Estimating channel‐forming discharge in urban watercourses
Bibliographic record
Abstract
Abstract Several methods of estimating channel‐forming discharge were conducted on 12 quasi‐stable urban stream channels ranging from 9 to 99% urban land use to test their applicability in the urban condition. Bankfull stage was identified at a series of locations along each study reach and it was found that the most consistent observations of bankfull discharge occurred during flood conditions where bankfull stage was identified at the top of point bars along the convex arc of bends. The largest errors in estimation occurred at gauge stations where cross‐sectional geometry had been altered to conform to bridges or culverts rather than the channel morphology. Independent evaluations of channel forming discharge were conducted by 11 practitioners ranging from 10 years to 43 years of experience with similar findings and errors. Various methods of relating frequency return periods were evaluated using annual peak series discharge observations and continuous 15‐min systematic discharge records using partial duration series analysis. Bankfull discharge was observed to occur more than once a year in all of the urban streams studied and often averaged from 4 to 8 bankfull discharge or larger events per year. In one particular case in a single given year 18 events exceeding bankfull discharge were observed. No specific correlations were identified between frequency return periods and land use change. However, based upon the findings of this study, the applicability of employing annual series peak discharge data to evaluate bankfull frequency return in urban stream channels is highly discouraged. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 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".