Integrated UAS and LiDAR reveals the importance of land cover and flood magnitude on the formation of incipient chute holes and chute cutoff development
Bibliographic record
Abstract
Abstract Meandering river sinuosity increases until the channel erodes into itself (neck cutoff) or forms a new channel over the floodplain (chute cutoff) and sinuosity is reduced. Unlike neck cutoff, which can be measured or modelled without considering overbank processes, chute cutoff must be at least partially controlled by channel‐forming processes on the floodplain. Even though chute cutoff controls meandering river form, the processes that cause chute cutoff are not well understood. This study analyses the morphology of two incipient chute cutoffs along the East Fork White River, Indiana, USA, using high temporal and spatial resolution UAS‐based LiDAR and aerial photography. LiDAR and aerial imagery obtained between 1998 and 2019 reveals that large scour holes formed in the centre of both chutes sometime after chute channel initiation. A larger analysis within the study watershed reveals that scour holes within incipient chutes can be stable or unstable, and tend to stabilize when the chute is colonized by native vegetation and forest. When the scour holes form in farmed floodplain, they enlarge rapidly after initial formation and contribute to complete chute cutoff. In addition, this study shows that the formation of scour holes can occur in response to common, relatively low‐magnitude floods and that the amount of incipient chute erosion does not depend on peak flood magnitude. The role of scour holes in enlarging chute channels could be an important mechanism for chute channel evolution in meandering rivers. This study also confirms that understanding the relationships among flow, land cover, and cutoff morphology is substantially improved with on‐demand remote sensing techniques like integrated UAS and LiDAR. © 2020 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".