Experiment on morphological and hydraulic adjustments of step‐pool unit to flow increase
Bibliographic record
Abstract
Abstract Exceptional flood events with a return period of about 50 years can be destructive to step‐pool channel segments. However, field investigations and flume experiments have not examined the hydraulic and morphological feedbacks of step‐pool morphology during unsteady hydrographs of exceptional flood events. We performed a series of flume experiments with a manually constructed step model, perturbed with three hydrographs that varied in the rate of water supply change. The bed texture, topography, flow regimes, surface flow field and water depth were characterized and measured as the flow rate was increased during the experiments. A distinct pool feature emerged downstream of the manually constructed step when the flow rate exceeded the threshold scaled to the peaks of ordinary flood events in well‐graded mountain streams. The pool feature was modified in several different ways with flow rate increase. The bed surface steadily coarsened, micro‐bedforms developed and became more pronounced, the bed topography became more spatially complex based on analysis using the Hurst exponent, and last, pool depth steadily increased. Pool modification was also linked to the flow regime: the impinging jet regime led to grain size segmentation in the pool while the jump regime contributed to decelerating flow velocity. The steeper rising limb of hydrograph led to a less developed pool feature, with smaller sized micro‐bedforms in the pool bottom to outlet, and higher discharge threshold for distinct coarsening and scouring in the pool. The estimated energy dissipation within the step‐pool unit decreased as a power function from low to high flow, quantified as the ratio h c / H S , where h c is the critical water depth and H S is scour depth. Our results highlight the interaction between morphology, hydraulics, and energy dissipation of step‐pool unit and the crucial role of hydrograph shape on the interaction during flow increase © 2019 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".