[1] There is increasing interest in the use of multiple measurement types, including indirect (geophysical) methods, to constrain hydrologic interpretations. To date, most examples integrating geophysical measurements in hydrology have followed a three-step, uncoupled inverse approach. This approach begins with independent geophysical inversion to infer the spatial and/or temporal distribution of a geophysical property (e.g., electrical conductivity). The geophysical property is then converted to a hydrologic property (e.g., water content) through a petrophysical relation. The inferred hydrologic property is then used either independently or together with direct hydrologic observations to constrain a hydrologic inversion. We present an alternative approach, coupled inversion, which relies on direct coupling of hydrologic models and geophysical models during inversion. We compare the abilities of coupled and uncoupled inversion using a synthetic example where surface-based electrical conductivity surveys are used to monitor onedimensional infiltration and redistribution. Through this illustrative example, we show that the coupled approach can provide significant reductions in uncertainty for hydrologic properties and associated predictions if the underlying model is a faithful representation of the hydrologic processes. However, if the hydrologic model exhibits structural errors, the coupled inversion may not improve the hydrologic interpretation. Despite this limitation, our results support the use of coupled hydrogeophysical inversion both for the direct benefits of reduced errors during inversion and because of the secondary benefits that accrue because of the extensive communication and sharing of data necessary to produce a coupled model, which will likely lead to more thoughtful use of geophysical data in hydrologic studies.

Electromagnetic parameters of the subsurface such as electrical conductivity are of great interest for non‐destructive determination of soil properties (e.g., clay content) or hydrologic state variables (e.g., soil water content). In the past decade, several non‐invasive geophysical methods have been developed to measure subsurface parameters in situ. Among these methods, electromagnetic (EM) induction appears to be the most efficient one that is able to cover large areas in a short time. However, this method currently does not provide absolute values of electrical conductivity due to calibration problems, which hinders a quantitative analysis of the measurement. In this study, we propose to calibrate EM induction measurements with electrical conductivity values measured with electrical resistivity tomography (ERT). EM induction measures an apparent electrical conductivity at the surface, which represents a weighted average of the electrical conductivity distribution over a certain depth range, whereas ERT inversion can provide absolute values for local conductivities as a function of depth. EM induction and ERT measurements were collected along a 120‐metre‐long transect. To reconstruct the apparent electrical conductivity measured with EM induction, the inverted ERT data were used as input in an electromagnetic forward modelling tool for magnetic dipoles over a horizontally layered medium considering the frequencies and offsets used by the EM induction instruments. Comparison of the calculated and measured apparent electrical conductivities shows very similar trends but a shift in absolute values, which is attributed to system calibration problems. The observed shift can be corrected for by linear regression. This new calibration strategy for EM induction measurements now enables the quantitative mapping of electrical conductivity values over large areas.

[1] The relevance of aquifer heterogeneity for flow and transport is recognized broadly; however, its characterization is hampered by the inaccessibility of the subsurface. Time-lapse electrical resistivity tomography (ERT) offers the possibility of imaging noninvasively subsurface transport. We present results of two tracer tests that were carried out successively in a shallow aquifer at the Krauthausen test site (Germany). The breakthroughs of an electrically conductive and a resistive tracer were monitored with ERT and local multilevel groundwater samplers (MLS) along two cross sections perpendicular to the mean flow direction. Sinking of the conductive salt tracer due to density effects was observed with ERT. We applied a stream tube model to characterize the spatially variable transport. ERT-derived stream tube parameters showed similar patterns for the two tracer experiments, reflecting the effect of aquifer heterogeneity on transport. MLS data did not show similar spatial patterns, which indicates that these measurements may be prone to subtle changes of the flow field in the small sampling volume and mixing within screened wells. Between 50% and 10% of the tracer was recovered in the ERT-derived breakthrough curves. Compared with transport simulations in a homogeneous aquifer, ERT-derived time-integrated changes in electrical conductivity were locally larger but focused in a smaller area. MLS data indicated that in this area, ERT did not underestimate the tracer recovery. The relatively low tracer recovery was attributed to undetected tracer breakthrough in regions with low ERT sensitivity and in regions where the length of the tracer plume and the electrical conductivity contrast were small.

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Abstract. Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown.