When tasked with accurately modeling a water body in a cold climate environment, the complexity of the system being simulated and the numerous parameters affecting the observable outcome pose an arduous task for any modeling effort. The task is increasingly complicated when the body of water is serving as a cooling pond for a power plant and can become partially frozen. The introduction of a heat effluent into the water creates a highly dynamic system whose physical state is not only reactionary to the surrounding environmental conditions, but the industrial facility\u27s operating parameters as well. Both calibrated thermal and visible imagery offer a powerful and unique source of validation data for these hydrodynamic modeling codes when trying to simulate these industrial processes in cold climates. This work presents an approach which uses an evolutionary optimization algorithm to drive the inputs of a hydrodynamic modeling code simulating a power plant cooling pond through imagery validation. The result of this process is an optimized set of functional parameters to the hydrodynamic model that best simulates the observed conditions. While applied to a hydrodynamic code for this work, the process created introduces a unique infrastructure for solving multi-dimensional, multi-system problem sets in a modular and evolutionary framework
