A Decision Support Tool for Constructing Rainfall Intensity-Duration-Frequency Relations in the Context of Climate Change
Bibliographic record
Abstract
Intensity-duration-frequency (IDF) relations are essential for estimating extreme rainfalls for design of various hydraulic structures. The construction of these relations represents however a challenging and tedious task since it involves the uncertainty analysis of different probability models and the frequency analyses of a large amount of extreme rainfall data for different durations at a given site or over many different locations. This paper proposes hence a decision-support tool, herein referred to as SMExRain, that can readily be used to identify in an objective and systematic manner the most suitable distribution(s) for accurate and robust estimation of design rainfalls. In addition, in the context of a changing climate, the proposed tool include a statistical downscaling procedure for describing the linkage between climate predictors given by global climate models and the daily and sub-daily extreme rainfalls at a given site. Results of an illustrative application using climate simulations from different global climate models and extreme rainfall data for Ontario region, Canada, has demonstrated the accuracy and practical usefulness of the SMExRain for establishing reliable IDF relations at a given site for present and future climates.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".