HR: 15:30h
AN: U33B-07 [Abstracts]
TI: Vulnerability Assessment of Population to Water Related Hazards in Venezuela
AU: * Guenni, L
EM: lbravo@cesma.usb.ve
AF: Universidad Simon Bolivar, Centro de Estadistica y Software Matematico. APDO. 89.000,
Caracas, 1080-A, Venezuela
AU: Rodriguez, J
EM: jrodriguez@cesma.usb.ve
AF: Universidad Simon Bolivar, Centro de Estadistica y Software Matematico. APDO. 89.000,
Caracas, 1080-A, Venezuela
AU: Prieto, J R
EM: jprieto@cesma.usb.ve
AF: Universidad Simon Bolivar, Centro de Estadistica y Software Matematico. APDO. 89.000,
Caracas, 1080-A, Venezuela
AU: Moreno, E
EM: elisboa@cesma.usb.ve
AF: Universidad Simon Bolivar, Centro de Estadistica y Software Matematico. APDO. 89.000,
Caracas, 1080-A, Venezuela
AB:
In order to quantify the vulnerability of the population to water related hazards, specifically floods and landslides, it
is important to count on reliable data about the impacts of events, population exposure and geophysical factors
contributing to the magnitude of damage. In this work we present the steps followed to build a vulnerability map of
Venezuela with the purpose of identifying the most vulnerable districts across the country. This district based
map is important for decision makers and local governments to set priorities in vulnerability reduction programs.
Data on the number of events and the number of people affected death or injured was collected from international
data sets and from local newspapers for the period 1960-2000. Different data sources at different spatial scales
were used to get information about population density, topography, river network, rainfall anomalies and other
geophysical variables. A hierarchical log linear regression model was used to represent the percentage of
people affected at each district relative to the national figure, also called "Relative Risk". This was considered as
a measurement of vulnerability. The main geophysical factors contributing to significantly explain the spatial
distribution of the Relative Risk were selected by using a model selection strategy. We also discuss the issue of
using multiple scale data sources and the importance of the scale in the final results.
DE: 1616 Climate variability (1635, 3305, 3309, 4215, 4513)
DE: 1630 Impacts of global change (1225)
DE: 1699 General or miscellaneous
DE: 1821 Floods
DE: 1834 Human impacts
SC: Union [U]
MN: 2007 Joint Assembly