2.4.1.Existing Flood
Vulnerability Index
Connor and Hiroki (2005) presented a
methodology to calculate a Flood Vulnerability Index (FVI) for river basins,
using eleven indicators grouped into four components. The index uses two
sub-indices for its computation: the human index, which corresponds to the
social effects of floods; and the material one, which covers the economic
effects of floods. The purpose of the FVI is to serve as a tool for assessing
flood risks due to climate change in relation to underlying socio-economic
conditions and management policies.
An elaborated methodology to calculate FVI was developed by
Balica (2007), using indicators which aims at assessing the condition that
favour flood damages at various levels: river basin, sub-catchments and urban
area. The methodology focused on two concepts: factors of vulnerability based
on three elements, including exposure, susceptibility and resilience on one
hand, and components of vulnerability including actual flooding and
establishing the elements of a system that suffer from this natural disaster on
the other hand. The methodology has been applied at different scales and has
resulted in interesting observations as to how quantifiable indicators can
reflect backs. Balica defines vulnerability as a function of exposure,
susceptibility, and resilience.
The Seventh Framework Programme (2011) defined the FVI in terms
of the following factors: exposure, susceptibility, and lack of coping
capacity. The methodology included a step of converting the indicators into
non-dimensional units, by interpolating the maximum and minimum of the series
of data obtained. The FVI values oscillate between 0 and 1, where 1 means the
highest flood vulnerability and 0 represents the lowest vulnerability to
floods. The methodology was tested in Japan river basins and in 18 river basins
in Philippines.
Depending on the equation
used, the indicators will have to have a different format, but the result of
the FVI remains the same. The goal of the equation of the FVI is to compare
different communes to one another in overall vulnerability, but also in its
separate factors exposure, susceptibility and resilience. To make it possible
to visualize these separated factors, a summation relationship is more
useful. Also, it is preferred if the resilience is negatively formulated, and a
higher score causes the vulnerability to be higher, conform other factors. With
the chosen equation, the indicators have to be measured on a scale from 0-100%
or 0-1, like Balica et al. (2012). Then, the indicators have to be normalised.
The method of normalization has to take into account the functional
relationship between the variable and vulnerability. If the functional relation
is ignored and if the variables are normalized simply, the resulting index will
be misleading. After computing the normalized scores the index is constructed
by giving either equal weights to all indicators/components or unequal weights.
These factors are then summed up according to the equation, and the result is a
0-100% or
0-1 number for vulnerability.
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