Confronted with poor model performance, the Hydrologist has blamed data errors, non-Gaussianities, model nonlinearities, parameter uncertainty, and just about everything else from Pandorra's box. Moreover, recent work has suggested astonishing numerical artefacts may arise from poor model implementation. Yet progress in hydrology requires reducing predictive errors and disentangling individual sources of uncertainty. How can this be accomplished? First, robust and efficient numerical methods are needed to avoid unnecessary artefacts. Second, the formidable interaction between data and structural errors, irresolvable in the absence of independent knowledge, can be approached using statistical analysis of rain- and stream- gauge networks. Structural errors, the key unresolved challenge, can then be explored using flexible model configurations, paving the way for more stringent hypothesis-testing. Importantly, informative diagnostic measures are available for each component of the analysis. This paper surveys several recent developments along these research directions and indicates areas of ongoing and future interest.