Flood envelope curves for the estimation of design flood magnitudes for highway bridges
Bibliographic record
Abstract
The rising costs and safety concerns associated with flood-induced infrastructure damages in Canada underscores the critical need for adapting design flood magnitudes to future climate change. Creager flood envelope curves, which serve as the upper bound/limit of observed extreme flows for different drainage areas within a specific region, are widely employed by practitioners to estimate design flood magnitudes, which in the case of most river-crossing highway bridges is considered as 75-year flood magnitude. A framework for adapting Creager curves to future changes in streamflow is proposed in this study. To this end, Creager curves, for the current 1951–2020 period, are developed using regional frequency analysis (RFA) on annual maximum daily mean streamflow, 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 is the main parameter that defines flood envelope curves for different regions, under the current climate, exhibits considerable variability, ranging from 1 to 45, across the studied river basins.To adapt Creager curves for future changes, a correction factor, RC, defined as the ratio of future to current period C values is proposed. Two RFA approaches were employed to calculate the ratio using simulated streamflow data, derived using a cell-to-cell routing scheme, applied to an ensemble of five-member Regional Climate Model (RCM) GEM (Global Environmental Multiscale) simulated runoff for the current reference 1951–2020 and future 2021–2099 periods for the observation sites. The first RFA approach, considering only the GEM grid cells where the stations are located, suggests RC in the 0.3 to 1.6 range, with St John and St Lawrence River basins showing values less than 1. The second approach, considering all GEM cells for a given region, produces comparable results but yields a wider range for RC and adds useful information in that RC values can also be established at ungauged locations, with RC values higher than 1.6 in various regions especially over western Canada. An evaluation of the level of confidence for RC , based on the GEM ensemble, reveal a higher level of confidence for most parts of the study domain. The second approach is likely to be a better choice for longer return periods considering the larger pooling of data. From a practical viewpoint, the proposed method for estimating future design floods is robust and transferrable to other basins but can benefit from using streamflow projections from other models for better quantification of uncertainty.
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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 | 0.001 | 0.008 |
| 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) | 0.001 | 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".