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In the last decade, modelling hurricanes in potentially affected areas using geographical information systems (GIS) and geospatial cyberinfrastracture (GCI) has become a major topic of research. Despite some basic approaches, some unsolved questions are still under discussion. The disastrous effects of hurricanes on communities are well known, however there is a need to better understand the hazard contributions of the different components related to a hurricane, such as storm surges, floods and high winds. In this paper, the selected approach is to determine an onset zoning from a set of attributes that are considered to govern the hurricane while examining the influence of each individual component that produces the final exposure. To this end, this study assesses the different components using parameters derived from topography, bathymetry and hurricane physical indexes. Key attributes are the river network, the topography, the wetness index and the offline bathymetry. Complementary data include the CMORPH rain dataset and the hurricane track together with its structure model, both based on National Oceanic and Atmospheric Administration (NOAA) datasets. Total hazard results were then overlaid with population data in the overall assessment of elements at risk. The approach, which made use of a number of available global and free datasets, was then validated on a regional basis using ground data collected by the World Food Programme (WFP) over the study area (Central America region) for a specific hurricane. [ABSTRACT FROM AUTHOR]

The shuttle radar topography mission (SRTM), was flow on the space shuttle Endeavour in February 2000, with the objective of acquiring a digital elevation model of all land between 60° north latitude and 56° south latitude, using interferometric synthetic aperture radar (InSAR) techniques. The SRTM data are distributed at horizontal resolution of 1 arc-second (∼30 m) for areas within the USA and at 3 arc-second (∼90 m) resolution for the rest of the world. A resolution of 90 m can be considered suitable for the small or medium-scale analysis, but it is too coarse for more detailed purposes. One alternative is to interpolate the SRTM data at a finer resolution; it will not increase the level of detail of the original digital elevation model (DEM), but it will lead to a surface where there is the coherence of angular properties (i.e. slope, aspect) between neighbouring pixels, which is an important characteristic when dealing with terrain analysis. This work intents to show how the proper adjustment of variogram and kriging parameters, namely the nugget effect and the maximum distance within which values are used in interpolation, can be set to achieve quality results on resampling SRTM data from 3” to 1”. We present for a test area in western USA, which includes different adjustment schemes (changes in nugget effect value and in the interpolation radius) and comparisons with the original 1” model of the area, with the national elevation dataset (NED) DEMs, and with other interpolation methods (splines and inverse distance weighted (IDW)). The basic concepts for using kriging to resample terrain data are: (i) working only with the immediate neighbourhood of the predicted point, due to the high spatial correlation of the topographic surface and omnidirectional behaviour of variogram in short distances; (ii) adding a very small random variation to the coordinates of the points prior to interpolation, to avoid punctual artifacts generated by predicted points with the same location than original data points and; (iii) using a small value of nugget effect, to avoid smoothing that can obliterate terrain features. Drainages derived from the surfaces interpolated by kriging and by splines have a good agreement with streams derived from the 1” NED, with correct identification of watersheds, even though a few differences occur in the positions of some rivers in flat areas. Although the 1” surfaces resampled by kriging and splines are very similar, we consider the results produced by kriging as superior, since the spline-interpolated surface still presented some noise and linear artifacts, which were removed by kriging. [ABSTRACT FROM AUTHOR]