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We present a workflow for efficient construction and calibrationof large-scale groundwater models that includes the integration of airborneelectromagnetic (AEM) data and hydrological data. In the first step, the AEMdata are inverted to form a 3-D geophysical model. In the second step, the3-D geophysical model is translated, using a spatially dependentpetrophysical relationship, to form a 3-D hydraulic conductivitydistribution. The geophysical models and the hydrological data are used toestimate spatially distributed petrophysical shape factors. The shape factorsprimarily work as translators between resistivity and hydraulic conductivity,but they can also compensate for structural defects in the geophysical model.  The method is demonstrated for a synthetic case study with sharp transitionsamong various types of deposits. Besides demonstrating the methodology, wedemonstrate the importance of using geophysical regularization constraintsthat conform well to the depositional environment. This is done by invertingthe AEM data using either smoothness (smooth) constraints or minimum gradientsupport (sharp) constraints, where the use of sharp constraints conforms bestto the environment. The dependency on AEM data quality is also tested byinverting the geophysical model using data corrupted with four differentlevels of background noise. Subsequently, the geophysical models are used toconstruct competing groundwater models for which the shape factors arecalibrated. The performance of each groundwater model is tested with respectto four types of prediction that are beyond the calibration base: a pumpingwell's recharge area and groundwater age, respectively, are predicted byapplying the same stress as for the hydrologic model calibration; and headand stream discharge are predicted for a different stress situation.  As expected, in this case the predictive capability of a groundwater model isbetter when it is based on a sharp geophysical model instead of a smoothnessconstraint. This is true for predictions of recharge area, head change, andstream discharge, while we find no improvement for prediction of groundwaterage. Furthermore, we show that the model prediction accuracy improves withAEM data quality for predictions of recharge area, headchange, and stream discharge, whilethere appears to be no accuracy improvement for the prediction of groundwaterage

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy