Using System-Inspired Metrics to Improve Water Quality Prediction in Stratified Lakes

Despite the growing use of Aquatic Ecosystem Models for lake modeling, there is currently no widely applicable framework for their configuration, calibration, and evaluation. Calibration is generally based on direct data comparison of observed versus modeled state variables using standard statistical techniques, however, this approach may not give a complete picture of the model's ability to capture system-scale behavior that is not easily perceivable in observations, but which may be important for resource management. The aim of this study is to compare the performance of “naïve” calibration and a “system-inspired” calibration, an approach that augments the standard state-based calibration with a range of system-inspired metrics (e.g., thermocline depth, metalimnetic oxygen minima), to increase the coherence between the simulated and natural ecosystems. A coupled physical-biogeochemical model was applied to a focal site to simulate two key state-variables: water temperature and dissolved oxygen. The model was calibrated according to the new system-inspired modeling convention, using formal calibration techniques. There was an improvement in the simulation using parameters optimized on the additional metrics, which helped to reduce uncertainty predicting aspects of the system relevant to reservoir management, such as the occurrence of the metalimnetic oxygen minima. Extending the use of system-inspired metrics when calibrating models has the potential to improve model fidelity for capturing more complex ecosystem dynamics.