Bibliographic record
Abstract
Abstract The majority of hydrologic engineering applications deal with ungauged watersheds. Various empirical methods are available for synthesizing unit hydrographs for ungauged watersheds from information obtained from maps or field inspection of the watershed. An alternative to the employment of generalized synthetic unit hydrographs is to develop a regional dimensionless hydrograph to characterize the local rainfall‐runoff processes better. The derivation of such a dimensionless hydrograph is described for Alberta foothills, based on the analysis of 31 basins and 61 rainfall‐runoff events. The analysis shows that it is possible to derive a representative dimensionless hydrograph for the region with reasonable accuracy by averaging the observed direct runoff hydrographs converted into a dimensionless form. However, considerable uncertainty is associated with the estimation of the excess rainfall duration and the lag time of the events analysed. The lag time is the key parameter needed to convert the regional dimensionless hydrograph into an ungauged watershed unit hydrograph. Possible reasons for unexplained lag time variations are discussed. The regional dimensionless hydrograph developed and lag time curve were used to regenerate the original 61 hydrographs. Results were compared with the generalized Soil Conservation Service dimensionless unit hydrograph which tended to produce larger errors in predicted peak flows. Error analysis indicates the limits of accuracy that may be expected from the method. Copyright © 2003 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".