Cette réflexion sur la modélisation en hydrologie, bien que se référant souvent à ce domaine précis de l'hydrologie, se veut d'ordre plus général et propose une classification très réductrice des modèles en deux genres: ceux établis à partir de données d'observation des processus étudiés, et ceux pour lesquels il n'existe aucune observation du phénomène à modéliser, à l'échelle étudiée.A partir de cette classification et des règles bien connues de la tragédie classique (unité de lieu, de temps, d'action), une pratique de la modélisation est proposée et des pistes de recherche sont dégagées.On conclut en rappelant qu'il doit exister aussi, dans la communauté scientifique, en sus de recherches en modélisation à caractère « utilitaire », d'autres travaux portant sur des modèles qui ne servent à rien.Ces quelques réflexions, à caractère quelques peu polémique, ont pour objet d'initier si possible une discussion; d'où la rubrique « tribune libre » où elles paraissent.This discussion article addresses the issue of the nature of models used in hydrolory. Although its emphasis is on contaminant transport in groundwater, I believe it is relevant to most areas of hydrologic modelling. It proposes a minimalist classification of models into two categories: models built on data from observations of the processes involved and those for which there are no observation data on any of these processes, at the scale of interest.The argument is that the former should (or rather, ought to, since the question seems to attract little interest) obey serious working constraints, well-known from classical tragedy:- unity of place,- unity of time,- unity of action.The meaning of these rules, in terms of model calibration, validation and extrapolation, is analysed. They impose very strong limitations on the applicability of such models.As to the models in the latter category which, in my opinion, are the more interesting and useful ones, several suggestions are made for their development and application.1. MODELLING OBSERVABLE OR OBSERVED PHENOMENAObservable phenomena such as nitrate or pesticide pollution are there to be measured and obserrved, although this might in practice involve considerable effort. Modelling is then used to forecast future behaviour of these pollutants.The archetypal model for observable phenomena is that of the « black box ». If one can provide the box with one or several inputs and outputs and place something numerical inside, it will produce results. The modeller's task is to introduce a serviceable « engine » into the box, if possible. However, the least demanding of approaches is protrably the neural network method. Here, an engine is not even necessary: the series of observed inputs and outputs is given to the network which itself carries out a « weighting » of the input data resulting in the given output. The « engine » in the black box is created by the data, whereas in more familiar black boxes, the modeller decides on a form of relationship between the input and the output (e.g. a convolution equation, a gro...