social vulnerability to floods

8/16/2019 Social Vulnerability to Floods

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Social vulnerability to floods: Review of casestudies and implications for measurement

Highlights

•

We analyze drivers of social vulnerability in 67 case studies of flood disasters.

•

We profile the social vulnerability drivers that occurred most frequently.

•

Indicator influence varied by disaster stage and national setting.

•

Research needs and recommendations are provided to contextually tailor indicators.

Abstract

leading challenge in measuring social vulnerability to hazards is for output metrics to better

reflect the context in !hich vulnerability occurs. "hrough a meta#analysis of 67 flood disastercase studies $%&&7'()%*+, this paper profiles the leading drivers of social vulnerability to floods.

"he results identify demographic characteristics, socioeconomic status, and health as the leading

empirical drivers of social vulnerability to damaging flood events. -o!ever, ris perception andcoping capacity also featured prominently in the case studies, yet these factors tend to be poorly

reflected in many social vulnerability indicators. "he influence of social vulnerability drivers

varied considerably by disaster stage and national setting, highlighting the importance of context

in understanding social vulnerability precursors, processes, and outcomes. "o help tailorquantitative indicators of social vulnerability to flood contexts, the article concludes !ith

recommendations concerning temporal context, measurability, and indicator interrelationships.


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Keywords

• /ocial vulnerability0

• 1lood0

• 2ase studies0

• Indicators

1. Introduction

3ver the past decade, social vulnerability indices have emerged as a leading tool to quantify and

map human dimensions of hazards vulnerability. 1rom a set of seminal studies 4%)5, 4(7, 4*%,4** and 4*7, social vulnerability modeling research has expanded to address questions of scale

4%, 455 and 486, temporal change 4*(, specific hazards 48, 487, 4&% and 4&7, uncertainty

47 and 4&*, validation 4(% and 45(, and integration !ith physical vulnerability 4%8,

4(* and 4%. 9espite these diverse developments, social vulnerability indices continue toexhibit a large degree of uniformity in index construction approaches. "his homogeneity reflects

gro!ing methodological consensus among modelers, but also highlights limitations in the ability

to translate social vulnerability processes into composite indicators. /uch uniformity may resultin misleading conclusions if dimensions of social vulnerability pertinent to specific hazards are

excluded, or by contrast if !ealy influential dimensions are overrepresented. mong the ma:or

challenges is to better incorporate the context in !hich social vulnerability occurs 4(, 4(8,

46, 46% and 46(.

2ontext distinguishes generalized notions of social vulnerability, such as those often reflected in

indicator studies, from its manifestations in specific disasters. Interacting contextual aspects

explain, underlie, amplify, and attenuate the exposure, susceptibility, and coping capacity ofvulnerable populations. 2ontext is multifaceted and includes the geographic setting of the

disaster, pre#existing social, economic and political conditions, hazard characteristics, degree of

exposure, scales of impacts and responses, and disaster phase $e.g., before, during, after+. 3therimportant contexts may include cultural and institutional norms, societal net!ors, governance,

and historical processes. "hese geographically and temporally varying characteristics are ey for

deconstructing vulnerability, because they describe the human and environmental precursors and

interactions that mae individual disasters unique. /ocial vulnerability theorists and case studyresearchers have long made this point 465 and 46&, yet the vast ma:ority of social vulnerability

indices employ equal !eighting and additive models based on the same leading indicators,

regardless of context. "here is a large gap bet!een the contextual complexity revealed throughqualitative studies and generalized, quantitative metrics produced by social vulnerability indices

47).

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9espite design and contextual shortcomings, quantitative indicators offer many benefits for

vulnerability reduction efforts. ;uantifying social vulnerability can help identify !hich places

are most vulnerable, and !hich dimensions of social vulnerability are the ey drivers. "he abilityof !ell#designed indicators to simplify multidimensional complexity into aggregate measures

maes them !ell suited for use in decision maing, resource allocation, and pro:ect prioritization

4%*. "his policy relevance is increasingly transforming the development of indicators fromacademic exercises into political necessities 4. sing the Web

of /cience, the follo!ing search terms !ere applied to identify peer#revie!ed :ournal articles

published bet!een the years ())) and ()%*?

4@floodA 3R @floodingA

=9

4@social vulnerabilityA 3R @vulnerabilityA 3R @copingA

"he article selection process is illustrated on 1ig. %. We began by collecting the %( articles $top

arro!s+. fter reading through the abstracts, !e selected those !ith a specific focus on the socialvulnerabilities of individuals and households $second level arro!s+. -ence, !e excluded articles

primarily focused on the physical aspects of flooding, built environment exposure, multi#hazard

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vulnerability, or climate change. 3thers !ere removed that centered on disaster management,

quantitative indicators, or computer simulation. /ome articles !ere later added in a sno!ball

fashion based on citations in the papers revie!ed. We then read the full papers, retaining thosethat investigated case studies through intervie!s, surveys, participant observation, focus groups,

and literature revie! $third level arro!+. t the conclusion of this process, !hat remained !ere

sixty#seven empirical studies of social vulnerability to flood disasters. We coded them in a matrixfor in#depth analysis.

1ig. %.

3vervie! of the systematic literature revie!.

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"he locations of the case studies are sho!n in 1ig. (. "he article count is highest for the >nited

/tates $dominated by investigations of -urricane Catrina+, Western Durope, and /outh sia.

Eean!hile, there !ere fe!er studies situated in Dast sia, frica, and 2entral and /outhmerica, despite the occurrence of floods across these regions. /tudies in the >nited /tates and

Dngland comprise approximately half of the total articles analyzed. "he result of our Dnglish

ey!ord selection is a bias favoring Dnglish#speaing settings. 1or countries such as


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encountered. In particular, !e focused on the flooding type, disaster phase, and national setting.

Whenever a specific indicator !as described in an article as influencing social vulnerability, the

article !as tallied in the matrix under the context$s+ in !hich it occurred.

"able %.

"heoretical indicators of social vulnerability.

"hematic indicators Specific indicators

#oping capacity

Individual capacity

Household capacity

Social capital

$emographic characteristics

ge

Race and ethnicity

%amily structure

&ender

%unctional needs

'anguage proficiency

Health

ccess

Stress

$isease

!ortality

Sanitation

'and tenure

3!ners

Renters

S(uatters

)eighborhood characteristics

"ransportation

*opulation density

Housing

Resource dependency

Ris+ perception

!areness

*rior e,perience

Knowledge of flood protection measures

Ris+ denial-acceptance

"rust in officials

Socioeconomic status Income

ealth


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"hematic indicators Specific indicators

/ducation

0ccupation

1ull#size table

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. Results

summary of the results is presented in "able (. "he thematic indicators are sorted by theirfrequency of appearance $highest to lo!est+ in the case studies, and characterized by their

percentage of citations !ithin a given disaster stage and development context. Gecause some

articles may include findings spanning multiple disaster stages, or involve cases in multiplecountries, the percentage sums may exceed one hundred for some indicators. 9emographic

characteristics !ere the most frequently appearing indicators of social vulnerability to floods,

especially in the disaster response and recovery stages. Indicators of socioeconomic status had

the second highest frequency of occurrence !ith the ma:ority of instances involving the response phase. Fining demographic and socio#economic characteristics !ith social vulnerability to

floods suggests that processes involving characteristics such as race, gender, age, and income are

principal drivers of a populations ability to prepare for, respond to, and recover from damagingflood events. 3ther important drivers include health, coping capacity, ris perception, land

tenure, neighborhood characteristics, and governance.

"able (.

Feading empirical indicators of social vulnerability to floods.

$river

0verall

fre(uen

cy 234

%lood type 234 $isaster stage 234$evelopment

conte,t 234

Rive

r

#oast

al

5rba

n

Region

al

!itigati

on

Respon

se

Recove

ry

'ess

develop

ed

!ore

develope

d

$emographi

ccharacterist

ics

8 5( (& 5% (6 *( 66 8 *( 76

Socioecono

mic status 55 *& * (6 %7 * 5( *& 6%

Health 57 58 %6 5 *% %& ( 5( *( 68

#oping

capacity*& 8 (* * (5 %6 58 58 5 56

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$river

0verall

fre(uen

cy 234

%lood type 234 $isaster stage 234$evelopment

conte,t 234

Rive

r

#oast

al

5rba

n

Region

al

!itigati

on

Respon

se

Recove

ry

'ess

develop

ed

!ore

develope

dRis+

perception*6 6( ** 58 () 56 5 (% ** 67

)eighborho

od (uality

of life

*) ) () 6) 5 ( 5 * * 6

'and tenure *) 6 5 % (& % *) 5 * 6

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3ne !ay to interpret the frequency of vulnerability drivers and dimensions in "able ( is as a

measure of importance. -o!ever, frequency might also be dependent on research focus $i.e. theless frequent might attract fe!er studies+, previous routine in the field $i.e. path dependence,

demographics !ere first lined to data availability and then became commonplace in

vulnerability analysis+ and theoretical frame!ors $i.e. some dimensions are less easy tointegrate or less often taen into account+.

Hisualizing the drivers through their interactions provides another perspective on indicator

importance $1ig. *+. "he seven segments of the circle are associated !ith the leading dimensions

of drivers of social vulnerability, !hile the !idth of the connections signifies their intensity. 3nlythose drivers that co#occurred in more than five articles are depicted. "he circular plot echoes the

frequency statistics in "able (, !ith indicators of socio#economic status and demographic

characteristics occurring most often in interactions. Gy frequency alone, land tenure is the least

influential of the social vulnerability drivers. -o!ever, it also has one of the most bet!een#driver connections, indicating that land tenure processes operate as a highly interactive

dimension. Gy contrast, social vulnerabilities associated !ith ris perception have fe!er

connections and can be interpreted to operate more independently. "he findings for each of theleading drivers are discussed in the follo!ing sections.

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1ig. *.

2onnectivity !ithin and among social vulnerability drivers.

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.1. $emographic characteristics

9emographic characteristics are among the most commonly applied social vulnerabilityindicators, yet the literature often diverges in describing the contribution of certain demographic

variables to socially vulnerable groups. 1or example, !hile some research argues that children

are among the most vulnerable segment of the population, they can also serve as resilience

drivers by bringing together community net!ors through their schooling 4%)), or by providingassistance to the household during recovery processes 46%. /imilarly, !omen and the elderly are

often considered among the most vulnerable, yet historical data on flood fatalities reveal that

young 4%) and 46 and middle aged men are also vulnerable due to ris#taing behavior 4*6,rescue activities, and temporary impairment due to alcohol or drugs 48. "hese discrepancies

require reexamination of the typical demographic drivers. "able * highlights some ey case

studies and findings regarding demographic characteristics.

"able *.

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Cey case studies involving demographic characteristics.

Study %lood event Study design Key findings

6on+man et

al. 789

-urricane

Catrina, ())

9ataset for 77%

fatalities in the >/state of Fouisiana,

"he ma:ority of victims !ere elderly,unableJun!illing to evacuate, incapable of

surviving the physical flood effect andJorsuffered from deterioration of basic publichealth services inside and outside flooded areas.

al+er et

al. 71;;

>C severe

flooding inKune ())7

Eixed methods

and !orshop

!ith 56 flood#affected children

2hildren are not only flood Lvictims, but play a

ey role in recovery, bringing together

community net!ors through schooling, leisureand friendship net!ors.

./.$"exas+, %&&7'

())%

-istorical data on

%%( floodcasualties

Boor communities of color suffereddisproportionately in human death and in:ury.

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"able % provides a detailed classification of the citation frequency for demographic drivers ofsocial vulnerability to floods. "he most frequently cited demographic characteristics are age $the

elderly and the young+, gender, race, recent migrants and single parent families. ge is the

leading demographic driver of social vulnerability based on the number of citations !ithin the

literature. Dxtremes along the age spectrum affect mobility out of harms !ay and increase the burden of care follo!ing a damaging event. "his is partially the result of reductions in services

that may mae recovery especially difficult for age dependent populations 4), 4& and 4&. "he contribution of age to social vulnerability can be offset by previous disaster experience and

anticipatory behavior during the mitigation phase0 ho!ever, 477 and only a fe! linear

relationships bet!een age and vulnerability have emerged from studies suggesting that only the

Lvery old and the Lvery young tend to be more vulnerable because of their dependency statusand physical conditions 46%.

mong the most commonly cited drivers !ere special needs populations, !hich include

institutionalized people, those !ith lo! capacity for self#care, long#term or chronically ill

patients needing continued care, and nursing home residents. 1or example, studies sho! thatevacuation and in situ sheltering !ere challenging for nursing home and hospital patients 4&,

and in extreme cases, family members might prevent those needing self#care from evacuating

4&&. Fimited mobility, dependence of care, and reliance on medication and other services areimpediments to evacuation. 2onversely, recovery processes are impeded !hen disruption of

services maes caring for special needs populations difficult 4).

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In addition to considering age dependent and institutionalized populations, flood vulnerability is

lined to gender status !here !omen disproportionately accept family care responsibilities 4&&.


11/34

"able 5.

Cey case studies involving socioeconomic characteristics.


A=ibade et al.

78 =igeria, ()%%

Intervie!s

$nN*6+, survey

$nN5*+, focusgroups $nN6+


12/34


13/34

Study%lood

eventStudy design Key findings

poor communities of color experience moreflood#related health casualties.

'owe et al.

7@8

Fiterature

revie!

Fiterature revie! of

*8 studies of floodsin the 3D29

"arget populations differ for morbidity and

mortality effects, and differ pre#, during, and post#flood time periods.

!ason et al.

7@@

>C ())7

flooding

2ross#sectional

survey $nN555+ 6

months follo!ingthe flood

1emales, children, people in poor health and

evacuees had higher mean scores on B"/9,anxiety and depression after the flood.

1ull#size table

"able options


• 9o!nload as 2/H

lthough ey drivers of health#related vulnerability to floods have been identified, studies arenot convergent on the demographic and societal factors associated !ith health outcomes that are

the product of flood events. /tudies are also inconclusive on the role of flood context on health

issues and mortality. >sing flood mortality, in:ury, and illness as an example, the literature sho!sthat factors related to health vary by timing rather then flood context. -ere, adverse health effects

are lined to phases that constitute?

•

pre-flood phase !here the main issues are heart attacs !hile performing strenuousactivity such as relocating furniture or sandbagging0

•

during-flood phase !here adverse impacts are mainly attributed to dro!ning, vehicular

accidents, flood#related in:uries, and carbon monoxide poisoning. 3ther adverse effects

include gastrointestinal illness, diarrhea, and psychological distress0

•

post-flood recovery phase !here reductions in adverse health effects may result fromdisplacement of flood#affected individuals $particularly those at increased ris of dying+

to non#flooded areas, or increased support from care net!ors follo!ing a damagingevent 46.

.. #oping capacity

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14/34

lthough social vulnerability analyses typically focus on the social characteristics that influence

susceptibility to adverse impacts, social vulnerability is also a function of the capacity of people

to cope !ith hazard impacts in the short term, and adapt in the longer term 4%6. "he case studiesdeconstruct coping capacity in terms of preventativeJadaptive actions taen before the onset of

the flood, but primarily as a set of reactive strategies adopted in the immediate aftermath 4%)*.

1or this paper, !e define coping capacity as the aggregate of resources available to people tocontest the negative effects of hazards, and the practices used to deploy them 4%% and 4%(. It

includes both the capacities exerted by individuals and households, and those accessed through

social net!ors. 2oping strategies tend to be successful !hen they involve accessing orallocating resources to overcome immediate needs, !ithout sacrificing long#term viability of

assets and livelihoods, !ith the particular strategies adopted varying !ith social, physical, and

geographic contexts 477. "able 6 highlights some ey case studies and findings regarding coping

capacity, !hile detailed citation frequency data are provided in "able 5

"able 6.

Cey case studies involving coping capacity.


#hatter=ee 7 India, ())

-ousehold surveys

$nN)+ in t!o urban

slum settlements

Eitigation at city and household levels!as not protective0 assistance for long#

term recovery and adaptation occurred

mostly at local scales via bonding

social capital.

*aul and

Routray 7BB

Gangladesh,

())7

-ousehold survey

$nN**%+ and secondary

data collection in *

coastal and inlandvillages

doption of coping strategies can

substantially reduce flood vulnerability,

but their effectiveness varies

temporally, spatially, and across socio#demographic settings.

Steinf?hrer and

Kuhlic+e 79;


15/34

valuables and protect household members. /tudies in


16/34


17/34

or quality of life drivers found !ithin the literature are lined to the prevalence of transportation

access, illegal andJor uncontrolled urbanization, housing quality, schools, and neighborhood

intersectionality. "ransportation dependence is the foremost#cited driver affecting quality of lifeat the neighborhood level. -urricane Catrina demonstrated ho! a lac transportation access

inhibits large#scale mobility and increases social vulnerability 4(&. -ere, it is not strictly an

issue of individual car o!nership 4&% that affects social vulnerability. Rather, unequal access totransportation alternatives 45) and collective dependence on public transportation 4%)%

explained the failure of evacuation plans since immobility is a ey factor guiding decisions to

stay prior to an event, or to return home follo!ing an event 45%. "able 8 highlights some eycase studies and findings regarding neighborhood characteristics, !ith detailed citation results

included in "able 6.

"able 8.

Cey case studies involving neighborhood characteristics.


#homsri

and Sherer

7@

()%% Eega

1lood in"hailand

=arrative

intervie!s,

participantobservation $nN%)+,

focus group

Beople in slums and in the rural areas felt inferior,

and criticized the information presented.

/lliott et

al. 79

-urricaneCatrina

/urvey 6 monthsafter -urricane

Catrina $nN5%8+

Fac of adequate transportation explains the

failure of evacuation plans? immobility is a eyfactor in decisions to stay or for challenges

returning home.

hittle et

al. 71;1

Kune ())7

flood in the

>C

Intervie!s $nN%8+,

%8#month diaries

$nN55+, staeholder participation

Baradoxically, the efficiency of insurers and

builders may explain evacuation behavior andlength to recover. Insurance tends to monopolize

all available rental accommodation after the

flood, the resulting lac of affordable housingavailable for rent hinders reconstruction in the

moderate income neighborhoods.



• 9o!nload as 2/H

neighborhoods population density, urbanicity, and legitimacy of settlements also impact social

vulnerability to floods. /ome authors question the historic bias to!ards positioning and

permitting lo!er income housing in floodplain areas 4&&, and it may be impossible for populations occupying lo!er income housing in floodplain areas to return follo!ing a damaging

flood event. "his is partially because affordable housing that is often rented can undergo serious

inflation as rents are being paid by insurance companies, allo!ing rental prices to syrocet

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18/34

overnight 4%)%. Informal or uncontrolled neighborhoods and illegal settlements generate mental

suffering, especially in flood prone areas, !ith populations having a general feeling of being

neglected 4(6. In these neighborhoods, residents !ere also faced !ith poor drainage andinfrastructure 4&(, as !ell as exclusion from participatory processes and political leverage,

leaving them unable to access mechanisms to reduce their social vulnerability 47&. Bopulation

and built environment density are ey drivers of social vulnerability that often correspond !ithlo!er income settlements. "hey may introduce evacuation difficulties prior to an event 45*,

increase the ris of disease transmission during and after a flood event, and hamper post#event

relief and recovery processes 4*.

3ther drivers of flood#related social vulnerability include population gro!th and urban spra!l,the number of schools per resident, and neighborhood intersectionality considering race, gender,

and class. Dspecially in the developing burgeoning metropolises, rapid urbanization and

population gro!th are associated !ith the unregulated spra!l, often !ith informal settlementsand !ea infrastructural and economic bases 48%. In the developing !orld, the number of

schools per resident has been used as a proxy for educational bacground, access to damage

compensation, and satisfaction !ith damage regulation 45*.

=eighborhood intersectionality is a concept constructed to foster recognition that perceivedgroup membership can mae people socially vulnerable to various natural hazards.

Intersectionality, particularly bet!een race, gender, and class, means that no single dimensions

can be reduced to the other !hen seeing to understand the !ide array of populations abilities to prepare for, respond, and recover from floods 45%. "he latter suggests that the neighborhoods

context has to be grasped as a !hole to assess social vulnerability.

.B. 'and tenure

Broperty o!nership can strongly influence the level of control a resident has over the adoption of protective measures and access to post#disaster assistance, leading to differences in flood

susceptibility among o!ners, renters, squatters, and the homeless. 2ompared to property o!ners,renters !ere associated !ith higher inundation levels 4(), more adverse health impacts

4%)% and 4&, lo!er economic loss 4%, and higher rates of displacement and :ob loss 45%.

lthough such disproportionate impacts are often associated !ith the lo!er social status of

renters, the causal relationship bet!een tenure and social vulnerability is culturally fluid 4&). 1or example, in


19/34


Catrina, ())government disaster

assistance programs

assistance programs favored property

o!ners over renters.

Steinf?hrer and

Kuhlic+e 79;


20/34

to ran and compare the social vulnerability of different places, yet the studies often lac context

and rarely attempt to validate findings.

1or social vulnerability to floods, a fe! studies have integrated case study and indicatordevelopment approaches 45(, 456, 475 and 4&%. Gut overall, connections bet!een case study

no!ledge and choices made in the modeling process are largely tenuous. "ypically, therationale for decisions regarding variable selection, analysis scale, !eighting, and aggregation is

either unstated or :ustified based on simplicity or choices made in previous studies. In manycases, no :ustification is provided at all. Getter integration of context can improve the ability of

social vulnerability indices to represent observed conditions. "he results of this study highlight

several gaps in no!ledge regarding the construction of social vulnerability indicators. mongthe leading research needs for social vulnerability indicators are accounting for temporal context,

improving the measurability of influential drivers, and understanding interactions bet!een

indicators.

.1. "emporal conte,t

leading conclusion of the meta#analysis is that social vulnerability drivers can vary

considerably !ith the stage of disaster. "his reinforces the understanding of social vulnerabilityas a dynamic situation of !hich people can move in and out 47& and 4&&. "he importance of

temporal characteristics !as succinctly captured by $46%, p. 8)*+

@The same group may be vulnerable in certain event phases and not vulnerable in others.

This means that the same indicator may have explanatory power in more than one phaseof the event but with opposite meanings in terms of social vulnerability.A

1indings from the revie! of demographic and health#related studies are particularly instructive.

2hildren and non#!hites appeared to be the more vulnerable before the flood due to lac ofa!areness and preparedness 4*& and 466. 9uring the flood, men and middle#age populations!ere more vulnerable due to ris#taing behavior 48 and involvement in rescue and emergency

operations 4%)%0 as !ell as children and the elderly due to their difficulty to s!im and reach

shelter or safety 4%). fter the flood, !omen, single#parent families, and the elderly !ere foundto be more vulnerable due to resource availability and difficulties coping !ith disruptions to

long#term care and services 4).

Incorporating the phase of the flood disaster is a ey to improving the contextual validity of

social vulnerability indicators and maps. "o account for temporal context, one approach is todifferentiate indicator development according to preparedness, response and recovery phases of a

flood disaster. Gorro!ing from /teinfPhrer and Cuhlice 4&), "able %) provides a demonstrationof this approach, !ith particular indicators evaluated based on their directional effect onvulnerability $e.g., Q increases, decreases+ for each disaster phase.

"able %).

Bhase#oriented approach to indicator selection.

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*otential indicators *reDflood-mitigation $uring flood-response *ost flood-recovery

#hildren E F

Goung adults F F

/lderly F F



• 9o!nload as 2/H

/uch a phase#oriented approach could inform variable selection $e.g., !hat are the ey

vulnerability drivers for flood recoveryS+, !eighting $!hat is the relative importance of

indicators for flood preparednessS+, and aggregation $!hat is the individual and combined effect

of individual indicators of flood responseS+. It could also mae social vulnerability analysis moresalient for emergency managers, !hose responsibilities are liely to be organized around the

emergency management cycle. 2urrently, the most common internal structure for social

vulnerability modeling and mapping is the thematic organization of indicators into sub#indices457 and 47) or statistical factors 4*%, 45( and 48* derived from themes such as those sho!n in

"able %. Gased on the findings of this research, an alternative thematic structure based on disaster

phase should also be considered.

.. !easurability

lthough indicators are increasingly recognized as useful tools for policy formulation and public

communication, they are sub:ect to measurability limitations 4%5. "he use of social vulnerabilityindicators may mislead decision#maing if practical considerations of cost, data availability, and

measurability are prioritized over validity? does the indicator faithfully represent vulnerability

processesS s one article put it , “understanding vulnerability and flood recovery is not as

straightforward as mapping socio-economic characteristics $ 4%)%, p. %7+.A

Improving measurability is particularly important for social capital, ris perception, and

psychosocial dimensions of health, !hich stymie standardized measurement because they are

often situationally dependent and may require quantification at scales $e.g., individual, net!or+different from other indicators. Indicators for these aspects typically cannot be computed from

publicly available databases $e.g., national censuses+ and require the use of qualitative methods,

targeted surveys, and participatory approaches. "o address this caveat, scorecards have become a

popular survey mechanism, especially for researchers interested in understanding urbanresilience $see 4&5, 47, 4&6+. Research is needed, ho!ever, to better integrate the findings of

studies employing such methods. In particular, the potential of participatory approaches to

generate salient quantitative data is still underestimated 468.

Eeasurability is also constrained by limited understanding of underlying social vulnerability

processes. 1or example, being a child 4%)) and 46%, an elder 477 and 4&, a !oman

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4*6 and 4&) and a member of a minority 4(8 and 4&8 !ere protective factors in some studies.

mbiguity and nuance in the effect on social vulnerability !ere particularly pronounced for ris

perception. 1ielding 45 found scale effects to ris perception, !ith variation bet!een, but not!ithin neighborhoods, regardless of socioeconomic status and flood ris. lthough structural

flood protection and institutional management can reduce flood exposure, higher levels of trust

in these elements may lead to erosion of a!areness and self#protective sills 4*5 and 46%. "he belief that flood protection is an institutional as opposed to a private responsibility !as

associated !ith reduced individual agency 476 and 4&). 2ollectively, many results regarding

ris perception are too contradictory to mae generalizations for indicator selection in the floodcontext. "he development and testing of ne! geospatial indicators of social capital is also ey

research need 48, and should be augmented by continued search for suitable existing proxy

measures.

3verall, measurability challenges are important to consider !hen interpreting the ranings andspatial distributions of output indicators. In contexts !here social vulnerability drivers that are

difficult to measure are particularly important, !hat is the meaning of the index !hen they are

not includedS "o !hat extent are assertions valid that the resultant indicators measure socialvulnerabilityS n important step in social vulnerability indicator development is to consider the

meaning of gaps in the input information.

.. Indicator interrelationships

@Some of these categories intersect in complex ways $ for instance disabled people are

disproportionately likely to be poor, as are members of minority ethnic groups, women

and older people+0 not all within them are equally vulnerable and vulnerability is a

dynamic rather than a static quality $ people can move in and out of vulnerability+A $ 4&&, p.((*+.

Eore research is needed to further explore ho! social vulnerability drivers interact, particularly

across geographic and temporal scales. Dxamples from the case studies include examination ofconnections bet!een demographic characteristics, !ealth, land tenure, and social capital 4&),

race and class 45%, and age, income, and social isolation 46% and 48%. "he issue of flood

insurance highlights the interrelationships of social vulnerability drivers and effects. t an

individual level, the purchasing of an insurance policy is strongly correlated !ith income, homeo!nership, and mitigation behavior 4*8. Fac of insurance magnified flood impacts 46 and

slo!ed house rehabilitation due to delays in public or federal payments 4). Eean!hile, being

uninsured or having problems !ith insurers !ere among the leading precursors to psychologicalimpacts, inducing stress or B"/9 4&.

finer understanding of the relationships bet!een social vulnerability drivers could benefit the

!eighting and aggregation stages of composite indicator development. "he !eighting of

indicators should ideally reflect their relative importance in affecting social vulnerability. In practice, ho!ever, equal !eighting has become the norm for modelers, !ith typical :ustification

similar to the follo!ing?

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@T each factor was viewed as having an equal contribution to the countys overall

vulnerability. !n the absence of a defensible method for assigning weights, we felt this

was the best option. $ 4*), p. (5+.A

-o!ever, it is more liely than not, that individual indicators differ in their degree of influence

on social vulnerability. Brevious research has demonstrated that hierarchical and inductiveindices of social vulnerability are highly sensitive to the !eighting approach employed 4&*. "he

development and testing of additional defensible methodologies for indicator !eightingrepresents a ey research need. 1or indicators applied to resource allocation and planning

processes, the use of context#specific !eights developed using participatory and survey methods

475 is one path to!ard better !eighting schemes. -o!ever, for applications focused on first# pass identification of vulnerable populations, comparing places, and advocacy, the time and

resource investment required for such an approach might be too high.

Berhaps it is possible to generate !eighting schemes applicable to broad categories of flood

contexts, for instance, leading to one set of !eights for mitigation of coastal flooding in

Gangladesh, and another for recovery from river flooding in Dngland. If sho!n to be moderatelyvalid, such an approach could represent a reasonable intermediary bet!een the default

assumption of equal !eights and methods involving primary data collection and analysis. >sing participatory methods that incorporate the opinion of experts !ithin the respective regions could

foster such actions !hile assuring local context and insight is considered. "his local

contextualization can be fulfilled using !eb#surveys or !orshops in !hich community leaders,local governments, and other relevant staeholders !or together to guide the indicator

!eighting process. 1or an in#depth discussion on participatory methods see 47( and 47*.

Regarding indicator aggregation, additive methods are still applied by a large proportion of

social vulnerability indicators. While such an approach has the advantage of simplicity, it is

based on the mathematical assumption that each vulnerability driver operates independently andthat a deficit in one dimension of social vulnerability can be offset $or compensated+ by a surplus

in another. -o!ever, the numerous indicator interactions profiled in the meta#analysis $1ig.* and 1ig. 5+ mae clear that such an assumption is untenable in the context of social

vulnerability to floods, strengthening the argument in favor of social vulnerability modeling and

mapping approaches that focus on interactions bet!een drivers 48*. 1ig. *, for instance,

illustrates the interactions bet!een social vulnerability drivers emerging from the qualitativestudies? !hen a paper mentions a demographic driver $e.g. age+ it is far more liely to be

subsequently associated !ith socioeconomic driving factors $e.g. income+ rather than factors

such as ris perception. 2onversely, case studies focused on ris perception are more liely toassociate coping capacity !ith social vulnerability rather than any other underlying cause. "he

heatmap in 1ig. 5 is based on a Bearson linear correlation of driversM cited in each of our

respective studies. "he latter taes the interaction bet!een drivers one step further by revealing precisely !hich drivers are positively $red+ or negatively $dar blue+ correlated. "his does not

necessarily mean that there is some causal relation bet!een the drivers, they :ust tend to emerge

together from the empirical field!or, and in most cases the empirical studies discuss the more

prominent interactions.

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8. #onclusions

"his paper has profiled the leading drivers of social vulnerability to floods, !ith the underlyinggoal of shedding light on the development of social vulnerability indicators. In our vie!, the field

of social vulnerability measurement has entered some!hat of a transitional period. 9ebates

regarding definitions of social vulnerability have been largely settled, and the need for reliable

metrics is !ell established. -o!ever, the results of this meta#analysis demonstrate that muchmore !or needs to be done to reflect the contextual characteristics of social vulnerability

processes in measurement and mapping. "he findings highlight the situational variability of

social vulnerability drivers. =ot all drivers have a consistent influence on social vulnerability,even for the most !idely agreed upon characteristics such as age and class. /ome factors

contribute to vulnerability in one context, yet detract from it in another. nd there can be

considerable variation in the identity and effect of vulnerability drivers throughout the temporal progression of a flood disaster.

Improved incorporation of context !ill help produce indicators that not only reflect vulnerability

as a state, but also as a situation. Dmpirical case studies are a rich source of situational


25/34

understanding of the root causes of social vulnerability, their relative importance, interactions

bet!een drivers, and scales $geographic, administrative, and temporal+ of operation. "his

understanding can be of great value for decisions during quantitative indicator construction,involving variable selection, scale of analysis, internal structure, !eighting, and aggregation.

Eoreover, greater consideration of context, measurability, and interaction bet!een drivers can

help highlight not only !hat is reflected in resultant vulnerability indices, but also !hat is absent.

"he measurability and simplicity features of indicators mean that they !ill never be able to fullyrepresent the complexity of vulnerability processes. -o!ever, strengthening linages bet!een

empirical studies and quantitativeJgeospatial modeling has the potential to result in more valid

metrics that are suitable for decision#maing. "he state of no!ledge and research needs profiledin this revie! represent one step in that direction.

Ac+nowledgment

"his research !as partially supported by funds from the >./. =ational /cience 1oundation

$%***%&)+, Infrastructure Eanagement and Dxtreme Dvents.

Appendi, A

/ee "able %, "able (, "able *, "able 5, "able , "able 6 and "able 7

"able %.

1requency of demographic vulnerability drivers.

$river

Arti

cle

cou

nt

Impact on

vulnerability

!ain disaster

stage %lood type

$evelopment

conte,t

Increa

sing

234

$ecrea

sing

234

!itiga

tion

234

Resp

onse

234

Reco

very

234

Riv

er

23

4

#oa

stal

234

%la

sh

flo

od

23

4

5rb

an

floo

d

234

Regi

onal

234

$evel

oped

234

$evelo

ping

234

/lderly B 8% % *) 58 *7 58 5% %& 58 *) 7) 5%

#hildren 1 8% %) %5 ( ** ** 5* %& ** (5 ( 7

%emale 18 7) % % ( ) () 5 % )

>lac+ 9 6 ) ** 6 78 (( ) ) %% 6 %)) )

Single parent

familiesC %)) ) ( *8 ) *8 *8 %* *8 ) 88 %*

%emale

headed

households

with children

B 86 ) (& 5* 7% (& 7 %5 (& ) 7% 5*

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$river

Arti

cle

cou

nt

Impact on

vulnerability

!ain disaster

stage%lood type

$evelopment

conte,t

Increa

sing234

$ecrea

sing234

!itiga

tion234

Resp

onse234

Reco

very234

Riv

er23

4

#oa

stal234

%la

sh

flood

23

4

5rb

an

flood

234

Regi

onal234

$evel

oped234

$evelo

ping234

Recent

immigrantsB 7 5* (& 5* 7% 7 %5 %5 7 %5 %)) %5

Handicapped-

disabledB %)) ) (& 7 5* (& (& ) 5* %5 7% (&

$ependency @ %)) ) ) %7 ** %7 %7 ) ** ** 67 **

'ow capacity

for selfDcare@ %)) ) %7 8* ) ) ** ) ) ** 67 **

)on white @ 67 %7 ** 8* 67 ** %7 ) ** %7 %)) )

!iddle age @ 67 %7 ** ) ** ) %7 %7 %7 %7 %)) )

!ale 8 %)) ) () %)) () 6) 5) 5) () 5) %)) )

"wenties %)) ) ) ) ) ) ) ) ) ) %)) )

Institutionali

ed %)) ) ( ( ( ( ( ) ) ( %)) (

)onDnative

spea+ers-lang

uage barriers

7 ) ) ) 7 ) ) ( 7 ( %)) )

Hispanic ) ) ) ( %)) ) ) ) ) ) %)) )

Acculturation 67 ** ) ** 67 ** ** ) %)) 67 %)) ))ursing

home

residents

%)) ) ) ) ) ) ) ) ) ) %)) )

*eople per

housing unit %)) ) ) ) ) %)) ) ) %)) ) %)) )

Social

security

beneficiaries

%)) ) ) %)) %)) ) )U ) ) ) %)) )

Households

with small

children

1 %)) ) %)) %)) ) %)) ) ) ) ) %)) )

'iving space

per person1 ) %)) %)) ) ) %)) %)) ) %)) ) %)) )

Rooms per

housing unit1 ) %)) %)) ) ) %)) ) ) ) %)) %)) )

"otal C B9 1 ; 1 8 ; 1 1 @ 8 9

1ull#size table

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"able options


• 9o!nload as 2/H

"able (.

1requency of socioeconomic vulnerability drivers.

$river%re(u

ency

Impact on

vulnerability!ain disaster stage %lood type

$evelopment

conte,t

Increa

sing234

$ecrea

sing234

!itiga

tion234

Respo

nse234

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ery234

Riv

er

234

#oas

tal234

%la

sh

flo

od23

4

5rb

an

flood

234

Regio

nal234

$evelo

ped234

$evelo

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*overty

and

deprivati

on

; &) ) () ) 5) 5) * % 6 *) 5

Househol

d or per

capita

income

18 8) ) ) 5) (7 57 () ) ** () 5) 6)

5nemplo

yed11 8( ) %8 %8 *6 (7 %8 & 5 (7 8( %8

*overty

rate1; 8) ) () 5) *) 5) %) ) 7) () 5) 6)

Adults

with no

high

school

diploma

1; 6) ) () 6) 5) ) *) ) ) () 5) 6)

%ishingJ

agricultureJ

forestry

C ( ) %* ) *8 ( *8 ) ( %* ( 7

'iteracy

rate@ 67 ) ) ** ) %7 %7 ) ** ** %7 8*

Informal

sector-da

y laborer

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28/34

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29/34

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30/34

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31/34

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32/34

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33/34

$river%re(ue

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!ain disaster stage %lood

social vulnerability to floods

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