Probabilistic approach to estimating the effects of channel reaches on flood frequencies
Bibliographic record
Abstract
A host of physical parameters and characteristics of catchments and channel reaches are normally needed in watershed planning and stormwater management studies. Some of these are also design variables, such as channel cross‐section size, shape, roughness, and (to a lesser extent) bed slope. Conventional channel routing techniques employ continuity and some form of the momentum equation to determine the downstream impacts of individual flood events. With the introduction of the concept of storage‐induced delay time, a probabilistic approach is developed wherein the role of a given channel reach on the frequency distribution of floods from the catchment upstream can be directly determined. The approach uses the same kinds of channel‐reach parameters as are typically used by many conventional flood routing algorithms. Its physically based nature makes it suitable for watershed planning and stormwater management studies wherein little or no flow data are available for parameter estimation or flow frequency analysis. The validity of this probabilistic approach is demonstrated by comparing its outcomes with the results of a suite of conventional continuous simulations using rainfall data from Halifax, Canada.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".