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Title: Global typology of coastal urban vulnerability under rapid

urbanization

Article Type: Research paper

Keywords: Coastal cities; cluster analysis; vulnerability-generating

mechanism; socio-ecological system; quantitative indication

Corresponding Author: Dipl-Geogr. Till Sterzel,

Corresponding Author's Institution: climate-babel

First Author: Till Sterzel

Order of Authors: Till Sterzel; Matthias Lüdeke, Dr.; Carsten Walther;

Marcel T Kok; Diana Sietz, Dr.; Paul L Lucas

Manuscript Region of Origin: Southeast Asia

Abstract: Coastal urban areas are urbanizing at unprecedented rates -

particularly in non-OECD countries. Combinations of long-standing and

emerging challenges generate vulnerability by threatening human well-

being and ecosystems alike. On an intermediate scale of complexity this

study provides a first spatially explicit global systematization of these

heterogeneous challenges into typical urban vulnerability profiles using

largely sub-national data. A cluster analysis of urban expansion, urban

population growth, marginalization, government effectiveness, exposures

and sensitivities to climate-related extreme events, low-lying

settlement, and wetland prevalence reveals a global typology of seven

clearly distinguishable urban vulnerability profiles. These profiles have

typical combinations of vulnerability-generating mechanisms and processes

assigned to them. Using ample city case studies for testing the

plausibility, we show that combinations of such mechanisms in extreme

forms are driving vulnerability in the profiles, and which coastal

locations are similar in this regard. Climate extreme vulnerability and

government effectiveness further differentiate the huge asymmetries in

coastal urban vulnerability. Against the background of underlying global

trends we point out profile characteristics that can have implications

for prioritizing systemic responses and future case studies, and then

propose entry points for generic profile-based vulnerability reduction.

Our findings contribute new insights into opportunities for sharing

experience and vulnerability reducing measures between socio-ecologically

similar urban areas in the rapidly urbanizing coastal fringe.

Till Sterzel Lindenstraße 11 14467 Potsdam +49 (0)170 9333 581 till@climate-babel.org

Global Environmental Change Editorial Office

Potsdam, October 1st, 2015

Dear Editorial Office:

This letter is to request the review of the submitted manuscript „Global typology of

coastal urban vulnerability under rapid urbanization“ for publication in Global

Environmental Change.

The manuscript is the result of a research collaboration of researchers from the Potsdam

Institute for Climate Impact Research (PIK), the Netherlands Environmental Assessment

Agency (PBL), Wageningen University, and climate-babel.

I have suggested four reviewers based on content-related and methodological expertise

regarding the submitted manuscript.

Thank you for your time and consideration.

Sincerely,

Till Sterzel

Cover Letter

Global typology of coastal urban vulnerability under rapid

urbanization

Till Sterzel#*, Matthias K.B. Lüdeke*, Carsten Walther*, Marcel T. Kok+, Diana Sietz++, Paul L. Lucas+

#climate-babel, Lindenstraße 11, 11467 Potsdam

*Potsdam Institute for Climate Impact Research, Research Domain II – Climate Impacts and Vulnerabilities,

Telegraphenberg, 14473 Potsdam, Germany

+ PBL Netherlands Environmental Assessment Agency, 3720 AH Bilthoven, The Netherlands

++ Wageningen University and Research Centre, Sociology of Development and Change, Hollandseweg 1, Bode 18, 6706 KN

Wageningen, The Netherlands

Corresponding author: Till Sterzel, till@climate-babel.org

Acknowledgements

The authors acknowledge the financial support from the Federal Ministry for the Environment, Nature

Conservation, and Nuclear Safety of Germany who supports this work within the framework of the

International Climate Protection Initiative. The funders had no role in study design, data collection and analysis,

decision to publish, or preparation of the manuscript.

This research was funded in part by the Netherlands Environmental Assessment Agency (PBL) Project E555075.

We thank Henk Hilderink for helpful discussions on the methodology. We also thank Steffen Kriewald for

valuable support in comparing different global digital elevation models.

*Title page (with author details, acknowledgements or affiliations)

Global typology of coastal urban vulnerability under rapid

urbanization

Abstract

Coastal urban areas are urbanizing at unprecedented rates – particularly in non-OECD countries. Combinations

of long-standing and emerging challenges generate vulnerability by threatening human well-being and

ecosystems alike. On an intermediate scale of complexity this study provides a first spatially explicit global

systematization of these heterogeneous challenges into typical urban vulnerability profiles using largely sub-

national data. A cluster analysis of urban expansion, urban population growth, marginalization, government

effectiveness, exposures and sensitivities to climate-related extreme events, low-lying settlement, and wetland

prevalence reveals a global typology of seven clearly distinguishable urban vulnerability profiles. These profiles

have typical combinations of vulnerability-generating mechanisms and processes assigned to them. Using

ample city case studies for testing the plausibility, we show that combinations of such mechanisms in extreme

forms are driving vulnerability in the profiles, and which coastal locations are similar in this regard. Climate

extreme vulnerability and government effectiveness further differentiate the huge asymmetries in coastal

urban vulnerability. Against the background of underlying global trends we point out profile characteristics

that can have implications for prioritizing systemic responses and future case studies, and then propose entry

points for generic profile-based vulnerability reduction. Our findings contribute new insights into opportunities

for sharing experience and vulnerability reducing measures between socio-ecologically similar urban areas in

the rapidly urbanizing coastal fringe.

Keywords: Coastal cities, cluster analysis, vulnerability-generating mechanism, socio-ecological system,

quantitative indication

Introduction

Urbanization is a defining phenomenon of our time (Hoornweg et al. 2011). Having reached 3.2 bn in 2011

(UNDESA 2012), urban population has increased by over factor four since 1950, and has lead to a concentration

of 40% of the world’s population on a narrow coastal band that takes up 7% of the Earth’s surface (McGranahan

et al. 2007). The locus of the most rapid urbanization is the developing world, where more than 95% of the net

global population increase is projected to be concentrated in cities in the coming decades (UNDESA 2012;

UNDESA 2008). Cities are disproportionately located along rivers and coastlines (Grimm et al. 2008). Urban

areas are generally more coastal than rural areas (McGranahan et al. 2007), and urban areas are growing faster

on coasts than inland (Seto et al. 2011a; McGranahan et al. 2007). Although the bulk of world population

growth is taking place in small and medium-sized cities (Grubler et al. 2012), they are commonly overlooked in

favor of larger and more iconic cities in case studies and meta-analyses.

Rapid urbanization is further contextualized by biophysical and socioeconomic characteristics operating on

multiple temporal and spatial scales. Urban life is radically changing its coastal environments through

unmanaged population increase (Grimm et al. 2008), urban expansion (Seto et al. 2011a), and resource demand

(Bloom 2011). At the same time, coastal cities are heavily influenced by global change, and particularly at risk of

flooding due to tropical storms and sea-level rise (Handmer et al. 2012). All of these characteristics can assume

extreme forms in coastal urban areas.

Altogether, long-standing and emerging challenges are compounding in coastal cities due to the magnitude

and acceleration of transitions (Tanner et al. 2009). These challenges risk to outpace efforts to reduce

vulnerability in urban areas in rapidly growing low- and middle-income nations (O’Brien et al. 2012; UN-HABITAT

2011a; UNISDR 2011) by increasing exposures and sensitivities in urban populations and ecosystems alike

(McGranahan et al. 2007), and overstretching municipal management and planning capacities (Grubler et al.

2012; Prasad et al. 2009).

Manuscript (including figures and tables, w/o author details)Click here to view linked References

Despite these converging characteristics in coastal urban areas across the developing world, and the resulting

increases in vulnerability of their populations to them, there is a lack of integrating studies on a global scale

that systematize these human-environment interactions. For example, Garschagen & Romero-Lankao (2013)

point out the greater scientific attention the linkages between different components of vulnerability deserve

under urbanization, because it can identify entry points to enhance adaptive capacity at the national level.

Regional and local case study literature shows that vulnerability in fast-growing urban coastal zones is

heterogeneous, and that the challenges outlined above are unevenly distributed geographically. This makes it

challenging to scale up successful vulnerability-reducing measures (Sietz et al. 2011). At the same time, there is

a need for regional or global-coverage vulnerability (reduction) analysis, which requires generic insights that

acknowledge local specifics and still constitute a global picture. Highlighting in how far outcomes of case

studies are also characteristic or relevant for similar urban areas elsewhere could contribute to scaling up

successful vulnerability-reducing measures through transfer between urban areas that are systematically

similar.

In view of this scaling challenge and the need for an integrated perspective on the forces shaping the

vulnerability of cities, a number of studies have used integrated qualitative and quantitative methods for

systematizing generic global overviews of socio-ecological problems. On a subnational level, Kok et al. (2015;

2010) and Sietz et al. (2011) characterize and map the typical mechanisms and processes influencing livelihoods

in global drylands regarding their socio-ecological vulnerability. The typical mechanisms and processes provide

entry points for scaling up vulnerability reducing measures. Focusing on large, densely populated, rapidly

growing urban areas, yet on a national scale, Kropp et al (2001) and Lüdeke et al (2004) characterize a typical

cause-effect “favela syndrome” and categorize countries according to the dynamics and intensity of this

exemplary non-sustainable human-environment interaction. Crona et al (2015) use social–ecological syndromes

to understand impacts of international seafood trade on small-scale fisheries. They identify three distinct

syndromes, with the degree of institutional enforcement playing a key role throughout.These approaches are

based on the hypothesis that it is possible to identify a limited number of typical dynamic cause-effect

relationships at an intermediate level of complexity which allow to subsume comparable case studies of socio-

ecological problems across the globe. Other studies focus on human-environment problems based on present

or projected future climatic risks in a limited number of coastal cities or urban areas. McGranahan et al (2007)

assess the geographic- and climate change-related risks for urban settlement in the heavily populated low

elevation coastal zone (LECZ). Hanson et al (2011) and Hallegatte et al (2013) quantify current and future

economic impacts of climate change-induced sea level rise and flooding for 3o and 136 coastal cities using

future climate projections, respectively. Tanner et al. (2009) assess climate change resilience in ten rapidly

urbanizing cities in predominantly coastal settings in developing countries. De Sherbinin et al (2007) examine

vulnerabilities of three coastal megacities to current and future climate hazards using a vulnerability

framework considering multiple synergistic stresses and socio-ecological characteristics.

To our knowledge, this paper is the first spatially explicit study to systematize urban vulnerability under rapid

urbanization in the global coastal fringe. We present a global systematization of rapidly growing urban coasts

according to typical manifestations of mechanisms and processes that increase urban vulnerability under

forces of global change. Using sub-national and national data we systematically profile the current situation in

these urban areas on an intermediate level of complexity based on quantifying similarities in their socio-

ecological problems. For this we apply a formalized method based on clustering (Kok et al. 2015; Janssen et al.

2012) which has been applied before to identify and interpret general mechanisms which similarly create

vulnerability in global drylands on regional and local scales (Sietz 2014; Sietz et al. 2012; Sietz et al. 2011; Kok et

al. 2010). We address and answer the following research questions:

(1) In how far do urban coasts share characteristic, typical mechanisms making them vulnerable under rapid

urbanization?

(2) How are these areas positioned to deal with these mechanisms and reduce vulnerability?

Methods and data

Our method for systematizing how and where vulnerability is typically generated follows a method of

vulnerability analysis on an intermediate level of complexity and spatial scale proposed by Kok et al (2015). It

consists of the following steps: Drawing from a multitude of vulnerability-generating mechanisms documented

in the literature we list and briefly characterize well-documented mechanisms that have been found to typically

generate vulnerability in coastal urban areas. Then we identify indicator datasets that render information on

the most important dimensions of the vulnerability-creating mechanisms. Finally, we subject these indicators

to an established cluster analysis (Janssen et al. 2012; Lüdeke et al. 2014) to address in how far and where

typical combinations of the vulnerability-creating mechanisms occur. Thereby each resulting cluster signifies an

urban vulnerability profile.

Vulnerability-generating mechanisms and processes

First we clarify the use of specific terminology. We understand a vulnerability-generating mechanism as a

cause-effect relationship in which a vulnerability relevant process a drives b (e.g. rapid urban population

increase a drives urban expansion b). Thereby the mechanism is that a drives b, while b can be driven by

multiple processes. A vulnerability-generating process is a progressing vulnerability-relevant phenomenon, and

can generate vulnerability through one or multiple mechanisms. The introduction of this paper shows that the

process of rapid urban population increase (a) generates urban vulnerability through a variety of mechanisms.

Through an extensive literature review we now specify seven typical mechanisms that generate vulnerability in

situations of rapid coastal urbanization, and give examples of cities where they have been documented. They

reflect the complex interplay between socioeconomic and biophysical factors, and show linkages between

each other. We continue the numeration from above, notwithstanding rapid urban population increase’s role

as the main process and driver.

Urban expansion (b) - Urban land expansion is driving large-scale land cover change in developing countries

across the globe (Seto et al. 2012; Seto et al. 2011a). Less-developed countries are generally experiencing much

higher levels of both urban expansion and its main driver – rapid population growth (Angel 2011). Importantly

so, urban expansion is faster in low elevation coastal zones than in other places (Seto et al. 2011a). Thereby

exposure to climate extremes is typically increased (UN-HABITAT 2011a). This mechanism has been exemplarily

documented for coastal cities including Accra, Bangkok (Shlomo 2011), Tel Aviv, Algiers, and Manila (Angel et

al. 2010).

Wetland loss (c) - Wetlands and floodplains provide important functions and services for the urban and

surrounding populations, e.g. flood regulation functions for attenuating negative consequences of climate

extremes (Costanza et al. 2008). Unchecked urban expansion is leading to unprecedented degradation and

destruction of such coastal ecosystems through increased demand for land (Baird 2009) and encroachment

(Seto et al. 2012; McGranahan et al. 2007; Bravo de Guenni et al. 2005). The degradation of these functions

through encroachment has increased urban inhabitants’ exposure and sensitivity to floods (Bravo de Guenni et

al. 2005; Hardoy et al. 2001), and has serious implications for the livelihoods of societies dependent on their

functions or services (Nicholls 2004). This was illustrated in New Orleans through Hurricane Katrina in 2005

(Törnqvist & Meffert 2008).

d) Management overstretch - Rapid urbanization is increasing the vulnerabilities in urban populations by

overstretching municipal management and planning capacities (Grubler et al. 2012; Prasad et al. 2009; Tanner

et al. 2009). The rapid endogenous and exogenous urban growth can overwhelm basic urban services,

especially if municipal adaptive capacity is initially low. This comes as new challenges arise before long-standing

ones have been dealt with (Tanner et al. 2009). For example, future increase of climate change is poised to

further stretch management capacities in coastal areas (Alam & Rabbani 2007, Dodman et al. 2011, De Sherbinin

et al. 2007). This mechanism of overstretched management and planning has exemplarily been illustrated in

case studies from Dhaka (Alam & Rabbani 2007), Dar es Salaam (Dodman et al. 2011), and Mumbai (De

Sherbinin et al. 2007). We subsume management and planning under “management” in the following.

e) Marginalization - Rapid population increase is often absorbed into the urban fabric through an increase of

densely populated informal settlements (Prasad et al. 2009). The growth of informal settlements has been

largely driven by poverty and marginalisation of poor and less equipped populations in and around megacities

in many developing countries (Bravo de Guenni et al. 2005; Douglas et al. 2008) and is frequently

underestimated (Kit & Lüdeke 2013). Such settlements have less capacities to deal with shocks, e.g. climate

extremes (Handmer et al. 2012; Huq et al. 2007; Hardoy et al. 2001) such as tropical cyclones and floods

(Handmer & Dovers 2007), and less capacities to deal with subsequent negative impacts (Bull-Kamanga 2003).

This particularly holds true where poor management, and low building and infrastructure quality coincide with

densely populated areas (Prasad et al. 2009). This mechanism has been observed in numerous coastal cities in

India (Revi 2008), or Iliolo City in the Phillipines (Rayos Co 2010).

f) Vulnerable settlement development – Under inadequate urban planning and unchecked growth informal

settlements outlined above commonly encroach more risk-prone areas where exposure to floods and cyclones

is high (Cardona et al. 2012; Kit et al. 2011; Satterthwaite 2007). These areas are avoided by wealthier groups

due to their higher exposure (Prasad et al. 2009). This leads to an increase in vulnerable populations and

population density with low building qualities in floodplains. This mechanism has been observed in cities such

as Lagos (Adelekan 2010), Mumbai (Chatterjee 2010), and Esmeraldas, Ecuador (Luque et al. 2013), and for

many urban poor in large African coastal cities (Douglas et al. 2008).

g) Generation of exposure and sensitivity to climate extremes - Rapid and unplanned urbanization is a key

driver of vulnerability to climate extremes (Cardona et al. 2012). Coastal cities are already disproportionately

exposed and sensitive to climate extremes, e.g. cyclones (Hanson et al. 2011; Nicholls et al. 2007) and floods

(Mondal & Tatem 2012; Hanson et al. 2011), which threaten human well-being (Handmer et al. 2012). Sections of

most of the largest cities on the African coast are currently at risk of flooding (IPCC 2012; Adelekan 2010; Awuor

et al. 2008). Exemplary cities subject to climate extremes include Dhaka (storm surges, UN-HABITAT 2011a)

Sorsogon City, Philippines (Taifuns, Button et al. 2013), Mumbai, Rio de Janeiro, Shanghai (Floods, De Sherbinin

et al. 2007).

h) Sea-level rise driven settlement exposure - While future estimates of the number of additional people at

risk from coastal flooding vary widely (Hinkel et al. 2014), all indicate a considerable increase due to surging

populations in low-lying areas and to sea-level rise. The ongoing superimposition of sea-level rise (driven by

subsidence and climate change) on current flood levels increases climate extreme vulnerability in coastal cities

(Frazier et al. 2010), regardless of a change of e.g. the tropical cyclone climate (Törnqvist & Meffert 2008). This

poses a major challenge to coastal management both in terms of adapting to rising storm surge levels and to

rising flood levels (UN-HABITAT 2011a; Wilbanks et al. 2007), and compounds existing vulnerabilities of low-

lying populations. This mechanism has been exemplarily illustrated for cities such as Cotonou, Benin (Dossou &

Glehouenou-Dossou 2007) or Dar Es Salaam (Dodman et al. 2011).

This paper formalizes the current situation in coastal urban areas. It is important to note that climate change is

expected to regionally increase damages from climate extremes in the coming decades, with far reaching

implications (IPCC 2013; Seneviratne et al. 2012; UN-HABITAT 2011a; Nicholls 2004). We acknowledge this

projected increase in the characterization of each urban vulnerability profile we identify with a broad

qualitative outlook on climate change-driven increases of cyclone and flood exposures, and sea-level. This

increase could dramatically compound vulnerability through mechanisms documented above under

urbanization (UN-HABITAT 2011a), e.g. through further wetland loss through sea-level rise and urban expansion

(Syvitski et al. 2009).

Data for indication and cluster analysis

On the basis of the rapid urban population increase and the seven documented mechanisms we choose 11

quantitative indicators with global coverage for coastal urban vulnerability under rapid growth (Table 1).

Indicators are chosen that capture the most important processes or conditions that describe the vulnerability-

generating mechanisms. Following Kok et al (2015) we do not impose a hypothesized predefined relationship

between the indicators, but let the available indicator data for the vulnerability mechanisms tell their own

story: By exploring the structure in the data-space we hope to (inductively) obtain clues on underlying

vulnerability patterns. We assign each indicator to one of the three vulnerability components which are

commonly used in frameworks for vulnerability analysis: exposure, sensitivity and adaptive capacity (Birkmann

2013; Patt et al. 2008; Schröter et al. 2005). Thereby we understand “damage”, e.g. from floods, as “exposure

times sensitivity”.

Indicator Vulnerability component

Related to mechanism

Dataset Aggre- gation

Relative urban population increase

Exposure b, c, d, e, f, g, h

Urban population change from 1990 to 2000 in percent of 1990 (Klein Goldewijk et al. 2010)

Subnat.

Urban area increase

Exposure b, c, e, f, g, h Urbanized area change

from 1990 to 2000 in percent of 1990 (Klein Goldewijk et al. 2010) Subnat.

Government effectiveness

Adaptive capacity

d, e, f, c Government effectiveness, aggregate and individual governance indicators (Kaufmann et al. 2010)

National

Average per capita income

Adaptive capacity

d, e, f Per capita GDP (UNSTAT 2005; The World Bank 2006) National

Urban population in poverty

Sensitivity e, d, g, f Slum population in 2000 in percent of urban population (UN-HABITAT 2008)

National

Prevalence of surrounding wetlands

Sensitivity c, g, f Combination of prevalence of key wetlands, and percentage to which wetlands immediately surround urban areas (Lehner & Doell 2004; CIESIN 2005)

Subnat.

Cyclone exposure

Exposure g, f, d Average relative frequency and distribution of cyclones (Dilley et al. 2005), aggregated to 0.5° resolution

Subnat.

Flood exposure

g, f, d Average relative frequency and distribution of floods (Dilley et al. 2005), aggregated to 0.5° resolution

Subnat.

Cyclone sensitivity

Sensitivity g, f, d, e Average relative mortality rate from cyclones(Dilley et al. 2005), aggregated to 0.5° resolution

Subnat.

Flood sensitivity

g, f, d, e Average relative mortality rate from floods (Dilley et al. 2005), aggregated to 0.5° resolution

Subnat.

Low-lying urban population

Exposure h, d, f, g Total urban population currently living 2m or less above sea-level - calculated using the digital elevation model SRTM v4.1 (Jarvis et al. 2008) and urban population data (Klein Goldewijk et al. 2010)

Subnat.

Table 1: Indicators and datasets used, including their assignment to vulnerability components, the mechanisms they are related to, and the level of spatial data aggregation. These datasets with predominantly subnational spatial resolution (0.5° x 0.5°) are motivated and explained in detail in the Annex, followed by details on data resolution and data treatment.

We applied an established cluster analysis method to integrate the 11 datasets indicating vulnerability, and to

identify typical combinations in the data structure (Lüdeke et al. 2014; Janssen et al. 2012). The optimal number

of clusters is determined by using a formalized method described in Kok et al (2015, see Annex for details on

the cluster analysis).

Subsequently, robust clusters, i.e. profiles, of typical indicator value combinations are each characterized and

interpreted. This is done in the light of the documented vulnerability-creating mechanisms, and by using their

spatial distribution (Figure 1), the combinations of indicator values (Figure 2), and box plots (Figure 3). Thereby

indicator values and value combinations for each profile indicate which mechanisms are active, and how severe

they are (Table 3). We used the Fraiman measure (Figure 4) to investigate the importance of single indicators

for shaping the typology and for partitioning the profiles. This measure determines how sensitive the profile

typology is to omitting each respective dataset from the analysis by fixing it at its mean-value (so-called

‘blinding’ of indicators), and comparing this ‘blinded’ profiling with the partition including all indicators

(Fraiman et al. 2008).

Results and characterization

Our analysis shows that 84 out of 196 countries (43%) and 153 countries with a coastline (55%) are experiencing

rapid coastal urbanization (>2.25%/a, see Annex). The overwhelming majority of this rapid urban growth (and

thus the spatial distribution of the profiles) is taking place in low and middle-income countries (Figure 1). Table

2 summarizes key characteristics, examples of cities, geographical distribution, and population statistics for

each of the seven profiles.

The Fraiman measure shows a differentiated picture of the indicators’ relative importance, and an even

distribution between 0.5 and 1 (Figure 4). Climate extreme exposures and sensitivities, and government

effectiveness are the most important indicators for partitioning the profiles and shaping the typology. The

most important indicators are exposure and sensitivity to floods, followed by sensitivity and exposure to

tropical cyclones. Government effectiveness and income are the next most important indicators. Next is urban

population increase, even though our analysis is confined to fast growing urban areas: Even within these areas

the degree of increase is an important cluster-separating indicator. Average income and urban population in

poverty are similarly important. Ecosystem degradation, urban extent change, and low-lying urban population

are less distinctive. Overall, the environmental indicators are slightly more important for separating the

clusters than the socioeconomic indicators.

Figure 1: Spatial distribution of the seven urban vulnerability profiles under rapid coastal urbanization, and examples of cities located in these profiles. See Figure 2 for the respective profiles.

Profile group and profile

Key characteristics Examples of countries and geographical regions Examples of cities located in profile No. of coun-tries

% of world

total of urban pop.

Urban pop. (m)

% of typology (No. of

0.5° grid cells)

I: Fastest population growth hitting pronounced poverty under overstretched management

I.1 "Tacloban"

profile

Extreme concatenated biophysical and socioeconomic mechanisms under widespread poverty

Most prevalent in cyclone-prone Madagascar and Mozambique; Myanmar, Central Philippines, Belize; absent in South America

Small and middle-sized cities such as Toamasina, Mahajanga, Quelimane, Angoche, Sittwe, Tacloban, Labasa, Belize City

16 0.5 14.9 6.9 (140)

I.2 "Monrovia

" profile

Most rapid urbanization and most severe poverty under lowest adaptive capacity

Most prevalent in Least Developed Countries, Sub-Saharan West African and on Equatorial coasts; Yemen, Eritrea, Pakistan, Myanmar, Eastern Indonesian Archipelago, Solomon Islands, Vanuatu; Nonexistent in Latin America

Monrovia (Liberia), Conakry (Guinea), Lomé (Togo), Libreville (Gabon), Swakopmund (Namibia), Massawa (Eritrea), Rangoon (Myanmar), Krong Koh Kong (Cambodia), Balikpapan (Indonesia)

41 1.8 51.4 18 (366)

II: Rapid population growth and most rapid expansion intensify high flood damages under moderate adaptive capacity

II.1 "Manila" profile

Extreme flood and cyclone damages are hitting fastest expansion and largest low lying populations

Subtropical coasts under tropical cyclone influence in Asia and Central America, majority of Philippines, China, Vietnam, and Bangladesh; India (Bay of Bengal); nonexistent in Africa, virtually nonexistent in the southern hemisphere

Manila, Guangzhou, Shanghai, Fuzhou, Chittagong, Da Nang, Kolkata, Santo Domingo, San Juan, Puerto Cabezas

12 5.2 149.1 11.6 (237)

II.2 "Dhaka" profile

Highest sensitivities to climate extremes, and highest sea-level-rise-driven settlement exposure under less effective governments

Subtropical coasts under less tropical cyclone exposure in Asia (Southern Philippines, Indian - Bay of Bengal) and Central America (Belize, Guatemala, Honduras), The Caribbean (Southern Haiti and Dominican Republic)

Dhaka and Khulna, Chennai, Karachi, Maputo, Bhubaneshwar, Haiphong, Jacmel, Santa Marta (Columbia)

23 2.7 78.3 10.6 (215)

II.3 "Rio de Janeiro” profile

High flood damages from rapid urban expansion and reduced natural protection

Southern Brazil, Ecuador, Peru, Columbia, Venezuela, Algeria, South Africa, Lebanon, NW India, SE India, Mekong Delta, Indonesia (Java, Sumatra), Southern Malaysia

Rio de Janeiro, Sao Paolo, Maracaibo, Caracas, Cartagena, Algiers, Istanbul, Oran, Durban, Accra, Luanda, Beirut, Ho Chi Minh City, Jakarta, Kuala Lumpur

39 7.3 209.6 21 (427)

III: Few and less extreme mechanisms under slower growth and high adaptive capacity

III.1 "Izmir" profile

Extreme flood sensitivity under relative wealth and least rapid growth

Prevalent in South America, (e.g. Panamá, El Salvador, and NE Brazil) Magreb countries (e.g. Morocco, and Tunisia); Turkey, South Africa, West-Indian coast

Esmeraldas, Fortaleza, Natal, Belém, Rabat, Tunis, Cape Town, Izmir, Antalya, Mumbai, Surat

43 3.6 102.9 21 (428)

III.2 "Abu Dhabi" profile

No critical mechanisms under less rapid growth and highest adaptive capacity

High-income countries on the Arabian Peninsula, Morocco, Tunisia, Guyana, Central Brazil, Turkey, Israel, Brunei, Malaysia

Abu Dhabi, Dubai, Doha, Muscat, Tel-Aviv, Bandar Seri Begawan (Brunei)

23 1 30.1 11 (223)

SUM 26.6 209.6 2036

Table 2: Vulnerability profiles - Key characteristics, city examples, and geographic distribution.

Figure 2: Vulnerability profiles and average indicator values constituting them. The colored dots show the average indicator values of the respective cluster centers. X shows where the value zero is for each indicator. The indicator values are normalized between 0 and 1 using the minimum and maximum values for the different indicators. The colors are identical to those used in Figure 1 to depict the spatial distribution of the profiles. Each profile is given a name of a characteristic city located in it for illustrative and referential purposes.

Figure 3: Box plots of the vulnerability profiles. They show the variation in indicator values (all indicator values are between 0 and 1) in each profile. The order of the indicators is identical to Fig. 3. The boxes present the 25-75 percentile range of the indicator values; the circles at the end of the dotted lines indicate the 5- and 95-percentile, while the larger black circle indicates the arithmetic mean; the band near the middle of the box indicates the median value. The number of grid cells in each profile is indicated at the top of each frame.

Figure 4: The Fraiman measure for each indicator. Values between 0 and 1 to express the relative importance of each indicator for separating the clusters. The smaller the value, the more important the indicator is, as it shows the percentage of grid cells identically assigned when the corresponding indicator is blinded.

All profiles are experiencing rapid urbanization (by definition), and sea-level rise driven

settlement vulnerability increase (Table 3). Exposure and sensitivity to climate extremes (five

profiles), management overstretch (four), and marginalization (four) are also prevalent. Two

mechanisms are prevalent in the Abu Dhabi profile. Four or more are prevalent in the seven

other profiles.

In the following profile characterization we distinguish between three distinct groups of

profiles. They have with similar manifestations of mechanisms involving indicators with the

lowest Fraiman measures. Each profile is given a name of a characteristic city located in it for

illustrative and referential purposes.

Group I: Fastest population growth hitting pronounced poverty under overstretched

management

The profiles in Figure 2 show that the “Tacloban” (black) and “Monrovia” (red) profiles have

the most rapidly increasing coastal populations, highest slum population levels. This appears

to be pronouncedly overstretching management judging by the lowest averages of

government effectiveness and income. Widespread urban poverty and very low average

incomes alone put these urban areas in a challenging position to reduce vulnerability. Totals of

low-lying population are very low.

Notably, the most rapid urban population increase coincides with the least increases in urban

area. Taking the severe poverty into the equation, this points to a large-scale absorption of

burgeoning population growth in dense informal settlements.

Profile I.1: “Tacloban” profile (black) – Extreme concatenated biophysical and

socioeconomic mechanisms under widespread poverty

The relatively rare Tacloban profile, named after a city in the Phillipines which was

devastatingly hit by the Taifun Haiyan in November 2013, hosts few large cities and peer

review case studies. This profile is characterized by extreme (i.e. severe) forms of multiple

connected mechanisms that are overstretching adaptive capacity – including marginalization

with dense informal settlement development, rapid population growth, and exposure and

sensitivity to climate extremes (Table 3). This combination of mechanisms is illustrated in the

case study of Sorsogon City (Button et al. 2013).

Under such a combination of extreme circumstances high cyclone exposure translates into

high cyclone sensitivity. In fact, the African cities listed in Table 2 are considered the cities the

most at risk to cyclones in all of Africa (Brecht et al. 2013). This suggests that on a global scale

hydrometeorological climate extremes are not stalling rapid growth and urban expansion in

risk-prone areas. Klose and Webersik (2010; 2011) demonstrated this in the case of tropical

storms in Haiti over time. Under these circumstances, the lowest urban expansion rate of any

profile additionally indicates an absorption of burgeoning population increase through dense

informal settlement, which worsens existing risks for poor households – a well-developed

argument in developing countries (McCarney et al. 2011; Tanner et al. 2009). Given this type of

climate-extreme sensitive settlement under widespread poverty, and the lowest prevalence

of surrounding wetlands, flood sensitivity appears to be relatively moderate. This is explained

by low exposure to floods apart from cyclone-driven storm surges due to low totals of low-

lying urban population. Relatively low flood damages and do not act as an ‘early warning sign’

for the potential increases of climate extreme frequency and damages (due to the sensitivity)

to be expected under future climate change.

Profile I.2: “Monrovia” profile (red) – Most rapid urbanization and most severe poverty

under lowest adaptive capacity

The Monrovia profile is prevalent in Least Developed Countries, and nonexistent in Latin

America. It is particularly prevalent on Sub-Saharan West African and equatorial coasts. Three

mechanisms are discernible in the profile which is most adversely positioned to reduce

existing vulnerabilities. The profile shows a characteristic combination of extreme forms of

growth, marginalization, and management overstretch: Judging by the least effective

governments and very high poverty levels the world’s fastest coastal population increase is

overstretching management. This is in line with findings for the confluence of poverty, most

rapid urbanization, and overstretched urban response options in Least Developed Countries

(Garschagen & Romero-Lankao 2013; Cohen 2006). Under these circumstances the lowest

expansion rate indicates an absorption of the soaring urban population increase through

dense informal settlement. Exposure and sensitivity to climate extremes is characteristically

low and show the minimal indicator value spread in the box plot. This signifies particularly

distinct and influential profile characteristics (Figure 3). According to Hanson et al. (2011),

cities in this profile such as Rangoon and Lomé will have some of the highest proportional

increases of population exposed to flooding by the 2070s.

Group II: Rapid population growth and fastest expansion intensify high flood damages

under moderate adaptive capacity

Under low to moderate income and government effectiveness the Manila, Dhaka, and Rio de

Janeiro profiles display extreme forms of three inextricably linked processes and conditions:

the highest flood exposures and sensitivities, the largest low-lying population totals, and the

fastest urban expansion rates. In mechanistic terms, this hints to a vast urban expansion into

flood-prone low-lying areas. If this mechanism would prevail under future sea-level rise and a

potential climate change-driven increase of flood extremes and frequency, then flood

damages would further increase without more effective regulation of rapid urban expansion.

High sensitivity to cyclones in two of the profiles further complicates this combination of

mechanisms for local authorities. Surrounding wetlands exist as flood-regulating buffers, but

need effective protection from degradation through rapid urban expansion.

Profile II.1: “Manila” profile (yellow) – Extreme flood and cyclone damages are hitting

fastest expansion and largest low-lying populations

The particularly distinct Manila profile is prevalent in tropical cyclone-prone urban coasts in

subtropical South and Southeast Asia. The extreme exposure and sensitivity to two climate

extremes distinguish this profile from the related Dhaka and Rio de Janeiro profiles. The

narrow indicator value distributions in the box plot signify a particularly distinct profile in the

data space.

The profile is affected by all seven mechanisms, indicating a severe confluence of problems.

Urban coasts with the fastest urban expansion and largest low-lying populations are suffering

the highest overall damages from climate extremes, and therefore appear to highly

vulnerable to future sea-level rise. Both this combination and the severe forms are markedly

illustrated in the case study of Shanghai (De Sherbinin et al. 2007).

Relatively high marginalization, low average income, and rapid growth suggest that prevalent

low-lying areas are also inhabited by vulnerable marginalized populations in informal

settlements. In fact, in Manila informal settlements at risk to coastal flooding make up 35% of

the population (UN-HABITAT 2007). This combination is also illustrated in the aforementioned

case study for Shanghai. The large low-lying populations are line with a global ranking of

population exposed to current coastal flooding in large port cities (Hanson et al. 2011; Nicholls

et al. 2007), of which three of the four highest ranked cities are located in this profile

(Shanghai, Guangzhou, and Kolkata). The low prevalence of surrounding wetlands shows that

few natural flood-regulating ecosystems exist, possibly because they have already been

diminished by urban expansion. Overall, the extreme flood sensitivity in the Manila profile

indicates a high vulnerability to sea-level rise due to its superimposition on coastal flood and

storm surge levels. (Hanson et al. 2011; Nicholls et al. 2007) find that other cities in this profile,

i.e. Shanghai, Guangzhou, Kolkata, Chittagong, and Ningo, will be among the cities most

exposed to coastal flooding in the 2070s due to sea-level rise and storm surge.

However, the notably high level of government effectiveness for these socioeconomic

conditions does suggest a greater efficacy of management. Its level is comparable to profiles

with much higher average income, less poverty and less challenging combinations of

mechanisms. This may explains why climate extreme sensitivity is comparable to profiles with

much lower climate extreme exposures and lower government effectiveness (Dhaka and

Tacloban profiles).

Profile II.2: “Dhaka” profile (purple) – Highest sensitivities to climate extremes, and

highest sea-level-rise driven settlement exposure under less effective governments

The Dhaka profile is prevalent in subtropical coasts under cyclone exposure in Asia, Central

America, and The Caribbean. Differential analysis with the Manila profile (II.1) reveals the most

important distinguishing feature of this profile: Despite its significantly lower exposures to

climate extremes, and slightly lower adaptive capacity, its sensitivities are similarly extreme.

This can be explained by a combination of multiple severe manifestations of mechanisms.

Seven mechanisms are discernible. Fast expansion, high marginalization and large low-lying

populations are exerting considerable pressure on urban areas with ineffective governments

and poor adaptive capacity. Under these circumstances moderate climate extreme exposure

translates into very high sensitivity. This is in line with insights into tropical storm impact on

population growth in Haïti, especially in densely populated hazard-prone urban areas (Klose

2011; Klose & Webersik 2010). The combination of pressures with climate extreme sensitivity

hints to markedly overstretched management. This is illustrated in the case of Chennai where

uncontrolled urban expansion, blockage and encroachment of natural drainage systems,

which are largely inhabited by slum settlements, have increased coastal and riverine flooding.

The flooding overstretches a lacking flood control response and drainage systems (Gupta &

Nair 2010). Under these circumstances the relative abundance of flood-regulating wetlands

alone does not significantly reduce high flood and cyclone-related sensitivity.

Such extreme sensitivity to floods and storm surges has also been observed in Dhaka (UN-

HABITAT 2011a), and also in Maputo (Douglas et al. 2008), where even moderate flooding

events largely affect the urban poor. This brings us to the conclusion that the urban coastal

fringes in the Dhaka profile are most adversely positioned to deal with climate extremes and

and future sea-level rise. This suggests that the protection of relatively abundant existing

wetlands is all the more important, but must be linked with additional vulnerability reducing

measures. Again, present damages from cyclones are not acting as an ‘early warning’ signal

for potentially greater frequency and intensity, and thus larger damages to larger populations,

expected under climate change.

Profile II.3.: “Rio de Janeiro” profile (grey) - High flood damages from rapid urban

expansion and reduced natural protection

The Rio de Janeiro profile is prevalent in flood prone deltas and monsoonal climates. The

profile is discernable in clusterings yielding two to six clusters as well, making it a particularly

distinct, robust structure in the data space (Figure 3). The high flood exposure and sensitivity

are similar to the Manila and Dhaka profiles, but there is essentially no tropical cyclone

activity.

Five mechanisms are discernible. The high flood damages appear to be a result of

combinations of very high flood exposure, concomitant fast-paced urban expansion which

may be responsible for reduced natural flood protection, and relatively prevalent settlement

in low-lying areas. This combination has been observed in a case study in Rio de Janeiro on

vulnerability to current climate hazards (De Sherbinin et al. 2007). At the same time, the

socioeconomic situation in cities such as Rio, Durban, or Kuala Lumpur is clearly more

favorable than in the related Manila and Dhaka profiles: Socioeconomic disparities are low,

and adaptive capacity is comparable to the wealthiest profiles. This explains the lower flood

vulnerability: While low-lying settlement is prevalent, marginalization is markedly low (a

distinguishing feature of the profile). Nevertheless, the Rio case study illustrates that leaving

combinations of vulnerability-generating mechanisms unchecked may accentuate flood

vulnerability through future climate change, and sea-level rise in particular.

Group III: Few and less extreme mechanisms under slower growth and high adaptive

capacity

Mechanisms and combinations thereof are less severe or absent in the Izmir and Abu Dhabi

profiles. Characteristically, the “slowest” population growth rates coincide with the highest

average income and government effectiveness. Climate extreme exposures are low. Under

these conditions the dark and light blue profiles are under the least pressure from

vulnerability-generating mechanisms, and most favorably positioned to reduce existing

vulnerabilities. This suggests that management is not as overstretched on the one hand, and is

responsible for less severe manifestations of mechanisms on the other. For example, there is

less pressure on abundant wetlands - possibly due to low expansion rates, or the effective

restriction of settlement and degradation. In addition, marginalization is less evident.

Profile III.1: “Izmir” profile (light blue) - Extreme flood sensitivity under least rapid

growth and relative wealth

The Izmir profile is prevalent in middle-income countries. The three mechanisms that ware

discernible are all linked to high flood sensitivity, resembling combinations observable in other

profiles. However, what distinguishes this profile from the wealthier Abu Dhabi profile and

other flood-sensitive profiles is the sensitivity under low exposure, relatively high income, and

relatively effective governments. The profile-based explanation for such flood sensitivity is the

high level of informal slum populations in flood-sensitive areas, which has been illustrated for

Esmeraldas, Ecuador (Luque et al. 2013) and Mumbai. In fact, the greatest socioeconomic

disparities in any profile hint to a marginalization of poor populations in flood-prone areas and

a high differential impact of floods. This is exemplified by cities such as Cape Town and

Mumbai (cities with pronounced social disparities), where informal low-lying settlements lack

drainage infrastructure ( u heibir iervogel 200 ; evi, atterthwaite, Arag n-Durand, et

al. 2014). Under these circumstances the high prevalence of surrounding wetlands alone are

insufficient to reduce major flood sensitivity.

Urban areas in this profile show a discrepancy between their relatively high capacity to adapt

and relatively high poverty rates and flood sensitivity, showing that capacity to adapt does

not necessarily mean adapting. On this basis present damages do not act as an ‘early warning’

for the much larger flood damages to be expected under climate change.

Profile III.2: “Abu Dhabi” profile (dark blue) – No critical mechanisms under less rapid

growth and highest adaptive capacity

The Abu Dhabi profile is prevalent in high-income countries. Two vulnerability-generating

mechanisms are discernible, which is the smallest amount for any profile. No severe forms of

mechanisms are discernible. Population growth is relatively slow. Effective government and

high average income indicate a significantly higher adaptive capacity than in any other profile.

At the same time exposure and sensitivity to cyclones and floods are the lowest, emphasizing

an advantageous position from a managerial point of view. Therefore this profile starkly

contrasts the fast-growing and managerially overstretched profiles in low-and middle income

countries.

However, in the future, flood vulnerability can increase through the combination of an

increase of rapid current urban expansion into wetlands (and other low-lying areas) and sea-

level rise. This combination has been pointed out for unplanned rapid urbanization in coastal

cities on the Arabian Peninsula (El-Raey 2009). While the profile would be best positioned to

contain this combination of mechanisms, its advantageous position may lead to ignoring

climate change adaptation requirements. This has been described for the urban planning

regulations in the Arab region (Tolba & Saab 2009).

Table 3: Typical mechanisms and processes, and combinations thereof, generating vulnerability in each vulnerability profile. “x” means discernible. “xx” indicates an extreme form of activity, e.g. extremely rapid urbanization. a) is a process, while b) through g) are mechanisms related to a).

Profile Number of mechanisms (out of 7)

Typical combinations of mechanisms described and interpreted in text

a) Rapid urban

population increase

b) Urban expansion

c) Wetland

loss

d) Management overstretch

e) Margina- lization

f) Vulnerable settlement

development

g) Exposure and

sensitivity to climate

extremes

h) Sea-level rise driven settlement exposure

Tacloban 6

a,e,g drive d (management overstretch) g,e,h drive d (no “early warning”) a,e,f,h,c drive g (large flood damages)

xx x xx xx x xx x

Monrovia 4 a,e drive d (management overstretch) a,d,e drive f (dense informal settlement development)

xx xx xx xx x

Manila 7

a,b,e,g drive d (management overstretch) b,e,f,h drive g (large flood damages) a,b,e,h,c drive g (“perfect storm” of flood vulnerability) a,b,h drive g (rapid expansion and large low-lying settlement)

x xx xx x x xx xx xx

Dhaka 6

a,b,e,g drive d (management overstretch) b,e,f,h drive g (large flood damages) a,b,e,f,h drive g (large flood damages) g,e,h drive d (no “early warning”) a,b,h drive g (rapid expansion and large low-lying settlement)

x x xx x xx xx xx

Rio 5 b,f,h drive g (large flood damages) a,b,h,c drive g (large flood damages)

x xx x x x x

Izmir 3 a,f,g,h drive g (large flood damages) g,e,h drive d (no “early warning”)

x x xx x

Abu Dhabi 2 a,b drive g (rapid expansion risks sea-level rise driven settlement development) x x

x

Discussion and conclusions

Combinations of extreme forms of mechanisms are driving urban vulnerability

Compounding forces of global change can risk to outpace vulnerability reduction in rapidly growing low- and

middle-income countries (UNI D 2011; O’Brien et al. 2012). If management overstretch is an indication of this

risk, then our study suggests such an outpacing is in effect in rapidly urbanizing coasts in over 50 countries. The

three profiles that show the clearest signs of overstretch (Tacloban, Monrovia, and Dhaka profiles) account for

36% of all rapidly growing urban coasts, and 145mn people (5% of the global urban population) in 57 countries.

Adding the less overstretched Manila profile increases the numbers to 47%, 294mn, and 10% in 58 countries,

respectively.

The profile-based explanation for management overstretch is combinations of multiple mechanisms - as

opposed to a single mechanism – whereas each mechanism shows an extreme (i.e. severe, or very adverse)

form (Table 3). “Combinations” signifies that the mechanisms are connected through cause-effect

relationships, while we do not investigate the strength of the connections. The most impoverished urban

coasts (Monrovia profile) indicate the activity of four mechanisms – a relatively low number. However, the

extreme forms explain why the respective governments are significantly overstretched. What drives

vulnerability here is the combination of particularly rapid urbanization (a key vulnerability-generating process

in itself), and severe forms of poverty, marginalization, and ineffective governments. A variety of case studies

in the Results section amply illustrates such combinations throughout profiles on a local scale.

Combinations of extreme mechanisms also drive vulnerability to floods under rapid urbanization throughout

the typology. Flood vulnerability has been largely driven by socioeconomic processes and factors in the past -

noted causes include poverty, ecosystem degradation, and poorly governed rapid urbanization (Ranger et al.

2011; Hanson et al. 2011; Revi 2008; Huq et al. 2007). Our results show where extreme forms of each of these

causes are typically combined to drive high flood vulnerability, namely in the impoverished Tacloban profile. In

the Manila, Rio, and Dhaka profiles fast expansion is a more critical indicator for high flood vulnerability. In the

Manila profile extreme forms of multiple mechanisms are concatenated to a “perfect storm”: The highest

urban expansion (mechanism b) is leading to wide-spread flood-prone settlement in low-lying areas (h) and

massive wetland loss (c) that leads to less attenuation of damage under high flood exposure (g) and very rapid

urban population growth (a).

In these four profiles this documented chain of mechanisms in extremis suggests a further accentuation of

extreme flood vulnerability through sea-level rise and potential increases of extreme event exposure. That is

unless mindful responses are successfully integrated in development plans ( evi, atterthwaite, Arag n-

Durand, et al. 2014; Lavell et al. 2012). This requires cross-level building of socio-ecological resilience (Adger et

al. 2005), and systemic integration of disaster risk management, poverty reduction and adaptation policy

(Lavell et al. 2012; O’Brien et al. 2012). Given the vast spatial distribution of urban areas in question, we suggest

that this also requires replicating good practices in systemically similar situations.

Climate extremes and ineffective governments are additionally driving urban vulnerability

Our results show that indicators for climate extreme vulnerability are key for globally systematizing coastal

urban vulnerability. Exposure and sensitivity to climate extremes play a direct role in five profiles and three

mechanisms. The corresponding datasets allow for differentiating combinations of exposure, sensitivity and

adaptive capacity to floods and cyclones between profiles. This confirms the large differences in adaptive

capacity in urban centers pointed out by Revi et al. (2014) for the rapidly urbanizing coastal fringe.

Based on our results we suggest that profiles are particularly threatened by climate-change driven future

increases in flood and cyclone exposures and sensitivities when these components do not currently act as an

“early warning signal”. This exemplarily applies to profiles with high sensitivity to cyclones (Dhaka profile) or

floods (Tacloban and Izmir profiles) under low to moderate exposure, and moderate to extreme levels of

urban population in poverty. This combination is discernible in 38.5% of the rapidly growing urban coast (196mn

people, 6.8% of the global urban population) in 58 countries. Moderate flood and cyclone exposures in these

profiles are already translating into damages that are associated with much higher exposures in other profiles.

This suggests a relatively low capacity for natural disaster prevention or post-disaster management. These

findings are particularly concerning in view of sea-level-rise and large-scale settlement expansion of vulnerable

communities into low-lying areas.

From this viewpoint we argue that the Monrovia profile will encounter serious problems if the very low current

climate extreme exposure increases through sea-level rise. The lowest capacity for vulnerability reduction

suggests that these urban areas have little experience, and the fewest means, to respond to a flood exposure

increase with (tropical cyclone increase is limited physically because the profile is currently largely confined to

equatorial areas).

Entry points for vulnerability reduction- examples from profiles vulnerable to floods

Rapid urbanization can play an important role in economic development (Tacoli et al. 2008), and offer

opportunities for vulnerability reduction (Birkmann et al. 2010; Garschagen & Romero-Lankao 2013).

Combinations of mechanisms hint at entry points for reducing vulnerability, which we explore for flood

vulnerability reduction under rapid growth in selected profiles in the following. Given the generic nature of this

study, these entry points target national and international institutions which focus on urban risk reduction and

cities interested in contextualizing their city among peers with systemically similar generation of vulnerability.

Ultimately, measures based on these entry points need to be contextualized locally.

The combination of the most severe forms of multiple flood-related mechanisms in the Manila profile calls for

integrated measures to reduce extreme exposure and sensitivity to floods and cyclones. Formidable measures

include channeling extremely rapid expansion, and protecting scarce remaining wetlands and widespread low-

lying settlements. This would require a complex, effective orchestration of action on all levels of government

and stakeholders. And we do suggest, based on its profile, that the Manila profile shows a unique capacity for

flood and cyclone vulnerability reduction to build on. Governments in this profile appear to be relatively

efficient at translating their limited adaptive capacity into reduction of flood vulnerability in a highly

disadvantageous situation: Sensitivity is comparable to values in other profiles showing significantly less

frequent flood and cyclone activity. This characteristic suggests entry points for flood vulnerability reduction

that utilize or require this kind of efficacy for leverage. This initailly suggests prioritizing large-scale, national

projects that integrate „hard“ infrastructural adaptation, such as di e-building, land use-planning, and “soft”

measures such as wetland protection in view of sea-level rise and unprecedented urban expansion. But beyond

this, experience can explain the efficacy, and appears to have accumulated to another unique advantage to

build on for flood reduction in the Manila profile: Given the geographic concentration of these urban areas in

subtropical cyclone paths, “soft” disaster responses have been refined under regular climate extremes

through decades of experience on the ground. An example for the development of effective responses

following major disasters is the community-based initiative the homeless People’s Federation of the Philippines

(Carcellar et al. 2011; evi, atterthwaite, Arag n-Durand, et al. 2014). This approach can be a resource for other

urban profiles in comparable situations to draw from.

The Izmir, Dhaka and Tacloban profiles are characterized by high flood sensitivity and much lower flood

exposure. In view of potential increases under climate change, we suggest entry points for flood vulnerability

reduction that jointly enhance adaptive capacity of flood sensitive communities and increase the effectiveness

of government-led flood vulnerability reduction on the ground. Huq et al. (2007) indicate that vulnerability

reduction requires pro-poor urban climate adaptation policies, which is particularly effective under

involvement of households, communities, and various levels of government (UN-HABITAT 2011b). However,

the evident existence of pronounced social disparities in the wealthy Izmir profile suggests poor top-down

organizational abilities for implementing such measures. In a context where governments lack resources or

will, collective organization in the affected communities may be a more promising way of reducing vulnerability

(De Sherbinin et al. 2007). Taking overstretched planning, widespread poverty and intense population pressure

in the Dhaka and Tacloban profiles into account, flood vulnerability reduction measures may simply be lacking

in many areas, thus requiring additional endogenous and exogenous measures: Regarding measures involving

collective organization, multiple cities in Africa and Asia have shown the potential of upgrading informal

settlements for vulnerability reduction to extreme events (Revi et al. 2014).

Discussion of the method

Like all statistical analyses this study comes with its limitations and caveats. The first limitation is the specificity

of a few indicators which show a large spread around their cluster center value (i.e. streteched central

quartiles, which always span up to 50% of the data range, see Figure 3). This means that the cluster

membership of a grid cell might not say much about this specific indicator - here it is important that the cluster

center value of such an indicator should not play a decisive role in the interpretation of the cluster. A second

limitation is the separate treatment of each urban area grid cell. Hence our results are not distinctly assigned to

urban agglomerations, or cities, according to their administrative boundaries. However, the reason for this

design is clear: It is not feasible to unambiguously assign comparable subnational data to all rapidly urbanizing

coastal cities with global coverage. The third limitation is the use of national-level data when subnational data

was either unavailable (e.g. for government effectiveness), or unfeasible (for GDP per cap), for capturing local

vulnerability-generating phenomena in urban areas. At the same time, national level GDP per cap helps to

differentiate low-, middle, and high-income countries and indicate the resources a national economy can

mobilize for vulnerability reduction, e.g. for climate change adaptation (Revi, Satterthwaite, Aragón-Durand, et

al. 2014). Besides this we ruled out using the potentially feasible subnational dataset of ‘‘gross cell product’’

per cap (Nordhaus 2006), because numerous coastal countries with rapid growth lack the data. Fourth, there

are other human-environment interactions that conceivably generate vulnerability in cities, such as landslides.

Although landslides are also noted as climate related-hazards in some urban areas, they are of significantly less

overall importance judging by the literature. In addition, we found that data on frequency and mortality

revealed a significantly high positive correlation with the respective flood datasets.

Conclusions

Using an established cluster analysis on largely sub-national data, we have constructed and discussed a

spatially explicit global systematization of the heterogeneous, yet characteristic, processes and mechanisms

that typically generate urban vulnerability under rapid urbanization. Our analysis advances the knowledge on

links of global trends and vulnerabilities in coastal urban areas under rapid urbanization in low- and middle

income countries, and where different combinations thereof happen similarly. This analysis shows that local

vulnerability-generating mechanisms recurrently formulated in a multitude of city-based case studies can be

robustly identified across regions that are socio-ecologically similar for a more generic global overview.

The main drivers of vulnerability under rapid coastal urbanization in our global overview are a) chain-like

combinations of extreme, i.e. severe, biophysical and socio-economic pressures (that we labeled mechanisms),

and b) vulnerability to floods and cyclones. Huge asymmetries exists between different urban profiles

regarding these drivers, and how they are positioned reduce vulnerability. The different combinations we

identify are useful for contextualizing fast-growing urban coasts against a background of the underlying global

trends of urbanization and urban expansion, and future climate extreme increase. First, income and

government effectiveness decrease with the rate of urban population growth, while urban poverty increases.

Second, interrelated mechanisms generating vulnerability are dramatically stacked against urban areas with

the least capacity to reduce vulnerability (Monrovia and Tacloban profiles). These areas are experiencing the

world’s most rapid coastal population growth rates and lowest urban expansion rates, and the most prevalent

poverty. Third, these extremes are combined with high vulnerabilities to climate extremes in specific urban

areas in Madagascar, Northern Mozambique, Small Island Developing States, Myanmar, and parts of the

Philippines (“Tacloban” profile). Fourth, the fastest urban area expansion is occurring in urban areas with the

most populous settlement of low-lying areas, and where large damages from floods, cyclones, or both, prevail.

This appears to be in line with the finding that expansion is growing faster in the low-elevation coastal zone

(Seto et al. 2011b). Fifth, these characteristics mean that patterns of rapid coastal urban development - i.e.

most rapid growth, and most rapid urban expansion - are in direct conflict with climate extreme vulnerability

reduction. In order to reduce risk, which is the critical issue for climate adaptation in low- and middle-income

nations (Satterthwaite 2009), this calls for a mindful, integrated approach of urban land use planning and

climate adaptation.

Response options are usually identified for a specific place or identified at a generic level (Jäger & Kok 2007;

Mortimore 2009). We suggest that a systems-based, typological approach on a generic level can facilitate

scaling up successful place-based vulnerability reduction. Vulnerability profiles may aid in exploring how

specific urban areas across the globe, which share similar socio-ecological systems and vulnerability-creating

contexts, could share the same response options, as corroborated by Kok et al (2015) and Sietz (2011). Thereby

the similar problem structure initially suggests a similar response to the same intervention.

Given the limited amount of resources available for vulnerability reduction strategies, a context specific “city

matching” may be useful for climate adaptation funds for more targeted and effective reduction efforts in

cities under rapid coastal urbanization. This can aid in the urgently needed upscaling and replication of

vulnerability-reducing measures that were successful in specific coastal urban areas by showing which other

areas they may be transferrable to (Kok et al. 2015; Sietz et al. 2011). For example, experience from pioneering

cities regarding climate risk management in the Manila profile may be transferrable to multiple urban areas in

this profile, or to a profile with similar combinations of mechanisms.

Follow-up studies should corroborate the value of this study for integrating development and climate

adaptation in certain types of cities. First, despite the lack of attention it receives in urban vulnerability studies,

clarifying the influence of city size on combined vulnerabilities would be valuable for an effective placement of

adaptation measures. This is important because the world’s population growth is in fact concentrated in small

and medium-sized cities in low and middle-income countries (UNDESA 2012). Second, this leads to questions of

the scaling of vulnerability-related properties with city size in view of socio-ecological change. This could be a

valuable investigation into predicting social properties using scaling relations of urban systems according to

Bettencourt (2013) and Bettencourt and West (2010). Finally, this study can provide the basis for analyzing

dynamics of the urban vulnerability profiles under projected socio-economic and environmental change, and

what this means for future risk management. Using the same methodology, Luedeke et al. (2014) have

conducted such a study for detecting and understanding change in patterns of drylands vulnerability.

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Annex

Indication

Some indicator datasets quantify changes over time (rather than a state) in order to accommodate for rapid

coastal urbanization as a process. The selection of the temporal resolution to indicate change was guided by

the most recent, globally complete time frame available for this ensemble of datasets. Data addressing a state

is predominantly from 2000, and data addressing change is exclusively for the period from 1990 to 2000. A

complete set of the georeferenced datasets for a more recent time frame was not available, partly because

some datasets are not updated.

We differentiate urban population pressure into rapid urban population increase (rapid urbanization) and

urban area increase (urban expansion) as a key process and a key mechanism in coastal urban areas. The

increase of urban dwellers over time relative to the existing urban population is the best indication of the scale

of the unprecedented coastal urbanization (UNDESA 2006). We indicate this process using relative urban

population increase (Table 1) by calculating the urban population increase from 1990 to 2000, expressed in per

cent, based on subnational urban population data on a 0.1° x 0.1° scale (Klein Goldewijk et al. 2010). Urban area

increase, i.e. the rate at which urban areas are encroaching surrounding non-urban areas (including coastal

ecosystems), is analogously indicated using urbanized area change per grid cell from 1990 to 2000 with a

spatial resolution of 0.1° x 0.1° (Klein Goldewijk et al. 2010). These two indicators are also employed in

establishing the areas of interest for this study (see Spatial delineation).

A crucial factor for determining the differential vulnerability of the urban poor (as opposed to non-poor) is the

percentage of urban population living in slums (Mehrota et al. 2011). For an urban-specific quantification of

marginalized populations we use the percentage of urban population living in poverty, proxied with a dataset

of slum population in per cent of urban population in the year 2000 (UN-HABITAT 2008). The change of slum

population from 1990 to 2000 was not used because of a high amount of country values missing for 1990,

which would have excluded these areas from the entire study and thus compromised the goal of a global

overview.

Being an important determinant of livelihoods and urban vulnerability, we use a measure of financial welfare to

contextualize the urban population living in poverty through average national income. For this we apply a

second indicator for poverty, i.e. average per capita income expressed by average per capita GDP (The World

Bank 2006; UNSTAT 2005). Combinations of these indicator values provide interpretable insights into the

prevalence of more vulnerable settlement types and livelihoods, and distributions of income and inequality.

We assume a measurement of how effectively governments handle managerial and planning challenges with

its services and policies determines how urban governments are positioned to deal with novel challenges

compounding with long-standing ones. Therefore we quantify planning and management capacity with the

government effectiveness dataset from the Worldwide Governance Indicators (WGI) dataset (Kaufmann et al.

2010). Government effectiveness captures perceptions of the quality of public services, the quality of the civil

service and the degree of its independence from political pressures, the quality of policy formulation and

implementation, and the credibility of the government's commitment to such policies. Interpreting values from

this indicator with per capita GDP also provides interpretable insights into the efficacy of planning and

management in translating relative wealth into government effectiveness in the face of rapid global change.

Recognizing a lack of global datasets on ecosystems services for coastal urban contexts, we compiled a

dataset on wetland prevalence surrounding urban areas. We combined the prevalence of key wetlands per grid

cell with the percentage to which wetlands immediately surround urban areas using wetland distribution and

urban-rural population distribution datasets (CIESIN 2005; Lehner & Doell 2004). While changes in prevalence

are not accounted for due to a lack of further time slices for the input data, combinations of this indicator with

the urban area change rate indicator help understand the distribution of the pressure of urban expansion on

ecosystem functions and services of wetlands.

Next, quantifying climate-related floods and cyclones is key, because lives and livelihoods are particularly

exposed and sensitive to them in coastal cities (Handmer et al. 2012). We use the high-resolution database of

natural hazard frequency and mortality distributions from Dilley et al., 2005. Scrutiny of the definitions for the

database’s frequency and mortality distributions for these two extreme events concluded that they

respectively denote exposure and sensitivity. This allows for quantifying these components of extreme event

vulnerability on a comparable basis. The exposures to cyclones and floods are indicated by using the respective

frequency datasets (Dilley et al. 2005). Cyclone frequency is based on storm tracks, and flood frequency is

based on ocean-based and river-based flood events. The sensitivities to cyclones and floods are indicated by

using the respective datasets of relative cyclone and flood hazard mortality rates (Dilley et al. 2005). These

datasets are based on local and regional hazard specific mortality records from 1981-2000. The data was

aggregated and averaged from the original spatial resolution of 2.5’ x 2.5’ to 0.5° x 0.5° grid cells.

Finally, we take into account that surging population growth in low-lying areas is increasing exposure by

putting an increasing number of settlements at risk to sea level rise and sea level extremes. Factors generating

vulnerability to inundation include long-term climate change related sea level rise, but also the related increase

of base heights of climatic extreme event-related coastal floods (Seneviratne et al. 2012). Acknowledging

further influences on relative sea level rise observed in coastal urban areas, e.g. from ground-related processes

such as subsidence and salt water intrusion, we broadly indicate overall population exposure to sea level rise

beyond climate change-related factors alone. For this we calculated the urban population percentage per grid

cell living in areas up to 2m above sea level, using urban population data (Klein Goldewijk et al. 2010), urban

land cover data from MODIS, and the digital elevation model STRM90 v4.1 (Jarvis et al. 2008). While sea level

rise is projected to rise by 0.52 to 0.98 m by 2100 compared to 1985-2005 for RCP8.5 (Church et al. 2013), an

additional increased incidence and/or magnitude of extreme high sea level is considered likely in the early 21st

century and very likely by the late 21st century (IPCC 2013), with a high confidence that extremes will increase

with mean sea level (Seneviratne et al. 2012). Regional differences in the aforementioned forces determining

sea level rise were not considered due to limitations of datasets with global coverage.

Data resolution and preparation

In this paper we understand urbanization demographically as an increase in urban population through

exogenous and endogenous growth. In addition to being the driving force behind urban expansion, this

population-driven definition allows for the use of high resolution urban population and area change data that

applies consistent definitions over time (UNDESA 2012; Grubler et al. 2012). In principle, our approach takes

cities and urban areas of all population sizes into account. Motivated by attaining a global overview, and by the

availability of subnational data for the selected indicators, we specify urban areas using established high-

resolution rasterized urban population data as opposed to using administrative boundaries. Furthermore,

there is a lack of city-specific datasets with global coverage for the majority of both our socioeconomic and

biophysical indicators, particularly for small- and medium-sized cities.

We delineate the area of interest the - global coastal fringe facing rapid urbanization - using data for 0.5°lon x

0.5°lat grid cells with above average urban population growth, coastal proximity, and minimum urban

population (see Annex section “spatial delineation” for details). This reduced 66,663 land grids cells to a mask

of 2036 cells (2.7 % of total land area, see the annex for details on the criteria applied for this reduction).

A high, subnational spatial resolution of the datasets is desirable to allow for sufficient differentiation within

heterogeneous coastal zones, and to resolve distinctive properties that set these zones apart from the

hinterland and non-urbanized coastal zones. These criteria are met for all biophysical datasets and, crucially, for

datasets indicating urban population and area increase. As a result subnational datasets were used for 8 out of

11 indicators (Table 1). For more details regarding the use of three datasets with national values see the annex.

All datasets with a different initial resolution that 0.5°lon x 0.5°lat resolution used for the cluster analysis were

aggregated or disaggregated to this resolution. For the proxy datasets with higher spatial resolution this

meant averaging or summing up values within each 0.5° grid cell. For the three datasets with national values,

this meant assigning the country value to each grid cell within the country.

Only grids cells with a complete set of 11 indicators are admitted to the cluster analysis. The 11 datasets were

checked for correlation. Two out of 55 pairs revealed a correlation coefficient above 0.6 which are significant

at the .05 level: Flood exposure and flood sensitivity (0.62), and cyclone exposure and cyclone sensitivity

(0.73). This can be explained by the multitude of cyclones-sensitive low-income countries with limited means

for adaptation exposed to tropical cyclones and flooding, Including both components of vulnerability for

floods and cyclones was crucial in this study with a focus on vulnerability, and multiple profiles reveal distinct

differences between exposure and sensitivity. GDP per cap and government effectiveness have a correlation of

0.54 at the .05 level, showing that higher income countries tend to have more effective governments. Income

is not used to construct the government effectiveness indicator. Both signify important aspects of adaptive

capacity in view of urbanization, and show different degrees of importance for shaping the typology, so the

correlation does not outweigh including both in the cluster analysis.

Socioeconomic data with subnational resolution and global coverage is not always readily available. For this

reason national datasets were used for average per capita income, urban population in poverty, and

government effectiveness. We also motivate per capita GDP on a national level to enable a differentiation

between coastal urban fringes in low- or middle income countries.

Spatial delineation of rapidly urbanizing coastal fringe

First, all grid cells intersecting a buffer reaching 50 ilometers inland from the world’s coastlines were selected

using the IMAGE land-ocean coastline (Stehfest et al. 2014). From these coastal grid cells we selected cells with

a total urban population exceeding 1000. This ruled out both purely rural coastal populations and cells with

mar edly low country thresholds for defining what an “urban” settlement is, such as 250 inhabitants for

Norway, from unbalancing more commonly applied higher thresholds (Svirejeva-Hopkins 2008). Finally, only

coastal urban grid cells with an annual urban population change of at least 2.25% from 1990 to 2000 were

selected. This change rate denotes above average, or rapid, urban population increase of at least 25% from

1990 to 2000 according to the UN World Urbanization Prospects (UNDESA 2008). The resulting mask

overwhelmingly comprises areas in low- and middle-income countries. This is in line with them being the locus

of rapid urbanization (UNDESA 2012; UNDESA 2008).

Cluster analysis

Before the cluster analysis is applied all the indicators were normalized to a 0-1-range using their minimum and

maximum values. Each indicator is fed in to the cluster analysis with the same weight. We applied the widely

known partitioning K-means method (Macqueen 1967). The initial partition for the algorithm was delivered by a

hierarchical clustering, using the Ward-method on a subset of the data (Ward 1963).

The data points, meaning the grid cell within the rapidly urbanizing coastal mask with the features described

above, are assigned to the k given initial clusters. Then the centers of the clusters are calculated and

subsequently the objects are assigned to the nearest cluster centers. This procedure minimizes the total

within-cluster sum-of-squares (TSS) criterion until a breakup criterion is reached.

The optimal number of clusters is determined by analyzing the stability of the different partitions for various

numbers of clusters. Thereby the algorithm is repeated 200 times with changing initial conditions, and by

comparing pairs of cluster partitions we calculate a consistency measure, showing how much the two cluster

results vary. A lower variety and a higher consistency measure for a pre-given number of clusters imply a higher

similarity between to the underlying structure in the data. The method belongs to the stability based methods

(Ben-Hur et al. 2002; Roth et al. 2002).