Journal of Geographic Information System, 2010, 2, 129-146
doi:10.4236/jgis.2010.23020 Published Online July 2010 (
Copyright © 2010 SciRes. JGIS
An Information System for Risk-Vulnerability
Assessment to Flood
Subhankar Karmakar1, Slobodan P. Simonovic2, Angela Peck2, Jordan Black2
1Centre for Environmental Science and Engineering, Indian Institute of Technology Bombay, Mumbai, India
2Department of Civil and Environmental Engineering, The University of Western Ontario, London, Canada
Received May 18, 2010; revised June 20, 2010; accepted June 25, 2010
An exhaustive knowledge of flood risk in different spatial locations is essential for developing an effective
flood mitigation strategy for a watershed. In the present study, a risk-vulnerability analysis to flood is per-
formed. Four components of vulnerability to flood: 1) physical, 2) economic, 3) infrastructure and 4) social;
are evaluated individually using a Geographic Information System (GIS) environment. The proposed meth-
odology estimates the impact on infrastructure vulnerability due to inundation of critical facilities, emer-
gency service stations and bridges. The components of vulnerability are combined to determine an overall
vulnerability to flood. The exposures of land use/land cover and soil type (permeability) to flood are also
considered to include their effects on severity of flood. The values of probability of occurrence of flood,
vulnerability to flood, and exposures of land use and soil type to flood are used to finally compute flood risk
at different locations in a watershed. The proposed methodology is implemented for six major damage cen-
ters in the Upper Thames River watershed, located in the South-Western Ontario, Canada to assess the flood
risk. An information system is developed for systematic presentation of the flood risk, probability of occur-
rence of flood, vulnerability to flood, and exposures of land use and soil type to flood by postal code regions
or Forward Sortation Areas (FSAs). The flood information system is designed to provide support for differ-
ent users, i.e., general public, decision-makers and water management professionals. An interactive analysis
tool is developed within the information system to assist in evaluation of the flood risk in response to a
change in land use pattern.
Keywords: Flood Management, Flood Risk, Geographic Information System, Risk Management,
Vulnerability Analysis, Information System
1. Introduction
Records of loss of life and damage caused by floods
worldwide show that these have continued to rise stead-
ily during recent years. Understandably, the response has
been to call for increased efforts to protect life and prop-
erty. The sustainable and effective management of floods
demands a holistic approach—linking socio-economic de-
velopment with the protection of natural ecosystems and
appropriate management links between land and water
uses. It is recognized that a watershed is a dynamic sys-
tem in which there are many interactions between human
population, land use and water bodies. Assessment and
mapping of “flood risk” [1-7] and “vulnerability to flo-
od”, and dissemination of the appropriate information to
different stakeholders is a very important part of the flo-
od management process. The general public may use the
information in purchasing a house, or in selecting a site
to start a business. Knowledge of flood risk could aid dec-
ision-makers in: developing land development plans and
land use zoning; planning emergency response strategies;
waste disposal site selections; preparing infrastructure
budgetary decisions; developing guidelines for operation
of existing infrastructure; and general policy develop-
ment at all levels. Water management professionals can
utilize the flood risk information in planning, design,
construction, and operation & maintenance of flood pro-
tection infrastructure (e.g., reservoirs, dikes, drainage
pipes, etc). Flood risk mapping has been performed ex-
tensively for effective flood management, starting with
the pioneering work of Garrett [8]. The risk of flooding
to towns and villages along 200 km of the River Thames
Copyright © 2010 SciRes. JGIS
and its tributaries are assessed using a mathematical
model developed for Thames Water Rivers Division, UK.
The River Thames Strategic Flood Defence Initiative
examines the vulnerability of floodplain development
along the river. The achievements of the Flood Risk
Mapping Program, New Brunswick, Canada, are sum-
marized by Burrell and Keefe [9]. The procedures used
to produce flood risk maps are outlined very clearly
along with an assessment of the accuracy achieved.
Floyd [1] performs a flood risk assessment on the city of
Bombay (Mumbai), India. The results provide an initial
indication of the cost-effectiveness of different remedial
measures. Morris and Flavin [10] present maps of Eng-
land and Wales showing the built-up areas that would be
at flood risk. Shrubsole [11] mentions government re-
sponsibilities in flood management of the Saguenay and
Red River valley and provides alternative flood man-
agement strategies considering ecosystem management,
partnerships and the role of science. Hall et al. [12]
represents the processes of fluvial and coastal flooding
over linear flood defence systems in sufficient detail to
test alternative policy options for investment in flood
management. Potential economic and social impacts of
flooding are assessed using national databases of flood-
plain properties and demography. A case study of the
river Parrett catchment and adjoining sea defences in
Bridgwater Bay in England demonstrates the application
of the method and presentation of results using Geo-
graphic Information System (GIS). Barredo et al. [13]
aims to illustrate a framework for flood risk mapping at
pan-European scale produced by the Weather-Driven
Natural Hazards (WDNH). The threatening natural event
is represented as the hazard component, and furthermore,
exposure and vulnerability are considered as anthropo-
genic factors that contribute also to flood risk. The flood
risk is considered on the light of exposure, vulnerability
and hazard, and mathematically considered as product of
hazard, exposure and vulnerability.
Vulnerability assessments have been undertaken to un-
derstand the “potential for loss”, traditionally they focu-
sed on the nature of the hazard and who and what are
exposed [14]. More recently, vulnerability assessments
have explored the social, economic, and political condi-
tions that are likely to affect the capacity of individuals
or communities to cope with or adapt to hazards [15].
Bender [16] discusses the development and use of natu-
ral hazard vulnerability assessment techniques in the
Americas. He emphasizes how and why a thorough vul-
nerability analysis is required for physical, economic and
social planning in a watershed. There are numerous
studies that have addressed contemporary vulnerability
of different communities worldwide to flooding from the
natural hazards perspective of understanding exposure
and the number of people and structures affected [17,18]
but few that explore the socio-economic aspects of flo-
oding vulnerability [19-22]. Recently, the conceptualiza-
tion on social vulnerability has gained prominence in the
literature. It is related to characteristics that influence an
individual’s or group’s ability or inability to anticipate,
cope with, resist, and recover from or adapt to any ex-
ternal stress such as the impact of flooding [23-25]. Cut-
ter et al. [26] present a method for assessing vulnerabil-
ity in spatial terms using both biophysical and social in-
dicators. Their results suggest that the most biophysically
vulnerable places do not always spatially interact with
the most vulnerable populations. Flax et al. [27] develop
a risk-vulnerability assessment methodology named as
Community Vulnerability Assessment Tool (CVAT),
which assists emergency managers in their efforts to re-
duce vulnerabilities through hazard mitigation, compre-
hensive land use and development planning. Cutter et al.
[28] list factors that have gained consensus among social
scientists as contributing to social vulnerability to envi-
ronmental hazards. Blong [29] introduces a new damage
index for estimating the replacement cost of damaged
buildings in vulnerability analysis. Carter [30] analyzes
flood risk as a combination of threat, consequence, and
vulnerability. He discusses the federal role in investment
decisions for flood control infrastructure. Chakraborty et
al. [31] develop two new quantitative indicators, i.e., a
geophysical risk index, based on National Hurricane
Center and National Flood Insurance Program data, and a
social vulnerability index, based on census information.
Rygel et al. [32] focus on constructing a social vulner-
ability index and its application to a case study of hurri-
cane storm hazard. They demonstrate a method of ag-
gregating vulnerability indices for different indicators
using Pareto ranking that results in a composite index of
vulnerability, which avoids the problems associated with
assigning weights. Werritty et al. [33] discuss the social
impacts of flood events in Scotland including attitude
and behavior toward flooding events, warnings, evacua-
tions and consequences. The study suggests ways for
enhancing social resilience for sustainable flood man-
agement in Scotland.
GIS is considered as a key tool by many researchers
[34-38] to map the spatial distribution of flood risk and
vulnerability to flood. A GIS facilitates the input, storage,
management, analysis, integration, and output of spatial
data which can aid with real time decision making and
strategic planning for effective risk management and
hazard preparedness [39]. GIS can improve warning,
evacuation, and emergency response systems by helping
route emergency response vehicles and locating emer-
gency response facilities [39-40]. Exposures of soil and
geology to flood, urban infrastructure, and socioeco-
nomic data, can be input and stored in a GIS and then
analyzed to identify areas prone to flood, identify vul-
nerable populations, and forecast flood events, and aid in
land use zoning decisions to improve flood mitigation
and management [17,39].
Copyright © 2010 SciRes. JGIS
The flood risk mapping and analysis on various flood
prone watersheds have been performed by many resea-
rchers throughout the world. In a recent study on Roman-
ian national strategy, Varga et al. [41] provide basic in-
formation for preliminary flood risk assessments and
flood hazard mapping in all areas with a significant flood
risk, according to the Flood Directive. The technical and
scientific approach and the main steps in setting up the
plan for flood prevention, protection and mitigation at
the river basin level are presented. Apel et al. [7] per-
form flood risk analyses in Eilenburg, Germany, espe-
cially in urban areas and tested a number of combina-
tions of models of different complexity both on the haz-
ard (probability of occurrence) and on the vulnerability.
Chang et al. [42] examine the anthropogenic and natural
causes of flood risks in six representative cities in the
Gangwon Province of Korea. Tran et al. [43] explore the
impacts of floods on the economy, environment and so-
ciety; and tries to clarify the rural community’s coping
mechanism to flood disasters in Central Viet Nam. They
reveal that flooding is an essential element for a coastal
population, whose livelihood depends on productive
functions of cyclical floods. Forster et al. [44] assess
flood risk for a rural detention area, alongside the Elbe
River in Germany. They find that the losses in agricul-
tural areas exhibit a strong seasonal pattern, and the
flooding probability also has a seasonal variation. The
flood risk is assessed for a planned detention area based
on loss and probability estimation approaches of differ-
ent time frames, namely a monthly and an annual ap-
The present research study is motivated by the Hots-
pots project [45,46] completed by the Center for Hazards
and Risk Research (CHRR) at Columbia University and
the World Bank’s Disaster Management Facility [DMF),
now the Hazard Management Unit (HMU). In the Hot-
spots project, the risk levels are estimated by combining
hazard or probability of occurrence with historical vuln-
erability for two indicators of risk—population and
Gross Domestic Product (GDP) per unit area—for six
major natural hazards: earthquakes, volcanoes, landslides,
floods, droughts, and cyclones. The relative risks for
each grid cell, rather than country as a whole, is calcu-
lated at sub-national scales. Such information can inform
a range of disaster prevention and preparedness measures,
and development of long-term land-use plans and multi-
hazard risk management strategies. Hotspots global
analysis and case studies stimulate additional research,
particularly at national and local levels. The present
study develops an information system for risk-vulner-
ability analyses to flood and facilitates vulnerability
mitigation by providing various flood information to
different users. The information system is designed to
provide selective access to information on the bases of
user needs. This reduces the misuse of data and promotes
data security. A set of suitable vulnerability indicators
and the procedure for their integration into an overall
vulnerability index with high spatial density represent the
major analysis tool within the information system. The
additional innovations of the information system include:
1) the computation of selected flood risk-vulnerability
indicators organized into themes from four components
of vulnerability to flood, i.e., physical, economic, infra-
structure, and social vulnerability [15], 2) the spatial in-
frastructure vulnerability analysis to flood due to inunda-
tion of main communication routes and road bridges, 3)
the spatial flood impacts due to inundation of critical
facilities (schools, hospitals, and fire stations) and 4)
quantification of exposures of land use/land cover and
soil permeability to flood. The postal codes or Forward
Sortation Areas (FSA) are considered for spatial discre-
tization of the region and flood risk evaluation. An in-
teractive analysis tool is also developed for calculation of
flood risk as a function of change in land use. The pro-
posed information system is implemented to six major
damage centers in the Upper Thames River watershed,
located in the South-Western Ontario, Canada. The study
focuses only on floods which are caused by the overflow
of river banks that are characteristics for the region of
As a prerequisite, some relevant technical definitions
are provided in the next section. The third section cont-
ains a detailed description of the study area—the Upper
Thames River basin in South-Western Ontario, Canada.
The fourth, fifth and sixth sections provide the details on
determination of probability of occurrence, vulnerability
and exposures of land use and soil permeability to flood,
respectively; and summarize the results obtained. The
seventh section describes the features of developed in-
formation system for risk-vulnerability analyses to flood.
The eighth section summarizes the conclusions from the
2. General Definitions
The most common approach to define “flood risk” is the
definition of risk as the product of “hazard”, i.e., the phy-
sical and statistical aspects of the actual flooding (e.g.,
return period of the flood, extent and depth of inunda-
tion), and the “vulnerability”, i.e., the exposure of people
and assets to floods and the susceptibility of the elements
at risk to suffer from flood damage [7,47,48]. This defi-
nition is adopted in the Flood Directive [49]. According
to Forster et al. [44], flood risk is a combination of po-
tential damage and probability of flooding. More pre-
cisely, risk is considered as the product of hazard and
vulnerability of a region [47]. However, in this study
flood risk is the product of probability of occurrence (pe),
vulnerability to flood (V), and exposures of land use
(ELand) and soil permeability (ESoil) to flood, following
Copyright © 2010 SciRes. JGIS
the concept of Kron [50] and Barredo et al. [13], where
flood risk is expressed as a function of the hazard, vul-
nerability and exposed values:
)()()( SoilLand
 (1)
Hazard may be defined as a threatening event, or the
“probability of occurrence (pe)” of a potentially damag-
ing phenomenon within a given time period and area [31,
47]. It frequently encompasses hydrological and hydrau-
lic analyses and the mapping of flood lines on floodplain.
Vulnerability to flood (V) is defined as a measure of a
regions’ or population susceptibility to flood damages
[51-53]. Exposures of land use (ELand) and soil perme-
ability (ESoil) to flood quantify their effect on the severity
of flood. When the exposures of land use/land cover and
soil permeability to flood are considered, these denote
how land use and soil permeability affects the severity of
flood. For example, urbanized land use pattern results in
an impervious soil layer increasing the severity of flood
and thus the exposure of land use pattern to flood in an
urban area is high. The exposure of soil permeability to
flood is also directly related to flood flow. The more
permeable soil has more infiltration capacity and there-
fore reduces surface runoff, whereas less permeable soil
has less infiltration capacity and is more prone to water
logging [54].
In the present study, all the information on the above
mentioned three components of flood risk are effectively
presented and processed using GIS. The layout for col-
lecting and integrating the data, along with the sequential
procedural steps for data processing and representation
are outlined in Figure 1. The vulnerability section in
Figure 1, illustrates the concept of layering data using a
GIS, as well as combining vulnerability components to
assess the overall vulnerability to flood. The next section
presents the detailed characteristics and geography of the
study area.
Impact of Exposures Vulnerability
Flood Risk
Risk Components
Land Use Pattern
Soil Drainage
Flood Lines
- 100 yr
- 250 yr
Risk Indices
General Public
(Descriptors –
(Descriptors - age, differential, access,
household structure, social status,
ethnicity, economic)
Layer 1
Layer 2
Layer P
Layer 1
Layer 2
Layer E
Layer 1
Layer 2
Layer I
Layer 1
Layer 2
Layer S
(Descriptors –
(Descriptors -
critical facilities,
Vulnerability to Flood
Figure 1. Organization of the flood information system.
Copyright © 2010 SciRes. JGIS
3. Study Area
The Upper Thames river basin lies in the middle of south
western Ontario; drains an area of 3,500 km2; and is
populated by approximately 422,000 people. The basin is
nested between the Great Lakes Huron and Erie. The
basin has a well documented history of flooding events
dating back to the 1700s. A detailed map of the Upper
Thames River watershed in Ontario, Canada with a loca-
tion map (inset) is shown in Figure 2. Two main tribu-
taries of the Thames River, referred to as the North
(1,750 km2) and South (1,360 km2) branches, join at a
location in London known as “The Forks” (Figure 2).
The Forks region has served as a historical landmark for
London, and is characterized by both commercial and
residential structures. Major flood damage centers in the
watershed include communities of London, St. Marys,
Ingersoll, Mitchell, Stratford and Woodstock. The Upper
Thames river basin is an area of special importance for
the sustainable socio-economic development of Ontario.
This is a large and fertile area, and plays an important
role in agriculture production from, fishing and aquacul-
ture to perennial fruit trees. The flooding in the Upper
Thames river watershed has the great effect on the fertil-
ity of soil and increase in the natural aquatic production.
It is also the most dangerous natural disaster hazard af-
fecting the socio-economic development and the life of
the people in the area. Several studies have already been
done to estimate the economic damage in the watershed
due to flooding [55].
U T River Watershed
The Forks of the
Thames River in
7 3.5 0 kms
Figure 2. Detailed map and location map (inset) of the Upper Thames River watershed (Source: <http://www.thames->).
Copyright © 2010 SciRes. JGIS
The study area consists of a number of major postal
code regions, some of which extend beyond the water-
shed bo- undaries. The Forward Sortation Areas (FSAs)
are distinguished by the first three characters of their
postal code designation. The regions which historically
experience more frequent flooding events are selected as
areas of particular significance and the FSAs of these
regions are selected for analysis. A total of 25 FSAs from
these cities have been considered in the study, and are
listed in Table 1. These FSAs are the smallest spatial
geographic units considered in this study. The cities of
important FSAs include London (17 FSAs), Woodstock
(3 FSAs), Mitchell (1 FSA), St. Marys (1 FSA), Ingersoll
(1 FSA) and Stratford (2 FSAs); with a particular em-
phasis on the city of London. Table 1 shows that the
largest FSA by area in London is N6P; with an area of
103 km2 and in the entire study area is N0K; with an area
of 1510.0 km2. The smallest FSA by area in London is
N6B; with an area of 3.2 km2, and in the entire study
area is N4Z; with an area of 0.5 km2. The average area of
an FSA in London is 29.5 km2 and the average area of all
FSAs in the study area is 122.2 km2.
Numerical data necessary for the development of a
flood information system has been collected from Statist-
ics Canada, which provides updated national statistics
consistently every five years following a Census of the
population. Data is available for areas of various sizes,
including FSAs as small census divisions which remain
relatively stable over many years. Various layers and
datasets compatible with the GIS software are collected
from Statistics Canada, The Ontario Fundamental Data-
set, Upper Thames River Conservation Authority (UTR-
CA), Surficial Geology of Southern Ontario (SGSO)
dataset, and Route Logistics (RL). These datasets are
available online or obtained from the Serge A. Sawyer
map library and the Internet Data Library System (IDLS)
at the University of Western Ontario (UWO), London,
Canada. Table 2 presents the detailed information on
different data layers with their formats and sources used
in the present case study. All three components of the
flood risk are assessed and represented in the information
system separately in dissimilar ways. Each component is
represented graphically, numerically, or using a combi-
nation of both. Next two sections describe the method-
ologies for determination of probability of occurrence
and vulnerability to flood.
4. Probability of Occurrence
Probability of occurrence of flood (pe) [31] describes a
physical threat from a flood occurring and a region beco-
ming inundated. The consideration of probability of occu-
rrence as a flood risk component is essential, since flood
Table 1. List of the municipalities and FSAs (PSEPC* 2005).
Municipality FSAArea
(in km2)Municipality FSAArea
(in km2)
N5V46.7 N6L38.0
N5W15.7 N6M28.7
N5X27.0 N6N70.0
N5Z11.0 Mitchell N0K1510.0
N6A7.1 N4S326.0
N6B3.2 N4T4.1
N6E20.6 St. Marys N4X327.0
N6G25.8 N4Z0.5
N6H41.5 Stratford N5A233.0
N6K28.4 Ingersoll N5C149.0
*Public Safety and Emergency Preparedness Canada.
Table 2. Information on data layers used in GIS.
Indicator Source* Layer Type
Wetlands OFD wetlands_unitPolygon
Roads OFD road Line
Railways OFD transport_line Line
Unpaved roads OFD road Line
Intersections RL ren Point
Critical facilities OFD building_symbolPoint
Bridges RL rll Line
Land use RL ONland_use Polygon
Soil permeability SGSO sgu_poly Polygon
100-year flood line UTRCA -- Line
250-year flood line UTRCA -- Line
Grid BM obmindex Polygon
FSAs RL zip Polygon
*OFD—Ontario Fundamental Dataset (OGDE-Alymer_Guelph.mdb)
from the Serge A. Sauer Map Library at UWO; RL—CanMaps Route
Logistics, 2006 dataset from the UWO IDLS; SGSO—Surficial Geolo-
gy of Southern Ontario, 2003 cd-rom dataset from Serge A. Sauer Map
Library aUWO; UTRCA—the layers obtained from the Upper Thames
River Conservation Authority in 2007; BM—Ontario Base Map Sheet,
2001 from the UWO IDLS.
risk of a highly vulnerable population to flood is neglig-
ent if there is less probability or chance of occurrence of
flood. The probability of occurrence (i.e., the extreme
events and associated probability) of flood is also termed
as “flood hazard” by many researchers [7,13,47,50,56].
In the present study however, the available results on pe
performed by the UTRCA are used. Already available
100-year and 250-year flood lines are used for risk-vul-
nerability analysis.
Copyright © 2010 SciRes. JGIS
The probability or likelihood of occurrence of flooding
is described as the chance that a location will be flooded
in any one year. For example, 1.3% chance of flooding
each year implies 1 in 75 chance of flooding at that loca-
tion in any year. Exceedance probability (pe) of a flood is
represented as [31,57]:
)(1][ xFpxXP e
 (2)
where F(x) denotes the value of Cumulative Distribution
Function (CDF) of river flow x. The return period (Tx) of
flood flow x is the reciprocal of exceedance probability,
which is mathematically represented as [57]:
 (3)
A flood line of a particular return period is the extreme
boundary of the region exposed to a flood of the same re-
turn period. It represents the spatial extent of threat from
the flood of a particular return period. The flood lines for
a particular return period are evaluated by using physical,
hydraulic and hydrologic characteristics of a particular
location in the watershed. The present study utilizes 250-
year flood line data for all FSAs being considered and
100-year flood line data for FSAs within the City of Lon-
don, as per the availability. A flood hazard map with 100
and 250-years flood lines is used as one of risk compon-
ents depicting spatial extent of flooding with exceedance
probability of 0.01 and 0.004, respectively. The follow-
ing procedural steps are followed in GIS for incorporat-
ing the information on probability of occurrence in this
study: 1) the FSA, 100 and 250-years flood line shapefile
layers are imported into ArcMap [58]; 2) the FSA layer
and 100-yr flood line layers are turned “on” so that they
are displayed in the Data Viewing window; 3) twenty
five FSAs of interest in this study are highlighted using
the selection tool and then converted into their own layer
(FSA layer of interest); 4) these map features are then
observed in the “layout” view where it is possible to in-
sert map elements such as north arrows, legends and
scale bars using the Insert Map Elements feature; 5) the
map is then exported to “.jpeg” format. The same proce-
dure is repeated for the 250-year flood line layer.
5. Vulnerability to Flood
Vulnerability to flood is defined as measure of a region’s
susceptibility to flood damage [51-53]. It also includes
population susceptibility to physical, mental or emotional
damage due to flooding. Vulnerability could be influen-
ced by individual emotions, seriousness of the current
situation, and previous experiences with natural disasters.
Traditionally, vulnerability has considered only biophy-
sical factors. More recently, social factors have also been
incorporated into assessment of vulnerability to disasters
In this study, vulnerability to flood has been defined as
a combination of four distinctive types of vulnerabilities:
physical, economic, infrastructure and social [15]. The
physical vulnerability generally incorporates only those
indicators susceptible to biological sensitivity. Wetlands
are for example, considered regions of physical vulnera-
bility in this study. Wetlands are among the most produ-
ctive ecosystems on earth. The richness of these transi-
tional ecosystems relates mostly to the diversity of eco-
logical niches created by the variability of seasonal and
interannual cycles. Modifications in the hydrologic re-
gime that disturb these cycles have been found to be the
main stress factor threatening shoreline wetlands in all
the world's major rivers [59]. The regulation of water
levels has also caused the shrinkage of wetlands, and an
incidental reduction in the diversity of plant communities
and the number of plant species [60]. These regions have
high biodiversity and sensitive life, which would experi-
ence higher damages, longer, slower recovery times due
to flooding. Economic vulnerability includes flood dam-
age indicators which can be expressed in monetary terms.
Infrastructure vulnerability includes civil structure such
as road networks, railways, and road bridges. Infrastruc-
ture components are important to movement of popula-
tion, communications, and safety. Their inundation im-
pedes traffic and hinders communications, increasing
stress in the exposed population. Inundation may also
block important emergency routes and cause physical
damage to roads. Social vulnerability focuses on the re-
action, response, and resistance of a population to a dis-
astrous event. Vulnerable population may require special
attention in an evacuation situation for example. The
indicators are chosen based on a review of existing lit-
erature assessing vulnerability to current hazards [25-27,
The vulnerability index (VIi) corresponding to each
indicator for ith FSA is calculated using the following
equation, which standardizes [61] each vulnerability in-
dex value ranging from 0.0 to 1.0 as done by Wu et al.
VI i
where Vmin and Vmax are the minimum and maximum
values of the indicator for all FSAs, respectively, and Vi
is the actual value of the indicator for ith FSA. All physi-
cal, economic, infrastructure (including the numbers of
critical facilities and road bridges) and social vulnerabil-
ity indices are directly calculated using (4). For example,
vulnerability index for ith FSA of the social vulnerability
indicator “Population under 20yrs of age” is calculated
from the data set of 25 values (for 25 FSAs) on populat-
ion under 20yrs of age using (4). The GIS is not used for
determination of economic and social vulnerability indi-
ces, as they are directly calculated in the spreadsheet us-
ing Statistics Canada Census data. The economic and so-
Copyright © 2010 SciRes. JGIS
cial vulnerability maps are created in ArcMap on the
basis of calculated values of vulnerability indices. The
following procedure is followed for calculating the sum
of the length of roadways/railways: 1) roads/railways
layer is imported into GIS; 2) the length of each
road/railway is contained in the attributes table; 3) those
road/railway segments from the attribute table are se-
lected which fall within and intersect a particular FSA; 4)
the “statistics” option from the “length” field in the at-
tribute table options is selected; 5) the program calcu-
lates the sum of lengths and display them in that particu-
lar FSA; 6) the values are stored in a table and finally 4)
is used for determining the vulnerability indi-
ces—“length of roads” and “length of railways”. The
similar procedure is followed for the vulnerability in-
dex—“unpaved roads”, but the unpaved, dirt and gravel
roads are selected for each FSA individually using the
“Select by Location” or “Select by Attributes” tool.
This calculation of vulnerability index using 4) [61]
offers an improvement over the traditional calculation
[26,31,51] of vulnerability index, i.e., dividing all values
by the maximum value, )( max
, as it considers
both the maximum and minimum values and ensures that
the vulnerability indices are within [0, 1] interval and
always non-negative. It is not necessary to use the scale
[0, 1] for standardization. Montz and Evans [25],
Grosshans et al. [54] and Odeh et al. [62] standardize the
values of vulnerability and plotted the maps considering
[0, 10], [0, 5] and [0, 100] scales, respectively. In the
assessment of infrastructure vulnerability, the present
study considers: a) the impact of flooding of critical fa-
cilities (schools, hospitals, and fire stations) and b) the
spatial impact of flooding of main communication routes
and road bridges. The developed methodologies for con-
sidering these impacts are discussed in next two subsec-
tions. The main objectives of the analysis presented in
these two subsections are: 1) to model the impact of in-
undation of critical facilities (e.g., schools, hospitals and
fire stations) of an FSA on its total infrastructure vulner-
ability, which is achieved by considering the “number of
critical facilities (i.e., number of schools or hospitals
within an FSA)” as vulnerability indicators [determined
by using 4), as done for physical, economic, social and
other infrastructure vulnerability indicators]; and 2) to
model the impact of inundation of an FSA (which may
contain one or more than one critical facilities) on its
surrounding FSAs, which is modeled using a grid system.
There are numerous studies that have addressed the im-
pact of inundation of critical facilities on infrastructure
vulnerability based on the number of critical facilities
within an FSA [27,51,62], but none that explore the im-
pact on surrounding FSAs, because people dwelling out-
side the flooded FSA also may depend on the critical
facilities situated within the flooded FSA. This impact on
infrastructure vulnerability of surrounding FSAs is mod-
eled using a grid system.
5.1. Infrastructure Vulnerability Due to
Inundation of Critical Facilities
Vulnerability of critical facilities is an indicator of infra-
structure vulnerability [27,51,62]. Emergency shelters,
nursing homes, public buildings, schools, hospitals, fire
and rescue stations, police stations, water treatment or
sewage processing plants, utilities, railroad stations, air-
ports and government facilities are identified as critical
facilities by Odeh et al. [62] and Flax et al. [27]. Critical
facilities are those that play an integral role in public
safety, health, and provision of aid [27]. As per the
availability of data, the critical facilities considered in
this study include schools, fire stations and hospitals, and
are given special attention in vulnerability analysis in
order to provide a more accurate estimate of flood risk.
Schools can be used for both education and as a place
of refuge and a center for aid distribution during a flood.
Fire stations provide the source of response to an emer-
gency in the area near the station, and aid in disaster re-
lief. Flooding of a fire station causes the population in
close proximity to be more vulnerable. Hospitals repre-
sent another type of critical facilities that require special
attention during flooding. Hospitals may have patients
that need special attention in the case of an emergency.
Procedure for assessment of infrastructure vulnerability
due to inundation of critical facilities includes the use of
a GIS tool. As per the availability of data, the FSAs of
London are considered for the demonstration of the
methodology. The impact of inundation of critical facili-
ties of an FSA on its total infrastructure vulnerability is
determined by considering the “number of critical facili-
ties (i.e., number of schools or hospitals within an FSA)”
as vulnerability indicators [determined by using 4), as
done for physical, economic, social and other infrastruc-
ture vulnerability indicators]. To model the impact of
inundation of an FSA (which may contain one or more
than one critical facilities) on its surrounding FSAs, a
6x6 grid layer is placed over the FSAs of London, which
breaks the entire city into 36 cells, as illustrated in Fig-
ure 3(a). The size of each grid cell is 25 km2 (5 km × 5
km). The cell area for each FSA is calculated using area
calculation function provided by the GIS tool. The layer
containing the information on critical facilities is placed
onto the grid layer and FSA layer to determine areas
more susceptible to damage. The process used in assign-
ing infrastructure vulnerability due to the inundation of
critical facilities is based on the assumption that the peo-
ple closest to the facility are its primary users. Thus, the
spatial shape, termed as “vulnerability shape” in this
study, is square as shown in Figure 3(b). There are four
different color designations (red, orange, yellow and
white) representing assigned Degree of Importance (DI).
Copyright © 2010 SciRes. JGIS
The presence of just one of these is sufficient to classify
the cell as important. All “important” cells are equally
important. The DI values indicating vulnerability levels
decrease equally in all directions with the distance from
the inundated cell. Procedure implemented using GIS
tool is as follows:
1) Divide the area under consideration into a grid—the
grid should be regular in shape. In this analysis, a 6 6
square grid is used for demonstration purpose.
2) Use the DI to quantify the importance of a critical
facility for each FSA. Red, orange, yellow and white
color codes correspond to 1.0, 0.75, 0.2 and 0.0 DI val-
ues, respectively. The colors are reflecting the DI of each
cell: red (high), orange (medium), yellow (low), white
(no influence), which indicates the importance of the pre-
sence of critical facilities. The grid cells within an FSA
that contain one or more critical facilities are identified.
These grid cells are assigned red color, the highest DI of
1, assuming that the people closest to the facility are
its primary users.
3) Assign a white color, indicating “zero” DI value to
the remaining grid cells. The result is a square-shaped
representation of vulnerability, which decreases with
distance from the red (center) cell.
4) Following the previous three steps, assign DI values
for all grid cells separately for each case of a grid cell
with red color. For example, if 10 (ten) grid cells contain
critical facilities, the grids cells would be assigned ap-
propriate DI values 10 times. Finally, the Overall DI
(ODI) for a grid cell is calculated by averaging these ten
DI values.
5) The vulnerability for an FSA—area shown in bold
solid line in Figure 3(c) —is calculated as:
iFSA ()
where ODIk is overall DI for kth grid cell, Ak is the area of
ith FSA.
Grid line
Area under k
grid cell
grid cell with over all degree of
importance, ODI
Figure 3. Determination of the vulnerability due to inundation of critical facilities and bridges. (a) 6 × 6-grid layered over the
FSAs of London; (b) square vulnerability shape; (c) example FSA region divided in grid cells; (d) vulnerability shapes for
cells with 1-5 bridges; (e) with 6-10 bridges (not to scale).
Copyright © 2010 SciRes. JGIS
6) Determine the standardized vulnerability index val-
ue using:
Vul i
where Vule
max and Vule
min are the maximum and minim-
um vulnerability values of critical facilities, i
Vul is the
value of vulnerability for critical facilities pertaining to
the ith FSA. Thus the standardized infrastructure vulner-
ability values are obtained at FSA level.
5.2. Infrastructure Vulnerability Due to
Inundation of Road Bridges
The infrastructure vulnerability is also affected by the
inundation of road bridges. Vulnerability of an area due
to the inundation of a bridge includes the interruption of
traffic and formation of communication barriers between
different locations in the affected region. Inundation of,
or damage to a particular bridge affects not only the FSA
in which it is located, but also all other FSAs that depend
on the use of the bridge. In this study, only bridges over
the water bodies are considered significant, because the-
se bridges have limited alternate routes associated with
them, and are necessary for safe crossing of the water
body. They are frequently used as means for transporting
commercial goods, a route to and from the workplace,
and as emergency routes in case of a disaster.
The impact of inundation of road bridges of an FSA
on its total infrastructure vulnerability is modeled by con-
sidering the “number of road bridges” as vulnerability
indicator [determined by using 4), as done for physical,
economic, social and other infrastructure vulnerability
indicators]. To model the impact of inundation of an
FSA (which may contain one or more than one road
bridges) on its surrounding FSAs, the same procedure
(steps 1 through 6) as described in previous subsection is
followed, with the use of the new vulnerability shapes as
shown in Figures 3(d) and (e). Again, 6 6 grid is used
in the assessment of vulnerability as shown in Figure
3(a). However, the shape used in assessing vulnerability
due to the inundation of road bridges is not a box, but
rather cross-like. The shape varies with the number of
bridges in any particular grid cell. Figures 3(d) and (e)
illustrate the shapes of vulnerability for cells containing
1-5 and 6-10 road bridges, respectively. The number of
bridges over water that is contained in each cell deter-
mines the shape that would be used in assessment of
vulnerability. As the number of bridges increases, the
more likely it is that inundation of that cell would affect
more people. The vulnerability shape due to inundation
of bridges is mainly based on a basic assumption: the
need for crossing any given bridge decreases with dis-
tance from the bridge (i.e., the need for crossing the
bridge is highest in areas that are closest to the bridge),
because people further away from a particular bridge
may have other alternatives for crossing the water body
with equal convenience. The proposed method assumes
that the whole cell being considered is flooded, and that
bridges in that cell are unavailable for use. The cells are
assigned a DI based on the vulnerability mapping in
proximity to the inundated cell. The DI assignment is
similar to the one used in assessing the infrastructure
vulnerability due to inundation of critical facilities.
However, the road bridges scenario designates a DI as
either red/high (1.0) or yellow/low (0.2) for demonstra-
tion of the proposed methodology. In both analyses it
was assumed that the whole grid cell is equally affected
by the flooding, thus damage is assumed to be uniform
across the cell area. The population density within a por-
tion of the FSA covered by a grid cell is not known.
Therefore a uniform distribution of population is as-
sumed throughout each FSA.
The following procedural steps are followed in GIS
for incorporating the information on infrastructure vuln-
erability due to inundation of critical facilities: 1) the
“buildings” layer is imported; 2) in the attribute table the
type of building is specified. In the options for the attrib-
ute table “select by attribute” is used and then the cate-
gory (e.g., school/hospital/fire station) is used to select
only those buildings which are schools/hospitals/fire
stations; 3) these buildings are saved as a separate layer
for referencing in the critical facilities analysis of the
study. The same procedure is followed for road bridges
but the “bridge” layer is imported.
The calculation of the vulnerability indices following
the procedures described in this section provides input
for mapping each category of vulnerability in GIS. Table
3 shows the values of four components of vulnerability
in the Upper Thames River basin. The seventh column of
Table 3 indicates the standardized average vulnerability
values of four vulnerability components (presented in co-
lumns 3-6) for all FSAs. In physical vulnerability, the
FSA-N0K in Michell is identified as the most vulnerable
due to large wetland areas in the region. The FSAs with
“zero” values in the column of physical vulnerability
indicate absence of wetlands. The FSA–N4S in Wood-
stock is the most vulnerable in economic sense due to the
presence of large number of older houses in this region.
The FSA–N0K in Michell is also identified as the most
vulnerable in regards to infrastructure component due to
its largest land area, which includes the longest road and
railway networks. It is also identified that the FSA–N4Z
in Stratford is the least vulnerable due to the absence of
railway and minimum length of paved and unpaved ro-
ads. The column for social vulnerability shows high val-
ues for most of the FSAs within the city of London due
to high population in these FSAs. The FSA–N5Y is the
most vulnerable in social sense due to high values of
indicators such as “differential access to resources”, “so-
Copyright © 2010 SciRes. JGIS
cial status” and “ethnicity”.
The present study incorporates a unique consideration
of inundation of road bridges and critical facilities in ass-
essing infrastructure vulnerability.
Figure 4 displays the difference in infrastructure vul-
nerability due to inundation of critical facilities and road
bridges. In most cases, the standardized values of infra-
structure vulnerability of the FSA increase with the addi-
tion of impacts due to the inundation of road bridges and
critical facilities. The GIS generated map may be produ-
ced for each component of vulnerability values as pre-
sented in Table 3 for identifying spatial variability of
vulnerabilities to flood. More details of the processed
numerical data and graphical results of the present vul-
nerability analysis to flood can be obtained from Peck
et al. [63].
5.3. Calculation of the Vulnerability to Flood
Although maps of individual component of vulnerability
can be useful, it is easiest to assess vulnerability throug-
hout the watershed if the multidimensional components
can be integrated into a single measure [32]. In the pres-
ent study, the simplest way to combine the four compo-
nents of vulnerability into a single measure would be to
average the values of indices [31,51] for each component
[as given in the seventh column of Table 3]. Figure 5
shows the GIS generated map of vulnerability to flood
obtained by averaging and standardizing the four comp-
onents of vulnerability. The darker color indicates larger
vulnerability. Map in Figure 5 provides for easy comp-
arison of vulnerability between different FSA regions of
six major damage centers, and insight into the spatial
Table 3. Vulnerability analysis to flood of the Upper Tham-
es River basin.
Components of Vulnerability
to Flood
Center FSA
Phy.Eco.Infras. Soc.
vul* Rank
N6A0.000 0.264 0.432 0.383 0.29615
N6B0.000 0.282 0.467 0.399 0.31612
N6C0.014 0.613 0.477 0.794 0.4986
N6E0.000 0.128 0.256 0.767 0.30313
N6G0.000 0.495 0.556 0.703 0.4719
N6H0.015 0.828 0.356 0.784 0.5035
N6J0.000 0.876 0.447 0.732 0.29614
N6K0.004 0.457 0.299 0.613 0.5274
N6L0.000 0.000 0.022 0.000 0.35311
N6M0.009 0.234 0.172 0.012 0.00025
N6N0.044 0.004 0.055 0.020 0.11019
N6P 0.005 0.084 0.089 0.072 0.03222
N5V0.002 0.815 0.305 0.813 0.06120
N5W0.000 0.515 0.421 0.608 0.4868
N5X0.034 0.288 0.293 0.390 0.40510
N5Y0.001 0.707 0.461 1.000 0.26816
N5Z0.005 0.539 0.502 0.736 0.5623
Michell N0K1.0000.7491.000 0.632 1.0001
N4S 0.000 1.000 0.510 0.715 0.5722
N4T0.000 0.110 0.011 0.082 0.04121
N4V0.004 0.027 0.008 0.030 0.01023
St. MarysN4X0.0020.2660.299 0.181 0.20018
N4Z0.000 0.048 0.000 0.021 0.00924
Stratford N5A0.134 0.673 0.384 0.690 0.4947
IngersollN5C0.092 0.293 0.275 0.261 0.24917
*Overall vulnerability: standardized average values
4 8
12 16
0.00 – 0.09
0.10 – 0.29
0.30 – 0.49
0.50 –0.69
0.70 – 0.89
0.90 – 1.00
(a) (b)
Figure 4. Infrastructure vulnerability for the FSAs of London. (a) infrastructure vulnerability not considering the impact
of critical facilities and bridges; (b) infrastructure vulnerability including the critical facilities and bridges.
Copyright © 2010 SciRes. JGIS
variability of vulnerability. It is identified from Table 3
and Figure 5 that FSA–N0K in Michell is the most vul-
nerable in the basin, as the area under this postal code is
much larger than other FSAs and consequently it con-
tains a number of wetlands and more infrastructure. It is
found that the vulnerability values for FSAs in London
vary between 0 and 0.562. The FSA–N5Z is identified as
the most vulnerable, whereas N6M is identified as the
least vulnerable within the city of London. The Table 3
and Figure 5 give a general description of region’s vul-
nerability, and can be used for emergency flood man-
agement, disaster mitigation activities and planning fu-
ture disaster protection infrastructure.
It should be noted for clarification that, there may be
some correlation among vulnerability indicators under
different components of vulnerability. In this study the
chance of involving correlated indicators is very less, as
the indicators are chosen based on a review of existing
literature [25-27,31,32,51]. If the number of indicators is
too high Principal Components Analysis (PCA) [32] can
be applied to select the set of uncorrelated indicators.
The basic aim of a PCA is to reduce a complex set of
many correlated variables into a set of fewer, uncorre-
lated components.
Ingersol l
St. Marys
0 kms 12
0.00 – 0.09
0.10 – 0.29
0.30 – 0.49
0.50 – 0.69
0.70 – 0.89
0.90 –1.00
Figure 5. GIS generated map of standardized average vuln-
erability to flood.
6. Exposures of Land Use and Soil
Permeability to Flood
The present study utilizes 250-year flood line data for all
FSAs and 100-year flood line data for FSAs within the
City of London, as obtained from UTRCA, Canada, for
considering the values of probability of occurrence in the
calculation of flood risk. The impacts of land use and soil
type are not considered during generation of the flood
lines and probability of occurrences. To incorporate the
impact of exposures of land use and soil permeability
into the analysis a separate component of the flood risk is
considered as expressed in (1), following Kron [50] and
Barredo et al. [13]. The indices of vulnerability to flood,
as discussed in previous section, have no influence on
flood flow and river channel characteristics. The expo-
sures of land use and soil permeability are two physical
watershed characteristics which affect the flood flow
[64], and are considered as the important characteristics
of flood risk in the Upper Thames River watershed. This
study considers the impact of exposures of land use and
soil permeability only for those FSAs within the munici-
pality of London as per the availability of data. An ex-
posure value of 1 is assigned to the regions outside of the
City of London.
6.1. Land Use
The land use map used in the present study is obtained
from the UWO Internet Data Library System (IDLS).
Their CanMap Route Logistics 2006 dataset contains the
Ontario land use GIS layer. This layer is designated as
“ONland_use” and is of type “polygon” as mentioned in
Table 2. The available land use data include seven dif-
ferent categories of use: open space, commercial, resi-
dential, parks and recreational, government and institu-
tional, resource and industrial, and water body. Each of
these land use categories has been assigned a DI value.
These values, while estimated by the research team, can
be changed by decision-makers with more extensive kn-
owledge on how different land use influences flood run-
off characteristics. Overdeveloped and highly commer-
cialized areas include more pavement and impervious
surfaces. They increase runoff quantity and shorten the
time of concentration. On the other side, open land (in-
cluding agricultural land) is exposed to direct infiltration
of rainfall which decreases runoff quantity and lengthens
the time of concentration. With this knowledge, the DI
values are assigned to each category of land use, which
are as follows: water body (0.1), parks & recreational
(0.2), open area (0.3), Government and institutional (0.7),
commercial (0.8), residential (0.8), resources & indus-
trial (0.8).
Area under each land use type is expressed as a frac-
tion of the FSAs total area. Summation of the fraction of
Copyright © 2010 SciRes. JGIS
each type multiplied by its DI provides an exposure
value representative of the land use for an FSA. There-
fore, mathematically the exposures of land use to flood
for ith FSA is expressed as:
Eis the exposure of land use to flood, DIl is
the DI of land use type “l”. “l” may be any of the land
use types. Area under each land use type l is expressed as
i for ith FSA. Total area of the ith FSA is denoted as Ai.
6.2. Soil Permeability
Soil permeability refers to the hydrological drainage cha-
racteristic of soil to allow water movement through its
pores, which is inversely proportional to soil density.
The more permeable the soil is, the more water can be
transmitted through it. A soil with low permeability, such
as clay, doesn’t permit much water flow. This could ca-
use “puddling” of water and thus higher accumulation of
water on the soil surface. Regions which are composed
primarily of these types of soils are prone to a higher
flood risk because the water requires a longer time to
drain or infiltrate into the ground [54]. Using a GIS
dataset known as Surficial Geology of Southern Ontario,
it was possible to spatially assess the soil permeability
characteristics of the region. The data is available with
different designations of permeability: low, medium-low,
high or variable. A DI is assigned to each permeability
category based on the ability of soil to infiltrate water,
facilitate its transmission, and decrease flooding. DI val-
ues assigned to each category of soil permeability are as
follows: low (0.8), low-medium (0.6), variable (0.5),
high (0.3).
Area under each permeability category is expressed as
a fraction of the FSAs total area. Summation of the frac-
tion of each category multiplied by its DI provides an
exposure value representative of soil permeability for an
FSA. Therefore, mathematically the exposure of soil
permeability to flood for ith FSA is expressed as:
where Soil
Eis the exposure of soil permeability to flood,
DIP is the DI of permeability category “p”. “p” may be
low, medium-low, variable and high. Area under each
permeability category (p) is expressed as p
for ith FSA.
Total area of the ith FSA is denoted as Ai. The standardi-
zation is being performed following the equation similar
to (4) and (6), expressed as:
where Emax and Emin are the maximum and minimum ex-
posure values pertaining to land use/soil permeability, Ei
is the value of exposure of land use/soil permeability
pertaining to the ith FSA. The following procedural steps
are followed in GIS for incorporating the information on
exposures of soil permeability and land use to flood: 1)
the Ontario Surficial Geology dataset for Zone 17 in On-
tario is imported into ArcMap; 2) the layer features are
symbolized by categorical attributes; 3) to symbolize
each soil type with its own colour, the Layer Properties
dialogue box is opened, “Symbology” and “Categories”
tabs are selected from the left menu, and “unique values”
is highlighted; 4) in the Value Field, “SINGLE_PRI” is
selected, which represents the single primary material of
the soil composition. A colour scheme is selected and
then each soil is represented by its own colour in the
ArcMap data viewer; 5) the “Select by Location” tool of
GIS is used to isolate those soils which fall within a par-
ticular FSA; 6) an area calculation is then performed on
these “soils of interest” which provides the area of each
soil type and the FSA boundaries that the soil area is
within. An additional field is entered into the layers at-
tribute table; the name of the field (column) and its type
(floating point, integer etc.) are specified. All values are
initialized to 0. The calculator option is then selected to
compute the areas of the selected attributes. An advanced
Visual Basics Application (VBA) area statement is used
to calculate the required areas; 7) the type of each soil
can be found in the layers attribute table along with the
soils characteristic permeability which varied from “low”
to “high”; 8) the results are summarized in a table. A
similar procedure is followed for land use, but “cate-
gory” is symbolized by: Commercial, Government &
Institutional, Open area, Parks and Recreational, Resi-
dential, Resource and Industrial, and Water body. The
impacts of exposures of land use and soil permeability to
flood for FSAs of London are tabulated in Table 4. All
Table 4. Impact of exposures of land use and soil perme-
ability to flood.
Impact of Exposures (standardized value)
FSA Based on land use Based on soil permeability
N6A0.7362 0.2139
N6B1.0000 0.0000
N6C0.7562 0.9403
N6E0.4311 0.9497
N6G0.3954 0.5836
N6H0.2481 0.5144
N6J 0.7067 0.8668
N6K0.3339 0.4643
N6L0.0000 1.0000
N6M0.0386 0.7239
N6N0.0125 0.9149
N6P 0.0122 0.7982
N5V0.3049 0.3906
N5W0.7427 0.1372
N5X0.2357 0.3267
N5Y0.8250 0.2760
N5Z0.7938 0.4290
Copyright © 2010 SciRes. JGIS
the values are standardized with in [0, 1] to produce an
indicator scores. Table 4 indicates that the exposure of
land use for FSA-N6B, which is located in the central
part of London, has maximum impact to flood due to
presence of more commercial, residential and industrial
areas; whereas the exposure of soil permeability has
minimum impact to flood, as N6B has high permeable
soil with high drainage capacity. The table also indicates
that the exposure of land use for FSA-N6L, which is lo-
cated in the southern part of London, has minimum im-
pact to flood due to presence of more open and recrea-
tional areas; whereas the exposure of soil permeability
has more impact to flood, as N6L has low permeable soil
with less drainage capacity.
7. Development of the Information System
Providing a website for people to access flood risk infor-
mation is an effective way of informing the public about
the susceptibility to flooding that they may otherwise not
be aware of. The study of Barredo et al. [13] is a contri-
bution to the discussion about the need for communica-
tion tools between the natural hazard scientific com- mu-
nity and the political & decision making players in this
field. The website can serve as an information center and
may provide analysis tools for interactive processing of
available flood information. It also provides the opp-
ortunity to tailor the presentation of the same information
to different types of users according to their needs. Acc-
ording to the program evaluation glossary of USEPA
[65], an information system is an organized collection,
storage, and presentation system of data and other know-
ledge for decision making, progress reporting, and for
planning and evaluation of programs. It can be either
manual or computerized, or a combination of both. The
information from the present risk-vulnerability analysis
to flood is systematically kept in a computerized inform-
ation system for more efficient use. The Adobe Dreamw-
eaver Creative Sweet 3 software (
ap/products/dreamweaver) is used for creating the flood
information system. The whole website is based off the
Cascading Style Sheets (CSS) template provided in
Adobe CS3. The developed information system is easy to
navigate. The process starts by providing access to dif-
ferent FSAs of 6 damage centers in the Upper Thames
watershed. After selecting the damage center, typing in
the first three digits of an FSA will direct the user to in-
formation about that FSA. Selected three digits of the
FSA activate the search engine that is created using a
search engine composer. Information page is available
for every FSA region. The information that is displayed
for all the users; includes maps, numerical data, and an
analysis tool in Microsoft excel spreadsheet format for
calculation of flood risk as a function of change in land
use. After typing the first three digits of an FSA in an
identified cell of the spreadsheet, the user will be di-
rected to the information on flood risk of the FSA, con-
sidering the present land use pattern and area under dif-
ferent categories of land use. Area under each land use
type can be changed by the user to find out the flood risk
under future scenario of land use pattern. It will calculate
risk by using (1).
The prototype information system created for this risk-
vulnerability analysis to flood targets 3 different user
categories: 1) general public, 2) decision-makers and 3)
water management professionals. The general public has
access to a simple explanation of flood risk terminology,
tables providing values of vulnerability to flood and a de-
scription of what they mean, 100-year and 250-year flo-
od lines, as well as a simple analysis tool for flood risk
calculation. Decision-makers are provided with a more
detailed description of flood risk terminology and the
implications of flooding. They have access to the same
flood hazard maps as the general public. Decision-mak-
ers are provided with a more detailed and flexible analy-
sis tool which allows the user to change the land use and
compare the present level of flood risk with the one ob-
tained under changed land use scenario. This may assist
in the analyses of different land development initiatives
and their consequences on flood risk. Water management
professionals are presented with the most detailed de-
scriptions and the most technical flood related informa-
tion. They are provided a very detailed numerical break-
down of vulnerability and exposures of land use and soil
permeability, including a list of all indicators used in the
analyses. They also have access to the flood hazard maps
similar to those provided to the general public and the
decision-makers. The analysis tool available to professio-
nals is the same as one provided to the decision-makers.
The professionals are the only user with access to a “raw
data” containing all of the numerical data used for the
flood risk analyses. Screenshots of the opening page of
prototype information system and analysis tool are
shown in Figures 6(a) and (b). The information system
is user-friendly and the details can be found in Black
et al. [66].
8. Conclusions
The present study analyzes flood risk and vulnerability to
flood in the Upper Thames River basin, Ontario, Canada.
It deals with a large region as a case study with six major
damage centers in the watershed for flood risk mapping
considering probability of occurrence, four components
of vulnerability and exposures of land use and soil per-
meability to flood. The impact of inundation of critical
facilities and road bridges on infrastructure vulnerability
is analyzed. New indices are introduced in the infra-
structure vulnerability to flood, for example—length of
railway, length of road, number of major intersections,
Copyright © 2010 SciRes. JGIS
Figure 6. (a) Opening page of the information system; (b) analysis tool for decision makers.
Copyright © 2010 SciRes. JGIS
number of critical facilities and road bridges. Typically,
exposures of land use and soil permeability have been
included as a component of risk. The minimum and max-
imum values of vulnerability are considered in the stand-
ardization process instead of using the conventional for-
mula for standardization. A user-friendly information sys-
tem is designed to systematically represent all flood info-
rmation. The study provides an “analysis tool” for estima-
tion of flood risk as a consequence of change in land use.
The present study has some limitations and offers so-
me benefits for future development. In the flood inform-
ation system all the indices of infrastructure vulnerability
for critical facilities are not considered due to unavaila-
bility of data. For example, emergency shelters, nursing
homes, public buildings, police stations, water treatment
or sewage processing plants, utilities, railroad stations,
airports and government facilities; which are identified
critical facilities [27,62]. The assignment of Degree of
Importance (DI) for calculation of impact inundation of
important service buildings, emergency service stations
and road bridges across the river on infrastructure vuln-
erability, and in calculation for exposures of land use and
soil permeability is dependent on the perspective of deci-
sion-makers or floodplain planners, which introduces
some uncertainty due to vagueness or imprecision in the
model. This uncertainty due to imprecision in the assi-
gnment of DI may be addressed in the flood risk calcula-
tion. In the present system only two flood lines are ava-
ilable, e.g., 100- and 250-years flood lines, which limit
the calculation of flood risk. The impact of critical facili-
ties and road bridges across the river on infrastructure
vulnerability is calculated only for the City of London as
per the availability of data. The same analysis may be
performed for other damage centers in the watershed. In
future studies, different shapes and sizes of “vulnerabil-
ity shapes” with finer grid system and actual population
density can be considered for determining the impact of
inundation of critical facilities and road bridges. The
impact of climate change is not considered in the current
version of the system. The hazard maps or the position of
flood lines will change if the climate change impacts are
taken into consideration [67]. The values of flood risk for
different postal codes may be easily updated to include
the impact of climate change. No hydrologic calculation
is performed in the present study to find out current posi-
tion of flood lines. A sophisticated hydrologic modeling
may be implemented for finding out the current positions
of flood lines and result in more accurate calculation of
flood risk. The proposed methodologies of flood risk ma-
pping are not limited to the present case study and may
be easily applied to other watersheds.
9. Acknowledgements
The authors sincerely thank the Upper Thames River Co-
nservation Authority, Statistics Canada, Canadian Hom-
ebuyers Guide, Serge A. Sawyer map library & the IDLS
library at The University of Western Ontario for provid-
ing data used in this study. Work presented in this paper
has been conducted under the research grant by the Natu-
ral Sciences and Engineering Research Council of Canada.
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