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IUGG XXIV General Assembly July 2-13, 2007 Perugia, Italy
(S) - IASPEI - International Association of Seismology and Physics of the Earth's
Interior
JSS003 Oral Presentation 1854
Natural Hazard Monitoring: a early warning method to delineate
potentially affected areas by Hurricane using a GIS model
Dr. Andrea Taramelli
Dipartimento di Scienze della Terra Università degli Studi di Perugia
Laura Melelli, Massimiliano Pasqui, Alessandro Sorichetta, Bernardo Gozzini
This research integrates the concept that the subject of natural hazards and the use of existing remote
sensing system in the different phases of a disaster management for a specific hurricane hazard, is
based on the applicability of GIS model for increasing preparedness and providing early warning. The
modelling of an hurricane event in potentially affected areas by GIS has recently become a major topic
of research. In this context the disastrous effects of hurricanes on coastal communities and
surroundings areas are well known, but there is a need to better understand the causes and the
hazards contributions of the different events related to an hurricane, like storm surge, flooding and high
winds. This blend formed the basis of a semi-quantitative and promising approach in order to model the
spatial distribution of the final hazard along the affected areas. The applied model determines a sudden
onset zoning from a set of available parameters that include topography, bathymetry, storm track into
coast proximity and river network. For all these parameters, key attributes based on SRTM and
bathymetry data, are the river network delineation (based on the Strahler methodology) the slope data
and coastline bathymetry identification. Complementary data for the final model includes remote sensed
density rain dataset, elevation datasets for selected coastal drainage basins, and existing hurricane
tracks inventories together with hurricane structure model (different buffers related to wind speed
hurricane parameters in a GIS environment). To assess the overall susceptibility, the hazard results
were overlaid with population dataset and landcover. The approach, which made use of a number of
available global data sets, was then validated on a regional basis using past experience on hurricane
frequency study over an area that covers both developed and developing countries in the Caribbean
region. As a final result we can state that remote sensing data analysed together with meteorological
and environmental data in an integrated GIS system give a spatially resolved picture of the surface
conditions and, in our context, information on the occurrence, extent and severity of hurricane hazard.
The applied GIS model has then given rise to a long-lead system that can be set-up to allow such a
early warning to go ahead.
Keywords: hurricane, earlywarning, gis