Economic Assessment of Climate Change Scenarios on Drainage Infrastructure Design
Bibliographic record
Abstract
The potential impacts of climate change on the ability of existing and planned drainage infrastructure to safely convey flood flows and prevent off-site erosion, is causing municipalities to examine the recent shifts in storm patterns (rainfall volume, intensity and frequency) and the associated implication to design approaches. This paper examines the approaches taken by different southern Ontario centres, specifically the communities of Cambridge and Milton. Major flooding problems were caused by severe storms in excess of a 50 year return period in 2005 and again in 2006 in the City of Cambridge. In response, a plan was prepared to alleviate the existing flood risk by way of increasing conveyance infrastructure capacity and providing designated flood storage. Various scenarios were costed and the economic implications provided to City of Cambridge administrators to review in terms of risk versus costs. In the Town of Milton case study, an on-going subwatershed study supporting future planned development of a Major Business Park provided the opportunity to apply meteorologic time series related to climate change scenarios. A calibrated hydrologic model was used to assess the impact of modified meteorologic conditions (reflecting potential climate change scenarios) on off-site flood and erosion susceptibility, as well as the design of stormwater management infrastructure. The economic impacts, in terms of land consumption and capital costs for construction, were assessed and compared for stormwater management systems designed under current (standard) and future climate change scenarios.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".