Sediment transport‐storage relations for degrading, gravel bed channels
Bibliographic record
Abstract
In a drainage network, sediment is transferred through a series of channel/valley segments (natural sediment storage reservoirs) that are distinguished from their neighbors by their particular capacity to store and transport sediment. We propose that the sediment transport capacity of each reservoir is a unique positive function of storage volume, which influences sediment mobility and availability through variations in bed surface texture, channel gradient, and availability of valley floor sediments for erosion. Examinations of the form of transport‐storage relations of degrading alluvial reservoirs using published field studies, flume experiments, and simulations support a conceptual model that includes two phases. In phase I, filled channels respond to variations in supply primarily by changes in stored sediment volume, with little change in transport rate. In phase II, channel mobility is responsive to supply through armoring and form roughness. Although these phases could represent idealized transport‐limited (phase 1) or supply‐limited (phase II) states, we propose that every alluvial reservoir responds to changes in sediment inputs by changing both storage and transport rate, the propensity for either depending on reservoir characteristics and the sediment exchange processes in the channel. Transport‐storage relations for phase II are approximately linear, but examination of numerical simulations and flume experiments indicates that armoring imparts positive curvature. Simulations of degradation of an alluvial reservoir with channel and valley floor surfaces indicate that interactions between channel lowering and lateral erosion are critical in the manifestation of a transport‐storage relation. Better knowledge of transport–storage relations could lead to improved sediment‐routing models for drainage basins wherein component sediment reservoirs dynamically adjust to varying sediment loads.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".