2009

Glo

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Assessing risk – innovation and progress at the global levelIdentifying who and what is at risk, where and

from which hazard type is an essential step that

underpins all other disaster risk reduction actions

– starting with decisions on whether the benefits

of reducing risk outweigh the costs through to the

practicalities of where to stockpile relief items and

related intervention.

Poor communities in developing countries

are usually more at risk because they are both

more exposed to hazard and because they

are more vulnerable. They are also usually less

resilient because they do not have insurance

or access to assets that can buffer the loss.

Accordingly, the risk of mortality or economic loss

in disasters is usually considered to be a function

of four key variables:

hazard�� , which is the probability of a damaging

event such as an earthquake occurring in a

given period of time;

exposure�� , which is the number of people or

economic assets exposed to the hazard in

question;

vulnerability�� , which is the susceptibility of the

people or assets to suffer loss; and

resilience�� , which refers to the capacity to

absorb the loss and recover.

The Report draws on innovative methodology

– albeit with limitations at the national and local

level – to generate a global overview of disaster

risk across a number of major natural hazards,

primarily floods, tropical cyclones, earthquakes

and landslides.

As a first step, global data sets were

updated on each of the hazards, permitting

the calculation of the degree of hazard in each

area of the Earth’s surface. Secondly, global

data on population and gross domestic product

(GDP) distribution was processed to enable

the calculation of exposure to each hazard.

Sub-national data on vulnerability factors such

as building quality are scarce or non-existent,

meaning that proxies have to be used, for

example country-level indicators on government

accountability or per capita income.

The data on hazard, vulnerability and

exposure was then computed for each disaster

that had occurred since 1975. This was then

linked with losses documented in the EMDAT

database 1. Analyzing the mortality and economic

loss experienced in past disasters allows a

calibration of the weight of each of the three

main risk factors in configuring risk: hazard event

characteristics, exposure and vulnerability. Once

data is gathered for each of these risk factors for

many individual disaster events then their relative

importance can be statistically analysed. For

instance, taking into account cyclones of different

intensities, size of the population or economy

in the area affected, it is possible to measure

how vulnerability factors (such as a country’s

institutional quality) affect mortality or the size

of economic losses.

Once the weight of each of the variables in

the model have been statistically calibrated, risk

levels can then be calculated for any area, using

exposure and vulnerability data from 2007. This

provides the expected modelled losses – whether

or not there are recorded disaster losses. This is

important because while a particular area may not

have suffered a major cyclone or earthquake in

recent decades, it does not mean that that there

is no risk. It is also possible to calculate what

would happen to risk in a given area if any of the

variables are changed, for example population

exposure increases – due to infrastructure

development – or governance improves.

While there is now a better understanding

of the distribution and dynamic of global

disaster risk, data limitations combined with

the unpredictable and unique nature of hazard

still mean that much uncertainty remains.

Despite improvements in disaster reporting, loss

information for individual events is incomplete

and suffers from inconsistent measurement of

damages and broader losses, particularly in the

case of economic losses. There may also be rapid

increases in vulnerability and in the exposure of

population and economic assets, as well as the

Invest today for a safer tomorrow

possibility of shifting climatic conditions affecting

hazard location, frequency or magnitude.

Given these constraints, the model shows

average and relative differences in risk – valuable

information for planning purposes – but of course

cannot predict disaster losses from specific

events. If, for instance, the model predicts an

annual average mortality of 1,000 people for a

given hazard type globally, there could be one

event killing 10,000 people followed by nine years

of almost no casualties.

As a case in point, in August 2008, a dyke

breach led to a large flood in Bihar, India. The red

areas on the map below are those that actually

flooded, while the blue areas represent the flood

hazard indicated by the model. It follows that the

global model cannot take into account locally

specific risk factors, such as the strength of dykes,

even though these have a critical influence on the

distribution and magnitude of losses. Even so,

there could be no doubt as to where the general

areas of risk lay, providing the authorities, if so

minded, with firm pointers on overall targets for

remedial action.

Given these limitations and uncertainties

the estimates of exposure and risk provided in

the Report can only be taken as indicative. They

do not describe and cannot predict disaster risk

in specific locations. As such, while many of the

results can be displayed at quite high geographic

resolutions, these should not be used for planning

or decision making at the national or local levels.

The purpose of this global risk analysis is to

decipher global patterns and trends in risk and

it does not and cannot substitute for detailed

national and local-level risk assessments.

1 The OFDA/CRED International Disaster Database: http://www.emdat.net.

Ar u

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Siliguri

Muzaffapur

Dharbhanga

Jalpaiguri

Kathmandu

Bhagalpur

Rangpur

Dinajpur

Purnea

Binnaguri

Bihar

Saidpur

Monghyr

Katihar

Biratnagar

Jamalpur

Mokameh

Parbatipur

Hilli

Saharsa

Barhiya

Kishanganj

Balurghat

Sitamarhi

Raiganj

Kaliaganj

Doma

Birpur

Jogbani

Samastipur

Laukaho

Forbesganj

English Bazar

Manihari

Basantpur

Jaynagar

Islampur

Madhubani

Phulbari

Madhipur

Teghra

Nirmalj

Patan

Madhepur

Janakpur

KasbaMurliganj

Kadambini

Nilphamari

Matabhanga

Kurseong

Belakoba

Ranjganj

BaruniKhagaria

Shaikhpura

KALIMPONGDARJEELING

Supaul

Ghoraghat

Mainaguri

Changrabandha

Malangwa

Thakurgaon

Bhadrapur

Sahibganj

Raghunathpur

Bijaipur

K o s h i

W e s t B e n g a l

M e c h i

J a n a k p u r

S a g a r m a t h

H a

B a g m a t i

W e s t B e n g a l

Na

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I N D I AB i h a r

B A N G L A D E S HR a j s h a h i

88° E86° E

86° E

26° N 26°N

N

Bihar Flooding18 August, 2008

Permanent water

18 August 2008 flood

Flooded areas as modelled

Major urban areas

0 40 8020

Kilometres

Example of one limitation

of the modelCartography and

GIS analysis: UNEP/

GRID-Europe,

data source for

detected Bihar flood

event, courtesy of

Dartmouth Flood

Observatory.