Modelled Water Temperature Patterns and Energy Balance of a Threatened Coastal Lagoon Ecosystem
Bibliographic record
Abstract
ABSTRACT Coastal water temperatures control physical, chemical, and biological processes and are expected to rise due to future changes in freshwater temperature and flow rates, heat exchange with the warming atmosphere, and thermal interactions with a changing ocean. However, the thermal sensitivity of transitional, coastal water bodies to climate change remains poorly understood, due partly to a lack of knowledge on present‐day thermal controls in these settings. Accordingly, we applied a coastal hydrodynamic model (MIKE 3 FM), with a coupled thermal module to simulate hydrodynamics and water temperature variability in the Basin Head lagoon, a federally protected coastal ecosystem in the Canadian province of Prince Edward Island. Field data from the lagoon were used to calibrate and assess the numerical model, while atmospheric, oceanic, and hydrologic data were used to form the thermal and hydrodynamic boundary conditions. The model successfully reproduced tidal water level oscillations as well as diurnal and semi‐diurnal (tidal) temperature fluctuations. Model results show longitudinal, cross‐shore, and vertical thermal variability within the lagoon, including pronounced thermal variability near the bed and near the inlet due to tidal pumping. Model results and field data highlight the thermal sensitivity of the lagoon during heat waves; however, distinct cold‐water plumes at freshwater inputs (springs and groundwater‐dominated streams) persisted, with temporally averaged temperatures in these zones up to 18 °C colder than the ambient lagoon. Although, these freshwater inflows can dominate local energy budgets, the surface heat fluxes, especially shortwave radiation, exert the dominant control on the lagoon‐wide energy budget. Collectively, the model findings emphasise the interacting effects of atmospheric, hydrologic, and oceanic forcing on the spatiotemporal patterns of water temperatures in this threatened coastal ecosystem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".