Urban flood management and disaster in Canada : incidence, recovery strategy, and environmental resilience
Bibliographic record
Abstract
This dissertation investigates how cities can improve flood management relationships with riverine landscapes. It develops new data, analysis, and tools to address the need for systematic research on floods and flood management at the municipal scale. In Canada, floods remain the most frequent disaster type, and it is municipalities that are responsible for related land-use planning, emergency response, and often, flood management. However, municipal-scale information on flood disasters and flood risk management remains limited. To examine where in Canada flooding is a problem for municipalities, I developed and analyzed two databases: the All Floods Database (n=149), and a more detailed Riverine Floods Database (n=43), on municipal flood disasters from 2001 to 2013. Data were compiled from the Canadian Disaster Database, municipal surveys, staff interviews, and provincial and territorial disaster financial assistance records. According to the database, 15% of Canadian urban municipalities experienced flood disasters, most of which were non-riverine. Among riverine flood disasters, medium-sized population centres experienced disproportionately more events, and Alberta and British Columbia accounted for over half of the total. Next, I considered flood recovery as a window of opportunity for building resilience, focusing on environmental resilience in terms of flood management relationships with riverine landscapes. Are municipalities re-creating pre-flood conditions during recovery, or are they working to improve resilience and build back better? I created a typology of approaches to riverine flood management and applied it to 20 case study municipalities using survey, interview, and document data. Overall, 85% employed a primarily non-structural approach through land-use regulation. Comparing pre- and post-flood approaches, as many as 45% of municipalities modified their approach to improve resilience, and 30% chose a strategy that would in theory improve environmental resilience, particularly after large flood events; however, the majority retained a return to normal approach. Finally, I developed a tool, the Connection Workbook, to provide municipalities with a rigorous yet practical approach to operationalize assessment of environmental resilience. The tool was applied to three Alberta municipalities, and the results provided insights for actionable guidance to improve municipal flood management through the lens of riverine connection with the landscape.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".