Identification of a dynamic lake model by the group method of data handling: An application to lake ontario

A revised algorithm of the group method of data handling (GMDH) is used to model and identify water quality dynamics in Lake Ontario. The state variables in the model are corrected chlorophyll a, volume-averaged over the top 20 m, and soluble reactive phosphorus in three layers, 0–20 m, 20–40 m and from 40 m to bottom. Inputs are the average quarterly temperatures for each layer and the total phosphorus loadings. Two kinds of model are developed: recursive and iterative; they are used to simulate long-term predictions for three different hypothetical rates of phosphorus loading. Results show that GMDH can be usefully employed to develop lake models with a very low expense of manpower and computer time. This exercise compares well with other large-scale modelling efforts of Lake Ontario, even if necessarily more limited in scope.