This paper presents a brief historical excursus on the development of hydrological catchment models together with a number of possible future perspectives. Given the wide variety of available hydrological models which, according to the embedded level of prior physical information, vary from the simple input–output lumped models to complex physically meaningful ones, the paper suggests how to accommodate and to reconcile the different approaches. This can be performed by better clarifying the roles and the limitations of the different models through objective benchmarks or test-beds characterizing the diverse potential hydrological applications. Furthermore, when dealing with hydrological forecasting, the reconciliation can be obtained in terms of forecasting uncertainty, by developing Bayesian frameworks to combine together models of different nature in order to assess and reduce predictive uncertainty.
Keywords: conceptual, data-driven, hydrological models, physically based, predictive uncertainty, validation uncertainty