Hydraulic validation of two-dimensional simulations of braided river flow with spatially continuous aDcp data
Bibliographic record
Abstract
Gravel-bed braided rivers are characterized by shallow, branching flow across low relief, complex, and mobile bed topography. These conditions present a major challenge for the application of higher dimensional hydraulic models, the predictions of which are nevertheless vital to inform flood risk and ecosystem management. This paper demonstrates how high-resolution topographic survey and hydraulic monitoring at a density commensurate with model discretization can be used to advance hydrodynamic simulations in braided rivers. Specifically, we detail applications of the shallow water model, Delft3d, to the Rees River, New Zealand, at two nested scales: a 300 m braid bar unit and a 2.5 km reach. In each case, terrestrial laser scanning was used to parameterize the topographic boundary condition at hitherto unprecedented resolution and accuracy. Dense observations of depth and velocity acquired from a mobile acoustic Doppler current profiler (aDcp), along with low-altitude aerial photography, were then used to create a data-rich framework for model calibration and testing at a range of discharges. Calibration focused on the estimation of spatially uniform roughness and horizontal eddy viscosity, νH, through comparison of predictions with distributed hydraulic data. Results revealed strong sensitivity to νH, which influenced cross-channel velocity and localization of high shear zones. The high-resolution bed topography partially accounts for form resistance, and the recovered roughness was found to scale by 1.2–1.4 D84 grain diameter. Model performance was good for a range of flows, with minimal bias and tight error distributions, suggesting that acceptable predictions can be achieved with spatially uniform roughness and νH.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".