Utilizing a DUALEM‐421 and inversion modelling to map baseline soil salinity along toposequences in the Hunter Valley Wine district

In the oldest commercial wine district of Australia, the Hunter Valley, there is the threat of soil salinization because marine sediments underlie the area. To understand the risk requires information about the spatial distribution of soil properties. Electromagnetic (EM) induction instruments have been used to identify and map the spatial variation of average soil salinity to a certain depth. However, soils vary with depth dependent on soil forming factors. We collected data from a single‐frequency and multiple‐coil DUALEM‐421 along a toposequence. We inverted this data using EM4Soil software and evaluated the resultant 2‐dimensional model of true electrical conductivity (σ – mS/m) with depth against electrical conductivity of saturated soil pastes (EC_p – dS/m). Using a fitted linear regression (LR) model calibration approach and by varying the forward model (cumulative function‐CF and full solution‐FS), inversion algorithm (S1 and S2), damping factor (λ) and number of arrays, we determined a suitable electromagnetic conductivity image (EMCI), which was optimal (R² = 0.82) when using the full solution, S2, λ = 3.6 and all six coil arrays. We conducted an uncertainty analysis of the LR model used to estimate the electrical conductivity of the saturated soil‐paste extract (EC_e – dS/m). Our interpretation based on estimates of EC_e suggests the approach can identify differences in salinity, how these vary with parent material and how topography influences salt distribution. The results provide information leading to insights into how soil forming factors and agricultural practices influence salinity down a toposequence and how this can guide soil management practices.     

Mapping soil salinity in 3‐dimensions using an EM38 and EM4Soil inversion modelling at the reconnaissance scale in central Morocco

Large areas of Morocco require irrigation and although good quality water is available in dams, farmers augment river water with poorer quality ground water, resulting in salt build‐up without a sufficient leaching fraction. Implementation of management plans requires baseline reconnaissance maps of salinity. We developed a method to map the distribution of salinity profiles by establishing a linear regression (LR) between calculated true electrical conductivity (σ, mS/m) and electrical conductivity of the saturated soil‐paste extract (ECe, dS/m). Estimates of σ were obtained by inverting the apparent electrical conductivity (ECa, mS/m) collected from a 500‐m grid survey using an EM38. Spherical variograms were developed to interpolate ECa data onto a 100 m grid using residual maximum likelihood. Inversion was carried out on kriged ECa data using a quasi‐3d model (EM4Soil software), selecting the cumulative function (CF) forward modelling and S2 inversion algorithm with a damping factor of 3.0. Using a ‘leave‐one‐out cross‐validation' (LOOCV), of one in 12 of the calibration sites, the use of the q‐3d model yielded a high accuracy (RMSE = 0.42 dS/m), small bias (ME = ?0.02 dS/m) and Lin's concordance (0.91). Slightly worse results were obtained using individual LR established at each depth increment overall (i.e. RMSE = 0.45 dS/m; ME = 0.00 dS/m; Lin's = 0.89) with the raw EM38 ECa. Inversion required a single LR (ECe = 0.679 + 0.041 × σ), enabling efficiencies in estimating ECe at any depth across the irrigation district. Final maps of ECe, along with information on water used for irrigation (ECw) and the characterization of properties of the two main soil types, enabled better understanding of causes of secondary soil salinity. The approach can be applied to problematic saline areas with saline water tables.     

Detecting a leachate plume in an aeolian sand landscape using a DUALEM‐421 induction probe to measure electrical conductivity followed by inversion modelling

Solid waste poses a serious health risk when it is disposed of inadequately because water‐based solutions derived from the decomposition of solid waste products (leachate) can enter groundwater systems via plumes. To assess the public health risk and potential ecological impacts, we require knowledge on the pedological and hydrogeological settings in which waste is disposed. This is particularly the case in coarse textured highly permeable soil. To rapidly collect data, geophysical methods such as direct current (dc) resistivity techniques have been used. Moreover, non‐contact electromagnetic (EM) induction instruments have also been employed. The aim of this research was to demonstrate how the inversion using a 1‐dimensional inversion algorithm with lateral constraints of the apparent electrical conductivity (σ_a) measured in the horizontal coplanar (HCP) and perpendicular co‐planar arrays (PRP) of a DUALEM‐421 EM induction probe can be used to develop a two‐dimensional model of the true electrical conductivity (σ) within a Quaternary aeolian sand in the Tuggerah Soil Landscape southeast of Sydney in Australia. Our results from 2D models of σ accord with estimates of bulk electrical conductivity (σ_b) of a leachate plume and uncontaminated groundwater, the stratigraphy of the Tuggerah soil landscape unit and the depth of sand used to landscape the decommissioned landfill. Further research is needed to determine the origin of the plume and a quasi‐3D modelling approach is applicable.