Development of Appropriate Synthetic Design Storms for Small Catchments in Gauteng, South Africa
Bibliographic record
Abstract
Synthetic design storms are often used as input in dynamic rainfall-runoff simulation models. A number of methods to generate synthetic design storms are described in the literature. However, the selection of an inappropriate synthetic design storm will generate unrealistic simulations. Therefore, the aim of this study was to develop appropriate synthetic design storms for small urban catchments in Gauteng, South Africa. This study evaluated the applicability of the SCS method adapted for South Africa (SCS-SA), the Chicago Design Storm method and the Rectangular Hyetograph method. The performance of each method was evaluated compared to observed rainstorm events. Storm shape and intensity were used for the evaluation. As expected, the Rectangular Hyetograph was the least representative of naturally occurring storm events. The Chicago Design Storm and SCS-SA distribution curves initially performed poorly. Adjustment of the timing of the peak storm intensity to the start of the event resulted in a significant improvement for both methods. A novel approach was used to generate intermediate site-specific SCS-SA rainfall distribution curves anywhere in the study area.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".