historical aerial
imagery and lidar survey with UAV surveys was tested in
a buffer zone around the glacier, always using snow- and
ice-free zones in both years of comparison. This means that
the comparison of the 1981 and 2020 point clouds was per-
formed in a buffer zone with a 300% larger extent than the
1981 glacier boundaries (over stable terrain); the coregistra-
tion error between the 2011 and 2020 point clouds was deter-
mined in the same way. In the first case for the Aneto glacier
the RMSE is 0.06 m and in the second case 0.4m (Vidaller et
al., 2021).
3 Results
The extent of the Aneto glacier has decreased significantly
in the last few decades, from 135.7 ha (1.36 km2) in 1981 to
48.1 ha (0.48 km2) in 2022, i.e. by −64.7 %. The surface and
thickness losses of the glacier continues, resulting in changes
in area and the division of the glacier into two bodies. It is
noteworthy that the secondary body today shows signs of
stagnant dynamics (Table S5).
In 1981, the surface of the Aneto glacier was 135.7 ha
(1.36 km2); in 2011, the surface decreased to 69.3 ha
(0,69 km2), a loss of 49.0 %. Between 2015 and 2016, the
Aneto glacier divided into two bodies; in 2020 the main body
was 47.8 ha (0.48 km2) and the secondary body was 4.2ha
(0.04 km2), a total of 52.0 ha (0.52 km2). Table S5 shows
that in the last 40 years the losses were 63.1 % of its surface
(−1.6 % yr−1). In 2022, the surface had decreased to 48.1 ha
(0.48 km2) (44.6 ha (0.45km2) for the main body and 3.52ha
(0.03 km2) for the secondary body), a decrease of 64.7 %
compared to 1981 (Fig. 2). This decrease represents a retreat
of the lowest glacier front (the front of the main body) from
2828 ma.s.l. in 1981 to 2939ma.s.l. in 2011, 3011ma.s.l. in
2020, 3014 m a.s.l. in 2021 and 3026 m a.s.l. in 2022.
A comparison of the 1981 and 2022 point clouds (differ-
ence calculated normal to surface) shows a mean ice thick-
ness loss of 30.51 m (Figs. 3, S3 and S4) during this period,
and considering only the area covered by the glacier in 2022
(considering the 1981 glacier extent, the ice thickness loss is
24.1 m; considering height surface changes, the loss is 45.3 m
(for more information, see Table S6 and Fig. S4)). Note these
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I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022 3183
Figure 2. Appearance of the Aneto glacier during the study period. (a) Photo (Fernando Biarge, Fototeca DPH) corresponding to the Aneto
glacier in 1982. (b) Photo corresponding to the Aneto glacier in 2022. The red stars refer to the same location in both photos. (c) Map
showing the differences in the area of the glacier during the study period; the purple line delineates the extent of the glacier in 1981, the green
line in 2011 and the orange line in 2020. The shading of the terrain was calculated from the 2011 lidar. The yellow triangle represents the
summit of the Aneto peak. (d) Cumulative area change plot of the Aneto glacier for the years 1981 (purple), 2011 (green) and 2022 (orange).
mean ice thickness losses are the mean values of differences
in glacier surfaces (normally computed) for the entire pe-
riod computed. This means that the glacier lost on average
0.6 m yr−1over the entire glacier and 0.7myr−1in the cur-
rently glaciated area during the 1981–2022 period. The thick-
ness losses are not evenly distributed. The highest ice thick-
ness loss is in the middle of the main body, while the lowest
changes are in the secondary body (Fig. 3a). More than 41 %
of the 2022 glacier area has lost more than the mean (30.5 m)
(Fig. 3b).
The results indicate an acceleration in glacier ice thick-
ness loss in the last decade. The mean ice thickness loss for
the period 1981–2011 was 17.8 m (0.6 m yr−1) and 12.6 m
(1.1 m yr−1) for the period 2011–2022, representing an in-
crease in ice thickness loss in the later period of 200 %
compared to 1981–2011. The available information for the
2020–2021 and 2021–2022 annual comparisons highlights
the high interannual variability in ice thickness loss, with
mean ice thickness loss of 1.5 and 3.2 m, respectively. As for
the specific mass balance, the changes are −0.6 m w.e. yr−1
for the period 1981–2022, −0.5 m w.e. yr−1for the pe-
riod 1981–2011, −1.0 m w.e. yr−1for the period 2011–
2022, −1.2 m w.e. yr−1for the period 2020–2021 and
−2.7 m w.e. yr−1for the period 2021–2022 (data are always
calculated within the most recent glacier surface).
The GPR survey of the main body of the glacier in 2020
reveals a mean glacier thickness of 15.0m, with a maximum
glacier thickness of 44.7 m (Fig. 4a). This maximum glacier
thickness was measured in the western part of the glacier,
near the Maldito (3354 ma.s.l.) and del Medio (3349ma.s.l.)
peaks. The greatest thickness was measured in the upper
parts of the glaciers in the elevation range between 3200 and
3350 m a.s.l. (Fig. 4a and b). In some elevation ranges (be-
tween 3100 and 3180 m a.s.l.), the glacier thickness is lower
than expected, considering the trend of increase with increas-
ing elevation. This is mainly due to the presence of a rela-
tively thick sector (up to 39m) between 3000 and 3100m in
the western part of the glacier, which affects the mean values
observed in this elevation range. Figure 4a also shows the
presence of very narrow and shallow ice sectors (light blue
areas) adjacent to the cirque wall in two places, indicating an
imminent separation of the glacier into three ice bodies.
In 1981 (Fig. 5a), the pattern of ice thickness distribution
shows some differences compared to recent periods. In 1981,
the maximum glacier thickness was found in the middle ele-
vations of the western part, where ice thickness reached 90 m.
Below the del Medio pass, the glacier thickness was also very
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3184 I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022
Figure 3. (a) Thickness loss of the Aneto glacier from 1981 to 2022. In the upper map, the black line delineates the glacier in 2022, while the
grey line represents the glacier in 1981. The arrow indicates the north direction (see the maps in Fig. S3 for each period of the UAV surveys).
(b) Distribution of thickness loss considering elevation bands (mean of each band) of 20m.
thick, almost 70 m. In 1981, the maximum thickness was
96.5 m, and the mean thickness of the glacier was 32.9 m.
In 2011 (Fig. 5b), the distribution pattern of ice thickness on
the Aneto glacier was very similar to that of 2020 (Fig. 4a);
the maximum ice thickness was measured below the Maldito
peak and in the lower western part of the glacier. The max-
imum ice thickness at that time was 52.5 m, while the mean
ice thickness of the glacier was 19.2 m. In 2022 (Fig. 5c), the
ice thickness distribution had not changed markedly, and the
greatest thickness was also under the Maldito pass and peak,
as well as in the middle of the main body of the Aneto glacier.
In this latter year, the average ice thickness was 11.9 m, and
the maximum ice thickness was 44.0 m, but although the
maximum ice thickness exceeded 44 m, 43.0 % of the Aneto
glacier in 2022 had an ice thickness of less than 10 m.
Glaciers erode the surface beneath the ice mass so that
the subglacial topography is not a flat surface (Palacios et
al., 2022). Glacial erosion creates thresholds and depres-
sions, which in some cases are filled by meltwater from the
glacier, forming glacial lakes (Shugar et al., 2020; Yao et al.,
2018). This is the case with Ibón Innominato, a new, small
proglacial lake formed in 2015 as a result of the retreat of
the Aneto glacier. Today, it is the highest mountain lake in
the Pyrenees (3150 m a.s.l.). Due to the continuous surface
loss of the glacier, this lake grows simultaneously with the
retreat of the Aneto glacier, although it is ice free only 3–
4 months a year (July–October). In 2020, its area was 0.4 ha
and in 2022 it was 0.5 ha, an increase in area of 26.5 % for
the period 2020–2022, mainly due to the frontal retreat of the
Aneto glacier by about 15.2 m.
The TPI spatial distribution depicts depression areas that
could fill with water after the ice disappears (blue colours in
Fig. 6). For example, under the del Medio pass and peak a
remarkable depression for 150 and 200 m search distances
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I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022 3185
Figure 4. Ice thickness of the Aneto glacier in 2020. In map (a), the blue colour represents the zones of lesser ice thickness that are about
to disappear, in contrast to the purple colours that represent the greatest ice thickness. The secondary body of the Aneto glacier is coloured
grey because no data are available for this glacier body, and therefore no interpolation is possible. The boxplot (b) shows the mean glacier
thickness in 2020 for each elevation band (20m). A GPR profile is shown in the Supplement as an example of the longitudinal radargram
(SE–NW) of the glacier (Fig. S5).
Figure 5. Reconstruction of the ice thickness of the Aneto glacier at different times during the study period. Panel (a) shows the thickness
in 1981, (b) shows the thickness in 2011 and (c) shows the thickness in 2022. The blue colour represents the zones of lower ice thickness
that are about to disappear, in contrast to the red colours that represent the greatest ice thickness. The secondary body of the Aneto glacier is
coloured grey because no data are available for this glacier body, and therefore no interpolation is possible. (d) Comparison of the thickness
of the Aneto glacier in 1981, 2011 and 2022, with structures in elevation bands of 20m.
is observed. This spatial distribution of the lowest value of
TPI is confirmed by radargram 1062 (Fig. S5), in which the
left side coincides with the overdeepening area below the
del Medio pass and also with the second depression below
the Maldito peak. These areas nowadays have the highest
ice thicknesses, and thus lakes could be found in these areas
when the glacier has completely disappeared.
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3186 I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022
Figure 6. TPI 70, 100, 150 and 200 m based on the basal topography derived from the GPR data from 2020. Negative (positive) values (blue
(red) colours) represent locations that are lower (higher) than their surroundings.
4 Discussion
4.1 Recent changes in the Aneto glacier: a
foreshadowing of the future evolution of European
glaciers
Annual surface loss has decreased uniformly over time
(−2.2 ha yr−1). However, it must be noted that the relative
changes are larger in the latter years, since the losses occur-
ring in the most recent period are measured with respect to a
progressively smaller surface. Thus, there has been no recent
acceleration in surface loss per year, but the relative surface
loss has increased. Oppositely, the rates of glacier thickness
loss have increased during the study period (−0.6 m yr−1
from 1981 to 2011 and −1.1 m yr−1from 2011 to 2022),
indicating an acceleration of glacier ice thickness loss, es-
pecially in the last decade, and more pronounced in the last
3 years. In terms of specific mass balance (considering only
changes at the smallest surface glacier, the most recent year
of comparison), the losses are 0.6 m w.e.yr−1for the pe-
riod 1981–2022, 0.5 m w.e. yr−1for the period 1981–2011,
1 m w.e. yr−1for the period 2011–2022, 1.2m w.e.yr−1for
the period 2020–2021 and 2.7 m w.e.yr−1for the period
2021–2022. Based on these results, two inflexion points can
be identified, one after 2011 and another after 2020 – in
both cases the thickness loss has accelerated sharply. This
ice thickness loss is mainly accelerated (among other fac-
tors) by the fact that the accumulation zone over the glacier
in summer is negligible, especially during very hot summers
as in 2022, and the ablation zone covers the entire glacier,
as no ELA is observed for some years. Unfortunately, due
to the small extent of this glacier no reliable satellite obser-
vations of sufficient resolution are available for the Aneto
glacier ELA in late summer, and this absence of accumula-
tion area is based on field work observations of UAV opera-
tors.
Various studies of other glaciers in the Pyrenees have also
shown a continuous increase in glacier thickness and area
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I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022 3187
losses, with a high interannual variability but a clear neg-
ative trend over longer time periods. These works focused
on the Monte Perdido glacier (López-Moreno et al., 2019),
Ossoue glacier (Gascoin and René, 2018), Maladeta glacier
(Pastor Argüello, 2013) and La Paul glacier (Rico et al.,
2015). Hugonnet et al. (2021) also determined a mean ice
thinning of −0.96 m yr−1for the Pyrenean glaciers for the
period 2000–2019. Although this work focuses on the period
1981–2022, the glaciers of the Aneto–Maladeta massif had
about 610 ha at the end of the LIA, so they lost about 338 ha
from 1850 to 1984 (Rico et al., 2017).
The mean annual specific mass balance values of
−0.6 m w.e. yr−1on the Aneto glacier determined for the pe-
riod 1981–2022 are similar to those in other studies in the
Alps, such as Davaze et al. (2020), who estimated an annual
mass balance of −0.7 m w.e. yr−1from 2000 to 2016 for 239
Alpine glaciers. Similarly, Carturan et al. (2016) determined
the mean annual mass balance of nine Italian glaciers from
2004 to 2013, which ranged from −1.8 to −0.8 m w.e. yr−1.
This is also supported by other climatic data showing an in-
crease in air temperatures over the past century (Bolch et al.,
2012; Rabatel et al., 2013), particularly a sharp rise in tem-
peratures at high elevations and low latitudes (Vuille et al.,
2008; Pepin and Mountain Research Initiative EDW Work-
ing Group, 2015), accompanied by a shorter duration of sea-
sonal snow cover (Brown and Mote, 2009).
Vidaller et al. (2021) describe the changes in ice thickness
of the Aneto glacier (among other glaciers of the Pyrenees)
based on an ice thickness decrease of 8.5 m during the period
2011–2020. Based on the ice thickness reconstruction data of
the Aneto glacier presented in this study, the mean ice thick-
ness in 2020 was 15.0 m, while in 2011 it was 19.2 m, so
the loss is 4.2 m. This difference is due to the fact that the
mean ice thickness of 2011 was calculated based on the ex-
tent of 2011, and the ice thickness of 2020 was calculated
based on the area of 2020, while in the case of Vidaller et
al. (2021) the ice thickness loss was calculated considering
only the ice thickness loss within the glacier area of 2020.
A similar problem exists when comparing the remaining ice
thickness in 1981 (32.9 m) and in 2022 (11.9 m) with the ice
thickness losses for the period 1981–2022 (30.5 m). The re-
maining ice thickness in 1981 is similar to those losses cal-
culated for the period 1981–2022; meanwhile the remaining
average ice thickness was 11.9m. The mean ice thickness for
a particular year was calculated based on the extent observed
for that year.
4.2 The importance of the methods
Remote sensing techniques have developed rapidly in recent
years, allowing observation of the Earth’s surface with a spa-
tial resolution that was previously impossible. This work ex-
ploits historical aerial photographs to reconstruct a digital
surface model for the year 1981 and provides a comparison
to observe changes in landscapes and surfaces in detail.
Campos et al. (2021) calculated changes in the Aneto
glacier from the LIA to 2017 using data from 1957, 1983,
2000, 2006, 2015 and 2017. In 1983, they reported an area
of 103.2 ha (1.03km2), in contrast to the 135.7 ha (1.36km2)
for 1981 described in this work. The large difference may be
due in part to the fact that they did not consider the slope an-
gle of the terrain in their calculations (2D vs. 3D surface).
Nonetheless, considering our delineation, but ignoring the
effect of slope angle on the area estimate, we would have
reported a value of 115.5 ha (1.16 km2) for 1981, which un-
derestimates our value by 20 %. This study also uses the Na-
tional Fly photograms to convert to point clouds, accounting
for stable GCP during the study period. This is a more ac-
curate method because it avoids distortion of the Plan Na-
cional de Ortofotografía Aérea (PNOA) orthophotos used by
Campos et al. (2021), who acknowledge a source of uncer-
tainty: “The extension for the 1983 stage should be con-
sidered with caution. Due to the lower quality of the 1983
aerial image (especially in the southeast part of the glacier)”.
The area determined in our study is closer to that reported
by Arenillas-Parra et al. (2008), who reported an extent of
136 ha (1.36 km2) for the Aneto glacier in 1982 based on
aerial photographs of a specific flight in the glaciated areas
of the Pyrenees.
The values of ice thickness from the GPR reported in Cam-
pos et al. (2021) also show significant differences not consis-
tent with our results. In 1994, the ERHIN programme esti-
mated a maximum ice thickness of 52 m using 17 transects
spaced 100 m apart (Arenillas-Parra et al., 2008; Jiménez-
Vaquero, 2016). In 2008, those authors determined a maxi-
mum ice thickness of 30 m calculated from 31 GPR transects
(Jiménez-Vaquero, 2016). Considering these data, Campos
et al. (2021) reconstructed the subglacial topography of the
Aneto glacier, and based on this topography they determined
a maximum ice thickness of 55 m for 1983, 37 m for 2006
and 29 m for 2015. These values are in stark contrast to
our estimates (maximum of 96.5 m in 1981, 52.5 m in 2011,
44.7 m in 2020, 43.5 m in 2021 and 41.8 m in 2022). Com-
paring the values of remaining thickness reported in 2008
(maximum ice thickness of 30 m; Jiménez-Vaquero, 2016)
and the rate of ice thickness loss (−1.0 m yr−1) established
by Vidaller et al. (2021) for the period 2011–2020, the ex-
pected maximum thickness in 2020 would be 18 m instead
of the 44.7 m we observed in 2020. Additionally, large areas
currently covered by the glacier would be ice free according
to the previous ice thickness loss estimates. Considering that
we used comparable values for wave propagation velocity of
GPR signal to those used in the above-cited work, the dif-
ferences between previous literature studies and the glacier
thicknesses reported here are likely related to the more mod-
ern and accurate antennas used in our survey and the much
denser net of transects conducted in the 2020 campaign. This
methodology significantly reduces the uncertainties associ-
ated with the interpolation process, making the results ob-
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3188 I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022
tained here more robust, and also permits a better understand-
ing of the glacier’s dynamic and its future behaviour.
4.3 Future perspectives
The rate of surface and ice thickness losses calculated in this
study and the reconstruction of ice thickness for the year
2022 indicate the critical situation of this glacier. There are
no signs of slowdown in glacier surface and thickness loss
rates; on the contrary, we have observed the high vulnerabil-
ity of the Aneto glacier to the occurrence of extremely hot
summers in recent years, as in 2022, when summer tempera-
tures were 0.5 ◦C above the mean for the period 2007–2022,
according to the Renclusa station (2140 m a.s.l.), and almost
2◦C in general in the Iberian Peninsula (AEMET). Thus, the
continued loss of surface area and thickness could be due to
an increase in temperature.
Taking into account the average current glacier thick-
ness of 11.9 m, we can affirm that the Aneto glacier is in-
deed in its terminal stage, with evident fragmentation into
smaller ice bodies, the absence of a significant accumula-
tion zone and obvious signs of ice stagnation. In this context,
glacier retreatment is exposing new areas of unconsolidated
bedrock material (granite boulders and debris) and destabil-
ising cirque walls in many areas. This process is also ac-
companied by a degradation of surrounding wall permafrost
(Rico et al., 2021). Under this situation the occurrence of
unusual warm periods, such as those observed in the 2021–
2022 period, triggered hazardous rockfalls, as were also no-
ticed in other mountain areas (Huggel et al., 2010; Kellerer-
Pirklbauer et al., 2012). This behaviour could also anticipate
the behaviour of other temperate mountain glaciers in their
final deglaciation phases.
Another aspect that determines the evolution of the Aneto
glacier is the darkening of the glacier surface. However, a
detailed quantification of the darkening of the glacier surface
and its effect on the energy and mass balance has not been
carried out yet. Early spring (summer) snowmelt and glacier
thickness loss result in a grey (dark) appearance of the glacier
surface, which reduces the albedo effect and increases the
absorption of thermal energy, leading to an acceleration of
glacier surface and thickness losses (Shaw et al., 2021). The
obvious similarities with the remaining glaciers of the range
suggest that the Pyrenees may become an ice-free mountain
range in the next few decades.
The rise in temperature in recent decades, combined with a
slight decrease in precipitation, has resulted in less snow ac-
cumulation during the winter months. This results in longer
exposure of the glacier during the ablation season, which in-
creases the melting of the glacier from year to year. Com-
pared to Pyrenean glaciers that have a minimal contribution
to water resources in downstream areas (López-Moreno et
al., 2020; Milner et al., 2017), changes in snowpack can lead
to severe changes in the downstream water regime (García-
Ruiz et al., 2011).
Also of note is the presence and development of new
proglacial lakes, as in the case of Ibón Innominato. This
small lake is in constant change due to the surface and thick-
ness of the glacier, where the retreat of the glacier front has
opened new outlets beneath the glacier, and consequently the
water level of the lake decreases. Similarly, as the Aneto
glacier shrinks, other lakes would be formed in the depres-
sion areas derived from the subglacial topography. The pres-
ence of proglacial lakes negatively affects the glacier’s equi-
librium by acting as an energy collector and accelerating the
rate of thawing at the front of the glacier (Otto, 2019). In ad-
dition, the dark appearance of the glacier surface caused a
decrease in albedo and therefore an increase in the surface
and thickness losses of the glacier (Yue et al., 2020).
On the other hand, the maximum ice thickness (>44 m)
is located under the Maldito pass, a protected area fed by
avalanche channels and protected by the shadow of the
Maldito peak. In these areas, longer persistence of the ice
body is expected.
The fast surface loss of the Aneto glacier in the last few
decades and the relatively low ice thickness observed to-
gether with the potential development of new lakes clearly
show the consequences of climate change in mountain ar-
eas. Those changes happening nowadays in most mountain
glaciers (Kääb et al., 2021; Barrand et al., 2017; DeBeer and
Sharp, 2009) will have a major impact on mountain land-
scapes and ecosystems (Huss et al., 2017), showing the ne-
cessity of monitoring and understanding the recent fast evo-
lution of these environments.
How long the glacier will maintain the ice movement and
a surface greater than 2 ha to still be considered a glacier is
a very uncertain issue to be estimated. The duration of the
glacier depends on several factors, such as the temperature
evolution in the next few years, the evolution of precipita-
tion (mainly snowfall in winter), the ability of the glacier
to transport the debris fallen from the headwalls (and avoid
the darkening of the surface), possible events of dust deposi-
tion (which may be frequent in winter and spring) and many
other factors. In addition, according to the study by Vidaller
et al. (2021), it is possible that these very small glaciers, once
they become smaller than 10 ha, will have a greater topocli-
matic control, so their preservation could be prolonged if
there are no more very hot summers, as in 2022. Otherwise,
glacier extinction could be imminent if there are a few sum-
mers like 2022 in the next decade. However, more detailed
studies are needed to answer such a simple question to reduce
the uncertainty in observations and simulations and also to
provide a deeper understanding of those processes that gov-
ern small and very small glaciers.
5 Conclusions
The Aneto glacier, although it is considered a very small
glacier, is the largest glacier in the Pyrenees and also the
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I. Vidaller et al.: The Aneto glacier’s evolution from 1981 to 2022 3189
largest in southern Europe. However, climate change has
accelerated its disappearance, in line with other glaciers in
the range. The evolution of close-range remote sensing tech-
niques allowed us to observe the glacier surface in a very
high level of detail that permits comparison between differ-
ent years’ surface (DEMs) of the glacier and evaluation of its
changes.
For the period 1981–2022 the Aneto glacier surface has
diminished 64.7 % (from 135.7 ha (1.36 km2) to 48.1 ha
(0.48 km2)), and its front has shifted from 2828 to 3026 m.
It has also been divided into two bodies between 2015 and
2016, and a proglacial lake has appeared in front of it in the
last few years. The annual rate of surface loss has been con-
stant over time (−2.2 ha yr−1), but the relative surface loss of
the glacier surface has increased during the study period.
The mean ice thickness loss was estimated at 30.5 m for
this 41-year period (with maximum losses over 80 m), with
a specific mass balance of −0.7 m w.e. yr−1. However, the
annual specific mass balance ratio has been increasing; in
fact it quadrupled (−2.7 m w.e. yr−1for the period 2021–
2022) over the period 1981–2022. Using GPR measure-
ments, we have estimated a mean of 44.7m of ice thickness
in 2020. GPR data and ice thickness loss estimated with UAV
data have been used to infer the actual mean ice thickness,
which was 11.9 m. The ice thickness distribution shows ar-
eas around the glaciers with very little thickness (<2 m), so
these zones are very close to becoming deglaciated during
the coming summers. The surface and thickness losses of the
Aneto glacier indicate the critical situation of this ice mass. It
is in its terminal stage, displaying fragmentation into smaller
ice bodies and the presence of debris cover in some areas.
Data availability. At the time of publication, the database
of glacier thickness changes and glacier delimitation in
1981, 2020, 2021 and 2022 will be available through
https://doi.org/10.5281/zenodo.7472185 (Vidaller et al., 2022).
Supplement. The supplement related to this article is available on-
line at: https://doi.org/10.5194/tc-17-3177-2023-supplement.
Author contributions. Conceptualisation: IV, JILM, EI, JR;
methodology: IV, EI, LMdR, JR; software: IV, EI, JR; validation:
IV, EI, LMdR, JR, JILM; formal analysis: IV, JR, EI; investigation:
all authors; resources: JILM; data acquisition: all authors; writing
(original draft preparation): IV; writing (review and editing): all
authors; visualisation: IV; funding acquisition: JILM. All authors
have read and agreed to the published version of the paper.
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Acknowledgements. We thank the Spanish National Geographic In-
stitute (IGN) for the collection, archiving and distribution of the
aerial photographs. We also thank the NextGIS/QuickMapServices
plugin (Original Work Published in 2014), available online at https:
//github.com/nextgis/quickmapservices (last access: 2 June 2021).
Thanks are also owed to AEMET for sharing the climatic data of
Renclusa hut.
Financial support. This work was supported by the Interreg-
POCTEFA project OPCC ADAPYR and Spanish Ministry of
Economy and Competitiveness project (project no. CGL2017-
82216-R), and the Spanish Ministry of Science and Innovation
(grant nos. PID2020-113247RB-C21 and PID2021-124220ob-
100/MARGISNOW). Jesús Revuelto has been supported by the
projects Juan de la Cierva I (project no. IJC2018-036260-I) and
Ramón y Cajal (project no. RYC2021-033859-I). Ixeia Vidaller
is enrolled in the PhD programme at the University of Zaragoza
(grant no. FPU18/04978). Eñaut Izagirre is supported by the
UPV/EHU (grant no. PPGI19/02) and the Consolidated Research
Group IT1678-22 (Basque Country Government). Esteban Alonso-
González has been funded by the CNES postdoctoral fellowship.
We acknowledge support of the publication fee by the CSIC
Open Access Publication Support Initiative through its Unit of
Information Resources for Research (URICI).
Review statement. This paper was edited by Nicholas Barrand and
reviewed by Pierre Pitte and one anonymous referee.
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