Joint Occurrence of Extreme Water Level and River Flows in St. Lawrence River Coasts Under Present and Sea Level Rise Conditions
Bibliographic record
Abstract
Abstract In low‐lying coastal regions, the joint occurrence of high river flow and high water levels can cause coastal flooding with substantial economic and social implications. Recent studies over Canada's coasts have shown that neglecting the interdependency between flood drivers can underestimate the risk of flooding by up to 50%. However, to date, such interdependency has not been investigated for the coasts of the St. Lawrence River, Estuary and Gulf system (StL), where Sea Level Rise (SLR), along with intensified river peaks, are already threatening these communities. In this study, a copula‐based bivariate frequency analysis was applied to quantify the likelihood of occurrence of flooding events under dependent and independent assumptions, for 26 sites along the StL. Furthermore, to quantify the impact of anthropogenic climate change, the joint return period in historical period was compared with that of projected SLR associated with RCP 8.5 for the year 2100. Results show that (a) the independence assumption can underestimate the likelihood of occurrence of flooding event in the Fluvial Section of the StL by up to 30 times and (b) the SLR can increase the likelihood of occurrence of flooding event by up to 50 times in the Estuary and the Gulf and by up to 5 times in the Fluvial Section of the StL. This study highlights the need for explicit consideration of the dependence between flood drivers and of SLR in the delineation of flood maps along the coast of the St. Lawrence.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".