Experimental evidence for the effect of hydrographs on sediment pulse dynamics in gravel‐bedded rivers
Bibliographic record
Abstract
Gravel augmentation is a river restoration technique applied to channels downstream of dams where size‐selective transport and lack of gravel resupply have created armored, relatively immobile channel beds. Augmentation sediment pulses rely on flow releases to move the material downstream and create conditions conducive to salmon spawning and rearing. Yet how sediment pulses respond to flow releases is often unknown. Here we explore how three types of dam releases (constant flow, small hydrograph, and large hydrograph) impact sediment transport and pulse behavior (translation and dispersion) in a channel with forced bar‐pool morphology. We use the term sediment “pulse” generically to refer to the sediment introduced to the channel, the zone of pronounced bed material transport that it causes, and the sediment wave that may form in the channel from the additional sediment supply, which can include input sediment and bed material. In our experiments, we held the volume of water released constant, which is equivalent to holding the cost of purchasing a water volume constant in a stream restoration project. The sediment pulses had the same grain size as the bed material in the channel. We found that a constant flow 60% greater than the discharge required to initiate sediment motion caused a mixture of translation and dispersion of the sediment pulse. A broad crested hydrograph with a peak flow 2.5 times the discharge required for entrainment caused pulse dispersion, while a more peaked hydrograph >3 times the entrainment threshold discharge caused pulse dispersion with some translation. The hydrographs produced a well‐defined clockwise hysteresis effecting sediment transport, as is often observed for fine‐sediment transport and transport‐limited gravel bed rivers. The results imply a rational basis for design of water releases associated with gravel augmentation that is directly linked to the desired sediment behavior.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".