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Critical Infrastructures (CIs) are interdependent and, thus, vulnerable to scenarios of cascading effects, e.g., initiated by natural hazardous events like flooding, windstorms, heat waves, etc, which can lead to partial or complete inoperability. Traditional approaches for CI inoperability assessment are based on historical data of natural event occurrence and deterministic climate projections, which often neglect spatial correlations, thus limiting the analysis to the footprint of the direct failures on the CI in the location of occurrence of the hazardous event. Additionally, for long-term CI projections lifespans, it is necessary to consider the changes in natural hazards frequency and intensity due to climate change. In this work, we use climate projections to generate spatially coherent natural hazard scenarios through Stochastic Fields (SFs) via Karhunen-Loe`ve Expansion (KLE), so that spatial dependencies and correlations across the CIs are preserved also accounting for climate change on the long-term. The generated hazard scenarios are integrated within the Dynamic Inoperability Input-output Model (DIIM) framework to evaluate the cascading effects on multi-state interdependent CIs. The overall modeling methodology and simulation framework is applied to multi-state interdependent power and water networks positioned in the climate conditions of North of Italy, considering to climate change projections based on Representative Concentration Pathways (RCPs), specifically RCP 4.5 and RCP 8.5. The analysis performed allows considering how climate change alters inoperability patterns and how systemic risks propagate across CIs. This provides insights for the design of mitigation and prevention strategies.
Mesa et al. (Fri,) studied this question.