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