Statistical estimation of probable maximum precipitation
Bibliographic record
Abstract
Civil engineers design infrastructure exposed to hydrometeorological hazards, such as hydroelectric dams, using probable maximum precipitation (PMP) estimates. Current PMP estimation methods have several flaws: some required variables are not directly observable and rely on a series of approximations; uncertainty is not always accounted for and can be complex to quantify; climate change, which exacerbates extreme precipitation events, is difficult to incorporate; and subjective choices increase estimation variability. In this paper, we derive a statistical model from the World Meteorological Organization's PMP definition and use it for estimation. This novel approach leverages the Pearson Type-I distribution, a generalization of the Beta distribution over an arbitrary interval, allowing for uncertainty quantification and the incorporation of climate change effects. Multiple estimation procedures are considered, including the method of moments, maximum likelihood, and Bayesian estimation. However, statistical PMP estimation remains challenging because a short-tailed model is applied to typically heavy-tailed precipitation data. The performance of the proposed approach is assessed through a simulation study and applied to actual precipitation data from two nearby stations in Canada. Finally, we provide and discuss recommendations for best practices in PMP estimation.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".