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

Despite the growing use of Aquatic Ecosystem Models (AEMs) for lake modelling, there is currently no widely applicable framework for their configuration, calibration, and evaluation. To date, calibration is generally based on direct data comparison of observed vs. modelled state variables using standard statistical techniques, however, this approach may not give a complete picture of the model’s ability to capture system-scale behaviour that is not prevalent in the state 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, a new approach that augments the standard state-based calibration with a range of system-inspired metrics (e.g. thermocline depth, metalimnetic oxygen minima), in an effort to increase the coherence between the simulated and natural ecosystems. This was achieved by applying a coupled physical-biogeochemical model to a focal site to simulate temperature and dissolved oxygen. The model was calibrated according to the new system-inspired modelling convention, using formal calibration techniques. There was a clear improvement in the simulation using parameters optimised on the additional metrics, which helped to focus calibration on aspects of the system relevant to reservoir management, such as the metalimnetic oxygen minima. Extending the use of system-inspired metrics for the calibration of models of nutrient cycling, algal blooms, and greenhouse gas emissions has the potential to greatly improve the prediction of complex ecosystem dynamics.