A series of recent flood events in Canada affecting areas around lakes and reservoirs have highlighted the need to explicitly represent such features in large scale flood models. Water level fluctuations in lakes are traditionally modelled using detailed hydrological models designed – as far as possible – to represent the actual physical processes that take place. This approach, while appropriate for local-scale studies in data-rich areas, is not applicable for large-scale flood modelling where data availability for model calibration and validation is often severely limited. This paper explores two methodologies, one statistical and one physically based, designed to approximately predict the increase in the water level of lakes in Quebec (Canada) using only limited morphological information about the lakes and the estimated discharge entering the water body during a flood event. Of the two methods, the statistical approach proved to be the most applicable to a large-scale modelling framework as it exhibited lower errors whilst being considerably easier to implement in a semi-automated modelling chain.