Risk-based framework to determine climate-informed design storms for road drainage infrastructure
Bibliographic record
Abstract
Climate change is amplifying extreme precipitation events in many regions and imposes substantial challenges for the resilience of road drainage infrastructure. Conventional design storm methodologies, which rely on historical trends of rainfall data under a stationarity assumption, may not adequately account for future climate change and variability. This study introduces a risk-based framework for determining climate-informed design storms tailored to road drainage systems. The proposed framework integrates climate model projections with risk assessment to quantify the potential impacts of future extreme rainfall on drainage performance and adjust the future design storm, with a focus on the province of Ontario, Canada. Projected precipitation changes for mid- and late-century time horizons are quantified using statistically downscaled CMIP6 General Circulation Models (GCMs). The risk level is defined as a function of hazard and vulnerability, where hazard combines both physiographic and meteorological factors. Vulnerability is composed of socioeconomic, transportation, and environmental considerations. To systematically integrate these components, a weighting scheme is developed based on a sensitivity analysis of the criteria, which provides flexibility in assigning relative importance to each factor. The estimated risk level is then applied to adjust projected design storm accordingly. The proposed workflow is demonstrated through both province-wide and site-specific applications across Ontario's road network to better highlight its scalability and adaptability. The findings signify the necessity of shifting from static, stationarity-based design methodologies to dynamic, risk-informed approaches that enhance the long-term resilience of transportation networks. • A structured and efficient hierarchical framework for flood risk assessment in road drainage design. • Integrating rainfall projections keeps risk relevant for future storm design. • Socioeconomic, transport, and environmental data inform local infrastructure vulnerability. • A dynamic, non-stationary framework provides a more realistic approach to climate risk assessment. • Proposed risk-based adjustment method supports cost-effective and resilient drainage upgrades.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".