Quantitative Methods for Estimating Flood Fatalities
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Transcript of Quantitative Methods for Estimating Flood Fatalities
ORI GIN AL PA PER
Quantitative methods for estimating flood fatalities:towards the introduction of loss-of-life estimationin the assessment of flood risk
Manuela Di Mauro • Karin M. De Bruijn • Matteo Meloni
Received: 1 June 2011 / Accepted: 18 April 2012 / Published online: 6 May 2012� Springer Science+Business Media B.V. 2012
Abstract Risk, including flood risk, can be defined as ‘the combination of the probability
of an event and its consequences’. Assessing and managing the risk from flooding should
explicitly include the estimation of impacts to people. Extensive research is currently
ongoing looking at both quantitative and qualitative approaches for assessing flood impacts
on people. Although there is some literature available on such approaches, examples of
methodological and routinely applications of these methodologies as part of flood risk
assessments are rare. This paper focuses on quantitative approaches for estimating impacts
of flooding to people, notably on methods for assessing fatality numbers associated with
flooding. Three methods for assessing losses of life are discussed in detail. The methods
discussed here constitute the forefront of research in Canada, UK and The Netherlands.
These methods provide an assessment of the physical consequences of flooding on people
and can be used to introduce the impacts to people as quantitative metric for the assessment
of flood risk. In this paper, the three methodologies are discussed and applied in a UK case
study reproducing the 1953 East Coast flood event. This study aims to provide a com-
prehensive comparison on both the reliability and the applicability of the methods. We
analyse possible added values on using of these methods in systematic analyses, aiming to
provide guidelines for applying these methods for flood fatality risk assessment.
Keywords Flood risk � Fatalities � Casualties � Loss of life � Flood � Flood damages �Impact assessment � Intangible impacts
M. Di Mauro (&)Earth Observatory of Singapore, Nanyang Technological University, N2-01A-14, 50 Nanyang Avenue,Singapore 639798, Singaporee-mail: [email protected]
M. Di Mauro � M. MeloniHR Wallingford Ltd, Howbery Park, Wallingford, Oxfordshire OX10 8BA, UK
K. M. De BruijnDeltares, PO. Box 177, 2600 MH Delft, The Netherlands
123
Nat Hazards (2012) 63:1083–1113DOI 10.1007/s11069-012-0207-4
1 Introduction
The International Standard Organisation (ISO) defines risk as ‘the combination of theprobability of an event and its consequences’ (ISO 2002). The UK government defines the
risk of flooding as ‘a combination of the probability of flooding and potential flood con-sequences’ (HM Government 2010). Assessing the impact of a possible event requires the
evaluation of possible economic, social, cultural and environmental damages (Samuels and
Gouldby 2009). In this paper, we will focus on the assessment of the direct impact on
people.
Floods may affect people in many ways. In general, evaluating the impacts of flooding
implies assessing direct, indirect and intangible\non-market impacts, where the latter
includes all the losses that ‘cannot be measured in monetary terms’ (Parker et al. 2009).
Assessing impacts on people means looking at the effects that the flood event causes on
people as individuals, such as direct (physical), indirect and intangible impacts (e.g.
psychological impacts), and societal impacts, affecting people and the community
(Messner and Meyer 2006). Societal impacts can be monetary losses (direct and indirect),
such as loss of infrastructure (e.g. schools, hospitals, telecommunication network), and
intangible impacts, such as loss of community cohesion and identity. Environmental losses
can also result in indirect long-term damages to the community, such as damages to
business and resources, whose loss can have serious impact on the communities’ liveli-
hood, but also intangible damages, as they might intrinsically influence community
resilience.
This paper, however, focuses on one aspect only, namely the fatalities caused by
flooding events. The number of fatalities resulting from a flood depends on many factors
such as (De Bruijn et al. 2009; De Bruijn 2010):
• Number of inhabitants in the area
• Possibilities for preventive evacuation (this determines the proportion of the population
present during the flooding)
• ‘Mortality’ of the people present during the flooding, defined as function of:
• Flood characteristics (depth, flow velocity, onset of flooding)
• Possibilities for fleeing or sheltering
• Vulnerability and behaviour of the inhabitants and the environment (type of houses,
health, knowledge of the area, etc.)
This paper discusses different methodologies for estimating flood fatalities, providing
an analysis of the reliability and applicability of the methods, as well as indications for
applying these methods for assessing flood fatality numbers within the context of flood risk
assessments.
The aim of this paper is to appraise methodologies that can be used for estimating
impacts of flooding on people in a rigorous, repeatable, methodical and quantitative
manner. For that, this paper analyses and compares three methods for assessing fatalities
associated with flooding and their use in flood risk assessments.
The rationale for comparing these three methods, despite their differences, lies in the
requirement for each method to provide results that are consistent with the real event as
well as with those produced by other ‘reliable’ methods. The consistency in the results can
be seen as an indicator on the reliability of these methods. This study draws conclusions on
the reliability and applicability of the analysed methods and provides guidelines for
applying them for flood risk assessment and management.
1084 Nat Hazards (2012) 63:1083–1113
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1.1 State of the art of the research on assessing flood casualty figures
The literature on assessing flood casualties figures presents many micro- and macro-scale
approaches for assessing the consequence of flooding to people (e.g. Peduzzi et al. 2009;
European Environment Agency 2001; Ashby et al. 2003) and specifically for evaluating
flood fatality figures (De Bruijn and Klijn 2009; Jonkman et al. 2008; Priest et al. 2007).
Micro-scale methods focus on single individuals. The circumstances and behaviour of
each individual are represented in detail so that the impacts to each person are explicitly
assessed. Examples of this type of model, specifically for floods, are rare and include the
Canadian agent-based Life Safety Model (Johnstone et al. 2006) and the US LifeSim
(Aboelata and Bowles 2005). These models take into account the time-based evolution of
the flood event and the statistics on the population in each sub-area of the modelled
domain. In micro-scale models, the values of the parameters representing the flood impact
to people change as the flood event evolves. These models, to different extents, dynami-
cally account for those who evacuate the area, thus the number of people in the modelled
area also changes as the event evolves.
Macro-scale methods do not focus on individuals but instead study the characteristics of
the population as a whole. They can be applied to a larger area. Some of such methods
were developed for assessing impacts of
• dam breaks; through the use of loss functions that depend on the characteristic of the
flood wave, the warning time, the population characteristics and the dam features
(Aboelata et al. 2003; Graham 1999; RESCDAM 2001)
• tsunamis (Koshimura et al. 2009; Marchand et al. 2009)
• river and coastal flooding, in natural valleys or due to breaches in embankments. In
these methods, the mortality is introduced as a function of the characteristic of the
areas, the receptors and the maximum flood depth and velocity (De Bruijn 2005;
Jonkman et al. 2008; De Bruijn and Klijn 2009; HR Wallingford and Middlesex
University Flood Hazard Research Centre 2006).
Macro-scale models are usually based on the analysis of the most important factors that
determine the number of fatalities (De Bruijn and Klijn 2009) (see Fig. 1). Two types of
macro-scale methods to assess flood casualties can be distinguished: methods based on
expert judgements; and method based on statistical relationships between historical fatality
rate and the characteristics of the flood and the floodplain.
Fig. 1 Factors influencing the number of fatalities due to flooding (source: De Bruijn 2010)
Nat Hazards (2012) 63:1083–1113 1085
123
In the first class of methods, each of the factors in Fig. 1 is estimated per region. Fatality
assessments are made by multiplying the number of inhabitants with the percentage of the area
affected by the flood, the percentage of people who do not evacuate before the flooding and the
fatality rate. Klijn et al. (2007) estimated the average fatality rate for the Netherlands to be
0.3 %. Jonkman et al. (2008) estimated an average worldwide rate to be around 1 %, meaning
that about 1 % of the people exposed to the flooding are killed on average in past events.
A second class of methods includes those based on statistical relationships between the
fatality rate, the flood and the area characteristics. These relationships are derived from past
floods. This approach was implemented in the Risk to People method (HR Wallingford and
Middlesex University Flood Hazard Research Centre 2006) that uses relations between risk
parameters (describing the hazard, the area vulnerability and the people vulnerability) and
the number of casualties, both as injuries and fatalities. Another example of this type of
methods is the set of Dutch Mortality Functions, in which the fatalities occurring in the 1953
flood were related to flood parameters such as water depth, flow velocity and the rate of rise
of water level (Jonkman et al. 2008). Area and people vulnerabilities are not considered in
the Mortality Functions. This means, in fact, that the influence of the area and people
vulnerability to the fatality rate is assumed to be the same now as in 1953.
The methods analysed in this paper are the Mortality Functions method (Jonkman et al.
2008; Kok et al. 2005; Di Mauro and De Bruijn 2012), the Life Safety Model (Johnstone et al.
2006, 2005) and the Flood Risk to People method (HR Wallingford and Middlesex University
Flood Hazard Research Centre 2006). Each method involves applying specific models to
represent the interactions between the flood hazard and the exposed population. Among them,
only the Life Safety Model is implemented in a dedicated stand-alone modelling platform.
These methods were all applied on the 1953 flooding of Canvey Island in the Thames
Estuary. The results of the application of these methods are discussed, together with the
outcomes from the analysis of the sensitivity of the methods to various parameters.
2 Methods for assessing fatalities from flooding
2.1 Approach 1: Mortality Functions method
The Mortality Functions method was derived to estimate the fatalities in areas exposed to
potential flooding caused by the failure of flood defences (Jonkman et al. 2008). These
functions aim to calculate the mortality among the exposed population by relating the
characteristics of the flood event to the expected number of fatalities.
The Mortality Functions were derived based on historic flood events, such as the 1953
flooding in the Netherlands and on expert judgement. The Mortality Functions have been
developed as dose–response functions and divide the flow path into hazard zones.
These hazard zones are typically distinguished as (Jonkman et al. 2008):
• Breach zones that are the zones affected by the highest velocities and the highest
product of depth and velocity;
• Zones with rapidly rising water; and
• Remaining zones.
It is possible to associate a dominant cause of death to each of these zones. This
association was derived from an empirical-based analysis and does not aim to constitute a
one-to-one relationship (Jonkman et al. 2008), but describes the particular combination of
depth and velocities that are likely to cause mortality due to
1086 Nat Hazards (2012) 63:1083–1113
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1. instantaneous building collapse or people not able to stand in the water (breach zones),
2. building collapse, people trapped in the flow path or in flooded buildings (rapid rise
water zones) and
3. extended exposure (remaining zones).
The application of the Mortality Function method involves:
1. Dividing the study area into zones based on the flood characteristics (depth, velocity
and rate of water level raise). These can be assessed through hydrodynamic modelling
or historical data, if available, according to the scenario considered.
2. Applying the Mortality Function appropriate to each zone.
3. Combining the obtained fatality rate with the actual population figures to assess the
expected number of fatalities.
The criteria for identifying the three zones are listed below (Jonkman et al. 2008). Each
criterion has an associated equation to calculate the mortality factor FD. This factor rep-
resents the proportion of fatalities within the exposed population. The total number of
fatalities can be calculated by multiplying this factor by the number of people present in
the flooded area at the onset of flooding.
1. Breach zone: zone with high flow velocities:
FD ¼ 1 if dv� 7 m2=s and v� 2 m=s ð1Þ
where FD is the mortality (the fraction of those killed as a proportion of all the people
present at the onset of flooding); d is the water depth; v the flow velocity.
2. Zone with rapidly rising water, in which the average rise rate of the first 1.5 m of
water is considered:
FDðdÞ ¼ UNlnðdÞ � lN
rN
� �lN ¼ 1:46 rN ¼ 0:28
if ðd� 2:1 m and w� 0:5 m/hourÞ and ðdv\7 m2=s or v\2 m/sÞð2Þ
where FD is the mortality (the fraction killed of all people present at the onset of flooding);
UN is the lognormal distribution with parameters lN and rN; lN the average of ln(d); rN is
the standard deviation of ln(d); d is the water depth; v the flow velocity; w is the water level
rise rate (over the first 1.5 m).
3. For the remaining area, Eq. 3 is used:
FDðdÞ ¼ UNlnðdÞ � lN
rN
� �lN ¼ 7:60 rN ¼ 2:75
ifw\0:5 m/hour or
ðw� 0:5 m/hour and d\2:1 mÞ
� �and ðdv\7 m2=s or v\2 m/sÞ
ð3Þ
where FD is the mortality (the fraction killed of all people present at the onset of flooding),
UN is the lognormal distribution with parameters lN and rN; lN the average of ln(d); rN is
the standard deviation of ln(d); d is the water depth; v the flow velocity.
2.1.1 Validation and reliability of the fatality rate functions
As mentioned previously, the Mortality Functions were derived from historical data of the
1953 flooding in the south-western part of the Netherlands. They were validated for the
Nat Hazards (2012) 63:1083–1113 1087
123
Canvey Island flooding of 1953 (Di Mauro and De Bruijn 2012). During this event,
breaches in the costal flood defences resulted to a severe inundation of the island. When
applied to the 1953 event, the functions provided a good estimate of the number of
fatalities. These functions can be less reliable if applied to other types of flood (e.g. river
floods), or to a present-day scenario characterised by stronger houses, better communi-
cation options and a higher population density. Since disasters are rare and every area and
flood event has its own specific characteristics, it is difficult to validate, test or improve
these functions. They are considered to be the best functions available in the Netherlands
and seem to produce reasonable results that enable the identification of high-risk areas.
2.1.2 Current use of the method
The Mortality Functions are commonly used in the Netherlands in flood risk assessments.
They have been included in the Standard Dutch Damage and Casualty Model and are
frequently incorporated into risk assessments (Kok et al. 2005; De Bruijn and Klijn 2009).
2.2 Approach 2: Flood Risk to People method
The Flood Risk to People (FRTP) is a method developed in the UK by HR Wallingford and
Middlesex University Flood Hazard Research Centre for the UK Department of Envi-
ronment, Food and Rural Affairs (DEFRA) and the Environment Agency (HR Wallingford
and Middlesex University Flood Hazard Research Centre 2006). This method aims to
evaluate ‘death or serious harm to people that occurs as a direct result of the flood eitherduring or up to one week after the event’ (HR Wallingford and Middlesex University Flood
Hazard Research Centre 2006).
It also provides measures of ‘annual average fatality risk’ that can be used in combi-
nation with the assessment of expected annual damages and other impacts to enhance flood
risk management. In this paper, however, we focus on the assessment of numbers of
fatalities and not on the fatality risks. The latter also requires assessments of flood
probabilities.
The Flood Risks to People method considers the physical characteristics of the flood and
the vulnerability of the receptors to determine possible physical consequences to people.
The method consists of the following steps:
1. The flood hazard is calculated for each point on the floodplain, based on expected
maximum depth and velocity. These values can be obtained from a hydrodynamic
model or be assessed following alternative procedures (as explained in HR
Wallingford and Middlesex University Flood Hazard Research Centre 2006). The
flood hazard is calculated according to Eq. 4.
2. The vulnerability of the area is assessed in terms of land use, exposure of the area and
flood warning procedures.
3. The vulnerability of the people is estimated based on age and summary information on
health conditions.
4. The rate of people at risk is then calculated following Eq. 6. This value corresponds to
the percentage of people likely to be injured by the flood.
5. The calculated rate of people at risk is finally applied to the actual number of people in
the floodplain (Eqs. 8, 9) to obtain the expected number of injuries and deaths for the
area considered.
1088 Nat Hazards (2012) 63:1083–1113
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The parameters and equations are described in detail in HR Wallingford and Middlesex
University Flood Hazard Research Centre (2006). A summary of these variables is pre-
sented below.
(i) Flood hazard. This is calculated based on the inundation depth, the flow velocity and
the ‘debris factor’. The resulting hazard level is categorised into four classes:
• ‘Caution level’, corresponding to shallow flowing water or deep standing water
• ‘Dangerous for some’, corresponding to deep or fast flowing water
• ‘Dangerous for most’, characterised by deep and fast flowing water
• ‘Dangerous for all’, related to a very deep and fast or deep and very fast flowing water
The ‘hazard rating’ is calculated with Eq. 4:
HR ¼ dmaxðvmax þ 0:5Þ þ DF ð4Þ
in which HR = (flood) hazard rating; dmax = maximum water depth inside the area (m);
vmax = water velocity inside the area in which the maximum depth occurs (m/s);
DF = debris factor, whose value depends on the likelihood that debris will lead to a
significantly greater hazard.
In this study, the ‘debris factor’ was calculated as:
DF ¼ 0 if dmax\0:25 m
1 if dmax [ 0:25 m
�ð5Þ
This assumes that the influence of the debris is not relevant for a flood depth less than
0.25 m.
(ii) Area vulnerability. The parameter ‘area vulnerability’ aims to classify the floodplain
according to
• Flood warning, including properties covered by the flood warning system, warnings
meeting 2-h target and people taking effective action.
• Speed of onset of a flood, assumed to be very gradual, gradual or rapid.
• Area characteristics, consisting of multi-storey apartments, typical residential/
commercial/industrial properties, bungalows, mobile homes, campsites, schools, etc.
Each factor is scored on a simple 1, 2, 3 scale. The parameter ‘area vulnerability’ is then
obtained as the sum of scores for ‘flood warning’, ‘speed of onset’ and ‘area
characteristics’.
(iii) People vulnerability, which is calculated according to
• % residents aged 75 years or over.
• % residents suffering from long-term illness.
The ‘people vulnerability’ parameter is simply defined as the sum of percentages from
the two census categories above.
Once the ‘hazard rating’ and the ‘area vulnerability’ have been calculated, the pro-
portion of people at risk (‘rate of people at risk’) can be evaluated using the equation:
X ¼ HR AV ð6Þ
where X rate of people at risk, HR hazard rating calculated with Eq. 4, AV area
vulnerability
Given the population N(Z)i, it is possible to determine the number of people exposed to
the risk in each hazard zone N(ZE)i:
Nat Hazards (2012) 63:1083–1113 1089
123
NðZEÞ ¼ NðZÞ X
100ð7Þ
The number of injuries Nin is calculated as function of the number of people exposed
(N(ZE)) and the ‘people vulnerability’ (Y):
Nin ¼ 2Y
100NðZE) ð8Þ
The number of fatalities is then obtained as:
Ndeaths ¼frate2HR
100Ninj ð9Þ
Further details on the method, its derivation and calculation can be found in HR
Wallingford and Middlesex University Flood Hazard Research Centre (2006).
Expanding Eq. 9, we obtain
Ndeaths ¼4 � HR
100
Y
100NðZEÞ
� �¼ 4 � HR
100
Y
100NðZÞ X
100
� �¼ 4 � HR
100
Y
100NðZÞHR � AV
100
� �
¼ 4
106HR2 Y AV
� �NðZÞ ð10Þ
The fatality rate, defined as the ration of fatalities among the exposed population, can be
expressed as:
FFRTP ¼4
106HR2 Y AV ð11Þ
Equation 11 shows that the fatality rate is a function of the square of the ‘hazard rating’,
and thus to the square power of the product of the water depth and flow velocity.
2.2.1 Validation and reliability of the fatality rate functions
The Flood Risk to People method was developed in the UK, based on historical data on
area vulnerability, physical test of people stability to flow conditions (depth and velocity),
literature data on structural strength of buildings and consequence of flooding, and expert
judgement. The final report describing the model (HR Wallingford and Middlesex Uni-
versity Flood Hazard Research Centre 2006) states that: ‘(the) uncertainty in the results ishigh, particularly in the number of people who will be exposed to a flood and the widerange of site specific factors that affect whether people are injured or killed. The results dohowever provide a guide to flood risks to people, and can be used to compare the impactsof different options’.
2.2.2 Current use of the method
Currently, the method used in the UK is mainly limited to the ‘hazard rating’ parameter
(Eq. 4) to establish the flow conditions that can constitute ‘danger for some’, ‘danger for
most’ and ‘danger for all’ (Udale-Clarke et al. 2005)
2.3 Approach 3: Life Safety Model
The Life Safety Model (LSM, www.lifesafetymodel.net) is a model developed by BC
Hydro in Canada (Sakamoto et al. 2004) to estimate loss of life and damage to buildings
1090 Nat Hazards (2012) 63:1083–1113
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and vehicles due to a flood. The tool was initially designed for simulating dam break
scenarios, but it has been subsequently tested for other types of floods such as failure of
levees (Di Mauro et al. 2011) and tsunamis (Lumbroso et al. 2010).
The LSM includes flood wave propagation, the movement of people (as pedestrian or
vehicular traffic) and the dissemination of the warning. It determines whether a population
at risk in a floodplain can evacuate safely or find a safe shelter during an event (see Fig. 2).
In the LSM, the flood wave is simulated as it evolves over time, and this evolution affects
people that (i) get warned, (ii) start evacuating, (iii) reach safety, (iv) get stuck in traffic or
floating cars or (v) are killed. The time-varying flood condition (depth and velocity)
encountered by the people affects their health and therefore their survival capacity, and
also the speed at which they move. Their survival depends on the flood condition at a
particular moment (e.g. simulating people drowning because they are swept away by the
flood) but also on the continuous exposure to the water (e.g. to simulate exhaustion). If
people evacuate by car, the flood wave interacts with the vehicle such that an engine would
stop when it encounters a certain depth of water or alternatively the car would get swept
away. The buildings are also modelled as receptors. Their capacity to withstand the flood is
also modelled depending on the flood characteristics, so that they would collapse directly
after being hit by the flood wave or as consequence of a continuous exposure to the flood.
People can be modelled as individuals and also as groups (e.g. family), so they would not
separate during an evacuation. In the model, people can receive warning from a ‘warning
centre’, from other people or they can be warned by the observation of the incoming flood
wave itself. Vehicular movement is modelled by a traffic algorithm that can reflect the
reduced speed due to congestion and bottlenecks.
The LSM can assist in simulating emergency scenarios and enable resource planning
and the exploration of the impacts of different decisions, for example in terms of
i. time needed for a safe evacuation compared with the available time (e.g. the arrival
time of the flood wave);
ii. loss of life and vehicles due to the flood characteristics, evacuation procedures (e.g.
warning time or decisions to close roads) and impacts on buildings.
The relationship between the condition of people and the flow characteristics is
determined as dose–response function. The people’s health parameter (PPC) varies with
time as function of the variation in flow depth and velocity, as shown in Eq. 12 (Sakamoto
et al. 2004). When the variation in flow depth and velocity is above another threshold (the
‘cumulative loss’ threshold, PCDVM), the person is considered to be dead.
Fig. 2 Conceptual diagram of the LSM
Nat Hazards (2012) 63:1083–1113 1091
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PPC(t þ DtÞ ¼ PPC(tÞ 1� dvðtÞ þ dvðt þ DtÞ2
Dt1
PCDVM
� �ð12Þ
where Dt time step of the simulation, PPC people’s health index (which varies between 0
and 1); dv product depth times velocity; PCDVM cumulative threshold for death due to
exhaustion (expressed in m2/s)
The conceptual diagram of the LSM is presented in Fig. 2.
LSM requires many detailed input data including:
• the type and location of individual properties, vehicles and people, with their
characteristics such as physical strength and escape mode;
• flood depths and velocities evolving in time;
• details of the road network and other evacuation pathways;
• location of safe heavens; and
• warning mode and rate of warning dissemination.
The method involving the application of the LSM model can be schematically repre-
sented as follows:
1. First, detailed time-varying information on the flood hazard needs to be obtained in
terms of depth and velocities by applying a 2D hydrodynamic model.
2. The required data of the population include information of the initial location of each
person, the escape mode (by foot or vehicle), the time of reaction to the first warning
and the initial ‘health condition’.
3. The required data of the buildings include information of their location, average egress
time and the initial value of the ‘building health’ variable.
4. The road network should be modelled as a connected graph (a set of nodes and arches)
with their characteristics in terms of road type, width and speed limit.
5. The locations of the warning centres, if any, have to be specified together with the time
in which they start disseminating the warning and the maximum distance that this
warning is expected to reach.
6. Once the above input data are prepared, the model can be run. The results contain
information on the number of people evacuated, deceased or safe. Detailed snapshots
of each time step are available in separate files for people, buildings and vehicles.
2.3.1 Validation and reliability of the fatality rate functions
The fatality rate functions embedded in the LSM are loss functions whose values are
calculated for each modelled individual. These functions specify the ability of people to
resist the impact of the flood wave, in terms of depth and velocity, and how this ability
changes during an event. These functions were developed through the analysis of people’s
instability curves and literature regarding human stability in water (Johnstone et al. 2006)
and calibrated and validated through (i) a review of forensic papers, which analysed and
summarised the impacts of flood events, (ii) the direct gathering and analysis of docu-
mentary evidence of events by witnesses and (iii) the modelling of historical events such as
the Malpasset Dam failure, in 1959 (Sakamoto et al. 2004). Some sensitivity analyses were
also performed to assess how the variation of the input parameters can influence the results
(Sakamoto et al. 2004), thus how the uncertainty in the input data would spread to the
model’s outcome. The sensitivity of the model to some of the parameters, not always
directly related to the fatality rate functions, has proven to be relevant (Di Mauro and
1092 Nat Hazards (2012) 63:1083–1113
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Lumbroso 2008). This agrees with the physics of the system and the characteristics of the
areas, for example, the collapse of a tall and populated building is more likely to cause
more fatalities than the collapse of a single-dwelling structures. However, this makes the
model sensitive to the reliability of the input data.
2.3.2 Current use of the method
The LSM was developed by BC hydro (Canada) as tool for estimating consequences of
dam breaks. This tool is currently used in British Columbia for performing risk assess-
ments related to dam safety. Research on the applicability in other contexts is ongoing (Di
Mauro and Lumbroso 2008). Its use as a tool to inform emergency management is being
developed (Di Mauro et al. 2010, 2011) and its use not widespread.
3 Application of the three methods
The three methods have been applied to the same case study area, Canvey Island in the
Thames Estuary. Canvey Island was flooded during the 1953 flood surge that caused many
fatalities in the Netherlands and the UK. This section gives a brief overview of Canvey
Island and the flooding event, the hydrodynamic modelling carried out to reproduce the
flood parameters corresponding to the event and the number of fatalities as assessed by the
three methods discussed in the previous section.
3.1 Overview of Canvey Island and the 1953 event
Canvey Island is a flat, low-lying alluvial island in the Thames Estuary, covering an area of
18.5 km2. The island has an average height of approximately 1 m below the mean high
water level. The location of Canvey Island is shown in Fig. 3. Canvey Island is protected
from the sea by a network of flood defences. The island was inundated in 1953 by the
‘Great North Sea Flood’ that caused multiple breaches in the island’s defences. This event
resulted in the death of 58 people and the destruction of several hundred houses. The
consequences of the 1953 floods led to the construction of new flood defences (Brown et al.
2007). In 1953, Canvey Island was sparsely developed with some 5,200 houses on the
island below the normal high tide level. Access to Canvey Island is currently only possible
by two roads both of which are connected to the same roundabout. In 1953, the access
constituted a single road.
The total population of Canvey Island in 1953 was around 13,000 people, based on the
1951 census data (Southall 2005). The distribution of the population was derived from the
distribution of buildings. The locations of buildings were reconstructed by analysing his-
torical maps available in hard copy from the Bodleian Library, Oxford (www.bodleian.
ox.ac.uk). Without more detailed information, the population was assumed to be equally
distributed between the buildings. The building (and therefore the population) locations are
shown in Fig. 4. It can be noted that the majority of the buildings were located on the
eastern part of the island. As the flood event occurred during the night, the population was
assumed to be at home for the entire duration of the flooding. This approximation leads to a
conservative factor in the final results, as it is likely that part of the population was moving
after receiving flood warnings.
The 1953 flood was caused by a storm surge, and the resulting consequences were
described as ‘the worst peacetime disaster that the UK has known’ (Canvey Island
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archive). The tidal surge reached Canvey Island at around 1.00 am on Sunday 1 February
1953 and around this time the first breach of the flood defences occurred (Elford 2006). In
1953, there was no tidal surge forecasting system in place, and there was no single
authority responsible for flood warnings (Canvey Island archive). Fifty-eight people died
during the 1953 flood in Canvey Island (Baxter 2003). Baxter (2003) states that of the 58
people who died, 53 of the fatalities occurred in the north-eastern part of the island where
the first breaches occurred.
3.2 Hydrodynamic model
The hydrodynamic model of the 1953 flood in Canvey Island was developed by HR
Wallingford Ltd as part of a previous study (Di Mauro and Lumbroso 2008) and was used
in this study to obtain the conditions that occurred during the event. This model is a two-
dimensional hydrodynamic model of the island that was developed using the finite dif-
ference hydrodynamic software TUFLOW. The ground elevation information used in the
model was obtained from a recent Light Detection and Ranging (LIDAR) topographic
survey. The elevation model was modified in order to represent the situation of 1953.
Canvey Island was discretised into a regular 20 m 9 20 m grid, covering a total of
18 km2.
The shape of the tidal surge was available from data in HR Wallingford’s archive,
integrated with newspaper and police reports from 1953 that indicated that the peak tidal
surge level was 4.6 m. This peak was recorded at between 1.00 and 1.30 am, and the
breaching of the flood defences occurred shortly before the peak tidal surge (Canvey Island
archive). The location of the breaches in the flood defences that occurred in 1953 (Fig. 5)
was established from the literature (Allen et al. 1954). The two main breaches, whose
depths were estimated to be 100 and 140 m (Allen et al. 1954), were represented in the
hydraulic model by two main openings in the flood embankments.
Fig. 3 Location of Canvey Island within the UK
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Allen et al. (1954) presents a record of the maximum flood depths, an estimate of the
flood velocities and the inflow volume. This information was used to calibrate and validate
the hydrodynamic model of the flood.
Figure 5 shows a map of the peak flood extent resulting from the hydrodynamic model.
The resulting flood, covering most of the eastern part of Canvey Island, shows that the
water depth was around 2–3 m at the point closest to the breach, with a mean depth
between 0.8 and 1.0 m. The highest velocities were in the vicinity of the breaches with the
peak water velocity being about 2.3 m/s. These results were obtained using a 2D finite
difference model, which implies a slight underestimation in the velocities, mainly in the
transition dry/wet cells (Lhomme et al. 2010). This factor needs to be noted as the velocity
of the flood is very important in assessing flood fatalities. The maximum flow velocity,
maximum flow depth and the time series of depth and velocity have been extracted. The
total model duration is 8 h, whilst the results have been extracted every 0.5 h. More
detailed information about the model can be found in Di Mauro and Lumbroso (2008).
3.3 Application of the Mortality Functions (Approach 1)
An extensive analysis and validation on the Mortality Functions is presented in Di Mauro
and De Bruijn (2012). Here, the main findings of that paper are highlighted, and further
analysis is carried out to enable a better comparison of this method with the other two
methods. The three criteria constituting the Mortality Functions were applied to each cell
of the modelled area, to define the ‘hazard zones’, each of these corresponding to a specific
Mortality Function. There were no cells with a maximum product depth velocity larger
than 7 m2/s, which is the threshold for applying Eq. 1. Therefore, Eq. 1 was not consid-
ered. Breach zones are expected to be small, about 100–200 m around a breach. People
Fig. 4 Location of the buildings reconstructed for 1953 (background Ordinance Survey (OS) map)
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located at about 100 m behind a breaching embankment have a very high probability of
dying, as an area close to a breach (depending on land use, elevation, soil conditions) flood
depths, currents and velocities are likely to be very high.
The second and third criteria sets (Eqs. 2, 3) did apply in Canvey Island. Thus,
according to the Mortality Functions, people died due to rapidly rising water levels and due
to ‘other causes’.
3.3.1 Evaluating the number of fatalities
The maximum value of the ‘mortality factor’, which represents the likely percentage of
fatalities, was approximately 40 %. The mean value for this factor was 0.7 %. This value
corresponds well with the mean value calculated based on the historical data, which results
in a fatality rate of 0.4 % (58 for a total population of 13000 people).The results from the
Mortality Functions show good agreement with the real event in terms of number of
fatalities. The total number of deaths found by applying the Mortality Functions is 71,
which is slightly more than the 58 fatalities that occurred in 1953.
The Mortality Functions also give an indication of the probable cause of death. They
indicate that 35 people drowned in areas with fast rising water depths (second criterion)
and that another 36 people died due to exhaustion, hypothermia and other causes. Most
(42) fatalities were calculated as occurring in the north-eastern part of the island. In the
south-eastern part of the island, the model calculated 18 fatalities, whilst the other 11
fatalities occurred in the central area. Figure 6 shows the calculated locations of the
fatalities.
Fig. 5 Location of the breaches used in the hydrodynamic model and the resulting peak flood extent(background OS map)
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Little information is available from literature about the real cause of fatalities in Canvey
Island in 1953. However, different people were reported to have died in their homes, and
that seems to point to the first (house collapse) and second cause (surprise due to a fast
water level rise rate) as the dominant causes (Canvey Island archive). Furthermore, wit-
nesses stated that in some cases the people died due to exposure to icy water (Canvey
Island archive), and this supports the hypothesis that some fatalities could have occurred as
a consequence of hypothermia which may support the third cause of death (‘other causes’).
3.3.2 Sensitivity analysis
A sensitivity analysis was undertaken with regard to the (i) population distribution and the
(ii) parameters of the three criteria (Eqs. 1, 2, 3). In this paper, a summary of the results is
presented for comparison with the other two methods presented. The detailed sensitivity
analysis can be found in Di Mauro and De Bruijn (2012).
i. Sensitivity to the population distribution
The literature shows that 91 % of the fatalities occurred in the north-eastern area (Baxter
2003). The Mortality Function method calculated that, in the same area, approximately
59 % of the fatalities occurred. These results are strictly related to the assumed distribution
of the population, which was concentrated in the south-east. This underlines the sensitivity
of the results to the assumption of the population distribution. Assuming, for example, a
uniform population density of 722 people per square kilometre, the calculated fatality
density is definitely sparser and the total number of fatalities is higher (91 fatalities).
Fig. 6 Location of the fatalities calculated with the Mortality Functions method (background OS map)
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ii. Sensitivity and assessment of the three criteria (Eqs. 1, 2, 3)
For this case study, none of the cells fulfil the first criterion, thus it was not possible to
evaluate the significance of this criterion.
The second criterion related to the rate of rise of the water level was met at many
locations in this study. The adopted value of rise rate threshold (0.5 m/s) might be very
sensitive to the type of buildings and therefore differs for each case study area. For that, the
application of the second criterion might be questionable (De Bruijn et al. 2009). The
fatality rate was recalculated using only Eq. 3 (third criterion). This is equivalent to assume
that only depth is important to estimate loss of life. This resulted in a total number of
fatalities of 59, which is slightly closer to reality. However, the estimated location of the
fatalities does not fully agree with the case history: the resulting fatality rate is quite
homogeneously distributed within the cells characterised by high water depth (eastern and
central part of the island) and results in a more even distribution of fatalities over the
inhabited areas (southern and north-eastern part of the island), whilst the case history
indicates a concentration of fatalities in the north-eastern area.
The water lever rise threshold used in Eq. 2 (0.5 m/s) can be questionable: as Jonkman
et al. (2008) state, this threshold could have been chosen anywhere between 0.5 and 4 m/h.
Jonkman et al. (2008)’s range was used to investigate the sensitivity of this threshold,
analysing the variation in the number of fatalities subsequent to the variation of the chosen
threshold. This resulted in a variation in the number of fatalities that ranges from 71, using
a water level rise rate of 0.5 m/h and converges to 59, using the maximum water level rise
threshold of 3–4 m/h. This shows that the model is not very sensitive to the variation in the
water level rise rate threshold. This is also due to the distribution of the modelled water
level rise gradient that is concentrated between 0.5 and 2 m/h, so we can expect that the
main variation in the results would be obtained using a water level rise threshold varying
between these two values (Di Mauro and De Bruijn 2012).
3.3.3 Discussion
The Mortality Functions mainly aim to represent the hazard in the floodplain, rather than to
provide a precise estimation of the number of fatalities (Jonkman et al. 2008). The fatality
rate can be expressed in terms of hazard zones and possible causes of death.
The results of the application to Canvey Island compared well with the 1953 observed
distribution of the fatalities (Di Mauro and De Bruijn 2012), and there seems to be
accordance between these results and the historical references about the causes of death:
both assess that the majority of fatalities occurred by drowning in the northeaster part of
the island (Canvey Island archives; Baxter 2003).
In the Mortality Functions method, the vulnerability of the people is incorporated
implicitly. The functions were originally derived for the 1953 flood event in the Nether-
lands (Jonkman et al. 2008). By using these curves, one thus supposes that the vulnerability
of the area and the people affected in that event is representative also, for other events,
locations and times.
3.4 Application of the Flood Risk to People methodology (Approach 2)
The FRTP method includes scoring the ‘hazard rating’ (HR), ‘area vulnerability’ (AV) and
‘people vulnerability’ (PV).
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The ‘hazard rating’ was here calculated based on the results of the hydrodynamic
model, according to Eq. 4.
The ‘area vulnerability’ is determined by ‘flood warning’, ‘speed of onset’ of a flood
and ‘area characteristics’. The ‘flood warning’ was scored as 2 as only an inefficient alert
was issued through an acoustic signal and the direct actions of the police and the fire
brigade that had time to reach a limited number of people (Canvey Island archive). The
parameter ‘speed of onset’ was assessed by calculating the water arrival time. If the water
arrived within half an hour, a score of 3 was assigned to the ‘speed of onset’, if the water
arrived within 1 h, a score of 2 was assigned and otherwise a score of 1 was assigned. The
‘area characteristics’ were given a score of 2, because Canvey Island was mostly char-
acterised by typical residential properties (two-storey homes) (Barsby 2001).
The ‘people vulnerability’ was calculated based on the 1951 census of Canvey Island
(Southall 2005). These figures only include the percentage of population aged below 15,
between 15 and 64 and those aged above 65. Thus, the 1951 census does not provide the
percentage of residents aged 75 or over, which is the figure required by the method.
Assessing this figure from the population pyramid might lead to an imprecise assessment,
as only three age ranges are available to reconstruct the pyramid. For that, the percentage
of residents aged 65 or over was used (15 %). This percentage indeed includes those aged
75 or over, thus our assumption is expected to lead to a conservative estimation.
We were not able to retrieve the percentage of residents suffering from long-term illness
in 1951, whilst the percentage taken from the 2001 census (UK Office for National Sta-
tistics) is 1.62 %. Assuming that this percentage is the same as in 1953 can lead to an
underestimation, as the percentage of invalid residents was likely higher in 1953 than in
2001, possibly due to the consequences of the war. The fact that the population age
distribution in 2001 is similar to that of 1953 also supports the assumption that the per-
centage of invalid persons is similar in both years. Without more precise data, the per-
centages of 15 and 1.62 % were considered to calculate the ‘people vulnerability’. The
entire population was assumed to be at home, since the flood happened at night (1 am)
(Canvey Island archive).
3.4.1 The resulting number of fatalities
The application of the FRTP method resulted in 455 injuries and 18 fatalities. The ratios
between injuries (and deaths) and the total population are 3.5 and 0.15 %, respectively.
The calculated number of fatalities, mainly located in the north-eastern part of the island
(Fig. 7), is low compared to the 58 reported fatalities in 1953. However, the model esti-
mated the number of fatalities within the same order of magnitude as the actual number of
fatalities, with an underestimation of 0.3 % if compared with the total population. As
expected, due to the influence of the ‘hazard rating’ (based on a combination of velocity,
depth and the presence of debris), the model identified the areas with the highest depths
and flow velocities as the areas with the most fatalities.
3.4.2 Sensitivity analysis
A brief sensitivity analysis was carried out to evaluate the influence of the parameters that
are considered to have higher uncertainties, including the ‘flood warning’ and people
vulnerability.
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The ‘flood warning’ parameter was chosen as it is difficult to forecast (or to assess, in
case of post-event analysis) the actual response rate to a warning. Therefore, a low sen-
sitivity to this variable would be particularly significant to the robustness of the method.
The ‘people vulnerability’ should be calculated as the sum of the percentage of people
aged above 75 and the percentage of people with long-term illnesses. As explained above,
a precise assessment of both these percentages was not possible, and, or this reason, we
chose to include this parameter in the sensitivity analysis.
(i) Sensitivity to the ‘flood warning’ parameter
Changing the ‘flood warning’ score from 1 (corresponding to the assumption that people
receive the warning and act upon it) to 3 (simple acknowledgement of the presence of any
warning system), the number of deaths increases linearly, ranging from 15 to 21 fatalities.
The same trend can be found in the calculated injuries.
The ‘flood warning’ parameter is part of the ‘area vulnerability’ parameter, which
should take into account the possibility for people to escape (through the ‘flood warning’
and the ‘speed of onset’ values) and the resilience of buildings. Whatever combinations of
scores are assigned to the three components, the value of ‘area vulnerability’ can range
between 3 and 9. Varying the ‘area vulnerability’ within this range resulted in the number
of fatalities varying between 12 and 37. The model thus shows a very low sensitivity, not
only to the ‘flood warning’ variable, but to the whole ‘area vulnerability’ parameter.
(ii) Sensitivity to the ‘people vulnerability’
The range of variation in the ‘people vulnerability’ parameter was chosen by selecting the
plausible range of variation of the two percentages constituting this parameter: the
Fig. 7 Location of the fatalities calculated with the Flood Risk to People method (background OS map)
1100 Nat Hazards (2012) 63:1083–1113
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percentage of population above 75, which can be assumed to vary between 0 and 15 %
(corresponding to the population aged above 65 years, thus including those aged above
75 years), and the population with long-term illnesses, whose variability was arbitrarily
assumed between 1 and 15 %. The range covering this possible variation is therefore
1–30 %. We did not consider the lower most value (1 %), which would have led to a very
low number of fatalities. Varying the ‘people vulnerability’ from 5 to 30 %, the number of
fatalities also increases linearly, ranging from 5 to 33 deaths.
This analysis shows that, for a given scenario, the performance of the ‘flood warning’ as
well as the calculation of ‘people vulnerability’ could be a source of uncertainty, and the
variation of the two parameters could lead to changes on the final estimate of loss of life.
However, the simplicity of the model allows easy calculations of the results with varying
parameters, also obtaining uncertainty bands.
3.4.3 Discussion
The FRTP methodology resulted in an estimated number of fatalities lower than those that
occurred in reality, but within the same order of magnitude. The location of the estimated
fatalities was consistent with the real data. The method thus provides an indication of the
areas that are most at risk.
On the whole, the FRTP method can be considered an effective starting point in a risk
assessment study, because it provides an approximate number of deaths for every given
scenario (thus allowing their comparison). This method can also be useful to map the
highest-risk zones for a particular area. However, the method presents some aspects that
require further investigation, regarding the underlying assumptions, and its area of
application.
The FRTP method was developed based on a combination of evidence and expert
judgement (HR Wallingford and Middlesex University Flood Hazard Research Centre
2006). The approach to derive these functions, therefore, was not systematic. For that, it is
not possible to draw specific recommendations regarding punctual changes in the equations
governing the FRTP method.
The FRTP method was tested against different historical events (HR Wallingford and
Middlesex University Flood Hazard Research Centre 2006). The validation test case events
varied in temporal scale, including events occurring in 1901 as well as 2000. On the other
hand, all the case studies were located in the UK. Further calibration of the method might
be necessary to apply this method to other countries.
When comparing our results with those obtained in the validation case studies, the
underestimation of the number of fatalities in our Canvey Island model does not seem to
occur consistently in the original case studies (HR Wallingford and Middlesex University
Flood Hazard Research Centre 2006), and, therefore, it might be specific to this particular
data set.
The influence of ‘flood warning’ identified by this method seems to be limited, although
real cases suggest differently (for example, Parker et al. 2009). Further research should be
carried out to look at revising the model by studying another parameterisation that reflects
the importance of the presence or absence of warning, and the effectiveness of warnings for
fatality-rate assessments.
The overall low sensitivity to changes of the ‘area vulnerability’ parameters needs to
be studied as well. It seems unrealistic that the flood impact to people living in, for
example, multi-storey dwellings is similar to the impact on people living in bungalows or
campsites.
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The fatality rate assumed by the FRTP method (Eq. 11) is a function of the square
power of the flow depth and velocity. In the Mortality Functions method, a logarithmic
relationship is assumed, only involving the flow depth (Eqs. 3, 4), whilst in the LSM a
linear relationship is proposed (Eq. 12).1 In the FRTP method, the fatality rate is assumed
to increase exponentially with the flow depth and velocity. However, comparing the shape
of the Mortality Functions and the Flood Risk to People fatality rate function, the fatality
rate resulting from the FRTP method is consistently lower than those obtained with the
Mortality Functions, until higher values of flood depth occur (Fig. 8). This caused the
lower number of fatalities obtained with this method compared to the Mortality Functions
method. If the exponent is varied, the results change. A linear relationship between
mortality and the ‘hazard rating’ ([4] in Fig. 8) results in low fatality numbers at high
depths and follows a similar trend of Eq. 3 (which, in fact, is applied for low flow depth
and velocity). Changing the exponent of the ‘hazard rating’ from 2 to 2.2, we obtain the
fatality rate function as shown in Fig. 8, [5]. However, the number of fatalities resulting
from the modified function is 19, which is very close to the 18 fatalities obtained using the
original curve. It is important to note that this can be due to the specific data set, and further
research should be carried out on this subject.
We, however, do not consider the underestimation of the number of fatalities the main
issue of the method, as the calculated fatality figures are of the same order of magnitude
that those occurred in reality. On the other hand, the low sensitivity of the method to the
‘flood warning’ and ‘area characteristic’ parameter should not be ignored. In fact, the focus
of further research should not be on reproducing the exact number of fatalities of a
historical case, but to correctly reproduce the casual relationships between external factors
and flood fatalities. This casual relationship between the ‘area vulnerability’ and the
fatality rate might not be correctly represented by the current method.
Fig. 8 Comparison between the functions to calculate the fatality rate proposed in the Mortality Functionmethod [1, 2] and the one proposed in the Flood Risk to People method [3, 4, 5] are possible variation of theFlood Risk to People function
1 In the Life Safety Model the fatality rate is not modelled as an explicit function expressing the chance ofbeing killed by the flooding. However, Eq. 12 represents how the flood affects the individual’s health, thusthe individual’s mortality.
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3.5 Application of the Life Safety Model (Approach 3)
The Life Safety Model (LSM) was applied using the same population distribution and
building locations used for the previous applications. Time-evolving flood depth and
velocities were obtained from the results of the hydrodynamic model. Historical maps from
1939 to 1961 were used to assess the roads at the time of the flood, whilst the safe haven
was allocated at the exit point of the island, which also coincides with the higher ground to
the north-west side of the island.
To simulate the flood warning, historical evidence of the event was considered. From
the 1953 police reports and other sources, it appeared that when the first breaches in the
flood defences occurred, some sirens were sounded in the hope of raising the alarm (Elford
2006). Firemen and the police also commenced warning people by going from door to door
(Canvey Island Archive). The warning centre in the model was located in accordance with
the position of the old fire station. The rate of the warning dissemination in the LSM was
set to 0.6 m/s, as this is equivalent to a slow walking pace.
3.5.1 The resulting number of fatalities
Using the default parameters in the LSM, 38 fatalities resulted from drowning, 13 as a
result of exhaustion (i.e. time exposed to the floodwater) and 14 fatalities as consequence
of building collapse. In total, the model calculated 65 deaths. The results of the recon-
struction of the 1953 flood event slightly overestimate the total number of fatalities in
comparison with the available historical data. However, the error in the estimate of
fatalities is 0.13 % of the total population. The majority (51) of the fatalities were located
in the eastern part of the island, mostly in the north-eastern part close to the breaches,
whilst the remaining fatalities were calculated to be in the central-northern part, along one
of the main roads. Figure 9 shows the locations of the calculated fatalities.
3.5.2 Sensitivity analysis
The number of fatalities and injuries depends on the ‘resilience factors’ applied to both
people and buildings and other parameters, such as the rate of warning dissemination. An
analysis of the sensitivity of the results to various parameters was undertaken, and this is
discussed below. These include
(i) physical condition of the people;
(ii) the rate of the warning dissemination;
(iii) the proximity warning distance.
(iv) building ‘strength’
(i) Sensitivity to the physical condition of the people
The physical condition of the people is determined by a parameter (PPC) whose value is
calculated through a human ‘Object Damage and Loss Function’ (ODLF) (Sakamoto et al.
2004). The value of PPC decreases with the time in which a person is exposed to depth and
velocities (Eq. 12), until it reaches a critical threshold, determining whether a person is
‘knocked over’. An individual that is ‘knocked over’ is assumed not to be able to move
through the water. This causes his\her strength to continue to decline until the PPC reaches
a second threshold, and the person is considered to be deceased.
Variations in the initial value of the PPC where tested. An increase of 30 % of the
default value of PPC has little impact on the number of fatalities as a result of drowning.
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However, a 10 % reduction in PPC doubles the number of modelled deaths as a result of
‘exhaustion’ and ‘continuous’ exposure. A 30 % decrease in PPC increases the number of
fatalities from exhaustion from 13 (default value of PPC) to 623.
(ii) Sensitivity to the rate of the warning dissemination
The rate at which the warning is disseminated is implemented in the LSM through a
parameter that affects the awareness of people towards the incoming flood (thus the
number of fatalities). The ‘warning centres’ are modelled in the LSM as points from which
the warning is disseminated radially. Thus, the timely dissemination of the warning is both
a function of the location of the warning centres and the rate of warning dissemination.
Differences in these parameters can result in different number of fatalities. Decreasing the
warning dissemination rate from 0.6 m/s to 0.35 m/s increases the number of fatalities
from 65 to 208. Increasing the warning dissemination rate up to 0.7 m/s decreases the
calculated number of fatalities. Further increase in this parameter does not result in a
further decrease in fatalities. A warning rate of 0.7 m/s means that within 1 h after the
occurrence of the first breach, most of the population at risk are aware that a flood is
occurring and start evacuating. Evacuation cannot be quicker since the maximum capacity
of the evacuation routes has been reached.
(iii) Sensitivity to the proximity of the warning centre
The LSM accounts for the possibility for people who are aware of the incoming flood to
warn other people. This is implemented through a parameter that represents the maximum
distance over which two people can communicate to each other. A person unaware that the
flood is happening can be warned by someone that is aware if the distance between the two
people is less than a user-defined ‘proximity warning distance’. Setting up this parameter to
Fig. 9 Location of the fatalities calculated with the Life Safety Model (background OS map)
1104 Nat Hazards (2012) 63:1083–1113
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zero corresponds to assume that an ‘aware’ person is unable to warn anyone else. In the
model, a proximity warning distance of 150 m was used. Varying this parameter from 150
to 50 m, the calculated number of fatalities increases with a factor of about 2.5. It was also
noted that the number of fatalities that resulted from drowning and exhaustion is not
sensitive to the proximity warning distance. On the other hand, the number of fatalities due
to the collapse of buildings is very sensitive to variation of this parameter. In fact, when the
proximity warning distance is low, people tend not to receive the warning and to remain
where they were at the beginning of the simulation, thus inside their houses. This caused a
higher number of fatalities in areas in which many building collapsed due to the flood
wave.
(iv) Sensitivity to the ‘strength’ of the buildings
In the LSM, the impact of flooding to buildings is modelled through a ‘building strength
index’. The value of this parameter, compared with user-defined threshold values, deter-
mines whether the building is ‘standing’ or ‘collapsed’. Once the building is considered
destroyed, the people located in the building are assumed to be killed instantaneously.
Similarly, to the people’s health index (Eq. 12), this parameter decreases with time, pro-
portional to the depth and velocity of the flow. Using the default value for two-storey
dwellings suggested by Sakamoto et al. (2004), the model calculated the collapse of around
600 buildings, which resulted in 369 fatalities. An intensive calibration was necessary to
obtain an acceptable value of 70 collapsed buildings, by increasing the ‘building strength
index’ by the 12 %. This corresponds to 88 % decrease in the resulting number of col-
lapsed buildings.
4 Discussion
The LSM was used to model each of the 13,000 people living Canvey Island in 1953 and
their response to the flood wave. The 51 fatalities predicted by the LSM as a result of
drowning and exhaustion for the 1953 event compared well with the 58 observed fatalities,
most of whom are reported to have died in these ways. The LSM also estimated that
approximately 14 people died during the 1953 flood as a result of structural failure of their
homes. Seventy collapsed buildings resulted from the model, which compares well with
the historical data. However, this result was obtained after an intensive calibration of the
building parameters, to which the model showed to be very sensitive. It is concluded that
the LSM currently underestimates the resilience to floodwater of some of the British, and
possibly European, house types and overestimates the number of people who die instan-
taneously when their houses collapse.
The high sensitivity to the change in the initial value of the ‘building strength index’ is
probably caused by the type of relationship between this index and the flow depth and
velocity. The presence of people in the collapsed buildings may also be wrongly assessed,
which may have led to an overestimation of the number of casualties. On the other hand,
considering that people have no chance to survive the collapse of their building might also
lead to an over conservative assessment. In Canvey Island, some people were rescued after
clinging on the remaining walls of their partly destroyed house (Canvey Island Archive).
After the 2004 Indian Ocean tsunami, many stories emerged of people saved from the flow
after the house in which they were in collapsed (Murata 2010). This might depend on the
building type, as collapse of a lightweight building is relatively less likely to trap or injure
its occupants.
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Once the LSM has been set up, it is relatively simple to make changes to the model to
assess the impact of different interventions and management strategies on loss of life.
Other loss-of-life models, including the Mortality Functions and the FRTP methods ana-
lysed in this paper, generally provide only first-order magnitude estimates of fatalities and
do not include spatial–temporal variations and people’s movements that are crucial to
assessing the number of flood fatalities accurately. In fact, the LSM could be used to assess
the effectiveness of new evacuation routes, flood warning systems and safe havens in
reducing the potential loss-of-life within areas at risk of flooding. The LSM also allows
evacuation times to be assessed. However, the results were found to be sensitive to the
input parameters and initial conditions. The large number of parameters needed also
contributes to a large variation in outcomes. Therefore, the results need to be carefully
interpreted.
Looking at further developing the model, the sensitivity of the results to the initial
values of the ‘building strengths’ and to the ‘people’s health’ indices should be revised to
improve the robustness of the method. A high variance in the results corresponding to a
slight variation of the input parameters introduces a significant uncertainty in the results.
This makes them more difficult to use in flood risk assessments.
It is recommended to consider a reduction in the number of user-defined parameters.
This might mean that some of the ‘accuracy of the representation of the real world’ is
sacrificed in order to make the results of different applications more comparable and less
sensitive to minor changes.
5 Comparison of the results and discussion
This study demonstrated that the results of the application of the three methods agree to
some extent with the historical event examined. Furthermore, and possibly more signifi-
cantly in terms of reliability of the three models, they compare well with each other,
especially in terms of localising possible fatalities (Table 1).
This agreement between the results of these methods, in terms of number and location
of the fatalities, does indeed demonstrate coherence between the three methods and
therefore increases the confidence in using these methods. In fact, although developed in
very different countries and for different flood types, these methods strongly depend on the
flood characteristics, and this explains the coherence of results.
The methods incorporate different factors related to flood fatalities in very different
ways, which explains the differences in results. The main difference is in the way in which
the methods account for the dissemination of the warning, the characteristics of the area
and people’s physical characteristics. There was no evidence of a direct relationship
between the degree of accuracy in which the area is described and the accuracy in the
estimation of the number of fatalities in this case study area. The Mortality Function is the
Table 1 Number and location of the fatalities calculated with the three methods
No of fatalities Location of fatalities
1953 event 58 Mainly in the north-eastern part of the island
Mortality functions 71 Mainly in the north-eastern part of the island
Flood Risk to People 18 Mainly in the north-eastern part of the island
Life Safety Model 65 Mainly in the north-eastern part of the island
1106 Nat Hazards (2012) 63:1083–1113
123
method that requires the least prior knowledge of the modelled area; the LSM involves a
detailed modelling of the receptors; the FRTP method sits somewhere in between in terms
of the required level of details on the characteristics of the area and the population, but was
found to be the least accurate.
The methods were developed for different areas and time periods and their performance
will be the best for the areas and time periods for which they were developed. The results
of the Mortality Functions will be less accurate in areas that differ a lot from the south-
western part of the Netherlands in 1953. The Flood Risk to People method was developed
and validated on various case studies in the UK, dating from the early twentieth to the early
twenty-first century. The primary validation case study for the LSM is the Malpasset
disaster occurred in 1959. However, the model was developed building on extensive results
from physical experiments, rather than historical data (Sakamoto et al. 2004); therefore, it
cannot be expected to be related to a particular historical data set but rather to the setting of
the laboratory experiments. The comparability of the results for only one and an ‘old’ case
study cannot be considered representative for their comparability in all case study areas
and all time periods. However, this comparison proved to be very valuable for illustrating
the differences and agreements between the three methods.
The methods are not equally easy to be used. The Mortality Functions and FRTP
methods are easy to apply, and their sensitivity is easy to explore to provide uncertainty
boundaries. The LSM, however, requires a lot of detailed data and is therefore more
difficult to set up. The sensitivity and uncertainty of the LSM is also more difficult to study
since many parameters can be varied and are co-dependent.
The LSM method therefore serves another purpose compared to the other two methods.
It may be used to study the effect of flood warning improvements, varying evacuation
routes, house strength improvements and other non-structural measures. The effects of
these measures are not explicitly incorporated in the other two methods and can thus not be
assessed with the other two methods. The LSM is thus a useful tool for flood event
management or for evaluating non-structural strategies, but may be too data demanding for
a simple impact assessment.
The FRTP method is a useful tool for providing a quick assessment of many scenarios
(future scenarios, climate change impacts, etc.) and can be used when exact flood prop-
agation data are not available. The FRTP method can take into account many aspects of the
modelled area, but does not require these to be estimated in great detail, making it easier to
retrieve the essential information. The method is quite flexible to adaptation (for example,
see a related approach in De Bruijn and Klijn 2009).
6 Recommendations and guidelines for the application of the three approaches
In general, modelling fatality numbers will always be uncertain since each flood and each
area at risk are different. In countries such as the UK and the Netherlands, events causing
many fatalities are rare, and, therefore, the validation of methods and models is difficult.
Every model requires great care in setting up the modelling assumptions, accounting for
the quality of the input data and in interpreting the results. This is true for every numerical
model, even for those where the physical laws are known, such as hydrodynamic models.
This consideration is particularly important for models which simulate human behaviour
and response. The results of such models should not be considered as absolute numbers of
fatalities. Instead, they should be applied to search for an order of magnitude of fatalities to
inform flood risk managers, or to find places that are more at risk.
Nat Hazards (2012) 63:1083–1113 1107
123
To take into account the uncertainty that is intrinsic to the estimation of the parameters,
to which it is also very difficult (and potentially misleading) to assign a specific probability
distribution, we recommend performing sensitivity analyses by varying the input param-
eters, and considering the results in terms of mean and variance, which gives a good
assessment of the uncertainty boundaries. As a general indication, the higher the obtained
variance, the less reliable are the results.
All three methods showed to be sensitive to the distribution of the population in the
floodplain. The actual number of people present at the onset of a flooding depends on a
number of variables including the number of inhabitants, travellers, the flood forecasting,
the evacuation possibilities and so on. These are difficult to assess in detail. The analysis of
multiple scenarios allows accounting for this uncertainty to some extent. Probabilistic
distribution of the population can be used if the shape and parameters of the distribution are
reliable. Most important is to provide an accurate characterisation of the area, through the
analysis of the historical data and by working closely with the people who know the
communities and are therefore best placed to characterise them.
In general, all the three methods can be used for
(i) the identification of locations that are particularly dangerous for people and
therefore where to target possible interventions
(ii) a comparison between different structural interventions for managing the flood risk,
for example the construction of a levee versus the abandonment of floodplains.
The Mortality Functions and the FRTP methods could potentially be also used for
(iii) broad-scale flood risk assessments
(iv) broad-scale comparison of non-structural intervention (e.g. warning strategies)2
The Life Safety Model can be also used for
(v) detailed flood risk assessments
(vi) detailed comparison of non-structural intervention (e.g. warning and evacuating) for
managing flood risk
(vii) emergency planning and flood event management
It is recommended not to use the Flood Risk to People and Mortality Function
methods for localised (property\parish\small town level) flood risk assessments, as in
this case the flood risk assessment should strongly account for evacuation strategies and
a detailed assessment of buildings’ resilience. It should be good practice to assess the
benefits of particular evacuation strategies, as well as the consequences of not having
any strategy in place for densely populated cities. On the other hand, the Life Safety
Model should not be used for broad-scale assessments, as the large number of required
parameters can produce results that are patchy and unstable, and therefore lack
reliability.
A summary of the comparison and the recommendations is presented in Table 2.
2 In the Flood Risk to People method, this can be modelled by changing the parameter describing thecoverage of the warning system. In the Mortality Functions method, this has to be explicitly modelled byaccounting for those who would potentially respond to a warning and decide to evacuate, thus physicallyleaving the area.
1108 Nat Hazards (2012) 63:1083–1113
123
Ta
ble
2C
om
par
iso
nb
etw
een
the
thre
ean
alyse
dm
od
els
and
sum
mar
yo
fth
ere
com
men
dat
ions
Met
ho
d\m
od
elO
rig
inal
sco
pe
Cou
ntr
yIn
pu
td
ata
Eas
eo
fap
pli
cati
on
Mai
nst
ren
gth
sM
ain
wea
kn
esse
sR
eco
mm
end
edu
se
Mo
rtal
ity
Fu
nct
ion
sE
val
uat
ing
Flo
od
Ris
kto
Peo
ple
du
eto
lev
yfa
ilure
Net
her
lan
ds
Req
uir
ed:
Hy
dro
dy
nam
icin
form
atio
nO
pti
onal
:P
op
ula
tio
nd
istr
ibu
tio
n
Sim
ple
Ver
yea
syan
dq
uic
kto
app
ly,
giv
enin
form
atio
no
nth
eh
yd
rod
yn
amic
Dev
elo
ped
for
the
19
53
flo
od
inth
eN
eth
erla
nd
s,an
din
clu
din
gim
pli
citl
yth
ein
form
atio
no
nth
ear
ea,
itm
igh
tn
ot
pro
vid
eg
oo
dre
sult
sif
app
lied
tov
ery
dif
fere
nt
area
so
rm
od
ern
dat
ase
t
Iden
tifi
cati
on
of
dan
ger
ous
loca
tion
sB
road
-sca
lefl
oo
dri
skas
sess
men
tC
om
par
iso
nb
etw
een
dif
fere
nt
stru
ctu
ral
inte
rven
tio
ns
for
man
agin
gfl
oo
dri
sks
Rou
gh
esti
mat
ion
so
fth
eef
fect
so
fn
on
-st
ruct
ura
lin
terv
enti
on
s(e
.g.
war
nin
g,
see
Note
2)
Flo
od
Ris
kto
Peo
ple
Ev
alu
atin
gF
loo
dR
isk
toP
eople
du
eto
lev
yfa
ilure
UK
Req
uir
ed:
Are
ach
arac
teri
stic
s(i
ncl
ud
ing
dem
og
rap
hy
)an
das
sess
men
to
fth
eso
urc
eso
ffl
oo
dh
azar
dO
pti
onal
:H
yd
rody
nam
icin
form
atio
n
Av
erag
eQ
uit
eco
mp
reh
ensi
ve.
Pu
shth
ean
aly
stto
con
sid
erm
any
fact
ors
and
ther
efo
red
oa
more
com
pre
hen
siv
eas
sess
men
to
fth
ear
eaC
anb
eap
pli
edw
ith
ou
ta
det
aile
dh
yd
rod
yn
amic
mod
el
Man
ypar
amet
ers
nee
dto
be
esti
mat
ed,
alth
ou
gh
itp
rov
edto
be
lin
earl
yse
nsi
tiv
eto
the
var
iati
on
of
the
par
amet
ers.
The
sen
siti
vit
yto
the
chan
ge
infl
ood
war
nin
gp
oli
cies
and
inth
ev
uln
erab
ilit
yo
fth
ear
ea(i
ncl
ud
ing
typ
eo
fb
uil
din
gs)
app
ears
too
low
,th
us
no
tw
ell
cap
ture
db
yth
em
od
el
Iden
tifi
cati
on
of
dan
ger
ous
pla
ces
Bro
ad-s
cale
flo
od
risk
asse
ssm
ent
Com
par
iso
nb
etw
een
dif
fere
nt
stru
ctu
ral
inte
rven
tio
ns
for
man
agin
gfl
oo
dri
sks
Rou
gh
esti
mat
ion
so
fth
eef
fect
so
fn
on
-st
ruct
ura
lin
terv
enti
on
s(e
.g.
war
nin
g,
see
Note
2)
Nat Hazards (2012) 63:1083–1113 1109
123
Ta
ble
2co
nti
nu
ed
Met
ho
d\m
od
elO
rig
inal
sco
pe
Cou
ntr
yIn
pu
td
ata
Eas
eo
fap
pli
cati
on
Mai
nst
ren
gth
sM
ain
wea
kn
esse
sR
eco
mm
end
edu
se
Lif
eS
afet
yM
odel
Ass
essm
ent
of
loss
of
life
du
eto
dam
bre
ach
Can
ada
Req
uir
ed:
Hy
dro
dy
nam
icin
form
atio
n,
nu
mb
ero
fp
eop
le,
char
acte
rist
icof
the
bu
ild
ing
sO
pti
onal
:D
etai
led
dem
og
rap
hy
Co
mple
xV
ery
det
aile
din
pre
sen
tin
gth
eo
utp
uts
.A
cco
un
tsfo
rp
oss
ible
no
n-
stru
ctu
ral
inte
rven
tio
n(e
.g.
war
nin
gan
dev
acuat
ion
stra
teg
ies)
.P
rovid
ea
dy
nam
icm
od
elan
dvis
ual
isat
ion
of
the
even
t.
Dat
a-h
ung
ry,
and
sen
siti
ve
toso
me
of
the
par
amet
ers,
esp
ecia
lly
bu
ild
ing
resi
lien
ce.
So
me
of
thes
ep
aram
eter
sm
igh
tb
eel
imin
ated
tore
du
ceth
ev
aria
bil
ity
inth
ere
sult
sV
alid
ated
for
No
rth
Am
eric
a.C
auti
on
isn
eed
edw
hen
app
lied
else
wher
e.
Iden
tifi
cati
on
of
dan
ger
ous
pla
ces
Det
aile
dfl
ood
risk
asse
ssm
ent
Com
par
iso
nb
etw
een
dif
fere
nt
stru
ctu
ral
inte
rven
tio
ns
for
man
agin
gfl
oo
dri
sks
Det
aile
dco
mp
aris
on
of
no
n-s
tru
ctu
ral
inte
rven
tio
n(e
.g.
war
nin
gan
dev
acuat
ion)
for
man
agin
gfl
oo
dri
skE
mer
gen
cyp
lann
ing
and
even
tm
anag
emen
t
1110 Nat Hazards (2012) 63:1083–1113
123
7 Conclusions
This paper presents a comparison between three methods for estimating fatalities due to
flooding, using the 1953 Canvey Island (UK) flood as a case study. This analysis shows
that these methods can be used as part of flood risk assessments to assess consequences and
estimate damages for an assessment of consequence and quantitative estimate of the
damage as part of flood risk assessments. However, these assessments should account for
the uncertainty of these methods by providing order of magnitude estimates instead of
absolute numbers of fatalities, and performing sensitivity tests to take into account how the
uncertainty in the input data is transferred into the estimated number of fatalities.
The studied methods can be particularly useful for scenario comparison, and for
assessing the benefit of structural and non-structural options for managing the flood risk.
The methods produce maps of dangerous locations and fatalities and could, therefore, be
potentially powerful if used for awareness-raising.
Each of the methods has an appropriate scale for the application that should be
respected; the results should not be extrapolated to a different scale for which they have
been calculated. Also, the application of these methods requires a characterisation of the
area, including making assumption on people’s responses. This should be therefore done
by working with people who have detailed knowledge of the communities at risk.
Evaluating risk to people should be part of the good practice in assessing flood risk. The
methods analysed in this paper, if applied appropriately (and accounting for their
respective limitations), can be fit this purpose, as well as informing flood risk managers and
emergency planners on potential benefits of structural and non-structural measures for
managing flood risk.
Acknowledgments The authors would like to thank Dr Sally Priest (Flood Hazard Research Centre,Middlesex University) and Dr Christos Gouramanis (Earth Observatory of Singapore, Nanyang Techno-logical University) for providing their valuable comments on the research work and the paper.
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