Quantitative imaging of solute transport in an unsaturated and undisturbed soil monolith with 3‐D ERT and TDR

Abstract

[1] Electrical resistivity tomography (ERT) has proved to be a valuable tool for imaging solute transport processes in the subsurface. However, a quantitative interpretation of corresponding ERT results is constrained by a number of factors. One such factor is the nonuniqueness of the ERT inverse problem if no additional constraints are imposed. In the vadose zone, further problems arise from the general ambiguity of the imaged bulk electrical conductivity in terms of water content and solute concentration. In this study we address these issues in detail for a solute tracer experiment conducted in an undisturbed unsaturated soil monolith where the tracer transport was monitored by means of 3-D smoothness-constrained ERT and time domain reflectometry (TDR) measurements. The experimental design allowed the determination of solute tracer concentrations directly from imaged bulk electrical conductivity. Independent TDR data and effluent tracer concentrations provided a ''ground truth'' for the ERT-derived apparent convection-dispersion equation transport parameters. The apparent transport velocity calculated from the ERT results was consistent with that based on TDR data and the sampled effluent, independent of the degree of smoothness imposed in the ERT inversion. On the other hand, the apparent dispersivity calculated from the ERT results was larger than that estimated from TDR data but smaller than that estimated from the sampled effluent, with the magnitude of deviations dependent on the degree of smoothing. Importantly, no mass balance problems were observed in the ERT results. We believe that this is largely a consequence of the uniform application of the tracer as a front and of the configuration of the electrode array with respect to the main transport direction. In conclusion, the study demonstrates that ERT can yield unprecedented quantitative information about local-and column-scale solute transport characteristics in natural soils.

Monitoring hillslope moisture dynamics with surface ERT for enhancing spatial significance of hydrometric point measurements

“…Many studies show the potential of electrical resistivity tomography (ERT) for hydrological investigation by means of synthetic case studies for aquifer transport characterization (Kemna et al, 2004;Vanderborght et al, 2005), imaging water flow on soil cores (Bechtold et al, 2012;Binley et al, 1996a, b;Garré et al, 2010Garré et al, , 2011Koestel et al, 2008Koestel et al, , 2009a, cross-borehole imaging of tracers (Daily et al, 1992;Oldenborger et al, 2007;Ramirez et al, 1993;Singha and Gorelick, 2005;Slater et al, 2000), or imaging of tracer injection or irrigation with surface ERT (Cassiani et al, 2006;De Morais et al, 2008;Descloitres et al, 2008a;Michot et al, 2003;Perri et al, 2012). However, some research has been conducted under natural conditions to characterize water content change, infiltration or discharge by use of cross-borehole ERT (French and Binley, 2004), surface ERT (Brunet et al, 2010;Benderitter and Schott, 1999;Descloitres et al, 2008b;Massuel et al, 2006;Miller et al, 2008) or a combined surface cross-borehole ERT array (Beff et al, 2013;Zhou et al, 2001).…”

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

“…TDR devices are designed to measure the dielectric properties of soils. More precisely, they measure the apparent electrical permittivity, from which not only the dielectric constant but also the effective electrical conductivity can be deduced (e.g., Dalton et al, 1984;Topp et al, 1988;Weerts et al, 2001;Noborio, 2001;Robinson et al, 2003;Lin et al, 2007Lin et al, , 2008Thomsen et al, 2007;Huisman et al, 2008;Koestel et al, 2008;Bechtold et al, 2010). In general, TDR measurements might be difficult to use to recover the electrical conductivity with the desired accuracy.…”

Subsurface fluid distribution and possible seismic precursory signal at the Salse di Nirano mud volcanic field, Italy

“…Appropriately adjusting the error model that describes data uncertainty in the inversion (e.g. Koestel et al 2008) is important to avoid overfitting the data and thus the creation of artefacts in the obtained images. From a careful analysis of the data and the inversion results obtained for different error model parameters, we set the data error to a combination of 2 per cent relative and 5 × 10 −4 absolute resistance error.…”