Comparison of regional and at-site frequency analysis methods for the estimation of southern Alberta extreme rainfall
Bibliographic record
Abstract
At least one national meteorological organization has updated their rainfall intensity–duration–frequency (IDF) estimates using a regional frequency analysis approach. In this study, the regional frequency analysis L-moments approach was applied to data from 12 rain gauge sites in southern Alberta, for rainfall durations from 1 hour to 24 hours. Five candidate frequency distributions were tested, and the best-fit regional frequency distribution was used to estimate rainfall quantiles for return periods up to 1:1000 years. The quantiles were compared to the quantiles from the conventional at-site Gumbel approach. The results showed that the conventional approach tends to underestimate the quantiles relative to the regional approach, mainly for the sub-daily rainfall durations. The accuracy of the two approaches was tested using Monte Carlo simulations, and the results showed that the root mean square error (RMSE) using the conventional approach is approximately 2–2.5 times greater than the error using the regional approach, for the 1:100-year return period. The full benefits of regional frequency analysis have not yet been realized in Canada, which could benefit from a national program to update rainfall IDF estimates using these more statistically robust approaches.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".