Managing Hydrological Risks with Extreme Modeling: Application of Peaks over Threshold Model to the Loukkos Watershed, Morocco
Bibliographic record
Abstract
The peaks over threshold (POT) is a widely used technique to describe the exceedances of hydrological data above a threshold. It is well known that, under some conditions, the exceedances distribution can be approximated by a generalized Pareto distribution (GPD). The lack of a generally accepted methodology for selecting the optimal threshold is a major issue of the POT technique. In this paper an integrated approach is proposed that combines some graphical approaches with some analytical approaches to identify the optimal threshold and estimate the shape parameter of the exceedances distribution. Such a combination intends to reduce the subjectivity in graphical methods, and to refine their finding by using rigorous mathematical tools of analytical methods. First, a statistical test is used to select the appropriate GPD fitting the exceedances. Then, three numerical approaches, namely the likelihood ratio test, square error method, and multiple threshold method, are applied to detect the optimal threshold above which exceedances can be approximated by a GPD. These techniques are illustrated in a case study of Loukkos basin, a water resource of great importance in Morocco.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".