Variability in bedload and morphological active widths of gravel-bed rivers across timescales
Bibliographic record
Abstract
The morphodynamics of alluvial river systems arise from complex interactions between sediment transport and the resulting morphological changes. The variability of sediment transport across space and time makes predicting the geomorphic trajectories of alluvial rivers challenging, yet essential for improved river management. Advances in high-resolution monitoring techniques now enable continuous tracking of riverbed activity in flume experiments with unprecedented detail. These developments offer new opportunities to unravel the links between bedload transport patterns and morphological changes across various spatial and temporal scales, potentially improving predictions of future river geomorphic behavior.A key metric in assessing alluvial river dynamics is the active width—the portion of the channel actively involved in sediment transport—which directly connects sediment movement to morphological changes. Despite its importance, active width is often inferred from areas of observed morphological change and has not yet been systematically compared to the actual two-dimensional patterns of bedload transport. This gap limits our understanding of the relationship between sediment transport and channel morphology. Additionally, the definition, quantification, and interpretation of active width are highly dependent on the timescale of analysis (e.g., instantaneous, single flood events, or cumulative flood events). The absence of a robust method to account for this timescale dependency complicates comparisons across different hydrological eventsThis study investigates the temporal and spatial variability of both the bedload active width (BAW) and the morphological active width (MAW) by simulating flood events for different gravel-bed river types using a physical modelling approach. The flume is 24m long and 0.6 m wide, is filled with a uniform grain size sediment of 1mm of diameter, and the slope is set to 0.01. The flume is equipped with a laser scanner allowing to perform topographic surveys and two cameras taking photos every minute during each experiment. Using both a recently developed time-lapse imagery technique and topographic surveys, 2D spatio-temporal information of sediment transport and morphological changes occurrence and intensity can be obtained.The experiments explore variability across timescales ranging from instantaneous (minute-by-minute) to multiple flood events. Each experimental duration is designed to maintain consistency in terms of volume of sediment transported under various flow conditions, guided by the conservation of sediment mass (Exner equation), which depends on water depth, wetted width, and sediment flux.The experiments simulated different river morphologies (braided, transitional, and alternating bar) with varying dimensionless stream power (w*). Results show that MAW systematically underestimates BAW by around 30%, regardless of river type, including braided systems. Laboratory experiments also reveal that the relationship between MAW, the timescale of analysis and w* is best described by a power law with coefficients varying by w* and thus the river type. Quantifications of morphological changes on the Tagliamento (Italy), the Sunwapta (Canada), and the Rees (NZ) rivers corroborate laboratory findings.These insights enhance our understanding of sediment transport and morphological response of alluvial rivers to hydrological events, with implications in improving future river geomorphic trajectories, river management and flood risk assessment.
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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 | 0.000 |
| 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".