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: manuela@ntu.edu.sg

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.

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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)

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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

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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

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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.

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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:

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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

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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

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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

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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)

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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)

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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

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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

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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.

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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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