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Title Atlas of the Human Planet 2017. Global Exposure to Natural Hazards

Abstract

The Atlas of the Human Planet 2017. Global Exposure to Natural Hazards summarizes the global multi-temporal analysis of exposure to six major natural hazards: earthquakes, volcanoes, tsunamis, floods, tropical cyclone winds, and sea level surge. The exposure focuses on human settlements assessed through two variables: the global built-up and the global resident population. The two datasets are generated within the Global Human Settlement Project of the Joint Research Centre. They represent the core dataset of the Atlas of the Human Planet 2016 which provides empirical evidence on urbanization trends and dynamics.

The figures presented in the Atlas 2017 show that exposure to natural hazards doubled in the last 40 years, both for built-up area and population. Earthquake is the hazard that accounts for the highest number of people potentially exposed. Flood, the most frequent natural disaster, potentially affects more people in Asia (76.9% of the global population exposed) and Africa (12.2%) than in other regions. Tropical cyclone winds threaten 89 countries in the world and the population exposed to cyclones increased from 1 billion in 1975 up to 1.6 billion in 2015. The country most at risk to tsunamis is Japan, whose population is 4 times more exposed than China, the second country on the ranking. Sea level surge affects the countries across the tropical region and China has one of the largest increase of population over the last four decades (plus 200 million people from 1990 to 2015). The figures presented in the Atlas are aggregate estimates at country level.

The value of the GHSL layers used to generate the figures in this Atlas is that the data are available at fine scale and exposure and the rate of change in exposure can be computed for any area of the world. Researchers and policy makers are now allowed to aggregate exposure information at all geographical scale of analysis from the country level to the region, continent and global.

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JRC 106292

EUR 28556 EN

PDF | ISBN 978-92-79-67959-9 | ISSN 1831-9424 | doi:10.2760/19837
Print | ISBN 978-92-79-67958-2 | ISSN 1018-5593 | doi:10.2760/709471

Luxembourg: Publications Office of the European Union, 2017

© European Union, 2017

Reproduction is authorised provided the source is acknowledged.

How to cite: Martino Pesaresi, Daniele Ehrlich, Thomas Kemper, Alice Siragusa, Aneta J. Florczyk, Sergio Freire, Christina Corbane, Atlas of the Human Planet 2017: Global Exposure to Natural Hazards, EUR 28556 EN, doi: 10.2760/19837

All images © European Union 2017, except:

Pag. 27 © spumador, Fotolia.com, Nepal Earthquake, 2015

Pag. 36 © hello_kosmos, Flickr.com, Isla de Ometepec, Nicaragua, 2014

Pag. 43 © Kariochi, Fotolia.com, Tsunami in Japan, 2011

Pag. 49 © Bartsadowski, Fotolia.com, Illinois, USA, 2007

Pag. 58 © jon11, Fotolia.com, Australia, 2016

Pag. 63 © kmiragaya, Fotolia.com, Havana, Cuba, 2016

(Unless otherwise specified)

Atlas of the Human Planet 2017

Global Exposure to Natural Hazards

Martino Pesaresi, Daniele Ehrlich, Thomas Kemper, Alice Siragusa, Aneta J. Florczyk, Sergio Freire, Christina Corbane

2017

ACKNOWLEDGEMENTS

This Atlas has been authored and edited by a group of experts at JRC: the GHSL team includes several areas of expertise, such as remote sensing, demography, statistics, informatics engineering, data management, risk and disaster management, planning, and urban sciences.

We would like to acknowledge the activity of the GHSL project team, led by Thomas Kemper, which has been working for many years to produce the data sets used in this Atlas. In 2017, the team comprises Donato Airaghi, Christina Corbane, Daniele Ehrlich, Aneta Florczyk, Sergio Freire, Fernand Haag, Luca Maffenini, Martino Pesaresi, Panagiotis Politis, Alice Siragusa, Filip Sabo and Luigi Zanchetta.

The preparation of the Human Planet Atlas included: a) preliminary stage for the preparation of datasets; b) investigation of databases, data mining and analysis; c) elaboration of findings and drafting of chapters; d) verification and ranking of main findings; e) final editing. The GHSL geospatial data products are one of the outcomes of the GHSL Framework, as illustrated in the second chapter.

We would like to acknowledge the work of our JRC colleagues that provided us with data and information on the hazard data: especially Luca Vernaccini and Tom De Groeve (INFORM), and Francesco Dottori and Lorenzo Alfieri (GloFAS).

EXECUTIVE SUMMARY

Policy context

The Atlas of the Human Planet 2017 highlights the importance of exposure in the context of risk analysis by reporting on the global exposure and its changes over time to six major natural hazards: earthquakes, volcanos, tsunamis, tropical cyclone winds, tropical cyclone storm surge and floods. The exposure is measured as built-up surface and population. Both are global datasets produced by the Global Human Settlement Layer (GHSL) of the European Commission, Joint Research Centre.

The exposure data and the findings of the Atlas aim at supporting the monitoring of the implementation of the Sendai Framework for Disaster Risk Reduction 2015-2030 (DRR). The GHSL baseline data provides a framework that allows learning from the last 40 years and closely monitoring the impact of policies of today and for the future. It aims at supporting the monitoring of the implementation of the post-2015 international frameworks: the UN Framework Convention on Climate Change, the Sendai Framework for Disaster Risk Reduction 2015-2030 (DRR), the Sustainable Development Goals (SDGs), and the New Urban Agenda (Habitat III).

The Atlas of the Human Planet 2017 is the second outcome of the GEO Human Planet Initiative. Launched in October 2014 under the GEO programme, this initiative supports the implementation of the Agenda 2030 by enabling the testing and the collective discussion of alternative options in operationalization of the indicators for monitoring post-2015 frameworks.

Key conclusions

The Atlas sheds new light on the global exposure to natural hazard and its evolution. Often the discussion on changing disaster risk was dominated by the impact of climate change related hazards. This Atlas highlights the importance of major changes of exposure to global population and economic growth. This is possible thanks to the global, fine-scale, synoptic, and multi-temporal datasets that provides a historical record of the past 40 years.

The GHSL layers used to generate the figures enable scientists and policy makers to aggregate exposure information at different scales ranging from the city level to national, regional and global levels. It will put them in the position to evaluate better the impact of disaster risk reduction measures and policies.

Main findings

The empirical evidences supporting this release of the Atlas have been collected and processed within the Global Human Settlement Layer (GHSL) of the European Commission, Joint Research Centre. The GHSL dataset has been combined with the best available global hazard maps to measure the potential exposure to natural hazards over time. The analysis is based on a single return period for each hazard, in order to focus the attention on the change over time.

According to this analysis, the global exposure of population and built-up surface to natural hazards increased in the last 40 years. Some hazards, due to their nature and characteristics, pose a threat to a large number of people in different regions of the world. Earthquake is the hazard that accounts for the highest number of exposed population. The number of people living in seismic areas has increased by 93% in 40 years (from 1.4 billion in 1975 to 2.7 billion in 2015). In 2015, 414 million people lived near one of the 220 most dangerous volcanoes and could suffer from the consequences of eruptions. Tsunamis affect coastal areas in many regions, but dangerous areas are more concentrated in Asia. Japan has by far the highest amount of built-up surface exposed to tsunamis, followed by China and by the United States of America. Flood, the most frequent natural disaster, potentially affects more people in Asia (76.9% of the global population exposed) and Africa (12.2%) than in other regions. The world population potentially exposed to flood is around 1 billion in 155 countries in 2015. 11% of the area built-up on Earth is potentially exposed to this hazard, too. Cyclone winds pose a threat to 89 countries in the world and exposed population increased from 1 billion in 1975 up to 1.6 billion in 2015, (about 24% of the world population). In 2015, 640 million people are exposed to extremely strong cyclone winds. China is by far the country with the largest number of people potentially exposed to storm surge as consequence of tropical cyclones: 50 million of Chinese people live in coastal areas included in the hazard area and this number increased by a factor of 1.5 in the last 40 years.

Related and future work

The GHSL is one of the core datasets used in the GEO Human Planet initiative, and is the main baseline used in releases the Atlas of the Human Planet 2016 and 2017. GHSL activities are currently supported by the JRC scientific working plan 2016-2019 in the frame of the JRC Directorate E “Space, Security & Migration”. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General (DG) for Internal Market, Industry, Entrepreneurship and SMEs (DG GROW) are working towards a regular and operational monitoring of global built-up surface and population based on the processing of Sentinel Earth Observation data produced by the European Copernicus space program. At the JRC, the GHSL framework of data and tools supports the Knowledge Centres for Disaster Risk Management, Sustainable Development, Territorial Modelling, and Security & Migration, but also the Index for Risk Management (INFORM), the Global Flood Awareness System (GloFAS), the Global Disaster Alert and Coordination System (GDACS) and the Copernicus Emergency Management Service (Copernicus EMS). Moreover, the GHSL is one key test case contributing to the JRC Earth Observation and Social Sensing Big Data Pilot project in the frame of the JRC Text & Data Mining Competence Centre.

Quick guide

The present Atlas of the Human Planet 2017 is based on evidences collected by the GHSL project of the JRC. GHSL combines satellite and census data to produce high resolution, global open information on built-up surface and population. In the current release supporting the Atlas 2017, it covers the epochs 1975, 1990, 2000 and 2015 combined with hazard maps. The data sets are used to understand, where and in which built environment people live in hazard-prone areas, and how settlements and population changed over time. This knowledge can be used to assess the increment in exposure to natural disasters.

The first chapter introduces the topic and main challenging in measuring exposure. The second illustrates the GHSL key elements, concepts and methodology. The third chapter presents the main findings per each hazard. In the conclusion, final remarks regarding both limitation and future development of this work are presented. The annexes contain technical details and references.

1. INTRODUCTION

Exposure represents the people and assets at risk of potential losses or that may suffer damage to a hazard impact. It covers several dimensions like the physical (e.g. the built-up environment), the social (e.g. population distribution) and the economic dimensions. The first two dimensions typically describe human settlements which patterns have been shaped by dynamic and complex socio-economic and ecological processes.

Particular attention to understanding exposure is required for the formulation of policies and actions to reduce disaster risk (UNISDR 2015a) as highlighted by the Sendai Framework for Disaster Risk Reduction: “Policies and practices for disaster risk management should be based on an understanding of disaster risk in all its dimensions of vulnerability, capacity, exposure of persons and assets, hazard characteristics and the environment. Such knowledge can be leveraged for the purpose of pre -disaster risk assessment, for prevention and mitigation and for the development and implementation of appropriate preparedness and effective response to disasters.

The article 17 of the Sendai Framework clearly calls for actions to avoid the creation of “new risk” and reduce the existing: the aim of the Framework is to “Prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience.” (UNISDR 2015c, 12)

Exposure is one of the drivers of disaster together with the frequency and intensity of hazardous events and the effectiveness of protection measures or any other form of adaptation (Stevens et al. 2015). All of these drivers can change over time so a full analysis of disaster risk should consider the evaluation of how these drivers evolve both historically and into the future (via scenario analysis). While there are many studies on changes in hazards and future hazard projections, retrospective analysis in the analysis of exposure is still missing. Assessing changes and trends in exposure to disaster risk is typically very complex due the interdependent and dynamic dimensions of exposure and their variability across spatial and temporal scales: human settlements – where people live, work, and move – experience variations that census and administrative geographical unit definitions often are unable to depict. The tools and methods for defining exposure need to consider the dynamic nature of human settlements which evolves over time as a result of often unplanned urbanization, demographic changes, modifications in building practice, and other socio-economic, institutional and environmental factors (World Bank, GFDRR 2014).

Among the different tools for collecting information on exposure and monitoring its changes over time, earth observation represents an invaluable source of up-to-date information on the extent and nature of human settlements ranging from city level (using very high spatial resolution data) to the global level (using global coverage of satellite data) (Deichmann et al. 2011; Dell’Acqua, Gamba, and Jaiswal 2013; Ehrlich and Tenerelli 2013). Besides, change detection techniques based on satellite images can provide timely information about changes to the built-environment (Bouziani, Goïta, and He 2010). The coupling of recent remote sensing technologies and spatial modelling offers the opportunity to deliver worldwide geodatasets depicting built-up surfaces and population distribution that are consistent for global risk modelling, impact analysis, and policy-making in the field of disaster risk reduction.

Earth observation together with spatial modelling techniques are the cornerstone of the Global Human Settlement Layer (GHSL) which is the first global, fine scale, multi-temporal, open data on the physical characteristics and the dynamics of human settlements. Drawing from 40 years of satellite observations, multi-temporal grids describing the built-up environment and population distribution have been produced for the periods 1975, 1990, 2000, 2015 epoch (Martino Pesaresi, Melchiorri, et al. 2016).

At present, the GHSL datasets represent an unprecedented source of information for understanding global changes and trends in exposure to natural disasters (Martino Pesaresi, Melchiorri, et al. 2016). The availability of such consistent information on physical and human exposure and its changes over time at a fine spatial resolution is the driving force behind this report.

Thus acknowledging the need for detailed, updated, and consistent geodata on exposure and building on the GHSL baseline data released in 2016 in the First Atlas of the Human Planet, this second Atlas presents the global status and trends of human settlements exposure to selected natural hazards. The purpose is to shed light on the spatiotemporal patterns of exposure and their relation to socio-economic vulnerability. The analysis brings together the best available global hazard data and multi-temporal exposure data on built-up surface and population with the aim of drawing attention to geographical areas or hotspots where necessary refinements are needed for a comprehensive understanding of disaster risks.

2. THE GHSL TO MEASURE EXPOSURE

2.1 Remote sensing data to map human settlements

Human settlements are typically measured based on the amount of population and on the size of the built environment, two information aspects that are also used to quantify exposure to disaster risk. Remote sensing technologies combined with spatial modelling are one of the most cost-effective tools for monitoring human settlements at the global level.

The first attempts to map settlements globally using satellite images relied on coarse and medium scale resolution imagery available since the 1990’s (300m - 1000m spatial resolution) (Potere and Schneider 2007) and with figures that vary significantly (Schneider, Friedl, and Potere 2010). Over time, changes in the physical size of settlements have been mapped and measured from a combination of coarse and moderate resolution imagery as well as from medium resolution imagery. In 2016, the JRC published the first public release of the GHSL, the most complete, consistent, global, free, and open dataset on human settlements. The GHSL maps all human settlements from the village to the megacity. By applying a specific spatial disaggregation methodology, the GHSL provides information about the number of inhabitants and their density at a fine scale. Thanks to the use of historical input imagery data (Landsat series for circa 1975, 1990, and 2000 and 2015), both population and built-up layers are produced for the four epochs, allowing to measure the expansion of human settlements over the last forty years in a consistent way. Using homogenous and wall-to-wall grid, the GHSL provides information on much of the Earth’s surface is covered by settlements, where, how much and how fast are settlements growing. These new sets of information on physical size of cities and their growth impact societal processes at all levels, and are necessary to guide country development plans towards more sustainable societies (United Nations, General Assembly 2015). Producing such information from the field observations has usually a high cost and that is why earth observation is the most promising and cost-effective technology to address the assessment of human settlements from local to national and global scale (Martino Pesaresi et al. 2013).

Box 1 Sendai Framework for Disaster Risk Reduction – UNISDR

The Sendai Framework was adopted by UN Member States on 18 March 2015 at the Third UN World Conference on Disaster Risk Reduction in Sendai City, Miyagi Prefecture, Japan.

The Sendai Framework is a 15-year, voluntary, non-binding agreement which recognizes that the State has the primary role to reduce disaster risk but that responsibility should be shared with other stakeholders including local government, the private sector and others. It aims for the following outcome:

The substantial reduction of disaster risk and losses in lives, livelihoods and health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries.

The Sendai Framework is the successor instrument to the Hyogo Framework for Action (HFA) 2005-2015: Building the Resilience of Nations and Communities to Disasters. It is the outcome of stakeholder consultations initiated in March 2012 and inter-governmental negotiations held from July 2014 to March 2015, which were supported by the UNISDR upon the request of the UN General Assembly.

UNISDR has been tasked to support the implementation, follow-up and review of the Sendai Framework.

Source: http://www.unisdr.org/we/coordinate/sendai-framework

The need for global settlement information goes beyond scientific enquiries and has practical implications related to local and global sustainability.

Human settlement information are used for improving our disaster risk knowledge and for monitoring the four post-2015 international frameworks including:

— Sendai framework for Disaster Risk Reduction (DRR) (United Nations 2015) (see Box 1),

— Sustainable Development Goals (SDGs) with particular focus on Goal 11 (make cities and human settlements inclusive, safe, resilient, sustainable),

— Paris Climate Agreements

— New Urban Agenda (adopted in Quito, Ecuador in October 2016).

In particular, the implementation of the SDGs, is contingent to the availability and access to data and statistics, to ensure that no one is left behind in the information gaps.

As highlighted during the Habitat III preparatory process, up-to-date information about land use and cover, cadastral systems and vulnerable areas should be incorporated in the planning process, especially at local level. “Open and easily accessible geospatial data can support monitoring in many aspects of development, from health care to natural resource management. They can be particularly effective especially in spatial analyses and outputs that can also be compared worldwide. Considering the challenge of handling large amounts of data (both in terms of know-how and costs), local and regional authorities can work together with national and international institutions and research centres to make the most effective use of open, easily accessible data.” (Preparatory Committee for the United Nations Conference on Housing and Sustainable Urban Development (Habitat III) 2016).

Information on location and size of human settlements can be used to measure exposure (to natural / man-made hazards, disasters, and pollution). In fact, global human settlement information are in demand by a number of institutions operating globally including the European Commission Services for Development and Humanitarian Aid¹, the United Nations agencies and programs, the World Bank, as well as the donor countries that require quantitative variables to prioritize their humanitarian and development aid or their national investments. The different phases of crisis management, including risk assessment, alerting of disaster and emergency response, all require exposure information and all at fine detail, something that is not available to the degree of detail. Global alert systems such, as the Global Disaster Alert and Coordination System (GDACS)², and INFORM (De Groeve, Vernaccini, and Poljansek 2015) (see Box 2), rely on models with exposure and vulnerability. The more precise the information, the better will be the outcome of the alert. Similarly, disaster risk models rely on the same exposure variables with the difference that they may need to take into account also the expanding settlements in the coming age. These are some of the reasons why is import to have accessible, homogenous and free data on settlements.

Box 2 INFORM – Index for Risk Management

INFORM is a composite indicator that identifies countries at risk of humanitarian crisis and disaster that would overwhelm national response capacity. The INFORM index supports a proactive crisis and disaster management framework. The INFORM initiative began in 2012 as a convergence of interests of UN agencies, donors, NGOs and research institutions to establish a common evidence-base for global humanitarian risk analysis. The INFORM model is based on risk concepts published in scientific Literature and envisages three dimensions of risk: Hazards & Exposure, Vulnerability and Lack of Coping Capacity. The INFORM model is split into different Levels to provide a quick overview of the underlying factors leading to humanitarian risk and builds up the picture of risk by 53 core indicators. The INFORM 2016 was mainly changed to incorporate new disaster risk data published by GAR 2015.

Source: http://www.inform-index.org/

2.2 The GHSL dataset

The GHSL operates in an open and free data access policy including the full data production and dissemination cycle (open input, open processing methods, open outputs, open sharing platforms). The GHSL consists of three main information components hierarchically placed at three different levels of abstraction:

— Global Human Settlement built-up areas (GHS-BU),

— GHS population grids (GHS-POP)

— GHS settlement classification model (GHS-SMOD).

The first two products have been used in this report.

Global Human Settlement built-up areas (GHS-BU) is a layer providing information on observable presence of built-up structures or buildings. The “building” constitutes the physical part of the human settlement fabric or spatial extension that is observable and measurable using the available global sensors. The GHSL reports about built-up areas (GHS-BU, resolution 38m), as areas (spatial units) where buildings can be found (Martino Pesaresi et al. 2013). The concept of “buildings” formalized by the GHSL are enclosed constructions above ground which are intended or used for the shelter of humans, animals, things or for the production of economic goods and that refer to any structure constructed or erected on its site (Martino Pesaresi et al. 2013). Since this definition excepts the condition of the permanency of the structure the GHSL allows for inclusion of refugee camps, informal settlements, slums and other temporary settlements and shelters in the notion of built-up area in the GHSL paradigm.

The GHSL population grid or GHS-POP (250m resolution). This layer is derived from the combination of global collections of national population census data and global built-up areas (GHS-BU). In the approach taken by the GHSL, the population data collected by national censuses with heterogeneous criteria and heterogeneous update time are harmonized in the same space and time domains as the GHS-BU grids, by systematic and consistent application of the same set of data interpolation and spatial disaggregation methods to the best available global spatial baseline data (Freire Sergio et al. 2016).

The following sections help the reader to understand the extraction of information from satellite imagery, the built-up surface definition (2.2.1), the process of combining built-up grids with census data to produce the population grids (2.2.2), and an example (2.2.3).

2.2.1 From Earth’s surface to built-up surface

2.2.2 From Built-up surface to population grid

2.2.3 An example from the city of Madrid, Spain

2.3 Key concepts to measure exposure

This paragraph introduces the methodology used to measure exposure combing the GHSL and the best available global hazard maps. Before presenting the methodology, some key concepts related to risk and natural disaster are presented as they have been treated in the Atlas. These key concepts are presented both we the international agreed definitions and as they have been integrated in the report.

RISK

The potential disaster losses, in lives, health status, livelihoods, assets and services, which could occur to a particular community or a society over some specified future time period

(United Nations General Assembly 2016)

When addressing the risk from natural hazards we may comprise three elements to compute the risk: Hazard intensity, Exposure, and Vulnerability.

EXPOSURE

The situation of people, infrastructure, housing, production capacities and other tangible human assets located in hazard-prone areas

(United Nations General Assembly 2016)

The Atlas of the Human Planet 2017 focuses on the exposure to natural hazards. This Atlas addresses changes over time in exposure of human settlements expressed as population and built-up surface) to six natural hazard types (earthquakes, tsunamis, volcanic eruptions, floods, tropical cyclone winds, and tropical cyclone storm surge).

VULNERABILITY

The conditions determined by physical, social, economic and environmental factors or processes which increase the susceptibility of an individual, a community, assets or systems to the impacts of hazards.

(United Nations General Assembly 2016)

With a growing population and urbanizing area, also exposure is expected to increase. With growing exposure, also risk is likely to increase unless vulnerabilities are reduced. Despite the fact that vulnerability is widely discussed, it is not measured globally, mostly because of the lack of global and reliable data. The measurement of vulnerability represents the next global challenge in terms of disaster risk assessment. This element of the risk assessment is not discuss in this report.

HAZARD AREAS

Hazard maps are produced using probabilistic methods based on different return periods. These hazard layers illustrate the probabilistic model and represent the probability that a hazardous event will occur in the future in a given geographical area.

PROBABILISTIC HAZARD MODEL

all potential hazardous events within the return period

RETURN PERIOD

Average frequency with which a particular event is expected to occur

(UNISDR 2015)

Disaster probability and related potentially affected area depend on a time frame considered and are usually provided for several return periods, according to the nature of the hazard and the selected probabilistic model.

The approach applied in the case of the Atlas 2017 was to use only one return period for each hazard. This simplification allows focusing the analysis on the increment of exposure in relation to urbanisation processes that come with the increment of global population, improving of living conditions, economy and changes in lifestyle, as well as migrations to cities. This approach has been chosen to make a call to the international arena for addressing the consequences of increasing exposure as currently occurs with the other part of the “risk equation”, intensity of the hazard, increment of the events and vulnerability. Given that climate change might have a significant effect on the frequency and severity of some hazards (IPCC 2012), such as future flood events, more variables should be considered in multi-return-period analysis (Jongman, Ward, and Aerts 2012). In this analysis we did not include disaster risk reduction strategies or defences that countries could or have put in place (such as the Netherlands for flood, i.e.). Also coping capacity, is the ability of people, organizations and systems, using available skills and resources, to manage adverse conditions, risk or disasters³, was not considered in the study.

2.4 Methodology and input data

Image 1 Method applied to calculate exposure to natural hazards

The analysis of the exposure in this Atlas benefits from the global hazard data produced by different research teams for purposes of global hazard and risk analysis. GHSL data on built-up surface and population have been combined with geospatial datasets on natural hazards commonly used at international level or developed at the JRC. The methodology adopted for the Atlas 2017 prescribes to overlay hazard maps for a selected return period with population grids (GHS-POP) and built-up layer (GHS-BU) in order to derive the total population living in the hazard zone and the total built-up surface potentially exposed to the specific hazard. This method has been repeated both for population and built-up surface for the four GHSL available epochs (1975-1990-2000-2015)⁴.

The hazard zones are obtained from the best available hazard maps for the specific hazard type⁵ (see Table 1). For each hazard, the input hazard maps with descriptive information are detailed in the technical annexes (see annex).

Data for the seismic hazard elaborated for the GAR 2013 (UNISDR 2013) at global level are presented for the four levels of risk, derived from the Modified Mercalli Intensity Scale (MMI). The 475 year RP used in this analysis is prescribed by the national building codes in Europe for standard buildings (“Eurocode 8: Seismic Design of Buildings Worked Examples” 2012, 7). Besides, it is the most common standard used in the insurance industry for assessing seismic risk, and it is also the basis for most building codes for seismic design.

JRC elaborated the hazard map for volcano by creating a buffer zone of 100km around the 220 volcanoes included in the NOAA database⁶. This analysis does not include underwater volcanoes that mainly cause tsunami, that have been studied separately.

For the analysis of exposure to tsunamis, the GAR dataset has been used with 500 year RP: this RP has been considered a common standard, and even though the GAR 2015 has been produced for more RPs, it has been highlighted that longer return periods imply more uncertainties and limitations, since the model includes estimations on infrequently occurring tsunami causing earthquakes, lacking of reliable long records (UNISDR 2014).

The JRC elaborated a high-resolution global hazard map for floods, called Global Flood Awareness System (GloFAS) that has been used for measuring exposure to this hazard⁷. 100 year RP selected to analysis this hazard is the RP used for the preparation of the flood hazard and flood risk maps, set forth in Article 6 of the European Flood Directive (European Parliament and the Council of the European Union 2007, para. 7).

Results for tropical cyclone wind provide information on two levels of hazard: the lowest correspond to strong winds up to 177 km\h (SS1-2), the highest refers to extreme strong winds greater than 178 km\h (SS3-5). To measure the exposure to tropical cyclone storm surge, JRC has elaborated the hazard map by using different input data, as illustrated in the technical annex. For both cyclone hazards 250 year return period has been used. Higher return periods (500 and 1000 year), covering the maximum potential exposure as people and buildings, were available for cyclones. However, the definition of the areas exposed would become more uncertain, due to the extrapolation error in fitting the extreme value distribution to such high return periods.

Table 1 Synthesis of the input hazard maps and selected return period

Hazard

Source

Return period

Earthquake

GAR13

475 years

Volcano

JRC (baseline: NOAA Significant Volcanic Eruption Database)

---

Tsunami

GAR15

500 years

Flood

JRC - GloFAS

100 years

Tropical Cyclone Wind

GAR15

250 years

Tropical Cyclone Storm Surge

JRC (baseline: GAR15)

250 years

Box 3 GAR15 - Global Assessment Report 2015

The 2015 Global Assessment Report on Disaster Risk Reduction (GAR15) is the fourth in the series coordinated by the United Nations Office for Disaster Risk Reduction (UNISDR) in the context of the Hyogo Framework for Action 2005-2015: Building the Resilience of Nations and Communities to Disasters (HFA). The HFA is an international framework adopted by 168 UN member States in Kobe, Japan in January 2005 to achieve an expected outcome of the substantial reduction of disaster losses, in lives and in the social, economic and environmental assets of communities and societies. Every biennium governments have self-assessed their progress towards the achievement of this outcome using the online HFA Monitor. In 2007 UNISDR published Disaster Risk Reduction: Global Review 2007, which assessed progress in the first two years of the HFA. Shortly afterwards, work began on the first edition in the GAR series, which has compiled and analysed data and information on disaster risk patterns and trends, government self-assessments of progress, and critical challenges to disaster risk reduction since 2009.

Source: https://www.unisdr.org/we/inform/gar

3. EXPOSURE TO NATURAL HAZARDS

This chapter analyses the change in exposure to six different hazards of one return period each in the past 40 years (1975-1990-2000-2015).

The analysis is carried out by hazard for one return period for different geographical scales at global, regional⁸, and country level. It takes into account only a single return period per each hazard in order to focus the attention on the change in exposure.

The selected return periods and the data sources are reported in the annexes.

In the following paragraphs, every natural hazard is briefly introduced and the key elements of the input data are presented

For each hazard, a global outlook of exposure is introduced both for population and built-up surfaces. Then a regional breakdown is presented, to identify which regions of the world are more prone to a specific hazard. For some hazards, a breakdown by income group⁹ is also reported.

A specific level of income could be used as input to estimate the vulnerability: economic capacity of a community is in fact one of the components to be considered in vulnerability evaluation.

Finally, two lists are illustrated and commented: the first is the list of top ten countries ranked by the number of people potentially exposed to that specific hazard in 2015; the second is the list of top ten countries with the highest amount of built-up surface potentially exposed in 2015

3.1 Earthquake

The term earthquake is used to describe any seismic event that generates seismic waves. Earthquakes are caused mostly by the rupture of geological faults and sometimes also by volcanic activity, landslides, etc., and are concentrated within specific areas around the world, mainly in geologically active areas such as the Pacific coast on North and South America, Indonesia, Japan, Himalayas, etc. (UNISDR 2015). Map 1 shows the hazard map of the seismic area in the world where the geographical distribution of the hazards areas is clearly visible.

The seismic hazard map used in this analysis (EMMI-GSHAP, more details in Annex) classifies the area exposed to earthquakes into four classes according to earthquake shaking intensity and relative damages.

These classes refer to earthquake classes following the MMI scale. During an earthquake of class MMI 5, for example, the shaking is felt by nearly everyone and many awake, some dishes and windows get broken and unstable objects are overturned. The extreme events result in some well-built wooden structures being destroyed, while most masonry and frame structures are destroyed with foundations, and even rails may be bent.

Dataset used for the Seismic hazard

Return Period: 475 years

Semantic of the final map: Earthquake intensity classification based on the Mercalli Modified Intensity Scale

Source: EMMI-GSHAP hazard map

Map 1 Earthquake Hazards Map – Classes defined for the analysis

Figure 1 Global population potentially exposed to seismic hazard of class from 5 to 8, 475 years RP (1975-1990-2000-2015)

Figure 2 Global Built-up potentially exposed to seismic hazard of class from 5 to 8, 475 years RP (1975-1990-2000-2015)

The population potentially exposed to earthquakes has increased from 1.4 to 2.7 billion in the last 40 years (increment of 93%) (Figure 1), considering 475 years RP and any earthquake of class five or higher, i.e., from moderate to extreme event (see annex). In 2015, the total number of people living in hazard areas in 145 countries was 37% of the global population, concentrated in Asia, Pacific Islands, Middle East Asia, and Eastern Europe and on the western part of the Americas (Map 1). If the population potentially exposed to earthquakes doubled in the last 40 years, the built-up surface increased by 145% during the same period, from 97,000 to 238,000 km², corresponding to 31% of the global built-up surface (Figure 2).

Figure 4 Population potentially exposed to seismic hazard by hazard class, 475 years RP (1975-1990-2000-2015)

Figure 4 illustrates the distribution of potentially exposed population living in the different class of hazard areas over time (475 years RP). Half billion people, one fourth of the potentially exposed population in 2015, lives in areas falling within the most dangerous classes (class 7 and 8).

Figure 3 Built-up potentially exposed to seismic hazard by hazard class, 475 years RP (1975-1990-2000-2015)

A similar proportion can be found also in the share of built-up surface exposed, about 48,000 km² of the 230,000 km² potentially exposed to earthquake are in hazard zones falling within class 7 and 8 (Figure 3). The amount of built-up surface in hazard zones has more than doubled in the last 40 years, similarly to the exposed population.

Figure 5 Share of Population and Built-up potentially exposed to earthquake by region, 475 years RP (1975-1990-2000-2015)

Table 2 Population and Built-up surface potentially exposed to earthquake by region, 475 years RP (1975-1990-2000-2015)

|  | Africa | Asia | Europe | Latin America and the Caribbean | Northern America | Oceania | N\A | total
Exposed Population (inhabitants) | 1975 | 86,684,308 | 955,772,881 | 159,251,310 | 151,012,600 | 45,885,420 | 10,478,829 | 205,129 | 1,409,290,477
1900 | 130,835,883 | 1,328,541,996 | 172,206,826 | 211,507,247 | 55,277,532 | 13,265,922 | 270,280 | 1,911,905,686
2000 | 165,500,872 | 1,569,470,634 | 171,767,285 | 250,151,172 | 62,948,799 | 15,558,069 | 334,362 | 2,235,731,194
2015 | 233,325,052 | 1,912,205,773 | 173,475,388 | 304,362,164 | 73,971,099 | 20,080,067 | 408,209 | 2,717,827,753
Exposed Built-up surface (Km2) | 1975 | 2,794 | 44,464 | 19,360 | 12,511 | 14,758 | 3,400 | 18 | 97,306
1900 | 6,212 | 81,652 | 34,739 | 18,391 | 22,588 | 4,721 | 4 | 168,308
2000 | 7,877 | 96,600 | 39,885 | 21,734 | 25,099 | 5,240 | 73 | 196,508
2015 | 11,102 | 121,814 | 46,257 | 25,167 | 27,895 | 5,888 | 83 | 238,207

The region with the highest number of people exposed to earthquake in 2015 is Asia. 1.9 billion Asians live in seismic areas, increasing from 40% of the regional population in 1975 to 44% in 2015. In the same period, the share of built-up surface exposed decreased from 56% to 47%, but the total amount has increased of about 3 times (Table 2). Latin America and the Caribbean is the one with the highest share of population potentially exposed to this hazard: In 2015, 300 million people are exposed to seismic hazard (Table 2), corresponding to 48% of the regional population (Figure 5). While the exposed population and built-up surface doubled in the last 40 years in terms of absolute values in Latin America and the Caribbean, the share of exposed population increased while the share of exposed built-up surface has been slightly decreasing. Exposure to earthquake in Africa is decreasing in relative terms and increased in absolute number faster than in other regions. In Africa, the share of exposed population and built-up surfaces exposed to earthquakes decreased between 1975 and 2015, but in the absolute terms they have increased by a factor of 4 during the same period (Table 2).

Figure 6 Share of Population and Built-up potentially exposed to earthquake by income group, 475 years RP (1975-1990-2000-2015)

Table 3 Population and Built-up surface potentially exposed to earthquake by income group, 475 years RP (1975-1990-2000-2015)

| Year | High Income Countries | Upper-Middle Income Countries | Lower-Middle Income Countries | Low Income Countries | Not Assigned | Total
Exposed Population | 1975 | 511,753,475 | 256,092,584 | 468,871,721 | 148,794,043 | 23,778,654 | 1,409,290,477
1990 | 629,022,532 | 351,983,759 | 684,864,929 | 216,320,685 | 29,713,781 | 1,911,905,686
2000 | 690,050,716 | 403,786,543 | 833,336,178 | 275,858,974 | 32,698,783 | 2,235,731,194
2015 | 771,467,002 | 483,103,954 | 1,056,255,081 | 370,695,235 | 36,306,481 | 2,717,827,753
Km2Exposed Built-up surface (Km2) | 1975 | 56,227 | 18,097 | 17,374 | 1,810 | 3,799 | 97,306
1990 | 94,915 | 33,251 | 31,534 | 3,718 | 4,889 | 168,308
2000 | 108,639 | 39,355 | 37,807 | 4,976 | 5,731 | 196,508
2015 | 128,173 | 47,385 | 48,098 | 8,106 | 6,445 | 238,207

Figure 6 shows potentially population and built-up surface exposed to earthquake by income groups over time (475 years RP). Exposed population increased in countries of all groups in the last 40 years. The one billion of people that live in hazard areas in Low Income Countries (LMC) representing 42% of the total population living in those countries (Figure 6). In Upper-Middle Income Countries (UMC) 43% of population is also living in hazards areas corresponding to almost half billion of people (Table 3). In Low Income Countries, the share of population potentially exposed over the total population is slightly decreasing in the last forty years (Figure 6). The built-up surface potentially exposed to earthquake in High Income Countries (128.000 km² in 2015), has more than doubled between 1975 and 2016 (Table 3), and its share increased from 26% to 28% in the same period (Figure 6). In the last forty years, the built-up surface potentially exposed increased by 128%, while the population increased by 51%. Looking at the share of built-up surface potentially exposed over total built-up surface, it can be observed that in Upper-Middle and Lower Middle Income Countries this share is significantly higher than in High Income Countries (42-43% and 26% in 2015). In fact, in UMCs and LMCs built-up surface in hazard areas are similar, about 47.000 km² and they increased in the last forty years respectively by 162% and 177%. Despite the fact that the amount of built-up surface potentially exposed in Low Income Countries is relatively small (8.000 km² and about 16% of the total in 2015), it is important to highlight that it increased by 348% in the last forty years.

Figure 7 Ten countries with the highest number of people potentially exposed to seismic hazard in 2015, compared to total population, 475 years RP (1975-1990-2000-2015)

Figure 8 Ten countries with the highest amount of built-up potentially exposed to seismic hazard in 2015, compared to total built-up, 475 years RP (1975-1990-2000-2015)

In Figure 7, the 10 countries with the highest number of people living in hazard areas in 2015 are ranked by exposed population. India and China have both more than 380 million of people potentially exposed to earthquakes (475 years RP); Indonesia has more than 200 million people in the same condition, Pakistan and Bangladesh, and Japan follow in the ranking with more than 100 million each. Apart from India and China, all other countries in this ranking have more than 2/3 of the country population potentially exposed to earthquake hazard. In the case of Pakistan and Iran, this share is more than 95%. All of those countries are in Asia, apart from Mexico. Only China and Japan are High Income Countries (HIC)¹⁰. In Figure 8 the 10 countries with the highest amount of built-up surface potentially exposed to seismic hazard are ranked. Only three of them are HIC, while the others are LMC or UMC. In this top ten list, two European countries appear: Italy and Romania with respectively 84% and 92% of built-up surfaces in hazard zones. Turkey, Romania, and Iran have very high share of exposed population over total population in all four periods having with most of the land mapped in the hazard areas.

3.2 Volcano

Comprehensive volcanic hazard maps are difficult to generate because each volcano produces hazards that can be modelled only locally: volcano hazards depends on the topography of the volcano, on precipitation, on wind direction, factors that are all local in nature.

In addition, some volcanic hazards have local impact while others have wider geographical impact even if with less intensity: the fall of pyroclastic blocks and lava flows occur in the immediate vicinity of the volcano; volcanic ashes and smoke plumes can travel farther and affect people and economic activities at wider distances and for longer periods.

In the absence of global volcanic hazard maps, in this report we assess population and built-up within 100 km of 220 volcanoes present in the SVED database (more details in Annex), as one proxy for potential exposure. A distance of 100 km is relevant for assessing direct effects of volcanic eruptions, since lethal pyroclastic flows and surges (Nakada 2000), and lahars (Rodolfo 2000) may occasionally extend to these distances. Chester et al. have estimated that among twelve destructive phenomena associated with volcanic eruptions, seven can potentially reach such a distance (Chester et al. 2000).

Dataset used for the volcano hazard

Return Period: Not Available

Semantic of the final map: Zone within 100 km of volcanoes

Source: NOAA Significant Volcanic Eruption Database (SVED)

Map 2 Exposure analysis within 100 km radial distance from the 220 volcanoes included in the study

Figure 9 Population within 100 km of volcanoes (1975-1990-2000-2015)

Figure 10 Area of built-up within 100 km of volcanoes (1975-1990-2000-2015)

Figure 9 shows evolution of total population within a range of 100 km from the 220 volcanoes in the SVED database (see Annexes). According to these results, the proportion of the global population living within 100 km has remained relatively stable from 1975 to 2015 (at around 5.5%), although absolute values have increased by 82% in this period to a total 414 million people, following the rate of global population growth (Table 4). This translates into additional 186 million potentially exposed since 1975.

Figure 10 shows evolution of total area of built-up surface within 100 km of the 220 volcanoes in the SVED database. According to these results, the amount of built-up surface in proximity of these volcanoes has been considerably increasing, reaching 39,000 km² in 2015. This represents an increase of 139% since 1975 and of 23% between 2000 and 2015. However, increase rates were significantly higher from 1975 to 1990 (4.4% mean annual growth) compared to later periods (1.7 and 1.5% in 1990-2000 and 2000-2015 respectively). Still this increase has been in line with global increase in built-up surface in these periods, keeping the proportion of the global built-up surface potentially exposed stable at around 5%.

Table 4 Population and Built-up surface potentially exposed to volcano hazard (1975-1990-2000-2015)

Exposed Population

Share of exposed population over total

Exposed Built-up surface (km²)

Share of exposed Built-up surface over total

1975

227,483,973

5.6%

16,312

5.3%

1990

302,524,355

5.7%

27,167

5.1%

2000

348,945,818

5.7%

31,837

5.0%

2015

413,616,012

5.6%

39,063

5.0%

increment 1975-2015

82%

139%

Figure 11 Share of Built-up and Population potentially exposed to volcano hazards by region (1975-1990-2000-2015)

Table 5 Built-up surface and Population potentially exposed to volcano hazards by region (1975-1990-2000-2015)

Africa
Asia
Europe
Latin America and
the Caribbean
Northern America
Oceania

Exposed
Population
1975
13,620,897
154,429,010
11,475,989
46,186,646
108,518
1,372,174

1990
20,930,719
205,011,711
11,551,298
62,712,143
152,654
1,782,165

2000
26,820,637
234,452,573
11,371,037
73,519,785
192,383
2,136,493

2015
38,754,783
273,066,505
11,571,822
86,686,733
259,073
2,752,380

Exposed
Built-up
surface (km2)
1975
412
10,623
1,592
3,485
13
162

1990
714
18,200
2,407
5,594
47
177

2000
1,016
21,441
2,640
6,441
62
205

2015
1,758
26,716
2,969
7,290
75
221

Figure 11 and Table 5 show the evolution over time of total population and built-up surface within 100 km of the 220 volcanoes in the SVED database, by continental region. Results show that Asia concentrates most of the potentially exposed population, in all epochs, followed by Latin America and the Caribbean which is the region with the highest share of exposed over total population. Those are also the regions with the highest share of built-up surface potentially exposed in all four epochs analysed.

In 2015, these two regions the share of global exposure compared to their share of global population are significantly different (namely 66% exposed vs 59% of global population and 21% exposed vs 9% of global population, respectively). Potential exposure of people is much lower in other regions, but still amounting to 38.7 million in Africa and 11.5 million in Europe in 2015.

Concerning the temporal trends, potential exposure for both built-up surface and population has not increased since 1975 in Europe (about 2%), whereas it has increased significantly in the other regions, especially in Africa and Northern America. From 1975 to 2015, potential exposed population almost tripled in Africa (+185%) to 38.7 million and in Northern America, where it more than doubled, although totalling only 259,000 in 2015. Results in Figure 11 and Table 5 also highlight the concentration in Asia of most of the potentially exposed built-up surface, in all epochs, followed by Latin America and the Caribbean, and Europe. In 2015 these two former regions still present a share of global exposure significantly different from their share of global built-up surface (namely 68% vs 33% and 19% vs 8%, respectively).

Potential exposure of built-up surface is much lower in other regions, and lower than their share of global built-up surface. This mismatch is especially significant in Europe, which in 2015 concentrates only 8% of global exposure while accounting for 25% of all built-up surface. There are also significant regional differences regarding the global share of exposure of built-up surface respect to population. While in Africa this share is much lower for built-up surface than for population (4% vs 9% in 2015), in Europe the opposite situation occurs, with exposure of built-up surfaces being much higher than population’s (8% vs 3% in 2015).

Regarding the temporal trends, potential exposure of built-up surface has grown substantially in all regions except Oceania (37%), and above population exposure. Greatest increases were observed in Northern America (472%) and Africa (327%). In Europe the significant growth in exposed built-up surface occurring in all periods (overall rise of 86%) contrasts with unchanging population exposure between 1975 and 2015.

Figure 12 Ten countries with the highest number of people potentially exposed to volcano hazards (1975-1990-2000-2015)

Figure 13 Ten countries with the highest amount of built-up potentially exposed to volcano hazards (1975-1990-2000-2015)

Figure 12 shows evolution of total population within a range of 100 km from the 220 volcanoes in the SVED database for the 10 countries with the overall highest exposure. These 10 countries have been accounting for 88% of the globally exposed population since 1975, or 361 million in 2015. Results show that the three countries with highest overall potential exposure are located in Asia, followed by countries in Latin America and Africa. Indonesia clearly leads the ranking, with 38% of the globally exposed population in 2015. Currently, Indonesia and the Philippines account for 52% of total exposed world population (217 million). In Europe, Italy has by far the highest potential exposure, with close to 10 million people. Concerning the temporal trends, potential exposure has not had the same behaviour in all countries due to differing population growth rates and tendencies. While exposure in Italy and Japan have decreased from 2000 to 2015 (in Italy also in all other epochs), in all other countries highlighted in Figure 12 it has increased significantly. In Yemen and Ethiopia, it has increased by more than 50% between 1975 and 1990 and between 2000 and 2015.

Figure 13 depicts the evolution of total area of built-up surface within 100 km of the 220 volcanoes in the SVED database, for the ten countries with the overall highest such exposure. These 10 countries have been accounting for about 90% of the globally exposed built-up surface since 1975, or 35 thousand km² in 2015 (out of 39 thousand). Results show that the rank of potentially exposed built-up surface is different from that regarding population (Figure 12). Two Asian countries, Indonesia and Japan, clearly lead the built-up surface ranking and together comprise about 60% of the global exposure. Japan and Italy concentrate a much larger share of global exposed built-up surface respect to population, while Philippines is in the opposite situation (6% vs 14% in 2015). Indonesia has been leading the ranking since 1990, whereas in 1975 it was led by Japan (with 32% of globally exposed built-up surface). Italy has the highest potential exposure in Europe, but changes regarding built-up surface and population are decoupled: while population exposure has been decreasing since 1975, built-up surface exposure has continued to increase, by significant rates (12% in period 2000-2015). In last 40 years, potentially exposed built-up surface has been increasing significantly in all countries considered, and it has grown above population growth (143% vs 80%).

3.3 Tsunami

Tsunamis are waves set in motion by large and sudden forced displacements of the seawater, having characteristics intermediate between tides and swell waves. Although tsunamis are infrequent (ca. 5-10 events reported globally pr. year), they do represent a serious threat to the coastal population in many areas. (UNISDR 2015)

The frequency of tsunamis is linked to seismic activity, and areas historically affected by tsunamis are is the ring of fire of the Pacific Rim both in Asia and in the Americas and Indonesia, even though also Mediterranean costs have been hit by tsunamis in past times. As coastline is a preferred place to live and to conduct human activities and tsunamis mainly hit coastal zones, this hazard as a relevant impact on global exposure, especially with growing world population and built-up surface in coastal areas.

For the purpose of this report, the hazard map for tsunami produced for the last Global Assessment Report on Disaster Risk Reduction with the 500 years return period has been used.

Dataset used for the Tsunami hazard

Return Period: 500 years

Semantic of the final map: Area flooded by tsunami run-up

Source: Tsunami Run-up hazard map (GAR 2015)

Figure 14 Population potentially exposed to tsunami hazard, 500 years RP (1975-1990-2000-2015)

In 2015, 116 countries in the world have area exposed to tsunami hazard. According to the analysis done combining GHSL data and hazard maps, in 2015 42 million people were potentially exposed to this natural hazard, considering a 500 year return period. In 1975, there were 28 million, meaning that the global exposed population has increased by 51% in forty years (Figure 14).

Figure 15 Built-up potentially exposed to tsunami hazard, 500 years RP (1975-1990-2000-2015)

Built-up surface potentially exposed to tsunami had increased more rapidly than population, by 68% between 1975 and 2015. In 1975, 3,850 square kilometres of built-up surfaces were exposed in the world. In 2015, this value reached 6,490 square kilometres (Figure 15).

Table 6 Population and built-up surface exposed to tsunami hazard by region, 500 years RP (1975-1990-2000-2015)

EXPOSED POPULATION (inhabitants)

EXPOSED BUILT-UP SURFACE (km2)

1975

1990

2000

2015

1975

1990

2000

2015

Africa

106,752

132,164

181,980

266,534

12

24

32

37

Asia

25,499,171

29,143,289

33,302,269

37,920,629

3,321

4,396

4,893

5,541

Europe

357,834

348,098

351,052

345,568

55

88

100

110

Latin America and the Caribbean

1,354,945

1,894,868

2,257,014

2,737,349

148

192

225

264

Northern America

214,982

249,646

276,136

311,798

299

395

436

508

Oceania

65,119

74,943

85,522

107,124

16

21

24

28

N\A

118

265

586

118

0.2

1

1

2

TOTAL

27,598,921

31,843,274

36,454,560

41,689,119

3,851

5,116

5,711

6,488

Table 6 shows the trends of population and built-up surface potentially exposed to tsunami by region between 1975 and 2015. The region with the highest number of people potentially exposed is Asia (37.99 million), followed by Latin America and the Caribbean (2.77 million). Concerning temporal changes, exposed population has been especially increasing in Asia (from 26 million to almost 38 million) in Latin America and the Caribbean (from 1.3 to 2.7 million). This figure also shows that Asia is the most exposed region in terms of built-up surface assets (5.540 km² in 2015), followed by Northern America (500 km²) or 7.82% share over total built-up surface, while the share of population potentially exposed to tsunami in 2015 (500 years RP) was 0.75%.

Figure 16 illustrates the regional distribution of exposure to tsunamis in 2015. Asia concentrates almost 91% of the world’s exposed population, with Africa, Europe, and Northern America representing less than 1% of total each. Differences emerge when comparing the shares of built-up surface and population. Only 0.75% of the world population exposed to tsunamis live in Northern America¹¹, but 7.82% of the total built-up surface exposed to the same hazard is located in the that region. Significant differences can be highlighted also in Europe (0.83% vs 1.69) and in Oceania (0.26% vs 0.43%). The opposite case is represented by Latin America and the Caribbean where the share of built-up surface exposed is lower than the share of exposed population (6.57% vs 4.07%).

Figure 16 Share of Population and built-up exposed to tsunami hazard by region, 500 years RP (2015)

Figure 17 Ten countries with the highest number of people potentially exposed to tsunami hazard in 2015, 500 years RP (1975-1990-2000-2015)

Figure 18 Ten countries with the highest built-up area potentially exposed to tsunami hazard in 2015, 500 years RP (1975-1990-2000-2015)

According to data obtained combined the GHSL and the hazard map, Japan is the country with the highest number of people potentially exposed to tsunami in 2015 for 500 years RP (Figure 17). The exposed population increased from about 16 million in 1975 to almost 19 million in 2015.

Having experienced a big increment in population in coastal areas in the last decades, China has seen also the number of population exposed to tsunami become bigger, from 2 to 6.5 million. Peru is the only non-Asian country in the top 10 of the most exposed countries, among them Indonesia, India, Philippines, Bangladesh, Hong Kong, Oman and Myanmar. Japan, along with China and Indonesia concentrate 74% of the total population exposed. However, Japan is a major exposure hotspot for tsunamis, concentrating 45% of global exposure in 2015. This share has been decreasing since 1975 when it was accounting for 61% of global exposure.

Figure 18 shows the ten countries with the highest amount of built-up surface potentially exposed to tsunami with 500 years RP. These ten countries account for 94% of global built-up surface exposure in 2015, and this share has been only slightly decreasing since 1975 (when it was amounting to 96%). The figure also shows that Japan has by far the highest amount of built-up surface exposed in 2015 (4,000 km² of built-up surface), or 63% of total global exposure, followed by China. Together these two countries concentrate 75% of the total global exposure. The United States of America, which were not included in the top ten by population, are third with 7% of total. Two European countries appear in this rank, Italy and Greece (50 and 42 thousand square kilometres respectively). This happens because these countries have a significant amount of built-up surfaces potentially exposed to tsunami, but these areas have lower population densities than those of other countries in Figure 17. Exposure has been increasing in all countries from 1975 to 2015, with highest rates observed in India, China, and Indonesia.

3.4 Flood

Flooding is the most damaging hazard globally (Jongman, Ward, and Aerts 2012). In many countries, it is the most frequent hazard and in some, it is a recurrent event. Floods are triggered by various phenomena and there are different types of floods: e.g. flash floods, river floods, and urban floods, all of which are caused by a combination of heavy precipitation and poor drainage. The severity of these flood types depends on rainfall intensity, spatial distribution of rainfall, topography and surface conditions. (UNISDR 2015)

Flooding often occurs in flat areas and in proximity to river networks, which are typically the areas that favor the development of human settlements. In fact, proximity of the river network facilitates movement of people, transport of goods and flat areas agricultural practices, and industries. The growth of settlements due to population increase, urbanization and the development of infrastructure are aggravating flood factors. In fact, soil sealing associated with urbanization hampers water retention and accelerates run off.

Dataset used for Flood hazard

Return Period: 100 years

Semantic of the final map: Flooded area (by 1cm or more)

Source: Flood hazard map (GloFAS 2015)

Map 3 Global map of the areas exposed to flood

Figure 19 Population potentially exposed to flood hazard, 100 years RP (1975-1990-2000-2015)

Figure 20 Built-up potentially exposed to flood hazard, 100 years RP (1975-1990-2000-2015)

More than 1 billion people globally were potentially exposed to a hundred year RP floods in 2015. That is more than 14% of the global population. The figure doubled compared to 1975 (Figure 19). In the same period, the area of built-up surface potentially exposed to a hundred year flood almost triplicated from 28,677 km² to 80,483 km² (Figure 20). Similar differences in growth rate of population and built-up surface are also observed at global level. In the last forty years the population increased by a factor of 1.8, while the built-up surface increased by a factor of 2.5 (Martino Pesaresi, Melchiorri, et al. 2016, 35). However, with factors of 2 for population and 2.8 for the built-up surface the increase is stronger in potentially exposed areas.

Although significantly less people are potentially exposed to a hundred year RP floods compared to earthquakes [see 3.1], exposure to this hazard is significantly relevant because flooding is the most frequent natural disaster. According to the CRED/EMDAT/UNISDR report ‘The human cost of weather related disasters’, between 1995-2015 floods were by far the most occurring disaster with 43% (CRED and UNISDR 2015).

Figure 21 Share of Built-up and Population potentially exposed to floods by region, 100 years RP (1975-1990-2000-2015)

Table 6 Built-up surface and Population potentially exposed to floods by region, 100 years RP (1975-1990-2000-2015)

Year
Africa
Asia
Europe
Latin America and
the Caribbean
Northern America
Oceania

Exposed
Population
1975
46,809,067
384,765,697
52,299,761
22,320,393
11,285,911
524,067

1990
69,325,866
525,072,781
55,884,148
27,333,928
12,085,131
654,176

2000
88,143,275
637,094,193
56,927,008
32,360,381
12,972,284
722,268

2015
126,566,129
797,601,275
58,693,079
39,188,090
14,286,300
913,088

Exposed Built-up
surface (Km2)
1975
3,514
10,072
7,884
1,962
4,951
223

1990
5,457
23,222
12,069
2,807
6,843
375

2000
6,747
29,496
13,601
3,332
7,609
419

2015
9,135
42,123
16,224
3,843
8,556
470

In 2015, people in potentially exposed areas were living in 155 countries (out of 251). Although these areas are distributed in all continents (Map 3), flood exposed areas potentially affect people in Asia and Africa more than other continents (Table 6). The regional distribution of potentially exposed population and built-up surface shows significant differences. The majority of the population potentially exposed to floods, almost 800 million people, live in Asian countries, followed by Africa with 126 million. Figure 21 illustrates the different share of built-up surface and population potentially exposed to floods by region over time. In Asia 18% of the population in exposed, as well of 16% of the built-up surface (both shares increased since 1975). The other regions have smaller share of exposed population. Table 6 illustrated also the built-up surface exposed over time. Asia is still the region with the highest amount of built-up surface potentially exposed to floods in 2015 (42,120 km² corresponding to 16% of the total built-up surface), followed by Europe (16,220 km²) and by Africa (9,140 km²). In all regions, these values have been increasing between 1975 and 2015, mostly in Asia with an increase of 4.2 times more than the global average).

Figure 22 Ten countries with the highest number of people potentially exposed to floods in 2015, compared to total population, 100 years RP (1975-1990-2000-2015)

Figure 23 Ten countries with the highest amount of built-up potentially exposed to floods in 2015, compared to total built-up, 100 years RP (1975-1990-2000-2015)

Figure 22 illustrates the ten countries with the highest number of people exposed to floods in 2015, compared to total population over time. China has the highest number of people exposed to floods, 260 million (19% of the country population), followed by India with 220 million (16.8%), and by the Bangladesh with 71 million (44.6%). In 2015, sixteen countries had more than one quarter of the population exposed to floods. Of these countries only the Netherlands is in High Income Countries; Suriname, Turkmenistan, Iraq, and Thailand are Upper-Middle Income Countries; Egypt, Pakistan, and Vietnam are Lower-Middle Income Countries; Bangladesh, Cambodia, Chad, Laos, Myanmar, South Sudan, Sudan are Low Income and Less Developed countries¹². In countries such as Pakistan, Thailand and Nigeria the increment of population in hazard areas is bigger than the increment of the national population (242% against 183% in Pakistan, 140% against 61% in Thailand, and 239% against 187% in Nigeria), suggesting that population is increasing more in coastal and riverside areas, typically more exposed to flood hazard.

In Figure 23 the ten countries with the highest amount of built-up surface potentially exposed to floods in 2015 are ranked, compared to total built-up surface over time. China is by far the country with the highest amount of built-up surface exposed to floods in 2015 (23,000 of km²), followed by the United States with 7,600 km² (not included in the list of the countries with the highest number of people exposed) and by India with 4,100 km². In all the countries included in this list, the built-up surface exposed to floods increased in the last 40 years. In China and Thailand, the exposed built-up surface has increased by factors of 5 and 4 respectively between 1975 and 2015.

Figure 24 shows the EU countries with more than 50,000 people potentially exposed to floods ranked by exposed population in 2015. Germany is the EU country with the highest number of people exposed to floods, about 8 million (10% of the national population), followed by France with 5.7 million (9%).

The Netherlands, third in this ranking, has an exposed population of 5.3 million that is one third of the national population. In this country, the exposed population increased to 44% between 1975 and 2015, while the national population increased to 13% (Table 7). In the same period, in Italy the exposed population increased by 28%, compared to national population increment of 5% only. In Hungary and Romania, while the national population decreased between 1975 and 2015, the exposed population has been increasing. In some EU countries, such as Italy, Poland, United Kingdom, Slovakia, and Greece, while national populations register slight increment between 1975 and 2015, figures indicates that the population exposed to floods is increasing significantly from 20 to 44% (Table 7). Countries as Hungary, Austria, Slovakia, and Latvia register high shares of population exposed over the total population in 2015, between 15% and 19%.

Figure 24 EU countries with more than 50,000 people potentially exposed to flood in 2015, 100 years RP (1975-1990-2000-2015)

Table 7 Comparison between national and population exposed to floods in EU countries, 100 years RP (ranked by exposed population in 2015)

Country

Population exposed to floods (2015)

National population (2015)

Increment of exposed population between 1975 and 2015

Increment of national population between 1975 and 2015

Share of exposed population over national population (2015)

Germany

8,012,900

80,746,785

12%

2%

10%

France

5,727,264

64,378,728

19%

13%

9%

Netherlands

5,396,798

16,908,820

44%

13%

32%

Italy

2,477,981

59,762,191

28%

5%

4%

Spain

2,312,117

46,085,657

24%

18%

5%

Hungary

1,666,455

9,855,867

13%

-5%

17%

Romania

1,495,473

19,514,874

13%

-17%

8%

Austria

1,445,487

8,541,414

28%

11%

17%

Poland

1,422,384

38,591,013

39%

1%

4%

United Kingdom

1,190,047

64,662,475

37%

13%

2%

Belgium

798,834

11,300,151

22%

13%

7%

Slovakia

790,427

5,426,123

20%

3%

15%

Croatia

750,374

4,237,095

-4%

-11%

18%

Sweden

717,462

9,756,555

-3%

14%

7%

Finland

478,649

5,446,337

1%

10%

9%

Czech Republic

439,759

10,544,758

3%

2%

4%

Latvia

379,525

1,969,769

-26%

-26%

19%

Bulgaria

282,390

7,123,263

-35%

-19%

4%

Lithuania

221,022

2,878,296

-19%

-22%

8%

Slovenia

191,166

2,067,334

-4%

3%

9%

Greece

145,006

10,946,801

44%

8%

1%

Portugal

101,591

10,354,470

-9%

5%

1%

Ireland

52,450

4,691,951

-33%

32%

1%

Luxembourg

8,548

566,219

26%

48%

2%

3.5 Tropical Cyclone Wind

Tropical cyclones are unevenly spread around the globe as their development depends on specific climatic and oceanic conditions. A tropical cyclone has multiple impacts on the affected areas, including: extremely powerful winds; torrential rains leading to floods and/or landslides; high waves and damaging storm surge, leading to extensive coastal flooding. The complexity of the multiple forms of impact triggered by tropical cyclones would call for integrated modelling of wind, rain, storm surge and landslides. However, given the limited time available for the present study, priority was given to modelling the winds and storm surge. (UNISDR 2015)

Tropical cyclones form with a combination of oceanic and atmospheric processes. The processes include warm sea surface temperature, vortices at tropical latitudes induced by Earth’s rotation, rising air converted over a large area, and high air pressure. A wide range of scientific evidences points to an increase of frequency and intensity of cyclone occurrence due to the climate change (IPPC 2007). Cyclones damages are caused by heavy rain fall, strong winds and sea level surge. Despite the fact that heavy rains can cause landslides and flooding, the most damaging effect of cyclone hazard are wind and storm surge. Exposure to tropical cyclone wind is illustrated in this paragraph, storm in the following one (3.6).

In order to analyse exposure to cyclone wind, the Saffir-Simpson Hurricane Wind Scale used in the GAR 2015 has been adopted. The Saffir-Simpson (SS) Hurricane Wind Scale is a 1 to 5 rating based on a hurricane's sustained wind speed. This scale estimates potential property damage. Hurricanes reaching Category 3 and higher are considered major hurricanes because of their potential for significant loss of life and damage. For the purpose of this analysis, the cyclone wind hazard is presented in two categories: SS1-2 and SS3-5 from the Saffir-Simpson Scale. Strong Cyclone Wind (category SS1-2) are very dangerous. They reach up to 177 km/h and produce some damage: for example, well-constructed frame homes could have damage to roof, shingles, vinyl siding and gutters; large branches of trees will snap and shallowly rooted trees may be toppled; extensive damage to power lines and poles likely will result in power outages that could last a few to several days. Extreme Cyclone Winds (category SS3-5) exceed 178 km/h and cause devastating damage: well-built framed homes may incur major damage or removal of roof decking and gable ends; many trees will be snapped or uprooted, blocking numerous roads; electricity and water will be unavailable for several days to weeks after the storm passes. Above category SS3-5 the damage is catastrophic leaving most of the impacted area uninhabitable for weeks or months.

Dataset used to Tropical Cyclone wind Hazard

Return Period: 250 years

Semantic of the final map: Area affected by cyclone wind of Saffir-Simpson category 1 or higher

Source: Cyclone wind hazard map (GAR 2015)

Map 4 Tropical Cyclone wind hazard map

Figure 25 Population potentially exposed to Tropical cyclone wind, compared to total, 250 years RP (1975-1990-2000-2015)

The global trend of population potentially exposed to tropical cyclone wind is shown in Figure 25. This hazard threats 89 countries in the world, of which 45 are exposed to also the devastating hurricanes (both SS1-2 and SS3-5). Both, the total population potentially exposed to categories SS1-2 and to categories SS3-5 increased in the last 40 years: from 1 billion in 1975 up to 1.6 billion in 2015 (one billion in class SS1-2 and 640 million in SS3-5), which represents about the 24% of the world population.

Figure 26 Built-up potentially exposed to Tropical cyclone wind, compared to total, 250 years RP (1975-1990-2000-2015)

In Figure 26 the global trend of built-up surface potentially exposed to tropical cyclone wind is presented. Both built-up surface potentially exposed to categories SS1-2 and to categories SS3-5 increased in the last 40 years, from 72.000 km² in 1975 to 185.000 km² which represents about the 24% of the global built-up surface stock. The built-up surface in SS3-5 increased from 32.000 to 74.000 km² (increment 131% from 1975 to 2015), while built-up surface in SS1-2 increased from 40.000 in 1975 to 111.000 km²in 2015 with an increment of 177% in the last 40 years.

Figure 27 Share of Built-up and Population potentially exposed to tropical cyclone wind SS1 and SS3 by region, 250 years RP (1975-1990-2000-2015)

Table 8 Built-up surface and Population potentially exposed to tropical cyclone wind SS1 and SS3 by region, 250 years RP (1975-1990-2000-2015)

Africa
Asia
Europe
Latin America and the Caribbean
Northern America
Oceania
N\A

Strong Tropical Cyclone Wind

Exposed
Population
1975
14,697,187
816,648,698
2,819,200
94,964,371
75,779,017
4,059,133
16,278,945

1990
20,338,839
1,059,183,769
2,911,723
130,479,067
88,148,763
5,184,770
20,200,422

2000
26,996,934
1,194,974,452
2,721,071
155,794,498
98,859,034
5,932,066
22,846,971

2015
39,858,377
1,348,043,284
2,404,450
189,893,623
113,644,071
7,258,941
25,171,005

Exposed
Population
1975
552
36,486
195
6,512
25,418
1,288
1,148

1990
760
67,264
264
12,959
39,545
2,019
1,504

2000
916
81,257
280
15,721
46,308
2,317
1,639

2015
1,102
104,722
308
18,557
55,851
2,715
1,852

Extreme Tropical Cyclone Wind

Exposed
Population
1975
3,683,521
300,394,511
-
32,628,663
4,755,179
262,213
10,740

1990
5,286,904
410,024,651
-
45,698,450
5,817,091
348,744
23,252,080

2000
6,888,062
476,215,615
-
54,396,343
6,677,242
410,111
24,877,828

2015
9,535,065
562,215,490
-
66,166,353
7,773,321
444,251
30,432

Exposed
Population
1975
306
26,803
-
2,479
2,184
33
3

1990
332
45,195
-
5,108
2,951
58
7

2000
338
52,784
-
6,107
3,222
70
10

2015
379
63,090
-
7,146
3,419
102
15

The majority of people potentially exposed to tropical cyclone winds lives in Asia (1.3 billion in 2015) which is by far the most potentially exposed region. More than half billion of people is potentially exposed to tropical cyclone wind of categories SS3-5 (Table 8). The second most exposed region is Latin America and the Caribbean in which the population potentially exposed to tropical cyclone wind increased from 95 million to 190 million between 1975 and 2015. While population potentially exposed to tropical cyclone winds in Asia is evident from the previous figure, a great amount of built-up potentially exposed to this hazard is also located in other regions. Figure 27 also provides a similar regional distribution for the built-up surface potentially exposed to tropical cyclone wind of categories SS1-2 and categories SS3-5. Asia is by far the region with the highest amount of built-up surface potentially exposed to both classes, while Northern America has a very high amount of built-up surface exposed but mostly in categories SS1-2.

Figure 28 Population potentially exposed to tropical cyclone winds SS1 and SS3 by income group, compared to total population, 250 years RP (1975-1990-2000-2015)

In Figure 28 population potentially exposed to tropical cyclone wind by income groups is reported for the four analysed epochs. Also in this case, trends show that in both categories SS1-2 and categories SS3-5 the population is increasing. The group of high-income countries (which includes USA, China, Japan, and Australia, among others) is the one with the highest number and share of population exposed, almost one billion in 2015, representing 36% of the total population of the same group. In the other groups, the share of exposed population over total population is around 15%.

Figure 30 Ten countries with the highest number of people potentially exposed to tropical cyclone winds SS1 in 2015, compared to total population, 250 years RP (1975-1990-2000-2015)

Figure 29 Ten countries with the highest amount of built-up potentially exposed to tropical cyclone winds SS1 in 2015, compared to total built-up, 250 years RP (1975-1990-2000-2015)

Figure 30 reports the 10 countries with the highest number of population potentially exposed to tropical cyclone wind of class SS1 and SS3 in 2015 compared with the total population of the country. The ranking shows that China is in first position both for SS1 and SS3, followed by India for SS3 and Japan, in which 95% of population is exposed to tropical cyclone winds in class SS3 and the remaining 5% only to SS1. Also in the Philippines almost all population is potentially exposed to tropical cyclone wind (99%) of which 79% to the most dangerous class of hazard. All population of North Korea is potentially exposed to SS1, while in South Korea 75% of the population is exposed to tropical cyclone wind SS1 and 25% at SS3. Almost half of Chinese population (664 million) and more than one third of the Mexican population (113 million) live in hazard areas of class SS1. In the same countries, 287 million and 42 million are potentially exposed to class SS3. Figure 29 reports the ten countries with the highest amount of built-up surface potentially exposed to class SS1 and SS3 in 2015 compared to the total built-up surface. The United States of America are the country with the highest amount in SS1, 55.000 km² corresponding to 37% of the total built-up surface, but China and Japan are the ones with the highest amount of built-up surface in class SS3 (28.000 km² and 26.000 km² respectively). In Japan, in fact 95% of the built-up surface is in hazard areas of class SS3 and only 5% only in class SS1.

3.6 Tropical Cyclone Storm Surge

Storm surges and strong wind (described in previous section) are the most damaging hazards that unfold from a tropical cyclone. Storm surge turns into a coastal flooding in low lying coastal zones and shallow bathymetry. Surges penetrate land based on their height and damage the infrastructure on its path. They also exert lateral pressure on buildings and building structures. In addition, the flooding that ensues can then affect both settlements and agricultural land. Storm surge generates water lateral pressure that may be sufficiently strong to devastate coastal settlements.

Dataset used for Tropical Cyclone Storm Surge Hazard

Return Period: 250 years

Semantic of the final map: Inundated area (area affected by storm surge)

Source: Storm Surge hazard map (GAR 2015)

Map 5 Tropical Cyclone Storm Surge hazard map

Figure 31 Population potentially exposed to tropical cyclone storm surge, 250 years RP (1975-1990-2000-2015)

In 2015, 162 million of people in the world were exposed to tropical cyclone storm surge in 79 countries (Figure 31). The population potentially exposed to tropical cyclone storm surge doubled in the last forty year, it was 83 million in 1975.

Figure 32 Built-up potentially exposed to tropical cyclone storm surge, 250 years RP (1975-1990-2000-2015)

The built-up surface in hazard areas also increased with by 104% with respect to 1975, (from 12 to 24 thousand km²) (Figure 32). Both these figures, as all the ones contained in this paragraphs, have been produced considering a 250 year return period.

Figure 33 Share of Population and built-up surface exposed to tropical cyclone storm surge by region over total exposed population and built-up surface, 250 years RP (2015)

Table 9 Population and built-up surface exposed to tropical cyclone storm surge by region over time, 250 years RP (1975-1900-200-2015)

Africa

Asia

Europe

Latin America and the Caribbean

Northern America

Oceania

N\A

Total

Population exposed

1975

879,180

68,247,894

92,330

2,550,298

10,415,126

395,336

186,243

82,766,407

1990

1,120,275

93,203,252

101,334

3,368,884

11,445,015

495,678

4,999,277

114,733,716

2000

1,548,971

113,359,870

96,018

4,409,197

12,564,833

588,383

5,322,027

137,889,300

2015

2,231,988

138,764,515

93,490

5,540,910

14,092,042

756,330

353,438

161,832,715

Built-up surface exposed (km²)

1975

28

6,116

8

377

5,193

177

19

11,918

1990

61

9,463

11

545

7,237

296

539

18,153

2000

103

11,112

12

659

8,086

352

641

20,964

2015

121

13,859

15

752

9,185

419

33

24,382

Figure 33 illustrates the geographical distribution of people and built-up surface potentially exposed to tropical cyclone storm surge in 2015. Asia is by far the region with the highest number of people potentially exposed to tropical cyclone storm surge: almost 86% of the total exposed world population live in Asia (139 million), and 8.7% in Northern America (14 million). The exposed population in Asia increased in the last forty years of 70 million, doubling between 1975 and 2015 (Table 9). The geographic distribution of the built-up surface potentially exposed to tropical cyclone storm surge is significantly different from the one of the population. Almost 14,000 km² of built-up surface exposed are in Asia and more than 9,000 km² in Northern America¹³. In fact, despite the relatively small share of exposed population living in Northern America, almost 38% of the share of global built-up surface potentially exposed to tropical cyclone storm surge (250 year RP) is located in this region.

Figure 34 Ten countries with the highest number of people potentially exposed to cyclone surge in 2015, 250 years RP (1975-1990-2000-2015)

Figure 35 Ten countries with the highest amount of built-up potentially exposed to tropical cyclone storm surge in 2015, 250 years RP (1975-1990-2000-2015

China is by far the country with the highest number of people potentially exposed to cyclone storm surge (Figure 34). 50 million of Chinese live in coastal areas prone to tropical cyclone storm surge. Their number increased by 1.5 times in the last forty years. This is not surprising if we consider the growth of Chinese population since 1975 and the urbanization rate in this country (88.2% in 2015). India, Japan and the Philippines have similar population potentially exposed (about 18 million each), followed by the United States and Bangladesh (13-14 million).

In Figure 35, the ten countries with the highest amount of built-up surface potentially exposed to tropical cyclone storm surge in 2015 are ranked and the increment over time of this value is reported. The United States of America have the highest amount (9,000 km²), followed by China and Japan (6,000 and 4,000 respectively in 2015). The other countries have all less than 1,000 km² of built-up surface potentially exposed (India, the Philippines, Taiwan, Indonesia, Australia, Vietnam and Mexico). All countries report an increment of built-up surface in the last forty years: in 2015 in China it is four times the amount of 1975.

4. CONCLUSIONS

Population growth, urbanization, and socioeconomic development drive the evolution of exposure, and have been the primary factor of disaster losses in recent decades (GFDRR 2016). The effect of exposure on increasing disaster losses is strong, and has been established with much more confidence than the effect of hazard and vulnerability (Visser, Petersen, and Ligtvoet 2014). In order to achieve the goal set by the Sendai Framework to avoid the construction of “new risk” and reducing the existing one (UNISDR 2015c), global multi-temporal analysis of exposure is essential for a better understanding of the spatial and temporal patterns of disaster risk drivers and for identifying effective policy actions for more resilient communities.

This Atlas sheds light on the spatiotemporal changes in exposure to natural hazards in the last four decades. The analysis is based on datasets produced for the whole globe from a single data source – remote sensing data – with a consistent methodology that enables a systematic quantification of exposure and its changes over time.

The findings presented in the Atlas were in part expected and anticipated by other global analysis, such as the GAR (UNISDR 2013, 2015b), or the Intergovernmental Panel on Climate Change (IPCC) (IPCC 2014), among others. However, some new aspects of exposure of human settlements to natural hazards were unveiled through the analysis of the Global Human Settlements data in combination with maps of natural hazards with a worldwide coverage:

- In built-up surface and population increased in all regions and results show that the increase of exposure is in line with the

- Global exposure, both for population and built-up surface, has doubled for all hazards between 1975 and 2015.

- Flood, the most frequent natural disaster, potentially affects more people in Asia (76.9% of the global population exposed) and Africa (12.2%) than in other regions.

- Tropical cyclone winds threaten 89 countries in the world and the population exposed to cyclones increased from 1 billion in 1975 up to 1.6 billion in 2015.

- The country most at risk to Tsunamis is Japan whose population is potentially 4 times more exposed than the second potentially affected country.

- Sea level surge affects the countries across the tropical region and China has the largest increase of population over the last four decades.

The value of the GHSL layers used to generate the figures in this Atlas is that the data are available at fine scale and with a wall-to-wall coverage. Researchers and policy makers are now able to aggregate exposure information at all geographical scales of analysis from the city level to the region, continent and global. As start, this Atlas produces new information on exposure to natural disaster at country level only. We also provide continental aggregations and a grouping of countries according to the economic classification of the UN statistical Division. However, disaster risk practitioners and scientist can generate statistics also at local or regional level to assess exposure to natural hazards. In order fully exploit the potential of the resolution of the GSHL, institutions involved in disaster risk reduction are invited to produce hazard maps at local scale.

The present study is a first attempt to quantify global exposure and it can be improved in a number of ways. First, exposure is calculated for each hazard separately and based on one single return period. We relied on the available open hazard data for this. The return periods are different for each hazard and that prevented us to generate a multi-hazard exposure map. Secondly, the Atlas 2017 focuses on two variables only, population and built-up surface. Other exposed assets including agricultural land, infrastructure, pasture land, water resources, and ecosystem services should be included in future analysis. Other emerging hazards such as slow onset disasters (droughts, desertification, etc.) might as well be included.

The exposure information illustrated in the Atlas 2017 reports on built-up surface and population. This information should eventually be complemented with information on socioeconomic characteristics of population including age group, fertility, and built-up surface characteristics such as infrastructure typologies, building taxonomy, structural characteristics, and replacement values. All those information would support a proper definition of the vulnerability of the assets at risk.

The measure of the built-up surface and population itself can be greatly improved. That improvement already started. The new GHSL Built-up surface layers that will be available in 2018 are computed using Sentinel-1 and Sentinel-2 data, which improve the resolution of the product. In addition, the provision of open data such as Sentinel data allows generating continuous update of the GHSL baseline data. The new GHSL datasets will allow improving the capacity to measure exposure also at local level. Researchers and policy makers may thus take advantage to generated exposure and risk analysis at the geographical scale they are operating whether at local, regional, national, continent or global level.

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Potere, David, and Annemarie Schneider. 2007. “A Critical Look at Representations of Urban Areas in Global Maps.” GeoJournal 69 (1–2): 55–80. doi:10.1007/s10708-007-9102-z.

Preparatory Committee for the United Nations Conference on Housing and Sustainable Urban Development (Habitat III). 2016. “Policy Paper 6: Urban Spatial Strategies: Land Market and Segregation.” A/CONF.226/PC.3/19.

Rodolfo, Kelvin S. 2000. “The Hazard from Lahars and Jökulhlaups.” Encyclopedia of Volcanoes. Academic Press, San Diego, 973–995.

Schneider, Annemarie, Mark A. Friedl, and David Potere. 2010. “Mapping Global Urban Areas Using MODIS 500-M Data: New Methods and Datasets Based on ‘urban Ecoregions.’” Remote Sensing of Environment 114 (8): 1733–46. doi:10.1016/j.rse.2010.03.003.

Stevens, A. J., D. Clarke, R. J. Nicholls, and M. P. Wadey. 2015. “Estimating the Long-Term Historic Evolution of Exposure to Flooding of Coastal Populations.” Natural Hazards and Earth System Sciences Discussions 3 (2): 1681–1715.

UNISDR. 2013. “From Shared Risk to Shared Value –The Business Case for Disaster Risk Reduction. Global Assessment Report on Disaster Risk Reduction.” Geneva, Switzerland: United Nations Office for Disaster Risk Reduction (UNISDR).

———. 2014. “Tsunami Methodology and Result Overview.” Norwegian Geolotechnical Institute (NGI) and Geoscience Australia (GA).

———. 2015a. “Global Assessment Report 2015.” Geneva: United Nations. http://www.preventionweb.net/english/hyogo/gar/2015/en/gar-pdf/GAR2015_EN.pdf.

———. 2015b. “Making Development Sustainable: The Future of Disaster Risk Management. Global Assessment Report on Disaster Risk Reduction.” Geneva, Switzerland: United Nations Office for Disaster Risk Reduction (UNISDR).

———. 2015c. “Sendai Framework for Disaster Risk Reduction 2015-2030.” United Nations International Strategy for Disaster Risk Reduction. http://www.unisdr.org/files/43291_sendaiframeworkfordrren.pdf.

United Nations. 2015. “Sendai Framework for Disaster Risk Reduction 2015–2030. In: UN World Conference on Disaster Risk Reduction, 2015 March 14–18, Sendai, Japan. Geneva: United Nations Office for Disaster Risk Reduction; 2015. Available from: http://www.wcdrr.org/uploads/Sendai_Framework_for_Disaster_Risk_Reduction_2015-2030.pdf [Cited 2015 May 11].” http://www.wcdrr.org/uploads/Sendai_Framework_for_Disaster_Risk_Reduction_2015-2030.pdf [cited 2015 May 11].

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6. ANNEXES

6.1 Methodology and input data

The potential human and physical exposure has been estimated per each hazard separately and then aggregated at country level. The human exposure is based on the estimated number of people exposed to given hazard. It results from the combination of the hazard zones and the total population living in the spatial unit. It thus indicates the expected number of people exposed in the hazard zone. Similarly, the physical exposure is based on the estimated total built-up surface exposed to given hazard. It results from the combination of the hazard zones and the total built-up surface in the spatial unit. It thus indicates the expected number of built-up surface exposed in the hazard zone.

The dataset on hazard zones used in this work vary in data type and formats. Therefore, a common working grid has been selected. All analysis has been performed using a global grid at 250 m resolution in World Mollweide projection, which is one of the global area-equal projections.

6.1.1 Exposure data

The physical and human exposure has been analysed using built-up surface and population grids, respectively. The built-up surface density maps have been aggregated from a 38 × 38 m (approx.) multi-temporal classification of built-up surface presence (GHS_BUILT_LDSMT_GLOBE_R2015B) derived from four Landsat image collections. The population data (GHS_POP_GPW4_GLOBE_R2015A) have been produced by disaggregating census information into built-up surface maps respecting the targeted nominal temporal signature (Freire Sergio et al. 2016). The table below presents the technical details of the exposure maps used. The datasets are available in the working grid.

Table 10 The exposure grids used in this work.

Exposure data

Dataset ID

Product ID

Resolution / Projection

Temporal characteristic

Built-up surface 1975

GHS_BUILT_LDS1975_GLOBE _R2016A_54009_250_v1_0

GHS_BUILT_LDSMT_GLOBE_R2015B

250 m / World Mollweide

1975

Population 1975

GHS_POP_GPW41975_GLOBE _R2015A_54009_250_v1_0

GHS_POP_GPW4_GLOBE_R2015A

250 m / World Mollweide

1975

Built-up surface 1990

GHS_BUILT_LDS1990_GLOBE _ R2016A_54009_250_v1_0

GHS_BUILT_LDSMT_GLOBE_R2015B

250 m / World Mollweide

1990

Population 1990

GHS_POP_GPW41990_GLOBE _R2015A_54009_250_v1_0

GHS_POP_GPW4_GLOBE_R2015A

250 m / World Mollweide

1990

Built-up surface 2000

GHS_BUILT_LDS2000_GLOBE _ R2016A_54009_250_v1_0

GHS_BUILT_LDSMT_GLOBE_R2015B

250 m / World Mollweide

2000

Population 2000

GHS_POP_GPW42000_GLOBE _R2015A_54009_250_v1_0

GHS_POP_GPW4_GLOBE_R2015A

250 m / World Mollweide

2000

Built-up surface 2015

GHS_BUILT_LDS2014_GLOBE _ R2016A_54009_250_v1_0

GHS_BUILT_LDSMT_GLOBE_R2015B

250 m / World Mollweide

2014

Population 2015

GHS_POP_GPW42015_GLOBE _R2015A_54009_250_v1_0

GHS_POP_GPW4_GLOBE_R2015A

250 m / World Mollweide

2015

6.1.2 Hazard data

One hazard map has been created per each natural hazard. For the purpose of the analysis, all maps have been produced as grids at 250 m in World Mollweide projection. Table below summarises the details of the hazard maps, and their production is explained in more details in this Annex.

Table 11 The natural hazard maps used in this work (RP: return period)

Natural Hazard

Dataset

Source

Data type

Selected RP

Earthquake

EMMI-GSHAP (multiclass)

GAR 2013

Raster, 11km (approx.) WGS-84

475 RP

Volcano

JRC-SVED

NOAA SVED, adapted byJRC

Vector (point), WGS-84

NA

Tsunami

Tsunamis Run-up

GAR 2015

Raster, 80 m (approx.) WGS-84

500 RP

Flood

Flood map

JRC GloFAS

Raster, 1 km (approx.) WGS-84

100 RP

Cyclone Wind

JRC-GAR, wind category (multiclass)

GAR 2015, adapted by JRC

Raster, 1 km (approx.) WGS-84

250 RP

Cyclone Storm Surge

JRC-GAR, inundated area

GAR 2015, adapted by JRC

Vector (point), WGS-84

250 RP

Earthquake

The information on seismic hazard used in this work is based on the Earthquakes Modified Mercalli Intensity (EMMI) dataset produced by CIESIN Columbia University for the GAR 2013¹⁴. This simulation-based dataset is derived from Global Seismic Hazard Assessment Program (GSHAP) dataset that was converted to MMI scales based on the methodology described by Wald et al. (1999). The GSHAP project (1992-1999) depicts Peak-Ground-Acceleration (PGA) with 10% chance of exceedance in 50 years, corresponding to a return period of 475 years. This EMMI-GSHAP grid is provided at 11 × 11km grid cells (approx.) in WGS-84, and it has been brought into the final seismic hazard map in the working grid (250 m, World Mollweide projection).

The EMMI scale classifies the area exposed to earthquakes into ten classes¹⁵ according to earthquake shaking intensity and relative damages (see Table below). The lower numbers of the intensity scale generally deal with the manner in which the earthquake is felt by people. The higher numbers of the scale are based on observed structural damage. Structural engineers usually contribute information for assigning intensity values of VIII or above.

Table 12 Earthquakes Modified Mercalli Intensity scale and produced damage

Intensity

Shaking

Description/Damage

I

Not felt

Not felt except by a very few under especially favourable conditions.

II

Weak

Felt only by a few persons at rest, especially on upper floors of buildings.

III

Weak

Felt quite noticeably by persons indoors, especially on upper floors of buildings. Many people do not recognize it as an earthquake. Standing motor cars may rock slightly. Vibrations similar to the passing of a truck. Duration estimated.

IV

Light

Felt indoors by many, outdoors by few during the day. At night, some awakened. Dishes, windows, doors disturbed; walls make cracking sound. Sensation like heavy truck striking building. Standing motor cars rocked noticeably.

V

Moderate

Felt by nearly everyone; many awakened. Some dishes, windows broken. Unstable objects overturned. Pendulum clocks may stop.

VI

Strong

Felt by all, many frightened. Some heavy furniture moved; a few instances of fallen plaster. Damage slight.

VII

Very strong

Damage negligible in buildings of good design and construction; slight to moderate in well-built ordinary structures; considerable damage in poorly built or badly designed structures; some chimneys broken.

VIII

Severe

Damage slight in specially designed structures; considerable damage in ordinary substantial buildings with partial collapse. Damage great in poorly built structures. Fall of chimneys, factory stacks, columns, monuments, walls. Heavy furniture overturned.

IX

Violent

Damage considerable in specially designed structures; well-designed frame structures thrown out of plumb. Damage great in substantial buildings, with partial collapse. Buildings shifted off foundations.

X

Extreme

Some well-built wooden structures destroyed; most masonry and frame structures destroyed with foundations. Rails bent.

In this analysis the multi-temporal exposure estimates have been calculated using four hazard zones: class 5, class 6, class 7, and class 8. Table below outlines how this schema can be mapped into MMI scale, and Map 1 (pg.26) shows the geographical extent of these hazard zones.

Table 13 Mapping between classes of the hazard zone map and MMI scale

Earthquake hazard map encoding

MMI class

Class 5

V-VI-VII

Class 6

VII-VIII

Class 7

VIII-IX

Class 8

X

Volcano

The hazard map of volcanoes is based on the NOAA Significant Volcanic Eruption Database¹⁶ (SVED), a global listing of over 500 significant eruptions. In this data, a significant eruption is classified as one that meets at least one of the following criteria: caused fatalities, caused moderate damage (approximately USD $1 million or more), with a Volcanic Explosivity Index (VEI) of 6 or higher, caused a tsunami, or was associated with a major earthquake.

In this work, the volcano hazard map is understood as the potentially affected area within the distance of 100 km or closer to any potentially dangerous volcano. The volcano hazard map has been produced as follows. First, a unique list of volcanoes has been derived from the SVED. The underwater volcanoes have been excluded because it is assumed that they mainly cause tsunamis (which is analysed using a dedicated hazard map which does not consider volcanogenic tsunamis). The final SVED volcano list contains 220 volcanoes of which only less than ten had the last know eruption in BCE (the oldest event around -4360 BCE). Then, a buffer of 100km was constructed per each volcano from the SVED list. Finally, the resulted buffered point layer was converted into the working grid (i.e., 250 m, World Mollweide projection).

Tsunami

The tsunami hazard map has been derived from the GAR 2015 tsunami map¹⁷ that depicts the estimate of tsunami Run-up. The applied tsunami hazard model uses a Probabilistic Tsunami Hazard Assessment (PTHA) methodology, which quantifies the probability of the tsunami run-up height in various areas, combined with the method of amplification factor to estimate maximum shoreline water elevations. This dataset was modelled using global data, and is based on two a comprehensive list of reports and scientific papers compiled and utilized in producing tsunami hazard maps as well as finding return periods of future events. In this map, each cell (of 80 m approx.) represents a tsunami run-up over a minimum return period of 500 years.

Map 6 Tsunami Run-up (GAR 2015) on example of Japan

The hazard map used in this work is the result of the transformation of the GAR 2015 tsunami frequency map into the working grid, i.e. 250 m in World Mollweide projection.

Flood

The hazard data for flood exposure are derived from the Flood hazard map of the World (GloFAS 2015)¹⁸ (Dottori et al. 2016). These flood hazard maps are based on streamflow data from the European and Global Flood Awareness System (EFAS and GloFAS) and have been computed using two-dimensional hydrodynamic models. There are several hazards maps, according to the return period used to derive the data, i.e, 10, 20, 50, 100, 200 and 500 years. These maps can be used to assess flood exposure and risk of population and assets. However, this dataset is based on JRC elaborations and is not an official flood hazard map.

For purpose of this analysis, the 100-RP map has been used, which depicts flood prone areas at global scale for flood events with 100-year return period: this is usually adopted in national flood risk assessment documents and maps as a reference for rare and severe flood events

Resolution is 30 arcseconds (approx. 1km). Cell values indicate water depth (in m). The derived hazard map represents areas flooded with 1cm or more (that includes all affected area, independently by the height of water), per each cell of 250m grid in World Mollweide projection.

The limit of the minimum water depth in the flood map was set at 1cm as it was in other previous experiments (Alfieri et al. 2017).

Tropical Cyclone Wind

The cyclone wind hazard map used in this analysis, has been derived from the GAR map on Tropical Cyclonic Wind. The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry (CIMNE and INGENIAR Ltda 2015). There are several maps offered at 30 arc-seconds resolution (approx. 1km) for different return periods, i.e. 50, 100, 250, 500, and 1000 years.

In this work, the 250-RP map has been used. The GAR cyclone wind hazard data represents the gasp wind speed per cell. This map has been reclassified to express the Saffir-Simpson Hurricane Wind Scale¹⁹, a 1 to 5 hurricane rating based on a sustained wind speed²⁰. This scale estimates potential property damage. Hurricanes reaching Category 3 and higher are considered major hurricanes because of their potential for significant loss of life and damage, while category 1 and 2 storms are still dangerous and require preventative measures. The exposure has been estimated per each category (i.e., SS class), and the values have been aggregated into two classes for the analysis (SS1-2 and SS3-5).

Table 14 The Saffir-Simpson Hurricane Wind Scale is a 1 to 5 rating based on a hurricane's sustained wind speed. Source: NOAA

Category

Sustained Winds

Types of Damage Due to Hurricane Winds

1

119-153 km/h

Very dangerous winds will produce some damage: Well-constructed frame homes could have damage to roof, shingles, vinyl siding and gutters. Large branches of trees will snap and shallowly rooted trees may be toppled. Extensive damage to power lines and poles likely will result in power outages that could last a few to several days.

2

154-177 km/h

Extremely dangerous winds will cause extensive damage: Well-constructed frame homes could sustain major roof and siding damage. Many shallowly rooted trees will be snapped or uprooted and block numerous roads. Near-total power loss is expected with outages that could last from several days to weeks.

3

178-208 km/h

Devastating damage will occur: Well-built framed homes may incur major damage or removal of roof decking and gable ends. Many trees will be snapped or uprooted, blocking numerous roads. Electricity and water will be unavailable for several days to weeks after the storm passes.

4 (major)

209-251 km/h

Catastrophic damage will occur: Well-built framed homes can sustain severe damage with loss of most of the roof structure and/or some exterior walls. Most trees will be snapped or uprooted and power poles downed. Fallen trees and power poles will isolate residential areas. Power outages will last weeks to possibly months. Most of the area will be uninhabitable for weeks or months.

5 (major)

252 km/h or higher

Catastrophic damage will occur: A high percentage of framed homes will be destroyed, with total roof failure and wall collapse. Fallen trees and power poles will isolate residential areas. Power outages will last for weeks to possibly months. Most of the area will be uninhabitable for weeks or months.

Table 15 Mapping between classes of the hazard zone map and the Saffir-Simpson Hurricane Wind Scale

Tropical cyclone wind hazard map encoding

SS classes

SS1-2

1, 2

SS3-5

3, 4, 5

Tropical Cyclone Storm Surge

The baseline data used to create the storm surge hazard map is the global GAR 2015 dataset of the wave high. These data have been created by the tropical cyclonic strong wind and storm surge model, which uses information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry (CIMNE et al., 2015a). GAR 2015 Storm Surge hazard maps are expressed in points, and are made available for different return periods, i.e. 50, 100, 250, 500, and 1000 years.

Map 7 SRTM tiles covering the terrestrial land masses. The land masses above 60-degree latitude North are not covered

The data used in this analysis has been produced for return period of 250 years. This dataset consists of more than 165,000 points along the coast representing the expected storm surge level. In order to estimate the inundated area affected by storm surge, a global elevation model have been used by applying method described in Hoque and Khan, 1997 (Hoque and Khan 1997) adopted by JRC INFORM. First, the point layer was converted in a raster. Then for each pixel the information of surge level was compared with the terrain elevation. The pixels where the expected surge level is higher or equal than the Digital Elevation Model (DEM), define the hazard zone. The final hazard map represents the potentially inundated areas (see Map 5 Tropical Cyclone Storm Surge hazard map).

The DEM was taken from the Shuttle Radar Topography Mission (SRTM) v4.1 (Jarvis, Reuter, and Guevara 2008), which provides terrain elevation grids at a 90 meters resolution (approx.). The SRTM radar data were processed into a Digital Surface Model (DSM) and available as open source at 3 arc-seconds for the entire land masses between latitude 60 North and 50 South.

The storm surge impact calculated is an estimation that is affected by uncertainties in three variables at least, the surge model, the SRTM dataset (from which the elevation data are derived) and the exposure (population and built up). Despite the SRTM dataset limitations (e.g., the height measures include that of buildings, and vegetation canopy) this dataset was ultimately used because it is the one that best matches the resolution of the built up layers, and for its geographical scale, that covers most of the inhabited place of the earth. However, there are limitations and assumptions associated with the data that were taken into account during the analysis.

6.1.3 Geospatial aggregation for analysis

In this work, the potential exposure to natural hazards were analysed using several spatial aggregation of the world. The main layer was the country layer, and then geographical and income aggregation of the country data.

Country layer

Map 8 Country layer used for the analysis

The global data used in this work were analysed and aggregated at country level (i.e., 251 entities) using the Database of Global Administrative Areas (GADM v2)²¹. This dataset is freely available for non-commercial use, which enables users to recreate the analysis.

Although it is a public database, GADM has a higher spatial resolution than other free or commercial databases. The GADM project created the spatial data for many countries from spatial databases provided by national governments, NGO, and/or from maps and lists of names available on the Internet (e.g. from Wikipedia).

Geographical classification

Map 9 Country classification per regions of the world

This report includes multi-temporal global data. Data are also presented in aggregated formats.

Country data for analysis purposes have been grouped according to the country classification by Major Area and Regions of the World as per the United Nations Population Division Department of Economic and Social Affairs World Population Prospects, 2015 Revision.

Countries are grouped in 6 regions: Africa, Asia, Europe, Latin America and the Caribbean, Northern America and Oceania.

The following countries are not listed under any group: French Southern Territories, Bouvet Island, Heard Island and McDonald Islands, British Indian Ocean Territory, Paracel Islands, North Korea, British Virgin Islands, Akrotiri and Dhekelia, Caspian Sea, Clipperton Island, Northern Cyprus, United States Minor Outlying Islands.

Income classification

Map 10 Country classification per income class

Countries are divided in 4 income classes: High Income, Upper-Middle, Lower-Middle and Low Income Countries.

Classification of countries per regions and income classes is inspired by The Classification Of Countries By Major Area And Region Of The World (World Population Prospects: The 2015 Revision)²².

The following countries are not listed under any income group: Anguilla, Åland, French Southern Territories, Bonaire, Sint Eustatius and Saba, Saint-Barthélemy, Bouvet Island, Cocos Islands, Cook Islands (the), Christmas Island, Western Sahara, Falklands, Guernsey, Gibraltar, Guadeloupe, French Guiana, Heard Island and McDonald Islands, Isle of Man, British Indian Ocean Territory, Jersey, Saint-Martin, Montserrat, Martinique, Mayotte, New Caledonia, Norfolk Island, Niue, Nauru, Pitcairn Islands, Paracel Islands, North Korea, eunion, South Georgia and the South Sandwich Islands, Saint Helena, Svalbard and Jan Mayen, Spratly islands, Saint Pierre and Miquelon, Taiwan, Vatican City, British Virgin Islands, Wallis and Futuna Islands, Akrotiri and Dhekelia, Caspian Sea, Clipperton Island, Kosovo, Northern Cyprus, United States Minor Outlying Islands, Tokelau.

6.2 Disclaimer

The disclaimer informs readers about specific arrangements adopted in the analysis of data published in this Atlas and other specifications related to information and views contained in this report.

The baseline data used to produce the Atlas have been organized in four epochs, namely 1975, 1990, 2000, 2015. Each epoch integrates satellite and census data that best approximate the nominal year: information about the exact dates of the satellite data and census data integrated in the product can be found at (M Pesaresi et al. 2016).

The empirical evidences about built-up surfaces and population supporting this release of the Atlas are based on the compilation of the best available open satellite data records collected since 1975 by the Landsat space program, the best available methods for automatic satellite data classification and the best available globally-harmonized national census spatial statistics collected by the CIESIN SEDAC.

Despite the best efforts done, unavoidable information gaps in specific locations of the Earth surface and specific points in time can result from unavailability of suitable satellite data or census data. Moreover, because the method for mapping built-up surfaces is based on physical observable characteristics as collected from space orbiting sensors, some settlements may be hardly detectable or simply invisible. Just to mention typical cases: settlement carved in rock cliffs, underground settlement, or settlements made by straw huts under large tree canopies are nearly invisible with the data technology used to support the Atlas.

Accordingly to the quality control procedures implemented so far using validated fine-scale cartographic reference data, the built-up surfaces quantities as estimated by GHSL are the best estimation available today using global open remote sensing data (Martino Pesaresi, Ehrlich, et al. 2016). The reader interested in understanding if specific issues or reported spatial-temporal data anomalies may be present in the global satellite-derived baseline data supporting the Atlas are invited to access the quality control information layers GHS built-up confidence grid “GHS_BUILT_LDSMTCNFD_GLOBE_R2015B” and GHS built-up data mask grid “GHS_BUILT_LDSMTDM_GLOBE_R2015B” that are included in the current open data release of the GHSL (Annex 4 GHSL Instructions for data access).

Maps and country borders

The term ‘country’ as used in this Report refers to territories or areas; the designations employed and the presentation of the material do not imply the expression of any opinion whatsoever on the part of the European Commission concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. In addition, the designations of country groups are intended solely for statistical or analytical convenience and do not necessarily express a judgement about the stage of development reached by a particular country or area in the development process. The boundaries, names, and designations used on the maps presented in this publication do not imply official endorsement of acceptance by the European Commission. The views expressed in this publication are those of the authors and do not necessarily reflect those of the European Commission or its senior management, or of the experts whose contributions are acknowledged.

If not otherwise indicated, all maps have been created by European Commission - Joint Research Centre. The boundaries and names shown on maps do not imply official endorsement or acceptance by the European Union. Kosovo: This designation is without prejudice to positions on status, and is in line with UNSCR 1244/1999 and the ICJ Opinion on the Kosovo Declaration of Independence.

City names have been used for the only purpose of the Atlas and do not imply any official status recognition by the European Union.

The analysis included in the Atlas, not necessarily include statistics for the following countries (ISO Country Codes): ABW, AIA, ALA, AND, ASM, ATF, ATG, BES, BLM, BLZ, BMU, BVT, CCK, COK, CPV, CUW, CXR, CYM, DMA, ESH, FLK, FRO, FSM, GGY, GRD, GRL, GUF, GUM, HMD, IMN, IOT, JEY, KIR, KNA, LCA, MAF, MDV, MKL, MNP, MSR, MYT, NCL, NFK, NIU, NRU, PLW, PYF, SGS, SHN, SJM, SLB, SMR, SP-, SPM, SWZ, SYC, TCA, TGO, TKL, TON, TUV, UMI, VCS, VCT, VIR, VUT, WSM, XAD, XCN.

The exclusion of the above-mentioned countries can be due to incomplete input data (such as population, built-up surface, area of settlement, and detection of Urban Centres) or missing continuous values across time.

Use constraints

This Atlas was generated using a selection of open global datasets. The main purpose of this Atlas is to highlight the importance of the exposure factor in the global risk assessment, in support to international policy processes, and to broadly identify areas where more detailed data should be collected for local policy agendas. The Atlas is not intended to be used as-it-is for local-scale applications such as land use planning, in-situ planning, or emergency and life-saving operations. The European Commission - Joint Research Centre and collaborators should in no case be liable for misuse or misinterpretation of the presented results. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of European Commission - Joint Research Centre concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries

LIST OF ABBREVIATIONS

CRED | Centre for Research on the Epidemiology of Disasters
DRR | Disaster Risk Reduction
ECHO | European Commission - Humanitarian Aid and Civil Protection
GAR | Global Assessment Report
GDACS | Global Disaster Alert and Coordination System
GloFAS | Global Flood Awareness System
GHSL | Global Human Settlement Layer
GSHAP | Global Seismic Hazard Assessment Program
HFA | Hyogo Framework for Action
INFORM | Index for Risk Management
MMI | Modified Mercalli Intensity Scale (I-XII)
NOAA | National Oceanic and Atmospheric Administration, the United States
OCHA | Office for the Coordination of Humanitarian Affairs
OECD | Organisation for Economic Co-operation and Development
PGA | Peak Ground Acceleration
RP | Return Period
SDG | Sustainable Development Goals
SFDRR | Sendai Framework for Disaster Risk Reduction
SS | Saffir-Simpson Hurricane Scale
SRTM | Shuttle Radar Topography Mission
UNEP | United Nations Environment Programme
UNISDR | United Nations Office for Disaster Risk Reduction

DEFINITIONS

Built-up surface per capita
Ratio between area of built-up land and population

Built-up surface
Built up area is typically expressed with a continuous value representing the proportion of building footprint area within the total size of the cell. (Martino Pesaresi, Melchiorri, et al. 2016)

Disaster
A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts. (United Nations General Assembly 2016)

Exposure
The situation of people, infrastructure, housing, production capacities and other tangible human assets located in hazard-prone areas. (United Nations General Assembly 2016)

Geodata
An image that has geographic information embedded in the file, like GeoTIFF

Hazard
A process, phenomenon or human activity that may cause loss of life, injury or other health impacts, property damage, social and economic disruption or environmental degradation. (United Nations General Assembly 2016)

Land mass
Land mass is the total surface of continental land excluding inland water bodies (Martino Pesaresi, Melchiorri, et al. 2016)

Megacity
A megacity is an urban settlement hosting more than 10 million people

Natural hazard
Natural hazards are predominantly associated with natural processes and phenomena. (United Nations General Assembly 2016)

Nightlight
Emission of light measured in watt per m²

Population
Resident population accounted in national censuses

Raster
An image composed of a complete grid of pixels

Return period
Average frequency with which a particular event is expected to occur. (UNISDR 2015a)

Disaster Risk
The potential loss of life, injury, or destroyed or damaged assets which could occur to a system, society or a community in a specific period of time, determined probabilistically as a function of hazard, exposure, vulnerability and capacity. (United Nations General Assembly 2016)

Vulnerability
The conditions determined by physical, social, economic and environmental factors or processes which increase the susceptibility of an individual, a community, assets or systems to the impacts of hazards. (United Nations General Assembly 2016)

LIST OF FIGURES

Figure 1 Global population potentially exposed to seismic hazard of class from 5 to 8, 475 years RP (1975-1990-2000-2015)

Figure 2 Global Built-up potentially exposed to seismic hazard of class from 5 to 8, 475 years RP (1975-1990-2000-2015)

Figure 3 Built-up potentially exposed to seismic hazard by hazard class, 475 years RP (1975-1990-2000-2015)

Figure 4 Population potentially exposed to seismic hazard by hazard class, 475 years RP (1975-1990-2000-2015)

Figure 5 Share of Population and Built-up potentially exposed to earthquake by region, 475 years RP (1975-1990-2000-2015)

Figure 6 Share of Population and Built-up potentially exposed to earthquake by income group, 475 years RP (1975-1990-2000-2015)

Figure 7 Ten countries with the highest number of people potentially exposed to seismic hazard in 2015, compared to total population, 475 years RP (1975-1990-2000-2015)

Figure 8 Ten countries with the highest amount of built-up potentially exposed to seismic hazard in 2015, compared to total built-up, 475 years RP (1975-1990-2000-2015)

Figure 9 Population within 100 km of volcanoes (1975-1990-2000-2015)

Figure 10 Area of built-up within 100 km of volcanoes (1975-1990-2000-2015)

Figure 11 Share of Built-up and Population potentially exposed to volcano hazards by region (1975-1990-2000-2015)

Figure 12 Ten countries with the highest number of people potentially exposed to volcano hazards (1975-1990-2000-2015)

Figure 13 Ten countries with the highest amount of built-up potentially exposed to volcano hazards (1975-1990-2000-2015)

Figure 14 Population potentially exposed to tsunami hazard, 500 years RP (1975-1990-2000-2015)

Figure 15 Built-up potentially exposed to tsunami hazard, 500 years RP (1975-1990-2000-2015)

Figure 16 Share of Population and built-up exposed to tsunami hazard by region, 500 years RP (2015)

Figure 17 Ten countries with the highest number of people potentially exposed to tsunami hazard in 2015, 500 years RP (1975-1990-2000-2015)

Figure 18 Ten countries with the highest built-up area potentially exposed to tsunami hazard in 2015, 500 years RP (1975-1990-2000-2015)

Figure 19 Population potentially exposed to flood hazard, 100 years RP (1975-1990-2000-2015)

Figure 20 Built-up potentially exposed to flood hazard, 100 years RP (1975-1990-2000-2015)

Figure 21 Share of Built-up and Population potentially exposed to floods by region, 100 years RP (1975-1990-2000-2015)

Figure 22 Ten countries with the highest number of people potentially exposed to floods in 2015, compared to total population, 100 years RP (1975-1990-2000-2015)

Figure 23 Ten countries with the highest amount of built-up potentially exposed to floods in 2015, compared to total built-up, 100 years RP (1975-1990-2000-2015)

Figure 24 EU countries with more than 50,000 people potentially exposed to flood in 2015, 100 years RP (1975-1990-2000-2015)

Figure 25 Population potentially exposed to Tropical cyclone wind, compared to total, 250 years RP (1975-1990-2000-2015)

Figure 26 Built-up potentially exposed to Tropical cyclone wind, compared to total, 250 years RP (1975-1990-2000-2015)

Figure 27 Share of Built-up and Population potentially exposed to tropical cyclone wind SS1 and SS3 by region, 250 years RP (1975-1990-2000-2015)

Figure 28 Population potentially exposed to tropical cyclone winds SS1 and SS3 by income group, compared to total population, 250 years RP (1975-1990-2000-2015)

Figure 29 Ten countries with the highest amount of built-up potentially exposed to tropical cyclone winds SS1 in 2015, compared to total built-up, 250 years RP (1975-1990-2000-2015)

Figure 30 Ten countries with the highest number of people potentially exposed to tropical cyclone winds SS1 in 2015, compared to total population, 250 years RP (1975-1990-2000-2015)

Figure 31 Population potentially exposed to tropical cyclone storm surge, 250 years RP (1975-1990-2000-2015)

Figure 32 Built-up potentially exposed to tropical cyclone storm surge, 250 years RP (1975-1990-2000-2015)

Figure 33 Share of Population and built-up surface exposed to tropical cyclone storm surge by region over total exposed population and built-up surface, 250 years RP (2015)

Figure 34 Ten countries with the highest number of people potentially exposed to cyclone surge in 2015, 250 years RP (1975-1990-2000-2015)

Figure 35 Ten countries with the highest amount of built-up potentially exposed to tropical cyclone storm surge in 2015, 250 years RP (1975-1990-2000-2015

LIST OF MAPS

Map 1 Earthquake Hazards Map – Classes defined for the analysis

Map 2 Exposure analysis within 100 km radial distance from the 220 volcanoes included in the study

Map 3 Global map of the areas exposed to flood

Map 4 Tropical Cyclone wind hazard map

Map 5 Tropical Cyclone Storm Surge hazard map

Map 6 Tsunami Run-up (GAR 2015) on example of Japan

Map 7 SRTM tiles covering the terrestrial land masses. The land masses above 60-degree latitude North are not covered

Map 8 Country layer used for the analysis

Map 9 Country classification per regions of the world

Map 10 Country classification per income class

LIST OF TABLES

Table 1 Synthesis of the input hazard maps and selected return period

Table 2 Population and Built-up surface potentially exposed to earthquake by region, 475 years RP (1975-1990-2000-2015)

Table 3 Population and Built-up surface potentially exposed to earthquake by income group, 475 years RP (1975-1990-2000-2015)

Table 4 Population and Built-up surface potentially exposed to volcano hazard (1975-1990-2000-2015)

Table 5 Built-up surface and Population potentially exposed to volcano hazards by region (1975-1990-2000-2015)

Table 6 Built-up surface and Population potentially exposed to floods by region, 100 years RP (1975-1990-2000-2015)

Table 7 Comparison between national and population exposed to floods in EU countries, 100 years RP (ranked by exposed population in 2015)

Table 8 Built-up surface and Population potentially exposed to tropical cyclone wind SS1 and SS3 by region, 250 years RP (1975-1990-2000-2015)

Table 9 Population and built-up surface exposed to tropical cyclone storm surge by region over time, 250 years RP (1975-1900-200-2015)

Table 10 The exposure grids used in this work.

Table 11 The natural hazard maps used in this work (RP: return period)

Table 12 Earthquakes Modified Mercalli Intensity scale and produced damage

Table 13 Mapping between classes of the hazard zone map and MMI scale

Table 14 The Saffir-Simpson Hurricane Wind Scale is a 1 to 5 rating based on a hurricane's sustained wind speed. Source: NOAA

Table 15 Mapping between classes of the hazard zone map and the Saffir-Simpson Hurricane Wind Scale

LIST OF BOXES

Box 1 Sendai Framework for Disaster Risk Reduction – UNISDR

Box 2 INFORM – Index for Risk Management

Box 3 GAR15 - Global Assessment Report 2015

Notes

¹ http://ec.europa.eu/europeaid/about-development-and-cooperation-europeaid_en http://ec.europa.eu/echo/

² http://www.gdacs.org/

³ https://www.unisdr.org/we/inform/terminology#letter-c

⁴ GHSL data area free and open. The whole collection is available for download: http://data.jrc.ec.europa.eu/collection/GHSL

⁵ The input hazard maps for each hazard with relative technical information is illustrated at the end of each paragraph (for the technical details, see 0).

⁶ https://www.ngdc.noaa.gov/hazard/volcano.shtml

⁷ http://globalfloods.jrc.ec.europa.eu

⁸ For the regional grouping see Geographical classification

⁹ For the income grouping see Income

¹⁰ See Income Grouping pg.91

¹¹ Note than in this regional grouping, Northern America includes the United States and Canada.

¹² French Guiana is not listed under any income group but it has 33.2% of the 2015 population potentially exposed to flood.

¹³ Please note that Asia includes 51 countries; Northern America includes Canada and the United States of America (see Geographical classification)

¹⁴ http://preview.grid.unep.ch/index.php?preview=data&events=earthquakes&evcat=3&lang=eng

¹⁵ https://earthquake.usgs.gov/learn/topics/mercalli.php

¹⁶ https://www.ngdc.noaa.gov/hazard/volcano.shtml

¹⁷ http://preview.grid.unep.ch/index.php?preview=data&events=tsunamis&evcat=2&lang=eng

¹⁸ [Dataset] PID: http://data.europa.eu/89h/jrc-floods-floodmapgl_rp100y-tif

¹⁹ http://www.nhc.noaa.gov/aboutsshws.php

²⁰ The re-classification rules consider that wind gasp speed is around 30% higher than sustained wind speed.

²¹ Global administrative areas (boundaries). University of Berkeley, Museum of Vertebrate Zoology and the International Rice Research Institute (2012).

²² https://esa.un.org/unpd/wpp/General/Files/Definition_of_Regions.pdf