Linking geomorphic change due to floods to spatial hydraulic habitat dynamics
Bibliographic record
Abstract
Abstract Large flood events have the capacity to induce geomorphic restructuring that can impact riverine ecosystems. However, the detailed morphodynamics associated with infrequent, high‐magnitude floods are variable and difficult to capture, and more research is needed into potential relationships between geomorphic change, flow organization, and aquatic habitat dynamics. In this study, we focus on the reach‐scale response of a gravel bed river to a large flood, employing a combined remote sensing, field measurement, and numerical modelling approach to measure and interpret conditions bracketing the flood. Documented geomorphic turnover was extensive, reworking low‐flow channel patterns and causing widespread bank erosion and sediment deposition. This resulted in a shift to wide, shallow flow conditions in the post‐flood morphology and a loss of hydraulic diversity, particularly in ecologically important pool and riffle units identified using a fuzzy statistical classification method. These impacts are most evident at low flows; higher discharges display relatively similar hydraulic conditions despite geomorphic change. Smaller‐scale adjustments in the year following the flood appear to be driving the reintroduction of hydraulic diversity, which is interpreted as beneficial for in‐stream brown trout. Results from this study highlight the utility of applying flexible and objective remote sensing and modelling methods to measure fluvial change and provide a real‐world example that can inform broader theoretical understanding of large flood ecohydrology.
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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 | 0.001 | 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) | 0.001 | 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 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".