Three-dimensional subsurface architecture and its influence on the spatiotemporal development of a retrogressive thaw slump in the Richardson Mountains, Northwest Territories, Canada

Abstract

The development of retrogressive thaw slumps (RTS) is known to be strongly influenced by relief-related parameters, permafrost characteristics, and climatic triggers. To deepen the understanding of RTS, this study examines the subsurface characteristics in the vicinity of an active thaw slump, located in the Richardson Mountains (Western Canadian Arctic). The investigations aim to identify relationships between the spatiotemporal slump development and the influence of subsurface structures. Information on these were gained by means of electrical resistivity tomography (ERT) and ground-penetrating radar (GPR). The spatiotemporal development of the slump was revealed by high-resolution satellite imagery and unmanned aerial vehicle–based digital elevation models (DEMs). The analysis indicated an acceleration of slump expansion, especially since 2018. The comparison of the DEMs enabled the detailed balancing of erosion and accumulation within the slump area between August 2018 and August 2019. In addition, manual frost probing and GPR revealed a strong relationship between the active layer thickness, surface morphology, and hydrology. Detected furrows in permafrost table topography seem to affect the active layer hydrology and cause a canalization of runoff toward the slump. The three-dimensional ERT data revealed a partly unfrozen layer underlying a heterogeneous permafrost body. This may influence the local hydrology and affect the development of the RTS. The results highlight the complex relationships between slump development, subsurface structure, and hydrology and indicate a distinct research need for other RTSs.

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Arctic, Antarctic, and Alpine Research
An Interdisciplinary Journal
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/uaar20
Three-dimensional subsurface architecture and
its influence on the spatiotemporal development
of a retrogressive thaw slump in the Richardson
Mountains, Northwest Territories, Canada
Julius Kunz, T. Ullmann, C. Kneisel & R. Baumhauer
To cite this article: Julius Kunz, T. Ullmann, C. Kneisel & R. Baumhauer (2023) Three-
dimensional subsurface architecture and its influence on the spatiotemporal development of a
retrogressive thaw slump in the Richardson Mountains, Northwest Territories, Canada, Arctic,
Antarctic, and Alpine Research, 55:1, 2167358, DOI: 10.1080/15230430.2023.2167358
To link to this article: https://doi.org/10.1080/15230430.2023.2167358
© 2023 The Author(s). Published with
license by Taylor & Francis Group, LLC.
Published online: 27 Feb 2023.
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Three-dimensional subsurface architecture and its inuence on the
spatiotemporal development of a retrogressive thaw slump in the Richardson
Mountains, Northwest Territories, Canada
Julius Kunz
a
, T. Ullmann
b
, C. Kneisel
a
, and R. Baumhauer
a
a
Department of Physical Geography, Institute of Geography and Geology, University of Wuerzburg, Wuerzburg, Germany;
b
Department of
Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, Wuerzburg, Germany
ABSTRACT
The development of retrogressive thaw slumps (RTS) is known to be strongly inuenced by relief-
related parameters, permafrost characteristics, and climatic triggers. To deepen the understanding
of RTS, this study examines the subsurface characteristics in the vicinity of an active thaw slump,
located in the Richardson Mountains (Western Canadian Arctic). The investigations aim to identify
relationships between the spatiotemporal slump development and the inuence of subsurface
structures. Information on these were gained by means of electrical resistivity tomography (ERT)
and ground-penetrating radar (GPR). The spatiotemporal development of the slump was revealed
by high-resolution satellite imagery and unmanned aerial vehicle–based digital elevation models
(DEMs). The analysis indicated an acceleration of slump expansion, especially since 2018. The
comparison of the DEMs enabled the detailed balancing of erosion and accumulation within the
slump area between August 2018 and August 2019. In addition, manual frost probing and GPR
revealed a strong relationship between the active layer thickness, surface morphology, and hydrol-
ogy. Detected furrows in permafrost table topography seem to aect the active layer hydrology and
cause a canalization of runo toward the slump. The three-dimensional ERT data revealed a partly
unfrozen layer underlying a heterogeneous permafrost body. This may inuence the local hydrol-
ogy and aect the development of the RTS. The results highlight the complex relationships
between slump development, subsurface structure, and hydrology and indicate a distinct research
need for other RTSs.
ARTICLE HISTORY
Received 27 April 2022
Revised 17 October 2022
Accepted 8 January 2023
KEYWORDS
Retrogressive thaw slump;
permafrost; spatiotemporal
slump development; near-
surface geophysics; remote
sensing
Introduction
Ongoing global warming and the associated changes in
regional climatic patterns have strong impacts on arctic
regions, leading to significantly faster warming com-
pared to the global average and changing precipitation
patterns (Intergovernmental Panel on Climate Change
2021). Especially in areas characterized by the presence
of permafrost, these changes lead to hydrological and
thermal feedbacks in the subsurface, accompanied by an
increasing active layer thickness (ALT) and changing
water pathways. Along with this, an increasing number
and activity of retrogressive thaw slumps (RTS) has been
detected in many parts of the Arctic and especially in
northwestern Canada (Lantuit and Pollard 2008;
S. V. Kokelj et al. 2015; Segal, Lantz, and Kokelj 2016;
Lewkowicz and Way 2019) and Alaska (Balser, Jones,
and Gens 2014; Balser 2015; Swanson and Nolan 2018).
RTSs are backwasting thermokarst features and repre-
sent one of the most rapid erosion processes in the
present periglacial environments on earth (French
2017). In comparison to other periglacial landforms,
they are rather short-lived, with an average activity per-
iod of thirty to fifty years (French and Egginton 1973),
but in this time, they lead to sometimes enormous mass
redistributions. Extreme rainfall events have major
impacts on RTS initialization and their growth rates.
Accordingly, in wet summers with more heavy rainfall
events, the formation of new RTSs is more frequent and
the growth rates of existing slumps are significantly
higher (S. V. Kokelj et al. 2015; Lewkowicz and Way
2019).
Recent studies on RTSs focused on the impact of slumps
on river systems regarding material transport (S. V. Kokelj
et al. 2013, 2021) and geo- and biochemistry (Littlefair,
CONTACT Julius Kunz julius.kunz@uni-wuerzburg.de Department of Physical Geography, Institute of Geography and Geology, University of Wuerzburg,
Wuerzburg 97072, Germany.
ARCTIC, ANTARCTIC, AND ALPINE RESEARCH
2023, VOL. 55, NO. 1, 2167358
https://doi.org/10.1080/15230430.2023.2167358
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.

Tank, and Kokelj 2017; Shakil et al. 2020; Zolkos et al. 2020;
Bröder et al. 2021) but also on the spatiotemporal slump
development using Earth observation data (Brooker et al.
2014; Segal, Lantz, and Kokelj 2016; Lewkowicz and Way
2019; Nitze et al. 2021). Further studies highlighted the
influence of various terrain parameters, deduced from
digital elevation models (DEMs), on the slump develop-
ment (Lacelle et al. 2015; Ramage et al. 2017; Ward Jones,
Pollard, and Jones 2019). Studies of RTSs employing geo-
physical surveying are rare so far but the methods are able
to provide key information on the subsurface properties;
for example, in regions adjacent to retreating headwalls.
This subsurface information increases the knowledge
about local permafrost properties and (permafrost) hydrol-
ogy. Both factors have been inadequately studied in RTS
environments, although they have major influences on
spatiotemporal slump development. Local hydrology is of
particular interest because it links both catchment areas of
slump sites and downstream water bodies and can increase
the spatial influence of local slump sites to larger scales due
to sediment cascades and geo- and biochemical impacts
(S. V. Kokelj et al. 2021).
The current study uses a combined approach of
remote sensing and geophysical methods to investigate
the spatiotemporal development and local subsurface
structures adjacent to the retreating headwall of a RTS
at the edge of the Richardson Mountains (Canada). The
spatiotemporal development is investigated using satel-
lite images as well as high-resolution unmanned aerial
vehicle (UAV)-based orthoimages and digital DEMs.
Manual frost probing, electrical resistivity tomography
(ERT), and ground-penetrating radar (GPR) are used to
reveal subsurface structures and to gather information
on the ALT and ice contents of the permafrost in the
surrounding of the slump. The investigations focus on
the relationships between local hydrology, subsurface
structure, and the spatiotemporal slump behavior, thus
providing a holistic view on the different environmental
conditions and processes.
Study site
The Peel Plateau, located in the southwest of the
Mackenzie Delta (Northwest Territories, Canada), is
well known for widespread and ice-rich permafrost
often containing ground ice of glacial origin. This is
due to its former position at the marginal zone of the
Laurentide Ice Sheet (LIS), which reached its maximum
limit at the edge of the Richardson Mountains at
approximately 18 ka cal yr BP (Lacelle et al. 2013,
2015). Consequently, glaciofluvial, glaciolacustrine, and
glacial sediments of a hummocky moraine terrain dom-
inate the surficial geology of the Peel Plateau up to an
elevation of approximately 750 m.a.s.l. (Duk-Rodkin
1992; S. V. Kokelj et al. 2015). The adjacent
Richardson Mountains, reaching an altitude of up to
1,575 m.a.s.l., were ice-free and belonged to the ice-free
sector of Beringia (Lacelle et al. 2013). The geology of the
mountain range is characterized by Cretaceous
(Jurassic) and Devonian shales and siltstones (Wheeler
et al. 1996; Yukon Geological Survey 2020) and the
slopes and valleys are mostly covered by colluvial mate-
rial. The current climate at the climate station Fort
McPherson (approximately 50 km northeast of the
study site, 35 m.a.s.l.) is characterized by a mean annual
air temperature of −7.3°C and a mean annual precipita-
tion of 298 mm (rainfall: 145.9 mm; snowfall: 152.5 cm,
1981–2010; Government of Canada 2020). Within the
Richardson Mountains there are no long-term climatic
measurements, but the data of a climate station installed
14 km to southwest of the study site suggests a slightly
warmer mean annual air temperature of −5.4°C ± 0.9°C
at an altidue of 660 m.a.s.l. between 2014 and 2021
(S. A. Kokelj et al. 2022). Recent studies indicated that
atmospheric inversion settings during winter months
caused average temperatures in the Peel Plateau and
adjacent Richardson Mountains to be warmer than in
Fort McPherson, which is located on the Peel Plain
(O’Neill et al. 2015). In the last three decades, the total
amount of summer precipitation and also the number of
extreme precipitation events increased significantly in
this region (S. V. Kokelj et al. 2015). Permafrost is
continuous in this region and permafrost temperatures
derived from large-scale data sets are between −2°C and
−5°C (Brown, Sidlauskas, and Delinski 1997; Henry and
Smith 2001). Recent studies recorded warmer perma-
frost temperatures (−1°C to −3°C) than expected by the
aforementioned large-scale models due to the inversion-
related warmer winter temperatures and a rather fast
snow accumulation on tundra vegetation (shrubs;
O’Neill et al. 2015). The thickness of permafrost ranges
between 120 m close to Fort McPherson (Mackay 1967)
and up to 625 m in higher elevations in the Richardson
Mountains close to the Yukon–Northwest Territories
border. Therefore, permafrost thicknesses of about
300 m are suspected for the higher elevated parts of the
Peel Plateau region (Smith, Meikle, and Roots 2004).
The region is known for the widespread occurrence of
RTSs due to the degradation of ice-rich permafrost. At
least ten of them are classified as megaslumps due to
their size of more than 20 ha (Lacelle et al. 2015).
The investigated RTS (67°10′59″, −135°48′41″;
Figure 1) is located at the eastern flank of the
Richardson Mountains in the transition zone toward
the Peel Plateau and close to the Dempster Highway
(km 25 from NWT border). It is situated on
2J. KUNZ ET AL.

Figure 1. (a) Location of the study site in the transition zone between the Richardson Mountains and the Peel Plateau indicated by the
red dot. The cloud-free Landsat-8 RGB true-color image composite was provided by Nill et al. (2019). (b), (c) High-resolution unmanned
aerial vehicle (UAV)-based orthoimage and digital elevation model. The area of erosion by the slump is surrounded by the red line. The
white dots in (c) mark the electrode positions of the electrical resistivity tomography (ERT) measurements. (d) A soil profile (image
curtesy: L. Nill) and (e) a ground-based photo of the retrogressive thaw slumps (RTS) and its surrounding taken on 4 September 2019.
Both the active slump in the central part of the image and the stabilized and already vegetation-covered slump in the right part of the
image are visible. The position of (d) is marked in (e).
ARCTIC, ANTARCTIC, AND ALPINE RESEARCH 3

a southeast-exposed slope at an altitude of about 730 m.
a.s.l. and is separated by a small creek (Creek A; cf.
Figure 1) from an older slump scar. The old scar is
already covered with vegetation and seems to be stable
(see Figure 1e). The creek is part of the Vitrekwa Creek
catchment, is deeply incised in some areas, and currently
runs right through the slump floor. The area of the
slump is just under 6,000 m
2
(September 2021) and the
retreating headwall is up to 6.5 m in height. Therefore,
the investigated slump is rather small compared to other
slumps in the region, which are characterized by an
average size of 5.4 ha (Lacelle et al. 2015). However,
because the slump is comparatively young and the
slump type and initiation seem to be similar to others
in the region, the slump appears to be quite representa-
tive. After Duk-Rodkin (1992), the location of the slump
is exactly at the edge of the LIS and, therefore, the slump
developed in glaciogenic materials containing ice-rich
permafrost. According to the ground ice map of O’Neill,
Wolfe, and Duchesne (2022), the study site is underlain
by ice-rich permafrost with an ice content between 20
and 30 percent. The exposed permafrost at the headwall
shows strongly varying ice contents on a small scale,
which, however, are higher than the abovementioned
20 to 30 percent in some areas. Due to slope-parallel
stone stripes, the near-surface subsurface is character-
ized by alternating substrates. Whereas the dominating
part is characterized by clay and silt dominated fine-
grained material, the other part mainly consists of
loamy sediments containing also pebbles. Whereas the
fine-grained material forms hummock-like ridges, the
coarser material is situated in troughs inbetween. After
the World Reference Base classification, the soil type has
to be classified as Cambic Turbic Cryosol. The frost
sorting-related substrate differences also affect the vege-
tation coverage. The fine-grained ridges are covered by
a mixture of dwarf shrubs (willows and birches), grass,
herbs, and lichens, whereas the troughs underlain by
loamy sediments are covered by moss almost exclu-
sively. Recent remote sensing–based studies indicated
a greening trend within the region caused by shrub
expansion (Fraser et al. 2014; Nill et al. 2019, 2022),
which can also affect the thermal regime in the subsur-
face on a long-term scale.
Materials and methods
A combined approach of geophysical and remote sen-
sing methods was applied to investigate the development
of the RTS and its relationship to the prevailing subsur-
face properties such as active layer thickness, substrate
properties, and ice content.
Remote sensing methods
The remote sensing methods include a UAV-based
structure from motion approach performed two times
(2018 and 2019) in the area of the slump as well as
a manual mapping of the slump area based on optical
satellite images (2014–2021).
UAV-based structure from motion
During two field campaigns in August 2018 and
August 2019, two UAV surveys were conducted to gen-
erate aerial images of the slump area. The UAV used was
a DJI Phantom 3 Pro equipped with a standard camera
(12.4 MP, 1/2.3-in. complementary metal oxide semicon-
ductor sensor). The flights were each piloted manually so
that an overlap of 80 percent in flight direction with
a sidelap of approximately 60 percent was reached.
Three different flight heights above ground (approxi-
mately 30, 60, and 90 m) were accomplished during
both surveys to reach an acceptable spatial resolution
and coverage. After sorting out blurred or distorted
images, a total number of 180 (25 August 2018) and 937
(23 August 2019) images were used for further processing,
taking into account that the surveyed area was much
larger in 2019. The aerial images were used in
a structure from motion approach using the software
Agisoft PhotoScan Professional (Agisoft 2016). Six well-
visible and unchanged positions in the study area were
used as reference points for the calibration and coregis-
tration of the two models. For this purpose, features like
striking boulders or distinct features in microrelief were
used, which seem to be stable throughout the period of
investigation. Nevertheless, a detailed survey of these
points using real-time kinematic positioning was not pos-
sible. Therefore, there is a certain uncertainty in this
regard that should be discussed later on. The subsequent
processing contained the image alignment, the identifica-
tion of ground control points (GCPs), and the camera
optimization. Afterward a dense point cloud was created,
out of which a mesh, a DEM, and an orthoimage were
computed. The derived models were characterized by
spatial resolutions between 3 and 4 cm and were
resampled to an resolution of 5 cm to enable
a comparison of the data sets.
To investigate the changes between the two timesteps,
morphometric analysis was performed in ArcMap (ESRI
Inc. 2018) as well as in the software Geomorphic Change
Detection 7 (GCD 7; Bailey et al. 2020), v7.5.0.0. The
detailed methodological approach of the GCD 7 soft-
ware is described in Wheaton (2008), Wheaton,
Brasington, Darby, and Sear (2010), and Wheaton,
Brasington, Darby, Merz et al. (2010).
4J. KUNZ ET AL.

Slump development monitoring
The development of the slump was investigated using high-
resolution optical satellite images from RapidEye and
PlanetScope (Planet Labs PBC). Based on eight scenes
acquired between 2014 and 2021, the yearly development
of the slump was mapped manually using true-color RGB
(red–green–blue) composites. The scenes (Table 1) pro-
vided a spatial resolution of 3 × 3 m or 5 × 5 m per pixel,
were cloud-free, and were acquired in late August or early
September. No data at appropriate resolution were available
before 2014. Because the erosion processes are of particular
interest in this study, the cumulative area and the annual
change were restricted to the scar zone of the slump.
Changes in the accumulation zone further downslope
were not taken into account when looking at spatial
changes.
Hydrological modeling
Based on the TanDEM-X DEM (10-m resolution; created
in 2011/2012) and on the UAV-derived DEM, a modeling
of the surface hydrology was conducted using the System
for Automated Geoscientific Analyses (SAGA) software
v7.2.0 (Conrad et al. 2015). The processing included fill
sinks after L. Wang and Liu (2006), flow accumulation
(flow tracing; Gruber and Peckham 2009), and SAGA wet-
ness index calculation after Böhner et al. (2002). The same
processing was applied to two elevation models of the
permafrost table topography derived from (1) the frost
probing and (2) GPR measurements to investigate the
influence of the local hydrology. Therefore, permafrost
table was picked manually in the GPR data and its depth
values were exported every 0.5 m along the profiles. The
generated point cloud and the data points derived from
frost probing were used to build two separate raster data
sets containing the ALT. In a first step, the ALT was sub-
tracted from the real elevation derived from the UAV-
based DEM. The interpolation method used for subsequent
generation of the raster data was spline interpolation con-
ducted in ArcMap (ESRI Inc. 2018). The resolution of both
data sets was set to 0.2 m taking into account that the
original point density of the two data sets was quite
different.
In situ and geophysical methods
In addition to the remote sensing analysis and the
hydrological modeling, in situ measurements were con-
ducted to investigate the subsurface structure adjacent to
the retreating headwall of the RTS.
Frost probing
Manual frost probing was performed using a 120-cm-long
steel rod to measure the ALT in the surrounding of the RTS
and to calibrate the geophysical models. The steel rod was
pushed into the soil until the frost table was reached.
Because the measurements were carried out at the end of
thaw season in late August and early September, it was
assumed that the active layer had reached its seasonal
maximum. As such, the measured depth was interpreted
to represent the depth of the permafrost table. The mea-
surements were rounded to an accuracy of 5 cm.
Ground-penetrating radar
The use of GPR is widespread in the field of permafrost
research and the method has often been used for ALT
mapping (Hinkel et al. 2001; Moorman, Robinson, and
Burgess 2003; Chen et al. 2016; Campbell et al. 2021) but
also for the investigation of subsurface structures such as ice
wedges within ice-rich permafrost (Munroe et al. 2007). The
method is well suited for the detection of ALT because it is
sensitive to dielectric differences between unfrozen and fro-
zen state. A bistatic setup with a pair of antennas (i.e.,
transmitter and receiver) was employed using a PulsEKKO
Pro System with unshielded 100-MHz antennas and
a NOGGIN System with shielded 250-MHz antennas, both
from Sensors and Software Inc. The three-dimensional (3D)
grid consists of twenty-six parallel common-offset profiles
performed with 250-MHz antennas and twenty-four com-
mon-offset profiles performed using 100-MHz antennas. For
the best adjustment of signal propagation velocities during
processing, they were first calculated by alignment with
colocated frost probing results using the known two-way
travel time of the signal and ALT. The subsequent GPR
data processing included a time zero correction, background
removal, dewow filtering, migration, SEG2 gaining, and
a topographic correction of the data.
Table 1. List of used remote sensing scenes for long-term slump development analysis.
Date Satellite Scene ID Resolution
9 September 2014 RapidEye-5 20140909_214033_870113_RapidEye-5 5 × 5 m
6 July 2015 RapidEye-5 20150706_213703_870113_RapidEye-5 5 × 5 m
24 August 2016 Planet Scope 20160824_194908_0e20 3 × 3 m
16 August 2017 Planet Scope 20170816_200004_0e0f 3 × 3 m
29 August 2018 Planet Scope 20180829_200733_1012 3 × 3 m
9 September 2019 Planet Scope 20190909_173053_1054 3 × 3 m
29 August 2020 Planet Scope 20200829_184412_62_106a 3 × 3 m
5 August 2021 Planet Scope 20210805_194447_14_245d 3 × 3 m
ARCTIC, ANTARCTIC, AND ALPINE RESEARCH 5

Electrical resistivity tomography
The ERT method is common in the field of permafrost
research and is based on the different electrical resistiv-
ities of different materials. It enables the delineation of
frozen and unfrozen materials as well as the detection of
relative changes in water content or ice saturation
(Kneisel et al. 2008; Reynolds 2011). Recently, the use
of quasi-three-dimensional (q3D) approaches has
enabled large-scale 3D surveying of subsurface hetero-
geneities within periglacial landforms (Emmert and
Kneisel 2021; Kunz and Kneisel 2021). As such, the
approach allows the investigation of the internal struc-
ture of geomorphological features and landforms. The
q3D approach describes the measurement of several par-
allel or orthogonal profiles that are merged and inverted
together afterward. This approach enables a detailed 3D
investigation of larger areas in comparison to real 3D
approaches (Rödder and Kneisel 2012; Hauck 2013).
To investigate the area surrounding the RTS, a grid of
twenty-three profiles was surveyed using a Syscal Pro
Switch 72 device from IRIS Instruments Inc. The measure-
ment setup consists of ten longitudinal profiles measured
with seventy-two electrodes and two additional longitudi-
nal profiles as well as eleven cross-profiles measured with
thirty-six electrodes. The electrode spacing used was 3 m
and a dipole–dipole array was applied. The chosen interline
spacing was 6 m between longitudinal profiles and 12 m
between cross-profiles. The location of the individual pro-
file lines is visible in Figure 1c. The data processing was
conducted using the software packages RES2DINVx64
v4.05.41 (Geotomo Software SDN BHD 2015) and
RES3DINVx64 v3.11.73 (Geotomo Software SDN BHD
2016). It included the removal of poor datum points
based on a 5 percent threshold of the stack deviation and
a two-dimensional (2D) trial inversion with subsequent bad
datum point filtering based on a 100 percent root mean
square error threshold as promoted by Loke (2019).
Afterward, all 2D data were collated into a 3D data set
and topography derived from UAV surveys was added.
For the inversion, the L1-norm (robust inversion) with an
initial damping factor of 0.1 and a vertical to horizontal
flatness filter ratio of 1 was applied to the data.
Results
Spatiotemporal slump development
Long-term development
Based on the remote sensing data sets, the spatial devel-
opment of the slump could be delineated for the period
from August 2014 to July 2021 (Figure 2). The cumula-
tive area in Figure 2 refers exclusively to the scar zone,
the area in which erosion has taken place due to slump
processes. The change describes the annual change in
this area. At the beginning of the time series, the extent
of the slump was around 160 m
2
. That represents an
initial state of the RTS and is close to the lowermost limit
that could be detected visually using optical satellite data
with the available resolutions. In the following years, the
slump extended in a western and southwestern direc-
tion, but since 2018, the expansion has taken place along
a small creek (Creek A) in a northwestern and northern
direction. In contrast, the southward growth decreased
significantly since 2018 and south of the initial slump
scar. Since 2018, the annual spatial growth of the slump
has increased significantly. This is likely due to the
increasing area of the retreating headwall especially
along Creek A. Along the incised creek, headwall retreat
has taken place on both sites of the creek since 2018,
which is why the headwall length was much higher and
annual change has increased significantly since then.
The cumulative area of the slump shows an exponential
growth that can be seen in Figure 2d by the dotted line
and the corresponding growth function, due to the
increasing annual change. In the case of the growth
shown for 2021, it should be noted that the extent was
already measured in July and thus in the middle and not
toward the end of the thaw season.
High-resolution change detection
The two UAV surveys from August 2018 and August 2019
allowed the detection of small-scale variabilities of slump
development and indicated the differences regarding ero-
sion and accumulation. Differences between the two high-
resolution DEMs of the entire slump area as well as the
changes along two profiles are shown in Figure 3. The
backward erosion of the headwall between the two surveys
is colored in dark red and clearly visible. In this area,
a negative vertical change of the surface of up to 6.5 m is
observed. This also corresponds to the height of the head-
wall in August 2019. In contrast, in parts of the 2018 slump
floor as well as along the course of the creek, an accumula-
tion of material mainly took place, raising the slump floor
by 1.0 to 1.4 m. This change also matches the change in
headwall height, which was up to 7.5 m in August 2018 and
thus 1 m higher than in 2019. Other portions of the slump
floor, as well as much of the downslope areas outside the
current stream channel, were balanced/stable during the
observation period. However, individual features in the
slump floor, representing larger root balls of former vegeta-
tion that have broken off the headwall, were slightly dis-
placed toward the east. This is visible due to an increase in
elevation of the eastern side and a decrease in elevation of
the western side. This is also visible in profile A–A′ in
Figure 3b. In addition, the two profiles in Figures 3b and
3c indicate the displacement of the headwall. The
6J. KUNZ ET AL.

horizontal displacement was in a range between 4 and 7 m
along the entire headwall length. At the spur protruding
from the slump, the horizontal retreat was up to 11 m,
because erosion in this area occurred from two sides.
Profile B–B′ additionally shows the distinct widening of
the erosion channel along both sides of the creek.
A numerical analysis of the two terrain models using
GCD 7 software showed that the described changes corre-
spond to an erosion of about 6,170 m
3
and a redeposition of
4,970 m
3
of material. Accordingly, about 1,200 m
3
of mate-
rial has been eroded and deposited outside the investigated
area (approximately 20 percent). This volume includes the
meltwater of the former ground ice draining into the chan-
nel and eroded sediment.
Subsurface architecture
Active layer characteristics and hydrology
The spatial distribution of the ALT in the area southwest
of the slump adjacent to its retreating headwall was
investigated using manual frost probing and GPR
measurements. In Figure 4, the results of frost probing
are shown as point data on the left and as interpolated
spatial data on the right. The main result of these mea-
surements is a clear zonation of the ALT depending on
the relief position. The higher elevated positions on the
slope between the two incised creeks are characterized
by low ALTs between 15 and 60 cm, whereas the creeks
in the southwestern part (Creek B) and also at the north-
ern most edge of the grid (Creek A) are characterized by
higher ALTs of up to 120 cm and more. The ALTs at the
slope dipping toward Creek B are in a medium range
between 60 and 85 cm but are separated from the creek
by a small band of lower ALTs between 20 and 60 cm (cf.
P1 in Figure 4b). This band runs parallel to the creek,
approximately 10 m away from its center and is just
a few meters wide. In the area of the slope ridge, which
is predominantly characterized by low ALTs, a smaller
cluster of somewhat higher ALTs is noticeable in the
direction of the slump. This apparent gully-like struc-
ture in the permafrost table is also evident in the inter-
polated data, where it stands out as a brighter greenish to
Figure 2. Spatial development of the retrogressive thaw slumps (RTS) between August 2014 and July 2021 derived from optical
satellite data. At the top are two optical satellite images from (a) September 2014 (Image © 2014 Planet Labs PBC) and (b) August 2021
(Image © 2021 Planet Labs PBC). A red line in both images marks the 2021 extent of the area where erosion has taken place. (c) The
temporal development of the slump and statistics on the annual change and (d) the cumulative spatial change.
ARCTIC, ANTARCTIC, AND ALPINE RESEARCH 7

yellowish path running in the direction of the slump (P2
in Figure 4b). Similar structures are visible further
downslope, one of which also drains toward the slump
(P3 in Figure 4b).
Comparing the frost probing data with GPR results
(Figure 5), it is noticeable that the GPR data appear much
more heterogeneous. At this point, it should be noted that
the two data sets do not show a significant correlation (cf.
Figure 5c), which will be discussed further. Especially in
the 2D GPR profiles, a strongly undulating structure of
the permafrost table is visible, which can be recognized in
the data as an intense continuous reflector. This undulat-
ing structure coincides with the impressions from the
field, where strong small-scale differences could also be
detected during frost probing and within the soil pit
shown in Figure 1d. However, these are not visible in
the comparatively coarse-scaled grid in Figure 3. GPR
data measured perpendicular to the slope direction indi-
cate that the permafrost table has a system of furrows and
ridges orientated in the direction of the slope. In addition
to the topography of the permafrost table, the reflection
characteristics within the active layer are striking. On top
of the ridges, the reflections are much more intense than
over the depressions in the permafrost table. These differ-
ences are indicative of substrate variations within the
active layer occurring in a more or less regular pattern,
which is the result of frost sorting processes and repre-
sents the already mentioned stone stripes. In particular,
this changing pattern and the ridge and depression struc-
tures occur in the central part of the slope but not in the
area immediately to the retreating headwall. In this area
(approximately 5 m from the headwall), the permafrost
table is more regular and the ALT is much lower
(approximately 30–60 cm).
Figure 3. (a) Surface elevation changes within the affected area between 25 August 2018 and 23 August 2019 derived from two
unmanned aerial vehicle (UAV)-based surveys. (b), (c) The surface elevation through the most active parts of the retrogressive thaw
slumps (RTS) along the profile lines indicated in (a).
8J. KUNZ ET AL.

Figure 6 shows the results of hydrological modeling
based on the UAV-derived DEM as well as on the
permafrost table topography derived from frost probing
and GPR. In Figures 6g and 6h, a large-scale analysis for
the entire catchment area is shown to demonstrate the
overall hydrological situation (from TanDEM-X DEM).
The modeling based on the permafrost table topography
derived from both methods (frost probing and GPR)
shows a strong runoff canalization toward the RTS. In
the permafrost table–based models, more runoff flows
into the slump than in the models derived from the
UAV surface models. The GPR-derived model presents
a much more heterogeneous structure and seems more
similar to the UAV-derived surface model due to the
Figure 4. Manual frost probing results of the area adjacent to the retreating headwall in early September 2019 shown (a) as point data
and (b) spatially interpolated data. Positions referred to in the text are marked by dashed lines and labeled (P1–P3).
Figure 5. Results of ground-penetrating radar (GPR)-based active layer measurements. (a) A GPR profile of the 250 MHz antennas is
shown for example. A red line highlights the continuous reflector of the frost table. The location of the profile in comparison to the
electrical resistivity tomography (ERT) grid is shown on the right. (b) A map of derived active layer depth data (scale as in Figure 4). (c)
A comparison between the frost probing results and the closest GPR-derived data points is visible as a scatterplot.
ARCTIC, ANTARCTIC, AND ALPINE RESEARCH 9

high resolution. Nevertheless, the flow directions are
closer to those in the coarser permafrost table model
derived from frost probing, so the GPR results demon-
strate the altered hydrology due to the permafrost table
topography.
Permafrost characteristics
The underlying permafrost, investigated by ERT, shows
a heterogeneous structure similar to the ALTs. Figure 7
presents selected depth slices of the q3D ERT model as
a stacked layer graphic. Based on the frost probing
results, the threshold value for the delineation between
frozen and unfrozen ground seems to be around 600
Ωm. The first depth slice covers the uppermost 1.05 m of
the model and seems to be affected by the varying active
layer characteristics. Hence, this layer shows spatial vari-
able patterns comparable to those revealed by frost
probing. The small, incised channel (Creek B) is char-
acterized by very low resistivity values (<100 Ωm), and
the adjacent hillslope (P1 in Figure 7) also shows com-
parably low resistivity values (100–250 Ωm). In contrast,
most parts of the slope (especially, the uppermost part;
Figure 6. (a)–(c) Calculated flow accumulation and (d)–(f) topographic wetness index based on the (a), (d) unmanned aerial vehicle
(UAV)-derived digital elevation models (DEM), (b), (e) frost probing–derived frost table topography, and (c), (f) ground-penetrating
radar (GPR)-derived frost table topography. The extent of the GPR data is marked in (a)–(f) by the dashed line. (g), (h) The hydrology of
the larger catchment area based on the TanDEM-X DEM.
10 J. KUNZ ET AL.

P2 in Figure 7) are characterized by resistivities between
600 and 1,800 Ωm. These differences might be due to
different soil moistures within the active layer and also
due to ALT variations. The uppermost model layer of
the ERT integrates over the uppermost 1.05 m of the
subsurface. In areas with ALTs higher than this, the
model layer is unfrozen, whereas some parts of the
model layer are frozen in areas with ALTs lower than
the model layer thickness. In the upper hillslope area
(P2), the higher resistivity values therefore might be
affected by the lower ALTs in this area. Close to Creek
B, where the uppermost model layer represents only
unfrozen and saturated material, the resistivity values
are distinctly lower.
In the layers beneath (from 1.05 to 5.24 m), an area-wide
increase of the resistivity values is observed (exceeding
1,500 Ωm), except for the region below Creek B. Here,
the resistivity values remain at a low level (<250 Ωm),
indicating unfrozen conditions, also in greater depths. It
is noticeable that the resistivity rises above 10 kΩm in some
areas, especially at depths between 2.26 and 3.65 m. This
high resistivity can be interpreted as ice-rich permafrost.
However, these are not large-scale, continuous structures
but individual separated anomalies elongated in slope
direction (P3 in Figure 7). The area in between these
high-resistivity anomalies seems to be frozen but has to
be considered as less ice-rich as resistivity values range
between 1,500 and 3,200 Ωm (i.e., are of lower magnitude).
In the depth layer (5.24–7.08 m) as well as in the underlying
layer (7.08–9.19 m, not shown), a clear area-wide drop in
resistivity to well below 600 Ωm can be seen. According to
the low resistivity values, it is assumed that this layer is
partially unfrozen and contains a high amount of liquid
water. In some parts, the resistivity values drop even below
250 Ωm (P4 in Figure 7). Below a depth of approximately
9 m, there is a slight increase in electrical resistivity again,
followed by a sharp increase from about 11.5 m depth
downwards. The lower model layers most likely represent
ice-rich permafrost or frozen bedrock, because of the resis-
tivity values exceeding more than 10 kΩm.
In summary, these results indicate a four-layered struc-
ture consisting of the active layer, a partly ice-rich layer of
permafrost, an underlying layer of partly unfrozen sedi-
ments, and a lowermost high-resistivity layer, most likely
representing ice-rich permafrost or frozen bedrock.
A detailed discussion of this rather unusual structure fol-
lows in the section “Spatiotemporal development and pos-
sible relationships to subsurface structure” on this article.
The structure is consistent over the entire grid except from
the area below the incised Creek B. Here, the low-resistivity
surface anomaly continues down to a depth of about 5 m
(cf. P1 in Figure 8a). In the longitudinal profile L4
(Figure 8b), it can be seend that the multilayered structure
extends along the entire slope. The third layer, represented
by lower resistivity values, appears to be frozen over large
parts of the profile, because the resistivity values remain
above the assumed threshold value. However, the lower
slope area starting at 150-m profile length is an exception,
because there, at depths between 6 and 10 m, the resistivity
values drop below 250 Ωm (P2). The location of the low-
resistivity anomaly is striking here, because it lies along the
profile in the area closest to the retreating slump headwall.
Further, the depth position from 6- to 7-m depth onwards
corresponds to the height of the slump headwall and thus
also to the approximate depth of the slump floor.
Discussion
Methodological approach
The methodological approach combines a set of remote
sensing and geophysical methods to investigate the for-
mer and recent development of the RTS. The remote
Figure 7. Selected depth slices of the 3D electrical resistivity
tomography (ERT) model. The threshold for delineation of frozen
and unfrozen areas is around 600 Ωm. Positions referred to in the
text are labeled (P1–P4).
ARCTIC, ANTARCTIC, AND ALPINE RESEARCH 11

sensing–based analysis of the slump development is
based on a simple manual mapping of the slump area
using optical satellite data. Only one single image
per year could be used for the derivation of yearly spatial
changes, because the data availability was low due to the
frequent cloud coverage, shading, and rather short
snow-free period. Whenever possible, images acquired
in late August or early September were chosen, because
thaw season is presumed to end in early/mid-September,
thus reaching the maximum extent of the slump in
a respective year.
The use of UAV-based DEMs for change detection
analysis is common in the field of permafrost research
(cf. Gaffey and Bhardwaj 2020; Śledź, Ewertowski, and
Piekarczyk 2021) and for investigations of RTSs
(Armstrong et al. 2018; van der Sluijs et al. 2018;
Turner, Pearce, and Hughes 2021). A main problem
with this method in permafrost regions is often a lack
of stable ground control points due to, for example,
permafrost-related surface displacements, vegetation
coverage, or missing bedrock outcrops. In this study,
prominent positions recognizable in both images were
used, and it is assumed that the selected GCPs allowed
for a reliable coregistration of the 2018 and 2019 DEMs.
Unfortunately, the GCPs could only be measured in
the second year, which is why the coregistration of the
UAV models was subsequently performed on the basis
of these points. The comparison of the two DEMs indi-
cated area-wide changes of less than 5 cm for the
(unchanged) vegetated patterns adjacent to the slump.
Instead, the vertical and horizontal changes in the area
of the slump, related to its development, were between
−6.5 and +1.5 m, showing a much higher order of
magnitude. The small changes in the undisturbed areas
show that at least the internal position accuracy of the
two models fits well and the error caused by this is much
smaller than the changes to be detected in the area of the
slump.
Similar to the use of UAVs, the usage of geophysical
methods is common in permafrost research. The appli-
cation of ERT enables the delineation of frozen and
unfrozen areas as well as the derivation of relative ice
content changes within the subsurface (Fortier, Allard,
and Seguin 1994; Kneisel et al. 2008). The delineation of
frozen and unfrozen areas is based on the sharp increase
in electrical resistivity at the transition from unfrozen to
frozen conditions (Kneisel et al. 2008). Therefore,
a threshold value for the delineation should be deter-
mined for the site-specific conditions. In the present
study, the threshold was set as 600 Ωm based on
a cross-check to direct manual frost probing. This
threshold is in range detected by other studies in similar
arctic and subarctic environments (300–1,000 Ωm;
Fortier, Allard, and Seguin 1994; Dobiński 2010;
Lewkowicz, Etzelmüller, and Smith 2011; Emmert and
Kneisel 2021) but is much lower than in other mountai-
nous regions (Kneisel and Hauck 2008). Varying resis-
tivities within the permafrost highlight small-scale
heterogeneity and may be due to different substrates
Figure 8. Two 2D electrical resistivity tomography (ERT) profiles of the measured q3D grid. Profile C5 presents a cross-profile measured
perpendicular to the slope direction, whereas the longitudinal profile L4 was measured in slope direction. The red lines in the sketch
map, which presents all electrode positions on a hillshade map of the unmanned aerial vehicle (UAV)-based digital elevation models
(DEM) from 2019, indicate the positions of these profiles. Positions referred to in the text are labeled (P1–P2).
12 J. KUNZ ET AL.

but especially to different ice and water contents in the
permafrost. Higher resistivity values point to higher ice
contents and lower (pore) water contents within the
permafrost (Fortier, Allard, and Seguin 1994; Hauck
2002; Oldenborger and LeBlanc 2018). Whithout dril-
lings, these variations could not be validated in detail,
but exposed permafrost at the slump headwall shows
distinct heterogeneities in ice content and fits to the
detected subsurface structures based on the ERT. The
chosen setup with 3-m electrode spacing, up to seventy-
two electrodes, and the dipole–dipole array used allowed
a detailed and high-resolution investigation of the upper
10 m of the subsurface in the slump environment, which
was of particular interest with regard to the slump depth.
Further, the chosen interline spacing of 6 m between
longitudinal profiles and 12 m between the cross-profiles
contributed to the high model resolution of the 3D
model. Regardless of the model resolution, the small
model error of 5.34 percent (mean absolute error)
using the fifth iteration indicates good data quality.
Differences between the 2D measurements and the gen-
erated 3D model were small and did not affect the
interpretation of the results but were in line with find-
ings revealed in previous research (Emmert and Kneisel
2017).
The active layer delineation based on frost probing
and GPR delivered area-wide estimates of the ALTs in
the area adjacent to the retreating headwall. It should be
noted that all measurements were taken in late August
and early September close to the end of thaw season. In
both years, freeze-back started only few days after the
measurements. Nevertheless, it could not be ruled out
that the ALT slightly increased during the short period
of several days after the measurement. Because the mea-
surements were rounded to an accuracy of 5 cm and the
focus of this study was more on spatial heterogeneities
than on the temporal ALT development, the mentioned
uncertainty was accepted and the detected frost table
was interpreted as permafrost table. Both methods are
associated with several uncertainties. The manual frost
probing results were rounded to an accuracy of 5 cm due
to the undefined surface in case of soft vegetation, such
as moss cushions and lichens. In addition, the partly
coarse-grained material likely did not always allow
detecting the permafrost table without doubt. In turn,
the GPR results could be calibrated using the frost prob-
ing results, but a comparison of the calibrated GPR data
and frost probing results showed a poor correlation
between the two data sets (Figure 5c). Calibration
revealed average signal propagation velocities in the
active layer of 0.043 to 0.056 m/ns with decreasing
velocity in the downslope direction. In general, these
values are in line with values from other studies for
a moist active layer in similar environments (Brosten
et al. 2009; Kunz and Kneisel 2021) and are also within
the theoretical range (Davis and Annan 1989; Moorman,
Robinson, and Burgess 2003; Reynolds 2011). However,
the calibration enables only a rough approximation due
to the small-scale variability in soil moisture. These
strong soil moisture variations as well as the inaccuracy
in coregistration of the two data sets of approximately
0.5 m affects the correlation of the two data sets. The
mentioned velocity decrease in the downslope direction
is possibly due to an increase in soil moisture downslope
and ensures that the time–depth conversion in the
slope-parallel profiles could not be performed with
a single standard value. Instead, the average velocity
was calculated for each profile covered by GPR and
frost probing and the propagation velocity of the closest
calibrated profile was used for the intervening GPR
profiles. The mentioned inaccuracies in the methodolo-
gical approaches to localize the permafrost table also
affect the hydrological modeling results based on these
data. The permafrost table model derived from frost
probing is comparatively coarse-scaled. The distance
between the original data points was 6 m and, therefore,
much higher than in the GPR-derived data set. The
GPR-derived permafrost table model is much more
detailed and the resulting hydrological data look much
more heterogenous. This heterogeneity is more in line
with the actual small-scale variability of both ALTs and
permafrost table topography, also proven by digging,
although the absolute values do not show a good
correlation.
Overall, the combined methodological approach
allowed a comprehensive investigation of the slump
environment in terms of subsurface structures and per-
mafrost hydrology, as well as the spatiotemporal evolu-
tion of the RTS.
Spatiotemporal development and possible
relationships to subsurface structure
The initialization of the slump likely originated from
a small stream channel (Creek A) and is therefore likely
due to erosion of the insulating active layer in the riparian
area. This is considered a typical origin of RTSs (French
2017). The slump was first detected in summer 2014 in the
remote sensing imagery with a rather small area of
approximately 160 m
2
. Nevertheless, the initial rupture
ARCTIC, ANTARCTIC, AND ALPINE RESEARCH 13

of the slump in the riparian area of the creek could already
have occurred some years before. It is possible that
increased precipitation, and possibly higher ALTs, caused
increased erosion in the streambed, which in turn con-
tributed to the initialization or reactivation of the slump.
The expected runoff volumes for the RTS under investiga-
tion might be comparatively low, considering the rather
small size of the catchment (2.16 ha). However, the runoff
is further influenced by thermal erosion in the slightly
incised streambed and is therefore dynamic in nature.
Permafrost table topography affects the local hydrology
due to a canalization of water on top of the permafrost
table. The detected furrows, revealed by GPR and frost
probing and also visible in the soil pit (Figure 1d), concen-
trate the runoff in the downslope direction and in some
parts toward the slump. This is highlighted by the hydro-
logical modeling and is indicated in a simplified model of
the subsurface structures in Figure 9. Furthermore, the
subsurface structures detected by ERT could have
a strong influence on the hydrology. Especially the partially
unfrozen layer at a depth of about 6 to 10 m could be
directly connected to the slump floor because the anomaly
continues also in the profiles closest to the slump. This low-
resistivity layer could therefore represent a possible water
pathway, potentially providing an underground connec-
tion between Creek B in the southwest of the grid and the
slump floor. A possible connection to Creek B further
upslope is also supported by the 2D profiles. At the profile
position of approximately 35 m in profile C5 (Figure 8), it
can be seen that there is a small intersection (green, P1) of
the low-resistivity structure (yellow and brownish) at
a depth between 6 and 7 m below the surface.
Nevertheless, the intersection area is also characterized by
resistivity values close to 600 Ωm and, therefore, close to
the assumed threshold value between frozen and unfrozen
ground. Hence, the unfrozen layer provides a potential
connection between the creek and the slump floor that
may affect the hydrology. The formation of a talik at this
depth is rather atypical but could be related to the detected
talik below the creeks. These might affect subsurface
hydrology and thus cause a thermal impact also at greater
depths. This impact could have propagated along flow
paths and thus contributed to the formation of this partly
unfrozen layer. The subsurface structure including this
layer and possible water pathways are shown in the sim-
plified block model in Figure 9. This detected subsurface
structure fits to the observations of other studies where
comparatively warm permafrost temperatures (−1°C to
−3°C) were measured (O’Neill et al. 2015), and icing events
were detected in this region (Crites, Kokelj, and Lacelle
2020), indicating water pathways and occurrences within
the permafrost. The partly unfrozen layer also affects the
slump development and represents a kind of lower limit for
the slump headwall due to the rather low ice content or
even the absence of ice. This layer would not be affected or
only slightly affected by permafrost thaw and ground ice
melt and therefore would probably not be subject to such
rapid erosion as the overlying ice-rich layer.
Within the frozen layer, the ERT measurements also
revealed varying ice contents, which were also visible at the
exposed slump headwall. In some areas, nearly pure ice was
exposed at the headwall, whereas in other parts frozen
sediment without visible ice lenses was exposed.
A comparison of the observations with the ERT results
Figure 9. Simplified illustration of the detected subsurface structures as well as derived water pathways (blue arrows). The figure is not
drawn to scale and is shown vertically exaggerated.
14 J. KUNZ ET AL.

supports the interpretations, because the areas where high
resistivities were detected were in close proximity to the
areas where high ice contents or massive ice were visible in
the headwall. This varying ice content may also affect the
spatial development of the slump. Whereas the slump
extends especially in a southern and southwestern direction
during the first years, it has expanded in a western and
northwestern direction since 2018. This is possibly due to
lower ice content in the area south of the slump, whereas
higher electrical resistivities were measured in the area to
the west adjacent to the slump, indicating a higher ice
content. Erosion is therefore progressing toward areas
characterized by higher ice content (cf. Lewkowicz 1987)
and would thus be directly influenced by subsurface prop-
erties as already assumed in previous studies (e.g.,
Lewkowicz 1987; Lacelle et al. 2015; Bernhard et al. 2022;
Hayes et al. 2022). This suspicion was also confirmed by
comparison with an ERT reconnaissance profile surveyed
in August 2018 that was measured along the headwall
diagonally across the slope. The profile showed higher
resistivity values in the area west of the slump, whereas
the resistivity values south and southwest of the slump
indicated low ice contents or partly unfrozen conditions.
Erosion up to the 2019 measurements then occurred in
exactly those areas where high electrical resistivities, and
thus high ice contents, were detected in 2018. In the direc-
tion of the areas that had lower resistivity values, no or only
minor expansion of the slump took place. This suggests
that in addition to hydrology and general relief character-
istics, subsurface properties (in particular ice content) have
a distinct influence on spatiotemporal slump development.
Other RTSs in the region typically show an eastern
exposure of the slope (cf. Lacelle et al. 2015; Bernhard
et al. 2022). The RTS investigated in this study shows
a slope with southeastern orientation. It should be
noted that the slump was initiated in the northeast-
exposed riparian area of the creek and subsequently
extended toward the south and west. The elevation of
the slump is close to the uppermost limit of RTSs in
the region, which is due to the expected limited dis-
tribution of ice-rich permafrost or massive ground ice
up to an elevation of 750 m.a.s.l. This distribution is
closely related to the outermost limit of the LIS
(Lacelle et al. 2013, 2015). The size of the slump
(approximately 6,000 m
2
), its annual increase in
thawed area, and the volume of material eroded
annually were lower than the regional average and
were in the lower limit of the regional spectrum.
Nevertheless, based on the UAV surveys conducted
in 2018 and 2019, an area-to-volume scaling factor of
1.285 can be determined from the annual change in
thawed area (891 m
2
) and the thawed volume eroded
during the same period (6,170 m
3
). This value
describes the scaling exponent of the power law rela-
tionship between the disturbed area and the volumetric
change. A similar value (1.27) was calculated as regio-
nal average (from 438 RTSs) by Bernhard et al. (2022)
for the Peel Plateau region, whereas S. V. Kokelj et al.
(2021) calculated a distinctly higher average scaling
factor (1.42) for seventy-one slumps in the Peel
Plateau, the Anderson Plain, and the Tuktoyaktuk
Coastland region. However, the scaling factors were
calculated in different ways in these studies. Whereas
S. V. Kokelj et al. (2021) calculated the factor using the
entire slump scar volume and area, Bernhard et al.
(2022) used a simplified approach using calculated
annual area and volume changes derived from head-
wall retreat. We also used the simplified approach and
thus only the changes in area and volume but were
able to use very high-resolution data to calculate these
parameters. A more precise calculation would need
a predisturbance relief data set as recommended by
van der Sluijs, Kokelj, and Tunnicliffe (2022).
The influence on downstream areas is likely rather
small due to the comparatively small size. As men-
tioned above, the material was mostly redeposited in
the direct vicinity of the headwall and only a very
small part was transported away via the headwaters.
Nevertheless, the occurrence of RTSs also affects the
water chemistry. Therefore, a certain influence on the
downstream waters is still to be expected at the inves-
tigated RTS. A geochemical analysis of samples from
the headwall, debris, and runoff conducted by Bröder
et al. (2021) showed that the slump did not differ
significantly from other slumps in the vicinity regard-
ing different geochemical parameters. The redeposi-
tion of the eroded material (cf. Figure 2) led to a rise
of the slump floor level and a relative decrease in the
headwall height. In the long run, accumulation in the
slump floor area could potentially lead to
a stabilization of the slump so that, similar to the
adjacent slump scar, no more material is removed.
This would also fit with observations at other slumps
in the Mackenzie Delta region that demonstrated
a positive relationship between headwall height and
erosion rates (B. Wang, Paudel, and Li 2016).
A decreasing headwall height would therefore also
indirectly indicate a decreasing erosion rate and sup-
port the stabilization (S. V. Kokelj et al. 2015).
Conclusion
The presented study employs a multimethod approach to
investigate the past and present development of
a retrogressive thaw slump and to characterize subsurface
structures in regions adjacent to the retreating headwall.
ARCTIC, ANTARCTIC, AND ALPINE RESEARCH 15

The analysis of high-resolution satellite data evidenced the
initialization of the slump in summer 2014 at the latest and
an increasing growth of the retrogressive thaw slump since
then. In particular, annual growth rates have increased
significantly since the summer of 2018, when the slump
also started to expand toward a local creek. By means of two
UAV-based digital elevation models, a horizontal retreat of
the headwall between 4 and 11 m was detected during the
period August 2018 to August 2019. A simultaneous accu-
mulation of eroded material in the slump floor led to its rise
and thus to a decrease in the headwall height from 7.5 to
6.5 m. A clear relationship between the ALTs and the relief
position in the vicinity of the slump could be identified
employing manual frost probing and at least partially by
GPR surveying. In addition, GPR revealed material sorting
within the active layer affecting the ALTs and the local
hydrology. In combination with the varying ALTs, it caused
a canalization of water on top of the permafrost table. By
means of q3D electrical resistivity tomography,
a heterogeneous layer of ice-rich permafrost was detected,
which was underlain by a very low-resistivity (probably
partially unfrozen) layer at a depth of about 6 to 10 m.
This layer represents a possible water pathway within the
permafrost body and possibly forms a connection to an
unfrozen area below an adjacent creek. A comparison of the
slump development with the detected subsurface structures
indicated that the spatial development of the slump was
affected not only by hydrological and relief-related para-
meters but also by the subsurface structures and in parti-
cular by the ice content. However, the spatial and temporal
development of the slump must be further investigated to
make more precise statements in this regard.
To the authors’ knowledge, this is the first study
conducting a detailed 3D investigation of subsurface
structures adjacent to an active slump headwall.
Previous studies have mostly relied on drillings, but
these cannot detect the observed heterogeneities to this
extent. To gain further insight into the interactions
between subsurface structures and slump development,
the approach would be useful to transfer the approach to
other slumps in the region and combine it with drillings
as absolute validation. Especially in the context of accel-
erated climate warming, this could help to better under-
stand the complex interactions in RTSs and to better
predict their future development.
Acknowledgments
We thank the Education and Research Program by Planet Lab
for providing free access to PlanetScope satellite imagery for
scientific purposes and Sebastian Buchelt for data aquisition.
We are grateful to Franziska Losert, Blair Kennedy, Andreas
Bury, Leon Nill, and Tim Wiegand for their untiring support
in the field. Thanks also to the Northwest Territories for the
issued research permits and Aurora Research Institute
(Inuvik, NWT, Canada) and K&K TruckRentals
(Whitehorse, YT, Canada) for logistical support. We also
thank the two reviewers for their constructive and fair reviews,
which contributed to a significant improvement of the article.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This study was funded by the German Research Foundation
(DFG 329721376). The Digital Elevation Model of the
TanDEM-X Mission is shown under the permission of the
German Aerospace Center (DLR), Germany, ©DLR, 2016
(proposal “IDEM_HYDRO0182” and TanDEM-X Science
proposal “DEM_OTHER1323”). This publication was sup-
ported by the Open Access Publication Fund of the
University of Wuerzburg.
ORCID
Julius Kunz http://orcid.org/0000-0003-3689-9575
T. Ullmann http://orcid.org/0000-0002-6626-3052
C. Kneisel http://orcid.org/0000-0002-5348-9001
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