Future flood envelope curves for the estimation of design flood magnitudes for highway bridges at river crossings
Bibliographic record
Abstract
Creager flood envelope curves, which serve as the upper bound/limit of observed extreme flows, are commonly used by practitioners to estimate design flood magnitudes, which in the case of most river-crossing highway bridges is 75-year flood magnitude in Canada. This study proposes a novel framework for climate change adaption of Creager curves for estimating future design floods. These curves, for the current period, are assessed considering 417 observation stations, located in seven major Canadian river basins (i.e., Fraser, Nelson, Mackenzie, Yukon, Churchill, St Lawrence and St John). The Creager coefficient C, which defines flood envelope curves, varies between 1 and 45 across the studied river basins. To adapt Creager curves for future changes in streamflow, a correction factor, RC, which is the ratio of future to current period C values, is proposed. These factors are obtained for observation sites, using streamflow data from an ensemble of Regional Climate Model (RCM) simulations for current and future periods, through two Regional Frequency Analysis approaches. The first approach, considering only the RCM cells where the stations are located, suggests RC in the 0.3–1.6 range, with southeasterly basins showing values < 1. The second approach, considering all RCM cells for a given region, yields a wider range for RC and adds useful information in that RC values can also be established at ungauged locations. From a practical viewpoint, the proposed framework for estimating future design floods is robust and transferrable to other basins, but can benefit using streamflow projections from other models for better uncertainty quantification.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".