HR: 1340h
AN: OS23D-1343    [Abstracts]
TI: GIS Development of Probabilistic Tsunami Hazard Maps
AU: * Wong, F L
EM: fwong@usgs.gov
AF: U.S. Geological Survey, 345 Middlefield Road, MS 999, Menlo Park, CA 94025 United States
AU: Geist, E L
EM: egeist@usgs.gov
AF: U.S. Geological Survey, 345 Middlefield Road, MS 999, Menlo Park, CA 94025 United States
AU: Venturato, A J
EM: Angie.J.Venturato@noaa.gov
AF: Joint Institute for the Study of the Atmosphere and Ocean (JISAO), University of Washington Box 354235, Seattle, WA 98115 United States
AB: Probabilistic tsunami hazard mapping is best performed using geographic information systems (GIS), where multiple model-based inundation maps can be combined according to assigned probabilities. To test these techniques, hazard mapping is performed at Seaside, Oregon, the site of a pilot study that is part of the Federal Emergency Management Agency's (FEMA) effort to modernize its Flood Insurance Rate Maps (FIRMs). Because of the application of the study to FIRMs, we focus on developing aggregate hazard values (e.g., inundation area, flow depth) for the 1% and 0.2% annual probability events, otherwise known as the 100-year and 500-year floods. Both far-field and local tsunami sources are considered, each with assigned probability parameters. For an assumed time-independent (Poissonian) model, the only probability parameter needed is the mean inter-event time of the source under consideration. For a time-dependent model, the probability parameters include the time to the last event, the mean inter-event time, and a measure of recurrence aperiodicity. The main input for the model consists of far-field and local inundation maps, which represent maximum inundation values on land modeled for different combinations of earthquake magnitude and distance to earthquake source. The maps are rendered as raster grids, which lend themselves to algebraic functions as numerical arrays. One approach to determine the 100-year or 500-year inundation line is to calculate the maximum spatial extent of the input inundation maps. Alternatively, probabilistic flow depths can be determined by estimating a frequency-flow depth regression relationship for all of the layers at any given spatial point and interpolating the 100-year or 500-year value. The flow depths and accompanying inundation lines will be provided as map data layers reflecting the impact of tsunamis on the process of modernizing the FEMA Flood Insurance Rate Maps. In addition this type of analysis can be expanded to other hydrodynamic parameters for estimating probabilistic wave impacts. Finally, another important aspect of using GIS is to map historic inundation zones (e.g., from the 1964 Great Alaska tsunami) and to spatially analyze tsunami deposits for comparison with model results.
UR: http://walrus.wr.usgs.gov/tsunami/
DE: 7223 Seismic hazard assessment and prediction
DE: 4255 Numerical modeling
DE: 4564 Tsunamis and storm surges
SC: Ocean Sciences [OS]
MN: 2004 AGU Fall Meeting