Hydrodynamic Modeling of the St. Lawrence Fluvial Estuary. I: Model Setup, Calibration, and Validation
Bibliographic record
Abstract
In this study, a high-resolution, two-dimensional (2D), time-dependent hydrodynamic model of the St. Lawrence fluvial estuary was developed with the objective of documenting the tidal hydrodynamics of this complex yet poorly understood region. The hydrodynamic model solves the shallow-water equations over a finite-element–discretized domain, with an average spatial resolution of 50 m, and includes a drying–wetting component for the treatment of shallow intertidal areas. The numerical terrain model is composed of high-density topographic data and detailed friction fields associated with bottom substrate and macrophytes. Calibration and validation were carried out using recently acquired data for water level and velocity. Results show very good accuracy in water levels, with prediction skills higher than 0.99 at all stations (where a skill of 1 means perfect agreement between model and observations in terms of their relative average error) and root-mean-square errors (RMSEs) less than 5% of local tidal ranges downstream; at upstream stations where tidal ranges are significantly reduced, RMSEs were lower than 6 cm. Discharges were reproduced with similarly good accuracy, with errors lower than 6% of the maximum observed discharges at 11 of the 13 surveyed transects; the two remaining sections are subject to larger interpolation and bathymetric uncertainties. In this paper, critical aspects of model development are discussed, including the 2D approximation, temporal and spatial resolution, bathymetric uncertainty, error in the boundary conditions, and calibration under nonstationary conditions. This work is the first part of a two-part investigation serving as a methodological framework for model setup, calibration, and validation in large tidal rivers.
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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 | 0.001 |
| 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".