Social vulnerability assessment of flood prone areas in Flanders.
Ingrid Coninx and Kris Bachus
Research group Environmental Policy and Sustainable Development, HIVA - Katholieke Universiteit
Leuven, Belgium
Correspondence:
Ingrid Coninx, Parkstraat 47 bus 5300, 3000 Leuven, Belgium. Email: Ingrid.Coninx@hiva.kuleuven.be
Abstract:
Flood risks are expected to increase in North-western European countries, due to (among others)
climate change. Acknowledging that full avoidance of floods is difficult to reach; current flood risk
management is directed to give ‘space to the river’ while reducing the probability of harmful flooding
and the consequences of flooding, like tangible (e.g. material losses) and intangible impacts (e.g.
non-material losses). The focus of this paper is on intangible impacts. Empirical research has
demonstrated that some population groups are more vulnerable towards intangible flood impacts
than others, like elderly, ill people, immigrants, single parents, financially deprived people and
people living in one-storey houses (Tapsell and Tunstall 2001; Werrity et al. 2007; DEFRA/EA 2003;
Cutter et al. 2003; Morrow 1999; Trush et al. 2005). Localization of these people is a prerequisite to
develop policy that aims to reduce the intangible impacts. In this paper, the Belgian social flood
vulnerability index is developed and applied to five flood prone areas in Flanders. This index
enables relative comparison in space and time. Geographical comparison identifies socially
vulnerable areas and is carried out at the level of the river catchments (macro), at the level of the
municipalities (meso) and at the level of the districts (micro). The change of social flood vulnerability
over time is analyzed by the periodic comparison. Linear regression is used to understand the
driving forces of current social flood vulnerability and social flood vulnerability change over time.
Key words: intangible impact; social vulnerability; flood.
Introduction
Scientists are warning that the number and intensity of floods are expected to increase in Northwestern European countries. These changes are explained by the growth in human settlements, the
human interferences in river systems, the land use changes and the rainfall increase and sea level
rise due to climate change. When water reaches society, floods can result in disasters. During several
decades, flood management aimed to prevent flooding by technical measures, like dike heightening or
river deepening. Many of these measures did not solve the flood problem, but has transferred it to
downstream areas. Becoming aware that complete avoidance of floods is not a realistic objective,
flood risk management is shifting towards a policy of ‘giving space to the river’. The existence of
residual flood risks is acknowledged and flood risk management is focused on both preventing the
probability of harmful flooding and limiting the consequences of the residual flood risks through
technical and non-technical flood measures.
Two types of impacts are caused by floods: tangible and intangible impacts. A tangible impact
is defined as a material loss that is directly or indirectly caused by flooding like damage to the house,
private property, business or public infrastructure (Werrity et al. 2007). Intangible impact refers to nonmaterial loss or emotional loss, like stress, health impacts, change in risk perception, or loss of social
cohesion (Werrity et al. 2007). Avoiding material losses is the main focus of current flood risk
management. However, several studies have demonstrated that intangible impacts are experienced
as severe as well (Werrity et al., 2007; Grinwis and Duyck, 2001). Furthermore, it is stated that
particular population groups suffer more from these intangible impacts than others due to inability to
cope with flooding (Thrush et al., 2005; Tapsell and Tunstall 2001; Morrow 1999). To understand and
to forecast the potential intangible impacts of residual flood risks, it is necessary to identify the social
vulnerability of people.
A composite index is a methodology used to identify these vulnerable people (Tapsell et al.
2002; Cutter et al. 2003). This methodology is a quantitative approach that aggregates vulnerability
indicators and enables relative comparison of vulnerability in time and space. Based on this social
flood vulnerability index, areas are ranked and areas with high vulnerability can be indicated, which
are called ‘social hotspot areas’. To limit intangible flood impacts, flood policy should pay extra
attention to people in these areas before, during and after flooding.
The aim of the paper is firstly, to explore the concepts of intangible impacts and social flood
vulnerability in developed countries. And secondly, to apply the social flood vulnerability index to five
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flood prone areas in Flanders, which is a region in Belgium. Geographical comparison of the indices at
the level of the catchments (macro), at the level of the municipalities (meso) and at the level of the
districts (micro) will reveal the areas requiring special attention from flood risk managers and
emergency managers. The periodic comparison of the indices will clarify the social flood vulnerability
change over time. To conclude, driving forces of the social vulnerability in the five flood prone areas
are explored.
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Social flood impacts
Let us take a look at the disaster area and explore the tangible and intangible impacts that flood
victims often experience in developed countries. Tangible impacts are defined as material losses,
(Werrity et al. 2007), for example damage to wall paper, ground floor furniture, damaged crops and
destroyed roads. Intangible impacts are impacts that are related to non-material loss or emotional loss
(Werrity et al. 2007). Most of these intangible impacts are social impacts, since they are experienced
by people and they change the way people live and relate to each other (based on Burdge 1998).
These impacts are experienced at the level of the individual, the household or the community. Both
tangible and intangible impacts can be caused directly or indirectly by flooding and some of these
impacts reinforce other impacts. We will elaborate more on current knowledge of these intangible
(social) impacts.
Intangible impacts emerge as soon as flooding destroys residential, business, agricultural or
public property. Considering a few intangible flood impacts, we start with loss of life. Loss of life is one
of the few thoroughly investigated intangible impacts. Jonkman and Kelman (2005) have identified six
main causes of death during or after flooding: drowning, physical trauma, heart attack, electrocution,
carbon monoxide poisoning and fire. Other impacts are health impacts, like physical health impacts
(e.g. injury, colds, skin irritation, exhaustion…) and mental health (e.g. stress, anxiety and depression)
(Tapsell and Tunstall 2001; Lamothe et al. 2005). Mental health impacts are experienced as being
more severe than physical health impacts and can last longer (Ahern et al. 2005). The inability of
meeting basic needs, like water and electricity supply, because of disruption of infrastructure and
public services is another intangible impact. The fall-out of electricity and phone contact may hamper
emergency and recovery support (Roos et al. 2003). Also, the ordeal of negotiating with insurers and
builders has a rather stressful impact. In particular, people have to deal with the slowness of
reimbursing claims and conflicting advices (Tapsell and Tunstall 2001). Many people also suffer from
time disruption, which refers to the time ‘lost’ because of personal recovery and repairs of the house,
cancellation of holiday, time off from work or the lack of leisure time (Grinwis and Duyck 2001). Beside
time disruption, several flood victims are exposed to disruption of the financial situation as well, which
has two causes. On the one hand, the income cut, due to days off from work or due to business
interruption. On the other hand, the occurrence of extensive costs like clean-up costs, costs of living in
temporary accommodation, costs of health care and repairing (Twigger-Ross 2005; Tapsell and
Tunstall 2001; Walker et al. 2006). Recovery stress, time disruption and financial disruption also affect
personal relations. Some studies argue that they positively affect relations, while in other cases the
effect is negative (Tapsell et al. 2002; Ketteridge and Fordham 1998). All these intangible impacts may
affect people’s attitude and behaviour. Negative attitude emerges when flooding increases distrust
regarding governing authority and results in loss of confidence in existing flood protection, flood
forecasting and support provision (Defra/EA 2004; Lamothe et al. 2005; Tapsell et al. 2002). However,
the flood experience may encourage people to take precautionary measures themselves, which is
positive behaviour (Grothmann and Reusswig 2006).
Intangible impacts that affect the community are for instance migration, loss of social cohesion
and changing policy. During and after the flood, people may leave the flood area. Some might relocate
temporarily, while others might decide to migrate forever. Evacuation affects people’s sense of place
and therefore also people’s sense of attachment, self-identity and their health (Ohl et al. 2000;
Fullilove 1996). Remarkably, flooding may result in a twofold impoverishment of the neighbourhood.
Firstly, material impoverishment due to the material damage of infrastructure, rubbish on the streets
and damply and dirty living environment. Secondly, the social impoverishment because of the empty
houses. It makes people feel insecure and anxious. Both types of impoverishment affect social
cohesion of the community (Tapsell et al. 2002). A last intangible impact mentioned here, is the
change in policy, which is in particular triggered by large and unexpected floods (Johnson et al. 2003).
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Social vulnerability of people
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As we have argued in the beginning, some people suffer more or experience more severe intangible
flood impacts than others. This is mainly explained by their inabilities to cope with the flooding. In spite
of equal material damage, people suffer differently from intangible impacts because some people are
more vulnerable towards flooding than others. Vulnerability of people, in this paper is also called social
vulnerability and refers to the degree to which an individual or a group of individuals is limited to
anticipate, cope with, resist and recover from flooding due to social, economic, political and cultural
characteristics of the individuals (based on Blaikie et al. 1994). Aysan (1993) has defined 8 causes
that make people more vulnerable to floods than others:
 Lack of resources
 Disintegration of social patterns
 Degradation of the environment and the inability to protect it
 Lack of access to information and knowledge
 Lack of public awareness
 Limited access to political power and representation
 Certain beliefs and customs
 Weak buildings or weak individuals
In other words, social groups that are considered to be vulnerable towards flood impacts in developed
countries are elderly, financially deprived people, ill people, single parents, immigrants and residents
of one-storey properties (Tapsell et al. 2002; Cutter et al. 2003). It should be noted that vulnerability is
a dynamic concept, since it can change over time and between regions.
Why these social groups are they vulnerable towards floods and from which intangible impacts
may they suffer disproportionally? Elderly are more vulnerable because of limited mobility, reduced
hearing and visual capacities (Tapsell et al. 2002). Many older people have lost their partners and are
living on their own or in a home for the elderly. Often, they have limited social networks and few
resources (Thrush et al. 2005). As a consequence, they are more susceptible towards physical health
impacts and loss of life (Ahern 2005). Furthermore, many older people are unable or unwilling to leave
the flood risk area, which increases their risk to live in an impoverished environment temporarily. The
recovery of the house is difficult as well, because elderly are dependent on others to clean the dirt and
to negotiate with insurers and builders (Walker et al. 2006; Morrow 1999).
Financially deprived people are often stuck in a vicious circle of lack of money. On one hand, a
budget to increase protection by insurances or private precautionary measures is absent. Therefore,
they are more susceptible to material damage. On the other hand, the limited financial resources
hamper prosperous recovery. The flood may even aggravate their financial situation, in particular in
the absence of insurances. Poor people are most vulnerable to poverty and difficulties in recovery
(Walker et al. 2006; Thrush et al. 2005; Morrow 1999). Furthermore, they suffer more from physical
and mental health impacts due to worries about financial scarcity and high levels of anxiety. This may
affect the family relations of these people (Thrush D. et al. 2005; Walker et al. 2006; Werrity et al.
2007; Morrow 1999).
Ill people are hampered by immobility and poor pre-existing health conditions. They are
restricted in protecting properties after flood warning and are more susceptible for health impacts, loss
of life and problems related to evacuation and recovery (Ramsbottom et al. 2003; defra/EA 2004).
Single parents are disproportionally affected by floods because of limited finance and because
they have to bear with the flood and care of the children on their own. As a consequence, single
parents have a larger risk to stress and depression, recovery problems and difficulties in meeting
basic needs like food, housing and emotional support to the children (Tapsell et al. 2002 ; Green 1987
et al. Cited by Walker et al. 2006).
Immigrants, and in particular immigrants from developing countries, are more vulnerable
because of language problems, lack of flood risk awareness, different culture and traditions and
limited economic resources. Therefore they are expected to experience difficulties in understanding
flood warnings and difficulties in evacuation and recovery (Thrush et al. 2005).
Mobile homes and one-storey houses are housing types that are more vulnerable to intangible
impacts like the loss of irreplaceable items, difficult evacuation and recovery and physical health
impacts. This is in particular because these houses seldom resist floods and because of the absence
of a buffer to move properties upstairs (Thrush et al. 2005; Tapsell et al. 1999; Walker et al. 2006;
Tapsell et al. 2001).
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Methodology: the Belgian social flood vulnerability index
We aim to identify people which are vulnerable towards floods in Flanders by means of a social flood
vulnerability index. This index enables the indication of social hotspot areas, which are areas occupied
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by a high proportion of vulnerable people. These areas request special consideration by flood risk
managers and emergency managers in order to limit intangible impacts. The higher the social flood
vulnerability index, the higher the proportion of vulnerable people, the more attention should be paid
from the perspective of intangible impact reduction.
3.1 Indicator selection
The development of the Belgian social flood vulnerability index is mainly based on studies carried out
by Tapsell et al. (2002) and Cutter et al. (2003). In accordance with the above review and the data
available in Belgium, we have selected indicators for each vulnerable population group, as there are
the elderly, ill people, single parents, the immigrants, the financially deprived people and people living
in one-storey houses.
<Table 1> Indicators of the Belgian social flood vulnerability index
Belgian social flood vulnerability index
Population group
indicator
Elderly
Residents aged 75 and over as a percentage of
all residents
Ill people

mobility problems: proportion of people
suffering from restrictions in daily activities
due to long term illness, handicap or
chronic diseases

psychological distress: proportion of people
suffering from psychological distress
Single parents
Single parent families as a proportion of all
families
Immigrants
Strata 3 nationalities as a proportion of all
residents (Verhoeven 2000)
Financially deprived people

no basic comfort: cobb-douglas calculation
of houses without toilet, bathroom or
central heating

no-car ownership: proportion of houses
without a car

no-house ownership: proportion of houses
that are rented
People living in one-storey
one-storey houses: residents of one-storey
houses
houses as a proportion of all residents
In accordance with the social flood vulnerability index of Tapsell et al. (2002), the indicator for elderly
includes the proportion of people of 75 years or older. Tapsel et al. (2002) argue that based ‘on
epidemiological research, it was clarified that people of 75 years or older have a larger risk to suffer
from arthritis, which can be caused by the wet and cold conditions after flooding’ (Tapsell et al. 2002).
It is noticed that while most social groups are assessed by one indicator, ill people and financially
deprived people are measured by multiple indicators. The proportion of ill people is measured by a
aggregation of two indicator used in the national health survey (Scientific Institute of Public Health
2004). Financially deprivation is measured based on three indicators: the proportion of people without
house ownership, without car ownership and without basis comfort. The indicator on people without
basis comfort is often used in poverty surveys and is based on the proportion of houses without toilet,
without bathroom or without heating (FPS Economy s.d.). Immigrants are assessed based on the
theory of ethno-stratification of Verhoeven (2000) who has argued that in Flanders, nationalities can
be distinguished based on their position at the labour market. Most vulnerable are the nationalities in
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the third stratum which are people from Eastern-Europe and non-European nationalities, except USA,
Canada, Australia and Japan.
3.2 Data collection
Data on the indicators of the elderly, the financial deprivation, the single parents and the immigrants is
collected from the censuses of 1991 and 2001 (FPS Economy 1991 and 2001). Information on the
proportion of ill people is collected by the national health survey (Scientific Institute of Public Health
2004)). Data on the one-storey houses are gathered by FPS Economy (s.d).
The geographical analysis is based on data at two levels. First level is the level of the
municipality. Second level is the district level. This is the lowest possible level to gather census data.
A district is a subdivision of a municipality and it includes a number of streets. It should be remarked
that districts differ in surface and in number of inhabitants (from 1 up till 3586 inhabitants). The
periodic comparison is based on data from 1991 and from 2001. One of the main bottlenecks in this
analysis is the unavailability of data for some indicators at the year 1991 or at district level. Therefore,
the indicators of ill people and people living in one-storey houses are not included in this research.
3.3 Standardization, weighting and aggregation
Every indicator concerns data on the proportion of people and is expressed in percentages (X1, X2,
…Xn) = [0 - 1]. Therefore, standardization is not required. Equal weights are attached to each of the
indicators, since currently scientific evidence on the interrelationships between the indicators is not yet
available. The indicators are aggregated by the geometric mean since the variables are noncomparable and ratio scale.1
n
X = social vulnerability indicator
I(Y) = ∏ (1-Xi)Wi *100 Wi >0 for i= 1,….n;
Y = non-social vulnerability index
i=1
S = social vulnerability index
I(S) = 100-I(Y)
W= indicator weight
The Belgian social flood vulnerability index is a figure between 0 and 100 that is attached to a
geographical area and that enables relative comparison. The higher the index, the more vulnerable
the people in this area are towards floods.
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Study areas
River flooding is the focus of this research. However, the social flood vulnerability index can also used
in cases of pluvial and coastal flooding. Several areas in Flanders have been confronted with river
flooding in the past, with most severe flooding in 1993, 1995, 1998 and 2002-2003. The surface
flooded during 1988 and 2000 is estimated on 43,164 ha, which equals 3.2% of the total surface in
Flanders (Van Orshoven, 2001). Urban areas, as well as industrial, agriculture and nature area were
exposed. Based on flooded surface and frequency of flood event, 5 areas are selected, which
represent about 78% of the total recently flooded area in Flanders. (Van Orshoven 2001) It is the first
time social vulnerability assessment is applied to Flanders.
<Figure 1> Selected study areas
1. Yser catchment
During 1988-2000 about 6,186 ha was inundated in the Yser catchment. Most severe flooding took
place in December 1993 and January 1995. The municipalities taken into consideration in this study
are the main cities near the Yser: Poperinge, Diksmuide and Nieuwpoort.
2. Dender catchment
The Dender is a rain-fed river that enters the Flemish region in Geraardsbergen. The area near the
Dender was exposed to severe flooding in December 1993, January 1995, December 1999, 2001 and
1 Ebert and Welsh; Böhringer and Jochem 2006.
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2002-2003. Twelve floods have taken place during 1988 and 2000. The municipalities of
Geraardsbergen, Ninove and Denderleeuw are considered.
3. Demer catchment
The Demer catchment is characterised by the largest surface recently flooded: 13,559 ha during 22
flood events since 1988. The area near the Demer estuary in Rotselaar has been severely affected
during the floods of 1965 and 1966. Many measures are implemented to give space to the rivers in
this catchment, while many more planned in the next years. Municipalities in this analysis are HerkDe-Stad, Halen, Scherpenheuvel-Zichem, Diest, Aarschot and Rotselaar.
4. Nete catchment
The Nete catchment has been inundated about 8 times during 1988 and 2000. The municipalities
selected for this study are Lier, Heist-Op-Den-Berg, Berlaar, Grobbendonk, Zandhoven and Hulshout.
5. Meuse catchment
In 1993 and 1995, the Meuse municipalities were shocked by severe flooding of the river. Most of the
housing has disappeared from the Meuse riverbed, what limits the consequences of low return periods
floods. Examined Meuse municipalities are Lanaken, Maasmechelen, Maaseik and Kinrooi.
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Results
5.1
Geographical comparison
5.1.1
Macro-level comparison: catchments
The first analysis compares the social flood vulnerability of the 5 river catchments. This social flood
vulnerability is based on the average vulnerability of the municipalities in each catchment. The
average vulnerability for each catchments is calculated according:
Σ(SVIm*INHABm)/ INHABc
SVIm = social vulnerability index of municipality
INHABm=number of inhabitants of the municipality
INHABc = number of inhabitants in the catchment
<Figure 2> Average social flood vulnerability of river catchments (2001)
Based on the results presented in Figure 1 it is concluded that the average social vulnerability of the
total study area equals 10.32. This is the degree of social vulnerability in the area. The social flood
vulnerability in the Dender (SVI: 10.42) and the Yser (10.39) basins are highest, while the indices in
the Nete (SVI: 9.65), the Demer (SVI: 8.99) and the Meuse (SVI: 8.73) basins are lower. What is high
social flood vulnerability and what is low social flood vulnerability? In theory, high social flood
vulnerability are indices near 100. However, this means that almost all inhabitants are old, immigrants,
single parents and financially deprived. This rarely happens at the level of the municipality. Therefore,
it is argued to use the average vulnerability of the total study area (10.32) as the threshold level. This
means that inhabitants of the Yser and Dender basin are rather highly vulnerable, and require extra
consideration in intangible impact reduction.
Which vulnerable population group is determining this vulnerability? To answer this research
question, data at the level of the districts are analysed by means of a linear regression. 2
<Figure 3> Driving forces of social vulnerability
2 To prevent the influence of outliers on the regression, every district with standardised residuals
below -3 and above 3 are eliminated from the dataset.
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The standardized beta coefficients in Figure 3 illustrates the same pattern in each catchment. In
particular the high proportion of financially deprived people and single parents are driving forces of
social flood vulnerability in these catchments. Elderly are contributing to a smaller extent. The
proportion of immigrants seems to be rather low in the Yser and Dender basin, but higher in the
Demer and Meuse basin. Thus, in order to reduce the intangible flood impacts, flood measures are
advised to be directed in the first place to single parents and financially deprived people in order to
decrease their exposure and/or increase their coping capacities.
5.1.2
Meso-level comparison: municipalities
Several researchers have warned for the bottlenecks of aggregating data on vulnerability, since
aggregation may conceal underlying social hotspots. (O’Brien et al. 2004) <Figure 4 reveals this
bottleneck by presenting the social vulnerability index of each separate municipality. Although at the
macro scale the Dender basin is most vulnerable, meso-scale comparison demonstrates that
municipalities of other catchments, like Nieuwpoort, Lier and Diest are more vulnerable than the
Dender municipalities (Geraardsbergen, Ninove and Denderleeuw). Furthermore, it can be concluded
that there is a large variety in vulnerability in each basin, except for the Dender basin. In specific,
vulnerability ranges from 9.55 to 12.42 in the Yser basin, from 7.39 to 12.36 in the Nete basin, from
7.39 to 10.58 in the Demer basin, from 10.07 to 10.58 in the Dender basin and from 6.34 to 10.09 in
the Meuse basin. The most vulnerable municipalities are Nieuwpoort (Yser basin), Lier (Nete basin),
Diest (Demer basin), Geraardsbergen (Dender basin) and Ninove (Dender basin). These figures
indicate a rather high proportion of vulnerable people what may result in more severe intangible
impacts given the same flood characteristics.
<Figure 4> Social flood vulnerability of municipalities (2001)
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5.1.3
Micro-level comparison: districts
The last geographical comparison is based on the smallest level of available data, the district level.
The comparison aims to identify areas the policymakers should pay attention to. As mentioned above,
we will use the average vulnerability and average population per district as threshold levels to argue
what is high or low vulnerability. Two types of areas distinguished. The first type of areas are called
social hotspot areas, which refer to areas with high vulnerability index and large population, thus
districts with an vulnerability larger than 10.3 and more than 374 inhabitants. These districts are
located in the upper right rectangle in the below scatter plots. The second type of area is ‘alert area’,
which are districts with high vulnerability (>10.3), but are inhabited by a few people (<374). These
districts are situated in the upper left rectangle of the scatter plots below. Social hotspot areas are
considered to be more critical, since many people are involved.
(scatterplots)
<Table 2> number of people in social hotspot areas and alert areas
<Table 2 presents the number of people in the social hotspot and alert areas. About 36,700 persons
are living in the social hotspot areas in the Dender basin, which equals 44% of the population in the
three selected municipalities. The second largest proportion of people in social hotspot areas are in
the Yser basin and the Nete basin. The proportion of people living in alert areas is about 6.6% in the
Dender basin, 2.5% in the Yser basin, 2.4% in the Demer basin, 2% Nete basin and 1.7% in the
Meuse basin. The full use of these indices at the micro level will be clear when this information is
combined with flood maps, revealing the actual number of people that is socially vulnerable towards
floods and that is actual at risk of flooding. This is one of the objectives of our further research.
5.2
Periodic comparison
In the context of climate change, where it is expected that flooding will occur more frequently and
more intensely, it is important to understand how vulnerability might change. Therefore, we start to
consider the vulnerability change in the past by comparing the census data at the level of districts in
1991 and in 2001. Although we expected average social vulnerability to increase because of
expectations in increasing proportions of single parents, elderly and foreigners, <Insert> Figure 5
reveals the opposite in all catchments. The largest decrease in social vulnerability is in the Yser and
the Dender basin.
<Insert> Figure 5 comparison of social vulnerability in 1991 and 2001 3
Vulnerability has increased in some districts, and decreased in others. Another regression analysis is
carried out to examine the explaining variables of change.
<Insert> Figure 6 driving forces of social flood vulnerability change
In the districts where vulnerability has increased since 1991, this is mainly caused by the increasing
proportion of single parents and financially deprived people, except for the Nete basin, where in
particular the proportion of elderly has contributed the most to the vulnerability increase. In the districts
that have known a decrease in social vulnerability, this is caused by a decrease in the proportion
single parents and financially deprived people, except for the Meuse basin, where in particular the
decreasing proportion of immigrants and single parents has contributed the most to the vulnerability
decrease.
Conclusion and discussion
The paper starts from the acknowledgment that the assessment of people which are vulnerable
towards floods is of importance in order to estimate intangible flood impacts and to develop policy
aiming to reduce the intangible flood impacts. In Flanders, research on the social aspects of flood
3 Social flood vulnerability index is expressed from 0 to 1.
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impacts is rather limited and this paper is the first to apply the social flood vulnerability index to flood
prone catchments in Flanders.
In this paper, the Belgian social flood vulnerability index is developed based on previous
research and on data available in Belgium. One of the bottlenecks in current research is the
unavailability of data on some indicators. From the geographical analysis it is concluded that social
vulnerability is highest in the Dender and Yser basin among the 5 selected flood prone areas. The
regression analysis clarifies that the driving forces of social vulnerability are mainly the proportion of
single parents and financially deprived people. Those population groups have a larger risk to suffer
from intangible impacts like disruption of physical and mental health, disruption of time spending and
financial situation, difficulties in meeting basic needs and recovering their house. Towards
policymakers, these results indicate that measures to reduce intangible impacts should be directed to
these population groups in the first place, for instance by extra financial support, risk communication
and spatial planning. Meso-level analysis reveals the bottlenecks of aggregation, as none of the
Dender municipalities belong to the top 3 of most vulnerable municipalities. Two types of areas are
distinguished in the micro-level analysis: the social hotspot areas which include districts with high
social vulnerability and high population number, and alert areas which include districts with high social
vulnerability and low population number. Results of the micro-level analysis are useful for flood risk
managers when combined with detailed flood and land-use maps. The periodic comparison of
vulnerability in 1991 and in 2001 has shown that social flood vulnerability is decreasing overall. In
districts where vulnerability has increased, this is mainly due to increasing proportion of single parents
and financially deprived people. Both variables seem to be the driving forces in districts where
vulnerability has decreased over time.
Further research will focus on the methodological improvement of the social flood vulnerability
index by assigning appropriate weights to the indicators. Another scientific challenge is the validation
of this methodology in Flanders, which is difficult because of the limited data availability on the
intangible impacts of historical floods.
The Belgian social flood vulnerability index presented here is only one part in the integrated
assessment of flood impacts. The findings should be integrated in the work carried out by hydrologists,
economists and ecologists in order to be of full use to assist policymakers in setting policy priorities.
This methodology is currently in development within the Belgian ‘Adapt project”.
Acknowledgment
The work reported in this paper will be carried out within the ‘Adapt project’ and is financed by the
Belgian Science Policy. (http://www.ulb.ac.be/ceese/ADAPT/Home.html) I am also grateful to my
colleague Wim Van Opstal for the support in statistics.
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