Are we unnecessarily constraining the agility of complex process-based models?

We argue that the relatively poor performance of such models can occur due to restrictions on their ability to refine their portrayal of physical processes, in part because of strong a priori constraints in: (i) the representation of spatial variability and hydrologic connectivity, (ii) the choice of model parameterizations, and (iii) the choice of model parameter values.

We provide a specific example of problems associated with strong a priori constraints on parameters in a land surface model. Moving forward, we assert that improving hydrological models requires integrating the strengths of the "physics-based" modeling philosophy (which relies on prior knowledge of hydrologic processes) with the strengths of the "conceptual" modeling philosophy (which relies on data driven inference). Such integration will accelerate progress on methods to define and discriminate among competing modeling options, which should be ideally incorporated in agile modeling frameworks and tested through a diagnostic evaluation approach.