Identification of Design Rainfall Changes Using Regional Frequency Analysis: A Case Study in Ontario, Canada
Bibliographic record
Abstract
Historical rainfall records at 32 gauges located in Southern Ontario, Canada were analyzed to assess if temporal changes in design rainfall intensities were evident in this geographical area.Rainfall records were split into two time periods and design rainfalls of 2 y, 5 y and 10 y return periods were estimated using partial duration series instead of the conventionally used annual maximum series.However, due to limited available record lengths, uncertainties in design rainfall estimates were substantial and determinations of whether storm intensities were increasing were challenging.To reduce uncertainties, the gauges were grouped into regions and a regional L-moment algorithm was used for each region.We found that the regional algorithm produced more accurate rainfall estimates (i.e.lower relative root mean square error) and less uncertainty (i.e.narrower confidence intervals) than at-site models.This approach shows, for example, that for 10 y return storms, the regional model reduced relative root mean square error by 26% for the first period and 35% for the second period .The findings indicate that this procedure indeed identified increasing trends in rainfall intensities for the design rainfalls for 30% of the sites (11 of 32).Further, the regional models showed 66% of the sites have at least one location of increasing design rainfalls.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".