Understanding sensitive wetlands often requires non-invasive methods to characterize their complex geological structure and hydrogeological parameters. Here, geoelectrical characterization is explored by employing frequency-domain electromagnetic induction (EMI) at a site previously characterized by extensive intrusive measurements and 3D electrical resistivity tomography (ERT). This work investigates the performance of several approaches to obtain structural information from EMI data and sharp and smooth inversions. Additionally, the hydrological information content of EMI data is investigated using correlation with piezometric measurements, established petrophysical relationships, and synthetic modeling. EMI measurements were dominated by peat thickness and were relatively insensitive to both topography and depth to bedrock. An iso-conductivity method for peat depth estimation had a normalized mean absolute difference (NMAD) of 23.5%, and although this performed better than the sharp inversion algorithm (NMAD = 73.5%), a multi-linear regression approach achieved a more accurate prediction with only 100 measurements (NMAD = 17.8%). In terms of hydrological information content, it was not possible to unravel correlation causation at the site, however, synthetic modeling demonstrates that the EMI measurements are predominantly controlled by the electrical conductivity of the upper peat pore-water and not the thickness of the unsaturated zone or the lower peat pore-water conductivity. Additionally, a priori information significantly improves the potential for time-lapse applications in similar environments. This study provides an objective overview and insights for future EMI applications in similar environments. It also covers areas seldom investigated in EMI studies, e.g. error quantification and the depth of investigation of ERT models used for EMI calibration.

Southern Africa is facing unprecedented strains on its agriculture, including a rapidly increasing population and demand for cereals. The global issues of climate change, water scarcity, and soil erosion are also affecting southern Africa, which expects a drier climate in the future. A promising tool in the fight for food security is Conservation Agriculture (CA), a technique based on minimum soil disturbance, mulching using crop residues, and crop rotation and/or intercrops. CA is promoted by organisations including the United Nations due to its potential to increase crop yields in arid/semi-arid climates; increase drought resilience; and increase infiltration of rainwater, reducing flooding and erosion. Despite its benefits and promotion, little is understood of the hydrodynamics of soils under CA cultivation. In order to investigate these hydrological processes, we installed Electrical Resistivity Tomography (ERT) monitoring systems (PRIME, developed by BGS) at three agricultural research sites in southern Africa (Zambia, Malawi, & Zimbabwe) under CA and conventional tillage systems. The sites are also instrumented with soil temperature, moisture, and matric potential sensors, as well as monitored groundwater boreholes, enabling comparison between monitoring techniques and the tracking of water from the ground surface to depth. ERT deployments for the respective sites include surface 2D, shallow cross-borehole 3D, and surface 3D electrode arrays. Each PRIME system is configured for twice daily data collection, and uses data telemetry for remote data retrieval. ERT monitoring allows us to monitor the hydrodynamics from the root zone, through the soil profile and vadose zone, to the aquifer. Initial results show variability between the sites, and heterogeneous nature of the vadose zone within the sites. This heterogeneity has been shown to influence preferential fluid flow pathways in the vadose zone. Monitoring over rainfall events has shown a strong, rapid response of pronounced, shallow wetting fronts, with limited changes at depth. We are beginning the process of comparing the hydrodynamics between CA and conventional plots, and the procedure of optimising data processing to enable better imaging of soil moisture changes at depth in the presence of rapid near surface changes.