Rapid Damage Scenario Assessment for Earthquake Emergency Management

Abstract

AbstractThe rapid availability of reliable damage statistics, after the occurrence of a major earthquake, is an essential mitigation strategy to drive and support emergency intervention operations. Unfortunately, the latency in collecting and organizing actual damage information has a substantial impact on the efficiency of the initial phases of the intervention framework. To speedup preliminary management operations, a quick, although, coarse prediction of the expected damage is highly desirable.For this purpose, we have developed a system for rapid damage estimation. The system, presently implemented for the Friuli Venezia Giulia region, relies on the existing seismological monitoring infrastructure of the National Institute of Oceanography and Applied Geophysics (OGS), which is responsible for delivering earthquake alerts in northeastern Italy.In case of a major earthquake event, the predicted damage is automatically computed using the OpenQuake software engine by means of ad hoc structural exposure and fragility models developed for the region. Damage calculations rely on a combination of actual observed ground motion from the stations of the OGS seismological network and empirical prediction using the ShakeMaps software developed by the U.S. Geological Survey. The resulting damage scenario, aggregated at municipality level, is finally delivered to the control room of the regional civil protection in support of early intervention activities.Although, the system is presently still under active development, a number of experimental trials have confirmed the reliability and the usefulness of the proposed approach. We are confident that the current research will contribute in mitigating the impact of possible future damaging earthquakes by (1) guiding targeted postevent emergency interventions, (2) increasing the preparedness and response capacity of emergency teams and population through preparatory training activities, and (3) supporting the decision-making process during the recovery phase, hence enhancing resilience.