Multifractal Flood Frequency Analysis
Bibliographic record
Abstract
Hydrology and more generally sciences involved in water resources management, researches and technological or operational development face a fundamental difficulty: the extreme variability of hydrological fields. It clearly appears today that this variability is a function of the observation scale and yield natural hazards such as floods or droughts. The estimation of return periods for extreme precipitation and flooding events requires a model of the natural (unperturbed) statistical behaviour of the probability tails and the possible clustering (including possible long-range dependencies) of the extremes. Appropriate approaches for handling such non classical variability over wide ranges of time and space scale do exist. They are based on a fundamental property of the non-linear equations: scale invariance. Its specific framework is that of multifractals. In this framework hydrological variability builds up scale by scale leading to non-classical statistics; this provides the key element needed to better understand and predict floods. Scaling is a verifiable physical principle which can be exploited to model hydrological processes and estimate their statistics over wide ranges of space-time scales. We first present the Multifractal Flood Frequency Analysis (MFFA) tool and illustrate some results of its application to a large database (for more than 16000 selected stations over USA and Canada). We then discuss its efficiency by showing how the mean flow information - coupled with universal multifractal parametrizations with power law tails - can be used to estimate return times for extreme flood events.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".