Identifying Vulnerability Pattern in a Flood Prone Micro ... - ipcbee

2011 2nd International Conference on Environmental Science and Development IPCBEE vol.4 (2011) © (2011) IACSIT Press, Singapore

Identifying Vulnerability Pattern in a Flood Prone Micro-Hotspot of Mumbai, India Subhajyoti Samaddar

Bijay Anand Misra

GCOE-HSE Project, Mumbai Base Disaster Prevention Research Institute, Kyoto University Kyoto, Japan [email protected]

GCOE-HSE Project, Mumbai Base Kyoto University Mumbai, India [email protected]

Roshni Chatterjee

Hirokazu Tatano

GCOE-HSE Project, Mumbai Base Kyoto University Mumbai, India [email protected]

Disaster Prevention Research Institute Kyoto University Kyoto, Japan [email protected]

Abstract— The paper attempts to examine and identify the household vulnerability patterns in a flood prone microhotspot slum of Mumbai named Premnagar. Considering household profile and physical conditions of the house and the site are the two major factors influencing flood vulnerability, five types of loss due to flood are examined by using “two-step cluster analysis” and “two way ANOVA”. It is found that monetary loss is invariably higher among the economically & culturally developed groups than other groups irrespective of the physical conditions of the house and the site. Households who are economically and culturally less developed have higher damage in terms of loss to food stored in house affecting health and sustenance. Household characteristics and physical condition of the house and the site, it is observed, impact all kinds of damage, but the level of impact across groups varies from damage to damage. As a result, no particular pattern of common impact on vulnerability has emerged. However, in majority of the cases it is found that the economically and culturally poor people have reported maximum loss of all types due to flood. Keywords-vulnerabiliy pattern, micro-hotspot, flood disaster, mumbai

I.

INTRODUCTION AND BACKGROUND

Mumbai, located on the west coast of India facing the Arabian Sea, is the land of 12 million people in an area of 437 Sq.Km. The city which is the financial capital of India contributes over 25% of the country’s tax revenues and generates about 5% of India’s Gross Domestic Product (GDP) [1]. Unprecedented change in rainfall pattern along with rapid urbanization, inadequate city management and planning make the financial capital of India highly prone to floods, the severest one was on 26th July, 2005. According to the Flood Fact Finding Committee of Government of Maharashtra State [2], the city received 940 mm rainfall in 24 hours on 26 July 2005. The financial cost of the flood was unprecedented and the flood halted the entire commercial, trading, and industrial activity for days. According to Municipal Corporation of Greater Mumbai (MCGM), at least

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419 persons died directly due to the flood and subsequent landslide in the Mumbai municipal area alone; moreover. Another 216 were dead after the event due to water born diseases that followed during and after the flood. It is reported that 100,000 residential and commercial buildings collapsed, 30,000 vehicles were damaged, the entire railway system and telephone lines had collapsed and more than 60 % of the city area was directly or partially affected due to the 2005 flood [1], [3]. The poor people, who are forced to live in slums comprising the half of the population of the city huddled only on 10% of the entire city’s land area, were most severely affected by the 2005 flood. Limited economic and social resources and capital often, we know, put the livelihood of the poor into risks. For example, living on hazard prone land, engaged in hazardous occupation, high unemployment and economic insecurity, illiteracy and poor health conditions all add to vulnerability. In addition, the problem is compounded because the city lacks a sustainable urban planning practice, where often decisions for short-term gains destroy the natural environmental safeguards and neglect the needs of majority of the city dwellers. Result, unplanned development weakens the natural safety valves of the terrain from hazards and helps persistence of slums and poverty that in turn exacerbate the risks and vulnerability. The city needs crash and coordinated efforts toward flood risk reduction and management. In order to promote disaster resilience in a community, sensitive planning and initiatives are required focusing not only for the engineering based solutions which focus mostly on the structural measures, but also non-structural social engineering solutions which focus on capacity building and reduction of social vulnerability of the community. Especially, a detailed vulnerability analysis at micro-hotspot is required which will help sharpen and pinpoint the prescriptions/solutions in risk management that are feasible to implement. Keeping this in mind, the vulnerability pattern study analysis in Premnagar, a flood prone micro-hotspot in Mumbai was taken up. Premnagar a sub-cluster of a larger slum pocket, Dharavi, in G-North Ward, Mumbai is the case study.

II.

VULENERABILITY PATTERN : CONCEPTUAL FRAMEWORK AND DEFINATION

The promotion of disaster resilient society requires a paradigm shift away from the primary focus on natural hazards and their quantification towards the identification, assessment and ranking of various vulnerabilities [4], [5]. Therefore, instead of defining disaster primarily as physical occurrence, requiring largely technological solutions, disasters should be viewed as a result of complex interaction between a potentially damaging physical event and the vulnerability of the society as manifest in quality of infrastructure, economy, environmental stability, which are always determined by human interaction and behavior. Thus, measuring vulnerability is increasingly being seen as a key step towards effective risk reduction and the promotion of a culture of disaster resiliency. There is no universal definition of vulnerability, rather different scholars defined vulnerability differently, though a well established and accepted definition of vulnerability is that vulnerability is an intrinsic predisposition to be affected by or to be susceptible to damage, that means vulnerability represents the system or the community’s physical, economic, social or political susceptibility to damage as the result of a hazardous event of natural or anthropogenic origin [6]. Carren˜o et al. [7] mentioned two aspects of vulnerability – socioeconomic fragilities and lack of resiliency as social context conditions (that favor the second order impacts) on the one hand, and the physical damage caused by exposure and physical susceptibility of the built environment on the other hand (related to first order impacts). Generally speaking, an element at risk becomes more vulnerable when more exposed to hazard and more susceptible to its force and impacts. Therefore, three analects or factors are important in order to assessing or measuring the vulnerability pattern as shown in Figure 1. First, it is important to identify the element at risks which specify the amount of social, economic or ecological units or system which are at risk of being affected by the kind of hazard. In the present study, a cluster of households more particularly a household is considered as the unit or element to assess the level of vulnerability as the household is considered as the most generic or primary social group or unit of the society and therefore assessing its level of vulnerability may significantly guide the planners and policy makers for the urban diagnosis of disaster risks. Therefore, one major influencing factor is taken as the household profile. The second major influencing factor in vulnerability is the physical conditions of the house and the site where it is located. The conditions influence the probability and severity

Figure 1. A conceptual framework of household vulnerability elements

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of the hazard, for example, duration, velocity and frequency of the hazard. Ample examples are reported that people are more vulnerable who stay in physically vulnerable area or highly flood prone areas, for example, distance and height from the river bed, nature of flood, velocity of water etc. All these conditions significantly influence the level of damage or loss which in turn the level of vulnerability of the community. Third important of element of the vulnerability assessment is the susceptibility indicator which is a composition of the socio-economic characteristics of the community or the element at risk and also the coping capacity or resiliency power of the element which are at risk due to the hazard. Nearly all concepts of vulnerability view it as an “internal side of risk” that means the conditions of the exposed element or community (susceptibility) at risk are seen as core characteristics of vulnerability [6], [8]. To simplify the problem, in this study, we did not consider or examine the coping capacity of the community, rather a strong focus has been put on the household characteristics (susceptibility indicator), and the vulnerability pattern has been assessed in relation to the household profile of the community and the physical conditions of the house and the site. TABLE I.

TABLE REPRESENTING THE RESEARCH QUESTIONS OF THE PRESENT STUDY FOCUSING ON EXAMING HOUSEHOLD FLOOD VULNERABILITY PATTERN Physical Conditions of the House and the Site

Household Profile

High prone

Low prone

Economic ally and culturally prosperous

Level of vulnerability (?)

Level of vulnerability (? )

Economic ally and culturally less prosperous

Level of vulnerability (? )

Level of vulnerability (? )

Since disaster is considered as the outcome or reflection of the vulnerability, the level of damage or loss is taken as a reflection of the level of vulnerability of the community. Based on the above discussion, the research questions examined in this paper are represented in table 1. III.

METHODS

A. Field Survey and Data Collection To systematically identify the flood vulnerability pattern in the micro-hotspot of Mumbai, the data was collected primarily by questionnaire survey based on face to face structured interview. Data were collected principally on three aspects – a) Household profile data - were collected by focusing on three main elements of household features including socio-economic characteristics, housing typology and infrastructure facility. Under this three main elements of household profile various information were collected as

mentioned in Table III and Table IV. b) Physical conditions of the house and the site - Three types of data on physical conditions of the house and the site including level of flood water inside the house, duration of water inside the house, and duration of flood water at immediate vicinity of the house (see table VI) were collected. c) Flood damage/Loss finally, to measure the household damage or loss, two types of self reported data were collected – first is the total estimated loss in terms of money and secondly the respondents were asked to report self evaluation on the level of household damage to food, clothes, household durable assets (furniture) and damage to building. These questions were measured on a 4 point scale including no loss, little loss, major loss, and total loss. Weights on the various level of damage were put as - no loss – 0, little loss – 33.33, major loss – 66.66 and total loss – 100. Field survey was conducted from February to March, 2010. It took 14 days to cover the entire settlement for the present study area. It took 35 to 45 minutes to conduct one interview. Most of the cases, the head of the household was interviewed, though in case where the head of the household was not available, the other member of the household was interviewed. The survey was conducted by the hired surveyors who were trained and oriented for the survey prior to the field survey. All of the surveyors attended the survey orientation workshop organized by the GCOE-HSE (Kyoto University Global Center of Excellence Program) Mumbai Research team. The hired surveyors were demonstrated about the details of questionnaires and survey techniques, particularly how to conduct interview. In this interactive workshop, surveyors also shared their ideas, problems and understanding about the survey. The orientation workshop continued for two days. After the first training or orientation, the surveyors were taken for rehearsal interview to 5 people. In addition, in the actual field, the first two sample surveys of each surveyor were conducted under the direct supervision and guidance of Kyoto university GCOE-HSE Mumbai research expert member including the first author of the paper. Apart from that, the personals and experts of GCOEHSE Mumbai were continuously present in the field to monitor and guide the surveyors.

two factors household profile and physical condition of the house and site.

B. Survey Population The survey samples were selected randomly. We first divided the entire settlement into four similar clusters/zones according to the size of area. Then from each cluster, same numbers of households were selected for interview. In total, 208 households were interviewed, but 206 households are considered as valid sample for the study. Other samples are excluded because of incomplete information.

“Two-step cluster analysis” is performed to categorize the households based on household profile and physical condition of the site and house. Table II shows that based on household profile; there are two different groups or clusters of households can be found in Premnagar. Cluster – 1 is comprised by the group of households having relatively higher income, more than one storied building with concrete housing (Pucca house) and the group also consists of people from different culture (See table III). Whereas, cluster 2 consists of people who have relatively low income, with only ground floor semi-concrete house and they are having homogeneous cultural background. Based on these findings, we considered and called cluster 1 and cluster 2 of table 2 as “economically and culturally prosperous” and “economically and culturally less prosperous” respectively.

C. Analytical Techniques Various statistical tools are used of which two major statistical techniques are – “Two Step Cluster analysis” is used for categorizing the people based on household profile and physical condition of the house and site. “Two -way Analysis of Variance” (Two-Way ANOVA) is exploited to identify the pattern of vulnerability based on

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IV.

CASE STUDY

A. Premnagar – An Overview The present case study area, called Premnagar, is a part of the Dharavi slum, the biggest slum of Asia, which is located in the bank of Mithi River. Though, there is no official record about the population and growth of the settlement, yet according to the field engineers and field officers of Municipal Corporation of Greater Mumbai (MCGM), the settlement started to develop 30 to 35 years ago in the low laying marshy land abounded by mangrove forest. Most of present inhabitants of the settlement are the migrated labor from the Uttar Pradesh and Bihar, economically weaker provinces of India. At present, there are approximately 15000 people in Premnagar. The community people are mainly engaged in small scale factory, wage laborer, and various others unorganized sector of economy. Running small recycling factory is very common in the area. Both Hindu and Muslim community are observed in Premnagar. Predominantly mixed landuse is observed in the settlement, particularly the ground floors along the main approaching road of the settlement are used for commercial activities including small bottle making factory, recycling factory etc. whereas the upper floors are used for residential purpose. This low lying settlement (2 to 3 feet below from the main road) is prone to flood; particularly on 2005 the magnitude of the flood was huge. Though no official record is available on 2005, our survey found that in an average there was 5 to 6 feet water inside the house for 36 hours. Apart from flood, the area is vulnerable to various kinds of environmental risks accelerated by narrow lanes, very poor ventilation, inadequate infrastructure facility, hazardous garbage and waste generated by recycling factories. So far, apart from the routine maintenance of drainage system, no initiative has been taken by the local government or by any non governmental organization. V.

RESULTS AND DISCUSSION

TABLE II.

CLUSTER DISTRIBUTION OF HOUSEHOLD PROFILE N

% of Combined 69.5 % 30.5 % 100%

73 32 105 101 206

Cluster – 1 Cluster – 2 Combined Excluded Cases Total

clothes. Apart from these two main effects, there is no main effect observed for any category of damage. It depicts economically and culturally less prosperous people reported higher loss or damage to food stored in house than the prosperous people irrespective of physical condition of the house and the site. In case of damage to cloths, households who are high prone to flood is more vulnerable irrespective of their household profile background. No such relations are observed in case of damage to building and durable assets. c) An interaction between the physical condition of the house and site and the household profile is observed in all kinds of damage which indicates that there are common impact of household profile and physical condition of the house and the site in all kinds of damage. Though a detailed observation of the graph (see figure 3) shows that interaction impact varies in all categories of damage. Economically and culturally less prosperous households of the high prone category have much higher damage for food and clothes than the less prosperous people of the low prone category. But such finding varies in case of damage to durable assets and damage to building. Therefore, a combined impact of both factors is observed in all types of damage, but no particular pattern of vulnerability can be observed for all types of damage. In majority of the cases it is found that economically and culturally less prosperous people of high prone to flood category has reported maximum flood loss, except in case of damage to household durable assets.

% of Total 35.4% 15.5% 51.0% 49.0% 100%

Two step cluster analysis of the physical conditions of the house and the site shows (see table V and VI) that Clusters 1 is more flood prone than cluster 2, for example the flood water level, duration of water inside and outside the house is much higher in case of cluster 1 than cluster 2. Therefore, we consider cluster 1 emerged from the cluster analysis of the physical condition of the house and the site as high prone and cluster 2 is low prone to flood. To examine the vulnerability pattern of Premnagar, two way ANOVA was performed to considering two factors including household profile and physical condition of the house and the site determining the level of vulnerability of the community. As depicted in figure 3 and 4, following vulnerability pattern of Premnagar slum are observed – a) In case of total estimated loss it is found that there is a household profile main effect, but no main effect of physical condition of the house and the site, and no interaction of two factors are observed (see figure 2). It indicates that the total amount of loss or damage of the prosperous people is much higher than the socio-economically less prosperous people irrespective of physical conditions of the house and the site. b) Figure 3 shows household profile main effect is observed at damage to food, and a main effect of physical condition of the house and the site is observed at damage to TABLE III.

DETAILED DESCRIPTION AND SCORE OF CLUSTER DISTRIBUTION OF HOUSEHOLD PROFILE Household profile Score

Cluster – 1

Religion Education

Monthly Income Household Size Period of Staying Building height Building Structure

Description of Household Profile

Cluster – 2

Cluster - 1

Cluster – 2

Hindu Muslim

47.3 % 52.7 %

Hindu Muslim

100 % 0%

Hindus & Muslims uniformly distributed

Illiterate Only Can read and write Up to Class 4 Up to Class 8 Up to Class 10 Up to Class 12 Graduation

18.9 % 13.5 %

Illiterate Only Can read and write Up to Class 4 Up to Class 8 Up to Class 10 Up to Class 12

15.6 % 18.8 %

Many are illiterate, most have studied till class 8 (not much variation with cluster 2)

Many are illiterate but there are relatively more higher educated people (not much variation with cluster 1)

16.2 % 25.7 % 18.9 % 4.1 % 2.7 %

Graduation

25.0 % 15.6 % 12.5 % 6.25 % 6.25 %

are

Predominantly Hindus

6067

3859

More income

Less income

6.48

5.50

Larger household size

Smaller household size

21

27

Relatively newer

Relatively older migrants

Most of the higher storey structures concentrated here, also ground storey structures Mostly pucca structures

Predominantly storey buildings

Ground G+1 G+2 Pucca Semi-pucca Kachcha

40.5 % 39.2 % 20.3 % 81.1 % 17.6 % 1.4 %

Ground G+1 G+2 Pucca Semi-pucca Kachcha

96.9 % 0.0 % 3.1 % 3.2 % 90.6 % 6.2 %

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ground

Predominantly semi kachha building

TABLE IV.

COMPONENETS NOT CONSIDERED FOR TWO STEP CLUSTER ANALYSIS OF HOUSEHOLD PROFILE

Components

Reason for not considering for cluster analysis

Language

94 % people are Hindi-speaking; so not considered for cluster analysis 93 % people are from U.P.; so not considered for cluster analysis Source & quantity of water supply is same for an area ; so not considered Sanitation facilities are same for a particular area; so not considered

Native Place Water Supply Sanitation Facility

TABLE V.

house and the site is observed in all kinds of damage, but such common impact varies from damage to damage, therefore, no particular pattern of common impact on vulnerability has emerged. However, in majority of the cases , it is found that poor people of high hazardous category has reported maximum flood loss, except in case of damage to household durable assets. The results of the present study are obvious and prematured as the study is based on limited components or factors of vulnerability assessment. Therefore, the present study may not be able to provide holistic and generalized image of the vulnerability of the community which may help

CLUSTER DISTRIBUTION OF PHYSICAL CONDITIONS OF THE HOUSE AND THE SITE N

Cluster – 1 Cluster – 2 Combined Excluded Cases Total

93 70 163 43 206

% of Combined 57.1 % 42.9 % 100%

% of Total 45.1 % 34.0% 79.1% 20.9% 100%

TABLE VI. DETAILED DESCRIPTION AND SCORE OF CLUSTER DISTRIBUTION OF PHYSICAL CONDITIONS OF THE HOUSE AND THE SITE Indicator of Physical Conditions of the House and the Site

Score of Physical Conditions of the House and the Site Cluster : 1

Average Level of Flood Water Average Duration of flood (in hour) inside the house Average Duration of flood water (in hours) outside the house

Cluster : 2

Description of Physical Conditions of the House and the Site Cluster : 1

Cluster : 2

7 feet

5 feet

High

Low

42 hours

22 hours

Long period

Short period

49 hours

25 hours

Long period

Short period Figure 2. Estimated Marginal Means of Total Estimated Loss (Self Reported)

VI.

the policy formulation of the flood prone micro-hotspots of Mumbai , however, since till date there is no micro-level field survey has been done on Mumbai floods and its impacts on community, the present study will help and guide future studies aiming on more holistic analysis of vulnerability assessment for the urban diagnosis of the Mumbai disaster risks and the study also contributes to collect baseline information on flood impact on the slum dwellers at micro hot-spot level in Mumbai which is presently unavailable from any governmental and non-governmental agency. The present study is limited only in considering household profile and the physical condition of the house determining vulnerability pattern, while the coping capacity of the household is not considered for the analysis. Therefore, a future study is proposed which will consider community’s coping capacity level for vulnerability assessment in order to examine the holistic perspective of community’s vulnerability pattern. In addition, similar study needs to conduct in other settlements of Mumbai in order to develop a

CONCLUSIONS

Based on two factors including household profile and , and physical condition of the house and the site, our study examined the distribution of flood damage or loss in order to identify the vulnerability pattern in a flood prone microhotspot slum of Mumbai. Three major findings have emerged from this analysis – firstly, it is found that self reported total amount of loss in terms of money is higher among the prosperous group than the less prosperous group irrespective of the physical conditions of the house and the site. Secondly Households who are economically and culturally less prosperous have more damage to food stored in house than prosperous group irrespective of their level of hazard. Household of high flood prone category reported more loss to clothes than low flood prone category irrespective of their household profile. No such relations (factor main effect) are observed in case of damage to building and damage to durable assets. Thirdly, combined impact of household profile and physical condition of the

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generalized picture of vulnerability pattern for the policy formulation of the Mumbai disaster risks management.

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Figure 3. Estimated Marginal Means of various types of flood damage (self – reported)

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