Stage discharge estimation using a 1d river hydraulic model and spatially-variable roughness
Bibliographic record
Abstract
Stage-discharge relations (rating curves) are integral to stream gauging, yet the existing empirical calibration methods are expensive, particularly in remote areas, and are limited to low flows. Numerical modelling can provide stage-discharge relations from a single site survey, reducing the overall cost, and can be fit to changing surface conditions. This study explores a one-dimensional model to calculate theoretical stage-discharge relations for four field sites in British Columbia that range in bed stability, bed structure, hydrology and sediment supply. However, due to the non-linear relation between flow and roughness we do not assume the conventional reach-averaged roughness and instead employ a spatially-distributed roughness model. Furthermore, based on local grain size distribution and refined field survey technique, new formulae for wetted perimeter, flow area, and flow depth were developed that eliminate commonly held modelling assumptions and reduce topographic error. The results show (1) good agreement with Water Survey of Canada measurements, (2) distributed roughness provided an improvement over spatially-averaged roughness, (3) spatial variability of the geomorphology within the channel reach leads to shifts in the stage-discharge relations and high sediment amplifies those shifts, and (4) the relations must be re-evaluated following events that mobilize the bed. The method can be used to estimate high flows and flows in remote locations and it does not require calibration.
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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 | 0.001 |
| 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) | 0.001 | 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".