Resilience to climate change‐caused flooding—Metro Vancouver case study
Bibliographic record
Abstract
Abstract Climate variability, together with other drivers of global change (like population growth, land‐use change, etc.), is affecting the management of floods. Traditional approaches are no longer sufficient to address the increased pressures that areas vulnerable to flooding are facing. A paradigm shift from flood risk reduction to flood resilience‐building strategies is required. An analytical framework is developed to help quantify, compare, and visualize dynamic resilience to flooding to address some shortcomings in current resilience assessment research. The proposed methodological framework for flood resilience combines physical, economic, engineering, health, and social spatio‐temporal impacts and adaptive capacities of flood‐affected systems. To capture the dynamic spatio‐temporal characteristics of resilience and gauge the effectiveness of potential climate change adaptation options, a flood resilience simulation tool (FRST) is developed to use the analytical framework. The FRST is applied to a case study in Metro Vancouver, British Columbia, Canada. The simulation model focuses on the impacts of climate change‐influenced riverine flooding and sea‐level rise. Simulation results suggest that various adaptation options, such as access to emergency funding, mobile hospital services, and managed retreat can all help to increase resilience to flooding. Results also suggest that, at a regional scale, Metro Vancouver is rather resilient to climate change‐influenced flood hazards.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".