ABSTRACT
In the context of climate-related hazards, there has been increasing recognition of spatial and spatiotemporal correlation and tail dependence among extreme events. Therefore, understanding the dependence structure among extreme events is important to understand impacts of these stressors on infrastructure and natural systems, and human lives and economies. In case of infrastructure systems, both built and natural, the network-based knowledge needs to be complemented and supplemented with data acquisition, data representation, information management, real-time data ingestion, and offline data analyses. Network science-based methods have been used for change detection, correlative and predictive analyses in weather and climate, robustness and resilience of infrastructures and ecosystems, including percolations and cascades of failures, as well as for characterization of social systems. Thus, network science methods may act as a connective technology across the stressors, stressed, and the impacted systems, even though a comprehensive treatment of each system can require other approaches as well.
