Sediment Storage and Fluvial Sediment Transport Linkages Across an Experimental Flood Sequence
Bibliographic record
Abstract
Abstract River channels are maintained by coordination between flow hydraulics, sediment supply, riparian vegetation, and sediment transport. This coordination is challenging to understand in natural flow regimes, where climatic and environmental drivers produce episodic flood and sediment supply events. To better understand the response of channels to flood sequences, we have undertaken laboratory flume experiments on sediment storage and export across a sequence of alternating hydrographs. Our experiments indicate that accumulated sediment storage before floods predicts sediment transport during floods, with sediment storage depletion during floods causing a nonlinear variation of sediment‐transport rates through time. Likewise, sediment storage between floods follows a growth‐to‐saturation pattern, whereby the sediment transport gradually increases toward the sediment feed rate depending on the occupation of available sediment storage zones. To describe these non‐linear variations, we developed a mathematical model which represents sediment transport and storage as a coupled dynamical system. This work highlights the crucial role that within‐channel sediment storage and its history play in determining sediment export in rivers.
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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 | 0.001 |
| 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".