## 1. Introduction

[2] Anomalous transport behavior, including solute concentration rebound after pumping stops and long tailing, has been noted in numerous field and laboratory experiments [*Feehley et al*., 2000; *Harvey and Gorelick*, 2000]. Such behavior is a principal control on the efficiency of aquifer remediation [*Harvey et al*., 1994] and water resource management using aquifer storage and recovery [*Culkin et al*., 2008]. Several mathematical models have been proposed to explain such behavior, including dual-domain mass transfer (DDMT) [*van Genuchten and Wierenga*, 1976], multirate mass transfer [*Haggerty and Gorelick*, 1995], fractional dispersion [*Benson et al*., 2000], and continuous time random walk [*Berkowitz et al*., 2006]. Although these models can reproduce observed transport behavior, identification of appropriate model parameters (e.g., mass transfer rate coefficient) is impeded by the lack of experimental methods that can directly interrogate the immobile pore space. Commonly, model parameters are identified by history matching and model calibration to mobile fluid tracer concentrations [*Singha et al*., 2007]. Because the occurrence of anomalous-transport phenomena depends strongly on experiment time scales [*Haggerty et al*., 2001], and the relative rates of advection and diffusion, parameters estimated based on a single flow path averaged experiment may not prove effective under different conditions.

[3] Recent work at pilot field [*Singha et al*., 2007] and laboratory scales [*Swanson et al*., 2012] has demonstrated that anomalous solute transport has an observable geoelectrical signature that, in principle, can be exploited to directly determine mass transfer parameters (i.e., rate coefficient, mobile porosity, and immobile porosity) [*Day-Lewis and Singha*, 2008]. Whereas, fluid sampling preferentially draws from the mobile domain, electrical measurements are sensitive to solute tracer in both mobile and immobile domains. The relation between bulk and fluid conductivity appears hysteretic, or time dependent, in the presence of rate-limited mass transfer. Hysteresis between fluid and bulk conductivity is observed because bulk conductivity (mobile and immobile domains) lags behind relatively quick changes in fluid conductivity (mobile domain) on both the rising and falling limbs of a solute breakthrough curve (BTC) due to rate-limited mass transfer between mobile and immobile pore space. The electrical signature of mass transfer was observed first in field-experimental data by *Singha et al*. [2007], but only limited sensitivity analysis was performed for that data set, and rigorous parameter estimation was not attempted. *Swanson et al*. [2012] recently inferred mass transfer parameters using parametric sweeps for controlled homogeneous column experiments with independently evaluated immobile porosity. Forward simulations of transport were run for several thousand combinations of mass transfer parameters, and the fit to experimental data was evaluated. The previous work detailed above relied on either (1) analysis based on temporal moments [*Day-Lewis and Singha*, 2008], which are subject to error in the presence of measurement noise or (2) parametric sweeps [*Swanson et al*., 2012], which provide limited insight into parameter sensitivity or information content.

[4] Here, we extend the use of electrical measurements to inference of field-scale mass transfer parameters at both the effective flow path and local scales by adopting a model-calibration strategy to match both fluid conductivity and geoelectrical tracer signatures simultaneously. Our approach facilitates rigorous sensitivity analysis to evaluate the information content of the geophysical data, and provides insight to describe the true variability in local mass transfer parameters. We apply our approach to conservative field tracer data from a former uranium mill tailings site near Naturita, Colorado [e.g., *Curtis et al*., 2006; *Brosten et al*., 2011]. At this site, the fluid sample-geoelectrical measurement data exhibit the expected signature of mass transfer. We calibrate a series of one-dimensional DDMT models, for various sampling locations, to tracer and geophysical data using nonlinear regression. For transport simulation, we use the code Modular Three-Dimensional Multispecies model (MT3DMS) [*Zheng and Wang*, 1999]. For model calibration, the transport model is linked to a computer code for universal inverse modeling (UCODE_2005) [*Poeter et al*., 2005] nonlinear regression package, which also performs sensitivity analysis. Finally, we introduce an Eulerian-based Damköhler number useful for planning experiments and evaluating whether tracer injection duration was sufficient to observe hysteresis and determine local-scale mass transfer.