Modelling fluvial responses to episodic sediment supply regimes in mountain streams
Notice bibliographique
Résumé
Large, episodically occurring sediment supply events may temporarily dominate channel morphology and sediment transport in mountain streams. Field studies of channel response to these events are challenging to undertake, as a long data record is needed to reasonably assess a system's state of response in the context of episodic supply. Greater confidence in the observed state of response of a system can be achieved with flume experiments where fluvial response can be observed in detail after episodic events are introduced in a controlled fashion. Yet, the amount of work necessary to carry out these experiments is large, which limits the number of experimental conditions that can be studied, and thus their utility for addressing applied problems of channel adjustment. To overcome this limitation, I developed the 1-D morphometric sediment transport model BESMo, which allows large numbers of simulations to be run in batches, generating ensemble results. This model was used to recreate results from flume experiments, after which the experimental conditions were extended to include a broader range of simulated pulse frequencies, magnitudes, and grain size compositions. It was shown that the sequencing of pulse events of different magnitudes has only a short term effect on the slope and grain size response of the channel. Furthermore, thresholds were identified that allow for the categorization of fluvial response to episodic sediment supply regimes into one of (a) constant-feed-like, or (b) pulse-dominated. The practical utility of BESMo for studying fluvial response to large sediment supply events was demonstrated through the study of potential geomorphic effects following the removal of a dam in the Carmel River, California, USA. This showed the advantage of BESMo for simulating many different future scenarios, as stochasticity could be explicitly included through varied hydrographs. This allowed results to be interpreted in light of the uncertainty in future flood occurrence. Finally, to overcome data limitations on surface grain size distributions, I developed machine-learning based methods to detect grain size distributions from images. Collectively, this work has advanced our understanding and ability to characterise downstream channel response to episodic supply events, and to better obtain data needed for this characterisation.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| 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 tête enseignante, 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 ».