A Physically Based Method for Real‐Time Monitoring of Tidal River Discharges From Water Level Observations, With an Application to the St. Lawrence River
Bibliographic record
Abstract
Abstract A fast, physically based, fully nonlinear, and nonsteady reach‐averaged river model is developed to provide instantaneous freshwater discharge rates in the tidally influenced stretch of the St. Lawrence River (Canada) from water level measurements alone at two tide gauge stations. The model does not require any a priori knowledge of the river geometry (width and depth) or hydraulic conditions (hydraulic slope and friction coefficient) as those parameters are inferred by inverse modeling. The model is fast in that several decades of hourly discharges can be reproduced in few seconds on any modern desktop computer. The method is accurate in that it reproduces to within 3% the observed tidal discharge variability at 8 different cross‐sections distributed along the tidally influenced portion of the St. Lawrence River. Although much simpler, the new method performs as well as, or better than, much more sophisticated models for reproducing daily and monthly discharge averages. Being physically based, it is also shown that the model can perform well outside the limited range of measurements that are generally available for calibration. The method is simple and easy to manage in that the main function only contains a few lines of code such that it could be readily implemented as a tool for real‐time discharge monitoring of the St. Lawrence River near Québec City. Being physically based, the model developed here could likely be applied more generally to highly unsteady tidal rivers with flow reversals.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".