2.3.2 Indicators for Measuring Vulnerability
Indicators are widely recognized as useful measurement tools in
distinct fields of research (Damm 2010, p 42) but researchers disagree on their
definitions. According to Gallopin (1997, p 14) indicator is defined as a sigh
that summarizes information relevant to a particular phenomenon. Some authors
(Adriaanse, 1995) define indicators in relation to an aggregation process
starting with variables or basic data, followed by processed information and
indicators, finally ending up with highly aggregated indices. While others view
them as a single variable or an output value from a set of data that describes
a system or process. According to Birkman ( 2013, p 88), defining indicator in
terms of the level of aggregation neglects an essential aspect: goals. For this
researcher, every indicator-development process needs to be related to goals,
or at least to a vision which serves as a basis for defining the state or
characteristic of interest.
The Hyogo Framework for Action (HFA) 2005-2015 stresses the need
to develop systems of indicators of disaster risk and vulnerability at national
and sub-national levels that will enable decision-makers to assess the impact
of disasters (UN/ISDR 2005). An indicator, or set of indicators, can be defined
as an inherent characteristic that quantitatively estimates the condition of a
system (Balica et al. 2012). «Indicators necessarily limit themselves to
the sphere of the measurable» (Moldan and Dahl 2007: 9). A vulnerability
indicator can be defined as a variable which is an operational representation
of a characteristic or quality of an object or subject able to provide
information regarding the susceptibility, coping and adaptive capacity and
resilience of a system (Birkman, 2013, p 87).
Vulnerability indicators are widely used in vulnerability
assessment. The first step in an indicator-based vulnerability assessment is
the selection of the study area; second, one has to select indicators based on
criteria, such as the availability of data, personal judgement or previous
research. The procedures for indicator selection follow two general approaches.
These are deductive and inductive approaches (Adger et al.,2004). In deductive
approach, indicators are selected based on relationships established from
theories and conceptual frameworks, whilst inductive approach involves
statistical procedures to relate a large number of variables to vulnerability
in order to identify the factors that are statistically significant. While a
range of widely-accepted relevant characteristics and indicators is being
presented in literature, (Adriaanse, 1995; World Bank, 2005.), the actual
conditions that determine flood vulnerability are, to a certain degree, very
site-specific, location, and hazard-dependent (Muller et al, 2011, p 2113). It
can be expressed in terms of functional relationships between expected damages
regarding all systems and exposure, susceptibility and resilience
characteristics of the affected system, referring to all the different types of
possible flood hazards (Balica, 2007).
A total of 30 indicators have been identified under the three
factors of vulnerability through various literature. Exposure and
susceptibility both have a positive influence on vulnerability, and resilience
has a negative influence on vulnerability "Table 1"
|
Table 1: Flood indicators
information
|
|
|
No
|
Defined indicator
|
Factors
|
Unit
|
Functional relationship with vulnerability
(+ or -)
|
References
|
|
1
|
Flood frequency
|
Exposure
|
year
|
Higher is the number of flood events, higher is the
vulnerability (+)
|
Balica (2007)
|
|
2
|
Flood duration
|
Exposure
|
days
|
The higher the flood duration, the higher the vulnerability
(+)
|
Balica (2007)
|
|
4
|
Flood water depth
|
Exposure
|
m
|
The higher the flood water level, the higher the vulnerability
(+)
|
Balica (2007)
|
|
5
|
Proximity of the village to the water body
|
Exposure
|
m
|
The Closer is the place to the river, the higher is the
vulnerability (+)
|
Balica (2007)
|
|
7
|
population in the flood area
|
Exposure
|
#
|
The higher the number of population, the higher the vulnerability
(+)
|
Balica (2012); Fekete (2009);
|
|
8
|
Heavy rainfall
|
Exposure
|
mm
|
The higher the value of the variance, the higher the
vulnerability (+)
|
Balica (2012)
|
|
9
|
Maximum discharge in the past ten years
|
Exposure
|
m3/s
|
The higher the discharge, the higher the vulnerability (+)
|
Balica (2012);
|
|
10
|
Land use: Farmland
|
Exposure
|
%
|
The higher the %, the higher the vulnerability (+)
|
Balica (2012); Fekete (2010); Bowen and Riley (2003)
|
|
11
|
Gender
|
Susceptibility
|
%
|
The higher the % of women, the higher the vulnerability (+)
|
Wisner et al. (2004); Haki et al. (2004); Cutter et al. (2003);
Muller et al. (2011)
|
|
12
|
Elderly
|
Susceptibility
|
%
|
The higher the % of elderly, the higher the vulnerability (+)
|
Clark et al. (1998); Muller et al (2011); Steinführer and
Kuhlicke (2007); Thieken et al. (2007); Birkmann et al. (2008)
|
|
13
|
Children under 15
|
Susceptibility
|
%
|
The higher the % of children, the higher the vulnerability
(+)
|
Schneiderbauer (2007); Cutter et al. (2003); Muller et al.
(2011); Birkmann et al. (2008)
|
|
14
|
Agriculture workers
|
Susceptibility
|
%
|
The higher the % of household having agriculture activity the
higher the vulnerability (+)
|
Fekete (2010)
|
|
15
|
Female headed household
|
Susceptibility
|
%
|
The higher the %, the higher the vulnerability (+)
|
McLanahan (1983); Snyder et al. (2006);
|
|
16
|
Literacy Level
|
Susceptibility
|
%
|
The higher the %, the higher the vulnerability (+)
|
Fekete (2010); Schneiderbauer (2007); Haki et al. (2004);
Steinführer and Kuhlicke 2007
|
|
17
|
Household size
|
Susceptibility
|
%
|
The higher the %, the higher the vulnerability (+)
|
Haki et al. (2004); Cutter et al. (2003); Muller et al. (2011);
Martens and Ramm (2007)
|
|
18
|
Number of houses with poor material (wall, roof, floor)
|
Susceptibility
|
#
|
The higher the number of houses with poor material, the higher
is the vulnerability (+)
|
Schneiderbauer (2007); Clark et al. (1998);
Cutter et al. (2003); Muller et al (2011)
|
|
19
|
Past experience
|
Susceptibility
|
%
|
The lower the %, the higher the vulnerability (+)
|
Balica (2007); Birkmann (2005a); Velasquez and Tanhueco (2005);
Wisner et al (2004); Muller (2011)
|
|
20
|
Preparedness
|
Susceptibility
|
%
|
The lower the % of people with flood experience, the higher the
vulnerability (+)
|
Balica (2012); Birkmann (2005a); Velasquez and Tanhueco (2005);
Wisner et al. (2004); Cardona (2003); Muller (2011)
|
|
21
|
Awareness
|
Susceptibility
|
%
|
The lower the % of people, the higher the vulnerability (+)
|
Balica (2007)
|
|
22
|
Emergency services
|
Resilience
|
%
|
The higher the % of people reported to get help from government
or institution during and after flood, the lower the vulnerability
(-)
|
Balica (2007)
|
|
23
|
Ability to evacuate
|
Resilience
|
%
|
The higher the %, the lower the vulnerability
(-)
|
Cardona (2003); Muller (2011); Balica (2012); Birkman et al
(2013)
|
|
24
|
Knowledge about private protection measures
|
Resilience
|
%
|
The higher the %, the lower the vulnerability
(-)
|
Muller et al (2011)
|
|
25
|
Knowledge about flood hazard
|
Resilience
|
%
|
The higher the percentage, the lower the vulnerability
(-)
|
Cardona (2003); Muller (2011)
|
|
26
|
Warning system
|
Resilience
|
%
|
The existence of warning system lowers the vulnerability
(-)
|
Balica (2007); Balica(2012); Veenstra (2013)
|
|
27
|
Recovery Time to flood
|
Resilience
|
%
|
The faster the recovery time, the lower the vulnerability
(-)
|
Balica (2012)
|
|
28
|
Emergency service
|
Resilience
|
%
|
The higher the %, the Lower the vulnerability
(-)
|
Balica (2012); Aall and Norland (2005); Veenstra (2013)
|
|
29
|
Long term residents
|
Resilience
|
%
|
The higher the %, the lower the vulnerability
(-)
|
Fekete (2010)
|
|
30
|
Environmental recovery
|
Resilience
|
%
|
The higher the %, the lower the vulnerability
(-)
|
Balica (2007)
|
|
|
