Local Non-Stationary Flood-Duration-Frequency Modelling
Bibliographic record
Abstract
La modélisation débit-durée-fréquence est une généralisation de l'analyse fréquentielle classique des crues, qui tient compte de la multi-durée des hydro grammes de crue. Cette approche est principalement basée sur l'hypothèse suivante : les paramètres de la distribution des crues, pour n'importe quelle durée de crue, ne changent pas au cours du temps. Cependant, en réalité, et comme conséquence aux activités anthropogéniques locales et/ou globales, la stationnarité des variables hydrologiques ne peut souvent pas être assumée. De nouvelles méthodes, qui tiennent compte de la non-stationnarité des séries hydrologiques et qui considèrent les paramètres de la distribution des crues comme des fonctions du temps, devraient être développées et utilisées en pratique. Cette étude introduit des aspects formels de la modélisation débit-durée-fréquence non-stationnaire. L'approche proposée considère une analyse de la tendance pour identifier les composantes temporelles du modèle et pour prédire leurs changements dans le futur. La signification des paramètre de la dépendance temporelle est représentée par la structure du modèle. L'approche est illustrée pour un basin versant de la Colombie-Britannique, Canada. \n \n<h2>Abstract</h2> \nFlood-duration-frequency modelling is an extension of standard flood frequency analysis that takes into account the multi-duration aspect of flood hydrographs. The key assumption of this approach is that the parameters describing the flood frequency distribution for any flood duration do not change over time. In reality, however, as a consequence of local and/or global anthropogenic activities, stationarity of hydrologic records cannot be assumed. New methods that take into account the non-stationarity of hydrologic records and that can properly deal with time-dependent parameters of flood frequency distributions need to be developed and used in practice. This study introduces formal aspects of non-stationary flood-duration-frequency modelling. The presented approach uses trend analysis to identify time-dependent components of the model and to predict their changes in the future. The significance of the time-dependent parameters is reflected in the structure of the model. The approach is illustrated on a study catchment in British Columbia, Canada.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".