The measurement and mapping of transportation network vulnerability to natural hazards constitute subjects of global interest for a sustainable development agenda and as means of adaptation to climate change. During a flood, some elements of a transportation network can be affected, causing the loss of lives. Furthermore, impacts include damage to vehicles, streets/roads, and other logistics services - sometimes with severe economic consequences. The Network Science approach may offer a valuable perspective considering one type of vulnerability related to network-type critical infrastructures: the topological vulnerability. The topological vulnerability index associated with an element is defined as reducing the network’s average efficiency due to removing the set of edges related to that element. In this paper, we present the results of a systematic literature overview and a case study applying the topological vulnerability index for the highways in Santa Catarina (Brazil). We produce a map considering that index and areas susceptible to urban floods and landslides. Risk knowledge, combining hazard and vulnerability, is the first pillar of an Early Warning System and represents an important tool for stakeholders of the transportation sector in a disaster risk reduction agenda.
Cities increasingly face flood risk primarily due to extensive changes of the natural land cover to built‐up areas with impervious surfaces. In urban areas, flood impacts come mainly from road interruption. This article proposes an urban flood risk map from hydrological and mobility data, considering the megacity of São Paulo, Brazil, as a case study. We estimate the flood susceptibility through the Height Above the Nearest Drainage algorithm; and the potential impact through the exposure and vulnerability components. We aggregate all variables into a regular grid and then classify the cells of each component into three classes: Moderate, High, and Very High. All components, except the flood susceptibility, have few cells in the Very High class. The flood susceptibility component reflects the presence of watercourses, and it has a strong influence on the location of those cells classified as Very High.
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