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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T i s t a Ri v
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T a lB a r a i l a
G a n g e s R i v e r
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K o s i R i v e r
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.