Modern environmental management and decision-making is based on the use of increasingly complex numerical models. Such models have the advantage of allowing representation of complex processes and heterogeneous system property distributions inasmuch as these are understood at any particular study site. The latter are often represented stochastically, this reflecting knowledge of the character of system heterogeneity at the same time as it reflects a lack of knowledge of its spatial details. Unfortunately, however, complex models are often difficult to calibrate because of their long run times and sometimes questionable numerical stability. Analysis of predictive uncertainty is also a difficult undertaking when using models such as these. Such analysis must reflect a lack of knowledge of spatial hydraulic property details. At the same time, it must be subject to constraints on the spatial variability of these details born of the necessity for model outputs to replicate observations of historical system behavior. In contrast, the rapid run times and general numerical reliability of simple models often promulgates good calibration and ready implementation of sophisticated methods of calibration-constrained uncertainty analysis. Unfortunately, however, many system and process details on which uncertainty may depend are, by design, omitted from simple models. This can lead to underestimation of the uncertainty associated with many predictions of management interest. The present paper proposes a methodology that attempts to overcome the problems associated with complex models on the one hand and simple models on the other hand, while allowing access to the benefits each of them offers. It provides a theoretical analysis of the simplification process from a subspace point of view, this yielding insights into the costs of model simplification, and into how some of these costs may be reduced. It then describes a methodology for paired model usage through which predictive bias of a simplified model can be detected and corrected, and postcalibration predictive uncertainty can be quantified. The methodology is demonstrated using a synthetic example based on groundwater modeling environments commonly encountered in northern Europe and North America.