Conclusions

Even though the physically based approach shows some predictive skill in estimating lake water level fluctuations, the small difference in precision when compared to using an average distribution inevitably leads to the question of whether it is worth implementing it in a large-scale modelling framework. Including it in the automated process of flood simulation and deriving all the data needed as input (especially the outlet channel width, which needs to be measured manually) would require a considerable amount of effort. Moreover, the results suggest that the physical model is not suitable to simulate the complexity of the processes that take place during flood routing of a streamflow in lakes. Although it performs reasonably well when accurate streamflow data is provided, it is not reliable enough when run with synthetic hydrographs across all Quebec. It is likely that similar findings would have been obtained in other geographical contexts.
The statistical approach on the contrary provides a lower RMSE than the one obtained using the physical based model and eliminates the need for measuring the outflow channel width for every lake, thus simplifying the process. This procedure can be easily implemented in a more extensive large-scale modelling framework to provide first-order approximations of water levels associated with extreme floods. These levels could be used as boundary conditions for two-dimensional hydraulic simulations of river flow into the lake, a very common situation in Canada but also in many other regions affected by the Laurentide or Scandinavian ice sheets, as well as to define flood prone areas around lakes where detailed hydrological models are not available.