Glacial landscape configuration influences channel response to flooding
Bibliographic record
Abstract
Abstract The influence of glaciation‐induced watershed configuration on channel response to flooding remains a topic of comparatively little research despite the prevalence of paraglacial landscapes. This study examines the response of Guichon Creek, a 1,200 km2 catchment in the southern interior of British Columbia, to major flood events during consecutive freshets with return periods of 20–40 years. The glacial history of the Guichon Creek landscape has influenced the spatial pattern of sediment availability to the channel, with erosion of thick glaciofluvial deposits located adjacent to the mainstem channel leading to extensive destabilization of downstream reaches. Over the two flood events, Guichon Creek widened by a factor of eight, but only downstream of sediment sources. Where sediment was not available to enter the channel, major changes in width were not observed. At least 163,000 ± 83,000 m3 and 73,890 ± 11,752 m3 of sediment entered the stream channel during the 2017 and 2018 freshets, respectively, representing an input more than 200 times the mean annual transport capacity. Between 40% and 60% of this sediment was of a sufficient caliber to be transported as bedload. While aggradation and degradation exceeding several meters in depth was observed at some sites, net increase in storage was small, indicating substantial sediment export. Specific patterns of channel change downstream of sediment input sites correspond to gradients in stream power. We present a conceptual model contrasting profile form, sediment availability, and transport capacity between a previously glaciated and a non‐glaciated basin of similar size and relief. These findings illustrate the broad implications of glacial landscape history on channel response to flooding, and have direct implications for river management, particularly as it pertains to channel stability and aquatic habitat.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".