Numerical modelling of the hydrodynamics driven by tidal flooding of the land surface after dyke breaching
Bibliographic record
Abstract
Managed dyke realignment is a method of creating more coastal wetland environments, by breaching constructed dykes (levees) to allow seawater driven by tides to flood the land surface and enable re-establishment of salt marshes over time. However, coastal land regions that are protected by dykes experience major hydrodynamic changes after breaching. To investigate these dynamics, a dyke in Atlantic Canada was purposefully breached and the adjacent land surface allowed to flood with the tides. Field measurements pre- and post-breach provide a rare opportunity to model the hydrodynamics of early dyke realignment in a hypertidal estuary in the Bay of Fundy. These include measurements of water levels and current velocities at spring tide collected across of field site. A numerical model with an unstructured flexible mesh (Delft3D-FM) was applied to examine the impacts of tidal flooding from a river channel, through the dyke breach and across the previously agricultural landscape that was historically a salt marsh. The model was used to simulate the hydrodynamics inside and around the breach before and after seawater flooding during spring tides, to evaluate the initial impacts of this nature-based method of managed dyke realignment. The results indicate that the breach was not wide enough to influence water levels within the Missaguash River. The depth-averaged current speeds can exceed 1 m s−1 within the breach and are typically <0.3 m s−1 across the flooded area with an average depth of 0.66 m over the simulation period with six tidal cycles. The model results also highlight the importance of high-resolution computational grids and variable bottom roughness for simulating the hydrodynamics of small-scale salt marsh restoration projects. Overall, the results may provide insight to researchers and practitioners in applying nature-based solutions to improve coastal resilience.
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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 | 0.000 |
| 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 | 0.002 | 0.001 |
| 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".