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Natural disasters and climate change an economic perspective

Stéphane Hallegatte

Natural Disasters
and Climate Change
An Economic Perspective


Natural Disasters and Climate Change



Stéphane Hallegatte

Natural Disasters
and Climate Change
An Economic Perspective

123


Stéphane Hallegatte

Sustainable Development Network
World Bank
Washington, DC, USA

ISBN 978-3-319-08932-4
ISBN 978-3-319-08933-1 (eBook)
DOI 10.1007/978-3-319-08933-1
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Prologue

New Orleans was founded in 1718 and by the early nineteenth century had grown
into the largest city in the southern United States. Its land area protected by natural
levees was very small, so as the city expanded, it spread into marshland that was
drained using pumps, drainage canals, and artificial levees. More reliable electric


pumps and the development of better levees at the start of the twentieth century
allowed for accelerated development. In the years since, however, weather-related
catastrophes have become common in and around the city of New Orleans.
In 1915, a hurricane overflowed the protection system along the city’s Lake
Pontchartrain shore. Water levels reached 4 m in some districts, and it took 4 days
to pump the water from the city. The government responded by upgrading pump
stations and raising levees along the drainage canals. In 1947, another hurricane hit
the city, and the levees failed again. Thirty square miles flooded, and 15,000 people
had to be evacuated. Again, major improvements to the protection system followed
in the immediate aftermath of the disaster, with levees being raised and extended.
In 1965, Hurricane Betsy made landfall, and New Orleans flooded again. About
13,000 homes filled with water, leaving 60,000 people homeless and causing 53
deaths and more than $1 billion in damage. This led to the passing of the Flood
Control Act of 1965 by the U.S. Congress and to an ambitious plan to protect
New Orleans. The plan was to be fully implemented within 13 years, but in the
face of numerous difficulties, including conflicts with environmental protection
movements, it remained stalled for about two decades. It was eventually revised
into the “high level plan.” The implementation of that plan was 60–90 % complete
when Hurricane Katrina struck in 2005, leading to the flooding of 80 % of the
city and unprecedented human and economic damages. The complete failure of the
protection system in 2005 demonstrated that both construction and maintenance had
not been adequately supervised and monitored.
Over the past 100 years and four disasters, the New Orleans region has
experienced large socioeconomic and environmental changes. In particular, the local
sea level rose by 5 cm per decade, about 50 cm in all, because of geological factors:
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Prologue

the soil was (and still is) sinking, a process referred to as “subsidence.” Failure to
protect the New Orleans population from disasters, illustrated by a decrease in the
city’s population since its peak in 1965, can provide important lessons regarding
how to manage risks in other locations.
Indeed, the global sea level rise due to climate change will affect all coastal cities
in the future, and this rise is expected to be of the same order of magnitude as what
was experienced in New Orleans in the last 100 years. Over the coming decades,
many cities around the world will thus experience the same changes in risk as New
Orleans did in the past, and one can hope they do not have to go through a similar
series of disasters.
Fortunately, risk management also offers more positive stories. In the Netherlands, subsidence also made the local sea level rise by about 2 cm per decade during
the twentieth century. A flood there in 1953 caused more than 1,800 deaths and
extensive damage. The response to this event went beyond just engineering more
and better protection. The Delta committee was created to manage the response from
institutional, legal, and technical perspectives. In 1960, the committee published
the Delta Plan, which included an engineering section, the Delta Works, but also a
new approach to the management of flood risks. The Delta committee determined
an acceptable level of flood risk in different regions of the country through a
combination of economic analyses and political decisions. From there, it derived
an optimum level of protection, which could then be used by engineers to design
protection systems.
Risk management in the Netherlands does not exhibit the same cycle as in New
Orleans, where defense improvements have been driven by disasters demonstrating
the weakness of protections. The Dutch Law on Water Defences requires that water
levels and wave heights used in risk analyses and in the design of protections
be updated every 5 years and that water defenses be evaluated for these new
conditions. Such a response does not reduce risk to zero, and the Netherlands dealt
with flooding again in the 1990s. But the 5-year updates ensure that changing
demographic, economic, and environmental conditions are taken into account in
the design, maintenance, and upgrades of flood defenses, even if no disaster has
occurred.
New Orleans’ history shows how socioeconomic and environmental changes can
increase both the risk and the damage when storms strike. The Netherlands example
suggests that good risk management can reduce the losses. With the right policies
and decisions, future risks can be managed, even as climate change increases
vulnerability in some places. Strengthening risk management will not eliminate
disasters, but it will avoid many crises, save lives, and reduce losses and suffering.
We cannot predict how well we will be able to manage future risks in the face of
climate change, but much can be done to increase the odds of a scenario in which
ever-changing socioeconomic and environmental conditions are accounted for,
disaster risks are reduced as much as possible, affected populations are supported in
post-disaster situations, and climate change impacts are as limited as possible.
This book provides insights into how to manage natural risks in a changing
environment. Many remarkable books investigate the social, health, and psycho-


Prologue

vii

logical aspects of catastrophes. This book tries to complement them by taking an
economist’s point of view and providing economic tools to inform policymakers for
taking better decisions regarding risk management so we can prevent the avoidable
catastrophes and cope with the unavoidable ones.



Acknowledgments

This book is based on articles published between 2007 and 2013, with many
coauthors and collaborators. My main collaborators on this work are Philippe
Ambrosi, Jan Corfee-Morlot, Patrice Dumas, Michael Ghil, Susan Hanson, Fanny
Henriet, Jean-Charles Hourcade, Robert Lempert, Olivier Mestre, Nicolas Naville,
Robert Nicholls, Valentin Przyluski, Nicola Ranger, Ankur Shah, Lionel Tabourier,
and Vincent Viguié.
During these years, hundreds (thousands?) of hours of discussion with Patrice
Dumas played a critical role in shaping my ideas. My modeling work also benefited
immensely from the support and insights provided by Jean-Yves Grandpeix and
Alain Lahellec from the Laboratoire de Météorologie Dynamique and by the TEFZOOM modeling community. The book was completed while I was part of the
core writing team of the 2014 World Development Report, entitled “Risk and
Opportunity,” and the entire team provided very influential ideas, especially about
the process of risk management and the institutional side of the issues.
Other friends and colleagues have to be acknowledged for the exchanges we
had over these years and the support they have provided to me: Paolo Avner,
Anthony Bigio, Auguste Boissonnade, Laurens Bouwer, Jean-Louis Dufresne, Kris
Ebi, Ottmar Edenhofer, Kerry Emanuel, Sam Fankhauser, Chris Field, Francis
Ghesquière, Colin Greene, Goeffrey Heal, Jean Jouzel, Nidhi Kalra, Howard Kunreuther, Norman Loayza, Reinhart Mechler, Erwann Michel-Kerjan, Robert MuirWood, Roger Pielke Jr., Julie Rozenberg, Reimund Schwarze, Eric Strobl, Richard
Tol, Vincent Viguié, Adrien Vogt-Schilb, and Gary Yohe. Several benevolent (and
pitiless) reviewers helped improve the manuscript, including Laura Bonzanigo,
Fabrice Chauvin, Jun Rentschler, Julie Rozenberg, Vincent Viguié, and Adrien
Vogt-Schilb. They offered very important suggestions to improve the organization
and content of the manuscript and provided critical feedback on the framework used
in this book. Stacy Morford kindly edited the prologue and introduction.
Most of the work presented in this book has been done in the context of
my research at the Centre International de Recherche sur l’Environnement et le
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Acknowledgments

Développement (CIRED), directed by Jean-Charles Hourcade. CIRED offered a
great environment for academic research, through frequent and invaluable informal
interactions and discussions, on the topic of this book and on many others. I thank
the entire team for the ideas that were shared during my time there.
From 2007 to 2012, I was a researcher for Météo-France and professor at the
Ecole Nationale de la Météorologie, led by François Lalaurette. During that time
and beyond, I benefited from the continuous support of Alain Ratier, deputy director
of Météo-France. Additional financial support was provided by the European
Commission through multiple projects (E2-C2, Ensembles, Weather, ConHaz), by
Risk Management Solutions, and by the OECD through the leadership of Jan
Corfee-Morlot.
The book was completed while I was at the World Bank, in the office of the
Sustainable Development Network Chief Economist, Marianne Fay. Her friendly
support and advice have been critical for finding the time and energy to complete
this project.


Contents

1

Introduction and Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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2 What Is a Disaster? An Economic Point of View . . . . .. . . . . . . . . . . . . . . . . . . .
2.1 Defining the Economic Cost of Extreme Events . .. . . . . . . . . . . . . . . . . . . .
2.1.1 Direct and Indirect Costs . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.1.2 Defining a Baseline . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.1.3 Assessment Purpose and Scope . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.2 Output Losses and Their Drivers .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.2.1 From Asset Losses to Output Losses . . . . . .. . . . . . . . . . . . . . . . . . . .
2.2.2 “Ripple Effects” . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.2.3 Non-linearity in Output Losses and Poverty Traps.. . . . . . . . . . .
2.2.4 Building Back Better? The Productivity Effect . . . . . . . . . . . . . . .
2.2.5 The Stimulus Effect of Disasters . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.3 From Output Losses to Welfare Losses . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.4 Assessing Disaster Losses . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.4.1 Measuring Indirect Losses Using Econometric Analyses . . . .
2.4.2 Modeling Indirect Losses. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
2.5 Conclusion and the Definition of Resilience . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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3 Disaster Risks: Evidence and Theory .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.1 Defining Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.2 The Current Patterns of Risk . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.3 Current Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.4 “Good” and “Bad” Risk-Taking .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.4.1 Good Risk-Taking . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.4.2 Bad Risk-Taking . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.4.3 Consequences of Risk Management Policies. . . . . . . . . . . . . . . . . .

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3.5 Technical Insight: Economic Growth and Disaster Losses. . . . . . . . . . . .
3.5.1 Risk and Development.. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.5.2 A Balanced Growth Pathway.. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
3.5.3 The Safety vs. Productivity Trade-Off .. . . .. . . . . . . . . . . . . . . . . . . .
3.5.4 Optimal Protection and Risk-Taking .. . . . . .. . . . . . . . . . . . . . . . . . . .
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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4 Trends in Hazards and the Role of Climate Change .. . . . . . . . . . . . . . . . . . . .
4.1 Scenarios for Climate Change Analysis . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.2 Climate Change Scenarios .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.2.1 Changes in Average Climate Conditions . .. . . . . . . . . . . . . . . . . . . .
4.2.2 Forecasting Natural Variability.. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.3 “Downscaling” Global Climate Scenarios to Extreme
Event Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.3.1 Statistical Methods . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.3.2 Physical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.4 Consequences in Terms of Extremes .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.4.1 Heat Waves and Cold Spells . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.4.2 Droughts.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.4.3 Storms and High Winds . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.4.4 River Floods.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.4.5 Coastal Floods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.4.6 Can We Attribute Extreme Events to Climate Change? . . . . . .
4.5 How Would These Changes in Hazard Translate
into Changes in Losses? . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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5 Climate Change Impact on Natural Disaster Losses . . . . . . . . . . . . . . . . . . . . .
5.1 Methodology for Local Assessment of Climate Change
Impacts on Disaster Risks . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.2 Case Study: Hurricanes and the U.S. Coastline . . .. . . . . . . . . . . . . . . . . . . .
5.2.1 The Hazard: Climate Change and Hurricanes .. . . . . . . . . . . . . . . .
5.2.2 Exposure, Vulnerability and Resilience: Climate
Change and Hurricane Losses . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.2.3 Adaptation Options . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.3 Case Study: Sea Level Rise and Storm Surges in Copenhagen .. . . . . .
5.3.1 The Hazard: Extreme Sea Levels in Copenhagen .. . . . . . . . . . . .
5.3.2 The Exposure: Population and Assets at Risk . . . . . . . . . . . . . . . . .
5.3.3 The Vulnerability: Flood Direct Losses . . .. . . . . . . . . . . . . . . . . . . .
5.3.4 The Resilience: Direct and Indirect Losses .. . . . . . . . . . . . . . . . . . .
5.3.5 Adaptation Options . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.4 Case Study: Heavy Precipitations in Mumbai . . . . .. . . . . . . . . . . . . . . . . . . .
5.4.1 The Hazard: Heavy Precipitations and Extreme
Run-offs in Mumbai . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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5.4.2 The Exposure: Population and Assets in Mumbai . . . . . . . . . . . .
5.4.3 The Vulnerability: Direct Losses . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.4.4 The Resilience: Indirect and Total Losses .. . . . . . . . . . . . . . . . . . . .
5.4.5 Adaptation Options . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.4.6 Impact on Marginalized Populations . . . . . .. . . . . . . . . . . . . . . . . . . .
5.5 Lessons from the Case Studies . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
5.6 Conclusion on the Future of Natural Disasters
and the Role of Climate Change.. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6 Methodologies for Disaster Risk Management
in a Changing Environment.. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.1 The Disaster Risk Management “Policy Mix” .. . . .. . . . . . . . . . . . . . . . . . . .
6.2 Disaster Risk Management for Climate Change Adaptation .. . . . . . . . .
6.2.1 Reactive vs Proactive Risk Management . .. . . . . . . . . . . . . . . . . . . .
6.2.2 The Adaptation Gap . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.3 Case Study: A Cost-Benefit Analysis of New Orleans
Coastal Protections .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.3.1 A First Cost-Benefit Assessment. . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.3.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.3.3 Cost-Benefit Analysis Under Uncertainty .. . . . . . . . . . . . . . . . . . . .
6.4 Case Study: Early Warning Systems in Developing Countries . . . . . . .
6.4.1 Benefits from Early Warning and Preparation Measures . . . . .
6.4.2 Economic Benefits from Hydromet Information .. . . . . . . . . . . . .
6.4.3 How to Improve Early Warning, and at What Cost? . . . . . . . . . .
6.4.4 Conclusions on Investments
in Hydrometeorological Information
and Early Warning .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7 Decision Making for Disaster Risk Management
in a Changing Climate .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.1 Methodologies for Robust Decision-Making . . . . . .. . . . . . . . . . . . . . . . . . . .
7.1.1 Robust Decision-Making .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.1.2 Advantages over Other Approaches . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.2 Robust Strategies for Disaster Risk Management .. . . . . . . . . . . . . . . . . . . .
7.2.1 No-Regret Strategies.. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.2.2 Reversible Strategies.. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.2.3 Safety-Margin Strategies . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.2.4 Soft Strategies .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
7.2.5 Strategies That Reduce Decision-Making Time Horizons .. . .
7.2.6 Taking into Account Conflicts and Synergies . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

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List of Figures

Fig. 1.1

Fig. 2.1

Fig. 2.2
Fig. 2.3
Fig. 2.4

The overall losses and insured losses from weatherand climate-related disasters worldwide (in 2010
US$). These data for weather- and climate-related
‘great’ and ‘devastating’ natural catastrophes are
plotted without inclusion of losses from geophysical
events. A catastrophe in this data set is considered
‘great’ if the number of fatalities exceeds 2,000, the
number of homeless exceeds 200,000, the country’s
GDP is severely hit, and/or the country is dependent
on international aid. A catastrophe is considered
‘devastating’ if the number of fatalities exceeds
500 and/or the overall loss exceeds US$650 million
(in 2010 values) .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Production as a function of time, without disaster or in
a scenario with disaster and no reconstruction. In the
latter case, the discounted value of the lost production
(from the disaster to the infinity) is equal to the value
of lost assets. The production decrease is equal to the
value of lost assets multiplied by the interest rate.. . . . . . . . . . . . . . . . . .
Production with respect to productive capital for
different modeling assumptions .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Supply and demand curves in the pre- and post-disaster
situations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Wages for qualified workers involved in the
reconstruction process (roofer and carpenter), in two
areas where losses have been significant after the 2004
hurricane season in Florida.. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

2

20
23
26

28

xv


xvi

Fig. 2.5

Fig. 2.6

Fig. 3.1

Fig. 3.2
Fig. 3.3

Fig. 3.4
Fig. 3.5

Fig. 3.6
Fig. 3.7

Fig. 3.8
Fig. 3.9
Fig. 4.1

Fig. 4.2

List of Figures

The direct losses – indirect (output) losses as a function
of direct (asset) losses, in Louisiana for Katrina-like
disasters of increasing magnitude .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Amplifying feedback loop that illustrates how natural
disasters could become responsible for macro-level
poverty traps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
The tropical storm hazard in the US can be estimated
using the density of past tracks (here from 1851 to
2013), according to NOAA . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Average physical exposure to tropical cyclones
assuming constant hazard (in thousands of people per year) .. . . . . .
Relation between flood depth and damage factor for
houses, distinguishing between damage to building and
house content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Number of victims of natural disasters per 100,000
inhabitants over the 1976–2005 period .. . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Distribution by income group (1980–2011); Left: Total
number of loss events; Middle: Fatalities due to natural
catastrophes; Right: Absolute economic losses in 2011 values . . . .
Economic losses as percentage of GDP, 1980–2011 .. . . . . . . . . . . . . . .
Hurricane losses in the US from 1929 to 2005. The
top panel presents the hurricane losses with only
the inflation removed; the middle panel presents the
normalized losses, in which the effect of increasing
population and wealth in the US has been removed; the
bottom panel shows the normalized losses when trends
in local GDP and population in hurricane-prone area
has been removed. The absence of a trend in the bottom
panel shows that the increase in hurricane losses in the
US is fully explained by socio-economic drivers . . . . . . . . . . . . . . . . . . .
A set of screens for assessing obstacles to risk
management, and formulating policy responses . . . . . . . . . . . . . . . . . . . .
A simple risk framework to analyze the link between
economic growth and risk-taking in a normative setting . . . . . . . . . . .
The four scenarios used in the IPCC (2013) to
characterize the impact of manmade emissions on the
climate. The four scenarios represent different levels of
“radiative forcing” (equivalent to an additional flux of
energy at the top of the atmosphere) .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
For different GHG emission scenarios (the four
RCP scenarios), climate models can derive climate
scenarios, here the corresponding increase in global
temperature for the RCP2.6 and RCP8.5 scenarios .. . . . . . . . . . . . . . . .

34

35

52
53

53
56

58
59

61
67
68

78

80


List of Figures

Fig. 4.3

Fig. 4.4

Fig. 4.5

Fig. 4.6

Fig. 4.7

Fig. 4.8

Fig. 5.1
Fig. 5.2
Fig. 5.3
Fig. 5.4

Geographical pattern of warming, for the average
model (i.e. the average of all available models) and for
1ı C of global warming . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Precipitation changes (in mm/day per degree C of
global temperature change) for the average model.
Stippled regions are those where the mean signal is
larger than the 95 percentile of the model dispersion,
suggesting a strong signal .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
The number of tropical cyclones that should be
expected in the North Atlantic, as a function of
two large-scale climate parameters: the sea surface
temperature in the North Atlantic, and the Southern
Oscillation Index (a proxy for El Niño) . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Average June-to-September temperature over France
according to observations up to 2003, and from
the IPSL (green line) and CNRM (red line) model
simulations in the A2 emission scenario . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Return period under climate change associated with the
present time 100-year return period level. The future
time period is 2035–2064. Black dots represent places
where the 100-year flood will have a return period
lower than 50 years (i.e. a doubling in likelihood);
white dots represent places where it will have a return
period larger than 180 years (i.e. almost a division by two) .. . . . . . .
Increase between 2005 (“today”) and the 2070s in
population exposed to the 100-year coastal floods in
coastal cities (of more than one million inhabitants in
2005). The figure shows the role of climate change
(assumed to lead to 50 cm of sea level rise and a 10 %
increase in storm frequency) and subsidence, and
the role of socio-economic change (from an OECD
scenario). At the global scale, climate change and
subsidence are responsible for one third of the total
increase, but this ratio varies depending on countries . . . . . . . . . . . . . .
The different components necessary to assess climate
change impacts at the local level .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Extension of Paris agglomeration between 2010 and
2100, according to one scenario . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Assessing the benefits from mitigation . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Annual probability of landfall of a hurricane of a given
category, according to historical data (HURDAT), and
synthetic tracks in the present (PC) and modified (MC)
climate. Climate change effect on hurricane direct losses . . . . . . . . . .

xvii

80

81

84

86

89

91
101
103
106

108


xviii

Fig. 5.5

Fig. 5.6

Fig. 5.7

Fig. 5.8

Fig. 5.9

Fig. 5.10

Fig. 5.11

Fig. 6.1

Fig. 6.2

List of Figures

Mean damage per track and per landfall from historical
data, from synthetic tracks generated for the present
climate (PC), and from the ten 570-track samples
extracted from the 3,000 Present-Climate synthetic tracks .. . . . . . . .
Storm surge return water level (cm) corresponding to
various return-periods, up to 1,000 years. Note: The
117 years of data are reproduced with circles. The
presented data was de-trended for extreme analysis . . . . . . . . . . . . . . . .
Population density (top panel) and total asset exposure
(bottom panel) situated in areas with an elevation
below (orange) and above (green) 2 m elevation above
sea level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Total losses caused by the flooding of Copenhagen, as
a function of the rise in mean sea level, and for various
event return times, in absence of protection .. . . .. . . . . . . . . . . . . . . . . . . .
Illustrative example assuming a homogenous
protection at 180 cm above current mean sea level (in
the ‘No SLR’ and ‘50 cm SLR’ cases). The vertical
arrow shows the cost of SLR in absence of adaptation.
The horizontal arrow shows the need for adaptation to
maintain mean annual losses unchanged . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Flood map corresponding to the 200-year return period
precipitation event, in the Mithi basin, in Mumbai,
today (left panel) and in the 2080s in one climate
scenario (right panel) .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
The estimated total (direct C indirect) losses for a
1-in-100 year flood event in Mumbai under five
scenarios (from left to right): (i) present-day; (ii) 2080s
– using the one ‘high-end’ scenario considered in this
study and an unchanged city; (iii) 2080s, assuming
properties are made more resilient and resistant to
flooding (e.g. through building codes); (iv) 2080s,
assuming the drainage system is improved such that
it can cope with a 1-in-50 year rainfall event; and
(v) combined property and drainage improvements . . . . . . . . . . . . . . . .

110

114

116

119

119

121

124

An example of risk-management policy mix, in
which physical protections avoid frequent events,
land-use planning limit losses in these protections are
overtopped, and early warning, evacuation, insurance
and crisis management cope with the largest events .. . . . . . . . . . . . . . . 132
Policies to cope with correlated risks, depending on the
spatial correlation .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 133


List of Figures

Fig. 6.3

Fig. 6.4

Fig. 6.5
Fig. 6.6

Fig. 7.1

Flood safety standards under Dutch national law (From
Netherlands Environmental Assessment Agency,
National Institute for Public Health and the Environment) .. . . . . . . .
Two risk-management strategies. On the top panel, a
reactive strategy, as observed in New Orleans, where
flood defenses are improved after each flood. On the
bottom panel, a proactive strategy, applied in the
Netherlands, where a political process defines the
maximum acceptable risk and regular risk-analysis and
defense improvements make sure that the actual risk
never exceeds the acceptable level. In such a situation,
floods are still possible, since risk is not zero, but the
risk is known and kept below a pre-determined level .. . . . . . . . . . . . . .
Illustration of the adaptation gap and of different
definition of climate change adaptation . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Number of people reported killed by weather-related
natural disasters (1975–2011), in developing countries
and at the world level. There is no significant trend in
these series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

xix

135

136
138

163

Steps in Robust Decision Making (RDM) analysis . . . . . . . . . . . . . . . . . 181



List of Tables

Table 2.1 Losses in the housing sector after the 2010 floods in
Pakistan (US$ million) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Table 2.2 Reason for businesses to close following the
Northridge earthquake.. . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Table 3.1 Disasters by fatalities (1980–2012) .. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Table 3.2 Disasters by absolute economic losses (in US$ m,
original values, 1980–2012) . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Table 3.3 Loss-to-GDP ratio elasticity to GDP per capita for a
selected set of countries and regions . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Table 4.1 Estimated change in extreme weather losses in 2040
due to climate change and exposure change, relative to
the year 2000 from 21 impact studies . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .

14
29
57
57
62

92

Table 5.1 Components of the total flood losses, as a function of
water level above current mean level, in absence of protection . . . . 118
Table 5.2 Upper estimation of total losses (direct C indirect,
including loss in housing services) due to various types
of events in present-day and future conditions ... . . . . . . . . . . . . . . . . . . . 123
Table 6.1 Potential benefits from avoided asset losses
thanks to early warning (with European-standard
hydro-meteorological services), and share of these
benefits actually realized with current services .. . . . . . . . . . . . . . . . . . . . 162
Table 6.2 Potential economic benefits from improved
hydrometeorological services, and share of these
benefits actually realized with current services (these
benefits exclude the benefits from early warning,
presented in Table 6.1) . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 168

xxi


xxii

List of Tables

Table 6.3 Summary of benefits from and costs of upgraded
hydro-meteorological services . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 172
Table 7.1 Sectors for which climate change should be taken into
account as of now, because of time scale or sensitivity
to climate conditions. Sensitivity is estimated by the author .. . . . . . 178
Table 7.2 Examples of adaptation options in various sectors, and
their assessment in light of the strategies proposed by
this article . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 187


Chapter 1

Introduction and Summary

Abstract This chapter introduces the book by summarizing three major debates
regarding the link between disaster risk management and climate change adaptation,
namely the existence and magnitude of long-term economic impacts of natural
disasters, the idea that development is the solution to excessive natural risks and
climate change adaptation challenge, and the link between trends in natural disasters
and climate change. The chapter also presents the main messages of the book,
stressing the existence of large potential gains and synergies from a combination of
disaster-risk-reduction, resilience-building and adaptation policies. It stresses that
disaster risk management policies need to shift from a purely negative stance and
follow a more positive and holistic approach that is fully integrated in development
planning and aims at a more resilient development.
Keywords Disaster risk management • Climate change adaptation • Resilience •
Development

The amount of economic damage caused by natural disasters has increased over the
past three decades (Fig. 1.1). Moreover, a few recent natural disasters have changed
our perception of socioeconomic vulnerability. The 2005 landfall of Katrina in
New Orleans showed that large-scale destruction and large losses of lives are not
limited to developing countries, and that one event can lead to a local economic
collapse. The 2004 tsunami in Asia demonstrated that many countries can be
affected by a single exceptional event. The 2010 earthquake in Haiti showed how an
entire country can be paralyzed by a disaster, making recovery and reconstruction
extremely challenging. The 2010 fires in Russia illustrated how a disaster in
one country can have global consequences through commodity markets and food
prices. Further, the Icelandic volcano Eyjafjallajökull forced the cancellation of
thousands of flights, showing that, even in the absence of destruction, a hazard can
create heavy perturbations to the functioning of the global economic system. The
earthquake in Japan in March 2011 showed that even the best-prepared regions can
be overwhelmed by an exceptionally intense event, and that global supply chains
can be heavily affected by a disaster. And the landfall of Superstorm Sandy in 2012
in New York City proved that even the richest cities are sometimes under-prepared
for large-scale weather events.

© Springer International Publishing Switzerland 2014
S. Hallegatte, Natural Disasters and Climate Change,
DOI 10.1007/978-3-319-08933-1__1

1


2

1 Introduction and Summary
250

Overall Losses in 2010 Values
Of Which Insured in 2010 Values
200

US$ billions

150

100

50

0
1980

1985

1990

1995

2000

2005

2010

Fig. 1.1 The overall losses and insured losses from weather- and climate-related disasters
worldwide (in 2010 US$). These data for weather- and climate-related ‘great’ and ‘devastating’
natural catastrophes are plotted without inclusion of losses from geophysical events. A catastrophe
in this data set is considered ‘great’ if the number of fatalities exceeds 2,000, the number of
homeless exceeds 200,000, the country’s GDP is severely hit, and/or the country is dependent
on international aid. A catastrophe is considered ‘devastating’ if the number of fatalities exceeds
500 and/or the overall loss exceeds US$650 million (in 2010 values) (Data from Munich Re (2011).
Source: IPCC (2012))

Concerns about future vulnerability to disasters have been raised repeatedly
in recent years, in both the scientific and policy communities. Decision-makers
and policy leaders promise resolute action after each large event. But beyond
recognizing the need for action, little has been said about what should be done
and what can be done. This book investigates these questions to help design policy
responses.
To do so, a better understanding of disaster consequences is urgently needed, and
three main scientific debates must be resolved.
The first debate concerns the economic impact of natural disasters on development. Beyond the human toll and the unquestionable immediate impact on
welfare, should we be really concerned about long-term economic consequences
of disasters? Albala-Bertrand claimed in 1993 that disasters were a problem of
development, but not a problem for development. In other terms, he estimated
that disasters are not a macroeconomic threat, and that their long-term impacts
are generally overestimated. This view has been supported by a few scholars
(e.g., Skidmore and Toya 2002, 2007), and challenged by many others (e.g., Benson
and Clay 2004; Noy and Nualsri 2007; Noy 2009; Hochrainer 2009; Jaramillo 2009;
Raddatz 2009; Strobl 2011; Felbermayr and Gröschl 2013). The recent increase in


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