Urban Water Infrastructure Design in Climate Change Context: Advances and Challenges in Developing Engineering Practice Guidelines
Bibliographic record
Abstract
There exists an urgent need to assess the possible impacts of climate change on the design storm for improving the design of urban water infrastructure in the context of a changing climate. This design storm is commonly estimated from the intensity-duration-frequency (IDF) relations at the location of interest. Consequently, the derivation of IDF relations in the climate change context for a given location has been recognized as one of the most challenging tasks in current engineering practice. The main challenge is how to establish the linkages between the climate projections given by global/regional climate models at global/regional scales and the observed extreme rainfalls at a given local site or at many sites concurrently over an urban catchment area. If these linkages could be established, then the projected climate change conditions given by climate models could be used to predict the resulting changes of local extreme rainfalls and related runoff characteristics. Hence, innovative downscaling approaches are needed in the modeling of extreme rainfall processes over a wide range of temporal and spatial scales and given the high uncertainty in climate projections by different climate models. Therefore, the overall objective of the present paper is to provide an overview of some recent progress and shortcomings in the modeling of extreme rainfall processes in a changing climate from both theoretical and practical viewpoints. Another focus of this paper is to introduce the recently published technical guide by the Canadian Standards Association to provide some guidance to water professionals in Canada on how to consider the climate change information in the design of urban water infrastructure.
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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 | 0.038 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".