Variability in bedload and morphological active widths of gravel-bed rivers across timescales
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».