RDG5230014_s01.pdf

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RDG5230014

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Email address : [email protected]

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Vulnerability-based approach Hazard-of-place (HOP) model of vulnerability : Community-based Vulnerability Index (CoVI) CoVI 3 Intergovernmental Panel on Climate Change (IPCC) 1) Exposure 2) Sensitivity 3) Adaptive capacity

CoVI CoVI

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CoVI

CoVI 3 CoVI Social-ecological system Decision Supporting System (DSS)

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Abstract

Project Code : RDG5230014

Project Title : Assessment of Extreme Weather Events of Thailand: risk and hotspot vulnerability

analysis.

Phase 2 : Vulnerability Analysis and Risk Assessment from Rainfall Extreme in Hotspot

Areas.

Researchers : Sangchan Limjirakan1, Atsamon Limsakul2

1 Environmental Research Institute, Chulalongkorn University 2 Environmental Research and Training Center, Department of Environmental Quality

and Promotion

Email address : [email protected]

Project duration : May 2009 October 2010

The community-level vulnerability to rainfall extremes and climate-related disasters in

Nakhon Sithammarat and Chachoengsao provinces was studied and analyzed. The vulnerability-

based approach and Hazard-of-place (HOP) model of vulnerability were applied to develop

Community-based Vulnerability Index (CoVI). The CoVI is a composite index representing the overall

communitys vulnerability in the context of biophysical and socioeconomic dimensions. Based on the

Intergovernmental Panel on Climate Change (IPCC), the CoVIs structure consists of three major

components namely 1) Exposure, 2) Sensitivity and 3) Adaptive capacity. The criteria used to select

indicators for each component were considered mainly on the basis of scale consistency of data, as

well as primary and secondary data which were collected from relevant institutes and organizations in

the study area. Furthermore, multivariate statistical techniques were employed to analyze

multidimensional indicator matrix in order to reduce the number of variables but still exemplify most of

the overall communitys vulnerability.

The results showed that the CoVI is able to represent characteristics and spatial patterns of

vulnerability in Nakhon Sithammarat and Chachoengsao provinces in consistence with overall contexts

of communities. Spatial patterns of each major component and vulnerability level of individual

community in both provinces were generally different. These reflect differences in community contexts

and factors determining the overall vulnerability of those communities. When considered as a whole, it

was found that the communities in Chachoengsao province had higher vulnerability than those in

Nakhon Sithammarat province. These results would indicate the differences in each of major

component that was aggregated from a number of indicators. However, maps of each major

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components and their CoVI consistently indicated similar communities vulnerability in both provinces.

The communities explicitly located close to the Gulf of Thailand such as communities in Bangpakong

district of Chachoengsao province and in Pakphanang, Mung, Thasala and Sichon districts of Nakhon

Sithammarat province had greater risk to climate-related hazards and were more sensitive and

vulnerable than others. The results of CoVI analysis and major components of vulnerability were

generally consistent with the previous studies, illustrating that coastal communities are particularly

vulnerable to climate-related disasters, coastal erosion, high wave and storm surge.

The communities data base of the CoVI, including major vulnerable components, outcome

risk from climate hazards, as well as socioeconomic, environment and disaster data used to calculate

vulnerability index are large data base covering community-level resolution and multidimensional

coverage of social-ecological system. This is a fundamental unit of sustainable development. The

major output data base from this study, therefore, is not only undoubtedly useful for community-level

vulnerability and risk analysis from climate-related disasters, but also be applied as a supporting tool

for policy decision making processes at provincial level to prioritizing the communities needed

immediate actions for socioeconomic development, in parallel with adaptation to climate change and

disasters. Moreover, this data base can be integrated as a part of Provincial Decision Supporting

System (DSS) and other existing data bases at province, district and sub-district levels to support the

measures adapting to adverse impacts of climate change and disaster management in the future.

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Vulnerability-based approach Hazard-of-place (HOP) model of vulnerability Community-based Vulnerability Index (CoVI)

CoVI 3 Intergovernmental Panel on Climate Change (IPCC) 1) Exposure 4 17 2) Sensitivity 5 30 3) Adaptive capacity 4 24

CoVI Principal Component Analysis (PCA) PCA PCA 17 , Sensitivity 30 Adaptive capacity 24 8, 13, 9 7, 11, 10 62 % 59 % PCA PCA PC score Kaiser criterion PC score Exposure, Sensitivity Adaptive capacity

CoVI Exposure, Sensitivity Adaptive capacity (Outcome risk) 0 - 1

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Human Development Index (HDI) Water Poverty Index (WPI)

3-4 .. 2548-2551 .. 2548-2551 3-4

CoVI Exposure, Sensitivity Adaptive capacity GIS Exposure, Sensitivity Adaptive capacity CoVI CoVI

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CovI

CoVI Exposure, Sensitivity Adaptive capacity CoVI 80 0.8 9 CoVI 80 0.8 10 spider plot 9 10 Exposure Sensitivity

CoVI 3 CoVI 76 layer Social-ecological system Decision Supporting System (DSS)

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iii iv viii xiii xv

1-1 1.1 1-1 1.2 1-3 1.3 1-3 1.4 1-4

(Literature Reviews) 2-1 2.1 2-1 2.2 2-5 2.3 - 2.4 - 2.5 - 2.6. / 2-32

3-1 3.1 - 3.2 / -

3.3 -

CoVI 3.4 // -

3.5 / 3-30

COVI

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()

. CoVI 3-31

- 4.1 4-1

4.1.1 - 4.1.2 4-2

4.2 - (Human Achievement Index; HAI)

4.3 CoVI 4-9 4.4 CoVI 4-10

4.4.1 Exposure 4-10 4.4.2 Sensitivity 4-21 4.4.3 Adaptive capacity 4-21

4.5 4-37 4.6 CoVI PCA - 4.7 Exposure, Sensitivity Adaptive capacity - 4.8 Exposure, Sensitivity Adaptive capacity 4-54 4.9 CoVI 4-65

5-1 6 6-1

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2.1 2- 2.2 2-20 2.3 - 2.4 7 2-30 2.5 ESPON Hazard - . (Cutter et al., 2003) 2-36 2.7 2-39 . -

. 2-42 . - . / 2-43 . 2-44 . Livelihood Vulnerability Index (LVI) -

Mozambique . IPCC -

. Flood Risk Index (FRIc) - 2.16 GRAVITY 2-55 2.17 GRAVITY 2-55 2.18 Climate Vulnerability Index (CVI) 2-56 .1 Community-based Vulnerability Index 3-6

(CoVI)

3. - 3.3 CoVI - 3.4 / (2) - 3.5 3-20 3.6 .. 2007-2009 -

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3.7 .. 2007-2009 -

3.8 // 3-29 3.9 // 3-30 3.10 F(t,x) 6 CoVI -

4.1 HAI 8 -

4.2 PCA CoVI -

4.3 PCA CoVI -

4.4 loading PCA -

exposure, sensitivity adaptive capacity

4.5 loading PCA - exposure, sensitivity adaptive capacity

4.6 Exposure score 10 4-62 . Sensitivity score 10 4-62 4.8 Adaptive capacity score 10 -

4.9 Exposure score 10 4-63 4.10 Sensitivity score 10 4-64 4.11 Adaptive capacity score 10 - 4.12 CoVI 80 4-71 4.13 CoVI 80 4-71

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2.1 2-2 2.2 -

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= subset of , = not a subset of,R = resilience, V = vulnerability, AC = adaptive capacity CR =capacity of response

2.4 2-5

2.5 2-9 2.6 2-9 2.7 Pressure and Release (PAR) Model 2-11 2.8 Hazards-of-Place model of vulnerability 2-11 2.9 Vulnerability framework of nested scales and coupled system - 2.10 -

feedbacks 2.11 Vulnerability-led approach 2-15 2.12 - 2.13 -

2.14 - - 2.15 2-18 2.16 Human Development Index Sub-indices 2-23

HDI 2.17 Human Well-being Index Sub-indices 2-23

HWI 2.18 Environmental Sustainability Index (ESI) -

Sub-indices ESI 2.19 Prevalent Vulnerability Index (PVI) -

Sub-indices PVI 2.20 Index of Social Vulnerability to Climate Change -

Sub-indices SVA

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2.21 Disaster Risk Index (DRI) 2-28 Sub-indices DRI Euler constant (base of the natural logarithm)

2.22 Predictive Vulnerability Indicators - Sub-indices PIV

2.23 Environmental vulnerability Index (EVI), CC = Climate change, D = Exposure to 2-33 natural disasters, HH = Human health, AF = Agriculture & Fisheries, W = Water,

CCD = Desertification CBD = Biodiversity 2.24 Integrated vulnerability ESPON Hazard 2-35 2.25 2-42 (Resilience baseline assessment) 2.26 -

GIS 2.27 Flood Risk Index (FRIc) 2-49 2.28 FAO/FIVIMS -

2.29 Core indicator Additional indicator FAO/ FIVIMS -

3.1 -

3.2 3-5

. CoVI 3-7 3.4 Data-indicator-goal 3-8

model( Cabri-Volga Project Deliverable D3) 3.5 Data-indicator-goal model 3-9

( Cabri-Volga Project Deliverable D3) 3.6 Community-based Vulnerability Index 3-10

/

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3.7 / 3-16

2 2550)

3.8 3-19

3.9 3-22 3.10 3-23 3.11 -

.. 2007-2009 3.12 3-25

.. 2007-2009 3.13 CoVI 3-33 4.1 4-4 4.2 (Climatological means) 4-5

4.3 (Climatological means) 4-5

4.4 4-6 4.5 (Climatological means) 4-6

4.6 (Climatological means) 4-7

4.7 4-8 4.8 Histogram 4-11

CoVI 4.9 Histogram -

CoVI 4.10 -

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4.11 - 4.12 // - 4.13 // - 4.14 - 4.15 4-17 4.16 - 4.17 4-19 4.18 4-22 4.19 4-23 4.20 - 4.21 - 4.22 - 4.23 - 4.24 - 4.25 4-29 4.26 - 4.27 - 4.28 - 4.29 - 4.30 - 4.31 - 4.32 .. 2548-2551 4-40

4.33 .. 2548-2551 4-41

4.34 .. 2548-2551 4-42

4.35 .. 2548-2551 4-43

4.36 eigenvalue PCA -

Exposure, Sensitivity Adaptive capacity

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4.37 eigenvalue PCA 4-47 Exposure, Sensitivity Adaptive capacity

4.38 Histogram normalized summed PC scores 4-55 4.39 Exposure score - 4.40 Sensitivity score - 4.41 Adaptive capacity score - 4.42 Exposure score - 4.43 Sensitivity score - 4.44 Adaptive capacity score - 4.46 CoVI histogram 4-67 4.48 CoVI Exposure-Adaptive capacity score 4-68

4.49 CoVI - 4.50 CoVI - 4.51 Spider plot Exposure score, Sensitivity score Adaptive capacity 4-72

score CoVI 80 4.52 Spider plot Exposure score, Sensitivity score Adaptive capacity score 4-74

CoVI 80 5.1 (building resilient community) 5-8 5.2 5-9 5.3 community-based adaptation -

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1.1

(Climate variability and change) (Climate extreme and disaster) (IGES, 2006; ADB, 2009; Yusuf and Francisco, 2009) (World Bank, 2008) ( , 2553; , 2552; Limjirakan et al., 2009; Limsakul et al., 2010) .. 2532 - 2551 ( , 2552) 57 ( . . 2494 - 2550) 63 .. 2526 - 2550 .. 2500 - 2525 (, 2551)

( , 2549; Gupha-Sapir et al., 2004) .. 2532-2547 65,497 .. 2546 - 2550 23,758,577 2,679,021 8,341,002,953 (, 2553)

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(Gregory et al., 1997; Ziegler, et al., 2003; IPCC, 2007a) El Nio-Southern Oscillation (ENSO), Asian Monsoon Indian Ocean Dipole (IOD) (Saji et al., 1999; Timmermann et al., 1999; Lau and Nath, 2003; Chang et al., 2005; Juneng and Tangang, 2005)

ENSO (Limsakul et al., 2007; Limsakul and Goes, 2008) 170 ( , 2553) (Alexander et al., 2006; IPCC, 2007a) 10 Empirical Orthogonal Function (EOF) Principal score EOF 72.5 10 (Composite index) Total rainfall, Simple daily intensity, Heavy rainfall amount, Number of days above 50 mm, Number of heavy rainfall days, Maximum 1-day rainfall amount Maximum 5-day rainfall amount ()

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Principal score () ( , 2553)

1

1. 1. . 1. . (sensitivity)

(coping/adaptive capacity)

1.2.3

1.3

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. . Outcome risk & Social vulnerability indices


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. . Outcome risk indices

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. . (Social vulnerability)

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1.3.11

1.4

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1.4.4 (Social vulnerability)

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1.4.8

1.4.9 // /

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2 (Literature Reviews)

2.1 (Vulnerability)

(Hazard) (Cutter, 1996; Cutter et al., 2003; Adger et al., 2004; Brooks et al, 2005; Adger, 2006; Gallopin, 2006;

IPCC, 2007b; Cutter et al., 2008) (Adger et al., 2004; Adger, 2006; Gallopin, 2006; Cutter et al., 2008) (Socio-ecological system) (Gallopin, 2006; Cutter et al., 2008)

(Adger, 2006; Gallopin, 2006) (Adger et al., 2004; Adger, 2006; Smit and Wandel, 2006)

(Intergovernmental Panel on Climate Change; IPCC) 3 ( 2.1)

/ (Exposure; who or what is at risk) (Sensitivity; the degree to which people or places can be harmed) (Adaptive capacity) (UNFCCC, 2004; IPCC, 2007b;

Leary et al., 2008)

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2.1 : UNFCCC, 2004 IPCC, 2007

(Adger et al., 2004; UNFCCC, 2004; Adger, 2006; IPCC, 2007b; Cutter et al., 2008; Leary et al., 2008)

(Adger, 2006; Gallopin, 2006; IPCC, 2007b) (UNFCCC, 2004; Adger, 2006; Brooks et al., 2005; Gallopin, 2006; IPCC, 2007b; Cutter et al., 2008; Leary et al., 2008)

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Pallopin (2003) (disturbance) ( 2.2)

2.2

: Gallopin, 2003

Pallopin (2006) (resilience) 2.3

(Adger, 2006; Folke, 2006; Gallopin, 2006) Adger

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(2006) 1) 2) 3)

2.3 = subset of , = not a subset of, R = resilience, V = vulnerability, AC = adaptive capacity CR = capacity of response

: Gallopin, 2006

2.4 2.4A (Adger, 2006; Folke, 2006) 2.4B (Burton, 2002; OBrien et al., 2004) 2.4C (Turner et al., 2003; Gallopin, 2006) 2.4D (Manyena, 2006; Cutter et al., 2008) 2.4E (Paton and Johnston, 2006; Tierney and Bruneau, 2007) Cutter et al. (2008)

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(Hazard-of-place (HOP) model of vulnerability) 2.4E

2.4

: Cutter, et. al., 2008

2.2

2 1)

(Brooks, 2003; Adger et al., 2004)

2) (Allen, 2003)

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IPCC (IPCC, 2007b) (physical or biophysical vulnerability)

(first-order) (Brooks, 2003; Adger, et al., 2004) Jone Boer (2003)

(Allen, 2003) (inherent or social vulnerability) (Allen, 2003; Brooks, 2003; Adger et al., 2004; IPCC, 2007b)

(IPCC, 2007b) (Adger et al., 2004)

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( ) (Brooks, 2003; Adger et al., 2004; UNFCCC,2004)

Biophysical vulnerability =f (Hazard x Social vulnerability) (1)

(1) Biophysical vulnerability (Indicator) IPCC (risk) 2.1 1) 2) (probability) (consequence) (2)

Risk = Hazard x Social vulnerability = Probability of hazard occurrence x Consequences (2)

(outcome risk) 2.5 2.6

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2.1

Author(s) Risk definition

Smith, 1996 Probability x loss (probability of a specified hazard occurrence), Hazard = potential threat.

IPCC, 2001 Function of probability and magnitude of different impacts.

Morgan and Henrion, 1990

Risk involves an exposure to a chance injury or loss.

Adams, 1995 A compound measures combing the probability and magnitude of an adverse affect.

Jones and Boer, 2003 Probability x consequences Hazard: an event with the potential to cause harm, e.g., tropical cyclones, drought, floods or conditions leading to an outbreak of disease-causing organisms.

Downing et.al., 2001 Expected losses (of lives, persons injured, property damaged, and economic activity disrupted) due to a particular hazard for a given area and reference period. Hazard: a threatening event, or the probability of occurrence of a potentially damaging phenomenon within a given time period and area.

Downing et.al., 2001 Probability of hazard occurrence Hazard : potential threat to human and their welfare.

Crichton, 1999 Risk is the probability of a loss, and depends on three elements, hazard, vulnerability and exposure.

Stenchion, 1997 Risk might be defined simply as the probability of occurrence of an undesired event [but might] be better described as the probability of a hazard contributing to a potential disasterimportantly, it involves consideration of vulnerability to the hazard.

UNDHA, 1992 Expected losses (of live, person injured, property damaged, and economic activity disrupted) due to a particular hazard for a given area and reference period. Based on mathematical calculations, risk is the product of hazard and vulnerability.

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2.5

2.6

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2.3

(Cutter et al., 2009)

1) 2) 3) 4) (Moss et al., 2001)

(Kates, 1971; Burton et al., 1993) Multilayer Multidimension / (Bohle et al., 1994) Watts Bohle (1993) 3

1) (Entitlement) 2) (Empowerment) 3) - (Political economy) Blaikie et al. (1994) Pressure and Release (PAR) Model ( 2.7)

PAR () ( ) () PAR Feedback (scale) Cutter (1996) Cutter et al. (2003) (Hazard-of-place (HOP) model of vulnerability) / ( 2.8)

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Hazard potential

2.7 Pressure and Release (PAR) Model : Blaikie et. al., 1994

2.8 Hazards-of-Place model of vulnerability

: Cutter, 1996; Cutter et. al., 2003

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Turner et al. (2003) PAR Hazard-of-place model ( 2.9) Clark, Stockholm Environmental Institute Stanford (De Sherbinin, 2007) / Feedback ( 2.10) (mega city) (De Sherbinin, 2007)

United Nations Framework Convention on Climate Change (2004, 2008)

1) Top-down approach (Impact-based approach) Scenario (General Circulation Model; GCM) Scenario (UNFCCC, 2004,2008) (Projection) Biophysical Scenario Socio-economic scenario (Threshold effect) Top-down approach

2) Bottom-up approach (Vulnerability-based approach) (UNFCCC, 2004, 2008) (Participatory

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2.9 Vulnerability framework of nested scales and coupled system : Turner et al., 2003

2.10 feedbacks : De Sherbinin et al., 2007

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approach) - (UNFCCC, 2004, 2008) - (Disaster Risk Management; DRM) National Adaptation Programmes of Action (NAPA) Vulnerability-based approach (UNFCCC, 2004 2008) Bottom-up approach Assessment of Impacts and Adaptation to Climate Change (AIACC) (Leary et al., 2008) IPCC (IPCC, 2007a) (National Communication) (UNFCCC, 2008) Vulnerability-led approach 2.11 (Dolan and Walker, 2004) Downing Patwardhan (2004) 5 ( 12) Polsky et al. (2003) 8 ( 2.13) (Polsky et al., 2003)

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2.11 Vulnerability-led approach

: Dolan and Walker, 2004

2.12

: Downing and Patwardhan, 2004

Structuring the vulnerability assessment : Definition, framework and objectives

Identifying vulnerable groups :Exposure and assessment boundaries

Assessing sensitivity: Current vulnerability of the selected system and vulnerable group

Assessing future vulnerability

Linking vulnerability assessment outputs with adaptation policy

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- 2.14 - (Indicator) (Indices) - (Driver) Downing et al. (2001) ( 2.15)

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(Cutter et al., 2009) (indicator) (Cutter et al., 2008, 2009) (indices) (Gall, 2007; Cutter et al., 2008, 2009) (Rossi and Gilmartin, 1980; Babbie and Mouton, 2001; Gall, 2007; Cutter et al., 2008, 2009) (Rossi and Gilmartin, 1980; Gall, 2007)

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2.14 - : UNFCCC, 2008

2.15 : Downing et. al. 2001

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1960 1970s 1990 (Cutter et al., 2008) (Hill and Cutter, 2002) ( ) ( ) 2.15 (sectoral index) (Dowing et al., 2001)

Gall (2007) ( 2.2) (Diener and Suh, 1997) Vincent (2004) UNDP (2004) Cutter et. al. (2003) 250 42 normalize multicollinearity Adger et. al. (2004) - 3 1) 2) 3) (Dowing et al., 2001; UNFCCC, 2008)

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2.2

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2.5 Gall (2007) 7

4 Prevalent Vulnerability Index (PVI), Social Vulnerability to Climate Change for Africa (SVA), Disaster Risk Index (DRI), Predictive Indicators of Vulnerability (PIV) 3 Human Development Index (HDI), Human Well-being Index (HWI) Environmental Sustainability Index (ESI) 2.3 -

2.3

: Gall, 2007

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Human Development Index (HDI) (UNDP, 2004,2005) HDI 3 4 Life expectancy, Adult literacy rate, Educational enrollment GDP per capita(UNDP, 2004,2005; Cutter et al., 2009) ( 2.16) HDI 177 HDI standardize min-max normalization (goalpost point of reference) UNDP HDI

Human Well-being Index (HWI) HDI (political rights, crime, Internet users peace and order) (Gall, 2007; Cutter et al., 2009) HWI 33 5 (health, population, wealth, knowledge, community equity) 2.17 HWI 2 5 HWI HWI HDI HWI Bellagio Principal of Sustainability (Gall, 2007)

Environmental Sustainability Index (ESI) HWI ESI (Esty et al., 2005) ESI 146 1) Environmental system 2) Environmental stress 3) Human vulnerability to environmental stress 4) Human capacity to respond to environmental change 5) Global stewardship ESI Driving Force-Pressure-State-Impact-Response (DPSRI) ESI ESI HWI 1)

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2.16 Human Development Index Sub-indices HDI

2.17 Human Well-being Index Sub-indices HWI

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2) GDP HDI 3) ESI 76 ESI ( 2.18) ESI ESI ESI 2 31 Sub-indices 7 ESI Sub-indices 1) standardization 2) imputation of missing variables 3) scale transformation 4) winsorization 5) directional adjustment 6) Z-score transformation

Prevalent Vulnerability Index (PVI) (Cardona, 2005) PVI PVI Inter-American Development Bank (Gall, 2007) PVI 24 0-1 min-max normalization ( 2.19) 3 Sub-indices Weighted aggregation

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2.18 Environmental Sustainability Index (ESI) Sub-indices ESI

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2.19 Prevalent Vulnerability Index (PVI) Sub-indices PVI

Index of Social Vulnerability to Climate Change (SVA) PVI SVA (Adger, 2006; Gallopin, 2006) SVA SVA (baseline) SVA 8 - 9 ( 2.20) SVA Weighted Unweighted averaging Aggregation Weighted Index 5 Sub-indices

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2.20 Index of Social Vulnerability to Climate Change Sub-indices SVA

Disaster Risk Index (DRI) UNDP Bureau for Crisis Prevention and Recovery (UNDP/BCRP) DRI 4 1) 2) 3) 4) (Gall, 2007) DRI DRI

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DRI (Risk) = Hazard * Population * Vulnerability Hazard * Population Vulnerability - DRI 4 Sub-indices 4 2.21 DRI 4 6 26 Stepwise linear regression

2.21 Disaster Risk Index (DRI) Sub-indices DRI Euler constant (base of the natural logarithm)

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Predictive Indicators of Vulnerability (PIV) (Adger et al., 2004; Brooks et al., 2005) PIV SVA PIV PIV () DRI (Brooks et al., 2005) PIV (Adger et al., 2004) PIV SVA SVA Framework-driven approach PIV Outcome-driven approach EM-DAT .. 1971-2000 standardize PIV 45 11 11 ( 2.22) PVI 2.4 7

2.22 Predictive Vulnerability Indicators Sub-indices PIV

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- 31

2.4

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. . Page 2- 32

Environmental Vulnerability Index (EVI) South Pacific Applied Geoscience Commission (SOPAC) (Kaly, 1999; SOPAC, 2005) (Small Island Developing States (SIDS) EVI EVI 50 3 Sub-indices (Hazard, Resistance Damage) 2.23 1) 2) 3) 4) 5) 6) 7)

2.6. / 2.6.1 ESPON Hazard Project EU 27+2

Risk =Hazard potential * Vulnerability (Damage potential +Coping capacity)

2 aggregated hazard integrated vulnerability (ESPON Hazard project, 2005) damage potential coping capacity

Vulnerability = {economic (economic damage potential), social (people, livelihood, resilience, coping capacity),

ecological (ecosystem, environmental vulnerability or fragility)}

ESPON Hazard 2.5 2.4 integrated vulnerability 2.24

2.6.2 Cutter et al. (2003) ( 2.6) (Social Vulnerability Index; SoVI) SoVI - 42 factor analysis ( 2.7)

. . Page 2- 33

2.23 Environmental vulnerability Index (EVI), CC = Climate change, D = Exposure to natural disasters, HH = Human health, AF = Agriculture & Fisheries, W = Water, CCD =

Desertification CBD = Biodiversity

. . Page 2- 34

2.5 ESPON Hazard

Indicator dp/cc1 Econ/soc/ecol2 Description GDP/capita dp Econ High GDP/capita measures the value of endangered

physical infrastructure and the extent of possible damage to the economy. Insurance company point of view

Population density dp Econ/soc Measures the amount of people in danger Tourism (e.g. number of tourists/number of hotel beds

dp/cc Econ/soc Tourists or people outside their familiar environmental are especially vulnerable for two main reasons. First, they are generally unaware of the risks and dont necessarily understand the seriousness of hazardous situation. They dont necessarily know the local language and thus they are likely to miss important information. Secondly, tourist dwellings are often located in high-risk areas and might not meet the requirements of structural risk mitigation

Culturally significant sites

dp Ecol Such sites are unique and important for the cultural and historical identity of people e.g. sites on the UNESCO world heritage list

Significant natural areas

dp Ecol Areas with special natural values (e.g. national parks or other significant natural areas) can be considered vulnerable because they are unique and possibly home to rare species of flora of fauna

Fragmental natural areas

dp Ecol Natural areas that are small and fragmented are vulnerable, since they are likely to be locally destroyed if a hazard strikes

GDP/capita cc Soc Low GDP/capita measures the capacity of people or region to cope with a catastrophe. In the hazard project, the national GDP/capita was used because the presumption was that coping capacity is weak in poor countries and strong in rich countries. It was further presumed that there are no marked differences in coping capacity inside a country

Education rate cc soc Measures peoples ability to understand and gain information. The presumption is that people with a low education level do not find, seek or understand information concerning risks as well as others, and are therefore vulnerable

Dependency ratio cc soc Measures the proportion of strong and weak population groups. A region with high dependency ratio is especially vulnerable for two reasons. First, elderly people and young children are physically frail and thus vulnerable to hazards. Secondly, elderly people and children may not be able to help themselves but need help in the face of a hazard. A region with a high dependency ratio is dependent on help from the outside.

. . Page 2- 35

Indicator dp/cc1 Econ/soc/ecol2 Description Risk perception

cc

soc

Indicates how people perceive a risk and what their efforts have been to mitigate the effects of a hazard

Institutional preparedness

cc Indicates the level of mitigation of a region

Medical infrastructure

cc Indicates how a region is able to respond to a hazard (e.g. number of hospital beds per 1000 inhabitants or number doctors per 1000 inhabitants)

Technical infrastructure

cc Indicates how a region is able to respond to a hazard (e.g. number of fire brigades, fire men, helicopters etc)

Alarm systems cc Indicates the level of mitigation of a region Share of budget spent on civil defence


Share of budget spent on research and development


1dp = damage potential, cc = coping capacity 2econ=economic dimension, soc=social dimension, ecol=ecological dimension of vulnerability

2.24 Integrated vulnerability ESPON Hazard

. . Page 2- 36

2.6 (Cutter et al., 2003) Concept Description Increase (+)/or

decrease (-) social vulnerability

Socio-economic status (income, political power, prestige)

The ability to absorb losses and enhance resilience to hazard impacts. Wealth enables communities to absorb and recover from losses more quickly due to insurance, social safety nets and entitlement programs.

High status (+/-) Low income or status (+)

Gender Women can have a more difficult time during recovery than men, often due to sector-specific employment, lower wages and family care responsibilities.

Gender (+)

Race and ethnicity Imposes language and cultural barriers that affect access to post-disaster funding and residential locations in high hazard areas.

Nonwhite (+) Non-Anglo(+)

Age Extreme of the age spectrum affect the movement out of harms way. Parents lose time and money caring for children when daycare facilitate are affected; elderly may have mobility constraints or mobility concerns increasing the burden of care and lack of resilience.

Elderly (+) Children (+)

Commercial and industrial development

The value, quality and density of commercial and industrial buildings provides and indicator of the state of economic health of a community, and potential losses in the business community, and longer-term issues with recovery after an event.

High density (+)

High value (+/-)

Employment loss The potential loss of employment following a disaster exacerbates the number of unemployed workers in a community, contributing to a slower recovery from the disaster.

Employment loss (+)

Rural/urban Rural residents may be more vulnerable due to lower incomes and more dependent on locally based resource extraction economies (e.g. farming, fishing). High density areas (urban) complicate evacuation out of harms way.

Rural(+)/Urban(+)

Residential property The value, quality and density of residential construction affects potential

Mobile homes (+)

. . Page 2- 37

losses and recovery. Expensive homes on the coast are costly to replace; mobile homes are easily destroyed and less resilient to hazards.

Infrastructure and lifelines Loss of sewers, bridges, water, communication, and transportation infrastructure compounds potential disasters losses. The loss of infrastructure may place an insurmountable financial burden on smaller communities that lack the financial resources to rebuild.

Extensive infrastructure (+)

Renters People that rent do so because they are either transient or do not have the financial resources for home ownership. They often lack access to information about financial aid during recovery. In the most extreme cases, renters lack sufficient shelter options when loading becomes uninhabitable or too costly to afford.

Renter (+)

Occupation Some occupation, especially those involving resource extraction, may be severely impacted by a hazard event. Self-employed fisherman suffer when their means of production is lost and may not have the requisite capital to resume work in a timely fashion and thus will seek alternative employment. Those migrant workers engaged in agriculture and low-skilled service jobs (housekeeping, childcare, and gardening) may similarly suffer, as disposable income faded and the need for services declines. Immigration status also affects occupational recovery.

Professional or managerial (-)

Clerical or laborer (+)

Service sector (+)

Family structure Families with large number of dependents or single-parent households often have limited finance to outsource care for dependents, and thus must juggle work responsibilities and care for family members. All affect the resilience to and recovery from hazards.

High birth rates (+) Large families (+) Single-parent households (+)

Education Education is linked to socio-economic status, with higher educational attainment resulting in greater lifetime

Little education (+) Highly educated

. . Page 2- 38

earnings. Lower education constraints the ability to understand warning information and access to recovery information.

(-)

Population growth Countries experiencing rapid growth lack available quality housing, and the social services network may not have had time to adjust to increased populations. New migrants may not speak the language and not be familiar with bureaucracies for obtaining relief or recovery information, all of which increase vulnerability.

Rapid growth (+)

Medical services Health care providers including physicians, nursing homes and hospitals, are important post-event sources of relief. The lack of proximate medical services will lengthen immediate relief and longer-term recovery from disasters.

Higher density of medical (-)

Social dependence Those people who are totally dependent on social services for survival are already economically and socially marginalized and required additional support in the post-disaster period.

High dependence (+) Low dependence (-)

Special needs populations Special needs populations (infirm, institutionalized, transient, homeless), while difficult to identify and measures, and disproportionately affected during disasters and because of their invisibility in communities, mostly ignored during recovery.

Large special needs population (+)

. . Page 2- 39

2.7

(Cutter et al., 2003)

. . Page 2- 40

2.6.3 Brook et al. (2005) 46 2.8

2.6.4 Community and Regional Resilience Initiative (CARRI) Community resilience baseline (Cutter et al., 2008) (Resilience baseline assessment) 4 ( 2.25) 2.9 - 2.12 (Layer) Hazard of place model of vulnerability GIS ( 2.26)

. . Page 2- 41

2.8 (Brook et al., 2005)

. . Page 2- 42

2.25 (Resilience baseline assessment)

: Cutter et al., 2008)

2.9 (Cutter et al., 2008)

Category Variables Race and ethnicity % African American; % Native American; % Asian or Pacific Islanders; % Hispanic Age % population under 5 years old; % population 65 or older; median age Socioeconomic status

Per capita income; % families earning more than $100,000; median dollar value of owner-occupied housing

Gender %female; % females in civilian labor force Employment % of the civilian labor force umemployed; % civilian labor force participation Education % population over 25 with less than high school education Household structure

Average number of people per household; % families living in poverty; % female-headed household, no spouse present

Access to services Number of physicians per 100,000 population; % rural farm population; % urban population

Occupation % employed in fishing, farming, forestry; % employed in transportation, communications, public utilities, % employed in service occupation

Housing % housing units that are mobile homes; % renter-occupied housing units; median gross ($) for renters

Special needs Nursing home residents per capita; % social security recipients; % migrate to the United States from abroad in last 5 years

. . Page 2- 43

2.10 (Cutter et al., 2008) Category Variables

Residential Median age for housing units, Housing units built before 1960, Density of housing units, Density of mobile homes, Number of building permits for new housing units, Daily water usage, Value of all residential property

Commercial and industrial development

# commercial establishments, # manufacturing establishment, Value of sales for all businesses ($), value of all sales for all farms, Industrial earning ($), Banking offices, Private non-farm business establishments, Hazardous materials facilities, # Small businesses , # marinas

Lifelines Hospitals, Schools, Electric power facilities, Potable water facilities, Wastewater facilities, Dams, Police stations, Fire station, Oil and natural gas facilities, Nuclear facilities, Emergency centers, Number of hospital beds, Communications towers/antennae

Transportation infrastructure

Airports, Bus terminals, Ferry facilities, Interstate miles, Other principal arterial miles, Fixed transit and ferry network miles, Rail miles, Highway and rail bridges, Ports

Monuments and icons

Churches, Landmark and historic registry building parks, social organizations

2.11 / Variables

- Area of dunes - Average dune height - Average beach width

- Erosion rates - Acreage of wetland - Wetland/habitat loss (% change from previous decade) - Acreage of undisturbed habitat - Coastal subsidence (rate per year) - Sediment supply (estimated berms and offshore bars) - # and location of coastal defense (groins, jetties, seawalls, revetments)

- # and size of storm water detention basins - Water contamination (surface and ground) - 100-year and 500-year flood zones delineations - Storm surge inundation zones - Land cover classification - Amount of impervious surfaces - Projected sea level rise from Intergovernmental Panel on Climate Change reports

. . Page 2- 44

2.12

Variables

- Disasters/emergency response plans (household and community) - Building standard, codes and enforcement - Hazard mitigation plans and hazard vulnerability assessments (required by the disaster

mitigation act of 2000

- Comprehensive plans (land use and growth management) - Zoning ordinances prohibiting development of high hazard areas - Continuity of operations plans for local governments - Interoperable communications among police, fire and emergency responders - Disaster recovery plans - Participation in the National Flood Insurance Program (NFIP) - Coastal setbacks for development

- Dune management districts - Transfer of development rights to discourage development in sensitive areas - Fiscal policies to shift public infrastructure costs (water, sewer, roads) to developers - Provision of risk/hazard information to the public - Tabletop and nock exercise and drills for disaster response

. . Page 2- 45

2.26 GIS

. . Page 2- 46

2.6.5 Hahn et. al. (2009) Livelihood Vulnerability Index (LVI) Mabote Mona Mozambique 200 socio-demographics, livelihoods, social networks, health, food/water security natural disaster climate variability ( 2.13) Composite index standardize HDI

minmax

min

SSSSindex dSd ,

Sd= d Smin Smax= standardize

n

iindexMni Sd

d1 ,

Md = d [socio-demographic profile (SDP), livelihood strategies (LS), social networks (SN), health (H), food (F), water (W), natural disasters and climate variability (NDCV)], indexSdi = n LVI 7

NDCWFSHHLSSDP

vNDCdwdFdHdSNdLSdSDPd wwwwwww

NDCVwWwFwHwSNwLSwSDPwLVI

, Hahn et al. (2009) LVI IPCC 2.14 7 LVI-IPCC

. . Page 2- 47

6 6 socio-demographic profile, livelihood strategies social networks health, food, water 2.14

ni Mi

ni diMi

d wMw

CF1

1 ,

CFd = IPCC (Exposure, Sensitivity Adaptive capacity) d, Mdi d I, wMi (weight) n

dddd saeIPCCLVI *)(

LVI-IPCCd = LVI IPCC, e = exposure score, a = adaptive capacity score s = sensitivity score

.

.

Pa

ge 2

- 48

2.1

3

Live

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al.,

200

9)

.

.

Pa

ge 2

- 49

2.1

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)

.

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Pa

ge 2

- 50

2.1

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)

. . Page 2- 51

2.14 IPCC LVI-IPCC (Hahn et al., 2009)

2.6.6 Kannami Taniyoshi (2008) Flood Risk Index (FRIc) PAR model FRIc 5 Hazard, Exposure, Basic vulnerability, Capacity soft countermeasure Capacity hard countermeasure Sub-index 3 Sub-index ( 2. 27) FRIc

CVEHFRIcIndexRiskFlood /**)( ,

H = Hazard index, E = Exposure index, V = Basic vulnerability index, C = Capacity index [Capacity index = (Capacity hard countermeasures index + Capacity soft

countermeasures index)/2]

)}(()((/{))}(()({ xMINLNxMAXLNxMINLNxLNIndicator

Sub-index = Indicator1 + Indicator2 + Indicator3

International Center for Water Hazard and Risk Management (ICHARM) 2.15

. . Page 2- 52

2.27 Flood Risk Index (FRIc) (Kannami and Taniyoshi, 2008)

2.15 Flood Risk Index (FRIc) (Kannami and Taniyoshi, 2008)

. . Page 2- 53

2.6.7 Disaster Risk Index (DRI) Division of Early Warming and Assessment (DEWA) GRID-Geneva United Nations Environment Programme (UNEP) Global Risk and Vulnerability Trends per Year (GRAVITY) (Peduzzi et al., 2001,2002,2003; Dao, 2003) DRI GRID-Geneva (PREVIEW) GRAVITY 4

Risk =Frequency * Population* Vulnerability

(Risk) = /

(Frequency) = (Population) = / (Vulnerability) =

Physical exposure (PhExp) = Frequency * Population Risk = PhExp * Vulnerability

(Proxy)

PhExpimpactsVul proxy

impact ( GRAVITY) = CRED .. 1980-2000

Risk = f (PhExp,Vulnerability Factors)

. . Page 2- 54

- GRAVITY 2.16 2.17 EM-DAT

2.6.8 Climate Vulnerability Index (CVI) Oxford Center for Water Research CVI Water Poverty Index (WPI) (Oxford Center for Water Research) CVI CVI 6 32 2.18

2.6.9 Food Insecurity and Vulnerability Information and Mapping System (FIVIMS) Food and Agriculture Organization (FAO) (FIVIMS, 2000) FIVIMS .28 (FIVIMS) Core indicator Additional indicator / 2.29 (FIVIMS, 2002)

. . Page 2- 55

2.16 GRAVITY

2.17 GRAVITY

. . Page 2- 56

2.18 Climate Vulnerability Index (CVI)

.

.

Pa

ge 2

- 57

2

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/FIV

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.

.

Pa

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- 58

2

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. . Page -1

3

3.1 : : - Vulnerability-led approach (UNFCCC, 2004) The Famine Early Warning System, FEWS; Vulnerability Analysis Mapping, VAM (USAID, 1997), Global Risk and Vulnerability Trend per Year, GRAVITY (Peduzzi et al., 2001,2002,2003), Vulnerability indices (Dowing

et al., 2001), Global Natural Disaster Risk Hotspots (Dilley et al., 2005; Arnold et al., 2006), Food

Insecurity and Vulnerability Information and Mapping Systems (FIVIMS), Indicators of disaster risk and

risk management: Program for Latin America and the Caribben (Cardona, 2005, 2007), Social

vulnerability to climate and environmental hazards (Cutter et al., 2003, 2009) Climate change variability mapping for Southeast Asia (Yusuf and Franciso, 2009) (/) (Baseline data) (Future threat) ( ) - Vulnerability-lead approach (UNFCCC, 2004) Hazard-of-place model of vulnerability (Cutter et al., 2006,2008) 2 2.5

. . Page -2

( Vulnerability hotspot) (Geographic Information System, GIS)

3.1 ( . )

1) / 2) 3) 4) 5) 6) 7) Sensitivity Adaptive capacity 8)

3.2 /

-

2 2.6 Community-based Vulnerability Index (CoVI) CoVI 1) Representativeness 2) Data availability 3) Sensitivity 4) Feasibility 5) Reliability 6) Validity (Dowing et al., 2001; UNFCCC, 2008)

. . Page -3

CoVI 3 IPCC Hahn et al. (2009) LVI Mozambique Exposure, Sensitivity Adaptive capacity Exposure 4 (Sub-component) Sensitivity 5 Adaptive capacity 4

CoVI 3.1 CoVI 3.3 Data-Indicator-Goal Model CoVI ( 3.4) 9 3.5 (UNUIEHS & NNSUACE) CoVI 3.2 (UNUIEHS & NNSUACE, EEA-European Environment Agency, 2004; Birkmann, 2006) CoVI CoVI CoVI 3.6

.

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10. D

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Cr

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Z-s

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(mea

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0, s

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= 1)

and

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11. I

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lysis

(PCA

)

.

.

Pa

ge -

7

3

.3

Com

mun

it-ba

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Vuln

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x (C

oVI)

.

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4 6

3 4

6 7

10

4

3

8 3

5 8

.

.

Pa

ge -

8

3

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Data

-indi

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(

Cab

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; w

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. . Page -9

3.5 Data-indicator-goal model ( Cabri-Volga Project Deliverable D3; www.cabri-volga.org)

3.2

Standard criteria for indicator development 1. Measurable

2. Relevant, represent an issue that is important to the relevant topic

3. Policy-relevant

4. Only measure important key-elements instead of trying to indicate all aspects

5. Analytically and statistically sound

6. Understandable

7. East to interpret

8. Sensitivity, be sensitive and specific to the underlying phenomenon

9. Validity/accuracy

10. Reproducible

11. Based on available data

12. Data comparability

13. Appropriate scope

14. Cost effective

.

.

Pa

ge -

10

3

.6

Com

mun

ity-b

ased

Vul

nera

bility

Inde

x

/

. . Page -11

3.3 CoVI

- CoVI 3.3

(Missing value) CoVI

1. / (2) / / 2 2 22 2530

/ (2 2550) 3.7 20 2 8 2 2

10 (.. 2550-2554) 2 8 3.4

. . Page -12

2

- - /

- - / - - - // 2 /

7-10

2 2552 2 5.3.4 2 5.3.4 Excel CoVI

2. ( 3.8) ACC Programme Classification Administrative Committee on Coordination, United Nations (General Statistics) 3 23

- 8 - 13 - 2

3.5

. . Page -13

4 3.9

.

.

Pa

ge -

14

3.3

Co

VI

/

1.

(GIS

)

laye

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2.

. 2

25

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(

)

2

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G

IS

4.

/

/

5.

.

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11.

.

.

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. . Page -17

3.4 / (2)

. . . . / . / . . . . .

. . . . . . . . . . . . . . . . . .

. . Page -18

3.4 ()

. . . . . . . .

. . .

. . . . . . . - . .

. . .

. . . . .

. . Page -19

3.4 ()

. . . . . .

3.8 :

. . Page -20

3.5

. . Page -21

3.5 ()

. . Page -22

3.9 (: )

3. (GIS) (Overlay) (Spatial analysis) 4 3.10

- - - - - - -

. . Page -23

7 (GIS) (Weighting) (Rating)

- 80

- 56-80

- 30-55

- 30

CoVI

3.10

. . Page -24

4. GIS/MIS 3 .. 2550 - 2551 CoVI

5.

6.

7.

8.

9. 2007 - 2009 29 17 3.11 3.12 3.6 3.7

,)1(

)( 21

n

XXSD

j

n

iij

jk (1)

jkSD = j (j=1,,12) k (k =2007, 2008 2009); ijX = i j ; jX = j

),(_ jkSDaverageSDMean (2)

SDMean_ = .. 2007-2009 (Hahn et al., 2009)

. . Page -25

3.11 .. 2007-2009

3.12 .. 2007-2009

. . Page -26

3.6 .. 2007-2009

() UTM (x) UTM (y) . 900943.7 552005 600117.4 957382.8 552008 544049.4 915274.6 552009 593378.3 1017074.5

552010 608013.4 878898.1

552011 626703.1 896638.0

552012 588083.0 924186.2

552013 558403.5 886546.6

552016 567127.8 946260.8

552017 566078.1 913091.1

552018 589149.8 940773.1

552022 563860.9 923038.2

552024 594618.5 956264.3

552201 605687.6 929753.9

552301 618924.2 920942.6

552401 555043.8 929659.6

552006 596741.2 989439.9

. . Page -27

3.6 ()

() UTM (x) UTM (y) 552007 589240.1 896547.5

594760.8 891030.7

644369.6 885633.4

27042 . 900943.7

27013 604589.3 928645.6

27062 574909.5 900943.7

27082 588080.8 925291.8

27122 593378.3 1017074.5

27142 610218.0 878903.3

27401 580300.0 963973.9

27442 560565.3 917505.5

27452 563824.7 947361.3

. . Page -28

3.7 .. 2007-2009

() UTM (x) UTM (y) 423003 741152.3 1518980.2

423004 721547.1 1534299.4

423007 719728.2 1493338.9

423002 727212.9 1504395.2

423001 723890.3 1513293.2

423008 748704.6 1521264.8

423006 762880.4 1510334.7

423005 754135.5 1519103.5

423010 811960.1 1479867.3

738988.6 1518960.3

721566.1 1532086.3

730206.2 1533268.5

725048.2 1504395.2

718553.9 1504395.2

724972.3 1513293.2

746509.5 1524564.4

765034.0 1511463.3

. . Page -29

,)1(

)( 21

n

XXSD

k

n

ik

k (3)

kSD = k (k=2007, 2008 2009); kX = k; kX = k

),(_ kSDaverageSDMean (4)

SDMean_ = .. 2007-2009 (Hahn et al., 2009)

3.4 //

( ) // // 23 163 1531 // 11 93 876 // 3.8 3.9

3.8 //

AmpName AmpID TamName TamID VILL_Name Vill_ID Point_X Point_Y

8001 800106 80010601 610945.13 930258.00

80010602 618361.50 931063/75

80010603 613394.63 932570.19

800107 80010702 610563.38 930633.19

80010704 610963.06 928788.81

80010705 60965.06 928788.81

8002 800201 80020103 589296.00 941042.63

80020104 591167.69 940860.69

. . Page -30

3.9 //

AmpName AmpID TamName TamID VILL_Name Vill_ID Point_X Point_Y

2401 240102 24010201 723857.81 1515534.75

24010202 722051.56 1518142.75

24010204 721409.00 1519098.75

240103 24010302 727842.06 1516114.25

24010303 727498.63 1515777.00

24010305 727936.56 1515791.00

2402 240204 24020402 735593.88 1516731.75

24020403 734578.19 1515940.75

24020404 735587.31 1514201.75

3.5 / COVI

CoVI Excel Z scroe standardize CoVI Principal Component Analysis (PCA) Z scroe

itititit SDXXZ /)( (5)

. . Page -31

itZ = Z scroe i, itX = i, itX = I, itSD = i t = ()

3.6 CoVI Principal Component Analysis (PCA) Social

Vulnerability Index (SoVI) Cutter et al. (2003, 2009) Hazard & Vulnerability Research Institute CoVI

PCA (Descriptive multivariate statistic) Empirical Orthogonal Function (EOF) Singular Value Decomposition (SVD) (Hotelling, 1935; Hannachi et al., 2007) (Hannachi et al., 2007) PCA Orthogonal (Eigenvalue/ Eigenvector) (Preisendorfer, 1988; Hannachi et al., 2007) PCA (Matrix) F(t,x) t (t = t1, t2, t3,,tn) x (x1, x2, x3,,xp) 6

(6)

p = uj(x), aj(t) = Principal score j, a1u1 = 1 1 F , a2u2 = 2 2 F 2 Orthogonality PCA (Preisendorfer, 1988; Jolliffe, 2002; Hannachi et al., 2007) PCA (Covariance matrix) Eigenvalue, Eigenvector Principal Score PCA

Z*E =E*L (Z-L)*E=0 (7)

)),()((),(1

xutaxtF jp

jj

. . Page -32

A=Z*E (8)

E*ET=ET*E=I (9)

AT*A=L (10)

Z = n x p n= p=

E = Eigenvector p x p

L = Eigenvalue p x p Off-diagonal

A = Principal Score p x p

I = Diagonal Off-diagonal

PCA CoVI (Exposure, Sensintivity Adaptive capacity) 3.10 CoVI 3.13 PCA (Cutter et al., 2003) Varimax rotation Kaiser criterion PCA (Hazard & Vulnerability Research Institute, 2010) PCA normalize Min.-Max. normalization Human Development Index (HDI) PCA ( 3.13)

3.10 F(t,x) 6 CoVI

Exposure Sensitivity Adaptive capacity

(17, 1531) (30, 1531) (24,1531)

(17, 875) (30, 875) (24, 875)

.

.

Pa

ge -

33

3

.13

CoVI

Norm

alize

d ex

posu

re c

ompo

nent

(4

sub

-com

pone

nts/

17in

dica

tors

) No

rmal

ized

sens

itivity

com

pone

nt

(5 s

ub-c

ompo

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s/30

indi

cato

rs)

Norm

alize

d ad

aptiv

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paci

ty c

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(4

sub

-com

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24in

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)

Dire

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nal a

djus

tmen

ts o

f som

e in

dica

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(mul

tiple

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-1) s

uch

that

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pres

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g hi

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risk,

hig

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nsitiv

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city

whi

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alue

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and

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PCA

perfo

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Plac

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ach

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Min

.-Max

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mal

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x min /

x max

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of o

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for e

ach

com

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Map

ping

nor

mal

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over

all s

core

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CoVI

cal

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of C

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mal

ized

CoVI

. . Page 4-1

4

4.1 4.1.1

2 10,169 14.9 % 193 (, 2553) 3

-

- 95

-

21 2 165 1,509 ( 4.1) 1,533,894 764,615 (49.8%) 769,279 (50.2%) 154.3 (, 2553; , 2553)

( 4.2)

. . Page 4-2

75 % ( 4.3) 550 ( 4.2) 25 2505 (, 2550)

48.2% 17.2 % (, 2550; , 2553; , 2553)

4.1.2

( 4.4) , 13.8 (, 2553)

- . %

- . %

. . Page 4-3

- . % (, 2553; , 2553)

3 . % . % .4 % (, 2553)

(, 2551) 200 ( 4.5) ( 4.6) ( ) ( 4.7)

(, 2551) 70.4 % 15.9 % 5 % (, 2553) 4.2

(Human Achievement Index; HAI) (United Nations Development Programme)

.. 2550 (, 2550) HAI .. 2546 HAI 8 ( )

. . Page 4-4

4.1

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

. . Page 4-5

4.2 (Climatological means)


. . Page 4-6

4.4


. . Page 4-7


40 8 HAI (, 2550) HAI 8 46 29 ( 4.7 4.1) HAI

. . Page 4-8

4.7

:

. . Page 4-9

4.1 HAI 8

/ HAI 46 29 20 19 18 40 72 68 41 14 32 63 39 33 59 36 61 38

4.3 CoVI

CoVI 3 IPCC CoVI CoVI 3.3 exposure 4 (sub-component) 17 (indicator) sensitivity 5 30 adaptive capacity 4 24 CoVI 71 CoVI 1 2 // CoVI 3 Multicollinearity Cutter et al. (2003)

. . Page 4-10

Multicollinearity Multicollinearity 4 5 CoVI -0.5 0.5 -0.4 0.5 ( 1,531 875 CoVI ( 4 5) 4.8 4.9 CoVI

CoVI -0.1 0.1 Multicollinearity CoVI PCA

CoVI 6 7 normalize Cutter (1996) Cutter et al. (2003) 4.4 CoVI 4.4.1 Exposure

( 4.10)

. . Page 4-11

4.8 Histogram CoVI

4.9 Histogram CoVI

. . Page 4-12

4.10

. . Page 4-13

4.11

. . Page 4-14

4.12 //

. . Page 4-15

4.13 //

. . Page 4-16

4.14

. . Page 4-17

4.15

. . Page 4-18

4.16

. . Page 4-19

4.17

. . Page 4-20

( 4.11) ( 4.12)

4.13

( 4.14) ( 4.12 4.14) ( 4.13 4.15) ( 4.15)

Exposure ( 4.16 4.17) 1 ( 4.16) 0.2 ( 4.16) ( 4.17) 0.2 ( 4.17)

. . Page 4-21

4.4.2 Sensitivity

( 4.18) ( 4.19) 1-5 10 ( 4.20) ( 6 ) 6-10 10 ( 4.21)

75% ( 4.22) ( 4.23) ( 4.24 4.25) 4.4.3 Adaptive capacity

(poverty incidence) (, 2553) (Cutter, 1996; ESPON Hazard project, 2005; Cardona, 2005,2007) (povetry line) (, 2553) .. 2550 1,443 // (, 2551) .. 2550 0.2 44.8% 8.4% (8.5%) (

. . Page 4-22

4.18

. . Page 4-23

4.19

. . Page 4-24

4.20

. . Page 4-25

4.21

. . Page 4-26

4.22

. . Page 4-27

4.23

. . Page 4-28

4.24

. . Page 4-29

4.25

. . Page 4-30

, 2551) 20% ( 4.26) 5.7% 4.27

(Gini index) (Brooks et al., 2005; Cardona, 2005; ESPON Hazard project, 2005; Gall, 2007) 0 1 0 (, 2551)

0.39 - 0.66 ( 0.48) ( 4.28) 0.4 0.25 0.55 0.36 ( 4.29)

(dependency ratio) 15 65 19 60 (Cardona, 2005; Hahn et al., 2009) 0.6 - 0.7 0.7 - 0.8 0.6 ( 4.30) 0.6-0.7 0.6 0.8 ( 4.31)

. . Page 4-31

4.26

. . Page 4-32

4.27

. . Page 4-33

4.28

. . Page 4-34

4.29

. . Page 4-35

4.30

. . Page 4-36

4.31

Documents

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