No abstract

Interactions between groundwater (GW) and surface water (SW) have important implications for water quantity, water quality, and ecological health. The subsurface region proximal to SW bodies, the GW-SW interface, is crucial as it actively regulates the transfer of nutrients, contaminants, and water between GW systems and SW environments. However, geological, hydrological, and biogeochemical heterogeneity in the GW-SW interface makes it difficult to characterise with direct observations. Over the past two decades geophysics has been increasingly used to characterise spatial and temporal variability throughout the GW-SW interface. Geophysics is a powerful tool in evaluating structural heterogeneity, revealing zones of GW discharge, and monitoring hydrological processes. Geophysics should be used alongside traditional hydrological and biogeochemical methods to provide additional information about the subsurface. Further integration of commonly used geophysical techniques, and adoption of emerging techniques, has the potential to improve understanding of the properties and processes of the GW-SW interface, and ultimately the implications for water quality and environmental health.

The cumulative sensitivity forward model is limited in some cases.EMagPy is an open-source Python API and GUI for 1D EMI modeling/inversion. Application of EMagPy is illustrated through cases studies with real and synthetic data. Both Maxwell-based and cumulative sensitivity forward models are implemented. Inversion algorithms include deterministic and stochastic methods.

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.

(1.2-7.0%). There are large differences between the median DR values (rescaled to 10 <500 µm) for soils over the PM types, which when used as a predictor (in combina-11 tion with SOC concentration) accounted for 53% of the variation in DR. There was no 12 evidence for including an interaction term between PM class and SOC concentration 13 for the prediction of DR. After applying the aggregate stability tests using the sixty 14 regional soil samples, they were stored for nine months and the tests were repeated 15 resulting in a small but statistically significant increase in DR for samples from some, 16but not all, PM types. We show how a palaeosol excavated from a site in southern 17