Multi-decadal (1953 – 2017) rock glacier morphodynamics analysed by high-resolution topographic data in the Upper Kauner Valley, Austria

Permafrost is being degraded worldwide due to the change in external forcing caused by climate change. This has also been shown to affect the morphodynamics of active rock glaciers. We studied these changes, depending on the analysis, on nine or eight active rock glaciers with different characteristics in multiple epochs between 1953 and 2017 in Kauner Valley, Austria. A combination of historical aerial photographs and airborne laser scanning data and their derivatives are used to 15 analyse surface movement and 3D displacements. In general, the studied landforms show a significant acceleration of varying magnitude in the epoch 1997–2006 and a volume loss to varying degrees throughout the investigation period. Besides, we detect rock glaciers that show indication of inactivation. By analysing meteorological data (temperature, precipitation and snow cover onset and duration), we are able to identify possible links to these external forcing parameters. The combined investigation of horizontal and vertical 3D displacements shows that these are temporally decoupled on some rock glaciers. 20 The catchment-wide survey further reveals that, despite the general trend, timing, magnitude and temporal peaks of morphodynamic changes indicate a slightly different sensitivity, response or response time of individual rock glaciers to fluctuations and changes in external forcing parameters. 25 https://doi.org/10.5194/tc-2021-77 Preprint. Discussion started: 14 April 2021 c © Author(s) 2021. CC BY 4.0 License.

temperatures close to 0 °C move faster and are more sensitive to thermal forcing than colder ones. More recent studies highlighted the role of liquid water, especially in the shear horizon, and attribute little or no significance to the change in permafrost temperature to explain the deformation variations on a multi-annual, inter-annual, seasonal and shot-term scale. 65 (Wirz et al., 2016a;Kenner et al., 2017;Buchli et al., 2018;Cicoira et al., 2019a). Kenner et al. (2020) synthesise these findings by showing that water availability in the rock glacier is governed by ground temperature which is a function of mean annual air temperature and onset as well as duration of snow cover and thus correlate with rock glacier deformation as well. Although rock glaciers normally move at rates ranging from a few cm/yr to a few m/yr, some studies show destabilization of rock glaciers leading to rates of movement of several tens of meters per year Scotti et al., 2017). Besides, rock glacier 70 dynamics can also be influenced by other factors like topography, temporal and vertical variations in ice content, rheology of the ice-debris mixture, thickness, and input of ice and debris to the system. The present and former response of rock glacier morphodynamics to atmospheric warming and climate change observed in many high mountain regions (Hock et al., 2019) is of large scientific interest for climate change projections and landscape evolution models. But an understanding of these landforms has also implications for natural hazard protection (Schoeneich et 75 al., 2015) or future water availability (Jones et al., 2019). Although there are several studies investigating rock glacier morphodynamics on different time scales, the number of studies is low compared to ice glaciers. Groh and Blöthe (2019) investigated the recent flow velocities of Kauner Valley rock glaciers and ascertained slightly lower flow velocities between 2001/2003-2009 than between 2010-2015. They also noted that the velocity of rock glaciers in the study area mainly depends on parameters describing the general inclination and that their activity status is controlled by their size and the topoclimate. 80 Apart from Roer et al. (2005), who investigated multi-decadal catchment wide rock glacier morphodynamics in Turtman Valley, Swiss Alps, most studies investigating rock glacier morphodynamics on a decadal time scale investigate just one or two large and prominent rock glaciers (Scapozza et al., 2014;Scotti et al., 2017;Kaufmann et al., 2019;Kenner et al., 2020).
In this study, we focus on long-term

morphodynamic investigations rock glaciers, located in the Upper Kauner 85
Valley, Ötztal Alps, Austria, displaying different characteristics in order to understand their reaction to climate change under similar climatic forcing. We do this by analysing surface movement (flow velocity) of eight rock glaciers by means of imagecorrelation techniques on the base of orthophotos and hillshades. In addition, multitemporal 3D displacements are derived for nine rock glaciers by a 3D distance analysis using photogrammetric as well as Airborne Laser Sanning (ALS) point clouds.
The identified changes in rock glacier morphodynamics will be discussed with regard to rock glacier characteristics, elevation 90 classes and changes in the meteorological forcing by investigating different climate parameters recorded directly in the catchment and nearby meteorological stations.  carried out in this area (Dusik et al., 2015;Groh and Blöthe, 2019;Altmann et al., 2020). But the road and the associated ski area also cause anthropogenic influences on natural systems, which have to be considered.
A rock glacier inventory was compiled for the study area (c.f. Sect.3.1). Within the catchment, 54 rock glaciers can be found, which were classified as active (20), inactive (16) and fossil (19). Due to poor image quality or snow cover (in one or more 110 epochs of the historic data) and the activity status, the vast majority of these rock glaciers had to be excluded from the following analyses. However, eight active rock glaciers representing different characteristics and conditions were investigated in detail regarding flow velocities and nine rock glaciers regarding 3D displacements. Detailed characteristics of these rock glaciers can be found in Table A1. The most prominent of those is the well-studied and largest (0.237 km²) rock glacier Innere Ölgrube (RG 01) (Fig. 2). 115 On the Ölgrube, the first velocity studies were already carried out by Finsterwalder (1928) and Pillewizer (1957) and more recent studies continue their research and contribute additional information about its hydrology, internal and external structure and morphodynamics (Berger et al., 2004;Krainer and Mostler, 2006;Hausmann et al., 2012;Groh and Blöthe, 2019). The area of the investigated rock glaciers ranges from 0.02 km² (RG 07) to 0.237 km² (RG 01). They show expositions of N, NE, 120 E and W, with minimum elevation ranging from 2446 m to 2727 m. The elongated rock glacier RG 06 spans heights from 2702 m to 3093 m and thus reaches the highest elevation. Without geophysical, geochemical or petrographic information, interpretations about the genesis and internal structure are difficult (Berthling, 2011;Clark et al., 1998). In the case of the rock glaciers RG 03, RG 04 and RG 09 a complete or partial covering of the rock glaciers by the LIA glacial extent (Fischer et al., 2015) suggests a glacial genesis after 1850 or at least a glacial influence during and after this time as described by Dusik et al. 125 (2015) for RG 09. Some detailed studies about Innere Ölgrube (RG 01) exist, revealing information on internal structure (Hausmann et al., 2012) and genesis (Berger et al., 2004). Berger et al. (2004) show that both lobes of the rock glacier developed from debris covered glaciers after the peak of the Little Ice Age (LIA) (~1850). The rock glacier is composed of a 4 m to 6 m active layer, a 20 m to 30 m thick ice rich permafrost body and a underlying 10 m to 15 m ice free sediment layer and has an ice content of 40% to 60% (Hausmann et al., 2012). 130

Rock glacier inventory
Although manual mapping of rock glacier landforms is shown to be highly subjective (Brardinoni et al., 2019), we tried to minimize the heterogeneity in the inventory by incorporating the guide lines for inventorying rock glaciers (IPA Action Group Rock glacier inventories and kinematics 2020) and only mandate one operator to compile the inventory on the basis of Krainer 135 & Ribis (2012). Rock glacier outlines were corrected and additional landforms were mapped on the basis of the most recent hillshade derived from the 2017 ALS campaign of the DFG founded project PROSA and an orthoimage of 2015 (data source: Land Tirol -data.tirol.gv.at). Activity status was assigned according to morphological characteristics in combination with a DEM of difference (DoD) of the 2012 and 2017 ALS campaigns and image correlation analysis on the derived hillshades without local alignment of the data. In the case of active rock glacier complexes, which are overflowing fossil ones, they were 140 considered as two separate units.

Meteorological data
For the analysis of rock glacier morphodynamics over a decadal time period, a reference to climatic conditions that influence such systems in various ways is indispensable. However, long time series data in the high alpine areas are only very sporadically available, as early climate monitoring stations tended to be located in population centres. This also applies to our 145 catchment area, where the meteorological station Weißsee (2540 m) (data source: TIWAG) is recording data since 2006. For this reason, we use additional data from nearby meteorological stations, which have longer time series available, to provide information on the approximate climatic development in the catchment area. The locations of these stations are shown in Figure 1, while an overview of the stations and the used data is given in Table 1.
In earlier studies, it is shown that changes in rock glacier morphodynamics can be related to the warming of permafrost through 150 heat conduction caused by an increase in air temperature (Roer, 2005;Kääb et al., 2007;Delaloye et al., 2010;Kellerer-Pirklbauer and Kaufmann, 2012;Cicoira et al., 2019b). But especially on a shorter timescale, it is very likely that rock glacier morphodynamics are also controlled by liquid water availability and snow cover parameters Wirz et al., 2016a;Kenner et al., 2017;Buchli et al., 2018;Cicoira et al., 2019a;Kenner et al., 2020). We https://doi.org/10.5194/tc-2021-77 Preprint. Discussion started: 14 April 2021 c Author(s) 2021. CC BY 4.0 License. Table 1. Overview of the meteorological stations used. Distance gives the distance to the center of the study area. T -Temperature; Pr -Precipitation, SC -Snow cover. The data were provided by the "Federal Misistry of Agriculture, Regions and Tourism" (BMLRT), the "Central Institute for Meteorology and Geodynamics" (ZAMG), "Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region" (HISTALP), "Autonomous Province of Bozen/Bolzano " (Province BZ) and TIWAG 160 therefore analyse changes, anomalies and trends in air temperature, precipitation and snow cover for the whole period of investigation and between the individual epochs. In addition, we analyse temperature and precipitation on a seasonal basis.

Station
Snow cover onset and duration were determined according to Peng et al. (2013).

Airborne Laser Scanning (ALS) data
To analyse rock glacier flow velocities on hillshades (see Sect. 3.50) and 3D displacements in point clouds (see Sect. 3.6) in 165 the two most recent epochs 2006-2012 and 2012-2017, we used data from different ALS campaigns (see Table 2). The most recent one was acquired on the 5 th of June 2017 by a helicopter and a mounted mobile laser scanning system VuxSys-LR from Riegl (www.riegl.com). This ALS flight mission was carried out by the Chair of Physical Geography at the Catholic University of Eichstätt-Ingolstadt during the DFG-funded project (PROSA), achieving a mean point density of 20.0 pts/m² on the studied rock glaciers. Due to weather conditions and organizational problems on the part of the contracted company, which made an 170 area-wide data acquisition on one day impossible, the 2012 ALS data were recorded (also during the PROSA project) on the 4th and 18th of July. An LMS Q680i-S laser scanner from Riegl mounted on a helicopter was used for data recording.
Depending on the date of recording, the average point density ranges between 12.3 pts/m² and 12.7 pts/m². Furthermore, an additional ALS data set from the 5th of September 2006 with an average point density of 5.0 pts/m² was provided by the TIWAG. All datasets were georeferenced with parameters optimised by an automatic strip adjustment (Glira et al., 2015). To 175 account for possible variable errors throughout the catchment, we locally fine registered the 2006 and 2012 point clouds to the 2017 data set for every single rock glacier by using an iterative closest point algorithm (ICP) (Besl and McKay, 1992)

Generation of point clouds and orthoimages from historical aerial images
In order to extend the time series of ALS data (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) and to quantify the morphodynamics of the rock glaciers that occurred in the previous century, we used aerial photos acquired at irregular intervals over the entire Austrian territory. 185 The useable aerial photos for the Kauner Valley catchment were collected at six separate epochs between 1953 and 1997. The epochs were chosen based on data availability, similarity in acquisition date (i.e. late summer), image quality, and sufficient image overlap. Note that the photos of 1953 were collected in three different flights on different days. However, considering these three datasets separately was not possible for the 3D reconstruction due to lacking image overlap. Therefore, all the images were processed together to generate one 1953 DEM. 190 The historical aerial photos used in this study were scanned and provided in tiff-format by "Office of the Tyrolean Government-Department of Geoinformation" (https://www.tirol.gv.at/en/) and "Austrian Federal Office of Surveying and Metrology" (BEV) (https: //www.bev.gv.at) along with the camera calibration protocols if available (Table 3).
Using advances in digital photogrammetry, particularly Structure from Motion (SfM) with Multi-View Stereo (MVS), the reconstruction of 3D information in form of point cloud from scanned historical photos does not require specialized knowledge 195 (Bakker and Lane, 2017;Fawcett et al., 2019). The aerial images were processed in Agisoft Methashape (v.1.6.1) using the film camera tool, which estimates the camera calibration parameters based on the fiducial marks. The software automatically derived the locations of the fiducial mark in the images. Their distance in mm and the focal length were available from the calibration protocol. Having defined the camera interior orientation, the camera exterior orientation, the 3D point cloud reconstruction and the orthophoto generation follow the standard SfM-MVS workflow. This includes ground control points 200 (GCPs) measurement for georeferencing and dense image matching. The 3D coordinates of the GCPs were chosen from the ALS 2017 point cloud on stable terrain and were evenly distributed throughout the catchment. The resulting average point density on the studied rock glaciers varies from 1.2 pts/m² to 11.9 pts/m². The ground resolution of the orthoimages varies between 0.2 m and 0.5 m. The photogrammetric point clouds were co-registered with the reference 2017 ALS data in order to minimize inherent 205 systematic errors (Bakker and Lane, 2017) and by using again an iterative closest point (ICP) algorithm (Besl and McKay, 1992) on mapped stable areas around the rock glaciers. Fine registration was performed for all individual rock glaciers and epochs separately to account for any variable errors throughout the catchment. To account for possible shifts in the orthoimages, we resampled them at a resolution of 0.5 m and we locally co-registered all 215 individual rock glaciers for each epoch to the 1953/54 orthoimage. We used 9 to 29 co-registration points equally distributed around the rock glaciers. We obtained co-registration root mean squared errors (RSMEs) between 0.225 m and 0.549 m with an average of 0.316 m.

Calculation of horizontal flow velocities 220
Horizontal flow velocities of the rock glaciers were calculated for the six processed time steps between 1953 and 2017. For this purpose, an image correlation approach was chosen, which is a common method to derive glacier and rock glacier velocity from orthoimage, hillshades and satellite images (Scambos et al., 1992;Kääb and Vollmer, 2000;Heid and Kääb, 2012;Monnier and Kinnard, 2017;. In this study, orthoimage and hillshade image pairs  (Scambos et al., 1992) within SAGA-GIS software was applied. The algorithm attempts to match small sub-scenes from two images by applying a fast Fourier transform-based version of a cross-correlation.
It can locally adjust the intensity values between two image pairs and therefore compensate for differences in illumination.
Using this algorithm, sub-pixel precision of displacement vectors can be achieved. We used search and reference chip size 230 combinations of 64/32, 128/64 and 256/128 with a fixed spacing of 5 m. The combinations were calculated for all image pairs and the most reasonable was chosen for further analysis. This was done by visually analysing the resulting displacement vectors in combination with the input data. In general, larger chip sizes were chosen for faster moving rock glaciers and/or long time spans between the image pairs. The resulting raw vector maps can contain erroneous displacement measurements or decorrelation, where no measurement is possible, due to snow, strong shading effects, areas where displacements are 235 dominated by rock fall and large displacements, which cause a change of texture. These vectors were excluded manually for all time steps with the help of the matching orthoimages or hillshades. Subsequently, a mask was created for the areas where measurements were possible in all time steps and just measurements in these areas were used for further analysis to make the individual time steps comparable.
The combination of orthoimages and hillshades has to be chosen because low point densities in some of the aerial images 240 derived point clouds resulted in low details in the resulting DEMs. Tests regarding image correlation on these DEMs showed very poor results. We are aware that the low point densities also affect the accuracy of the resulting orthoimages and outline the variable errors in Sect. 4.1. On the other hand, we decided not to use orthoimages for the more recent epochs from 2006 to 2017, available from "Office of the Tyrolean Government-Department of Geoinformation" (https://www.tirol.gv.at/en/) for the reason that they are orthorectified utilizing the most up to date DEM with a resolution of 5 m, which could result in 245 https://doi.org/10.5194/tc-2021-77 Preprint. Discussion started: 14 April 2021 c Author(s) 2021. CC BY 4.0 License. erroneous displacement measurements. If a non-matching DEM is used, it would lead to orthorectification errors particularly on moving landforms, like rock glaciers (Kaufmann and Kellerer-Pirklbauer, 2015).
The measurement of horizontal flow velocities of rock glaciers on remote sensing data, especially when using historical aerial images and their derivatives, is prone to errors. As described by Kääb et al. (2020), the error budget is composed of the following components: 1) overall shifts between the orthorectified data 2) lateral shifts in the orthoimages due to errors in the 250 DEM used for orthorectification 3) distortions in the aerial images or in the sensor model that propagate into the orthoimages 4) image matching uncertainties and errors. We minimized the shifts between the orthoimages by local co-registration of the orthoimages. By using the matching DEMs of the individual years for orthorectification, we addressed error type 2). However, quality of the DEMs varies locally in a single epoch and more crucially between the epochs and therefore are still a source of error. The DEMs with the lowest quality were the epochs 1982 and 1997. These were also the years with the worst quality of 255 the raw aerial images (error type 3). Another source of error when working with historical aerial images are scratches and alterations on the original image film caused by storage and age. These can lead to problems in the processing and thus were masked out before processing. Errors of type 4) contain errors caused by the image correlation method itself. The measurement errors as consequence of image correlation vary with the image quality like resolution, shadow, contrast and noise of the image pairs (Kääb et al., 2020). We removed both directional and magnitudinal gross outliers manually by counterchecking the 260 resulting displacement vectors with the corresponding orthoimage and hillshade pairs.

Error assessment for horizontal flow velocities
To quantify the overall error budget for horizontal flow velocities, we mapped close stable areas of similar texture/roughness and exposition on the single rock glaciers for all time steps. Due to snow and shading effects, these stable areas had to be adjusted slightly for some time steps. Subsequently, displacement vectors in these areas were analysed for all individual epochs 265 and rock glaciers. As no gross outliers were found in these areas, we used the mean value ( ) added by two times the standard deviation ( ) as measure for error budget ( ) of flow velocity measurements.

270
This measure was also applied by Fey and Krainer (2020) to determine a level of detection (LoD) for rock glacier flow velocity and recommended as a statistical measure of flow velocity error by Paul et al. (2017). We have decided not to use a LoD for calculating rock glacier flow velocity statistics for the fact that even in areas below the LoD there might be actual displacement (Anderson, 2019). We therefore rather illustrate the errors as red bars in Figure

3D displacements on rock glaciers
We used both photogrammetric and ALS point clouds to measure the 3D displacements on the rock glaciers, which represent the surface change normal to the surface. The method described in the following can be used as a simple and robust alternative to a DEM of difference analysis in the 2D case, but offers some advantages in complex 3D cases, particularly on vertical to near vertical and rough surfaces or if point densities are variable (c.f. Lague et al. 2013). The datasets for the 3D reconstruction 280 differ slightly from the datasets used for the velocity analysis on the rock glaciers as the processing of the aerial photographs did not lead to sufficient point cloud resolutions for all of them. Thus the 1982 and 1997 epochs had to be excluded and the analysis could only be performed for the 1953-1970/71, 1970/71-2006, 2006epochs, where the 2006datasets represent ALS data. In 1970and 1971, only a portion of the valley was covered in each case, so this epoch had to be composed of two partial data sets. 285 As already described, the point clouds were locally fine-registered (c.f. Sect. 3.4) and then thinned (0.5 m) during the import process into LIS SAGA in order to produce homogeneous point densities for all epochs. To account for the sometimes very long time intervals between individual data sets and the expected high 3D displacements, we used the 3D distance between points approach by Fey & Wichmann (2017), which is based on Lague et al. (2013) and is recommended as a robust distance measurement for geomorphological change detection in a complex terrain (Fig. A1). For each point, the normal vector for a 290 best fitting plane (including the point neighborhood) with a radius of 5 m around the point was calculated in a first step (module "point cloud features" in LIS SAGA). Using this normal information of individual points, the approach (implemented in LIS SAGA) determines corresponding points in two point clouds of different epochs along the normal or flipped normal vector and derives the 3D distance (distance perpendicular to surface) and thus the 3D displacement between these corresponding point pairs by using a distance threshold (10 m) and a threshold for the maximum normal difference (15°). The relatively high 295 threshold for the radius was chosen to account for the expected high 3D displacements over the long investigation periods. In addition, we decided to use the same limit for all data sets to ensure comparability of the data. A detailed description of the workflow can be found in Fey & Wichmann (2017). Analogous to the rock glaciers, the calculations were also performed on the stable areas around the single rock glaciers for all epochs in order to calculate the measurement error. At the end of this workflow, the 3D displacements for the rock glacier areas as well as for the stable areas were assigned as an attribute to the 300 respective point clouds and were used for the following analyses. As for the flow velocities, we have omitted an LoD and have included the error values as additional information in the corresponding figures. The volume calculations were also performed based on the point cloud information. For this purpose, raster data sets (1m) were aggregated from the mean annual 3D displacment point cloud attribute and the volume was calculated from this raster dataset. in the orthoimage are not taken into account or can even lead to incorrect measurements. In other cases, the variability of the errors may be related to the accuracy of the co-registration, but also to differences in the quality of the image in terms of contrast, illumination, and resolution.
To assert the validity of our results we performed a qualitive comparison with dGPS measurements, which were taken by 320 Krainer and Mostler (2006)  Due to the generation of masks with the aim of only taking into account areas in which measurements were possible in all epochs in order to ensure better comparability, some areas of the rock glaciers had to be excluded (Table 4). In the case of the 330 flow velocity analysis, between 27.39% and 80.00% of the rock glacier area could be taken into account. In the case of the 3D distance analysis, it was 50.50% to 95.67%.

Changes in meteorological forcing
As it is known from many studies that the changes in the external (meteorological) forcing (temperature, precipitation, snow 350 cover) play an important role for changes in the periglacial system and thus rock glaciers, we analysed climate data from weather stations within and close to our catchments, which was challenging due to the large temporal scale . All stations show similar patterns, even if the manifestation of the anomalies are slightly different in some cases. We note that the positive trend of temperature increase is slightly higher for stations of higher elevation in the study period. In the case of temperature and snow cover, we mainly present data from the stations Obergurgl-Vent (1938 m a.s.l.) and Obergurgl (1942 m 355 a.s.l.), as these are located at the highest elevation and only about 21 km away from the centre of our study area. In the case of precipitation, we mainly present data from the Plangeroß station (1605 m a.s.l.), because although it is located at a significantly lower elevation than the Weißsee station (2540 m a.s.l.) and the studied rock glaciers, it offers the best agreement with the Weißsee station data in terms of monthly precipitation (r= 0.892, p<0.001). Wherever possible, we try to confirm the observations with the limited time series (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) of the Weißsee station. In the following sections, we describe the general 360 trend of meteorological forcing which is further discussed with regard to changes in flow velocity in Sect. 4.4.

Temperature
During the period of investigation , the temperature trend shows an increase of 1.92 °C in 65 years at the Obergurgl-Vent (1938 m a.s.l.) station (Fig. A2). This is a stronger increase than at the lower elevated station Nauders ( Miller (2012). In general, the temperature increase goes well in line with the alpine wide temperature increase, which has a significantly higher amplitude than the global average (Beniston, 2006). The seasonal development of the temperature trends shows a stronger increase in temperatures in spring and summer of 2.73 °C and 2.64 °C, respectively, in 65 years compared to 370 the winter and autumn temperatures. For these, the positive trend is clearly weakened and amounts to only 1.6 °C for winter and 0.69 °C for autumn. If one compares the seasonal temperatures with the reference period 1961 -1990, it is noticeable that in the case of summer and spring temperatures, only positive anomalies occur from the beginning of the 1990s onwards ( For the period before the beginning of the 1990s, periods/years with positive and negative anomalies are visible, whereby the strong anomalies are mostly in the negative range. The summer temperature anomalies show the lowest variance. Particularly striking is the period between 1970-1982 with its continuously comparatively low summer and autumn temperatures and a period of relatively cold winters from 1962-1970.

Precipitation 385
All considered meteorological stations, except for the station Nauders in summer precipitation, an increase in winter precipitation and an increase in the frequency and intensity of extreme precipitation events are predicted for the European Alps (Gobiet et al., 2014). 395 Looking at the precipitation anomalies, there is a clear increase from the mid-1990s for positive summer and autumn precipitation anomalies, which is particularly pronounced in the period 1995-2002. For the 1999-2002 period and the most recent time step from 2013-2017, this can also be seen in the spring precipitation (Fig. 6). Another period with clustered positive precipitation anomalies can be observed in the case of spring and summer precipitation between 1963 and 1967 and for autumn precipitation between 1972 and 1981, with both showing single years with just slightly positive or negative 400 anomalies. Relatively dry summers were recorded from 1954-1984 and 1990-1994, dry autumns from 1984-1991. In the case of winter precipitation, there are some positive and negative anomalies, but these do not occur in clusters. Nevertheless, the winters with high precipitation in 2011 and 2012 should be mentioned here.

Snow cover
The onset and duration of snow cover is described below for the Obergurgl station (1942 m a.s.l.), as this is the closest and 405 highest station with a long data series. As with the other parameters, the values are difficult to transfer to the study area, but can provide an indication of general trends and anomalies in snow cover onset and duration (Fig. A4). Looking at the entire study period from 1953 to 2017, there is a slightly negative trend both for the start of the snow cover and for its duration. This fits in with the results of Olefs et al. (2020) , who found an elevation-dependent reduction in snow depth and duration in Austria between 1961 and 2020, but this only applies to elevations below 2000 m. 410 Although this is not always the case, in general, the data show that when the onset takes place earlier, the duration of snow cover is longer and vice versa (Fig. 6). Particularly striking is the period from 2007-2016 where snow cover onset anomalies show consistently negative values and are also associated with a long duration of snow cover, particularly between 2007 and 2011. Although no long time series data is available for Weißsee station, the early onset and long duration of the snow cover for this period can also be observed here. This effect is also evident in a period between 1956 and 1962 and in two shorter 415 periods from 1971 to 1974 and 1978 to 1981. The opposite is visible for the epoch 1983-1997, as the snow cover tends to set in late and often melts again more quickly.
Exceptions to the general trend can particularly be seen in the case of RG 04. This rock glacier is characterised by very low and relatively constant flow velocities, which even decrease slightly in the two periods following 2006. Many studies 435 mentioned periods of slight decrease or constant flow velocities following the strong acceleration in the 1990s (Delaloye et al., 2010;Kellerer-Pirklbauer and Kaufmann, 2012;Hartl et al., 2016). But there is no known example in the literature which shows a decrease in flow velocities over the entire surface area of the rock glacier in recent years, relative to the period at the beginning of the 1950s with no increase in the 1990s.
Although there is a general trend towards higher flow velocities for all rock glaciers, apart from RG 04, regarding flow velocity 440 patterns and trends, there are different characteristics observable on the individual rock glaciers. and 383.20%, respectively. Beside the rock glacier destabilisation that has been observed for some rock glaciers in recent years Scotti et al., 2017), these are the highest relative movement changes compared to other studies (c.f. Roer, 2005;Kaufmann, 2012, 2018;Kenner et al., 2020). In contrast, in the case of RG 04, the average and 455 maximum flow velocity is reduced by -12.51% and -47.14%, respectively. Since rock glaciers RG 02, RG 07 and RG 04 show similar exposition, size, and elevation ranges, we assume that the different behaviour is explained by a topographic or structural control rather than different external forcing.
The relative changes regarding the remaining rock glaciers ranges between 23.45% and 271.87% for mean flow velocity and 21.77% and 348.20% for maximum flow velocity. If RG 04, which has a very low slope, is not taken into account, one could 460 say that higher elevated rock glaciers change their relative flow velocity to a greater extent. Apart from this observation, none of the topographic factors could explain the different magnitude of the change.

Local temporal peaks -sensitivity of rock glaciers to external forcing
On rock glaciers RG 01 and RG 08, higher flow velocities have been measured between 1953/54 and 1970/71 compared to the subsequent periods. Although there are not many studies covering this period, this phenomenon has also been observed in 465 case studies on other individual rock glaciers in the Austrian (Kellerer-Pirklbauer and Kaufmann, 2012; and Swiss Alps (Kenner et al., 2020) and is explained by decennial variations in mean annual air temperature (Delaloye et al., 2010). In the epoch 1997 to 2006, higher flow velocities were measured compared to the epochs before and after. This is especially the case for rock glaciers RG 01, RG 03, RG 06 and RG 08, although caution is required in the

Patterns of flow velocity and elevation dependency 480
To better place the general trends in a spatial and temporal context, flow velocities in individual epochs and rock glaciers were analyzed for altitudinal zones of 20 meter along the rock glacier surface. Fig. 8 clearly shows the different size and height coverage of the various rock glaciers. Furthermore, it becomes apparent that many rock glaciers do not move uniformly, but have zones with higher and lower flow velocities. The zones of higher flow velocity are usually, but not always, located in the rock glacier front. Exceptions to this are mainly RG 03, which shows a gradual change in its flow pattern over time (see Sect. If we look at the percentage increase in the mean values of the last two epochs, the two lowest elevated rock glaciers, RG 01 and RG 08, show the smallest increases, while the sharpest increase was measured on the highest elevated rock glacier RG 05, 490 whereas the remaining rock glaciers showing relatively similar increase rates. This could point to the fact that from 2006 to 2017 higher elevated rock glaciers enter an unstable state as a reaction of changes in the external forcing. Since rock glaciers tend to react with a certain time lag to changes in e.g. temperature, our data cannot be used to make any statements about e.g. temperature limits or similar. As it is very likely that such a time lack differs between single rock glaciers, we cannot find a general elevation-dependent temporal change regarding the elevation classes.   Kenner et al. (2017) synthesise findings for external factors controlling rock glacier flow velocity. Accordingly, an increase in the permafrost temperature, which changes the viscosity, hardness, and shear-and crushing strength of the permafrost ice, can thus increase its internal plastic deformation. Another factor would be the increase in water availability and water pressure, which reduces the friction resistance in the shear zone. The former is primarily determined by changes in air temperature 505 leading to changes in ground temperature and the timing and duration of snow cover. The latter can be controlled by precipitation, snowmelt, the formation of new drainage systems and melting permafrost ice. As our analysis covers periods of 5 to 17 years, it is difficult to identify individual meteorological factors that cause changes in rock glacier morphodynamics, as possible influences and reactions superimpose. In addition, in situ measurements of permafrost temperature, water availability and the formation of new drainage systems were not the goal of this study. Nevertheless, the following sections 510 describe possible implications of changes in the meteorological forcing (see Sect. 4.2) based on the development of the flow velocity for the six epochs between 1953 and 2017 (Fig. 6).

Temperature
As described by numerous studies, this development of temperatures fits well with the development of flow velocities (e.g. Roer, 2005;Kääb et al., 2007;Delaloye et al., 2010;Scapozza et al., 2014;Hartl et al., 2016;Kenner et al., 2017;Kenner et 515 al., 2020). Even though our rock glacier analysis started with the aerial images from 1953, we had a look at temperature data before this date since rock glacier dynamics can have a significant temporal delay. Although not covered in Fig. 4, we observed exclusively positive temperature anomalies ranging between 0.5 °C and 1°C between 1946-1951. Relatively warm temperatures were measured throughout the Alps during this period (Beniston, 2006). This could be a possible explanation for the local peak in flow velocities of RG 01 and RG 08 between 1953 and 1971 and is also suggested as an explanation by 520 Delaloye et al. (2010). However, this would also imply either that these rock glaciers take longer to react to the increase in temperature or that they take longer to slow down after this increase compared to the other rock glaciers studied. It could also indicate that the remaining rock glaciers have not yet reached a certain system state and have therefore hardly or not at all reacted to the increased temperatures of this period.
When looking at the strong increase in flow velocities from 1997 onwards, it turns out that the spring and autumn temperatures 525 may primarily be responsible for the increase, as the average winter time temperature actually decreases and the autumn mean  This could be due to a delayed warming of the permafrost ice or to the duration of the formation of new drainage systems (Kenner et al., 2017;Kenner et al., 2020), which also might explain the varying magnitude of the increases. The local peak of some rock glaciers between 1997 and 2006 could be explained by the particularly strong increase in spring temperatures or by 540 the heatwave in the summer of 2003, which has also led to very high flow velocity rates in annual studies (e.g.Kellerer-Pirklbauer and Kaufmann, 2012.

Precipitation
While many studies, especially recent ones, emphasise the role of liquid water in rock glacier movement , especially in the shear horizon (e.g. Kenner et al., 2017;Cicoira et al., 2019a;Kenner et al., 2020), only some show a correlation between precipitation and movement (Micheletti et al., 2015;Hartl et al., 2016;Eriksen et al., 2018), while others find no or only a weak connection (Kenner et al., 2017;Kenner et al., 2020). In the latter, rock glacier acceleration is explained, among 550 other things, by an increase in runoff efficiency due to the formation of new drainage pathways in the permafrost body.

Snow Cover 560
Snow cover onset and duration have been shown to be important factors in the development of rock glacier flow velocities, as it controls the time span of liquid water availability as well as the temperature in the subsurface due to the winter cooling intensity (Kenner et al., 2017;Kenner et al., 2020). As for the other two parameters, temperature and precipitation, links can be found between the temporal development of snow cover onset and duration and the evolution of flow velocities in the rock glaciers studied. In the last three epochs from 1997 to 2017, the snow cover sets in relatively early. In combination with the 565 amount of snow and the temperature, this can decrease the rock glacier deceleration in winter, by isolating the rock glacier relatively long duration of the snow cover, which leads to a shorter availability of liquid water. In the period before 1997, it is 570 more difficult to establish a connection. This may be due to the fact that the time periods are larger and thus positive and negative anomalies balance each other out, but possibly also to the fact that the factor snow cover must always be seen in connection with the temperature, which only changed drastically from the beginning of the 1990s. This possibly has led to the formation of new drainage systems, causing a tipping point of flow velocities to a higher level, which in turn might change the value on the influence of the snow cover and precipitation on the flow velocities. The calculation of the mean annual 3D displacements could be carried out on a total of 9 rock glaciers in four epochs. Figure  580 7 shows the values of these 3D displacements on the rock glaciers and additionally the values calculated on stable areas, in order to give an estimation of the accuracy of the measurements. The mean values range from 0.031 (RG 05, epoch 1970(RG 05, epoch -2006 to -0.047 (RG 04, epoch 2006(RG 04, epoch -2012 of the 3D displacements. This is particularly evident in rock glaciers RG 01 and RG 04, where both the mean values and the scatter are significantly more pronounced compared to the others, especially for the epochs since 2006. Both rock glaciers 595 differ with respect to size, slope, spanned altitudinal zones and relief, so that these parameters do not seem to be causal for this behavior. In contrast, the other rock glaciers show more or less similar scatter and similar magnitudes of 3D displacements.
Also with regard to the temporal course, the rock glaciers cannot be divided into different subgroups. Here, one main group it can be assumed that RG 02 changes significantly later in time than the other rock glaciers.

Temporal and spatial trends of the single rock glaciers 610
To better place the general trends in a spatial and temporal context, 3D displcements in individual epochs were analyzed for individual elevation zones. Figure 9 shows the measured 3D displacements in the individual epochs for 20 m elevation zones along the rock glacier surface and the corresponding maps. In addition, based on the maximum extent of each rock glacier, the volume change within each epoch was calculated. The data show that the individual rock glaciers span different elevation levels and that some of the studied objects have very different sizes. Within each rock glacier, there are zones of positive 3D displacements as well as areas of negative change. These changes are mostly spatially clustered, but in some cases they also show a clear temporal clustering.
Overall, the picture already described for the general trends is confirmed. Thus, all rock glaciers show a very clear dispersion of 3D displacements into the positive and into the negative value range. The fact that this scattering can be explained by the movement of the rock glacier becomes very clear when looking at the spatial patterns on the maps. Thus, areas with negative 620 changes are followed by positive changes downslope. Therefore, the characteristic topography for rock glaciers is formed (Frehner et al., 2015) or the rock glacier advances. Furthermore, Fig. 7 and Fig. 9 show that changes in activity occur between the individual epochs. These changes in activity correspond to the general trends already described, but are again spatially more finely resolved in Figure 9. This change between epochs is particularly clear in RG 08. Thus, after a high activity phase in the area of the front of the rock glacier in the first two epochs, these zones become almost inactive in the epochs after 2006 625 (only subsidence are still visible later). On the other hand, RG 02 and RG 07 show hardly any 3D displacements in the epochs before 1970 and 2006, respectively, and show subsidence thereupon (c.f. Sect. 4.6.2).
Nevertheless, the trend towards stronger negative 3D displacements is also confirmed for the other rock glaciers. Here, spatial patterns also exist, suggesting that active and inactive areas are shifting, but a clear elevation dependence is not apparent. The shift in patterns is apparently more due to the dynamics of the individual rock glaciers and less a consequence of elevation. An 630 exception is RG 05, whose rock glacier area extends over a large altitudinal range and whose highest areas are above 3000 m.
Here, areas exist that apparently show very clear activity in terms of subsidence only in the last two epochs starting in 2006.
This fits well with the statements about flow velocities, but a general altitude-dependent trend in the rock glaciers cannot be derived from the 3D displacement data.
Looking at the absolute volume change in the individual epochs, it becomes clear that all rock glaciers show negative volume 635 balances in almost all epochs. Exceptions here are RG 02 and RG 07, which show almost no volume loss in the first epoch, and RG 05, for which this was the case in the second epoch. Overall, however, the negative volume changes increase very significantly from epoch to epoch. This trend is almost linear for the rock glaciers RG 02, RG 03, RG 06, RG 08, RG 09, but with different characteristics. RG 01 as the largest rock glacier also shows the highest volume changes overall, although a jump in the magnitude of the volume changes can be identified here after the second epoch. RG 04, on the other hand, shows 640 a linear trend from the first to the third epoch, which then reverses from 2012 onwards. A temporal outlook is not possible since none of the rock glaciers became completely inactive during the observation period and could be used as a reference. Rather, some of the rock glaciers have shown that an inactive phase can be followed by more active phases. An exception may be at RG 04, where a reversal of the trend in volume changes is visible after 2012, which could indicate that here the ice body could thaw in the medium term and thus the rock glacier could become inactive. 645 A better picture regarding trends would be obtained if the analysis of 3D displacements were combined with the analysis of movement rates. This would allow conclusions to be made as to whether rock glaciers tend to successively sink, followed by a decrease in velocity or whether subsidence is in line with velocities. This synopsis will be done in the following chapter.

Antropogenic influence on rock glacier morphodynamics
In addition to the influences of natural external forcing, the Kauner Valley also provides a good insight into the consequences of anthropogenic interventions in high mountain landscapes. This is also visible with regard to the rock glacier dynamics. As 655 for example we attribute the change in flow velocity pattern on RG 03 to such anthropogenic influences. In the time between 1979 and 1982, the Kauner Valley glacier road was built which intersects the rock glacier in its upper part. This lead to a separation of the upper and lower parts of the rock glacier and results in considerably higher flow velocities as well as increased 3D displacements being measured in the area directly below the constructed road in the following periods, especially from 1997 onwards. We suspect that this deep disturbance of the rock glacier has enabled a more efficient heat transfer, even into 660 the deeper layers of the rock glacier, and that new and more efficient drainage systems have developed below the road. This disturbance of the rock glacier system due to a change in the thermal and hydrological regime in combination with a change of external meteorological forcing could explain the change in the flow velocity and 3D displacements pattern. Although a change can already be observed in the 1982-1997 epoch, a strong increase in flow velocity was measured since 1997, which makes a delayed reaction of the rock glacier to the road construction 17 years before very likely. 665

Inactivation vs reactivation of rock glaciers
It is known that rock glaciers can become inactive due to topographic effects or the loss of ice. Both factors may be linked to the velocities and also to negative 3D displacements (subsidence), which are governed by external forcing as well as internal forcing (slope, altitude and the presence of ice). Regarding topography and elevation, it is evident that there are rock glaciers in our watershed that tend to become inactive due to low slope and/or volume loss since 1953. This can be clearly observed in 670 the example of RG 04, which shows strong subsidence accompanied by comparatively low and constant flow velocities throughout the study period and has even slightly decelerated since 2006. Another example shows that also parts of a glacier can become inactive. This is observable on RG 08, where the flow velocities exceed 1. Whether changes in external forcing or the strong movements at the rock glacier front before 2006 are the cause of these changes cannot be answered conclusively with our data. 680 In contrast, RG 07 and to a lesser extent RG 02 show flow velocities until 1997 that are barely above the error value and furthermore show hardly any detectable 3D displacements at least in the first epoch. This is followed by the strongest relative acceleration that is present in our catchment. Such a behaviour has already been described by Michelleti et al. (2015)  can be seen as a reactivation, although flow patterns on the rock glacier surface suggest that flow velocity before 1997 was 685 probably just too small to be detected by our method. The sudden and sharp change might indicate a change in the internal structure of the rock glacier system and mechanism of flow.

Thermokarst phenomena
Thermokarst is a widespread phenomenon in periglacial landscapes, and such thermokarst depressions can sometimes be filled with water forming lakes (Soare, 2021). This might become a more common feature on rock glaciers due to warming and 690 degradation of permafrost which favours the formation of such depressions also in alpine terrain (Kääb and Haeberli, 2001).
Thermokarst depressions can be observed on RG 04 and particularly on RG 06. Here, in the course of the observation period since 1953, a thermokarst lake developed, which subsequently changed in extent and shape and shifted its location by about 40 m, meaning that this thermokarst depression is part of the moving rock glacier system. These thermokarst depressions are not only characterised by severe subsidence, but also affect the flow velocity of the 695 surrounding areas when they occur on active rock glaciers. This is evident on RG 06, where high flow velocities were measured in the area above and below the thermokarst lake during the entire study period and especially from 1997 onwards. This phenomenon can also be observed on RG 04 where a hot spot of higher flow velocities is found around a thermokarst depression, which decrease since 1997. Here, displacements directions are measured contrary to the main flow direction towards the depression, a phenomenon also described in . 700 This indicates that the more efficient heat conduction also in deeper permafrost layers through the depression in combination with the sinking motion and the liquid water, released by the strong melting of the permafrost leads to an increase in the flow velocities in such areas. In the case of RG 06, the thermal influence of the lake water and the intermittent drainage play surely a role in the development of flow velocities especially in the areas below the lake.

Analysis of velocity and 3D displacements 705
On the one hand, the movement of the rock glacier body can result in a change in the complex surface topography, as negative 3D displacements are often linked to positive 3D displacements and characteristic flow lobes are formed and/or the rock glacier advances in the area of the front. In the case of flowing rock glaciers in the area of shear surfaces, despite the movement of parts or the whole rock glacier body, there should be no or only a slight volume changes of the rock glacier. On the other hand, the movement of a rock glacier can also be the consequence of melting of the ice body or the pore ice. If such melting 710 zones are at zones with higher slope inclinations, the rock glacier tends to both move and subside, but with a tendency to a negative volume balance. If slope inclinations are low, the rock glacier tends to subside with no or only slight movement, which also leads to a negative volume balance. Therefore, it seems reasonable to analyze flow velocities and 3D displacements together, as such an analysis can provide clues to the system state of a rock glacier and regarding a multi-temporal analysis possibly the trends in its behavior.
Thus we plotted the mean values of the 3D displacements against the mean values of the flow velocities, where the scatter of the data is represented by the circles and ellipses (Fig. 10). For this analysis, data from the 1953-1970/71, 1970/71-2006, 2006-2012, and 2012-2017 epochs were used in order to make changes of this interaction over the entire study period visible.
From the analyses of flow velocities and 3D displacements in the previous chapters, it was expected that a majority of the rock glaciers would have overall flow velocities and 3D displacements of a similar magnitude. This was also expected for the 720 relationship between flow velocity and 3D displacements. Both are visible in Fig. 10 for RG 02-RG 07. Exceptions are RG 01 and RG 04, which show complete different magnitude as well as a different relationship between velocity and 3D displacements compared to the other rock glaciers. While RG 01 as the largest rock glacier of the catchment shows the highest velocities and the highest 3D displacements at all, RG 04 shows no or only a slight velocity, but very high 3D displacements. It is very obvious that in the case of RG 04 thawing 730 permafrost is involved and that this thawing has successively increased from 1953 to 2012. Particularly striking then is the https://doi.org/10.5194/tc-2021-77 Preprint. Discussion started: 14 April 2021 c Author(s) 2021. CC BY 4.0 License. reversing trend in the epoch 2012 to 2017, where the 3D displacements are lower than in the previous epoch, but are still at a higher level than in the epochs between 1953 and 2012. This fits also well with the volume losses in Fig. 10. The changes in flow velocities, on the other hand, are minimal and move along the zero value with very little scatter, which fits very well with the low slope gradients (20°) in the area of this rock glacier, which probably prevent movement of the rock glacier despite 735 thawing permafrost. Although it remains to be seen whether the emerging trend will continue since 2017, a successive inactivation of this rock glacier seems to be very likely due to the available data and the visible trend. and 2017. Both, the significant jump in magnitude as well as the delay could be the consequence of the changes in the above described external forcing in combination with the known ice body of the rock glacier (Hausmann et al., 2012). It seems that the external forcing leads to a significant melting of the permafrost body, which "stimulates" the rock glaciers to increase the Regarding the other rock glaciers, we have included a zoomed section of the diagram for better visibility (Fig. 10b). The graph clearly shows that there are differences between the individual rock glaciers in this area. RG 02, RG 03, RG 05 and RG 08 show a tendency towards increasing flow velocities and increasing negative 3D displacements, which is also reflected in a 750 clearly visible increase in the scattering both in terms of 3D displacements and in terms of flow velocities. Here, neither the mean ratio nor the ratio in the scatter (shape of the circles and ellipses) between flow velocity and 3D displacement seems to change visibly. But there are differences between the rock glaciers in terms of the first epoch as well as in terms of temporal changes. While the trend regarding the relation between 3D displacements and velocity seems to be constant for all three rock glaciers, the temporal course of changes is quite different. While the temporal change for RG 02 and RG 05 is relatively 755 constant and shows only minor activity in the first epoch, RG 05 shows a much greater increase in terms of velocity and 3D displacements compared to the other rock glaciers. Phases with significantly increased activity and phases with only slightly increased activity alternate for RG 03 and RG 08 with an already existing activity in the first epoch of RG 08 and minor activity of RG 03. This alternation, in the case of RG 03, is the consequence of the delayed response to road construction (c.f. Sect. 4.6.1). In the case of RG 08, it is a shifting of activity zones from the area of the tongue to the upper part of the rock glacier, 760 with some time lag between these activity phases, with only a slight increase in 3D displacements and velocity in the third epoch.
RG 06 and RG 07 do not show a stable relationship between 3D displacements and velocity over the study period. RG 07 indicates a clear increase in velocity in the second epoch, which is also accompanied by a negative 3D displacement. After the second epoch, the rock glacier appears to accelerate, but this acceleration is not associated with an increased 3D displacement; rather, they decrease or remain at about the same level. Here, it appears to have been an activation (or reactivation) of the rock glacier in the second epoch, causing the system to move and the rock glacier to advance overall, resulting in an overall decrease in volume loss after a period of higher volume loss in epoch 2. RG 06 shows an increase in velocity in epoch 2, which is not accompanied by higher 3D displacements. In the third epoch, the 3D displacementsincreased with only a slight increase in velocity. The increase in 3D displacements hold on in the epoch between 2012 and 2017, but is accompanied by an increase 770 in velocity. It is very likely that this temporal behaviour is the consequence of a thermokarst phenomenon including the appearance of a lake (c.f. Sect. 4.6.3). This lake indicates the melting of permafrost in the third epoch and the following activation of this part of the rock glacier regarding flow velocity, probably due to the heat transfer by the water and the building of subsurface flow routes.
In summary, the temporal decoupling of vertical and horizontal movements observed by Ulrich et al. (2021) over a seasonal 775 period can also be observed over a longer period for some rock glaciers. This indicates that both processes have a different sensibility to external forcing parameters, are governed by different external forcing parameters or have a varying time lag.

Conclusions
The aim of this study was to investigate the multi-decadal and catchment wide morphodynamic changes of rock glaciers, based on a spatial analysis and by using high resolution topographic information from aerial images and LiDAR acquisitions. These 780 data were analysed in the context of the climate variability.
It could be demonstrated that the approach of the combination of different remote sensing techniques for the detection of vertical and horizontal 3D displacements is well suited to extend the study period back into the mid of the 20 th century and thus identify trends in rock glacier dynamics and relate them to climate changes evident over such a long period, as the derived changes were above the determined error values. 785 As a general result, we were able to demonstrate a significant increase in flow velocities in the epoch 1997 to 2006 and an increase in subsidence to varying degrees over the entire study period. Both observations can be explained by changes in external forcing. The sharp increase especially in spring and summer temperatures since the 1990s leads to a change in the flow properties of the permafrost body due to a warming of the permafrost ice. Although the thawing of permafrost ice cannot be distinguished from compaction due to a loss of pore space, trends to negative mass balances suggest a progressive thaw of 790 the permafrost body throughout the study period. Furthermore, the melting of the ice body might create new drainage systems.
This results in more water being available to the system, which is crucial for horizontal movement in shear zones. Flow velocity in this catchment area can also be linked to changes in precipitation pattern, which again governs water availability and the onset and duration of snow cover, which controls the time span of liquid water availability as well as the temperature in the subsurface due to the winter cooling intensity. 795 Although we were able to identify a general trend in rock glacier morphodynamics, the catchment wide view also shows a slightly different response of individual rock glaciers to similar external forcing regarding timing, magnitude and local https://doi.org/10.5194/tc-2021-77 Preprint. Discussion started: 14 April 2021 c Author(s) 2021. CC BY 4.0 License. temporal peaks and the relationship between 3D displacements and flow velocities. No characteristic could be identified that explains the different responses to external forcing over the entire study period. Elevation is suspected to play a role evidenced by some observations, although the investigation of altitudinal zones of the individual rock glaciers did not yield an altitude 800 dependent temporal trend. The different behaviour could be explained by different sensitivity, response or response time of individual rock glaciers to intra-annual, inter-annual or multi-annual fluctuations and changes in external forcing parameters.
For some rock glaciers internal structure and topography might explain different reactions, as two rock glaciers of similar size, exposition, elevation and elevation range showed contrasting reactions of inactivation and reactivation.
Beside the detection of two rock glaciers showing signs of full or partial inactivation, we were also able to show the influence 805 of thermokarst on rock glaciers. Both phenomena will become more frequent during the 21 st century as climate change progresses and permafrost degrades as a result.
We can also conclude that future investigations are necessary to better understand the climate forcing on rock glacier morphodynamics. Therefore, the analysis should be transferred to other catchments in order to identify differences and similarities within the Alps. 3D displacements and flow velocity should be combined with downscaled reanalysis data to better 810 understand catchment wide differences in external forcing on a longer timescale. If possible, future studies should combine borehole measurements or geophysical investigation to shed light on the internal structure of rock glaciers and clarify some of the assumptions and possible explanations of their behavior given in this study.
As a last important perspective, historical terrestrial images (if available) should be used with monoplotting tools. Mapping on such images would help to shorten the time span of the individual epochs which is crucial to better differentiate the influence 815 of individual forcing parameters, as it is very likely that there are changes within our analysed epochs. Beside this, historical terrestrial images would offer the opportunity to expand the analysis back to the 19 th century and thus closer to the LIA in order to study an important period in terms of massive system changes in the glacial and periglacial regions of the Alps. Table A 1: Characteristics of the rock glaciers studied. Permafrost occurrence gives the pseudo-probability of permafrost (Otto et al. 2020). Area covered by 1850 glacier extent is ascertained according to LIA glacier extends (Fischer et al. ,2015) if not specified otherwise.* As described in (Berger et al. 2004); ** as described in (Dusik et al. 2015

Code availibility 840
The image correlation algorithm (IMCORR) is used for the calculation of the rock glacier flow velocities is implemented in the open source geoinformation system SAGA GIS. Furthermore, some modules of the commercial SAGA GIS extension SAGA LIS PRO 3D were used to calculate the 3D displacements of the rock glaciers. The software which was used to create digiatal elevation models and orthophotos from historical aerial images was the commercial software Agisoft Metashape.

Data availibility 845
The analysed metrological data is availible from the "Federal Misistry of Agriculture, Regions and Tourism" (BMLRT), the "Central Institute for Meteorology and Geodynamics" (ZAMG), the "Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region" (HISTALP), the "Autonomous Province of Bozen/Bolzano" and "Tyrolean Hydropower AG " (TIWAG). The aerial images used to create digital elevation models and orthopotos are availible from the "Office of the Tyrolean Government-Department of Geoinformation" (https://www.tirol.gv.at/en/) and the "Austrian Federal Office of 850 Surveying and Metrology" (BEV) (https: //www.bev.gv.at). The self-collected ALS data will presumably be made available after completion of the SEHAG ("Sensitivity of High Alpine Geosystems to Climate ChangeSince 1850") research project.

Conflicts of Interest:
The authors declare that they have no conflict of interest. The funders had no role in the design of thestudy; in the collection, 860 analyses, or interpretation of data; in the writing of the manuscript, or in the decision topublish the results.