Imaging groundwater infiltration dynamics in the karst vadose zone with long-term ERT monitoring

Water infiltration and recharge processes in karst systems are complex and difficult to measure with conventional hydrological methods. In particular, temporarily saturated groundwater reservoirs hosted in the vadose zone can play a buffering role in water infiltration. This results from the pronounced porosity and permeability contrasts created by local karstification processes of carbonate rocks. Analyses of time-lapse 2-D geoelectrical imaging over a period of 3 years at the Rochefort Cave Laboratory (RCL) site in south Belgium highlight variable hydrodynamics in a karst vadose zone. This represents the first long-term and permanently installed electrical resistivity tomography (ERT) monitoring in a karst landscape. The collected data were compared to conventional hydrological measurements (drip discharge monitoring, soil moisture and water conductivity data sets) and a detailed structural analysis of the local geological structures providing a thorough understanding of the groundwater infiltration. Seasonal changes affect all the imaged areas leading to increases in resistivity in spring and summer attributed to enhanced evapotranspiration, whereas winter is characterised by a general decrease in resistivity associated with a groundwater recharge of the vadose zone. Three types of hydrological dynamics, corresponding to areas with distinct lithological and structural features, could be identified via changes in resistivity: (D1) upper conductive layers, associated with clay-rich soil and epikarst, showing the highest variability related to weather conditions; (D2) deeper and more resistive limestone areas, characterised by variable degrees of porosity and clay contents, hence showing more diffuse seasonal variations; and (D3) a conductive fractured zone associated with damped seasonal dynamics, while showing a great variability similar to that of the upper layers in response to rainfall events. This study provides detailed images of the sources of drip discharge spots traditionally monitored in caves and aims to support modelling approaches of karst hydrological processes

Assessment of magnetic data for landfill characterization by means of a probabilistic approach

The sustainable vision of the Dynamic Landfill Management (DLM) deals not only with present but also with long-term waste management. In this context, DLM enhances the environmental assessment of landfills after closure as well as the recovery of materials and energy resources, for which, a proper characterization is required. To this end, geophysical methods have demonstrated their suitability for landfill exploration, characterization and monitoring. Due to the complexity of these sites and challenges in data acquisition and/or processing, the use of multiple methods is the best approach for landfill investigations. In this work, we used multiple geophysical methods, co-located with several trial pits and boreholes, to estimate the structure of a waste disposal site located in a quarry, and to better delineate the underlying geology composed of limestone. We applied electrical resistivity tomography (ERT), time-domain induced polarization (IP), H/V spectral ratio from microtremor records and magnetometry. We made a structural joint interpretation using the different datasets and the ground truth data. First, the ERT and IP data were individually inverted, and a first structural model was derived. Afterwards, we followed a parametric analysis of the H/V data to corroborate the thickness of some layers at the position of the seismic stations. Then, this model was used to compute synthetic magnetic data and by comparing them with the observed total field magnetic anomalies, a refined model was produced. We evaluated the improvement of including magnetic modelling by using a probabilistic approach previously reported. This approach is based on the computation of conditional probabilities by comparing the inverted models with the co-located data from trial pits and boreholes. Overall, we delineated the lateral and vertical extension of the waste body, the distribution of ash and lime deposits and estimated the upper limit structure of the bedrock

Exploring the use of underground gravity monitoring to evaluate radar estimates of heavy rainfall

The radar-based estimation of intense precipitation produced by convective storms is a challenging task and the verification through comparison with gauges is questionable due to the very high spatial variability of such types of precipitation. In this study, we explore the potential benefit of using a superconducting gravimeter as a new source of in situ observations for the evaluation of radar-based precipitation estimates. The superconducting gravimeter used in this study is installed in Membach (BE), 48&thinsp;m underneath the surface, at 85&thinsp;km distance from a C-band weather radar located in Wideumont (BE). The 15-year observation record 2003–2017 is available for both gravimeter and radar with 1 and 5&thinsp;min time steps, respectively. Water mass increase at ground due to precipitation results in a decrease in underground measured gravity. The gravimeter integrates soil water in a radius of about 400&thinsp;m around the instrument. This allows capture of rainfall at a larger spatial scale than traditional rain gauges. The precision of the gravimeter is a few tenths of nm&thinsp;s−2, 1&thinsp;nm&thinsp;s−2 corresponding to 2.6&thinsp;mm of water. The comparison of reflectivity and gravity time series shows that short-duration intense rainfall events produce a rapid decrease in the underground measured gravity. A remarkable correspondence between radar and gravimeter time series is found. The precipitation amounts derived from gravity measurements and from radar observations are further compared for 505 rainfall events. A correlation coefficient of 0.58, a mean bias (radar–gravimeter)/gravimeter of 0.24 and a mean absolute difference (MAD) of 3.19&thinsp;mm are obtained. A better agreement is reached when applying a hail correction by truncating reflectivity values to a given threshold. No bias, a correlation coefficient of 0.64 and a MAD of 2.3&thinsp;mm are reached using a 48&thinsp;dBZ threshold. The added value of underground gravity measurements as a verification dataset is discussed. The two main benefits are the spatial scale at which precipitation is captured and the interesting property that gravity measurements are directly influenced by water mass at ground no matter the type of precipitation: hail or rain.</p

Brief communication: The role of geophysical imaging in local landslide early warning systems

We summarise the contribution of geophysical imaging to local landslide early warning systems (LoLEWS), highlighting how the design and monitoring components of LoLEWS benefit from the enhanced spatial and temporal resolutions of time-lapse geophysical imaging. In addition, we discuss how with appropriate laboratory-based petrophysical transforms, geophysical data can be crucial for future slope failure forecasting and modelling, linking other methods of remote sensing and intrusive monitoring across different scales. We conclude that in light of ever-increasing spatiotemporal resolutions of data acquisition, geophysical monitoring should be a more widely considered technology in the toolbox of methods available to stakeholders operating LoLEWS

Hydrogeological effects on terrestrial gravity measurements

For the 20 last years, terrestrial and satellite gravity measurements have reached such a precision that they allow for identification of the signatures from water storage fluctuations. In particular, hydrogeological effects induce significant time-correlated signature in the gravity time series. Gravity response to rainfall is a complex function of the local geologic and climatic conditions, e.g., rock porosity, vegetation, evaporation, and runoff rates. The gravity signal combines contributions from many geophysical processes, source separation being a major challenge. At the local scale and short-term, the associated gravimetric signatures often exceed the tectonic and GIA effects, and monitoring gravity changes is a source of information on local groundwater mass balance, and contributes to model calibrations. Some aquifer main characteristics can then be inferred by combining continuous gravity, geophysical and hydrogeological measurements. In Membach, Belgium, a superconducting gravimeter has monitored gravity continuously for more than 24 years. This long time series, together with 300 repeated absolute gravity measurements and environmental monitoring, has provided valuable information on the instrumental, metrological, hydrogeological and geophysical points of view. This has allowed separating the signal sources and monitoring partial saturation dynamics in the unsaturated zone, convective precipitation and evapotranspiration at a scale of up to 1 km², for signals smaller than 1 nm/s², equivalent to 2.5 mm of water. Based on this experience, another superconducting gravimeter was installed in 2014 in the karst zone of Rochefort, Belgium. In a karst area, where the vadose zone is usually thicker than in other contexts, combining gravity measurements at the surface and inside accessible caves is a way to separate the contribution from the unsaturated zone lying between the two instruments, from the saturated zone underneath the cave, and the common mode effects from the atmosphere or other regional processes. Those experiments contribute to the assessment of the terrestrial hydrological cycle, which is a major challenge of the geosciences associated with key societal issues: availability of freshwater, mitigation of flood hazards, or measurement of evapotranspiration

Detecting hydrological connectivity using causal inference from time series: synthetic and real karstic case studies

We investigate the potential of causal inference methods (CIMs) to reveal hydrological connections from time series. Four CIMs are selected from two criteria, linear or nonlinear and bivariate or multivariate. A priori, multivariate, and nonlinear CIMs are best suited for revealing hydrological connections because they fit nonlinear processes and deal with confounding factors such as rainfall, evapotranspiration, or seasonality. The four methods are applied to a synthetic case and a real karstic case study. The synthetic experiment confirms our expectation: unlike the other methods, the multivariate nonlinear framework has a low false-positive rate and allows for ruling out a connection between two disconnected reservoirs forced with similar effective precipitation. However, for the real case study, the multivariate nonlinear method was unstable because of the uneven distribution of missing values affecting the final sample size for the multivariate analyses, forcing us to cope with the results' robustness. Nevertheless, if we recommend a nonlinear multivariate framework to reveal actual hydrological connections, all CIMs bring valuable insights into the system's dynamics, making them a cost-effective and recommendable comparative tool for exploring data. Still, causal inference remains attached to subjective choices, operational constraints, and hypotheses challenging to test. As a result, the robustness of the conclusions that the CIMs can draw always deserves caution, especially with real, imperfect, and limited data. Therefore, alongside research perspectives, we encourage a flexible, informed, and limit-aware use of CIMs without omitting any other approach that aims at the causal understanding of a system

Imaging mass‐wasting sliding surfaces within complex glacial deposits along coastal cliffs using geophysics

This study presents a multidisciplinary survey combining geological fieldwork and geophysical data to better constrain the parameters influencing the morphology and behaviour of a retreating coastal cliff. Erosion rates are spatially highly variable and hard to predict because of the manifold parameters acting on them. Among these parameters, rock resistance exerts a paramount influence on cliff retreat. Characterizing the rock resistance distribution along a coastal region requires the mapping of several key subsurface properties including the bulk lithology, faulting, fracturing, or weathering. This is a difficult and expensive task because of the high spatial variability of these factors linked to the spatial complexity of the geology. Geophysical methods can be used to tackle this challenge by quickly providing the 3D visualization and distribution of these parameters within the subsurface. A fast-eroding portion of the Norfolk coast (UK) at West Runton is investigated using a multidisciplinary approach, combining ground-penetrating radar, electrical resistivity tomography (ERT), cone penetration tests, and outcrop studies. The results allowed us to build a 3D geological and geophysical model of a highly complex area of glacial geology. It forms part of a relict glaciotectonic thrust-tip moraine and sand basin sequence. The surfaces interpreted on radar data are associated with strong resistivity contrasts on the ERT data. These contrasts have been attributed to petrophysical variations between the lithological units. The base of the sand basin is marked by a low-permeability clay bed. Its low shear strength is likely to be more susceptible to failure, hereby accelerating the erosion rate of an already fast-eroding sand basin. The resulting model can be used as input for locally constraining the ground parameters in coastal recession and erosion models

Retrieving hydrological connectivity from empirical causality in karst systems

onderzoeksrappor

Retrieving hydrological connectivity from empirical causality in karst systems

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