A Novel Scale-Invariance Generalized Extreme Value Model Based on Probability Weighted Moments for Estimating Extreme Design Rainfalls in the Context of Climate Change
Bibliographic record
Abstract
The estimation of extreme design rainfalls in the context of possible climate change impacts (CCIs) has become essential in current engineering practices due to recent recognition of climate variability. This estimation requires hence a new rainfall modelling approach that could establish an accurate linkage between climate projections from global or regional climate models and observed extreme rainfall processes at a local site. Furthermore, outputs from these climate models are mostly available at the daily timestep because of current limitations on detailed physical modelling and computational capability. Hence, rainfall data of high temporal resolutions are often limited or unavailable at the location of interest while those of daily scale are widely available. Recently, statistical models based on the scale-invariance (or scaling) concept has increasingly become a new modeling tool since these scaling models could be used to derive short-duration extreme rainfalls based on those of longer durations. Therefore, the main objective of the present study is to propose a novel scaling GEV/PWM model for modeling extreme rainfall process over a wide range of time scales (e.g., several minutes to one day). The feasibility and accuracy of the GEV/PWM model was assessed and compared with the three existing popular models using IDF data from a network of 21 raingages located in Ontario, Canada. Results based on different statistical criteria have indicated the superior performance of the proposed GEV/PWM as compared to these existing models. The proposed model was used for constructing IDF relations for Ontario using climate projections from different climate models.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".