SPECIAL REPORT OF THE

INTERGOVERNMENTAL PANEL

ON CLIMATE CHANGE

MANAGING THE RISKS OF EXTREME

EVENTS AND DISASTERS TO ADVANCE

CLIMATE CHANGE ADAPTATION

Managing the Risks of Extreme Events

and Disasters to Advance

Climate Change Adaptation

Special Report of the

Intergovernmental Panel on Climate Change

Extreme weather and climate events, interacting with exposed and vulnerable human and natural systems, can lead to disasters. This

Special Report explores the challenge of understanding and managing the risks of climate extremes to advance climate change adaptation.

Weather- and climate-related disasters have social as well as physical dimensions. As a result, changes in the frequency and severity of

the physical events affect disaster risk, but so do the spatially diverse and temporally dynamic patterns of exposure and vulnerability.

Some types of extreme weather and climate events have increased in frequency or magnitude, but populations and assets at risk have

also increased, with consequences for disaster risk. Opportunities for managing risks of weather- and climate-related disasters exist or

can be developed at any scale, local to international. Some strategies for effectively managing risks and adapting to climate change

involve adjustments to current activities. Others require transformation or fundamental change.

The Intergovernmental Panel on Climate Change (IPCC) is the leading international body for the assessment of climate change, including

the physical science of climate; impacts, adaptation, and vulnerability; and mitigation of climate change. The IPCC was established by the

United Nations Environment Programme (UNEP) and the World Meteorological Organization (WMO) to provide the world with a

comprehensive assessment of the current state of knowledge of climate change and its potential environmental and socioeconomic

impacts.

Managing the Risks of Extreme Events

and Disasters to Advance

Climate Change Adaptation

Special Report of the

Intergovernmental Panel on Climate Change

Edited by

Christopher B. Field

Co-Chair Working Group II

Carnegie Institution

for Science

Vicente Barros

Co-Chair Working Group II

CIMA / Universidad de

Buenos Aires

Thomas F. Stocker

Co-Chair Working Group I

University of Bern

Qin Dahe

Co-Chair Working Group I

China Meteorological

Administration

David Jon Dokken

Gian-Kasper Plattner

Kristie L. Ebi

Simon K. Allen

Michael D. Mastrandrea

Melinda Tignor

Katharine J. Mach

Pauline M. Midgley

CAMBRIDGE UNIVERSITY PRESS

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Cambridge University Press

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ISBN 978-1-107-60780-4 Paperback

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Use the following reference to cite the entire volume:

IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate

Change Adaptation. A Special Report of Working Groups I and II of the

Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker,

D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen,

M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK,

and New York, NY, USA, 582 pp.

Section I Foreword ............................................................................................................................................................vi

Preface

...............................................................................................................................................................vii

Section II Summary for Policymakers

...............................................................................................................................3

Section III Chapter 1

Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience ....

...

Chapter 2 Determinants of Risk: Exposure and Vulnerability ....

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

Chapter 3

Changes in Climate Extremes and their Impacts on the Natural Physical Environment ....

........

109

Chapter 4 Changes in Impacts of Climate Extremes: Human Systems and Ecosystems ....

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

231

Chapter 5

Managing the Risks from Climate Extremes at the Local Level ....

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

291

Chapter 6 National Systems for Managing the Risks from Climate Extremes and Disasters....

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

339

Chapter 7

Managing the Risks: International Level and Integration across Scales....

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

393

Chapter 8 Toward a Sustainable and Resilient Future....

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

437

Chapter 9

Case Studies....

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

487

Section IV Annex I

Authors and Expert Reviewers....

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

545

Annex II

Glossary of Terms ....

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

555

Annex III

Acronyms.....................................................................................................................................565

nnex IV

List of Major IPCC Reports....

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

569

Index

................................................................................................................................................................573

Contents

I Foreword and Preface

viii

This Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation

(SREX) has been jointly coordinated by Working Groups I (WGI) and II (WGII) of the Intergovernmental Panel on

Climate Change (IPCC). The report focuses on the relationship between climate change and extreme weather and

climate events, the impacts of such events, and the strategies to manage the associated risks.

The IPCC was jointly established in 1988 by the World Meteorological Organization (WMO) and the United Nations

Environment Programme (UNEP), in particular to assess in a comprehensive, objective, and transparent manner all the

relevant scientific, technical, and socioeconomic information to contribute in understanding the scientific basis of risk

of human-induced climate change, the potential impacts, and the adaptation and mitigation options. Beginning in

1990, the IPCC has produced a series of Assessment Reports, Special Reports, Technical Papers, methodologies, and

other key documents which have since become the standard references for policymakers and scientists.

This Special Report, in particular, contributes to frame the challenge of dealing with extreme weather and climate

events as an issue in decisionmaking under uncertainty, analyzing response in the context of risk management. The

report consists of nine chapters, covering risk management; observed and projected changes in extreme weather and

climate events; exposure and vulnerability to as well as losses resulting from such events; adaptation options from the

local to the international scale; the role of sustainable development in modulating risks; and insights from specific

case studies.

Success in developing this report depended foremost on the knowledge, integrity, enthusiasm, and collaboration of

hundreds of experts worldwide, representing a very wide range of disciplines. We would like to express our gratitude

to all the Coordinating Lead Authors, Lead Authors, Contributing Authors, Review Editors, and Expert and Government

Reviewers who devoted considerable expertise, time, and effort to produce this report. We are extremely grateful for

their commitment to the IPCC process and we would also like to thank the staff of the WGI and WGII Technical

Support Units and the IPCC Secretariat, for their unrestricted commitment to the development of such an ambitious

and highly significant IPCC Special Report.

We are also very grateful to the governments which supported their scientists’ participation in this task, as well as to

all those that contributed to the IPCC Trust Fund, thereby facilitating the essential participation of experts from the

developing world. We would also like to express our appreciation, in particular, to the governments of Australia,

Panama, Switzerland, and Vietnam for hosting the drafting sessions in their respective countries, as well as to the

government of Uganda for hosting in Kampala the First Joint Session of Working Groups I and II which approved the

report. Our thanks are also due to the governments of Switzerland and the United States of America for funding the

Technical Support Units for WGI and WGII, respectively. We also wish to acknowledge the collaboration of the

government of Norway – which also provided critical support for meetings and outreach – and the United Nations

International Strategy for Disaster Reduction (ISDR), in the preparation of the original report proposal.

We would especially wish to thank the IPCC Chairman, Dr. Rajendra Pachauri, for his direction and guidance of the

IPCC process, as well as the Co-Chairs of Working Groups II and I, Professors Vicente Barros, Christopher Field, Qin

Dahe, and Thomas Stocker, for their leadership throughout the development of this Special Report.

Foreword

M. Jarraud

Secretary-General

World Meteorological Organization

A. Steiner

Executive Director

United Nations Environment Programme

Preface

This volume, Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, is a Special

Report of the Intergovernmental Panel on Climate Change (IPCC). The report is a collaborative effort of Working Group I

(WGI) and Working Group II (WGII). The IPCC leadership team for this report also has responsibility for the IPCC Fifth

Assessment Report (AR5), scheduled for completion in 2013 and 2014.

The Special Report brings together scientific communities with expertise in three very different aspects of managing

risks of extreme weather and climate events. For this report, specialists in disaster recovery, disaster risk management,

and disaster risk reduction, a community mostly new to the IPCC, joined forces with experts in the areas of the physical

science basis of climate change (WGI) and climate change impacts, adaptation, and vulnerability (WGII). Over the

course of the two-plus years invested in assessing information and writing the report, scientists from these three

communities forged shared goals and products.

Extreme weather and climate events have figured prominently in past IPCC assessments. Extremes can contribute to

disasters, but disaster risk is influenced by more than just the physical hazards. Disaster risk emerges from the interaction

of weather or climate events, the physical contributors to disaster risk, with exposure and vulnerability, the contributors

to risk from the human side. The combination of severe consequences, rarity, and human as well as physical determinants

makes disasters difficult to study. Only over the last few years has the science of these events, their impacts, and

options for dealing with them become mature enough to support a comprehensive assessment. This report provides a

careful assessment of scientific, technical, and socioeconomic knowledge as of May 2011, the cut-off date for literature

included.

The Special Report introduced some important innovations to the IPCC. One was the integration, in a single Special

Report, of skills and perspectives across the disciplines covered by WGI, WGII, and the disaster risk management com-

munity. A second important innovation was the report’s emphasis on adaptation and disaster risk management. A

third innovation was a plan for an ambitious outreach effort. Underlying these innovations and all aspects of the

report is a strong commitment to assessing science in a way that is relevant to policy but not policy prescriptive.

The Process

The Special Report represents the combined efforts of hundreds of leading experts. The Government of Norway and

the United Nations International Strategy for Disaster Reduction submitted a proposal for the report to the IPCC in

September 2008. This was followed by a scoping meeting to develop a candidate outline in March 2009. Following

approval of the outline in April 2009, governments and observer organizations nominated experts for the author team.

The team approved by the WGI and WGII Bureaux consisted of 87 Coordinating Lead Authors and Lead Authors, plus

19 Review Editors. In addition, 140 Contributing Authors submitted draft text and information to the author teams. The

drafts of the report were circulated twice for formal review, first to experts and second to both experts and governments,

resulting in 18,784 review comments. Author teams responded to every comment and, where scientifically appropriate,

modified drafts in response to comments, with Review Editors monitoring the process. The revised report was presented

for consideration at the First Joint Session of WGI and WGII, from 14 to 17 November 2011. At the joint session,

delegates from over 100 countries evaluated and approved, by consensus, the Summary for Policymakers on a

line-by-line basis and accepted the full report.

Structure of the Special Report

This report contains a Summary for Policymakers (SPM) plus nine chapters. References in the SPM point to the

supporting sections of the technical chapters that provide a traceable account of every major finding. The first two

chapters set the stage for the report. Chapter 1 frames the issue of extreme weather and climate events as a challenge

Preface

in understanding and managing risk. It characterizes risk as emerging from the overlap of a triggering physical event

with exposure of people and assets and their vulnerability. Chapter 2 explores the determinants of exposure and

vulnerability in detail, concluding that every disaster has social as well as physical dimensions. Chapter 3, the major

contribution of WGI, is an assessment of the scientific literature on observed and projected changes in extreme weather

and climate events, and their attribution to causes where possible. Chapter 4 assesses observed and projected

impacts, considering patterns by sector as well as region. Chapters 5 through 7 assess experience and theory in

adaptation to extremes and disasters, focusing on issues and opportunities at the local scale (Chapter 5), the national

scale (Chapter 6), and the international scale (Chapter 7). Chapter 8 assesses the interactions among sustainable

development, vulnerability reduction, and disaster risk, considering both opportunities and constraints, as well as the

kinds of transformations relevant to overcoming the constraints. Chapter 9 develops a series of case studies that

illustrate the role of real life complexity but also document examples of important progress in managing risk.

Acknowledgements

We wish to express our sincere appreciation to all the Coordinating Lead Authors, Lead Authors, Contributing Authors,

Review Editors, and Expert and Government Reviewers. Without their expertise, commitment, and integrity, as well as

vast investments of time, a report of this quality could never have been completed. We would also like to thank the

members of the WGI and WGII Bureaux for their assistance, wisdom, and good sense throughout the preparation of

the report.

We would particularly like to thank the remarkable staffs of the Technical Support Units of WGI and WGII for their

professionalism, creativity, and dedication. In WGI, thanks go to Gian-Kasper Plattner, Simon Allen, Pauline Midgley,

Melinda Tignor, Vincent Bex, Judith Boschung, and Alexander Nauels. In WGII, which led the logistics and overall

coordination, thanks go to Dave Dokken, Kristie Ebi, Michael Mastrandrea, Katharine Mach, Sandy MacCracken, Rob

Genova, Yuka Estrada, Eric Kissel, Patricia Mastrandrea, Monalisa Chatterjee, and Kyle Terran. Their tireless and very

capable efforts to coordinate the Special Report ensured a final product of high scientific quality, while maintaining an

atmosphere of collegiality and respect.

We would also like to thank the staff of the IPCC Secretariat: Renate Christ, Gaetano Leone, Mary Jean Burer, Sophie

Schlingemann, Judith Ewa, Jesbin Baidya, Joelle Fernandez, Annie Courtin, Laura Biagioni, and Amy Smith Aasdam.

Thanks are also due to Francis Hayes (WMO), Tim Nuthall (European Climate Foundation), and Nick Nutall (UNEP).

Our sincere thanks go to the hosts and organizers of the scoping meeting, the four lead author meetings, and the

approval session. We gratefully acknowledge the support from the host countries: Norway, Panama, Vietnam,

Switzerland, Australia, and Uganda. It is a pleasure to extend special thanks to the government of Norway, which

provided untiring support throughout the Special Report process.

Preface

Vicente Barros and Christopher B. Field

IPCC WGII Co-Chairs

Qin Dahe and Thomas F. Stocker

IPCC WGI Co-Chairs

II Summary for Policymakers

Drafting Authors:

Simon K. Allen (Switzerland), Vicente Barros (Argentina), Ian Burton (Canada),

Diarmid Campbell-Lendrum (UK), Omar-Dario Cardona (Colombia), Susan L. Cutter (USA),

O. Pauline Dube (Botswana), Kristie L. Ebi (USA), Christopher B. Field (USA),

John W. Handmer (Australia), Padma N. Lal (Australia), Allan Lavell (Costa Rica),

Katharine J. Mach (USA), Michael D. Mastrandrea (USA), Gordon A. McBean (Canada),

Reinhard Mechler (Germany), Tom Mitchell (UK), Neville Nicholls (Australia),

Karen L. O’Brien (Norway), Taikan Oki (Japan), Michael Oppenheimer (USA), Mark Pelling

(UK), Gian-Kasper Plattner (Switzerland), Roger S. Pulwarty (USA), Sonia I. Seneviratne

(Switzerland), Thomas F. Stocker (Switzerland), Maarten K. van Aalst (Netherlands),

Carolina S. Vera (Argentina), Thomas J. Wilbanks (USA)

This Summary for Policymakers should be cited as:

IPCC, 2012: Summary for Policymakers. In: Managing the Risks of Extreme Events and Disasters to Advance

Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea,

K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups

I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and

New York, NY, USA, pp. 3-21.

SPM

Summary

for Policymakers

Summary for Policymakers

Context

This Summary for Policymakers presents key findings from the Special Report on Managing the Risks of Extreme

Events and Disasters to Advance Climate Change Adaptation (SREX). The SREX approaches the topic by assessing the

scientific literature on issues that range from the relationship between climate change and extreme weather and

climate events (‘climate extremes’) to the implications of these events for society and sustainable development. The

assessment concerns the interaction of climatic, environmental, and human factors that can lead to impacts and

disasters, options for managing the risks posed by impacts and disasters, and the important role that non-climatic

factors play in determining impacts. Box SPM.1 defines concepts central to the SREX.

The character and severity of impacts from climate extremes depend not only on the extremes themselves but also on

exposure and vulnerability. In this report, adverse impacts are considered disasters when they produce widespread

damage and cause severe alterations in the normal functioning of communities or societies. Climate extremes,

exposure, and vulnerability are influenced by a wide range of factors, including anthropogenic climate change, natural

climate variability, and socioeconomic development (Figure SPM.1). Disaster risk management and adaptation to

climate change focus on reducing exposure and vulnerability and increasing resilience to the potential adverse impacts

of climate extremes, even though risks cannot fully be eliminated (Figure SPM.2). Although mitigation of climate

change is not the focus of this report, adaptation and mitigation can complement each other and together can

significantly reduce the risks of climate change. [SYR AR4, 5.3]

Figure SPM.1 | Illustration of the core concepts of SREX. The report assesses how exposure and vulnerability to weather and climate events determine impacts and the likelihood

of disasters (disaster risk). It evaluates the influence of natural climate variability and anthropogenic climate change on climate extremes and other weather and climate events

that can contribute to disasters, as well as the exposure and vulnerability of human society and natural ecosystems. It also considers the role of development in trends in exposure

and vulnerability, implications for disaster risk, and interactions between disasters and development. The report examines how disaster risk management and adaptation to climate

change can reduce exposure and vulnerability to weather and climate events and thus reduce disaster risk, as well as increase resilience to the risks that cannot be eliminated.

Other important processes are largely outside the scope of this report, including the influence of development on greenhouse gas emissions and anthropogenic climate change,

and the potential for mitigation of anthropogenic climate change. [1.1.2, Figure 1-1]

Summary for Policymakers

Box SPM.1 | Definitions Central to SREX

Core concepts defined in the SREX glossary

and used throughout the report include:

Climate Change: A change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean

and/or the variability of its properties and that persists for an extended period, typically decades or longer. Climate change may be due

to natural internal processes or external forcings, or to persistent anthropogenic changes in the composition of the atmosphere or in

land use.

Climate Extreme (extreme weather or climate event): The occurrence of a value of a weather or climate variable above (or below)

a threshold value near the upper (or lower) ends of the range of observed values of the variable. For simplicity, both extreme weather

events and extreme climate events are referred to collectively as ‘climate extremes.’ The full definition is provided in Section 3.1.2.

Exposure: The presence of people; livelihoods; environmental services and resources; infrastructure; or economic, social, or cultural

assets in places that could be adversely affected.

Vulnerability: The propensity or predisposition to be adversely affected.

Disaster: Severe alterations in the normal functioning of a community or a society due to hazardous physical events interacting with

vulnerable social conditions, leading to widespread adverse human, material, economic, or environmental effects that require immediate

emergency response to satisfy critical human needs and that may require external support for recovery.

Disaster Risk: The likelihood over a specified time period of severe alterations in the normal functioning of a community or a society

due to hazardous physical events interacting with vulnerable social conditions, leading to widespread adverse human, material,

economic, or environmental effects that require immediate emergency response to satisfy critical human needs and that may require

external support for recovery.

Disaster Risk Management: Processes for designing, implementing, and evaluating strategies, policies, and measures to improve the

understanding of disaster risk, foster disaster risk reduction and transfer, and promote continuous improvement in disaster preparedness,

response, and recovery practices, with the explicit purpose of increasing human security, well-being, quality of life, resilience, and

sustainable development.

Adaptation: In human systems, the process of adjustment to actual or expected climate and its effects, in order to moderate harm or

exploit beneficial opportunities. In natural systems, the process of adjustment to actual climate and its effects; human intervention may

facilitate adjustment to expected climate.

Resilience: The ability of a system and its component parts to anticipate, absorb, accommodate, or recover from the effects of a

hazardous event in a timely and efficient manner, including through ensuring the preservation, restoration, or improvement of its

essential basic structures and functions.

Transformation: The altering of fundamental attributes of a system (including value systems; regulatory, legislative, or bureaucratic

regimes; financial institutions; and technological or biological systems).

____________

Reflecting the diversity of the communities involved in this assessment and progress in science, several of the definitions used in this Special Report differ in breadth or

focus from those used in the Fourth Assessment Report and other IPCC reports.

This definition differs from that in the United Nations Framework Convention on Climate Change (UNFCCC), where climate change is defined as: “a change of climate

which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability

observed over comparable time periods.” The UNFCCC thus makes a distinction between climate change attributable to human activities altering the atmospheric

composition, and climate variability attributable to natural causes.

Summary for Policymakers

This report integrates perspectives from several historically distinct research communities studying climate science,

climate impacts, adaptation to climate change, and disaster risk management. Each community brings different

viewpoints, vocabularies, approaches, and goals, and all provide important insights into the status of the knowledge

base and its gaps. Many of the key assessment findings come from the interfaces among these communities. These

interfaces are also illustrated in Table SPM.1. To accurately convey the degree of certainty in key findings, the report

relies on the consistent use of calibrated uncertainty language, introduced in Box SPM.2. The basis for substantive

paragraphs in this Summary for Policymakers can be found in the chapter sections specified in square brackets.

Exposure and vulnerability are key determinants of disaster risk and of impacts when risk is realized.

[1.1.2, 1.2.3, 1.3, 2.2.1, 2.3, 2.5] For example, a tropical cyclone can have very different impacts depending on where

and when it makes landfall. [2.5.1, 3.1, 4.4.6] Similarly, a heat wave can have very different impacts on different

populations depending on their vulnerability. [Box 4-4, 9.2.1] Extreme impacts on human, ecological, or physical

systems can result from individual extreme weather or climate events. Extreme impacts can also result from non-

extreme events where exposure and vulnerability are high [2.2.1, 2.3, 2.5] or from a compounding of events or their

impacts. [1.1.2, 1.2.3, 3.1.3] For example, drought, coupled with extreme heat and low humidity, can increase the risk

of wildfire. [Box 4-1, 9.2.2]

Extreme and non-extreme weather or climate events affect vulnerability to future extreme events by modifying

resilience, coping capacity, and adaptive capacity. [2.4.3] In particular, the cumulative effects of disasters at local

Figure SPM.2 | Adaptation and disaster risk management approaches for reducing and managing disaster risk in a changing climate. This report assesses a wide range of

complementary adaptation and disaster risk management approaches that can reduce the risks of climate extremes and disasters and increase resilience to remaining risks as they

change over time. These approaches can be overlapping and can be pursued simultaneously. [6.5, Figure 6-3, 8.6]

or sub-national levels can substantially affect

livelihood options and resources and the capacity

of societies and communities to prepare for and

respond to future disasters. [2.2, 2.7]

A changing climate leads to changes in the

frequency, intensity, spatial extent, duration,

and timing of extreme weather and climate

events, and can result in unprecedented

extreme weather and climate events. Changes

in extremes can be linked to changes in the mean,

variance, or shape of probability distributions, or all

of these (Figure SPM.3). Some climate extremes (e.g.,

droughts) may be the result of an accumulation of

weather or climate events that are not extreme

when considered independently. Many extreme

weather and climate events continue to be the

result of natural climate variability. Natural variability

will be an important factor in shaping future

extremes in addition to the effect of anthropogenic

changes in climate. [3.1]

Observations of

Exposure, Vulnerability,

Climate Extremes,

Impacts, and Disaster

Losses

The impacts of climate extremes and the potential

for disasters result from the climate extremes

themselves and from the exposure and vulnerability

of human and natural systems. Observed changes

in climate extremes reflect the influence of

anthropogenic climate change in addition to natural

climate variability, with changes in exposure and

vulnerability influenced by both climatic and non-

climatic factors.

Exposure and Vulnerability

Exposure and vulnerability are dynamic, varying across temporal and spatial scales, and depend on

economic, social, geographic, demographic, cultural, institutional, governance, and environmental factors

(high confidence). [2.2, 2.3, 2.5] Individuals and communities are differentially exposed and vulnerable based on

inequalities expressed through levels of wealth and education, disability, and health status, as well as gender, age,

class, and other social and cultural characteristics. [2.5]

Settlement patterns, urbanization, and changes in socioeconomic conditions have all influenced observed

trends in exposure and vulnerability to climate extremes (high confidence). [4.2, 4.3.5] For example, coastal

Summary for Policymakers

Without climate change

With climate change

extreme cold

extreme hot

cold

Probability of OccurrenceProbability of OccurrenceProbability of Occurrence

less

extreme cold

weather

extreme cold

weather

less

cold

weather

near constant

extreme cold

weather

near constant

cold

weather

extreme hot

weather

extreme hot

weather

extreme hot

weather

cold

weather

Shifted Mean

Increased Variability

Changed Symmetry

Mean:

without and with weather change

Figure SPM.3 | The effect of changes in temperature distribution on

extremes. Different changes in temperature distributions between present and

future climate and their effects on extreme values of the distributions:

(a) effects of a simple shift of the entire distribution toward a warmer climate;

(b) effects of an increase in temperature variability with no shift in the mean;

asymmetry toward the hotter part of the distribution. [Figure 1-2, 1.2.2]

Summary for Policymakers

settlements, including in small islands and megadeltas, and mountain settlements are exposed and vulnerable to

climate extremes in both developed and developing countries, but with differences among regions and countries.

[4.3.5, 4.4.3, 4.4.6, 4.4.9, 4.4.10] Rapid urbanization and the growth of megacities, especially in developing countries,

have led to the emergence of highly vulnerable urban communities, particularly through informal settlements and

inadequate land management (high agreement, robust evidence). [5.5.1] See also Case Studies 9.2.8 and 9.2.9.

Vulnerable populations also include refugees, internally displaced people, and those living in marginal areas. [4.2, 4.3.5]

Climate Extremes and Impacts

There is evidence from observations gathered since 1950 of change in some extremes. Confidence in

observed changes in extremes depends on the quality and quantity of data and the availability of studies

analyzing these data, which vary across regions and for different extremes. Assigning ‘low confidence’ in

observed changes in a specific extreme on regional or global scales neither implies nor excludes the

possibility of changes in this extreme. Extreme events are rare, which means there are few data available to make

assessments regarding changes in their frequency or intensity. The more rare the event the more difficult it is to identify

long-term changes. Global-scale trends in a specific extreme may be either more reliable (e.g., for temperature

extremes) or less reliable (e.g., for droughts) than some regional-scale trends, depending on the geographical uniformity

of the trends in the specific extreme. The following paragraphs provide further details for specific climate extremes

from observations since 1950. [3.1.5, 3.1.6, 3.2.1]

It is very likely that there has been an overall decrease in the number of cold days and nights,

and an overall increase

in the number of warm days and nights,

at the global scale, that is, for most land areas with sufficient data. It is likely

that these changes have also occurred at the continental scale in North America, Europe, and Australia. There is medium

confidence in a warming trend in daily temperature extremes in much of Asia. Confidence in observed trends in daily

temperature extremes in Africa and South America generally varies from low to medium depending on the region. In

many (but not all) regions over the globe with sufficient data, there is medium confidence that the length or number

of warm spells or heat waves

has increased. [3.3.1, Table 3-2]

There have been statistically significant trends in the number of heavy precipitation events in some regions. It is likely

that more of these regions have experienced increases than decreases, although there are strong regional and

subregional variations in these trends. [3.3.2]

There is low confidence in any observed long-term (i.e., 40 years or more) increases in tropical cyclone activity (i.e.,

intensity, frequency, duration), after accounting for past changes in observing capabilities. It is likely that there has been

a poleward shift in the main Northern and Southern Hemisphere extratropical storm tracks. There is low confidence in

observed trends in small spatial-scale phenomena such as tornadoes and hail because of data inhomogeneities and

inadequacies in monitoring systems. [3.3.2, 3.3.3, 3.4.4, 3.4.5]

There is medium confidence that some regions of the world have experienced more intense and longer droughts, in

particular in southern Europe and West Africa, but in some regions droughts have become less frequent, less intense,

or shorter, for example, in central North America and northwestern Australia. [3.5.1]

There is limited to medium evidence available to assess climate-driven observed changes in the magnitude and

frequency of floods at regional scales because the available instrumental records of floods at gauge stations are

limited in space and time, and because of confounding effects of changes in land use and engineering. Furthermore,

there is low agreement in this evidence, and thus overall low confidence at the global scale regarding even the sign of

these changes. [3.5.2]

____________

See SREX Glossary for definition of these terms: cold days / cold nights, warm days / warm nights, and warm spell – heat wave.

Summary for Policymakers

It is likely that there has been an increase in extreme coastal high water related to increases in mean sea level.

[3.5.3]

There is evidence that some extremes have changed as a result of anthropogenic influences, including

increases in atmospheric concentrations of greenhouse gases. It is likely that anthropogenic influences have led

to warming of extreme daily minimum and maximum temperatures at the global scale. There is medium confidence

that anthropogenic influences have contributed to intensification of extreme precipitation at the global scale. It is

likely that there has been an anthropogenic influence on increasing extreme coastal high water due to an increase in

mean sea level. The uncertainties in the historical tropical cyclone records, the incomplete understanding of the physical

mechanisms linking tropical cyclone metrics to climate change, and the degree of tropical cyclone variability provide

only low confidence for the attribution of any detectable changes in tropical cyclone activity to anthropogenic

influences. Attribution of single extreme events to anthropogenic climate change is challenging. [3.2.2, 3.3.1, 3.3.2,

3.4.4, 3.5.3, Table 3-1]

Disaster Losses

Economic losses from weather- and climate-related disasters have increased, but with large spatial and

interannual variability (high confidence, based on high agreement, medium evidence). Global weather- and

climate-related disaster losses reported over the last few decades reflect mainly monetized direct damages to assets,

and are unequally distributed. Estimates of annual losses have ranged since 1980 from a few US$ billion to above

200 billion (in 2010 dollars), with the highest value for 2005 (the year of Hurricane Katrina). Loss estimates are lower-

bound estimates because many impacts, such as loss of human lives, cultural heritage, and ecosystem services, are

difficult to value and monetize, and thus they are poorly reflected in estimates of losses. Impacts on the informal or

undocumented economy as well as indirect economic effects can be very important in some areas and sectors, but are

generally not counted in reported estimates of losses. [4.5.1, 4.5.3, 4.5.4]

Economic, including insured, disaster losses associated with weather, climate, and geophysical events

are

higher in developed countries. Fatality rates and economic losses expressed as a proportion of gross

domestic product (GDP) are higher in developing countries (high confidence). During the period from 1970 to

2008, over 95% of deaths from natural disasters occurred in developing countries. Middle-income countries with rapidly

expanding asset bases have borne the largest burden. During the period from 2001 to 2006, losses amounted to about

1% of GDP for middle-income countries, while this ratio has been about 0.3% of GDP for low-income countries and

less than 0.1% of GDP for high-income countries, based on limited evidence. In small exposed countries, particularly

small island developing states, losses expressed as a percentage of GDP have been particularly high, exceeding 1% in

many cases and 8% in the most extreme cases, averaged over both disaster and non-disaster years for the period from

1970 to 2010. [4.5.2, 4.5.4]

Increasing exposure of people and economic assets has been the major cause of long-term increases in

economic losses from weather- and climate-related disasters (high confidence). Long-term trends in economic

disaster losses adjusted for wealth and population increases have not been attributed to climate change,

but a role for climate change has not been excluded (high agreement, medium evidence). These conclusions

are subject to a number of limitations in studies to date. Vulnerability is a key factor in disaster losses, yet it is not well

accounted for. Other limitations are: (i) data availability, as most data are available for standard economic sectors in

developed countries; and (ii) type of hazards studied, as most studies focus on cyclones, where confidence in observed

trends and attribution of changes to human influence is low. The second conclusion is subject to additional limitations:

(iii) the processes used to adjust loss data over time, and (iv) record length. [4.5.3]

____________

Economic losses and fatalities described in this paragraph pertain to all disasters associated with weather, climate, and geophysical events.

Summary for Policymakers

Disaster Risk Management and Adaptation to Climate

Change: Past Experience with Climate Extremes

Past experience with climate extremes contributes to understanding of effective disaster risk management and

adaptation approaches to manage risks.

The severity of the impacts of climate extremes depends strongly on the level of the exposure and

vulnerability to these extremes (high confidence). [2.1.1, 2.3, 2.5]

Trends in exposure and vulnerability are major drivers of changes in disaster risk (high confidence). [2.5]

Understanding the multi-faceted nature of both exposure and vulnerability is a prerequisite for determining how

weather and climate events contribute to the occurrence of disasters, and for designing and implementing effective

adaptation and disaster risk management strategies. [2.2, 2.6] Vulnerability reduction is a core common element of

adaptation and disaster risk management. [2.2, 2.3]

Development practice, policy, and outcomes are critical to shaping disaster risk, which may be increased

by shortcomings in development (high confidence). [1.1.2, 1.1.3] High exposure and vulnerability are generally

the outcome of skewed development processes such as those associated with environmental degradation, rapid and

unplanned urbanization in hazardous areas, failures of governance, and the scarcity of livelihood options for the poor.

[2.2.2, 2.5] Increasing global interconnectivity and the mutual interdependence of economic and ecological systems

can have sometimes contrasting effects, reducing or amplifying vulnerability and disaster risk. [7.2.1] Countries more

effectively manage disaster risk if they include considerations of disaster risk in national development and sector plans

and if they adopt climate change adaptation strategies, translating these plans and strategies into actions targeting

vulnerable areas and groups. [6.2, 6.5.2]

Data on disasters and disaster risk reduction are lacking at the local level, which can constrain improvements

in local vulnerability reduction (high agreement, medium evidence). [5.7] There are few examples of national

disaster risk management systems and associated risk management measures explicitly integrating knowledge of and

uncertainties in projected changes in exposure, vulnerability, and climate extremes. [6.6.2, 6.6.4]

Inequalities influence local coping and adaptive capacity, and pose disaster risk management and adaptation

challenges from the local to national levels (high agreement, robust evidence). These inequalities reflect

socioeconomic, demographic, and health-related differences and differences in governance, access to livelihoods,

entitlements, and other factors. [5.5.1, 6.2] Inequalities also exist across countries: developed countries are often better

equipped financially and institutionally to adopt explicit measures to effectively respond and adapt to projected

changes in exposure, vulnerability, and climate extremes than are developing countries. Nonetheless, all countries face

challenges in assessing, understanding, and responding to such projected changes. [6.3.2, 6.6]

Humanitarian relief is often required when disaster risk reduction measures are absent or inadequate

(high agreement, robust evidence). [5.2.1] Smaller or economically less-diversified countries face particular

challenges in providing the public goods associated with disaster risk management, in absorbing the losses caused by

climate extremes and disasters, and in providing relief and reconstruction assistance. [6.4.3]

Post-disaster recovery and reconstruction provide an opportunity for reducing weather- and climate-related

disaster risk and for improving adaptive capacity (high agreement, robust evidence). An emphasis on rapidly

rebuilding houses, reconstructing infrastructure, and rehabilitating livelihoods often leads to recovering in ways that

recreate or even increase existing vulnerabilities, and that preclude longer-term planning and policy changes for

enhancing resilience and sustainable development. [5.2.3] See also assessment in Sections 8.4.1 and 8.5.2.

Risk sharing and transfer mechanisms at local, national, regional, and global scales can increase resilience

to climate extremes (medium confidence). Mechanisms include informal and traditional risk sharing mechanisms,

micro-insurance, insurance, reinsurance, and national, regional, and global risk pools. [5.6.3, 6.4.3, 6.5.3, 7.4] These

mechanisms are linked to disaster risk reduction and climate change adaptation by providing means to finance relief,

recovery of livelihoods, and reconstruction; reducing vulnerability; and providing knowledge and incentives for reducing

risk. [5.5.2, 6.2.2] Under certain conditions, however, such mechanisms can provide disincentives for reducing disaster

risk. [5.6.3, 6.5.3, 7.4.4] Uptake of formal risk sharing and transfer mechanisms is unequally distributed across regions

and hazards. [6.5.3] See also Case Study 9.2.13.

Attention to the temporal and spatial dynamics of exposure and vulnerability is particularly important

given that the design and implementation of adaptation and disaster risk management strategies and

policies can reduce risk in the short term, but may increase exposure and vulnerability over the longer

term (high agreement, medium evidence). For instance, dike systems can reduce flood exposure by offering

immediate protection, but also encourage settlement patterns that may increase risk in the long term. [2.4.2, 2.5.4,

2.6.2] See also assessment in Sections 1.4.3, 5.3.2, and 8.3.1.

National systems are at the core of countries’ capacity to meet the challenges of observed and projected

trends in exposure, vulnerability, and weather and climate extremes (high agreement, robust evidence).

Effective national systems comprise multiple actors from national and sub-national governments, the private sector,

research bodies, and civil society including community-based organizations, playing differential but complementary

roles to manage risk, according to their accepted functions and capacities. [6.2]

Closer integration of disaster risk management and climate change adaptation, along with the incorporation

of both into local, sub-national, national, and international development policies and practices, could provide

benefits at all scales (high agreement, medium evidence). [5.4, 5.5, 5.6, 6.3.1, 6.3.2, 6.4.2, 6.6, 7.4] Addressing

social welfare, quality of life, infrastructure, and livelihoods, and incorporating a multi-hazards approach into planning

and action for disasters in the short term, facilitates adaptation to climate extremes in the longer term, as is increasingly

recognized internationally. [5.4, 5.5, 5.6, 7.3] Strategies and policies are more effective when they acknowledge multiple

stressors, different prioritized values, and competing policy goals. [8.2, 8.3, 8.7]

Future Climate Extremes, Impacts, and Disaster Losses

Future changes in exposure, vulnerability, and climate extremes resulting from natural climate variability, anthropogenic

climate change, and socioeconomic development can alter the impacts of climate extremes on natural and human

systems and the potential for disasters.

Climate Extremes and Impacts

Confidence in projecting changes in the direction and magnitude of climate extremes depends on many

factors, including the type of extreme, the region and season, the amount and quality of observational

data, the level of understanding of the underlying processes, and the reliability of their simulation in

models. Projected changes in climate extremes under different emissions scenarios

generally do not strongly diverge

in the coming two to three decades, but these signals are relatively small compared to natural climate variability over

this time frame. Even the sign of projected changes in some climate extremes over this time frame is uncertain. For

projected changes by the end of the 21st century, either model uncertainty or uncertainties associated with emissions

scenarios used becomes dominant, depending on the extreme. Low-probability, high-impact changes associated with

Summary for Policymakers

____________

Emissions scenarios for radiatively important substances result from pathways of socioeconomic and technological development. This report uses

a subset (B1, A1B, A2) of the 40 scenarios extending to the year 2100 that are described in the IPCC Special Report on Emissions Scenarios

(SRES) and that did not include additional climate initiatives. These scenarios have been widely used in climate change projections and

encompass a substantial range of carbon dioxide equivalent concentrations, but not the entire range of the scenarios included in the SRES.

Summary for Policymakers

Full model range

Central 50%

intermodel range

Median

B1 A1B A2

Scenarios:

2046−65 2081−00

Return period (Years)

Decrease in return period implies more frequent extreme temperature events (see caption)

Legend

2046−65 2081−00

Alaska/N.W. Canada - 1

2046−65 2081−00

E. Canada/Greenl./Icel. - 2

2046−65 2081−00

W. North America - 3

2046−65 2081−00

C. North America - 4

2046−65 2081−00

E. North America - 5

2046−65 2081−00

Central America/Mexico - 6

2046−65 2081−00

Amazon - 7

2046−65 2081−00

N.E. Brazil - 8

2046−65 2081−00

W. Coast South America - 9

2046−65 2081−00

S.E. South America - 10

2046−65 2081−00

N. Europe - 11

2046−65 2081−00

C. Europe - 12

2046−65 2081−00

S. Europe/Mediterranean - 13

2046−65 2081−00

Sahara - 14

2046−65 2081−00

W. Africa - 15

2046−65 2081−00

E. Africa - 16

2046−65 2081−00

S. Africa - 17

2046−65 2081−00

N. Asia - 18

2046−65 2081−00

E. Asia - 22

2046−65 2081−00

Tibetan Plateau - 21

2046−65 2081−00

C. Asia - 20

2046−65 2081−00

W. Asia - 19

2046−65 2081−00

S. Asia - 23

2046−65 2081−00

S.E. Asia - 24

2046−65 2081−00

N. Australia - 25

2046−65 2081−00

S. Australia/New Zealand - 26

2046−65 2081−00

Globe (Land only)

Figure SPM.4A | Projected return periods for the maximum daily temperature that was exceeded on average once during a 20-year period in the late 20th century (1981–2000). A decrease in return period implies more

frequent extreme temperature events (i.e., less time between events on average). The box plots show results for regionally averaged projections for two time horizons, 2046 to 2065 and 2081 to 2100, as compared to the late

20th century, and for three different SRES emissions scenarios (B1, A1B, A2) (see legend). Results are based on 12 global climate models (GCMs) contributing to the third phase of the Coupled Model Intercomparison Project

(CMIP3). The level of agreement among the models is indicated by the size of the colored boxes (in which 50% of the model projections are contained), and the length of the whiskers (indicating the maximum and minimum

projections from all models). See legend for defined extent of regions. Values are computed for land points only. The ‘Globe’ inset box displays the values computed using all land grid points. [3.3.1, Figure 3-1, Figure 3-5]

the crossing of poorly understood climate thresholds cannot be excluded, given the transient and complex nature of

the climate system. Assigning ‘low confidence’ for projections of a specific extreme neither implies nor excludes the

possibility of changes in this extreme. The following assessments of the likelihood and/or confidence of projections are

generally for the end of the 21st century and relative to the climate at the end of the 20th century. [3.1.5, 3.1.7, 3.2.3,

Box 3-2]

Models project substantial warming in temperature extremes by the end of the 21st century. It is virtually

certain that increases in the frequency and magnitude of warm daily temperature extremes and decreases in cold

extremes will occur in the 21st century at the global scale. It is very likely that the length, frequency, and/or intensity

of warm spells or heat waves will increase over most land areas. Based on the A1B and A2 emissions scenarios, a

1-in-20 year hottest day is likely to become a 1-in-2 year event by the end of the 21st century in most regions, except

in the high latitudes of the Northern Hemisphere, where it is likely to become a 1-in-5 year event (see Figure SPM.4A).

Under the B1 scenario, a 1-in-20 year event would likely become a 1-in-5 year event (and a 1-in-10 year event in

Northern Hemisphere high latitudes). The 1-in-20 year extreme daily maximum temperature (i.e., a value that was

exceeded on average only once during the period 1981–2000) will likely increase by about 1°C to 3°C by the mid-21st

century and by about 2°C to 5°C by the late 21st century, depending on the region and emissions scenario (based on

the B1, A1B, and A2 scenarios). [3.3.1, 3.1.6, Table 3-3, Figure 3-5]

It is likely that the frequency of heavy precipitation or the proportion of total rainfall from heavy falls will

increase in the 21st century over many areas of the globe. This is particularly the case in the high latitudes and

tropical regions, and in winter in the northern mid-latitudes. Heavy rainfalls associated with tropical cyclones are likely

to increase with continued warming. There is medium confidence that, in some regions, increases in heavy precipitation

will occur despite projected decreases in total precipitation in those regions. Based on a range of emissions scenarios

(B1, A1B, A2), a 1-in-20 year annual maximum daily precipitation amount is likely to become a 1-in-5 to 1-in-15 year

event by the end of the 21st century in many regions, and in most regions the higher emissions scenarios (A1B and A2)

lead to a stronger projected decrease in return period. See Figure SPM.4B. [3.3.2, 3.4.4, Table 3-3, Figure 3-7]

Average tropical cyclone maximum wind speed is likely to increase, although increases may not occur in

all ocean basins. It is likely that the global frequency of tropical cyclones will either decrease or remain

essentially unchanged. [3.4.4]

There is medium confidence that there will be a reduction in the number of extratropical cyclones averaged

over each hemisphere. While there is low confidence in the detailed geographical projections of extratropical

cyclone activity, there is medium confidence in a projected poleward shift of extratropical storm tracks. There is low

confidence in projections of small spatial-scale phenomena such as tornadoes and hail because competing physical

processes may affect future trends and because current climate models do not simulate such phenomena. [3.3.2, 3.3.3,

3.4.5]

There is medium confidence that droughts will intensify in the 21st century in some seasons and areas, due

to reduced precipitation and/or increased evapotranspiration. This applies to regions including southern Europe

and the Mediterranean region, central Europe, central North America, Central America and Mexico, northeast Brazil,

and southern Africa. Elsewhere there is overall low confidence because of inconsistent projections of drought changes

(dependent both on model and dryness index). Definitional issues, lack of observational data, and the inability of models

to include all the factors that influence droughts preclude stronger confidence than medium in drought projections.

See Figure SPM.5. [3.5.1, Table 3-3, Box 3-3]

Projected precipitation and temperature changes imply possible changes in floods, although overall there

is low confidence in projections of changes in fluvial floods. Confidence is low due to limited evidence and

because the causes of regional changes are complex, although there are exceptions to this statement. There is medium

confidence (based on physical reasoning) that projected increases in heavy rainfall would contribute to increases in

local flooding in some catchments or regions. [3.5.2]

Summary for Policymakers

Full model range

Central 50%

intermodel range

Median

B1 A1B A2

Scenarios:

Return period (Years)

2046−65 2081−00

Decrease in return period implies more frequent extreme precipitation events (see caption)

Legend

2046−65 2081−00

Globe (Land only)

2046−65 2081−00

S. Australia/New Zealand - 26

2046−65 2081−00

N. Australia - 25

2046−65 2081−00

2.4

S.E. Asia - 24

2046−65 2081−00

S. Asia - 23

2046−65 2081−00

W. Asia - 19

2046−65 2081−00

C. Asia - 20

2046−65 2081−00

Tibetan Plateau - 21

2046−65 2081−00

E. Asia - 22

2046−65 2081−00

N. Asia - 18

2046−65 2081−00

S. Africa - 17

2046−65 2081−00

E. Africa - 16

2046−65 2081−00

W. Africa - 15

2046−65 2081−00

Sahara - 14

2046−65 2081−00

S. Europe/Mediterranean - 13

2046−65 2081−00

C. Europe - 12

2046−65 2081−00

N. Europe - 11

2046−65 2081−00

S.E. South America - 10

2046−65 2081−00

W. Coast South America - 9

2046−65 2081−00

N.E. Brazil - 8

2046−65 2081−00

Amazon - 7

2046−65 2081−00

Central America/Mexico - 6

2046−65 2081−00

E. North America - 5

2046−65 2081−00

C. North America - 4

2046−65 2081−00

W. North America - 3

2046−65 2081−00

E. Canada/Greenl./Icel. - 2

2046−65 2081−00

2.4

Alaska/N.W. Canada - 1

Figure SPM.4B | Projected return periods for a daily precipitation event that was exceeded in the late 20th century on average once during a 20-year period (1981–2000). A decrease in return period implies more frequent

extreme precipitation events (i.e., less time between events on average). The box plots show results for regionally averaged projections for two time horizons, 2046 to 2065 and 2081 to 2100, as compared to the late 20th

century, and for three different SRES emissions scenarios (B1, A1B, A2) (see legend). Results are based on 14 GCMs contributing to the CMIP3. The level of agreement among the models is indicated by the size of the colored

boxes (in which 50% of the model projections are contained), and the length of the whiskers (indicating the maximum and minimum projections from all models). See legend for defined extent of regions. Values are computed

for land points only. The ‘Globe’ inset box displays the values computed using all land grid points. [3.3.2, Figure 3-1, Figure 3-7]

Summary for Policymakers

It is very likely that mean sea level rise will contribute to upward trends in extreme coastal high water

levels in the future. There is high confidence that locations currently experiencing adverse impacts such as coastal

erosion and inundation will continue to do so in the future due to increasing sea levels, all other contributing factors

being equal. The very likely contribution of mean sea level rise to increased extreme coastal high water levels, coupled

with the likely increase in tropical cyclone maximum wind speed, is a specific issue for tropical small island states.

[3.5.3, 3.5.5, Box 3-4]

There is high confidence that changes in heat waves, glacial retreat, and/or permafrost degradation will

affect high mountain phenomena such as slope instabilities, movements of mass, and glacial lake outburst

floods. There is also high confidence that changes in heavy precipitation will affect landslides in some regions. [3.5.6]

There is low confidence in projections of changes in large-scale patterns of natural climate variability.

Confidence is low in projections of changes in monsoons (rainfall, circulation) because there is little consensus in climate

models regarding the sign of future change in the monsoons. Model projections of changes in El Niño–Southern

-0.6 -0.2 0.2 0.60

Standard DeviationStandard Deviation

-0.75 -0.25 0.25 0.7500.4-0.4 -0.50 0.50

2046 - 2065

Change in consecutive dry days (CDD)

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2046 - 2065

Soil moisture anomalies (SMA)

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2081 - 2100

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2081 - 2100

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Dryness

−

Dryness

−

Figure SPM.5 | Projected annual changes in dryness assessed from two indices. Left column: Change in annual maximum number of consecutive dry days (CDD: days with

precipitation <1 mm). Right column: Changes in soil moisture (soil moisture anomalies, SMA). Increased dryness is indicated with yellow to red colors; decreased dryness with

green to blue. Projected changes are expressed in units of standard deviation of the interannual variability in the three 20-year periods 1980–1999, 2046–2065, and 2081–2100.

The figures show changes for two time horizons, 2046–2065 and 2081–2100, as compared to late 20th-century values (1980–1999), based on GCM simulations under emissions

scenario SRES A2 relative to corresponding simulations for the late 20th century. Results are based on 17 (CDD) and 15 (SMA) GCMs contributing to the CMIP3. Colored shading

is applied for areas where at least 66% (12 out of 17 for CDD, 10 out of 15 for SMA) of the models agree on the sign of the change; stippling is added for regions where at least

90% (16 out of 17 for CDD, 14 out of 15 for SMA) of all models agree on the sign of the change. Grey shading indicates where there is insufficient model agreement (<66%).

[3.5.1, Figure 3-9]

Summary for Policymakers

Oscillation variability and the frequency of El Niño episodes are not consistent, and so there is low confidence in

projections of changes in this phenomenon. [3.4.1, 3.4.2, 3.4.3]

Human Impacts and Disaster Losses

Extreme events will have greater impacts on sectors with closer links to climate, such as water, agriculture

and food security, forestry, health, and tourism. For example, while it is not currently possible to reliably project

specific changes at the catchment scale, there is high confidence that changes in climate have the potential to seriously

affect water management systems. However, climate change is in many instances only one of the drivers of future

changes, and is not necessarily the most important driver at the local scale. Climate-related extremes are also expected

to produce large impacts on infrastructure, although detailed analysis of potential and projected damages are limited

to a few countries, infrastructure types, and sectors. [4.3.2, 4.3.5]

In many regions, the main drivers of future increases in economic losses due to some climate extremes will

be socioeconomic in nature (medium confidence, based on medium agreement, limited evidence). Climate

extremes are only one of the factors that affect risks, but few studies have specifically quantified the effects of

changes in population, exposure of people and assets, and vulnerability as determinants of loss. However, the few

studies available generally underline the important role of projected changes (increases) in population and capital at

risk. [4.5.4]

Increases in exposure will result in higher direct economic losses from tropical cyclones. Losses will also

depend on future changes in tropical cyclone frequency and intensity (high confidence). Overall losses due to

extratropical cyclones will also increase, with possible decreases or no change in some areas (medium confidence).

Although future flood losses in many locations will increase in the absence of additional protection measures (high

agreement, medium evidence), the size of the estimated change is highly variable, depending on location, climate

scenarios used, and methods used to assess impacts on river flow and flood occurrence. [4.5.4]

Disasters associated with climate extremes influence population mobility and relocation, affecting host and

origin communities (medium agreement, medium evidence). If disasters occur more frequently and/or with greater

magnitude, some local areas will become increasingly marginal as places to live or in which to maintain livelihoods. In

such cases, migration and displacement could become permanent and could introduce new pressures in areas of

relocation. For locations such as atolls, in some cases it is possible that many residents will have to relocate. [5.2.2]

Managing Changing Risks

of Climate Extremes and Disasters

Adaptation to climate change and disaster risk management provide a range of complementary approaches for

managing the risks of climate extremes and disasters (Figure SPM.2). Effectively applying and combining approaches

may benefit from considering the broader challenge of sustainable development.

Measures that provide benefits under current climate and a range of future climate change scenarios,

called low-regrets measures, are available starting points for addressing projected trends in exposure,

vulnerability, and climate extremes. They have the potential to offer benefits now and lay the foundation

for addressing projected changes (high agreement, medium evidence). Many of these low-regrets strategies

produce co-benefits, help address other development goals, such as improvements in livelihoods, human well-being,

and biodiversity conservation, and help minimize the scope for maladaptation. [6.3.1, Table 6-1]

Potential low-regrets measures include early warning systems; risk communication between decisionmakers and local

citizens; sustainable land management, including land use planning; and ecosystem management and restoration.

Other low-regrets measures include improvements to health surveillance, water supply, sanitation, and irrigation and

drainage systems; climate-proofing of infrastructure; development and enforcement of building codes; and better

education and awareness. [5.3.1, 5.3.3, 6.3.1, 6.5.1, 6.5.2] See also Case Studies 9.2.11 and 9.2.14, and assessment in

Section 7.4.3.

Effective risk management generally involves a portfolio of actions to reduce and transfer risk and to

respond to events and disasters, as opposed to a singular focus on any one action or type of action (high

confidence). [1.1.2, 1.1.4, 1.3.3] Such integrated approaches are more effective when they are informed by and

customized to specific local circumstances (high agreement, robust evidence). [5.1] Successful strategies include a

combination of hard infrastructure-based responses and soft solutions such as individual and institutional capacity

building and ecosystem-based responses. [6.5.2]

Multi-hazard risk management approaches provide opportunities to reduce complex and compound hazards

(high agreement, robust evidence). Considering multiple types of hazards reduces the likelihood that risk reduction

efforts targeting one type of hazard will increase exposure and vulnerability to other hazards, in the present and

future. [8.2.5, 8.5.2, 8.7]

Opportunities exist to create synergies in international finance for disaster risk management and adaptation

to climate change, but these have not yet been fully realized (high confidence). International funding for

disaster risk reduction remains relatively low as compared to the scale of spending on international humanitarian

response. [7.4.2] Technology transfer and cooperation to advance disaster risk reduction and climate change adaptation

are important. Coordination on technology transfer and cooperation between these two fields has been lacking, which

has led to fragmented implementation. [7.4.3]

Stronger efforts at the international level do not necessarily lead to substantive and rapid results at the

local level (high confidence). There is room for improved integration across scales from international to local. [7.6]

Integration of local knowledge with additional scientific and technical knowledge can improve disaster

risk reduction and climate change adaptation (high agreement, robust evidence). Local populations document

their experiences with the changing climate, particularly extreme weather events, in many different ways, and this self-

generated knowledge can uncover existing capacity within the community and important current shortcomings. [5.4.4]

Local participation supports community-based adaptation to benefit management of disaster risk and climate

extremes. However, improvements in the availability of human and financial capital and of disaster risk and climate

information customized for local stakeholders can enhance community-based adaptation (medium agreement, medium

evidence). [5.6]

Appropriate and timely risk communication is critical for effective adaptation and disaster risk management

(high confidence). Explicit characterization of uncertainty and complexity strengthens risk communication. [2.6.3]

Effective risk communication builds on exchanging, sharing, and integrating knowledge about climate-related risks

among all stakeholder groups. Among individual stakeholders and groups, perceptions of risk are driven by psychological

and cultural factors, values, and beliefs. [1.1.4, 1.3.1, 1.4.2] See also assessment in Section 7.4.5.

An iterative process of monitoring, research, evaluation, learning, and innovation can reduce disaster risk

and promote adaptive management in the context of climate extremes (high agreement, robust evidence).

[8.6.3, 8.7] Adaptation efforts benefit from iterative risk management strategies because of the complexity, uncertainties,

and long time frame associated with climate change (high confidence). [1.3.2] Addressing knowledge gaps through

enhanced observation and research can reduce uncertainty and help in designing effective adaptation and risk

management strategies. [3.2, 6.2.5, Table 6-3, 7.5, 8.6.3] See also assessment in Section 6.6.

Table SPM.1 presents examples of how observed and projected trends in exposure, vulnerability, and

climate extremes can inform risk management and adaptation strategies, policies, and measures. The

Summary for Policymakers

Table SPM.1 | Illustrative examples of options for risk management and adaptation in the context of changes in exposure, vulnerability, and climate extremes. In each example, information is characterized at the

scale directly relevant to decisionmaking. Observed and projected changes in climate extremes at global and regional scales illustrate that the direction of, magnitude of, and/or degree of certainty for changes may

differ across scales.

The examples were selected based on availability of evidence in the underlying chapters, including on exposure, vulnerability, climate information, and risk management and adaptation options. They are intended

to reflect relevant risk management themes and scales, rather than to provide comprehensive information by region. The examples are not intended to reflect any regional differences in exposure and vulnerability, or in

experience in risk management.

The confidence in projected changes in climate extremes at local scales is often more limited than the confidence in projected regional and global changes. This limited confidence in changes places a focus on

low-regrets risk management options that aim to reduce exposure and vulnerability and to increase resilience and preparedness for risks that cannot be entirely eliminated. Higher-confidence projected changes in

climate extremes, at a scale relevant to adaptation and risk management decisions, can inform more targeted adjustments in strategies, policies, and measures. [3.1.6, Box 3-2, 6.3.1, 6.5.2]

Observed: Low conﬁdence at global scale

regarding (climate-driven) observed changes in

the magnitude and frequency of ﬂoods.

Projected: Low conﬁdence in projections of

changes in ﬂoods because of limited evidence

and because the causes of regional changes are

complex. However, medium conﬁdence (based on

physical reasoning) that projected increases in

heavy precipitation will contribute to

rain-generated local ﬂooding in some

catchments or regions.

[Table 3-1, 3.5.2]

Rapid expansion of poor people living

in informal settlements around

Nairobi has led to houses of weak

building materials being constructed

immediately adjacent to rivers and to

blockage of natural drainage areas,

increasing exposure and vulnerability.

[6.4.2, Box 6-2]

Observed: Low conﬁdence regarding

trends in heavy precipitation in East

Africa, because of insufﬁcient evidence.

Projected: Likely increase in heavy

precipitation indicators in East Africa.

[Table 3-2, Table 3-3, 3.3.2]

Limited ability to provide local ﬂash

ﬂood projections.

[3.5.2]

Low-regrets options that reduce exposure and

vulnerability across a range of hazard trends:

• Strengthening building design and regulation

• Poverty reduction schemes

• City-wide drainage and sewerage improvements

The Nairobi Rivers Rehabilitation and Restoration

Programme includes installation of riparian buffers,

canals, and drainage channels and clearance of existing

channels; attention to climate variability and change in

the location and design of wastewater infrastructure; and

environmental monitoring for ﬂood early warning.

[6.3, 6.4.2, Box 6-2, Box 6-6]

Observed: Likely increase in extreme coastal

high water worldwide related to increases in

mean sea level.

Projected: Very likely that mean sea level rise

will contribute to upward trends in extreme

coastal high water levels.

High conﬁdence that locations currently

experiencing coastal erosion and inundation will

continue to do so due to increasing sea level, in

the absence of changes in other contributing

factors.

Likely that the global frequency of tropical

cyclones will either decrease or remain

essentially unchanged.

Likely increase in average tropical cyclone

maximum wind speed, although increases may

not occur in all ocean basins.

[Table 3-1, 3.4.4, 3.5.3, 3.5.5]

Sparse regional and temporal coverage

of terrestrial-based observation

networks and limited in situ ocean

observing network, but with improved

satellite-based observations in recent

decades.

While changes in storminess may

contribute to changes in extreme coastal

high water levels, the limited

geographical coverage of studies to date

and the uncertainties associated with

storminess changes overall mean that a

general assessment of the effects of

storminess changes on storm surge is

not possible at this time.

[Box 3-4, 3.5.3]

Low-regrets options that reduce exposure and

vulnerability across a range of hazard trends:

• Maintenance of drainage systems

• Well technologies to limit saltwater contamination of

groundwater

• Improved early warning systems

• Regional risk pooling

• Mangrove conservation, restoration, and replanting

Speciﬁc adaptation options include, for instance,

rendering national economies more climate-independent

and adaptive management involving iterative learning. In

some cases there may be a need to consider relocation,

for example, for atolls where storm surges may

completely inundate them.

[4.3.5, 4.4.10, 5.2.2, 6.3.2, 6.5.2, 6.6.2, 7.4.4, 9.2.9,

9.2.11, 9.2.13]

Observed: Tides and El Niño–Southern

Oscillation have contributed to the more

frequent occurrence of extreme coastal

high water levels and associated

ﬂooding experienced on some Paciﬁc

Islands in recent years.

Projected: The very likely contribution

of mean sea level rise to increased

extreme coastal high water levels,

coupled with the likely increase in

tropical cyclone maximum wind speed, is

a speciﬁc issue for tropical small island

states.

See global changes column for

information on global projections for

tropical cyclones.

[Box 3-4, 3.4.4, 3.5.3]

Small island states in the Paciﬁc,

Indian, and Atlantic Oceans, often

with low elevation, are particularly

vulnerable to rising sea levels and

impacts such as erosion, inundation,

shoreline change, and saltwater

intrusion into coastal aquifers. These

impacts can result in ecosystem

disruption, decreased agricultural

productivity, changes in disease

patterns, economic losses such as in

tourism industries, and population

displacement – all of which reinforce

vulnerability to extreme weather

events.

[3.5.5, Box 3-4, 4.3.5, 4.4.10, 9.2.9]

Flash ﬂoods in

informal

settlements in

Nairobi, Kenya

Options for risk management and

adaptation in the example

Exposure and vulnerability

at scale of risk management

in the example

Example

GLOBAL

Observed (since 1950) and projected

(to 2100) global changes

REGIONAL

Observed (since 1950) and projected

(to 2100) changes in the example

SCALE OF RISK MANAGEMENT

Available information for the

example

Information on Climate Extreme Across Spatial Scales

Inundation related

to extreme sea

levels in tropical

small island

developing states

Continued next page

Summary for Policymakers

Table SPM.1 (continued)

Observed: Medium conﬁdence that the length

or number of warm spells or heat waves has

increased since the middle of the 20th century, in

many (but not all) regions over the globe.

Very likely increase in number of warm days and

nights at the global scale.

Projected: Very likely increase in length,

frequency, and/or intensity of warm spells or

heat waves over most land areas.

Virtually certain increase in frequency and

magnitude of warm days and nights at the global

scale.

[Table 3-1, 3.3.1]

Observations and projections can

provide information for speciﬁc urban

areas in the region, with increased heat

waves expected due to regional trends

and urban heat island effects.

[3.3.1, 4.4.5]

Low-regrets options that reduce exposure and

vulnerability across a range of hazard trends:

• Early warning systems that reach particularly

vulnerable groups (e.g., the elderly)

• Vulnerability mapping and corresponding measures

• Public information on what to do during heat waves,

including behavioral advice

• Use of social care networks to reach vulnerable

groups

Speciﬁc adjustments in strategies, policies, and measures

informed by trends in heat waves include awareness

raising of heat waves as a public health concern; changes

in urban infrastructure and land use planning, for

example, increasing urban green space; changes in

approaches to cooling for public facilities; and

adjustments in energy generation and transmission

infrastructure.

[Table 6-1, 9.2.1]

Observed: Medium conﬁdence in

increase in heat waves or warm spells in

Europe.

Likely overall increase in warm days and

nights over most of the continent.

Projected: Likely more frequent, longer,

and/or more intense heat waves or

warm spells in Europe.

Very likely increase in warm days and

nights.

[Table 3-2, Table 3-3, 3.3.1]

Factors affecting exposure and

vulnerability include age, pre-existing

health status, level of outdoor

activity, socioeconomic factors

including poverty and social isolation,

access to and use of cooling,

physiological and behavioral

adaptation of the population, and

urban infrastructure.

[2.5.2, 4.3.5, 4.3.6, 4.4.5, 9.2.1]

Observed: Low conﬁdence in any observed

long-term (i.e., 40 years or more) increases in

tropical cyclone activity, after accounting for past

changes in observing capabilities.

Projected: Likely that the global frequency of

tropical cyclones will either decrease or remain

essentially unchanged.

Likely increase in average tropical cyclone

maximum wind speed, although increases may

not occur in all ocean basins.

Heavy rainfalls associated with tropical cyclones

are likely to increase.

Projected sea level rise is expected to further

compound tropical cyclone surge impacts.

[Table 3-1, 3.4.4]

Limited model capability to project

changes relevant to speciﬁc settlements

or other locations, due to the inability of

global models to accurately simulate

factors relevant to tropical cyclone

genesis, track, and intensity evolution.

[3.4.4]

Low-regrets options that reduce exposure and

vulnerability across a range of hazard trends:

• Adoption and enforcement of improved building

codes

• Improved forecasting capacity and implementation of

improved early warning systems (including

evacuation plans and infrastructures)

• Regional risk pooling

In the context of high underlying variability and

uncertainty regarding trends, options can include

emphasizing adaptive management involving learning

and ﬂexibility (e.g., Cayman Islands National Hurricane

Committee).

[5.5.3, 6.5.2, 6.6.2, Box 6-7, Table 6-1, 7.4.4, 9.2.5,

9.2.11, 9.2.13]

See global changes column for global

projections.

Exposure and vulnerability are

increasing due to growth in

population and increase in property

values, particularly along the Gulf and

Atlantic coasts of the United States.

Some of this increase has been offset

by improved building codes.

[4.4.6]

Options for risk management and

adaptation in the example

Exposure and vulnerability

at scale of risk management

in the example

Example

GLOBAL

Observed (since 1950) and projected

(to 2100) global changes

REGIONAL

Observed (since 1950) and projected

(to 2100) changes in the example

SCALE OF RISK MANAGEMENT

Available information for the

example

Information on Climate Extreme Across Spatial Scales

Impacts of heat

waves in urban

areas in Europe

Increasing losses

from hurricanes in

the USA and the

Caribbean

Observed: Medium conﬁdence that some

regions of the world have experienced more

intense and longer droughts, but in some regions

droughts have become less frequent, less intense,

or shorter.

Projected: Medium conﬁdence in projected

intensiﬁcation of drought in some seasons and

areas. Elsewhere there is overall low conﬁdence

because of inconsistent projections.

[Table 3-1, 3.5.1]

Less advanced agricultural practices

render region vulnerable to increasing

variability in seasonal rainfall,

drought, and weather extremes.

Vulnerability is exacerbated by

population growth, degradation of

ecosystems, and overuse of natural

resources, as well as poor standards

for health, education, and

governance.

[2.2.2, 2.3, 2.5, 4.4.2, 9.2.3]

Observed: Medium conﬁdence in an

increase in dryness. Recent years

characterized by greater interannual

variability than previous 40 years, with

the western Sahel remaining dry and the

eastern Sahel returning to wetter

conditions.

Projected: Low conﬁdence due

to inconsistent signal in model

projections.

[Table 3-2, Table 3-3, 3.5.1]

Sub-seasonal, seasonal, and interannual

forecasts with increasing uncertainty

over longer time scales.

Improved monitoring, instrumentation,

and data associated with early warning

systems, but with limited participation

and dissemination to at-risk populations.

[5.3.1, 5.5.3, 7.3.1, 9.2.3, 9.2.11]

Low-regrets options that reduce exposure and

vulnerability across a range of hazard trends:

• Traditional rain and groundwater harvesting and

storage systems

• Water demand management and improved irrigation

efﬁciency measures

• Conservation agriculture, crop rotation, and livelihood

diversiﬁcation

• Increasing use of drought-resistant crop varieties

• Early warning systems integrating seasonal forecasts

with drought projections, with improved

communication involving extension services

• Risk pooling at the regional or national level

[2.5.4, 5.3.1, 5.3.3, 6.5, Table 6-3, 9.2.3, 9.2.11]

Droughts in the

context of food

security in West

Africa

Summary for Policymakers

importance of these trends for decisionmaking depends on their magnitude and degree of certainty at the temporal

and spatial scale of the risk being managed and on the available capacity to implement risk management options

(see Table SPM.1).

Implications for Sustainable Development

Actions that range from incremental steps to transformational changes are essential for reducing risk from

climate extremes (high agreement, robust evidence). Incremental steps aim to improve efficiency within existing

technological, governance, and value systems, whereas transformation may involve alterations of fundamental attributes

of those systems. Transformations, where they are required, are also facilitated through increased emphasis on adaptive

management and learning. Where vulnerability is high and adaptive capacity low, changes in climate extremes can

make it difficult for systems to adapt sustainably without transformational changes. Vulnerability is often concentrated

in lower-income countries or groups, although higher-income countries or groups can also be vulnerable to climate

extremes. [8.6, 8.6.3, 8.7]

Social, economic, and environmental sustainability can be enhanced by disaster risk management and

adaptation approaches. A prerequisite for sustainability in the context of climate change is addressing the

underlying causes of vulnerability, including the structural inequalities that create and sustain poverty and

constrain access to resources (medium agreement, robust evidence). This involves integrating disaster risk

management and adaptation into all social, economic, and environmental policy domains. [8.6.2, 8.7]

The most effective adaptation and disaster risk reduction actions are those that offer development benefits

in the relatively near term, as well as reductions in vulnerability over the longer term (high agreement,

medium evidence). There are tradeoffs between current decisions and long-term goals linked to diverse values,

interests, and priorities for the future. Short- and long-term perspectives on disaster risk management and adaptation

to climate change thus can be difficult to reconcile. Such reconciliation involves overcoming the disconnect between

local risk management practices and national institutional and legal frameworks, policy, and planning. [8.2.1, 8.3.1,

8.3.2, 8.6.1]

Progress toward resilient and sustainable development in the context of changing climate extremes can

benefit from questioning assumptions and paradigms and stimulating innovation to encourage new

patterns of response (medium agreement, robust evidence). Successfully addressing disaster risk, climate

change, and other stressors often involves embracing broad participation in strategy development, the capacity to

combine multiple perspectives, and contrasting ways of organizing social relations. [8.2.5, 8.6.3, 8.7]

The interactions among climate change mitigation, adaptation, and disaster risk management may have a

major influence on resilient and sustainable pathways (high agreement, limited evidence). Interactions

between the goals of mitigation and adaptation in particular will play out locally, but have global consequences.

[8.2.5, 8.5.2]

There are many approaches and pathways to a sustainable and resilient future. [8.2.3, 8.4.1, 8.6.1, 8.7] However, limits

to resilience are faced when thresholds or tipping points associated with social and/or natural systems are exceeded,

posing severe challenges for adaptation. [8.5.1] Choices and outcomes for adaptive actions to climate events must

reflect divergent capacities and resources and multiple interacting processes. Actions are framed by tradeoffs between

competing prioritized values and objectives, and different visions of development that can change over time. Iterative

approaches allow development pathways to integrate risk management so that diverse policy solutions can be

considered, as risk and its measurement, perception, and understanding evolve over time. [8.2.3, 8.4.1, 8.6.1, 8.7]

Summary for Policymakers

Box SPM.2 | Treatment of Uncertainty

Based on the Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties,

this

Summary for Policymakers relies on two metrics for communicating the degree of certainty in key findings, which is based on author

teams’ evaluations of underlying scientific understanding:

• Confidence in the validity of a finding, based on the type, amount, quality, and consistency of evidence (e.g., mechanistic

understanding, theory, data, models, expert judgment) and the degree of agreement. Confidence is expressed qualitatively.

• Quantified measures of uncertainty in a finding expressed probabilistically (based on statistical analysis of observations or model

results, or expert judgment).

This Guidance Note refines the guidance provided to support the IPCC Third and Fourth Assessment Reports. Direct comparisons between

assessment of uncertainties in findings in this report and those in the IPCC Fourth Assessment Report are difficult if not impossible,

because of the application of the revised guidance note on uncertainties, as well as the availability of new information, improved

scientific understanding, continued analyses of data and models, and specific differences in methodologies applied in the assessed

studies. For some extremes, different aspects have been assessed and therefore a direct comparison would be inappropriate.

Each key finding is based on an author team’s evaluation of associated evidence and agreement. The confidence metric provides a

qualitative synthesis of an author team’s judgment about the validity of a finding, as determined through evaluation of evidence and

agreement. If uncertainties can be quantified probabilistically, an author team can characterize a finding using the calibrated likelihood

language or a more precise presentation of probability. Unless otherwise indicated, high or very high confidence is associated with

findings for which an author team has assigned a likelihood term.

The following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of

agreement: low, medium, or high. A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high. The

accompanying figure depicts summary statements for evidence and agreement and their relationship to confidence. There is flexibility in

this relationship; for a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of

evidence and degrees of agreement are correlated with increasing confidence.

The following terms indicate the assessed likelihood:

Term* Likelihood of the Outcome

Virtually certain 99–100% probability

Very likely 90–100% probability

Likely 66–100% probability

About as likely as not 33–66% probability

Unlikely 0–33% probability

Very unlikely 0–10% probability

Exceptionally unlikely 0–1% probability

* Additional terms that were used in limited circumstances in the Fourth

Assessment Report (extremely likely: 95–100% probability, more likely than

not: >50–100% probability, and extremely unlikely: 0–5% probability) may

also be used when appropriate.

____________

Mastrandrea, M.D., C.B. Field, T.F. Stocker, O. Edenhofer, K.L. Ebi, D.J. Frame, H. Held, E. Kriegler, K.J. Mach, P.R. Matschoss, G.-K. Plattner, G.W. Yohe, and F.W. Zwiers,

2010: Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. Intergovernmental Panel on Climate Change

(IPCC), Geneva, Switzerland, www.ipcc.ch.

High agreement

Limited evidence

High agreement

Medium evidence

High agreement

Robust evidence

Medium agreement

Robust evidence

Medium agreement

Medium evidence

Medium agreement

Limited evidence

Low agreement

Limited evidence

Low agreement

Medium evidence

Low agreement

Robust evidence

Evidence (type, amount, quality, consistency)

Agreement

Conﬁdence

Scale

A depiction of evidence and agreement statements and their relationship to

confidence. Confidence increases toward the top-right corner as suggested by the

increasing strength of shading. Generally, evidence is most robust when there are

multiple, consistent independent lines of high-quality evidence.

Summary for Policymakers

III Chapters 1 to 9

Coordinating Lead Authors:

Allan Lavell (Costa Rica), Michael Oppenheimer (USA)

Lead Authors:

Cherif Diop (Senegal), Jeremy Hess (USA), Robert Lempert (USA), Jianping Li (China),

Robert Muir-Wood (UK), Soojeong Myeong (Republic of Korea)

Review Editors:

Susanne Moser (USA), Kuniyoshi Takeuchi (Japan)

Contributing Authors:

Omar-Dario Cardona (Colombia), Stephane Hallegatte (France), Maria Lemos (USA),

Christopher Little (USA), Alexander Lotsch (USA), Elke Weber (USA)

This chapter should be cited as:

Lavell, A., M. Oppenheimer, C. Diop, J. Hess, R. Lempert, J. Li, R. Muir-Wood, and S. Myeong, 2012: Climate change: new

dimensions in disaster risk, exposure, vulnerability, and resilience. In: Managing the Risks of Extreme Events and Disasters to

Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J.

Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the

Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA,

pp. 25-64.

Climate Change:

New Dimensions in

Disaster Risk, Exposure,

Vulnerability, and Resilience

Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Executive Summary ...................................................................................................................................27

1.1. Introduction................................................................................................................................29

1.1.1. Purpose and Scope of the Special Report .........................................................................................................................29

1.1.2. Key Concepts and Definitions ............................................................................................................................................30

1.1.2.1. Definitions Related to General Concepts....................................................

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

.30

1.1.2.2. Concepts and Definitions Relating to Disaster Risk Management and Adaptation to Climate Change .............................................34

1.1.2.3. The Social Construction of Disaster Risk.............................................................................................................................................36

1.1.3. Framing the Relation between Adaptation to Climate Change and Disaster Risk Management...

....

..............................37

1.1.4. Framing the Processes of Disaster Risk Management and Adaptation to Climate Change .............................................38

1.1.4.1. Exceptionality, Routine, and Everyday Life ...................................................

......................................................................................38

1.1.4.2.

Territorial Scale, Disaster Risk, and Adaptation...................................................................................................................................39

1.2. Extreme Events, Extreme Impacts, and Disasters ......................................................................39

1.2.1. Distinguishing Extreme Events, Extreme Impacts, and Disasters ......................................................................................39

1.2.2. Extreme Events Defined in Physical Terms ........................................................................................................................40

1.2.2.1. Definitions of Extremes....................................................

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

.......................40

1.2.2.2. Extremes in a Changing Climate ........................................................................................................................................................40

1.2.2.3. The Diversity and Range of Extremes .................................................................................................................................................40

1.2.3. Extreme Impacts...

....

..........................................................................................................................................................41

1.2.3.1. Three Classes of Impacts ................................................................................................................................................

....................41

1.2.3.2. Complex Nature of an Extreme ‘Event’...............................................................................................................................................42

1.2.3.3. Metrics to Quantify Social Impacts and the Management of Extremes..............................................................................................42

1.2.3.4. Traditional Adjustment to Extremes....................................................................................................................................................43

1.3. Disaster Management, Disaster Risk Reduction, and Risk Transfer ..........................................44

1.3.1. Climate Change Will Complicate Management of Some Disaster Risks ...........................................................................46

1.3.1.1. Challenge of Quantitative Estimates of Changing Risks.....................................................................................................................46

1.3.1.2.

Processes that Influence Judgments about Changing Risks ...............................................................................................................46

1.3.2. Adaptation to Climate Change Contributes to Disaster Risk Management...

....

...............................................................47

1.3.3. Disaster Risk Management and Adaptation to Climate Change Share Many Concepts, Goals, and Processes................48

1.4. Coping and Adapting .................................................................................................................50

1.4.1. Definitions, Distinctions, and Relationships.......................................................................................................................51

1.4.1.1. Definitions and Distinctions...............................................................................................................................................

.................51

1.4.1.2. Relationships between Coping, Coping Capacity, Adaptive Capacity, and the Coping Range............................................................51

1.4.2. Learning...

....

.......................................................................................................................................................................53

1.4.3. Learning to Overcome Adaptation Barriers .......................................................................................................................54

1.4.4. ‘No Regrets,’ Robust Adaptation, and Learning.................................................................................................................56

References .................................................................................................................................................56

Chapter 1

Table of Contents

Disaster signifies extreme impacts suffered when hazardous physical events interact with vulnerable social

conditions to severely alter the normal functioning of a community or a society (high confidence). Social

vulnerability and exposure are key determinants of disaster risk and help explain why non-extreme physical events

and chronic hazards can also lead to extreme impacts and disasters, while some extreme events do not. Extreme

impacts on human, ecological, or physical systems derive from individual extreme or non-extreme events, or a

compounding of events or their impacts (for example, drought creating the conditions for wildfire, followed by heavy

rain leading to landslides and soil erosion). [1.1.2.1, 1.1.2.3, 1.2.3.1, 1.3]

Management strategies based on the reduction of everyday or chronic risk factors and on the reduction of

risk associated with non-extreme events, as opposed to strategies based solely on the exceptional or

extreme, provide a mechanism that facilitates the reduction of disaster risk and the preparation for and

response to extremes and disasters (high confidence). Effective adaptation to climate change requires an

understanding of the diverse ways in which social processes and development pathways shape disaster risk. Disaster

risk is often causally related to ongoing, chronic, or persistent environmental, economic, or social risk factors. [1.1.2.2,

1.1.3, 1.1.4.1, 1.3.2]

Development practice, policy, and outcomes are critical to shaping disaster risk (high confidence). Disaster

risk may be increased by shortcomings in development. Reductions in the rate of depletion of ecosystem services,

improvements in urban land use and territorial organization processes, the strengthening of rural livelihoods, and

general and specific advances in urban and rural governance advance the composite agenda of poverty reduction,

disaster risk reduction, and adaptation to climate change. [1.1.2.1, 1.1.2.2, 1.1.3, 1.3.2, 1.3.3]

Climate change will pose added challenges for the appropriate allocation of efforts to manage disaster

risk (high confidence). The potential for changes in all characteristics of climate will complicate the evaluation,

communication, and management of the resulting risk. [1.1.3.1, 1.1.3.2, 1.2.2.2, 1.3.1, 1.3.2, 1.4.3]

Risk assessment is one starting point, within the broader risk governance framework, for adaptation to

climate change and disaster risk reduction and transfer (high confidence). The assessment and analysis process

may employ a variety of tools according to management context, access to data and technology, and stakeholders

involved. These tools will vary from formalized probabilistic risk analysis to local level, participatory risk and context

analysis methodologies. [1.3, 1.3.1.2, 1.3.3, Box 1-2]

Risk assessment encounters difficulties in estimating the likelihood and magnitude of extreme events and

their impacts (high confidence). Furthermore, among individual stakeholders and groups, perceptions of risk are

driven by psychological and cultural factors, values, and beliefs. Effective risk communication requires exchanging,

sharing, and integrating knowledge about climate-related risks among all stakeholder groups. [Box 1-1, 1.1.4.1,

1.2.2.1, 1.3.1.1, 1.3.1.2, Box 1-2, Box 1-3, 1.4.2]

Management of the risk associated with climate extremes, extreme impacts, and disasters benefits from

an integrated systems approach, as opposed to separately managing individual types of risk or risk in

particular locations (high confidence). Effective risk management generally involves a portfolio of actions to

reduce and transfer risk and to respond to events and disasters, as opposed to a singular focus on any one action or

type of action. [1.1.2.2, 1.1.4.1, 1.3, 1.3.3, 1.4.2]

Learning is central to adaptation to climate change. Furthermore, the concepts, goals, and processes of

adaptation share much in common with disaster risk management, particularly its disaster risk reduction

component (high confidence). Disaster risk management and adaptation to climate change offer frameworks for, and

examples of, advanced learning processes that may help reduce or avoid barriers that undermine planned adaptation

efforts or lead to implementation of maladaptive measures. Due to the deep uncertainty, dynamic complexity, and

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Executive Summary

long timeframe associated with climate change, robust adaptation efforts would require iterative risk management

strategies. [1.1.3, 1.3.2, 1.4.1.2, 1.4.2, 1.4.5, Box 1-4]

Projected trends and uncertainty in hazards, exposure, and vulnerability associated with climate change

and development make return to the status quo, coping, or static resilience increasingly insufficient goals

for disaster risk management and adaptation (high confidence). Recent approaches to resilience of social-

ecological systems expand beyond these concepts to include the ability to self-organize, learn, and adapt over time.

[1.1.2.1, 1.1.2.2, 1.4.1.2, 1.4.2, 1.4.4]

Given shortcomings of past disaster risk management and the new dimension of climate change, greatly

improved and strengthened disaster risk management and adaptation will be needed, as part of

development processes, in order to reduce future risk (high confidence). Efforts will be more effective when

informed by the experience and success with disaster risk management in different regions during recent decades, and

appropriate approaches for risk identification, reduction, transfer, and disaster management. In the future, the

practices of disaster risk management and adaptation can each greatly benefit from far greater synergy and linkage in

institutional, financial, policy, strategic, and practical terms. [1.1.1, 1.1.2.2, 1.1.3, 1.3.3, 1.4.2]

Community participation in planning, the determined use of local and community knowledge and capacities,

and the decentralization of decisionmaking, supported by and in synergy with national and international

policies and actions, are critical for disaster risk reduction (high confidence). The use of local level risk and

context analysis methodologies, inspired by disaster risk management and now strongly accepted by many civil society

and government agencies in work on adaptation at the local levels, would foster greater integration between, and

greater effectiveness of, both adaptation to climate change and disaster risk management. [1.1.2.2, 1.1.4.2, 1.3.3, 1.4.2]

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

1.1. Introduction

1.1.1. Purpose and Scope of the Special Report

Climate change, an alteration in the state of the climate that can be

identified by changes in the mean and/or the variability of its properties,

and that persists for an extended period, typically decades or longer, is

a fundamental reference point for framing the different management

themes and challenges dealt with in this Special Report.

Climate change may be due to natural internal processes or external

forcings, or to persistent anthropogenic changes in the composition of

the atmosphere or in land use (see Chapter 3 for greater detail).

Anthropogenic climate change is projected to continue during this

century and beyond. This conclusion is robust under a wide range of

scenarios for future greenhouse gas emissions, including some that

anticipate a reduction in emissions (IPCC, 2007a).

While specific, local outcomes of climate change are uncertain, recent

assessments project alteration in the frequency, intensity, spatial extent,

or duration of weather and climate extremes, including climate and

hydrometeorological events such as heat waves, heavy precipitation

events, drought, and tropical cyclones (see Chapter 3). Such change, in

a context of increasing vulnerability, will lead to increased stress on

human and natural systems and a propensity for serious adverse effects

in many places around the world (UNISDR, 2009e, 2011). At the same

time, climate change is also expected to bring benefits to certain places

and communities at particular times.

New, improved or strengthened processes for anticipating and dealing

with the adverse effects associated with weather and climate events

will be needed in many areas. This conclusion is supported by the fact

that despite increasing knowledge and understanding of the factors

that lead to adverse effects, and despite important advances over

recent decades in the reduction of loss of life with the occurrence of

hydrometeorological events (mainly attributable to important advances

with early warning systems, e.g., Section 9.2.11), social intervention in

the face of historical climate variability has not kept pace with the rapid

increases in other adverse economic and social effects suffered during

this period (ICSU, 2008) (high confidence). Instead, a rapid growth in

real economic losses and livelihood disruption has occurred in many

parts of the world (UNISDR, 2009e, 2011). In regard to losses associated

with tropical cyclones, recent analysis has shown that, with the exception

of the East Asian and Pacific and South Asian regions, “both exposure

and the estimated risk of economic loss are growing faster than GDP

per capita. Thus the risk of losing wealth in disasters associated with

tropical cyclones is increasing faster than wealth itself is increasing”

(UNISDR, 2011, p. 33).

The Hyogo Framework for Action (UNISDR, 2005), adopted by 168

governments, provides a point of reference for disaster risk management

and its practical implementation (see Glossary and Section 1.1.2.2 for a

definition of this practice). Subsequent United Nations statements

suggest the need for closer integration of disaster risk management and

adaptation with climate change concerns and goals, all in the context of

development and development planning (UNISDR, 2008a, 2009a,b,c).

Such a concern led to the agreement between the IPCC and the United

Nations International Strategy for Disaster Reduction (UNISDR), with

the support of the Norwegian government, to undertake this Special

Report on “Managing the Risks of Extreme Events and Disasters to

Advance Climate Change Adaptation” (IPCC, 2009).

This Special Report responds to that concern by considering climate

change and its effects on extreme (weather and climate) events, disaster,

and disaster risk management; how human responses to extreme

events and disasters (based on historical experience and evolution in

practice) could contribute to adaptation objectives and processes; and

how adaptation to climate change could be more closely integrated

with disaster risk management practice.

The report draws on current scientific knowledge to address three

specific goals:

1) To assess the relevance and utility of the concepts, methods,

strategies, instruments, and experience gained from the management

of climate-associated disaster risk under conditions of historical

climate patterns, in order to advance adaptation to climate change

and the management of extreme events and disasters in the

future.

2) To assess the new perspectives and challenges that climate change

brings to the disaster risk management field.

3) To assess the mutual implications of the evolution of the disaster

risk management and adaptation to climate change fields,

particularly with respect to the desired increases in social resilience

and sustainability that adaptation implies.

The principal audience for this Special Report comprises decisionmakers

and professional and technical personnel from local through to national

governments, international development agencies, nongovernmental

organizations, and civil society organizations. The report also has relevance

for the academic community and interested laypeople.

The first section of this chapter briefly introduces the more important

concepts, definitions, contexts, and management concerns needed to

frame the content of this report. Later sections of the chapter expand on

the subjects of extreme events and extreme impacts; disaster risk

management, reduction, and transfer and their integration with

climate change and adaptation processes; and the notions of coping and

adaptation. The level of detail and discussion presented in this chapter

is commensurate with its status as a ‘scene setting’ initiative. The

following eight chapters provide more detailed and specific analysis.

Chapter 2 assesses the key determinants of risk, namely exposure and

vulnerability in the context of climate-related hazards. A particular focus is

the connection between near-term experience and long-term adaptation.

Key questions addressed include whether reducing vulnerability to

current hazards improves adaptation to longer-term climate change,

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

and how near-term risk management decisions and adjustments

constrain future vulnerability and enable adaptation.

Chapter 3 focuses on changes in extremes of atmospheric weather and

climate variables (e.g., temperature and precipitation), large-scale

phenomena that are related to these extremes or are themselves

extremes (e.g., tropical and extratropical cyclones, El Niño, and monsoons),

and collateral effects on the physical environment (e.g., droughts,

floods, coastal impacts, landslides). The chapter builds on and updates

the Fourth Assessment Report, which in some instances, due to new

literature, leads to revisions of that assessment.

Chapter 4 explores how changes in climate, particularly weather and

climate extremes assessed in Chapter 3, translate into extreme impacts

on human and ecological systems. A key issue is the nature of both

observed and expected trends in impacts, the latter resulting from

trends in both physical and social conditions. The chapter assesses

these questions from both a regional and a sectoral perspective, and

examines the direct and indirect economic costs of such changes and

their relation to development.

Chapters 5, 6, and 7 assess approaches to disaster risk management and

adaptation to climate change from the perspectives of local, national,

and international governance institutions, taking into consideration the

roles of government, individuals, nongovernmental organizations, the

private sector, and other civil society institutions and arrangements.

Each chapter reviews the efficacy of current disaster risk reduction,

preparedness, and response and risk transfer strategies and previous

approaches to extremes and disasters in order to extract lessons for the

future. Impacts, adaptation, and the cost of risk management are

assessed through the prism of diverse social aggregations and means

for cooperation, as well as a variety of institutional arrangements.

Chapter 5 focuses on the highly variable local contexts resulting from

differences in place, social groupings, experience, management,

institutions, conditions, and sets of knowledge, highlighting risk

management strategies involving housing, buildings, and land use.

Chapter 6 explores similar issues at the national level, where

mechanisms including national budgets, development goals, planning,

warning systems, and building codes may be employed to manage, for

example, food security and agriculture, water resources, forests,

fisheries, building practice, and public health. Chapter 7 carries this

analysis to the international level, where the emphasis is on institutions,

organizations, knowledge generation and sharing, legal frameworks and

practices, and funding arrangements that characterize international

agencies and collaborative arrangements. This chapter also discusses

integration of responsibilities across all governmental scales, emphasizing

the linkages among disaster risk management, climate change adaptation,

and development.

Chapter 8 assesses how disaster risk reduction strategies, ranging from

incremental to transformational, can advance adaptation to climate

change and promote a more sustainable and resilient future. Key

questions include whether an improved alignment between climate

change responses and sustainable development strategies may be

achieved, and whether short- and long-term perspectives may be

reconciled.

Chapter 9 closes this report by presenting case studies in order to

identify lessons and best practices from past responses to extreme

climate-related events and extreme impacts. Cases illustrate concrete

and diverse examples of disaster types as well as risk management

methodologies and responses discussed in the other chapters, providing

a key reference point for the entire report.

1.1.2. Key Concepts and Definitions

The concepts and definitions presented in this chapter and employed

throughout the Special Report take into account a number of existing

sources (IPCC, 2007c; UNISDR, 2009d; ISO, 2009) but also reflect the fact

that concepts and definitions evolve as knowledge, needs, and contexts

vary. Disaster risk management and adaptation to climate change are

dynamic fields, and have in the past exhibited and will necessarily

continue in the future to exhibit such evolution.

This chapter presents ‘skeleton’ definitions that are generic rather than

specific. In subsequent chapters, the definitions provided here are often

expanded in more detail and variants among these definitions will be

examined and explained where necessary.

A glossary of the fundamental definitions used in this assessment is

provided at the end of this study. Figure 1-1 provides a schematic

of the relationships among many of the key concepts defined here.

1.1.2.1. Definitions Related to General Concepts

In order to delimit the central concerns of this Special Report, a distinction

is made between those concepts and definitions that relate to disaster

risk and adaptation to climate change generally; and, on the other

hand, those that relate in particular to the options and forms of social

intervention relevant to these fields. In Section 1.1.2.1, consideration is

given to general concepts. In Section 1.1.2.2, key concepts relating to

social intervention through ‘Disaster Risk Management’ and ‘Climate

Change Adaptation’ are considered.

Extreme (weather and climate) events and disasters comprise the two

central risk management concerns of this Special Report.

Extreme events comprise a facet of climate variability under stable or

changing climate conditions. They are defined as the occurrence of a

value of a weather or climate variable above (or below) a threshold

value near the upper (or lower) ends (‘tails’) of the range of observed

values of the variable. This definition is further discussed and amplified

in Sections 1.2.2, 3.1.1, and 3.1.2.

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Disasters are defined in this report as severe alterations in the normal

functioning of a community or a society due to hazardous physical events

interacting with vulnerable social conditions, leading to widespread

adverse human, material, economic, or environmental effects that

require immediate emergency response to satisfy critical human needs

and that may require external support for recovery.

The hazardous physical events referred to in the definition of disaster

may be of natural, socio-natural (originating in the human degradation

or transformation of the physical environment), or purely anthropogenic

origins (see Lavell, 1996, 1999; Smith, 1996; Tobin and Montz, 1997;

Wisner et al., 2004). This Special Report emphasizes hydrometeorological

and oceanographic events; a subset of a broader spectrum of physical

events that may acquire the characteristic of a hazard if conditions of

exposure and vulnerability convert them into a threat. These include

earthquakes, volcanoes, and tsunamis, among others. Any one geographic

area may be affected by one, or a combination of, such events at the

same or different times. Both in this report and in the wider literature,

some events (e.g., floods and droughts) are at times referred to as

physical impacts (see Section 3.1.1).

Extreme events are often but not always associated with disaster. This

association will depend on the particular physical, geographic, and social

conditions that prevail (see this section and Chapter 2 for discussion of

the conditioning circumstances associated with so-called ‘exposure’

and ‘vulnerability’) (Ball, 1975; O’Keefe et al., 1976; Timmerman, 1981;

Hewitt, 1983; Maskrey, 1989; Mileti, 1999; Wisner et al., 2004).

Non-extreme physical events also can and do lead to disasters where

physical or societal conditions foster such a result. In fact, a significant

number of disasters registered annually in most disaster databases

are associated with physical events that are not extreme as defined

probabilistically, yet have important social and economic impacts on

local communities and governments, both individually and in aggregate

(UNISDR, 2009e, 2011) (high confidence).

For example, many of the ‘disasters’ registered in the widely consulted

University of Louvaine EM-DAT database (CRED, 2010) are not initiated

by statistically extreme events, but rather exhibit extreme properties

expressed as severe interruptions in the functioning of local social and

economic systems. This lack of connection is even more obvious in the

DesInventar database (Corporación OSSO, 2010), developed first in

Latin America in order to specifically register the occurrence of small-

and medium-scale disasters, and which has registered tens and tens of

thousands of these during the last 30 years in the 29 countries it covers

to date. This database has been used by the UNISDR, the Inter-American

Development Bank, and others to examine disaster occurrence, scale,

and impacts in Latin America and Asia, in particular (Cardona 2005,

2008; IDEA, 2005; UNISDR, 2009e, 2011; ERN-AL, 2011). In any one

place, the range of disaster-inducing events can increase if social

conditions deteriorate (Wisner et al., 2004, 2011).

The occurrence of disaster is always preceded by the existence of

specific physical and social conditions that are generally referred to

as disaster risk (Hewitt, 1983; Lewis, 1999, 2009; Bankoff, 2001;

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Greenhouse Gas Emissions

Vulnerability

Exposure

Weather and

Climate

Events

DISASTER

RISK

Anthropogenic

Climate Change

Disaster Risk

Management

Climate Change

Adaptation

Disaster

Natural

Variability

DEVELOPMENTCLIMATE

Figure 1-1 | The key concepts and scope of this report. The figure indicates schematically key concepts involved in disaster risk management and climate change adaptation, and

the interaction of these with sustainable development.

Wisner et al., 2004, 2011; ICSU, 2008; UNISDR, 2009e, 2011; ICSU-LAC,

2009).

Disaster risk is defined for the purposes of this study as the likelihood

over a specified time period of severe alterations in the normal

functioning of a community or a society due to hazardous physical

events interacting with vulnerable social conditions, leading to

widespread adverse human, material, economic, or environmental

effects that require immediate emergency response to satisfy critical

human needs and that may require external support for recovery.

Disaster risk derives from a combination of physical hazards and the

vulnerabilities of exposed elements and will signify the potential for

severe interruption of the normal functioning of the affected society once

it materializes as disaster. This qualitative statement will be expressed

formally later in this assessment (Section 1.3 and Chapter 2).

The definitions of disaster risk and disaster posited above do not include

the potential or actual impacts of climate and hydrological events on

ecosystems or the physical Earth system per se. In this assessment, such

impacts are considered relevant to disaster if, as is often the case, they

comprise one or more of the following, at times interrelated, situations:

i) they impact livelihoods negatively by seriously affecting ecosystem

services and the natural resource base of communities; ii) they have

consequences for food security; and/or iii) they have impacts on human

health.

Extreme impacts on the physical environment are addressed in Section

3.5 and extreme impacts on ecosystems are considered in detail in

Chapter 4. In excluding such impacts from the definition of ‘disaster’ as

employed here, this chapter is in no way underestimating their broader

significance (e.g., in regard to existence value) or suggesting they

should not be dealt with under the rubric of adaptation concerns and

management needs. Rather, we are establishing their relative position

within the conceptual framework of climate-related, socially-defined

‘disaster’ and ‘disaster risk’ and the management options that are

available for promoting disaster risk reduction and adaptation to climate

change (see Section 1.1.2.2 and the Glossary for definitions of these

terms). Thus this report draws a distinction between ‘social disaster,’

where extreme impacts on the physical and ecological systems may or

may not play a part, and so-called ‘environmental disaster,’ where direct

physical impacts of human activity and natural physical processes on

the environment are fundamental causes (with possible direct feedback

impacts on social systems).

Disaster risk cannot exist without the threat of potentially damaging

physical events. However, such events, once they occur, are not in and of

themselves sufficient to explain disaster or its magnitude. In the search

to better understand the concept of disaster risk (thus disaster) it is

important to consider the notions of hazard, vulnerability, and exposure.

When extreme and non-extreme physical events, such as tropical

cyclones, floods, and drought, can affect elements of human systems in

an adverse manner, they assume the characteristic of a hazard. Hazard

is defined here as the potential occurrence of a natural or human-

induced physical event that may cause loss of life, injury, or other

health impacts, as well as damage and loss to property, infrastructure,

livelihoods, service provision, and environmental resources. Physical

events become hazards where social elements (or environmental

resources that support human welfare and security) are exposed to

their potentially adverse impacts and exist under conditions that could

predispose them to such effects. Thus, hazard is used in this study to

denote a threat or potential for adverse effects, not the physical event

itself (Cardona, 1986, 1996, 2011; Smith, 1996; Tobin and Montz, 1997;

Lavell, 2003; Hewitt, 2007; Wisner et al., 2004).

Exposure is employed to refer to the presence (location) of people,

livelihoods, environmental services and resources, infrastructure, or

economic, social, or cultural assets in places that could be adversely

affected by physical events and which, thereby, are subject to potential

future harm, loss, or damage. This definition subsumes physical and

biological systems under the concept of ‘environmental services and

resources,’ accepting that these are fundamental for human welfare and

security (Crichton, 1999; Gasper, 2010).

Exposure may also be dictated by mediating social structures (e.g.,

economic and regulatory) and institutions (Sen, 1983). For example,

food insecurity may result from global market changes driven by

drought or flood impacts on crop production in another location. Other

relevant and important interpretations and uses of exposure are

discussed in Chapter 2.

Under exposed conditions, the levels and types of adverse impacts will

be the result of a physical event (or events) interacting with socially

constructed conditions denoted as vulnerability.

Vulnerability is defined generically in this report as the propensity or

predisposition to be adversely affected. Such predisposition constitutes

an internal characteristic of the affected element. In the field of disaster

risk, this includes the characteristics of a person or group and their

situation that influences their capacity to anticipate, cope with, resist, and

recover from the adverse effects of physical events (Wisner et al., 2004).

Vulnerability is a result of diverse historical, social, economic, political,

cultural, institutional, natural resource, and environmental conditions

and processes.

The concept has been developed as a theme in disaster work since the

1970s (Baird et al., 1975; O’Keefe et al., 1976; Wisner et al., 1977; Lewis,

1979, 1984, 1999, 2009; Timmerman, 1981; Hewitt, 1983, 1997, 2007;

Cutter, 1996; Weichselgartner, 2001; Cannon, 2006; Gaillard, 2010) and

variously modified in different fields and applications in the interim

(Adger, 2006; Eakin and Luers, 2006; Füssel, 2007). Vulnerability has

been evaluated according to a variety of quantitative and qualitative

metrics (Coburn and Spence, 2002; Schneider et al., 2007; Cardona,

2011). A detailed discussion of this notion and the drivers or root

causes of vulnerability are provided in Chapter 2.

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

The importance of vulnerability to the disaster risk management

community may be appreciated in the way it has helped to highlight the

role of social factors in the constitution of risk, moving away from purely

physical explanations and attributions of loss and damage (see Hewitt,

1983 for an early critique of what he denominated the ‘physicalist’

interpretation of disaster). Differential levels of vulnerability will lead

to differential levels of damage and loss under similar conditions of

exposure to physical events of a given magnitude (Dow, 1992; Wisner

et al., 2011).

The fundamentally social connotation and ‘predictive’ value of

vulnerability is emphasized in the definition used here. The earlier

IPCC definition of vulnerability refers, however, to “the degree to which

a system is susceptible to and unable to cope with adverse effects of

climate change, including climate variability and extremes. Vulnerability

is a function of the character, magnitude, and rate of climate change and

variation to which a system is exposed, its sensitivity, and its adaptive

capacity” (IPCC, 2007c, p. 883). This definition makes physical causes and

their effects an explicit aspect of vulnerability while the social context

is encompassed by the notions of sensitivity and adaptive capacity

(these notions are defined later). In the definition used in this report, the

social context is emphasized explicitly, and vulnerability is considered

independent of physical events (Hewitt, 1983, 1997, 2007;

Weichselgartner, 2001; Cannon, 2006; O’Brien et al., 2007).

Vulnerability has been contrasted and complimented with the notion of

capacity.

Capacity refers to the combination of all the strengths, attributes, and

resources available to an individual, community, society, or organization

that can be used to achieve established goals. This includes the conditions

and characteristics that permit society at large (institutions, local groups,

individuals, etc.) access to and use of social, economic, psychological,

cultural, and livelihood-related natural resources, as well as access to

the information and the institutions of governance necessary to reduce

vulnerability and deal with the consequences of disaster. This definition

extends the definition of capabilities referred to in Sen’s ‘capabilities

approach to development’ (Sen, 1983).

The lack of capacity may be seen as being one dimension of overall

vulnerability, while it is also seen as a separate notion that, although

contributing to an increase in vulnerability, is not part of vulnerability

per se. The existence of vulnerability does not mean an absolute, but

rather a relative lack of capacity.

Promoted in disaster recovery work by Anderson and Woodrow (1989)

as a means, among other objectives, to shift the analytical balance from

the negative aspects of vulnerability to the positive actions by people,

the notion of capacity is fundamental to imagining and designing a

conceptual shift favoring disaster risk reduction and adaptation to climate

change. Effective capacity building, the notion of stimulating and

providing for growth in capacity, requires a clear image of the future

with clearly established goals.

Adaptive capacity comprises a specific usage of the notion of capacity

and is dealt with in detail in later sections of this chapter and Chapters

2 and 8 in particular.

The existence of vulnerability and capacity and their importance for

understanding the nature and extent of the adverse effects that may

occur with the impact of physical events can be complemented with a

consideration of the characteristics or conditions that help ameliorate or

mitigate negative impacts once disaster materializes. The notions of

resilience and coping are fundamental in this sense.

Coping (elaborated upon in detail in Section 1.4 and Chapter 2) is

defined here generically as the use of available skills, resources, and

opportunities to address, manage, and overcome adverse conditions

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

FAQ 1.1 | Is there a one-to-one relationship between extreme events and disasters?

No. Disaster entails social, economic, or environmental impacts that severely disrupt the normal functioning of affected communities.

Extreme weather and climate events will lead to disaster if: 1) communities are exposed to those events; and 2) exposure to potentially

damaging extreme events is accompanied by a high level of vulnerability (a predisposition for loss and damage). On the other hand,

disasters are also triggered by events that are not extreme in a statistical sense. High exposure and vulnerability levels will transform

even some small-scale events into disasters for some affected communities. Recurrent small- or medium-scale events affecting the same

communities may lead to serious erosion of its development base and livelihood options, thus increasing vulnerability. The timing (when

they occur during the day, month, or year) and sequence (similar events in succession or different events contemporaneously) of such

events is often critical to their human impact. The relative importance of the underlying physical and social determinants of disaster risk

varies with the scale of the event and the levels of exposure and vulnerability. Because the impact of lesser events is exacerbated by

physical, ecological, and social conditions that increase exposure and vulnerability, these events disproportionately affect resource-poor

communities with little access to alternatives for reducing hazard, exposure, and vulnerability. The potential negative consequences of

extreme events can be moderated in important ways (but rarely eliminated completely) by implementing corrective disaster risk

management strategies that are reactive, adaptive, and anticipatory, and by sustainable development.

with the aim of achieving basic functioning in the short to medium

terms.

Resilience is defined as the ability of a system and its component parts

to anticipate, absorb, accommodate, or recover from the effects of a

potentially hazardous event in a timely and efficient manner, including

through ensuring the preservation, restoration, or improvement of its

essential basic structures and functions. As Gaillard (2010) points out,

this term has been used in disaster studies since the 1970s (Torry, 1979)

and has its origins in engineering (Gordon, 1978), ecology (Holling,

1973) and child psychology (Werner et al., 1971).

Although now widely employed in the fields of disaster risk management

and adaptation, resilience has been subject to a wide range of

interpretations and levels of acceptance as a concept (Timmerman,

1981; Adger, 2000; Klein et al., 2003; Berkes et al., 2004; Folke, 2006;

Gallopín, 2006; Manyena, 2006; Brand and Jax, 2007; Gaillard 2007;

Bosher, 2008; Cutter et al., 2008; Kelman, 2008; Lewis and Kelman,

2009; Bahadur et al., 2010; Aven, 2011). Thus, for example, the term is

used by some in reference to situations at any point along the risk

‘cycle’ or ‘continuum’, that is, before, during, or after the impact of the

physical event. And, in a different vein, some consider the notions of

‘vulnerability’ and ‘capacity’ as being sufficient for explaining the ranges

of success or failure that are found in different recovery scenarios and

are thus averse to the use of the term at all (Wisner et al., 2004, 2011).

Under this latter formulation, vulnerability both potentiates original loss

and damage and also impedes recovery, while capacity building can

change this adverse balance and contribute to greater sustainability

and reduced disaster risk.

Older conceptions of resilience, as ‘bouncing back,’ and its conceptual

cousin, coping (see Section 1.4), have implicitly emphasized a return to

a previous status quo or some other marginally acceptable level, such

as ‘surviving,’ as opposed to generating a cyclical process that leads

to continually improving conditions, as in ‘bouncing forward’ and/or

eventually ‘thriving’ (Davies, 1993; Manyena, 2006). However, the

dynamic and often uncertain consequences of climate change (as well as

ongoing, now longstanding, development trends such as urbanization)

for hazard and vulnerability profiles underscore the fact that ‘bouncing

back’ is an increasingly insufficient goal for disaster risk management

(Pelling, 2003; Vale and Campanella, 2005; Pendalla et al., 2010)

(high confidence). Recent conceptions of resilience of social-ecological

systems focus more on process than outcomes (e.g., Norris et al., 2008),

including the ability to self-organize, learn, and adapt over time (see

Chapter 8). Some definitions of resilience, such as that used in this

report, now also include the idea of anticipation and ‘improvement’ of

essential basic structures and functions. Section 1.4 examines the

importance of learning that is emphasized within this more forward-

looking application of resilience. Chapter 8 builds on the importance of

learning by drawing also from literature that has explored the scope for

innovation, leadership, and adaptive management. Together these

strategies offer potential pathways for transforming existing development

visions, goals, and practices into more sustainable and resilient futures.

Chapters 2 and 8 address the notion of resilience and its importance in

discussions on sustainability, disaster risk reduction, and adaptation in

greater detail.

1.1.2.2. Concepts and Definitions Relating to Disaster Risk

Management and Adaptation to Climate Change

Disaster risk management is defined in this report as the processes

for designing, implementing, and evaluating strategies, policies, and

measures to improve theunderstanding of disaster risk, foster disaster

risk reduction and transfer, and promote continuous improvement in

disaster preparedness, response, and recovery practices, with the explicit

purpose of increasing human security, well-being, quality of life, and

sustainable development.

Disaster risk management is concerned with both disaster and disaster

risk of differing levels and intensities. In other words, it is not restricted

to a ‘manual’ for the management of the risk or disasters associated with

extreme events, but rather includes the conceptual framework that

describes and anticipates intervention in the overall and diverse patterns,

scales, and levels of interaction of exposure, hazard, and vulnerability

that can lead to disaster. A major recent concern of disaster risk

management has been that disasters are associated more and more with

lesser-scale physical phenomena that are not extreme in a physical sense

(see Section 1.1.1). This is principally attributed to increases in exposure

and associated vulnerability (UNISDR, 2009e, 2011).

Where the term risk management is employed in this chapter and

report, it should be interpreted as being a synonym for disaster risk

management, unless otherwise made explicit.

Disaster Risk Management can be divided to comprise two related but

discrete subareas or components: disaster risk reduction and disaster

management.

Disaster risk reduction denotes both a policy goal or objective, and

the strategic and instrumental measures employed for anticipating

future disaster risk, reducing existing exposure, hazard, or vulnerability,

and improving resilience. This includes lessening the vulnerability of

people, livelihoods, and assets and ensuring the appropriate sustainable

management of land, water, and other components of the environment.

Emphasis is on universal concepts and strategies involved in the

consideration of reducing disaster risks, including actions and activities

enacted pre-impact, and when recovery and reconstruction call for

the anticipation of new disaster risk scenarios or conditions. A strong

relationship between disaster risk and disaster risk reduction, and

development and development planning has been established and

validated, particularly, but not exclusively, in developing country

contexts (UNEP, 1972; Cuny, 1983; Sen, 1983; Hagman, 1984; Wijkman

and Timberlake, 1988; Lavell, 1999, 2003, 2009; Wisner et al., 2004,

2011; UNDP, 2004; van Niekerk, 2007; Dulal et al., 2009; UNISDR,

2009e, 2011) (high confidence).

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Disaster management refers to social processes for designing,

implementing, and evaluating strategies, policies, and measures

thatpromote and improve disaster preparedness, response, and recovery

practices at different organizational and societal levels. Disaster

management processes are enacted once the immediacy of the disaster

event has become evident and resources and capacities are put in place

with which to respond prior to and following impact. These include the

activation of early warning systems, contingency planning, emergency

response (immediate post-impact support to satisfy critical human

needs under conditions of severe stress), and, eventually, recovery

(Alexander, 2000; Wisner et al., 2011). Disaster management is required

due to the existence of ‘residual’ disaster risk that ongoing disaster

risk reduction processes have not mitigated or reduced sufficiently or

eliminated or prevented completely (IDB, 2007).

Growing disaster losses have led to rapidly increasing concerns for post-

impact financing of response and recovery (UNISDR, 2009e, 2011). In this

context, the concept and practice of disaster risk transfer has received

increased interest and achieved greater salience. Risk transfer refers to

the process of formally or informally shifting the financial consequences

of particular risks from one party to another, whereby a household,

community, enterprise, or state authority will obtain resources from

the other party after a disaster occurs, in exchange for ongoing or

compensatory social or financial benefits provided to that other party.

Disaster risk transfer mechanisms comprise a component of both disaster

management and disaster risk reduction. In the former case, financial

provision is made to face up to the impacts and consequences of disaster

once this materializes. In the latter case, the adequate use of insurance

premiums, for example, can promote and encourage the use of disaster

risk reduction measures in the insured elements. Chapters 5, 6, 7, and 9

discuss risk transfer in some detail.

Over the last two decades, the more integral notion of disaster risk

management and its risk reduction and disaster management components

has tended to replace the unique conception and terminology of ‘disaster

and emergency management’ that prevailed almost unilaterally up to

the beginning of the 1990s and that emphasized disaster as opposed to

disaster risk as the central issue to be confronted. Disaster as such

ordered the thinking on required intervention processes, whereas with

disaster risk management, disaster risk now tends to assume an

increasingly dominant position in thought and action in this field (see

Hewitt, 1983; Blaikie et al., 1994; Smith, 1996; Hewitt, 1997; Tobin and

Montz, 1997; Lavell, 2003; Wisner et al., 2004, 2011; van Niekerk, 2007;

Gaillard, 2010 for background and review of some of the historical

changes in favor of disaster risk management).

The notion of disaster or disaster management cycle was introduced

and popularized in the earlier context dominated by disaster or emergency

management concerns and viewpoints. The cycle, and the later ‘disaster

continuum’ notion, depicted the sequences and components of so-called

disaster management. In addition to considering preparedness, emergency

response, rehabilitation, and reconstruction, it also included disaster

prevention and mitigation as stated components of ‘disaster management’

and utilized the temporal notions of before, during, and after disaster to

classify the different types of action (Lavell and Franco, 1996; van

Niekerk, 2007).

The cycle notion, criticized for its mechanistic depiction of the intervention

process, for insufficient consideration of the ways different components

and actions merge and can act synergistically with and influence each

other, and for its incorporation of disaster risk reduction considerations

under the rubric of ‘disaster management’ (Lavell and Franco, 1996;

Lewis, 1999; Wisner et al., 2004; Balamir, 2005; van Niekerk, 2007), has

tended to give way over time, in many parts of the world, to the more

comprehensive approach and concept of disaster risk management with

its consideration of distinct risk reduction and disaster intervention

components. The move toward a conception oriented in terms of disaster

risk and not disaster per se has led to initiatives to develop the notion

of a ‘disaster risk continuum’ whereby risk is seen to evolve and

change constantly, requiring different modalities of intervention over

time, from pre-impact risk reduction through response to new risk

conditions following disaster impacts and the need for control of new

risk factors in reconstruction (see Lavell, 2003).

With regard to the influence of actions taken at one stage of the ‘cycle’

on other stages, much has been written, for example, on how the form

and method of response to disaster itself may affect future disaster risk

reduction efforts. The fostering of active community involvement, the

use of existing local and community capacities and resources, and

the decentralization of decisionmaking to the local level in disaster

preparedness and response, among other factors, have been considered

critical for also improving understanding of disaster risk and the

development of future disaster risk reduction efforts (Anderson and

Woodrow, 1989; Alexander, 2000; Lavell, 2003; Wisner et al., 2004)

(high confidence). And, the methods used for, and achievements with,

reconstruction clearly have important impacts on future disaster risk

and on the future needs for preparedness and response.

In the following subsection, some of the major reasons that explain

the transition from disaster management, with its emphasis on disaster,

to disaster risk management, with its emphasis on disaster risk, are

presented as a background for an introduction to the links and options

for closer integration of the adaptation and disaster risk management

fields.

The gradual evolution of policies that favor disaster risk reduction

objectives as a component of development planning procedures (as

opposed to disaster management seen as a function of civil protection,

civil defense, emergency services, and ministries of public works) has

inevitably placed the preexisting emergency or disaster-response-oriented

institutional and organizational arrangements for disaster management

under scrutiny. The prior dominance of response-based and infrastructure

organizations has been complemented with the increasing incorporation

of economic and social sector and territorial development agencies or

organizations, as well as planning and finance ministries. Systemic, as

opposed to single agency, approaches are now evolving in many places.

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Synergy, collaboration, coordination, and development of multidisciplinary

and multiagency schemes are increasingly seen as positive attributes for

guaranteeing implementation of disaster risk reduction and disaster risk

management in a sustainable development framework (see Lavell and

Franco, 1996; Ramírez and Cardona, 1996; Wisner et al., 2004, 2011).

Under these circumstances the notion of national disaster risk

management systems or structures has emerged strongly. Such

notions are discussed in detail in Chapter 6.

Adaptation to climate change, the second policy, strategic, and

instrumental aspect of importance for this Special Report, is a notion

that refers to both human and natural systems. Adaptation in human

systems is defined here as the process of adjustment to actual or

expected climate and its effects, in order to moderate harm or exploit

beneficial opportunities. In natural systems, it is defined as the process

of adjustment to actual climate and its effects; human intervention may

facilitate adjustment to expected climate.

These definitions modify the IPCC (2007c) definition that generically

speaks of the “adjustment in natural and human systems in response to

actual and expected climatic stimuli, such as to moderate harm or

exploit beneficial opportunities.” The objective of the redefinition used

in this report is to avoid the implication present in the prior IPCC

definition that natural systems can adjust to expected climate stimuli.

At the same time, it accepts that some forms of human intervention may

provide opportunities for supporting natural system adjustment to

future climate stimuli that have been anticipated by humans.

Adaptation is a key aspect of the present report and is dealt with in

greater detail in Sections 1.3 and 1.4 and later chapters. The more

ample introduction to disaster risk management offered above derives

from the particular perspective of the present report: that adaptation is

a goal to be advanced and extreme event and disaster risk management

are methods for supporting and advancing that goal.

The notion of adaptation is counterposed to the notion of mitigation

in the climate change literature and practice. Mitigation there refers to

the reduction of the rate of climate change via the management of

its causal factors (the emission of greenhouse gases from fossil fuel

combustion, agriculture, land use changes, cement production, etc.)

(IPCC, 2007c). However, in disaster risk reduction practice, ‘mitigation’

refers to the amelioration of disaster risk through the reduction of existing

hazards, exposure, or vulnerability, including the use of different disaster

preparedness measures.

Disaster preparedness measures, including early warning and the

development of contingency or emergency plans, may be considered a

component of, and a bridge between, disaster risk reduction and disaster

management. Preparedness accepts the existence of residual, unmitigated

risk, and attempts to aid society in eliminating certain of the adverse

effects that could be experienced once a physical event(s) occurs (for

example, by the evacuation of persons and livestock from exposed and

vulnerable circumstances). At the same time, it provides for better

response to adverse effects that do materialize (for example, by

planning for adequate shelter and potable water supplies for the affected

or destitute persons or food supplies for affected animal populations).

In order to accommodate the two differing definitions of mitigation, this

report presumes that mitigation is a substantive action that can be

applied in different contexts where attenuation of existing specified

conditions is required.

Disaster mitigation is used to refer to actions that attempt to limit

further adverse conditions once disaster has materialized. This refers to

the avoidance of what has sometimes been called the ‘second disaster’

following the initial physical impacts (Alexander, 2000; Wisner et al.,

2011). The ‘second disaster’ may be characterized, among other things,

by adverse effects on health (Noji, 1997; Wisner et al., 2011) and

livelihoods due to inadequate disaster response and rehabilitation plans,

inadequate enactment of existing plans, or unforeseen or unforeseeable

circumstances.

Disaster risk prevention and disaster prevention refer, in a strict sense,

to the elimination or avoidance of the underlying causes and conditions

that lead to disaster, thus precluding the possibility of either disaster

risk or disaster materializing. The notion serves to concentrate attention

on the fact that disaster risk is manageable and its materialization is

preventable to an extent (which varies depending on the context).

Prospective (proactive) disaster risk management and adaptation

can contribute in important ways to avoiding future, and not just reducing

existing, risk and disaster once they have become manifest, as is the case

with corrective or reactive management (Lavell, 2003; UNISDR, 2011).

1.1.2.3. The Social Construction of Disaster Risk

The notions of hazard, exposure, vulnerability, disaster risk, capacity,

resilience, and coping, and their social origins and bases, as presented

above, reflect an emerging understanding that disaster risk and disaster,

while potentiated by an objective, physical condition, are fundamentally

a ‘social construction,’ the result of social choice, social constraints, and

societal action and inaction (high confidence). The notion of social

construction of risk implies that management can take into account the

social variables involved and to the best of its ability work toward

risk reduction, disaster management, or risk transfer through socially

sustainable decisions and concerted human action (ICSU-LAC, 2009).

This of course does not mean that there are not risks that may be too

great to reduce significantly through human intervention, or others

that the very social construction process may in fact exacerbate (see

Sections 1.3.1.2 and 1.4.3). But in contrast with, for example, many

natural physical events and their contribution to disaster risk, the

component of risk that is socially constructed is subject to intervention

in favor of risk reduction.

The contribution of physical events to disaster risk is characterized by

statistical distributions in order to elucidate the options for risk reduction

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

and adaptation (Section 1.2 and Chapter 3). But, the explicit recognition

of the political, economic, social, cultural, physical, and psychological

elements or determinants of risk leads to a spectrum of potential outcomes

of physical events, including those captured under the notion of

extreme impacts (Section 1.2 and Chapter 4). Accordingly, risk

assessment (see Section 1.3) using both quantitative and qualitative

(social and psychological) measures is required to render a more

complete description of risk and risk causation processes (Section 1.3;

Douglas and Wildavsky, 1982; Cardona, 2004; Wisner et al., 2004; Weber,

2006). Climate change may introduce a break with past environmental

system functioning so that forecasting physical events becomes less

determined by past trends. Under these conditions, the processes that

cause, and the established indicators of, human vulnerability need to be

reconsidered in order for risk assessment to remain an effective tool.

The essential nature and structure of the characteristics that typify

vulnerability can of course change without climate changing.

1.1.3. Framing the Relation between Adaptation to

Climate Change and Disaster Risk Management

Adaptation to climate change and disaster risk management both seek

to reduce factors and modify environmental and human contexts that

contribute to climate-related risk, thus supporting and promoting

sustainability in social and economic development. The promotion of

adequate preparedness for disaster is also a function of disaster risk

management and adaptation to climate change. And, both practices are

seen to involve learning (see Section 1.4), having a corrective and

prospective component dealing with existing and projected future risk.

However, the two practices have tended to follow independent paths of

advance and development and have on many occasions employed

different interpretations of concepts, methods, strategies, and institutional

frameworks to achieve their ends. These differences should clearly be

taken into account in the search for achieving greater synergy between

them and will be examined in an introductory fashion in Section 1.3 and

in greater detail in following chapters of this report.

Public policy and professional concepts of disaster and their approaches

to disaster and disaster risk management have undergone very significant

changes over the last 30 years, so that challenges that are now an

explicit focus of the adaptation field are very much part of current disaster

risk reduction as opposed to mainstream historical disaster management

concerns (Lavell, 2010; Mercer, 2010).These changes have occurred under

the stimuli of changing concepts, multidisciplinary involvement, social

and economic demands, and impacts of disasters, as well as institutional

changes reflected in international accords and policies such as the UN

Declaration of the International Decade for Natural Disaster Reduction

in the 1990s, the 2005 Hyogo Framework for Action, as well as the work

of the International Strategy for Disaster Reduction since 2000.

Particularly in developing countries, this transition has been stimulated

by the documented relationship between disaster risk and ‘skewed’

development processes (UNEP, 1972; Cuny, 1983; Sen, 1983; Hagman,

1984; Wijkman and Timberlake, 1988; Lavell, 1999, 2003; UNDP, 2004;

Wisner et al., 2004, 2011; Dulal et al., 2009; UNISDR, 2009e, 2011).

Significant differentiation in the distribution or allocation of gains from

development and thus in the incidence of chronic or everyday risk,

which disproportionately affect poorer persons and families, is a major

contributor to the more specific existence of disaster risk (Hewitt, 1983,

1997; Wisner et al., 2004). Reductions in the rate of ecosystem services

depletion, improvements in urban land use and territorial organization

processes, the strengthening of rural livelihoods, and general and specific

advances in urban and rural governance are viewed as indispensable to

achieving the composite agenda of poverty reduction, disaster risk

reduction, and adaptation to climate change (UNISDR, 2009e, 2011)

(high confidence).

Climate change is at once a problem of development and also a symptom

of ‘skewed’ development. In this context, pathways toward resilience

include both incremental and transformational approaches to development

(Chapter 8). Transformational strategies place emphasis on addressing

risk that stems from social structures as well as social behavior and

have a broader scope extending from disaster risk management into

development goals, policy, and practice (Nelson et al., 2007). In this

way transformation builds on a legacy of progressive, socially informed

disaster risk research that has applied critical methods, including that of

Hewitt (1983), Watts (1983), Maskrey (1989, 2011), Blaikie et al. (1994),

and Wisner et al. (2004).

However, while there is a longstanding awareness of the role of

development policy and practice in shaping disaster risk, advances in

the reduction of the underlying causes – the social, political, economic,

and environmental drivers of disaster risk – remain insufficient to

reduce hazard, exposure, and vulnerability in many regions (UNISDR,

2009e, 2011) (high confidence).

The difficult transition to more comprehensive disaster risk management

raises challenges for the proper allocation of efforts among disaster risk

reduction, risk transfer, and disaster management efforts. Countries

exhibit a wide range of acceptance or resistance to the various challenges

of risk management as seen from a development perspective, due to

differential access to information and education, varying levels of

debate and discussion, as well as contextual, ideological, institutional,

and other related factors. The introduction of disaster risk reduction

concerns in established disaster response agencies may in some cases

have led to a downgrading of efforts to improve disaster response,

diverting scarce resources in favor of risk reduction aspects (Alexander,

2000; DFID, 2004, 2005; Twigg, 2004).

The increasing emphasis placed on considering disaster risk management

as a dimension of development, and thus of development planning, as

opposed to strict post-impact disaster response efforts, has been

accompanied by increasing emphasis and calls for proactive, prospective

disaster risk prevention as opposed to reactive, corrective disaster risk

mitigation (Lavell, 2003, 2010; UNISDR, 2009e, 2011).

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

The more recent emergence of integrated disaster risk management

reflects a shift from the notion of disaster to the notion of disaster risk

as a central concept and planning concern. Disaster risk management

places increased emphasis on comprehensive disaster risk reduction.

This shifting emphasis to risk reduction can be seen in the increasing

importance placed on developing resistance to the potential impacts

of physical events at various social or territorial scales, and in different

temporal dimensions (such as those required for corrective or prospective

risk management), and to increasing the resilience of affected communities.

Resistance refers to the ability to avoid suffering significant adverse

effects.

Within this context, disaster risk reduction and adaptation to climate

change are undoubtedly far closer practically than when emergency or

disaster management objectives dominated the discourse and practice. The

fact that many in the climate change and disaster fields have associated

disaster risk management principally with disaster preparedness and

response, and not with disaster risk reduction per se, contributed to the

view that the two practices are essentially different, if complementary

(Lavell, 2010; Mercer, 2010). Once the developmental basis of adaptation

to climate change and disaster risk management are considered, along

with the role of vulnerability in the constitution of risk, the temporal

scale of concerns, and the corrective as well as prospective nature of

disaster risk reduction, the similarities between and options for merging

of concerns and practices increases commensurately.

Section 1.3 examines the current status of adaptation to climate

change, as a prelude to examining in more detail the barriers and

options for greater integration of the two practices. The historical frame

offered in this subsection comprises an introduction to that discussion.

1.1.4. Framing the Processes of Disaster Risk

Management and Adaptation to Climate Change

In this section, we explore two of the key issues that should be considered

in attempting to establish the overlap or distinction between the

phenomena and social processes that concern disaster risk management

on the one hand, and adaptation to climate change on the other, and

that influence their successful practice: 1) the degree to which the focus

is on extreme events (instead of a more inclusive approach that considers

the full continuum of physical events with potential for damage, the social

contexts in which they occur, and the potential for such events to generate

‘extreme impacts’ or disasters); and 2) consideration of the appropriate

social-territorial scale that should be examined (i.e., aggregations, see

Schneider et al., 2007) in order to foster a deeper understanding of the

causes and effects of the different actors and processes at work.

1.1.4.1. Exceptionality, Routine, and Everyday Life

Explanations of loss and damage resulting from extreme events that

focus primarily or exclusively on the physical event have been referred

to as ‘physicalist’ (Hewitt, 1983). By contrast, notions developed around

the continuum of normal, everyday-life risk factors through to a linked

consideration of physical and social extremes have been defined as

‘comprehensive,’ ‘integral,’ or ‘holistic’ insofar as they embrace the social

as well as physical aspects of disaster risk and take into consideration the

evolution of experience over time (Cardona, 2001; ICSU-LAC, 2009). The

latter perspective has been a major contributing factor in the development

of the so-called ‘vulnerability paradigm’ as a basis for understanding

disaster (Timmerman, 1981; Hewitt, 1983, 1997; Wisner et al., 2004;

Eakin and Luers, 2006; NRC, 2006).

Additionally, attention to the role of small- and medium-scale disasters

(UNISDR, 2009e, 2011) highlights the need to deal integrally with the

problem of cumulative disaster loss and damage, looking across the

different scales of experience both in human and physical worlds,

in order to advance the efficacy of disaster risk management and

adaptation. The design of mechanisms and strategies based on the

reduction and elimination of everyday or chronic risk factors (Sen, 1983;

World Bank, 2001), as opposed to actions based solely on the

‘exceptional’ or ‘extreme’ events, is one obvious corollary of this

approach. The ability to deal with risk, crisis, and change is closely

related to an individual’s life experience with smaller-scale, more

regular physical and social occurrences (Maskrey, 1989, 2011; Lavell,

2003; Wisner et al., 2004) (high confidence). These concepts point

toward the possibility of reducing vulnerability and increasing resilience

to climate-related disaster by broadly focusing on exposure, vulnerability,

and socially-determined propensity or predisposition to adverse effects

across a range of risks.

As illustrated in Box 1-1, many of the extreme impacts associated with

climate change, and their attendant additional risks and opportunities,

will inevitably need to be understood and responded to principally at

the scale of the individual, the individual household, and the community,

in the framework of localities and nations and their organizational and

management options, and in the context of the many other day-to-day

changes, including those of an economic, political, technological, and

cultural nature. As this real example illustrates, everyday life, history, and

a sequence of crises can affect attitudes and ways of approaching more

extreme or complex problems. In contrast, many agents and institutions

of disaster risk management and climate change adaptation activities

necessarily operate from a different perspective, given the still highly

centralized and hierarchical authority approaches found in many parts

of the world today.

Whereas disaster risk management has been modified based on the

experiences of the past 30 years or more, adaptation to anthropogenic

climate change is a more recent issue on most decisionmakers’ policy

agendas and is not informed by such a long tradition of immediate

experience. However, human adaptation to prevailing climate variability

and change, and climate and weather extremes in past centuries and

millennia, provides a wealth of experience from which the field of

adaptation to climate change, and individuals and governments, can

draw.

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

The ethnographic vignette in Box 1-1 suggests the way some individuals

may respond to climate change in the context of previous experience,

illustrating both the possibility of drawing successfully on past experience

in adapting to climate variability, or, on the other hand, failing to

comprehend the nature of novel risks.

1.1.4.2. Territorial Scale, Disaster Risk, and Adaptation

Climate-related disaster risk is most adequately depicted, measured, and

monitored at the local or micro level (families, communities, individual

buildings or production units, etc.) where the actual interaction of hazard

and vulnerability are worked out in situ (Hewitt, 1983, 1997; Lavell,

2003; Wisner et al., 2004; Cannon, 2006; Maskrey, 2011). At the same

time, it is accepted that disaster risk construction processes are not

limited to specifically local or micro processes but, rather, to diverse

environmental, economic, social, and ideological influences whose

sources are to be found at scales from the international through to the

national, sub-national and local, each potentially in constant flux (Lavell,

2002, 2003; Wisner et al., 2004, 2011).

Changing commodity prices in international trading markets and their

impacts on food security and the welfare of agricultural workers, decisions

on location and cessation of agricultural production by international

corporations, deforestation in the upper reaches of river basins, and land

use changes in urban hinterlands are but a few of these ‘extra-territorial’

influences on local risk. Moreover, disasters, once materialized, have ripple

effects that many times go well beyond the directly affected zones (Wisner

et al., 2004; Chapter 5) Disaster risk management and adaptation policy,

strategies, and institutions will only be successful where understanding

and intervention is based on multi-territorial and social-scale principles

and where phenomena and actions at local, sub-national, national, and

international scales are construed in interacting, concatenated ways

(Lavell, 2002; UNISDR, 2009e, 2011; Chapters 5 through 9).

1.2. Extreme Events, Extreme Impacts,

and Disasters

1.2.1. Distinguishing Extreme Events,

Extreme Impacts, and Disasters

Both the disaster risk management and climate change adaptation

literature define ‘extreme weather’ and ‘extreme climate’ events and

discuss their relationship with ‘extreme impacts’ and ‘disasters.’

Classification of extreme events, extreme impacts, and disasters is

influenced by the measured physical attributes of weather or climatic

variables (see Section 3.1.2) or the vulnerability of social systems (see

Section 2.4.1).

This section explores the quantitative definitions of different classes of

extreme weather events, what characteristics determine that an impact

is extreme, and how climate change affects the understanding of

extreme climate events and impacts.

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Box 1-1 | One Person’s Experience with Climate Variability in the Context of Other Changes

Joseph is 80 years old. He and his father and his grandfather have witnessed many changes. Their homes have shifted back and forth

from the steep slopes of the South Pare Mountains at 1,500 m to the plains 20 km away, near the Pangani River at 600 m, in Tanzania.

What do ‘changes’ (mabadiliko) mean to someone whose father saw the Germans and British fight during the First World War and

whose grandfather defended against Maasai cattle raids when Victoria was still Queen?

Joseph outlived the British time. He saw African Socialism come and go after Independence. A road was constructed parallel to the old

German rail line. Successions of commercial crops were dominant during his long life, some grown in the lowlands on plantations (sisal,

kapok, and sugar), and some in the mountains (coffee, cardamom, ginger). He has seen staple foods change as maize became more

popular than cassava and bananas. Land cover has also changed. Forest retreated, but new trees were grown on farms. Pasture grasses

changed as the government banned seasonal burning. The Pangani River was dammed, and the electricity company decides how much

water people can take for irrigation. Hospitals and schools have been built. Insecticide-treated bed nets recently arrived for the children

and pregnant mothers.

Joseph has nine plots of land at different altitudes spanning the distance from mountain to plain, and he keeps in touch with his children

who work them by mobile phone. What is ‘climate change’ (mabadiliko ya tabia nchi) to Joseph? He has suffered and benefited from

many changes. He has lived through many droughts with periods of hunger, witnessed floods, and also seen landslides in the mountains.

He is skilled at seizing opportunities from changes – small and large: “Mabadiliko bora kuliko mapumziko” (Change is better than resting).

The provenance of this story is an original field work interview undertaken by Ben Wisner in November 2009 in Same District, Kilimanjaro

Region, Tanzania in the context of the U.S. National Science Foundation-funded research project “Linking Local Knowledge and Local

Institutions for the Study of Adaptive Capacity to Climate Change: Participatory GIS in Northern Tanzania.”

1.2.2. Extreme Events Defined in Physical Terms

1.2.2.1. Definitions of Extremes

Some literature reserve the term ‘extreme event’ for initial meteorological

phenomena (Easterling et al., 2000; Jentsch et al., 2007), some include

the consequential physical impacts, like flooding (Young, 2002), and some

the entire spectrum of outcomes for humans, society, and ecosystems

(Rich et al., 2008). In this report, we use ‘extreme (weather or climate)

event’ to refer solely to the initial and consequent physical phenomena

including some (e.g., flooding) that may have human components to

causation other than that related to the climate (e.g., land use or land

cover change or changes in water management; see Section 3.1.2 and

Glossary). The spectrum of outcomes for humans, society, and physical

systems, including ecosystems, are considered ‘impacts’ rather than part

of the definition of ‘events’ (see Sections 1.1.2.1 and 3.1.2 and the

Glossary).

In addition to providing a long-term mean of weather, ‘climate’

characterizes the full spectrum of means and exceptionality associated

with ‘unusual’ and unusually persistent weather. The World Meteorological

Organization (WMO, 2010) differentiates the terms in the following way

(see also FAQ 6.1): “At the simplest level the weather is what is happening

to the atmosphere at any given time. Climate in a narrow sense is

usually defined as the ‘average weather,’ or more rigorously, as the

statistical description in terms of the mean and variability of relevant

quantities over a period of time.”

Weather and climate phenomena reflect the interaction of dynamic and

thermodynamic processes over a very wide range of space and temporal

scales. This complexity results in highly variable atmospheric conditions,

including temperatures, motions, and precipitation, a component of

which is referred to as ‘extreme events.’ Extreme events include the

passage of an intense tornado lasting minutes and the persistence of

drought conditions over decades – a span of at least seven orders of

magnitude of timescales. An imprecise distinction between extreme

‘weather’ and ‘climate’ events, based on their characteristic timescales,

is drawn in Section 3.1.2. Similarly, the spatial scale of extreme climate

or weather varies from local to continental.

Where there is sufficient long-term recorded data to develop a statistical

distribution of a key weather or climate variable, it is possible to find the

probability of experiencing a value above or below different thresholds

of that distribution as is required in engineering design (trends may be

sought in such data to see if there is evidence that the climate has not

been stationary over the sample period; Milly et al., 2008). The extremity

of a weather or climate event of a given magnitude depends on

geographic context (see Section 3.1.2 and Box 3-1): a month of daily

temperatures corresponding to the expected spring climatological daily

maximum in Chennai, India, would be termed a heat wave in France; a

snow storm expected every year in New York, USA, might initiate a

disaster when it occurs in southern China. Furthermore, according to the

location and social context, a 1-in-10 or 1-in-20 annual probability

event may not be sufficient to result in unusual consequences.

Nonetheless, universal thresholds can exist – for example, a reduction

in the incidence or intensity of freezing days may allow certain disease

vectors to thrive (e.g., Epstein et al., 1998). These various aspects are

considered in the definition of ‘extreme (weather and climate) events.’

The availability of observational data is of central relevance for defining

climate characteristics and for disaster risk management; and, while data

for temperature and precipitation are widely available, some associated

variables, such as soil moisture, are poorly monitored, or, like extreme

wind speeds and other low frequency occurrences, not monitored with

sufficient spatial resolution or temporal continuity (Section 3.2.1).

1.2.2.2. Extremes in a Changing Climate

An extreme event in the present climate may become more common, or

more rare, under future climate conditions. When the overall distribution

of the climate variable changes, what happens to mean climate may

be different from what happens to the extremes at either end of the

distribution (see Figure 1-2).

For example, a warmer mean climate could result from fewer cold days,

leading to a reduction in the variance of temperatures, or more hot days,

leading to an expansion in the variance of the temperature distribution,

or both. The issue of the scaling of changes in extreme events with respect

to changes in mean temperatures is addressed further in Section 3.1.6.

In general, single extreme events cannot be simply and directly attributed

to anthropogenic climate change, as there is always a possibility the

event in question might have occurred without this contribution (Hegerl

et al., 2007; Section 3.2.2; FAQ 3.2). However, for certain classes of

regional, long-duration extremes (of heat and rainfall) it has proved

possible to argue from climate model outputs that the probability of

such an extreme has changed due to anthropogenic climate forcing

(Stott et al., 2004; Pall et al., 2011).

Extremes sometimes result from the interactions between two unrelated

geophysical phenomena such as a moderate storm surge coinciding

with an extreme spring tide, as in the most catastrophic UK storm surge

flood of the past 500 years in 1607 (Horsburgh and Horritt, 2006).

Climate change may alter both the frequency of extreme surges and

cause gradual sea level rise, compounding such future extreme floods

(see Sections 3.5.3 and 3.5.5).

1.2.2.3. The Diversity and Range of Extremes

The specification of weather and climate extremes relevant to the

concerns of individuals, communities, and governments depends on the

affected stakeholder, whether in agriculture, disease control, urban

design, infrastructure maintenance, etc. Accordingly, the range of such

extremes is very diverse and varies widely. For example, whether it falls

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

as rain, freezing rain (rain falling through a surface layer below freezing),

snow, or hail, extreme precipitation can cause significant damage

(Peters et al., 2001). The absence of precipitation (McKee et al., 1993) as

well as excess evapotranspiration from the soil (see Box 3-3) can be

climate extremes, and lead to drought. Extreme surface winds are

chiefly associated with structured storm circulations (Emanuel, 2003;

Zipser et al., 2006; Leckebusch et al., 2008). Each storm type, including

the most damaging tropical cyclones and mid-latitude extratropical

cyclones, as well as intense convective thunderstorms, presents a

spectrum of size, forward speed, and intensity. A single intense storm

can combine extreme wind and extreme rainfall.

The prolonged absence of winds is a climate extreme that can also be a

hazard, leading to the accumulation of urban pollution and disruptive

fog (McBean, 2006).

The behavior of the atmosphere is also highly interlinked with that of

the hydrosphere, cryosphere, and terrestrial environment so that extreme

(or sometimes non-extreme) atmospheric events may cause (or contribute

to) other rare physical events. Among the more widely documented

hydroclimatic extremes are:

• Large cyclonic storms that generate wind and pressure anomalies

causing coastal flooding and severe wave action (Xie et al., 2004).

• Floods, reflecting river flows in excess of the capacity of the normal

channel, often influenced by human intervention and water

management, resulting from intense precipitation; rapid thaw of

accumulated winter snowfall; rain falling on previous snowfall (Sui

and Koehler, 2001); or an outburst from an ice, landslide, moraine,

or artificially dammed lake (de Jong et al., 2005). According to the

scale of the catchment, river systems have characteristic response

times with steep short mountain streams, desert wadis, and urban

drainage systems responding to rainfall totals over a few hours, while

peak flows in major continental rivers reflect regional precipitation

extremes lasting weeks (Wheater, 2002).

• Long-term reductions in precipitation, or dwindling of residual

summer snow and ice melt (Rees and Collins, 2006), or increased

evapotranspiration from higher temperatures, often exacerbated

by human groundwater extraction, reducing ground water levels

and causing spring-fed rivers to disappear (Konikow and Kendy,

2005), and contributing to drought.

• Landslides (Dhakal and Sidle, 2004) when triggered by raised

groundwater levels after excess rainfall or active layer detachments

in thawing slopes of permafrost (Lewcowicz and Harris, 2005).

1.2.3. Extreme Impacts

1.2.3.1. Three Classes of Impacts

In this subsection we consider three classes of ‘impacts’: 1) changes in

the natural physical environment, like beach erosion from storms and

mudslides; 2) changes in ecosystems, such as the blow-down of forests

in hurricanes, and 3) adverse effects (according to a variety of metrics)

on human or societal conditions and assets. However, impacts are not

always negative: flood-inducing rains can have beneficial effects on the

following season’s crops (Khan, 2011), while an intense freeze may

reduce insect pests at the subsequent year’s harvest (Butts et al., 1997).

An extreme impact reflects highly significant and typically long-lasting

consequences to society, the natural physical environment, or ecosystems.

Extreme impacts can be the result of a single extreme event, successive

extreme or non-extreme events, including non-climatic events (e.g.,

wildfire, followed by heavy rain leading to landslides and soil erosion),

or simply the persistence of conditions, such as those that lead to

drought (see Sections 3.5.1 and 9.2.3 for discussion and examples).

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Probability of OccurrenceProbability of OccurrenceProbability of Occurrence

less

record cold

weather

record cold

weather

less

cold

weather

near constant

record cold

weather

near constant

cold

weather

record hot

weather

record hot

weather

record hot

weather

cold

weather

hot

weather

hot

weather

hot

weather

Previous Climate

Future Climate

Shifted Mean

Increased Variability

Changed Shape

Cold HotAverage

Figure 1-2 | The effect of changes in temperature distribution on extremes. Different

changes in temperature distributions between present and future climate and their

effects on extreme values of the distributions: a) effects of a simple shift of the entire

distribution toward a warmer climate; b) effects of an increased temperature variability

with no shift of the mean; and c) effects of an altered shape of the distribution, in this

example an increased asymmetry toward the hotter part of the distribution.

Whether an extreme event results in extreme impacts on humans and

social systems depends on the degree of exposure and vulnerability to

that extreme, in addition to the magnitude of the physical event (high

confidence). Extreme impacts on human systems may be associated

with non-extreme events where vulnerability and exposure are high

(Sections 1.1.2.1 and 9.2.3). A key weather parameter may cross some

critical value at that location (such as that associated with heat wave-

induced mortality, or frost damage to crops), so that the distribution of

the impact shifts in a way that is disproportionate to physical changes

(see Section 4.2). A comprehensive assessment of projected impacts of

climate changes would consider how changes in atmospheric conditions

(temperature, precipitation) translate to impacts on physical (e.g.,

droughts and floods, erosion of beaches and slopes, sea level rise),

ecological (e.g., forest fires), and human systems (e.g., casualties,

infrastructure damages). For example, an extreme event with a large

spatial scale (as in an ice storm or windstorm) can have an exaggerated,

disruptive impact due to the systemic societal dependence on electricity

transmission and distribution networks (Peters et al., 2006). Links between

climate events and physical impacts are addressed in Section 3.5, while

links to ecosystems and human systems impacts are addressed in 4.3.

Disaster signifies extreme impacts suffered by society, which may also

be associated with extreme impacts on the physical environment and

on ecosystems. Building on the definition set out in Section 1.1.2.1,

extreme impacts resulting from weather, climate, or hydrological events

can become disasters once they surpass thresholds in at least one of

three dimensions: spatial – so that damages cannot be easily restored

from neighboring capacity; temporal – so that recovery becomes

frustrated by further damages; and intensity of impact on the affected

population – thereby undermining, although not necessarily eliminating,

the capacity of the society or community to repair itself (Alexander,

1993). However, for the purposes of tabulating occurrences, some

agencies only list ‘disasters’ when they exceed certain numbers of killed

or injured or total repair costs (Below et al., 2009; CRED, 2010).

1.2.3.2. Complex Nature of an Extreme ‘Event’

In considering the range of weather and climate extremes, along with

their impacts, the term ‘event’ as used in the literature does not

adequately capture the compounding of outcomes from successive

physical phenomena, for example, a procession of serial storms tracking

across the same region (as in January and February 1990 and December

1999 across Western Europe, Ulbrich et al., 2001). In focusing on the social

context of disasters, Quarantelli (1986) proposed the use of the notion of

‘disaster occurrences or occasions’ in place of ‘events’ due to the abrupt

and circumstantial nature of the connotation commonly attributed to

the word ‘event,’ which belies the complexity and temporality of disaster,

in particular because social context may precondition and extend the

duration over which impacts are felt.

Sometimes locations affected by extremes within the ‘same’ large-scale

stable atmospheric circulation can be far apart, as for example the

Russian heat wave and Indus valley floods in Pakistan in the summer of

2010 (Lau and Kim, 2011). Extreme events can also be interrelated

through the atmospheric teleconnections that characterize the principal

drivers of oceanic equatorial sea surface temperatures and winds in the

El Niño–Southern Oscillation. The relationship between modes of climate

variability and extremes is discussed in greater detail in Section 3.1.1.

The aftermath of one extreme event may precondition the physical

impact of successor events. High groundwater levels and river flows can

persist for months, increasing the probability of a later storm causing

flooding, as on the Rhine in 1995 (Fink et al., 1996). A thickness reduction

in Arctic sea ice preconditions more extreme reductions in the summer

ice extent (Holland et al., 2006). A variety of feedbacks and other

interactions connect extreme events and physical system and ecological

responses in a way that may amplify physical impacts (Sections 3.1.4

and 4.3.5). For example, reductions in soil moisture can intensify heat

waves (Seneviratne et al., 2006), while droughts following rainy seasons

turn vegetation into fuel that can be consumed in wildfires (Westerling

and Swetman, 2003), which in turn promote soil runoff and landslides

when the rains return (Cannon et al., 2001). However, extremes can also

interact to reduce disaster risk. The wind-driven waves in a hurricane

bring colder waters to the surface from beneath the thermocline; for the

next month, any cyclone whose path follows too closely will have a

reduced potential maximum intensity (Emanuel, 2001). Intense rainfall

accompanying monsoons and hurricanes also brings great benefits to

society and ecosystems; on many occasions it helps to fill reservoirs,

sustain seasonal agriculture, and alleviate summer dry conditions in arid

zones (e.g., Cavazos et al., 2008).

1.2.3.3. Metrics to Quantify Social Impacts

and the Management of Extremes

Metrics to quantify social and economic impacts (thus used to define

extreme impacts) may include, among others (Below et al., 2009):

• Human casualties and injuries

• Number of permanently or temporarily displaced people

• Number of directly and indirectly affected persons

• Impacts on properties, measured in terms of numbers of buildings

damaged or destroyed

• Impacts on infrastructure and lifelines

• Impacts on ecosystem services

• Impacts on crops and agricultural systems

• Impacts on disease vectors

• Impacts on psychological well being and sense of security

• Financial or economic loss (including insurance loss)

• Impacts on coping capacity and need for external assistance.

All of these may be calibrated according to the magnitude, rate, duration,

and degree of irreversibility of the effects (Schneider et al., 2007).

These metrics may be quantified and implemented in the context of

probabilistic risk analysis in order to inform policies in a variety of

contexts (see Box 1-2).

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Information on direct, indirect, and collateral impacts is generally

available for many large-scale disasters and is systematized and provided

by organizations such as the Economic Commission for Latin America,

large reinsurers, and the EM-DAT database (CRED, 2010). Information

on impacts of smaller, more recurrent events is far less accessible and

more restricted in the number of robust variables it provides. The

Desinventar database (Corporación OSSO, 2010), now available for 29

countries worldwide, and the Spatial Hazard Events and Losses

Database for the United States (SHELDUS; HVRI, 2010), are attempts to

satisfy this need. However, the lack of data on many impacts impedes

complete knowledge of the global social and economic impacts of

smaller-scale disasters (UNISDR, 2009e).

1.2.3.4. Traditional Adjustment to Extremes

Disaster risk management and climate change adaptation may be seen

as attempts to duplicate, promote, or improve upon adjustments that

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Box 1-2 | Probabilistic Risk Analysis

In its simplest form, probabilistic risk analysis defines risk as the product of the probability that some event (or sequence) will occur and

the adverse consequences of that event.

Risk = Probability x Consequence (1)

For instance, the risk a community faces from flooding from a nearby river might be calculated based on the likelihood that the river floods

the town, inflicting casualties among inhabitants and disrupting the community’s economic livelihood. This likelihood is multiplied by the

value people place on those casualties and economic disruption. Equation (1) provides a quantitative representation of the qualitative

definition of disaster risk given in Section 1.1. All three factors – hazard, exposure, and vulnerability – contribute to ‘consequences.’

Hazard and vulnerability can both contribute to the ‘probability’: the former to the likelihood of the physical event (e.g., the river flooding

the town) and the latter to the likelihood of the consequence resulting from the event (e.g., casualties and economic disruption).

When implemented within a broader risk governance framework, probabilistic risk analysis can help allocate and evaluate efforts to

manage risk. Equation (1) implies what the decision sciences literature (Morgan and Henrion, 1990) calls a decision rule – that is, a

criterion for ranking alternative sets of actions by their ability to reduce overall risk. For instance, an insurance company (as part of a risk

transfer effort) might set the annual price for flood insurance based on multiplying an estimate of the probability a dwelling would be

flooded in any given year by an estimate of the monetary losses such flooding would cause. Ideally, the premiums collected from the

residents of many dwellings would provide funds to compensate the residents of those few dwellings that are in fact flooded (and

defray administrative costs). In another example, a water management agency (as part of a risk reduction effort) might invest the

resources to build a reservoir of sufficient size so that, if the largest drought observed in their region over the last 100 years (or some

other timeframe) occurred again in the future, the agency would nonetheless be able to maintain a reliable supply of water.

A wide variety of different expressions of the concepts in Equation (1) exist in the literature. The disaster risk management community

often finds it convenient to express risk as a product of hazard, exposure, and vulnerability (e.g., UNISDR, 2009e, 2011). In addition, the

decision sciences literature recognizes decision rules, useful in some circumstances, that do not depend on probability and consequence

as combined in Equation (1). For instance, if the estimates of probabilities are sufficiently imprecise, decisionmakers might use a criterion

that depends only on comparing estimates of potential consequences (e.g., mini-max regret, Savage, 1972).

In practice, probabilistic risk analysis is often not implemented in its pure form for reasons including data limitations; decision rules that

yield satisfactory results with less effort than that required by a full probabilistic risk assessment; the irreducible imprecision of some

estimates of important probabilities and consequences (see Sections 1.3.1.1 and 1.3.2); and the need to address the wide range of factors

that affect judgments about risk (see Box 1-3). In the above example, the water management agency is not performing a full probabilistic

risk analysis, but rather employing a hybrid decision rule in which it estimates that the consequences of running out of water would be

so large as to justify any reasonable investment needed to keep the likelihood of that event below the chosen probabilistic threshold.

Chapter 2 describes a variety of practical quantitative and qualitative approaches for allocating efforts to manage disaster risk.

The probabilistic risk analysis framework in its pure form is nonetheless important because its conceptual simplicity aids understanding

by making assumptions explicit, and because its solid theoretical foundations and the vast empirical evidence examining its application

in specific cases make it an important point of comparison for formal evaluations of the effectiveness of efforts to manage disaster risk.

society and nature have accomplished on many occasions spontaneously

in the past, if over a different range of conditions than expected in the

future.

Within the sphere of adaptation of natural systems to climate, among

trees, for example, natural selection has the potential to evolve

appropriate resilience to extremes (at some cost). Resistance to

windthrow is strongly species-dependent, having evolved according to

the climatology where that tree was indigenous (Canham et al., 2001).

In their original habitat, trees typically withstand wind extremes expected

every 10 to 50 years, but not extremes that lie beyond their average

lifespan of 100 to 500 years (Ostertag et al., 2005).

In human systems, communities traditionally accustomed to periodic

droughts employ wells, boreholes, pumps, dams, and water harvesting

and irrigation systems. Those with houses exposed to high seasonal

temperatures employ thick walls and narrow streets, have developed

passive cooling systems, adapted lifestyles, or acquired air conditioning.

In regions unaccustomed to heat waves, the absence of such systems,

in particular in the houses of the most vulnerable elderly or sick,

contributes to excess mortality, as in Paris, France, in August 2003

(Vandentorren et al., 2004) or California in July 2006 (Gershunov et al.,

2009).

The examples given above of ‘spontaneous’ human system adjustment

can be contrasted with explicit measures that are taken to reduce risk

from an expected range of extremes. On the island of Guam, within

the most active and intense zone of tropical cyclone activity on Earth,

buildings are constructed to the most stringent wind design code in the

world. Buildings are required to withstand peak gust wind speeds of

76 ms

-1

, expected every few decades (International Building Codes,

2003). More generally, annual wind extremes for coastal locations will

typically be highest at mid-latitudes while those expected once every

century will be highest in the 10° to 25° latitude tropics (Walshaw,

2000). Consequently, indigenous building practices are less likely to be

resilient close to the equator than in the windier (and storm surge

affected) mid-latitudes (Minor, 1983).

While local experience provides a reservoir of knowledge from which

disaster risk management and adaptation to climate change are drawing

(Fouillet et al., 2008), it may not be available to other regions yet to be

affected by such extremes. Thus, these experiences may not be drawn

upon to provide guidance if future extremes go outside the traditional

or recently observed range, as is expected for some extremes as the

climate changes (see Chapter 3).

1.3. Disaster Management, Disaster Risk

Reduction, and Risk Transfer

One important component of both disaster risk management and

adaptation to climate change is the appropriate allocation of efforts

among disaster management, disaster risk reduction, and risk transfer,

as defined in Section 1.1.2.2. The current section provides a brief survey

of the risk governance framework for making judgments about such an

allocation, suggests why climate change may complicate effective

management of disaster risks, and identifies potential synergies

between disaster risk management and adaptation to climate change.

Disaster risks appear in the context of human choices that aim to satisfy

human wants and needs (e.g., where to live and in what types of

dwelling, what vehicles to use for transport, what crops to grow, what

infrastructure to support economic activities, Hohenemser et al., 1984;

Renn, 2008). Ideally, the choice of any portfolio of actions to address

disaster risk would take into consideration human judgments about

what constitutes risk, how to weigh such risk alongside other values

and needs, and the social and economic contexts that determine whose

judgments influence individuals’ and societal responses to those risks.

The risk governance framework offers a systematic way to help situate

such judgments about disaster management, risk reduction, and risk

transfer within this broader context. Risk governance, under Renn’s

(2008) formulation, consists of four phases – pre-assessment, appraisal,

characterization/evaluation, and management – in an open, cyclical,

iterative, and interlinked process. Risk communication accompanies all

four phases. This process is consistent with those in the UNISDR Hyogo

Framework for Action (UNISDR, 2005), the best known and adhered to

framework for considering disaster risk management concerns (see

Chapter 7).

As one component of its broader approach, risk governance uses

concepts from probabilistic risk analysis to help judge appropriate

allocations in level of effort and over time and among risk reduction,

risk transfer, and disaster management actions. The basic probabilistic

risk analytic framework for considering such allocations regards risk

as the product of the probability of an event(s) multiplied by its

consequence (see Box 1-2; Bedford and Cooke, 2001). In this formulation,

risk reduction aims to reduce exposure and vulnerability as well as the

probability of occurrence of some events (e.g., those associated with

landslides and forest fires induced by human intervention). Risk transfer

efforts aim to compensate losses suffered by those who directly experience

an event. Disaster management aims to respond to the immediate

consequences and facilitate reduction of longer-term consequences (see

Section 1.1).

Probabilistic risk analysis can help compare the efficacy of alternative

actions to manage risk and inform judgments about the appropriate

allocation of resources to reduce risk. For instance, the framework

suggests that equivalent levels of risk reduction result from reducing an

event’s probability or by reducing its consequences by equal percentages.

Probabilistic risk analysis also suggests that a series of relatively smaller,

more frequent events could pose the same risk as a single, relatively less

frequent, larger event. Probabilistic risk analysis can help inform decisions

about alternative allocations of risk management efforts by facilitating

the comparison of the increase or decrease in risk resulting from the

alternative allocations (high confidence). Since the costs of available

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

risk reduction, risk transfer, and disaster management actions will in

general differ, the framework can help inform judgments about an

effective mix of such actions in any particular case (see UNISDR, 2011,

for efforts at stratifying different risk levels as a prelude to finding the

most adequate mix of disaster risk management actions).

Probabilistic risk analysis is, however, rarely implemented in its pure

form, in part because quantitative estimates of hazard and vulnerability

are not always available and are not numbers that are independent of

the individuals making those estimates. Rather, these estimates are

determined by a combination of direct physical consequences of an

event and the interaction of psychological, social, institutional, and

cultural processes (see Box 1-3). For instance, perceptions of the risks of a

nuclear power plant may be influenced by individuals’ trust in the people

operating the plant and by views about potential linkages between

nuclear power and nuclear weapons proliferation – factors that may not

be considered in a formal risk assessment for any given plant. Given this

social construction of risk (see Section 1.1.2.2), effective allocations of

efforts among risk reduction, risk transfer, and disaster management

may best emerge from an integrated risk governance process, which

includes the pre-assessment, appraisal, characterization/evaluation, and

ongoing communications elements. Disaster risk management and

adaptation to climate change each represent approaches that already

use or could be improved by the use of this risk governance process, but

as described in Section 1.3.1, climate change poses a particular set of

additional challenges.

Together, the implications of probabilistic risk analysis and the social

construction of risk reinforce the following considerations with regard to

the effective allocation and implementation of efforts to manage risks

in both disaster risk management and adaptation to climate change:

• As noted in Section 1.1, vulnerability, exposure, and hazard are

each critical to determining disaster risk and the efficacy of actions

taken to manage that risk (high confidence).

• Effective disaster risk management will in general require a

portfolio of many types of risk reduction, risk transfer, and disaster

management actions appropriately balanced in terms of resources

applied over time (high confidence).

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Box 1-3 | Influence of Cognitive Processes, Culture, and Ideology on Judgments about Risk

A variety of cognitive, cultural, and social processes affect judgments about risk and about the allocation of efforts to address these

risks. In addition to the processes described in Section 1.3.1.2, subjective judgments may be influenced more by emotional reactions to

events (e.g., feelings of fear and loss of control) than by analytic assessments of their likelihood (Loewenstein et al., 2001). People

frequently ignore predictions of extreme events if those predictions fail to elicit strong emotional reactions, but will also overreact to

such forecasts when the events elicit feelings of fear or dread (Slovic et al., 1982; Slovic 1993, 2010; Weber, 2006). Even with sufficient

information, everyday concerns and satisfaction of basic wants may prove a more pressing concern than attention and effort toward

actions to address longer-term disaster risk (Maskrey, 1989, 2011; Wisner et al., 2004).

In addition to being influenced by cognitive shortcuts (Kahneman and Tversky, 1979), the perceptions of risk and extremes and reactions

to such risk and events are also shaped by motivational processes (Weber, 2010). Cultural theory combines insights from anthropology

and political science to provide a conceptual framework and body of empirical studies that seek to explain societal conflict over risk

(Douglas, 1992). People’s worldview and political ideology guide attention toward events that threaten their desired social order

(Douglas and Wildavsky, 1982). Risk in this framework is defined as the disruption of a social equilibrium. Personal beliefs also influence

which sources of expert forecasts of extreme climate events will be trusted. Different cultural groups put their trust into different

organizations, from national meteorological services to independent farm organizations to the IPCC; depending on their values, beliefs,

and corresponding mental models, people will be receptive to different types of interventions (Dunlap and McCright, 2008; Malka and

Krosnick, 2009). Judgments about the veracity of information regarding the consequences of alternative actions often depend on the

perceived consistency of those actions with an individual’s cultural values, so that individuals will be more willing to consider information

about consequences that can be addressed with actions seen as consistent with their values (Kahan and Braman, 2006; Kahan et al., 2007).

Factual information interacts with social, institutional, and cultural processes in ways that may amplify or attenuate public perceptions of

risk and extreme events (Kasperson et al., 1988). The US public’s estimates of the risk of nuclear power following the accident at Three

Mile Island provide an example of the socio-cultural filtering of engineering safety data. Social amplification increased public perceptions

of the risk of nuclear power far beyond levels that would derive only from analysis of accident statistics (Fischhoff et al., 1983). The public’s

transformation of expert-provided risk signals can serve as a corrective mechanism by which cultural subgroups of society augment a

science-based risk analysis with psychological risk dimensions not considered in technical risk assessments (Slovic, 2000). Evidence from

health, social psychology, and risk communication literature suggests that social and cultural risk amplification processes modify

perceptions of risk in either direction and in ways that may generally be socially adaptive, but can also bias reactions in socially

undesirable ways in specific instances (APA, 2009).

• Participatory and decentralized processes that are linked to higher

levels of territorial governance (regions, nation) are a crucial part

of all the stages of risk governance that include identification,

choice, and implementation of these actions (high confidence).

1.3.1. Climate Change Will Complicate

Management of Some Disaster Risks

Climate change will pose added challenges in many cases for attaining

disaster risk management goals, and appropriately allocating efforts to

manage disaster risks, for at least two sets of reasons. First, as discussed in

Chapters 3 and 4, climate change is very likely to increase the occurrence

and vary the location of some physical events, which in turn will affect

the exposure faced by many communities, as well as their vulnerability.

Increased exposure and vulnerability would contribute to an increase in

disaster risk. For example, vulnerability may increase due to direct climate-

related impacts on the development and development potential of the

affected area, because resources otherwise available and directed

towards development goals are deflected to respond to those impacts,

or because long-standing institutions for allocating resources such as

water no longer function as intended if climate change affects the

scarcity and distribution of that resource. Second, climate change will

make it more difficult to anticipate, evaluate, and communicate both

probabilities and consequences that contribute to disaster risk, in

particular that associated with extreme events. This set of issues,

discussed in this subsection, will affect the management of these risks

as discussed in Chapters 5, 6, 7, and 8 (high confidence).

1.3.1.1. Challenge of Quantitative Estimates of Changing Risks

Extreme events pose a particular set of challenges for implementing

probabilistic approaches because their relative infrequency often makes

it difficult to obtain adequate data for estimating the probabilities and

consequences. Climate change exacerbates this challenge because it

contributes to potential changes in the frequency and character of such

events (see Section 1.2.2.2).

The likelihood of extreme events is most commonly described by the

return period, the mean interval expected between one such event and its

recurrence. For example, one might speak of a 100-year flood or a 50-year

windstorm. More formally, these intervals are inversely proportional to

the ‘annual exceedance probability,’ the likelihood that an event

exceeding some magnitude occurs in any given year. Thus the 100-year

flood has a 1% chance of occurring in any given year (which translates

into a 37% chance of a century passing without at least one such flood

((1-0.01)

100

= 37%). Though statistical methods exist to estimate

frequencies longer than available data time series (Milly et al., 2002),

the long return period of extreme events can make it difficult, if not

impossible, to reliably estimate their frequency. Paleoclimate records

make clear that in many regions of the world, the last few decades of

observed climate data do not represent the full natural variability of

many important climate variables (Jansen et al., 2003). In addition,

future climate change exacerbates the challenge of non-stationarity

(Milly et al., 2008), where the statistical properties of weather events

will not remain constant over time. This complicates an already difficult

estimation challenge by altering frequencies and consequences of

extremes in difficult-to-predict ways (Chapter 3; Meehl et al., 2007; TRB,

2008; NRC, 2009).

Estimating the likelihood of different consequences and their value is at

least as challenging as estimating the likelihood of extreme events.

Projecting future vulnerability and response capacity involves predicting

the trends and changes in underlying causes of human vulnerability and

the behavior of complex human systems under potentially stressful and

novel conditions. For instance, disaster risk is endogenous in the sense that

near-term actions to manage risk may affect future risk in unintended

ways and near-term actions may affect perceptions of future risks (see

Box 1-3). Section 1.4 describes some of the challenges such system

complexity may pose for effective risk assessment. In addition, disasters

affect socioeconomic systems in multiple ways so that assigning a

quantitative value to the consequences of a disaster proves difficult (see

Section 1.2.3.3). The literature distinguishes between direct losses,

which are the immediate consequences of the disaster-related physical

events, and indirect losses, which are the consequences that result from

the disruption of life and activity after the immediate impacts of the

event (Pelling et al., 2002; Lindell and Prater, 2003; Cochrane, 2004; Rose,

2004). Section 1.3.2 discusses some means to address these challenges.

1.3.1.2. Processes that Influence Judgments

about Changing Risks

Effective risk governance engages a wide range of stakeholder groups

– such as scientists, policymakers, private firms, nongovernmental

organizations, media, educators, and the public – in a process of

exchanging, integrating, and sharing knowledge and information. The

recently emerging field of sustainability science (Kates et al., 2001)

promotes interactive co-production of knowledge between experts and

other actors, based on transdisciplinarity (Jasanoff, 2004; Pohl et al.,

2010) and social learning (Pelling et al., 2008; Pahl-Wostl, 2009; see also

Section 1.4.2). The literature on judgment and decisionmaking suggests

that various cognitive behaviors involving perceptions and judgments

about low-probability, high-severity events can complicate the intended

functioning of such stakeholder processes (see Box 1-3). Climate change

can exacerbate these challenges (high confidence).

The concepts of disaster, risk, and disaster risk management have very

different meanings and interpretations in expert and non-expert contexts

(Sjöberg, 1999a; see also Pidgeon and Fischhoff, 2011). Experts acting

in formal private and public sector roles often employ quantitative

estimates of both probability and consequence in making judgments

about risk. In contrast, the general public, politicians, and the media

tend to focus on the concrete adverse consequences of such events,

paying less attention to their likelihood (Sjöberg, 1999b). As described

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

in Box 1-3, expert estimates of probability and consequence may also

not address the full range of concerns people bring to the consideration

of risk. By definition (if not always in practice), expert understanding of

risks associated with extreme events is based in large part on analytic

tools. In particular, any estimates of changes in disaster risk due to

climate change are often based on the results of complex climate

models as described in Chapter 3. Non-experts, on the other hand, rely

to a greater extent on more readily available and more easily processed

information, such as their own experiences or vicarious experiences from

the stories communicated through the news media, as well as their

subjective judgment as to the importance of such events (see Box 1-1).

These gaps between expert and non-expert understanding of extreme

events present important communication challenges (Weber and Stern,

2011), which may adversely affect judgments about the allocation of

efforts to address risk that is changing over time (high confidence).

Quantitative methods based on probabilistic risk analysis, such as those

described in Sections 5.5 and 6.3, can allow people operating in expert

contexts to use observed data, often from long time series, to make

systematic and internally consistent estimates of the probability of

future events. As described in Section 1.3.1.1, climate change may

reduce the accuracy of such past observations as predictors for future

risk. Individuals, including non-experts and experts making estimates

without the use of formal methods (Barke et al., 1997), often predict the

likelihood of encountering an event in the future by consulting their

past experiences with such events. The ‘availability’ heuristic (i.e., useful

shortcut) is commonly applied, in which the likelihood of an event is

judged by the ease with which past instances can be brought to mind

(Tversky and Kahneman, 1974). Extreme events, by definition, have a

low probability of being represented in past experience and thus will be

relatively unavailable. Experts and non-experts alike may essentially

ignore such events until they occur, as in the case of a 100-year flood

(Hertwig et al., 2004). When extreme events do occur with severe and

thus memorable consequences, people’s estimates of their future risks

will, at least temporarily, become inflated (Weber et al., 2004).

1.3.2. Adaptation to Climate Change

Contributes to Disaster Risk Management

The literature and practice of adaptation to climate change attempts to

anticipate future impacts on human society and ecosystems, such as

those described in Chapter 4, and respond to those already experienced.

In recent years, the adaptation to climate change literature has introduced

the concept of climate-related decisions (and climate proofing), which

are choices by individuals or organizations, the outcomes of which can

be expected to be affected by climate change and its interactions with

ecological, economic, and social systems (Brown et al., 2006; McGray et

al., 2007; Colls et al., 2009; Dulal et al., 2009; NRC, 2009). For instance,

choosing to build in a low-lying area whose future flooding risk increases

due to climate change represents a climate-related decision. Such a

decision is climate-related whether or not the decisionmakers recognize

it as such. The disaster risk management community may derive added

impetus from the new context of a changing climate for certain of its

pre-existing practices that already reflect the implementation of this

concept. In many circumstances, choices about the appropriate allocation

of efforts among disaster management, disaster risk reduction, and risk

transfer actions will be affected by changes in the frequency and

character of extreme events and other impacts of a climate change on

the underlying conditions that affect exposure and vulnerability.

Much of the relevant adaptation literature addresses how expectations

about future deviations from past patterns in physical, biological, and

socioeconomic conditions due to climate change should affect the

allocation of efforts to manage risks. While there exist differing views

on the extent to which the adaptation to climate change literature has

unique insights on managing changing conditions per se that it can

bring to disaster risk management (Lavell, 2010; Mercer, 2010; Wisner

et al., 2011), the former field’s interest in anticipating and responding

to the full range of consequences from changing climatic conditions can

offer important new perspectives and capabilities to the latter field.

The disaster risk management community can benefit from the debates

in the adaptation literature about how to best incorporate information

about current and future climate into climate-related decisions. Some

adaptation literature has emphasized the leading role of accurate

regional climate predictions as necessary to inform such decisions

(Collins, 2007; Barron, 2009; Doherty et al., 2009; Goddard et al., 2009;

Shukla et al., 2009; Piao et al., 2010; Shapiro et al., 2010). This argument

has been criticized on the grounds that predictions of future climate

impacts are highly uncertain (Dessai and Hulme, 2004; Cox and

Stephenson, 2007; Stainforth et al., 2007; Dessai et al., 2009; Hawkins

and Sutton, 2009; Knutti, 2010) and that predictions are insufficient to

motivate action (Fischhoff, 1994; Sarewitz et al., 2000; Cash et al., 2003,

2006; Rayner et al., 2005; Moser and Luers, 2008; Dessai et al., 2009;

NRC, 2009). Other adaptation literature has emphasized that many

communities do not sufficiently manage current risks and that improving

this situation would go a long way toward preparing them for any

future changes due to climate change (Smit and Wandel, 2006; Pielke et

al., 2007). As discussed in Section 1.4, this approach will in some cases

underestimate the challenges of adapting to future climate change.

To address these challenges, the adaptation literature has increasingly

discussed an iterative risk management framework (Carter et al., 2007;

Jones and Preston, 2011), which is consistent with risk governance as

described earlier in this section. Iterative risk management recognizes

that the process of anticipating and responding to climate change does

not constitute a single set of judgments at some point in time, but rather

an ongoing assessment, action, reassessment, and response that will

continue – in the case of many climate-related decisions – indefinitely

(ACC, 2010). In many cases, iterative risk management contends with

conditions where the probabilities underlying estimates of future risk

are imprecise and/or the structure of the models that relate events to

consequences are under-determined (NRC, 2009; Morgan et al., 2009).

Such deep or severe uncertainty (Lempert and Collins, 2007) can

characterize not only understanding of future climatic events but also

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

future patterns of human vulnerability and the capability to respond to

such events. With many complex, poorly understood physical and

socioeconomic systems, research and social learning may enrich

understanding over time, but the amount of uncertainty, as measured

by observers’ ability to make specific, accurate predictions, may grow

larger (Morgan et al., 2009, pp. 114–115; NRC, 2009, pp. 18–19; see

related discussion of ‘surprises’ in Section 3.1.7). In addition, theory and

models may change in ways that make them less, rather than more,

reliable as predictive tools over time (Oppenheimer et al., 2008).

Recent literature has thus explored a variety of approaches that can

help disaster risk management address such uncertainties (McGray et

al., 2007; IIED 2009; Schipper, 2009), in particular approaches that help

support decisions when it proves difficult or impossible to accurately

estimate probabilities of events and their adverse consequences.

Approaches for characterizing uncertainty include qualitative scenario

methods (Parson et al., 2007); fuzzy sets (Chongfu, 1996; El-Baroudy

and Simonovic, 2004; Karimi and Hullermeier, 2007; Simonovic, 2010);

and the use of ranges of values or sets of distributions, rather than single

values or single best-estimate distributions (Morgan et al., 2009; see

also Mastrandrea et al., 2010). Others have suggested managing such

uncertainty with robust policies that perform well over a wide range of

plausible futures (Dessai and Hulme, 2007; Groves and Lempert, 2007;

Brown, 2010; Means et al., 2010; Wilby and Dessai, 2010; Dessai and

Wilby, 2011; Reeder and Ranger, 2011; also see discussion in Chapter 8).

Decision rules based on the concept of robust adaptive policies go

beyond ‘no regrets’ by suggesting how in some cases relatively low-

cost, near-term actions and explicit plans to adjust those actions over

time can significantly improve future ability to manage risk (World

Bank, 2009; Hine and Hall, 2010; Lempert and Groves, 2010; Walker et

al., 2010; Brown, 2011; Ranger and Garbett-Shiels, 2011; see also

Section 1.4.5).

The resilience literature, as described in Chapter 8, also takes an interest

in managing difficult-to-predict futures. Both the adaptation to climate

change and vulnerability literatures often take an actor-oriented view

(Wisner et al., 2004; McLaughlin and Dietz, 2007; Nelson et al., 2007;

Moser 2009) that focuses on particular agents faced with a set of

decisions who can make choices based on their various preferences;

their institutional interests, power, and capabilities; and the information

they have available. Robustness in the adaptation to climate change

context often refers to a property of decisions specific actors may take

(Hallegatte, 2009; Lempert and Groves, 2010; Dessai and Wilby, 2011).

In contrast, the resilience literature tends to take a systems view (Olsson

et al., 2006; Walker et al., 2006; Berkes, 2007; Nelson et al., 2007) that

considers multi-interacting agents and their relationships in and with

complex social, ecological, and geophysical systems (Miller et al., 2010).

These literatures can help highlight for disaster risk management such

issues as the tension between resilience to specific, known disturbances

and novel and unexpected ones (sometimes referred to as the distinction

between ‘specified’ and ‘general’ resilience, Miller et al., 2010), the

tension between resilience at different spatial and temporal scales, and

the tension between the ability of a system to persist in its current state

and its ability to transform to a fundamentally new state (Section 1.4;

Chapter 8; ICSU, 2002; Berkes, 2007).

Disaster risk management will find similarities to its own multi-sector

approach in the adaptation literature’s recent emphasis, consistent with

the concept of climate-related decisions, on climate change as one of

many factors affecting the management of risks. For instance, some

resource management agencies now stress climate change as one of many

trends such as growing demand for resources, environmental constraints,

aging infrastructure, and technological change that, particularly in

combination, could require changes in investment plans and business

models (CCSP, 2008; Brick et al., 2010). It has become clear that many

less-developed regions will have limited success in reducing overall

vulnerability solely by managing climate risk because vulnerability,

adaptive capacity, and exposure are critically influenced by existing

structural deficits (low income and high inequality, lack of access to

health and education, lack of security and political access, etc.). For

example, in drought-ravaged northeastern Brazil, many vulnerable

households could not take advantage of risk management interventions

such as seed distribution programs because they lacked money to travel

to pick up the seeds or could not afford a day’s lost labor to participate

in the program (Lemos, 2003). In Burkina Faso, farmers had limited

ability to use seasonal forecasts (a risk management strategy) because

they lacked the resources (basic agricultural technology such as plows,

alternative crop varieties, fertilizers, etc.) needed to effectively respond

to the projections (Ingram et al., 2002). In Bangladesh, however, despite

persisting poverty, improved disaster preparedness and response and

relative higher levels of household adaptive capacity have dramatically

decreased the number of deaths as a result of flooding (del Ninno et al.,

2002, 2003; Section 9.2.5).

Scholars have argued that building adaptive capacity in such regions

requires a dialectic, two-tiered process in which climatic risk management

(specific adaptive capacity) and deeper-level socioeconomic and political

reform (generic adaptive capacity) iterate to shape overall vulnerability

(Lemos et al., 2007; Tompkins et al., 2008). When implemented as part of

a systems approach, managing climate risks can create positive synergies

with development goals through participatory and transparent

approaches (such as participatory vulnerability mapping or local disaster

relief committees) that empower local households and institutions (e.g.,

Degg and Chester, 2005; Nelson, 2005).

1.3.3. Disaster Risk Management and

Adaptation to Climate Change Share

Many Concepts, Goals, and Processes

The efficacy of the mix of actions used by communities to reduce,

transfer, and respond to current levels of disaster risk could be vastly

increased. Understanding and recognition of the many development-

based instruments that could be put into motion to achieve disaster risk

reduction is a prerequisite for this (Lavell and Lavell, 2009; UNISDR,

2009e, 2011; Maskrey 2011; Wisner et al., 2011). At the same time,

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

some aspects of disaster risk will increase for many communities due to

climate change and other factors (Chapters 3 and 4). Exploiting the

potential synergies between disaster risk management and adaptation

to climate change literature and practice will improve management of

both current and future risks.

Both fields share a common interest in understanding and reducing the

risk created by the interactions of human with physical and biological

systems. Both seek appropriate allocations of risk reduction, risk

transfer, and disaster management efforts, for instance balancing pre-

impact risk management or adaptation with post-impact response and

recovery. Decisions in both fields may be organized according to the

risk governance framework. For instance, many countries, are gaining

experience in implementing cooperative, inter-sector and multi- or

interdisciplinary approaches (ICSU, 2002; Brown et al., 2006; McGray et

al., 2007; Lavell and Lavell, 2009). In general, disaster risk management

can help those practicing adaptation to climate change to learn from

addressing current impacts. Adaptation to climate change can help

those practicing disaster risk management to more effectively address

future conditions that will differ from those of today.

The integration of concepts and practices is made more difficult because

the two fields often use different terminology, emerge from different

academic communities, and may be seen as the responsibility of different

government organizations. As one example, Section 1.4 will describe

how the two fields use the word ‘coping’ with different meanings and

different connotations. In general, various contexts have made it more

difficult to recognize that the two fields share many concepts, goals, and

processes, as well as to exploit the synergies that arise from their

differences. These include differences in historical and evolutionary

processes; conceptual and definitional bases; processes of social

knowledge construction and the ensuing scientific compartmentalization

of subject areas; institutional and organizational funding and

instrumental backgrounds; scientific origins and baseline literature;

conceptions of the relevant causal relations; and the relative importance

of different risk factors (see Sperling and Szekely, 2005; Schipper and

Pelling, 2006; Thomalla et al., 2006; Mitchell and van Aalst, 2008;

Venton and La Trobe, 2008, Schipper and Burton, 2009; Lavell, 2010).

These aspects will be considered in more detail in future chapters.

Potential synergies from the fields’ different emphases include the

following.

First, disaster risk management covers a wide range of hazardous

events, including most of those of interest in the adaptation to climate

change literature and practice. Thus, adaptation could benefit from

experience in managing disaster risks that are analogous to the new

challenges expected under climate change. For example, relocation and

other responses considered when confronted with sea level change can

be informed by disaster risk management responses to persistent or

large-scale flooding and landslides or volcanic activity and actions with

pre- or post-disaster relocation; responses to water shortages due to loss

of glacial meltwater would bear similarities to shortages due to other

drought stressors; and public health challenges due to modifications in

disease vectors due to climate change have similarities to those

associated with current climate variability, such as the occurrence of

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

FAQ 1.2 | What are effective strategies for managing disaster risk in a changing climate?

Disaster risk management has historically operated under the premise that future climate will resemble that of the past. Climate change

now adds greater uncertainty to the assessment of hazards and vulnerability. This will make it more difficult to anticipate, evaluate, and

communicate disaster risk. Uncertainty, however, is not a ‘new’ problem. Previous experience with disaster risk management under

uncertainty, or where long return periods for extreme events prevail, can inform effective risk reduction, response, and preparation, as

well as disaster risk management strategies in general.

Because climate variability occurs over a wide range of timescales, there is often a historical record of previous efforts to manage and

adapt to climate-related risk that is relevant to risk management under climate change. These efforts provide a basis for learning via

the assessment of responses, interventions, and recovery from previous impacts. Although efforts to incorporate learning into the

management of weather- and climate-related risks have not always succeeded, such adaptive approaches constitute a plausible model

for longer-term efforts. Learning is most effective when it leads to evaluation of disaster risk management strategies, particularly with

regard to the allocation of resources and efforts between risk reduction, risk sharing, and disaster response and recovery efforts, and

when it engages a wide range of stakeholder groups, particularly affected communities.

In the presence of deeply uncertain long-term changes in climate and vulnerability, disaster risk management and adaptation to climate

change may be advanced by dealing adequately with the present, anticipating a wide range of potential climate changes, and promoting

effective ‘no-regrets’ approaches to both current vulnerabilities and to predicted changes in disaster risk. A robust plan or strategy that

both encompasses and looks beyond the current situation with respect to hazards and vulnerability will perform well over a wide range

of plausible climate changes.

El Niño. Moreover, like disaster risk management, adaptation to climate

change will often take place within a multi-hazard locational framework

given that many areas affected by climate change will also be affected

by other persistent and recurrent hazards (Wisner et al., 2004, 2011;

Lavell, 2010; Mercer, 2010). Additionally, learning from disaster risk

management can help adaptation, which to date has focused more on

changes in the climate mean, increasing its focus on future changes in

climate extremes and other potentially damaging events.

Second, disaster risk management has tended to encourage an expanded,

bottom-up, grass roots approach, emphasizing local and community-

based risk management in the framework of national management

systems (see Chapters 5 and 6), while an important segment of the

adaptation literature focuses on social and economic sectors and macro

ecosystems over large regional scales. However, a large body of the

adaptation literature – in both developed and developing countries – is

very locally focused. Both fields could benefit from the body of work on

the determinants of adaptive capacity that focus on the interaction of

individual and collective action and institutions that frame their actions

(McGray et al., 2007; Schipper, 2009).

Third, the current disaster risk management literature emphasizes the

social conditioning of risk and the construction of vulnerability as a causal

factor in explaining loss and damage. Early adaptation literature and

some more recent output, particularly from the climate change field,

prioritizes physical events and exposure, seeing vulnerability as what

remains after all other factors have been considered (O’Brien et al.,

2007). However, community-based adaptation work in developing

countries (Beer and Hamilton, 2002; Brown et al., 2006; Lavell and

Lavell, 2009; UNISDR, 2009b,c) and a growing number of studies in

developed nations (Burby and Nelson, 1991; de Bruin et al., 2009;

Bedsworth and Hanak, 2010; Brody et al., 2010; Corfee-Morlot et al.,

2011; Moser and Eckstrom, 2011) have considered social causation.

Both fields could benefit from further integration of these concepts.

Overall, the disaster risk management and adaptation to climate change

literatures both now emphasize the value of a more holistic, integrated,

trans-disciplinary approach to risk management (ICSU-LAC, 2009).

Dividing the world up sectorally and thematically has often proven

organizationally convenient in government and academia, but can

undermine a thorough understanding of the complexity and interaction

of the human and physical factors involved in the constitution and

definition of a problem at different social, temporal, and territorial

scales. A more integrated approach facilitates recognition of the complex

relationships among diverse social, temporal, and spatial contexts;

highlights the importance of decision processes that employ participatory

methods and decentralization within a supporting hierarchy of higher

levels; and emphasizes that many disaster risk management and other

organizations currently face climate-related decisions whether they

recognize them or not.

The following areas, some of which have been pursued by governments,

civil society actors, and communities, have been recommended or

proposed to foster such integration between, and greater effectiveness

of, both adaptation to climate change and disaster risk management

(see also WRI, 2008; Birkmann and von Teichman, 2010; Lavell, 2010):

• Development of a common lexicon and deeper understanding of

the concepts and terms used in each field (Schipper and Burton,

2009)

• Implementation of government policymaking and strategy

formulation that jointly considers the two topics

• Evolution of national and international organizations and institutions

and their programs that merge and synchronize around the

two themes, such as environmental ministries coordinating with

development and planning ministries (e.g., National Environmental

Planning Authority in Jamaica and Peruvian Ministries of Economy

and Finance, Housing, and Environment)

• Merging and/or coordinating disaster risk management and

adaptation financing mechanisms through development agencies

and nongovernmental organizations

• The use of participatory, local level risk and context analysis

methodologies inspired by disaster risk management that are now

strongly accepted by many civil society and government agencies

in work on adaptation at the local levels (IFRC, 2007; Lavell and

Lavell, 2009; UNISDR, 2009 b,c)

• Implementing bottom-up approaches whereby local communities

integrate adaptation to climate change, disaster risk management,

and other environmental and development concerns in a single,

causally dimensioned intervention framework, commensurate

many times with their own integrated views of their own physical

and social environments (Moench and Dixit, 2004; Lavell and

Lavell, 2009).

1.4. Coping and Adapting

The discussion in this section has four goals: to clarify the relationship

between adaptation and coping, particularly the notion of coping range;

to highlight the role of learning in an adaptation process; to discuss

barriers to successful adaptation and the issue of maladaptation; and

to highlight examples of learning in the disaster risk management

community that have already advanced climate change adaptation.

A key conclusion of this section is that learning is central to adaptation,

and that there are abundant examples (see Section 1.4.5 and Chapter 9)

of the disaster risk management community learning from prior experience

and adjusting its practices to respond to a wide range of existing and

evolving hazards. These cases provide the adaptation to climate change

community with the opportunity not only to study the specifics of learning

as outlined in these cases, but also to reflect on how another community

that also addresses climate-related risk has incorporated learning into

its practice over time.

As disaster risk management includes both coping and adapting, and

these two concepts are central for adaptation to climate change in both

scholarship and practice, it is important to start by clarifying the meanings

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

of these terms. Without a clear conception of the distinctions between

the concepts and overlaps in their meanings, it is difficult to fully

understand a wide range of related issues, including those concerned

with the coping range, adaptive capacity, and the role of institutional

learning in promoting robust adaptation to climate change. Clarifying

such distinctions carries operational significance for decisionmakers

interested in promoting resilience, a process that relies on coping for

immediate survival and recovery, as well as adaptation and disaster

risk reduction, which entail integrating new information to moderate

potential future harm.

1.4.1. Definitions, Distinctions, and Relationships

In both the disaster risk management and climate change adaptation

literature, substantial differences are apparent as to the meaning and

significance of coping as well as its relationship with and distinction

from adaptation. Among the discrepancies, for example, some disaster

risk management scholars have referred to coping as a way to engage

local populations and utilize indigenous knowledge in disaster

preparedness and response (Twigg, 2004), while others have critiqued this

idea, concerned that it would divert attention away from addressing

structural problems (Davies, 1993) and lead to a focus on ‘surviving’

instead of ‘thriving.’ There has also been persistent debate over whether

coping primarily occurs before or after a disastrous event (UNISDR,

2008b,c, 2009e). This debate is not entirely resolved by the current

UNISDR definition of coping, the “ability of people, organizations, and

systems, using available skills and resources, to face and manage

adverse conditions, emergencies or disasters” (UNISDR, 2009d). Clearly,

emergencies and disasters are post facto circumstances, but ‘adverse

conditions’ is an indeterminate concept that could include negative pre-

impact livelihood conditions and disaster risk circumstances or merely

post-impact effects.

The first part of this section is focused on parsing these two concepts.

Once the terms are adequately distinguished, the focus shifts in the

second part to important relationships between the two terms and other

related concepts, which taken together have operational significance for

governments and stakeholders.

1.4.1.1. Definitions and Distinctions

Despite the importance of the term coping in the fields of both disaster

risk management and adaptation to climate change, there is substantial

confusion regarding the term’s meaning (Davies, 1996) and how it is

distinguished from adaptation.

In order to clarify this aspect, it is helpful first to look outside of the

disaster risk and adaptation contexts. The Oxford English Dictionary

defines coping as “the action or process of overcoming a problem or

difficulty” or “managing or enduring a stressful situation or condition”

and adapting as “rendering suitable, modifying” (OED, 1989). As noted

in Table 1-1, contrasting the two terms highlights several important

dimensions in which they differ – exigency, constraint, reactivity, and

orientation – relevant examples of which can be found in the literature

cited.

Overall, coping focuses on the moment, constraint, and survival;

adapting (in terms of human responses) focuses on the future, where

learning and reinvention are key features and short-term survival is less

in question (although it remains inclusive of changes inspired by

already-modified environmental conditions).

1.4.1.2. Relationships between Coping, Coping Capacity,

Adaptive Capacity, and the Coping Range

The definitions of coping and adapting used in this report reflect the

dictionary definitions. As an example, a community cannot adapt its way

through the aftermath of a disastrous hurricane; it must cope instead.

Its coping capacity, or capacity to respond (Gallopín, 2003), is a function

of currently available resources that can be used to cope, and determines

the community’s ability to survive the disaster intact (Bankoff, 2004;

Wisner et al., 2004). Repeated use of coping mechanisms without

adequate time and provisions for recovery can reduce coping capacity

and shift a community into what has been termed transient poverty

(Lipton and Ravallion, 1995). Rather than leaving resources for adaptation,

communities forced to cope can become increasingly vulnerable to

future hazards (O’Brien and Leichenko, 2000).

Adaptation in anticipation of future hurricanes, however, can limit the

need for coping that may be required to survive the next storm. A

community’s adaptive capacity will determine the degree to which

adaptation can be pursued (Smit and Pilofosova, 2003). While there is

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Dimension Coping Adapting

Exigency

Survival in the face of immediate,

unusually signiﬁcant stress, when

resources, which may have been

minimal to start with, are taxed

(Wisner et al., 2004).

Reorientation in response to

recent past or anticipated future

change, often without speciﬁc

reference to resource limitations.

Constraint

Survival is foremost and tactics are

constrained by available

knowledge, experience, and assets;

reinvention is a secondary concern

(Bankoff, 2004).

Adjustment is the focus and

strategy is constrained less by

current limits than by

assumptions regarding future

resource availability and trends.

Reactivity

Decisions are primarily tactical and

made with the goal of protecting

basic welfare and providing for

basic human security after an event

has occurred (Adger, 2000).

Decisions are strategic and

focused on anticipating change

and addressing this proactively

(Füssel, 2007), even if spurred by

recent events seen as harbingers

of further change.

Orientation

Focus is on past events that shape

current conditions and limitations;

by extension, the focus is also on

previously successful tactics

(Bankoff, 2004).

Focus on future conditions and

strategies; past tactics are

relevant to the extent they might

facilitate adjustment, though

some experts believe past and

future orientation can overlap

and blend (Chen, 1991).

Table 1-1 | The various dimensions of coping and adapting.

some variability in how coping capacity and adaptive capacity are

defined, the literature generally recognizes that adaptive capacity

focuses on longer-term and more sustained adjustments (Gallopín, 2006;

Smit and Wandel, 2006). However, in the same way that repeatedly

invoking coping mechanisms consumes resources available for subsequent

coping needs, it also consumes resources that might otherwise be

available for adaptation (Adger, 1996; Risbey et al., 1999).

There is also a link between adaptation and the coping range – that

is, a system’s capacity to reactively accommodate variations in climatic

conditions and their impacts (a system can range from a particular

ecosystem to a society) (IPCC, 2007b). In the adaptation literature,

Yohe and Tol (2002, p. 26) have used the term to refer to the range of

“circumstances within which, by virtue of the underlying resilience of

the system, significant consequences are not observed” in response to

external stressors. Outside the coping range, communities will “feel

significant effects from change and/or variability in their environments”

(Yohe and Tol, 2002, p. 25). Within its coping range, a community

can survive and even thrive with significant natural hazards. This is

particularly the case when the historical distribution of hazard intensity

is well known and relatively stable (see Section 1.2.3.4). A community’s

coping range is determined, in part, by prior adaptation (Hewitt and

Burton, 1971; de Vries, 1985; de Freitas, 1989), and a community is most

likely to survive and thrive when adaptation efforts have matched its

coping range with the range of hazards it typically encounters (Smit and

Pilifosova, 2003). As climate change alters future variability and the

occurrence of extreme events, and as societal trends change human

systems’ vulnerability, adaptation is required to adjust the coping range

so as to maintain societal functioning within an expected or acceptable

range of risk (Moser and Luers, 2008).

Box 1-4 provides an example of this process in the region that is now

The Netherlands. As this box illustrates, the process of shifting a society’s

coping range both depends on and facilitates further economic

development (i.e., requires adaptive capacity and enhances coping

capacity). The box also illustrates that the process requires continuous

reassessment of risk and adjustment in response to shifting hazard

distributions in order to avoid increasing, and maladaptive, hazard

exposure. Successful adjustments, facilitated in part by institutional

learning, can widen and shift a community’s coping range, promoting

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Box 1-4 | Adaptation to Rising Levels of Risk

Before AD 1000, in the low-lying coastal floodplain of the southern North Sea and around the Rhine delta, the area that is now The

Netherlands, the inhabitants lived on dwelling mounds, piled up to lie above the height of the majority of extreme storm surges. By the

10th century, with a population estimated at 300,000 people, inhabitants had begun to construct the first dikes, and within 400 years

had ringed all significant areas of land above spring tide, allowing animals to graze and people to live in the protected wetlands. The

expansion of habitable land encouraged a significant increase in the population exposed to catastrophic floods (Borger and Ligtendag,

1998). The weak sea dikes broke in a series of major storm surge floods through the stormy 13th and 14th centuries (in particular in

1212, 1219, 1287, and 1362), flooding enormous areas (often permanently) and causing more than 200,000 fatalities, reflecting an

estimated lifetime mortality rate from floods for those living in the region in excess of 5% (assuming a 30-year average lifespan;

Gottschalk, 1971, 1975, 1977).

To adapt to increasingly adverse environmental conditions (reflecting long-term delta subsidence), major improvements in the technology

of dike construction and drainage engineering began in the 15th century. As the country became richer and population increased (to an

estimated 950,000 by 1500 and 1.9 million by 1700), it became an imperative not only to provide better levels of protection but also to

reclaim land from the sea and from the encroaching lakes, both to reduce flood hazard and expand the land available for food production

(Hoeksma, 2006). Examples of the technological innovations included the development of windmills for pumping, and methods to lift

water at least 4 m whether by running windmills in series or through the use of the wind-powered Archimedes screw. As important was

the availability of capital to be invested in joint stock companies with the sole purpose of land reclamation. In 1607, a company was

formed to reclaim the 72 km

Beemster Lake north of Amsterdam (12 times larger than any previous reclamation). A 50-km canal and

dike ring were excavated, a total of 50 windmills installed that after five years pumped dry the Beemster polder, 3 to 4 m below the

surrounding countryside, which, within 30 years, had been settled by 200 farmhouses and 2,000 people.

After the major investment in raising and strengthening flood defenses in the 17th century, there were two or three large floods, one in

1717 (when 14,000 people drowned) and two notable floods in 1825 and 1953; since that time the average flood mortality rate has

been around 1,000 per century, equivalent to a lifetime mortality rate (assuming a 50-year average lifetime) of around 0.01%, 500 times

lower than that which had prevailed through the Middle Ages (Van Baars and Van Kempen, 2009). This change reflects increased protection

rather than any reduction in storminess. The flood hazard and attendant risk is now considered to be rising again (Bouwer and Vellinga,

2007) and plans are being developed to manage further rises, shifting the coping range in anticipation of the new hazard distribution.

resilience to a wider range of future disaster risk (Yohe and Tol, 2002),

as illustrated in Box 1-4 and discussed further in Section 1.4.2 (high

confidence).

1.4.2. Learning

Risk management decisions are made within social-ecological systems

(a term referring to social systems intimately tied to and dependent on

environmental resources and conditions). Some social-ecological systems

are more resilient than others. The most resilient are characterized by

their capacity to learn and adjust, their ability to reorganize after

disruption, and their retention of fundamental structure and function in

the face of system stress (Folke, 2006). The ability to cope with extreme

stress and resume normal function is thus an important component of

resilience, but learning, reorganizing, and changing over time are also

key. As Chapter 8 highlights, transformational changes are required to

achieve a future in which society’s most important social-ecological

systems are sustainable and resilient. Learning, along with adaptive

management, innovation, and leadership, is essential to this process.

Learning related to social-ecological systems requires recognizing

their complex dynamics, including delays, stock-and-flow dynamics,

and feedback loops (Sterman, 2000), features that can complicate

management strategies by making it difficult to perceive how a system

operates. Heuristic devices and mental models can sometimes inhibit

learning by obscuring a problem’s full complexity (Kahneman et al., 1982;

Section 1.3.1.2) and complicating policy action among both experts and

lay people (Cronin et al., 2009). For instance, common heuristics (see

Section 1.3.1.2) lead to misunderstanding of the relationship between

greenhouse gas emission rates and their accumulation in atmospheric

stocks, lending credence to a ‘wait and see’ approach to mitigation

(Sterman, 2008). Through a variety of mechanisms, such factors can lead

to paralysis and failure to engage in appropriate risk management

strategies despite the availability of compelling evidence pointing to

particular risk management pathways (Sterman, 2006). The resulting

learning barriers thus deserve particular attention when exploring how

to promote learning that will lead to effective adaptation.

Given the complex dynamics of social-ecological systems and their

interaction with a changing climate, the literature on adaptation to climate

change (usually referred to here, as above, simply as ‘adaptation’)

emphasizes iterative learning and management plans that are explicitly

designed to evolve as new information becomes available (Morgan et

al., 2009: NRC, 2009). Unlike adaptation, the field of disaster risk

management has not historically focused as explicitly on the implications

of climate change and the need for iterative learning. However, the

field provides several important examples of learning, including some

presented in Chapter 9, that could be instructive to adaptation

practitioners. Before introducing these case studies in Section 1.4.5, we

will outline relevant theory of institutional learning and ‘learning loops.’

Extensive literature explores both the role of learning in adaptation

(Armitage et al., 2008; Moser, 2010; Pettengell, 2010) and strategies for

facilitating institutional and social learning in ‘complex adaptive systems’

(Pahl-Wostl, 2009). Some important strategies include the use of

knowledge co-production, wherein scientists, policymakers, and other

actors work together to exchange, generate, and apply knowledge

(van Kerkhoff and Lebel, 2006), and action research, an iterative process

in which teams of researchers develop hypotheses about real-world

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

CONTEXT FRAMES

Single-Loop Learning

Double-Loop Learning

Triple-Loop Learning

Reacting

Reframing

Transforming

ACTIONS OUTCOMES

• What strategies might facilitate more

effective future transboundary ﬂood

management?

• How should vulnerability to other

climate change impacts be included in

ﬂood management planning?

• Should dike height be increased by 10

or 20 cm?

• Should resources be allocated toward

protecting existing populations and

infrastructure at increasing risk in a

changing climate, or should these assets

be relocated or abandoned once certain

risk thresholds are crossed?

Figure 1-3 | Learning loops: pathways, outcomes, and dynamics of single-, double-, and triple-loop learning and applications to flood management. Adapted from Argyris and

Schön, 1978; Hargrove, 2002; Sterman et al., 2006; Folke et al., 2009; and Pahl-Wostl, 2009.

problems and revise management strategies based on the results

(List, 2006). Prior work on learning theories, for example, experiential

learning (Kolb, 1984) and transformative learning (Mezirow, 1995),

emphasize the importance of action-oriented problem-solving, learning-

by-doing, concrete learning cycles, and how these processes result in

reflection, reconsideration of meaning, and re-interpretation of value

structures. The learning loop framework (Kolb and Fry, 1975; Argyris and

Schön, 1978; Keen et al., 2005) integrates these theories and divides

learning processes into three different loops depending on the degree

to which the learning promotes transformational change in management

strategies. Figure 1-3 outlines this framework and its application to the

issue of flood management.

In single-loop learning processes, changes are made based on the

difference between what is expected and what is observed. Single-loop

learning is primarily focused on improving the efficiency of action

(Pelling et al., 2008) and answering the question of “whether things are

being done right” (Flood and Romm, 1996), that is, whether management

tactics are appropriate or adequate to achieve identified objectives. In

flood management, for example, when floodwaters threaten to breach

existing flood defenses, flood managers may ask whether dike and

levee heights are sufficient and make adjustments accordingly. As

Figure 1-3 indicates, single-loop learning focuses primarily on actions;

data are integrated and acted on but the underlying mental model used

to process the data is not changed.

In double-loop learning, the evaluation is extended to assess whether

actors are “doing the right things” (Flood and Romm, 1996), that is,

whether management goals and strategies are appropriate. Corrective

actions are made after the problem is reframed and different management

goals are identified (Pelling et al., 2008); data are used to promote

critical thinking and challenge underlying mental models of what works

and why. Continuing with the flood management example, double-loop

learning results when the goals of the current flood management

regime are critically examined to determine if the regime is sustainable

and resilient to anticipated shifts in hydrological extremes over a

particular time period. For instance, in a floodplain protected by levees

built to withstand a 500-year flood, a shift in the annual exceedance

probability from 0.002 to 0.005 (equivalent to stating that the likelihood

that a 500-year flood will occur in a given year has shifted to that seen

historically for a 200-year event) will prompt questions about whether

the increased likelihood of losses justifies different risk management

decisions, ranging from increased investments in flood defenses to

changed insurance policies for the vulnerable populations.

Many authors also distinguish triple-loop learning (Argyris and Schön,

1978; Hargrove, 2002; Peschl, 2007), or learning that questions deeply

held underlying principles (Pelling et al., 2008). In triple-loop learning,

actors question how institutional and other power relationships determine

perceptions of the range of possible interventions, allowable costs, and

appropriate strategies (Flood and Romm, 1996). In response to evidence

that management strategies are not serving a larger agreed-upon goal,

that is, they are maladaptive, triple-loop learning questions how the

social structures, cultural norms, dominant value structures, and other

constructs that mediate risk and risk management (see Box 1-3) might

be changed or transformed. Extending the flood control example, triple-

loop learning might entail entirely new approaches to governance and

participatory risk management involving additional parties, crossing

cultural, institutional, national, and other boundaries that contribute

significantly to flood risk, and planning aimed at robust actions instead of

strategies considered optimal for particular constituents (Pahl-Wostl, 2009).

Different types of learning are more or less appropriate in given

circumstances (Pahl-Wostl, 2009, p. 359). For example, overreliance

on single-loop learning may be problematic in rapidly changing

circumstances. Single-loop learning draws on an inventory of existing

skills and memories specific to particular circumstances. As a result,

rapid, abrupt, or surprising changes may confound single-loop learning

processes (Batterbury, 2008). Coping mechanisms, even those that

have developed over long periods of time and been tested against

observation and experience, may not confer their usual survival

advantage in new contexts. Double- and triple-loop learning are better

suited to matching coping ranges with new hazard regimes (Yohe and

Tol, 2002). Integrating double- and triple-loop learning into adaptation

projects, particularly for populations exposed to multiple risks and

stressors, is more effective than more narrowly planned approaches

dependent on specific future climate information (McGray et al., 2007;

Pettengell, 2010).

Easier said than done, triple-loop learning is analogous to what some

have termed ‘transformation’ (Kysar, 2004; see Section 1.1.3; Chapter

8), in that it can lead to recasting social structures, institutions, and

constructions that contain and mediate risk to accommodate more

fundamental changes in world view (Pelling, 2010). Translating double-

and triple-loop learning into policy requires not only articulation of

a larger risk-benefit universe, but also mechanisms to identify,

account for, and compare the costs associated with a wide range of

interventions and their benefits and harms over various time horizons.

Stakeholders would need also to collaborate to an unusual degree in

order to collectively and cooperatively consider the wide range of risk

management possibilities and their impacts.

1.4.3. Learning to Overcome Adaptation Barriers

Learning focused on barriers to adaptation can be particularly useful.

Resource limitations are universally noted as a significant impediment in

pursuing adaptation strategies, to a greater or lesser degree depending

on the context. In addition, some recent efforts to identify and categorize

adaptation barriers have focused on specific cultural factors (Nielsen

and Reenberg, 2010) or issues specific to particular sectors (Huang et

al., 2011), while others have discussed the topic more comprehensively

(Moser and Ekstrom, 2010). Some studies identify barriers in the specific

stages of the adaptation process. Moser and Ekstrom (2010), for instance,

outline three phases of adaptation: understanding, planning, and

management. Each phase contains several key steps, and barriers can

Chapter 1Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

impede progress at each. Barriers to understanding, for instance, can

include difficulty recognizing a changing signal due to difficulty with its

detection, perception, and appreciation; preoccupation with other

pressing concerns that divert attention from the growing signal; and

lack of administrative and social support for making adaptive decisions.

While this study offers a diagnostic framework and avoids prescriptions

about overcoming adaptation barriers, other studies, such as those

mentioned above, offer more focused prescriptions relevant to particular

sectors and contexts.

Research on barriers has generally focused on adaptation as a process,

recognizing the difficulty in furnishing a universally acceptable a priori

definition of successful adaptation outcomes (Adger et al., 2005). This

skirts potentially important normative questions, however, and some

researchers have considered whether particular activities should be

considered maladaptive, defined as an “action taken ostensibly to avoid

or reduce vulnerability to climate change that impacts adversely on,

or increases the vulnerability of other systems, sectors, or social

groups” (Barnett and O’Neill, 2009, p. 211). They identify activities

that increase greenhouse gas releases, burden vulnerable populations

disproportionately, and require excessive commitment to one path of

action (Barnett and O’Neill, 2009). Other candidates include actions that

offset one set of risks but increase others, resulting in net risk increase,

for example, a dam that reduces flooding but increases the threat of

zoonotic diseases, and actions that amplify risk to those who remain

exposed (or are newly exposed as a result of a maladaptive action), of

which there are abundant examples in the public health literature

(Sterman, 2006) and other fields.

These issues have a long history in disaster risk management. For instance,

in 1942, deriving from study and work in the 1930s, Gilbert White asserted

that levees can provide a false sense of security and are eventually

fallible, ultimately leading to increased risk, and advocated, among

other ‘adjustment’ measures, land use planning and environmental

management schemes in river basins in order to face up to flooding

hazards (see Burton et al., 1978). Such findings are among the early

advances in the field of ‘human adjustment to hazards,’ which derived

from an ecological approach to human-environmental relationships. In

the case of levees for example, the distinction between adaptive and

maladaptive actions depends on the time period over which risks are

being assessed. From a probabilistic perspective, the overall likelihood

of a catastrophic flood overwhelming a levee’s protective capacity is a

function of time. The wrinkle that climate change introduces is that

many climate-related hazards may become more frequent, shrinking the

timescale over which certain decisions can be considered ‘adaptive’ and

communities can consider themselves ‘adapted’ (Nelson et al., 2007).

While frameworks that help diagnose barriers to adaptation are helpful

in identifying the origin of maladaptive decisions, crafting truly adaptive

policies is still difficult even when the barriers are fully exposed. For

instance, risk displacement is a common concern in large insurance

systems when risk is not continuously reassessed, risk management

strategies and mechanisms for distributing risk across populations (such

as risk pricing in insurance schemes) are inadequately maintained, or if

new risk management strategies are not recruited as necessary. This

was the case with the levees in New Orleans prior to Hurricane Katrina,

wherein the levees were built to make a hazardous area safer but

paradoxically facilitated the exposure of a much larger population to a

large hazard. As a result of multiple factors (Burby, 2006), inadequate

levee infrastructure increased the likelihood of flooding but no other

adequate risk reduction and management measures were implemented,

resulting in catastrophic loss of life and property when the city was hit

with the surge from a strong Category 3 storm (Comfort, 2006). Some have

suggested that, as a result of the U.S. federal government’s historical

approach to disasters, those whose property was at risk in New Orleans

anticipated that they would receive federal recovery funds in the event of

a flooding disaster. This, in turn, may have distorted the risk management

landscape, resulting in improper pricing of flooding risks, decreased

incentives to take proper risk management actions, and exposure of a

larger population to flood risk than might otherwise have been the case

(Kunreuther, 2006).

This example illustrates how an adaptation barrier may have resulted in

an ultimately maladaptive risk management regime, and demonstrates

the importance of considering how risk, in practice, is assumed and

shared. One goal of risk sharing is to properly price risk so that, in the

event risk is realized, there is an adequate pool of capital available to

fund recovery. When risk is improperly priced and risk sharing is not

adequately regulated, as can occur when risk-sharing devices are not

monitored appropriately, an adequate pool of reserves may not

accumulate. When risk is realized, the responsibility for funding the

recovery falls to the insurer of last resort, often the public.

The example also illustrates how an insurance system designed to

motivate adaptation (by individual homeowners or flood protection

agencies) can function properly only if technical rates – rates that properly

reflect empirically determined levels of risk – can be established and

matched with various levels of risk at a relatively high level of spatial

and temporal resolution. Even in countries with free-market flood

insurance systems, insurers may be reluctant to charge the full technical

rate because consumers have come to assume that insurance costs

should be relatively consistent in a given location. Without charging

technical rates, however, it is difficult to use pricing to motivate adaptation

strategies such as flood proofing or elevating the ground floor of a new

development (Lamond et al., 2009), restricting where properties can be

built, or justifying the construction of communal flood defenses. In such

a case, barriers to adaptation (in both planning and management, in

this case) can result in a strategy with maladaptive consequences in the

present. In places where risk levels are rising due to climate change

under prevailing negative conditions of exposure and vulnerability,

reconsideration of these barriers – a process that includes double- and

triple-loop learning – could promote more adaptive risk management.

Otherwise, maladaptive risk management decisions may commit collective

resources (public or private) to coping and recovery rather than successful

adaptation and may force some segments of society to cope with

disproportionate levels of risk.

Chapter 1 Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

1.4.4. ‘No Regrets,’ Robust Adaptation, and Learning

The mismatch between adaptation strategies and projected needs has

been characterized as the potential for regret, that is, opportunity costs

associated with decisions (and related path dependence, wherein earlier

choices constrain future circumstances and decisions) that are optimal

for one or a small number of possible climate futures but not necessarily

robust over a wider range of scenarios (Lempert and Schlesinger, 2001).

‘No regrets’ adaptation refers to decisions that have net benefits over

the entire range of anticipated future climate and associated impacts

(Callaway and Hellmuth, 2007; Heltberg et al., 2009).

To address the challenge of risk management in the dynamically

complex context of climate change and development, as well as under

conditions where probabilistic estimates of future climatic conditions

remain imprecise, several authors have advanced the concept of

robustness (Wilby and Dessai, 2010), of which ‘no regrets’ adaptation is

a special case (Lempert and Groves, 2010). Robustness is a property of

a plan or strategy that performs well over a wide range of plausible

future scenarios even if it does not perform optimally in any particular

scenario. Robust adaptation plans may perform relatively well even if

probabilistic assessments of risk prove wrong because they aim to

address both expected and surprising changes, and may allow diverse

stakeholders to agree on actions even if they disagree about values and

expectations (Brown and Lall, 2006; Dessai and Hulme; 2007; Lempert

and Groves, 2010; Means et al., 2010; see also Section 1.3.2).

As Section 1.4.3 highlights, currently, in many instances risks associated

with extreme weather and other climate-sensitive hazards are often not

well managed. To be effective, adaptation would prioritize measures

that increase current as well as future resilience to threats. Robustness

over time would increase if learning were a central pillar of adaptation

efforts, including learning focused on addressing current vulnerabilities

and enhancing current risk management (high confidence). Single-,

double-, and triple-loop learning will all improve the efficacy of

management strategies.

The case studies in Chapter 9 highlight some important examples of

learning in disaster risk management relevant to a wide range of climate-

sensitive threats and a variety of sectors. Section 9.2 provides examples

of how single- and double-loop learning processes – enhancing public

health response capacity, augmenting early warning systems, and

applying known strategies for protecting health from the threat of

extreme heat in new settings – had demonstrable impacts on heat-

related mortality, quickly shifting a region’s coping range with regard to

extreme heat (Section 9.2.1). Other case studies, examining risk transfer

(Section 9.2.13) and early warning systems (Section 9.2.11), provide

instances of how existing methods and tools can be modified and

deployed in new settings in response to changing risk profiles – examples

of both double- and triple-loop learning. Similarly, the case studies on

governance (Section 9.2.12) and on the limits to adaptation in small

island developing states (Section 9.2.9) provide examples of third-loop

learning and transformative approaches to disaster risk management.

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Virginia Murray (UK), Mark Pelling (UK), Jürgen Pohl (Germany), Anthony-Oliver Smith (USA),

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This chapter should be cited as:

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Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA,

pp. 65-108.

Determinants of Risk:

Exposure and Vulnerability

Determinants of Risk: Exposure and Vulnerability

Executive Summary ...................................................................................................................................67

2.1. Introduction and Scope..............................................................................................................69

2.2. Defining Determinants of Risk: Hazard, Exposure, and Vulnerability ........................................69

2.2.1. Disaster Risk and Disaster .................................................................................................................................................69

2.2.2. The Factors of Risk.............................................................................................................................................................69

2.3. The Drivers of Vulnerability .......................................................................................................70

2.4. Coping and Adaptive Capacities ................................................................................................72

2.4.1. Capacity and Vulnerability .................................................................................................................................................72

2.4.2. Different Capacity Needs...................................................................................................................................................74

2.4.2.1. Capacity to Anticipate Risk.....................................................

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

..................74

2.4.2.2. Capacity to Respond...........................................................................................................................................................................74

2.4.2.3. Capacity to Recover and Change........................................................................................................................................................75

2.4.3. Factors of Capacity: Drivers and Barriers...

....

....................................................................................................................76

2.5. Dimensions and Trends of Vulnerability and Exposure ..............................................................76

2.5.1. Environmental Dimensions.................................................................................................................................................76

2.5.1.1. Physical Dimensions .................................................................................................................................................

..........................77

2.5.1.2. Geography, Location, Place ................................................................................................................................................................77

2.5.1.3. Settlement Patterns and Development Trajectories.............................................................................................................................78

2.5.2. Social Dimensions ...

....

.......................................................................................................................................................80

2.5.2.1. Demography ...................................................................................................................................................

....................................80

2.5.2.2. Education............................................................................................................................................................................................81

2.5.2.3. Health and Well-Being........................................................................................................................................................................82

2.5.2.4. Cultural Dimensions............................................................................................................................................................................84

2.5.2.5. Institutional and Governance Dimensions ..........................................................................................................................................85

2.5.3. Economic Dimensions...

....

..................................................................................................................................................86

2.5.4. Interactions, Cross-Cutting Themes, and Integrations.......................................................................................................87

2.5.4.1. Intersectionality and Other Dimensions.....................................................

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

.88

2.5.4.2. Timing, Spatial, and Functional Scales................................................................................................................................................88

2.5.4.3. Science and Technology......................................................................................................................................................................89

2.6. Risk Identification and Assessment ...........................................................................................89

2.6.1. Risk Identification ..............................................................................................................................................................90

2.6.2. Vulnerability and Risk Assessment.....................................................................................................................................90

2.6.3. Risk Communication...........................................................................................................................................................95

2.7. Risk Accumulation and the Nature of Disasters ........................................................................95

References .................................................................................................................................................96

Chapter 2

Table of Contents

The severity of the impacts of extreme and non-extreme weather and climate events depends strongly on

the level of vulnerability and exposure to these events (high confidence). [2.2.1, 2.3, 2.5] Trends in vulnerability

and exposure are major drivers of changes in disaster risk, and of impacts when risk is realized (high confidence). [2.5]

Understanding the multi-faceted nature of vulnerability and exposure is a prerequisite for determining how weather

and climate events contribute to the occurrence of disasters, and for designing and implementing effective adaptation

and disaster risk management strategies. [2.2, 2.6]

Vulnerability and exposure are dynamic, varying across temporal and spatial scales, and depend on economic,

social, geographic, demographic, cultural, institutional, governance, and environmental factors (high

confidence). [2.2, 2.3, 2.5] Individuals and communities are differentially exposed and vulnerable and this is based

on factors such as wealth, education, race/ethnicity/religion, gender, age, class/caste, disability, and health status. [2.5]

Lack of resilience and capacity to anticipate, cope with, and adapt to extremes and change are important causal factors

of vulnerability. [2.4]

Extreme and non-extreme weather and climate events also affect vulnerability to future extreme events,

by modifying the resilience, coping, and adaptive capacity of communities, societies, or social-ecological

systems affected by such events (high confidence). [2.4.3] At the far end of the spectrum – low-probability, high-

intensity events – the intensity of extreme climate and weather events and exposure to them tend to be more pervasive

in explaining disaster loss than vulnerability in explaining the level of impact. But for less extreme events – higher

probability, lower intensity – the vulnerability of exposed elements plays an increasingly important role (high

confidence). [2.3] The cumulative effects of small- or medium-scale, recurrent disasters at the sub-national or local

levels can substantially affect livelihood options and resources and the capacity of societies and communities to

prepare for and respond to future disasters. [2.2.1, 2.7]

High vulnerability and exposure are generally the outcome of skewed development processes, such as

those associated with environmental mismanagement, demographic changes, rapid and unplanned

urbanization in hazardous areas, failed governance, and the scarcity of livelihood options for the poor

(high confidence). [2.2.2, 2.5]

The selection of appropriate vulnerability and risk evaluation approaches depends on the decisionmaking

context (high confidence). [2.6.1] Vulnerability and risk assessment methods range from global and national

quantitative assessments to local-scale qualitative participatory approaches. The appropriateness of a specific method

depends on the adaptation or risk management issue to be addressed, including for instance the time and geographic

scale involved, the number and type of actors, and economic and governance aspects. Indicators, indices, and

probabilistic metrics are important measures and techniques for vulnerability and risk analysis. However, quantitative

approaches for assessing vulnerability need to be complemented with qualitative approaches to capture the full

complexity and the various tangible and intangible aspects of vulnerability in its different dimensions. [2.6]

Appropriate and timely risk communication is critical for effective adaptation and disaster risk management

(high confidence). Effective risk communication is built on risk assessment, and tailored to a specific audience, which

may range from decisionmakers at various levels of government, to the private sector and the public at large, including

local communities and specific social groups. Explicit characterization of uncertainty and complexity strengthens risk

communication. Impediments to information flows and limited awareness are risk amplifiers. Beliefs, values, and

norms influence risk perceptions, risk awareness, and choice of action. [2.6.3]

Adaptation and risk management policies and practices will be more successful if they take the dynamic

nature of vulnerability and exposure into account, including the explicit characterization of uncertainty

and complexity at each stage of planning and practice (medium evidence, high agreement). However,

approaches to representing such dynamics quantitatively are currently underdeveloped. Projections of the impacts of

Chapter 2 Determinants of Risk: Exposure and Vulnerability

Executive Summary

climate change can be strengthened by including storylines of changing vulnerability and exposure under different

development pathways. Appropriate attention to the temporal and spatial dynamics of vulnerability and exposure is

particularly important given that the design and implementation of adaptation and risk management strategies and

policies can reduce risk in the short term, but may increase vulnerability and exposure over the longer term. For

instance, dike systems can reduce hazard exposure by offering immediate protection, but also encourage settlement

patterns that may increase risk in the long term. [2.4.2.1, 2.5.4.2, 2.6.2]

Vulnerability reduction is a core common element of adaptation and disaster risk management (high

confidence). Vulnerability reduction thus constitutes an important common ground between the two areas of policy

and practice. [2.2, 2.3]

Chapter 2Determinants of Risk: Exposure and Vulnerability

2.1. Introduction and Scope

Many climate change adaptation efforts aim to address the implications

of potential changes in the frequency, intensity, and duration of weather

and climate events that affect the risk of extreme impacts on human

society. That risk is determined not only by the climate and weather

events (the hazards) but also by the exposure and vulnerability to these

hazards. Therefore, effective adaptation and disaster risk management

strategies and practices also depend on a rigorous understanding of the

dimensions of exposure and vulnerability, as well as a proper assessment

of changes in those dimensions. This chapter aims to provide that

understanding and assessment, by further detailing the determinants of

risk as presented in Chapter 1.

The first sections of this chapter elucidate the concepts that are needed

to define and understand risk, and show that risk originates from a

combination of social processes and their interaction with the environment

(Sections 2.2 and 2.3), and highlight the role of coping and adaptive

capacities (Section 2.4). The following section (2.5) describes the different

dimensions of vulnerability and exposure as well as trends therein.

Given that exposure and vulnerability are highly context-specific, this

section is by definition limited to a general overview (a more quantitative

perspective on trends is provided in Chapter 4). A methodological

discussion (Section 2.6) of approaches to identify and assess risk provides

indications of how the dimensions of exposure and vulnerability can be

explored in specific contexts, such as adaptation planning, and the

central role of risk perception and risk communication. The chapter

concludes with a cross-cutting discussion of risk accumulation and the

nature of disasters (Section 2.7).

2.2. Defining Determinants of Risk:

Hazard, Exposure, and Vulnerability

2.2.1. Disaster Risk and Disaster

Disaster risk signifies the possibility of adverse effects in the future. It

derives from the interaction of social and environmental processes, from

the combination of physical hazards and the vulnerabilities of exposed

elements (see Chapter 1). The hazard event is not the sole driver of risk,

and there is high confidence that the levels of adverse effects are in

good part determined by the vulnerability and exposure of societies and

social-ecological systems (UNDRO, 1980; Cuny, 1984; Cardona, 1986,

1993, 2011; Davis and Wall, 1992; UNISDR, 2004, 2009b; Birkmann,

2006a,b; van Aalst 2006a).

Disaster risk is not fixed but is a continuum in constant evolution. A

disaster is one of its many ‘moments’ (ICSU-LAC, 2010a,b), signifying

unmanaged risks that often serve to highlight skewed development

problems (Westgate and O’Keefe, 1976; Wijkman and Timberlake, 1984).

Disasters may also be seen as the materialization of risk and signify ‘a

becoming real’ of this latent condition that is in itself a social construction

(see below; Renn, 1992; Adam and Van Loon, 2000; Beck, 2000, 2008).

Disaster risk is associated with differing levels and types of adverse

effects. The effects may assume catastrophic levels or levels commensurate

with small disasters. Some have limited financial costs but very high

human costs in terms of loss of life and numbers of people affected;

others have very high financial costs but relatively limited human costs.

Furthermore, there is high confidence that the cumulative effects of

small disasters can affect capacities of communities, societies, or social-

ecological systems to deal with future disasters at sub-national or local

levels (Alexander, 1993, 2000; Quarantelli, 1998; Birkmann, 2006b;

Marulanda et al., 2008b, 2010, 2011; UNISDR, 2009a).

2.2.2. The Factors of Risk

As detailed in Section 1.1, hazard refers to the possible, future occurrence

of natural or human-induced physical events that may have adverse

effects on vulnerable and exposed elements (White, 1973; UNDRO,

1980; Cardona, 1990; UNDHA, 1992; Birkmann, 2006b). Although, at

times, hazard has been ascribed the same meaning as risk, currently it

is widely accepted that it is a component of risk and not risk itself.

The intensity or recurrence of hazard events can be partly determined

by environmental degradation and human intervention in natural

ecosystems. Landslides or flooding regimes associated with human-

induced environmental alteration and new climate change-related

hazards are examples of such socio-natural hazards (Lavell, 1996,

1999a).

Exposure refers to the inventory of elements in an area in which hazard

events may occur (Cardona, 1990; UNISDR, 2004, 2009b). Hence, if

population and economic resources were not located in (exposed to)

potentially dangerous settings, no problem of disaster risk would exist.

While the literature and common usage often mistakenly conflate

exposure and vulnerability, they are distinct. Exposure is a necessary,

but not sufficient, determinant of risk. It is possible to be exposed

but not vulnerable (for example by living in a floodplain but having

sufficient means to modify building structure and behavior to mitigate

potential loss). However, to be vulnerable to an extreme event, it is

necessary to also be exposed.

Land use and territorial planning are key factors in risk reduction. The

environment offers resources for human development at the same

time as it represents exposure to intrinsic and fluctuating hazardous

conditions. Population dynamics, diverse demands for location, and

the gradual decrease in the availability of safer lands mean it is

almost inevitable that humans and human endeavor will be located in

potentially dangerous places (Lavell, 2003). Where exposure to events is

impossible to avoid, land use planning and location decisions can be

accompanied by other structural or non-structural methods for preventing

or mitigating risk (UNISDR, 2009a; ICSU-LAC, 2010a,b).

Vulnerability refers to the propensity of exposed elements such as

human beings, their livelihoods, and assets to suffer adverse effects

when impacted by hazard events (UNDRO, 1980; Cardona, 1986, 1990,

Chapter 2 Determinants of Risk: Exposure and Vulnerability

1993; Liverman, 1990; Maskrey, 1993b; Cannon, 1994, 2006; Blaikie et

al., 1996; Weichselgartner, 2001; Bogardi and Birkmann, 2004; UNISDR,

2004, 2009b; Birkmann, 2006b; Janssen et al., 2006; Thywissen, 2006).

Vulnerability is related to predisposition, susceptibilities, fragilities,

weaknesses, deficiencies, or lack of capacities that favor adverse effects

on the exposed elements. Thywissen (2006) and Manyena (2006) car-

ried out an extensive review of the terminology. The former includes a

long list of definitions used for the term vulnerability and the latter

includes definitions of vulnerability and resilience and their relationship.

An early view of vulnerability in the context of disaster risk management

was related to the physical resistance of engineering structures (UNDHA,

1992), but more recent views relate vulnerability to characteristics of

social and environmental processes. It is directly related, in the context

of climate change, to the susceptibility, sensitivity, and lack of resilience

or capacities of the exposed system to cope with and adapt to extremes

and non-extremes (Luers et al., 2003; Schröter et al., 2005; Brklacich

and Bohle, 2006; IPCC, 2001, 2007).

While vulnerability is a key concept for both disaster risk and climate

change adaptation, the term is employed in numerous other contexts,

for instance to refer to epidemiological and psychological fragilities,

ecosystem sensitivity, or the conditions, circumstances, and drivers that

make people vulnerable to natural and economic stressors (Kasperson

et al., 1988; Cutter, 1994; Wisner et al., 2004; Brklacich and Bohle, 2006;

Haines et al., 2006; Villagrán de León, 2006). It is common to find

blanket descriptions of the elderly, children, or women as ‘vulnerable,’

without any indication as to what these groups are vulnerable to

(Wisner, 1993; Enarson and Morrow, 1998; Morrow, 1999; Bankoff,

2004; Cardona, 2004, 2011).

Vulnerability can be seen as situation-specific, interacting with a hazard

event to generate risk (Lavell, 2003; Cannon, 2006; Cutter et al., 2008).

Vulnerability to financial crisis, for example, does not infer vulnerability

to climate change or natural hazards. Similarly, a population might be

vulnerable to hurricanes, but not to landslides or floods. From a climate

change perspective, basic environmental conditions change progressively

and then induce new risk conditions for societies. For example, more

frequent and intense events may introduce factors of risk into new

areas, revealing underlying vulnerability. In fact, future vulnerability is

embedded in the present conditions of the communities that may be

exposed in the future (Patt et al., 2005, 2009); that is, new hazards in

areas not previously subject to them will reveal, not necessarily create,

underlying vulnerability factors (Alwang et al., 2001; Cardona et al.,

2003a; Lopez-Calva and Ortiz, 2008; UNISDR, 2009a).

While vulnerability is in general hazard-specific, certain factors, such as

poverty, and the lack of social networks and social support mechanisms,

will aggravate or affect vulnerability levels irrespective of the type of

hazard. These types of generic factors are different from the hazard-

specific factors and assume a different position in the intervention

actions and the nature of risk management and adaptation processes

(ICSU-LAC, 2010a,b). Vulnerability of human settlements and ecosystems

is intrinsically tied to different socio-cultural and environmental

processes (Kasperson et al., 1988; Cutter, 1994; Adger, 2006; Cutter and

Finch, 2008; Cutter et al., 2008; Williams et al., 2008; Décamps, 2010;

Dawson et al., 2011). Vulnerability is linked also to deficits in risk

communication, especially the lack of appropriate information that can

lead to false risk perceptions (Birkmann and Fernando, 2008), which

have an important influence on the motivation and perceived ability to

act or to adapt to climate change and environmental stressors

(Grothmann and Patt, 2005). Additionally, processes of maladaptation

or unsustainable adaptation can increase vulnerability and risks

(Birkmann, 2011a).

Vulnerability in the context of disaster risk management is the most

palpable manifestation of the social construction of risk (Aysan, 1993;

Blaikie et al., 1996; Wisner et al., 2004; ICSU-LAC 2010a,b). This notion

underscores that society, in its interaction with the changing physical

world, constructs disaster risk by transforming physical events into

hazards of different intensities or magnitudes through social processes

that increase the exposure and vulnerability of population groups, their

livelihoods, production, support infrastructure, and services (Chambers,

1989; Wilches-Chaux, 1989; Cannon, 1994; Wisner et al., 2004; Wisner,

2006a; Carreño et al., 2007a; ICSU-LAC, 2010a,b). This includes:

• How human action influences the levels of exposure and

vulnerability in the face of different physical events

• How human intervention in the environment leads to the creation

of new hazards or an increase in the levels or damage potential of

existing ones

• How human perception, understanding, and assimilation of the

factors of risk influence societal reactions, prioritization, and

decisionmaking processes.

There is high agreement and robust evidence that high vulnerability and

exposure are mainly an outcome of skewed development processes,

including those associated with environmental mismanagement,

demographic changes, rapid and unplanned urbanization, and the scarcity

of livelihood options for the poor (Maskrey, 1993a,b, 1994, 1998; Mansilla,

1996; Lavell, 2003; Cannon, 2006; ICSU-LAC, 2010a,b; Cardona, 2011).

Increases in disaster risk and the occurrence of disasters have been in

evidence over the last five decades (Munich Re, 2011) (see Section 1.1.1).

This trend may continue and may be enhanced in the future as a result

of projected climate change, further demographic and socioeconomic

changes, and trends in governance, unless concerted actions are enacted

to reduce vulnerability and to adapt to climate change, including

interventions to address disaster risks (Lavell, 1996, 1999a, 2003; ICSU-

LAC, 2010a,b; UNISDR, 2011).

2.3. The Drivers of Vulnerability

In order to effectively manage risk, it is essential to understand how

vulnerability is generated, how it increases, and how it builds up

(Maskrey, 1989; Cardona, 1996a, 2004, 2011; Lavell, 1996, 1999a;

Chapter 2Determinants of Risk: Exposure and Vulnerability

O’Brien et al., 2004b). Vulnerability describes a set of conditions of

people that derive from the historical and prevailing cultural, social,

environmental, political, and economic contexts. In this sense, vulnerable

groups are not only at risk because they are exposed to a hazard but as

a result of marginality, of everyday patterns of social interaction and

organization, and access to resources (Watts and Bohle, 1993; Morrow,

1999; Bankoff, 2004). Thus, the effects of a disaster on any particular

household result from a complex set of drivers and interacting conditions.

It is important to keep in mind that people and communities are not

only or mainly victims, but also active managers of vulnerability (Ribot,

1996; Pelling, 1997, 2003). Therefore, integrated and multidimensional

approaches are highly important to understanding causes of vulnerability.

Some global processes are significant drivers of risk and are particularly

related to vulnerability creation. There is high confidence that these

include population growth, rapid and inappropriate urban development,

international financial pressures, increases in socioeconomic inequalities,

trends and failures in governance (e.g., corruption, mismanagement),

and environmental degradation (Maskrey, 1993a,b, 1994, 1998; Mansilla,

1996; Cannon, 2006). Vulnerability profiles can be constructed that take

into consideration sources of environmental, social, and economic

marginality (Wisner, 2003). This also includes the consideration of the

links between communities and specific environmental services, and the

vulnerability of ecosystem components (Renaud, 2006; Williams et al.,

2008; Décamps, 2010; Dawson et al., 2011). In climate change-related

impact assessments, integration of underlying ‘causes of vulnerability’

and adaptive capacity is needed rather than focusing on technical

aspects only (Ribot, 1995; O’Brien et al., 2004b).

Due to different conceptual frameworks and definitions, as well as

disciplinary views, approaches to address the causes of vulnerability

also differ (Burton et al., 1983; Blaikie et al., 1994; Harding et al., 2001;

Twigg, 2001; Adger and Brooks, 2003, 2006; Turner et al., 2003a,b;

Cardona, 2004; Schröter et al., 2005; Adger 2006; Füssel and Klein, 2006;

Villagrán de León, 2006; Cutter and Finch, 2008; Cutter et al., 2008).

Thomalla et al. (2006), Mitchell and van Aalst (2008), and Mitchell et al.

(2010) examine commonalities and differences between the adaptation

to climate change and disaster risk management communities, and

identify key areas of difference and convergence. The two communities

tend to perceive the nature and timescale of the threat differently:

impacts due to climate change and return periods for extreme events

frequently use the language of uncertainty; but considerable knowledge

and certainty has been expressed regarding event characteristics and

exposures related to extreme historical environmental conditions.

Four approaches to understanding vulnerability and its causes can be

distinguished, rooted in political economy, social-ecology, vulnerability,

and disaster risk assessment, as well as adaptation to climate change:

1) The pressure and release (PAR) model (Blaikie et al., 1994, 1996;

Wisner et al., 2004) is common to social science-related vulnerability

research and emphasizes the social conditions and root causes of

exposure more than the hazard as generating unsafe conditions.

This approach links vulnerability to unsafe conditions in a continuum

that connects local vulnerability to wider national and global shifts

in the political economy of resources and political power.

2) The social ecology perspective emphasizes the need to focus on

coupled human-environmental systems (Hewitt and Burton, 1971;

Turner et al., 2003a,b). This perspective stresses the ability of

societies to transform nature and also the implications of changes

in the environment for social and economic systems. It argues that

the exposure and susceptibility of a system can only be adequately

understood if these coupling processes and interactions are

addressed.

3) Holistic perspectives on vulnerability aim to go beyond technical

modeling to embrace a wider and comprehensive explanation of

vulnerability. These approaches differentiate exposure, susceptibility

and societal response capacities as causes or factors of vulnerability

(see Cardona, 1999a, 2001, 2011; Cardona and Barbat, 2000;

Cardona and Hurtado, 2000a,b; IDEA, 2005; Birkmann, 2006b;

Carreño, 2006; Carreño et al.,2007a,b, 2009; Birkmann and Fernando,

2008). A core element of these approaches is the feedback loop

which underlines that vulnerability is dynamic and is the main

driver and determinant of current or future risk.

4) In the context of climate change adaptation, different vulnerability

definitions and concepts have been developed and discussed. One

of the most prominent definitions is the one reflected in the IPCC

Fourth Assessment Report, which describes vulnerability as a

function of exposure, sensitivity, and adaptive capacity, as also

reflected by, for instance, McCarthy et al. (2001), Brooks (2003),

K. O’Brien et al. (2004a), Füssel and Klein (2006), Füssel (2007),

and G. O’Brien et al. (2008). This approach differs from the

understanding of vulnerability in the disaster risk management

perspective, as the rate and magnitude of climate change is

considered. The concept of vulnerability here includes external

environmental factors of shock or stress. Therefore, in this view, the

magnitude and frequency of potential hazard events is to be

considered in the vulnerability to climate change. This view also

differs in its focus upon long-term trends and stresses rather than

on current shock forecasting, something not explicitly excluded but

rather rarely considered within the disaster risk management

approaches.

The lack of a comprehensive conceptual framework that facilitates a

common multidisciplinary risk evaluation impedes the effectiveness of

disaster risk management and adaptation to climate change (Cardona,

2004). The option for anticipatory disaster risk reduction and adaptation

exists precisely because risk is a latent condition, which announces

potential future adverse effects (Lavell, 1996, 1999a). Understanding

disaster risk management as a social process allows for a shift in focus

from responding to the disaster event toward an understanding of

disaster risk (Cardona and Barbat, 2000; Cardona et al., 2003a). This

requires knowledge about how human interactions with the natural

environment lead to the creation of new hazards, and how persons,

property, infrastructure, goods, and the environment are exposed to

potentially damaging events. Furthermore, it requires an understanding

of the vulnerability of people and their livelihoods, including the

Chapter 2 Determinants of Risk: Exposure and Vulnerability

allocation and distribution of social and economic resources that can work

for or against the achievement of resistance, resilience, and security (ICSU-

LAC, 2010a,b). Overall, there is high confidence that although hazard

events are usually considered the cause of disaster risk, vulnerability

and exposure are its key determining factors. Furthermore, contrary to

the hazard, vulnerability and exposure can often be influenced by policy

and practice, including in the short to medium term. Therefore disaster

risk management and adaptation strategies have to address mainly

these same risk factors (Cardona 1999a, 2011; Vogel and O’Brien, 2004;

Birkmann, 2006a; Leichenko and O’Brien, 2008).

Despite various frameworks developed for defining and assessing

vulnerability, it is interesting to note that at least some common causal

factors of vulnerability have been identified, in both the disaster risk

management and climate change adaptation communities (see

Cardona, 1999b, 2001, 2011; Cardona and Barbat, 2000; Cardona and

Hurtado, 2000a,b; McCarthy et al., 2001; Gallopin, 2006; Manyena,

2006; Carreño et al., 2007a, 2009; IPCC, 2007; ICSU-LAC 2010a,b;

MOVE, 2010):

• Susceptibility/fragility (in disaster risk management) or sensitivity

(in climate change adaptation): physical predisposition of human

beings, infrastructure, and environment to be affected by a dangerous

phenomenon due to lack of resistance and predisposition of society

and ecosystems to suffer harm as a consequence of intrinsic and

context conditions making it plausible that such systems once

impacted will collapse or experience major harm and damage due

to the influence of a hazard event.

• Lack of resilience (in disaster risk management) or lack of coping

and adaptive capacities (in climate change adaptation): limitations

in access to and mobilization of the resources of the human beings

and their institutions, and incapacity to anticipate, adapt, and

respond in absorbing the socio-ecological and economic impact.

There is high confidence that at the extreme end of the spectrum, the

intensity of extreme climate and weather events – low-probability,

high-intensity – and exposure to them tend to be more pervasive in

explaining disaster loss than vulnerability itself. But as the events get

less extreme – higher-probability, lower-intensity – the vulnerability of

exposed elements plays an increasingly important role in explaining the

level of impact. Vulnerability is a major cause of the increasing adverse

effects of non-extreme events, that is, small recurrent disasters that

many times are not visible at the national or sub-national level

(Marulanda et al., 2008b, 2010, 2011; UNISDR, 2009a; Cardona, 2011;

UNISDR, 2011).

Overall, the promotion of resilient and adaptive societies requires a

paradigm shift away from the primary focus on natural hazards and

extreme weather events toward the identification, assessment, and

ranking of vulnerability (Maskrey, 1993a; Lavell, 2003; Birkmann,

2006a,b). Therefore, understanding vulnerability is a prerequisite for

understanding risk and the development of risk reduction and adaptation

strategies to extreme events in the light of climate change (ICSU-LAC,

2010a,b; MOVE, 2010; Cardona, 2011; UNISDR, 2011).

2.4. Coping and Adaptive Capacities

Capacity is an important element in most conceptual frameworks of

vulnerability and risk. It refers to the positive features of people’s

characteristics that may reduce the risk posed by a certain hazard.

Improving capacity is often identified as the target of policies and projects,

based on the notion that strengthening capacity will eventually lead to

reduced risk. Capacity clearly also matters for reducing the impact of

climate change (e.g., Sharma and Patwardhan, 2008).

As presented in Chapter 1, coping is typically used to refer to ex post

actions, while adaptation is normally associated with ex ante actions.

This implies that coping capacity also refers to the ability to react to and

reduce the adverse effects of experienced hazards, whereas adaptive

capacity refers to the ability to anticipate and transform structure,

functioning, or organization to better survive hazards (Saldaña-Zorrilla,

2007). Presence of capacity suggests that impacts will be less extreme

and/or the recovery time will be shorter, but high capacity to recover

does not guarantee equal levels of capacity to anticipate. In other

words, the capacity to cope does not infer the capacity to adapt

(Birkmann, 2011a), although coping capacity is often considered to

be part of adaptive capacity (Levina and Tirpak, 2006).

2.4.1. Capacity and Vulnerability

Most risk studies prior to the 1990s focused mainly on hazards,

whereas the more recent reversal of this paradigm has placed equal

focus on the vulnerability side of the equation. Emphasizing that risk

can be reduced through vulnerability is an acknowledgement of the

power of social, political, environmental, and economic factors in driving

risk. While these factors drive risk on one hand, they can on the other

hand be the source of capacity to reduce it (Carreño et al., 2007a;

Gaillard, 2010).

Many approaches for assessing vulnerability rely on an assessment of

capacity as a baseline for understanding how vulnerable people are to

a specific hazard. The relationship between capacity and vulnerability

is described differently among different schools of thought, stemming

from different uses in the fields of development, disaster risk

management, and climate change adaptation. Gaillard (2010) notes

that the concept of capacity “played a pivotal role in the progressive

emergence of the vulnerability paradigm within the scientific realm.”

On the whole, the literature describes the relationship between

vulnerability and capacity in two ways, which are not mutually exclusive

(Bohle, 2001; IPCC, 2001; Moss et al., 2001; Yodmani, 2001; Downing

and Patwardhan, 2004; Brooks et al., 2005; Smit and Wandel, 2006;

Gaillard, 2010):

1) Vulnerability is, among other things, the result of a lack of

capacity.

2) Vulnerability is the opposite of capacity, so that increasing

capacity means reducing vulnerability, and high vulnerability

means low capacity.

Chapter 2Determinants of Risk: Exposure and Vulnerability

The relationship between capacity and vulnerability is interpreted

differently in the climate change community of practice and the

disaster risk management community of practice. Throughout the

1980s, vulnerability became a central focus of much work on disasters,

in some circles overshadowing the role played by hazards in driving risk.

Some have noted that the emphasis on vulnerability tended to ignore

capacity, focusing too much on the negative aspects of vulnerability

(Davis et al., 2004). Recognizing the role of capacity in reducing risk also

indicates an acknowledgement that people are not ‘helpless victims’

(Bohle, 2001; Gaillard, 2010).

In many climate change-related studies, capacity was initially subsumed

under vulnerability. The first handbooks and guidelines for adaptation

emphasized impacts and vulnerability assessment as the necessary steps

for determining adaptation options (Kate, 1985; Carter et al., 1994; Benioff

et al., 1996; Feenstra et al., 1998). Climate change vulnerability was often

placed in direct opposition to capacity. Vulnerability that was measured

was seen as the remainder after capacity had been taken into account.

However, Davis et al. (2004), IDEA (2005), Carreño et al. (2007a,b), and

Gaillard (2010) note that capacity and vulnerability are not necessarily

Chapter 2 Determinants of Risk: Exposure and Vulnerability

Box 2-1 | Coping and Adaptive Capacity: Different Origins and Uses

As set out in Section 1.4, there is a difference in understanding and use of the terms coping and adapting. Although coping capacity is

often used interchangeably with adaptive capacity in the climate change literature, Cutter et al. (2008) point out that adaptive capacity

features more frequently in global environmental change perspectives and is less prevalent in the hazards discourse.

Adaptive capacity refers to the ability of a system or individual to adapt to climate change, but it can also be used in the context of

disaster risk. Because adaptive capacity is considered to determine “the ability of an individual, family, community, or other social group

to adjust to changes in the environment guaranteeing survival and sustainability” (Lavell, 1999b), many believe that in the context of

uncertain environmental changes, adaptive capacity will be of key significance. Dayton-Johnson (2004) defines adaptive capacity as the

“vulnerability of a society before disaster strikes and its resilience after the fact.” Some ways of classifying adaptive capacity include

‘baseline adaptive capacity’ (Dore and Etkin, 2003), which refers to the capacity that allows countries to adapt to existing climate

variability, and ‘socially optimal adaptive capacity,’ which is determined by the norms and rules in individual locations. Another definition

of adaptive capacity is the “property of a system to adjust its characteristics or behavior, in order to expand its coping range under

existing climate variability, or future climate conditions” (Brooks and Adger, 2004). This links adaptive capacity to coping capacity,

because coping range is synonymous with coping capacity, referring to the boundaries of systems’ ability to cope (Yohe and Tol, 2002).

In simple terms, coping capacity refers to the “ability of people, organizations, and systems, using available skills and resources, to face

and manage adverse conditions, emergencies, or disasters” (UNISDR, 2009b). Coping capacity is typically used in humanitarian discourse

to indicate the extent to which a system can survive the impacts of an extreme event. It suggests that people can deal with some

degree of destabilization, and acknowledges that at a certain point this capacity may be exceeded. Eriksen et al. (2005) link coping

capacity to entitlements – the set of commodity bundles that can be commanded – during an adverse event. The ability to mobilize this

capacity in an emergency is the manifestation of coping strategies (Gaillard, 2010). Furthermore, Birkmann (2011b) underscores that

differences between coping and adaptation are also linked to the quality of the response process. While coping aims to maintain the

system and its functions in the face of adverse conditions, adaptation involves changes and requires reorganization processes.

The capacity described by the disasters community in the past decades does not frequently distinguish between ‘coping’ or ‘adaptive’

capacities, and instead the term is used to indicate positive characteristics or circumstances that could be seen to offset vulnerability

(Anderson and Woodrow, 1989). Because the approach is focused on disasters, it has been associated with the immediate-term coping

needs, and contrasts from the long-term perspective generally discussed in the context of climate change, where the aim is to adapt to

changes rather than to just overcome them. There has been considerable discussion throughout the vulnerability and poverty and climate

change scholarly communities about whether coping strategies are a stepping stone toward adaptation, or may lead to maladaptation

(Yohe and Tol, 2002; Eriksen et al., 2005) (see Chapter 1). Useful alternative terminology is to talk about ‘capacity to change and adjust’

(Nelson and Finan, 2009) for adaptive capacity, and ‘capacity to absorb’ instead of coping capacity (Cutter et al., 2008).

In the climate change community of practice, adaptive capacity has been at the forefront of thinking regarding how to respond to the

impacts of climate change, but it was initially seen as a characteristic to build interventions on, and only later has been recognized as

the target of interventions (Adger et al., 2004). The United Nations Framework Convention on Climate Change, for instance, states in its

ultimate objective that action to reduce greenhouse gas emissions be guided by the time needed for ecosystems to adapt naturally to

the impacts of climate change.

opposites, because communities that are highly vulnerable may in fact

display high capacity in certain aspects. This reflects the many elements

of risk reduction and the multiple capacity needs across them. Alwang

et al. (2001) also underscore that vulnerability is dynamic and determined

by numerous factors, thus high capacity in the ability to respond to an

extreme event does not accurately reflect low vulnerability.

2.4.2. Different Capacity Needs

The capacity necessary to anticipate and avoid being affected by an

extreme event requires different assets, opportunities, social networks,

and local and external institutions from capacity to deal with impacts

and recover from them (Lavell, 1994; Lavell and Franco, 1996; Cardona,

2001, 2010; Carreño et al., 2007a,b; ICSU-LAC, 2010a,b; MOVE, 2010).

Capacity to change relies on yet another set of factors. Importantly,

however, these dimensions of capacity are not unrelated to each other:

the ability to change is also necessary for risk reduction and response

capacities.

Just like vulnerability, capacity is dynamic and will change depending

on circumstances. The discussion in Box 2-1 indicates that there are

differing perspectives on how coping and adaptive capacity relate.

When coping and adapting are viewed as different, it follows that the

capacity needs for each are also different (Cooper et al., 2008). This

suggests that work done to understand the drivers of adaptive ex ante

capacity (Leichenko and O’Brien, 2002; Yohe and Tol, 2002; Brenkert and

Malone, 2005; Brooks et al., 2005; Haddad, 2005; Vincent, 2007; Sharma

and Patwardhan, 2008; Magnan, 2010) may not be similar with the

identified drivers of capacities that helped in the past (ex post) and are

associated more closely with experienced coping processes. Many of

these elements are reflected in local, national, and international

contexts in Chapters 5, 6, and 7 of this Special Report.

2.4.2.1. Capacity to Anticipate Risk

Having the capacity to reduce the risk posed by hazards and changes

implies that people’s ability to manage is not engulfed, so they are not

left significantly worse off. Reducing risk means that people do not have

to devote substantial resources to dealing with a hazard as it occurs, but

instead have the capacity to anticipate this sort of event. This is the type

of capacity that is necessary in order to adapt to climate change, and

involves conscious, planned efforts to reduce risk. The capacity to reduce

risk also depends on ex post actions, which involve making choices after

one event that reduce the impact of future events.

Capacity for risk prevention and reduction may be understood as a

series of elements, measures, and tools directed toward intervention in

hazards and vulnerabilities with the objective of reducing existing or

controlling future possible risks (Cardona et al., 2003a). This can range

from guaranteeing survival to the ability to secure future livelihoods

(Batterbury, 2001; Eriksen and Silva, 2009).

Development planning, including land use and urban planning, river basin

and land management, hazard-resistant building codes, and landscape

design are all activities that can reduce exposure and vulnerability to

hazards and change (Cardona, 2001, 2010). The ability to carry these out

in an effective way is part of the capacity to reduce risk. Other activities

include diversifying income sources, maintaining social networks, and

collective action to avoid development that puts people at higher risk

(Maskrey, 1989, 1994; Lavell, 1994, 1999b, 2003).

Up to the early 1990s, disaster preparedness and humanitarian response

dominated disaster practice, and focus on capacity was limited to

understanding inherent response capacity. Thus, emphasizing capacity to

reduce risk was not a priority. However, in the face of growing evidence

as to significant increases in disaster losses and the inevitable increase

in financial and human resources dedicated to disaster response and

recovery, there is an increasing recognition of the need to promote the

capacity for prevention and risk reduction over time (Lavell, 1994, 1999b,

2003). Notwithstanding, different actors, stakeholders, and interests

influence the capacity to anticipate a disaster. Actions to reduce exposure

and vulnerability of one group of people may come at the cost of

increasing it for another, for example when flood risks are shifted from

upstream communities to downstream communities through large-

scale upstream dike construction (Birkmann, 2011a). Consequently, it

is not sufficient to evaluate the success of adaptation or capacities

to reduce risk by focusing on the objectives of one group only. The

evaluation of success of adaptation strategies depends on the spatial

and temporal scale used (Adger et al., 2005).

2.4.2.2. Capacity to Respond

Capacity to respond is relevant both ex post and ex ante, since it

encompasses everything necessary to be able to react once an extreme

event takes place. Response capacity is mostly used to refer to the

ability of institutions to react following a natural hazard, in particular

ex post during emergency response. However, effective response

requires substantial ex ante planning and investments in disaster

preparedness and early warning (not only in terms of financial cost but

particularly in terms of awareness raising and capacity building; IFRC,

2009). Furthermore, there are also response phases for gradual changes

in ecosystems or temperature regimes caused by climate change.

Responding spans everything from people’s own initial reactions to a

hazard upon its impact to actions to try to reduce secondary damage. It

is worth noting that in climate change literature, anticipatory actions

are often referred to as responses, which differs from the way this term

is used in the context of disaster risk, where it only implies the actions

taken once there has been an impact.

Capacity to respond is not sufficient to reduce risk. Humanitarian aid

and relief interventions have been discussed in the context of their role

in reinforcing or even amplifying existing vulnerabilities (Anderson and

Woodrow, 1991; Wisner, 2001a; Schipper and Pelling, 2006). This does

not only have implications for the capacity to respond, but also for other

Chapter 2Determinants of Risk: Exposure and Vulnerability

aspects of capacity. Wisner (2001a) shows how poorly constructed

shelters, where people were placed temporarily in El Salvador following

Hurricane Mitch in 1998, turned into ‘permanent’ housing when

nongovernmental organization (NGO) support ran out. When two

strong earthquakes hit in January and February 2001, the shelters

collapsed, leaving the people homeless again. This example illustrates

the perils associated with emergency measures that focus only on

responding, rather than on the capacity to reduce risk and change.

Response capacity is also differential (Chatterjee, 2010). The most

effective ex ante risk management strategies will often include a

combination of risk reduction and enhanced capacity to respond to

impacts (including smarter response by better preparedness and early

warning, as well risk transfer such as insurance).

2.4.2.3. Capacity to Recover and Change

Having the capacity to change is a requirement in order to adapt to

climate change. Viewing adaptation as requiring transformation implies

that it cannot be understood as only a set of actions that physically

protect people from natural hazards (Pelling, 2010). In the context of

natural hazards, the opportunity for changing is often greatest during the

recovery phase, when physical infrastructure has to be rebuilt and can be

improved, and behavioral patterns and habits can be contemplated

(Susman et al., 1983; Renn, 1992; Comfort et al., 1999; Vogel and O’Brien,

2004; Birkmann et al., 2010a). This is an opportunity to rethink whether

the crops planted are the most suited to the climate and whether it is

worthwhile rebuilding hotels near the coast, taking into account what

other sorts of environmental changes may occur in the area.

Capacity to recover is not only dependent on the extent of a physical

impact, but also on the extent to which society has been affected,

including the ability to resume livelihood activities (Hutton and Haque,

2003). This capacity is driven by numerous factors, including mental and

physical ability to recover, financial and environmental viability, and

political will. Because reconstruction processes often do not take

people’s livelihoods into account, instead focusing on their safety, new

settlements are often located where people do not want to be, which

brings change – but not necessarily change that leads to sustainable

development. Innumerable examples indicate how people who have been

resettled return back to their original location, moving into dilapidated

houses or setting up new housing, even if more solid housing is

available elsewhere (e.g., El Salvador after Hurricane Mitch), simply

because the new location does not allow them easy access to their

fields, to markets or roads, or to the sea (e.g., South and Southeast Asia

after the 2004 tsunami).

Recovering to return to the conditions before a natural hazard occurs

not only implies that the risk may be the same or greater, but also does

not question whether the previous conditions were desirable. In fact,

recovery processes are often out of sync with the evolving process of

development. The recovery and reconstruction phases after a disaster

provide an opportunity to rethink previous conditions and address the

root causes of risk, looking to avoid reconstructing the vulnerability

(IDB, 2007), but often the process is too rushed to enable effective

reflection, discussion, and consensus building (Christoplos, 2006).

Pushing the recovery toward transformation and change requires taking

a new approach rather than returning to ‘normalcy.’ Several examples

have shown that capacity to recover is severely limited by poverty

(Chambers, 1983; Ingham, 1993; Hutton and Haque, 2003), where

people are driven further down the poverty spiral, never returning to

their previous conditions, however undesirable.

The various capacities to respond and to survive hazard events and

changes have also been discussed within the context of the concept of

resilience. While originally, the concept of resilience was strongly linked

to an environmental perspective on ecosystems and their ability to

maintain major functions even in times of adverse conditions and crises

(Holling, 1973), the concept has undergone major shifts and has been

enhanced and applied also in the field of social-ecological systems and

disaster risk (Gunderson, 2000; Walker et al., 2004; UN, 2005; Abel et al.,

2006). Folke (2006) differentiates three different resilience concepts

that encompass an engineering resilience perspective that focuses on

recovery and constancy issues, while the ecological and social resilience

focus on persistence and robustness and, finally, the integrated social-

ecological resilience perspective deals with adaptive capacity, trans-

formability, learning, and innovation (Folke, 2006). In disaster risk

reduction the terms resilience building and the lack of resilience have

achieved a high recognition. These terms are linked to capacities of

communities or societies to deal with the impact of a hazard event

or crises and the ability to learn and create resilience through these

experiences. Recent papers, however, also criticize the unconsidered use

or the simply transfer of the concept of resilience into the wider context

of adaptation (see, e.g., Cannon and Müller-Mahn, 2010). Additionally,

the lack of resilience has also been used as an umbrella to examine

deficiencies in capacities that communities encompass in order to deal

with hazard events. Describing the lack of resilience, Cardona and

Barbat (2000) identify various capacities that are often insufficient in

societies that suffer heavily during disasters, such as the deficiencies

regarding the capacity to anticipate, to cope with, and to adapt to

changing environmental conditions and natural hazards.

Other work has argued a different view on resilience, because the very

occurrence of a disaster shows that there are gaps in the development

process (UNDP, 2004). Lessons learned from studying the impacts of the

2004 Indian Ocean tsunami (Thomalla et al., 2009; Thomalla and Larsen,

2010) are informative for climate-related hazards. They suggest that:

• Social vulnerability to multiple hazards, particularly rare extreme

events, tends to be poorly understood.

• There is an increasing focus away from vulnerability assessment

toward resilience building; however, resilience is poorly understood

and a lot needs to be done to go from theory to practice.

• One of the key issues in sub-national risk reduction initiatives is a

need to better define the roles and responsibilities of government

and NGO actors and to improve coordination between them. Without

mechanisms for joint target setting, coordination, monitoring, and

Chapter 2 Determinants of Risk: Exposure and Vulnerability

evaluation, there is much duplication of effort, competition, and

tension between actors.

• Risk reduction is only meaningful and prioritized by local government

authorities if it is perceived to be relevant in the context of other,

more pressing day-to-day issues, such as poverty reduction,

livelihood improvement, natural resource management, and

community development.

2.4.3. Factors of Capacity: Drivers and Barriers

There is high confidence that extreme and non-extreme weather and

climate events also affect vulnerability to future extreme events, by

modifying the resilience, coping, and adaptive capacity of communities,

societies, or social-ecological systems affected by such events. When

people repeatedly have to respond to natural hazards and changes, the

capitals that sustain capacity are broken down, increasing vulnerability

to hazards (Wisner and Adams, 2002; Marulanda et al., 2008b, 2010,

2011; UNISDR, 2009a). Much work has gone into identifying what these

factors of capacity are, to understand both what drives capacity as

well as what acts as a barrier to it (Adger et al., 2004; Sharma and

Padwardhan, 2008).

Drivers of capacity include: an integrated economy; urbanization;

information technology; attention to human rights; agricultural capacity;

strong international institutions; access to insurance; class structure; life

expectancy, health, and well-being; degree of urbanization; access to

public health facilities; community organizations; existing planning

regulations at national and local levels; institutional and decisionmaking

frameworks; existing warning and protection from natural hazards; and

good governance (Cannon, 1994; Handmer et al., 1999; Klein, 2001;

Barnett, 2005; Brooks et al., 2005; Bettencourt et al., 2006).

2.5. Dimensions and Trends of

Vulnerability and Exposure

This section presents multiple dimensions of exposure and vulnerability

to hazards, disasters, climate change, and extreme events. Some

frameworks consider exposure to be a component of vulnerability (Turner

et al., 2003a), and the largest body of knowledge on dimensions refers

to vulnerability rather than exposure, but the distinction between them

is often not made explicit. Vulnerability is: multi-dimensional and

differential – that is, it varies across physical space and among and

within social groups; scale-dependent with regard to space and units of

analysis such as individual, household, region, or system; and dynamic

– characteristics and driving forces of vulnerability change over time

(Vogel and O’Brien, 2004). As vulnerability and exposure are not fixed,

understanding the trends in vulnerability and exposure is therefore an

important aspect of the discussion.

There is high confidence that for several hazards, changes in exposure

and in some cases vulnerability are the main drivers behind observed

trends in disaster losses, rather than a change in hazard character, and

will continue to be essential drivers of changes in risk patterns over the

coming decades (Bouwer et al., 2007; Pielke Jr. and Landsea, 1998;

UNISDR, 2009a). In addition, there is high confidence that climate change

will affect disaster risk not only through changes in the frequency,

intensity, and duration of some events (see Chapter 3), but also through

indirect effects on vulnerability and exposure. In most cases, it will do

so not in isolation but as one of many sources of possible stress, for

instance through impacts on the number of people in poverty or suffering

from food and water insecurity, changing disease patterns and general

health levels, and where people live. In some cases, these changes may

be positive, but in many cases, they will be negative, especially for many

groups and areas that are already among the most vulnerable.

Although trends in some of the determinants of risk and vulnerability are

apparent (for example, accelerated urbanization), the extent to which

these are altering levels of risk and vulnerability at a range of geographical

and time scales is not always clear. While there is high confidence that

these connections exist, current knowledge often does not allow us to

provide specific quantifications with regional or global significance.

The multidimensional nature of vulnerability and exposure makes any

organizing framework arbitrary, overlapping, and contentious to a

degree. The following text is organized under three very broad headings:

environmental, social, and economic dimensions. Each of these has a

number of subcategories, which map out the major elements of interest.

2.5.1. Environmental Dimensions

Environmental dimensions include:

• Potentially vulnerable natural systems (such as low-lying islands,

coastal zones, mountain regions, drylands, and Small Island

Developing States (Dow, 1992; UNCED, 1992; Pelling and Uitto,

2001; Nicholls, 2004; UNISDR, 2004; Chapter 3)

• Impacts on systems (e.g., flooding of coastal cities and agricultural

lands, or forced migration)

• The mechanisms causing impacts (e.g., disintegration of particular

ice sheets) (Füssel and Klein, 2006; Schneider et al., 2007)

• Responses or adaptations to environmental conditions (UNEP/

UNISDR, 2008).

There are important links between development, environmental

management, disaster reduction, and climate adaptation (e.g., van Aalst

and Burton, 2002), also including social and legal aspects such as

property rights (Adger, 2000). For the purposes of vulnerability analysis

in the context of climate change, it is important to acknowledge that

the environment and human beings that form the socio-ecological

system (Gallopin et al., 2001) behave in nonlinear ways, and are

strongly coupled, complex, and evolving (Folke et al., 2002).

There are many examples of the interactions between society and

environment that make people vulnerable to extreme events (Bohle et

Chapter 2Determinants of Risk: Exposure and Vulnerability

al., 1994) and highlight the vulnerability of ecosystem services (Metzger

et al., 2006). As an example, vulnerabilities arising from floodplain

encroachment and increased hazard exposure are typical of the intricate

and finely balanced relationships within human-environment systems

(Kates, 1971; White, 1974) of which we have been aware for several

decades. Increasing human occupancy of floodplains increases exposure

to flood hazards. It can put not only the lives and property of human

beings at risk but can damage floodplain ecology and associated

ecosystem services. Increased exposure of human beings comes about

even in the face of actions designed to reduce the hazard. Structural

responses and alleviation measures (e.g., provision of embankments,

channel modification, and other physical alterations of the floodplain

environment), designed ostensibly to reduce flood risk, can have the

reverse result. This is variously known as the levee effect (Kates, 1971;

White, 1974), the escalator effect (Parker, 1995), or the ‘safe development

paradox’ (Burby, 2006) in which floodplain encroachment leads to

increased flood risk and, ultimately, flood damages. A maladaptive

policy response to such exposure provides structural flood defenses,

which encourage the belief that the flood risk has been removed. This

in turn encourages more floodplain encroachment and a reiteration of

the cycle as the flood defenses (built to a lower design specification) are

exceeded. This is typical of many maladaptive policy responses, which

focus on the symptoms rather than the causes of poor environmental

management.

Floodplains, even in low-lying coastal zones, have the potential to

provide benefits and/or risks and it is the form of the social interaction

(see next subsection) that determines which, and to whom. Climate

variability shifts previous risk-based decisionmaking into conditions of

greater uncertainty where we can be less certain of the probabilities of

occurrence of any extreme event.

The environmental dimension of vulnerability also deals with the role of

regulating ecosystem services and ecosystem functions, which directly

impact human well-being, particularly for those social groups that

heavily depend on these services and functions due to their livelihood

profiles. Especially in developing countries and countries in transition,

poorer rural communities often entirely depend on ecosystem services

and functions to meet their livelihood needs. The importance of these

ecosystem services and ecosystem functions for communities in the

context of environmental vulnerability and disaster risk has been

recognized by the 2009 and 2011 Global Assessment Reports on

Disaster Risk Reduction (UNISDR, 2009a, 2011) as well as by the

Millennium Ecosystem Assessment (MEA, 2005). The degradation of

ecosystem services and functions can contribute to an exacerbation of

both the natural hazard context and the vulnerability of people. The

erosion of ecosystem services and functions can contribute to the decrease

of coping and adaptive capacities in terms of reduced alternatives for

livelihoods and income-generating activities due to the degradation of

natural resources. Additionally, a worsening of environmental services

and functions might also increase the costs of accessing these services,

for example, in terms of the increased time and travel needed to access

drinking water in rural communities affected by droughts or salinization.

Furthermore, environmental vulnerability can also mean that in the case

of a hazardous event occurring, the community may lose access to the

only available water resource or face a major reduction in productivity

of the soil, which then also increases the risk of crop failure. For

instance, Renaud (2006) underscored that the salinization of wells after

the 2004 Indian Ocean tsunami had a highly negative consequence for

those communities that had no alternative access to freshwater

resources.

2.5.1.1. Physical Dimensions

Within the environmental dimension, physical aspects refer to a location-

specific context for human-environment interaction (Smithers and Smit,

1997) and to the material world (e.g., built structures).

The physical exposure of human beings to hazards has been partly

shaped by patterns of settlement of hazard-prone landscapes for the

countervailing benefits they offer (UNISDR, 2004). Furthermore, in the

context of climate change, physical exposure is in many regions also

increasing due to spatial extension of natural hazards, such as floods,

areas affected by droughts, or delta regions affected by salinization.

This does not make the inhabitants of such locations vulnerable per se

because they may have capacities to resist the impacts of extreme events;

this is the essential difference between exposure and vulnerability. The

physical dimension of vulnerability begins with the recognition of a link

between an extreme physical or natural phenomenon and a vulnerable

human group (Westgate and O’Keefe, 1976). Physical vulnerability

comprises aspects of geography, location, and place (Wilbanks, 2003);

settlement patterns; and physical structures (Shah, 1995; UNISDR,

2004) including infrastructure located in hazard-prone areas or with

deficiencies in resistance or susceptibility to damage (Wilches-Chaux,

1989). Further, Cutter’s (1996) ‘hazards of place’ model of vulnerability

expressly refers to the temporal dimension (see Section 2.5.4.2), which,

in recognizing the dynamic nature of place vulnerability, argues for a

more nuanced approach.

2.5.1.2. Geography, Location, Place

Aggregate trends in the environmental dimensions of exposure and

vulnerability as they relate to geography, location, and place are given

in Chapters 3 and 4, while this section deals with the more conceptual

aspects.

There is a significant difference in exposure and vulnerability between

developing and developed countries. While a similar (average) number of

people in low and high human development countries may be exposed

to hazards each year (11 and 15% respectively), the average numbers

killed is very different (53 and 1% respectively) (Peduzzi, 2006).

Developing countries are recognized as facing the greater impacts and

having the most vulnerable populations, in the greatest number, who

Chapter 2 Determinants of Risk: Exposure and Vulnerability

are least able to easily adapt to changes in inter alia temperature, water

resources, agricultural production, human health, and biodiversity

(IPCC, 2001; McCarthy et al., 2001; Beg et al., 2002). Small Island

Developing States, a number of which are also Least Developed

Countries, are recognized as being highly vulnerable to external shocks

including climate extremes (UN/DESA, 2010; Chapter 3). While efforts in

climate change adaptation have been undertaken, progress has been

limited, focusing on public awareness, research, and policy development

rather than implementation (UN/DESA, 2010).

Developed countries are also vulnerable and have geographically

distinct levels of vulnerability, which are masked by a predominant focus

on direct impacts on biophysical systems and broad economic sectors.

However, indirect and synergistic effects, differential vulnerabilities, and

assumptions of relative ease of adaptation within apparently robust

developed countries may lead to unforeseen vulnerabilities (O’Brien et al.,

2006). Thus, development per se is not a guarantee of ‘invulnerability.’

Development can undermine ecosystem resilience on the one hand but

create wealth that may enhance societal resilience overall if equitable

(Barnett, 2001).

The importance of geography has been highlighted in an analysis of

‘disaster hotspots’ by Dilley et al. (2005). Hazard exposure (event

incidence) is combined with historical vulnerability (measured by

mortality and economic loss) in order to identify geographic regions

that are at risk from a range of geophysical hazards. While flood risk is

widespread across a number of regions, drought and especially cyclone

risk demonstrate distinct spatial patterns with the latter closely related

to the climatological pattern of cyclone tracks and landfall.

2.5.1.3. Settlement Patterns and Development Trajectories

There are specific exposure/vulnerability dimensions associated with

urbanization (Hardoy and Pandiella, 2009) and rurality (Scoones, 1998;

Nelson et al., 2010a,b). The major focus below is on the urban because

of the increasing global trend toward urbanization and its potential for

increasing exposure and vulnerability of large numbers of people.

2.5.1.3.1. The urban environment

Accelerated urbanization is an important trend in human settlement,

which has implications for the consideration of exposure and vulnerability

to extreme events. There has been almost a quintupling of the global

urban population between 1950 and 2011 with the majority of that

increase being in less developed regions (UN-HABITAT, 2011).

There is high confidence that rapid and unplanned urbanization

processes in hazardous areas exacerbate vulnerability to disaster risk

(Sánchez-Rodríguez et al., 2005). The development of megacities with

high population densities (Mitchell, 1999a,b; Guha-Sapir et al., 2004)

has led to greater numbers being exposed and increased vulnerability

through, inter alia, poor infrastructural development (Uitto, 1998) and

the synergistic effects of intersecting natural, technological, and social

risks (Mitchell, 1999a). Lavell (1996) identified eight contexts of cities

that increase or contribute to disaster risk and vulnerability and are

relevant in the context of climate change:

1) The synergic nature of the city and the interdependency of its parts

2) The lack of redundancy in its transport, energy, and drainage systems

3) Territorial concentration of key functions and density of building

and population

4) Mislocation

5) Social-spatial segregation

6) Environmental degradation

7) Lack of institutional coordination

8) The contrast between the city as a unified functioning system and

its administrative boundaries that many times impede coordination

of actions.

The fact that urban areas are complex systems poses potential

management challenges in terms of the interplay between people,

infrastructure, institutions, and environmental processes (Ruth and

Coelho, 2007). Alterations or trends in any of these, or additional

components of the urban system such as environmental governance

(Freudenberg et al., 2008) or the uptake of insurance (McLemand and

Smit 2006; Lamond et al., 2009), have the potential to increase exposure

and vulnerability to extreme climate events substantially.

The increasing polarization and spatial segregation of groups with

different degrees of vulnerability to disaster have been identified as an

emerging problem (Mitchell, 1999b). For the United States, where there is

considerable regional variability, the components found to consistently

increase social vulnerability (as expressed by a Social Vulnerability Index)

are density (urbanization), race/ethnicity (see below), and socioeconomic

status, with the level of development of the built environment, age,

race/ethnicity, and gender accounting for nearly half of the variability in

social vulnerability among US counties (Cutter and Finch, 2008). Social

isolation, especially as it intersects with individual characteristics (see

Case Study 9.2.1) and other social processes of marginalization

(Duneier, 2004) also play a significant role in vulnerability creation (or,

conversely, reduction).

Rapidly growing urban populations may affect the capacity of developing

countries to cope with the effects of extreme events because of the

inability of governments to provide the requisite urban infrastructure or

for citizens to pay for essential services (UN-HABITAT, 2009). However,

there is a more general concern that there has been insufficient attention

to both existing needs for infrastructure maintenance and appropriate

ongoing adaptation of infrastructure to meet potential climate extremes

(Auld and MacIver, 2007). Further, while megacities have been associated

with increasing hazard for some time (Mitchell, 1999a), small cities and

rural communities are potentially more vulnerable to disasters than big

cities or megacities, since megacities have considerable resources for

dealing with hazards and disasters (Cross, 2001) and smaller settlements

are often of lower priority for government spending.

Chapter 2Determinants of Risk: Exposure and Vulnerability

The built environment can be both protective of, and subject to, climate

extremes. Inadequate structures make victims of their occupants and,

conversely, adequate structures can reduce human vulnerability. The

continuing toll of deaths and injuries in unsafe schools (UNISDR,

2009a), hospitals and health facilities (PAHO/World Bank, 2004),

domestic structures (Hewitt, 1997), and infrastructure more broadly

(Freeman and Warner 2001) are indicative of the vulnerability of many

parts of the built environment. In a changing climate, more variable

and with potentially more extreme events, old certainties about the

protective ability of built structures are undermined.

The increase in the number and extent of informal settlements or slums

(UN-HABITAT, 2003; Utzinger and Keiser, 2006) is important because they

are often located on marginal land within cities or on the periphery

because of the lack of alternative locations or the fact that areas close

to river systems or areas at the coast are sometimes state land that can

be more easily accessed than private land. Because of their location,

slums are often exposed to hydrometeorological-related hazards such

as landslides (Nathan, 2008) and floods (Bertoni, 2006; Colten, 2006;

Aragon-Durand, 2007; Douglas et al., 2008; Zahran et al., 2008).

Vulnerability in informal settlements can also be elevated because of

poor health (Sclar et al., 2005), livelihood insecurity (Kantor and Nair,

2005), lack of access to service provision and basic needs (such as

clean water and good governance), and a reduction in the capacity of

formal players to steer developments and adaptation initiatives in a

comprehensive, preventive, and inclusive way (Birkmann et al., 2010b).

Lagos, Nigeria (

Adelekan, 2010), and Chittagong, Bangladesh (Rahman

et al., 2010), serve as clear examples of where an upward trend in the

area of slums has resulted in an increase in the exposure of slum

dwellers to flooding. Despite the fact that rapidly growing informal

and poor urban areas are often hotspots of hazard exposure, for a

number of locations the urban poor have developed more or less

successful coping and adaptation strategies to reduce their vulnerability

in dealing with changing environmental conditions (e.g., Birkmann et al.,

2010b).

Globally, the pressure for urban areas to expand onto flood plains and

coastal strips has resulted in an increase in exposure of populations to

riverine and coastal flood risk (McGranahan et al., 2007; Nicholls et al.,

2011). For example, intensive and unplanned human settlements in

flood-prone areas appear to have played a major role in increasing

flood risk in Africa over the last few decades (Di Baldassarre et al.,

2010). As urban areas have expanded, urban heat has become a

management and health issue (for more on this see Section 2.5.2.3 and

Chapters 3, 5, and 9). For some cities there is clear evidence of a recent

trend in loss of green space (Boentje and Blinnikov, 2007; Sanli et al.,

2008; Rafiee et al., 2009) due to a variety of reasons including planned

and unplanned urbanization with the latter driven by internal and external

migration resulting in the expansion of informal settlements. Such

changes in green space may increase exposure to extreme climate

events in urban areas through decreasing runoff amelioration, urban

heat island mitigation effects, and alterations in biodiversity (Wilby

and Perry, 2006).

While megacities have been associated with increasing hazard for some

time (Mitchell, 1999a), small cities and rural communities (see next

section) are potentially more vulnerable to disasters than big cities or

megacities, since megacities have considerable resources for dealing

with hazards and disasters (Cross, 2001) and smaller settlements are

often of lower priority for government spending.

Urbanization itself is not always a driver for increased vulnerability.

Instead, the type of urbanization and the context in which urbanization

is embedded defines whether these processes contribute to an increase

or decrease in people’s vulnerability.

2.5.1.3.2. The rural environment

Many rural livelihoods are reliant to a considerable degree on the

environment and natural resource base (Scoones, 1998), and extreme

climate events can impact severely on the agricultural sector (Saldaña-

Zorrilla, 2007). However, despite the separation here, the urban and

the rural are inextricably linked. Inhabitants of rural areas are often

dependent on cities for employment, as a migratory destination of last

resort, and for health care and emergency services. Cities depend on

rural areas for food, water, labor, ecosystem services, and other

resources. All of these (and more) can be impacted by climate-related

variability and extremes including changes in these associated with

climate change. In either case, it is necessary to identify the many

exogenous factors that affect a household’s livelihood security.

Eakin’s (2005) examination of rural Mexico presents empirical findings

of the interactions (e.g., between neoliberalism and the opening up of

agricultural markets, and the agricultural impacts of climatic extremes),

which amplify or mitigate risky outcomes. The findings point to economic

uncertainty over environmental risk, which most influences agricultural

households’ decisionmaking. However, there is not a direct and inevitable

link between disaster impact and increased impoverishment of a rural

population. In Nicaragua, Jakobsen (2009) found that a household’s

probability of being poor in the years following Hurricane Mitch was not

affected by whether it was living in an area struck but by factors such as

off-farm income, household size, and access to credit. Successful coping

post-Hurricane Mitch resulted in poor households regaining most of

their assets and resisting a decline into a state of extreme poverty.

However, longer-term adaptation strategies, which might have lifted them

out of the poverty category, eluded the majority and were independent

of having experienced Hurricane Mitch. Thus, while poor (rural) households

may cope with the impacts of a disaster in the relatively short term,

their level of vulnerability, arising from a complex of environmental,

social, economic, and political factors, is such that they cannot escape

the poverty trap or fully reinstate development gains.

In assessing the material on exposure and vulnerability to climate

extremes in urban and rural environments it is clear that there is no

simple, deterministic relationship; it is not possible to show that either

rural or urban environments are more vulnerable (or resilient). In

Chapter 2 Determinants of Risk: Exposure and Vulnerability

either context there is the potential that climate risks can be either

ameliorated or exacerbated by positive or negative adaptation processes

and outcomes.

2.5.2. Social Dimensions

The social dimension is multi-faceted and cross-cutting. It focuses

primarily on aspects of societal organization and collective aspects

rather than individuals. However, some assessments also use the

‘individual’ descriptor to clarify issues of scale and units of analysis

(Adger and Kelly, 1999; K. O’Brien et al., 2008). Notions of the individual

are also useful when considering psychological trauma in and after

disasters (e.g., Few, 2007), including that related to family breakdown

and loss. The social dimension includes demography, migration, and

displacement, social groups, education, health and well-being, culture,

institutions, and governance aspects.

2.5.2.1. Demography

Certain population groups may be more vulnerable than others to climate

variability and extremes. For example, the very young and old are more

vulnerable to heat extremes than other population groups (Staffogia et

al., 2006; Gosling et al., 2009). A rapidly aging population at the

community to country scale bears implications for health, social isolation,

economic growth, family composition, and mobility, all of which are

social determinants of vulnerability. However, as discussed further

below (Social Groups section), static checklists of vulnerable groups do

not reflect the diversity or dynamics of people’s changing conditions.

2.5.2.1.1. Migration and displacement

Trends in migration, as a component of changing population dynamics,

have the potential to rise because of alterations in extreme climate

event frequency. The United Nations Office for the Coordination of

Humanitarian Affairs and the Internal Displacement Monitoring Centre

have estimated that around 20 million people were displaced or

evacuated in 2008 because of rapid onset climate-related disasters

(OCHA/IDMC, 2009). Further, over the last 30 years, twice as many

people have been affected by droughts (slow onset events not included

in the previous point) as by storms (1.6 billion compared with

approximately 718 million) (IOM, 2009). However, because of the multi-

causal nature of migration, the relationship between climatic variability

and change in migration is contested (Black, 2001) as are the terms

environmental and climate refugees (Myers, 1993; Castles 2002; IOM,

2009). Despite an increase in the number of hydrometeorological

disasters between 1990 and 2009, the International Organization on

Migration reports no major impact on international migratory flows

because displacement is temporary and often confined within a region,

and displaced individuals do not possess the financial resources to

migrate (IOM, 2009).

Although there is also a lack of clear evidence for a systematic trend in

extreme climate events and migration, there are clear instances of the

impact of extreme hydrometeorological events on displacement. For

example, floods in Mozambique displaced 200,000 people in 2001,

163,000 people in 2007, and 102,000 more in 2008 (INGC, 2009; IOM,

2009); in Niger, large internal movements of people are due to

pervasive changes related to drought and desertification trends (Afifi,

2011); in the Mekong River Delta region, changing flood patterns appear

to be associated with migratory movements (White, 2002; IOM, 2009);

and Hurricane Katrina, for which social vulnerability, race, and class

played an important role in outward and returning migration (Elliott

and Pais, 2006; Landry et al., 2007; Myers et al., 2008), resulted in the

displacement of over one million people. As well as the displacement

effect, there is evidence for increased vulnerability to extreme events

among migrant groups because of an inability to understand extreme

event-related information due to language problems, prioritization of

finding employment and housing, and distrust of authorities (Enarson

and Morrow, 2000; Donner and Rodriguez, 2008).

Migration can be both a condition of, and a response to, vulnerability –

especially political vulnerability created through conflict, which can drive

people from their homelands. Increasingly it relates to economically and

environmentally displaced persons but can also refer to those who do

not cross international borders but become internally displaced persons

as a result of extreme events in both developed and developing countries

(e.g., Myers et al., 2008).

Although data on climate change-forced displacement is incomplete, it

is clear that the many outcomes of climate change processes will be

seen and felt as disasters by the affected populations (Oliver-Smith,

2009). For people affected by disasters, subsequent displacement and

resettlement often constitute a second disaster in their lives. As part

of the Impoverishment Risks and Reconstruction approach, Cernea

(1996) outlines the eight basic risks to which people are subjected by

displacement: landlessness, joblessness, homelessness, marginalization,

food insecurity, increased morbidity, loss of access to common property

resources, and social disarticulation. When people are forced from their

known environments, they become separated from the material and

cultural resource base upon which they have depended for life as

individuals and as communities (Altman and Low, 1992). The material

losses most often associated with displacement and resettlement are

losses of access to customary housing and resources. Displaced people

are often distanced from their sources of livelihood, whether land,

common property (water, forests, etc.), or urban markets and clientele

(Koenig, 2009). Disasters and displacement may sever the identification

with an environment that may once have been one of the principle

features of cultural identity (Oliver-Smith, 2006). Displacement for any

group can be distressing, but for indigenous peoples it can result in

particularly severe impacts. The environment and ties to land are

considered to be essential elements in the survival of indigenous societies

and distinctive cultural identities (Colchester, 2000). The displacement

and resettlement process has been consistently shown to disrupt and

destroy those networks of social relationships on which the poor

Chapter 2Determinants of Risk: Exposure and Vulnerability

depend for resource access, particularly in times of stress (Cernea, 1996;

Scudder, 2005).

Migration is an ancient coping mechanism in response to environmental

(and other) change and does not inevitably result in negative outcomes,

either for the migrants themselves or for receiving communities (Barnett

and Webber, 2009). Climate variability will result in some movement of

stressed people but there is low confidence in ability to assign direct

causality to climatic impacts or to the numbers of people affected.

2.5.2.1.2. Social groups

Research evidence of the differential vulnerability of social groups is

extensive and raises concerns about the disproportionate effects of

climate change on identifiable, marginalized populations (Bohle et al.,

1994; Kasperson and Kasperson, 2001; Thomalla et al., 2006). Particular

groups and conditions have been identified as having differential

exposure or vulnerability to extreme events, for example race/ethnicity

(Fothergill et al., 1999; Elliott and Pais, 2006; Cutter and Finch, 2008),

socioeconomic class and caste (O’Keefe et al., 1976; Peacock et al.,

1997; Ray-Bennett, 2009), gender (Sen, 1981), age (both the elderly and

children; Jabry, 2003; Wisner, 2006b; Bartlett, 2008), migration, and

housing tenure (whether renter or owner), as among the most common

social vulnerability characteristics (Cutter and Finch, 2008). Morrow

(1999) extends and refines this list to include residents of group living

facilities; ethnic minorities (by language); recent migrants (including

immigrants); tourists and transients; physically or mentally disabled (see

also McGuire et al., 2007; Peek and Stough, 2010); large households;

renters; large concentrations of children and youth; poor households;

the homeless (see also Wisner, 1998); and women-headed households.

Generally, the state of vulnerability is defined by a specific population

at a particular scale; aggregations (and generalizations) are often less

meaningful and require careful interpretation (Adger and Kelly, 1999).

One of the largest bodies of research evidence, and one which can be an

exemplar for the way many other marginalized groups are differentially

impacted or affected by extreme events, has been on gender and disaster,

and on women in particular (e.g., Neal and Phillips, 1990; Enarson and

Morrow, 1998; Neumayer and Plümper, 2007). This body of literature is

relatively recent, particularly in a developed world context, given the

longer recognition of gender concerns in the development field

(Fordham, 1998). The specific gender and climate change link including

self-defined gender groups has been even more recent (e.g., Masika,

2002; Pincha and Krishna, 2009). The research evidence emphasizes the

social construction of gendered vulnerability in which women and girls

are often (although not always) at greater risk of dying in disasters,

typically marginalized from decisionmaking fora, and discriminated and

acted against in post-disaster recovery and reconstruction efforts

(Houghton, 2009; Sultana, 2010).

Women or other socially marginalized or excluded groups are not

vulnerable through biology (except in very particular circumstances) but

are made so by societal structures and roles. For example, in the Indian

Ocean tsunami of 2004, many males were out to sea in boats, fulfilling

their roles as fishermen, and were thus less exposed than were many

women who were on the seashore, fulfilling their roles as preparers and

marketers of the fish catch. However, the women were made vulnerable

not simply by their location and role but by societal norms which did not

encourage survival training for girls (e.g., to swim or climb trees) and

which placed the majority of the burden of child and elder care with

women. Thus, escape was made more difficult for women carrying

children and responsible for others (Doocy et al., 2007).

The gender and disaster/climate change literature has also recognized

resilience/capacity/capability alongside vulnerability. This elaboration of the

vulnerability approach makes clear that vulnerability in these identified

groups is not an immutable or totalizing condition. The vulnerability

‘label’ can reinforce notions of passivity and helplessness, which obscure

the very significant, active contributions that socially marginalized

groups make in coping with and adapting to extremes. An example is

provided in Box 2-2.

2.5.2.2. Education

The education dimension ranges across the vulnerability of educational

building structures; issues related to access to education; and also

sharing and access to disaster risk reduction and climate adaptation

information and knowledge (Wisner, 2006b). Priority 3 of the Hyogo

Framework for Action 2005-2015 recommends the use of knowledge,

innovation, and education to build a ‘culture of safety and resilience’ at

all levels (UNISDR, 2007a). A well-informed and motivated population

can lead to disaster risk reduction but it requires the collection and

dissemination of knowledge and information on hazards, vulnerabilities,

and capacities. However, “It is not information per se that determines

action, but how people interpret it in the context of their experience,

beliefs and expectations. Perceptions of risks and hazards are culturally

and socially constructed, and social groups construct different meanings

for potentially hazardous situations” (McIvor and Paton, 2007). In addition

to knowledge and information, explicit environmental education programs

among children and adults may have benefits for public understanding

of risk, vulnerability, and exposure to extreme events (UNISDR, 2004;

Kobori, 2009; Nomura, 2009; Patterson et al., 2009; Kuhar et al., 2010),

because they promote resilience building in socio-ecological systems

through their role in stewardship of biological diversity and ecosystem

services, provide the opportunity to integrate diverse forms of knowledge

and participatory processes in resource management (Krasny and Tidball,

2009), and help promote action towards sustainable development

(Waktola, 2009; Breiting and Wikenberg, 2010).

Many lives have been lost through the inability of education infrastructure

to withstand extreme events. Where flooding is a recurrent phenomenon

schools can be exposed or vulnerable to floods. For example, a survey

of primary schools’ flood vulnerability in the Nyando River catchment

of western Kenya revealed that 40% were vulnerable, 48% were

Chapter 2 Determinants of Risk: Exposure and Vulnerability

marginally vulnerable, and 12% were not vulnerable; the vulnerability

status was attributed to a lack of funds, poor building standards, local

topography, soil types and inadequate drainage (Ochola et al., 2010).

Improving education infrastructure safety can have multiple benefits.

For example, the Malagasy Government initiated the Development

Intervention Fund IV project to reduce cyclone risk, including safer school

construction and retrofitting. In doing so, awareness and understanding

of disaster issues were increased within the community (Madagascar

Development Intervention Fund, 2007).

The impact of extreme events can limit the ability of parents to afford

to educate their children or require them (especially girl children, whose

access to education is typically prioritized less than that of boy children)

to work to meet basic needs (UNDP, 2004; UNICEF, 2009).

Access to information related to early warnings, response strategies,

coping and adaptation mechanisms, science and technology, and human,

social, and financial capital is critical for reduction of vulnerability and

increasing resilience. A range of factors may control or influence the

access to information, including economic status, race (Spence et al.,

2007), trust (Longstaff and Yang, 2008), and belonging to a social

network (Peguero, 2006). However, the mode of information transfer or

exchange must be considered because there is emerging evidence of a

growing digital inequality (Rideout, 2003) that may influence trends in

vulnerability as an increasing amount of information about extreme event

preparedness and response is often made available via the internet (see

Chapter 9). Evidence has existed for some time that people who have

experienced natural hazards (and thus may have information and

knowledge gained directly through that experience) are, in general,

better prepared than those who have not (Kates, 1971). However, this

does not necessarily translate into protective behavior because of what

has been called the ‘prison of experience’ (Kates, 1962), in which people’s

response behavior is determined by the previous experience and is not

based on an objective assessment of current risk. In the uncertain

context of climate-related extremes, this may mean people are not

appropriately educated regarding the risk.

2.5.2.3. Health and Well-Being

The health dimension of vulnerability includes differential physical,

physiological, and mental health effects of extreme events in different

regions and on different social groups (McMichael et al., 2003; van

Lieshout et al., 2004; Haines et al., 2006; Few, 2007; Costello et al.,

Chapter 2Determinants of Risk: Exposure and Vulnerability

Box 2-2 | Integrating Disaster Risk Reduction, Climate Adaptation, and Resilience-Building:

the Garifuna Women of Honduras

The Garifuna women of Honduras could be said to show multiple vulnerability characteristics (Brondo, 2007). They are women, the gender

often made vulnerable by patriarchal structures worldwide; they come from Honduras, a developing country exposed to many hazards;

they belong to an ethnic group descended from African slaves, which is socially, economically, and politically marginalized; and they

depend largely upon a subsistence economy, with a lack of education, health, and other resources. However, despite these markers of

vulnerability, the Garifuna women have organized to reduce their communities’ exposure to hazards and vulnerability to disasters

through the protection and development of their livelihood opportunities (Fordham et al., 2011).

The women lead the Comité de Emergencia Garifuna de Honduras, which is a grassroots, community-based group of the Afro-Indigenous

Garifuna that was developed in the wake of Hurricane Mitch in 1998. After Mitch, there was a lack of external support and so the Comité

women organized themselves and repaired hundreds of houses, businesses, and public buildings, in the process of which women were

empowered and trained in non-traditional work. They campaigned to buy land for relocating housing to safer areas, in which the poorest

families participated in the reconstruction process. Since being trained themselves in vulnerability and capacity mapping by grassroots

women in Jamaica, they have in turn trained 60 trainers in five Garifuna communities to carry out mapping exercises in their communities.

The Garifuna women have focused on livelihood-based activities to ensure food security by reviving and improving the production of

traditional root crops, building up traditional methods of soil conservation, carrying out training in organic composting and pesticide use,

and creating the first Garifuna farmers’ market. In collaborative efforts, 16 towns now have established tool banks, and five have seed

banks. Through reforestation, the cultivation of medicinal and artisanal plants, and the planting of wild fruit trees along the coast, they

are helping to prevent erosion and reducing community vulnerability to hazards and the vagaries of climate.

The Garifuna women’s approach, which combines livelihood-based recovery, disaster risk reduction, and climate change adaptation, has

had wide-ranging benefits. They have built up their asset base (human, social, physical, natural, financial, and political), and improved

their communities’ nutrition, incomes, natural resources, and risk management. They continue to partner with local, regional, and

international networks for advocacy and knowledge exchange. The women and communities are still at risk (Drusine, 2005) but these

strategies help reduce their socioeconomic vulnerability and dependence on external aid (Fordham et al., 2011).

2009). It also includes, in a link to the institutional dimension, health

service provision (e.g., environmental health and public health issues,

infrastructure and conditions; Street et al., 2005), which may be impacted

by extreme events (e.g., failures in hospital/health center building

structures; inability to access health services because of storms and

floods). Vulnerability can also be understood in terms of functionality

related to communication, medical care, maintaining independence,

supervision, and transportation. In addition individuals including children,

senior citizens, and pregnant women and those who may need additional

response assistance including the disabled, those living in institutionalized

settings, those from diverse cultures, people with limited English

proficiency or are non-English speaking, those with no access to transport,

have chronic medical disorders, and have pharmacological dependency

can also be considered vulnerable in a health context.

Unfortunately, the health dimensions of disasters are difficult to measure

because of difficulties in attributing the health condition (including

mortality) directly to the extreme event because of secondary effects; in

addition, some of the effects are delayed in time, which again makes

attribution difficult (Bennet, 1970; Hales et al., 2003). The difficulty of

collection of epidemiological data in crisis situations is also a factor,

especially in low-income countries. Further understanding the post-

traumatic stress disorder dimensions of extreme climate events and the

psychological aspects of climate change presents a number of challenges

(Amstadter et al., 2009; Kar, 2009; Mohay and Forbes, 2009; Furr et al.,

2010; Doherty and Clayton, 2011).

Health vulnerability is the sum of all the risk and protective factors

that determine the degree to which individuals or communities could

experience adverse impacts from extreme weather events (Balbus and

Malina, 2009). Vulnerabilities can arise from a wide range of institutional,

geographic, environmental, socioeconomic, biological sensitivity, and other

factors, which can vary spatially and temporally. Biological sensitivity

can be associated with developmental stage (e.g., children are at

increased mortality risk from diarrheal diseases); pre-existing medical

conditions (e.g., diabetics are at increased risk during heat waves);

acquired conditions (e.g., malaria immunity); and genetic factors

(Balbus and Malina, 2009). Vulnerability can be viewed both from the

perspective of the population groups more likely to experience adverse

health outcomes and from the perspective of the public health and

health care interventions required to prevent adverse health impacts

during and following an extreme event.

For some extreme weather events the vulnerable population groups

depend on the adverse health outcome considered. For example, in the

case of heat waves socially isolated elderly people with pre-existing

medical conditions are vulnerable to heat-related health effects (see

Chapter 9). For floods, children are at greater risk for transmission of

fecal-oral diseases, and those with mobility and cognitive constraints

can be at increased risk of injuries and deaths (Ahern et al., 2005), while

people on low incomes are less likely to be able to afford insurance

against risks associated with flooding, such as storm and flood damage

(Marmot, 2010). Flooding has been found to increase the risk of mental

health problems, pre- and post-event, in both adults and children

(Ginexi et al., 2000; Reacher et al., 2004; Ahern et al., 2005; Carroll et

al., 2006; Tunstall et al., 2006; UK Department of Health, 2009). A UK

study of over 1,200 households affected by flooding suggested that

there were greater impacts on physical and mental health among more

vulnerable groups and poorer households and communities (Werritty et

al., 2007). However, while there is evidence for impacts on particular

social groups in identified disaster types, there are some social groups

that are more likely to be vulnerable whatever the hazard type; these

include those at the extremes of the age range, those with underlying

medical conditions, and those otherwise stressed by low socioeconomic

status. The role of socioeconomic factors supports the necessity of a

social, and not just a medical, model of response and adaptation.

A number of public health impacts are expected to worsen in climate-

related disasters such as storms, floods, landslides, heat, drought, and

wildfire. These are highly context-specific but range from worsening of

existing chronic illnesses (which could be widespread), through possible

toxic exposures (in air, water or food), to deaths (expected to be few to

moderate but may be many in low-income countries) (Keim, 2008).

Public health and health care services required for preventing adverse

health impacts from an extreme weather event include surveillance and

control activities for infectious diseases, access to safe water and improved

sanitation, food security, maintenance of solid waste management and

other critical infrastructure, maintenance of hospitals and other health

care infrastructure, provision of mental health services, sufficient and

safe shelter to prevent or mitigate displacement, and effective warning

and informing systems (Keim, 2008). Further, it is important to consider

the synergistic effects of NaTech disasters (Natural Hazard Triggering a

Technological Disaster) where impacts can be considerable if only single,

simple hazard events are planned for. In an increasingly urbanized world,

interactions between natural disasters and simultaneous technological

accidents must be given attention (Cruz et al., 2004); the combination

of an earthquake, tsunami, and radiation release at the Japanese

Fukushima Nuclear Power plant in March 2011 is the most recent

example. Lack of provision of these services increases population

vulnerability, particularly in individuals with greater biological sensitivity

to an adverse health outcome. Although there is little evidence for

trends in the exposure or vulnerability of public health infrastructure,

the imperative for a resilient health infrastructure is widely recognized

in the context of extreme climate events (Burkle and Greenough, 2008;

Keim, 2008).

Deteriorating environmental conditions as a result of extremes (including

land clearing, salinization, dust generation, altered ecology; Renaud,

2006; Middleton et al., 2008; Ellis and Wilcox, 2009; Hong et al., 2009;

Ljung et al., 2009; Johnson et al., 2010; Tong et al., 2010) can impact key

ecosystem services and exacerbate climate sensitive disease incidence

(e.g., diarrheal disease; Clasen et al., 2007), particularly via deteriorating

water quality and quantity.

For some health outcomes, which have direct or indirect implications for

vulnerability to extreme climate events, there is evidence of trends. For

Chapter 2 Determinants of Risk: Exposure and Vulnerability

example, obesity, a risk factor for cardiovascular disease, which in turn

is a heat risk factor (Bouchama et al., 2007) has been noted to be on the

increase in a number of developed countries (Skelton et al., 2009;

Stamatakis et al., 2010). Observed trends in major public health threats

such as the infectious or communicable diseases HIV/AIDS, tuberculosis,

and malaria, although not directly linked to the diminution of long-term

resilience of some populations, have been identified as having the

potential to do so (IFRC, 2008). In addition to the diseases themselves,

persistent and increasing obstacles to expanding or strengthening

health systems such as inadequate human resources and poor hospital

and laboratory infrastructure as observed in some countries (Vitoria et

al., 2009) may also contribute indirectly to increasing vulnerability and

exposure where, for example, malaria and HIV/Aids occasionally reach

epidemic proportions.

However, trends in well-being and health are difficult to assess.

Indicators that characterize a lack of well-being and a high degree of

susceptibility are, for example, indicators of undernourishment and

malnutrition. The database for the Millennium Development Goals and

respective statistics of the Food and Agriculture Organization (FAO)

underscore that trends in undernourishment are spatially and temporally

differentiated. While, as but one example, the trend in undernourished

people in Burundi shows a significant increase from 1991 to 2005, an

opposite trend of a reduction in the percentage of undernourished

people can be observed in Angola (see UN Statistics Division, 2011;

FAOSTAT, 2011). Thus, evidence exists that trends in vulnerability, e.g.,

in terms of well-being and undernourishment change over time and are

highly differentiated in terms of spatial patterns.

In considering health-related exposure and vulnerability to extreme events,

evidence from past climate/weather-related disaster events (across a

range of hazard types for which lack of space precludes coverage)

makes clear the links to a range of negative outcomes for physical and

mental health and health infrastructure. Furthermore, there is clear

evidence (Haines et al., 2006; Confalonieri et al., 2007) that current and

projected health impacts from climate change are multifarious and will

affect low-income groups and low-income countries the most severely,

although high-income countries are not immune.

2.5.2.4. Cultural Dimensions

The broad term ‘culture’ embraces a complexity of elements that can

relate to a way of life, behavior, taste, ethnicity, ethics, values, beliefs,

customs, ideas, institutions, art, and intellectual achievements that

affect, are produced, or are shared by a particular society. In essence, all

these characteristics can be summarized to describe culture as ‘the

expression of humankind within society’ (Aysan and Oliver, 1987).

Culture is variously used to describe many aspects of extreme risks from

natural disasters or climate change, including:

• Cultural aspects of risk perception

• Negative culture of danger/ vulnerability/ fear

• Culture of humanitarian concern

• Culture of organizations / institutions and their responses

• Culture of preventive actions to reduce risks, including the creation

of buildings to resist extreme climatic forces

• Ways to create and maintain a ‘Risk Management Culture,’ a

‘Safety Culture,’ or an ‘Adaptation Culture.’

In relation to our understanding of risk, certain cultural issues need to

be noted. Typical examples are cited below:

• Ethnicity and Culture. Deeply rooted cultural values are a dominant

factor in whether or not communities adapt to climate change. For

example, recent research in Northern Burkina Faso indicates that

two ethnic groups have adopted very different strategies due to

cultural values and historical relations, despite their presence in

the same physical environment and their shared experience of

climate change (Nielsen and Reenberg, 2010).

• Locally Based Risk Management Culture. Wisner (2003) has argued

that the point in developing a ‘culture of prevention’ is to build

networks at the neighborhood level capable of ongoing hazard

assessment and mitigation at the micro level. He has noted that while

community based NGOs emerged to support recovery after the

Mexico City and Northridge earthquakes, these were not sustained

over time to promote risk reduction activities. This evidence

confirms other widespread experience indicating that ways still

need to be found to extend the agenda of Community-Based

Organizations into effective action to reduce climate risks and

promote adaptation to climate change.

• Conflicting Cultures: Who Benefits, and Who Loses when Risks are

Reduced? A critical cultural conflict can arise when private actions

to reduce disaster risks and adapting to climate change by one

party have negative consequences on another. This regularly applies

in river flood hazard management where upstream measures to

reduce risks can significantly increase downstream threats to

persons and property. Adger has argued that if appropriate risk

reduction actions are to occur, the key players must bear all the

costs and receive all the benefits from their actions (Adger, 2009).

However, this can be problematic if adaptation is limited to specific

local interests only.

Traditional behaviors tied to local (and wider) tradition and cultural

practices can increase vulnerability – for example, unequal gender norms

that put women and girls at greater risk, or traditional uses of the

environment that have not adapted (or cannot adapt) to changed

environmental circumstances. On the other hand, local or indigenous

knowledge can reduce vulnerabilities too (Gaillard et al., 2007, 2010).

Furthermore, cultural practices are often subtle and may be opaque to

outsiders. The early hazards paradigm literature (White, 1974; Burton et

al., 1978) referred often to culturally embedded fatalistic attitudes,

which resulted in inaction in the face of disaster risk. However,

Schmuck-Widmann (2000), in her social anthropological studies of char

dwellers in Bangladesh, revealed how a belief that disaster occurrence

and outcomes were in the hands of God did not preclude preparatory

activities. Perceptions of risk (and their interpretation by others) depend

Chapter 2Determinants of Risk: Exposure and Vulnerability

on the cultural and social context (Slovic, 2000; Oppenheimer and

Todorov, 2006; Schneider et al., 2007).

Research findings emphasize the importance of considering the role –

and cultures – of religion and faith in the context of disaster. This

includes the role of faith in the recovery process following a disaster

(e.g., Davis and Wall, 1992; Massey and Sutton, 2007); religious

explanations of nature (e.g., Orr, 2003; Peterson, 2005); the role of

religion in influencing positions on environment and climate change

policy (e.g., Kintisch, 2006; Hulme, 2009); and religion and vulnerability

(Guth et al., 1995; Chester, 2005; Elliott et al., 2006; Schipper, 2010).

The cultural dimension also includes the potential vulnerability of

aboriginal and native peoples in the context of climate extremes.

Globally, indigenous populations are frequently dependent on primary

production and the natural resource base while being subject to

(relatively) poor socioeconomic conditions (including poor health, high

unemployment, low levels of education, and greater poverty). This

applies to groups from Canada (Turner and Clifton, 2009), to Australia

(Campbell et al., 2008), to the Pacific (Mimura et al., 2007). Small island

states, often with distinct cultures, typically show high vulnerability and

low adaptive capacity to climate change (Nurse and Sem, 2001).

However, historically, indigenous groups have had to contend with many

hazards and, as a consequence, have developed capacities to cope

(Campbell, 2006) such as the use of traditional knowledge systems,

locally appropriate building construction with indigenous materials, and

a range of other customary practices (Campbell, 2006).

Given the degree of cultural diversity identified, the importance of

understanding differential risk perceptions in a cultural context is

reinforced (Marris et al., 1998). Cultural Theory has contributed to an

understanding of how people interpret their world and define risk

according to their worldviews: hierarchical, fatalistic, individualistic,

and egalitarian (Douglas and Wildavsky, 1982). Too often policies and

studies focus on ‘the public’ in the aggregate and too little on the needs,

interests, and attitudes of different social and cultural groups (see also

Sections 2.5.2.1.2 and 2.5.4).

2.5.2.5. Institutional and Governance Dimensions

The institutional dimension is a key determinant of vulnerability to

extreme events (Adger, 1999). Institutions have been defined in a broad

sense to include “habitualized behavior and rules and norms that govern

society” (Adger, 2000) and not just the more typically understood

formal institutions. This view allows for a discussion of institutional

structures such as property rights and land tenure issues (Toni and

Holanda, 2008) that govern natural resource use and management. It

forms a bridge between the social and the environmental/ecological

dimensions and can induce sustainable or unsustainable exploitation

(Adger, 2000). Expanding the institutional domain to include political

economy (Adger, 1999) and different modes of production – feudal,

capitalist, socialist (Wisner, 1978) – raises questions about the

vulnerability of institutions and the vulnerability caused by institutions

(including government). Institutional factors play a critical role in

adaptation (Adger, 2000) as they influence the social distribution of

vulnerability and shape adaptation capacity (Næss et al., 2005).

This broader understanding of the institutional dimension also takes us

into a recognition of the role of social networks, community bonds and

organizing structures, and processes that can buffer the impacts of

extreme events (Nakagawa and Shaw, 2004) partly through increasing

social cohesion but also recognizing ambiguous or negative forms

(UNISDR, 2004). For example, social capital/assets (Portes, 1998;

Putnam, 2000) – “the norms and networks that enable people to act

collectively” (Woolcock and Narayan, 2000) – have a role in vulnerability

reduction (Pelling, 1998). Social capital (or its lack) is both a cause and

effect of vulnerability and thus can result in either positive benefit or

negative impact; to be a part of a social group and accrue social assets

is often to indicate others’ exclusion. It also includes attempts to

reframe climate debates by acknowledging the possibility of diverse

impacts on human security, which opens up human rights discourses

and rights-based approaches to disaster risk reduction (Kuwali, 2008;

Mearns and Norton, 2010).

The institutional dimension includes the relationship between policy

setting and policy implementation in risk and disaster management. Top-

down approaches assume policies are directly translated into action on

the ground; bottom-up approaches recognize the importance of other

actors in shaping policy implementation (Urwin and Jordan, 2008). Twigg’s

categorization of the characteristics of the ideal disaster resilient

community (Twigg, 2007) adopts the latter approach. This guideline

document, which has been field tested by NGOs, identifies the important

relations between the community and the enabling environment of

governance at various scales in creating resilience, and by inference,

reducing vulnerability. This set of 167 characteristics (organized under five

thematic areas) also refers to institutional forms for (and processes of)

engagement with risk assessment, risk management, and hazard and

vulnerability mapping. These have been championed by institutions

working across scales to create the Hyogo Framework for Action (UNISDR,

2007a) and associated tools (Davis et al., 2004; UNISDR, 2007b) with

the goal to reduce disaster risk and vulnerability. However, linkages

across scales and the inclusion of local knowledge systems are still not

integrated well in formal institutions (Næss et al., 2005).

A lack of institutional interaction and integration between disaster risk

reduction, climate change, and development may mean policy responses

are redundant or conflicting (Schipper and Pelling, 2006; Mitchell and

van Aalst, 2008; Mitchell et al., 2010). Thus, the institutional model

operational in a given place and time (more or less participatory,

deliberative, and democratic; integrated; or disjointed) could be an

important factor in either vulnerability creation or reduction (Comfort et

al., 1999). Furthermore, risk-specific policies must also be integrated

(see the slippage between UK heat and cold wave policies, Wolf et al.,

2010a). However, further study of the role of institutions in influencing

vulnerability is called for (O’Brien et al., 2004b).

Chapter 2 Determinants of Risk: Exposure and Vulnerability

Governance is also a key topic for vulnerability and exposure.

Governance is broader than governmental actions; governance can be

understood as the structures of common governance arrangements and

processes of steering and coordination – including markets, hierarchies,

networks, and communities (Pierre and Peters, 2000). Institutionalized

rule systems and habitualized behavior and norms that govern society

and guide actors are representing governance structures (Adger, 2000;

Biermann et al., 2009). These formal and informal governance structures

also determine vulnerability, since they influence power relations, risk

perceptions, and constitute the context in which vulnerability, risk

reduction, and adaptation are managed.

Conflicts between formal and informal governance or governmental

and nongovernmental strategies and norms can generate additional

vulnerabilities for communities exposed to environmental change. An

example of these conflicts of formal and informal strategies is linked to

flood protection measures. While local people might expend resources

to deal with increasing flood events (e.g., adapting their livelihoods and

production patterns to changing flood regimes), formal adaptation

strategies, particularly in developing countries, prioritize structural

measures (e.g., dike systems or relocation strategies) that have severe

consequences for the vulnerability of communities dependent on local

ecosystem services, such as fishing and farming systems (see Birkmann,

2011a,b). These conflicts between formal and informal or governmental

and nongovernmental management systems and norms are an important

factor that increase vulnerability and reduce adaptive capacity of the

overall system (Birkmann et al., 2010b). Countries with institutional and

governance fragilities often lack the capacity to identify and reduce

risks and to deal with emergencies and disasters effectively. The recent

disaster and problems in coping and recovery in the aftermath of the

earthquake in Haiti or the problems in terms of managing recovery and

emergency management after the Pakistan floods are examples that

illustrate the importance of governance as a subject of resilience and

vulnerability.

In some developed countries, the last 30 years have witnessed a shift in

environmental governance practices toward more integrated approaches.

With the turn of the century, there has been recognition of the need to

move beyond technical solutions and to deal with the patterns and

drivers of unsustainable demand and consumption. This has resulted in

the emergence of a more integrated approach to environmental

management, a focus on prevention (UNEP, 2007), the incorporation of

knowledge from the local to the global in environment policies

(Karlsson, 2007), and co-management and involvement of stakeholders

from all sectors in the management of natural resources (Plummer, 2006;

McConnell, 2008), although some have also questioned the efficacy of

this new paradigm (Armitage et al., 2007; Sandstrom, 2009).

2.5.3. Economic Dimensions

Economic vulnerability can be understood as the susceptibility of an

economic system, including public and private sectors, to potential

(direct) disaster damage and loss (Rose, 2004; Mechler et al., 2010) and

refers to the inability of affected individuals, communities, businesses,

and governments to absorb or cushion the damage (Rose, 2004).

The degree of economic vulnerability is exhibited post-event by the

magnitude and duration of the indirect follow-on effects. These effects

can comprise business interruption costs to firms unable to access

inputs from their suppliers or service their customers, income losses of

households unable to get to work, or the deterioration of the fiscal

stance post-disasters as less taxes are collected and significant public

relief and reconstruction expenditure is required. At a macroeconomic

level, adverse impacts include effects on gross domestic product (GDP),

consumption, and the fiscal position (Mechler et al., 2010). Key drivers

of economic vulnerability are low levels of income and GDP, constrained

tax revenue, low domestic savings, shallow financial markets, and high

indebtedness with little access to external finance (OAS, 1991; Benson

and Clay, 2000; Mechler, 2004).

Economic vulnerability to external shocks, including natural hazards,

has been inexactly defined in the literature and conceptualizations

often have overlapped with risk, resilience, or exposure. One line of

research focusing on financial vulnerability, as a subset of economic

vulnerability, framed the problem in terms of risk preference and

aversion, a conceptualization more common to economists. Risk

aversion, in this context, denotes the ability of economic agents to

absorb risk financially (Arrow and Lind, 1970). There are many ways to

absorb the financial burdens of disasters, with market-based insurance

being one, albeit prominent, option, although more particularly in a

developed country context. Households as economic agents often use

informal mechanisms relying on family and relatives abroad or outside

a disaster area; governments may simply rely on their tax base or

international assistance. Yet, in the face of large and covariate risks,

such ad hoc mechanisms often break down, particularly in developing

countries (see Linnerooth-Bayer and Mechler, 2007).

Research on financial vulnerability to disasters has hitherto focused

on developing countries’ financial vulnerability describing financial

vulnerability as a country’s ability to access domestic and foreign

savings for financing post-disaster relief and reconstruction needs in

order to quickly recover and avoid substantial adverse ripple effects

(Mechler et al., 2006; Marulanda et al., 2008a; Cardona, 2009; Cummins

and Mahul, 2009). Reported and estimated substantial financial

vulnerability and risk aversion in many exposed countries, as well as the

emergence of novel public-private partnership instruments for pricing

and transferring catastrophe risks globally, has motivated developing

country governments, as well as development institutions, NGOs, and

other donor organizations, to consider pre-disaster financial instruments

as an important component of disaster risk management (Linnerooth-

Bayer et al., 2005).

There is a distinct scale aspect to the economic dimension of exposure

and vulnerability. While evidence of the economic costs of known

disasters indicate impacts may be under 10% of GDP (Wilbanks et al.,

Chapter 2Determinants of Risk: Exposure and Vulnerability

2007), at smaller and more local scales the costs can be significantly

greater. A lack of good data makes it difficult to provide meaningful and

specific assessments other than to acknowledge that, without investment

in adaptation and resilience building measures, the intensification or

increased frequency of extreme weather events is bound to impact GDP

growth in the future (Wilbanks et al., 2007).

Work and Livelihoods

At the individual and community levels, work and livelihoods are an

important facet of the economic dimension. These are often impacted

by extreme events and by the responses to extreme events.

Humanitarian/disaster relief in response to extreme events can induce

dependency and weaken local economic and social systems (Dudasik,

1982) but livelihood-based relief is of growing importance (Pantuliano

and Wekesa, 2008). Further, there is increasing recognition that

disasters and extreme events are stresses and shocks within livelihood

development processes (Cannon et al., 2003; see Kelman and Mather,

2008, for a discussion of cases applying to volcanic events).

Paavola’s (2008) analysis of livelihoods, vulnerability, and adaptation to

climate change in Morogoro, Tanzania, is indicative of the way extreme

events impact livelihoods in specific ways. Here, rural households are

found to be more vulnerable to climate variability and climate change

than are those in urban environments (see also Section 2.5.1.3). This is

because rural incomes and consumption levels are significantly lower,

there are greater levels of poverty, and more limited access to markets

and other services. More specifically, women are made more vulnerable

than men because they lack access to livelihoods other than climate-

sensitive agriculture. Local people have employed a range of strategies

(extensification, intensification, diversification, and migration) to

manage climate variability but these have sometimes had undesirable

environmental outcomes, which have increased their vulnerability. In

the absence of opportunities to fundamentally change their livelihood

options, we see here an example of short-term coping rather than long-

term climate adaptation (Paavola, 2008).

Human vulnerability to natural hazards and income poverty are largely

codependent (Adger, 1999; UNISDR, 2004) but poverty does not equal

vulnerability in a simple way (e.g., Blaikie et al., 1994); the determinants

and dimensions of poverty are complex as well as its association with

climate change (Khandlhela and May, 2006; Demetriades and Esplen,

2008; Hope, 2009). It is important to recognize that adaptation

measures need to specifically target climate extremes-poverty linkages

as not all poverty reduction measures reduce vulnerability to climate

extremes and vice versa. Further, measures are required across scales

because the drivers of poverty, although felt at a local level, may

necessitate tackling political and economic issues at a larger scale

(Eriksen and O’Brien, 2007; K. O’Brien et al., 2008).

Given the relationship between poverty and vulnerability, it can be

argued (Tol et al., 2004) that economic growth could reduce vulnerability

(with caveats). However, increasing economic growth would not

necessarily decrease climate impacts because it has the potential to

simultaneously increase greenhouse gas emissions. Furthermore,

growth is often reliant on critical infrastructure which itself may be

affected by extreme events. There are many questions still to be

answered by research about the impacts of varying economic policy

changes including the pursuit of narrow development trajectories

and how this might shape vulnerability (Tol et al., 2004; UNDP, 2004;

UNISDR, 2004)

2.5.4. Interactions, Cross-Cutting Themes, and Integrations

This section began by breaking down the vulnerability concept into its

constitutive dimensions, with evidence derived from a number of

discrete research and policy communities (e.g., disaster risk reduction;

climate change adaptation; environmental management; and poverty

reduction) that have largely worked independently (Thomalla et al.,

2006). Increasingly it is recognized that collaboration and integration is

necessary both to set appropriate policy agendas and to better

understand the topic of interest (K. O’Brien et al., 2008), although

McLaughlin and Dietz (2008) have made a critical analysis of the

absence of an integrated perspective on the interrelated dynamics of

social structure, human agency, and the environment.

Reviewing singular dimensions of vulnerability cannot provide an

appropriate level of synthesis. Considerable conceptual advances arose

from the early recognition that so-called natural disasters were not

‘natural’ at all (O’Keefe et al., 1976) but were the result of structural

inequalities rooted in political economy. This critique required analysis

of more than the hazard component (Blaikie et al., 1994). Further, it

demonstrated how crossing disciplinary and other boundaries (e.g.,

those separating disaster and development, or developed and developing

countries) can be fruitful in better understanding extremes of various

kinds (see Hewitt, 1983). If we consider food security/vulnerability (as

just one example), an inclusive analysis of the vulnerability of food

systems (to put it broadly), must take account of aspects related to, inter

alia: physical location in susceptible areas; political economy (Watts and

Bohle, 1993); entitlements in access to resources (Sen, 1981); social

capital and networks (Eriksen et al., 2005); landscape ecology (Fraser,

2006); human ecology (Bohle et al., 1994); and political ecology (Pulwarty

and Riebsame, 1997; Holling, 2001; see Chapter 4 for further discussion

of food systems and food security). More generally, in relation to hazards,

disaster risk reduction, and climate extremes, productive advances have

been made in research adopting a coupled human/social-environment

systems approach (Holling, 2001; Turner et al., 2003b) which recognizes

the importance of integrating often separate domains. For example, in

analyzing climate change impacts, vulnerability, and adaptation in

Norway, O’Brien et al. (2006) argue that a simple examination of direct

climate change impacts underestimates the, perhaps more serious and

larger, synergistic impacts. They use an example of projected climate

change effects in the Barents Sea, which may directly impact keystone

fish species. However, important as this finding is, climate change may

Chapter 2 Determinants of Risk: Exposure and Vulnerability

also influence the transport sector (through reduction in ice cover);

increase numbers of pollution events (through increased maritime

transport of oil and other goods); may risk ecological and other damages

as a result of competition from introduced species in ballast water;

which, in turn, are aggravated by increases in ocean temperatures.

Neither the potential level of impact nor the processes of adaptation are

best represented by a singular focus on a particular sector but must

consider interactions between sectors and institutional, economic,

social, and cultural conditions (O’Brien et al., 2006).

2.5.4.1. Intersectionality and Other Dimensions

The dimensions discussed above generate differential effects but it is

important to consider not just differences between single categories

(e.g., between women and men) but the differences within a given

category (e.g., ‘women’). This refers to intersectionality, where, for

example, gender may be a significant variable but only when allied with

race/ethnicity or some other variable. In Hurricane Katrina, it mattered

(it still matters) whether you were black or white, upper class or work-

ing class, home owner or renter, old or young, woman or man in terms

of relative exposure and vulnerability factors (Cutter et al., 2006; Elliott

and Pais, 2006).

Certain factors are identified as cross-cutting themes of particular

importance for understanding the dynamic changes within exposure,

vulnerability, and risk. In the Sphere Project’s minimum standards in

humanitarian response, children, older people, persons with disabilities,

gender, psychosocial issues, HIV and AIDS, and environment, climate

change, and disaster risk reduction are identified as cross-cutting

themes and must be considered, not as separate sectors, which people

may or may not select for attention, but must be integrated within each

sector (Sphere Project, 2011). Exactly which topics are selected as cross-

cutting themes, to be incorporated throughout an activity, is context-

specific. Below, we consider just two: different timing (diachronic

aspects within a single day or across longer time periods) and different

spatial and functional scales.

2.5.4.2. Timing, Spatial, and Functional Scales

Cross-cutting themes of particular importance for understanding the

dynamic changes within exposure, vulnerability, and risk are different

timing (diachronic aspects within a single day or across longer time

periods) and different spatial and functional scales.

2.5.4.2.1. Timing and timescales

Timing and timescales are important cross-cutting themes that need

more attention when dealing with the identification and management

of extreme climate and weather events, disasters, and adaptation

strategies. The first key issue when dealing with timing and timescales

is the fact that different hazards and their recurrence intervals might

fundamentally change in terms of the time dimension. This implies that

the identification and assessment of risk, exposure, and vulnerability

needs also to deal with different time scales and in some cases might

need to consider different time scales. At present most of the climate

change scenarios focus on climatic change within the next 100 or

200 years, while often the projections of vulnerability just use present

socioeconomic data. However, a key challenge for enhancing knowledge

of exposure and vulnerability as key determinants of risk requires

improved data and methods to project and identify directions and

different development pathways in demographic, socioeconomic, and

political trends that can adequately illustrate potential increases or

decreases in vulnerability with the same time horizon as the changes in

the climate system related to physical-biogeochemical projections (see

Birkmann et al., 2010b).

Furthermore, the time dependency of risk analysis, particularly if the

analysis is conducted at a specific point in time, has been shown to be

critical. Newer research underlines that exposure – especially the

exposure of different social groups – is a highly dynamic element that

changes not only seasonally, but also during the day and over different

days of the week (e.g., Setiadi, 2011). Disasters also exacerbate pre-

disaster trends in vulnerability (Colten et al., 2008).

Consequently, time scales and dynamic changes over time have to be

considered carefully when conducting risk and vulnerability assessments

for extreme events and creeping changes in the context of climate change.

Additionally, changes in the hazard frequency and timing of hazard

occurrence during the year will have a strong impact on the ability of

societies and ecosystems to cope and adapt to these changes.

The timing of events may also create ‘windows of vulnerability,’ periods in

which the hazards are greater because of the conjunction of circumstances

(Dow, 1992). Time is a cross-cutting dimension that always needs to be

considered but particularly so in the case of anthropogenic climate change,

which may be projected some years into the future (Füssel, 2005). In

fact, this time dimension is regarded (Thomalla et al., 2006) as a key

difference between the disaster management and climate change

communities. To generalize somewhat, the former group typically

(with obvious exceptions like slow-onset hazards such as drought or

desertification) deals with fast-onset events, in discrete, even if extensive,

locations, requiring immediate action. The latter group typically focuses on

conditions that occur in a dispersed form over lengthy time periods and

which are much more challenging in their identification and measurement

(Thomalla et al., 2006). Risk perception may be reduced (Leiserowitz,

2006) for such events remote in time and/or space, such as some climate

change impacts are perceived to be. Thus, in this conceptualization,

different time scales are an important constraint when dealing with the

link between disaster risk reduction and climate change adaptation (see

Thomalla et al., 2006; Birkmann and von Teichman, 2010).

However, it is important to also acknowledge that disaster risk reduction

considers risk reduction within different time frames; it encompasses

Chapter 2Determinants of Risk: Exposure and Vulnerability

short-term emergency management/response strategies and long-term

risk reduction strategies, for example, building structures to resist

10,000-year earthquakes or flood barriers to resist 1,000-year storm

surges. Modern prospective risk management debates involve security

considerations decades ahead for production, infrastructure, houses,

hospitals, etc.

2.5.4.2.2. Spatial and functional scales

Spatial and functional scales are another cross-cutting theme that is of

particular relevance when dealing with the identification of exposure and

vulnerability to extreme events and climate change. Leichenko and O’Brien

(2002) conclude that in many areas of climate change and natural hazards

societies are confronted with dynamic vulnerability, meaning that

processes and factors that cause vulnerability operate simultaneously at

multiple scales making traditional indicators insufficient. Leichenko and

O’Brien (2002) analyze a complex mix of influences (both positive and

negative) on the vulnerability, and coping and adaptive capacity of

southern African farmers in dealing with climate variability. These

include the impacts of globalization on national-level policies and local-

level experiences (e.g., structural adjustment programs reducing local-

level agricultural subsidies on the one hand, and on the other, trade

liberalization measures opening up new opportunities through

diversification of production in response to drought). Also Turner et al.

(2003a,b) stress that vulnerability and resilience assessments need to

consider the influences on vulnerability from different scales, however,

the practical application and analysis of these interacting influences on

vulnerability from different spatial scales is a major challenge and in most

cases not sufficiently understood. Furthermore, vulnerability analysis

particularly linked to the identification of institutional vulnerability has

also to take into account the various functional scales that climate

change, natural hazards, and vulnerability as well as administrative

systems operate on. In most cases, current disaster management

instruments and measures of urban or spatial planning as well as water

management tools (specific plans, zoning, norms) operate on different

functional scales compared to climate change. Even the various hazards

that climate change may modify encompass different functional scales

that cannot be sufficiently captured with one approach. For example,

policy setting and management of climate change and of disaster risk

reduction are usually the responsibility of different institutions or

departments, thus it is a challenge to develop a coherent and integrated

strategy (Birkmann and von Teichman, 2010). Consequently, functional

and spatial scale mismatches might even be part of institutional

vulnerabilities that limit the ability of governance system to adequately

respond to hazards and changes induced by climate change.

2.5.4.3. Science and Technology

Science and technology possess the potential to assist with adaptation

to extreme climate events, however there are a number of factors that

determine the ultimate utility of technology for adaptation. These

include an understanding of the range of technologies available, the

identification of the appropriate role for technology, the process of

technology transfer, and the criteria applied in selection of the technology

(Klein et al., 2006). For major sectors such as water, agriculture, and

health a range of possible so-called ‘hard’ and ‘soft’ technologies exist

such as irrigation and crop rotation pattern (Klein et al., 2006) or the

development of drought-resistant crops (IAASTD, 2009) in the case of

the agricultural sector.

Although approaches alternative to pure science- and technology-

based ones have been suggested for decreasing vulnerability (Haque

and Etkin, 2007; Marshall and Picou, 2008), such as blending western

science and technology with indigenous knowledge (Mercer et al., 2010)

and ecological cautiousness and the creation of eco-technologies with

a pro-nature, pro-poor, and pro-women orientation (Kesavan and

Swaminathan, 2006), their efficacy in the context of risk and vulnerability

reduction remain undetermined.

The increasing integration of a range of emerging weather and climate

forecasting products into early warning systems (Glantz, 2003) has

helped reduce exposure to extreme climate events because of an

increasing improvement of forecast skill over a range of time scales

(Goddard et al., 2009; Stockdale et al., 2009; van Aalst, 2009; Barnston

et al., 2010; Hellmuth et al., 2011). Moreover, there is an increasing use

of weather and climate information for planning and climate risk

management in business (Changnon and Changnon, 2010), food

security (Verdin et al., 2005), and health (Ceccato et al., 2007; Degallier

et al., 2010) as well as the use of technology for the development of a

range of decision support tools for climate-related disaster management

(van de Walle and Turoff, 2007).

2.6. Risk Identification and Assessment

Risk accumulation, dynamic changes in vulnerabilities, and the different

phases of crises and disaster situations constitute a complex environment

for identifying and assessing risks and vulnerabilities, risk reduction

measures, and adaptation strategies. Understanding of extreme events

and disasters is a pre-requisite for the development of adaptation

strategies in the context of climate change and risk reduction in the

context of disaster risk management.

Current approaches to disaster risk management typically involve four

distinct public policies or components (objectives) (IDEA, 2005; Carreño,

2006; IDB, 2007; Carreño et al., 2007b):

1) Risk identification (involving individual perception, evaluation of

risk, and social interpretation)

2) Risk reduction (involving prevention and mitigation of hazard or

vulnerability)

3) Risk transfer (related to financial protection and in public

investment)

4) Disaster management (across the phases of preparedness, warnings,

response, rehabilitation, and reconstruction after disasters).

Chapter 2 Determinants of Risk: Exposure and Vulnerability

The first three actions are mainly ex ante – that is, they take place in

advance of disaster – and the fourth refers mainly to ex post actions,

although preparedness and early warning do require ex ante planning

(Cardona, 2004; IDB, 2007). Risk identification, through vulnerability and

risk assessment can produce common understanding by the stakeholders

and actors. It is the first step for risk reduction, prevention, and transfer,

as well as climate adaptation in the context of extremes.

2.6.1. Risk Identification

Understanding risk factors and communicating risks due to climate

change to decisionmakers and the general public are key challenges.

These challenges include developing an improved understanding of

underlying vulnerabilities, and societal coping and response capacities.

There is high confidence that the selection of appropriate vulnerability

and risk evaluation approaches depends on the decisionmaking context.

The promotion of a higher level of risk awareness regarding climate

change-induced hazards and changes requires an improved understanding

of the specific risk perceptions of different social groups and individuals,

including those factors that influence and determine these perceptions,

such as beliefs, values, and norms. This also requires attention for

appropriate formats of communication that characterize uncertainty

and complexity (see, e.g., Patt et al., 2005; Bohle and Glade, 2008; Renn,

2008, pp. 289; Birkmann et al., 2009; ICSU-LAC, 2011a,b, p. 15).

Appropriate information and knowledge are essential prerequisites for

risk-aware behavior and decisions. Specific information and knowledge

on the dynamic interactions of exposed and vulnerable elements

include livelihoods and critical infrastructures, and potentially damaging

events, such as extreme weather events or potential irreversible

changes such as sea level rise. Based on the expertise of disaster risk

research and findings in the climate change and climate change

adaptation community, requirements for risk understanding related to

climate change and extreme events particularly encompass knowledge

of various elements (Kasperson et al., 2005; Patt et al., 2005; Renn and

Graham, 2006; Biermann, 2007; Füssel, 2007; Bohle and Glade, 2008;

Cutter and Finch, 2008; Renn, 2008; Biermann et al., 2009, Birkmann et

al., 2009, 2010b; Cardona, 2010; Birkmann, 2011a; ICSU-LAC, 2011a,b),

including:

• Processes by which persons, property, infrastructure, goods, and

the environment itself are exposed to potentially damaging events,

for example, understanding exposure in its spatial and temporal

dimensions.

• Factors and processes that determine or contribute to the

vulnerability of persons and their livelihoods or of socio-ecological

systems. This includes an understanding of increases or decreases

in susceptibility and response capacity, including the distribution of

socio- and economic resources that make people more vulnerable

or that increase their level of resilience.

• How climate change affects hazards, particularly regarding

processes by which human activities in the natural environment or

changes in socio-ecological systems lead to the creation of new

hazards (e.g., NaTech hazards), irreversible changes, or increasing

probabilities of hazard events occurrence.

• Different tools, methodologies, and sources of knowledge (e.g.,

expert/scientific knowledge, local or indigenous knowledge) that

allow capturing new hazards, risk, and vulnerability profiles, as well

as risk perceptions. In this context, new tools and methodologies

are also needed that allow for the evaluation, for example, of new

risks (sea level rise) and of current adaptation strategies.

• How risks and vulnerabilities can be modified and reconfigured

through forms of governance, particularly risk governance –

encompassing formal and informal rule systems and actor

networks at various levels. Furthermore, it is essential to improve

knowledge on how to promote adaptive governance within the

framework of risk assessment and risk management.

• Adaptive capacity status and limits of adaptation. This includes the

need to assess potential capacities for future hazards and for

dealing with uncertainty. Additionally, more knowledge is needed

on the various and socially differentiated limits of adaptation.

These issues also imply an improved understanding on how different

adaptation measures influence resilience and adaptive capacities.

2.6.2. Vulnerability and Risk Assessment

The development of modern risk analysis and assessments were closely

linked to the establishment of scientific methodologies for identifying

causal links between adverse health effects and different types of

hazardous events and the mathematical theories of probability (Covello

and Mumpower, 1985). Today, risk and vulnerability assessments

encompass a broad and multidisciplinary research field. In this regard,

vulnerability and risk assessments can have different functions and

goals.

Risk and vulnerability assessment depend on the underlying

understanding of the terms. In this context, two main schools of

thought can be differentiated. The first school of thought defines risk

as a decision by an individual or a group to act in such a way that the

outcome of these decisions can be harmful (Luhmann, 2003; Dikau and

Pohl, 2007). In contrast, the disaster risk research community views risk

as the product of the interaction of a potentially damaging event and

the vulnerable conditions of a society or element exposed (UNISDR,

2004; IPCC, 2007).

Vulnerability and risk assessment encompass various approaches and

techniques ranging from indicator-based global or national assessments

to qualitative participatory approaches of vulnerability and risk assessment

at the local level. They serve different functions and goals (see IDEA,

2005; Birkmann, 2006a; Cardona, 2006; Dilley, 2006; Wisner, 2006a;

IFRC, 2008; Peduzzi et al., 2009).

Risk assessment at the local level presents specific challenges related to

a lack of data (including climate data at sufficient resolution, but also

Chapter 2Determinants of Risk: Exposure and Vulnerability

socioeconomic data at the lowest levels of aggregation) but also the

highly complex and dynamic interplay between the capacities of the

communities (and the way they are distributed among community

members, including their power relationships) and the challenges they

face (including both persistent and acute aspects of vulnerability).

To inform risk management, it is desirable that risk assessments are

locally based and result in increased awareness and a sense of local

ownership of the process and the options that may be employed to

address the risks. Several participatory risk assessment methods, often

based on participatory rural appraisal methods, have been adjusted to

explicitly address changing risks in a changing climate. Examples of

guidance on how to assess climate vulnerability at the community level

are available from several sources (see Willows and Connell, 2003;

Moench and Dixit, 2007; van Aalst et al., 2007; CARE, 2009; IISD et al.,

2009; Tearfund, 2009). In integrating climate change, a balance needs to

be struck between the desire for a sophisticated assessment that includes

high-quality scientific inputs and rigorous analysis of the participatory

findings, and the need to keep the process simple, participatory, and

implementable at scale. Chapter 5 provides further details on the

implementation of risk management at local levels.

The International Standards Organization defines risk assessment as a

process to comprehend the nature of risk and to determine the level of

risk (ISO, 2009a,b). Additionally, communication within risk assessment

and management are seen as key elements of the process (Renn, 2008).

More specifically, vulnerability and risk assessment deal with the

identification of different facets and factors of vulnerability and risk, by

means of gathering and systematizing data and information, in order to

be able to identify and evaluate different levels of vulnerability and risk

of societies – social groups and infrastructures – or coupled socio-

ecological systems at risk. A common goal of vulnerability and risk

assessment approaches is to provide information about profiles, patterns

of, and changes in risk and vulnerability (see, e.g., IDEA, 2005; Birkmann,

2006a; Cardona, 2008; IFRC, 2008), in order to define priorities, select

alternative strategies, or formulate new response strategies. In this

context, the Hyogo Framework for Action stresses “that the starting

point for reducing disaster risk and for promoting a culture of disaster

resilience lies in the knowledge of the hazards and the physical, social,

economic, and environmental vulnerabilities to disasters that most

societies face, and of the ways in which hazards and vulnerabilities are

changing in the short and long term, followed by action taken on the

basis of that knowledge” (UN, 2005).

Vulnerability and risk assessments are key strategic activities that

inform both disaster risk management and climate change adaptation.

These require the use of reliable methodologies that allow an adequate

estimation and quantification of potential losses and consequences to

the human systems in a given exposure time.

Risk estimates are thus intended to be prospective, anticipating

scientifically possible hazard events that may occur in the future.

Usually technical risk analyses have been associated with probabilities.

Taking into account epistemic and aleatory uncertainties the probabilistic

estimations of risk attempt to forecast damage or losses even where

insufficient data are available on the hazards and the system being

analyzed (UNDRO, 1980; Fournier d’Albe, 1985; Spence and Coburn,

1987; Blockley, 1992; Coburn and Spence, 1992; Sheldon and Golding,

1992; Woo, 1999; Grossi and Kunreuther, 2005; Cardona et al., 2008a,b;

Cardona 2011). In most cases, approaches and criteria for simplification

and for aggregation of different information types and sources are used,

due to a lack of data or the inherent low resolution of the information. This

can result in some scientific or technical and econometric characteristics,

accuracy, and completeness that are desirable features when the risk

evaluation is the goal of the process (Cardona et al., 2003b). Measures

such as loss exceedance curves and probable maximum loss for different

event return periods are of particular importance for the stratification of

risk and the design of disaster risk intervention strategy considering risk

reduction, prevention, and transfer (Woo, 1999; Grossi and Kunreuther,

2005; Cardona et al., 2008a,b; ERN-AL, 2011; UNISDR, 2011). However,

it is also evident that more qualitative-oriented risk assessment

approaches are focusing on deterministic approaches and the profiling

of vulnerability using participatory methodologies (Garret, 1999).

Vulnerability and risk indicators or indices are feasible techniques for

risk monitoring and may take into account both the harder aspects of

risk as well as its softer aspects. The usefulness of indicators depends

on how they are employed to make decisions on risk management

objectives and goals (Cardona et al., 2003a; IDEA, 2005; Cardona, 2006,

2008, 2010; Carreño et al., 2007b).

However, quantitative approaches for assessing vulnerability need to be

complemented with qualitative approaches to capture the full complexity

and the various tangible and intangible aspects of vulnerability in its

different dimensions. It is important to recognize that complex systems

involve multiple variables (physical, social, cultural, economic, and

environmental) that cannot be measured using the same methodology.

Physical or material reality have a harder topology that allows the use

of quantitative measure, while collective and historical reality have a

softer topology in which the majority of the attributes are described in

qualitative terms (Munda, 2000). These aspects indicate that a weighing

or measurement of risk involves the integration of diverse disciplinary

perspectives. An integrated and interdisciplinary focus can more

consistently take into account the nonlinear relations of the parameters,

the context, complexity, and dynamics of social and environmental systems,

and contribute to more effective risk management by the different

stakeholders involved in risk reduction or adaptation decisionmaking.

Results can be verified and risk management/adaptation priorities can

be established (Carreño et al., 2007a, 2009).

To ensure that risk and vulnerability assessments are also understood,

the key challenges for future vulnerability and risk assessments, in the

context of climate change, are, in particular, the promotion of more

integrative and holistic approaches; the improvement of assessment

methodologies that also account for dynamic changes in vulnerability,

exposure, and risk; and the need to address the requirements of

Chapter 2 Determinants of Risk: Exposure and Vulnerability

Chapter 2Determinants of Risk: Exposure and Vulnerability

Box 2-3 | Developing a Regional Common Operating Picture of Vulnerability

in the Americas for Various Kinds of Decisionmakers

The Program of Indicators of Disaster Risk and Risk Management for the Americas of the Inter-American Development Bank (IDEA, 2005;

Cardona, 2008, 2010) provides a holistic approach to relative vulnerability assessment using social, economic, and environmental indicators

and a metric for sovereign fiscal vulnerability assessment taking into account that extreme impacts can generate financial deficit due to

a sudden elevated need for resources to restore affected inventories or capital stock.

The Prevalent Vulnerability Index (PVI) depicts predominant vulnerability conditions of the countries over time to identify progresses and

regressions. It provides a measure of direct effects (as result of exposure and susceptibility) as well as indirect and intangible effects of

hazard events (as result of socioeconomic fragilities and lack of resilience). The indicators used are made up of a set of demographic,

socioeconomic, and environmental national indicators that reflect situations, causes, susceptibilities, weaknesses, or relative absences of

development affecting the country under study. The indicators are selected based on existing indices, figures, or rates available from

reliable worldwide databases or data provided by each country. These vulnerability conditions underscore the relationship between risk

and development. Figure 2-1 shows the aggregated PVI (Exposure, Social Fragility, Lack of Resilience) for 2007.

Vulnerability and therefore risk are also the result of unsustainable economic growth and deficiencies that may be corrected by means of

adequate development processes, reducing susceptibility of exposed assets, socioeconomic fragilities, and improving capacities and

resilience of society (IDB, 2007). The information provided by an index such as the PVI can prove useful to ministries of housing and

urban development, environment, agriculture, health and social welfare, economy, and planning. The main advantage of PVI lies in its

ability to disaggregate results and identify factors that may take priority in risk management actions as corrective and prospective measures

or interventions of vulnerability from a development point of view. The PVI can be used at different territorial levels, however often the

indicators used by the PVI are only available at the national level; this is a limitation for its application at other sub-national scales.

On the other hand, future disasters have been identified as contingency liabilities and could be included in the balance of each nation.

As pension liabilities or guaranties that the government has to assume for the credit of territorial entities or due to grants, disaster

34.2

50.4

46.6

50.3

60.8

56.0

55.7

45.8

38.4

51.1

52.0

64.9

65.9

60.2

61.4

73.9

65.4

68.0

25.0

17.4

25.7

27.0

28.7

23.3

22.5

27.7

32.6

33.0

46.2

20.5

34.1

39.7

38.3

43.5

35.1

40.4

15.1

15.5

23.5

18.5

16.7

26.8

25.7

33.6

54.6

34.6

30.3

45.1

37.1

42.0

42.4

30.2

53.6

47.5

Prevalent Vulnerability Index (PVI) Evaluated for 2007

Exposure and Susceptibility

Socioeconomic Fragilities

Lack of Resilience

Chile

Argentina

Mexico

Colombia

Peru

Ecuador

Bolivia

Panama

Barbados

Costa Rica

Belize

Trinidad & Tobago

Dominican Republic

Honduras

El Salvador

Guatemala

Jamaica

Nicaragua

0 20 40 60 80 100 120 140 160

Figure 2-1 | Aggregate Prevalent Vulnerability Index (PVI) for 19 countries of the Americas for 2007. Source: Cardona, 2010.

Continued next page

decisionmakers and the general public. Many assessments still focus

solely on one dimension, such as economic risk and vulnerability. Thus,

they consider a very limited set of vulnerability factors and dimensions.

Some approaches, e.g., at the global level, view vulnerability primarily with

regard to the degree of experienced loss of life and economic damage (see

Dilley et al., 2005; Dilley 2006). A more integrative and holistic perspective

captures a greater range of dimensions and factors of vulnerability and

disaster risk. Successful adaptation to climate change has been based

on a multi-dimensional perspective, encompassing, for example, social,

economic, environmental, and institutional aspects. Hence, risk and

vulnerability assessments – that intend to inform these adaptation

strategies – require also a multi-dimensional perspective.

Chapter 2 Determinants of Risk: Exposure and Vulnerability

reposition costs are liabilities that become materialized when the hazard events occur. The Disaster Deficit Index (DDI) provides an

estimation of the extreme impact (due to hurricane, floods, tsunami, earthquake, etc.) during a given exposure time and the financial

ability to cope with such a situation. The DDI captures the relationship between the loss that the country could experience when an

extreme impact occurs (demand for contingent resources) and the public sector’s economic resilience – that is, the availability of funds

to address the situation (restoring affected inventories). This macroeconomic risk metric underscores the relationship between extreme

impacts and the capacity to cope of the government. Figure 2-2 shows the DDI for 2008.

A DDI greater than 1.0 reflects the country’s inability to cope with extreme disasters, even when it would go into as much debt as

possible: the greater the DDI, the greater the gap between the potential losses and the country’s ability to face them. This disaster risk

figure is interesting and useful for a Ministry of Finance and Economics. It is related to the potential financial sustainability problem of

the country regarding the potential disasters. On the other hand, the DDI gives a compressed picture of the fiscal vulnerability of the

country due to extreme impacts. The DDI has been a guide for economic risk management; the results at national and sub-national levels

can be studied by economic, financial, and planning analysts, who can evaluate the potential budget problem and the need to take into

account these figures in the financial planning.

0123456780 10,000 20,000 30,000

Chile

Mexico

Argentina

Trinidad & Tobago

Costa Rica

Ecuador

Bolivia

Colombia

Jamaica

Peru

Guatemala

Panama

El Salvador

Nicaragua

Belize

Dominican Republic

Barbados

Honduras

1.47

1.46

0.96

0.80

0.46

0.37

0.42

6.96

5.75

5.41

4.59

4.55

3.42

2.84

2.73

2.47

1.85

2.40

6,942

17,544

5,664

1,197

1,616

4,043

2,887

3,540

3,103

426

7,818

1,420

3,984

2,346

2,270

8,223

26,289

23,256

Disaster Deﬁcit Index (DDI) for 500-year Return Period

Evaluated for 2008

Probable Maximum Loss (PML) for 500-year Return Period

Evaluated for 2008

$US millions

Figure 2-2 | Disaster deficit index (DDI) and probable maximum loss (PML) in 500 years for 19 countries of the Americas for 2008. Source: Cardona, 2010.

Assessment frameworks with integrative and holistic perspectives have

been developed by Turner et al. (2003a), Birkmann (2006b), and Cardona

(2001). Key elements of these holistic views are the identification of

causal linkages between factors of vulnerability and risk and the

interventions (structural, non-structural) that nations, societies, and

communities or individuals make to reduce their vulnerability or exposure

to hazards. Turner et al. (2003a) underline the need to focus on different

scales simultaneously, in order to capture the linkages between different

scales (local, national, regional, etc.). The influences and linkages

between different scales can be difficult to capture, especially due to

their dynamic nature during and after disasters, for example, through

inputs of external disaster aid (Cardona, 1999a,b; Cardona and Barbat,

2000; Turner et al., 2003a; Carreño et al., 2005, 2007a, 2009; IDEA,

2005; Birkmann, 2006b; ICSU-LAC, 2011a,b).

Several methods have been proposed to measure vulnerability from a

comprehensive and multidisciplinary perspective. In some cases composite

indices or indicators intend to capture favorable conditions for direct

physical impacts – such as exposure and susceptibility – as well as indirect

or intangible impacts of hazard events – such as socio-ecological fragilities

or lack of resilience (IDEA, 2005; Cardona, 2006; Carreño et al., 2007a).

In these holistic approaches, exposure and physical susceptibility are

representing the ‘hard’ and hazard-dependent conditions of vulnerability.

On the other hand, the propensity to suffer negative impacts as a result

of the socio-ecological fragilities and not being able to adequately cope

and anticipate future disasters can be considered ‘soft’ and usually

non-hazard dependent conditions, that aggravate the impact. Box 2-3

describes two of these approaches, based on relative indicators, useful

for monitoring vulnerability of countries over time and to communicate

it to country’s development and financial authorities in their own

language.

To enhance disaster risk management and climate change adaptation,

risk identification and vulnerability assessment may be undertaken in

different phases, that is, before, during, and even after disasters occur.

This includes, for instance, the evaluation of the continued viability of

measures taken and the need for further or different adaptation/risk

management measures. Although risk and vulnerability reduction are the

primary actions to be conducted before disasters occur, it is important

to acknowledge that ex post and forensic studies of disasters provide a

laboratory in which to study risk and disasters as well as vulnerabilities

revealed (see Birkmann and Fernando, 2008; ICSU-LAC, 2011a,b).

Disasters draw attention to how societies and socio-ecological processes

are changing and acting in crises and catastrophic situations, particularly

regarding the reconfiguration of access to different assets or the role

of social networks and formal organizations (see Bohle, 2008). It is

noteworthy that, until today, many post-disaster processes and strategies

have failed to integrate aspects of climate change adaptation and long-

term risk reduction (see Birkmann et al., 2009, 2010a).

In the broader context of the assessments and evaluations, it is also

crucial to improve the different methodologies to measure and evaluate

hazards, vulnerability, and risks. The disaster risk research has paid more

attention to sudden-onset hazards and disasters such as floods, storms,

tsunamis, etc., and less on the measurement of creeping changes and

integrating the issue of tipping points into these assessments (see also

Section 3.1.7). Therefore, the issue of measuring vulnerability and risk, in

terms of quantitative and qualitative measures also remains a challenge.

Lastly, the development of appropriate assessment indicators and

evaluation criteria would also be strengthened if respective integrative

and consistent goals for vulnerability reduction and climate change

adaptation could be defined for specific regions, such as coastal,

mountain, or arid environments. Most assessments to date have based

their judgment and evaluation on a relative comparison of vulnerability

levels between different social groups or regions.

There is medium evidence (given the generally limited amount of

long-term evaluations of impacts of adaptation and risk management

interventions and complications associated with such assessments), but

high agreement that adaptation and risk management policies and

practices will be more successful if they take the dynamic nature of

vulnerability and exposure into account, including the explicit

characterization of uncertainty and complexity (Cardona 2001, 2011;

Hilhorst, 2004, ICSU-LAC, 2010, Pelling, 2010). Projections of the impacts

of climate change can be strengthened by including storylines of changing

vulnerability and exposure under different development pathways.

Appropriate attention to the dynamics of vulnerability and exposure is

particularly important given that the design and implementation of

adaptation and risk management strategies and policies can reduce risk

in the short term, but may increase vulnerability and exposure over the

longer term. For instance, dike systems can reduce hazard exposure by

offering immediate protection, but also encourage settlement patterns

that may increase risk in the long term. For instance, in the 40-year span

between Hurricanes Betsy and Katrina, protective works – new and

improved levees, drainage pumps, and canals – successfully protected

New Orleans and surrounding parishes against three hurricanes in 1985,

1997, and 1998. These works were the basis for the catastrophe of

Katrina, having enabled massive development of previously unprotected

areas and the flooding of these areas that resulted when the works

themselves were shown to be inadequate (Colten et al., 2008). For other

examples, see Décamps (2010).

The design of public policy on disaster risk management is related to the

method of evaluation used to orient policy formulation. If the diagnosis

invites action it is much more effective than where the results are limited

to identifying the simple existence of weaknesses or failures. The main

quality attributes of a risk model are represented by its applicability,

transparency, presentation, and legitimacy (Corral, 2000). For more

details see Cardona (2004, 2011).

Several portfolio-level climate risk assessment methods for development

agencies have paid specific attention to the risk of variability and

extremes (see, e.g., Burton and van Aalst, 1999, 2004; Klein, 2001; van

Aalst, 2006b; Klein et al., 2007; Agrawala and van Aalst, 2008; Tanner,

2009). Given the planning horizons of most development projects

(typically up to about 20 years), even if the physical lifetime of the

Chapter 2Determinants of Risk: Exposure and Vulnerability

investment may be much longer, and need to combine attention to current

and future risks, these tools provide linkages between adaptation to

climate change and enhanced disaster risk management even in light of

current hazards. For more details on the implementation of risk

management at the national level, see Chapter 6.

2.6.3. Risk Communication

How people perceive a specific risk is a key issue for risk management

and climate change adaptation effectiveness (e.g., Burton et al., 1993;

Alexander, 2000; Kasperson and Palmlund, 2005; van Sluis and van Aalst,

2006; ICSU-LAC, 2011a,b) since responses are shaped by perception of

risk (Grothmann and Patt, 2005; Wolf et al., 2010b; Morton et al., 2011).

Risk communication is a complex cross-disciplinary field that involves

reaching different audiences to make risk comprehensible, understanding

and respecting audience values, predicting the audience’s response to

the communication, and improving awareness and collective and

individual decisionmaking (e.g., Cardona, 1996c; Mileti, 1996; Greiving,

2002; Renn, 2008). Risk communication failures have been revealed in

past disasters, such as Hurricane Katrina in 2005 or the Pakistan floods

in 2010 (DKKV, 2011). Particularly, the loss of trust in official institutions

responsible for early warning and disaster management were a key

factor that contributed to the increasing disaster risk. Effective and

people-centered risk communication is therefore a key to improve

vulnerability and risk reduction in the context of extreme events,

particularly in the context of people-centered early warning (DKKV,

2011). Weak and insufficient risk communication as well as the loss of

trust in government institutions in the context of early warning or climate

change adaptation can be seen as a core component of institutional

vulnerability.

Risk assessments and risk identification have to be linked to different types

and strategies of risk communication. Risk communication or the failure

of effective and people-centered risk communication can contribute to

an increasing vulnerability and disaster risk. Knowledge on factors that

determine how people perceive and respond to a specific risk or a set

of multi-hazard risks is key for risk management and climate change

adaptation (see Grothmann and Patt 2005; van Aalst et al., 2008).

Understanding the ways in which disasters are framed requires more

information and communication about vulnerability factors, dynamic

temporal and spatial changes of vulnerability, and the coping and

response capacities of societies or social-ecological systems at risk (see

Turner et al., 2003a; Birkmann, 2006a,b,c; Cardona, 2008; Cutter and

Finch, 2008; ICSU-LAC, 2011a,b). ‘Framing’ refers to the way a particular

problem is presented or viewed. Frames are shaped by knowledge of

and underlying views of the world (Schon and Rein, 1994). It is related

to the organization of knowledge that people have about their world in

the light of their underlying attitudes toward key social values (e.g.,

nature, peace, freedom), their notions of agency and responsibility (e.g.,

individual autonomy, corporate responsibility), and their judgments about

reliability, relevance, and weight of competing knowledge claims (Jasanoff

and Wynne, 1997). ‘Early warning’ implies information interventions

into an environment in which much about vulnerability is assumed. In

this regard, risk communication is not solely linked to a top-down

communication process, rather effective risk communication requires

recognition of communication as a social process meaning that risk

communication also deals with local risk perceptions and local framing

of risk. Risk communication thus functions also as a tool to upscale local

knowledge and needs (bottom-up approach). Therefore, effective risk

communication achieves both informing people at risk about the key

determinants of their particular risks and of impending disaster risk (early

warning), and also engages different stakeholders in the definition of a

problem and the identification of respective solutions (see van Aalst et al.,

2008).

Climate change adaptation strategies as well as disaster risk reduction

approaches need public interest, leadership, and acceptance. The

generation and receipt of risk information occurs through a diverse array

of channels. Chapter 5 and others discuss the important role of mass

media and other sources (see, e.g., the case of Japan provided in Sampei

and Aoyagi-Usui, 2009). Within the context of risk communication,

particularly in terms of climate change and disasters, decisionmakers,

scientists, and NGOs have to act in accordance with media requirements

concerning news production, public discourse, and media consumption

(see Carvalho and Burgess, 2005). Carvalho (2005) and Olausson (2009)

underline that mass media is often closely linked to political awareness

and is framed by its own journalistic norms and priorities; that means

also that mass media provides little space for alternative frames of

communicating climate change (Carvalho, 2005; Olausson, 2009).

Boykoff and Boykoff (2007) conclude that this process might also lead

to an informational bias, especially toward the presentation of events

instead of a comprehensive analysis of the problem. Thus, an important

aspect of improving risk communication and the respective knowledge

base is the acceptance and admission of the limits of knowledge about

the future (see Birkmann and von Teichman, 2010).

2.7. Risk Accumulation and

the Nature of Disasters

The concept of risk accumulation describes a gradual build-up of disaster

risk in specific locations, often due to a combination of processes, some

persistent and/or gradual, others more erratic, often in a combination of

exacerbation of inequality, marginalization, and disaster risk over time

(Maskrey, 1993b; Lavell, 1994). It also reflects that the impacts of one

hazard – and the response to it – can have implications for how the

next hazard plays out. This is well illustrated by the example of El

Salvador, where people living in temporary shelters after the 1998

Hurricane Mitch were at greater risk during the 2001 earthquakes due

to the poor construction of the shelters (Wisner, 2001b). The concept of

risk accumulation acknowledges the multiple causal factors of risk by

the connecting development patterns and risk, as well as the links

between one disaster and the next.

Chapter 2 Determinants of Risk: Exposure and Vulnerability

Risk accumulation can be driven by underlying factors such as a decline

in the regulatory services provided by ecosystems, inadequate water

management, land use changes, rural-urban migration, unplanned

urban growth, the expansion of informal settlements in low-lying areas,

and an underinvestment in drainage infrastructure. Development and

governance processes that increase the marginalization of specific

groups, for example, through the reduction of access to health services

or the exclusion from information and power – to name just a few – can

also severely increase the susceptibility of these groups and at the same

time erode societal response capacities. The classic example is disaster

risk in urban areas in many rapidly growing cities in developing countries

(Pelling and Wisner, 2009b). In these areas, disaster risk is often very

unequally distributed, with the poor facing the highest risk, for instance

because they live in the most hazard-prone parts of the city, often in

unplanned dense settlements with a lack of public services; where lack

of waste disposal may lead to blocking of drains and increases the risk

of disease outbreaks when floods occur; with limited political influence

to ensure government interventions to reduce risk. The accumulation of

disaster risk over time may be partly caused by a string of smaller

disasters due to continued exposure to small day-to-day risks in urban

areas (e.g., Pelling and Wisner, 2009a), aggravated by limited resources

to cope and recover from disasters when they occur – creating a vicious

cycle of poverty and disaster risk. Analysis of disaster loss data suggests

that frequent low-intensity losses often highlight an accumulation of risks,

which is then realized when an extreme hazard event occurs (UNISDR,

2009a). Similar accumulation of risk may occur at larger scales in hazard-

prone states, especially in the context of conflict and displacement (e.g.,

UNDP, 2004).

A context-based understanding of these risks is essential to identify

appropriate risk management strategies. This may include better collection

of sub-national disaster data that allows visualization of complex patterns

of local risk (UNDP, 2004), as well as locally owned processes of risk

identification and reduction. Bull-Kamanga et al. (2003) suggest that

one of the most effective methods to address urban disaster risk in

Africa is to support community processes among the most vulnerable

groups so they can identify risks and set priorities – both for community

action and for action by external agencies (including local governments).

Such local risk assessment processes also avoid the pitfalls of planning

based on dated maps used to plan and develop large physical construction

and facilities.

Disaster risk is not an autonomous or externally generated circumstance

to which society reacts, adapts, or responds (as is the case with natural

phenomena or events per se), but rather the result of the interaction of

society and the natural or built environment. Thus disasters are often

the product of parallel developments that sometimes reach a tipping

point, where the cumulative effect of these parallel processes results in

disaster (Dikau and Pohl, 2007; Birkmann, 2011b). After that point,

recovery may be slowed by conflict between processes and goals of

reconstruction (Colten et al., 2008). In addition, there is often strong

pressure to restore the status quo as soon as possible after a disaster

has happened, even if that status quo means continued high levels of

disaster risk. Sometimes, however, disasters themselves can be a

window of opportunity for addressing the determinants of disaster risk.

With proactive risk assessment and reconstruction planning, more

appropriate solutions can be realized while restoring essential assets

and services during and after disasters (Susman et al., 1983, Renn,

1992; Comfort et al., 1999; Vogel and O’Brien, 2004).

Chapter 2Determinants of Risk: Exposure and Vulnerability

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Chapter 2Determinants of Risk: Exposure and Vulnerability

109

Coordinating Lead Authors:

Sonia I. Seneviratne (Switzerland), Neville Nicholls (Australia)

Lead Authors:

David Easterling (USA), Clare M. Goodess (United Kingdom), Shinjiro Kanae (Japan), James Kossin

(USA), Yali Luo (China), Jose Marengo (Brazil), Kathleen McInnes (Australia), Mohammad Rahimi (Iran),

Markus Reichstein (Germany), Asgeir Sorteberg (Norway), Carolina Vera (Argentina), Xuebin Zhang

(Canada)

Review Editors:

Matilde Rusticucci (Argentina), Vladimir Semenov (Russia)

Contributing Authors:

Lisa V. Alexander (Australia), Simon Allen (Switzerland), Gerardo Benito (Spain), Tereza Cavazos

(Mexico), John Clague (Canada), Declan Conway (United Kingdom), Paul M. Della-Marta (Switzerland),

Markus Gerber (Switzerland), Sunling Gong (Canada), B. N. Goswami (India), Mark Hemer (Australia),

Christian Huggel (Switzerland), Bart van den Hurk (Netherlands), Viatcheslav V. Kharin (Canada),

Akio Kitoh (Japan), Albert M.G. Klein Tank (Netherlands), Guilong Li (Canada), Simon Mason (USA),

William McGuire (United Kingdom), Geert Jan van Oldenborgh (Netherlands), Boris Orlowsky

(Switzerland), Sharon Smith (Canada), Wassila Thiaw (USA), Adonis Velegrakis (Greece), Pascal Yiou

(France), Tingjun Zhang (USA), Tianjun Zhou (China), Francis W. Zwiers (Canada)

This chapter should be cited as:

Seneviratne, S.I., N. Nicholls, D. Easterling, C.M. Goodess, S. Kanae, J. Kossin, Y. Luo, J. Marengo, K. McInnes, M. Rahimi,

M. Reichstein, A. Sorteberg, C. Vera, and X. Zhang, 2012: Changes in climate extremes and their impacts on the natural

physical environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation

[Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen,

M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate

Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109-230.

Changes in Climate Extremes

and their Impacts on the

Natural Physical Environment

Changes in Climate Extremes and their Impacts on the Natural Physical Environment

110

Executive Summary .................................................................................................................................111

3.1. Weather and Climate Events Related to Disasters ..................................................................115

3.1.1. Categories of Weather and Climate Events Discussed in this Chapter ...........................................................................115

3.1.2. Characteristics of Weather and Climate Events Relevant to Disasters ...........................................................................115

3.1.3. Compound (Multiple) Events............................................................................................................................................118

3.1.4. Feedbacks.........................................................................................................................................................................118

3.1.5. Confidence and Likelihood of Assessed Changes in Extremes ........................................................................................120

3.1.6. Changes in Extremes and Their Relationship to Changes in Regional and Global Mean Climate..................................121

3.1.7. Surprises / Abrupt Climate Change..................................................................................................................................122

3.2. Requirements and Methods for Analyzing Changes in Extremes............................................122

3.2.1. Observed Changes............................................................................................................................................................122

3.2.2. The Causes behind the Changes ......................................................................................................................................125

3.2.3. Projected Long-Term Changes and Uncertainties ............................................................................................................128

3.3. Observed and Projected Changes in Weather and Climate Extremes .....................................133

3.3.1. Temperature .....................................................................................................................................................................133

3.3.2. Precipitation.....................................................................................................................................................................141

3.3.3. Wind .................................................................................................................................................................................149

3.4. Observed and Projected Changes in

Phenomena Related to Weather and Climate Extremes..........................................................152

3.4.1. Monsoons .........................................................................................................................................................................152

3.4.2. El Niño-Southern Oscillation............................................................................................................................................155

3.4.3. Other Modes of Variability...............................................................................................................................................157

3.4.4. Tropical Cyclones..............................................................................................................................................................158

3.4.5. Extratropical Cyclones......................................................................................................................................................163

3.5. Observed and Projected Impacts on the Natural Physical Environment.................................167

3.5.1. Droughts...........................................................................................................................................................................167

3.5.2. Floods...............................................................................................................................................................................175

3.5.3. Extreme Sea Levels ..........................................................................................................................................................178

3.5.4. Waves ...............................................................................................................................................................................180

3.5.5. Coastal Impacts................................................................................................................................................................182

3.5.6. Glacier, Geomorphological, and Geological Impacts .......................................................................................................186

3.5.7. High-latitude Changes Including Permafrost...................................................................................................................189

3.5.8. Sand and Dust Storms......................................................................................................................................................190

References ...............................................................................................................................................203

Boxes and Frequently Asked Questions

Box 3-1. Definition and Analysis of Climate Extremes in the Scientific Literature .......................................................................116

FAQ 3.1. Is the Climate Becoming More Extreme? ........................................................................................................................124

FAQ 3.2. Has Climate Change Affected Individual Extreme Events? .............................................................................................127

Box 3-2. Variations in Confidence in Projections of Climate Change: Mean versus Extremes, Variables, Scale ...........................132

Box 3-3. The Definition of Drought................................................................................................................................................167

Box 3-4. Small Island States...........................................................................................................................................................184

Supplementary Material

Appendix 3.A: Notes and Technical Details on Chapter 3 Figures................................................................................Available On-Line

Chapter 3

Table of Contents

111

This chapter addresses changes in weather and climate events relevant to extreme impacts and disasters.

An extreme (weather or climate) event is generally defined as the occurrence of a value of a weather or climate

variable above (or below) a threshold value near the upper (or lower) ends (‘tails’) of the range of observed values of

the variable. Some climate extremes (e.g., droughts, floods) may be the result of an accumulation of weather or climate

events that are, individually, not extreme themselves (though their accumulation is extreme). As well, weather or

climate events, even if not extreme in a statistical sense, can still lead to extreme conditions or impacts, either by

crossing a critical threshold in a social, ecological, or physical system, or by occurring simultaneously with other

events. A weather system such as a tropical cyclone can have an extreme impact, depending on where and when it

approaches landfall, even if the specific cyclone is not extreme relative to other tropical cyclones. Conversely, not all

extremes necessarily lead to serious impacts. [3.1]

Many weather and climate extremes are the result of natural climate variability (including phenomena

such as El Niño), and natural decadal or multi-decadal variations in the climate provide the backdrop for

anthropogenic climate changes. Even if there were no anthropogenic changes in climate, a wide variety of natural

weather and climate extremes would still occur. [3.1]

A changing climate leads to changes in the frequency, intensity, spatial extent, duration, and timing of

weather and climate extremes, and can result in unprecedented extremes. Changes in extremes can also be

directly related to changes in mean climate, because mean future conditions in some variables are projected to lie

within the tails of present-day conditions. Nevertheless, changes in extremes of a climate or weather variable are not

always related in a simple way to changes in the mean of the same variable, and in some cases can be of opposite

sign to a change in the mean of the variable. Changes in phenomena such as the El Niño-Southern Oscillation or

monsoons could affect the frequency and intensity of extremes in several regions simultaneously. [3.1]

Many factors affect confidence in observed and projected changes in extremes. Our confidence in observed

changes in extremes depends on the quality and quantity of available data and the availability of studies analyzing

these data. It consequently varies between regions and for different extremes. Similarly, our confidence in projecting

changes (including the direction and magnitude of changes in extremes) varies with the type of extreme, as well as

the considered region and season, depending on the amount and quality of relevant observational data and model

projections, the level of understanding of the underlying processes, and the reliability of their simulation in models

(assessed from expert judgment, model validation, and model agreement). Global-scale trends in a specific extreme

may be either more reliable (e.g., for temperature extremes) or less reliable (e.g., for droughts) than some regional-

scale trends, depending on the geographical uniformity of the trends in the specific extreme. ‘Low confidence’ in

observed or projected changes in a specific extreme neither implies nor excludes the possibility of changes in this

extreme. [3.1.5, 3.1.6, 3.2.3; Box 3-2; Figures 3-3, 3-4, 3-5, 3-6, 3-7, 3-8, 3-10]

There is evidence from observations gathered since 1950 of change in some extremes. It is very likely that

there has been an overall decrease in the number of cold days and nights, and an overall increase in the number of

warm days and nights, at the global scale, that is, for most land areas with sufficient data. It is likely that these changes

have also occurred at the continental scale in North America, Europe, and Australia. There is medium confidence of a

warming trend in daily temperature extremes in much of Asia. Confidence in observed trends in daily temperature

extremes in Africa and South America generally varies from low to medium depending on the region. Globally, in many

(but not all) regions with sufficient data there is medium confidence that the length or number of warm spells or heat

waves has increased since the middle of the 20th century. It is likely that there have been statistically significant

increases in the number of heavy precipitation events (e.g., 95th percentile) in more regions than there have been

statistically significant decreases, but there are strong regional and subregional variations in the trends. There is

low confidence that any observed long-term (i.e., 40 years or more) increases in tropical cyclone activity are robust,

after accounting for past changes in observing capabilities. It is likely that there has been a poleward shift in the

main Northern and Southern Hemisphere extratropical storm tracks. There is low confidence in observed trends in

Chapter 3 Changes in Climate Extremes and their Impacts on the Natural Physical Environment

Executive Summary

112

small-scale phenomena such as tornadoes and hail because of data inhomogeneities and inadequacies in monitoring

systems. There is medium confidence that since the 1950s some regions of the world have experienced a trend to

more intense and longer droughts, in particular in southern Europe and West Africa, but in some regions droughts

have become less frequent, less intense, or shorter, for example, in central North America and northwestern Australia.

There is limited to medium evidence available to assess climate-driven observed changes in the magnitude and

frequency of floods at regional scales because the available instrumental records of floods at gauge stations are limited

in space and time, and because of confounding effects of changes in land use and engineering. Furthermore, there is

low agreement in this evidence, and thus overall low confidence at the global scale regarding even the sign of these

changes. It is likely that there has been an increase in extreme coastal high water related to increases in mean sea

level in the late 20th century. [3.2.1, 3.3.1, 3.3.2, 3.3.3, 3.4.4, 3.4.5, 3.5.1, 3.5.2, 3.5.3; Tables 3-1, 3-2]