Acceleration and interannual variability of creep rates in mountain permafrost landforms (rock glacier velocities) in the European Alps in 1995–2022

Abstract

Cryospheric long-term timeseries get increasingly important. To document climate-related effects on long-term viscous creep of ice-rich mountain permafrost, we investigated timeseries (1995–2022) of geodetically-derived Rock Glacier Velocity (RGV), i.e., spatially averaged interannual velocity timeseries related to a rock glacier unit or part of it. We considered 50 RGV from 43 rock glaciers spatially covering the entire European Alps. Eight of these rock glaciers are destabilized. Results show that RGV are distinctly variable ranging from 0.04 to 6.23 m a-1. Acceleration and deceleration at many rock glaciers are highly correlated with similar behaviour over 2.5 decades for 15 timeseries. In addition to a general long-term, warming-induced trend of increasing velocities, three main phases of distinct acceleration (2000–2004, 2008–2015, 2018–2020), interrupted by deceleration or steady state conditions, were identified. The evolution is attributed to climate forcing and underlines the significance of RGV as a product of the Essential Climate Variable permafrost. We show that RGV data are valuable as climate indicators, but such data should always be assessed critically considering changing local factors (geomorphic, thermal, hydrologic) and monitoring approaches. To extract a climate signal, larger RGV ensembles should be analysed. Criteria for selecting new RGV-sites are proposed.

Full text

Environ. Res. Lett. 19 (2024) 034022 https://doi.org/10.1088/1748-9326/ad25a4
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LETTER
Acceleration and interannual variability of creep rates in mountain
permafrost landforms (rock glacier velocities) in the European
Alps in 1995–2022
Andreas Kellerer-Pirklbauer1,∗, Xavier Bodin2, Reynald Delaloye3, Christophe Lambiel4,
Isabelle Gärtner-Roer5, Mylène Bonnefoy-Demongeot6, Luca Carturan7,8, Bodo Damm9,
Julia Eulenstein1, Andrea Fischer10, Lea Hartl10, Atsushi Ikeda11, Viktor Kaufmann12,
Karl Krainer13, Norikazu Matsuoka11, Umberto Morra Di Cella14, Jeannette Noetzli15,
Roberto Seppi16, Cristian Scapozza17, Philippe Schoeneich18, Martin Stocker-Waldhuber10,
Emmanuel Thibert6and Matteo Zumiani19
1Institute of Geography and Regional Science, Cascade—The Mountain Processes and Mountain Hazards Group, University of Graz,
Graz, Austria
2EDYTEM, CNRS, Université Savoie Mont Blanc, Chambéry, France
3Department of Geosciences, University of Fribourg, Fribourg, Switzerland
4Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
5Department of Geography, University of Zurich, Zurich, Switzerland
6Université Grenoble Alpes, INRAE, CNRS, IRD, Grenoble-INP, Institut des Géosciences de l’Environnement (IGE), Grenoble, France
7Department of Land, Environment, Agriculture and Forestry, University of Padova, Padova, Italy
8Department of Geosciences, University of Padova, Padova, Italy
9Department II—Applied Physical Geography, University of Vechta, Vechta, Germany
10 Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Innsbruck, Austria
11 Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
12 Institute of Geodesy, Working Group on Remote Sensing and Photogrammetry, Graz University of Technology, Graz, Austria
13 Institute of Geology, University of Innsbruck, Innsbruck, Austria
14 Regional Agency for the Protection of the Environment (ARPA) - Valle d’Aosta, Saint-Christophe, Italy
15 WSL Institute for Snow and Avalanche Research SLF; Alpine Environment and Natural Hazards/CERC, Davos, Switzerland
16 Department of Earth and Environmental Sciences, University of Pavia, Pavia, Italy
17 Institute of Earth Sciences, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland
18 Institute of Alpine Geography, University Grenoble Alpes, Grenoble, France
19 Geological Service, Autonomous Province of Trento, Trento, Italy
∗Author to whom any correspondence should be addressed.
E-mail: andreas.kellerer@uni-graz.at
Keywords: Rock Glacier Velocity (RGV), Essential Climate Variable (ECV), terrestrial geodetic monitoring, annual surveys,
European Alps, permafrost
Supplementary material for this article is available online
Abstract
Cryospheric long-term timeseries get increasingly important. To document climate-related effects
on long-term viscous creep of ice-rich mountain permafrost, we investigated timeseries
(1995–2022) of geodetically-derived Rock Glacier Velocity (RGV), i.e. spatially averaged
interannual velocity timeseries related to a rock glacier (RG) unit or part of it. We considered 50
RGV from 43 RGs spatially covering the entire European Alps. Eight of these RGs are destabilized.
Results show that RGV are distinctly variable ranging from 0.04 to 6.23 m a−1. Acceleration and
deceleration at many RGs are highly correlated with similar behaviour over 2.5 decades for 15
timeseries. In addition to a general long-term, warming-induced trend of increasing velocities,
three main phases of distinct acceleration (2000–2004, 2008–2015, 2018–2020), interrupted by
deceleration or steady state conditions, were identified. The evolution is attributed to climate
forcing and underlines the significance of RGV as a product of the Essential Climate Variable
(ECV) permafrost. We show that RGV data are valuable as climate indicators, but such data
© 2024 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
should always be assessed critically considering changing local factors (geomorphic, thermal,
hydrologic) and monitoring approaches. To extract a climate signal, larger RGV ensembles
should be analysed. Criteria for selecting new RGV-sites are proposed.
1. Introduction
Rock glaciers (RGs) are striking landforms of ice-
(super)saturated permafrost in periglacial mountain
environments [1–4]. According to the Rock Glacier
Inventories and Kinematics (RGIK) initiative of the
International Permafrost Association (IPA) [5], RGs
are defined as debris landforms generated by the
former or ongoing creep of perennially frozen ground
rich in ice. They are detectable in the landscape
with a distinctive front and lateral margins as well
as occasionally ridge-and-furrow surface topography.
Permafrost creep refers to the combination of both
internal deformation within the ice of the frozen
material (creep of permafrost body sensu stricto) and
shearing at one or more distinct horizons within or
near the base of the frozen body [4–6]. The latter
is not strictly related to the occurrence of RGs but
can also be the motion mechanism of further mass
movements in permafrost conditions. RGs are there-
fore key landforms for understanding the effects of
past, present, and projected climate change and thus
environmental conditions in the periglacial process
domain [7,8].
RGs are widespread in the European Alps (5769
RGs in Austria, [9]; 3261 RGs in France, [10]) and
globally [11]. Intact RGs consist of poorly sorted,
mostly angular rock material, ice from deep and
long-term freezing (mostly interstitial, segregation
or massive ice) with in cases remains of buried
surface ice (mostly from perennial ice patches or
small retreating glaciers; [12]), water, and air. RGs
develop over centuries to millennia as shown by rel-
ative or numerical dating approaches [13–16]. Even
though some RGs contain remains of buried surface
ice, local climate conditions keeping the subsurface
frozen for hundreds to thousands of years are indis-
pensable for their formation and preservation [17].
Environmental conditions are not stationary at such
time scales. Thus, RGs may experience strong vari-
ations in their rate of nourishment in debris and
their overall ice content over time [18]. Such vari-
ations might lead to multi-unit RG systems with sev-
eral simple or complex superimposed units ([19];
figure 1). The frozen materials of active RGs typic-
ally creep downslope with horizontal surface velocit-
ies of centimetres to metres per year [20,21]. In case
of the destabilization of RGs, velocities substantially
and heterogeneously increase impacting the surface
morphology forming cracks, scarps, or crevasses,
which might gradually lead to RG disintegration
[22–27].
Whereas debris and ice input to the RG system
is vital for its millennial development, the measured
decadal kinematics (i.e. horizontal and vertical sur-
face elevation change over time) has been discussed
jointly with other environmental parameters such as
ground temperature or hydrology to understand the
RG-climate-relationship [4,28,29]. The key relation-
ship is that the interannual variability of rock glacier
velocity in general as a generic term follows perma-
frost temperature and all related consequences, espe-
cially temperature-dependent viscosity and unfrozen
water content. In addition, availability and advection
of surface water into the RG sediments affects the
creep behaviour by changing the pore-water pressure
[30]. The combined geomorphological, soil thermal,
hydrological, and climatic forcing of rock glacier
creep highlights the environmental relevance of mon-
itoring such landforms. Therefore, ‘Rock Glacier
Velocity’ or (‘RGV’ became an associated product
of the Essential Climate Variable (ECV) permafrost
[31] within the Global Climate Observing System in
October 2022 [32,33]. RGV, contrasting to the gen-
eric usage of rock glacier velocity as described above,
is defined as a spatially averaged interannual hori-
zontal velocity timeseries related to a RG unit or a
part of it. Thus, RGV is a climate indicator that reflects
impacts from climate change on cold mountain envir-
onments without reducing climate change to only
temperature. Summary reports on RGV, presenting
this topic in a broader environmental framework,
have recently been published, covering such data from
different parts of the world [7]. This implies that
operational monitoring of RGs, as part of permafrost
observation, is increasingly important [21,34].
In this contribution we discuss 50 RGV meas-
ured at 43 RGs in the European Alps. We focus on
the time period 1995–2022 due to increased RGV
monitoring activities since the mid-1990s and dis-
cuss relations to climatic and environmental factors.
This joint research effort is a continuation of earlier
activities [35–37] but presents a more comprehens-
ive database for the entire European Alps. The aims
of this study are: (a) compilation of a homogenized
dataset of RGV based on geodetic methods for the
time period 1995–2022 for the European Alps, (b)
presentation of long-term monitoring results of the
collected timeseries, and (c) assessment of the influ-
ence of different environmental variables driving the
2

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Figure 1. Different examples of talus-connected RG systems delineated by the extended geomorphological footprint: (a)
mono-unit rock glacier system—simple unit; (b) mono-unit rock glacier system—complex unit with disintegration signs
(crevasses, cracks or scarps) and a partly-truncated front; (c) multi-unit rock glacier system—e.g. two adjacent units; (d)
multi-unit rock glacier system—e.g. two overlapping units (for explanation of the terms used in this graphic see [5,19]).
temporal and spatial patterns of RGV evolution in the
European Alps.
2. Studied rock glaciers
The European Alps (hereafter Alps) are the most
prominent mountain range entirely located in Europe
and reach a maximum elevation of 4808 m asl at
Mont Blanc/Monte Bianco. The mountain range sep-
arates the marine west-coast climates of Western
Europe from the Mediterranean areas to the south
related to the arch shape of the mountain range.
The Alps act as a barrier in relation to the mid-
latitude cyclones that cross Central Europe usu-
ally from west to east. Climatic features include
distinct gradients in all three spatial dimensions
of space (latitude, longitude, elevation) leading to
four main types of transitions: south-north (from
Mediterranean to temperate climate), west-east (from
humid-oceanic to dry-continental), peripheral-
central (humid-cool margin of the Alps to dry-warm
inner alpine climate), and hypsometric (altitudinal
zonation) [38,39]. Around 6220 km2of the Alps
are underlain by permafrost if a permafrost index of
⩾0.5 is used as a realistic threshold for permafrost
existence [40].
In this study, we selected 43 RGs (table 1)
with appropriate data located between latitude
45◦01′00′′N and 46◦59′38′′ N and between longit-
ude 6◦23′57′′E and 13◦17′06′′ E. The RG sites cover
a west-east distance of 570 km and a north-south
one of 220 km (figure 2). Our dataset includes 1
RG in France, 31 in Switzerland, 5 in Italy and 6
in Austria. Almost all the studied landforms are
considered as RGs as previously defined. The only
possible exception is a glacial-permafrost composite
landform I05/Amola [41]. Some RGs are also close to
present glaciers. The creep of such RGs may have been
influenced by changes in glaciation (e.g. C23/Gruben;
[42,43]).
The investigated RGs terminate at elevations from
2230 to 2810 m asl, extend over elevation ranges from
50 to 450 m, cover an area of 0.005 (C35/Lapires)
to 0.42 km2(A06/Äußeres-Hochebenkar), and
are mostly north-oriented (figure 3; table 1).
Metamorphic rocks build-up 34 RGs (mostly gneiss
and schist), 7 RGs are composed of igneous rocks
(granodiorite, granite), and two of sedimentary rocks
(limestone, shale). Eight RGs showed distinct signs of
destabilization in the observation period with cracks,
crevasses, or scarps (tables 1and 2; reaction type 2
[accelerated displacement rate of part or whole of a
RG] or 3 [dislocation and rupture of the lower part
of a RG] according to [24]).
Figure 4depicts examples of the 43
RGs. F01/Laurichard (figure 4(a)) is a well-
developed, talus-connected mono-unit RG system
3

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Table 1. Summary of the 43 studied rock glaciers with one key publication per site for more information. Relevant for the codes:
A=Austria, F =France, I =Italy, C =Switzerland; Regions: AA =Austrian Alps; FA =French Alps, IA =Italian Alps, LV =Lower
Valais, UV =Upper Valais, EN =Engadine, TI =Ticino.
Cod.
Rock glacier/
Region Lat N (◦) Long E (◦)
Elev. range
(m asl)
Area
(km2)a
Asp.
(class) Lith.bKey publication
A03 Weissenkar/AA 46◦57′29′′ 12◦45′11′′ 2610–2720 0.125 W ms Kellerer-Pirklbauer
and Kaufmann [28]
A04 Hinteres
Langtalkar/AAe
46◦59′11′′ 12◦46′48′′ 2455–2700 0.168 NW ms Kellerer-Pirklbauer
and Kaufmann [47]
A05 Dösen/AA 46◦59′11′′ 13◦17′06′′ 2340–2650 0.194 W og Kellerer-Pirklbauer
et al [48]
A06 Äußeres
Hochebenkar/AAe
46◦50′01′′ 11◦00′36′′ 2420–2870 0.423 NW ms,pg Hartl et al [49]
A08 Tschadinhorn/AA 46◦59′38′′ 12◦41′47′′ 2575–2800 0.061 NW am Kaufmann
et al [50]
A09 Leibnitzkopf/AAe46◦55′51′′ 12◦42′43′′ 2450–2580 0.062 W ms Kaufmann et al [51]
F01 Laurichard/FA 45◦01′00′′ 6◦23′57′′ 2420–2640 0.084 N gr Thibert and Bodin
[52]
I01 Lazaun/IA 46◦44′43′′ 10◦45′13′′ 2495–2810 0.175 NNE ms,pg Fey and Krainer [53]
I02 Gran Sometta/IA 45◦55′13′′ 7◦40′13′′ 2640–2770 M: 0.240
S: 0.077d
NNW cs Bearzot et al [54]
I03 Napfen/IA 46◦58′06′′ 12◦07′42′′ 2565–2842 0.410 NW ms,pg Damm [55]
I04 Maroccaro/IA 46◦13′02′′ 10◦34′28′′ 2750–2870 0.025 SW to Seppi et al [41]
I05 Amola/IAh46◦12′04′′ 10◦42′43′′ 2310–2580 0.124 NNE to Seppi et al [41]
C01cAget-Rogneux/LVf46◦00′35′′ 7◦14′24′′ 2810–2890 0.038 SE mm Wee and Delaloye
[56]
C02cMont-Gelé B/LVi46◦05′49′′ 7◦17′22′′ 2600–2740 0.022 NE gn Delaloye et al [21]
C03cMont-Gelé C/LVi46◦05′49′′ 7◦17′13′′ 2620–2820 0.035 N gn Delaloye et al [21]
C04cTsarmine/LVe46◦02′47′′ 7◦30′31′′ 2480–2650 0.052 W og Vivero et al [27]
C05cBecs-de-
Bosson/LVj
46◦10′23′′ 7◦30′41′′ 2610–2850 0.175 NW cs Delaloye et al [21]
C06cGemmi-
Furggentälti/UV
46◦24′23′′ 7◦37′53′′ 2440–2700 0.027 NNW ls Delaloye et al [21]
C07cHungerlitälli 1/UV 46◦11′14′′ 7◦43′28′′ 2630–2780 0.049 NNW og,ph Delaloye et al [21]
C08cHungerlitälli 3/UV 46◦11′11′′ 7◦43′00′′ 2515–2650 0.048 NW og,ph Delaloye et al [21]
C09 Büz North/EN 46◦32′00′′ 9◦49′03′′ 2775–2840 0.017 NNE sh Ikeda et al [57]
C10 Petit-Vélan/LVe45◦54′51′′ 7◦13′51′′ 2520–2810 0.078 NE gn Delaloye and
Morard [58]
C11 Lac des Vaux B/LV 46◦05′58′′ 7◦16′33′′ 2720–2800 0.007 NW gn Delaloye et al [21]
C12 Lués Rares/LV 46◦06′13′′ 7◦17′45′′ 2320–2450 0.029 NE gn Delaloye et al [21]
C13 Les Cliosses/LV 46◦08′40′′ 7◦30′10′′ 2450–2550 0.038 W cs Delaloye et al [21]
C14 Tsaté-Moiry 1/LV 46◦06′40′′ 7◦33′15′′ 2680–2850 0.033 NE cs Lambiel [59]
C15 Tsaté-Moiry 2/LV 46◦06′35′′ 7◦33′20′′ 2720–2850 0.056 NE cs Lambiel [59]
C17 Grosse Grabe/UVe46◦09′03′′ 7◦49′13′′ 2600–2740 0.064 WNW ag Delaloye et al [23]
C18 Gugla-Bielzug/UVe46◦08′20′′ 7◦49′06′′ 2600–2820 0.095 W ms Delaloye et al [23]
C19 Dirru/UVe46◦07′18′′ 7◦49′04′′ 2530–2940 0.077 WNW gn Delaloye et al [23]
C21cGrosses Gufer/UV 46◦25′31′′ 8◦04′57′′ 2380–2600 0.176 NW gn Delaloye et al [21]
C23cGruben/UV 46◦10′26′′ 7◦57′57′′ 2770–2890 0.364 WSW gn Gärtner-Roer et al
[42]
C24cMonte Prosa A/TI 46◦33′52′′ 8◦34′46′′ 2440–2570 0.046 N gr Mari [60]
C25cMonte Prosa B/TI 46◦33′46′′ 8◦34′28′′ 2450–2530 0.025 NW gr Mari [60]
C26cStabbio di
Largario/TI
46◦28′40′′ 8◦59′10′′ 2230–2550 0.116 N og Scapozza et al [61]
C27cPiancabella/TI 46◦27′02′′ 9◦00′10′′ 2450–2550 0.023 NE gn Scapozza et al [62]
C29 Ganoni di
Schenadüi/TI
46◦33′20′′ 8◦44′50′′ 2480–2640 0.067 N og Delaloye et al [21]
(Continued.)
4

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Table 1. (Continued.)
Cod.
Rock glacier/
Region Lat N (◦) Long E (◦)
Elev. range
(m asl)
Area
(km2)a
Asp.
(class) Lith.bKey publication
C30cMuragl/EN 46◦30′24′′ 9◦55′42′′ 2490–2750 0.151 NW gd,gr Cicoira et al [30]
C31cMurtèl-
Corvatsch/EN
46◦25′44′′ 9◦49′19′′ 2630–2800 0.070 WNW gd,gr Cicoira et al [30]
C32 Marmugnun/EN 46◦25′41′′ 9◦49′09′′ 2650–2700 0.044 NW gd,gr Delaloye et al [21]
C33 Chastelets/EN 46◦25′44′′ 9◦48′48′′ 2650–2700 0.034 NW gd,gr Delaloye et al [21]
C34 Tsavolire/LV 46◦09′57′′ 7◦30′30′′ 2800–2900 0.034 NW cs Delaloye and Staub
[63]
C35cLapires/LVg46◦06′00′′ 7◦17′00′′ 2360–2650 0.005 NNE ps Mollaret et al [64]
aarea derived from GIS-based delineation by the different authors.
bdominant lithology: ms =mica schist, og =ortho gneiss, pg =para gneiss, gn =gneiss, am =amphibolite, gr =granite,
cs =calcareous schist, to =tonalite, mm =various metamorphic rocks (e.g. quartzites), ls =limestone, ph =phyllite, sh =shale,
ag =augen gneiss, gd =granodiorite, ps =prasinite.
cpart of the Swiss Permafrost Monitoring Network (PERMOS), n=17 [65].
dM=main, S =secondary.
edistinct signs of destabilisation in 1995–2022 with larger cracks and crevasses (reaction type 2 or 3 according to Schoeneich et al [24].
fglacier forefield; back-creeping push moraine.
gtalus slope with a small rock glacier.
hglacial-permafrost composite landform.
ifurther name ‘Yettes Conj`
a’.
jfurther name ‘Réchy’.
Figure 2. Study sites: (a) location of the 43 investigated RGs in the Alps grouped into seven different regions. Inset map (b) refers
to south-western Switzerland and north-western Italy with permafrost distribution visualising that the studied RGs are
dominantly in marginal mountain permafrost positions. The four automatic weather stations considered in the text are localized
(Besse 1520 m asl, Säntis 2502 m asl, Zugspitze 2960 m asl, Sonnblick 3109 m asl). For explanation of RG-codes see table 1. Data
sources: [44] (topography), [45] (perimeter of Alps), [46] (glaciers in 2015), [40] (permafrost).
5

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Figure 3. Distribution and some morphometric characteristics of the 43 RGs studied: (a) dominant aspect and (b) upper and
lower elevation limit, elevation range, and surface area.
Figure 4. Four examples of investigated RGs: (a) Laurichard RG—mono-unit rock glacier system; (b) Muragl RG—multi-unit
rock glacier system; (c) Hinteres Langtalkar RG—multi-unit rock glacier system with disintegration features; (d) Tsarmine
RG—mono-unit rock glacier system with truncated front (Photos: (a) Xavier Bodin, (b) Isabelle Gärtner-Roer, (c) Andreas
Kellerer-Pirklbauer, (d) Christophe Lambiel).
(cf figure 1(a)). C30/Muragl (figure 4(b)) is a
multi-unit RG system with partly overlapping units
and several lobes (cf figures 1(c) and (d)). The
two other examples show other less common mor-
phological characteristics. A04/Hinteres-Langtalkar
(figure 4(c)) is a multi-unit RG with two root-
ing zones (both formerly occupied by a glacier)
with distinct disintegration features and an advan-
cing, partly collapsing front (figure 1(b)). Finally,
C04/Tsarmine (figure 4(d)) is a mono-unit RG system
6

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
with a well-developed truncated front, which supplies
a steep alpine torrent with debris (figure 1(b)).
3. Material and methods
3.1. Geodetic monitoring of rock glaciers
RG monitoring has a long tradition in the Alps
[66,67]. Different terrestrial and remote sensing
techniques have been applied to quantify the kin-
ematics of permafrost creep in the past [3]. [68]
began in 1938–1955 to quantify RGVs at the Äußeres
Hochebenkar RG (A06 in table 1) using terrestrial
photogrammetry. Since 1951, creep velocity has been
measured at this RG by terrestrial geodetic methods
and since 2008 by Global Navigation Satellite System
(GNSS) technology [69–73]. A06 has the world-
wide longest record of RGV data [48,70] although
several gaps exist [49,73]. Annual terrestrial geo-
detic surveys to derive RGV have been included
as part of the long-term observation strategy of
the Swiss Permafrost Monitoring Network PERMOS
since 2008 to complement the measurements of
permafrost temperature and changes in ground ice
content [74].
At many of the RGs studied, the monitoring
technique has changed similarly over time from tra-
ditional geodetic surveying with a total station to
satellite-based geodetic surveying using GNSS. The
technical change typically occurred between the mid-
2000s and mid-2010s. The real-time kinematic GNSS
approach is commonly applied [75]. At present,
GNSS is used for annual geodetic velocity monitoring
at 33 out of the 43 RGs. At eight RGs, geodetic mon-
itoring is accomplished with a total station (I04, I05,
C07, C08, C30-C33). We included one RG with a per-
manent GNSS instrument (C34/Tsavolire) for com-
parison with the nearby, well-monitored C05/Becs-
de-Bosson. The permanent GNSS station sends data
every hour for a new position calculation. The sys-
tem comprises another station on stable terrain next
to the RG allowing for a differential positioning cal-
culation. Further RGs in Switzerland are equipped
with permanent GNSS instruments [76] but they
were not further considered in this study. Finally, at
one RG (I03/Napfen) a laser distance measurement
device is used to measure RG frontal advancement
(i.e. RG advance) since 1992. This dataset was added
for long-term RGV comparison reasons. Table 2sum-
marises geodetic RGV monitoring backgrounds for
all 43 RGs.
Different space- and air-borne remote sensing
technologies have been used in addition at several
RGs in the past. In recent years unmanned aerial
vehicle (UAV)-based surveys of RGs gained import-
ance providing high-resolution photo-textured
3D models, i.e. digital twins, of the surveyed
RGs. Automatic processing, i.e. georeferencing, of
UAV-data is based on Structure-from-Motion tech-
nology, GNSS-derived photo locations, and a few
control points located on the ground of the area to be
mapped. Practical examples have already confirmed
that RGV derived from UAV-data, i.e. multi-temporal
digital orthophotos and/or surface models, compare
very well with geodetic point measurements if photos
taken are of high spatial resolution [49,51].
Two different coordinate systems are used to
describe surface velocity. In Eulerian coordinates, a
spatial coordinate is fixed in space (e.g. InSAR). In
Lagrangian coordinates, a spatial coordinate is fixed
in the material (e.g. geodetic observation point).
As far as possible, such geodetic observation points
should be distributed evenly at the surface of the
RG, on blocks embedded in the matrix of the act-
ive layer [74,75,77]. Persistent point definition is
either by simple hollows (dot, cross) engraved in the
surveyed boulder by means of chisel and hammer,
or by brass bolts rooted to the rock as known from
classical surveying [78]. A representative value for
the RGV is obtained by averaging the velocities of a
set of observation points, sometimes termed as ref-
erence points [65]. The selection of these points is
based on data quality and the assumption that they
represent the overall kinematics of the RG. In most
cases, such points are located in the central part of a
RG unit, although there is no standardized approach
for point selection. Inhomogeneous movement may
cause splitting the velocity dataset into two or even
more datasets, i.e. subunits of the RG with similar
movement (table 2). In some cases, only parts of the
RG are monitored due to destabilization processes or
inactive parts. Often, other (multi-annual) methods
complement the annual surveys, e.g. based on aerial
images, satellite data, or terrestrial laser scanning.
3.2. Data base and analyses
We used 2D (horizontal) velocity values, as the ver-
tical dimension is affected by changing strain rate
(extension/compression rate) over time and distance
and by potential annually varying ice melt-induced
subsidence [79]. The number of observation (all) and
reference (selected) points for each of the 43 RG are
listed in table 2. In most cases, RGV is calculated as the
arithmetic mean of all reference points. This applies
either to the entire RG system or to the respect-
ive areas or zones if the reference points have been
divided into two groups based on differing kinematic
behaviour (i.e. A04/Hinteres-Langtalkar, I02/Gran-
Sometta, C04/Tsarmine, C05/Becs-de-Bosson,
C19/Dirru, C26/Stabbio-di-Largario, C29/Ganoni-
di-Schenadüi). The number of reference points
per RG varies between 1 (C34/Tsavolire) and 66
(C05/Becs-de-Bosson). Missing values (i.e. reference
points that were not measured in some years) poten-
tially skew the RG average velocity up or down. As
changes in velocity from year to year are commonly
7

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Table 2. Summary of monitoring history and study design for each of the 43 investigated RGs with reference to the responsible
institutions. Data since 1995 are considered in this study. Data gaps are not listed in this table.
Cod. Rock glacier/Re
Monit
initiationa
Monit
unitsb
Geodetic
metho
(present) c
No. pts
all (n)d
No. pts
reference
(n)e
Years with
RGV data
(n) Res. inst.f
A03 Weissenkar/AA 1997 1 GNSS 18 18 19 12,1
A04 Hinteres
Langtalkar/AAi
1999 2 (U/L) GNSS 38 U9; L6 U23/L23 12,1
A05 Dösen/AA 1995 1 GNSS 34 11 25 12,1
A06 Äußeres
Hochebenkar/AAi
1951
(1938h)
1 GNSS 46 39 23 10
A08 Tschadinhorn/AA 2014 1GNSS 14 14 812,1
A09 Leibnitzkopf/AAi2010 1 GNSS 15 15 12 12,1
F01 Laurichard/FA 1979 GNSS 35 5 27 2,6,18
I01 Lazaun/IA 2006 GNSS 52 39 5 13
I02 Gran Sometta/IA 2012 (M)
2015 (S)
2 (M/S) GNSS 62 M16; S8 M10/S7 3,14
I03 Napfen/IA 1992/93 1 Laser dist. 95 5 21 9,13
I04 Maroccaro/IA 2001 1 Total st. 25 23 14 7,16,19
I05 Amola/IA 2001 1 Total st. 25 24 15 7,16,19
C01gAget-Rogneux∗/LV 2001 GNSS 79 9 21 3
C02gMont-Gelé B/LV 2000 1 GNSS 37 23 22 4
C03gMont-Gelé C/LV 2000 1 GNSS 34 19 20 4
C04gTsarmine/LVi2004 2 (U/L) GNSS 53 U4; L3 U18/L18 3,4
C05gBecs-de-Bosson/LV 2004 2 (G1/LG2/U) GNSS 206 G1/L 45;
G2/U 21
U18/L18 3
C06gGemmi-
Furggentälti/UV
1998 1 GNSS 70 5 24 3
C07gHungerlitälli 1/UV 2001 1 Total st. 24 24 17 5
C08gHungerlitälli 3/UV 2002 1 Total st. 27 14 18 5
C09 Büz North/EN 1998 1 GNSS 13 4 21 11,15
C10 Petit-Vélan/LVi2005 1 GNSS 52 5 17 3
C11 Lac des Vaux B/LV 2005 1 GNSS 31 8 15 4
C12 Lués Rares/LV 2006 1 GNSS 21 10 16 4
C13 Les Cliosses/LV 2006 1 GNSS 33 24 16 4
C14 Tsaté-Moiry 1/LV 2005 1 GNSS 53 6 15 4
C15 Tsaté-Moiry 2/LV 2005 1 GNSS 25 16 13 4
C17 Grosse Grabe/UVi2007 1 GNSS 12 3 14 3
C18 Gugla-Bielzug/UVi2007 1 GNSS 44 5 15 3
C19 Dirru/UVi2007 2 (U/L) GNSS 75 U10; L24 U15/L15 3
C21gGrosses Gufer/UV 2007 1 GNSS 65 16 15 3
C23gGruben/UV 2012 1 GNSS 43 10 10 3
C24gMonte Prosa A/TI 2009 1 GNSS 34 16 10 3
C25gMonte Prosa B/TI 2009 1 GNSS 24 2 13 3
C26gStabbio di
Largario/TI
2009 2 (M/S) GNSS 33 M21; S4 M12/S11 17
C27gPiancabella/TI 2009 1 GNSS 28 20 14 17
C29 Ganoni di
Schenadüi/TI
2009 2 (U/L) GNSS 35 U3; L15 U11/L11 17
C30gMuragl/EN 2009 1 Total st. 20 20 13 5
(Continued.)
homogeneous at all observation points (high auto-
correlation), missing values can be determined math-
ematically through comparative analyses [52]. In few
cases, this was applied in our dataset. If boulders
are missing over time (e.g. fallen from the RG front
or into crevasses), the number of reference points is
reduced and a new RGV for the entire timeseries must
be calculated (e.g. I04/Maroccaro, I05/Amola). In
one case (A06/Äußeres-Hochebenkar), RGV is calcu-
lated in a two-step approach. First the four arithmetic
mean values for each of the four cross profiles (P0-P3)
are determined, then an average of the four mean val-
ues is calculated. P0 to P3 have been repeatedly set
back to the original line over the past decades (1997:
P0-P3; 2008: P1; 2021: P0-4). Thus, in the latter case
a mixed Eulerian–Lagrangian approach was chosen.
The second part of August and mid-September
to mid-October are the main periods of the annual
terrestrial geodetic RG measurements. However, the
surveys are not carried out on the same date and
vary across the study region as well as for individual
sites. For instance, the geodetic surveys in 2022 took
8

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Table 2. (Continued.)
Cod. Rock glacier/Re
Monit
initiationa
Monit
unitsb
Geodetic
metho
(present) c
No. pts
all (n)d
No. pts
reference
(n)e
Years with
RGV data
(n) Res. inst.f
C31gMurtèl-
Corvatsch/EN
2009 1 Total st. 11 11 13 5
C32 Marmugnun/EN 2009 1 Total st. 9 9 11 5
C33 Chastelets/EN 2009 1 Total st. 5 5 11 5
C34 Tsavolire/LV 2012 1 GNSSp 1 1 9 3
C35gLapires/LV 2013 1 GNSS 11 4 15 3
ainitiation of RGV monitoring—data gaps are not indicated.
bnumber of monitored units—in case of two units: U =upper part; L =lower part, M =main unit, S =secondary unit, G1/L =group
1, mostly lower part, G2/U =group 2, mostly upper part.
cgeodetic method: GNSS =annual GNSS campaign, laser dist. =laser distance measurement device, total st. =total station,
GNSSp =permanent GNSS instrument.
dnumber of measurement points.
enumber of points to calculate the average value—reference points.
finstitution according to the affiliation list.
gconsidered in PERMOS.
hterrestrial photogrammetry monitoring initiation.
idistinct signs of destabilisation in 1995–2022 with larger cracks and crevasses.
place between 16.08.2022 (A05/Dösen, C06/Gemmi-
Furggentälti) and 13.10.2022 (C14/Tsaté-Moiry-1,
C15/ Tsaté-Moiry-2, C35/ Lapires) over a period of
58 d. At C10/Petit-Vélan, survey dates varied between
17.7. and 8.10. in the period 1998–2022, i.e. a range
of 83 d. To account for the different time periods
between two field surveys at a given RG, we normal-
ized all RGV data to 365.25 d (one monitoring year).
This approach cannot account for seasonal variations
in creep velocity, which might play an important role
for some RGs [21,80] in case the geodetic survey dates
vary substantially from year to year at a given RG.
For years missing geodetic work, we calcu-
lated RGV from averaging multi-year observations.
Another option to fill data gaps would be to use the
remaining data to look for the best correlation with
another site, and then estimate the missing annual
values. As the distance between some studied RGs
is quite large and to not introduce a circular ref-
erence, we did not choose the latter approach. Our
longest timeseries with annual data since 1995 con-
sists of 25 monitoring years (A05/Dösen), whereas
the shortest one of only five years (I01/Lazaun). The
monitored RGs in the Swiss Alps are in four geo-
graphic regions. 24 RG units are in the Western (9
in the Upper and 15 in the Lower Valais), 7 in the
Southern (Ticino), and 5 in the Eastern Swiss Alps
(Engadine). Topoclimatic differences of these regions
are small and mainly due to their position related to
the Alpine Arc. The Valais and Engadine are charac-
terized by a rather dry inner-alpine climate, with the
Engadine being drier. The Ticino in the South of the
Alps receives more precipitation.
Absolute RGV were converted to relative RGV to
study the similarity of the behaviour. To minimize
the potentially large interannual variations of RGV
related to specific regional or site-dependent events,
we used the average annual value for a multi-year
period as a reference value and calculated the devi-
ation from the reference. Limited by the data availab-
ility at I01/Lazaun, we used the two-year time frame
2016–2018.
RGV data were compared to monthly mean val-
ues of air temperature of four automatic weather sta-
tions/AWS distributed over the Alps (figure 2) loc-
ated at Sonnblick (Austria), Zugspitze (Germany),
Säntis (Switzerland), and Besse (France). Finally, the
relationship between RGV and ground surface tem-
perature (GST) at snow-influenced sites was con-
sidered using monthly GST data from the Central and
Western Swiss Alps in 1999–2022. Depending on the
year, between 2 and 21 sites (with 1–15 measurement
locations per site) were used for average GST calcula-
tion. A detailed comparison of the RGV with ground
temperature or snow data at specific RG locations was
beyond the scope of this paper.
4. Results
4.1. Absolute rock glacier velocities
RGVs for all 50 datasets covering the time period
1995–2022 are depicted in figure 5indicating a gen-
eral increase in velocity over time. Seven different
regions of the Alps are distinguished for practical pur-
poses based on national borders (A, F, I) and geo-
graphic regions (within CH; cf above). Years without
measurements at individual RGs are either indic-
ated as data gaps or as averaged multi-annual val-
ues (inverted triangle). Appendix 1 summarises the
RGV for each RG unit and year. Results of absolute
RGV are not discussed here related to space reasons.
The different graphs in figure 5show, nevertheless,
that absolute RGV values vary substantially, ranging
from few centimetres up to 14 m a−1and following a
9

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Figure 5. Horizontal RGV since 1995 of 50 timeseries related to 50 RG units in the Alps, divided into seven regions (French and
Italian RGs are combine). For abbreviations of the site names see tables 1and 2. Note the different scale of the ordinate axis. For
graphical reasons, an identical field campaign date (15.08.) was used in the graphs for all RGs. For data see appendix.
comparable pattern in some regions (figures 5(a), (c)
and (e)) whereas for other regions this pattern seems
to be more diverse (figures 5(b), (d) and (f)).
4.2. Relative rock glacier velocities
RGV relative to the reference period 2016–2018 are
depicted in figure 6. Most RGs in Austria have a con-
sistent movement pattern. A notable exception to this
is A06/Äußeres-Hochebenkar, where mean move-
ment in 2021/22 has almost tripled since 2016–2018
(+187%, figure 6(a)) related to destabilization. The
differences between the RGV (including RG frontal
advance-data at I03/Napfen) in the French and Italian
Alps are much larger compared to the Austrian
RGs (figure 6(b)). The west-east distance between
F01/Laurichard and I03/Napfen is about 500 km
compared to Austria with only 180 km. I01/Lazaun is
another challenging case due to data gaps. However,
a similar movement pattern is still evident for F01
and the two data series at I02/Gran-Sometta (i.e, the
10

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Figure 6. Horizontal RGV relative to the reference period 2016–2018 (grey area) since 1995 of the 50 different RG units in the
Alps, divided into seven regions. For abbreviations of site names see tables 1and 2. The black line represents the average for each
region (French and Italian RGs are combined). In addition, in (b) also the Italian average excluding I03 (RG advance monitoring)
is depicted.
westernmost RG in Italy) for the time period 2012–
2022. Two distinct time intervals of higher RGV
occurred around 2014/15 and 2019–2021 with lower
RGV in 2017/18. An even earlier peak of the RGV was
notable in 2002–2004. Abrupt decelerations occurred
after the 2004- and 2015-peaks followed each time
by more gentle and longer acceleration periods. The
average movement pattern of all observed French and
Italian RGs seems—despite the huge area covered—
to be approximately the same as that of the Austrian
RGs.
The 15 RGs attributed to the Lower Valais region
(Swiss Alps) are mostly comparable to the other
regions discussed so far (figure 6(c)). However, sev-
eral possible outliers exist such as C01/Aget-Rogneux,
C02/Mont-Gelé-B, C11/Lac-des-Vaux-B, C12/Lués-
Rares or C14/Tsaté-Moiry-1. C12 reached almost
200% of the average velocity of 2016–2018 in 2015/16.
In contrast, C02 accelerated rapidly after 2018 reach-
ing 192% of its former velocity in 2019/20 fol-
lowed by a rapid decelerating phase afterwards. C14
followed the common pattern before 2016–2018
11

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
but decelerated substantially afterwards reaching in
2021/22 only about 10% of the former RGV.
In the Upper Valais several RGs are considered
destabilized since the mid-2000s based on satellite
interferometry [23]. Three of them are included in
this study (C17/Grosse-Grabe, C18/Gugla-Bielzug,
C19/Dirru). At C17 and C18 only the respective upper
and lower parts are assessed. At C19, a distinction is
made between a fast lower (C19-L) and a slow upper
(C19-U) part. The nine RG units assigned to this
region (figure 6(d)) also show a comparable move-
ment pattern with peaks around 2003/04, 2014/15
and 2019–2021 and lows around 2005–2007, and
2016/17. Distinct differences to this regional pattern
were observed at C17. Here, the lower part is destabil-
ized since many years. Whereas the movement had
almost stopped, this part of C17 was almost fully
covered by the deposits of several large rock falls in
summers 2019 and 2020 [81]. The upper part (con-
sidered in our study) has destabilized as well since
2020 and no survey has been possible since 2022
because of the rock fall danger. Another ‘outlier’ is
C19/Dirru. Whereas the upper part of this RG (C19-
U) follows the average regional pattern, the lower part
was very fast particularly in 2008/09, with a second
peak in 2013–2015 and has progressively decelerated
since then.
RGV monitoring in Ticino started in 2009 [82].
The velocity pattern of the seven RGV based on five
different RGs in this region (figure 6(e)) is only par-
tially congruent. At some RGs, the first year of meas-
urement (2009/10) started with higher values (C29-
U/Ganoni-di-Schenadüi upper part), others started
with very low ones (C27/Piancabella). Most RGs
reached distinct peaks in 2014/15 and 2019–2021. The
lowest velocities were recorded in 2016–2018 in most
cases.
In the Eastern Swiss Alps (figure 6(f)), the move-
ment pattern of the five observed RGs is only mod-
erately comparable with those of other regions of
Switzerland and nearby Austria and Italy. In 1998–
2009, only one RG was observed (C09/Büz-North)
and no surveys were accomplished in 2006 and
2007. All five RGs reached their velocity peaks
in 2014–2017: one in 2013/14 (C09), three in
2014/15 (C31/Murtèl-Corvatsch, C32/Marmugnun,
C33/Chastelets), and one in 2016/17 (C30/Muragl).
A less pronounced second peak occurred in 2019/20
followed by distinct deceleration.
5. Discussion
5.1. Similar behaviour or outlier: where to draw the
line?
The averaging of many individual observations for
one geomorphic process (e.g. glacier recession; [46])
may help to better understand signals of landscape
adaptation to environmental changes. Figure 7and
table 3combine the mean of each of the six regions.
Averaged over all 50 RG units (black curve in figure 7),
the following alpine-wide RGV pattern emerged:
gentle acceleration phase in 1995–1999, a brief decel-
eration phase in 1999/2000, a faster acceleration phase
until 2003 with a distinct peak in 2003/04 (also
observed at other RGs, [83]), followed by a distinct
drop of RGV and stable conditions in 2005–2008.
This is followed by a general acceleration phase with a
second, much higher peak in 2014/15, followed again
by a distinct drop of RGV until 2016, stable condi-
tions until 2018, and a last remarkable velocity peak in
2019–2021. In 2021/22 velocities distinctly decreased
again.
The sample size was, however, far from uniform
over the time period 1995–2022, varying from less
than 10 before 2000 and 50 in the three observation
years 2015–2018. RGV of ⩾20 RGs have been avail-
able since 2004, >30 since 2007, and >40 since 2009
(figure 8). The longest dataset (excluding RG front-
monitoring at I03/Napfen) exists for the Austrian and
French Alps, the shortest for Ticino. Taking this data
limitation into account, the time period prior to 2009
is more uncertain in terms of an Alpine-wide state-
ment of RGVs.
Several questions arise from this general pattern,
such as: How do individual RGV correlate with each
other? Which RGs evolve similar to each other, and
which ones might be considered as outliers? How do
you even define an outlier in this context? Should out-
liers be used as climate indicators? In addition, a time
period with a movement pattern which is congru-
ent to others might transform into a subsequent time
interval with outlier behaviour. Finally, what drives
these patterns?
Figure 9summarises the correlations of all 50
RGV based on the relative change of the surface flow
velocities using the reference period 2016–2018. This
matrix reveals that many RGs correlate well with each
other whereas others do not. A total of 584 pairs
of values correlate significantly with each other, 23
of which negatively indicating opposite creep beha-
viour between two RGs. For clarity, a RG with a sig-
nificant correlation percentage of 50% means that
the timeseries of this RG is significantly correlated
with half of the other RG in the dataset. A positive
correlation indicates that the RGV of two compared
RGs is relatively harmonious with each other. A neg-
ative correlation indicates an opposite RGV pattern
between a pair. Such a pattern might be explained
by in-situ permafrost degradation (ice loss, increase
of frictional forces) or by topographic conditions.
Some RGs moved out of steep slopes onto flatter ter-
rain, resulting in an increase in internal friction and
thus deceleration (C11/Lac-des-Vaux and C14/Tsaté-
Moiry-1). Finally, two RG that are negatively correl-
ated with most other RGs might, in turn, correlate
positively with each other (e.g. C15/Tsaté-Moiry 2
12

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Table 3. Averaged RGV relative to the reference period 2016–2018 of the 50 different RG units in the Alps in 1995–2022, divided into the six regions as defined in figure 2as well as the total average. Values in %. bold and
italics =minimum; bold =maximum.
RGV relative to the mean of 2016–2018 (%) in different monitoring years
Sample (n) 95/96 96/97 97/98 98/99 99/00 00/01 01/02 02/03 03/04 04/05 05/06 06/07 07/08 08/09
Austr. Alps (7) −55.0 −54.2 −44.9 −44.8 −50.9 −39.2 −39.0 −30.9 −16.9 −35.5 −45.1 −49.6 −50.8 −42.4
Fr +Ital. Alps (7) −29.5 −29.5 −23.8 −23.8 −40.1 −24.1 −7.6 6.2 12.0 −23.0 −37.1 −11.3 −35.0 −18.6
Lower Val. (15) −25.3 −0.7 −0.7 52.6 −31.4 −36.7 −42.0 −40.4 −10.5
Upper Val. (9) −23.3 −28.4 −18.0 −39.3 0.7 12.8 −11.6 −58.1 −56.1 −29.6 −5.4
Engadine (5) −33.6 −20.5 26.6 −10.5 −8.5 11.9 −8.4 −7.8 −7.8 −7.8 26.6
Ticino (7)
All (50) −52.6 −52.1 −43.9 −38.7 −43.5 −28.3 −21.1 −8.1 12.1 −27.3 −39.1 −37.2 −38.1 −13.9
RGV relative to the mean of 2016–2018 (%) in different monitoring years
Sample (n) 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18 18/19 19/20 20/21 21/22
Austr. Alps (7) −29.7 −24.8 −17.9 −10.7 12.0 43.6 38.3 4.0 −4.0 20.1 53.3 84.1 69.6
Fr +Ital. Alps (7) −7.6 −1.2 19.5 11.0 20.9 49.0 27.3 6.7 −6.7 14.3 42.1 39.1 35.8
Lower Val. (15) −4.8 1.7 0.8 29.0 35.4 53.3 47.2 −3.9 3.9 26.4 52.6 27.6 −6.3
Upper Val. (9) −11.8 −24.2 −12.0 1.5 16.4 34.0 23.8 −3.4 3.4 24.3 54.7 43.1 −3.6
Engadine (5) −18.0 −20.1 0.8 0.4 12.6 40.1 −0.4 12.7 −12.7 −7.7 16.9 8.7 −6.4
Ticino (7) 48.8 20.3 25.0 47.5 48.7 135.4 69.0 3.6 −3.6 81.4 129.2 125.9 57.4
RGV all (50) −1.9 −6.2 1.9 16.4 27.0 59.1 37.9 1.5 −1.5 28.5 60.0 54.0 19.8
13

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Figure 7. Regional and total averages of horizontal RGV relative to the reference period 2016–2018 (grey area) since 1995. The
number of RGV (maximum per year) for each region is indicated in brackets. I01 (RG advance monitoring) was excluded here
from the Italian sample but considered in the total average.
Figure 8. Number of RGV in the time period 1995–2022 in the different regions of the Alps used in this study.
and C19_L/Dirru-lower part). A positive or negative
correlation, therefore, also depends on the viewpoint
of the observer.
In figure 9, we used the arbitrary threshold of
60% (i.e. 29 data pairs) of significant correlations as a
conservative value to define a non-outlier behaviour
(forming the reduced sample). In statistics, an out-
lier is an observed value that contrasts sharply with
the overall pattern of values in a dataset. Therefore,
one could argue that the 60% value might be too low
or too high but can serve, nevertheless, as a starting
point.
All seven RGV in Austria correlate well and pos-
itive with each other and many other times series
in the dataset. A08/Tschadinhorn has the weakest
correlation with 42.9% probably due to the rather
short timeseries. The highest correlation degree for
Austrian RGs is 75.5% at A05/Dösen. The only French
RG considered in our study (F01/Laurichard) is in
good agreement with many of the other RGV and
yields the highest percentage of significant correla-
tions (77.6%; jointly with two RGV in Switzerland:
C05-U/Becs-de-Bosson upper part and C10/Petit-
Vélan) of all 50 RGV. None of the Austrian or the
French RGV might be considered as ‘outlier’.
The correlation result for the timeseries of Italian
RGs reveals a complex picture. Only I01/Lazaun
(59.2%) and I04/Marocarro (73.5%) correlate signi-
ficantly with more than half of the other timeser-
ies. Fewer data gaps and fewer averaged multi-annual
values at I01 (5 years with annual data, 7 years with
averaged multi-annual data) would have presumably
14

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Figure 9. Correlation matrix for all 50 RGV based on the relative change of the surface flow velocities (reference period
2016–2018). Correlations: significant at the <0.05 level. Percentage of significant correlations (distinguished between two levels:
p<0.01, p<0.05) are additionally shown in the lower graph. 15 RGV (∗) have at least 29 significant correlations (i.e. 60%) and
form the reduced sample. Number of negative correlations for each RG is indicated.
resulted in an even higher correlation percentage. The
correlation results for three of the six Italian RGV are
similar with 37% to 49%. These rather low values are
either related to short timeseries (I02-M, I02-S/Gran-
Sometta both units) or to a different RGV behaviour
or landform origin (I05/Amola; composite perma-
frost landform formed in glacier-permafrost contact;
[41]). The lowest correlation percentage was quan-
tified for I03/Napfen with only 8.2% where frontal
advance is monitored.
Of the 36 Swiss RGV, three (C01/Aget-Rogneux,
C31/Murtèl-Corvatsch and C32/Marmugnun) cor-
relate significantly with fewer than 10% with the other
timeseries. The RGV of these three landforms must be
considered as distinct outlier in an Alpine-wide con-
text. C01 is a back-creeping push moraine in continu-
ous degradation and should be excluded from RGV
comparisons [56]. The two landforms C31 and C32
are talus-connected RGs but behave differently com-
pared to most others. However, some of their peaks
15

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
are comparable to other RGs. C31 is very ice rich [84],
possibly C32 as well. This could lead to a delayed
reaction or a less pronounced one. Thus, reasons for
the pattern of C31 and C32 appear to be site-specific
effects that overcome the climatic ones. Interestingly,
neighbouring C33/Chastelets is in rather good agree-
ment with the Alpine wide signal (55.1% of correla-
tions). The RGV of C31 and C32 should be treated
with caution if compared to others.
Between 10 and 30% of significant correla-
tions were quantified for seven Swiss RGV dis-
tributed in the Engadine (C09/Büz-North), Lower
Valais (C11/Lac-des-Vaux-B, C14/Tsaté-Moiry-1,
C15/Tsaté-Moiry-2), Upper Valais (C23/Gruben)
and Ticino (one RG with two timeseries—C29-L
and C29-U/Ganoni-di-Schenadüi—and low ground
temperatures, [65]). While in some cases the unusual
creep behaviour may be related to site-specific effects
(C09, C11, C14, C15), in others it may be more related
to the short timeseries (e.g. C23 since 2012). A site-
specific effect was the exceptionally thick and long-
lasting snow cover in 2000/01 at C09 accelerating this
RG more than the heatwave in 2003. Thus, a possible
site-specific effect is a topography favourable—or
unfavourable—for snow accumulation.
Between >30% and 60% of significant correl-
ations were revealed for 18 Swiss RGV distributed
all over the Swiss Alps (e.g. Engadine: C30/Muragl,
C33/Chastelets; Lower Valais: C12/Lués-Rares,
C13/Les-Cliosses; Upper Valais: C17/Grosse-Grabe,
C18/Gugla-Bielzug; Ticino: C26-M/Stabbio-di-
Largario main unit, C27/Piancabella). The 18 RGV
in this percentage range can neither be seen as out-
liers nor as optimal climate indicators. This is partly
due to site-specific effects (e.g. unstable front at C18
or C04-L/Tsarmine lower unit; scree slope starting
to form RG at C35/Lapires) or possibly also due to
data gaps and/or limited length of data series (e.g.
C34/Tsavolire).
Finally, eight RGV in Switzerland yield a signi-
ficant correlation percentage value of >60%. None
of the RGs in the Engadine exceed this value. In
all other regions in Switzerland the 60%-threshold
value is exceeded suggesting an (almost) nation-
wide rather homogenous RGV behaviour. Thus,
the following Swiss RGV may be considered as
the optimal ones for climate-related statements:
C21/Grosses-Gufer in the Upper Valais; C02/Mont-
Gelé-B, C04-U/Tsarmine upper part, C05-U/Becs-
de-Bosson upper part, C10/Petit-Vélan in the Lower
Valais, and C24/Monte-Prosa-A, C25/Monte-Prosa-
B, and C26-S/Stabbio-di-Largario secondary lobe in
Ticino. The timeseries of these eight RGs seem to be
the most reliable climate indicators for Switzerland
so far. On the Alpine-wide scale, 15 out of the 50
timeseries have at least 60% of correlations between
the different value pairs (figure 9). Spatially, these
15 RGs are distributed all over the Alps where active
RGs exist, although spatially biased (particularly
with CH).
A raster-like analytical approach for the entire
Alps considering a certain number of RGs per given
area was not feasible in our study as the ana-
lysis is restricted to long-term geodetic surveys and
related data availability. It is, nevertheless, appro-
priate that our Alpine-wide RG population might
be regarded as representative for the Alps as many
RGs (apart from disintegrating ones or other pos-
sibly unclear-permafrost landforms) show compar-
able RGV patterns.
5.2. Environmental response mechanisms: climate,
topography, and hydrology
Several environmental parameters influence the creep
of the RG permafrost, including gradient of the
slope, thickness, temperature, unfrozen water con-
tent and water pressure of the creeping frozen body,
ice-content, and grain-size of the debris [3,4,30].
A comparison of air temperature data (MAAT; here
from September to August of the following year) with
RGV gives some Alpine-wide insight into RG-thermal
aspects. Table 4depicts a correlation matrix between
RGV for different regions, the total sample and the
RGV with a correlation percentage >60% (reduced
sample) and air temperature (annual and seasonal
data). On an annual temperature time scale, signific-
ant results (i.e. higher temperatures leading to accel-
eration and vice-versa) were quantified for only two
regions (Austria, Italy) for the total (n=50) and
reduced (n=15) samples. On a seasonal timescale,
only the autumn and the summer temperatures seem
to play a significant role in the averaged RGV for some
regions but also for the total and reduced samples.
At least on an averaged sample scale, the winter and
the spring temperatures are of minor relevance for the
Alpine-wide RGV pattern. This implies that even on
an Alpine-wide, averaged scale, a significant relation-
ship between RGV and air temperature can be estab-
lished thanks to long-term data and despite a large
amount of noise in the data. This confirms the gen-
eral usability of RGV in an ECV- and thus general
environmental context. However, individual beha-
viour related to site-specific environmental factors
such as topographical, ground thermal, and hydrolo-
gical impacts (the latter two impacted by snow char-
acteristics) potentially influences this general rela-
tionship. Care must be taken, therefore, in choosing
RGs that might be suitable candidates for RG-climate
relationship establishments.
The averaged temperature anomaly (running
mean of the 12 previous months) of the four AWS
Sonnblick, Zugspitze, Säntis, and Besse (figure 2) and
the averaged RGV pattern of all 50 timeseries as well
as the 15 timeseries with more than 60% of signi-
ficant correlations is depicted in figure 10(a). Cold
periods distinctly reduced the RGV as in 1999/00, in
16

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Table 4. Correlation matrix between the different averaged RGV (regional, total, selected) and air temperature (arithmetic mean of
Besse, Säntis, Zugspitze, and Sonnblick) distinguishing between mean annual (MAAT; September to August of the following year) and
seasonal (SON, DJF, MAM, JJA) values. Only significant correlations (p<0.05) are shown.
Air temperature (◦C)
Sample (n)rfor MAAT rfor SON rfor DJF rfor MAM rfor JJA
Austrian Alps (7) 0.51 0.39 ns ns 0.48
French Alps (1) Ns ns ns ns ns
Italian Alps (6) 0.59 0.50 ns ns 0.58
Lower Valais (15) Ns ns ns ns ns
Upper Valais (9) Ns ns ns ns ns
Engadine (5) Ns ns ns ns ns
Ticino (7) Ns ns ns ns ns
RGV all (50) 0.46 0.44 ns ns 0.40
RGV >60% sig. cor. (15) 0.46 0.39 ns ns 0.47
Figure 10. RGV and temperature: total average of RGV (n=50) and average of the 15 RGV with more than 60% of significant
correlations between data pairs (cf figure 7) relative to the reference period 2016–2018 (grey area) since 1995 and: (a) 12 month
running mean of air temperature (MAAT) anomaly relative to 08.2016 to 07.2018 averaged over four AWS (data: Besse,
Météo-France; Säntis, MeteoSwiss; Zugspitze, Deutscher Wetterdienst; Sonnblick, GeoSphere Austria) during the time period
January 1995 to September 2022 (figure 2for locations); (b) 2 year running mean of ground surface temperature (M2AGST) at
snow-influenced sites in the Central and Western Swiss Alps (n=2 to >20) in 1999–2022 relative to 2017–2021 (data: Alpine
Geomorphology research group, University of Fribourg and PERMOS).
2004/05, in 2010/11, in 2015–2017, and in 2021/22.
In contrast, the velocity maxima correlate with warm
permafrost temperatures as recorded in boreholes at
10 m (e.g. [65]) and are likely a result of the con-
tinuously warm conditions in the ground during the
last years. This assumption is confirmed by long-term
GST data from the Swiss Alps (10b). RGV clearly
correlates with the 2-year running mean of GST
(M2AGST) at snow-influenced sites in the Central
and Western Swiss Alps in 1999–2022 relative to
2017–2021 [29]. Lower ground temperatures seem
to be more efficient in RGV deceleration compared
to higher temperatures which cause acceleration
[28]. Continuous ground and permafrost warming
since approximately 2010 caused a general increase
in RGV [34].
The topographic context of the RGs can play a fur-
ther significant role as for instance the average slope of
the landform or if the RG is developing on alternating
steep or flat terrain. Such a topographical influence
could lead to a significant destabilization of the RG
with non-reversible changes [24], which can lead to a
split of a RGV of one RG into two datasets assigned to
two different RG units. For some RGs, this separation
of observations points has been accomplished in the
past (e.g. A04, [47]). For others, such a separation will
possibly be introduced in the future, with a decorrel-
ating movement pattern of the upper and lower parts
(e.g. A06, [49]). For further RGs where such a separ-
ation has taken place already earlier, only one unit is
surveyed today, partly also due to field safety reasons.
[30] pointed out that interannual and seasonal
variability in RG flow cannot be explained by heat
conduction alone. Non-conductive processes linked
to water availability dominate the short-term vari-
ations in velocity. The advection of surface water into
17

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
the RG and its interaction over porewater pressure
with the creep rheology explain the short-term vari-
ations in velocity. [85] also concluded that seasonal
variations in the flow velocity of Furggwanghorn RG
do not depend directly on the temperature at depth
but are very likely controlled by hydrological pro-
cesses (indirectly by temperature). Thus, subsurface
water conditions play a further role in RGV [4] but
such data are commonly absent at the monitored RGs
and the related process understanding is still limited.
Recently, [86] observed daily variations in flow velo-
city on I01/Lazaun relating them to daily variations in
discharge, thus, suggesting velocity-control by water
at least during summer.
Water (precipitation or meltwater) may infilt-
rate into the unfrozen sediment layer below the
frozen body, increase the hydraulic pressure thus
reducing the frictional resistance in the shear hori-
zon, and transports latent heat into the RG. Factors
for RG acceleration but also destabilization have
been attributed to permafrost degradation caused
by environmental changes and feedback mechan-
isms influencing subsurface water conditions (e.g.
[22,30,57,87]). Thus, timeseries of destabilized RG
must therefore be carefully considered (e.g. which
time period and how intensive was/is a destabil-
isation phase? [24]), as in our case the ones of
(currently) A04/Hinteres-Langtalkar, A06/Äußeres-
Hochebenkar, A09/Leibnitzkopf, C04/Tsarmine,
C10/Petit-Vélan, C17/Grosse-Grabe, C18/Gugla-
Bielzug, and C19/Dirru.
6. Conclusions
The large number of 50 timeseries from 43 RGs in
the European Alps allows for a unique in-depth ana-
lysis of long-term velocity variations. The absolute
RGVs in the Alps in 1995–2022 varied substantially
(0.04–6.23 m a−1), but relative changes in velocit-
ies are often comparable despite distinct differences
in local settings. Gaps in annual timeseries and dif-
ferences in the date of measurement can reduce the
accuracy of RGV.
Averaged over all 50 timeseries, the follow-
ing pattern emerges: 1995–1999 gentle acceleration;
1999/2000 slight deceleration; 2000–2004 fast acceler-
ation with a distinct velocity peak in 2003/04; 2004–
2006 distinct deceleration; 2006–2008 steady velocit-
ies; 2008–2015 long acceleration period with a short
time interval of weakened acceleration in 2009–2011;
2014/15 distinct velocity peak in the RGV records fol-
lowed by a drastic drop; 2018–2020 renewed accelera-
tion leading to peak velocities in 2019–2020 compar-
able to the ones in 2014/15; 2021/22 strong alpine-
wide drop of RGV.
Fifteen out of 50 RGV correlate very well with
each other (i.e. >60% of significant correlations
between the different RGV). These RGs are most
suitable to be used to calculate an Alpine-wide
average for RGV. The 15 RGV are distributed all over
the Alps in Austria (A03/Weissenkar, A04/Hinteres
Langtalkar—upper and lower part, A05/Dösen,
A09/Leibnitzkopf), France (F01/Laurichard), Italy
(I04/Maroccaro) and Switzerland (C02/Mont-Gelé B,
C04/Tsarmine—upper part, C05/Becs-de-Bosson—
upper part, C10/Petit-Vélan, C21/Grosses Gufer,
C24/Monte Prosa A, C25/Monte Prosa B, C26-
S/Stabbio di Largario—lobe West). This correlation
may change over time as the RGs may evolve dif-
ferently related to local factors. Even some currently
disintegrating RGs (A04, A09, C04) show good cor-
relation with non-disintegrating ones because of only
very recent or weak disintegration.
A decelerating trend of a RG unrelated to the gen-
eral RGV pattern suggests in-situ permafrost degrad-
ation (loss of ground ice), whereas strong accelera-
tion relates to destabilization. Local environmental
factors such as topography, hydrology, temperature,
geometry, and ice content become dominant and the
RGV pattern detaches from the regional environ-
mental signal. A more detailed analysis of RGV and
hydro-meteorological conditions (including perma-
frost temperature, active layer thickness, snow char-
acteristics, hydrology) at the 43 RGs was beyond
the scope of the paper and should be considered
elsewhere.
A major effort of the RG monitoring community
should be to secure the long-term recording of at least
annual data (like glacier length-change and mass bal-
ance measurement programs) for particularly highly
correlating RGs. A continuation of velocity mon-
itoring at RGs with >30% and 60% of significant
correlations (24 timeseries) is also encouraged. RG
advance monitoring can provide additional interest-
ing information but must be strictly separated from
RGV monitoring.
An optimal candidate for the start of a new
RGV program using geodetic surveys could be char-
acterized as follows: (a) talus-connected RG sys-
tem; (b) mono-unit RG system—simple unit; (c)
no signs of destabilisation; (d) gently sloping RG
with an overall slope of 20◦–40◦except the front;
(e) evenly inclined longitudinal profile at the RG
base and in the pro-RG area reducing the chance
of future RG-surface rupture; (f) RG front located
at least in the ‘mostly in cold conditions’ perma-
frost class of [40]); (g) geodetic observation points
site not prone to rock falls and well-distributable
over the entire unit; (h) logistically easily accessible if
possible.
Data availability statement
All data that support the findings of this study are
included within the article (and any supplementary
files).
18

Environ. Res. Lett. 19 (2024) 034022 A Kellerer-Pirklbauer et al
Acknowledgments
Numerous projects funded by different sources sup-
port the long-term monitoring of RGV in the
European Alps during the last decades. To avoid any
unfairness in the naming of funding agencies, we
explicitly do not want to name individual institutions
but prefer to say a big thank to the various regional,
national, and European-wide research funding bod-
ies. One crucial exception to this is, nevertheless, the
comprehensive long-term funding of the PERMOS
network in Switzerland, supported by MeteoSwiss
in the framework of GCOS Switzerland, the Federal
Office for the Environment and the Swiss Academy
of Sciences. Finally, the authors acknowledge also the
financial support by the University of Graz.
Author contributions
The original idea of combining the Alpine-wide
annual geodetic data was developed in the mid-2000s
by several people in central Europe around IGR [35]
and RD [36]. Some 10 years later, AKP, together
with CL, IGR, RD and XB, took up this idea again,
developed it further and coordinated it for this paper.
AKP coordinated the content of the paper, compiled,
homogenised, and processed the different data, per-
formed most of the analyses, wrote the initial manu-
script and designed the figures including the map
with contributions of XB, BD, RD, JE, GR, AI, VK,
CL, RS, CS, MSW and ET. Almost all the 23 authors
carried out field work and data collection. Finally, all
the authors contributed to improve the first draft of
the manuscript.
ORCID iDs
Andreas Kellerer-Pirklbauer 
https://orcid.org/0000-0002-2745-3953
Xavier Bodin https://orcid.org/0000-0001-6245-
4030
Reynald Delaloye https://orcid.org/0000-0002-
2037-2018
Christophe Lambiel https://orcid.org/0000-0003-
0930-8178
Isabelle Gärtner-Roer https://orcid.org/0000-
0003-3621-488X
Mylène Bonnefoy-Demongeot 
https://orcid.org/0000-0003-0762-0248
Luca Carturan https://orcid.org/0000-0003-2134-
2686
Bodo Damm https://orcid.org/0000-0001-9307-
8672
Julia Eulenstein https://orcid.org/0000-0002-
1450-4782
Andrea Fischer https://orcid.org/0000-0003-1291-
8524
Lea Hartl https://orcid.org/0000-0001-5688-3760
Atsushi Ikeda https://orcid.org/0000-0002-7488-
2793
Viktor Kaufmann https://orcid.org/0000-0003-
2074-1992
Norikazu Matsuoka https://orcid.org/0000-0003-
2832-179X
Umberto Morra Di Cella https://orcid.org/0000-
0003-4250-9705
Jeannette Noetzli https://orcid.org/0000-0001-
9188-6318
Roberto Seppi https://orcid.org/0000-0003-1796-
0596
Cristian Scapozza https://orcid.org/0000-0002-
9003-7864
Philippe Schoeneich https://orcid.org/0000-0002-
3143-5547
Martin Stocker-Waldhuber 
https://orcid.org/0000-0002-1592-8173
Emmanuel Thibert https://orcid.org/0000-0003-
2843-5367
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