Overview oof ICHARM’s Case Studies
on Disaster Management

Junichi Yoshitani
yositani@pwri.go.jp

International Centre for Water Hazards and Risk Management
under the auspices of UNESCO
Disaster Prevention Research Team
Public Works Research InstituteAn ICHARM Challenge: localism
Localism is a principle that takes into account localdidiversity
it off natural,t l social
i l andd culturallt lconditions, being sensitive to local
needs priorities
needs,
priorities, development stagestage, etc
etc., within thecontext of global and regional experiences andtrends <snip> To be needs driven rather than supply
trends.
driven, responsive to respective local realities. (ICHARM
Strategiesgand Action Plan for 2006-2008,, October 2006)).

Research programs coupled with capacity
building
Closely work with local engineers/scientists

Local studies and related disaster studies
Takemoto: Factor analysis of flood risksygy to identifyy
To demonstrate a better analyticalmethodology
root causes of disasters
Chavoshian: Policy analysis of the past large floods in the world
T helpToh l new program/policy/ li making
ki bby governmentstAdikari: Vulnerability assessment with focus on sociodemography
To develop a new view and methodology for vulnerability
assessment and contribution to UN-WWAP
District Nepal
Osti: Local study for Banke District,
Nepal, MEXT Kakushin
Program
To support climate change adaptation planning
Watanabe: Demonstration of debrisflow dehydration break in thePhilippines, Asian Development Bank’s Pilot Demonstration Activities
Yoshitani:Y
hit i S
Seriesi off symposiumsi
on flfloodd managementt policies
li i iin
China, Thailand and Japan, JST/CREST ProjectGeneral study approach of risk
factor analysis
Country-scale study

Regionn/disaste
er speciffic studyyPre analysis
Pre-analysis- Selection of region and flood(Hatiya, 1991 cyclone)- Literature survey
Disaster Profile Sheets
Factor analysis- Setting hypothesis- Cause and effect- Rough assessment of capacity
Verification by field survey- Joint work with local NGO- Field survey- Interview with survivors- Disaster External Force Chart- State of Countermeasures Chart- Regional Characteristics Chart- State of Damage Chart

Hypotheses
yp
and findings
g
No.

H
How
sacrificed?
ifi d?

1
2

Hypotheses
Drowned when engulfed by the tidal
surge.

3

Killed when struck by flying objects.
Because many cyclone warnings had
been false alarms.

4

Because they feared that if theyevacuated, their livestock andproperty might be stolen, so theyremained to protect them.

Verification
ResultRemarks○

Verified by field survey.

○

Verified by field survey.

○

Verified by field survey.

●

Field survey discovered
a new fact (not fear oftheft, but fear theirassets will be scattered.)○―

Quantitatively verified
using numerical values.
Unverifiable.

―

Unverifiable.

―○

Unverifiable.
Verified by the fieldsurvey.

―Unverifiable.

LegendWhy people
decidedWhy
failedto
toremainevacuate?
home?

5
6
7
8

Whycould
peoplenot
wereWhyunable to evacuate?
evacuate?

Specific types ofpeople sacrificed?

9
10

11

12
13

Did not evacuate because there wereno cyclone shelters near their homes.
The cyclone is the will of Allah
Women cannot evacuate because ofgrestrictions.
social and religious
If they evacuate, their family
members might be separated.
Could not walk easily because theroads were muddy.
Because they would be asked to pay
a fee to use the shelter.
Most fatalities were concentrated
among poor people on the oceanside of the dike on the south side(high risk zone).
90% of victims were women andchildren.
From 30% to 40% of residents of adike protected island died.

△

Quantitatively verified
using numerical values.
Documents obtained
from the Japanese Red
Cross Society―

Unverifiable.

○○: Facts ≒hypothesis●:Facts andhypothesis
differed. New
facts werediscovered.
△: Can be
confirmed only
by using
documents―:: Hypotheses
for which data
and testimony
necessary for
confirmation
could not be
obtained by thissurvey.

Factor Analysis Series
http://www.icharm.pwri.go.jp/html/docu/report.html
Reports published•
Bangladesh
•
SiL
Sri
Lanka
k
•
The Philippines•
Hatiyay Island, BangladeshgReports to be published•Honduras•
Infanta, the Philippines•
Factor analysis study guidelineLocal Study Series

Large-scale Flood andPolicyy Effective Lessons
Ali Chavoshianali55@pwri.go.jp

International Centre for Water Hazards and Risk Management
under the auspices of UNESCO
Disaster Prevention Research Team
Public Works Research InstituteO ctive
Objec

Large
Largeg -scale Flood ReportpLarge scale floods have a significant effect onLarge-scale
relevant disaster management policies• Policy-related Lessons of Large-scale Floods

1) ICHARM Fl
FlooddYYear BBookk

Expected 2) Detail Report on the Selected Case Studies
Outcomes 3) MonographMh off WorldW ld Floods:
Fl d Lesson-learnedLldPolicy Effective Flood Report•

IIncluding
l di
any policy
li relevantlt iinformationftiregardingdioccurrence of flood such as
Legal re-arrangement
Institutional framework
Institutionalization
Resources and action Plan

Vietnam
After major floods in 2000, the Government of Vietnam
introduced the “Living with Floods” concept that became
the strategy
g for disaster risk reduction in the Mekonggriver delta .
Bangladesh
The 1998 flood prompted the government to adopt an
Integrated Water Resources Policy in the National Water
Policy. Roles and responsibilities were reviewed andemphasis shifted toward preparedness, early warningand planning response .

Rating Flood Magnitude
 Flood Return Period is widely
y used byy hydrologistyg It doesn’t give any sense of death toll and economic damage
 Flood Magnitude and Severity Classes (Dartmouth Flood Obs.)
Severity Class:
Class 1: large flood events: significant damage to structures or agriculture; fatalities; and/or
1-2 decades-long reported interval since the last similar event.
Class 1.5: very large events: greater than 20 yr but less than 100 year recurrence interval, and/or
a local recurrence interval of at 10-20 yr.
Class 2: Extreme events: with an estimated recurrence interval greater than 100 years

Flood Magnitude =LOG(Duration x Severity x Affected Area)

 There is little attention to death toll and flood damages.
 Estimation
E ti ti off severity
it class
l
iis b
based
d on personall jjudgmentdt Using the same weight for the factors
 EM-DATEM DAT In fact it is not a rating flood magnitude but criteria to register aflood event in the data base

ICHARM Rating Scale••••

Easy to estimate
Ubiquitous rating estimation
Considering data availability
Take into account policy related factors• Tangible damages (weight factor=4)– Death toll
– Economic damages• Intangible damages (Weight Factor=2)– Affected area
– Affected population• Mass media coverage (0 to 0.25)
FS=(4 x (∑Tangible DF)+ 2 x (∑Intangible DF)) x (1+ Mass Media CF)

Estimation Method• The factors are normalized between 0 to 1 using thefollowing Eq.
(ln( xi ) − ln(min( X ))(ln(max( X ) − ln(min( X ))• The ICHARM Flood Scale can be between 0.01 to 15 forthe most severe flood event.
event• Mass media coverage of each event is estimated using
number of related news in Google news.
newsEvent

S
Scale

C
Cause

Date

Nargis, Myanmar

13.35

Tropical cyclone May 2008

Cider, Bangladesh

10.75

Tropical cyclone Nov. 2007

China (Hanjiang, Beijiang, Xijiang,
Pearl, Fangcheng, Huaihe. Fuhe.
Xiaohong. Hongru)

9.98

Heavy rain

Jun ~ Aug 2007

Bangladesh, India

9.52

Monsoon rain

Jul ~ Oct. 2007

Africa (11 Countries)

8.78

Heavy rain

Jul ~ Oct. 2007

Philippines, Vietnam

8.67

UK Fl
Flood
d

8 45
8.45

H
Heavy
rain
i

J ~ Jul,Jun
J l 2007

Bolivia , Paraguay

8.32

Heavy rain

Dec. 07 to Apr. 08

Mexico8 21
8.21

Heavy rain

Oct ~ Dec 2007
Oct.

India, Bangladesh

8.09

Monsoon rain

July 2008

Tropical cyclone Sep. Oct. 2007

Based on all the reported events in 2007~08, on average, there were 475
affected people, $267,000 damage and 455 Sq.km affected area per death.

Policy effective local studies
Policy TypePrivatisation
Pi ti tiTax
Financial
Insurance & Subsidy
Gender issue
Immigration
Socio‐economics Energy
Development
Education
War & Militarism

G
Governance

Miscellaneous

Decentralized
Decentrali
ed AdminAdmin.
Political Instability
Climate Change
Int’l organization
Land use policy
Preparedness & Respon.

UK

DE

CN

IR

MX

KP

JP

AF

BD

IN

PH

US

VN

TH

2007 UK Floody It was in fact series of destructive floods that occurred in various

areas across the country during the summer of 2007.
y Two major
j flooding
g events occurred: one in late June, the otherin late July.
y heavy rain on 24–25 June. Up to 111 mm of rainfall fell, with someplaceslreceivingi i over ffour timesitheh average monthly
hl rainfall.
i f lly On 19–20 July, up to 157 mm of rain fell in 48 hours, with someplaces receiving nearly six times the average monthly rainfall
rainfall.

y It was Britain's wettest May–July since records began (in 1776).
y Civil and military authorities described the June and July rescue

efforts as the biggest in peacetime Britain.

A close look at the UK water privatisation policy
and 2007 Flood Water privatization in England was undertaken in 1989
by the government of Margaret Thatcher.
 Managing surface water flood risk is intrinsically linked to
managing surface water drainage at the local level.
 Who is responsible for drainage after privatisation?
 Responsibilities for surface water drainage systems are split
between various organisations, partly as a consequence ofthe privatisation of the water industry.

Source: Reported by House of Commons, April-2008

L
Lessonlearnedld The current fragmented responsibilities for surface water drainage meantthat measures to tackle flood risk were often applied in a piecemeal fashion.
The current situation meant that the various organisations would simply“shift the problem from one place to another”.
 It is also highlighted some areas of the confusion with the current system.
W t UK-who
Water
UK h ddescribed
ib d ththe systemt
as a ““muddle”ddl ” said
id it was iimpossibleibl
to determine when, for example, a highway drain (the responsibility of thelocal authority as a highways authority) became a public sewer (theresponsibility of a water company). Hull City Council had produced a mapof the city’s drainage system since the summer floods, which showed there
was “some ambiguity”
g y about ownershipp of certain assets.
 As the conclusion local authorities, wanted an organisation to “take thelead” on coordinating surface water drainage in local areas. The law should
be changed to grant top-level local authorities a power to take responsibility
for surface water drainage.

Water-related disaster vulnerability:ya socio-demographic challenge

Yoganath Adikari
adikari@pwri.go.jp

International Centre for Water Hazards and Risk Management
under the auspices of UNESCO
Disaster Prevention Research Team
Public Works Research InstituteSymposyum‐2008

Nuumber of dissasters
s

600

500

Flood

Windstorm

Drought

Slide

Wave/Surge

Water-related Epidemic

400

300

200

100

0

Three year trend of water‐related disasters since 1980 to 2006
2006.
(Technical Note of PWRI No. 4088)

Symposyum‐2008
Country population
increaseHighbirth rate
Where and who
is affected and how
to protect them?

Increased livelihoodcompetitionii
Economic migration
City population
increase

Disaster
Slums
dev..

Exposure
VulnerabilityHighbirth rate

The concept of exposure and vulnerability from theview point of population increase
1.
2.

Slums
Vulnerable group (women, kids & elderly)Symposyum‐2008

Definition: a slum household is a household that lacks any one of thefollowing five elements (UN-HABITAT 2003c, p.7):☞ Access to improved water,☞ Access to improved sanitation,☞ Security of tenure (the right to effective protection
by the state against arbitrary, unlawful eviction),☞ Durability of housing (including living in a non-hazardous
location) and☞ Sufficient living area (no overcrowding).

Symposyum‐2008
person/Km2
1400

Source: World Population Prospects: The 2006 Revision
Population Database, MIdium Variant
http://esa.un.org/unpp/

1200
1000

1950
1955
1960
1965
1970

800
600

1975
1980
1985

400
200

1990
1995
2000

0

2005
2010
2015

Population density increase in some selected countries since1950 to 2015.

Symposyum‐2008
x 100040,00035,00030,00025 00025,000Source: World Population Prospects: The 2006
Revision Population Database, MIdium
Variant,
http://esa.un.org/unpp/

1950
1955
1960
1965

20,0001970
1975

15,00010,000

1980
1985
1990
1995
2000

5,000

2005
2010

0

Major city population increase since 1950 to 2015

2015

Symposyum‐2008

%
100
90

% UrbanDeveloped
p countries % SlumSl

Developing countries

80
70
60
50
40
30
20
10
0

The percentage urban population of a country (blue) andpercent slums (red) population in urban areas.
Source: UN‐HABITAT

Symposyum‐2008

16

Millions of people exposed in each city (red bar)Pop
pulation (mmillion)

14
12
10
8
6
4
2
0

Top 20 cities ranked in terms of population exposed to coastal(
in red).
) Source: Nicholls et al,, 2007,, COED,, Parisfloodingg in the 2070 (barsSymposyum‐2008

When flooded who suffers where? to what extent the slums are
exposed/vulnerable?
is an unknown fact……
W haveWehfi
figures off ffatalities
t liti b
butt nott always
lcategorized into different groups or age classes….

Local realities are….

Symposyum‐2008[2004 Niigata flood fatalities distribution]
60

Devveloped countrry
JAPA
AN

50
40

% 30
20
10
0
0 ‐9

10's

20's

30's

40's

50's

60's

70's

80's

[Niigata prefecture population distribution]
%

16
14
12
10
8
6
4
2
0
0‐9

10s

20s

30s

40s

50s

60s

70s

Age distribution

80s

90s

100+

Percentag
ge of fatalitiess

60.00
40.00

Hurricane Katrina Fatalities

20.00
0.00

Age group frequency (Yr)

Perceentage popullation

D
Developped country exxample
USA
A

Symposyum‐2008

16.00
14.00
12.00
10.00
8.00
6.00
4.00
2.00
0.00

Population

Age group
gro p frequency
freq enc (Yr)The percentage frequency distribution of (A) fatalities and (B) total populationof *8 parishes hit by Hurricane Katrina , New Orleans, USA (note a significant
g A))
number of old fatalities in fig*8 Parishes: East Baton Rouge, Jefferson, Orleans, Plaquemines, St. Bernard,
St. John the Baptis, St. Tammany and Washington Parishes
Source: U.S. Census Bureau,Symposyum‐2008
Fatalities[Fatalities]

Deve
eloping countryy exam
mple
PA
AKISTAN
N

25
20

%

15
Male
Female
Total

10
5
0

%

[B]
Population

45
40
35
30
25
20
15
10
5
0
0‐14

15‐24

25‐49

50+

Age group
The age group and sex ratio of fatalities of 2001 flood [A] and population of Pakistan [B] in 1998.
(note: the ratio of female is very low but usually we hear that women and children are most vulnerable)

Source: JICA Report (A) and UN‐population

Percentage off fatalities
P

45.00
40.00
35.00
30.00
25 00
25.00
20.00
15.00
10.00
5.00
0 00
0.00

Fatalities

0‐4

5‐9

10 ‐ 18

19 ‐ 29

30 or more

Not Stated

Age group frequency (Yr)

Percentage poPopulation

Devvelopinng counntry exaample
SSRI LANNKAICHARM Symposyum‐2008

14.00
12.00
10.00
8.00
6 00
6.00
4.00
2.00
0.00

Population

Age group frequency (Yr)The percentage frequency distribution of (A) fatalities of 2004 Indian Ocean Tunami and (B) total
population of a small village in Ampara district,
district Sri Lanka
Lanka. Note more than 35% of the victims arechildren less than 10 years old.
Data source: Source: Final Report - Census on the Buildings and People Affected by the Tsunami
Disaster – 2004, Department of Census and Statistics of Sri Lanka

Symposyum‐2008

Finally:
there is a grave need to study the disaster vulnerability of slums
especially against floods because most of the slums are located1) along low lying flood plains in megacities and 2) coastal cities
grouping fatalities into various age groups gives us an idea thatwhat group of people are exposed and vulnerable in which locality, country
or region.
iWith thisthi informationi f
ti we will
ill beb ablebl tot recognize
i fatalities
f t liti pattern
ttthat are linked to local economic development which may help coin future
development planning; but the data until today is very scarce and unreliablethus we need to do an intensive study on these themes
themes….
➢ last but not the least, I like to emphasize that slums population increase in
low lying floodplains and high kids fatalities is one of the main factors forwater‐related disaster vulnerability in the cities and localities especiallyin developing countries➢ our examples clearly show that kids are affected in developing countries
whereas elderly in developed countries but is not sufficient to draw conclusions…
The way forward is a long and slippery dark trail……we just started to collectbasic information and considering further investigations……………………….

Th k youThank