From Reducing the Risk of Urban Flooding to Evaluating Changes in Rainfall Patterns and Impacts of Climate Change
Bibliographic record
Abstract
From precipitation and infiltration to runoff and evaporation, the urban hydrologic cycle (UHC) is comprised of various components, wherein changes in any individual part may affect the entire structure of UHC in the long term. This research studies rainfall retrospectively, which is an essential component in UHC, from assessing its impact on urban stormwater management systems to understanding the root causes of the increasing severity of rainfall events in a novel perspective.The thesis starts with a review which assesses the ‘One Water’ concept in urban flood management. This review highlights the paramount structured thinking about the connection between each dimension within the hydrologic cycle and generating a holistic view of urban water resources security. Following the conceptual review, the application of Low Impact Development (LID) is assessed with a case study in London, Ontario. This dimension demonstrates that the pressure on urban stormwater infrastructure will increase, and continuing, under the impact of climate change.While rainfall conditions (increasing magnitude and frequency) continue to worsen due to climate change, it is imperative and fundamental to evaluate how rainfall has changed over the years with the influence of climate change and make informed predictions on how rainfall patterns will continue to evolve. Instead of analyzing conventional rainfall patterns indicators such as magnitude and frequency, this research employed a novel perspective: the timing of the heavy rainfall events (based on the Annual Maximum Series – AMS) plays a critical role in revealing the range of impacts of climate change. The time of heavy rainfall events occurrence provides overwhelming evidence indicating there are changing rainfall patterns in time-of-year, which is supported by statistical analyses on the mean, variance, and coefficient of variation. This research also explored the relationship between the inter-event time (IET) between rainfall events and the annual numbers of rainfall events (λ) for a series of rainfall data measured from 5min to 12hr and intensities vary from 2 to 24 mm/hr. It shows that the IET between rainfall events is shortening, and the λ is increasing for the majority rainfall durations at various intensities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".