Abstract. This work focuses on the analysis and characterization of the flash flood events occurring during summer in Catalonia. To this aim, a database with information about the social impact produced by all flood events recorded in Catalonia between 1982 and 2007 has been built. The social impact was obtained systematically on the basis of news press data and, occasionally, on the basis of insurance data. Flood events have been classified into ordinary, extraordinary and catastrophic floods, following the proposal of Llasat et al. (2005). However, bearing in mind flash flood effects, some new categories concerning casualties and car damage have also been introduced. The spatial and temporal distribution of these flood events has been analyzed and, in an effort to better estimate the social impact and vulnerability, some indicators have been defined and analyzed for a specific region. These indicators allow an analysis of spacial and temporal trends as well as characterization of the events. Results show a flash-flood increase in summer and early autumn, mainly due to inter-annual and intra-annual changes in population density.
Reliability of the air transportation system heavily depends on the performance of communication, navigation, and surveillance facilities in the National Airspace System (NAS). These facilities are prone to outages caused by convective weather, such as lightning. Current lightning safety standards and risk assessments focus solely on lightning occurrence and omit the effect of lightning intensity from hazard characterization. We propose methods that incorporate lightning intensity and occurrence parameters to better understand the impact of lightning strike on the NAS using the National Lightning Detection Network and Federal Aviation Administration NAS facilities and equipment outage databases. Spatial analysis and clustering reveal different exposure profiles for 436 U.S. airports. Kernel Density estimation and Hot Spot analysis show that regardless of lightning intensity, Southern state airports are the most exposed to lightning hazards. K‐means clustering reveal five different lightning exposure profiles that mimic the spatial patterns produced by the Kernel Density estimation and Hot Spot analysis. A scoring system ranks all airports according to their exposure profile taking into consideration lightning occurrence and intensity. It is complemented with a rising trend exposure analysis, which identifies airports whose exposure could be underestimated under the current standards, identifying airports with fewer lightning occurrences but higher intensities. Finally, a comparison between the exposure patterns and lightning‐induced outages provide insights into U.S. lightning impact patterns. Similar patterns between lightning exposure and outages indicate that the results of the proposed lightning hazard assessment provide useful information for prioritizing airport hardening investments at the national scale and reducing lightning risk.
Sea level rise (SLR) and storm surge inundation are major concerns along the coast of the San Francisco Bay (the Bay Area), impacting both coastal communities and critical infrastructure networks. The oil industry comprises a complex and critical infrastructure network located in the Bay Area. There is an urgent need to assess consequences and identify risk-based solutions to increase the resilience of this industrial network in the Bay Area to SLR and storm surge. In this study, a comprehensive multi-modal network model representing the fuel supply system was built. A total of 120 coastal flooding scenarios, including four General Circulation Models, two Representative Concentration Pathways, three percentiles of future SLR estimates, and five planning horizons (20 year intervals from 2000 to 2100) were considered. The impact of coastal flooding on fuel transportation networks was studied at two different scales: regional and local. At the regional scale, basic network properties and network efficiency were analyzed across multiple flooding scenarios. At the local scale, cascading effects of individual node disruptions were simulated. Based on this research, smarter and more holistic risk-based adaptation strategies can be established which could lead to a more resilient fuel transportation network system.
Many of the world’s most disaster-prone cities are also the most difficult to model and plan. Their high vulnerability to natural hazards is often defined by low levels of economic resources, data scarcity, and limited professional expertise. As the frequency and severity of natural disasters threaten to increase with climate change, and as cities sprawl and densify in hazardous areas, better decision-making tools are needed to mitigate the effects of near- and long-term extreme events. We use mostly public data from landslide and flooding events in 2017 in Freetown, Sierra Leone to simulate the events’ impact on transportation infrastructure and continue to simulate alternative high-risk disasters. From this, we propose a replicable framework that combines natural hazard estimates with road network vulnerability analysis for data-scarce environments. Freetown’s most central road intersections and transects are identified, particularly those that are both prone to serviceability loss due to natural hazard and whose disruption would cause the most severe immediate consequences on the entire road supply in terms of connectivity. Variations in possible road use are also tested in areas with potential road improvements, pointing to opportunities to harden infrastructure or reinforce redundancy in strategic transects of the road network. This method furthers network science’s contributions to transportation resilience under hydrometeorological hazard and climate change threats with the goal of informing investments and improving decision-making on transportation infrastructure in data-scarce environments.
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