Multivariate Analysis of Compound Flood Hazard Across Canada's Atlantic, Pacific and Great Lakes Coastal Areas
Bibliographic record
Abstract
Abstract Compound flooding, caused by the simultaneous or successive occurrence of two or more flood mechanisms, is mainly associated with extreme precipitation, river overflows, and storm tides across coastal areas. The interdependencies between these components can increase the risks of flood impacts, threatening coastal communities and infrastructure systems. This study quantifies the corresponding multivariate hazard over Canada's coastal areas by characterizing the dependencies between multiple drivers of flooding based on the C‐vine copula statistical approach. The joint return periods of compound flooding considering the AND, OR, and Kendall scenarios are estimated and the corresponding failure probabilities are assessed. Further, the compound hazard ratio (CHR) index is applied to quantify possible under‐ or overestimations of the flood hazards when individual drivers are assessed independently. Analyses are performed at 41 locations across the Atlantic, Pacific, and the Great Lakes coasts, and the uncertainties are quantified based on the Bayes theorem. Results show that at approximately 50% of locations (mostly at the Great Lakes), the flood hazard associated with the AND scenario increases considerably when the dependencies are characterized compared to the (unrealistic) independence scenario, indicating the potential for compound flooding in these regions. Besides, at more than half of the studied locations, the CHR index exceeds one highlighting the interrelationships between drivers of flooding. The results of this study provide a deeper understanding of the flood mechanisms and their interdependencies across Canada's coasts, which support the development of resilient structures and improved coastal flood management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".