Climate-resilient railway networks: a resource-aware framework

Data availability: The inventory of bridges, embankments and network topology compiled for the case study analysis is available in Tafur et al. (2025)47, hosted on DesignSafe at https://doi.org/10.17603/ds2-vvtn-1h54. The storm surge hazard dataset is described in Bilskie et al. (2018)56, and available at https://doi.org/10.7289/v5fq9tvx.

Code availability: The code developed for the case study analysis is available at https://github.com/Padgett-Research-Group/Railway-Climate-Resilience.

Supplementary information is available online at: https://www.nature.com/articles/s44172-025-00493-4#Sec18 .

Coastal hazards and climate change significantly threaten the resilience of railway systems, increasing stresses on global freight transportation, supply chains and economic stability. When it comes to system resilience, resource availability and allocation have been proven to be leading contributors to downtime and losses, alongside the physical vulnerability to extreme loads. To support the quantification and pursuit of system resilience, here we present a probabilistic framework that addresses gaps in resilience modeling of railway systems. Specifically, it systematically integrates tailored structural damage and restoration models across an infrastructure portfolio, while comparatively assessing network-level functionality over time with alternative approaches to recovery resource allocation. Applied to the railway network in Mobile and Baldwin Counties, Alabama, the framework estimates damage states, restoration costs and times, modeling drop and recovery of network functionality. Findings indicate that sea-level rise considerably affects service reinstatement, reducing resilience index up to 80% when combined with hurricanes. Resource allocation strategies also impact resilience, with variations resulting in up to 75% differences in resilience estimates. These results underscore the need to consider resource constraints and sea-level rise in resilience planning, offering nuanced resilience quantification to support decision-making for mitigation and response strategies, benefiting policymakers, infrastructure managers, insurers, and agencies.

Anibal Tafur and Jamie E. Padgett gratefully acknowledge the support for this research by the Center for Risk-Based Community Resilience Planning of the National Institute of Standards and Technology (NIST), United States, under financial assistance awards 70NANB15H044 and 70NANB20H008. The Center for Risk-Based Community Resilience Planning is a NIST-funded Center of Excellence, funded through a cooperative agreement between the U.S. National Institute of Science and Technology and Colorado State University. Sotirios Argyroudis and Stergios Mitoulis received funding by the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee (grant agreement No: EP/X037665/1, EP/Y003586/1). This is the funding guarantee for the HORIZON-MSCA-2021-SE-01 (grant agreement No: 101086413) ReCharged - Climate-aware Resilience for Sustainable Critical and interdependent Infrastructure Systems enhanced by emerging Digital Technologies.