Unser Forschungsprogramm 2021-2027
Subtopic 3.4 "Umsetzung von Gefahrenwissen in Risikominderung"
One of the greatest challenges in protecting society from geohazards is converting the acquired knowledge about these hazards into practical means of mitigating against the most serious consequences of such events. Therefore Subtopic 3.4 is working towards 1) raising community awareness and preparing decisionmakers charged with long-term urban planning or site selection/design of critical facilities, 2) improving short-term (days, hours, seconds) warnings, and 3) contributing to rapid response and real-time information during the hours and days following a major event.
Three key themes are central to this Subtopic:
Awareness and long-term urban planning: Here we contributes to the next generation of tsunami, volcanic and seismic hazard maps and models tailored for enhancing public awareness and preparedness, refining zoning and building codes, and the design and siting of critical facilities. Low-cost sensors imbedded in buildings and the use of fiber-optic cables helps to locate faults in urban areas and to record earthquakes and other ground movements within cities. With new seismological records and 3D wave propagation models we improve the evaluation of future shaking scenarios taking into account local ground conditions, particularly important for cities and critical facilities in large basins or close to major faults.
Warning: A new generation of Tsunami Early Warning (TEW) systems integrates data from modern GNSS and undersea cable networks. This integration requires the development of real-time GNSS signal processing, local feasibility studies, and Observation System Simulation Experiments (OSSE). We evaluate decentralized earthquake early warning/rapid response architectures based on widely distributed smart sensors. We additionally improve forecasting of near-Earth space weather by developing forecasting tools for the near-Earth radiation environment.
Rapid response and post-event information: Information material is tailored for a wide range of users’ expertise, from scientific experts to policymakers, industry, first responders, and the general public. An internal rapid data exchange platform is developed with the goal of providing information about extreme events by exploiting data science and automatizing event-detection.