Climate Change Impacts on Design Storms and Urban Runoff Characteristics
Bibliographic record
Abstract
The present study proposes a statistical downscaling approach to the assessment of the climate change impacts on the estimation of design storms and the resulting runoff characteristics for a given urban watershed. The proposed approach is based on a combination of a spatial downscaling method to link large-scale climate variables given by general circulation model (GCM) simulations with daily extreme precipitations at a site and a temporal downscaling procedure to describe the relationships between daily and sub-daily extreme precipitations based on the scaling general extreme value (GEV) distribution. The proposed method was tested using simulations from two GCMs under the A2 scenario (HadCM3A2 and CGCM2A2) and annual maximum (AM) precipitation data available at the Dorval Airport raingauge in Quebec (Canada). It was found that AM precipitations and design storm rainfall intensities downscaled from the HadCM3A2 displayed small decreasing trends in the future, while those values estimated from the CGCM2A2 indicated large increasing trends. Similar variations were found for the estimated runoff characteristics for several selected urban watersheds of different shapes, area sizes, and imperviousness levels. Results of this illustrative application have indicated the feasibility of the proposed downscaling method.
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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) | 0.000 | 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".