Annual Journal of Hydraulic Engineering, JSCE, Vol.53, 2009, February
Annual Journal of Hydraulic Engineering, JSCE, Vol. 53, 2009, February

ASSESSING VULNERABILITY TO REGIONAL
FLOOD HAZARD THROUGH SPATIAL
MULTI-CRITERIA ANALYSIS IN THE HUAIHE
RIVER BASIN, CHINA
Nanshan ZHENG1, Kaoru TAKARA2, Yosuke YAMASHIKI3and Yasuto TACHIKAWA4
1Student Member of JSCE, Graduate student, Dept. of Urban and Environmental Eng., Kyoto University(Kyoto 615-8540, Japan) E-mail: zhengnanshan@z05rcl.mbox.media.kyoto-u.ac.jp
2Fellow of JSCE, Dr. Eng., Professor, DPRI, Kyoto University (Uji 611-0011, Japan)
3Member of JSCE, Dr. Eng., Associate Professor, DPRI, Kyoto University (Uji 611-0011, Japan)
4Member of JSCE, Dr. Eng., Associate Professor, Dept. of Urban and Environmental Eng., Kyoto University(Kyoto 615-8540, Japan)

The identification of regional vulnerability to flood hazard and the components related tovulnerability is crucial for emergency preparedness, immediate response, mitigation planning, andlong-term recovery from disasters. This research attempts to employ demographic and socio-economic
data as well as other hazard information in studying the vulnerability to flood hazard with quantitative
measures. Firstly the perspective of vulnerability to regional flood hazard is reviewed according toprevious literature. On the basis of the conceptual framework of flood hazard vulnerability analysis, thispaper develops a quantitative methodology to characterize the vulnerability of regional flood hazards,
which employs spatial multi-criteria analysis approach based on entropy weight determining and scenario
analysis of the flood event occurred in 2003. The utility of this approach in an assessment of thevulnerability is illustrated in the Huaihe River Basin, China.
Key Words: Flood hazard, vulnerability, entropy, multi-criteria analysis, geographical information
systems

1. INTRODUCTION
Floods are known as frequent and mostdevastating events worldwide. Asia continent ismuch affected by floods and the countries like
China, India, and Bangladesh are extremely
vulnerable1). The impacts of hazardous events areoften exacerbated by the interactions with otherhazards or by occurring in areas with a high density
populationandsocialvulnerability.
The
identification of regional vulnerability to hazard andthe components contributing to vulnerability is
crucial for emergency preparedness, immediate
response, mitigation planning, and long-term
recovery from disasters. Thus the need for a
spatially oriented vulnerability assessment ishighlighted. For example, Birkmann2) stressed theneed for a paradigm shift from the quantification
and analysis of the hazard to the identification,assessment and ranking of vulnerability. In thisregard, vulnerability has emerged as the mostcritical research field in disaster studies. This newemphasis on vulnerability marks a shift away fromtraditional scientific assessments, which limit
analysis to the stressors (e.g. climate change,
hurricanes) and the corresponding impacts, towards
an examination of the system being stressed and its
ability to respond3). The advantage of thevulnerability based approach is that it allows a broadarray of potential risk factors to be explored4). Thisis conducive to diagnosing the various factors andinteractions that contribute to vulnerability.
Although vulnerability is a key issue inunderstanding disaster risk, quantitative assessmentof vulnerability is often less advanced than hazard
mapping and impact analysis. To identify
spatio-temporal distribution of vulnerability, toimprove understanding of the underlying processes,
and to prioritize strategies to reduce vulnerability,
the index approach as a quantitative measure is- 127 -

2. METHODOLOGY(1) The perspective of vulnerability
Vulnerability is a concept that evolved out of thesocial science and was introduced as a response to
the purely hazard-oriented perception of disasterrisk in the 1970s. In the literature of vulnerability,
although there are different views on vulnerability
from different experts and examples of methods forvulnerability analysis that have been developed byinstitutions and researchers, vulnerability is often
viewed as an intrinsic characteristic of a system or
element2,7,8).
In recent years, many interdisciplinary researches
have begun to explore the vulnerability foraddressing vulnerable people and regions. The
concept of vulnerability has been continuously
broadened towards a more comprehensive
framework encompassing exposure, susceptibility,
coping capacity and adaptive capacity or resilience,as well as focusing on different contexts, such as
physical, social, economic, environmental andinstitutional vulnerability.
This research of vulnerability assessment traces
its roots to Cutter9) who introduced a conceptualmodel of locality’s vulnerability to hazards. It ishypothesized that the social and biophysicalelements interpenetrate and shape the overallvulnerability, which is represented by the
combination of biophysical and social vulnerability(Fig. 1). Biophysical vulnerability relies uponhydro-geologicalconditions,andsocialvulnerability
dependsonsocio-economic
characteristics. It should be noted that
eco-environmental vulnerability is not taken into

consideration in this study. However the previous
studies always laid emphasis on social vulnerability.
To employ the indicator-based approach,vulnerability is conceptualized as a function of
exposure to a stressor, susceptibility to associatedimpacts, and the ability of a system to adapt to thestressor’s impacts7,10). In other words, three
determinants of vulnerability can be identified,
which are exposure, susceptibility and resilience.
According to analysis of the actual conditions in the
Huaihe River basin, China, for the whole basin as amanagement zone, the national disaster mitigation
policy can be thought as the same, and thehouseholds have similar awareness because thefrequent flood disaster occurs. Noticeably it isnecessary to consider data availability for indicator
selection in developing country. Therefore bothactual conditions and data availability in the HuaiheRiver basin form a fundamental assumption that ten
kinds of indicators are the most significant factors ofvulnerability to regional flood hazard (Fig. 1),
which are population density, land use, GDP per
capita, dependents and inequality, income of farmer,
income of employment, social expenditures,household savings, hospital beds of 10k people, andinstitutional preparedness.
Although flood prevention standard indicates thelevel of flood prevention, in China to some extent it
also can be used to reflect the level of institutional
preparedness. Thus flood prevention standard isselected to represent the institutional preparedness.
Because in the Huaihe River basin the agriculture isdominant, the percentage of farmland is to reflect
the land use. Furthermore, enrollment rate is
selected to represent the dependents and inequality.
（2）Vulnerability assessment methodology
The study objective is to develop a quantitative
approach for assessing vulnerability that enables
combining some variables influencing vulnerability.
In which, mathematical quantification ofheterogeneous indicators enables the representationof their interrelations, and different indicators can beincorporated in one assessment context.
Component

Vulnerability analysis

potentially useful. Some previous studies illustrate
the composite index approach to measurevulnerability5, 6).
While the index approach aggregates a broad
range of potential factors that may contribute tovulnerability, the relative importance of indicator
generally has not been considered in part due to alack of knowledge regarding their relationships andcontributions of factors. The components are
summed and rescaled to estimate vulnerability.
Actually to create an integrated index ofvulnerability with the application of index approach,
it is significant to deal with the weight determiningof indicator.
Large scale flooding due to heavy rainfall anddrainage congestion has been regularly experienced
in the Huaihe River basin, China. The summer
dominant rainfall, special topographic andsocio-economic conditions make the region highly
vulnerable to flooding. Taking the Huaihe Riverbasin as case study, the main objective of thisresearch is to put forth a methodology for assessing
vulnerability to regional flood hazard.

Biophysical vulnerability

DeterminantExposure

Indicator

Population density
Land use
GDP per capita
Dependents and Inequality

Social vulnerability

Susceptibility

Income of farmer
Income of employment
Social expenditures

Eco-environmental
vulnerability

Resilience

Household Savings
Hospital beds per 10k people
Institutional preparedness

Fig. 1 The conceptual framework of vulnerability analysis forregional flood hazard.

- 128 -

As mentioned in the introduction, composite
index approach is selected to measure vulnerability,
which is based on the conceptual framework
developed in this study.
Constructing a vulnerability index from a suite ofindicators raises several problems in the aggregationof these selected indicators, including weight
determining and model structure. In this paper,
multi-criteria analysis method is employed for theconstruction of model, and entropy-based weight
determining approach is used to supply objective
indicator weights. More details are illustrated in
Section 4.

3. DESCRIPTIONS OF STUDY AREA ANDDATA AVAILABILITYThe Huaihe River basin (HRB)11) is situated ineastern China, which is one of the seven major river
basins in China (Fig. 2). Geographically, it islocated between the latitude 31°N-36°N andlongitude 112°E -121°E, covering an area of
270,000km2. This study area is composed of four
provinces, which are Jiangsu, Shandong, Anhui andHenan. HRB is composed of two water systems,
one is the Huaihe River to the south of the old
Yellow River and the other is the Yishusi River tothe north. The Huaihe River originates from the
Tongbo Mountain, and flows into the Yangtze River
with the length of approximately 1000 km. The
elevation usually is from 2 m to 200 m.
Climatologically, it lies in the warm
semi-humid monsoon region. Precipitation mainly
occurs in the period from May to October. Becauseof anomalies of the Meiyu front during the rainyseason influenced by the South Asian monsoon, andthe unique topography, the basin has been known
for its frequent disasters. Moreover, this region is
populous with the population density of 623 capita
per km2 in 2003, at the same time the total
population reached 168 million, accounting for 13%of the nation’s population. And there is 17% of thecountry’s cultivated land12).
Obviously it is significant to study vulnerability
to flooding for preparedness strategy making andgovernance. This paper takes a typical flood event
occurred in the year 2003 as a case, the relevant datasuch as demographical census, socio-economic datain the year 2003 are collected or generated from thecensus yearbooks of Jiangsu, Shandong, Anhui andHenan Provinces. In this study, 186 counties andcities have been selected as assessment units. The
land use data is generated by the MODIS Vegetation
Index composite imagery data obtained in 2003.
Meanwhile for larger area, the biographicalvulnerability is identified by remote sensing dataprocessing according to scenario analysis of flood
event occurred in 2003 (Fig. 3)13).

´

Rizhao(!

Jining

Linyi(!
Zaozhuang Lianyungang

(
!

Heze
(
!

(
!

ZhengzhouKaifeng
(
!

(
!

Suqian(!
Huai'an Yancheng(!

Huaibei(!
SuzhouXuchang
(
!

(
!

(
!

(
!

PingdingshanLuoheZhoukou
(
!

(
!

(
!

Zhumadian(!

Bengbu(!

Fuyang

The Huaihe River

(
!

(
!

(
!

200

300

Legend

(
!

Lu'an

(
!

50 100

Yangzhou

Huainan(!

Xinyang0(!

XuzhouShangqiu(!

400 Kilometers

City
River 4
River 2-4
lakeAnhui ProvinceHenan Province
Jiangsu Province
Shandong Province
Huaihe River basin

Fig. 2 The Huaihe River basin, China. The upper map shows its
location in China. In the bottom figure, main rivers, lakes anddistricts are shown.

´
LegendBiophysical vulnerability
Political zone
0

50

100

200

300

400
Kilometers

Fig. 3 The biophysical vulnerability to flood hazard in the
Huaihe River basin, China, 2003.

4. VULNERABILITY ASSESSMENT
THROUGH SPATIAL
MULTI-CRITERIA ANALYSIS
In this section social vulnerability metric bytranslating a framework of vulnerability analysis
into a mathematical expression is presented. A
numerical
weightingschemeisapplied.
Multi-criteria analysis model14) is receiving
increasing attention in dealing with complex- 129 -

problems. It was introduced to GIS science in avariety of applications using different calculation
methods. In essence, a multi-criteria method createssummary evaluation scores for each decision
alternative based on their combined performance inthe multiple criteria. Suppose there are n
indicators ( n = 10 , see Fig. 1) and m assessment
units ( m = 186 , see Fig. 3), the original indicator
data X is expressed as following Eq. (1).

x12
x22
M
xm 2

x1n ⎤
L x2 n ⎥⎥L M ⎥⎥L xmn ⎦
L

(1)

1
1+ e

n

(2)

j =1

where VI (i ) is the vulnerability index of the units
i . n denotes the number of indicator, w j is therelative weight of the indicator j . The weights
have to be non-negative and sum up to 1. xij is thevalue of the indicator j for the units i ( xi1 Population density, xi 2 - Percentage of farmland,xi 3 - GDP per capita, xi 4 - Enrollment rate, xi 5 Income of farmer, xi 6 - Income of employment,
xi 7 - Social expenditures, xi 8 - Household savings,
xi 9 - Hospital beds of 10k people, and xi10 Institutional preparedness). V ( xij ) stands for the
representation of indicator normalization for xij .
(1) Measuring key indicators for vulnerability
assessment
To make spatial multi-criteria analysis possible,
each vulnerability indicator is to be measured by its
impact and converted into a normalized range from0 (very low impact) to 1 (very high impact). The
indicators may have different measurement scales(nominal, ordinal, interval and ratio). For
standardizing indicators, a set of equations can beutilized to convert the original data to a range
between 0 and 1. In terms of literature analysis, thecontribution of individual indicator to vulnerability
is better to be represented by a nonlinear functionreferring to risk analysis15). In this study, S-shaped
transform function16) is selected to convert thequantitative indicator according to the feature ofvulnerability (Eq. (3)). Other percentage indicators
are used directly. Moreover flood prevention
standard ( Ti ) is selected to measure the institutional
preparedness. Subjectively it is represented by flood
prevention capacity of main river or city inassessment unit i . The function x i10 = 1 − (Ti / 100)is designed to quantify it while 0 ≤ Ti ≤ 100 , or else
x i10 = 0 while T > 100 . For example when Ti is(3)( a×( − x + b ))where a is used to determine the gradient of curve,
and estimated by the fitting of independent variable( x ) and dependent ( V (x) ) according to S-shaped
regression function (Eq. (3)). b is specified by amean (or median) value of indicator. This function
just shows the indicator is positive in relation tovulnerability. Additionally, the unfavorable
indicator can be converted by reverse S-shaped
function (Eq. (4)).

V ( x) = 1 −For dealing with quantitative measure of social
vulnerability, the method used is an additive model,
in which a vulnerability index is calculated based ona set of indicators (Eq. (2)).

VI (i ) = ∑ w j × V ( xij )V ( x) =

1
1+ e

(4)

( a×( − x + b ))For example, the original data of GDP per capita
in the Huaihe River basin, 2003 is shown in Fig. 4.
In order to estimate the parameter a , some empiricalinformation is considered because there is no a priorinformation about the relationship of indicator value
and normalized value. It is assumed that thenormalized value is higher while larger than 0.8 and
it is lower while less than 0.2. Thus after sorting
GDP per capita from minimum to maximum, thecorresponding indicator value is obtained while theorder in the sorted data series over total number
m in study area is 0.8, 0.5 and 0.2 respectively.
After fitting analysis16), the parameter a is estimated(Fig. 5). GDP per capita is converted by Eq. 4 (Fig.
6). To illustrate the assessment processes, someexamples are listed in Table 1.

´
LegendHuaihe River basin

GDP2003
Value
High : 24319
0

50

100

200

300

400
Kilometers

Low : 1712

Fig. 4 The example of origional data (GDP per capita in 2003).
1

1-1/(1+exp(0.0006*(-x+9030)))

0.9
0.8
0.7
0.6
V(x3)

⎡ x11
⎢x
X = ⎢ 21
⎢ M
⎢
⎣ xm1

25-year return period, it is specified as 0.75.

0.5
0.4
0.3
0.2
0.1
0

5000

10000
15000
GDP per Capita (CNY)

20000

25000

Fig. 5 The normalization function for GDP per capita in 2003.

i- 130 -

Table 1 The data used in the multi-criteria analysis (Examples from three assessment units)
Indicator*

x•1

x• 2

x•3

x• 4

x•5

x•6

x•7

x•8

x•9

x•10

x1•

344.2

61

24319

0.99450

2900

12123

649.014112.5

26.0

0.5

s1•

0.46

0.61

0.00

0.00550

0.44

0.11

0.45

0.18

0.38

0.5

x2•

460.7

62

8264

0.99630

2713

9885

451.913682.026.0

0.5

Unit No.
1

2

3

s2•
x3•

0.48

0.62

0.26

0.00370

0.47

0.32

0.42

0.34

0.38

0.5

584.0

67

11211

0.99900

3260

9885

318.642834.4

26.0

0.5

s3•

0.49

0.67

0.06

0.00100

0.39

0.32

0.40

0.74

0.38

0.5

Weight0.01677 0.03379
0.24105
0.06418
0.02677
0.11250 0.03432
0.112510.147730.21038* x•1 ― Population density (Capita/km2), x•2 ―Percentage of farmland (%), x•3 ―GDP per capita (CNY), x•4 ―Enrollment rate(%), x•5 ―Income of farmer (CNY), x•6 ―Income of employment (CNY), x•7 ―Social expenditures (CNY/Capita),x•8 ―Household savings (CNY), x•9 ―Hospital beds of 10k people, and x•10 ― Institutional preparedness(2) Entropy-based weighting determining
There are some approaches to determine weightof individual indicator composing of subjective andobjective methods. In previous researches about
vulnerability assessment, the relative importance ofindicator is assumed as the same. To deal with thisproblem, the study attempts to employ objective
method for judging their relative importance, which
is based on entropy17) described as follows.
a) Normalization of the original data matrix
The normalized matrix ( S ) is shown as Eq. (5).
S = ( sij ) m×n , i = 1,..., m , j = 1,..., n(5)where sij is the normalized data of the units i at
the indicator j (m=186, n=10), which is computedby the method mentioned in subsection (1) of
Section 4.
b) The definition of entropy value
According to the normalization result, the ratio( rij ) of the normalized value sij to the sum ofthem for the indicator j is shown as Eq. (6).
m

rij = sij / ∑ sij , i = 1,..., m , j = 1,..., n

(6)

i =1

The entropy measurement e for the indicator

j is computed by Eq. (7).

´
LegendGDP per captia normalization

GDP_PER_03.00010 - .35958.35959 - .66182.66183 - .81659
0

50

100

200

300

400
Kilometers.81660 - .92481.92482 - .98776

Fig. 6 The normalized value for GDP per capita in 2003.

ej = −

1 m∑ rij ln rij , j = 1,..., nln m i =1(7)c) The entropy-based weight determining
The objective weight w for the indicator j
is defined as Eq. (8).
1− ej
, j = 1,..., n
(8)
w =
j

n

∑ (1 − e )j =1jwhere 0 ≤ w j ≤ 1 and ∑ j =1 w j = 1 .
With the application of entropy-based weight
determining, the indicator weights used in themulti-criteria analysis model can be calculated,which are listed in Table 1. The results about
weighting determining indicate that GDP per
capital and institutional preparedness in study area
are most important, and the economic indicatorsplay much more role in this assessment.
n(3) Results and analysis
After the selection of the indicators, theirnormalizations and the definition of indicatorweights (Table 1), the social vulnerability index(SVI) is computed by Eq. (2). The social
vulnerability index map is generated by ArcGIS
environment (Fig. 7), in which the social
vulnerability index values range from 0.19 to 0.71.
The greater value shows the more vulnerable toflooding. This comprehensive quantitative index
can be used to characterize the social vulnerabilityof regional flood hazard in the Huaihe River basin.
According to spatial distribution of social
vulnerability index in the Huaihe River basin, thevulnerable area is located in the center andsouthwestern part. The most vulnerable region is
located in the southeastern of Henan Province andthe northern of Anhui Province. From the city of
Bengbu to the upstream along the main trunk of the
Huaihe River, it is found that social vulnerability is- 131 -

´
Legend0

50 100200

300

400 Kilometers

Biophysical vulnerability
Social vulnerability
SVI.19 - .31
.32 - .39
.40 - .50
.51 - .60
.61 - .71

Fig. 7 The distribution of vulnerability to flood hazard in the
Huaihe River basin, 2003.

relatively higher. Moreover, social vulnerability
index is lower in the eastern part and around
Zhengzhou city because the economic activities are
more active and institutional preparedness is well,which result in the resilience is stronger. It indicatesgreater socio-economic differences among politicalzones in developing country cause spatiallyheterogeneous. Because of temporal variability ofindicator value, the resultant vulnerability index isnot static. It is necessary to be updated regularly.
After overlay analysis of social vulnerability andbiophysical vulnerability, it is identified that from
Bengbu city to the upstream along main trunk of the
Huaihe River, the integrated vulnerability toregional flood hazard is much higher (Fig. 7). It is
helpful to prioritize strategies to reduce regional
vulnerability to flooding.

5．CONCLUSIONSThis study proposed a methodology forassessing vulnerability to flood hazard in the
Huaihe River basin, China, in which biophysicalvulnerability and social vulnerability are considered.
The analysis result shows that the proposed method
is applicable to regional flood hazard analysis. In
terms of quantitative information of vulnerability,
the vulnerable area can be identified for basin flood
management. However it is important to check theeffectiveness of vulnerability index for the decision
makers. The objective entropy-based weight
determining approach is very significant tovulnerability assessment while a lack of a priori
knowledge. The analysis shows that the approach isapplicable to better understand the vulnerability.
However in the further study, the sensitivity ofassessment model and the integrated approach ofsocial and biophysical vulnerability index should beanalyzed in detail.
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1) World Water Assessment Programme (WWAP): UN World- 132 -

(Received September 30, 2008)