A Method to Assess the Impacts of Climate Change on the Reliability of Stormwater Infrastructure Components
Bibliographic record
Abstract
In recent years, there has been a growing interest to understand how climate change will affect the management of urban drainage systems.The performance assessment of urban stormwater infrastructure requires precipitation data on a spatial scale of tens of square kilometres and on a range of time scales from 15 min to 24 h.However, the spatial and temporal averaging methods that are used by global climate models (GCMs) to predict precipitation are typically of the order of a few hundred square kilometers, and of months or seasons respectively.This mismatch of spatial and temporal scales has prompted research to bridge the gap and produce rainfall intensity-duration-frequency (IDF) relationships that can be used to assess the impacts of climate change on stormwater infrastructure.In this chapter, a method is presented that takes a different approach to address this gap.Instead of looking at the effects of climate change on the return period (frequency) of different intensity-duration rainfall events, the effects of climate change on component reliability are examined.This is accomplished by relating component reliability to intensity-duration values using the hydraulic risk function, the extreme value (type I) distribution (EVD-I), and historical rainfall data.To examine the effects of climate change on component reliability, historical rainfall data are used to ascertain the mean and standard deviation of the EVD-I for hourly rainfall extremes and then a correction factor, based on physical constraint governed by the Clausius-Clapeyron relation, is applied to
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".