Delineation of Homogeneous Regions Based on the Seasonal Behavior of Flood Flows: An Application to Eastern Canada
Bibliographic record
Abstract
We used the peaks over threshold (POT) approach to analyze the seasonal behavior of flood frequencies extracted from daily streamflow records of a group of hydrometric stations in Eastern Canada. For each record, we analyzed the distribution of the times of occurrence of flood flows above a threshold. The aim was to achieve a seasonal portioning of the year based on the time distribution of flood occurrences. It may be possible to assemble stations that are similar in their flood seasonality into geographical regions that exhibit some homogeneity. This broadly provides “homogeneous regions based on seasonality”. The number of significant “seasons” and the dates of beginning and end of each season jointly characterize the seasonal behavior of floods in a homogeneous region. We will provide examples of graphical analyses that help identify the seasons from a hydrometric record. The most important graph is based on plotting the mean number of exceedances in the time interval (zero, t] as a function of increasing t from zero to 365 days. One diagram may combine several plots for a number of threshold levels. Regional flood modeling based on grouping catchments with similar seasonal flood behavior should help reduce the uncertainty in flood forecasts at individual flood sites.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".