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Contents
List of tables and figures ix
List of acronyms xvi
Foreword xix
Hans van Ginkel/Rector UNU
Preface xxii
Salvano Briceneo/UN/ISDR
Acknowledgements xxv
Introduction 1
Janos J. Bogardi/Director UNU-EHS
Part I: Basic principles and theoretical basis 7
1 Measuring vulnerability to promote disaster-resilient societies: Conceptual frameworks and definitions 9
Jörn Birkmann/UNU-EHS
2 Indicators and criteria for measuring vulnerability: Theoretical bases and requirements 55
Jörn Birkmann/UNU-EHS
3 Social levels and hazard (in)dependence in determining vulnerability 78
Stefan Schneiderbauer, Daniele Ehrlich/JRC
4 User needs: why we need indicators 103
Angela Queste, Peter Lauwe/BBK
Part II: Vulnerability and environment 117
5 Environmental components of vulnerability 119
Fabrice Renaud/UNU-EHS
6 Human vulnerability to environmental change: An approach for UNEP's Global Environmental Outlook (GEO) 130
Marcel T.J. Kok, Vishal Narain, J. Wonink, Jill Jäger
Part III: Global, national and sub-national index approaches 151
7 Review of global risk index projects: Conclusions for sub-national and local approaches 153
Mark Pelling/King's College London
8 The Disaster Risk Index: Overview of a quantitative approach 176
Pascal Peduzzi/UNDP
9 Disaster risk hotspots: A project summary 187
Maxx Dilley/UNDP
10 A system of indicators for disaster risk management in the Americas 194
Omar D. Cardona/Universidad Nacional de Colombia,
Manizales
11 Multi-risk assessment of Europe's regions 216
Stefan Greiving/University of Dortmund
12 Disaster vulnerability assessment: The Tanzania experience 236
Robert B. Kiunsi and Meshak V. Minoris/UCLAS, Tanzania
13 A Human Security Index 256
Erich J. Plate/University of Karlsruhe
Part IV: Local vulnerability assessment 279
14 Community-based disaster risk index: Pilot implementation in Indonesia 281
Christina Bollin, Ria Hidajat/GTZ
15 Measuring vulnerability: The ADRC perspective for the theoretical basis and principles of indicator development 300
Masaru Arakida/Asian Disaster Reduction Center
16 Vulnerability assessment: The sector approach 311
Juan Carlos Villagr n de Le n/UNU-EHS
17 Self-assessment of coping capacity: Participatory, proactive, and qualitative engagement of communities in their own risk management 328
Ben Wisner/London School of Economics
18 Measuring vulnerability in Sri Lanka at the local level 341
Jörn Birkmann, Nishara Fernando, Siri Hettige
Part V: Institutional vulnerability, coping and lessons learned 373
19 Assessing institutionalised capacities and practices to reduce the risks of flood disaster 375
Louis Lebel, Elena Nikitina, Vladimir Kotov, Jesse Manuta
20 Public sector financial vulnerability to disasters: The IIASA CATSIM model 396
Reinhard Mechler, Stefan Hochrainer, Joanne Linnerooth-
Bayer, Georg Pflug
Text box Effective measurement of vulnerability is essential to help those most in harm's way 417
Simon Horner/ECHO
21 Overcoming the black hole: Outline for a quantitative model to compare coping capacities across countries 421
Peter Billing, Ulrike Madengruber
22 A methodology for lessons learning: Experiences at the European level 433
Elisabeth Krausmann, Fesil Mushatq/JRC
23 Conclusion 450
Jörn Birkmann/UNU-EHS
24 Core terminology of disaster reduction: A comparative glossary 466
Katharina Thywissen
List of contributors 515
Index 528
List of tables and figures
Materials in colour are indicated by (c), and are grouped together in the centre of the book.
Tables
2.1 Overview and systematisation of selected indicator approaches to measure vulnerability 70
3.1 Classification of groups and types of hazards 82
3.2 Selected hazard-independent parameters and potential indicators for vulnerability at different ``social levels'' 88
3.3 Selected hazard-dependent parameters and potential indicators for vulnerability at different ``social levels'' 90
6.1 Selected indicators of human insecurity comprising the standard set 136
6.2 Environmental causes and indicators related to categories of human vulnerability 139
9.1 Countries receiving emergency loans and reallocation of existing loans to meet disaster reconstruction needs, 1980-2003 192
10.1 PVI_{ES} estimation 202
10.2 PVI_{SF} estimation 203
10.3 PVI_{LR} estimation 203
10.4 RMI_{RI} estimation 206
10.5 RMI_{RR} estimation 206
10.6 RMI_{DM} estimation 207
10.7 RMI_{FP} estimation 207
10.8 Indicators of physical risk, social fragility and lack of resilience and their weights 210
11.1 Hazard indicators 223
11.2 Possible indicators for measuring vulnerability in Europe 224
12.1 Characteristics of agro-ecological zones 240
12.2 Aggregated coping strategies for drought according to agro-ecological zones 249
12.3 Hazards and other factors associated with loss of life 250
12.4 Ranking of zones according to hazard risk 252
12.5 Vulnerability index parameters by zone 254
14.1 Set of community-based disaster risk indicators 286
15.1 Ratio of amount of damage to GDP (Asia) (1975- 2002) 304
18.1 Time that households need to replace housing damage 359
19.1 Framework for assessing institutionalised capacities and practices with regard to flood-related disasters 382
19.2 Illustrations of scale-dependent actors, institutions and perceptions with regard to flood-related disasters 384
20.1 Ex-post financing sources for relief and reconstruction 405
22.1 Description of the information categories used for data collection, structuring and retrieval in MARS 441
22.2 The NEDIES lessons-learned report format 445
Figures
1.1 Key spheres of the concept of vulnerability 17
1.2 Bohle's conceptual framework for vulnerability analysis 20
1.3 The sustainable livelihood framework 21
1.4 The conceptual framework to identify disaster risk 23
1.5 Risk as a result of vulnerability, hazard and deficiencies in preparedness 23
1.6 The ISDR framework for disaster risk reduction 25
1.7 Turner et al.'s vulnerability framework 27
1.8 The onion framework 28
1.9 The Pressure and Release (PAR) model 30
1.10 Theoretical framework and model for holistic approach to disaster risk assessment and management 32
1.11 The BBC conceptual framework 35
1.12 The six principles of sustainability 42
1.13 Serageldin's triangle of sustainability 44
1.14 Egg of sustainable development 46
2.1 The data pyramid 59
2.2 The model of the three pillars: indicators, data and goals 60
2.3 Development process of vulnerability indicators 64
2.4 Example of the data gathered in the MRNatCatSERVICE 67
3.1 The risk triangle 80
3.2 Spectrum of hazards 83
3.3 Social levels and relevant characteristics of vulnerability 85
4.1(c) Germany: Elbe Flood 2002 104
4.2 Framework for risk assessment 106
4.3 The vulnerability of critical infrastructures (CI): holistic approach 111
4.4 Vulnerability of persons in high tide flood-prone areas of the Baltic Sea 112
4.5 Vulnerability of critical sites in high tide flood-prone areas of the Baltic Sea 113
5.1 Destruction along the coast, Galle, Sri Lanka 122
5.2 Tsunami-impacted paddy fields in Sri Lanka 124
5.3 Potential effects of land degradation on rural and urban vulnerabilities (economic, social and environmental vulnerability; coping capacity) 127
6.1 Vulnerability framework 135
6.2 Linkages between ecosystem services and human well-being, the MA framework 145
6.3 Risk factors for attributable DALYs for selected regions 146
7.1(c) Relative vulnerability for flooding, 1980-2000 155
7.2(c) Global distribution of flood mortality risk 158
7.3(c) Global distribution of flood economic loss risk 159
7.4(c) Global distribution of flood economic loss risk as a proportion of GDP 159
7.5(c) The global distribution of risk of mortality, by hazard type 160
7.6(c) The global distribution of risk of economic loss, by hazard type 160
7.7(c) The global distribution of risk of economic loss as a proportion of GDP, by hazard type 161
7.8 National financial exposure to catastrophic disaster 162
7.9 Absolute economic exposure to catastrophic disaster 163
7.10 Loss from locally and nationally recognised disasters, 1996-2000 164
7.11 Socioeconomic vulnerability in the Americas, 2000 165
7.12 Disaster risk management performance in the Americas, 2000 166
8.1(c) Physical exposure concerning flood events: a regional example 178
8.2 Comparing exposure and mortality rates in the most/least developed countries 179
9.1(c) Mask used to eliminate sparsely populated, non-agricultural areas 189
10.1 Diagram for DDI calculation 197
10.2 LDI estimation 200
10.3 PVI evaluation 204
10.4 RMI evaluation 208
11.1 Components of risk 220
11.2 Calculation of the Integrated Risk Index (IRI) 221
11.3 Integrated risk matrix 222
11.4(c) Aggregated hazard map 228
11.5(c) Vulnerability map 229
11.6(c) Aggregated risk map 231
12.1 Agro-ecological zones of Tanzania 241
12.2 Four main hazards compared according to different levels 244
12.3 Hazard occurrence in different agro-ecological zones 245
12.4 Drought occurrence in Tanzania 246
12.5 Coping strategy for drought and pests; disaster awareness and communication 247
12.6 Institutional set-up for disaster manageability at the village and district level 248
12.7 Interrelationships between distances from nearest dispensary and probability of death 251
13.1 Combination of resistance and vulnerability as function of time 260
13.2 Schematic distribution of resources and resources needed as function of population income group n 263
13.3 Resistance and vulnerability as indices derived from weighted indicators 265
13.4 Effect of globalisation and environmental change on human security 267
13.5 Time development of resistance, vulnerability and risk 269
13.6 Defining resilience 273
14.1 The conceptual framework to identify disaster risk 284
14.2 Indicator and index system 288
14.3 Indonesia: project location 292
Picture 14.1 Discussion with local stakeholders in the district of Sleman, Yogyakarta 293
Picture 14.2 The summit of the Merapi volcano lies only 30 km away from the capital city (left). The district of Kulon Progo is predominantly affected by landslides (right). 294
Picture 14.3 In 1992 a tsunami completely destroyed the village of Wuring (left picture), but a few years later the village was rebuilt at the same site and is still extremely exposed to earthquakes and tsunamis 294
14.4 Disaster Risk Index of a community in Kulon Progo district prone to landslides 295
14.5 Vulnerability score breakdown of a community in Kulon Progo district 296
14.6 Capacity score breakdown of a community in Kulon Progo district 296
14.7 Disaster Risk: Comparison of a community in Sikka district (left) and a community in Kulon Progo district (right) 297
15.1 Mechanism of natural disaster reduction 302
15.2 Percentage of world population affected by natural disaster in different regions (1975-2002) 303
15.3 Amount of damage caused worldwide by different disaster types (1975-2002) 303
15.4 Quake-resilience assessment of homes 305
15.5 Self-assessment of flood capacity: questionnaire 305
15.6 Self-assessment of flood capacity: results 306
15.7 Self-assessment for local Government: assessment process 307
15.8(c) Self-assessment for local Government 308
15.9 Matrix of indicators 308
15.10 3-D matrix of indicators 309
16.1 Hazards and vulnerability, and their relationship with disasters and coping capacities 315
16.2 The dimensions of vulnerability 316
16.3 Matrix to evaluate structural vulnerability with respect to eruptions 319
16.4(c) Vulnerability with respect to landslides in an urban settlement of Guatemala city 321
16.5 Matrix to calculate the functional vulnerability of a health centre with respect to floods 322
16.6 The composition of risk 323
16.7 Risk map associated with eruptions, Pacaya volcano in Guatemala 324
17.1 Timeline 333
17.2 Example of a seasonal calendar 333
17.3 Example of a Venn diagram 334
18.1 The BBC conceptual framework 343
18.2(c) Overview of the tsunami impact in Sri Lanka 347
18.3 Overview of selected sites in Galle for the questionnaire-based research 348
18.4(c) Spatial exposure of different critical infrastructures 350
18.5 Dead and injured people in the 100-metre zone and outside in the selected GN divisions in Galle (in %) 353
18.6 Housing damage in the 100-metre zone and outside in the selected GN divisions in Galle (in %) 353
18.7 Development of unemployment before and after the tsunami in selected GN divisions in Galle 356
18.8 Income levels of households in the selected GN divisions 357
18.9 The index applied to house damage and land title 360
18.10 Landownership and spatial exposure in the selected GN divisions 361
18.11 Landownership and squatting in the six GN divisions 362
18.12 Willingness to resettle in a safer location inland 365
19.1 Institutions modify vulnerabilities and hence risks of flood-related disasters through several pathways 378
20.1 Public sector financial vulnerability to natural hazards 399
20.2 Financing gap in India after the Gujarat earthquake 401
20.3 Financial vulnerability and the CATSIM methodology 403
20.4 Financial vulnerability to 100-year event in four Latin American countries 407
20.5(c) Simulated growth versus stability for El Salvador over a 10-year time horizon 409
20.6(c) Wind hazard in Honduras 411
20.7 Cumulative probability distribution of direct asset damages for storm and flood for Honduras 412
20.8(c) Assessing financial vulnerability to storm and flood risk in Honduras 413
Textbox, Simon Horner: Figure for Box 21.1: Water supply at Kasab camp 419
21.1 Coping capacity classification in descending order for selected countries 429
22.1 Schematic description of the lessons-learning methodology 436
22.2 Example of a quantitative analysis of the human and organisational causes of accidents contained in the MARS Full Reports 443
24.1 For earthquake hazard, the two lines represent the different magnitude - frequency relationships for two different fictitious regions, region x and region y. The two lines are region-specific. 504
24.2 Sample residential damage function for the hazard of a tornado 505
24.3 Coping capacity and resilience are hard to delineate. Resilience is understood to be the more encompassing term. 508
24.4 Risk seen as a function of hazard, vulnerability, exposure and resilience, while the mathematical relationship between the variables is unknown 509
Library of Congress Subject Headings for this publication:
Natural disasters.
Natural disaster warning systems.