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

2025-08-13

AI Summary

Climate change intensifies extreme precipitation, rendering conventional stationary-design storm methods—reliant on historical data—inadequate for ensuring road drainage system resilience. To address this, we propose a risk-driven, climate-adaptive design storm framework that abandons the stationarity assumption and establishes a dynamic, non-stationary mechanism for adjusting design storms, applicable at both provincial and site-specific scales. Methodologically, the framework integrates statistically downscaled precipitation projections from CMIP6 models, couples meteorological–geographic hazard factors with multidimensional vulnerability indicators (socioeconomic, transportation, and environmental), and employs sensitivity analysis to develop a weighted risk integration approach. Applied to Ontario’s provincial road network, the framework significantly enhances drainage infrastructure resilience against future extreme rainfall events. It demonstrates strong scalability and engineering practicality, offering a robust, transferable solution for climate-resilient urban drainage planning.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Scalability of ML Systems
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Sustainability and climate impact of web technologies

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.

Problem

• Develop climate-informed design storms for road drainage resilience
• Integrate climate models and risk assessment for future rainfall impacts
• Shift from static to dynamic risk-based drainage design methods

Innovation

• Integrates climate models with risk assessment
• Uses CMIP6 models for precipitation projections
• Applies dynamic risk-informed design methodology