Understanding ecologically sensitive wetlands often requires non‐invasive methods to characterize their complex structure (e.g., deposit heterogeneity) and hydrogeological parameters (e.g., porosity and hydraulic conductivity). Here, electrical conductivities of a riparian wetland were obtained using frequency domain electromagnetic induction (EMI) methods. The wetland was previously characterized by extensive intrusive measurements and 3D electrical resistivity tomography (ERT) surveys and hence offers an ideal opportunity to objectively assess EMI methods. Firstly, approaches to obtain structural information (e.g., elevation and thickness of alluvium) from EMI data and inverted models were assessed. Regularized and sharp inversion algorithms were investigated for ERT calibrated EMI data. Moreover, the importance of EMI errors in inversion was investigated. The hydrological information content was assessed using correlations with piezometric data and petrophysical models. It was found that EMI data were dominated by the thickness of peaty alluvial soils and relatively insensitive to topography and total alluvial thickness. Furthermore, although error weighting in the inversion improved the accuracy of alluvial soil thickness predictions, the multi‐linear regression method performed the best. For instance, an iso‐conductivity method to estimate the alluvial soil thickness in the regularized models had a normalized mean absolute difference (NMAD) of 21.4%, and although this performed better than the sharp inversion algorithm (NMAD = 65.3%), the multi‐linear regression approach (using 100 intrusive observations) achieved a NMAD = 18.0%. In terms of hydrological information content, correlations between EMI results and piezometric data were poor, however robust relationships between petrophysically derived porosity and hydraulic conductivity were observed for the alluvial soils and gravels.

In the past several decades, there has been considerable interest in groundwater–surface water interactions and their ability to regulate and cycle nutrients and pollutants. These interactions are spatially and temporally complex, but electrical resistivity imaging can be a useful tool for their characterization. Here, an electrical resistivity imaging monitoring array was installed laterally across a groundwater‐dominated Chalk river and into the adjacent riparian wetland; data were collected over a period of 1 year. Independent inversions of data from the entire transect were performed to account for the changing river stage and river water conductivity. Additionally, data from just the riparian zone were inverted using a temporally constrained inversion, and the correlation between the riparian zone resistivity patterns and river stage was assessed using time‐series analysis. The river stage and the Chalk groundwater level followed similar patterns throughout the year, and both exhibited a sharp drop following cutting of in‐stream vegetation. For the independent inversions, fixing the river resistivity led to artifacts, which prevented reliable interpretation of dynamics in the riverbed. However, the resistivity structure of the riparian zone coincided well with the intrusively derived boundary between the peat and the gravel present at the field site. Time‐series analysis of the inverted riparian zone models permitted identification of seven units with distinct hydrological resistivity dynamics (five zones within the peat and two within the gravel). The resistivity patterns in the gravel were predominantly controlled by up‐welling of resistive groundwater and the down‐welling of more conductive peat waters following the river vegetation cutting event. In comparison, although the vegetation cutting influenced the resistivity dynamics in the peat zones, the resistivity dynamics were also influenced by precipitation events and increasing pore‐water conductivity, likely arising from biological processes. It is evident that such approaches combining electrical resistivity imaging and time‐series analysis are useful for understanding the spatial extent and timing of hydrological processes to aid in the better characterization of groundwater‐surface water interactions.

Time-lapse geophysical studies have been used as noninvasive tools to study subsurface processes. However, compared to direct current resistivity methods, the transient electromagnetic (TEM) method has not been explored in much detail for time-lapse investigations. Here, we introduce the use of a monitoring TEM (mTEM) system and methodology for long-term groundwater monitoring purposes. The system can run measurements automatically and the modeling approach uses a time-constrained inversion (TCI) scheme based on an existing laterally constrained inversion scheme. Instead of lateral constraints that traditionally ensure lateral coherency between measurements, temporal constraints are used to control the temporal coherency between measurements. Synthetic analysis demonstrates that very tight time constraints on resistivities and a time constraint on the depth to the water table fixed at a fraction of the expected annual water table variation is needed to track water table changes at the decimeter level. Furthermore, the synthetic analysis indicates that the mTEM method can track annual variations in the water table with amplitudes as low as 0.6 m with a mean absolute error of 0.2 at depths between 16 m and 30 m. A field example consisting of 233 1D TEM measurements acquired over 10 months using the mTEM instrument supports the synthetic results. The estimated water table depth from the TCI scheme reproduces the variation in the water table accurately, as confirmed by measurements from a water level data logger in a nearby well. The differences between the results from the TEM monitoring system and the diver measurements are in the range of ±0.1 m with an average absolute difference of 0.05 m. This study demonstrates the potential to monitor changes in the groundwater level noninvasively.

Understanding hydrogeological processes often requires continuous measurements, which in many cases are not cost‐efficient. Time‐lapse geophysical studies are invaluable for investigating dynamic subsurface processes. They offer several advantages over traditional borehole‐based monitoring methods, such as being cost‐efficient and non‐intrusive. However, to be effective as an early warning tool, these studies must provide real‐time information on the processes. This is often not feasible with traditional geophysical methods, which typically do not offer real‐time monitoring. In this study, we present a windowed inversion approach for time‐lapse transient electromagnetic (TEM) monitoring. The advantages of this approach include: (i) immediate and continuous results as each time‐step dataset is acquired; (ii) no limitations on the size of the monitoring period or dataset; and (iii) the use of model information from previous inversion results for robust and continuous outcomes. The results from a synthetic study are followed by two field case studies, demonstrating the advantages of the windowed inversion compared to the inversion of the full dataset. We monitored the water table dynamics of a shallow unconfined aquifer over 8 months and the saltwater intrusion in a confined aquifer over 10 months using a monitoring TEM (mTEM) system while collecting nearly continuous daily measurements. With the windowed approach, the water table dynamics was recovered with an accuracy of 10 cm, and the changes in salinity were tracked with results comparable to those of an EC data logger.

Typically, transient electromagnetic (TEM) hydrogeological applications focus on spatial characterization. However, there is scope for utilizing time‐lapse TEM to monitor dynamic processes. Changes related to hydrogeological processes are subtle; consequently, measurements must be of exceptional quality, and the presence and influence of noise sources must be well understood. A potentially problematic noise source is radio waves operating in the 3–300 kHz range, as they introduce correlated error into TEM measurements. For instance, it was anticipated that such radio waves could introduce smooth perturbations in the data that could be erroneously interpreted in the TEM inversion results. This work evaluates the presence of correlated noise sources, their temporal dependence and their significance for TEM monitoring. This work combines fully sampled data and normalized covariance matrices of gated data to identify correlated noise sources. Radio signals operating in the low and very low frequency (LF and VLF) bands were found to vary across the investigation period; however, their influence on the TEM inversion results was minimal. Measurement stacking remains an effective way to improve the signal‐to‐noise ratio of TEM data affected by LF and VLF noise sources as they do not introduce bias, for example smooth perturbation, into the TEM data explored in this work.

Permafrost is a widespread phenomenon in the cold regions of the globe and is under‐represented in global monitoring networks. This study presents a novel low‐cost, low‐power, and robust Autonomous Electrical Resistivity Tomography (A‐ERT) monitoring system and open‐source processing tools for permafrost monitoring. The processing workflow incorporates diagnostic and filtering tools and utilizes open‐source software, ResIPy, for data inversion. The workflow facilitates quick and efficient extraction of key information from large data sets. Field experiments conducted in Antarctica demonstrated the system's capability to operate in harsh and remote environments and provided high‐temporal‐resolution imaging of ground freezing and thawing dynamics. This data set and processing workflow allow for a detailed investigation of how meteorological conditions impact subsurface processes. The A‐ERT setup can complement existing monitoring networks on permafrost and is suitable for continuous monitoring in polar and mountainous regions, contributing to cryosphere research and gaining deeper insights into permafrost and active layer dynamics.