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.