Optimal rainfall temporal patterns for urban drainage design in the context of climate change
Bibliographic record
Abstract
The main objective of the present study is to propose a method for estimating an optimal temporal storm pattern for urban drainage design in southern Quebec (Canada) in the context of climate change. Following a systematic evaluation of the performance of eight popular design storm models for different typical urban basins, it was found that the Canadian Atmospheric Environment Service (AES) storm pattern and the Desbordes model (with a peak intensity duration of 30 min) were the most accurate for estimating runoff peak flows while the Watt model gave the best estimation of runoff volumes. Based on these analyses, an optimal storm pattern was derived for southern Quebec region. The proposed storm pattern was found to be the most suitable for urban drainage design in southern Quebec since it could provide accurate estimation of both runoff peak flow and volume. Finally, a spatial-temporal downscaling method, based on a combination of the spatial statistical downscaling SDSM technique and the temporal scaling General Extreme Value distribution, was used to assess the climate change impacts on the proposed optimal design storm pattern and the resulting runoff properties.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".