[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.

Rock physics attempts to relate the geophysical response of a rock to geologic properties of interest, such as porosity, lithology, and fluid content. The geophysical properties estimated by field‐scale surveys, however, are impacted by additional factors, such as complex averaging of heterogeneity at the scale of the survey and artifacts introduced through data inversion, that are not addressed by traditional approaches to rock physics. We account for these field‐scale factors by creating numerical analogs to geophysical surveys via Monte Carlo simulation. The analogs are used to develop field‐scale rock physics relationships that are appropriate for transforming the geophysical properties estimated from a survey into geologic properties. We demonstrate the technique using a synthetic example where radar tomography is used to estimate water content.

Keywords:agent-based modeling Bayes' theorem emerging systems industrial ecology switchgrass technology diffusion Supporting information is available on the JIE Web site SummaryThis article presents a framework to evaluate emerging systems in life cycle assessment (LCA). Current LCA methods are effective for established systems; however, lack of data often inhibits robust analysis of future products or processes that may benefit the most from life cycle information. In many cases the life cycle inventory (LCI) of a system can change depending on its development pathway. Modeling emerging systems allows insights into probable trends and a greater understanding of the effect of future scenarios on LCA results. The proposed framework uses Bayesian probabilities to model technology adoption. The method presents a unique approach to modeling system evolution and can be used independently or within the context of an agent-based model (ABM). LCA can be made more robust and dynamic by using this framework to couple scenario modeling with life cycle data, analyzing the effect of decision-making patterns over time. Potential uses include examining the changing urban metabolism of growing cities, understanding the development of renewable energy technologies, identifying transformations in material flows over space and time, and forecasting industrial networks for developing products. A switchgrass-to-energy case demonstrates the approach.

A number of issues impact electrical resistivity tomography ͑ERT͒ inversions: how ERT measurements sample the subsurface, the nature of subsurface heterogeneity, the geometry selected for data collection, the choice of data-misfit criteria, and regularization of the inverse problem. Lab-scale rock-physics models, typically used to estimate solute concentration from ERT, do not accommodate or account for these issues and therefore produce inaccurate geophysical estimates of solute concentrations. In contrast, the influence of measurement sensitivity and inversion artifacts can be captured by pixel-based rock-physics relationships, determined using numerical analogs that recreate the field-scale ERT experiment based on flow and transport modeling and a priori data. In the 2D synthetic example presented here, where ERT is used to monitor the transport of a saline tracer through the subsurface, improved estimates of concentration are obtained when field-scale rock-physics relationships based on numerical analogs are used.