Inoperability assessment of interdependent critical infrastructures exposed to natural hazards considering climate change

Critical Infrastructures (CIs) are interdependent and, thus, vulnerable to scenarios of cascading effects initiated by natural hazardous events such as flooding, windstorms, and heat waves, which can lead to partial or complete inoperability. Traditional approaches for CI inoperability assessment rely on historical data and deterministic climate projections, which often neglect spatial correlations, limiting the analysis to the direct footprint of failures at the location of the hazardous event. Furthermore, for long-term CI projections, it is necessary to account for changes in the frequency and intensity of natural hazards driven by climate change. To address these limitations, this work uses climate projections to generate spatially coherent natural hazard scenarios through Stochastic Fields (SFs) via Karhunen–Loève Expansion (KLE), preserving spatial dependencies and correlations across CIs while accounting for long-term climate change. The generated hazard scenarios are then integrated within the Dynamic Inoperability Input-Output Model (DIIM) framework to evaluate cascading effects on multi-state interdependent CIs.

The findings of this study demonstrate how climate change alters inoperability patterns and drives the propagation of systemic risks across interdependent infrastructures. Applied to multi-state interdependent power and water networks in Northern Italy, the modeling framework incorporates climate change projections under Representative Concentration Pathways RCP 4.5 and RCP 8.5, enabling a comparative assessment of cascading failure dynamics under moderate and high emission scenarios. The results show that under RCP 8.5, the increased frequency and intensity of natural hazards significantly amplifies inoperability cascades across both networks, with power and water systems exhibiting strong mutual dependencies that accelerate and widen the impact of failures. The spatially coherent hazard scenarios generated through KLE reveal that neglecting spatial correlations in traditional approaches substantially underestimates systemic risk. Overall, the analysis provides actionable insights for the design of targeted mitigation and prevention strategies, supporting more resilient and climate-informed CI planning over long-term horizons.