Numerical Modelling of Wind-driven Hydrodynamics in Shallow Wastewater Ponds and Coastal Estuaries
Bibliographic record
Abstract
Throughout the world, shallow water (<5 m) environments, including ponds and estuaries, provide vital contributions to regional economies and environments. Despite their prevalence and importance, questions remain about the complex hydrodynamic processes that occur in these environments. In this thesis, two field sites, characterized by shallow depths and generally bounded by shorelines, are studied using observations and numerical modelling to improve understanding of hydrodynamics that occur in: a waste stabilization pond (WSP) in Ontario, Canada, and a back-barrier estuary in North Carolina, USA. WSPs are applied for wastewater treatment throughout the world. Typically, they are designed using simplified equations that may not incorporate complex hydrodynamics, and existing numerical models have lacked validation. To address this, field monitoring of water levels, current, and temperatures was used to validate a high-resolution three-dimensional Delft3D model. Hydrodynamics were primarily wind-driven, with smaller contributions from outflows, and circulation patterns were classified into four hydraulic regimes. Vertical temperature differences of up to 8.0°C over the 1.7 m depth between the surface and bed were observed during an 8-month monitoring period, inhibiting mixing through thermal stratification. Using a simulated tracer, the hydraulic retention time was ~22% shorter than predicted by design equations. A dimensionless empirical equation was developed relating the longitudinal current to wind speed, direction, and outflows, which represents an important step toward incorporating hydraulic complexity into design. During extreme storms, wind-driven changes in water levels and intense precipitation can contribute to flooding, particularly on low-lying coastal plains. To investigate the roles of rainfall and wind-driven storm surge on coastal flooding, two major 2016 storms, Tropical Storm Hermine and Hurricane Matthew were simulated using a coupled flow-wave model (Delft3D-SWAN). Results showed that different wind field inputs produced variations in coastal conditions, and that precipitation on the water surface inundated a larger area. This model was extended into a real-time forecast system, which provided generally accurate 36-hour forecasts during Hurricane Dorian, a major storm event that impacted coastal North Carolina in 2019. Collectively, these results emphasize the contributions of different processes to circulation and water levels, and provide guidance on atmospheric forcing impacts in back-barrier environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".