On Modeling of Extreme Rainfall Processes over a Wide Range of Time Scales
Bibliographic record
Abstract
Extreme rainfalls of short time scales are usually required for the design of urban infrastructure. These data, however, are often limited or unavailable at the location of interest, while those for the daily scale are widely available. Hence, an improved method for modeling of extreme rainfall processes over a wide range of time scales is necessary so that short-duration rainfalls can be estimated from those of longer durations. Scaling models such as the scaling Gumbel (GUM) and generalized extreme value (GEV) models have been proposed for addressing this issue. These models are based on the scale-invariance properties of the ordinary moments or non-central moments (NCMs) of the observed extreme rainfalls. However, the probability weighted moments (PWMs) have been known to provide more robust estimates of random variables for small sample sizes. The present study introduces therefore a PWM-based scaling GEV distribution (GEV/PWM) model. Results of a numerical application using historical rainfall records from a network of 74 raingauges located across Canada have indicated that the extreme rainfall estimates given by the GEV/PWM model are the most accurate as compared to those given by existing scaling models such as GEV/NCM, GUM/NCM, and GUM/PWM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".