Développement de modèles de queues et d'invariance d'échelle pour l'estimation régionale des débits d'étiage
Bibliographic record
Abstract
This paper proposes a methodology for the regional analysis of drought flows. This approach lies on the combination of two procedures. (i) The simple scale invariance method for regional series of drought flows, based on the analysis of the relation moments-surfaces or the relation quantiles-surfaces, explains the spatial variability of drought processes by their indexation on a series of scale parameters, essentially the size of the drainage basin. This procedure principally aims at delimitating homogeneous regions, and this characterizes the first condition of a regional estimation. (ii) Frequency analysis of minimum flows, with the approach using tail conditional models, consists in adjusting a probability distribution to values that are smaller than or equal to some given threshold. As a fact, this procedure gives some more weight to the lower part of the distribution by defining a priori a level of censure, mainly a threshold ui called ceiling value. This procedure establishes the second condition of a regional estimation, that is, the determination of a regional estimation model. This methodology has been applied to analyze drought flow characteristics of 187 hydrometric stations scattered in the province of Quebec. Results have shown that the first to the sixth order non-central moments follow a simple scale invariance with respect to the area of the watershed. As a result, analysis of the residuals of these six moments has allowed classifying hydrologically similar watersheds. On the basis of the results from the analysis on the residuals of the sixth moment, the province has been divided into two homogeneous regions. The behaviour of the minimum flows from these different regions has shown that they, too, followed a simple scale invariance.Key words: regionalization, flow, drought, scale invariance, tail model, non-central moment.[Journal translation]
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".