Storyline-Based Modelling of Cascading Critical Infrastructure Impacts and Recovery in Small Island Developing States

Small Island Developing States (SIDS) face complex and compounding climate risks, particularly tropical-cyclone winds and storm surges, which frequently disrupt tightly interconnected infrastructure systems, including electricity, transport, water, and telecommunications. Nevertheless, many current impact assessments are misaligned with practical adaptation requirements, relying predominantly on GDP-based exposure and loss metrics that fail to capture service disruptions, infrastructure interdependencies, or the dynamics of recovery. Moreover, the common assumption that infrastructure is fully restored within a single calendar year is often unrealistic in SIDS, where disruptions and recovery efforts may extend well beyond this timeframe. This underscores the need for more granular, service-oriented analyses.

We introduce a storyline-based, transparent, and data-efficient workflow to evaluate cascading infrastructure impacts and recovery processes under physically consistent, multi-hazard tropical-cyclone scenarios. Storylines are based on historical or plausible events and are translated into gridded hazard fields representing wind and storm-surge inundation. Leveraging CLIMADA for hazard–exposure–impact analysis, we combine compound hazard intensities with sector-specific fragility and recovery functions to estimate direct damage, functional reliability, and time-dependent restoration trajectories for infrastructure assets. Utilizing open exposure datasets (e.g., OpenStreetMap-derived assets) and demand layers, we capture cross-sector dependencies, such as electricity enabling water supply and telecommunications, or transport influencing repair access, to quantify service disruption over time for affected populations.

We emphasize the heterogeneous fragility and recovery capacities across SIDS, incorporating composite proxy indicators (including infrastructure condition, accessibility, response capacity) to derive comparable metrics such as time-to-restoration thresholds and service loss duration. This framework enables the stress-testing of adaptation pathways and informs Loss and Damage strategies, and resilience planning by aiming to identify adaptation limits and avoiding maladaptation, while generating evidence relevant to international finance and support mechanisms.