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2012

Living with Familiar Hazards: Flood Experiences and Human Vulnerability in Accra, Ghana

Dacosta Aboagye

Abstract

The paper explores demographic characteristics, migration history, and impact of flooding on households and communities. The main objective is to explore the different ways in which floods impact households and communities in Accra. Specifically, the paper analyzes how floods alter the set of resources available to households and communities. The results indicate that urbanization and governmental policies have rendered more people, especially the poor and recent migrants, homeless. These homeless people have become more vulnerable to flooding than the average Accra resident. The results also show that the homeless community contrast with the fixed community in terms of socio-economic characteristics, degree of social cohesion, and physical location. The paper concludes that the unchanging pattern of vulnerability shows the inability of a society to cope and adjust to familiar hazards.

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1Flooding is among the most significant issues affecting sustainable development in many countries. It affects many hundreds of thousands of households annually causing recurrent losses of material goods, endangered lives, thereby affecting progress towards sustainable human development. Hazards can become disasters and worsen when particular groups are precluded from obtaining access to social, political, economic, and physical resources. Awareness of the social and demographic characteristics of households is necessary to understand the strategies households employ to mitigate and to recover from hazards. Inequality in gender, education, employment, income, and migratory history are factors that help to shape households’ strategies to mitigate and recover from floods.

2Some researchers have argued that vulnerability analysis must draw upon patterns of everyday access to resources (Mustafa 2002, Cannon 1994). A households’ ability to mitigate hazards is to some degree a consequence of their ability to possess, control and extract benefits from a particular resource (Sen 1981). Blaikie et al. (1994) developed the access to resources concept, which focuses on differential availability of resources for subsistence. Different types of resources are available to individuals but accessibility to these resources determines how individuals respond to hazards. The access concept provides a framework for analyzing how households struggle over resources and develop coping strategies. Similarly, Moser (1996) hypothesized that poor communities’ vulnerability is linked to differential access to labor, social capital, and housing.

3Property and human loss can be reduced or even entirely eliminated through mitigation. This is because vulnerability to environmental hazards is a function of interaction of the physical environment, the human environment, and the adjustments made to cope with the hazard (Burton et al. 1993). The term mitigation is generally defined as “the wide array of actions that can be taken to reduce vulnerability” (Hague and Burton 2005: 341). In other words, mitigation implies, “sustained deliberative measures, implemented well in advance of impending disaster” (Hague and Burton 2005: 342).

4With regards to flooding, approaches to mitigate the negative impacts could be categorized into ‘structural’ and ‘nonstructural’ measures (Smith 2004, Tobin and Montz 2004, Parker 1999). Structural measures are engineering interventions, such as river channel modifications, embankments, reservoirs and barrages, designed to control the flow of rivers and spread of flooding. Nonstructural measures on the other hand seek to reduce the impact of hazards. They include formal flood warning systems, evacuation programs, land use controls on flood-prone sites, building regulations to prevent incursion of floodwaters, and insurance schemes (Parker 1999). Patton et al. (2000) commented that a deeper understanding of how demographic characteristics combine to influence a group’s level of vulnerability would be beneficial in establishing more effective mitigation programs to reduce vulnerability.

Background

5Even though academic interest in hazards is not a recent phenomenon, data constraints have limited the number and coverage of Ghana hazards studies. In general, studies of hazards in Ghana may be broadly categorized into two groups: physical systems (Møller-Jensen and Yankson 1994, Kasanga et al. 1996, Kufogbe 1996, Gough and Yankson 1997, 2000, Maxwell et al. 1999), and socio-economic characteristics (Boateng et al. 1992, Songsore and McGranahan 1998, GSS 2000, Pellow 2002). On the whole, the studies capture the environmental and socio-economic impact of growing urbanization, but a paucity of data at the micro level has led to an overwhelming focus on processes that determine levels of human vulnerability at the regional and national levels.

6For the purpose of assessing environmental impacts of urbanization, the literature on hazards in Ghana typically examines the impact of increasing population and associated land cover transformations. This usually entails the use of time series satellite images and Geographic Information Systems (GIS) software to observe patterns of land use and land cover change at a regional or national level (Møller-Jensen and Yankson 1994, Kufogbe 1996, Yankson and Gough 1999, Gough and Yankson 1997, 2000, Nyarko 2002), or relating regional volumes human waste generated to human health in the region (Kasanga et al. 1996). Using time series satellite images taken in 1985, 1991 and 2002, Yankson et al. (2005) showed that the rate of urban growth at the fringe areas of Accra increased from 10 km2 per year for the period 1985-1991 to 25 km2 per year for the period 1991-2002. This implies that there have been environmental consequences of the rapid spatial expansion of the metropolitan area into rural areas.

7The other common feature of many of the hazards studies in Ghana is a description of the pattern of social and demographic characteristics across various subgroups of the population (GSS 1995, 2000, 2005). These studies highlight the main socio-demographic characteristics based on cross tabulations computed from Ghana Living Standards Survey data and Ghana population and housing census data (Appiah et al. 2000, and GSS 2000). Other studies employ regression analysis to help determine the relative importance of social and demographic characteristics (Asenso-Okyere et al. 1997). By focusing on either a national or regional level analysis, these studies have provided insight on variations in changes of inequality over time and across different ecological zones.

8An inference that can be drawn from the literature on hazards in Ghana is that the country is experiencing rising human vulnerability- the differences in a household’s or community’s ability to foresee, adjust to, and recover from the impact of natural hazards. Even long time residents who have long experience with flooding continue to suffer heavy personal and property losses.

Objectives

9Most Ghanaians now recognize that hazards are more than a force of nature and have turned their attention to interventions in hopes of reducing loss. These interventions have included preventing floods, preventing the consequences of flood, or mitigating the consequences of flood. However, coping (resistance and resilience) depends on availability of resources. Thus, ability to absorb losses from floods and continue functioning and ability to recover from floods depends on the set of political, social, economic, and physical resources available to a household. The question that emerges from these observations is how urbanization has created inequality in access to these resources and how the process of urbanization has exacerbated human vulnerability to environmental hazards? Again, how effective is social capital in flood hazard mitigation and how has the erosion of social capital due to urbanization affected householder and community ability to mitigate hazards in the Accra Metropolitan area?

10The main objective of this paper is to analyze the different ways in which floods impact households in Accra, and the differences in flood experiences between two selected communities in Accra. I investigated the characteristics of households and communities that have frequently recorded higher losses from floods than national averages in order to better understand and document differential human vulnerability to flooding.

Methodology

11The data was collected from documents, through interviews, and direct observation. The study began with an informal conversation with relevant institutions and through this conversation I selected Alajo (Figure 1) and the homeless communities for thorough study. Alajo has been identified as a “hot spot” and highly vulnerable to flooding by the various institutions due to their history of flooding. The town is located in a flood prone area at the confluence of the Odaw River and one of its tributaries about 8 km from the Korle Lagoon where the river empties into the Gulf of Guinea. Its 80,000 occupy just 1 km2, making it one of the most densely settled places in Africa. The 2000 national population and housing census indicates that Alajo has just 1,813 houses. It is important to note that in a community such as Alajo, a single structure or house could contain more than one household unit. For example, a compound house could contain between four to ten rooms rented out to different households. With an average household size of 4.8 persons, there is a possibility that a single compound house could contain more than 40 persons. Although new houses are built each year, the pace of construction has been unable to keep up with population growth.

Figure 1: Map of Accra

Figure 1: Map of Accra

Source: Center for Remote Sensing and Geographic Information Systems, University of Ghana

12The units of analysis were the household and community. I adopted the definition of a household used by the Ghana Statistical Services (2005). Specifically, I define a household as a person or group of persons who live together, share the same living arrangements, and consider themselves a single unit. In this definition, household residents need not be related by blood or marriage, but simply cohabitate. It follows that the term family is not synonymous with household in this context, even though family members who live in different households often involve one another in decisions concerning production and consumption. I follow Morris-Oswald (2007) in defining a community as a group of households that interact frequently and have common interests, needs, and shared sense of identity. Communities are, thus, not spatially defined neighborhoods but social collectivities that share something in common and may or may not be place-based. Thus, communities in this study are not necessarily fixed in space nor are they based on family ties.

13Interviews were conducted with 180 sampled heads of households. The second sample comes from a mobile community that consists of the AMA homeless. According to the 2000 population and housing census, there are 709 homeless people in the AMA. The census defines a house as a structurally separate and independent place of abode such that a person or group of persons can isolate themselves from the weather. This definition covers any type of shelter used as living quarters, such as huts, kiosks, containers, tents, and compound houses. A household’s usual place of residence is where the household members spend most of the day or time. A homeless person, in this study, is a person whose usual place of residence is not a house. Interviews were also conducted with 70 homeless persons, representing approximately ten percent of the sampling frame.

14I used the first part of the interview guide to obtain socio-demographic data about respondents. I also used the second part to gather information about household mitigation activities. I asked the respondents to provide information about their physical infrastructure, and how they prepared for previous floods. My intention was to define their living conditions and relate them to the impacts of flooding. I used the third part of the questionnaire to obtain information about the impact of floods on households. I designed the questions to collect information on damages to persons, houses, and personal belongings in 1995, 2001, and 2007. Other questions also provided information on recovery and sources of help for mitigating and recovering from impacts of flooding.

15Based on the collected responses, people who were identified through the survey as very well informed about the history and current events in their community were later selected for an in-depth interview. Ten elderly people were interviewed in-depth to record their perspective on changes in their community. Data collected for the study was qualitative. The analysis involved some attempt to summarize and order the data by identifying themes and concepts. It also consisted of the search for patterns in data and for ideas that help explain the existence of those patterns.

Results and Discussions

Incidence of Floods

16Available data indicates that the severity of floods in Accra is growing. Floods have become increasingly more severe, especially during the rainy seasons. Over a decade after the 1995 major flood disaster, losses associated with subsequent flood events continue to increase. For example, on July 4, 1995, The Daily Graphic newspaper reported the death of over 17 people as a result of flooding. Over 1000 families were reportedly displaced in the process. Properties and infrastructures worth thousands of dollars were also reportedly destroyed, and economic activities disrupted. A similar report on June 28, 2001 said floods had led to the death of twenty people, displaced thousands of households, and destroyed millions of dollars worth of property. Between 1995 and 2007, more than ten incidents of flooding were recorded in Accra that resulted in human casualty, displacement of households, infrastructure damage, and disruption of economic activities.

17One of the probable climatic factors that are implicated in the incidence of floods in Accra has been erratic rainfall. For instance, the rainfall of 4th July 1995 was the highest recorded in one event since 1936. The Ghana Meteorological Services Agency (GMSA) recorded 258 mm of rain with a peak intensity of 64mm in 12minutes and lasted for approximately 5hrs. From the GMSA the last major rainfall recorded in Accra before this event was 192.8 mm which occurred in 1959. The response of the catchments to rainfall events in these areas has become quicker and this increases peak flows. However, increased flood impact in Accra cannot be attributed only to climatic factors. Socio-economic factors are also implicated in the increased impact of floods. These include rapid urbanization, absence and/or inadequate capacities of storm drains, lack of proper maintenance of storm drain, and poor enforcement of city planning and building regulations.

18The increased incidence of floods in Accra has resulted from the low lying nature of the land, rainfall intensity and duration, deposition of sediments in storm drains, dumping of refuse into stream and storm drains, construction of undersized drains and culverts, and building without permit in flood plains. Undoubtedly, there are high levels of vulnerability to floods in Accra but who is highly vulnerable. In other words, what socio-demographic group is likely to be impacted by the incidence of floods? One of the objectives of the study was to assess differential social vulnerability.

Socio-demographic Characteristics and Vulnerability to Floods

19Social vulnerability studies provide insight on the importance of deeper understanding of how demographic characteristics combine to influence human vulnerability to hazards. Inequalities in access to social, economic and political resources have impacts on households’ and communities’ ability to cope with hazards.

20Gender differences could determine disparities in the impact of floods and data collected from this study supports previous studies that households headed by women suffered disproportionately during flooding. The data from Table 1 suggests that 46% of the households frequently affected by flooding in both communities are headed by men while 54% are headed by women.

Table 1: Gender of Respondents

Gender

Frequency

Percentage

Male

115

46

Female

135

54

Total

250

100

Source: Author’s fieldwork.

21There are several reasons why women-headed households were more vulnerable to the risk of flooding. Details that emerged from in-depth interview revealed that most women in the study area are not able to compete favorably with men for available resources. This is because cultural norms inhibit their ability to obtain and keep a job in the formal sector of the economy. Another explanation was that most Ghanaian women are in retail trading in physical areas that are the most vulnerable to flooding and, thus, their income is usually the most affected. According to Maxwell (1997: 17) Ghanaian women are more interested in retailing: “Urban retail marketing and petty trading are sectors that have long been dominated by women in West Africa, so it is not surprising to note that these are the most common forms of female livelihood in Accra”. During the colonial period and to some extent during the early part of the independence period, women had limited access to formal sector jobs due to their limited educational background, and socio-cultural norms that discriminated against them (Obosu-Mensah 1998).

22Thus, gender discrimination plays an important role in gender disparities in mitigating and responding to flooding in Accra. This finding is in contrast to explanations given by some social scientists for a disproportional rate of female vulnerability to hazards (Drabek and Key 1984, Bolin 1986, Morrow 1999). These researchers often attribute higher female vulnerability to the role of women in caring for their family, and the fact that women would ensure that everyone is safe and together before evacuating during a disaster, which likely translates into higher mortality statistics for both women and children (Jones 2004). Such claims and more structural findings of this study only serve to reinforce the need to consider vulnerability cross-culturally and in comparative perspective.

23I observe from this study that the age of the head of household has implications for its vulnerability to flooding in the Accra Metropolitan Area. The information collected from respondents show that age of the head of household has an effect on the household’s ability to prepare for and recover from flooding. Figures in Table 2 show that 14% of interviewed heads of households 65 years or older have previously suffered severe damage to their homes and businesses. This observation is even more relevant considering that the elderly forms less than 5% of the population (Ghana Statistical Service 2002), and most elderly households have difficulty keeping a house up and providing the necessary repairs that would make it less vulnerable to disaster.

Table 2: Age Distribution of Respondents

Age Group

Frequency

Percentage

15-24

25

10

25-34

55

22

35-44

50

20

45-54

45

18

55-64

40

16

65 +

35

14

Total

250

100

Source: Author’s fieldwork.

24There are several reasons why the elderly are more vulnerable. How people prepare and respond to the prospect of disaster depends on physical mobility and health. The elderly are more likely than younger people to have difficulty in moving quickly because they are more likely to be physically challenged. Thus, the capacities to walk, communicate, or evacuate decreases as age advances. In sudden-onset disasters such as floods, the elderly may be physically unable to quickly evacuate a building and help other household members especially children. Conversely, it is plausible that the elderly are more attached to their homes and neighborhoods and less able than younger people to transport their valued possessions to safe places. They may feel more threatened with the loss of their home. It was observed from this study that, although none of the people aged 55 years or older had flood insurance, they were the ones that reported severe personal injuries. Also, the majority of the elderly interviewed indicated a lack of funds to obtain better housing because they were not working and do not receive any social security or welfare benefits. Table 2 also shows that most of the age group of people frequently affected by flooding falls between 25 and 54. This finding is not surprising since the youthful age group is the most dominant in the population composition but underscores the fact that although the elderly (65 plus) constitutes the minority age composition they are the group most vulnerable to flooding.

25While Alajo is a vibrant and socially resilient community several features inhibit the quality of life there. The neighborhood has recently been overcome by rapid urbanization, with its infrastructure failing to support the influx of residents from various ethnic groups. I selected Alajo as a study site not only because it was among the most vulnerable to flooding in the metropolis, but also because it contains a great diversity of peoples who have differential access to resources to mitigate flood hazards. Alajo is comprised of populations that have lived there for many generations but also of recent migrants. The combined circumstances of the physical hazards and the societal conditions at Alajo make it an ideal place to study human vulnerability and social response to flooding.

26One social demographic characteristic that has an influence on social vulnerability is migration history. People who have lived in a particular area a long time learn to cope and those who can’t cope tend to leave. I asked the 250 people who have been previously affected by flooding “how many years have you lived in Accra”. The result is presented in Table 3. The table shows that 60% of interviewees experienced a severe flood when they were recent migrants (i.e., less than five years) in the city. Very few people (22%) that had lived in Accra for over 10 years or were born in Accra experienced severe flooding.

Table 3 Length of Time in Accra

Number of years in Accra

Frequency

Percent

Less than 1 year

90

36

1-5 years

60

24

6-10 years

45

18

Over 10 years

35

14

Born in Accra

20

8

Total

250

100

Source: Author’s fieldwork.

27More often, differential availability of economic and social resources to recent migrants and long-time residents define their vulnerability to flooding and the links between risks of flooding. The situation confirms the assertion that people who have lived in an area for many generations develop a mechanism to cope with the risk at that particular place. Various researchers have come to a similar conclusion that certain categories of people, such as recent migrants, are at a greater risk throughout the disaster process (Morrow 1999). What constitutes a bigger problem in the case of Accra is the failure of institutions to assess attributes that characterize households that seem to cope differently, and for governmental mitigation strategies to address these apparent differential vulnerabilities

Impact of Flooding on Household

28A portion of the survey questionnaire was used to collect data on impacts of flooding on households. Random selections of households were surveyed using a household interview guide. Households were sampled following a selection pattern of one in every fiftieth house. For the homeless community, I used a purposeful selection to identify respondents for interview. In selecting the specific homeless community, I carefully examined the 2000 census data to identify typical areas that contained relatively high population of the homeless.

29A summary is presented in Tables 4 and 5. Of the 180 surveyed in Alajo, 56% suffered severe damage to their house during 1995 flood disaster, 61% during the 2001 flood, and 72% during the 2007 flood. With reference to the homeless, a different pattern is revealed. Of the seventy homeless that were sampled, 21% had their houses severely damaged in the 1995 flood, 29% had their houses severely damaged in the 2001 flood and none had their houses damaged in the 2007 flood. This suggests that the homeless are not pursuing mitigation activities. This also implies that not only are some of the homeless recent migrants, long time residents are also systematically rendered homeless by flooding. Data from Table 4 and 5 also shows that of the 180 households sampled in Alajo, 58% had their businesses damaged in the 1995 flood, 72% in 2001, and 77% in 2007. With the homeless, 42% had their businesses damaged in the 1995 flood and none in 2001 and 2007. Eighty percent of households in Alajo lost their belongings in 1995, 86% in 2001, and 77% in 2007.

Table 4: Impact Data for Respondents from Alajo, in %

Variable

Disaster Year

1995

2001

2007

Damage to house

56

61

72

Damage to business

58

72

77

Loss of belongings

80

86

88

Injury of household member

25

33

22

Loss of health

19

22

13

Loss of employment (at least 1 week)

83

86

89

Loss of income (at least 1 week)

83

86

89

Access to utilities affected

56

61

72

Never been affected by flooding

0

0

0

Took less than a month to recover

19

17

8

Took 1-6 months to recover

28

25

17

Took 6-12 months to recover

39

36

-

More than 12 months to recover

14

11

-

Have not recovered

0

7

75

Source: Author’s fieldwork.

30The figures for the homeless are a little lower in 1995 and 2007 but higher than Alajo for 2001. Sixty percent lost their belongings in 1995, 93% in 2001, and 86% in 2007.

Table 5: Impact Data for the Homeless, in %

Variable

Disaster Year

1995

2001

2007

Damage to house

21

29

0

Damage to business

42

0

0

Loss of belongings

64

93

86

Injury of household member

71

86

79

Loss of health

92

92

71

Loss of employment

0

7

14

Loss of income

86

100

79

Access to utilities affected

21

29

0

Never been affected by flooding

0

0

0

Took less than a month to recover

0

0

0

Took 1-6 months to recover

0

0

0

Took 6-12 months to recover

0

0

-

More than 12 months to recover

0

0

-

Have not recovered

21

29

0

Source: Author’s fieldwork.

31A causal factor for the variations in impact could be differing use of structural and non-structural mitigation activities by the fixed and mobile communities. This suggests that some groups in the Accra are disproportionately burdened by the impact of the perennial flooding. In all of the three flood disasters, some households sustained minor losses, while other households lost their livelihoods all together. Although the homeless households had fewer personal belongings, they lost the most from flooding. In-depth interviews revealed that spatial isolation and lack of participation in decision-making has exacerbated the vulnerability of the homeless to flooding.

32The health of households was also adversely affected by the three flood events (Tables 4 and 5). Numerous health problems emerge during flooding and when a population is exposed to flooding for prolonged periods compounded with inefficient waste management systems, the lack of clean or potable water, and electric power. The data collected shows that 19% of households in Alajo in 1995, 22% in 2001, and 13% in 2007 reported health problems resulting from water borne diseases as a result of flooding. Twenty-five percent in 1995, 33% in 2001, and 22% in 2007 also reported injury to a household member in Alajo. These figures contrast sharply with the homeless where 92% reported health problems in 1995, 92% in 2001, and 71% in 2007. Also casualty figures for the mobile community contrast with the fixed community. Seventy-one percent of the homeless reported injury as a result of flooding in 1995, 86% reported in 2001, and 79% reported in 2007. The difference in health impact suggests that the homeless retain fewer resources to retain good health after flood events.

33Employment and economic activity is crucial to overcome flood impact. Figures in Tables 4 and 5 shows that economic activities in the area were severely affected by flood disasters. Households in Alajo reported employment losses in 1995 (83%), 2001 (86%), and 2007 (89%). The same percentage reported income loss (Table 4). Information for the homeless indicates no one reported employment loss in 1995, 7% reported loss in 2001 and 14% reported loss in 2007. A greater number of the homeless reported income loss as opposed to loss of employment compared to Alajo. Eighty-six percent reported income loss in 1995, 100% reported in 2001, and 79% reported loss of income in 2007.

34Information in Tables 4 and 5 also show that while some households took a short time to recover, some took longer and others never recovered from the previous impact of flooding. The two communities had different recovery patterns after flooding. The fixed community appears to have recuperated faster after the 1995 flooding but the recovery rate seems to be deteriorating. In both communities, the recovery rate appears to be slower when you consider that 75% of the population at Alajo has not recovered from the 2007 flood and none of the homeless have recovered. Some of the few households that were able to rebuild quickly explained that it was due to insurance coverage, financial reserves, and social networks. These were also households that had effective mitigation activities in place prior to the floods. The higher recovery rate in Alajo, the fixed community, is also likely related to higher levels of assistance from the government and other non-governmental organizations. This is because governmental notion of a community as connected to both a place and a common landscape, such as a neighborhood, precludes the homeless from receiving disaster assistance. For the definition to cover the homeless, a community would have to be seen in both geographical and social terms. Although, the homeless had little properties, they took longer to recover from flooding due to heavy loss of personal belongings, health problems and lack of access to recovery aid.

Conclusions

35This paper explored demographic characteristics, mitigation activities, and impact of flooding on households that have suffered floods. First, the data from the study suggests that majority of the households frequently affected by flooding are headed by women. This data implies that households headed by women were more vulnerable than the average household. This supports findings by other researchers.

36Second, the information collected from respondents show that age of the head of household has an effect on the household’s ability to prepare for and recover from flooding. This observation supports findings by other researchers. Also, a majority of the elderly in Accra had no or very low income simply because they receive no social security or retirement benefits. As a result they lacked the needed funds to obtain better housing or improve their living conditions.

37Furthermore, the information from Alajo shows that although long-term residents are more resilient than recent migrants, there is evidence that over time there is a consistent lack of ability to reduce the impact of each successive flood. Several factors could explain such a pattern. The resident’s location of high susceptibility to flooding or the intensity of flooding in these areas may cancel the effectiveness of all responses. For instance, each annual flood may be causing more damage to a household’s means of raising additional income, such as backyard food production. Also, previous experience or awareness may not be good predictors of behavioral changes or changes in responses if other conditions are not present such as the availability of economic resources to be able to decrease vulnerability.

38Again, the impact of the flood disasters of 1995, 2001, and 2007 on Alajo households was overall less than on homeless people. A contributing factor to a better adjustment to these disasters could be their relative access to resources and demographic and social characteristics less prone to vulnerability. Another factor could be the differences in the socio-spatial organization of both communities: residents of Alajo obtain more assistance from the government and NGOs than do homeless. Also, the prevalence of urban poverty and rising urban unemployment has intensified the problem of human vulnerability to flooding in the Accra. This problem is complemented by a higher cost of living in the metropolitan area, especially the cost of food and housing. The majority of the households in flood prone areas in Accra live on less than 1 Cedi a day. This figure supports findings from some social science researchers that those who are financially poor are generally displaced to the margins of society, spatial margins that tend to be more hazardous (Susman et al. 1983, Wisner 1998).

39The mobile community contrast with Alajo in terms of socio-economic characteristics, degree of social cohesion, residence, and physical location, and respond differently to flooding than the fixed community. The unchanging pattern of vulnerability and impact is of concern as it shows the inability of a society to cope and adjust to familiar hazards.

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List of illustrations

Title Figure 1: Map of Accra
Credits Source: Center for Remote Sensing and Geographic Information Systems, University of Ghana
URL http://journals.openedition.org/articulo/docannexe/image/2110/img-1.png
File image/png, 1.2M
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References

Electronic reference

Dacosta Aboagye, Living with Familiar Hazards: Flood Experiences and Human Vulnerability in Accra, GhanaArticulo - Journal of Urban Research [Online], Briefings, Online since 30 October 2012, connection on 15 June 2025. URL: http://journals.openedition.org/articulo/2110; DOI: https://doi.org/10.4000/articulo.2110

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About the author

Dacosta Aboagye

Dr. Dacosta Aboagye is a lecturer at the Department of Geography and Rural Development, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. He obtained his PhD in Geography from the University of Oklahoma (USA). His current research interests include disaster management, climate change mitigation and adaptation, demographic economics, and development issues in emerging economies. Email: aboagyed@gmail.com

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Copyright

CC-BY-NC-ND-4.0

The text only may be used under licence CC BY-NC-ND 4.0. All other elements (illustrations, imported files) are “All rights reserved”, unless otherwise stated.

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