Investigation of pluvial flash flood loads on overpasses for the city of Montreal
Bibliographic record
Abstract
Warmer temperatures in a future climate can lead to more frequent high intensity short-duration rainfall events, which can lead to frequent and severe flash floods. Such events pose significant threats to urban infrastructure, including urban overpasses, which have not been adequately explored. This study combines high-resolution (4 km) regional climate simulations from the Global Environmental Multiscale (GEM) model with two-dimensional hydrodynamic modeling, based on HEC-RAS, at 25 m spatial resolution to assess vulnerability of overpasses in Montreal, the second-largest city in Canada, under flood-induced hydrostatic, drag, and debris impact loads for different greenhouse gas emission scenarios. HEC-RAS simulations for design storms, developed following the Huff curve and Chicago methods, corresponding to 100-year return levels of 15-min, 1-h, and 6-h rainfall events for current and future climates obtained from GEM, suggest future increases in inundated areas by 13 to 31%, with higher changes being associated with shorter-duration events. Moreover, classification of overpasses into various risk categories (i.e., low, medium, and high) based on flood loads indicates potential increases in the number of overpasses in both high- and medium-risk categories in future climate. Risk categorization shows that 6-h duration events in current climate have the highest number of overpasses (30) in the high-risk category, with far future projections indicating increases of 17-200% in the number of high-risk overpasses across all storm durations. This foundational work will form the basis for detailed investigations focused on individual overpasses and infrastructure design considerations that account for the intensification of flash flood loads under future climate conditions to ensure climate resiliency. Supplementary Information: The online version contains supplementary material available at 10.1007/s44327-025-00172-1.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".