Towards accurate quantification of ice content in permafrost of the Central Andes, part I: geophysics-based estimates from three different regions

In view of the increasing water scarcity in the Central Andes in response to ongoing climate change, the significance of permafrost occurrences for the hydrological cycle is currently controversial. The lack of comprehensive field measurements 10 and quantitative data on the local variability of internal structure and ground ice content further enhances the situation. We present field-based data from six extensive geophysical campaigns completed since 2016 in three different high-altitude regions of the Central Andes of Chile and Argentina (28 to 32° S). Our data cover various permafrost landforms ranging from ice-poor bedrock to ice-rich rock glaciers and are complemented by ground truthing information from boreholes and numerous test pits near the geophysical profiles. In addition to determining the thickness of the potential ice-rich layers from the 15 individual profiles, we also use a quantitative 4-phase model to estimate the volumetric ground ice content in representative zones of the geophysical profiles. The analysis of 52 geoelectrical and 24 refraction seismic profiles within this study confirmed that ice-rich permafrost is not restricted to rock glaciers, but is also observed in non-rock-glacier permafrost slopes in the form of interstitial ice as well as layers with excess ice, resulting in substantial ice contents. Consequently, non-rock glacier permafrost landforms, whose role 20 for local hydrology has so far not been considered in remote-sensing based approaches, may be similarly relevant in terms of ground ice content on a catchment scale and should not be ignored when quantifying the potential hydrological significance of permafrost. We state that geophysics-based estimates on ground ice content allow for more accurate assessments than purely remotesensing-based approaches. The geophysical data can then be further used in upscaling studies to the catchment scale in order 25 to reliably estimate the hydrological significance of permafrost within a catchment. https://doi.org/10.5194/tc-2021-206 Preprint. Discussion started: 23 August 2021 c © Author(s) 2021. CC BY 4.0 License.


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Introduction 30 (2015) emphasise in this context that practically no quantitative data on the hydrology of rock glaciers are available, but that most studies are qualitative instead.
To estimate the ground ice content of permafrost landforms such as a rock glacier, both the total volume of the landform, i.e., 65 horizontal and vertical extent, as well as the spatial variability of its ground ice content needs to be known. Both parameters can be derived from geophysical data. Compared to direct methods (core drillings or excavations/test pits), which are very costly and mostly restricted to point information or shallow depths, geophysical surveying can cover larger areas and depths, is cost-effective and comparatively easy to apply, and can be applied non-invasively, also in fragile and remote polar and high mountain terrain (e.g., Kneisel et al., 2008). Recent developments in the application of geophysical techniques to permafrost 70 problems have focused on quantitative estimates of volumetric ground ice content from electric, electromagnetic, seismic and gravimetric techniques, mostly applied in combination (Duvillard et al., 2018;Hauck et al., 2011;Hausmann et al., 2007;Mollaret et al., 2020;Oldenborger and LeBlanc, 2018;Wagner et al., 2019). Hauck et al. (2011), Wagner et al. (2019) and Mollaret et al. (2020) showed that the spatial distribution of the subsurface composition (ice, water, air and rock/soil content) can be derived from linking the measured electrical and seismic properties through petrophysical models and validated their 75 approach using borehole (core) data. In the Andes, such quantitative geophysical studies are still very rare and focused on individual rock glaciers (e.g., Halla et al., 2021;Monnier and Kinnard, 2013;de Pasquale et al., 2020).
To reduce the lack of comprehensive and quantitative field data on the local variability of ground ice content within rock glaciers but also on other ice-rich and ice-poor permafrost occurrences in the Central Andes, we conducted extensive geophysical measurement campaigns in different high-altitude regions of Chile and Argentina. We here present a large number 80 of geoelectric (Electrical Resistivity Tomography, ERT, 52 surveys) and seismic (Refraction Seismic Tomography, RST,24 surveys) data sets from several permafrost sites with different geomorphologic settings, including numerous ice-rich and icepoor permafrost occurrences (Table 1, Table 2). Borehole and test pit data are available for some of the sites, which are used to validate the quantitative estimates of ground ice contents by the 4-phase model (Hauck et al., 2011). The surveys were conducted during the years 2016 -2019 in four different regions between 28 and 32° S (Figure 1) in the framework of several 85 Environmental Impact Assessment studies.
With these data, we want to address the following objectives: (1) Demonstrate the potential and feasibility of geophysical surveys for the quantification of ground ice content of different permafrost landforms in the Central Andes; (2) compare the ground ice content in different rock glaciers with non-rock glacier permafrost occurrences; and (3) analyse the uncertainties of ground ice content estimates in the context of future studies of water availability from thawing permafrost under climate 90 change.
In the following, we will introduce our methodology to estimate the thickness of ice-rich permafrost layers and quantify ground ice contents from geophysical surveys, present the compiled data set, and comment on the implications of the results regarding potential water storage within permafrost systems in high mountain regions.

Methods
With respect to the conducted ERT (Electrical Resistivity Tomography) and RST (Refraction Seismic Tomography) surveys, we follow the well-established methodology described in Halla et al. (2021), Mewes et al. (2017), and Mollaret et al. (2019). 105 This methodology includes the conduction of the surveys, data processing with filtering of measured apparent resistivities (ERT), first break picking (RST), data inversion using the software RES2DINV (Aarhus Geosoftware) and Reflex-W (Sandmeier Geophysical Research) and, where applicable, running the 4-phase model (Hauck et al., 2011).
ERT data were obtained in the field using a SYSCAL multi-electrode instrument (Iris Instruments) with 48 electrodes. As ERT data acquisition quality often suffers from low signal-to-noise ratios, induced by the high contact resistances of galvanically 110 coupled electrodes in dry and coarse-blocky substrates, all measurements were performed in the Wenner configuration to ensure maximum signal strength. The spacing between the electrodes for the individual profiles varied between 1 and 8 m depending on the desired survey geometry and penetration depth. The obtained apparent resistivity data sets were filtered following the procedure described in Mollaret et al. (2019). Data inversion was conducted using the Software RES2DINV (Loke, 2020) and typical inversion parameters used for heterogeneous and highly resistive terrain (Hilbich et al., 2009;115 Mollaret et al., 2019). Inversions with other schemes such as the open-source library pyGIMLi (Rücker et al., 2017) gave comparable results (Mollaret et al., 2020). Refraction seismic data were recorded through a Geode system (Geometrics) with 24 geophones and a sledgehammer as source. First breaks were picked manually and afterwards inverted within the software REFLEXW (Sandmeier, 2020) to yield tomograms of P-wave velocity on co-located lines of specific ERT profiles. The resolution and data quality differ for each 125 profile and method; in general, the resulting root-mean-square errors of the ERT profiles were below 10 % (except for E17 with 22 %) and below 3 ms for the RST inversion (Table A1). See Table 2 for details on the individual profiles.
Regarding quantification of the volumetric ground ice content (ice content), Hauck et al. (2011) introduced a petrophysical approach by way of the so-called 4-phase model (4PM) using the obtained specific resistivity and P-wave velocity distributions as input variables. The 4PM consists of a combination of two basic mixing rules for electrical resistivity (Archie's Law, Archie, 130 1942) and seismic P-wave velocities (a modified Wyllie equation, see Timur, 1968), and the condition that the volumetric contents of ice, water, air and rock sum up to 1 for each model cell. Under the assumption of a site-specific porosity distribution, the 4PM estimates the ice-, water and air content for each model cell. Wagner et al. (2019) extended the approach to a petrophysical joint inversion (PJI) model, which yields physically consistent estimates of all 4 phases, i.e. without the necessity of prescribing porosity. Both model approaches were successfully applied to various permafrost occurrences (Halla et al., 135 2021;Mollaret et al., 2020;de Pasquale et al., 2020;Pellet et al., 2016;Schneider et al., 2013). However, the PJI still faces convergence problems in the absence of a priori knowledge, and its application to a large number of geologically and https://doi.org/10.5194/tc-2021-206 Preprint. Discussion started: 23 August 2021 c Author(s) 2021. CC BY 4.0 License. geomorphologically different profiles is therefore challenging. Therefore, we opted here for the application of the 4PM, which allows consistent ice content modelling for a large number of profiles.
In the 4PM, the largest uncertainties in absolute ground ice content values are due to the absence of reliable porosity 140 information and extreme values of pore water resistivities. The later are a factor in Archie's Law that must be prescribed (Hauck et al., 2011). Halla et al. (2021) established a procedure using ranges of porosity and pore water resistivity values to quantify the uncertainty in absolute volumetric ice content estimates of a rock glacier in the Argentinian Andes.
Within this study, we used ERT surveys to detect ground ice occurrences and delineate their vertical extent. As seismic surveys are much more time-consuming, they were conducted only at specific ERT profiles to get quantitative ice content estimates at 145 representative locations. As co-located ERT and RST profiles are necessary to provide input data for the 4PM, these model results are only available for 22 profiles (see Table 2). Ice content estimates and ground ice extent were estimated from ERT data alone for all other profiles. Hereby, resistivity averages and maxima were evaluated within so-called zones-of-interest (ZOI), i.e. the profile region, which is assumed to be representative for the landform and permafrost occurrence (Etzelmüller et al., 2020). Validation data are available for several profiles and ZOI's through drill cores, borehole temperature information 150 and test pits (see next section).

Study sites and data set
Between 2016 and 2019, five extensive geophysical campaigns were completed in three different regions of the Central Andes on both sides of the border between Chile and Argentina. In total 52 ERT and 24 RST profiles were acquired to characterize permafrost conditions regarding extent, active layer thickness and ground ice content. All field data were acquired in the austral 155 summer as part of characterising the periglacial environment. Profile locations were chosen according to the probable presence of frozen ground, but also according to easy access and safety. Apart from the fact that some of the considered permafrost landforms had surface disturbances (e.g., access roads or drilling platforms), the context of the projects has no further relevance for the scientific content of this paper. The available infrastructure, however, enabled access to high-altitude permafrost environments and made possible the collection of a large and unique data set, including in-situ validation data.  Due to the different locations (cf. Figure 1), a large variety of ground conditions ranging from sediment slopes (including gelifluction slopes, colluvial slopes, debris-covered bedrock, moraines, landslides) over talus slopes, protalus ramparts (also 165 called protalus rock glaciers, or embryonic rock glaciers, cf. Barsch, 1996;Hedding, 2011) to rock glaciers is covered by geophysical profiles. Table 1 summarises the main characteristics of the different study sites and geophysical profiles, and Figure 2 shows some typical examples of the considered landforms with the geophysical profile lines indicated. Many of the rock glaciers in the different investigation areas show initial or advanced signs of degradation (e.g., inactive front slopes, thermokarst depressions), but in the absence of kinematic data for most of the observed rock glaciers a reliable determination 170 of their activity state according to the guidelines of the IPA action group on rock glacier inventory and kinematics (RGIK, 2020) remains challenging. As the activity of a rock glacier is not directly linked to its ice content, which is the focus of this paper, we avoid any pre-classification of the rock glacier activity here, even if geomorphological indications and kinematic data are available in some cases.
In total 24 coinciding ERT and RST profiles were subsequently used for the estimation of the ground ice content and its spatial 175 variability based on the 4PM. The availability of undisturbed core drillings, borehole temperature measurements and numerous test pits enabled the validation of the methodological approach at 24 of the profile lines (availability of ground truthing data indicated in Table 2).

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Results 180 All available data (ERT/RST) have been quality-checked, processed and interpreted. An overview about data quality (filter statistics, RMS error) and a reference plot with all available ERT and RST tomograms is provided in the Appendix (cf. Table   A1, Figures A1 and A2). In the following, we will present exemplary results regarding different landforms characteristics.

General characteristics 185
In total, 19 ERT profiles were measured on ice-rich permafrost landforms, including rock glaciers (16) and protalus ramparts (3). These are shown in Figure 3 with the same dimensions and colour scales for all tomograms. All profiles have been analysed and interpreted independently Hilbich et al., 2018;Hilbich and Hauck, 2018a; here, we focus on a general and comparative analysis of all profiles, as a detailed discussion of each case study is beyond the scope of this paper. 190 Among our data, the resistivities of rock glaciers can be grouped into two parts: rock glaciers with resistivity maxima of the permafrost body below the active layer well above 100 kΩm, partly reaching 1 MΩm or more (RG I, Figure 3a), and rock glaciers with resistivity maxima mostly < 100 kΩm and/or shallower and more patchy resistive zones (RG II, Figure 3b). Rock

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Note, that rock glaciers with a very coarse-blocky and dry active layer (with air-filled voids) typically can have similarly high resistivities in the active layer as in the ice-rich permafrost layer, as both air and massive ice are electrical isolators (e.g., profiles A04, A15). However, at the bottom of the active layer, more fine-grained material typically accumulates, and moisture from snowmelt and seasonal active layer thawing may be retained on top of the impermeable frozen layer, often resulting in a more conductive intermediate layer (e.g., visible in A01, A03, A08, A09, cf. Figure 3a). 205 A high-resistive zone indicating ice-rich permafrost can usually be observed throughout the entire landform for rock glaciers of group RG I, but with varying thicknesses and specific resistivity values. We estimate the thickness of the ice-rich permafrost body of the rock glaciers from the thickness of this high-resistive zone. As the resolution of geophysical methods generally decreases with depth, the determination of the upper boundary is more reliable than its vertical extent. The resolution of the lower boundary depends on several factors: 210 a) survey geometry, defining the spatial resolution and the depth of investigation; b) the depth of the lower boundary in relation to the investigation depth (the shallower the boundary, the better its resolution); and c) the resistivity contrast between ice-rich permafrost layer and underlying layer (e.g., bedrock, the higher the contrast, the better the resolution). 215 We therefore use the onset of a decreasing resistivity gradient (below the maximum) as a conservative indicator for the lower limit of ice-rich permafrost. The investigation depth was sufficient to identify the bottom of the ice-rich permafrost layer for most rock glacier profiles.
Due to the spatial heterogeneity within the observed profiles, the thickness of the ice-rich permafrost layer cannot reliably be determined everywhere along the profile and remains an estimate. Figure 4 indicates the ERT-based minimum and maximum thickness of the ice-rich layer in all ice-rich permafrost profiles (i.e. rock glaciers and protalus ramparts). Note, that the 225 minimum thickness refers to the ice-rich zones within the tomograms, and that most profiles also contain zones without permafrost or ice-rich layers. The determined thicknesses mainly range between a few meters and do not exceed 25 m for all considered landforms. No clear difference is observed for the different categories (and was not expected).
As an overall observation, it can be noted that data quality is often worse on the coarse-blocky parts of the rock glaciers because of challenging conditions for sufficient galvanic coupling at the surface (Hilbich et al., 2009;Mollaret et al., 2019) than for 230 the generally more fine-grained surface material and lower resistivities of rock glaciers with advanced degradation (RG II).
This clearly affected the data quality in the first half of profile E17 (22 % data error, cf. Figure 3c), but had no severe impact on most other profiles (a few more profiles with insufficient data quality exist, but were not considered for this study).

Example data set: Rock glacier A16B
As an example, Figure 5 shows the geophysical results for profile A16B, which crosses two neighbouring rock glacier lobes, 235 with a borehole drilled in one of the lobes marked by the black vertical line. The active layer was largely removed through the construction of the borehole platform. Maximum resistivities of up to 1 MΩm are observed in two distinct anomalies corresponding to the two different lobes, and indicate high ground ice content occurrences of 5-18 m thickness, which is confirmed by the drilling results (cf. Figure 5a, Figure 6, Table 3). Based on the co-located ERT and seismic profiles, the volumetric fractions of the four phases rock, ice, water and air have been modelled using the 4PM (cf. section 2). Figure 5c shows the modelled ice content for profile A16B, with two anomalies 245 of > 60 % ground ice content, which is in good agreement with the previous interpretation and the results from the borehole stratigraphy. The thaw depth is around 3-5 m in both lobes (except for the disturbed area of the drilling platform).

4.2
Talus slopes 4.2.1 General characteristics ERT profiles were collected on three talus slopes, and all of them show a similar resistivity pattern: a layer of increased resistivity (~10 kΩm) within the talus material having a bulk resistivity of only a few kΩm (Figure 7). The resistive layer is 265 located at depths > 3 m, i.e. below a potential active layer, and has a maximum thickness of 10 m for the four measured profiles.
The resistivities are sufficiently high to support the hypothesis of frozen conditions within the talus slope , even if the expected ground ice content would be low. The resistive zone could also be explained by purely air-filled voids within the porous coarse-blocky substrate, similar to the resistive anomalies visible directly at the surface in most profiles. This ambiguity can, in general, be addressed through coinciding seismic profiles (available for A05, A16a, and A25) 270 and will be shown exemplary in section 4.2.2. Unfortunately, no ground truthing information is available for any of the talus slopes.

4.2.2
Example data set: Talus slope A05 Profile A05 is a longitudinal profile within a talus slope, located on an east-facing slope in the western part of a valley with numerous rock glaciers at its south-and west-facing slopes. The ERT results in Figure 8a show comparatively low resistivities 275 of < 10 kΩm in most parts of the profile, indicating no or very small ground ice content. A localised anomaly with higher resistivities ( ≥ 10 kΩm) exists between 80 -140 m horizontal distance and suggests a small possibility for potential ground ice at approximately 5 -12 m depth. Seismic velocities of vp < 1500 m/s in the same region point to loose blocks and debris with air-filled voids (Fig. 8c), rather than a layer with massive ground ice, except at larger depths (~25 m), where higher Pwave velocities (vp ~ 2000-3000 m/s) and coinciding low resistivities ( < 5 kΩm) strongly indicate the bedrock. No ground 280 truthing data are available for this profile. The anomaly with slightly larger resistivity values around 10 km between distances 80 -140 m could indeed indicate frozen conditions, but with volumetric ice contents, which are too small to be detected by our seismic survey set-up. Consequently, the 4PM-estimated ice content is close to zero within the whole model domain (    additional ERT profiles were measured in more fine-grained sedimentary substrate, including colluvial slopes (17 profiles), gelifluction slopes (4 profiles), and weathered bedrock covered with a shallow debris layer (9 profiles, cf. Figure A2). Some 300 of these ERT profiles on sediment slopes do not contain permafrost (e.g., E03, E13, E14, E15, cf. Table 2). However, all profiles on sediment slopes show significantly lower resistivities (mostly well below 1 kΩm) compared to rock glaciers, protalus ramparts and talus slopes (cf. Figure A1), including those, where ground ice was confirmed by test pits or outcrops (e.g., D07, D09, C06, C08). The reduced resistivity values are a result of the fine-grained and partly humid substrate and/or the weathered bedrock, as well as the generally lower volumetric ice content in sediment slopes in the form of interstitial ice 305 (i.e. << 50 %, except for excess ice in mostly thin ice lenses).
In addition, many of these profiles contain prominent conductive layers of < 100 Ωm ( Figure A2). We speculate that this is mainly caused by (a) conductive sediments stemming from eroded hydrothermally altered bedrock, which was transported downslope (in case of the colluvial slopes), (b) the altered/conductive bedrock itself, or partly also (c) liquid (supercooled) water due to freezing point depression by increased ion content related to hydrothermal alteration Hilbich 310 and Hauck, 2018a). Significant water flow was for example observed above an ice-rich layer in a 4-5 m deep test pit close to profile D07 and below a frozen layer in profile C08 (cf. Figure A2a). It is important to note that these conductive layers are with high probability features strongly amplified by the inversion process caused by preferential current flow through conductive layers, which strongly biases the inversion result towards this conductive layer. Various synthetic modelling studies (e.g., Hilbich et al., 2009;Mewes et al., 2017) have shown that the real thickness of such conductive layers may be more than 315 an order of magnitude smaller than illustrated in the resulting tomograms. In this case, the resistivity of layers below will be biased towards lower values (i.e. ice contents may well be higher than expected from the inverted values), while the depth of the deeper layers could be strongly overestimated.
The detection of permafrost occurrences is further complicated by the often thin or patchy ice-lenses, which cannot be detected with confidence because of the trade-off in the electrode spacing between reasonable large investigation depth/profile length 320 and the resulting reduced spatial resolution capacity. A reliable interpretation of these tomograms is therefore not straightforward, but experience from the synthetic modelling studies mentioned above and comparison with ground truthing information allows resistive anomalies caused by small ice lenses to be identified, even if absolute resistivity values are lower than commonly known to indicate frozen conditions. Similar cases are known from the European Alps, where the combination of low-resistive geologic host material, increased water content and temperatures close to the freezing point leads to similarly 325 low permafrost resistivities (Hilbich et al., 2008;Mollaret et al., 2019;Noetzli et al., 2019).
Similar values but within much smaller and thinner anomalies were found in profiles D06, D07, D08. Test pits and natural outcrops within incised channels confirm the presence of permafrost for profiles D04 -D07 and D09 (cf. Figure A2).

4.3.2
Example data set: Colluvial Slope D04 Figure 9 shows the results of profile D04 located within an east-facing slope, and consisting of mainly fine-grained colluvial sediments, cut by incised channels, which are active during snow melt (cf. Figure 2e). The slope shows a slightly convex form, indicating the potential for ice-rich conditions. The ERT tomogram in Figure 9a

345
Below this ice-rich layer, resistivities < 500 Ωm prove ice-poor conditions, while a reliable differentiation between sediment or bedrock is not possible without further information. Seismic P-wave velocities increase to > 5000 m/s at ~10 m depth and indicate a transition to more competent frozen rock at this depth (Figure 9b).

Mean resistivity and P-wave velocity
Comparing mean resistivity and P-wave velocities of the various profiles is a delicate task due to their dependence on (potentially very different) local geologic conditions, which may give rise to substrate-dependent resistivity/velocity variations 355 that may be misinterpreted as differences in ground ice content. Besides, uncertainties due to different measurement configurations and inversion errors may further impact a joint comparison. On the other hand, the dependence of resistivity and P-wave velocity on ice content is very strong, and its signal should be clearly detectable in such a large and comparatively homogeneous dataset presented in this study.
For a joint analysis of the representativeness of the measured geophysical parameters for the considered landforms, we selected 360 all profiles where the presence of permafrost a) has been identified, or b) is considered possible but not confirmed (e.g. in talus slopes, cf. Table 2). We defined a rectangular zone either within the presumed permafrost occurrence (representative permafrost zone), or within the zone most probable/indicative for permafrost in case of ambiguous interpretation and call this zone hereafter the zone of interest (ZOI). The ZOI was chosen to be representative for the confirmed (or unconfirmed possible) permafrost occurrence within the respective landform with minimal bias from potential inversion artefacts, thus resulting in 365 various sizes and positions of the ZOI for different profiles.
Mean and maximum resistivities/velocities within the ZOIs were then extracted, and they clearly show different resistivity and velocity regimes for different landforms and substrates (see Figure 10a,b). Figure 10c analyses the relationship between mean specific resistivity and P-wave velocity within the ZOI of co-located ERT and seismic profiles, and reveals a landform-specific clustering of resistivity-velocity pairs. Hereby, the resistivity/velocity pairs of rock glaciers cluster in two parts (green and 370 purple in Fig. 10c). The purple cluster (lower resistivity and P-wave velocity) is consistent with the rock glaciers of group RG II (see section 4.1.1), showing visible signs of advanced degradation and lower ground ice contents than the ones in the green cluster (RG I). Similarly, lower velocity mean values are present for protalus ramparts (PR) and talus slopes (TS), with the exception that TS show lower maximum resistivities than PR and RG II; probably due to their lesser ground ice contents.
While the two rock glacier groups clearly differ in their mean resistivities and velocities, their maximum values overlap 375 probably because most degrading rock glaciers still contain ice-rich zones with similar values as in intact rock glaciers ( Figure   10a,b). Sediment slopes (often reaching bedrock at shallow depths) have clearly differing characteristics from coarse-blocky sites, which is attributed to their lower porosity (higher velocity) and lower ground ice content (lower resistivity). Note, however, that Figure 10c only provides an incomplete picture biased towards ice-rich landforms, as seismic surveys have mainly been conducted on ERT profiles indicating ice-rich permafrost (cf.  The striking pattern in Figure 10c, with clustered and high resistivities for intermediate velocities (RG I), and low-intermediate resistivities for similarly high or even higher P-wave velocities (bedrock) is apparent and has already been noted by Hauck et al. (2007) for geophysical surveys on several permafrost landforms in the South Shetland Islands/Maritime Antarctica. Rock 390 glaciers with massive ice cause maximum resistivities, but P-wave velocities around 3500 m/s, close to the literature value for ice. Sites with vp > 4000 m/s usually indicate the presence of (unfrozen or frozen) bedrock, coinciding with lower resistivities due to the lower ice content. Mean seismic velocities in Figure 10b are all < 3000 m/s, which is certainly influenced by the limited investigation depth on some rock glaciers (bedrock not reached), but also represents the generally lower P-wave velocities of hydrothermally altered bedrock in some cases. 395

Figure 10: Landform-specific distribution of mean (black) and maximum (blue) values of inverted a) resistivity and b) velocity within the ZOIs of the respective ERT and RST tomograms. c) Scatter plot of mean resistivity and velocity for all co-located ERT and RST profiles, classified after landforms. Unfilled symbols in a) denote ERT profiles without co-located RST profiles. Unfilled symbols in c) denote
The systematic pattern observed in Figure 10 with a high consistency in the resistivity values over so many different surveys justifies the applicability of the geophysical approach to characterise different permafrost landforms, even in the absence of ground truthing. The seismic results further support and confirm the interpretation of the ERT data, but with a reduced overall representativeness due to a biased profile selection, fewer profiles and smaller profile dimension.

4.4.2
Volumetric ground ice contents Similarly to Figure 4 (section 4.1.1), Figure 11a shows the estimated minimum (dark grey) and maximum (light grey) thicknesses of the ground ice layer for all profiles, where permafrost is a) confirmed or probable (indicated by blue frames), or b) unconfirmed and uncertain, but possible. The permafrost base for non-rock glacier sites could not always be detected, in these cases the base of the surficial ice-rich layer was determined and is plotted instead. It is clear that ice-rich layers in 405 sediments are much thinner than in coarse-blocky substrates. Further, most ice-rich layers within our study are thinner than 25 m, including all rock glacier profiles. Quantitative model results for volumetric ice content (as presented exemplarily above) are available for a total of 21 profiles with co-located ERT and seismic surveys, including 12 rock glaciers, 2 protalus ramparts, 1 talus slope, and 6 sediment slopes. Figure 11b shows the modelled mean (dark grey) and maximum (light grey) volumetric ground ice contents within the defined ZOIs for all these profiles. The error bars give the uncertainty resulting from different 410 4PM runs spanning over the most probable porosity range for the respective landforms (SED: 30-45-60 %; TS: 40-50-60 %; RG: 40-60-80 %). The results indicate that maximum ice contents within the considered ZOIs are 51 -56 % (+-20 %), highest for rock glaciers of group RG I, and between 25 -49 % (+-20 %) for all other profiles. Note that anomalies with even higher ice contents can be present, but cannot explicitly be delineated if their size is smaller than detectable by the measurement configuration. More representative for the landform scale is, however, the mean ground ice content within the considered 415 ZOIs, which spans in the same order of magnitude for most considered profiles (11 -40 %) and shows that the ice content in the ice-rich layers of sediment slopes can be comparable to those of rock glaciers, even if the overall dimension of the ice-rich layer is very different.

Discussion
The uncertainty of the ice content estimation presented above depends first of all on the standard uncertainties of the geophysical data such as measurement data quality, resolution capacity, investigation depth, potential inversion artefacts, and representativeness of the geophysical profile for the whole landform. In addition, the uncertainties of the 4PM approach (rockice ambiguity, porosity range, Archie parameter and estimate of rock P-wave velocity) have to be taken into account. In the 430 context of mountain permafrost studies, 4PM-related uncertainties have already been addressed by Mewes et al. (2017) and Halla et al. (2021). In this study, we make additional use of the opportunity to compare our estimates with available ground truthing information, wherever possible, which when used as calibration reduces the uncertainty considerably. However, a large uncertainty remains regarding the representativeness of the individual profiles for a given landform. Depending on the local geomorphological setting, ground ice contents can vary strongly, especially in case of very large landforms (e.g., Halla 435 et al., 2021).

Comparison of results with ground truthing information
Since permafrost is thermally and temporally defined (Muller, 1943) and can be present in different substrates under various porosity and saturation conditions, it can exhibit a wide range of possible values in the geophysical parameters. Attribution of absolute electrical resistivity and P-wave velocity values to permafrost presence and specific ice contents can therefore be 440 ambiguous without additional information. Further, the inverted geophysical parameters within a tomogram are influenced by the resolution capacity of the survey geometry in relation to the observed structure, the data quality and the material contrasts, which may all lead to inversion artefacts (Day-Lewis et al., 2005;Hilbich et al., 2009;Mewes et al., 2017). Small-scale anomalies and thin ice layers may not become visible in the comparatively coarse survey geometries utilised in the majority of the profiles of our study. 445 Table 3 gives an overview over the different types of available ground truthing data (boreholes, test pits, natural outcrops), the respective depth range covered, and the type of validation provided by the different data.
In general, the interpretation of the tomograms (regarding presence/absence of ice-rich permafrost, cf. profiles highlighted in blue in Figure 11a) as well as the overall dimension of the active layer thickness (cf. ERT tomograms in Figure A1, A2) is confirmed by the ground truthing data, thus enabling the spatial analysis of ground ice occurrence and its quantification. 450 For some rock glaciers (A02, A06, A07, A08, A16b), borehole-derived ice content values (representing minimum and maximum values observed throughout the borehole) can be compared to 4PM-derived min/max ice contents within the pre-https://doi.org/10.5194/tc-2021-206 Preprint. Discussion started: 23 August 2021 c Author(s) 2021. CC BY 4.0 License. defined ZOIs (Figure 12). As thin ice-rich layers can be resolved by direct observations from boreholes or test pits but not necessarily by the relatively coarse survey geometries of the geophysical profiles, maximum ice content values observed in the drill cores are generally higher. In addition, the 4PM cannot model super-saturated conditions (i.e. ice contents exceeding 455 the assumed porosity), which further implies a bias towards underestimated maximum ice contents for the applied porosity ranges (cf. section 4.2.2). It is therefore not surprising, that the borehole-derived ice contents are mostly higher than the 4PMderived values. Where quantitative ground truthing information is available, the 4PM can be calibrated by minimising the difference between the estimate and the ground truth, resulting in more consistent ice content values, as illustrated exemplarily in Figure 13 for a profile with intermediate ice content (A02) and one with high ice content (A16b). Figure 13 further shows, 460 that the porosity models of 60 or 80 % lead to more realistic ice content values than the lower-bound porosity model of 40 %.
However, borehole validation provides highly valuable information on the point scale, but a direct comparison of boreholeand 4PM-derived ground ice contents remains challenging due to the different resolution capacities, dimensions (1-D vs. 2-D) and 4PM-related limitations. In the absence of such calibration data, ice content estimates of ice-rich permafrost layers may be underestimated (as a consequence of underestimated porosity ranges) and rather represent lower-bound estimates. This bias 465 is, however, also a direct consequence of the spatially averaging ZOIs, which also include zones with higher spatial variability and therefore smaller ice contents. On the contrary, boreholes represent single-point information and are usually placed where the maximum ground ice content is assumed.

Ice content of rock glaciers
Ground ice is present in the majority of all profiles, with ice contents ranging from a few percent by volume to clearly supersaturated conditions within various rock glaciers (Figure 3, Table 3). At sites with shallow sediment cover, small ice lenses are frequently present, which appear in the tomograms in the form of local resistive anomalies (cf. Table 3 and Fig. A2), and could be validated through various test pits and natural outcrops. Based on the estimates drawn from the 4PM simulations 480 (considering the 60 % and 80 % porosity models), the rock glaciers with resistivity maxima > 100 kΩm (RG I) within our study areas show on average ground ice contents between 35 and 55 % by volume and thicknesses of the ice-rich layer of 3 to 25 m, but with a considerable spatial heterogeneity (cf. min/max estimates for the thickness of the ice-rich layer in Figure 11a, or the example in Figure 5). Our results further suggest, that the detected maximum ice contents within the ZOIs (35 -75 %) roughly correspond to the general assumption on average ice contents within active rock glaciers found in the literature (40 -485 https://doi.org/10.5194/tc-2021-206 Preprint. Discussion started: 23 August 2021 c Author(s) 2021. CC BY 4.0 License. 60 %, cf. Arenson and Springman, 2005;Barsch, 1996), which implies, however, that this assumption may tend to overestimate mean ground ice contents on a landform scale. Care has therefore to be taken regarding general up-scaling approaches for quantitative estimates of the total ground ice content within a rock glacier. Several studies of the hydrologic role of rock glaciers in the Andes used an estimate of 50 % volumetric ice content as mean value for rock glacier bodies (e.g., Brenning, 2005;Perucca and Angillieri, 2011;Rangecroft et al., 2015). This commonly used estimate is often justified by borehole core 490 data from rock glaciers elsewhere (e.g., Haeberli et al., 1988;Mühll and Holub, 1992). However, boreholes are usually drilled at promising locations for massive ground ice occurrences and the recovery of undisturbed samples with high ice contents is easier than sampling ice-poor samples. Therefore, results from boreholes are often biased towards ice-rich conditions, hence, do not represent mean conditions for the entire landform. Estimates of volumetric ice content using a homogeneous value of 50 % can therefore easily lead to over-estimations. 495 In addition, published estimates of total ground ice volumes within rock glaciers have been based on simplified relations between the surface area and average rock glacier thickness (i.e. area-thickness relations introduced by Brenning, 2005). Figure   14 compares the area-thickness estimates according to the approach by Brenning (2005) with our geophysics-based estimates for the rock glaciers of our study. This comparison suggests that the thickness of the ice-rich permafrost layer as inferred from geophysical data is in most cases considerably smaller than the one approximated from commonly applied area-thickness-500 relations (cf. Azócar and Brenning, 2010;Janke et al., 2017;Rangecroft et al., 2015). Only for few of the very ice-rich landforms (e.g. E17 or A16b) the two approaches show comparable results. In addition, areal extents of rock glaciers are often not clear and very difficult to determine (Brardinoni et al., 2019;RGIK, 2020), especially in the case of complex landforms combining multiple rock glacier generations, resulting in a significant source of error when applying any rock glacier areathickness correlation. 505 Although previous assumptions of ground ice content within rock glaciers (40 -60 %, e.g., Brenning, 2005) roughly correspond to our field-based results, this is only true for their ice-rich zone. As rock glacier bodies also consist of zones with considerably smaller ice contents (cf. Fig. A1), large-scale model studies using the above-mentioned area-thickness relations will introduce a bias towards overestimation of total ice content with respect to total area. In the companion paper in part II, Mathys et al. 515 (n.d.) propose a new upscaling approach of geophysically-based estimates of the ice volume per landform, which is compared to standard approaches using area-thickness scaling and constant ground ice contents per rock glacier. Similar to our results presented in Figure 14 they find lower total ground ice volumes in rock glaciers when estimates are based on geophysical data in the field compared to simplified rock glacierice content relations.

5.3
Ice content of other landforms 520 In contrast to remote-sensing-based approaches, which can only delineate rock glaciers as indirect representations of permafrost bodies with unknown relevance for the hydrological cycle (Azócar and Brenning, 2010), the geophysically-based approach presented in this study is not restricted to rock glaciers, but allows the estimation of ground ice content in a variety of landforms that constitute the periglacial belt. Examples are given in sections 4.2 and 4.3. Neglecting landforms other than rock glaciers in most studies is due to the invisibility of their ground ice content from space (and during site visits) and the 525 corresponding difficulties in obtaining field data from remote areas. Rough approximations indicate that even thin ice-rich layers in permafrost slopes at high elevations (e.g. Figure 9) may add up to similar ice volumes per catchment as present in catchments in zones where individual rock glaciers are present and only a medium probability of permafrost exists.
To investigate this hypothesis, we exemplarily upscaled the geophysics-based ice content estimates to the landform scale for two sites, where ground truthing data is available. Based on geophysical results from six different profiles on a sediment slope 530 (D03, D04, D05, D06, D07, D08), and three different profiles from a rock glacier (A01, A02, A03), the average thickness and ice content of the ice-rich layer of both landforms was approximated in terms of a lower-bound and an upper-bound estimate. Figure 15 shows the two landforms, the lower-and upper-bound estimates of the thickness of the ice-rich layer, as well as the estimated total ground ice volumes for the sediment slope and the rock glacier. The area of the rock glacier is approximately 0.11 km 2 , which is about ten times smaller than the considered area of the colluvial slope (~1 km 2 ), but the rock glacier is 535 expected to have a substantially thicker ice-rich layer of 10 -15 m, compared to 0.5 -1.5 m for the sediment slope. Assuming an average volumetric ice content of 50 % for the ice-rich layer at both sites leads to an approximated lower-bound estimate of the total ice volume of 250 000 m 3 for the sediment slope and 550 000 m 3 for the rock glacier. Considering the upper-bound estimate, i.e. the upper-bound average value for the thickness of the ice-rich layer (as opposed to its maximum within the landform), estimated volumes range with 750 000 m 3 for the sediment slope and 825 000 m 3 for the rock glacier in the same 540 order of magnitude. This indicates that even thin ice layers in sediment slopes can contain similar dimensions of ice volume per catchment as rock-glacier-dominated catchments. A more detailed analysis of this hypothesis using a newly developed upscaling approach is presented and discussed in the companion paper, part II (Mathys et al., n.d.).

Conclusion and Outlook
Based on more than 50 geophysical surveys from various regions in the Central Andes, this study demonstrates the value of geophysical surveys to a) detect ice-rich permafrost occurrences in various landforms (also beyond prominent forms such as rock glaciers); and b) to estimate ground ice volumes in permafrost regions. The added value of combined ERT and RST 550 surveys lies in an increased reliability of the interpretation (e.g., regarding the identification of bedrock), and the potential for ice content quantification through coupled petrophysical relationships such as within the 4-phase model.
The availability of various ground truthing data (cores from boreholes, test pits, natural outcrops) in this study allows the validation of the geophysical results for many cases. The good agreement between independent validation data and interpreted geophysical profiles confirms the detection of ice-rich layers in various non-rock-glacier permafrost landforms, emphasizing 555 the value of geophysical data in the scientific debate on the role of ice-rich permafrost in the hydrological cycle. Further, we observe a substantial intra-and inter-site heterogeneity of the thickness of the ice-rich layer(s) and ice volumes, which is often wrongly inferred from visual inspections alone. Geophysics-based estimates on ground ice content therefore allow for more accurate assessments than purely remote-sensing-based approaches without a solid data basis. The data set presented in this paper is therefore one of the first available extensive set of field-based and validated data regarding the presence and total 560 quantities of ground ice in the Central Andes. The analysis of 52 ERT and 24 RST profiles within this study confirmed that ice-rich permafrost is not restricted to rock glaciers, but is also observed in non-rock-glacier permafrost slopes in the form of interstitial ice as well as layers with excess ice, resulting in substantial ice contents (e.g. D09, D04, C07), which can be close to the volumes observed in rock glaciers (D09). Consequently, non-rock glacier permafrost landforms, whose role for local hydrology has so far not been considered 565 in remote-sensing based approaches, may, depending on the catchment size of the watershed, be similarly relevant in terms of ground ice content on a catchment scale and should not be ignored when quantifying the potential hydrological significance of permafrost.
On the other hand, a realistic estimate of ground ice volume is only the first step towards the evaluation of the hydrological importance of permafrost within a catchment. Further factors, such as a) different response times of permafrost landforms to 570 observed and projected atmospheric changes in the Central Andes, and b) the dominance of the relevant hydrological processes (e.g. melting vs. sublimation, and discharge vs. evaporation), play a decisive role in the annual contribution to total runoff to downstream water resources from degrading permafrost (or to evaporation and sublimation) (e.g., Rivera et al., 2017).
According to Duguay et al. (2015) the contribution of degrading permafrost to the total runoff of a catchment is difficult to measure, hence quantify, and therefore remains basically unknown. Without a reliable determination of these factors (e.g., by 575 measuring or modelling the full energy balance over permafrost areas, cf. e.g., Harrington et al., 2018), the relevance of permafrost for the hydrological cycle remains strongly speculative. Preliminary modelling approaches suggest that this is negligible and would be non-measurable in the arid Andes (Arenson et al., 2013), and a recent analysis of mass-balance rates of ice masses in the Argentinian Central Andes confirms that rock glaciers showed almost zero mass balance rates from 2000 -2018 (Ferri et al., 2020). However, no publications exist so far, that have specifically calculated the contribution of rock 580 glaciers to streamflow in the semiarid Andes of Chile (Schaffer et al., 2019). Studies from other mountain environments (e.g., the European Alps, Marmy et al., 2016;Scherler et al., 2013) have shown that, depending on the snow cover and surface characteristics, the degradation of rock glaciers can be a very slow process because of the extremely efficient insulating effect of the active layer (coarse blocks) and the latent heat effect. Haeberli (1985) approximated the time needed for the complete decay of ice-rich permafrost in rock glaciers under a warming climate to be in the order of centuries to millennia, and Krainer 585 et al. (2015) showed that ~10.000 years old permafrost ice persisted until today even during warm periods of the Holocene.
The quantitative contribution of melting ground ice of degrading permafrost in rock glaciers to the annual discharge from the catchment can therefore be very small (Harrington et al., 2018;Krainer et al., 2015;Pruessner et al., 2021) and the relative contributions from other ice-poor permafrost landforms without blocky surfaces and thin but widespread ground ice layers still remain unknown. The geophysical data set presented in this study may therefore serve as input for modelling studies on the 590 overall amount of ground ice present within the periglacial belt and estimates regarding the relative contributions of rock glacier and non-rock glacier ground ice to runoff in the semi-arid regions of the Central Andes.

Acknowledgements
The acquisition of this comprehensive data set would not have been possible without the valuable support and hard work of numerous field helpers from Chile, Argentina and Switzerland. Therefore, we sincerely thank all field helpers for their efforts 595 in the field. The authors also would like to acknowledge the support from various private companies that agreed for having their data published, provided additional information, and logistically supported the various field campaigns.

Code/Data availability
The data that support the findings of this study are available from the corresponding author upon request and will be provided 600 through an online data repository after acceptance of the manuscript.

Author contribution
CHi planned, coordinated and participated at the geophysical campaigns, processed the geophysical data, conducted the 4phase modelling, wrote the major part of the text, and made all figures. CHa had the overall lead of the geophysical campaigns, and contributed to the study design. CM coordinated and participated at two of the geophysical field campaigns, and helped 605 with data processing. PW and LA coordinated the environmental impact assessment studies, which included the geophysical campaigns, but also borehole drilling, excavation of test pits and collection of other data. They planned and coordinated the field logistics of the geophysical campaigns together with CHi, and provided further background information. All authors contributed actively to the discussion and interpretation of all data sets, and the intermediate and final version of the manuscript.

Competing interests 610
The authors declare that they have no conflict of interest.