Geomorphological effectiveness of floods to rework gravel bars: Insight from hyperscale topography and hydraulic modelling
Bibliographic record
Abstract
Abstract Bars are key morphological units in river systems, fashioning the sediment regime and bedload transport processes within a reach. Reworking of these features underpins channel adjustment at larger scales, thereby acting as a key determinant of channel stability. Despite their importance to channel evolution, few investigations have acquired spatially continuous data on bar morphology and sediment‐size to investigate bar reworking. To this end, four bars along a 10 km reach of a wandering gravel‐bed river were surveyed with terrestrial laser scanning (TLS), comparing downstream changes in slope, bed material size and channel planform. Detrended standard deviations (σz) were extracted from TLS point clouds and correlated to underlying physically measured median grain‐size (D50), across a greater range of σz values than have hitherto been reported. The resulting linear regression model was used to create a 1 m resolution median grain‐size map. A fusion of airborne LiDAR and optical‐empirical bathymetric mapping was used to develop reach‐scale digital elevation models (DEMs) for rapid two‐dimensional hydraulic modelling using JFlow® software. The ratio of dimensionless shear stress over critical shear stress was calculated for each raster cell to calculate the effectiveness of a range of flood events (2.33–100 year recurrence intervals) to entrain sediment and rework bar units. Results show that multiple bar forming discharges exist, whereby frequent flood flows rework tail and back channel areas, while much larger, less frequent floods are required to mobilise the coarser sediment fraction on bar heads. Valley confinement is shown to exert a primary influence on patterns of bar reworking. Historical aerial photography, hyperscale DEMs and hydraulic modelling are used to explain channel adjustment at the reach scale. The proportion of the bar comprised of more frequently entrained units (tail, back channel, supra‐platform) relative to more static units (bar head) exerts a direct influence upon geomorphic sensitivity. © 2018 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".