Risk-based framework to determine climate-informed design storms for road drainage infrastructure

TL;DR

This paper presents a risk-based, climate-informed framework for designing storm events for road drainage, integrating climate projections with risk assessment to improve infrastructure resilience amid changing precipitation patterns.

Contribution

It introduces a novel framework combining climate model data and risk analysis to adapt design storms for road drainage systems, moving beyond traditional stationary methods.

Findings

Framework effectively incorporates climate projections into design storm planning.

Application to Ontario demonstrates scalability and adaptability.

Highlights need for dynamic, risk-informed infrastructure design approaches.

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 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. The risk level is defined as a function of hazard and vulnerability, where hazard combines both physiographic and meteorological factors. Vulnerability is comprised 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 the 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.