Challenging the standard dike freeboard: Methods to quantify statistical uncertainties in river flood protection
Bibliographic record
Abstract
In most developed and developing nations, a freeboard is being applied to flood defense structures as a margin of uncertainty in the estimated flood stage. In some jurisdictions, practice is shifting towards the use of confidence intervals based on the fitted flood probability distribution, albeit often relying on only one statistical distribution and on only annual maximum flows. In this paper, we argue that, independent of geotechnical, geomorphological or hydrological uncertainties pertaining to the estimation of flood stage, the application of standard freeboards ignores stochastic uncertainty, the inclusion of which would provide a more scientifically defensible measure for allowable freeboard. The river stage estimate is subject to multiple sources of uncertainty, including but not limited to model and parameter uncertainty. Consequently, freeboards should be determined via a frequency analysis that explicitly takes into consideration, as well as minimizes, the uncertainty of the estimate due to known factors. Confidence intervals are a common way to represent uncertainty of a statistical estimate such as for the river stage. The choice of the confidence level will be critical, and in many cases will be associated with significant cost implications for the construction or upgrade of flood defense structures. Quantitative flood risk assessments, which are emerging as a standard in developed nations, are well suited to address this issue by allowing loss and mitigation cost comparisons for different flood scenarios. Our paper provides guidance for confidence interval calculations of river stage using an extension of the classical peaks-over-threshold method for daily river levels.
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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 | 0.027 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".