When do aquatic systems models provide useful predictions, what is changing, and what is next?

This article considers how aquatic systems modelling has changed since 1995 and how it must change in future if we are to continue to advance. A distinction is made between mechanistic and statistical models, and the relative merits of each are considered. The question of "when do aquatic systems models provide accurate and useful predictions?" is addressed, implying some guidelines for model development. It is proposed that, in general, ecological models only provide management-relevant predictions of the behaviour of real systems when there are strong physical (as opposed to chemical or ecological) drivers. Developments over the past 15 years have included changes in technology, changes in the modelling community and changes in the context in which modelling is conducted: the implications of each are briefly discussed. Current trends include increased uptake of best practice guidelines, increasing integration of models, operationalisation, data assimilation, development of improved tools for skill assessment, and application of models to new management questions and in new social contexts. Deeper merging of statistical and mechanistic modelling approaches through such techniques as Bayesian Melding, Bayesian Hierarchical Modelling and surrogate modelling is identified as a key emerging area. Finally, it is suggested that there is a need to systematically identify areas in which our current models are inadequate. We do not yet know for which categories of problems well-implemented aquatic systems models can (or cannot) be expected to accurately predict observational data and system behaviour. This can be addressed through better modelling and publishing practices. Recent changes have resulted from changes technology and the modelling community.Active topics include integration, uncertainty, operationalisation and data assimilation.Emerging approaches more deeply combine statistical and mechanistic thinking.Conditions in which models are likely to provide useful predictions are discussed.We need to honestly and transparently report both model successes and failures.