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Abstract:
The need for a global approach to natural hazard and risk assessment is becoming increasingly apparent to the disaster risk reduction community. Different natural (e.g. earthquakes, tsunamis, tornadoes) and anthropogenic (e.g. industrial accidents) hazards threaten millions of people every day all over the world. Yet, while hazards can be so different from each other, the exposed assets are mostly the same: populations, buildings, infrastructure and the environment. Exposure should be regarded as a dynamic process, as best exemplified by rapid urbanization, depopulation of rural areas and all of the changes associated with the actual evolution of the settlements themselves. The challenge is thus to find innovative, efficient methods to collect, organize, store and communicate exposure data on a global scale, while also accounting for its inherent spatio-temporal dynamics. The aim of this paper is to assess the challenge of implementing an exposure model at a global scale, suitable for different geo-hazards within a dynamic and scalable framework. In this context, emerging technologies, from remote sensing to crowd-sourcing, are assessed for their usability in exposure modelling and a road map is laid out towards a global exposure model that will continuously evolve over time by the continuous input and updating of data, including the consideration of uncertainties. Such an exposure model would lay the basis for global vulnerability and risk assessments by providing reliable, standardized information on the exposed assets across a range of different hazards.
