CR2.1

Temporally continuous measurements of the snow water equivalent (SWE) are a key variable in many hydrological, meteorological and glaciological studies and are of particular importance in high mountain regions. Obtaining temporally continuous, accurate and reliable SWE observations in these harsh environments, however, remains a challenge. Recently, promising results have been achieved by using a neutronic cosmic ray snow gauge (n-CRSG). The n-CRSG device is deployed below the seasonal snowpack and counts fast neutrons from the secondary cascades of cosmic rays, which are efficiently moderated and absorbed by the hydrogen atoms contained in the snowpack. Based on the exponential relationship between neutrons and hydrogen atoms, we can infer SWE from the neutron count rate. We have installed and evaluated a n-CRSG on the Swiss Glacier de la Plaine Morte. Our validation with 22 manual measurements over five winter seasons (2016/17-2020/21) showed an average underestimation of -2% ±10% (one standard deviation).
In the present study, we explore the use of muons instead of neutrons to infer SWE. To this end, we deployed two muonic cosmic ray snow gauges (µ-CRSG), one below and one above the seasonal snowpack, for the winter season 2020/21 on the same glacier site in Switzerland. The difference in count rates between the top and bottom device can be related to the SWE of the snowpack. We derive a first-cut conversion function based on manual SWE observations by means of snow pits and snow cores. To evaluate the measurements by the µ-CRSG, we also compare them to SWE estimates by the n-CRSG. Over the winter season 2020/21, almost up to 2000 mm w.e. were observed. Overall, the µ-CRSG agrees well with the n-CRSG on the evolution of the snowpack at a high temporal resolution and thus demonstrates its great potential. Also, the inferred SWE measurements lie within the uncertainty of manual observations. Furthermore, the µ-CRSG has several advantages over the n-CRSG; It is cheaper, lighter and promises a higher measurement precision due to the improved counting statistics of the muon count rates. We conclude that the µ-CRSG has even greater potential than the n-CRSG to monitor SWE in remote high mountain environments.

How to cite: Gugerli, R., Desilets, D., and Salzmann, N.: Application of cosmic ray snow gauges to monitor the snow water equivalent on alpine glaciers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1002, https://doi.org/10.5194/egusphere-egu22-1002, 2022.

The seasonal snowpack influences the hydrology, ecology and economy of the areas where it is present. However, the real time monitoring of the seasonal snowpack is a still well known scientific challenge. In this study, we have explored the potential of muon scattering radiography (MSR) to infer the snow water equivalent (SWE) of the snowpack. We have used the energy and mass balance model Snowpack to realistically simulate the time evolution and microstructure of the snowpack. The ERA5-Land reanalysis was used as forcing of Snowpack, in a location close to the Monte Perdido massif (Central, Pyrenees) at an elevation of 2041m above sea level. The simulations cover the hydrologic year 2015/2016, approximately reaching up to 700mm of peak SWE. Then, we have coupled the Snowpack numerical simulations with the Geant4 model to simulate the propagation of the muons through the snow layers and to collect the deviation of the muon trajectories. We have measured these deviations with a virtual muon detector based in multiwire proportional chambers, replicating a real detection system designed by us. The obtained distributions of muon deviations have exhibited a strong correlation with the simulated SWE, showing a coefficient of determination of 0.99. This model presents a root-mean-square error (RMSE) of 23.9mm in the SWE estimation. In order to validate the simulation analysis results, we have replicated the numerical experiments under controlled conditions, measuring three artificial snow samples ranging from 0 to 200 mm of SWE in our laboratory. We have measured the samples with an experimental setup composed of the real muon detector whose hardware was virtually replicated for the numerical experiments. Then, we have applied the model derived from the numerical simulations to the muon deviations measured in our laboratory. We have calibrated the real measurements and we have obtained a RMSE of 38.4mm in the SWE estimation. These results show that MSR is a promising non-destructive technique that can be used for the deployment of accurate SWE monitoring networks and can eventually provide information from the internal layered structure of the snowpack.

How to cite: Orio, A., Alonso, E., Martínez, P., Díez, C., and Gómez, P.: Exploring the potential of cosmic muon scattering to measure the snow water equivalent, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12490, https://doi.org/10.5194/egusphere-egu22-12490, 2022.

Ground and space based GNSS-IR (Interferometric Reflectometry) has been used in the last 20 years for characterizing the Earth Surface, together with other remote sensing techniques. Among the physical quantities which can be monitored using these techniques, the characterization of the snow cover is of particular interest since it is an important source of freshwater. The increase of the global temperature due to anthropogenic climate changes is threatening the seasonal recharging, and for this reason monitoring the snow cover is crucial. Ground based GNSS-IR can be used for obtaining information on the height of the snowpack, with a precision of 0.04 m by using geodetic-grade GNSS instruments (such those involved in Continuously Operating Reference Stations - CORS). In the present study, the sensitivity of the retrieval of the snowpack height from data acquired with low cost non-geodetic grade instruments with the GNSS-IR technique is evaluated. The analysis is applied to a flat alpine area in the Lavarone plateau in the Province of Trento, Italy (1400 m above sea level), where GNSS field campaigns were carried out in 2018, 2019 for short time periods (90, 120 minutes) due to constraints of the study area. Single-frequency GPS observations were collected with u-blox M8T GNSS receivers and patch u-blox and Tallysman antennas. Leica antenna and receiver were also used for collecting GPS data in double frequency, in order to acquire reference data with geodetic grade instruments. Given the characteristics of the area, it is possible to consider that GPS signals reflect with specular reflection, and thus modelling the Signal to Noise Ratio (SNR) as a function of the distance between the reflecting snow surface above solid ground and the antenna. Multipath frequency associated with snowpack height is retrieved by applying the Lomb Scargle Periodogram on SNR data. The results show that, by applying GNSS-IR technique to data acquired with low-cost receivers and antennas, it is possible to retrieve the height of the snow pack with a standard deviation of about 0.05 m. This demonstrates the feasibility of GNSS-IR also with non-geodetic grade instruments.

How to cite: Graldi, G., Rover, S., and Vitti, A.: Single-frequency GNSS-IR for estimating snowpack height with consumer grade receivers and antennas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4573, https://doi.org/10.5194/egusphere-egu22-4573, 2022.

The Apennine mountain range is the backbone of the Italian peninsula, crossing it from North-West to South-East for approximately 1200 km. The main peaks are found in Central Apennines, especially in the Gran Sasso d’Italia massif, which hosts the highest Apennines peak, named Corno Grande, with its 2912 m a.s.l. During the winter season, Central Apennines are typically covered with snow, with thickness that can vary between a few centimeters to several meters. Despite the historical presence of snow in these territories, the Apennine snowpack is poorly studied and weather data coming from automatic measurement stations and manual snow measurements hardly coexist. Thus, within the SMIVIA (Snow-mantle Modeling, Inversion and Validation using multi-frequency multi-mission InSAR in Central Apennines) project, we identified the measurement sites of Pietrattina, at 1459 m a.s.l, and Campo Felice, at 1545 m a.s.l., both located in Central Apennines. There we collected automatic measurements using ad hoc installed automatic weather-snow stations (AWSS) and where we performed systematic manual measurements of the snowpack properties, from November 2020 till April 2021. The AWSS measures every 5 minutes air temperature, relative humidity, wind speed, wind direction, incoming short-wave radiation, reflected short-wave radiation, soil surface temperature, snow surface temperature and snow height. The manual part of the campaign included the digging of 10 and 8 snow pits at Pietrattina and Campo Felice sites, respectively, to measure vertical profiles of snow density, temperature, grain shape, grain size and fractional content of light absorbing impurities. Manual snow measurements provide important information on the state of the snowpack, and give the opportunity to reconstruct the history of the snowpack. Their proximity to automatic weather stations let us evaluate the impact of the very local atmospheric conditions on the snowpack evolution. These measurements were performed within the SMIVIA project to: i) evaluate the ability of the snow cover model SNOWPACK to reproduce the observed snow cover properties; ii) verify the possibility to infer snow height and snow water equivalent from the data retrieved with Earth observation satellites; iii) investigate whether the use of a combination of snow numerical models and remote sensing data may provide better results compared to using each of the aforementioned approach, separately. Nevertheless, the data collected during the SMIVIA campaign at the measurement sites of Pietrattina and Campo Felice during season 2020-2021 can also provide precious information for other fields of study, like hydrology, biology and chemistry.

How to cite: Raparelli, E., Tuccella, P., Lombardi, A., Palermo, G., Alvan Romero, N., Papa, M., Picciotti, E., Di Fabio, S., Pettinelli, E., Mattei, E., Lauro, S., Cosciotti, B., Petroselli, C., Cappelletti, D., Pecci, M., and Marzano, F. S.: Snow measurement campaign for snowpack model and satellite retrieval validation in Italian Central Apennines within SMIVIA project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12082, https://doi.org/10.5194/egusphere-egu22-12082, 2022.

In the Pyrenees, as in other mid latitude mountain ranges, sub alpine areas have a long lasting snow cover that affect different mountain processes, including river discharge timing, soil erosion, primary production or animal and plant phenology. This work presents and analyzes a complete snow depth and Normalized Difference Vegetation Index (NDVI) spatial distribution dataset, generated by Unmanned Aerial Vehicles (UAV) over two years. This study aims to increase the knowledge and understanding of the relationship of the duration and timing of snowmelt and vegetation cover and its annual cycle.

The dataset was obtained in Izas Experimental Catchment, a 55 ha study area located in Central Spanish Pyrenees ranging between 2000 to 2300 m a.s.l., which is mostly covered by grasslands. A total of 18 UAV snow depth and 14 NDVI observations were obtained by a fixed wing UAV equipped with RGB and multispectral cameras during 2020 and 2021. The melt out date for the different areas of the catchment has been obtained from the snow depth distribution dataset, which in turn has been used to analyze the NDVI evolution. The NDVI values for each UAV flight have been correlated with the snow depth distribution observed in previous dates and with different topographic variables as elevation, solar radiation, curvature (through the Topographic Position Index) or slope.

The maximum seasonal NDVI happens throughout the study area simultaneously in the entire study area; however those zones with the latest snow disappearance do not reach NDVI values as high as those observed in areas with earlier snow disappearance. Oppositely areas with the soonest snow melting (in late February) have lower maximum NDVI values that those observed in areas with snow melting occurring later (around May).  NDVI correlations have shown that the snow depth distribution observed about one month prior to each NDVI acquisition has a very important control on pasture phenology. This correlation is particularly evident on the free-snow areas during first melting weeks, with a lower influence in those areas where snow melts at the end of the snow season. This field study exemplifies how intensive UAV acquisitions allow understanding snow processes over extended areas with an unprecedented spatial resolution.

How to cite: Revuelto, J., Sobrino, J., Gómez, D., Rodriguez-López, G., Alonso-González, E., Rojas-Heredia, F., Izagirre, E., Montorio-Lloveria, R., Pérez-Cabello, F., and López-Moreno, J. I.: Annual development of subalpine grassland observed with UAV: how NDVI evolution is controlled by snow melting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3248, https://doi.org/10.5194/egusphere-egu22-3248, 2022.

How to cite: Semmling, M., Wickert, J., Kreß, F., Hoque, M., Divine, D., Gerland, S., and Spreen, G.: Arctic Sea-Ice Permittivity Derived from GNSS Reflectometry Data of the MOSAiC Expedition, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3030, https://doi.org/10.5194/egusphere-egu22-3030, 2022.

The thermal regime under lakes, ponds, and shallow near shore zones in permafrost zones in the Arctic is predominantly determined by the temperature of the overlying water body throughout the year.   Where the temperatures of the water are warmer than the air, unfrozen zones within the permafrost, called taliks, can form below the water bodies.

However, the presence of bottom-fast ice can decrease the mean annual bed temperature in shallow water bodies and significantly slow down the thawing or even refreeze the lake or sea bed in winter. Small changes in water level have the potential to drastically alter the sub-bed thermal regime between permafrost-thawing and permafrost-forming. The temperature regime of lake sediments is a determining factor in the microbial activity that makes their taliks hot spots of methane gas emission. Measurements of the sediment temperature below shallow water bodies are scarce, and single temperature-chains in boreholes are not sufficient to map spatial variability.

We present a new device to measure in-situ temperature-depth profiles in saturated soils or sediments, adapting the functionality of classic Bullard-type heat flow probes to the special requirements of the Arctic. The measurement setup consists of 30 equally spaced (5cm) digital temperature sensors housed in a 1.5 m stainless steel lance. The lance is portable and can be pushed into the sediment by hand either from a wading position, a small boat or through a hole in the ice during the winter. Measurements are taken continuously and 15 minutes in the sediment are sufficient to acquire in-situ temperatures within the accuracy of the sensors (0.01K after calibration at 0°C). The spacing of the sensors yield a detailed temperature-depth-profile of the near-surface sediments, where small-scale changes in the bottom water changes dominate the temperature field of the sediment. The short time needed for a single measurement allows for fine-meshed surveys of the sediment in areas of interest, such as the transition zone from bottom-fast to free water.

Test campaigns in the Canadian Arctic and on Svalbard have proven  the device to be robust in a range of environments. We present data acquired during winter and summer, covering non-permafrost, thermokarst lake and offshore measurements.

How to cite: Miesner, F., Cable, W., Boike, J., and Overduin, P. P.: In-situ measurements of sediment temperature under shallow water bodies in Arctic environments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7154, https://doi.org/10.5194/egusphere-egu22-7154, 2022.

Several icy moons of our Solar System like Jupiter’s moon Europa have a global ocean of liquid water below their icy crust. These ocean worlds are possible targets for space missions that aim to assess their potential for habitability or even to search for life. Cryobots (or ice melting probes) are suitable tools to reach the subglacial oceans for in-situ investigations. The necessary ice shell transit provides an excellent opportunity to investigate structure and composition of the ice itself by means of geophysical and other in-situ measurements. This will allow us to better understand the evolution of icy moons and their role in our solar system.

We present current ideas as well as first results from terrestrial analogue studies. Acoustic data obtained during a field test on Langenferner Glacier, Italy was used to conduct a travel time tomography, which yields insight into heterogeneities in the local acoustic wave propagation speed through the ice. The acoustic sensor set-up was originally designed for localization of the melting probe rather than an investigation of the ice structure. However, we can still show that such opportunity data can be used to obtain a wave velocity distribution which can be further interpreted with respect to ice properties like porosity.

While we already investigated the acoustic data, we evaluate the potential of other measurements. For example, Radar measurements in combination with the acoustics can be used to identify the ice-water boundary and, in addition, cracks and inclusions in the ice. Conductivity measurements provide information on the salinity. At ice-water interface regions, the salinity is in thermochemical equilibrium with the temperature and porosity of the ice. We present our concept for on-board electrical conductivity measurements and analyze its potential, for example, to constrain ice properties and to predict ice-water interfaces based on existing terrestrial field data and process models. Furthermore, some of the cryobot’s housekeeping data might be of interest for investigating the ambiance, too. For example, the temperature and the density of the ice affect the melting velocity of the cryobot, which constitutes an inverse problem to get further information on the ice.

How to cite: Boxberg, M. S., Simson, A., Chen, Q., and Kowalski, J.: Investigation of ice with geophysical measurements during the transit of cryobots, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10195, https://doi.org/10.5194/egusphere-egu22-10195, 2022.

Atmospheric warming has led to increased surface melting on glaciers in the Arctic. This meltwater can percolate into firn and refreeze to form ice layers. Depending on their thickness, low-permeability ice layers can act as barriers that inhibit subsequent vertical meltwater infiltration in deeper firn pore space and favor lateral meltwater runoff. Thus, characterizing ice layers in firn is key for understanding the near-surface hydrological conditions that could promote surface meltwater runoff and its contribution to sea level rise.

Airborne ice-penetrating radar (IPR) is a powerful tool for imaging subsurface structure, but only recently have these systems been applied to direct observations of the bulk properties of the near-surface. To evaluate the bulk permeability of the near-surface firn system of Devon Ice Cap (DIC), Canadian Arctic, we use the Radar Statistical Reconnaissance (RSR) technique, originally developed for accumulation studies in West Antarctica. This method utilizes both the coherent and incoherent components of the total surface return, which are predominately sensitive to near-surface permittivity/structure within the system’s vertical range resolution and surface roughness, respectively. Here, we apply RSR to IPR data collected over DIC with the High-Capability Airborne Radar Sounder 2 (HiCARS) system (60 MHz center-frequency, 15 MHz bandwidth), operated by the University of Texas Institute for Geophysics (UTIG). Guided by ground-based ice-penetrating radar data and firn core density measurements, we show that the near-surface heterogeneous firn structure, featuring ice layers, mainly affects the observed coherent component.

We further compare the coherent component of HiCARS with that derived from IPR data collected with the University of Kansas Multichannel Coherent Radar Depth Sounder (MCoRDS) 3 system (195 MHz center-frequency; 30 MHz bandwidth), to evaluate the utility of dual-frequency IPR for characterizing near-surface ice layers. We expect that each radar system is sensitive to a different scale of near-surface bulk properties (i.e., depth and thickness of ice layers of different vertical extents), governed by each radar systems’ center frequency and bandwidth-limited range resolution. We leverage these differences in range resolution to derive ice layer thickness constraints in the DIC firn zone containing meter-thick ice layers, which are consistent with ground-based observations. Our results suggest this dual-frequency approach does indeed show that ice layers are vertically resolvable, spatially extensive, and mostly impermeable to surface meltwater. Thus, we hypothesize that lateral flow over high elevation meter-thick ice layers may contribute to the total surface runoff routed through supraglacial rivers down-glacier in the ablation zone.

How to cite: Chan, K., Grima, C., Rutishauser, A., Young, D. A., Culberg, R., and Blankenship, D. D.: Ice layer detection, distribution, and thickness in the near-surface firn on Devon Ice Cap: a new dual-frequency radar characterization approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6414, https://doi.org/10.5194/egusphere-egu22-6414, 2022.

Measurements of the seismic properties of Antarctic ice streams are critical for constraining glacier dynamics and future sea-level rise contributions. In 2020, passive seismic data were acquired at the Rutford Ice Stream, West Antarctica, with the aim of imaging the near-surface firn layer. A DAS (distributed acoustic sensing) interrogator and 1 km of optic fibre were supplemented by 3-component geophones. Taking advantage of transient seismic energy from a petrol generator and seismicity near the ice stream shear margin (10s of km away from the DAS array), which dominated the ambient seismic noise field,  we retrieve Rayleigh wave signals from 3 to 50 Hz. The extracted dispersion curve for a linear fibre array shows excellent agreement with an active seismic surface wave survey (Multichannel Analysis of Surface Waves) but with lower frequency content. We invert the dispersion curves for a 1D S-wave velocity profile through the firn layer, which shows good agreement with the previously acquired seismic refraction survey. Using a triangular-array geometry we repeat the procedure and find no evidence of seismic anisotropy at our study site. Our study presents challenges and solutions for processing noisy but densely sampled DAS data, for noise interferometry and imaging. 

How to cite: Zhou, W., Butcher, A., Kendall, J. M., Kufner, S.-K., and Brisbourne, A.: S-wave velocity profile of an Antarctic ice stream firn layer with ambient seismic recording using Distributed Acoustic Sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7409, https://doi.org/10.5194/egusphere-egu22-7409, 2022.

In recent years radar polarimetry has re-surfaced as an ideal tool to determine ice-fabric patterns and linked mechanical ice anisotropy. The leap forward was facilitated by coherent data processing often collected by phase-sensitive Radio-Echo-Sounding (pRES) systems at fixed locations. The polarimetric response can either be synthesized from a set of quad-polarimetric measurements or obtained by manually rotating the antennas. Specifics of the data collection in the field varied between the different surveys, and no set of best practices has yet emerged.  Here we present a systematic study that includes more than fifty different combinations of how polarimetric data can be acquired, including:

• different distances between the transmitter and receiver (2, 4 and 8 m)
• different combinations in polarization orientation (22.5 deg)
• a comparison between discrete full azimuthal data collected every 22.5 degrees and synthesized data collected in a quad-pole setup
• the effect of 180-degree polarization orientation on repeat measurements, e.g., basal melt rate and polarimetric analysis, e.g., coherence phase
• definition of Horizontal (H) and Vertical (V) orientation is pRES antenna setup and its impact on synthesizing and analyzing data
• 90-degree fabric orientation ambiguity in polarimetric data

This study aims to provide best practices, considering that observation time in the field is limited. Ideally, this will lead to a unified setup and nomenclature, facilitating better compatibility from data collected by different groups on ice sheets, shelves, and glaciers.

How to cite: Eisen, O., Ershadi, R., Drews, R., Berger, S., Gong, D., Li, Y., Martin, C., and Zeising, O.: Best practices for collecting polarimetric data with ApRES for constraining ice-fabric orientation and its spatial variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1021, https://doi.org/10.5194/egusphere-egu22-1021, 2022.

The radar detection of bedrock interface and internal ice layers is a widely used technique for observing interiors and bottoms of ice sheets, which is also an important indicator of inferring the evolution of glaciers and explaining subglacial topographies. The conventional methods, such as the filtering denoise, are limited by the low contrast in ice radar image with noise and interferes and thus the automatic method in tracing and extracting layers' features is trapped. The manual and semiautomatic methods are widely applied but with large time-consuming especially for the large-scale radar image with continuous bedrock and internal layers. To extract and identify the bedrock interface and internal ice layers automatically, we propose EisNet, a fusion system consisting of three sub neural networks. Because of the limitations of conventional manual methods, it is relatively rare that the high-precision extraction of layer features, which can be applied as labels in training. To obtain sufficient radar images with high-quality training labels, we also propose a novel synthetic method to simulate the not only visual texture of the bedrock interface and internal layers but also the artifact noise and interference to match the feature in field data. EisNet is first verified on synthetic data and shows capacity on the extraction of multi types of layer targets. Second, the application on observational radar images reveals EisNet’s generalized performance from synthetic data to the CHINARE data. EisNet is also applied to extract bedrock interfaces from the radar film from the Antarctic. EisNet is now open open-accessing. We hope that EisNet could be applied in more ice radar images from other regions and different forms to promote glacial research.

How to cite: Dong, S., Tang, X., and Fu, L.: Using EisNet to Extract Bedrock and Internal layers from Digital and Analog Radiostratigraphy in Ice Sheets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6377, https://doi.org/10.5194/egusphere-egu22-6377, 2022.

Over the past decades, ground-penetrating radar (GPR) has become a fundamental tool in glaciological studies thanks to its tremendous capacity to provide high-resolution images in snow and ice. 3D acquisitions in particular can give detailed information on the internal structure, properties, and dynamics of glaciers. For imaging and highlighting important englacial and subglacial features such as meltwater tunnels and voids, an analysis of the spatial distribution of diffractions in the data holds great potential. However, the diffracted wavefield typically has low amplitude and is often masked by more prominent arrivals. Diffraction separation and imaging procedures have already become topics of significant interest in the field of exploration seismology, and may potentially open new possibilities for the analysis of glacier GPR data.

Here, we explore the potential of recent advances in diffraction imaging for the analysis of alpine glacier GPR data. To this end, we consider a 3D data set acquired on the Haut Glacier d’Arolla (Valais, Switzerland) using a 70-MHz single-antenna real-time-sampling GPR system. The approach we use coherently approximates the dominant reflected wavefield and subtracts it from the data. The remaining diffracted wavefield is then enhanced using local coherent stacking. We find that this methodology is highly effective at isolating diffractions in glacier GPR data and provides clean images of the diffracting structures. Current work includes investigation of the correlation between these structures and the englacial and subglacial hydrological network.

How to cite: Klahold, J., Schwarz, B., Bauer, A., and Irving, J.: Diffraction imaging of alpine glacier GPR data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5865, https://doi.org/10.5194/egusphere-egu22-5865, 2022.

The internal stratigraphy of alpine glaciers entails information about its past dynamics and accumulation rates. It further can be used for intercalibrating the age-depth scales of ice cores. The internal ice stratigraphy is often imaged using radar, but similar to polar ice sheets the deeper stratigraphy is often difficult to resolve with classical pulsed radar systems. For polar ice sheets, the introduction of phase coherent radars has illuminated this former echo-free zone (EFZ) and now patterns of folded, buckled and disrupted ice stratigraphy are clearly visible. Unfortunately, the new airborne and ground-based radar systems applied in polar regions are typically too heavy to be deployed in an alpine environment.

Here, we transfer the lightweight autonomous phase-sensitive radio-echo sounder (ApRES) to an alpine glacier targeting its echo-free zone (Colle Gnifetti, Italy/Switzerland). The ApRES is a coherent frequency modulated continuous wave radar with an integration time of 1 s per trace which we deployed in combination with a GNSS used in real time kinematic (RTK) mode. The latter allows repositioning of the antennas with sub-wavelength accuracy (approximately 5 cm) required to exploit the coherent signal. Like this, the radio-stratigraphy of the former EFZ at this site could be imaged using a matched filtering SAR method. The resulting radargrams cover former ice core sites (e.g., Ice Memory and KCC) and can be used to harmonize conflicting age-depth scales. This dataset will be analysed further in conjunction with ice-fabric measurements from ice cores to reveal how the anisotropic ice rheology imprints on the flow field of glaciers.

How to cite: Oraschewski, F., Koch, I., Ershadi, M., Hawkins, J., and Drews, R.: Illuminating the deeper radio-stratigraphy of an alpine glacier using SAR processing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7725, https://doi.org/10.5194/egusphere-egu22-7725, 2022.

Thanks to the excellent propagation characteristics of radar waves in ice, ground-penetrating radar (GPR) has been one of the key geophysical methods used in the field of glaciology over the last 50 years. Alpine glacier GPR surveys are typically performed either directly on the glacier surface (e.g., on foot, skis, or with snowmobiles), or by helicopter several tens of meters above the surface. Helicopter-based surveys allow the coverage of large areas safely and efficiently, but this comes at the expense of reduced resolution of glacier internal structures, particularly in the context of 3D surveys. On the other hand, ice-based acquisitions offer high-resolution opportunities, but are very time-consuming, often risky, and can be physically exhausting to perform. Recent advances in the development of drone technologies open new data acquisition possibilities for glacier GPR data, combining the advantages of both ice and air-based methods.

We have developed a drone-based GPR system that allows for safe and efficient high-resolution 3D and 4D data acquisition on alpine glaciers. Our custom-built GPR instrument uses real-time sampling to record traces of length 2800 ns, which corresponds to a depth of over 200 m in glacier ice. Each trace is stacked over 5000 times and acquired using a sampling frequency of 320 MHz, the latter of which is just enough to avoid aliasing with our single lightweight 70-MHz-center-frequency antenna. Traces are recorded at a rate of 14 Hz, meaning that a drone speed of at least 4 m/s can be considered while maintaining a sufficiently high trace density for high-resolution studies. This is at least four times faster than a conventional survey on foot. The total weight of our GPR system plus single transmit/receive antenna is around 2 kg. The drone used in our work has a maximum payload capacity of about 6 kg and is equipped with a radar-based ground sensor which enables us to follow the glacier surface topography during the flights. An independent differential GPS allows us to locate each recorded GPR trace with decimeter precision.

We performed initial testing of the above-described system in August 2021 on the Otemma glacier and successfully acquired around 70-line kilometers of 3D GPR data, over an 8-day period, covering a large portion of the glacier. In September 2021, we undertook additional fieldwork on the Tsanfleuron and Sex-Rouge glaciers and recorded 30-line kilometers of 3D GPR data in less than 3 days. We could then determine and model with high-precision the ice-thickness distribution over the Tsanfleuron pass. These first field results show the concrete benefit of drone-based GPR glacier surveys and motivate further development towards 3D and 4D studies.

How to cite: Ruols, B., Baron, L., and Irving, J.: Drone-based GPR system for 4D glacier data acquisition, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3073, https://doi.org/10.5194/egusphere-egu22-3073, 2022.

Geoelectrical methods are widely used for permafrost investigations by research groups, government agencies and industry. Electrical Resistivity Tomography (ERT) surveys are typically performed only once to detect the presence or absence of permafrost. Exchange of data and expertise among users is limited and usually occurs bilaterally. Neither complete information about the existence of geophysical surveys on permafrost nor the data itself is available on a global scale. Given the potential gain for identifying permafrost evidence and their spatio-temporal changes, there is a strong need for coordinated efforts regarding data, metadata, guidelines, and expertise exchange. Repetition of ERT surveys is rare, even though it could provide a quantitative spatio-temporal measure of permafrost evolution, helping to quantify the effects of climate change at local (where the ERT survey takes place) and global scales (due to the inventory).

Our International Permafrost Association (IPA) action group (2021-2023) has the main objective of bringing together the international community interested in geoelectrical measurements on permafrost and laying the foundations for an operational International Database of Geoelectrical Surveys on Permafrost (IDGSP). Our contribution presents a new international database of electrical resistivity datasets on permafrost. The core members of our action group represent more than 10 research groups, who have already contributed their own metadata (currently > 200 profiles covering 15 countries). These metadata will be fully publicly accessible in the near future whereas access to the resistivity data may be either public or restricted. Thanks to this open-access policy, we aim at increasing the level of transparency, encouraging further data providers and fostering survey repetitions by new users.

The database is set up on a virtual machine hosted by the University of Fribourg. The advanced open-source relational database system PostgreSQL is used to program the database. Homogenization and standardization of a large number of data and metadata are among the greatest challenges, yet are essential to a structured relational database. In this contribution, we present the structure of the database, statistics of the metadata uploaded, as well as first results of repetitions from legacy geoelectrical measurements on permafrost. Guidelines and strategies are developed to handle repetition challenges such as changing survey configuration, changing geometry or inaccurate/missing metadata. First steps toward transparent and reproducible automated filtering and inversion of a great number of datasets will also be presented. By archiving geoelectrical data on permafrost, the ambition of this project is the reanalysis of the full database and its climatic interpretation.

How to cite: Mollaret, C., Hilbich, C., Herring, T., Farzamian, M., Buckel, J., Dafflon, B., Draebing, D., Fossaert, H., Gugerli, R., Hauck, C., Kunz, J., Lewkowicz, A., Limbrock, J. K., Maierhofer, T., Magnin, F., Pellet, C., Pfaehler, S., Scandroglio, R., and Uhlemann, S. and the IDGSP IPA Action Group: Initiation of an international database of geoelectrical surveys on permafrost to promote data sharing, survey repetition and standardized data reprocessing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10565, https://doi.org/10.5194/egusphere-egu22-10565, 2022.

High Arctic regions are experiencing an accelerated rise in temperatures, about three times more than the global average. As a result, the glacier coverage over these landscapes is reducing, uncovering soils which start their development by sustaining emergent microbial communities. These new systems will have a significant impact on the global carbon budget, thus monitoring and understanding their evolution becomes a necessity.

Geoelectrical methods have emerged as a fast, cost-effective and minimally invasive way of imaging soil moisture dynamics in the shallow subsurface. BGS PRIME technology is designed to facilitate low-power remote geoelectrical tomography by using an array of sensor electrodes. We are using such technology to monitor the year-round variability of soil electrical resistivity in 4D on a glacier forefield in the vicinity of Ny-Alesund, Svalbard. Until now, such assessment of soil properties was confined to the summer period due to harsh Arctic winter conditions making site access very difficult.

Two PRIME systems were deployed during the summer of 2021 on Midtre Lovénbreen glacier forefield, which exhibits a soil chronosequence extending from the youngest soils near the glacier snout up to soils of approximately 120 years old. The two geophysical systems are monitoring electrical resistivity within the top 2m of soil of approximately 5 and 60 years of age respectively, recording soil moisture and freeze-thaw dynamics within the active layer above the permafrost.

We present early results, a timeseries of 3D soil electrical resistivity models, that captured several precipitation events during the summer and the progression of the freezing front when soil temperatures dropped below 0 °C in October 2021. These results reveal differences in the hydrodynamic activity between the 5- and 60-year-old sites determined by soil properties and their location on the glacier forefield. In addition, soil cores were sampled from the vicinity of the PRIME systems. These were subsequently subjected to laboratory tests to describe the changes in electrical resistivity as a function of moisture content and during successive freeze-thaw cycles. Furthermore, we are working towards an integrated analysis and a more comprehensive model of soil evolution at our sites by combining geoelectrical measurements with point measurements of environmental parameters and microbiological activity.

How to cite: Cimpoiasu, M. O., Harrison, H., Meldrum, P., Wilkinson, P., Chambers, J., Bradley, J., Sommers, P., Schmidt, S. K., Irons, T., Liljestrand, D., Oroza, C., and Kuras, O.: Year-round high-resolution geoelectrical monitoring to improve the understanding of deglaciated soil evolution in the High Arctic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10159, https://doi.org/10.5194/egusphere-egu22-10159, 2022.