Year: 2024
Type: article
Abstract: 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.
Source: Water Resources Research
DOI: 10.1029/2023wr036570
Institutions: The University of Western Australia, Virginia Tech
Language: English
Subfield: Environmental Chemistry
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,000
Awards: LTREB: Integrating real-time open data pipelines and forecasting to quantify ecosystem predictability at day to decadal scales, Global Centers Track 2: Building the Global Center for Forecasting Freshwater Futures, Collaborative Research: CIBR: Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting, Unravelling the drivers of greenhouse gas emissions in estuaries, Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management, Balancing estuarine and societal health in a changing environment, Unravelling how aquatic coastal networks regulate nitrogen removal , Between a hot place & hypoxia: Quantifying fish-kill risk in inland rivers
OpenAlex ID: W4401445176