Climate-induced warming of permafrost soils is a global phenomenon,
with regional and site-specific variations which are not fully
understood. In this context, a 2-D automated electrical resistivity
tomography (A-ERT) system was installed for the first time in
Antarctica at Deception Island, associated to the existing Crater
Lake site of the Circumpolar Active Layer Monitoring – South Program (CALM-S) – site. This setup aims to (i) monitor subsurface freezing and
thawing processes on a daily and seasonal basis and map the
spatial and temporal variability in thaw depth and to (ii) study the
impact of short-lived extreme meteorological events on active layer
dynamics. In addition, the feasibility of installing and running
autonomous ERT monitoring stations in remote and extreme
environments such as Antarctica was evaluated for the first
time. Measurements were repeated at 4 h intervals during
a full year, enabling the detection of seasonal trends and
short-lived resistivity changes reflecting individual meteorological
events. The latter is important for distinguishing between (1) long-term
climatic trends and (2) the impact of anomalous seasons on the
ground thermal regime.
Our full-year dataset shows large and fast temporal resistivity
changes during the seasonal active layer freezing and thawing and
indicates that our system setup can resolve spatiotemporal thaw
depth variability along the experimental transect at very high
temporal resolution. The largest resistivity changes took place during
the freezing season in April, when low temperatures induce an abrupt
phase change in the active layer in the absence of snow cover. The
seasonal thawing of the active layer is associated with a slower
resistivity decrease during October due to the presence of snow
cover and the corresponding zero-curtain effect. Detailed
investigation of the daily resistivity variations reveals several
periods with rapid and sharp resistivity changes of the near-surface
layers due to the brief surficial refreezing of the active layer in
summer or brief thawing of the active layer during winter as
a consequence of short-lived meteorological extreme events. These
results emphasize the significance of the continuous A-ERT
monitoring setup which enables detecting fast changes in the active
layer during short-lived extreme meteorological events.
Based on this first complete year-round A-ERT monitoring dataset on
Deception Island, we believe that this system shows high potential
for autonomous applications in remote and harsh polar environments
such as Antarctica. The monitoring system can be used with larger
electrode spacing to investigate greater depths, providing adequate
monitoring at sites and depths where boreholes are very costly and
the ecosystem is very sensitive to invasive techniques. Further
applications may be the estimation of ice and water contents through
petrophysical models or the calibration and validation
of heat transfer
models between the active layer and permafrost.
Introduction
Although permafrost soils show currently a clear global warming
trend due to climate change (Biskaborn et al., 2019), regional
differences can be pronounced, which are not only due to regional
climate differences but also due to heterogeneous soil
characteristics. One example is the Antarctic Peninsula (AP), where one
of the strongest air temperature increases has been recorded since the
1950s. In spite of this general air temperature increase, the
northwest of the Antarctic Peninsula showed a cooling trend
between 1999 and 2015 (Turner et al., 2016; Oliva et al.,
2016). Consequently, and contrary to the general trend, the
seasonal surficial thaw layer of the ground above the permafrost
(the active layer) decreased, indicating that the climate signal
is more complex than previously reported (Ramos et al., 2017).
The active layer of permafrost environments is not only a climate
change indicator; its dynamic is also of extreme importance to
terrestrial ecosystems, since it influences the hydrology, soil
nutrient and contaminant fluxes, and geomorphological
processes, such as polar erosion and mass wasting. Furthermore,
changes in active layer thickness may also affect infrastructure
due to the effects it shows on the rheological properties of the
perennially frozen soil (Williams and Smith, 1989).
In moist polar environments, the transition zone between the
active layer and the permafrost table is frequently characterized
by the presence of a high content of interstitial ice, forming an
ice-rich layer, some centimeters to decimeter thick. This relates
to the interannual variability in the active layer thickness. In
warmer summers, the active layer thickens and water percolates
downwards, concentrating at the permafrost table, refreezing at the
beginning of the cold season. In cooler summers, the active layer
is shallower and the previously formed ice does not melt. This
ice-rich layer, still poorly characterized but with significance
due to its impacts on soil behavior, is called the transient layer
(Shur et al., 2005). A continuous monitoring of the physical
properties of the active and transient layers is therefore
essential for understanding the permafrost dynamics and its potential
impacts on climate feedbacks and local ecology.
Deception Island in the South Shetland archipelago, off northern
Antarctic Peninsula, is an extraordinary natural laboratory for
studying active layer and permafrost dynamics. The island is an
active stratovolcano with widespread permafrost down to sea level
except at spatially restricted localities with geothermal
anomalies, generally along faults (Goyanes et al., 2014). The soil
surface is bare, with vegetation being almost completely absent, and
permafrost is close to its climatic limit, since mean annual air
temperatures are just below 0 ∘C (Bockheim et al.,
2013; Ramos et al., 2017). The soil is composed by a mix of lavas,
lapilli and pumice, which in some areas induce high thermal
insulation, with a resulting active layer thickness of only 40 cm.
The shallow active layer and soil characteristics of Deception
Island, the easy access to the permafrost table, and the
geographical setting in the maritime Antarctic and its geothermal
characteristics have made the island one of the best-studied
areas for permafrost research on the Antarctic Peninsula (e.g.,
Ramos et al., 2008, 2017; Vieira et al., 2010; Melo et al., 2012;
Bockheim et al., 2013; Goyanes et al., 2014).
Two permafrost and active layer monitoring sites within the
Circumpolar Active Layer Monitoring – South Program (CALM-S) – and
the Global Terrestrial Network for Permafrost (GTN-O/GCOS/IPA)
including ground temperature boreholes and meteorological stations
have been installed at Irizar and Crater Lake.
So far, monitoring of the active layer dynamics in Antarctica was
conducted using only 1-D borehole and meteorological
data, which restricted the analysis to point information that
often lacks representativeness at the field scale. In addition,
being an invasive technique, the drilling of boreholes disturbs
the subsurface and is not feasible to conduct over large areas,
especially in environmentally sensitive ecosystems such as the
Antarctic. Also, the drilling of boreholes to monitor temperature
in deeper layers is very expensive in Antarctica, which further
limits the application of boreholes for deep investigations and in
areas with very heterogeneous ground conditions. As
a cost-effective and ecologically non-hazardous alternative,
2-D geophysical monitoring, such as electrical
resistivity tomography (ERT), allows for monitoring the
spatiotemporal variability in the freezing and thawing
characteristics of the active layer and the permafrost, as has
been demonstrated in several applications in the European Alps
(e.g., Hauck, 2002; Hilbich et al., 2008; 2011; Krautblatter
et al., 2010; Ottowitz et al., 2011; Supper et al., 2014; Mewes
et al., 2017; Mollaret et al., 2019). ERT is a non-invasive
technique that is sensitive to the electrical conductivity (the
reciprocal of electrical resistivity) of materials. Due to the
large contrast between the resistivity of ice and water, the
method has become popular in permafrost investigation to
distinguish between frozen and unfrozen soil and thus to monitor
the active layer dynamics including freezing, thawing, water
infiltration and refreezing processes in a spatial context, which
is sometimes very difficult to assess with only temperature
boreholes. This technique is also being widely used to provide
non-invasive estimates of spatiotemporal unfrozen water content
distribution due to the strong dependence of electrolytic
conduction on the phase change of water to ice in earth materials
(e.g., Hauck, 2002).
Location of Deception Island and Crater Lake CALM-S site in
Antarctica.
Although individual ERT measurements in Antarctica have been
reported (e.g., McGinnis et al., 1973; Guglielmin et al., 1997;
Gugliemin and Dramis, 1999; Hauck et al., 2007; Goyanes et al.,
2014), no continuous and autonomous ERT monitoring has been
attempted yet at these remote and extreme environments, where
winter access is usually impossible. In these cases, maintenance
and repair, which have often become necessary in the autonomous ERT
studies reported from the European Alps (cf. Supper et al., 2014),
are not possible for most of the year. In this paper, we show that
continuous ERT monitoring of the active layer and shallow
permafrost is possible in Antarctica and that its results may
yield high-resolution 2-D data on freeze and thaw
characteristics on different timescales.
We installed and tested an autonomous and continuously measuring
ERT monitoring system in the vicinity of shallow boreholes at the
Crater Lake CALM-S site, Deception Island, with the objective of
evaluating its potential in a remote area without maintenance for
a full year. The Crater Lake CALM-S site is typical for conditions
found in Antarctica, where year-round stations are scarce, and
most research stations are only operated in summer. Data were
collected to monitor subsurface freezing and thawing processes on
a daily and seasonal basis and to detect seasonal trends as well
as the impact of short-lived extreme meteorological
events. Short-lived meteorological events are rarely addressed in
permafrost studies, but they reflect the impact of fast-changing
meteorological conditions on the upper soil horizons. In the
context of climate change, with increasing frequency of
atmospheric extreme events, these events may also become more
frequent. Being able to identify them in the ERT series allows
for a better characterization of the links between soil thermal
regimes and geomorphic dynamics.
Environmental monitoring setup at the Crater Lake CALM-S site.
Study area
Deception Island (62∘55′ S, 60∘37′ W) is
located about 100 km north of the AP, in the Bransfield Strait, and is part of the South
Shetland archipelago (Fig. 1).
The island is a stratovolcano with a horseshoe shape and
a diameter of 15 km, a 7 km wide caldera open to
the sea, and maximum elevation at Mount Pond
(539 m). About 57 % of the island is currently
glaciated and about 47 km2 is glacier-free (Smellie
and López- Martínez, 2002). The climate is cold oceanic, with
frequent summer rainfall, a moderate annual temperature range
and mean annual air temperatures close to -3∘C
at sea level. The weather conditions are dominated by the
influence of the polar frontal systems, and atmospheric
circulation is very variable, including the possibility for
winter rainfall (Styszynska, 2004). Deception Island is an
active volcano and is formed by intercalation of lava flows,
pyroclastic and ash deposits, with many of the present-day
glaciers being ash-covered. During the recent eruptions of 1967, 1969
and 1970, pyroclastic and ash deposits covered the snow mantle,
and buried snow is still present at some sites. Deposits are
very porous and insulating, with high ice content at the
permafrost table. The active layer is thin, varying from 30 to
96 cm depth across Deception Island in different soils
(Bockheim et al., 2013), and boreholes show the presence of warm
permafrost.
The Crater Lake CALM-S site is located in a small and relatively
flat plateau-like surface covered by volcanic and pyroclastic
deposits at 85 m a.s.l., north of Crater Lake
(62∘59′06.7′′ S, 60∘40′44.8′′ W). The site was selected due to its flat characteristics,
absent summer snow cover, large distance to known geothermal
anomalies, good exposure to the regional climate conditions
(mitigating site-specific effects and being representative in
a regional context) and vicinity to the Spanish
station Gabriel de Castilla. The ground surface is completely
devoid of vegetation, and the mean annual air temperature at the
Crater Lake CALM-S site between 28 January 2009 to 22 January
2014 was -3.0 ∘C. Permafrost temperatures are
-0.3 to -0.9∘C, with permafrost thickness
varying spatially from 2.5 to 5.0 m (Vieira et al.,
2008; Ramos et al., 2017) and active layer thickness in the
range of 25 to 40 cm. This spatial variability has not
been addressed in the literature, but it is possibly related to
differences in surface deposits and snow cover.
Material and methodsCrater Lake CALM-S environmental monitoring setup
The Crater Lake CALM-S site consists of a 100 m×100m grid with little topography (maximum of 6 m
variation in elevation) and was installed in January 2006 (Fig. 2),
with several upgrades since then. The site includes monitoring of
air temperature, permafrost and the active layer in boreholes, and
snow thickness. Thaw depth is measured manually once per year
during summer at 121 nodes spaced at 10 m intervals by
mechanical probing (Ramos et al., 2017).
Air temperatures are measured at 160 cm a.g.l. (above ground level),
monitored with hourly measurements since 2009. Ground temperatures
are measured in the shallow borehole S3,3 down to
160 cm (node 3,3; Fig. 2). This borehole has a diameter
of 32 mm and is cased with air-filled PVC pipes, and ground
temperatures are measured with iButton sensors at depths of 2.5, 5,
10, 20, 40, 80 and 160 cm. In addition, ground
temperatures are measured in 16 very shallow boreholes, regularly
distributed within the grid, with a single iButton sensor close to
the base of the active layer. Finally, snow thickness is estimated
using a so-called snow pole, with iButton miniloggers installed on
a vertical stake at 5, 10, 20, 40 and 80 cm height above the
ground (de Pablo et al., 2016). Snow distribution is mapped using
a Campbell CC640 time-lapse camera with daily pictures at 11:00,
12:00 and 13:00 (all times in local solar time). The combined approach of snow
pole and the time-lapse camera allows evaluating the snow
distribution in the study area.
(a) Overview of the CALM-S site and (b) A-ERT monitoring system
installation at CALM-S site. Electrodes are buried in the ground and are
connected to the resistivity meter box by buried cables. (c) Resistivity
meter box; the 4POINTLIGHT_10W instrument is connected to
a solar-panel-driven battery and multi-electrode connectors . (d) A schematic
display of the measured resistivity (pseudo-section) at the CALM-S site
using a Wenner electrode configuration.
Electrical resistivity tomography monitoring
ERT is the method for the
calculation of the subsurface electrical resistivity distribution
from multiple electrical resistance measurements made using
a quadrupole arrangement of electrodes. The electrodes are placed
on the ground surface, and a 2-D or 3-D image of the resistivity
distribution can be achieved by varying the location and spacing of
the electrodes. The relationship between the measured spatial
apparent-resistivity distribution and the true resistivity
distribution of the subsurface is complex and needs to be estimated
using inversion theory (Loke, 2002). Under the assumption that
general conditions (e.g., lithology, pore space) remain unchanged
during the observation period, repeated resistivity measurement can
provide a means for evaluation of freezing or thawing processes and
subsurface temperature variations (Hauck, 2002).
An automatic ERT (A-ERT) monitoring system using
a 4POINTLIGHT_10W (Lippmann) instrument was installed in the
vicinity of the ground temperature borehole S3,3 (see
Fig. 2) in 2010 in order to monitor active layer freezing and
thawing by ground surface time-lapse surveys (Fig. 3). The system
was installed close to the interfluve, in the most elevated zone
within the site, where stronger spatiotemporal subsurface
variations are expected. The individual readings of each quadruple
measurement were converted to apparent-resistivity values and
stored in the internal memory.
All ERT surveys were performed using the Wenner electrode
configuration to minimize energy consumption and measurement time
as well as to obtain the best signal-to-noise ratio in highly
resistive terrain (Kneisel, 2006; Hauck and Vonder Mühll,
2003). The Wenner array is also more sensitive to vertical changes
in the subsurface resistivity below the center of the array (Loke,
2002), which makes the configuration ideal for active layer imaging.
Twenty copper plates, which are connected by buried cables to the
active boxes, with an electrode spacing of 0.5 m were used
in this study (Fig. 3b). A robust, waterproof box was used and
buried, casing the 4POINTLIGHT_10W instrument, solar-panel-driven battery and multi-electrode connectors during data
acquisition (Fig. 3c). This setup yields 56 individual data points
for each monitoring dataset at six data levels (Fig. 3d). A-ERT
measurements were started at the beginning of 2010 and repeated
every 4 h during 1 full year.
A-ERT data processing and inversion
A-ERT data processing and inversion include several steps: data
filtering (outlier detection), spatial mean apparent-resistivity
analysis and resistivity data inversion. As a first step, the
apparent-resistivity data measured during 1 year were filtered by
removing data spikes and negative values. Furthermore, data points
with standard deviations of more than 2 % after nine stackings
were excluded. The quality of the apparent-resistivity data was
good in most datasets, and less than 0.5 % of all measurements
had to be eliminated. The measured apparent-resistivity data were
then averaged for each depth level, providing six horizontal mean
values for each dataset as shown in Fig. 3d and analyzed regarding
daily and monthly resistivity changes.
In the next step, the apparent-resistivity datasets were inverted
using the commercially available software RES2DINV. The robust
inversion option in RES2DINV, as well as a mesh refinement to half
of the electrode spacing, was applied to better resolve the
expected strong resistivity contrasts between unfrozen and frozen
subsurface materials. The objective function used in the robust
inversion algorithm attempts to minimize the absolute changes in
the resistivity values which produce models with sharp interfaces
between different regions with different resistivity values (Loke,
2002). In addition, a full 4-D inversion algorithm developed by Kim
et al. (2009) was used to better image the temporal resistivity
changes. The full 4-D inversion algorithm defines a subsurface
structure and the entire monitoring data in the space–time domain
to obtain a four-dimensional space–time model using just one
inversion process. In this approach, regularization is introduced
not only in the space domain but also in time, resulting in reduced
inversion artifacts and improved stability of the inverse problem
(Kim et al., 2009).
Virtual borehole analysis
A so-called virtual borehole analysis (e.g., Hilbich et al., 2011)
was used to investigate the active layer dynamics during 2010 in
more detail as well as to study the resistivity–temperature
relationship. Here, inverted resistivity values were extracted from
the tomogram along a 1-D depth transect, close to the
existing borehole S3,3. At this borehole, temperature sensors
are installed at different depths down to 160 cm, and
temperature data are available every 3 h during the
experiment. The maximum depth of the ERT investigation is hereby
almost equal to the deepest temperature sensor. The inverted
temporal vertical resistivity variations from the tomogram are then
compared to the corresponding temporal thermal variations obtained
from S3,3.
Results and discussionsAnalysis of observational data
Figure 4 shows air, surface, shallow and ground temperature
variations during the A-ERT monitoring period observed very close
to the A-ERT transect. Snow cover during winter is thin, with only
10 to 20 cm thickness and frequent snow-free
periods. Correspondingly, air and ground temperature are generally
well-coupled, with a slight phase lag in the presence of snow
(cf. the cooling events in August and September 2010).
(a) Snow thickness and air and soil surface temperature variability
during the A-ERT data acquisition in 2010. (b) Borehole temperature plotted
for the sensors installed at the node 3,3 (S3,3), covering the
investigation depth of the ERT transect. (c) Shallow borehole temperatures
plotted for the sensors installed at nodes 2,2 and 4,2 at the base of the
active layer. The dashed lines mark the selected dates for the ERT inversion
analysis shown in Fig. 7.
The ground temperature at S3,3 at shallow depths (Fig. 4b)
fluctuates significantly during the year, with temperatures ranging
from -8 to 5 ∘C, reflecting the snow cover
variability and air temperatures. Temperatures above zero are
delineated by the yellow to red colors, indicating active layer
thawing events. The temperature within the active layer falls below
0 ∘C at the end of April and stays below
0 ∘C until the beginning of November. The
zero-curtain phase in spring lasts around 1 month, whereas no
significant zero curtain can be seen in autumn due to the low air
and soil surface temperatures and the absence of a thick snow cover
during freezing. Short-lived meteorological events with quick and
superficial changes of the ground temperature around
0 ∘C are quite frequent during the study period, and
therefore, brief surficial refreezing (e.g., in March, April and
December) and thawing of the active layer (May) can be identified
in the summer and winter respectively.
The temperature variations in the shallow temperature boreholes at
nodes 2,2 and 4,2 (see Fig. 2 for the locations of the temperature
sensors) are shown in Fig. 4c to investigate the lateral
temperature changes along the A-ERT transect. Node 2,2 is closer to
the interfluve and is more exposed to the wind and sun. Consequently,
thinner snow cover, as well as a smaller number of days with snow,
was recorded at node 2,2 when compared to node 4,2, with
corresponding lower winter temperatures at node 2,2 due to the
stronger insulation effect of snow at node 4,2 (supported by
time-lapse camera observations). The stability of temperatures at
0 ∘C in the summer months reflects the ice content
and latent heat effects that limit thaw propagation.
Figure 5 shows the spatial distribution of thaw depth across the
Crater Lake CALM-S site, measured in January 2010. The thaw depth
varies between 25 to 40 cm, with shallower thaw depth in the
south of the study area where the area is less wind-exposed, and
shows a longer and more stable snow cover. The thaw depth is
approximately 30 cm along the A-ERT transect in January 2010.
Spatial distribution of thaw depth measured at the grid nodes
across the study area in January 2010. The location of the A-ERT transect is
delineated with the black dotted line (A–B).
(a) Mean apparent-resistivity data of the A-ERT profile during
2010 for different electrode spacing on a daily scale. (b, c) Mean apparent-resistivity data on the scale of individual events: (b) brief surficial
refreezing event from 14 to 28 March 2010 and (c) brief surficial thawing event
from 7 to 14 May 2010.
Apparent-resistivity data
The apparent-resistivity raw data of all surveys were processed as
discussed in Sect. 3.3, and the resulting mean daily apparent
resistivity change of each data level is shown in Fig. 6a. The
usefulness of investigating spatiotemporal apparent-resistivity
data over different timescales was demonstrated in several studies
(e.g., Hilbich et al., 2008, 2011). They allow insights into the
resistivity variability trend during the year as well as the
identification of the impact of specific meteorological events on
the subsurface thermal regime. For most of the year, resistivity
increase and decrease can be associated with freezing and thawing
processes.
The apparent-resistivity data collected at a=1 and 2 levels
(corresponding to 0.5 and 1 m electrode spacing
respectively) reveal a sharp resistivity rise on 19 April from
approximately 10–20 kΩm and reaching values
more than 500 kΩm on 5 May, suggesting the
beginning of the seasonal freezing of the active layer. Because of
the absence of snow cover during this period, the very low air
temperature provokes an abrupt phase change, which causes a sharp
resistivity rise in this period. The delayed response of deeper
levels (i.e., a=3, 4, 5 and 6, corresponding to 1.5, 2, 2.5 and
3 m electrode spacing respectively) indicates the advancing
freezing front and is coincident with the gradual decrease in the
active layer temperature with depth (see Fig. 4b). The freezing of
the active layer intensifies in June, July and August. The
beginning of the seasonal thawing phase is associated with the
steady decrease in apparent resistivity, starting on 4 October from
a value of approximately 200 kΩm to less than
40 kΩm at the end of October. During the seasonal
thawing of the active layer, the snow cover dampens the thawing
effect and provides water input to the active layer, which
refreezes again at the still-frozen active layer. Interestingly,
this zero-curtain phase, visible in the temperature record, was
reflected in the steady decrease in apparent resistivity, recorded
by the A-ERT system in this period. Deeper levels experience the
resistivity decrease with some delay.
(a) Inverted resistivity tomograms of 12 monthly spaced A-ERT
datasets between January and December 2010, based on data measured on the
28th of each month, and (b) relative resistivity changes based on the
first ERT dataset referred to January.
In general, the daily apparent-resistivity fluctuations are
relatively small. However, Fig. 6a reveals several significant
resistivity fluctuations during the observation period. These
fluctuations are associated with either brief surficial refreezing
of near-surface layers in summer or short thawing periods during
winter as a consequence of short-lived meteorological extreme
events with quick and superficial changes of the ground temperature
around 0 ∘C. Two examples of these daily apparent
resistivity changes during the short-lived events, events (I) and
(II), were selected for detailed investigation. Event (I), shown in
Fig. 6b, is an example of the surficial refreezing of the active
layer in the summer. A continuous increase in apparent resistivity
at shallower levels is evident with a total difference of
approximately 30 kΩm in 10 d. On the other hand,
event (II), shown in Fig. 6c, presents a very rapid apparent
resistivity decrease at the shallowest levels, a=1 and a=2, with
a total difference of approximately 400–600 kΩm in
3 d. The observation of such rapid changes of the apparent
resistivity proves the significance of the automatic ERT monitoring
system to record continuous resistivity changes.
Air and ground temperature fluctuations on the event scale. (a) Event (I): brief surficial refreezing event from 14 to 28 March 2010. (b) Event (II): brief surficial thawing event from 7 to 14 May 2010. (c) Time-lapse camera photos at 11:00, 12:00 and 13:00 on 9 May.
Monthly resistivity variations
A monthly selection of the modeled resistivity data in 2010 and the
resistivity changes relative to the first ERT dataset are shown in
Fig. 7. Data collected on the 28th day of each month at 12:00 were
used in this analysis, and all data were inverted using the full 4-D
inversion algorithm, described in Sect. 3.3. The corresponding
temperature profiles were marked with the dashed lines in Fig. 4b.
The resistivity pattern along the A-ERT monitoring transect at
CALM-S site is characterized by two vertical distinct resistivity
zones. The first zone, down to 20–40 cm depth in summer,
images the active layer. The resistivity of this layer changes
dramatically during freezing and thawing. The deeper zone images the
permafrost to a depth of 160 cm during the A-ERT
measurements.
The resistivity model plotted for January shows a more conductive
zone (less than 10 kΩm) for the first
30–40 cm, followed by a deeper zone with resistivity of
more than 30 kΩm. The shallow zone images the active
layer in summer, when this layer has not been frozen yet, and shows
a slight thickness increase from the left to the right. The
thickness and small lateral variability in this layer are in good
agreement with the thaw depth measurement using a mechanical probe
in January 2010 (cf. Fig. 5). The resistivity and thickness of the
active layer show a slight change during February and
March. However, a more significant resistivity decrease is evident
at depths of more than 50 cm due to the slight temperature
increase at depth during February and March (Fig. 4b), which
increases the unfrozen water content and consequently decreases the
subsurface resistivity.
The largest resistivity changes at the surface during the year take
place between March and April due to the freezing of the active
layer. Interestingly, the resistivity of the permafrost at more
than 1 m depth decreases slightly during this period. The
resistivity model behavior in April can be well explained by an
abrupt phase change during the active layer freezing in shallow
surface and delayed response of the deeper zone. This is in very
good agreement with the thermal transect shown in Fig. 4b, which
shows a slight temperature increase at more than 1 m depth
when compared to March. On the other hand, the resistivity model
plotted for May is characterized by a resistivity decrease at the
shallower zone (active layer) and a resistivity increase at depth
(permafrost) when compared to the resistivity model for April. The
resistivity increase in the permafrost is coincident with
a temperature decrease in the permafrost during May, which results in
lower unfrozen water content. On the other hand, the active layer
warming during this month provides more unfrozen water to the active
layer, which decreases the active layer resistivity during the same
period.
The freezing of the active layer and cooling of permafrost
intensifies during June, July and August, which is reflected by the
high resistivity values. The beginning of seasonal thawing is then
associated with the resistivity decrease in October. The average
resistivity of the active layer on 28 October is higher than the
corresponding zone during the thawing seasons (i.e., January,
February, March, November and December). This is due to the
zero-curtain phase in October, when the snow cover damps the thawing
effect and the temperature stays around zero. The steady resistivity
decrease in the active layer and permafrost down to a depth of
160 cm is evident along the A-ERT transect during November
and December due to the subsurface temperature increase. The monthly
subsurface resistivity behavior is consistent with the mean apparent
resistivity data shown in Fig. 6a.
Daily resistivity variations on the scales of
individual events: events (I) and (II)
Two short-lived meteorological events with fast and superficial
changes of the ground temperature around 0 ∘C are
selected for detailed A-ERT analysis to investigate how well the
A-ERT model can resolve the expected sharp subsurface changes
associated with the fast active layer freezing and thawing
processes.
Figure 8a and b show in detail the air and ground temperature
fluctuation during the selected events. Event (I) indicates
a surficial refreezing of the active layer in the
summer. A decrease in air temperature started on 16 March and
intensified on 20 March, with a subsequent increase starting on
22 March. The arrival of the cold air induced an impact in the
uppermost 5 cm starting on 17 March, when the ground
temperature at depths of 2.5 and 5 cm falls below zero. The
active layer refreezing intensified between 22 and 24 March, when
temperatures decreased and the advancing freezing front reached
10 cm. A very shallow subsurface phase change is expected
during this short-lived meteorological event, as no impact has been
recorded at ground temperature sensors deeper than 10 cm.
Relative resistivity changes of daily spaced A-ERT datasets on the
event scale. (a) Event (I): brief surficial refreezing event from 14 to 28 March 2010. (b) Event (II): brief surficial thawing event from 7 to 14 May 2010.
Event (II) presents a surficial thawing of the active layer in
summer. A drastic rise in the air temperature from -8.4 to
1.4 ∘C is evident on 9 May. The warm air influenced
the ground temperature immediately and generated an abrupt phase
change in the top 20 cm on 10 May, as evidenced by the
above-zero temperatures on 10 and 11 May at depths of 2.5, 5, 10 and
20 cm. The time-lapse camera photos taken on 9 May
(Fig. 8c) show clearly the fast snowmelt between 11:00 and 13:00
on this day, which might explain the quick subsurface temperature
rise due to the infiltration of the melted snow to the soil
subsurface and consequent advective heat transfer. The thermal
sensors at depths of 40 and 80 cm also recorded the
temperature increase during this event, although temperatures stay
below zero at these depths. Event (II) lasts for a shorter period
compared to the event (I). However, it caused stronger and deeper
subsurface temperature changes.
Figure 9 shows the time-lapse inversion results during events
(I) and (II). Data collected on 14 March and 7 May were used as the
reference for the events (I) and (II) respectively, and the
resistivity changes relative to the first ERT dataset are presented
in this figure. Data collected at 12:00 were used in this analysis,
and all data were inverted using the full 4-D inversion algorithm,
discussed in Sect. 3.3. A continuous resistivity increase at
shallow depth (less than 30 cm) is evident from 18
to 24 March in Fig. 9a. The resistivity of this zone started to
decrease again on 26 March, and there is no significant change
between resistivity models on 28 March and the reference model on
14 March. In addition, no significant change occurs at depths of
more than 30 cm during this event, suggesting that this
event provoked phase changes only within the shallow subsurface.
The results of the time-lapse resistivity models are in good
agreement with the air and ground temperature fluctuation shown in
Fig. 8a. The resistivity increase in the active layer is coincident
with the temperature decrease in the active layer at shallow
depth. The resistivity of the active layer reached its maximum
between 22 and 24 March, when the temperature reached its minimum
and a larger amount of the pore water is frozen.
Figure 9b shows a sharp resistivity decrease on 9 May, suggesting an
abrupt phase change during this day. In the following, the
resistivity of the active layer reached its minimum on 10 and
11 May. We anticipate that this is due to the infiltration of the
snowmelt water into the soil subsurface, which provides liquid
water to the active layer and decreases resistivity. A slight
increase in resistivity at depths of more than 1 m is
evident on 9 and 10 May. This can be explained by the slight
permafrost temperature decrease at a depth of 160 cm on
these days (cf. Fig. 8b). The continuous active layer refreezing
during the following 3 d is coincident with a slight
resistivity decrease at depth. This can be explained with the
delayed response of the permafrost to the temperature signal at the
surface. An increase in the permafrost temperature at depths of 40 and
80 cm was recorded in the ground thermal sensors on these days.
Temperature–resistivity relationship
The temperature–resistivity relationship for temperatures below
zero was studied during two periods: (i) the beginning of the
seasonal active layer freezing in April–May (P1) and (ii) the
beginning of the seasonal thawing in October (P2). The selected
A-ERT data were inverted using the full 4-D inversion algorithm,
described in Sect. 3.3. The virtual borehole analysis, described in
Sect. 3.4, was used to establish the temperature–resistivity
relationship.
Figure 10 shows the linear regression between resistivity and
temperature in the virtual borehole at S3,3 for three depths
(i.e., 20, 40 and 80 cm). These depths were selected
to study the resistivity–temperature behavior of the active
layer (20 cm), permafrost table (40 cm) and
permafrost (80 cm). The figure shows an excellent linear
regression between resistivity and temperature at all depths during
the seasonal thawing (P2) in October with R2 greater than
0.96. Small deviations from this linear relationship can be found
during the seasonal freezing phase (P1) due to the faster
subsurface temperature and therefore phase change (no zero curtain
present), where the mismatch between the volume measured by
resistivity and temperature can be larger. In addition, the effect
of downward ion migration upon freezing may further influence the
relationship; however, a strong linear regression between the
resistivity and temperature at all depths can be also seen (R2
greater than 0.86) for all depths during this period. The highest
resistivity (and probably ice contents) is found at
the 40 cm depth upon freezing, which may partly be due to more
frequent refreezing effects at the boundary between the active layer
and permafrost.
Resistivity values at the borehole location against borehole
temperatures in S3,3 during the seasonal active layer freezing in
April and May (P1) and thawing in October (P2).
Evaluation of the temporal resistivity variability in
the virtual borehole S3,3
Figure 11 shows the resistivity evolution with time in virtual
boreholes at the S3,3 location during 2010 using the
evaluation described in Sect. 3.4. As the RES2DINV software cannot
invert more than 21 ERT datasets simultaneously, no time-lapse
inversion algorithm was used for this analysis and apparent
resistivity data were inverted independently using a batch
routine. The 0 ∘C isotherm from the borehole temperatures
at S3,3 (Fig. 4b) is superimposed on the resistivity
tomogram. A resistivity cut-off value of 13 kΩm
was selected to delineate the temporal variability in the thaw
depth at the S3,3 location. This value was selected based
on our analysis of the individual resistivity tomograms as well as
the average thaw depth measured by a mechanical probe in
January. This value roughly corresponds to the resistivity
transition value between the unfrozen media at the surface and the
more resistive frozen zone at depth.
Evaluation of the temporal resistivity variability in virtual
borehole S3,3 inferred from inverted A-ERT data for the period
January 2010 to December 2010. The black line delineates the cut-off value
of 13 kΩm, and the white dashed line shows the 0 ∘C isotherm from
the borehole temperatures at S3,3.
The average of thaw depth at the end of January is about
30 cm, with a slight increase in February. The brief active
layer thinning between 20 and 24 February might have happened due
to the brief active layer cooling in this period, recorded by the
ground temperature sensors. Afterwards, the thaw depth increases
to an average of 40 cm at the beginning of March. The
maximum thaw depth is recorded during March, probably due to the
stronger active layer warming in this month. The sudden
resistivity rise in the middle of March is coincident with the
brief active layer freezing (event I), discussed in
Sect. 4.4. Thinning of the active layer starts in April due to the
active layer cooling and possible refreezing of the infiltrating
water above the permafrost table.
The largest resistivity changes in the active layer took place at
the end of April due to the active layer freezing. The active
layer stays frozen from May until October except during the brief
surficial thawing event between 7 and 14 May (cf. Fig. 9b). The
resistivity changes near the surface during the winter are
coincident with consecutive active layer cooling and warming
events. The resistivity values are greatest in winter and around
the permafrost table at depths around 40 cm. We anticipate
that this is where maximal ice contents are present due to the
repeated thawing and refreezing processes of water infiltrating
from snow and rain that accumulated on top of the permafrost table
(cf. the critical zone; Shur et al., 2005). During the
zero-curtain phase in October–November, ground temperatures are
still below zero and the active layer is still frozen. However,
unfrozen moisture is already present due to snowmelt and the warm
but subzero temperatures, which results in lower resistivity
values near the surface. The active layer thaws at the beginning
of November, when the temperature rises above zero and is
coincident with the strong resistivity decrease in this
period. The average thaw depth in November–December is
20 cm, with a slight increase at the end of December. The
sudden resistivity rise in December is coincident with the brief
active layer freezing in this month.
Discussion
The monitoring setup with a very small electrode spacing
(i.e., 50 cm) and dense measurements six times per day
was designed to generate subsurface resistivity maps with very
high spatial and temporal resolution. This enables detecting the
expected fast and sharp resistivity changes within the very narrow
active layer during the short-lived extreme meteorological events
at the study site. Since short-lived meteorological events may
induce phase change, they are potential generators of geomorphic
activity, such as cryoturbation, or even small debris-flows in
sloping terrains. These events are particularly important in
regions without a thick or continuous snow cover such as
Deception Island due to the quick response of the active layer to
the air temperature signal.
With this high-resolution setup, we were able to identify these
events in our A-ERT models. Looking more closely at the
resistivity and temperature changes during the brief active-layer
thawing events, we suggest that infiltration processes from the
melting snow cover are the dominating factor provoking the
observed resistivity decrease and temperature increase. This is in
agreement with Scherler et al. (2010), who simulated the active
layer thaw period using a 1-D fully coupled heat and
mass transfer model. They found that the water pool, formed at the
ground surface from the melting snow cover, may percolate and
reach greater depths, which results in fast water and advective
heat transfer to depth. The infiltration ends when the water pool
is emptied and/or the water refreezes. Such shallow active layer
dynamics show that there are freeze–thaw cycles during the
freezing season, which may result in cryoturbation and in sloping
terrain, that are responsible for increased superficial solifluction.
The resistivity of both active layer and permafrost zones
indicates only a slight lateral change along the transect, which is
indicative for a spatially homogeneous ground conditions in the
study area. However, the size of the A-ERT transect is
comparatively small compared to other A-ERT studies where stronger
lateral variations along the ERT transects are usually more
evident (i.e., Hilbich et al., 2011; Supper et al., 2014; Keuschnig
et al., 2017). In contrast, large lateral resistivity changes are
visible during the extreme short-lived meteorological events. An
example of such lateral changes is very evident during event (II) shown in Fig. 9b. The obtained resistivity models during this
event suggest the propagation of the thawing process from the left
(A) to the right (B) on 9 and 10 May (i.e., the active layer
resistivity decreased from the left to the right) and then
refreezing from the same direction on 11 May. Because the left
side of the A-ERT transect is closer to the interfluve and is more
exposed to the wind and sun, subsurface thaw and snowmelt are expected to
take place from left to right along the transect orientation after
the initial air temperature rise on 9 May. Similarly, active layer
refreezing starts from the same direction (left to right) when the
air cools down again on 11 May. On seasonal timescales, a similar
lateral resistivity variation is visible in Fig. 7. During the
freezing season, the resistivity of the active layer is higher on
the left side due to the enhanced cooling of the active layer in
this part of the profile. Similarly, the resistivity of the active
layer decreases from the left to the right during active layer
warming (i.e., September 2010) and thawing (i.e., October 2010).
We used a resistivity cut-off value of 13 kΩm to
distinguish the unfrozen media at the surface (i.e., active layer)
from the more resistive frozen zone (i.e., permafrost) at
depth. This value was selected empirically based on our analysis
of the individual resistivity tomograms and the average thaw depth
measured by a mechanical probe in January. The resistivity of the
unfrozen media in the study area is comparatively high compared to
other studies conducted in alpine and polar regions (e.g., Supper
et al., 2014; Keating et al., 2018) and could be due to the soil
being composed by very porous lapilli, with high air content and
large intergranular pore spaces that induce fast percolation of
snowmelt and rainwater. The dark surface of the soil- and
wind-exposed conditions promotes fast evaporation and favor
quick drying of the near-surface horizons. In accordance with
other studies (e.g., Oldenborger and LeBlanc, 2018) the obtained
linear relationship between resistivity and temperature also
implies the absence of large latent heat effects during phase
change, i.e., comparatively dry conditions. In addition, in
pyroclastic sediments, pores containing water can be disconnected
from each other, which further reduces the effect of phase change
between liquid and frozen water on the bulk resistivity. It is
worth mentioning that the resistivity of any subsurface material
is a complex function of soil properties (e.g., grain and pore
size, void ratio, degree of saturation, water content and
salinity, temperature, and water phase), and thus the cut-off value
cannot be used for other sites and a site-specific investigation
is required to estimate this value.
The detailed investigation of the resistivity tomograms indicates
that our A-ERT setup could better map the thaw depth compared to
the ground temperature sensor S3,3. In fact, the thaw depth
variability in the 10, 20 and 40 cm depth range, seen in
the resistivity tomogram (Fig. 11), is not reflected in the
borehole temperature data due to the lack of sensors between these
depths. Hence, the ground temperature tomogram (Fig. 4b) shows
a constant thaw depth of 40 cm in the first 3
months. These results reveal that our A-ERT setup allows for
accurate characterization of the active layer freeze–thaw process,
with a spatial resolution that can usually not be achieved with
temperature sensors, except for a very dense sensor setup. In
addition, the spatiotemporal resistivity variations show that the
resistivity values are greatest in winter and around the
permafrost table at depths around 40 cm (see Fig. 11),
indicating maximum ice contents at this depth. This is due to the
repeated thawing and refreezing processes of water infiltrating
from snow and rain that accumulated on top of the permafrost table
(cf. the transition zone; Shur et al., 2005), which forms an
ice-rich layer and increases the resistivity of this
layer. Resistivity at the borehole location compared to borehole
temperatures within S3,3 (Fig. 10) also shows remarkably
greater values during active layer freezing at a depth of
40 cm, indicating that A-ERT data can be used to study the
transition zone in the study area.
On the other hand, our resistivity models slightly overestimated
the thaw depth during several periods, when compared to the
borehole temperature data. Examples of such overestimations are
seen in March, when inverted resistivity suggests the thaw depth to
be slightly over 40 cm. This error becomes worse at the
beginning of the seasonal thawing in November, when the A-ERT-derived thaw depth is too small (5 cm). We used a small
electrode spacing of 50 cm to deal with the expected
abrupt changes close to the surface. However, the resolution of
the A-ERT within the first 10 cm (e.g., the active layer
condition at the beginning of the seasonal thawing) is still very
limited. In addition, the over-parameterized inverse problem and
the effects of smoothing from regularization applied in the
inversion algorithm overestimate the thaw depth in the resistivity
tomograms. In the virtual borehole analysis, each dataset was
inverted independently, and temporal resistivity changes of
individual quadrupoles were not accounted for in the
inversion. Using a time-lapse inversion algorithm as used in
Sect. 4.3 and 4.4 might enhance the temporal resolution of the
resistivity tomogram and reduce the uncertainty in the estimation
of the thaw depth.
Conclusion and outlook
An automated ERT (A-ERT) system with a solar-panel-driven battery
and multi-electrode configuration was installed at Deception
Island at the Crater Lake CALM-S monitoring site as the first
automatic resistivity monitoring system in Antarctica. Our analysis
of this combined geophysical and thermal monitoring approach
focused on (i) the ability of the A-ERT system to monitor the
spatiotemporal variability in the active layer along the
small-scale transect, (ii) the active layer freezing and thawing
processes on seasonal time scales, and (iii) the impact of extreme
short-lived meteorological events on the ground thermal regime.
Based on the comprehensive analysis of the A-ERT data, the
following main conclusions can be drawn:
The A-ERT system allows detailed monitoring of the spatiotemporal
variability in the active layer in summer. The maximum thaw depth in 2010
was recorded in March, with values slightly more than 40 cm.
The process of active layer freezing in autumn and thawing in spring was
well resolved by the A-ERT system. The absence of the snow cover and direct
influence of atmospheric processes during the seasonal freezing provoked
a drastic resistivity rise in April. On the contrary, the zero-curtain phase
during the seasonal thawing causes a continuous resistivity decrease during
several weeks in November.
Short-lived meteorological events during a few days provoked a fast and
dramatic resistivity change in the active layer due to the brief active
layer freezing and thawing, detected by the A-ERT system. Our study clearly
shows that without automatic and quasi-continuous measurements, short-term
active layer freezing and thawing, and the infiltrating water from
the melting snow cover to the ground during such extreme meteorological
events, could not be investigated.
The automated system developed in this study allows a free choice of
measurement interval as well as electrode configuration, and our
A-ERT setup with a small electrode spacing of 0.5 m and
dense measurements of six times per day enabled us to detect the
impact of the extreme short-lived meteorological events on the
active layer with a thickness as small as
20–40 cm. Interestingly, our A-ERT system could detect the
spatial directions of the thawing and freezing processes along such
a small transect. The A-ERT setup can also be applied with
larger-spaced configurations to investigate greater depths, enabling
permafrost monitoring in Antarctica, where boreholes are very costly
and the ecosystem is very sensitive to invasive techniques.
The consistency of our full-year results with previous studies in
more easily accessible alpine and polar regions (e.g., Hilbich
et al., 2011; Supper et al., 2014; Keuschnig et al., 2017;
Tomaskovicova, 2018; Oldenborger and
LeBlanc, 2018) suggests that the detailed studies of the Alps can be
transferred to setups in very remote environments, which would
allow for integrative process studies as well as coupled modeling of
A-ERT data with existing water content and temperature monitoring
systems in Antarctica. Examples of such studies include the
combination of data processing techniques, petrophysical models and
supporting information to estimate unfrozen water content from
electrical resistivity data (e.g., Hauck, 2002;
Fortier et al., 2008; Grimm and Stillman, 2015;
Dafflon et al., 2016) or combining electrical resistivity
data with seismic refraction data in a joint petrophysical model to
estimate ice and water content (e.g., Hauck et al., 2011). Such analyses also provide a tool for
monitoring the transient layer and studying the impact of fast-changing
meteorological conditions and the frequent freeze–thaw process on soil
behavior at the permafrost table. However, in the context of the
volcanic material at Deception Island, the link between pore water
resistivity and measured bulk resistivity should be assessed by
laboratory measurements prior to performing a quantitative
investigation on soil ice and water content. In addition, the type of
the electric conduction needs to be investigated, as in dry soils
with low salinity, surface conduction is the dominant process
(Duvillard et al., 2018), as opposed to
electrolytic conduction, which is usually assumed to calculate water
contents from resistivity values.
A long-term deployment of an A-ERT system in Antarctica would allow
a much more detailed analysis of the permafrost and active layer
evolution, which could be used as input data for hydrothermal
models simulating the future permafrost evolution (e.g., Marmy
et al., 2016; Rasmussen et al., 2018). In this context, joint A-ERT
and thermal modeling approaches such as the uncoupled modeling approach
(Scherler et al., 2010) and fully coupled electro-thermal modeling
approach (Tomaskovicova, 2018) can be used for calibration of the
thermal model that allows simulating heat transfer in active layer
and permafrost. On a more local scale, the specific characteristics
of Deception Island, where permafrost conditions are influenced also
by geothermal and even volcanic activity, would allow for detailed
investigations of the resulting hydrothermal interactions in
a cryospheric context. The fact that the monitoring occurs along
a transect allows for improving the spatial understanding of the
active layer dynamics with a minimal environmental disturbance in
comparison to boreholes. It allowed detecting high-temporal
resolution changes in freezing and thawing along the transect,
providing new insight also into the potential geomorphic dynamics
and its regime, for example, for processes such as cryoturbation or
solifluction.
Data availability
The A-ERT and ground temperature data can be found at 10.5281/zenodo.3635063 (Farzamian et al., 2020). For inquiries please contact Mohammad Farzamian (mohammadfarzamian@fc.ul.pt).
Author contributions
MF processed the A-ERT and borehole data, wrote the main part of the text,
and generated the main results. GV contributed to the experimental design,
installation and maintenance of the A-ERT system; processing the
borehole and snow cover data; and writing the text. FAMS designed the
A-ERT survey and contributed to the elaboration of the methodology and
writing of the text. BYT contributed to the processing of A-ERT data. CH
contributed to the elaboration of the methodology, the discussion of the
results, and the intermediate and final revision of the text. MCP contributed
to the processing of the A-ERT data and the generation of the figures. IB
built the A-ERT system and installed it at Deception Island. MR, MAdP and GV
installed the air, surface and ground temperature sensors; processed the
temperature data; and measured the thaw depth at the site. All authors
contributed to the revision of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The research was funded by the Fundação para a Ciência e
a Tecnologia under project PERMANTAR-2 (Permafrost and Climate
Change in the Maritime Antarctic – FCT – PTDC/AAC-CLI/098885/2008)
and the Portuguese Polar Program (PROPOLAR-FCT). We thank the
Spanish Antarctic Station Gabriel de Castilla, the BIO Hespérides personnel for logistical support and the continued
support of the Spanish Polar Committee for the research on Deception
Island. Gabriel Goyanes, Vanessa Baptista, José Miguel Cardoso,
Ana David, Alice Ferreira and Mário Neves are thanked for the
support in the maintenance of the A-ERT system. This work and
publication have been supported by the FCT – project UIDB/50019/2020
– IDL, FCT – project UIDB/00295/2020 – and the Swiss National
Science Foundation (project no. 178823). We are very grateful for
the helpful and constructive comments of the editor Christian Beer and two
anonymous reviewers, who helped to significantly improve the
paper.
Financial support
This research has been supported by the FCT (grant nos. UIDB/50019/2020 – IDL, FCT – project UIDB/00295/2020) and the Swiss National Science Foundation (project no. 178823).
Review statement
This paper was edited by Christian Beer and reviewed by two anonymous referees.
References
Biskaborn, B. K., Smith, S. L., Noetzli, J. et al.: Permafrost is warming at a global scale, Nat.
Commun., 10, 264, 2019.Bockheim, J., Vieira, G., Ramos, M., Lopez-Martinez, J., Serrano, E.,
Guglielmin, M., Wilhelm, K., and Nieuwendam, A.: Climate warming and
permafrost dynamics in the Antarctic Peninsula region, Global Planet. Change,
100, 215–223, 10.1016/j.gloplacha.2012.10.018, 2013.Dafflon, B., Hubbard, S., Ulrich, C., Peterson, J., Wu, Y., Wainwright, H.,
and Kneafsey, T. J.: Geophysical
estimation of shallow permafrost distribution and properties in an ice-wedge
polygon-dominated Arctic tundra region, Geophysics, 81, WA247–WA263, 10.1190/geo2015-0175.1, 2016.De Pablo, M. A., Ramos, M., Molina, A., Vieira, G., Hidalgo, M. A., Prieto, M., Jiménez, J. J., Fernández, S., Recondo, C., Calleja, J. F.,
Peón, J. J., and Mora, C.: Frozen ground and snow cover monitoring in
the South Shetland Islands, Antarctica: Instrumentation, effects on ground
thermal behaviour and future research, Cuad. Invest. Geográfica, 42, 475–495, 10.18172/cig.2917, 2016.Duvillard, P. A., Revil, A., Soueid Ahmed, A., Qi, Y., Coperey, A., and Ravanel, L.: Three dimensional electrical conductivity and induced polarization tomography of a rock glacier, J. Geophys. Res.-Solid Earth, 123, 9528–9554, 10.1029/2018JB015965, 2019.Farzamian, M., Vieira, G., Monteiro Santos, F. A., Tabar, B. Y., Hauck, C., Paz, M. C., Bernando, I., Ramos, M., de Pablo, M. A.: A-ERT and ground temperature data- 2010- Deception Island/Antarctica, Zenodo, 10.5281/zenodo.3635063, 2020.Fortier, R., LeBlanc, A.-M., Allard, M., Buteau, S., and Calmels, F.: Internal
structure and conditions of permafrost mounds at Umiujaq in Nunavik, Canada,
inferred from field investigation and electrical resistivity tomography,
Can. J. Earth Sci. , 45, 367–387, 10.1139/E08-004, 2008.Goyanes, G., Vieira, G., Caselli, A., Cardoso, M., Marmy, A., Santos, F.,
Bernardo, I., and Hauck, C.: Local influences of geothermal anomalies on
permafrost distribution in an active volcanic island (Deception Island,
Antarctica), Geomorphology, 225, 57–68,
10.1016/j.geomorph.2014.04.010, 2014.Grimm, R. E. and Stillman, D. E.: Field test of detection and characterization of
subsurface ice using broadband spectral-induced polarisation, Permafrost
Periglac., 26, 28–38, 10.1002/ppp.1833, 2015.Guglielmin, M. and Dramis, F.: Permafrost as a climatic indicator in
northern Victoria Land, Antarctica, Ann. Glaciol., 29, 131–135,
10.3189/172756499781821111, 1999.Guglielmin, M., Biasini, A., and Smiraglia, C.: The contribution of
geoelectrical investigations in the analysis of periglacial and glacial
landforms in ice free areas of the Northern Foothills (Northern Victoria
Land, Antarctica), Geogr. Ann. A, 79, 17–24, 10.1111/j.0435-3676.1997.00003.x, 1997.Hauck, C.: Frozen ground monitoring using DC resistivity tomography,
Geophys. Res. Lett., 29, 2016, 10.1029/2002GL014995, 2002.Hauck, C. and Vonder Mühll, D.: Inversion and interpretation
oftwo-dimensional geoelectrical measurements for detecting per-mafrost in
mountainous regions, Permafrost Periglac., 14, 305–318.
10.1002/ppp.462, 2003.Hauck, C., Vieira, G., Gruber, S., Blanco, J. J., and Ramos, M.: Geophysical
identification of permafrost in Livingston Island, maritime
Antarctica, J.
Geophys. Res., 112, F02S19, 10.1029/2006JF000544, 2007.Hauck, C., Böttcher, M., and Maurer, H.: A new model for estimating subsurface ice content based on combined electrical and seismic data sets, The Cryosphere, 5, 453–468, 10.5194/tc-5-453-2011, 2011.Hilbich, C., Hauck, C., Hoelzle, M., Scherler, M., Schudel, L., Völksch, I., Vonder Mühll, D., and Mäusbacher, R.: Monitoring mountain
permafrost evolution using electrical resistivity tomography: A 7-year study
of seasonal, annual, and long-term variations at Schilthorn, Swiss Alps,
J. Geophys. Res., 113, F01S90, 10.1029/2007JF000799, 2008.Hilbich, C., Fuss, C., and Hauck, C.: Automated Time-lapse ERT for Improved
Process Analysis and Monitoring of Frozen Ground, Permafrost Periglac., 22,
306–319, 10.1002/ppp.732, 2011.Keating, K., Binley, A., Bense, V., Van Dam, R. L., and Christiansen, H. H.:
Combined geophysical measurements provide evidence for unfrozen water in
permafrost in the Adventdalen valley in Svalbard, Geophys. Res.
Lett., 45, 7606–761, 10.1029/2017GL076508, 2018.Keuschnig, M., Krautblatter, M., Hartmeyer, I., Fuss, C., and Schrott, L.:
Automated Electrical Resistivity Tomography Testing for Early Warning in
Unstable Permafrost Rock Walls Around Alpine Infrastructure, Permafrost Periglac., 28, 158–171, 10.1002/ppp.1916
2017.Kim, J. H., Yi M. J., Park, S. G., and Kim, J. G.: 4-D inversion of DC
resistivity monitoring data acquired over a dynamically changing earth
model, J. Appl. Geophys., 68, 522–532, 10.1016/j.jappgeo.2009.03.002, 2009.Kneisel, C.: Assessment of subsurface lithology in mountain environments
using 2D resistivity imaging, Geomorphology, 80, 32–44, 10.1016/j.geomorph.2005.09.012, 2006.Krautblatter, M., Verlcysdonk, S., Flores-Orozco, A., and Kemna A.:
Temperature-calibrated imaging of seasonal changes in permafrost rock walls
by quantitative electrical resistivity tomography (Zugspitze,
German/Austrain Alps), Geophys. Res., 115, F02003,
10.1029/2008JF001209, 2010.
Loke, M. H.: Tutorial: 2D and 3D Electrical Imaging Surveys, Technical Note,
2nd edn., Geotomo Software, Malaysia, 2002.Marmy, A., Rajczak, J., Delaloye, R., Hilbich, C., Hoelzle, M., Kotlarski, S., Lambiel, C., Noetzli, J., Phillips, M., Salzmann, N., Staub, B., and Hauck, C.: Semi-automated calibration method for modelling of mountain permafrost evolution in Switzerland, The Cryosphere, 10, 2693–2719, 10.5194/tc-10-2693-2016, 2016.
McGinnis, L. D., Nakao, K., and Clark, C. C.: Geophysical identification of
frozen and unfrozen ground, Antarctica, in: Proceed. 2nd Internat. Conf.
Permafrost, 13–28 July, Yakutsk, Russia, 136–146, 1973.Melo, R., Vieira, G., Caselli, A., and Ramos, M.: Susceptibility modelling
of hummocky terrain distribution using the information value method
(Deception Island, Antarctic Peninsula), Geomorphology, 155–156, 88–95 10.1016/j.geomorph.2011.12.027, 2012.Mewes, B., Hilbich, C., Delaloye, R., and Hauck, C.: Resolution capacity of geophysical monitoring regarding permafrost degradation induced by hydrological processes, The Cryosphere, 11, 2957–2974, 10.5194/tc-11-2957-2017, 2017.Mollaret, C., Hilbich, C., Pellet, C., Flores-Orozco, A., Delaloye, R., and Hauck, C.: Mountain permafrost degradation documented through a network of permanent electrical resistivity tomography sites, The Cryosphere, 13, 2557–2578, 10.5194/tc-13-2557-2019, 2019.Oldenborger, G. A. and LeBlanc, A.-M.: Monitoring changes in unfrozen water
content with electrical resistivity surveys in cold continuous permafrost,
Geophys. J. Int., 215, 965–977,
10.1093/gji/ggy321, 2018.Oliva, M., Nývlt, D., and Pereira, P.: Recent regional climate cooling
on the Antarctic Peninsula and associated impacts on the cryosphere,
(December), STOTEN, 580, 210–223, 10.1016/j.scitotenv.2016.12.030,
2016.
Ottowitz, D., Jochum, B., Supper, R., Römer, A., Pfeiler, S., and
Keuschnig, M.: Permafrost monitoring at Mölltaler Glacier and
Magnetköp?, Berichte der Geologischen Bundesanstalt, 93, 57–64, 2011.Ramos, M., Vieira, G., Gruber, S., Blanco, J. J., Hauck, C., Hidalgo, M. A.,
Tome, D., Neves, M., and Trindade, A.: Permafrost and active layer
monitoring in the Maritime Antarctic: Preliminary results from CALM sites on
Livingston and Deception Islands, US Geological Survey and the National
Academies; USGS OF-2007–1047, Short Research Paper 070
https://pubs.usgs.gov/of/2007/1047/srp/srp070/ (last access: 15 February 2019), 2008.Ramos, M., Vieira, G., De Pablo, M. A., Molina, A., Abramov, A., and
Goyanes, G.: Recent shallowing of the thaw depth at Crater Lake, Deception
Island, Antarctica (2006–2014), Catena, 149, 519–528, 10.1016/j.catena.2016.07.019,
2017.Rasmussen, L., Zhang, W., Hollesen, J., Cable, S., Christiansen, H.,
Jansson, P. E., and Elberling, B.: Modelling present and future permafrost
thermal regimes in Northeast Greenland, Cold Reg. Sci. Technol.,
146, 199–213, 10.1016/j.coldregions.2017.10.011, 2018.Scherler, M., Hauck, C., Hoelzle, M., Stähli, M., and Völksch, I.:
Meltwater infiltration into the frozen active layer at an alpine permafrost
site, Permafrost Periglac., 21, 325–334, 10.1002/ppp.694,
2010.Shur, Y., Hinkel, K. M., and Nelson, F. E.: The transient layer: implications
for geocryology and climate change science, Permafrost Periglac., 16, 5–17, 10.1002/ppp.518, 2005.
Smellie, J. L. and López-Martínez, J.: Geological map of Deception
Island, in: Geology and Geomorphology of Deception Island, edited by:
Smellie, J. L., López-Martínez, J., Serrano, E., and Rey, J., Sheet 6-A, 1:25.000,
BAS GEOMAP Series, British Antarctic Survey, Cambridge, 2002.
Styszynska, A.: The origin of coreless winters in the South Shetlands area
(Antarctica), Pol. Polar Res., 25, 45–66, 2004.Supper, R., Ottowitz, D., Jochum, B., Romer, A., Pfeiler, S., Gruber, S.,
Keuschnig, M., and Ita, A.: Geoelectrical monitoring of frozen ground
andpermafrost in alpine areas: field studies and considerations towards an
improvedmeasuring technology, Near Surf. Geophys., 12, 93–115, 10.3997/1873-0604.2013057, 2014.
Tomaskovicova, S.: Coupled thermo-geophysical inversion for permafrost
monitoring, PhD thesis, Department of Civil Engineering, Technical
University of Denmark, Technical University of Denmark, Department of Civil Engineering, 2018.Turner, J., Lu, H.,White, I., King, J. C., Phillips, T., Hosking, J. S.,
Bracegirdle, T. J., Marshall, G. J., Mulvaney, R., and Deb, P.: Absence of 21st
century warming on Antarctic Peninsula consistent with natural variability,
Nature, 535, 411–415, 10.1038/nature18645, 2016.Vieira, G., López-Martínez, J., Serrano, E., Ramos, M., Gruber, S.,
Hauck, C., and Blanco, J. J., Geomorphological observations of permafrost and
ground-ice degradationon deception and Livingston Islands, Maritime
Antarctica, in: Proceedings of the 9th
International Conference on Permafrost, edited by: Kane, D. and Hinkel, K., 29 June–3 July 2008, Fairbanks,
Alaska, Extended Abstracts, Vol. 1, University of AlaskaPress, Fairbanks,
1839–1844, 10.5167/uzh-3320, 2008.Vieira, G., Bockheim, J., Guglielmin, M., Balks, M., Abramov, A. A.,
Boelhouwers, J., Cannone, N., Ganzert, L., Gilichinsky, D. A., Goryachkin, G., López-Martínez, J., Meiklejohn, J., Raffi, R., Ramos, M.,
Schaefer, C., Serrano, E., Simas, F., Sletten, R., and Wagner, D.: Thermal
state of permafrost and active-layer monitoring in the Antarctic: advances
during the International Polar Year 2007–2009, Permafrost Periglac.,
21, 182–197, 10.1002/ppp.685, 2010.Williams, P. J. and Smith, M. W.: The Frozen Earth. Fundamentals of Geocryology,
Cambridge University Press, Cambridge,
10.1017/CBO9780511564437, 1989.