A particle tracking model (PTM) is linked with a hydrodynamic model to evaluate mean seasonal circulation patterns in Lake Ontario, and also to provide a basis for predicting movement of algal blooms. The PTM is based on a random walk algorithm that combines a deterministic advective component with a stochastic component associated with the turbulent diffusivity field to calculate trajectories of neutrally buoyant particles, where both the advective and diffusive velocities are obtained from the hydrodynamic model. Mean circulation is calculated using 30-year average meteorological forcing data collected from five stations around the lake. Seasonal variations in lake circulation are demonstrated, and a clockwise flow in the eastern basin during summer and early fall is identified, contrary to some previous observations that suggest counterclockwise flow. The impacts of Niagara and St. Lawrence river flows on general lake circulation are found to be small, except within approximately 10 km of the river mouth. Development and application of the PTM demonstrate its potential to provide calculations of (Lagrangian) movements as determined from the hydrodynamic output, and to serve as a first step toward development of an algal transport model. Particle tracking helps to visualize flow patterns and provides a means of evaluating the probability a bloom will reach a specified area, given an initial position and the predicted velocity and diffusivity fields. This capability, when set up for real-time applications, can provide an important tool to support management decisions that may be needed when a bloom is observed, for example in predicting potential impacts of the bloom on a beach or a water intake.
Journal of Great Lakes Research
Vol. 33 • No. 4
Vol. 33 • No. 4