What controls river widening? Comparing large and extreme flood events
Bibliographic record
Abstract
Abstract Extreme (i.e., centennial‐scale) floods are by definition rare and can therefore be difficult to study. As a result, case studies of response to extreme floods can provide unique insights. On 15 November 2021, the Nicola River in British Columbia, Canada, experienced a 200‐year flood that increased the average width of the Nicola River by more than 50% (35 m), leaving the only highway in the region impassable for nearly 12 months. This research assesses the spatial variability of erosion along a 71‐km‐long segment of the Nicola River during a period with a large flood (2015–2018) and a second period containing the extreme flood in 2021 (2018–2021). We use a random forest statistical analysis to explore the most important valley and channel characteristics affecting relative widening during both periods. Unit stream power, gradient and valley confinement were the primary determinants of erosion during the extreme flood, whereas vegetation cover, channel pattern and surficial material were less important. Erosion during the large flood event was not related to these variables, with channel pattern providing a better indication of widening potential. As the Nicola River is flanked by erodible glacial deposits, this work provides important insight into river response in the paraglacial environments that are common throughout much of Canada and the northern United States, as well as Europe and Asia, but less intensely studied. In these settings, the geomorphic response to large floods may not be representative of the potential erosion (and infrastructure impacts) that can occur during extreme events, which destabilize glacial terraces in confined reaches. Historical observations should therefore be used with caution in paraglacial settings as extreme events may not be captured in available data, creating a perception of stability in confined reaches that may have the potential to erode dramatically during extreme events.
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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 | 0.001 |
| 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 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".