Fluvial response to changes in the magnitude and frequency of sediment supply in a 1-D model
Bibliographic record
Abstract
Abstract. In steep headwater reaches, episodic mass movements can deliver large volumes of sediment to fluvial channels. If these inputs of sediment occur with a high frequency and magnitude, the capacity of the stream to rework the supplied material can be exceeded for a significant amount of time. To study the equilibrium conditions in a channel following different episodic sediment supply regimes (defined by grain size distribution, frequency, and magnitude of events), we simulate sediment transport through an idealized reach with our numerical 1-D model “BESMo” (Bedload Scenario Model). The model performs well in replicating flume experiments of a similar scope (where sediment was fed constantly, in one, two, or four pulses) and allowed the exploration of alternative event sequences. We show that in these experiments, the order of events is not important in the long term, as the channel quickly recovers even from high magnitude events. In longer equilibrium simulations, we imposed different supply regimes on a channel, which after some time leads to an adjustment of slope, grain size, and sediment transport that is in equilibrium with the respective forcing conditions. We observe two modes of channel adjustment to episodic sediment supply. (1) High-frequency supply regimes lead to equilibrium slopes and armouring ratios that are like conditions in constant-feed simulations. In these cases, the period between pulses is shorter than a “fluvial evacuation time”, which we approximate as the time it takes to export a pulse of sediment under average transport conditions. (2) In low-frequency regimes the pulse period (i.e., recurrence interval) exceeds the “fluvial evacuation time”, leading to higher armouring ratios due to the longer exposure of the bed surface to flow. If the grain size distribution of the bed is fine and armouring weak, the model predicts a decrease in the average channel slope. The ratio between the “fluvial evacuation time” and the pulse period constitutes a threshold that can help to quantify how a system responds to episodic disturbances.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".