Current approaches for inverse modeling (IM) to estimate flow and transport properties in subsurface media implicitly assume that uncertainty in the input-output representation of the model arises from uncertainty in the parameter estimates. However, uncertainties in the modeling procedure stem not only from uncertainties in the parameter estimates, but also from measurement errors, from incomplete knowledge of subsurface heterogeneity, and from model structural errors arising from the aggregation of spatially distributed real-world processes in a mathematical model. In this paper we present an improved concept for IM of subsurface flow and transport. Studies using interwell reactive tracer test data demonstrate that this new method, called Simultaneous Optimization and Data Assimilation, results in parameter estimates and model prediction uncertainty bounds which more closely mimic the properties of the subsurface. Most important is the finding that explicit treatment of input, output and model structural errors during IM, significantly alters the optimal values of the model parameters.