Analysis of the Seasonal Nature of Extreme Floods Across Canada
Bibliographic record
Abstract
The purpose of this paper is to study the seasonal characteristics of floods in Canada. Its principal objective is to judge the relevance of seasonal analysis and to attempt to present a unified set of methodologies for handling the problems of seasonally varied flows. Another objective is to broadly identify various regions of Canada for which seasonal analysis would be appropriate. Partial duration series and the exceedance model are used because they are well suited to this type of analysis. The focus will be on improved estimation of low-frequency/large-magnitude flood events. Two methods are investigated as a means for grouping flood series on a seasonal basis in a meaningful way: One method is based on flood generating phenomena, and the other divides the year into “homogeneous” periods, which could be called “seasons.” Seasonal analyses were performed on 166 hydrometric stations in all regions of Canada. Synthesis of the results has made it possible to broadly identify regions for which an analysis of seasonal flood variations may be the most relevant. In general, these are the southern regions of the country. Specifically, there is an indication from the results obtained for the Maritime provinces, southern Ontario and the Prairie provinces, that these regions are well suited to seasonal analysis. Also, the results show that frequency analysis for stations recording one or more rare events, or having flood subpopulations that are clearly distinct, may be significantly improved by using a seasonal approach.
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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 | 0.001 |
| 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) | 0.000 | 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".