Recent Trends in Individual and Multivariate Compound Flood Drivers in Canada's Coasts
Bibliographic record
Abstract
Abstract Over half of the global population and the majority of the cities in coastal zones are at risk of coastal flooding. Changes in the occurrence of individual extremes and the interactions between hydrological and coastal variables can exacerbate flood risks. While extensive research has been conducted to understand and predict different types of flood hazards in isolation, spatial and temporal trends and variability of compound flooding, that is, flooding caused by multiple drivers, remain an open question. This study investigates the individual and joint temporal variations of multiple drivers that can cause compound flooding in Canada's coasts including total water level, storm surge, precipitation, and streamflow. Long‐term changes in the frequency and intensity of extremes are analyzed over the Atlantic, Pacific, and the Great Lakes regions. Univariate and multivariate trend tests including Mann Kendall, Covariance Inversion Test, Covariance Sum Test, and Covariance Eigenvalue Test are applied. In addition, a new multivariate index based on the contributing flood drivers transformed into a probability space is proposed, and its application to study compound flooding is investigated. Overall, results show increased risks of individual and compound flooding over the Atlantic coast and varying trends in the Pacific and Great Lakes regions. The multivariate trend indices show consistent results in most scenarios. The proposed index provides a simple and flexible measure to analyze the spatial and temporal variation of compound flooding risks at different thresholds. The results highlight the importance of considering nonstationary compound flood events to develop resilience strategies in coastal environments.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".