Climatic a priori information for the GEV distribution’s shape parameter of annual maximum flow series
Bibliographic record
Abstract
The Generalized Extreme Value (GEV) distribution encompasses various models with unique characteristics, such as upper or lower bounds, complicating the application of the maximum likelihood algorithm in hydrological frequency analysis. When proposed, the Generalized Maximum Likelihood (GML) approach addressed some computational challenges in maximum likelihood estimation but remains sensitive to constraints on the shape parameter. These constraints on the support of the shape parameter do not consider the variability on the tail behavior of annual maximum flow series in various hydroclimatic regions. To mitigate this, we introduce the Extended GML (EGML), which incorporates a priori information on the shape parameter to reduce model specification bias in annual maximum flows, particularly when working with short data records. Based on the statistical characteristics of the monthly flows for the training set of the data series and a classification by Fuzzy C-Means (FCM) we developed four classes representing similar hydrological behaviors. This classification analysis was then combined with the Koppen climate regions to propose the a priori distributions for the GEV shape parameter across the four classes to better characterize the tail behaviour of annual maximum flow series distribution. A comparison of the 100-year return period quantile estimated with the EGML and GML methods reveals significant differences, particularly for the arid climate class.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".