Mountain permafrost is sensitive to climate change and is
expected to gradually degrade in response to the ongoing atmospheric warming
trend. Long-term monitoring of the permafrost thermal state is a key task,
but problematic where temperatures are close to 0
Mountain permafrost is sensitive to climate change and has been affected by a significant warming trend in the European Alps for the last 2 decades (Noetzli et al., 2016). The warming trend is however not uniformly distributed (both spatially and temporally), as permafrost occurs in a large variety of complex landform settings. To understand the site-specific and regional evolution of mountain permafrost, detailed information about the spatio-temporal permafrost distribution and its monitoring is necessary. A better understanding of freeze and thaw processes is critical as thawing permafrost will affect mountain slope stability and trigger rock falls (Ravanel et al., 2017).
The thermal definition of permafrost (ground remaining at or below 0
Ground temperature monitoring alone cannot identify freezing and thawing
processes, which are of particular interest. At the freezing point, the
effect of latent heat flux maintaining temperatures near 0
Geophysical methods and especially geoelectrical techniques can accurately distinguish between frozen and unfrozen soil, as the electrical resistivity is particularly sensitive to the presence of liquid water and temperature permitting an indirect quantification of temperature changes in the subsurface with higher spatial resolution than point-scale temperature loggers (e.g. Hauck and Kneisel, 2008a). The electrical resistivity (or its reciprocal, the electrical conductivity) combines the contribution of three conduction mechanisms: particle conduction, surface conduction, and electrolytic conduction (e.g. Klein and Santamarina, 2003). Particle or electronic conduction is mainly related to metallic materials (due to the high number of free electrons). Surface conduction mechanisms take place at the electrical double layer formed at the mineral–ice–water interface (at the electrical double layer) and is controlled by the water content and the electrical properties of the interface (pH, zeta-potential, cation exchange capacity). Finally, the ionic or electrolytic conduction takes place through fluid-filled pores and is controlled by porosity, saturation, pore connectivity, and fluid conductivity. The flow of electrical current in soils/rocks is mainly controlled by ionic charge transport taking place through water-filled pores, and by surface conduction mechanisms at mineral–ice–water interfaces (especially in the case of clay-rich materials; e.g. Revil and Glover, 1998). Hence, spatial and temporal resistivity variations in the subsurface depend upon porosity, pore connectivity, mineralogy, water chemistry, water content, water saturation, and temperature (e.g. Duvillard et al., 2018; Hermans et al., 2014; Ward et al., 2010). Upon freezing, electrolytic conduction paths are severely reduced, reducing ion mobility and implying an increase in resistivity of (partially) frozen soil/rock, which can be monitored if the contact between electrodes and the ground (the so-called contact resistance) permits current injection and measurement of voltages (Hauck, 2002). According to Duvillard et al. (2018), the surface conduction dominates at low salinity instead of the electrolytic conduction.
Geoelectrical methods therefore permit detection and monitoring of both ice melting (through the phase change from ice to water) and thawing (through the higher mobility of ions with higher temperatures) processes (Hauck, 2002; Oldenborger and LeBlanc, 2018). The considerable resistivity change during freezing and thawing processes is (in addition to the temperature dependence of electrical resistivity; Hayley et al., 2007; Oldenborger and LeBlanc, 2018) mainly a consequence of the inverse conductivity characteristics of ice (characterized by high electrical resistivity values) and water (characterized by high electrical conductivity values). Geoelectric surveying adds a spatial dimension and it can detect changes occurring at temperatures close to the freezing point during the so-called zero curtain period (defined in 1990 by Outcalt et al.) when temperature does not vary significantly.
Electrical resistivity tomography (ERT) and its monitoring variant ERTM has been used for many years in other geoscientific fields such as landslide investigations, irrigation/infiltration studies, landfill monitoring, and remediation of contaminated sites (e.g. Barker and Moore, 1998; LaBrecque and Yang, 2001; Loke, 1999; Suzuki and Higashi, 2001; Flores Orozco et al., 2019). However, although ERT is today recognized as a standard method in permafrost surveying (e.g. Kneisel et al., 2008), long-term ERTM profiles have only relatively recently been introduced in this research domain (Doetsch et al., 2015; Farzamian et al., 2019; Hilbich et al., 2008a, b; Keuschnig et al., 2017; Kneisel et al., 2008, 2014; Lewkowicz et al., 2011; Oldenborger and LeBlanc, 2018; Supper et al., 2014; Tomaškovičová, 2018).
In a recent review of published permafrost ERTM approaches, Supper et al. (2014) presented the acquisition parameters of eight sites, of which two are
part of the ERTM network presented in this study. Since then, further ERTM
case studies on permafrost were published that focused, for example, on rock
wall monitoring (Keuschnig et al., 2017), combining direct-current
resistivity with induced polarization monitoring (Doetsch et al., 2015), or
analysing vegetation–permafrost interaction (e.g. Dafflon et al., 2017;
Stiegler et al., 2014). However, in contrast to borehole temperature
monitoring networks, most of the above-cited ERTM studies focused on single
field sites or on short-term monitoring (
Map of Switzerland with location of the ERTM network sites presented in this paper (in red) and other monitoring sites (in grey). Photographs show the location of the ERT profiles in red for each site of the network. Note that CER is located in Italy.
Mountain permafrost terrain is indeed challenging regarding the installation
and maintenance of long-term (
ERTM in Switzerland started in 1999 at Schilthorn, Berner Oberland (Hauck, 2002), as part of the European PACE project (Permafrost and Climate in Europe; Harris et al., 2009). Due to its promising results, the installation was maintained and later extended to a fully automated and continuously operating monitoring site (Hilbich et al., 2011). During the following decade, ERTM was initiated at an increasing number of sites; four of them were subsequently included in the Swiss permafrost monitoring network PERMOS (Hilbich et al., 2008a; Morard, 2011; PERMOS, 2019). Though other ERTM sites exist in the central Alps, the network presented here is composed of a selection of six monitoring sites located in different regions of the Swiss and Italian Alps located in Fig. 1. These six sites (out of the 14 sites where repeated measurements are available; see Table 1) were selected according to (a) length of ERTM time series, (b) existence of long borehole temperature time series for validation and process analysis, and (c) diversity of subsurface properties. Table 1 summarizes the general characteristics of the ERTM network in addition to other ERTM sites introduced below. In this paper, we present datasets until the end of 2017.
Key information of the geophysical monitoring installations.
Number of electrodes in an earlier setup is specified in brackets. W: Wenner
array; WS: Wenner–Schlumberger array. Data sources: Mewes et al., 2017;
Morard, 2011; Pellet et al., 2016; PERMOS, 2016.
Temperature key information of the monitoring sites, including mean
annual air temperature (MAAT), active layer thickness (ALT) of 2017, mean
annual ground temperature at 10 m depth (MAGT10 m), and the average
resistivity (
Overview of the available ERTM time series for the six stations of the network and comparison of their spatial mean resistivities.
The network covers a large variety of site characteristics regarding
substrate, ice content, geomorphology, thermal properties, and the observed
soil temperature range. The active layer thickness (ALT, i.e. the maximum
seasonal thaw layer that is here derived from temperature measurements)
given in Fig. 2 varies for all sites between 2.5 and almost 10 m, depending
on site characteristics and local climatic conditions (PERMOS, 2019;
Pogliotti et al., 2015). During the observation period, a general increase
in ground temperatures was observed in all boreholes (PERMOS, 2016;
Pogliotti et al., 2015). Figure 2 summarizes the main thermal
characteristics of all sites (mean annual air temperatures (MAATs), ground
temperature at 10 m depth, and ALT) compared to the average resistivity at
around 10 m depth (
The Schilthorn monitoring site is located in the Bernese Oberland, northern
Swiss Alps (46
The monitoring station comprises a meteorological station, three instrumented boreholes, three soil moisture sensors, and ERT profiles. Soil moisture has been monitored since August 2007 from 10 to 50 cm depth (Hilbich et al., 2011; Pellet et al., 2016). The meteorological station includes sensors measuring shortwave solar and longwave atmospheric and terrestrial radiation, air temperature, humidity, snow height, and wind speed and direction. Three boreholes, including two along the ERTM profile (14 m deep SCH-5198 drilled 1998 and 100 m deep SCH-5000 drilled 2000), are instrumented with temperature loggers (see PERMOS, 2016, and Vonder Mühll et al., 2000, for details of the borehole instrumentation). Permafrost depth extends to at least 100 m and ALT varies between 5 and 10 m (PERMOS, 2016). The ERTM profile at SCH is horizontal and almost parallel to the Schilthorn east–west-striking crest.
The Stockhorn monitoring site is located on a high-altitude plateau
surrounded by glaciers above Zermatt in the canton of Valais (45
The site comprises a full meteorological station, continuous soil moisture measurements, and a permanent ERTM profile (Hilbich et al., 2008a; Pellet and Hauck, 2017; PERMOS, 2019; Rosset et al., 2013). Figure 2 shows the considerable spatial variability of permafrost temperatures and active layer depth at the two boreholes. After initial geophysical measurements in 1998 during the PACE project (Hauck, 2001), a permanent ERTM profile was installed in 2005, extending from the relatively flat plateau to the south slope. In 2007, the profile was extended with seven additional electrodes drilled into the steep north face to get a more convex geometry (see Table 1).
The Lapires monitoring site is a high alpine talus slope located in the
Nendaz valley, canton of Valais (46
Ground temperatures have been monitored since 1999 (borehole LAP-0198, 19.6 m depth; PERMOS, 2016), and in three additional boreholes (up to 40 m depth) since 2008 (Scapozza et al., 2015), two of which are in permafrost (LAP-1108 and LAP-1208; PERMOS, 2016). Using nuclear well logging at these boreholes, Scapozza et al. (2015) quantitatively estimated the porosity (40 %–80 %) and the volumetric ice content (20 %–60 %) from 4 to 20 m depths. A meteorological station completes the temperature dataset (Staub et al., 2015). ERTM started in 2006 with a permanent horizontal profile (LAH; see Table 1), complemented by an additional, longer vertical cross-profile (LAV) in 2007. The LAV profile (not part of this paper) was damaged due to frequent rockfall and was finally uninstalled in 2017.
The Murtèl-Corvatsch monitoring site is a coarse blocky crystalline rock
glacier located on a northern slope below Piz Corvatsch in Upper Engadin,
eastern Swiss Alps (46
Similar to SCH and STO, full meteorological station data are available since 1997. ERT data were first acquired in 1998 as part of the PACE project along a longitudinal profile (Vonder Mühll et al., 2000), and at approximately the same position a long-term ERTM profile was installed in 2005.
The Cervinia monitoring site is located on a high-altitude plateau at Cime
Bianche in the upper part of Valtournenche (45
The Dreveneuse monitoring site is a low-altitude and vegetated northwestern
scree slope (Morard, 2011) located in the so-called Prealps, canton of
Valais (46
The site is further equipped with a meteorological station including soil moisture measurements (Pellet and Hauck, 2017). The permanent ERTM profile was installed in 2007. Initially, electrical resistivity measurements were conducted on a monthly basis (Morard, 2011); however since 2012 ERTM measurements have been recorded about once a year.
ERT is used to image the distribution of the electrical resistivity of the
subsurface, based on a four-electrode array, or quadrupole. The electrical
voltage (or potential difference)
Long-term ERTM requires consistency in all acquisition parameters for comparison purposes. Regarding the configuration of the quadrupoles, surveys were collected in early years using one of three standard configurations: Wenner (W), Wenner–Schlumberger (WS), and dipole–dipole (DD) (e.g. Hauck et al., 2003). Recently, the W configuration has been established as the main configuration used for the monitoring measurements because of a high signal-to-noise ratio. Even though logistical constraints have changed at some sites and new instrumentation became available since the earliest measurements, the same W configuration is still deployed to permit a fair comparison of the data from different periods. However, CER and DRE are exceptions as their monitoring procedure continues to use the initial WS configuration.
We use two different instruments for the acquisition of ERTM data: a
SyscalR1 instrument (Iris Instruments, France) with an internal resistivity
of 10
In the context of long-term and automated ERTM at several sites, a consistent processing of the data prior to the inversion is required to evaluate data quality. This has already been addressed for single automated stations in mountain permafrost terrain (Hilbich et al., 2011; Keuschnig et al., 2017; Supper et al., 2014). In a precursor study, a data filtering scheme for several ERTM datasets for the Swiss Alps has been developed (Rosset et al., 2013). Here, we extend this approach by analysing the seasonal and site-specific characteristics of contact resistance and the statistical distribution of measured apparent resistivities. By this, we identify and remove outliers before the inversion of the data to reduce possible artefacts.
Galvanic contact resistances between electrodes and the ground are recorded before each measurement at SCH, LAH, MCO, and STO, where the Geotom system is deployed. The recorded values are below the internal instrument resistance except for singular cases, where the filtering scheme identified them accordingly. As observed in Fig. 4, electrode contact resistance exhibits site-specific characteristics and seasonal variations. Within one profile, the contact resistance characteristics of individual electrodes show consistency in time, reflecting substrate characteristics (e.g. in Fig. 4c the first six electrodes placed in the steep north face always exhibit a higher resistance than most other electrodes of the profile). Similarly, the seasonal evolution of contact resistances is consistent between different sites and years, with low values in summer and high values in winter. The variations in contact resistance are most pronounced during seasonal surface freeze-up and melt (see mid-October in 2015 and mid-May in 2016 at LAH in Fig. 4e).
Temporal evolution of contact resistances and seasonal variations
in contact resistance histograms at
The median contact resistances exhibit a clear summer–winter duality at all
sites (Fig. 4b, d, and f). The lowest contact resistances are observed at
SCH (moist and fine-grained material) with median values varying from 1.5
A similar analysis can be performed with injected current (see the distribution of injected current in Fig. S1 in the Supplement). Narrower current distribution results from more homogeneous surface conditions, while low current injections are associated with high contact resistances, more outliers, and often limited data reliability.
A consistent data processing is required for a comparative analysis of data collected in different locations and under variable conditions. In relatively resistive terrain, anomalously high resistivity values can indicate ground ice occurrences and freeze or thaw processes, but such values could also be associated with systematic errors. Such systematic errors can relate to low current densities injected (associated with high contact resistances) below the resolution limit of the ERT instruments. In view of the huge number of datasets, automatic filtering procedures are essential to ensure consistent data processing. Several approaches with different levels of complexity exist. A time-lapse inversion scheme with time regularization (see e.g. Karaoulis et al., 2013; Lesparre et al., 2017; and the discussion section below) should in principle filter out the data, which are not correlated over time. The comparison of normal and reciprocal measurements (e.g. Flores-Orozco et al., 2012, 2018) is another common method to quantify data uncertainty. Reciprocals refer to the re-collection of the data after interchanging the electrodes used for current and potential dipoles. However, the collection of reciprocals also requires doubling the time necessary for the collection of the data, which is not well suited for monitoring measurements within the network. Except for singular tests, no reciprocal measurements to quantitatively estimate measurement noise levels were conducted, due to energy consumption and time constraints. In the absence of reciprocal measurements and 4-D inversion schemes, we use a three-step procedure. The first step filters single quadrupoles, while the second and third steps reject entire datasets if the resolution or representativeness of the obtained specific resistivity model with respect to the measured values (raw data) is considered too low.
Step 1 includes a systematic filter (SF) and a moving median filter (MMF).
This analysis is performed for a complete tomographic dataset and filters
individual quadrupoles. Following Hilbich et al. (2011), we apply a SF to
remove physically implausible measurements (negative potential differences
or apparent resistivity) and low-reliable measurements (i.e. low current
flow associated with high potentials;
Seasonal analysis after filter step 1 for all sites. Step 2 consists in a cut-off filter of 80 % (grey line). Summer (red) is defined here as the 3 months with thawing processes and an absence of snow cover (JAS), and winter conditions (blue) are represented by the nine other months (October–June). The symbol “#” denotes the number of datasets taken into account for each site.
Step 2 deletes tomographic datasets, from which more than 20 % of the
quadrupoles were removed in step 1. Rosset et al. (2013) defined a
cut-off limit of 80 % for acceptable datasets by comparing the ratio
of filtered data points with the subsequent inversion error. Only datasets
containing more than 80 % of the original data points after filter step 1
are therefore inverted. Figure 5 shows the distribution of the percentage of
data kept after filter step 1 for each dataset as a function of seasons for
each site. If the occurrence of few outliers (i.e. high percentages in Fig. 5) is considered as a quality criterion, SCH shows high-quality data
throughout the year. At LAH, high-quality data are possible throughout the
year but with the highest probability in summer. In addition, LAH shows also the
largest variability of percentage of filtered data from all sites, partly
because only a few winter measurements were attempted at STO and MCO. At STO,
only a few outliers were detected in summer datasets, as opposed to winter
data (as can be expected from the contact resistances, Fig. 4c and d),
resulting in a high filtering rate at step 1 (up to
After data inversion, step 3 excludes a dataset if its inversion error is greater than a user-defined site-specific threshold (see the threshold values presented in both Table 1 and Fig. S3 in the Supplement). Note that a low inversion error does not automatically mean the resulting tomogram has good resolution and reliability, as low inversion errors can also be obtained for high filter ratios (Rosset et al., 2013). However, such cases would already be excluded by step 2, and it is therefore essential to keep a high tomogram resolution.
We compute the distribution of the specific electrical resistivity for a finite element grid from filtered apparent resistivity datasets. We inverted our data using different inversion schemes and compared the obtained results (similarly to Hellman et al., 2016) and could not detect any significant differences between them that would be relevant for interpretation in a permafrost monitoring context. In addition, previous studies have also revealed that consistent temporal resistivity changes can be resolved using independent inversions of the data (e.g. Caterina et al., 2017; Hilbich et al., 2011; Oldenborger and LeBlanc, 2018; Supper et al., 2014), instead of using a time-lapse algorithm (designed to reduce systematic errors for time series with short time gaps, e.g. hours or days). In this paper, in accordance with previous ERT studies at our field sites, the inversion itself is conducted iteratively using the software RES2DINV (Loke, 2018) in batch mode. We use the robust inversion scheme (L1-norm) with the so-called extended model to minimize the edge effects.
More advanced time-lapse inversion schemes (as proposed e.g. by Karaoulis et al., 2013; Lesparre et al., 2017; Loke et al., 2014) can reduce the occurrence of inversion artefacts, which may originate from the underdetermined parts of the inversion problems of each individual tomogram. Difference plots of individual tomograms without explicit time constraints may therefore contain artefacts, especially in regions with low sensitivity. In the following, small-scale anomalies in the resulting tomograms are therefore excluded from interpretation.
Due to the large number of datasets, as well as the spatio-temporally varying surface and subsurface characteristics at the six sites affecting the rms, the choice of a consistent and comparable stopping criterion for the inversion is not obvious. For simplicity and based on experience, here we use the third iteration of the inversion, which provides a compromise between an efficient reduction in data misfit and avoiding overfitting potential measurement noise (e.g. Hilbich et al., 2011).
Representative end-of-summer tomograms for each site with interpretation of major characteristics. Boreholes along the resistivity profiles are represented in black, with horizontal lines indicating the thaw depth at the date of the ERT measurement, except for DRE, where the borehole is completely unfrozen at the time of ERT measurement. Note a second line for LAH at borehole LAP-1108 has been added to represent the permafrost base limit.
After the pre-processing and inversion, the tomograms with sufficient quality are selected for further analyses based on a user-defined site-specific threshold for the inversion error (see filter step 3). Inversion error histograms (see Fig. S3 in the Supplement) illustrate the distribution of the inversion errors for all inverted datasets per site and the site-specific thresholds. As a standardized and non-empirical histogram analysis can indeed not be conducted with such a low number of bins (statistically, the number of measurements is low; see Fig. S3), manually defined thresholds based on expert knowledge were used similarly to in other studies (see Supper et al., 2014).
The resulting set of accepted ERT tomograms amounts to a total of 901 (out
of 1013) for the network. One representative tomogram per site for
end-of-summer conditions is given in Fig. 6. Mean resistivity values
obtained for all sites span over 3 orders of magnitude as shown in Fig. 3,
and highlight the considerable variability of alpine permafrost conditions
ranging from fine-grained debris and weathered bedrock at SCH
(
The thaw depth derived from the respective borehole temperatures is used as validation of the inversion results and is shown as horizontal lines crossing each borehole in Fig. 6. At CER, LAH, and MCO the thaw depth is well represented by vertical resistivity contrasts, while the thaw depth at the ice-poor bedrock sites SCH and STO (plateau) does not necessarily cause significant resistivity contrasts. Here, resistivity variations may rather be influenced by variable ground properties (rock type, degree of fracturation) (e.g. SCH-5000 and STO-6000 boreholes), which can prevent clear relationships between absolute resistivity values and thermal state (frozen or unfrozen).
Percentage change of resistivity logarithm over the full observation period for comparable dates at end of summer for each site. Vertical lines represent borehole positions and horizontal lines the thaw depth (white and black lines correspond to the oldest and most recent dates, respectively). Change in ALT is expressed in metres near the boreholes. Missing white lines (for SCH and LAH) correspond to non-existent data.
As the borehole at DRE showed positive summer temperatures during most years
of the observation period, no thaw depth is indicated in Figs. 2, 6, and 7.
At the time of the ERT measurement shown in Fig. 6, the minimum temperature
observed in the borehole was
Within our network, ERTM series reach up to 19 years in 2017, which is rare
if not unique in the permafrost literature (Supper et al., 2014). Although
still short in a climate impact context, they allow the analysis of up to
almost 2 decades of resistivity changes in the context of permafrost thaw
and ground ice melt. Figure 7 presents the percentage change of resistivity
logarithm (i.e.
Amongst all sites, the strongest and spatially most consistent resistivity
decrease occurs at SCH and LAH, which are also characterized by high
permafrost temperatures (see MAGT
At STO, the resistivity decreased between 4 and 10 m depth on the plateau (left-hand side of the tomogram). Both boreholes situated on the plateau indicate a permafrost degradation (see ALT increase in Fig. 7 by 0.9 and 1.7 m respectively), but with a higher rate at the warmer and southern borehole. This is consistent with the thicker negative anomaly at STO-6100 seen in Fig. 7. The strong negative anomaly in the southern slope may also indicate permafrost degradation, but could also be related to increased water saturation at the time of the measurement.
At MCO, the increase of 0.8 m ALT over the 11-year period is the smallest increase rate of the whole network, which is difficult to resolve on the scale of the ERT resolution (5 m electrode spacing). A comparatively small ALT increase is expected considering the high ice content, and it is also consistent with the results from transient model studies (Marmy et al., 2016; Scherler et al., 2013) predicting a much faster permafrost degradation at SCH compared to MCO during the 21st century.
At CER, a significant short-term (during the 4-year observation period)
resistivity decrease can be observed along the entire profile at
At DRE, a significant resistivity decrease can be observed in the middle of
the profile (around 50–70 m) at around 5–15 m depth between 2007 and 2015, which corresponds to the zone interpreted as a blocky layer, which is
affected by the air circulation process (see Fig. 6). As the borehole
indicates unfrozen conditions for both measurement dates (minimum
temperature 0.7
Example of resistivity–temperature scatter plots at SCH, STO, and
LAH at shallow depth (left column, where the colour scale denotes months) and
within the permafrost (right column, where colour scale denotes the years).
For the sites, which are measured throughout the year (SCH, LAH, and DRE),
the seasonal sinusoidal variation in resistivity values demonstrates the
general relationship between resistivity and temperature data for the
near surface (Fig. 3, higher resistivity values in winter and lower values
in summer). Figure 8 shows selected resistivity–temperature scatter plots
for three sites (SCH, LAH, and STO), at both shallow depth in the active
layer (
Within the permafrost (Fig. 8, right column), seasonal cycles have much less
influence and an inter-annual signal with a clear thawing trend with
decreasing resistivity values is predominant. At 10 m depth at SCH, the
temperature seems to reach a maximum constant value of
Figure 8e presents a comparison of resistivity–temperature pairs at both
boreholes at STO for two depths within the permafrost (5 and 9 m).
Temperatures recorded in the southernmost borehole (STO-6100) are generally
higher and closer to the melting point. In both boreholes, at 5 m depth, the
variability of temperature and resistivity is higher than at 9 m depth, as
it is more influenced by surface variations. In borehole STO-6000, the
recorded temperatures (
Resistivity logarithm percentage change tomograms with respect to the median of the end-of-summer time series at SCH. Black lines represent borehole positions.
The best example of permafrost degradation detected by ERTM is seen in the
almost 20-year time series at SCH. Figure 9 shows the inter-annual evolution
of the long-term end-of-summer resistivity changes at SCH, highlighted by
the resistivity logarithm change with respect to the median of all
considered SCH datasets (shown for all other stations in the Supplement,
Figs. S4–S8). The resistivity evolution at SCH clearly shows the effect
of the heatwave in 2003 (strong resistivity decrease; see Hilbich et al.,
2008b) and the slow return (until 2008) to the initial state (except for the
zone between 2 to 6 m depth and 5 to 15 m horizontal distance, which did
not recover its original resistivity value). From 2010 onwards, the ground
below 5 m depth down to the bottom of the tomogram at 15 m depth exhibits
even smaller resistivity values than during the record heatwave of 2003.
The temporal evolution of both resistivity and temperature at 7 m depth at
the SCH-5198 borehole location (see Fig. S9 in the Supplement) shows this
evolution as well, but with continuous temporal details. Since 2009, and
coinciding with temperatures reaching the melting point for the second time,
the resistivity remained low (
Time series of the end-of-summer permafrost selected zone of
In contrast to the effect of the singular heatwave of 2003 (affecting relatively shallow depths), the sustained resistivity decrease since 2009 affects greater depths and extends over the entire length of the ERTM profile. It represents the cumulative resistivity decrease caused by and in agreement with the change in the ground temperature regime since 2009 (warmer summers and winters). The observed ongoing resistivity decrease therefore confirms the sustained increase in liquid water content within the permafrost layer, which is considered a direct consequence of ground ice degradation.
To facilitate inter-site comparison, we plotted the resistivity
values for each site determined from the spatial average of a manually
selected representative zone within the permafrost in Fig. 10. Remarkably, all six
sites follow a common general trend of long-term resistivity decrease, which
is modified by year-to-year variations (e.g. due to meteorological
year-to-year variability, irregular dates of the measurement, which may not
correspond to the annual maximum thaw depths, or inversion artefacts; see
further discussion below). Note the distinct reductions in resistivity that
occur simultaneously between
The reliability of ERT results is a constant concern, as inversion results can be ambiguous, especially where model sensitivity is low. Inverting monitoring datasets without an explicit time constraint in the inversion can produce errors due to noisy data (e.g. Karaoulis et al., 2013; Lesparre et al., 2017; Loke et al., 2014). The objectives of this study include the development and validation of a robust inversion approach that can detect the large-scale climate change signal (permafrost thawing) in the ERT data for very different field sites and conditions, rather than an optimal inversion algorithm for an individual case study. The consistency of the long-term series (e.g. Figs. 3 and 10) proves the high reliability of most ERT datasets presented in this study, even if single datasets may have a relatively high uncertainty (in the case of high electrode contact resistance, high inversion error, or random errors). However, regarding the interpretation of temporal resistivity changes, uncertainties may arise from the effects of temporally varying contact resistances and the number of quadrupoles used for inversion, but these are considered small compared to the amplitude of seasonal and long-term resistivity changes. The reliability of single ERT tomograms can be assessed by either analysing the resolution matrix (Friedel, 2003), the sensitivity uncertainty (e.g. Hermans et al., 2015), or the DOI index (depth of investigation; defined by Oldenburg and Li, 1999), all of which have been applied to datasets obtained on permafrost (e.g. Hauck et al., 2003; Hilbich et al., 2009). Although uncertainties remain, further comparison of resistivity values with borehole data, complementary geophysical data such as refraction seismics (e.g. Maurer and Hauck, 2007; Hilbich, 2010), and hydro-thermal model simulations (e.g. Pellet et al., 2016; Scherler et al., 2010) shows a generally good match regarding the depth of the thaw layer, the layer structure, and its spatio-temporal variability. Moreover, both apparent resistivities and inverted resistivities (Fig. 10a and b) show similar decreasing trends, supporting the assumption that existing inversion artefacts are smaller than the long-term measured variations.
Due to the technical difficulties of maintaining several high-altitude monitoring stations and obtaining high-quality data at the same time, our datasets presented in Fig. 3 have major data gaps and most sites are measured only a few times per year. However, data gaps in the resistivity time series may prohibit the reliable identification of extreme values related to specific thawing–freezing processes, and Hilbich et al. (2008b, 2011) proved the applicability of ERTM in investigating permafrost evolution on different timescales and validated the approach of long-term monitoring with only an annual end-of-summer dataset (at SCH, before the automatization of the site). Within the present network, we confirmed the feasibility of using end-of-summer measurements in a long-term evolution context for sites that are morphologically different than SCH and consider our data sufficient to justify our approach. With respect to resistivity, changes based on the temporal median of resistivity values of the end-of-summer dates (cf. Figs. 9 and 10) permit comparison to sites with only single annual measurements. However, a potential bias might be introduced by variations in the dates used for the end-of-summer measurements alone, or in combination with variations in the timing of the maximum thaw depth (Hilbich et al., 2011). Having continuously operating ERTM systems, as deployed at SCH, should therefore be the goal for future extensions and new installations of ERTM in permafrost areas.
As mentioned above, a further source of uncertainty may arise from seasonally changing electrode contact resistances and the correspondingly changing number of quadrupoles for the inversions of one ERTM profile. Because the filtering process is applied independently to each dataset, the filtered datasets may not consist of the same quadrupoles for each time instance. A time-lapse inversion approach to compute temporal differences would therefore be challenging, especially considering the low number of readings according to the Wenner configuration and the low time continuity of our datasets (gaps in time), which may lead to the occurrence of artefacts. However, for the long-term perspective according to the use of end-of-summer measurements as discussed above, this uncertainty is small as differences in quadrupole numbers are small (below 1 % for SCH, LAH, and CER and between 5 % and 10 % for the other sites for the dates considered in Fig. 7). Moreover, our filtering procedure excludes datasets with more than 20 % filtered quadrupoles (i.e. most winter measurements at STO and LAH; see Fig. 5), which avoids extreme cases of comparing inversion results obtained from a different number of readings. However, for detailed process studies on shorter timescales or at high temporal resolution, such as presented in Hilbich et al. (2011) for SCH, the same number of quadrupoles should be used for inversion to avoid inconsistencies and the occurrence of artefacts in the computed time-lapse differences. In such cases where datasets are derived from continuous monitoring at high temporal resolution, specific time-lapse inversion schemes may likely improve the tomograms significantly (as demonstrated by Doetsch et al., 2015). In our study, we excluded the interpretation of small-scale anomalies to reduce the uncertainty for the individual inversions.
Hysteresis loops in both directions can be observed within our dataset by plotting resistivity against temperature for each borehole location (see Fig. 8a, c). At SCH, visible hysteresis cycles are similar for both boreholes (13 m distant) close to the surface (0–1 m depths, not shown). However, at 1.2 and 1.6 m depths, only borehole SCH-5198 displays a similar (but less pronounced) hysteresis cycle, whereas borehole SCH-5000 exhibits a zero curtain during freezing that results in a hysteresis cycle in the other direction (see Fig. S10 in the Supplement). In sediments, Tomaškovičová (2018) recorded a zero-curtain effect only during the freezing period, similarly to Fig. 8c but in contrast to Fig. 8a, while a thawing zero-curtain effect similar to Fig. 8a is reported by Hilbich et al. (2011), Pellet and Hauck (2017), and Supper et al. (2014). Different local ground effects contribute to the freezing–thawing hysteresis, e.g. pore connectivity, metastable nucleation, capillarity, and pore blocking (Tian et al., 2014), as well as the mineralogy – the carbonate content being an important factor according to Krautblatter et al. (2010) – are assumed to be responsible for these differences.
On larger temporal scales, the comparison of borehole temperatures and resistivities shows that the last two extreme heatwaves in the Swiss Alps, in summers 2003 and 2015 (Scherrer et al., 2016), had a direct effect on ground temperatures and a more sustained effect on resistivities. Heatwaves accelerate the decrease in the ice content in the upper part of the permafrost, and the lost ice is not recovered (air temperatures too high to enable ice aggradation). Therefore, a subsequent warming has a higher effect in the years following a heatwave, as less energy is needed to thaw the decreased amount of ice, leaving more energy for warming of deeper layers (see also Scherler et al., 2013, for a summary of dominant influencing factors for active layer deepening). Figure 9 highlights this effect, which is well documented by the resistivity change below 5 m depth at SCH since 2015.
In their review paper on ERTM in the context of permafrost, Supper et al. (2014) compared the acquisition characteristics mentioned in the very few available ERTM studies at that time and described the SCH installation (Hilbich et al., 2011) as “exceptional”. This was because of the favourable conditions in terms of low contact resistances (due to lithology), permafrost temperatures close to the freezing point, and low resistivity values on the one hand and the unusually low ice content in comparison to other ERTM sites on the other hand. The results of our study confirm this observation in a broader context, with SCH having the lowest resistivities (Fig. 3), the lowest contact resistance values (Fig. 4), and the lowest inversion errors of the entire network. However, the results of the network demonstrate that long-term ERT monitoring is also possible for extremely resistive permafrost landforms such as MCO rock glacier (see Fig. 10a) with much higher contact resistances, but not significantly higher inversion misfits, at least for the summer period. Figure 10c also indicates that analysing several sites simultaneously with a similar methodology helps to increase the representativeness of observed changes in ground ice occurrences. With the ERTM network presented in this study, the SCH data can now be set in relation to ERTM results from other permafrost landforms such as rock glaciers and talus slopes.
The parallel evolution of mean resistivity within the permafrost layer in all parts of Switzerland (and the Italian Alps) confirms the high representativeness of ERTM data for the analysis of ongoing permafrost degradation. Importantly, this spatially averaged resistivity trend represents a much larger footprint of permafrost conditions than would otherwise have been determined according to the point information of borehole temperature profiles (see the standard PERMOS temperature time series plots shown in the Supplement, Fig. S11). Furthermore, ERTM results are directly relatable to ground ice melt without the masking effect found in temperature series due to latent heat release close to the melting point. The observed parallel resistivity evolution at all sites therefore clearly illustrates the ongoing permafrost degradation with a remarkable shift to another resistivity regime after 2009, in agreement with temperature evolution.
In addition to the top-down permafrost degradation observed at all sites, Fig. 7 shows a resistivity decrease at LAH below the ice-rich body. This may be explained by air circulation in the talus slope, particularly below the ice-rich resistive body (Delaloye and Lambiel, 2005; Wicky and Hauck, 2017), which might lead to bottom-up permafrost degradation. Thinning of the permafrost body from below is indeed confirmed by increasing temperatures around 20 m depth (the sensor at 21.5 m depth in borehole LAP-1108 measured constantly negative temperatures until 2012, and recorded positive temperatures since 2015; see PERMOS, 2016). The observed resistivity decrease between 15 and 20 m depth, although close to the limit of the sensitivity of the tomogram, may therefore result from an increase in the ratio between liquid water content and ice content or increasing ion mobility in the liquid pore water at negative temperatures (Oldenborger and LeBlanc, 2018).
The ERTM network is part of a broader permafrost monitoring framework, which includes other variables like air and ground temperature (e.g. Isaksen et al., 2011; PERMOS, 2019), as well as soil moisture (Pellet and Hauck, 2017; Wang et al., 2016), P-wave velocity (Hilbich, 2010; Krautblatter and Draebing, 2014), and permafrost creep velocity (Delaloye et al., 2010; Kellerer-Pirklbauer and Kaufmann, 2012; PERMOS, 2019). Figure 11 shows the simultaneous temporal evolution of electrical conductivity (the inverse of electrical resistivity) together with other monitoring variables from different sites in Switzerland, chosen for their long time series. We present here the conductivity instead of the resistivity for visual consistency with the trend of all other parameters. Interestingly, the increasing trends in rock glacier velocity and the ground temperature both show a break in 2016, while the conductivity trend continues to increase.
Evolution of the permafrost temperature (brown line), rock glacier horizontal velocity (purple line), and permafrost electrical conductivity (green dots and line) at selected PERMOS field sites in the context of long-term climate observations. Annual air temperature anomaly (relative to the 1981–2010 norm period) measured at Säntis (2500 m) is shown by blue and red bars, and its smoothed value is shown in black. The dashed purple line represents horizontal rock glacier velocity reconstructed from orthophotos (PERMOS, 2019).
Similar trends are also observed at almost all other PERMOS field sites and they represent general tendencies for the entire Swiss Alps (PERMOS, 2019). The observed decrease in ground temperature and creep velocity in 2017 is due to the late onset of snow during the 2016/17 winter, which enabled a more efficient cooling of the ground. This effect is not seen in the resistivity data at SCH because only end-of-summer measurements are considered. Deep ground cooling also occurred during that winter at SCH (shown in Fig. S9 in the Supplement), leading to a seasonal increase in the ice-to-water content ratio. However, this increase was countered by the especially warm spring and summer until the end-of-summer measurement in 2017. The temperature measured at 9 m depth in summer 2017 was indeed the second highest after 2015. The decrease in temperature in 2016/17 was more pronounced at sites with thinner snow cover (and late snow onset) and colder permafrost temperatures like at MCO (see Fig. S11 in the Supplement), and at STO and LAH where it caused small increases in the resistivities in 2017 (Fig. 10).
The fact that all measured variables in Fig. 11 (and similar data for other stations in the Alps) exhibit the same long-term evolution of permafrost, i.e. degradation due to climatic conditions, is very likely due to the current disequilibrium between atmospheric and ground thermal conditions. The resulting permafrost degrading trend is so dominant that it outweighs any impacts of morphological, topographical, climatological, or geological differences between the sites.
This study presents the analysis of a network of six electrical resistivity tomography monitoring (ERTM) sites in the central Alps, where at least one measurement per year at the end of summer was conducted for more than 1 decade (except CER, which has only 5 years of ERTM data). The network data are comprised of 1013 datasets in total (counting until the end of the year 2017). Systematic data processing includes (a) the analysis of contact resistances and (b) the application of automatic processing routines (filtering, data inversion, and time series analysis), which allows for a comparative analysis of the long-term resistivity evolution in the context of climate-induced permafrost degradation. Following previous studies in ERTM on mountain permafrost (Hilbich et al., 2008; Supper et al., 2014), we used independent inversions instead of time-lapse regularization of difference inversions to provide a general overview of the long-term trend in the electrical parameters.
The ERTM network covers representative permafrost landforms, including warm
permafrost with high temperatures and low ice content and cold permafrost
with massive ice as well as a high spatio-temporal variability of ground ice
occurrences. All permafrost sites show a clear permafrost degradation
signal, which is strongest for the longest time series at SCH, regarding
different parameters: temperature increase, active layer thickening, and
resistivity decrease within the permafrost body (i.e. liquid water-to-ice
content ratio increase). The correlation between temperature increase below
the freezing point and resistivity decrease is significant at all sites and
is especially pronounced around the permafrost table. Moreover, the observed
degradation trend is particularly visible between
Despite the difficulty to maintain long-term ERTM sites in mountain permafrost terrain, we believe the effort to be very valuable, as long-term time series are of high importance regarding permafrost monitoring and the understanding of the impacts of climate change and meteorological anomalies. ERTM provides the possibility to detect and monitor ground ice loss, even when borehole temperatures stagnate at the melting point (zero-curtain effect). ERTM further helps to evaluate the representativeness of borehole temperature information for the entire landform.
We demonstrated the advantages of combining data from several sites into an ERTM network. Similar to the existing global temperature network of permafrost GTN-P (Biskaborn et al., 2015), the ERTM installations currently initiated around the world could lead to a global ERTM database and be used as a complementary assessment of ongoing permafrost change on a global scale.
The application and subsequent validation of more advanced inversion strategies (e.g. 4-D) are beyond the scope of the present paper. In a future study, such an approach could first be applied at SCH, where resistivity data at high temporal resolution are available (Hilbich et al., 2011). This data availability is, however, the exception at operational permafrost monitoring stations, which currently favour a simple and robust data processing approach that is feasible for operational monitoring networks. Further electrical methods such as spectral induced polarization (e.g. Flores-Orozco et al., 2018) and spontaneous potential (e.g. Slater, 2007) may help to overcome limitations of the ERT method. However, applications of these methods to permafrost are still rare (e.g. Doetsch et al., 2015; Duvillard et al., 2018; Grimm and Stillman, 2015) and no long-term monitoring datasets exist yet.
The borehole temperature data from the PERMOS network are published under PERMOS (2016),
The supplement related to this article is available online at:
CM developed the automatized filtering process, processed all the ERT data, wrote the major part of the text, and made all figures (except Fig. 11, which was made by CP). CHi and CHa contributed to the elaboration of the methodology, the data acquisition, the discussion of the results, and the intermediate and final revision of the text. CP, RD, and AFO contributed to the discussion of the results and the final revision of the text, with AFO focusing on the discussion of the technical aspects.
The authors declare that they have no conflict of interest.
We are thankful to the PERMOS office for providing the temperature data and supporting our monitoring work and to all field helpers. We are grateful to the cable car companies Schilthornbahn AG, Télénendaz S.A., and Corvatsch AG for the logistical support to our research activities. We also thank our colleagues from ARPA (Italian Regional Environmental Protection Agency) for supplying the data from the Cervinia monitoring site. We acknowledge the reviewers Oliver Sass, Andre Revil, and Sara Bazin and the editor Peter Morse for their helpful comments and suggestions that improved the paper quality.
This paper was edited by Peter Morse and reviewed by Oliver Sass and Andre Revil.