HS8.1.10

Our study discusses imaging results from a spectral induced polarization (SIP) survey to identify concurring processes (such as aerobic respiration, denitrification, or sulfate- and iron reduction) in a biogeochemically active peat in a wetland located in the Lehstenbach catchment in Southeastern Germany. Terrestrial wetland ecosystems such as peatlands are a critical element in the global carbon cycle. Due to their role as natural carbon sinks and ecological importance for an array of flora and fauna, there is a growing demand to conserve and restore degraded peatlands. Biogeochemical processes occur with non-uniform reaction rates within the peat, making the environment sensitive to physical disturbances. To investigate biogeochemical processes in-situ, it is important to avoid disturbing the redox-sensitive conditions in the subsurface by bringing oxygen into anoxic areas.  Our previous study demonstrated that the induced polarization (IP) was able to identify biogeochemically active and inactive areas of the peat. The IP response was sensitive to the presence of carbon turnover and P release in the absence of iron sulfide. These highly polarizable areas have high iron concentrations, but most likely in an oxidized form. As most iron oxides are poor conductors, the strong polarization response is unlikely related to an electrode polarization process.

Here we also analyzed the frequency dependence of the SIP data to investigate whether iron oxides and carbon-iron complexes, two possible mechanisms for the high polarization response, can be distinguished. SIP imaging data sets covered the frequency range between 0.06 and 225 Hz and were collected with varying electrode spacing (20 and 50 cm) at different locations within the Waldstein catchment characterized by different properties, e.g., saturated and non-saturated soils. Our imaging results reveal variations of the IP effect within the peat layer, indicating substantial heterogeneities in the peat composition and biogeochemical activity. The frequency dependence allowed us to resolve a sharper contrast between the different features of the peat. Geochemical analyses on a freeze core and pore water samples are used to validate our results and find correlations between the Cole-Cole parameters of the SIP response and the geochemical parameters.

How to cite: Katona, T., Gilfedder, B., Frei, S., Aigner, L., Bücker, M., and Flores Orozco, A.: Determining frequency dependence of carbon turnover in peat using spectral induced polarization, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15653, https://doi.org/10.5194/egusphere-egu21-15653, 2021.

Nuclear magnetic resonance (NMR) has been widely used in near-surface geophysics due to its direct sensitivity to water. As a field form of NMR, borehole NMR has been applied to in situ hydrological investigations for decades. However, the recent implementations of borehole NMR to unsaturated zones face challenges due to the complex geology. Due to the fast operation speed and unsaturated conditions in critical zones, the raw NMR signals often suffer from limited relaxation time ranges and low signal to noise ratios. Such low quality of raw data can induce artifacts during inversion and following data interpretations. This study investigates the long-overdue evaluations of how the low borehole NMR data quality affects water distribution estimation in unsaturated zones. A synthetic analysis based on lab NMR data was first performed to simulate the inversion errors induced by the low-quality borehole NMR data. Lab NMR measurements were conducted on carbonate and shale samples from a well that has a corresponding borehole NMR profile. In order to match the low signal-to-noise ratio and data size of the low-quality borehole NMR data, lab NMR data points were reduced, deadtime was increased and normally distributed noise was added.  The inversion results of the synthetic data reveal that the low signal to noise ratio leads to an overestimation of signals at lower relaxation time while the limited relaxation time range does not significantly affect the total water estimation. To improve the water estimation from the low-quality borehole data, a peak decomposition and peak fusion method were then applied to the synthetic data. Relaxation time distribution of both lab and synthetic data were decomposed into multiple normally distributed peaks. The first peak with the shortest relaxation time from lab NMR was used to substitute the first peak of the synthetic borehole NMR relaxation time distribution. After peak decomposition and fusion, the predicted water contents were closer to lab NMR than original synthetic data. This study reveals the mispredictions of water distribution due to the low data quality of borehole NMR. The success of improving water content estimation on the synthetic study has clear implications that the peak decomposition and peak fusion method can be applied to actual borehole NMR data to improve water content and distribution estimation in unsaturated zones.   

How to cite: Zhang, F. and Zhang, C.: Improve NMR estimation of water content and distribution in unsaturated bedrocks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13549, https://doi.org/10.5194/egusphere-egu21-13549, 2021.

Understanding streaming potential generation in porous media is of high interest for hydrological and reservoir studies as it allows to relate water fluxes to measurable electrical potential distributions in subsurface geological settings. The evolution of streaming potential stems from electrokinetic coupling between water and electrical fluxes due to the presence of an electrical double layer at the interface between the mineral and the pore water. Two different approaches can be used to model and interpret the generation of the streaming potential in porous media: the classical coupling coefficient approach based on the Helmholtz-Smoluchowski equation, and the effective excess charge density. Recent studies based on both approaches use a mathematical up-scaling procedure that employs the so-called fractal theory. In these studies, the porous medium is represented by a bundle of tortuous capillaries characterized by a fractal capillary-size distribution law. The electrokinetic coupling between the fluid flow and electric current is obtained by averaging the processes that take place in a single capillary. In most cases, closed-form expressions for the electrokinetic parameters are obtained in terms of macroscopic hydraulic variables like permeability, saturation and porosity. In this presentation we propose a review of the existing fractal distribution models that predict the streaming potential in porous media and discuss their benefits compared against other published models.

How to cite: Jougnot, D., Thanh, L. D., Soldi, M., Vinogradov, J., and Guarracino, L.: Advances and benefits of fractal models to predict streaming potentials in partially saturated porous media, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10835, https://doi.org/10.5194/egusphere-egu21-10835, 2021.

Exploration of plant roots and monitoring their conditions during growth is of great importance. A promising method for the non-invasive investigation of plant roots is spectral induced polarization (SIP). To enhance understanding of the mechanism controlling the plant root’s induced polarization response, we have conducted a series of experiments and constructed a physical-based numerical model. We measured the SIP signal of wheat root grown in the nutrient solution. The experiments have demonstrated a relationship between the SIP parameters (chargeability and relaxation time) and the root biomass and surface area. Monitoring the SIP response of roots poisoned by cyanide has revealed that the root polarization source is the cell membrane potential. In addition, we modeled plant root as a collection of 2-dimensional individual cells surrounded by an electrolyte. The SIP signal was calculated based on the numerical solution of the Poisson-Nernst-Planck equation. The model has supported the experimental results with the correlation between the magnitude of polarization and the root surface area. According to the model, the root polarization magnitude is related to the root’s external surface area. The polarization length scale is the root’s diameter, not the cell diameter. Based on these results and data from the literature, we suggest that at the low-frequency range associated with the SIP method, passing the current through the plant results in polarization of the individual cells, a relatively high polarization and relaxation time that is related to the cell length. On the other hand, injecting current to the growing medium results in the polarization of the external surface area of the root and polarization length scale related to the root diameter.

How to cite: Tsukanov, K. and Schwartz, N.: Spectral induced polarization of the plant root: Experiments and numerical modeling., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7720, https://doi.org/10.5194/egusphere-egu21-7720, 2021.

The development, architecture, and activity of the plant root system has a key role in plant-soil-water interactions, and thus, in plant ecology both in agricultural and natural systems. The characterization of the flow of electric current in the root-soil system may provide non-invasive methodologies to observe the state and dynamics of this critical zone hidden in the shallow subsurface.

Inversion of Current source density (CSD) from Mise-a-la-masse (MALM) surveys provides a straightforward way to describe the shape of a conductive body that charges up. While numerous studies show a correlation between root mass density and electrical capacitance (Ehosioke et al., 2020), physical proofs of the underlying assumptions of such concepts are still missing. In particular, some authors questioned the hypothesis that the xylem behaves as a continuous conductive body with regard to its physiological state. Application of the MALM in conjunction with CSD helps distinguish the current pathway through the root system (Mary et al., 2019; Peruzzo et al., 2020).

As roots are electrically polarisable, their responses depend on the frequency of the current injection. Extending the CSD inversion to secondary voltages produced by secondary currents (after shutting down the primary current) may provide insights into transient phenomena associated with the polarization of the roots.

Based on a Self-Potential (SP) processing algorithm (Shao et al., 2018), we build and test a new inversion scheme of secondary voltages using synthetic models. Small-scale laboratory experiments are in progress on grapevine cuttings placed in water-filled rhizotrons. Root growth will be monitored using MALM in TDIP domain.  

How to cite: Mary, B., Iván, V., and Cassiani, G.: Root system monitoring using a mise-à-la-masse (MALM) extension to time-domain IP, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2839, https://doi.org/10.5194/egusphere-egu21-2839, 2021.

Knowledge of real time spatial distribution of soil moisture has great potential to improve yield and profit in agricultural systems. Rapid and precise quantification of water in crop fields is challenging due to the influence of highly variable soil properties such as texture and porosity.  Recent advances in non-invasive electromagnetic induction (EMI) techniques have created an opportunity to determine soil moisture content with high-resolution and minimal soil intrusion. So far, EMI has mainly been validated for homogenous soils, which are not common in agriculture. This study from a field site in Western Australia converts time series apparent electrical conductivity data recorded with a Dualem 1Hs EM-meter into spatiotemporal domains. A least square inversion algorithm was used to determine electric conductivities for individual soil layers (0-50cm, 50-80 cm and 80-160 cm) for two EMI surveys at a trial site, with different crop rotations and varying moisture conditions. A laboratory experiment under controlled conditions developed electric conductivity vs volumetric water content relations with power law functions for each layer with R² values between 0.98 and 0.99. Subsequently, EMI data were converted to volumetric water contents for each layer and predictions were spatially displayed. These EMI soil moisture predictions were compared with neutron moisture meter measurements, with R² values between 0.95 and 0.74 for the two surveys. The method is robust and offers a comparatively fast method to estimate the soil moisture status in fields and to subsequently make informed management decisions. 

How to cite: Shaukat, H., Flower, K., and Leopold, M.: Quasi-3D mapping of soil moisture for agriculture using electric conductivity sensing , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7117, https://doi.org/10.5194/egusphere-egu21-7117, 2021.

The soil is considered as a biological reactor or an outlet for treated domestic wastewater, respectively to reduce pollutant concentrations in the flows or because the surface hydraulic medium is too remote. In these cases, the saturated hydraulic conductivity of the soil is a key is a quantitative measure to assess whether the necessary infiltration capacity is available. To our knowledge, there is no satisfactory technique for evaluating the saturated hydraulic conductivity Ks of a heterogeneous soil (and its variability) at the scale of a parcel of soil. The aim of this study is to introduce a methodology that associates geophysical measurements and geotechnical in order to better described the near-surface saturated hydraulic conductivity Ks. Here we demonstrate here the interest of using a geostatistical approach, the BME "Bayesian Maximum Entropy", to obtain a 2D spatialization of Ks in heterogeneous soils. This tool opens up prospects for optimizing the sizing infiltration structures that receive treated wastewater. In our case, we have Electrical Resistivity Tomography (ERT) data (dense but with high uncertainty) and infiltration test data (reliable but sparse). The BME approach provides a flexible methodological framework to process these data. The advantage of BME is that it reduces to kriging as its linear limiting cases when only Gaussian data is used, but can also integrate data of other types as might be considered in future works. Here we use hard and Gaussian soft data to rigorously integrate the different data at hand (ERT, and Ks measurement) and their associated uncertainties. Based on statistical analysis, we compared the estimation performances of 3 methods: kriging interpolation of infiltration test data, the transformation of ERT data, and BME data fusion of geotechnical and geophysical data. We evaluated the 3 methods of estimation on simulated datasets and we then do a validation analysis using real field data. We find that BME data fusion of geotechnical and geophysical data provides better estimates of hydraulic conductivity than using geotechnical or geophysical data alone.

How to cite: Rabouli, S., Dubois, V., Serre, M., Gance, J., Henine, H., Molle, P., Truffert, C., and Clement, R.: Spatialization of physical variables in soils by geophysical, geotechnical and geostatistic methods: the Bayesian maximum entropy data fusion approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4779, https://doi.org/10.5194/egusphere-egu21-4779, 2021.

Nowadays, tourism and sport activities make the Alps high mountain environment widely populated. As example, the Dolomites (UNCESCO site, North-East Italy) host millions of tourists every year. Consequently, many infrastructures (e.g. roads, cable cars and hotels) have been built in these areas, and are subject to instabilities hazards as landslide, avalanches or frozen soils problems.  Mountain permafrost is in fact one of the many aspects to be considered for the natural hazards and risk management in high mountains environment. Due to the atmospheric warming trend, mountain permafrost is thawing and its degradation is influencing the triggering and the evolvement of natural hazards processes such as rockfalls, landslides, debris flows and floods. We have nearly 5000 rock glaciers in the alps, as highlighted in the inventory of the PermaNET project (2011), therefore the study and monitoring of these periglacial forms has both a scientific and economic importance. Geophysical surveys have been historically applied in this kind of environment, in particular the Electrical Resistivity Tomography (ERT) for the characterization of the active layer thickness (ALT). The technique exploits the high electrical resistivity contrast between frozen and non-frozen debris, and, over the last years, has allowed the researchers to achieve very relevant results. However, performing these measurements is expensive both in terms of time and equipment, particularly considering that the rock glaciers are often very difficult to reach. Thus, usually we are not able to perform many investigation lines and, as the results are 2D resistivity sections, it is very difficult to obtain enough information to completely characterize a heterogeneous environment such as a rock glacier. For this reason, we tried to apply the EMI method (in the frequency domain) for the characterization of the ALT. EMI method, in fact,  theoretically allows us to define the distribution of electrical resistivity in the first subsoil in a very quick way, simply by transporting the device over the interested area. Compared to ERT, it is potentially able to characterize much larger areas of a rock glacier, albeit with a lower resolution and penetration. On the other hand, because the high resistivities of the frozen ground, EMI do not guarantee an optimal working and rigorous acquisition protocol must be adopted. We tested ERT and EMI measurements along the same investigation lines, in two different sites of the Dolomites area (the Murfreit and Biz Boè rock glaciers). Finally, we discussed the advantages and disadvantages of both the techniques.

How to cite: Pavoni, M. and Boaga, J.: Frozen soils characterization by the use of ERT and EMI methods: cases in the Dolomites (Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2146, https://doi.org/10.5194/egusphere-egu21-2146, 2021.

Continuous monitoring of soil salinity/sodicity is of prime importance in environments such as the B-XII irrigation district (SW Spain) where a shallow saline water table and intensive irrigated agriculture create a fragile equilibrium between salt accumulation and leaching in the topsoil. We evaluate to which extend electromagnetic induction (EMI) sensing and inversion with limited calibration can be used to accomplish such monitoring purposes, given that widespread soil sampling and laboratory analyses are prohibitive for economic and technical reasons.

Detailed EMI surveys were performed in 2017 and 2020 in a 4-ha tile-drained field with a heavy clay soil. Soil samples were taken at different locations and depths along a transect and analyzed for salinity/sodicity-related parameters. Inversion of the EMI signals along the investigated transect yielded consistent conductivity images for both years and showed a strong relation (R²<0.95) with saturated paste extract conductivity. The observed spatial conductivity patterns persisted from 2017 to 2020, although the obtained absolute values of the salinity/sodicity parameters changed slightly. This indicates that salinity hotspots persist in time and are mainly associated with wet locations, where salt movement towards the topsoil is promoted, possibly as a result of deficiencies in the performance of the drainage system.

Our results show that inversion of EMI signals offers a powerful means for accurately monitoring spatial and temporal changing salinity/sodicity under the specific conditions of the B-XII irrigation district.

Acknowledgement

This work is funded by the Spanish State Agency for Research through grant PID2019-104136RR-C21 and by IFAPA/FEDER through grant AVA2019.018.

How to cite: Gómez Flores, J. L., Ramos Rodriguez, M., González Jiménez, A., Farzamian, M., Herencia Galán, J. F., Salvatierra Bellido, B., Cermeño Sacristán, P., and Vanderlinden, K.: Monitoring changes in salinity and sodicity in a tile-drained field in the B-XII irrigation district (SW Spain) using electromagnetic induction sensing and inversion., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6465, https://doi.org/10.5194/egusphere-egu21-6465, 2021.

In order to prevent further soil degradation, it is important to understand the processes controlling salinization. Salt related problems in soils can refer to an excess of soluble salts (saline soils), a dominance of exchangeable sodium in the soil exchange complex (sodic soils), or a mixture of both situations (saline-sodic soils). These categories are important because the impacts and management vary accordingly. Traditional soil sampling methods –which require boreholes for soil sampling and analysis– difficultly lead to a comprehensive answer to this problem. This is because they cover only small and localized sites and may not be representative of the soil properties at the management scales. Furthermore, they are highly time and work consuming, resulting in costly surveys. Geophysical techniques such as electromagnetic induction (EMI) provide enormous advantages compared to soil sampling because they allow for in-depth and non-invasive analysis, covering large areas in less time and at a lower cost.

EMI surveys were performed in several regions in Portugal with historic soil salinity and sodicity problems to evaluate the salinization risk. We inverted field apparent conductivity data (σ_a) in order to obtain electromagnetic conductivity images (EMCI) of the real soil electrical conductivity (σ) in depth. We evaluated the potential of EMCI in the estimation of soil salinity, sodicity, and other soil properties over large areas across regions with a very different range of salinity and sodicity.

Acknowledgments

This work was developed in the scope of SOIL4EVER “Sustainable use of soil and water for improving crops productivity in irrigated areas” project supported by FCT, grant no. PTDC/ASP-SOL/28796/2017.

How to cite: Farzamian, M., Martinez Moreno, F. J., Ramos, T. B., Castanheira, N., Paz, A. M., Monteiro Santos, F. A., Alexandre, C. A., Paz, M. C., Rodríguez, M. R., Vanderlinden, K., and Gonçalves, M. C.: Evaluation of soil salinity and sodicity using electromagnetic conductivity imaging, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2872, https://doi.org/10.5194/egusphere-egu21-2872, 2021.

Electromagnetic conductivity imaging (EMCI) is a state-of-the-art methodology for soil salinity assessment over large areas. It involves the following rationale: (1) use of the electromagnetic induction (EMI) geophysical technique to measure the soil apparent electrical conductivity (EC_a, mS m⁻¹) over an area; (2) inversion of EC_a to obtain EMCI, which provides the spatial distribution of the soil electrical conductivity (σ, mS m⁻¹); (3) calibration process consisting of a regression between σ and the electrical conductivity of the saturated soil paste extract (EC_e, dS m⁻¹), used as a proxy for soil salinity; and (4) conversion of EMCI into salinity maps using the obtained calibration equation.

In this study, we applied EMCI and a regional calibration in Lezíria Grande de Vila Franca de Xira, located in Portugal. The study area is an important agricultural system where soil faces the risk of salinization due to climate change, as the level and salinity of groundwater are likely to increase as a result of the rise of the sea water level and consequently of the estuary. These changes can also affect the salinity of the irrigation water which is collected upstream of the estuary.

EMI surveys and soil sampling were carried out between May 2017 and October 2018 at four locations with different salinity levels across the study area. A regional calibration was developed and its ability to predict EC_e from EMCI was evaluated. The validation analysis showed that EC_e was predicted with a root mean square error of 3.14 dS m⁻¹ in a range of 52.35 dS m⁻¹, slightly overestimated (−1.23 dS m⁻¹), with a strong Lin’s concordance correlation coefficient of 0.94 and high linearity between measured and predicted data (R² = 0.88). It was also observed that the prediction ability of the regional calibration is more influenced by spatial variability of data than temporal variability of data.

Because of the transient nature of data, it was also possible to perform a preliminary qualitative analysis of soil salinity dynamics in the study area, revealing salinity fluctuations related to the input of salts and water either through irrigation, precipitation, or level and salinity of groundwater.

How to cite: Paz, M. C., Farzamian, M., Paz, A. M., Castanheira, N. L., Gonçalves, M. C., and Monteiro Santos, F.: Regional calibration and electromagnetic conductivity imaging for assessing the dynamics of soil salinity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16296, https://doi.org/10.5194/egusphere-egu21-16296, 2021.

Agrogeophysics comprises the use of geophysical methods applied to near surface agricultural problems. It is an interdisciplinary field which has been gaining momentum given the many advantages of geophysical tools for agriculture: non-invasiveness, large volume sample with reasonable spatial resolution, high-throughput, time-lapse possibility. In order to federate the agrogeophysical community and provide an overview of the field to researchers, we developed the catalog of agrogeophysical studies (CAGS). The catalog and its content is available under open licences and promotes practices that implement the FAIR Data Principles. These principles encourage progress toward sharing data and codes that are Findable, Accessible, Interoperable, and Reusable. The biggest strength of the CAGS is that it provides an overview of the current research state while providing metadata, associated datasets, and computational notebooks connected to the articles in which they were published. In this way, CAGS encourages reproducible research by providing the datasets and processing steps to reproduce the results of the papers. The ambition is to ultimately unite the agrogeophysical community around common standards for data processing and data interpretation. The website is hosted on GitHub (https://agrogeophy.github.io/catalog/). The open nature of CAGS and the possibility for everyone to contribute to it makes it a great platform to increase knowledge exchange across the different various international research teams.

Benjamin Mary, and Guillaume Blanchy. 2020. CAGS: Catalog of Agrogeophysical Studies. doi: 10.5281/zenodo.4058524.

How to cite: Iván, V., Mary, B., Blanchy, G., Weigand, M., and Garré, S.: Supporting successful data and codes sharing practices in agrogeophysics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12526, https://doi.org/10.5194/egusphere-egu21-12526, 2021.

Most, if not all, models of real aquifers go through a calibration process to adjust their hydraulic and solute transport parameters in order to bring the simulations outputs closer to the field observations. In coastal aquifers, the datasets are commonly composed of head time series, solute concentrations from water samples, and water and formation electrical conductivity, these last being of particular importance in coastal settings due to their relevance for seawater detection. Argentona is a well-instrumented field site of a coastal alluvial aquifer located 40 km NE of Barcelona, where a 2-year Cross-Hole Electrical Resistivity Tomography (CHERT) experiment was performed. CHERT provided high resolution electrical resistivity data in depth and allowed the visualization of dynamic aquifer processes. In the present work, we test the calibration of the Argentona SWI model using both the hydrological and the geophysical datasets. To do so, a density-dependent groundwater model was combined with CHERT forward modeling within a parameter calibration framework. In the process we pay attention to the CHERT capacity to recover aquifer salinities, to the coupling of the hydrological and geophysical simulations through petrophysics, to the use of the field specific relations and to the inverse problem parametrization, among other things. Pre-calibration analysis showed the sensitivity of the formation electrical resistivities to the porosities and to the petrophysical parameters, so the inverse problem solves for hydraulic transmissivities, porosities and petrophysical parameters. From the comparison of the preliminary results from the hydrological and the hydrogeophysical calibration, we observe that they point towards a better calibration of model porosities when the electrical resistivity is included in the inverse problem. The results will be compared to other parameter estimation methods, such as laboratory tests, the tidal method and heat tests, also performed at the Argentona site. We will conclude on the added value of the geophysical dataset in the calibration process, the possible improvements and drawbacks of the method.

How to cite: Palacios, A., Goyetche, T., Linde, N., and Carrera, J.: Hydrogeophysical coupled inversion in coastal aquifers: the Argentona case, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11535, https://doi.org/10.5194/egusphere-egu21-11535, 2021.

Characterization of groundwater aquifers plays an important role in addressing the increasing demand for freshwater and low carbon energy. Specifically, hard rock aquifers that have been neglected in the past due to their overall low productivity, are increasingly recognised as important aquifers for local water supplies, sustaining environmental flows, and low enthalpy geothermal resources. Groundwater flow and, more so storage, in these aquifers are still poorly understood creating a necessity to quantify their properties and role in sustaining human and ecosystem needs. This study aims to quantify groundwater storage properties, and their spatial variability, in weathered/fractured hard rock aquifers using near-surface geophysical techniques and further evaluate the associated uncertainties. To do so, we analysed 2D electrical resistivity tomography (ERT) and induced polarization (IP) data in combination with 1D magnetic resonance sounding (MRS) and borehole geophysical logging from a metamorphic rock catchment in Gortinlieve, Ireland. The geophysical data comprised a challenging dataset that includes information at different resolution scales: a low-resolution ERT profile of 1,3 km of length, a high-resolution ERT+IP profile of 70 m of length, 8 MRS logs distributed along the study area, borehole logs (gamma ray, temperature and caliper) and petrological analysis at borehole locations. Aquifers storativity data derived from application of petrophysical model to the geophysical data showed good accuracy and reasonable uncertainty of estimated properties. ERT porosities derived from Archie´s model revealed that this model overestimates the porosity for the study site whereas estimates derived from the Waxman & Smits (WS) model, which accounts for the influence of the cation exchange capacity (CEC) of clay minerals on the ERT measurements, were closer to specific yield values obtained from pumping test in boreholes, MRS water content estimates and the typical ranges of hard rock aquifers. The superiority of WS over Archie demonstrated that the clay content cannot be neglected when characterizing storage properties in weathered/fractured basement rock aquifers. Water content profiles from MRS corroborated the results with a particularly good match at three locations across the study area characterised by deep weathering/fracturing associated with regional fracture zones. Results demonstrated that the methodology provides a reasonable estimate of storage heterogeneity which is consistent with weathering/fracturing patterns as described in accepted conceptual models of hard rock aquifers. To further challenge the ERT porosity models, we tested an alternative approach based on the differential effective medium (DEM) theory applied to time-domain IP data to recover CEC and porosity tomograms. Preliminary results show promise, through yielding porosity values close to both 2D WS porosities and 1D MRS water contents and, importantly, the approach may provide a mean to bypass the requirement for having direct clay data of the study site. Taken together, the results confirmed that near-surface geophysical techniques are key instruments to assess groundwater conditions in hard rock aquifers and quantify the spatial heterogeneity of their storage properties at larger scales. The approach can be applied in similar hard rock environments affected by weathering and fracturing.

How to cite: Mézquita González, J. A. and Comte, J.-C.: Quantification of groundwater storage heterogeneity in a hard rock aquifer using near-surface ERT and IP geophysical techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8455, https://doi.org/10.5194/egusphere-egu21-8455, 2021.

Advanced modeling of hydrological processes in mountain catchments requires accurate characterization of the shallow subsurface, and in particular the depth to the soil/bedrock interface. Frequency domain electromagnetic induction (EMI) methods are well suited to this challenge as they have short acquisition times and do not require direct coupling with the ground; consequently they can be highly productive. Moreover, although traditionally used for revealing lateral electrical conductivity changes, EMI inversion is increasingly used to quantitatively resolve both lateral and vertical changes. These quantitative models can then be used to inform several properties relevant for hydrological modelling (e.g. water content, permeability).

In this work the open-source software EMagPy is used to compare between EMI data collected with a multi-coil device (i.e. a single frequency device with multiple receiver coils) and a multi-frequency device (i.e. a single inter-coil distance and multiple frequencies). The latter instrument is easier to handle because of its shorter length and lower weight, and thus it is potentially more suitable for the rugged topography of mountain slopes. However it is important to compare the value of information (e.g. sensitivity patterns and data quality) obtained from both instruments.

To begin with, the performance of both devices is assessed using synthetic modeling. Following from this the analysis is focused on two mountainous catchments: one located in the Alpine region above 2000 m a.s.l., the other in a Mediterranean catchment in Southern Italy. Both sites have differing geological and hydrological conditions and provide a useful comparison to determine the suitability of multi-frequency and multi-coil devices, and highlight necessary considerations of EMI acquisition.

How to cite: Blanchy, G., McLachlan, P., Censini, M., Boaga, J., Binley, A., and Cassiani, G.: EMI characterization in mountain catchments: multi-frequency versus multi-coil inversion using EMagPy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5169, https://doi.org/10.5194/egusphere-egu21-5169, 2021.

Detailed 3D structural information of the subsurface is fundamental for the development of both hydrological and geochemical models. This structural information is often derived from geophysical mapping results. Some parts of a catchments areas are however inaccessible for the geophysical mapping or might suffer from low data quality, which results in information gaps. Multipoint statistics can be used to remediate these data gaps and incorporate uncertainty in the construction of the hydrogeological models. This results in an ensemble of plausible 3D hydrogeological models.

This project focusses on nitrate retention mapping. The approach taken is to start from the resistivity models that are obtained from the tTEM measurement campaign. These resistivity datasets are combined with borehole lithological data from the Danish national well-database in an automated procedure that estimates resistivity-to sand/clay translator functions. This results in a clay fraction – resistivity data pair for every point in the subsurface where resistivity data is collected. These clay fraction – resistivity data pairs are converted to discrete hydrogeological units through clustering. This procedure is performed because the groundwater model that uses the end-product of this workflow, uses hydrogeological units rather than resistivity values or clay fractions to define zones of similar hydrogeological behavior.

Direct sampling is used to go from the cluster information obtained at the resistivity model location to fill out the full model volume and generate multiple plausible model realizations. This method allows, at the same time, for incorporating uncertainty through separation of data into a hard  data set for the cluster information with higher probability, and a soft data set for the cluster information with lower probability. Since the redox conditions in the subsurface are related to the hydrogeological conditions, we are using this method to co-simulate hydrogeological units and redox conditions by merging the cluster training dataset with a redox condition training dataset that is constructed based on the cluster dataset and hydrogeochemical samples that are collected across the catchment. We combine the three training images: resistivity, cluster and redox condition, to simultaneous simulate the three variables in each grid point as a vector, instead of simulating them as separate variables.  The resulting set of  3D hydrogeologic structural models and redox condition models retains the complex geostatistical spatial relationships that can exists between the different type of datasets within the training image, making them suitable for nitrate retention modeling at catchment scale.

How to cite: Claes, N., Frederiksen, R. R., Vilhelmsen, T. N., Foged, N., Kim, H., and Christiansen, A. V.: Merging tTEM data and borehole lithological information to generate hydrogeologic structural models and redox conditions through Direct Sampling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7420, https://doi.org/10.5194/egusphere-egu21-7420, 2021.

Groundwater in volcanic islands is usually the main source of freshwater, and it is essential for sustainable development. In Tenerife Island, groundwater extraction occurs by drilling horizontal water tunnels, called water galleries, as well as numerous coastal wells. Since around 1900, but especially since the 1960s, hundreds of water tunnels have been drilled for agriculture and freshwater supply. This has resulted in a sustained extraction of groundwater larger than the natural recharge, leading to a general water table decline, locally up to 200 m of down drop. Since 2000, satellite radar interferometry (InSAR) applied to measure surface deformation has located several subsidence bowls (e.g., Fernandez et al., 2009). The localized surface deformation patterns have been correlated with water table changes and hence aquifer compaction. However, no further investigations have been carried out to confirm which characteristics (chemical composition, texture, porous network, alterations, etc.) of the volcanic materials can control compaction process, and to which extent porous volcanic units, the most abundant material in Tenerife, can compact to explain the observed surface deformation. This lack of knowledge might affect the effectiveness of water management policies.

To investigate the compaction processes affecting the volcanic aquifer, we propose to set up a passive hydrogeophysical monitoring network composed of geodetic and seismological instruments. However, considering logistic constrains it is desirable to have as low as possible number of observation sites, whist maximizing the detection and characterization of the aquifer dynamics. Here, we explore different network configurations to maximize the spatial and temporal characterization of the compaction processes using machine learning methods (low-rank matrix techniques). We pose the network design as an optimization process with the aim to parsimoniously have as fewer as possible ground station sites, and have a low error on reconstructing spatiotemporal land subsidence observations. Land subsidence rates were estimated using Sentinel-1 radar interferometric observations from October 2014 to December 2020. This method allows for an optimal network configuration, with respect to the dual penalty function, which facilitate the decision making. Nevertheless, this type of network design should be regarded as proposals because some station site conditions are a priori unknown. Although, one could modify the penalty function to optimize the network considering additional types of information, e.g., geological materials, groundwater table time series, etc.

Fernandez, J., et al. (2009), Gravity-driven deformation of Tenerife measured by InSAR time series analysis, Geophys. Res. Lett., 36, L04306, doi:10.1029/2008GL036920.

How to cite: Gonzalez, P. J., Charco, M., Eff-Darwich, A., Lamur, A., Marrero, R., and De Angelis, S.: Optimal design of an hydrogeophysical monitoring system of compacting volcanic aquifers (Tenerife Island), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10263, https://doi.org/10.5194/egusphere-egu21-10263, 2021.

Full-waveform inversion (FWI) using ground-penetrating radar (GPR) is gaining momentum as a powerful hydrogeological tool for inferring the hydraulic properties of soils between boreholes [1]. Nonetheless, the large computational requirements of FWI make it often unattainable with limited practical uptake [2]. In addition, the inability to accurate reconstruct the loss mechanisms and the need for a good initial model, further reduce the applicability of FWI [1], [2].

In order to overcome the aforementioned limitations, we suggest a novel framework that substantially reduces the optimization space of FWI which consequently reduces the overall computational requirements [2]. This methodology assumes that the water fraction of the investigated medium follows a fractal distribution [3]. Based on that, using a principal components analysis on 3000 randomly generated fractals, we build an orthonormal basis that is fine-tuned for fractal correlated noise. Furthermore, it is proven [2], that fractal correlated noise is highly compressible and can be sufficiently represented with just 30-40 principal components. This reduces the optimization space since now FWI needs to fine-tune just these 30-40 parameters instead of every cell of the investigated medium [2].

The involved fractals describe the distribution of the water fraction that is subsequently transformed to dielectric properties via a semi-empirical formula that relates readily available soil properties to the frequency depended complex electric permittivity [4], [5]. Via this approach, we overcome the need for a simultaneous FWI for both permittivity and conductivity [6]. This further reduces the optimization space and overcomes pitfalls associated with reconstructing loss mechanisms [2].

References

[1] Klotzsche, A., Vereecken, H., & Kruk van der J., (2019), Review of Crosshole Ground-Penetrating Radar Full-Waveform Inversion of Experimental Data: Recent Developments, challenges, and Pitfalls, Geophysics, vol. 84, pp. H13-H28.

[2] Giannakis, I, Giannopoulos, A., Warren, C. & Sofroniou, A., (2021), Fractal-Constrained Crosshole/Borehole-to-Surface Full Waveform Inversion for Hydrogeological Applications Using Ground-Penetrating Radar, IEEE Transactions on Geoscience and Remote Sensing, Early Access.

[3] Turcotte, L. (1992), Fractal and Chaos in Geology and Geophysics, Cambrige, UK: The Press Syndicate of the University of Cambridge.

[4] Peplinski, N. R., Ulaby, F. T., & Dobson, M. C., (1995), Dielectric Properties of Soils in the 0.3-1.3 GHz Range, IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 3, pp. 803-807.

[5] Giannakis, I., Realistic Numerical Modelling of Ground Penetrating Radar for Landmine Detection, (2016), PhD Thesis Submitted at The University of Edinburgh.

[6] Meles, G. A., Kruk, van der J., Grennhalgh, S. A., Ernst, J. R., Maurer, H & Green, A. G., (2010), A New Vector Waveform Inversion Algorithm for Simultaneous Updating of Conductivity and Permittivity Parameters from Combination Cross/Borehole-to-Surface GPR Data, IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 9, pp. 3391-3407.

How to cite: Giannakis, I., Warren, C., Giannopoulos, A., and Sofroniou, A.: Fractal-Based Orthonormal Basis for Compressing and Reducing the Dimensionality of Full-Waveform Inversion for Hydrogeological Applications Using Ground-Penetrating Radar, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16464, https://doi.org/10.5194/egusphere-egu21-16464, 2021.