Abstract. Permafrost degradation poses increasing risks to infrastructure in high-mountain regions, yet quantitative and spatially distributed assessments remain scarce in data-limited areas such as Central Asia. This study focuses on a 1,563 km² area in Inner Tien Shan, Kyrgyzstan, including the Kumtor gold mine and the Arabel plateau. We applied the numerical permafrost model CryoGrid to simulate permafrost thermal conditions and thaw depth across the study area, informed by geophysically derived stratigraphies and assessed against borehole temperatures and ground-based observations. To represent subsurface heterogeneity, surface-type-specific stratigraphies were developed for six landscape classes derived from petrophysical joint inversion of co-located Electrical Resistivity Tomography (ERT) and Refraction Seismic Tomography (RST) data. Legacy borehole measurements from 1986–1992 were incorporated to provide a multi-decadal validation of ground thermal conditions. The resulting 30 m resolution maps indicate continuous permafrost across the study area, with mean ground temperatures at 5 m depth ranging from −0.1 °C to −6.5 °C and a spatial mean of −2.9 °C. Mean thaw depth at the end of August 2024 is 2.0 m, with surface type exerting a strong control on spatial patterns. Fine-grained and vegetated sediments on the Arabel plateau show comparatively deep thaw, coinciding with thermokarst features and ground subsidence, whereas coarse-blocky debris and rock glaciers exhibit shallow thaw depths. Comparison with ERT-derived thaw depths from 15 profiles shows agreement generally within about 1 m. Simulated ground temperatures show warming trends of 0.20 °C and 0.18 °C per decade at 5 m and 20 m depth since 1975, corresponding to approximately 1 °C warming since the 1980s. These results demonstrate the value of integrating geophysical data and surface-type-specific stratigraphies into modeling studies to represent spatial variability of mountain permafrost in data-scarce regions.

Continuous monitoring of permafrost dynamics remains challenging in remote high-mountain environments due to logistical constraints, harsh climatic conditions, and the limited availability of spatially distributed observations. In addition to direct temperature observations in boreholes, Autonomous Electrical Resistivity Tomography (A-ERT) offers significant potential for long-term monitoring by providing high temporal resolution observations of subsurface electrical properties, which are highly sensitive to freeze/thaw processes. This study presents the field validation of a low-power A-ERT system designed for long-term autonomous operation in extreme environments. The system was deployed at a high-altitude permafrost site near the Kumtor gold mine in the Central Tien Shan, Kyrgyzstan, representing the first application of continuous A-ERT monitoring in the Central Asian mountain ranges. The system operated continuously under harsh environmental conditions with air temperatures as low as −30 °C. Data quality remained consistently high throughout the monitoring period, with less than 1% of measurements removed during filtering, and inversion results with root-mean-square errors generally ranging between 3% and 4%. Time-lapse resistivity observations revealed strong seasonal freeze–thaw dynamics within the active layer and continued seasonal resistivity variations within the underlying permafrost despite permanently frozen conditions. Analysis of depth-dependent resistivity–temperature relationships revealed increasingly pronounced hysteresis behavior below the active layer, indicating that subsurface electrical properties were not controlled solely by temperature. This behavior likely reflects variations in unfrozen water content and pore connectivity within the fine-grained permafrost, where liquid water can persist at sub-zero temperatures. The results demonstrate the capability of the A-ERT system for reliable long-term autonomous monitoring in remote permafrost environments and investigation of coupled thermal and hydrological processes in permafrost systems.

Central Asian Mountain regions (Tien Shan and Pamir) are expected to be significantly impacted by climate change, affecting water availability and natural hazards. The cryosphere plays a crucial role in many watersheds of the region by providing water for hydropower station, irrigation, and domestic use downstream. At the same time, retreating glaciers and thawing permafrost increase the risk of natural hazards. Therefore, cryosphere monitoring systems are necessary to provide baseline data for estimating future water availability and detecting dangerous hazard zones. Despite the large areas underlain by permafrost in the Tien Shan and Pamir Mountain ranges, data on permafrost distribution, characteristics and evolution are scarce. However, quantitative estimations of permafrost subsurface components, especially water and ice contents, are needed to evaluate the consequences of current climate change on mountain permafrost environments.Recent field-based investigations have emphasised the coupled use of geophysical techniques, e.g., by employing the Petrophysical Joint Inversion scheme (PJI, Wagner et al., 2019) that combines electrical resistivity and seismic refraction p-wave velocity data to estimate the four phases present in the subsurface (volumetric contents of air, water, ice, and rock). The traditional PJI implementation relies on Archie’s law (Archie, 1942) as one of the primary petrophysical equation to link resistivity to porosity and water content. Archie's law is generally considered valid when electrolytic conduction dominates, a condition that is not universally justified for dry and coarse blocky substrates and landforms in mountainous terrain. Recognizing this limitation, Mollaret et al. (2020) introduced the electrical Geometric Mean Model as an alternative implementation in the PJI. The Geometric Mean Model assumes random distributions of the four phases and offers the advantage of including the fractions of ice and air in the petrophysical equation for resistivity, which are not present in Archie’s law. In this study, we assess the feasibility and effectiveness of using the Geometric Mean Model within the PJI framework across an extensive geophysical dataset comprising 22 profiles in Central Asia (Kyrgyzstan and Tajikistan). Our research encompasses diverse landforms, including moraines, rock glaciers, talus slopes, and fine-grained sediments. Our goals are to (i) evaluate the performance of the Geometric Mean Model in comparison to Archies law across different landforms and (ii) address the existing data gap concerning mountain permafrost and ground ice contents in the Central Asian region.ReferencesArchie, G. E. (1942). The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics. Transactions of the AIME, 146(01), 54–62. https://doi.org/10.2118/942054-GMollaret, C., Wagner, F. M., Hilbich, C., Scapozza, C., & Hauck, C. (2020). Petrophysical Joint Inversion Applied to Alpine Permafrost Field Sites to Image Subsurface Ice, Water, Air, and Rock Contents. Frontiers in Earth Science, 8, 85. https://doi.org/10.3389/feart.2020.00085Wagner, F. M., Mollaret, C., Günther, T., Kemna, A., & Hauck, C. (2019). Quantitative imaging of water, ice and air in permafrost systems through petrophysical joint inversion of seismic refraction and electrical resistivity data. Geophysical Journal International, 219(3), 1866–1875. https://doi.org/10.1093/gji/ggz402

In the Central Asian Tien Shan and Pamir mountain ranges, permafrost is extensive, but in situ data on permafrost remain scarce. Quantitative analysis of permafrost's subsurface components – ice, water, air, and rock – is vital for not only discerning the impact of climate change on increased slope instability due to permafrost degradation, but also understanding its role as a potential water resource in high-altitude environments. Recent studies have employed a petrophysical joint inversion (PJI) approach combining geoelectrical and seismic refraction data to model the subsurface's four phases (fractions of air, water, ice, and rock). However, most of these studies primarily rely on Archie’s law, which has limitations in coarse blocky substrates typical of mountainous terrains. Recognizing this limitation, the electrical geometric mean (PJI-GM) model may be used as an alternative implementation within PJI. In this study, we assess the suitability of using the PJI-GM model across an extensive geophysical dataset comprising 22 profiles in Central Asia (Kyrgyzstan and Tajikistan). Our goals are to (i) address the existing data gap concerning mountain permafrost and ground ice contents in the Tien Shan and Pamir of Central Asia and (ii) evaluate the performance of the PJI-GM model in comparison to Archie's law within the PJI framework across the different landforms at remote sites. The findings reveal that the ground ice content is more specific to landform types than to the different geographic regions surveyed, with rock glaciers exhibiting the highest mean ice contents (38 %–60 %), followed by moraines (18 %–40 %), talus slopes (20 %–40 %), and fine-grained sediments (0 %–20 %). The PJI-GM model performed especially well for ice-rich landforms such as rock glaciers, accurately reflecting high ice contents with minimal variability between different model runs. The quality of a model result was here assessed by comparing a multitude of different model runs with different sets of inversion parameters and petrophysical variables using a clustering approach. This research provides one of the first comprehensive (geophysical) in situ datasets on permafrost on various landforms and sites in Central Asia, highlighting the potential of the PJI-GM model as a more suitable alternative to Archie’s law, particularly for rock glaciers and other ice-rich landforms. These findings significantly advance our understanding of permafrost in the Tien Shan and Pamir and serve as a baseline dataset for future modeling studies.

In a time of increasing global warming and interconnected environmental changes, the assessment and detection of moving landforms are of growing importance for implementing successful adaptation and mitigation strategies concerning geohazards. Understanding the factors and processes governing slope movements, as well as their annual and seasonal variability, is of crucial significance. In recent years, advances in remote sensing applications and the availability of satellite data have been made, leading to an increased use of remotely sensed data for hazard monitoring and detection. Additionally, the use of machine learning enables the application of these methods to larger areas. Here, we concentrate on the Ala Archa catchment in Kyrgyzstan (Central Asia), located approximately 40 kilometres south of the capital city Bishkek, with a long history of glacier monitoring at Golubin Glacier. This study aims to identify the predominant environmental factors influencing slope movements within the Ala Arch catchment and investigates the contribution of variations in environmental conditions to annual fluctuations in slope movement. We first present an average velocity map for the area of interest using InSAR (Interferometric Synthetic Aperture Radar) with Sentinel-1 data from both ascending and descending orbits between 2018 and 2023. The data is processed using ISCE (InSAR Scientific Computing Environment) and MintPy (Miami InSAR Time-series software in Python). Additionally, we use statistical modelling to estimate the influence of selected environmental variables such as relief, permafrost distribution, vegetation cover and landform classification. To determine the influence on seasonal/annual variations, we incorporate fluctuating variables like air temperature, precipitation and the duration of snow cover. In a final step, we examine how the identified relationships can be applied to generate an upscaled regional susceptibility map for slope movement. In conclusion, our objective is to demonstrate the potential use of openly available satellite data for detecting hazardous or moving areas in regions where in-situ measurements are impractical or the necessary resources are unavailable.

Coarse-blocky landforms are assumed to be the most resilient permafrost occurrences due to low thermal conductivity and their seasonal asymmetric internal convection processes and have been addressed in many field and modelling studies. In this contribution we will put a specific focus on the most general of these landforms, the ubiquitous talus slopes, which are understudied compared to other mountain permafrost landforms such as rock glaciers. Talus slopes exist in all mountain ranges and at different elevations, including middle mountains where they give rise to specific undercooled micro-climatic conditions. In many cases, internal convection processes are the main reason that the cool micro-climatic conditions could be preserved over long time scales. The ice content can be variable, ranging from zero at low elevations to the presence of ice cores at elevations where permafrost is widespread. However, the ice content in most talus slopes is generally unknown, as boreholes are extremely scarce and standard geophysical techniques (such as Electrical Resistivity Tomography and Seismic Refraction techniques) exhibit problems in detecting medium to small ice contents in coarse blocky substrates. In this contribution we use a compilation of data from a large number of different talus slopes in Europe, the Central Andes and Central Asia to attempt to (1) quantify the influence of slope angle, substrate and thickness of the talus on the internal air circulation and its cooling effect and (2) address the application of emerging geophysical techniques to improve the quantification of ice content in these substrates.

Over the recent years, there have been focused international efforts to coordinate the development and compilation of rock glacier inventories. Nevertheless, in some contexts, identifying and characterizing rock glaciers can be challenging as complex conditions and interactions, such as glacier–rock-glacier interactions, can yield landforms or landform assemblages that are beyond a straightforward interpretation and classification through ordinary visual means alone. To gain a better understanding of the spatial and temporal complexity of the ongoing processes where glacier–permafrost interactions have occurred, the characterization of the subsurface of the Gruben rock glacier and its adjacent complex contact zone with the then more extended Little Ice Age Gruben glacier is quantitatively assessed using a petrophysical joint inversion (PJI) scheme, based on electrical resistivity (ERT) and refraction seismic (RST) data. Surface dynamics are assessed using both in situ and close-range remote sensing techniques to monitor daily and seasonal displacements and to monitor landform-wide surface changes at high spatial resolution, respectively. Both the geophysical and geodetic surveys allowed two zones to be identified: the rock glacier zone and the complex contact zone where both permafrost and embedded surface ice are present. In the complex contact zone extremely high ice contents (estimated up to 85 %) were found. Widespread supersaturated permafrost conditions were found in the rock glacier zone. Surface displacement rates in this zone are typical of permafrost creep behaviour, with a gradual acceleration in late spring and a gradual deceleration in winter. Moreover, the coherent nature of the rock glacier zone surface deformation contrasts with the back-creeping and slightly chaotic surface deformation of the complex contact zone. Favouring a multi-method approach allowed a detailed representation of the spatial distribution of ground ice content and origin, which enabled us to discriminate glacial from periglacial processes as their spatio-temporal patterns of surface change and geophysical signatures are (mostly) different.

Climate change modifies the water and energy fluxes between the atmosphere and the surface in mountainous regions such as the Qinghai–Tibet Plateau (QTP), which has shown substantial hydrological changes over the last decades, including rapid lake level variations. The ground across the QTP hosts either permafrost or is seasonally frozen, and, in this environment, the ground thermal regime influences liquid water availability, evaporation and runoff. Consequently, climate-induced changes in the ground thermal regime may contribute to variations in lake levels, but the validity of this hypothesis has yet to be established. This study focuses on the cryo-hydrology of the catchment of Lake Paiku (southern Tibet) for the 1980–2019 period. We process ERA5 data with downscaling and clustering tools (TopoSCALE, TopoSUB) to account for the spatial variability of the climate in our forcing data (Fiddes and Gruber, 2012, 2014). We use a distributed setup of the CryoGrid community model (version 1.0) to quantify thermo-hydrological changes in the ground during this period. Forcing data and simulation outputs are validated with data from a weather station, surface temperature loggers and observations of lake level variations. Our lake budget reconstruction shows that the main water input to the lake is direct precipitation (310 mm yr−1), followed by glacier runoff (280 mm yr−1) and land runoff (180 mm yr−1). However, altogether these components do not offset evaporation (860 mm yr−1). Our results show that both seasonal frozen ground and permafrost have warmed (0.17 ∘C per decade 2 m deep), increasing the availability of liquid water in the ground and the duration of seasonal thaw. Correlations with annual values suggest that both phenomena promote evaporation and runoff. Yet, ground warming drives a strong increase in subsurface runoff so that the runoff/(evaporation + runoff) ratio increases over time. This increase likely contributed to stabilizing the lake level decrease after 2010. Summer evaporation is an important energy sink, and we find active-layer deepening only where evaporation is limited. The presence of permafrost is found to promote evaporation at the expense of runoff, consistently with recent studies suggesting that a shallow active layer maintains higher water contents close to the surface. However, this relationship seems to be climate dependent, and we show that a colder and wetter climate produces the opposite effect. Although the present study was performed at the catchment scale, we suggest that this ambivalent influence of permafrost may help to understand the contrasting lake level variations observed between the south and north of the QTP, opening new perspectives for future investigations.

Ground thermal regime of high mountain catchments impacts the partition between infiltration and runoff, latent and sensible heat fluxes, frozen and liquid subsurface water and the presence (or absence) of permafrost. In the context of global warming, hydrological modifications associated to ground thermal changes are of critical importance for extensive headwater regions such as the Qinghai-Tibet Plateau (QTP) and the Himalayas, which are major water towers of the world. Improving our ability to quantify these changes is therefore a key scientific challenge both regarding basic science and continental-scale water resource management. Many watersheds of the QTP have seen their hydrologic budget modified over the last decades as evidenced by strong lake level variations observed in endorheic basins. Yet, the role of ground thermal changes in these variations has not been assessed. Lake Paiku (central Himalayas, southern TP) has exhibited important level decreases since the 70s and thus offers the possibility to test the potential role of ground thermal changes and permafrost thaw on these hydrologic changes. We present distributed ground thermo-hydric simulations covering the watershed over the last four decades to discuss their implications on the lake level changes. We use the Cryogrid model to simulate the surface energy balance, snow pack dynamics and the ground thermo-hydric regime while accounting for the phase changes and the soil water budget. Because the surface radiative, sensible and latent heat fluxes in alpine environments are strongly dependent on the physiography, the model is forced with distributed downscaled forcing data produced with the TOPOSCALE model to account for this spatial variability. Simulated surface conditions are evaluated against meteorological data acquired within the basin, ground surface temperature loggers and remotely sensed surface temperatures. The simulations show that, contrary to large scale estimates of permafrost occurrence probability, an significant part of the basin is underlaid by permafrost (>20%). We also show that over the 1980-2020 period, ground temperature warmed up by 1.5 to 2°C per centuries. The permafrost limit rose from 5100 to 5300 m asl (in 40 years). Unfrozen surface conditions increased by around 25 days per century and evaporation increasing by +22% over the period. To represent the impact of these changes on the lake level, we included them in a simple hydrological budget calculation including the contribution of glacier melt and lake evaporation. This approach shows that ground thermo-hydric changes in the catchment have significantly contributed to the lake level changes. These first results highlight the potential of thermo-hydric simulation to better quantify hydrological changes to come in the QTP.

Abstract. With ongoing climate change, there is a pressing need to better understand how much water is stored as ground ice in areas with extensive permafrost occurrence and how the regional water balance may alter in response to the potential generation of melt water from permafrost degradation. However, field-based data on permafrost in remote and mountainous areas such as the South-American Andes is scarce and most current ground ice estimates are based on broadly generalised assumptions such as volume-area scaling and mean ground ice content estimates of rock glaciers. In addition, ground ice contents in permafrost areas outside of rock glaciers are usually not considered, resulting in a significant uncertainty regarding the volume of ground ice in the Andes, and its hydrological role. In part I of this contribution, Hilbich et al. (submitted) present an extensive geophysical data set based on Electrical Resistivity Tomography (ERT) and Refraction Seismic Tomography (RST) surveys to detect and quantify ground ice of different landforms and surface types in several study regions in the semi-arid Andes of Chile and Argentina with the aim to contribute to the reduction of this data scarcity. In part II we focus on the development of a methodology for the upscaling of geophysical-based ground ice quantification to an entire catchment to estimate the total ground ice volume (and its estimated water equivalent) in the study areas. In addition to the geophysical data, the upscaling approach is based on a permafrost distribution model and classifications of surface and landform types. Where available, ERT and RST measurements were quantitatively combined to estimate the volumetric ground ice content using petrophysical relationships within the Four Phase Model (Hauck et al., 2011). In addition to introducing our upscaling methodology, we demonstrate that the estimation of large-scale ground ice volumes can be improved by including (i) non-rock glacier permafrost occurrences, and (ii) field evidence through a large number of geophysical surveys and ground truthing information. The results of our study indicate, that (i) conventional ground ice estimates for rock-glacier dominated catchments without in-situ data may significantly overestimate ground ice contents, and (ii) substantial volumes of ground ice may also be present in catchments where rock glaciers are lacking.

Permafrost has a crucial influence on sub-surface water flow and thus on the hydrology of catchments. Its thawing drives the release of frozen water and a transition from surface-water-dominated systems to groundwater-dominated systems. In the context of global warming, these hydrological modifications are of critical importance for extensive headwater regions such as the Qinghai-Tibet Plateau (QTP) and the Himalayas. Permafrost covers a significant proportion of these regions (40% of the QTP), which are major water towers of the world. Therefore, improving our understanding and ability to quantify these changes are a key scientific challenge. Many watersheds of the QTP have seen their hydrologic budget modified over the last decades as evidenced by strong lake level variations observed in endorheic basins. Yet, the possible contribution of permafrost thaw to these variations has not been assessed. The Paiku basin (central Himalayas, southern TP) finds itself in a similar situation. The Paiku lake at the lowest point of this endorheic basin has exhibited important level decreases since the 70’s and thus offers the possibility to test the potential role of permafrost thaw on these hydrologic changes. We present permafrost simulations at the scale of the basin over the last four decades that reproduce its degradation as result of regional climatic change. We use the Cryogrid model to simulate the surface energy balance, snow pack dynamics and the ground thermal regime while accounting for the phase changes and the soil water budget. Because the surface radiative, sensitive and latent heat fluxes in alpine environments are strongly dependent on the physiography the model is forced with distributed downscaled forcing data produced with the TOPOSCALE model to account for this spatial variability. Simulated surface conditions are evaluated against meteorological data acquired within the basin and remotely sensed surface temperatures. The simulations show that, contrary to large scale estimates of permafrost occurrence probability, an important part of the basin is underlaid by permafrost. During the simulated period, permafrost distribution and active layer exhibit limited variations (active layer deepening neighboring 10 cm) yet deeper ground temperatures (7-8 m) show a warming close to 0.8 degree (0.2 degree per decade). These first results tend to indicate a limited contribution of permafrost to the catchment hydrology over the last decades, a trend that could be significantly modified in the future if the simulated warming rates persist and lead to increased permafrost thawing.

The manuscript submitted by Mathys et al. describes a new method to extrapolate the quantification of ice contents in permafrost areas based on another paper submitted. By the combination of multiple observations, modelling tools, remote sensing data, they evaluated the ice contents in study sites in the Central Andes. This quantification is helpful for the scientific community to better understand hydrological processes occurring in permafrost-affected catchments.

The surface energy balance is one of the most important influencing factors for the ground thermal regime. It is therefore crucial to understand the interactions of the individual heat fluxes at the surface and within the subsurface layers as well as their relative impacts. A unique set of high-altitude meteorological measurements has been analysed to determine the energy balance at three mountain permafrost sites in the Swiss Alps, where data is being collected since the late 1990s in collaboration with the Swiss Permafrost Monitoring (PERMOS). The three stations have a standardized equipment with sensors for four-component radiation, air temperature, humidity, wind speed and direction as well as ground temperatures and snow height. The three sites differ considerably by their surface and ground material composition ranging from a coarse blocky active layer above ice supersaturated permafrost at rock glacier Murtèl-Corvatsch to deeply weathered micaceous shales, which are covered by fine grained debris of sandy and silty material with a low ice content at the Northern slope of Schilthorn summit. The third site at the Stockhorn plateau shows intermediate ice contents and heterogeneous surface conditions with medium-size debris, fine grained material and outcropping bedrock. Ice content estimation and general ground characterisation are based on geophysical surveying and borehole drilling. The energy fluxes are calculated based on around two decades of field measurements. While the determination of the radiation budget and the ground heat flux is comparatively straightforward (by the four-component radiation sensor and thermistor measurements within the boreholes, respectively), larger uncertainties exist for the determination of sensible and latent turbulent heat fluxes. They are therefore determined on the one hand by the bulk aerodynamic method using the bulk Richardson number to describe the stability of the surface layer relating the relative effects of buoyancy to mechanical forces and on the other hand by the bowen ratio method. Results show that mean air temperature at Murtèl-Corvatsch (1997–2018, elevation 2600 m asl.) is –1.66°C and has increased by about 0.7°C during the observation period. The Schilthorn (1999–2018, elevation 2900 m asl.) site shows a mean air temperature of –2.48°C with a mean increase of 1.0°C and the Stockhorn (2003–2018, elevation 3400 m asl.) site shows lower air temperatures with a mean of –5.99°C with an increase of 0.6°C. Measured net radiation, as the most important energy input at the surface, shows substantial differences with mean values of 33.41 Wm-2 for Murtèl-Corvatsch, 40.65 Wm-2 for Schilthorn and 24.88 Wm-2 for Stockhorn. The calculated turbulent fluxes show values of around 7 to 12 Wm-2 using the bowen ratio method and 8 to 18 Wm-2 using the bulk method at all sites. Large differences are observed regarding the energy used for melting of the snow cover: at Schilthorn a value of 12.41 Wm-2, at Murtèl-Corvatsch of 7.31 Wm-2 and at Stockhorn of 3.46 Wm-2 is calculated reflecting the differences in snow height at the three sites.

With climate change and the associated continuing recession of glaciers, water security, especially in regions depending on the water supply from glaciers, is threatened. In this context, the understanding of permafrost distribution and its degradation is of increasing importance as it is currently debated whether ground ice can be considered as a significant water reservoir and as an alternative resource of fresh water that could potentially moderate water scarcity during dry seasons in the future. Thus, there is a pressing need to better understand how much water is stored as ground ice in areas with extensive permafrost occurrence and how meltwater from permafrost degradation may contribute to the hydrological cycle in the region. Although permafrost and permafrost landforms in the Central Andes are considered to be abundant and well developed, the data is scarce and understanding of the Andean cryosphere lacking, especially in areas devoid of glaciers and rock glaciers. In the absence of boreholes and test pits, geophysical investigations are a feasible and cost-effective technique to detect ground ice occurrences within a variety of landforms and substrates. In addition to the geophysical surveys themselves, upscaling techniques are needed to estimate ground ice content, and thereby future water resources, on larger spatial scales. To contribute to reducing the data scarcity regarding ground ice content in the Central Andes, this study focuses on the permafrost distribution and the ground ice content (and its water equivalent) of two catchments in the semi-arid Andes of Chile and Argentina. Geophysical methods (Electrical Resistivity Tomography, ERT and Refraction Seismic Tomography, RST) were used to detect and quantify ground ice in the study regions in the framework of environmental impact assessments in mining areas. Where available, ERT and RST measurements were quantitatively combined to estimate the volumetric ground ice content using the Four Phase Model (Hauck et al., 2011). Furthermore, we developed one of the first methodologies for the upscaling of these geophysical-based ground ice quantifications to an entire catchment in order to estimate the total ground ice volume in the study areas. In this contribution we will present the geophysical data, the upscaling methodology used to estimate total ground ice content (and water equivalent) of permafrost areas, and some first estimates of total ground ice content in rock glacier and rock glacier free areas and compare them to conventional estimates using remotely sensed data. Hauck, C., Böttcher, M., and Maurer, H. (2011). A new model for estimating subsurface ice content based on combined electrical and seismic datasets, The Cryosphere, 5: 453-468.

•Cryosphere plays a key role for future water availability in Central Asia.•Cryosphere monitoring has to be combined with remote sensing and modelling.•New snow monitoring tools are essential for sound quantification of snow resources.•Resumed glacier monitoring data improves understanding of the glacier role in runoff.•Continuous permafrost warming for the past 9 years.