Surface water flooding risk to urban
communities: Analysis of vulnerability,
hazard and exposure
Aleksandra Kazmierczak
azmierczak and Gina Cavan
October 2011
EcoCities is a joint initiative between the University of Manchester and office
provider Bruntwood, and seeks to build capacity to adapt to climate change in
Greater Manchester and beyond.
School of Environment and Development
University of Manchester
Oxford Road
Manchester
M13 9PL
This report is a pre-print version of an article published in the
Landscape and Urban Planning Journal. It should be referenced as:
Kazmierczak, A. and Cavan, G. (2011) ‘Surface water flooding risk to urban
communities: Analysis of vulnerability, hazard and exposure.’ Landscape and
Urban Planning 103(2), 185-197.
The full, edited version of the article can be accessed at the Elsevier website:
http://www.sciencedirect.com/science/article/pii/S0169204611002404
2
Table of Contents
Summary
5
1
6
2
3
4
5
Introduction
1.1
Vulnerability, hazard and exposure as elements of risk
6
1.2
Hazard: surface water flooding
7
1.3
Exposure: land use and housing
8
1.4
Vulnerability of people and communities to surface water flooding
9
Methods
11
2.1
Study area
11
2.2
Analysis of vulnerability
13
2.3
Analysis of associations between vulnerability, hazard and exposure
14
Results
16
3.1
Vulnerability of people and communities
16
3.2
Hazard: distribution of surface water flooding
19
3.3
Exposure of communities to surface water flooding
20
3.4
Associations between vulnerability, hazard and exposure
21
3.4.1
Vulnerability of communities and hazard
21
3.4.2
Vulnerability of communities and exposure
22
3.4.3
Hazard and exposure
25
3.4.4
Comparing hazard and exposure among the most vulnerable
communities
26
3.4.5
28
Risk of surface water flooding to communities
Discussion and conclusions
28
4.1
Developing responses tailored to exposure and vulnerability
32
4.2
Concluding remarks
34
References
35
3
List of Figures
Figure 1. Risk triangle.
7
Figure 2. Urban, suburban and peri-urban areas in Greater Manchester.
12
Figure 3. Principal component scores for Lower Super Output Areas.
18
Figure 4. Proportion of LSOAs susceptible to surface water flooding.
19
Figure 5. Spatial distribution of the total green space and gardens in Greater
Manchester.
20
Figure 6. Spatial distribution of the risk of surface water flooding to urban
communities.
28
List of Tables
Table 1. Principal component (PC) loadings for the indicators of vulnerability. 17
Table 2. Housing type and condition in Greater Manchester.
21
Table 3. Spearman’s rank correlations between the level of vulnerability and the
proportion of LSOAs exposed to surface water flooding in Greater Manchester.
22
Table 4. Spearman’s rank correlations between the percentage of different land
use types in LSOA, the percentage of areas exposed to surface water flooding
in LSOAs and the scores of principal components.
23
Table 5. Values of Spearman’s rank correlation coefficient between the principal
component scores and presence of green space in LSOAs.
25
Table 6. Comparison of variables of exposure and hazard between the most
vulnerable areas.
27
4
Summary
Developing appropriate responses to address and prevent surface water flooding
requires an analysis of interactions between elements of a risk framework
encompassing hazard, vulnerability and exposure. This paper explores the
spatial distribution of surface water flooding, the vulnerability of communities to
flooding, and the characteristics of physical environment and land use that affect
people’s exposure to flooding, particularly concerning green cover in Greater
Manchester, UK.
A set of 26 indicators known to influence the vulnerability of people to flooding
was identified for spatial census units in Greater Manchester, and simplified with
Principal Component Analysis to four underlying factors relating to material
situation, diversity of communities and high proportion of children or elderly in
the population. This was followed by an analysis of the presence and spatial
distribution of surface water flooding areas, land use types, green cover and
housing. Finally, the spatial associations between hazard, vulnerability and
exposure were analysed.
The results indicate that some of the most vulnerable people in Greater
Manchester, namely the culturally diverse and materially deprived communities,
are at high risk of flooding due to a convergence of factors related to socioeconomic characteristics of the population, spatial distribution of the hazard, and
the land use and housing types present in the area. Some adaptation responses
tailored to the characteristics of the community and the environment they live in
are discussed.
5
1 Introduction
Weather and climate are inextricably linked to many aspects of human life.
Extreme weather events in recent decades have emphasised the significance of
hazards associated with climate and weather, such as high temperatures or
flooding following intense precipitation (Beniston and Stephenson 2004). Climate
change is likely to result in increased incidences of extreme weather events
(Smith et al 2009), and it is these, rather than gradual change over long timeperiods, that cause the most significant risk to humans and natural systems
(EEA, JRC and WHO 2008; Meehl et al 2000). Adaptation to the projected
increasingly frequent occurrence of extreme weather events is particularly
important in cities, where the high density of populations
combined with
urbanised land cover, can exacerbate climate change risks to people (Beniston
and Stephenson 2004; Lindley et al 2006).
Preparing to an uncertain future can be approached through improving
adaptation to current climate variability and extreme weather events (Burton
1997). In order to provide appropriate adaptation responses, it is necessary to
understand, what the nature of risk and its spatial distribution are. This paper,
based on the case study of Greater Manchester, UK, aims to assess the risk of
surface water flooding for urban communities, by investigating the spatial
associations between the vulnerability of people, the occurrence of surface water
flooding, and the physical characteristics of the natural and built environment.
1.1
Vulnerability, hazard and exposure as elements of risk
The risks associated with climate and weather can be understood as an
interaction of hazard, exposure, and vulnerability, forming a ‘risk triangle’
(Crichton 1999; 2007). Specifically in relation to flood risk, a source – pathways
– receptors model is being used (DEFRA and EA 2006). These models are
combined in Figure 1 in relation to the risk of surface water flooding to people in
urban areas. Climate hazard, or ‘source’, in the risk triangle framework relates to
extreme weather events, such as intense rainfall causing surface water flooding.
Vulnerability refers to the intrinsic characteristics of the hazards’ receptors
(which can be people, infrastructure, economic activities, or other), and defines
the extent to which these receptors are susceptible to harm from, or unable to
cope with, hazards. The term ‘exposure’ can be defined as the nature and
degree to which a receptor (the urban communities in this study) is exposed to
6
climate or weather hazards (Parry et al 2007). Thus, exposure, closely related to
the concept of a flooding ‘pathway’ (DEFRA and EA 2006), refers to the
geographical location of a receptor, as well as the characteristics of the specific
location that can exacerbate or reduce the magnitude of a hazard’s impact.
According to this framework, for risks to be realised, the receptors and hazard
need to coincide spatially. Further, the magnitude of risk depends on the level of
vulnerability of the receptors, the nature of the hazard, and the physical
characteristics of the environment defining the exposure (Lindley et al 2006).
Figure 1. Risk triangle adapted from Crichton (1999) and DEFRA and EA (2006).
1.2
Hazard: surface water flooding
Surface water flooding describes the combined flooding in urban areas during
heavy rainfall. As such, it includes pluvial flooding (that results from rainfallgenerated overland flow and ponding before the runoff enters any watercourse,
drainage system or sewer, or cannot enter it because the network is full to
capacity), sewer flooding, flooding from small open-channel and culverted urban
watercourses, and overland flows from groundwater springs (Falconer et al
2009). Surface water flooding is predominantly caused by short duration intense
rainfall, occurring locally. Therefore, such floods are difficult to forecast, warn
against and prepare for (Falconer et al 2009; Golding 2009). The UK Office of
Science and Technology (Evans et al 2004) estimate that 80,000 urban
properties in the UK are currently at risk from surface water flooding, yielding
average annual damages of £270 million, and that these numbers are likely to
7
increase in the future as a consequence of climate change. Thus, the widespread
character of this hazard suggests that it is important to analyse the risks of
surface water flooding to people in urban areas.
1.3
Exposure: land use and housing
The management of surface water flooding is hindered by the characteristics of
urban drainage. The presence of large sealed surfaces in urban areas (such as
buildings, roads, and car parks) raises the volume of surface water runoff (Ripl
1995; Sanders and Phillipson 2003). Conversely, green spaces reduce runoff
volumes and rates by facilitating the infiltration of water into the ground, and
through evapotranspiration of water back into the air (Ripl 1995). Gill et al.
(2007) modelled surface water runoff from different types of land use and found
that runoff increases with the proportion of built-up areas. For example, on
sandy soils, for an 18 mm precipitation event in North West England, low-density
residential areas (66% vegetated cover) were characterised by 32% runoff,
compared with 74% runoff in town centres (20% vegetated cover). Therefore, it
is apparent that land use, and the proportion of evapotranspiring surfaces in
particular, has a significant influence on surface water behaviour, and therefore
the exposure of the area to flooding. Further, whilst the focus of this study is on
the risks to people, it is important to note that specific land uses and types of
infrastructure can have a negative impact on the functioning of urban areas if
they are affected by flooding. Flooding of critical infrastructure, such as transport
routes, electricity distribution substations, waste management facilities, water or
sewage treatment works, can be both an inconvenience and a threat to urban
residents. The presence of facilities such as hospitals or schools in areas at risk
of flooding may put them at risk, but they can also function as places for shelter
or as temporary accommodation in an emergency situation, if they are protected
from flooding.
Another element of exposure in the context of flooding is housing type. Houses
with the lowest floor at or below ground level are more exposed than dwellings
located on higher floors, and occupants and their belongings may be more
significantly affected (Thieken et al 2005). Solid masonry buildings can
withstand flooding without suffering major structural damage, while lightweight
constructions may be more easily damaged (Sanders and Phillipson 2003).
Housing type also affects the flood protection measures that could be provided.
Detached houses are relatively easy to protect from shallow flooding at the
property-level with the use of door guards, air brick covers and waterproof
8
skirts, whilst in high-density terraced housing, water can seep under flooring and
through
walls
between
adjacent
properties
(Bowker
2002).
Thus,
only
community-level flood protection measures (structural defences; landscaping)
would be effective and financially feasible for terraced housing (Johnson and
Priest 2008). In this paper, the housing and land use types (with a particular
focus on green spaces and presence of vegetation) are investigated as
components of exposure to surface water flooding.
1.4
Vulnerability of people and communities to surface water
flooding
Knowledge of hazards aids the understanding of the physical aspects of
disasters, but it may also result in a perception that extreme weather events are
the disasters, indiscriminately affecting all residents of the location where it
occurs (McEntire 2005). However, some people are more susceptible to harm
than others due to their different capacities to deal with hazards (Clark et al
1998).
Consequently, alongside the knowledge of hazards, awareness of the
vulnerabilities to disasters is increasingly seen as a crucial factor in the reduction
of disaster risk (McEntire 2005; UNISDR 2005). Vulnerability of people to
flooding is sometimes understood simply as their ability to respond to a flood by
being able to physically withstand the flood water’s velocity and depth (DEFRA
and EA 2006). However, in a broader sense, vulnerability of people to flooding is
a function of the characteristics of people and households, which influence the
following four types of issues:
•
Access to information. This has an impact on an individual’s awareness of
living in an area at risk, on knowing what to do in the event of surface
water flooding and understanding what help is available in the aftermath
of a flood. Those without strong social networks and unfamiliar with their
area (for example, as a result of short residence in the area or renting)
may have less access to information (Cutter et al 2003). Inability to
understand the information due to illiteracy (Cutter et al 2003), no
knowledge of the official language (McGeehin and Mirabelli 2001), age or
mental health, also increases vulnerability.
•
Ability to prepare for flooding. Those on low incomes may not be able to
invest in flood insurance (Tapsell et al 2002) or in property-level flood
protection measures (Clark et al 1998). Those physically less able,
9
including the elderly, disabled or those suffering from a chronic illness
(Clark et al 1998) may find it difficult to secure their belongings from
flooding once warnings are issued or in the early stages of a flood.
•
Ability to respond to flooding. The capacity of people to act in the event of
flooding can be negatively affected by their young or old age (Clark et al
1998; Cutter et al 2003; Fernandez et al 2002), pre-existing health
problems (Rygel et al 2006), whether they care for others (e.g. lone
parents) (Cutter et al 2003; Tapsell et al 2002), or if they own a car and
can leave the area (Clark et al 1998). Isolated and housebound people
(especially the elderly) may wait longer for help when service providers
cannot reach them due to impassable roads affected by the flood event
(Fernandez et al 2002). Similarly, those in need of additional support and
resources
(infirm,
institutionalised,
transient,
or
homeless)
are
disproportionately affected during disasters (Cutter et al 2003). In areas
of high density housing and where households are overcrowded, the
emergency services responding to an event may be overstretched (Cutter
et al 2003). In the case of some ethnic minorities, cultural differences
may hamper support from emergency services due to misunderstandings
and problems with communication (Cutter et al 2003).
•
Ability to recover. The populations in need of additional support and
resources are often overlooked during recovery due to their relative
invisibility in communities (Cutter et al 2003). Those on lower incomes,
carers, single parents, or pensioners may find it difficult to find the
resources, energy and mental strength to start again (Clark et al 1998;
Tapsell et al 2002). Children and the elderly are more susceptible to
health-related impacts of floods (injuries, water pollution and other). Preexisting problems with physical and mental health have been found to
significantly affect the ability of people to recover after flooding (Tapsell et
al 2002). Also, older people and children have been found to suffer
considerable psychological trauma following flood events (Fernandez et al
2002; Rygel et al 2006; Tapsell et al 2002).
Whilst spatial mapping of the vulnerability of people to extreme weather events
has been the subject of studies in North America (Chakraborty et al 2005; Clark
et al 1998; Cutter et al 2000, 2003; Rygel et al 2006; Wu et al 2002), fewer
such studies have been undertaken to date in the UK context (Haynes et al
2008; Lindley et al 2006; Tapsell et al 2002). The UK Cabinet Office (2008)
10
emphasised that spatial representation of vulnerability with the use of GIS can
be particularly useful for targeting emergency responses. DEFRA and EA (2006)
offer an example of a methodology for assessment of the flood risk of people.
However, this guidance only focused on the danger of people being swept over
or drowned in a flood. Thus, there is a need for studies exploring the broader
issues of vulnerability and their spatial relation to hazard and exposure.
Consequently, this
paper aims
to
spatially analyse the
vulnerability
of
communities to surface water flooding - considering their ability to prepare for,
respond to and recover after flooding - in Greater Manchester, UK. It then
analyses the associations between the spatial distribution of vulnerability,
exposure and hazard in the context of surface water flooding, in order to
investigate the risk to communities.
2 Methods
2.1
Study area
The conurbation of Greater Manchester in the North West of England (53°30’N;
2°15’W) covers 1276km2. It is located on a river basin flanked by the Pennine
hills in the north and east and stretching to lowland areas to the south and west
(Gill et al 2007; Ravetz 2000). Greater Manchester is a home to over 2.5 million
people and it comprises ten districts: the cities of Manchester and Salford and
eight metropolitan boroughs.
The conurbation includes urban areas (town
centres, retail and industrial areas), suburban housing areas, as well as periurban fringe (Figure 2).
Greater Manchester evolved from free standing industrial towns, during the
industrial revolution associated with the cotton industry in the 19th century
(Barlow 1995). The conurbation continued to grow until 1960s until, following
the global economic turn, it went through the process of post-industrial change,
including significant job losses, rises in unemployment and poverty, and an
increase in the magnitude of social problems (Barlow 1995). In recent decades,
urban regeneration in Greater Manchester has made the conurbation the largest
cluster for finance, law, media, research and higher education in England outside
of London. However, due to the out-movement of wealthier residents to the
suburbs and peri-urban areas, the conurbation is still characterised by great
income inequalities (Ravetz 2000). Moreover, throughout its history, Greater
Manchester has been a multi-ethnic centre for many groups, more recently
11
including South Asian, Caribbean and Chinese (Ravetz 2000). Therefore, many
aspects of the socio-economic vulnerability of individuals and households in this
post-industrial conurbation can be transferable to other British and European
cities.
Figure 2. Urban (over 66% built-up areas), suburban (34-66% build up areas)
and peri-urban (up to 33% built-up areas) areas in Greater Manchester. Based
on Gill et al. (2007). Base map is © Crown Copyright/database right (2009). An
Ordnance Survey/EDINA supplied service.
An analysis of media reports for the period 1961 - 2009 in Greater Manchester
found that flooding was a dominant climate impact within the conurbation, and
the principal cause of damage to property and infrastructure (Smith and Lawson
2011). Also, there seems to be an increase in the number of flood events over
the decades. Whilst between 1961 and 1970 six pluvial flood events were
recorded, their number increased to eight in the period 1981-1990 and 14
between 2001 and 2009 (Smith and Lawson 2011). Whilst the press is
inconsistent in recording weather, and recording of weather events by other
organisations (e.g. local authorities) is generally not comprehensive, these
findings seem to reflect the effects of recent changes in weather and climate.
12
Also, future climate change projections suggest increased frequency and
magnitude of precipitation events. Palmer and Räisänen (2002)
estimate that
the probability of total winter precipitation exceeding two standard deviations
above normal will increase by a factor of five over the North West of England.
Specifically for Greater Manchester, analysis of the UK Climate Projections 2009
indicate that by the 2050s, under the high emissions scenario, changes in
average winter precipitation across Greater Manchester are unlikely to be less
than 0.3% (10th percentile) and unlikely to be more than 36% (90th percentile),
and changes in rainfall on the wettest day in winter are unlikely to be less than
1.4% (10th percentile) and unlikely to be more than 38% (90th percentile)
(Cavan 2010). Therefore, the problems associated with heavy rainfall causing
surface water flooding in Greater Manchester are likely to intensify.
2.2
Analysis of vulnerability
Table 1 lists the 26 indicators describing vulnerability of people and communities
to surface water flooding, reflecting their access to information and their ability
to prepare for, respond to, and recover after flooding (see section 1.4). The
indicators were obtained from Census 2001 (ONS 2002), and the Indices of
Multiple Deprivation 2007 (CLG 2008) for the Lower Super Output Area (LSOA)
level. LSOAs are compact areas of homogenous socio-economic characteristics
constrained by the boundaries of the electoral wards used by the Office of
National Statistics to report small area statistics across England and Wales.
LSOAs contain on average a population of around 1500 people (circa 600
households), and a minimum population of 1000 residents (400 households)
(ONS, 2008). There are 1646 LSOAs in Greater Manchester.
In order to avoid double-counting of inter-correlated variables and to ensure
against the redundancy of data, the dataset was reduced with the use of
Principal Component Analysis (PCA). PCA is a vector space transformation, which
helps to identify patterns in high-dimensional data and reveals the underlying
factors (principal components, further referred to as PCs) that best describe
variations in the data through identification and clustering of variables that
measure the same theme. Varimax rotation was used in order to maximise the
variance of loadings, thus aiding the classification of variables to PCs (Rygel et al
2006). This data reduction method results in zero correlations between the
principal components. As a result of the PCA, each of the LSOAs in Greater
Manchester is characterised by the values of a limited number of PCs, rather
13
than by values of 26 indicators. Low values of the emergent PCs indicate low
vulnerability, and high values represent high vulnerability.
2.3
Analysis of associations between vulnerability, hazard and
exposure
The geospatial map of Areas Susceptible to Surface Water Flooding 2009
(produced by JBA Consulting and licensed by the Environment Agency for
emergency planning purposes) was used as the best spatial representation of
areas at risk of surface water flooding available at the time of the study. The
map is based on the Digital Terrain Model (5m by 5m resolution) and was
produced by simulating a 1 in 200 year event for a 6.5 hour duration rainfall
event, as such rainfall would overwhelm even the most modern drainage system
and so any impact from the drainage system can be ignored and is not
accounted for in the model (GeoStore, no date). The map presents four
categories indicating areas of increasing natural susceptibility to surface water
flooding, based upon flooded depths of up to 0.1m (no flooding); 0.1-0.3m; 0.31.0m and over 1.0m (GeoStore, no date). In this paper, all areas where the
depths of 0.1m are exceeded are considered susceptible to surface water
flooding, as even shallow floods can cause significant damage to housing and
incur costs, thus making it difficult for people to recover after flooding (ABI, no
date; Penning-Rowsell and Green 2000). The areas where the flood depth may
exceed 1m represent the most extreme surface water flooding scenario, and are
referred to as highly susceptible to flooding. Flood depths greater than 1m may
damage the structure of buildings (Soetanto and Proverbs 2004), and thus
displace vulnerable populations. Further, depending on the velocity of the flood
water, depths between 0.5m and 1.5m may make it impossible for a person to
stand (DEFRA and EA, 2006), thus presenting a direct risk to lives. Whilst, in the
model, overland flow routes and areas where surface water ponds were
identified using advanced 2D hydraulic modelling techniques (Geostore no date),
the model does not include the velocity of flood waters. This could be seen as a
limitation if the study focused exclusively on the ability of people to withstand
the flood waters (DEFRA and EA 2006), rather than the broader understanding
of vulnerability, where it is likely to play a lesser role. In addition, Soetanto and
Proverbs (2004) observe that the velocity of flood waters in the UK is not very
high in general, and is not considered very destructive to properties by building
surveyors (compared to its depth and contents), thus suggesting that it does not
affect the material aspects of the ability to recover after flooding.
14
The Greater Manchester Urban Morphology Type (UMT) dataset was used in
order to gain an understanding of land use types present, as well as to analyse
the proportion of green space in LSOAs. UMTs are homogenous urban land use
types (Table 4), which were identified by the Centre for Urban and Regional
Ecology at the University of Manchester within the Adaptation Strategies for
Climate Change in the Urban Environment (ASCCUE) project (2003-2006). Gill et
al (2007) used aerial photograph interpretation of random points to estimate the
mean percentage of nine land cover types (trees, shrubs, mown grassland,
rough grassland, cultivated, water, bare soil, building, and other impervious
surfaces) in each UMT category. Here, the total area of green space in each UMT
was calculated by summing the proportion of trees, shrubs, mown grassland,
rough grassland, cultivated land, and water.
The percentage of the territory of the individual LSOAs at risk of flooding was
calculated by overlaying the Areas Susceptible to Surface Water Flooding 2009
with LSOAs in ESRI ArcGIS. Land use characteristics for each LSOA were
investigated by overlaying this information with the UMT dataset, with a
particular focus on green spaces, and with Ordnance Survey Mastermap (garden
data). Data on the proportions of housing types in LSOAs (semi- and detached
houses, terraced houses, houses with the lowest floor at or below ground level)
was obtained from the Census 2001 (ONS 2001). The percentage of houses in
poor condition at the LSOA level was obtained from the Indices of Multiple
Deprivation 2007 (CLG 2008).
The association between the vulnerability principal component scores, the
proportion of areas at risk of flooding, and different types of housing, land use,
green space and gardens in LSOAs was analysed using Spearman’s rank
correlation. A one-way ANOVA was used to compare the mean proportion of
areas of exposure and hazard in LSOAs between the most vulnerable areas (top
quantile of the principle component scores). This was done in order to learn
whether the areas with high vulnerability, exposure and hazard coincide, and
which types of communities are at highest risk of surface water flooding. The
overall risks for communities, characterised by selected aspects of vulnerability,
were spatially mapped.
15
3 Results
3.1
Vulnerability of people and communities
The Principal Component Analysis identified four principal components, which
explained over 81% of variance in the data (Table 1). The components were
named: poverty (PC1), which grouped variables associated with unemployment,
material deprivation and poor health; diversity (PC2), which grouped variables
associated with non-British origin, high density populations and private renting;
children (PC3); and old age (PC4). One of the 26 original variables – the
percentage of people living in residential care homes - did not load heavily on
any of the principal components. This may be because a very low percentage of
people live in care homes in the majority of the LSOAs.
It is important to note that some of the variables are associated with more than
principal component. For example, the percentage of children aged 0-4, whilst
contributing the most strongly to PC3: children, was also associated with PC1:
poverty and PC2: diversity (loadings exceeding 0.3). Similarly, the health related
indicators (% people with limiting long-term illness, % households with at least
one person with a limiting long term illness; % people whose health was not
good), whilst loading the most heavily on the first factor associated with poverty
and poor health, also contributed to PC4: old age. Thus, whilst the names of the
principal components refer to the main reasons for vulnerability, careful
interpretation of the results in table 1 also reveals that these also include
secondary aspects.
The LSOAs with high scores of the poverty component in Greater Manchester are
in general spatially concentrated around town centres, but there seem to also be
pockets of deprivation and poor health in peri-urban areas (Figure 3; compare
Figure 2). The LSOAs with high diversity component scores are even more visibly
associated with the urban centres across Greater Manchester. Conversely, areas
characterised by high children component scores (PC3) are more concentrated in
sub- and peri-urban parts of the conurbation. The LSOAs with high scores of the
old age component (PC4) are scattered throughout the suburban areas of
Greater Manchester.
16
Table 1. Principal component (PC) loadings for the indicators of vulnerability.
The most significant loadings for each variable are in bold. Aspect of
vulnerability: 1 – access to information; 2 – ability to prepare; 3 – ability to
respond; 4 – ability to recover. Source of data: ONS (2001) and * (DCLG, 2007).
Aspects of
vulnerability
Variables
% households with at least one person with a
limiting long term illness
2, 3, 4
% people deprived in terms of their income*
2, 4
% people deprived
employment*
2, 4
in
terms
of
their
% households with no car
% people with no or level 1 qualifications
0.807
0.017
0.192 0.434
0.864
0.387
0.211 0.024
0.947
0.078
0.003 0.084
3
0.891
0.360
-0.156 0.072
1, 2, 3, 4
0.807
-0.112
0.368 0.131
0.838
0.368
0.921
0.096
0.799
0.307
0.368
0.175
0.821
0.015
0.292
0.236
% of unemployed people among economically
active 16 to 74 years old
% households rented from social landlords
PC4:
PC1:
PC2:
PC3:
Old
Poverty Diversity Children
age
2, 4
1, 2, 3, 4
0.062
0.116
-0.030 0.046
% households with no adults in employment
and dependent children
2, 3, 4
% lone parent households with dependent
children
2, 3, 4
% people with limiting long-term illness
2, 3, 4
0.797
-0.056
-0.124 0.523
% people whose health was not good
2, 3, 4
0.869
0.027
-0.108 0.372
0.088
0.523
0.105
0.072
0.377
0.814
-0.144
0.110
0.145
0.657
-0.499
0.206
0.019
0.936
0.114
0.052
0.067
0.918
0.202
0.048
0.384
0.317
0.653
0.300
0.317
0.029
0.828
0.203
0.001
0.034
0.898
0.360
0.192
0.335
0.773
0.352
0.509
0.380
-0.630
0.248
-0.007
-0.055
-0.285 0.756
3
Number of people per hectare
2, 3, 4
% overcrowded households
% households rented from private landlords
1, 2, 3, 4
1, 3
% people born outside the Great Britain
1, 3
% ethnic minorities
3, 4
% children under 4 years old in the population
3, 4
% people 5 to 17 years old in the population
% households with dependent children 5-18
years old
3
% households with dependent children under 4
years old
3
1, 3
% single person households (non-pensioner)
% people aged over 75 in the population
1, 3, 4
% people aged 65 to 75 in the population
1, 3, 4
0.150
-0.141
-0.215 0.889
% single pensioner households
1, 3, 4
0.390
-0.039
-0.233 0.788
% all pensioner households
1, 3, 4
-0.463
-0.377
0.093 0.629
0.581
0.075
-0.342 0.656
0.010
0.335
-0.335 0.138
Initial eigenvalues
10.49
6.09
3.73
1.63
Percentage of variance explained (%)
38.87
22.55
13.82
6.03
% households with no adults in employment
(no children)
2, 4
% residents living in residential care
2, 3, 4
17
Figure 3. Principal component scores for Lower Super Output Areas in Greater
Manchester (classified into quantiles). Base map is © Crown Copyright/database
right (2009). An Ordnance Survey/EDINA supplied service.
18
3.2
Hazard: distribution of surface water flooding
Over 14.2% of the Greater Manchester area is susceptible to surface water
flooding, and 2.2% is highly susceptible. The mapping of surface water flooding
against the territorial units suggests that the hazard is widespread (Figure 4):
only five of 1646 LSOAs are not affected by surface water flooding and over 78%
of LSOAs in Greater Manchester include areas highly susceptible to flooding. The
areas exposed to surface water flooding constitute up to 72.3% of individual
LSOAs’ territory (mean=16.3; SD=10.4), while the percentage of individual
LSOAs highly susceptible to flooding range between 0 and 20.2% (mean=1.9;
SD=2.7).
The spatial distribution of the percentage of LSOAs affected by surface water
flooding across Greater Manchester (Figure 4) indicates that the highest
proportion of areas highly susceptible to flooding are located to the north of the
conurbation, while the south-west and areas around urban centres are at lower
risk of flooding. This varied distribution suggests that the levels of risk of surface
water flooding are determined by factors associated with topography and land
use.
Figure 4. Proportion of LSOAs susceptible to surface water flooding (classified
using natural breaks). Base map is © Crown Copyright/database right (2009).
An Ordnance Survey/EDINA supplied service.
19
3.3
Exposure of communities to surface water flooding
The predominant land use category in Greater Manchester is green and open
space, a large percentage of which is farmland (Table 4; see Figure 2).
Residential areas, predominantly of medium density development, form another
important land use, hence the high proportion of suburban areas in Figure 2.
The total green space cover in Greater Manchester, calculated as the percentage
of green and open space in all UMTs, is 71.7%, ranging 20.1–94.1%
(mean=58.7; SD=14.6) in individual LSOAs. The highest percentage areas of
green space are associated with peripheral areas of Greater Manchester (Figure
5).
Specifically,
private
gardens
constitute
17.7%
of
the
total Greater
Manchester area, and their proportion in LSOAs ranges between 0.2 and 63.7%
(mean=25.9; SD=14.2). The highest proportion of gardens is present in the
suburban parts of the conurbation, in particular to the south of the conurbation
(Figure 5; compare Figure 2).
Figure 5. Spatial distribution of the total green space and gardens in Greater
Manchester (classified using natural breaks). Base map is © Crown
Copyright/database right (2009). An Ordnance Survey/EDINA supplied service.
20
Over half of the housing in Greater Manchester is comprised of semi-detached
and detached housing, and terraced housing accounts for a third of the housing
stock (table 2). Consequently, the vast majority of housing across the
conurbation has the lowest floor at or below ground level, which is significant
when considering flood risk. Just over a quarter of housing in Greater
Manchester is described as in poor condition.
Table 2. Housing type and condition in Greater Manchester (% of total housing
in LSOAs; N=1646).
Type of houses
Minimum Maximum Mean
Std.
Deviation
% semi- and detached houses
1.0
99.6
53.4
27.1
% terraced houses
0.0
95.4
32.0
22.1
28.3
100.0
88.2
11.5
8.0
57.0
27.0
8.1
% houses with lowest floor at/ below ground
level
% houses in poor condition
3.4
Associations between vulnerability, hazard and exposure
This section analyses the associations between the elements of the risk triangle
(see Figure 1): vulnerability of communities, the hazard of surface water
flooding and the exposure factors relating to the land use and housing
characteristics in order to identify the character of risks present.
3.4.1 Vulnerability of communities and hazard
The poverty and diversity component scores (PC1 and PC2) are positively
associated with the proportion of LSOAs susceptible to surface water flooding
(table 3). However, in the case of PC1, the correlation is not significant with
regard to high susceptibility to flooding. Further, in the case of PC2, the more
vulnerable that the communities are, the lower the proportion of areas highly
susceptible to flooding is within LSOAs. The children component scores (PC3) are
negatively correlated with the proportion of LSOAs at risk of flooding (including
high susceptibility); and, in the case of the old age component scores (PC4), this
holds true for all areas susceptible to flooding. Thus, the spatial distribution of
communities characterised by poverty and poor health and high diversity
coincides spatially with the areas at risk of surface water flooding.
21
Table 3. Spearman’s rank correlations between the level of vulnerability and
the proportion of LSOAs exposed to surface water flooding in Greater
Manchester (N=1646).
Principal components
PC1: Poverty
All areas susceptible to
flooding
0.056*
High susceptibility
Ns
PC2: Diversity
0.139***
-0.234***
PC3: Children
-0.099***
-0.059*
PC4: Old age
Significant at 0.01 level;
not significant.
***
**
-0.111***
Significant at 0.05 level;
*
Ns
Significant at 0.10 level; Ns -
3.4.2 Vulnerability of communities and exposure
Analysis of Spearman’s rank correlations between the scores of principal
components and the percentage of different types of land use in LSOAs (table 4)
reveals that an increase in vulnerability associated with poverty (PC1) is
correlated with a decreasing proportion of different types of green space (except
for formal open space, which increases with vulnerability), as well as with
decreasing peri-urban characteristics (such as the presence of farmland). With
increased scores of the poverty component, the proportion of high density
residential areas, roads, retail areas, and town centres in LSOAs increased. In
addition, higher scores of the poverty component are associated with greater
proportions
of
derelict
and
underused
land,
as
well
as
with
existing
manufacturing areas. Similarly, an increase in scores of the diversity factor
(PC2) is associated with the increase in urban land use characteristics
(proportion of high density housing, town centre and offices) and a decrease in
farmland.
Increasing scores of the children component (PC3) are associated with the
increase in the proportion of medium- and low-density housing and with a
decreasing proportion of ‘urban’ land uses such as retail, offices, manufacturing
or distribution, and transport infrastructure. The communities characterised by a
high score of the old age component (PC4) are associated with lower amounts of
both peri-urban land use types (farmland), as well as high density housing,
manufacturing, derelict land and office areas. An increase in the score of the old
age component is also associated with an increase in the proportion of medium22
Table 4. Spearman’s rank correlations between the percentage of different land
use types in LSOA, the percentage of areas exposed to surface water flooding in
LSOAs and the scores of principal components (N=1646)
Land use
category
(% in GM)
% of LSOA susceptible
to surface water
flooding
All areas
High
susceptibl susceptibilit
e
y
Principal Components
Land use type
0.087**
0.071**
*
0.127***
0.195***
Ns 0.101***
-0.057*
0.113***
0.083**
Ns
*
Offices
and
Retail
Town centre
Improved farmland
PC4:
Old
age
-0.050*
0.198**
Manufacturing
Industry and Distribution
business storage
(8.8%)
Mineral
works/quarries
PC1:
PC2:
PC3:
Povert Diversit Childre
y
y
n
Ns -0.083** -0.074**
Ns
0.092***
0.074**
Ns -0.123***
Ns
Ns
0.114***
Ns
0.089***
0.088***
Ns
0.070**
Ns
Ns -0.058*
-0.239***
0.181***
Ns 0.111**
-0.152***
0.078**
0.073**
0.215***
0.103**
Ns
*
0.134**
*
0.306***
0.248** -0.532***
Ns
0.092***
0.183***
*
Unimproved farmland 0.091** -0.195***
*
Disused/derelict land
0.167**
*
*
-0.247***
Ns 0.124**
*
***
Green/open Remnant countryside
** -0.186
0.065
space
(55.5%)
Woodland
0.182** -0.218***
Ns
Ns
Ns
0.148***
Ns
Ns
-0.171***
0.228***
Ns 0.063**
- 0.102**
0.066** 0.104***
***
*
0.087
Ns -0.057*
Ns
Ns
Ns
Ns
Ns
Ns
Ns -0.108***
0.107*** 0.084**
0.049*
0.057*
Ns
Ns
0.074**
0.055*
0.106***
Ns
0.209***
0.274***
*
Formal recreation
Formal open space
Informal open space
Allotments
Rivers and canals
-0.054*
Ns
Energy
production
Ns -0.089***
and distribution
Critical
infrastructur Water
-0.193***
e (1.0%) storage/treatment
0.077**
Refuse disposal
Residential High density
areas
(30.0%) Medium density
Ns
0.214**
*
Ns
Ns
Ns
0.053*
0.091***
Ns
Ns
0.048*
0.173***
Ns
0.058**
Ns
0.058*
0.052*
-0.057*
Ns
-0.230***
-0.062*
0.158***
0.114**
Ns 0.181***
*
0.413***
-0.051*
23
Land use
category
(% in GM)
Principal Components
Land use type
PC1:
PC2:
PC3:
Povert Diversit Childre
y
y
n
0.372** -0.081**
Low density
PC4:
Old
age
-0.053* 0.077**
% of LSOA susceptible
to surface water
flooding
All areas
High
susceptibl susceptibilit
e
y
-0.074**
Ns
*
Cemeteries/cremator
Social
ia
infrastructur
Schools
e (2.3%)
Hospitals
Transport Major roads
infrastructur Airports
e (2.4%)
Rail
***
Significant at 0.01 level;
significant.
GM – Greater Manchester
**
Ns
Ns
-0.053*
Ns
Ns
Ns
Ns
Ns
*
*
**
Ns
ns
Ns
Ns
0.077*
Ns
Ns -0.080**
Ns
Ns
Ns
Ns
0.136***
Ns
0.110***
Ns
Ns
Ns -0.067**
Ns
0.162***
0.194***
-0.057
Ns
0.063 0.085
-0.064* 0.049*
Significant at 0.05 level;
*
Significant at 0.10 level; Ns - not
and low density residential areas in LSOA, as well as formal recreation and green
spaces, schools and hospitals.
Analysis of the correlations between the component scores and the amount of
green space in LSOAs (table 5) reveals that increasing scores of components of
poverty and diversity are associated with a reduction in green space. In addition,
in areas characterised by high scores of the poverty component, the presence of
gardens is lower than in less vulnerable areas. Conversely, in areas inhabited by
communities characterised by a high score of the diversity component, the
percentage of gardens is higher than in areas characterised by lower scores.
There are more gardens in areas with higher proportions of families with
dependant children and elderly populations (high scores of PC3 and PC4). Thus,
whilst the urban character of the environment in areas inhabited by the
communities characterised by high vulnerability due to poverty or diversity can
increase their exposure to surface water flooding, the exposure of elderly
populations and populations with a high proportion of children may be reduced
due to a high proportion of green space.
24
Table 5. Values of Spearman’s rank correlation coefficient between the
principal component scores and presence of green space in LSOAs (N=1646)
% total green
space
% gardens
% houses with
lowest
floor
at/
below
ground level
% semi- and
detached
houses
%
terraced
houses
% houses in
poor condition
***
Significant at
not significant.
PC1:
Poverty
PC2:
Diversity
PC3:
Children
PC4:
Old age
Ns
% area
at
risk of
flooding
-0.152***
% area
highly
susceptible
to flooding
0.237***
0.269***
0.269***
0.347***
-0.559***
0.056*
0.199***
0.231***
0.113***
Ns
-0.365***
-0.553***
0.457***
0.110***
-0.131***
0.064**
-0.076**
Ns
Ns
0.048*
0.069**
Ns
0.337***
0.617***
-0.449
0.535***
0.209***
-0.080**
0.382***
0.543***
-0.372***
0.01 level;
**
***
0.055
*
0.130***
Ns
Significant at 0.05 level;
*
Significant at 0.10 level; Ns -
Increasing scores of the poverty, diversity and old age components are
associated with decreasing proportions of houses with the lowest floor at ground
level (table 5). Conversely, communities vulnerable due to a high proportion of
children in the population (high scores of PC3) are more likely to live in areas
where houses have the lowest floor at ground level, comprising mostly of semiand detached housing. The scores of poverty and diversity factors (PC1 and
PC2), are positively correlated with the percentage of terraced housing and
houses in poor condition in LSOAs. In contrast, the children component is
negatively associated with the proportion of houses in poor condition. Thus,
whilst the exposure of communities with a high proportion of children is
increased by the ground-level character of housing, the actual damage may be
reduced by the good quality of housing.
3.4.3 Hazard and exposure
Significant positive associations are present between the percentage of LSOA
susceptible to surface water flooding and the percentage of areas covered by the
land use types associated with industry and business, critical infrastructure and
transport-related land use types (table 4). In the case of critical infrastructure,
25
these correlations are stronger regarding high susceptibility to flooding. The
proportion of LSOA susceptible to flooding shows an inverse relationship with the
proportion of residential areas; this is particularly visible in the case of medium
density residential areas and areas highly susceptible to flooding.
LSOAs with a high percentage of houses with the lowest floor at or below ground
level are located in areas highly susceptible to flooding rather than in areas with
a general risk of flooding (table 5). Also, although correlations are weak, results
suggest that larger proportions of housing in poor quality and terraced housing
are at greater risk of flooding than other housing types. This may suggest that
the hazard and exposure factors coincide spatially, potentially exacerbating the
risks.
Table 5 suggests that the areas with a large proportion of land susceptible to
surface water flooding tended to have less green space; however, the larger the
proportion of a LSOA that is highly susceptible to flooding, the more green space
and fewer gardens are present. The associations between the proportion of area
at risk of flooding and proportion of green space are explained for individual
green space types in table 4: while the percentage of woodland and farmland in
LSOAs decreases with the increasing proportion for areas highly susceptible to
surface water flooding; the association is positive for derelict and disused land,
formal recreation sites and informal open space.
3.4.4 Comparing hazard and exposure among the most vulnerable
communities
A one-way ANOVA comparing the exposure and hazard characteristics between
the LSOAs occupied by communities classified as the most vulnerable to surface
flooding due to the highest scores of the four principal components supports the
results of the Spearman’s rank correlation (table 6). The communities vulnerable
due to high percentages of elderly (PC4) and children (PC3) are located in LSOAs
characterised by the lowest proportionate area susceptible to flooding; the
highest proportion of green space and gardens and the lowest proportion of
houses in poor condition. The only factor of exposure that situated them at a
disadvantage (in flood risk terms) is the high proportion of detached housing,
and high proportion of houses with the lowest floor at or below ground level.
Conversely, LSOAs occupied by communities vulnerable due to poverty (PC1)
and diversity (PC2) are characterised by a significantly larger proportion of area
26
Table 6. Comparison of variables of exposure (mean proportion of types of
housing and green spaces) and hazard (mean proportion of areas susceptible to
flooding) between the most vulnerable areas (top quantile of each factor).
Factors of exposure and hazard
Highest vulnerability (top 20% of PC
scores)
PC1
PC2
PC3
F
PC4
% houses with lowest floor at/ below
ground level
83.50
77.56
91.46
86.38 75.079***
% semi- and detached houses
35.41
32.73
56.97
55.78 85.312***
% terraced houses
38.72
40.35
33.76
25.14 30.951***
% houses in poor condition
28.02
34.12
25.30
26.91 77.067***
% green space
54.04
47.73
56.64
58.64 41.800***
% gardens
22.85
25.97
29.29
28.91 14.158***
% area susceptible to flooding
16.62
19.29
15.79
14.48 11.349***
1.89
1.34
1.67
% area highly susceptible to flooding
***
Significant at 0.01 level; Ns - not significant
1.62
Ns
Figure 6. Spatial distribution of the risk of surface water flooding to urban
communities (classified into quantiles). Base map is © Crown
Copyright/database right (2009). An Ordnance Survey/EDINA supplied service.
27
highly susceptible to flooding, combined with a higher proportion of houses in
poor condition and lower proportion of green space (in particular PC2) and
gardens (in particular PC1).
3.4.5 Risk of surface water flooding to communities
The three elements of risk: vulnerability, hazard and exposure, were combined
in order to demonstrate how the overall risk could be calculated and presented
at the conurbation level. Figure 6 provides example risk maps, based on the
spatial coincidence of hazard, exposure and high levels of vulnerability of urban
communities. Figure 6 indicates that risk is highest where the largest proportions
of area are highly susceptible to surface water flooding (see figure 4), high
vulnerability of communities due to poverty and poor health or high cultural
diversity (see figure 3), and high exposure (low proportions of green space [see
Figure 5], and high proportions of houses with the lowest floor at or below
ground level) coexist.
4 Discussion and conclusions
The research findings presented in this paper have revealed different facets of
vulnerability of people and communities to surface water flooding through a
Principal Component Analysis. This sets this research apart from the previous UK
studies focused on vulnerability, which integrated social and financial factors into
one index (e.g. Haynes et al 2008; Lindley et al 2006; Tapsell et al 2002). The
approach used here allows evaluation of not only where the most vulnerable
people live, but also what makes them vulnerable. This can help local
authorities, emergency services and other agencies, to provide information and
assistance in preparation for, response to and recovery from flooding,
appropriate to the community characteristics.
Some aspects of vulnerability, namely poverty and diversity of communities,
reflect the general socio-economic inequality of urban areas represented in
spatial terms, and these two principal components together explained the
highest proportion of the variance in the dataset (table 1). However, this study
has also emphasised that the percentage of children and elderly people in the
population will also influence the ability of communities to cope with surface
28
water flooding. Therefore, the vulnerability of populations in Greater Manchester
to surface water flooding does not seem to be entirely synonymous with social
exclusion or material deprivation, as previous studies in the UK suggested (see
e.g. Haynes et al 2008; Tapsell et al 2002).
The spatial distribution of different aspects of vulnerability varies across the
conurbation. The poor and diverse communities tend to be associated with urban
centres, and the communities characterised by higher proportions of wealthier
families and the elderly are more linked to suburban and more peri-urban
locations (Figure 3). This is consistent with the associations between the level of
vulnerability and the proportion of different land use types in LSOAs (Table 4);
whilst high scores of components of poverty and diversity are associated with
urban conditions (town centres, manufacturing areas, high density housing), the
children component is clearly suburban in character (largely medium-density
residential areas with little industry and business-oriented land use). This
pattern neatly reflects the processes of socio-spatial diversification of cities
present in most of Western Europe and North America, but particularly visible in
British cities, and associated with the continuing trend of wealthier populations
moving out of inner-city areas to more attractive, suburban locations, and the
subsequent void being filled by private renters and immigrants (Dorling and
Rees 2003; Healey et al 1988). Further, the industrial history of Greater
Manchester is visible in the relationships between the distribution of poor
communities and the location of current manufacturing areas and derelict and
underused land, which is frequently associated with previous industrial uses. The
consistency between the distribution of vulnerable communities across Greater
Manchester and these recognised urban processes suggests that similar patterns
of vulnerability may be found in other cities. Although variables such as local
climate, topography and institutional frameworks are also significant driving
factors, the responses to surface water flooding in relation to these types of
areas in Greater Manchester are likely to be transferable. Further, close
associations between different aspects of vulnerability in particular geographic
locations emphasises the potential role that spatial planning could perform in
moderating exposure as factors increasing or decreasing flood risk.
Analysis of the correlations between the vulnerability of communities and the
proportion of areas at risk of surface water flooding suggests that communities
vulnerable due to poverty and diversity are more exposed to the widespread risk
of surface water flooding than wealthier and more homogenous communities
(table 3; table 6). This suggests that the material loss and inconvenience
associated with surface water flooding affect those with the least material means
29
and the lowest ability to act or receive help in the event of disaster and in its
aftermath (Tapsell et al 2002). Thus, whilst this research has emphasised that
vulnerability is a multi-faceted phenomenon, strong links remain between social
inequality and climate change risk. In contrast, the negative relationship
between the vulnerability of populations associated with a high proportion of
elderly and dependent children shows that, whilst these populations can be
considered vulnerable due to their socio-demographic characteristics, the low
threat of surface water flooding means that they are at relatively lower risk in
comparison to other vulnerable groups.
The high proportion of built-up areas, associated with typically urban land uses
(table 4), may exacerbate the risk of flooding in the event of extreme
precipitation (Gill et al 2007; Sanders and Phillipson 2003), and significant
inequalities between different vulnerable groups were found in relation to the
proportion of green space in LSOAs (table 5). Whilst the communities vulnerable
due to a high proportion of children or elderly in the population enjoy more
green spaces and gardens in their vicinity, the areas vulnerable due to poor and
diverse communities in general contain less green space (excluding gardens in
the case of diverse communities). This may have repercussions not only for the
risk of surface water flooding in these areas, but also for the general well-being
of communities. Green spaces provide multiple benefits for urban communities,
including: improvement in physical and mental health, contribution to building
stronger communities, and improved image of the neighbourhood (Tzoulas et al
2007). Therefore, this pattern of distribution of green space strongly suggests
environmental injustice in Greater Manchester. In addition, only the areas
identified as vulnerable due to a high proportion of elderly people seemed to be
positively associated with the presence of schools and hospitals, which could be
used as temporary shelters in the event of a flood. This may place the other
vulnerable communities at a disadvantage in the event of a flood due to a lack of
access to such facilities. However, more detailed assessment is needed in order
to assess the distribution of infrastructure associated with social services and
care in relation to flooding and vulnerability.
It is also notable that the proportion of land in LSOAs linked to economic
functions, such as critical infrastructure and transport, is associated with the
percentage of LSOAs at risk of flooding (table 4). This highlights the risk of
considerable economic losses for manufacturing, retail, distribution and storage,
and businesses in town centres in the case of extreme precipitation and surface
water flooding events. Further risks include that flooding of strategic transport
routes can paralyse a city, and flooding of refuse disposal sites can result in
30
contamination
of
water
supplies,
creating
additional
hazards
to
urban
populations.
The negative correlations between green space cover and the proportion of an
area susceptible to flooding (table 5) concur with previous studies (e.g. Gill et al
2007), suggesting that the increasing amount of sealed surfaces in an area
exacerbates the problem of flooding through increased runoff and reduced
infiltration capacity. Positive correlations between the proportion of green space
and the areas at high risk of flooding in LSOAs (table 5) may be a result of some
areas being known as prone to flooding and, therefore, considered unsuitable for
development and maintained as open space. Further, the positive association
between the proportion of areas at risk of surface flooding with formal recreation
sites and informal open space (table 4) indicates that these places may already
be performing important functions as rain water collection, storage and
infiltration areas, and should be afforded further protection from development
for this reason.
The role of private gardens in mediating the risk of flooding can be seen as
particularly important, as they are negatively associated with the risk of flooding
(table 5). Gardens form a significant proportion of the green space in Greater
Manchester and many other cities, and are the most widespread type of green
space in the urbanised neighbourhoods of Greater Manchester (Gill et al 2007).
Gardens therefore have a great impact on the environmental performance of the
conurbation. Approximately 40% of all the evapotranspiring surfaces in
‘urbanised’ Greater Manchester occur in residential areas, with medium-density
residential areas accounting for the majority of such surfaces (Gill et al. 2007).
The fact that the poorest communities have the fewest gardens (table 6) again
stresses the environmental injustice associated with unequal distribution and
provision of green space across the conurbation and suggests that the capacity
of the areas inhabited by these communities to deal with intense rainfall may be
compromised.
The risk of surface water flooding to communities vulnerable due to a high
proportion of children in the population could be exacerbated by the high
proportion of houses with the lowest floor at or below ground level in the most
vulnerable areas (table 6). However, as a lower proportion of houses tend to be
in poor condition in these areas than elsewhere, the damage caused by flood
events may be less than expected. Conversely, communities vulnerable due to
31
poverty and their diverse character are more likely to live in areas with a high
proportion of houses in poor condition.
Various aspects of the vulnerability of people and communities to surface water
flooding in Greater Manchester have been found to correspond with different
levels of hazard and exposure to such events (table 6). The communities
vulnerable due to a high proportion of children or elderly in the population are
found to be at significantly lower risk of flooding than other types of vulnerable
communities, due to lesser flood hazard where they live, and the fact that their
exposure to surface water flooding is buffered by a higher proportion of
vegetation and better quality housing than in other vulnerable areas. In general,
the communities vulnerable due to their diverse character are also living in areas
most affected by surface water flooding. These communities are also the most
exposed to flooding as a result of the low proportion of green space and the poor
condition of housing in their area. The communities vulnerable due to poverty
and poor health are characterised by only marginally lower occurrence of
hazards and exposure. Therefore, while the study has revealed different aspects
of vulnerability, it can be concluded that the risk of surface water flooding
(composed of the associations between vulnerability, exposure and hazard) is
the highest for the communities in urban areas that are already socioeconomically disadvantaged.
4.1
Developing responses tailored to exposure and vulnerability
The vulnerability associated with a high percentage of children and elderly in the
population coincided with a high proportion of houses exposed to flooding due to
their lowest floor being located at or below ground level, and a high proportion
of semi-detached and detached houses in the area. This, combined with the fact
that the vulnerability of these communities is not related to material deprivation,
suggests that the risk of surface water flooding in these areas could be lowered
through the promotion of property-level flood protection measures, such as flood
gates or air brick covers (Kazmierczak and Bichard 2010). Conversely, in areas
where poor or ethnically diverse, transient populations live in terraced houses
and houses in poor condition, actions aimed at promoting the protection of
individual properties against flooding may not succeed due to the tenure
dominated by social and private renting, and the high costs associated with such
solutions (Kazmierczak and Bichard 2010). Also, the protection of individual
houses in these areas may be counterproductive because water can seep
through the walls between adjoining properties, and the options for flood32
proofing internal walls are limited (Bowker 2002). Consequently, wider flood
measures to protect entire streets and neighbourhoods should be promoted in
such areas, and the vulnerability of people may be considerably reduced through
the provision of relevant information about reducing risk of flooding to residents,
in appropriate languages and format.
Green spaces are considered to be an important measure for reducing the
surface water runoff, thus limiting the impacts of extreme weather events on
cities (Gill et al 2007). Greening may include development and conservation of
parks and woodlands, planting of moisture-retaining tree and shrub species, or
construction of sustainable urban drainage systems, including ponds and
reservoirs (Williams et al 2010). The positive associations between the
susceptibility to flooding and the proportion of informal open spaces and formal
recreation sites in LSOAs (table 4) suggest that these types of green spaces
could potentially be used to provide the functions which help to manage surface
water flooding. Where green space is absent, and the density of development
does not allow for provision of new green areas, green roofs could provide an
additional measure to reduce surface water flooding. Gill et al (2007) found that
adding green roofs in high density residential areas can reduce total runoff by up
to 20%.
Williams et al (2010) recommend removal of non-porous driveways and
restoration of green front gardens to reduce the risk of surface water flooding.
This is particularly important in Greater Manchester as domestic gardens account
for nearly one-fifth of the entire conurbation, and in many suburban areas they
are the main type of green space (Gill et al 2007). However, gardens are often
lost as urban areas densify. For example, Pauleit et al (2005) observed a 5%
loss in garden areas in Merseyside between 1975 and 2000 due to gardengrabbing by developers. The role of vegetation in surface water management
recognised by Gill et al (2007) emphasises the importance of ‘leafy suburbs’
remaining as such, and calls for the introduction of anti-densification measures.
The recent policy change in the UK removes gardens from the list of land use
types classified as brownfield land, where development is encouraged (CLG,
2010); therefore, their further loss may be slowed down or stopped.
The GIS analysis at the conurbation scale can successfully highlight areas that
will require action in relation to climate change related risks in the urban
environment (Lindley et al 2006). However, it needs to be emphasised that the
results of the statistical analysis identified the principal reasons for vulnerability
33
in a given area, rather than providing a detailed picture of the community
characteristics. Thus, the use of principal components allows for strategic
planning of adaptation actions across the conurbation, and a more detailed
property-level analysis of vulnerability is needed to provide assistance to
individual people and households. Similarly, more detailed flood modelling,
incorporating velocity of flood water and debris content (see DEFRA and EA,
2006) for smaller areas, and also including the drainage system response under
flooding scenarios less extreme than the 1 in 200 years event, which considered
here due to the paucity of other data, would be advisable to estimate the
magnitude of hazard more precisely.
4.2
Concluding remarks
This research has helped to identify spatial patterns and associations between
elements of hazard, vulnerability, exposure and risk across Greater Manchester,
which could be indicative of a situation in other cities. Whilst different aspects of
vulnerability have been identified, it was found that particularly for poor and
diverse communities vulnerability coincides with the occurrence of the hazard of
surface water flooding, and is exacerbated by exposure factors linked to land use
characteristics and housing types. Surface water flooding already causes
considerable damage in England (Evans et al 2004) and Greater Manchester
(Smith and Lawson 2011), and the continuing urbanisation combined with the
projected future climate changes highlight that the problem of surface water
flooding is likely to be grow (Cavan 2010; Evans et al 2004; Pitt 2008), and
therefore urgent adaptation responses are needed. These should take into
account the social and demographic characteristics of populations, together with
the land use and housing characteristics of the areas they occupy, in order to
develop responses tailored to communities and considering both land use and
housing improvements.
Acknowledgments
The authors would like to acknowledge the Environment Agency for providing
surface water flooding data, and Ordnance Survey for the use of spatial data,
including MasterMap.
34
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