From hazard to disruption: forecasting direct and indirect tropical cyclone impacts on infrastructure in Mozambique

Critical infrastructure (CI), such as healthcare facilities, schools, and the road network, plays a vital role in society by providing essential services that sustain the functioning of communities. Disruptions to this infrastructure can have profound consequences, affecting public health, safety, economic activities, and general well-being. Weather extremes, including tropical cyclones (TCs), are major drivers of such disruptions, causing widespread failures to power, communication, transportation, and healthcare. Forecasting the potential impact of weather events on these services in the weeks to days before landfall is crucial to enhance preparedness and enable effective anticipatory actions. Unlike previous efforts that focused primarily on estimating the potentially affected population, this research shifts attention to evaluating direct and indirect impacts on CI, and to capture the uncertain nature of impact forecasts depending on lead-time. The methodology, which relies entirely on open-source code and data, yields several metrics quantifying the impact of ensemble-based tropical cyclone (TC) wind forecasts on healthcare access, including the number of hospitals directly affected and the number of people indirectly affected due to disrupted access to healthcare facilities. We apply this approach to TCs Idai, Kenneth, and Freddy, which have struck Mozambique since 2019. The results highlight the extent of indirect effects on the population from infrastructure disruptions. Uncertainty arises from lead time, disruption threshold assumptions, and the challenge of capturing impact magnitude, especially for rapidly intensifying TCs. These findings underscore the importance of including indirect impacts into Impact-Based Forecasting (IBF) frameworks, which could enhance decision making. This research aligns with the development of IBF and situational awareness mechanisms promoted by the World Meteorological Organization (WMO). Building on this, the work supports international organizations in activating early warning protocols and delivering more targeted aid, such as financial resources, blankets, medical supplies, and volunteer personnel by identifying where hospitals are likely to be disrupted and which populations may lose access to healthcare. The visualizations generated further assist decision-makers in prioritizing areas that require immediate support.