Dr. Ian Wilderspin (Vietnam) (PDF) - Tokyo Development Learning ...

Common Goals, Collective Action
The identification of vulnerable communes
for the community-based disaster risk
management (CBDRM) programme of the
Government of Viet Nam
Dr. Ian Wilderspin
Technical Specialist, Disaster Risk Management, UNDP Viet Nam
and
Dr. Le Dang Trung
Indochina Research and Consulting (IRC), Viet Nam
Presentation for The World Bank
Disaster Risk Management in East Asia and the Pacific
Distance Learning Seminar Series 2013
SESSION 1 Community-Based Disaster Risk Management (CBDRM)
Lessons learned and way forward
January 17, 2013
http://www.un.org.vn

Overview
• Objectives
• Risk assessment methodology:
components of the Integrated Risk Index
and data sources
• Calculation process
• Example of results of Integrated Risk
Index for some provinces
• Expected future outputs

Objectives
• To develop a scientific, objective and
feasible methodology for ranking risk in
all 11,112 communes of the 63 provinces
of Viet Nam
• To identify 6,000 communes out of the
total as the most vulnerable communes
for particular attention under the
Government CBDRM programme

Conceptualization of vulnerability to
disasters
• The characteristics and circumstances of a community,
system or asset that make it susceptible to the
damaging effects of a hazard. There are many aspects
of vulnerability, arising from various physical, social,
economic, and environmental factors. (UNISDR, 2009)
• Vulnerability indices must cover impacts of climate
change to economic, environmental and social systems
(IPCC 1995, 2001, 2007)
…We need a comprehensive vulnerability index that is
aggregated from economic, environmental and social
vulnerability indices

Methodology
What we need…
An Integrated Risk Index that is able to
utilize all nationally available data to
capture important dimensions of natural
hazards in Viet Nam and that is
computable at the commune level

Potential approaches to the
selection of communes
• Self-proposed approach
• Systematic ranking approach

Self-proposed approach
Technical characteristics
o Vulnerability ranking is implemented independently within
each province, through selection by provincial authorities
based on various criteria („self selected‟ or „predetermined‟)
o It is likely to be based on a subjective judgment of local
experts and politicians
Potential limitations
o There is no foundation for comparability across provinces
o Vulnerability may not be defined well enough or defined
according to the same national definition
o Vulnerability and selected communes are identified based on
„quotas‟ rather than objective criteria and can be strongly
affected by unwanted factors
o It can take a very long time to implement

Systematic ranking approach
Technical characteristics
o Vulnerability ranking is implemented equally and with same
methodology for all the communes in the country
o It is based on objective evaluation using scientific methods
and with extensive commune disaggregated data
Strengths
o Vulnerability is well defined and it is easy to compare
between communes, districts and provinces
o All dimensions that contribute to vulnerability can be
included if data is available
o Communes are selected based on their vulnerability
characteristics rather than „quotas‟
o It is objective, scientific and not affected by unwanted
factors

Systematic ranking approach
Certain limitations
o Need for data disaggregated by communes
o Data sets need to be accessible for the whole country
o Selection of indicators in composite index can be
subjective BUT can be avoided through participatory
process and discussion
o Weighting depends on preference of user, therefore
equal weighting preferred, with exceptions, e.g. on
hazard prevalence per region*
o Scientific method not always easy to comprehend

Methodology
Hazard
Measured by
intensity and
probability
Hazard Potential
Vulnerability
Hazard Exposure
Risk Index
Coping Capability
Source: Greiving (2006), originally adopted by Schmidt-Thomé (2005)

Vulnerability index
Risk Index
Methodology
Tropical storms
(Wind > 35knot, probability 0 ≤ p ≤ 1)
„Disastrous’ rainfall
(Daily rainfall > 51mm, probability 0 ≤ p ≤ 1)
Drought
(Yearly rainfall < 250mm, probability 0 ≤ p ≤ 1)
Other hazards
(Probability, 0 ≤ p ≤ 1)
Hazard Potential
Population
(0 ≤ value ≤ 1)
Population density
(0 ≤ value ≤ 1)
Hazard Exposure
Asset index
(0 ≤ value ≤ 1)
Proportion of temporary houses
(0 ≤ value ≤ 1)
Proportion of female population
(0 ≤ value ≤ 1)
Dependency ratio
(0 ≤ value ≤ 1)
GDP at the province level
(0 ≤ value ≤ 1)
Coping Capacity
Source: developed from Greiving (2006)

Components of Integrated Risk Index
Hazard Potential
INDICATOR
• Tropical storms: measured by the
likelihood of being hit by storms
• Rainfall floods: measured by the
likelihood of being affected by heavy
rainfall (day-total rainfall > 51mm)
• Rainfall droughts: measured by the
likelihood of experiencing periods without
rainfall (month-total rainfall < 30mm)
• Other hazards: measured by the
likelihood of experiencing other disasters
(whirlwind, landslides, flash floods and
run-off floods)
DATA SOURCE
• Tropical storms: best-track archives of
all storms that hit Viet Nam between
1951 and 2010
• Floods and droughts: daily rainfall from
172 weather stations all over the country
for the period from 1975 to 2006
• Other hazards: Viet Nam Central
Committee for Flood and Storm Control
disaster database that records all
disasters that happened from 1989 to
2010

Components of Integrated Risk Index
Hazard Exposure
INDICATORS
• Population size: total persons in the
community
Coping Capacity
DATA SOURCE
• Population and assets:
Population Census 2009
• Literacy ratio: Proportion of literate population
• Asset index: Possession of main assets, including:
telephone, TV, motorbike, refrigerator, washing
machine and air-conditioner
• Temporary house: main pier, roof and walls of the
house made of temporary materials
• Dependency ratio: proportion of population aged
< 15 or > 64 years
• District-level poverty rate: poverty headcounts at
the district level
• Poverty rate: Results of poverty
mapping (World Bank and
General Statistics Office of Viet
Nam)

Hazard potential
This index constructed from four hazard components, including: storms,
rainfall flood, rainfall drought and other hazards (such as hail, flash flood,
landslides and whirlwind)
Equal weights were not used as:
o frequencies and impacts of these components can be different -
storms appear to be the most devastating both in terms of
frequency and damage
o weights are also affected by the accuracy level (or measurement
errors) of the data
The component “other hazards” calculated using CCFSC‟s database
available only at provincial level
This results in a lot of „noise‟ when assuming that all the communes in a
particular have the same level of risk of “other hazards”
By contrast, storms, rainfall flood and rainfall drought components are
calculated at the commune level

Hazard potential
Relied on the CCFSC‟s database to calculate the weights for these four components of
Hazard Potential
Took into consideration the following aspects to assign weights to each component the
share of:
o human losses due to each type of hazard
o houses destroyed or damaged by each type of hazard
o bridges destroyed or damaged by each type of hazard
o communication poles destroyed or damaged by each type of hazard
Argued that human losses and house losses should play more important role in capturing
the aftermath of a disasters than bridge and communication pole damage
Accordingly, assigned weights for each of these sub-components as follows:
o Human losses: 35%
o House losses: 35%
o Bridge losses: 15%
o Communication pole losses: 15%
To account for in-country regional heterogeneity of disasters, weights of risk components
of the Integrated Risk Index were calculated at the regional level

Hazard exposure
There are four sub-components including the:
i. proportion of dependent population
ii.
proportion of illiterate population
iii. proportion of female population, and
iv. size of population normalized to ensure its value
ranges between zero and unity
Equal weights were assigned to the four
components

Coping capacity
The coping capacity index is constructed
from three indicators, including:
o poverty headcount at the district level
o the asset index
o the proportion of temporary houses.
Equal weights were assigned to these three
components

Calculations
Step 1: Hazard potential
Step 2: Hazard exposure
Step 3: Coping capability
HP i
=
HE i
=
CC i
=
åw hp j
*h ij
åw he k
*e ik
åw cc l
*c ic
Step 4: Risk index
Risk i
= W hp
* HP i
+W he
* HE i
+W cc
*CC i

The Integrated Risk Index
The risk index is calculated using the
following formula:
Risk = 0.65*(Hazard Potential) +
0.15*(Hazard Exposure) + 0.20*(Coping
Capacity)

A distributional summary
..of the Integrated Risk Index of all 11,112
communes in Viet Nam
Density
Integrated Risk Index

Integrated Risk Index
for Viet Nam

Integrated Risk
Index for Gia
Lai province

Expected future outputs
• Online atlas of all communes in the 63 provinces of Viet Nam
comprising:
o A set of maps per province showing the Integrated Risk
Index for all communes colour coded (for low, media and
high risk)
o Identification of the most vulnerable communes
(communes with largest values of integrated risk index)
o Baseline indicators (of nationally available statistics for
each province)
o Collation of all available data sets per provinces (including
digital hazard maps, links to other data sources, contact
details of Provincial government authorities and main
stakeholders, etc.)
• Online hub for non-government and development agencies to
gather and input information from their operation
• Publications including guidance notes and specific reports

Thank you very much
For further information please contact:
Dr. Ian Wilderspin
and
Dr. Le Dang Trung