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Front cover photo: Vendors trying to save their products while a 60cm flood inundates their kiosks in South
Jakarta on January 2013 as a result of heavy rains causing the Pesanggrahan River to overflow, by Afriadi
Hikmal under creative commons license at https://www.flickr.com/photos/25684785@N02/8382845341
Estimation of River Flood Damage in Jakarta:
The Case of Pesanggrahan River
Pini Wijayanti
Tono
Hastuti
Danang Pramudita
February, 2015
Comments should be sent to:
Pini Wijayanti, Department of Environmental Economics and Resources,
Faculty of Economics and Management, Bogor Agricultural University, Bogor, Indonesia
Email: [email protected]; [email protected]
The Economy and Environment Program for Southeast Asia (EEPSEA) was established in May 1993
to support training and research in environmental and resource economics. Its goal is to strengthen local
capacity in the economic analysis of environmental issues so that researchers can provide sound advice to
policymakers.
To do this, EEPSEA builds environmental economics (EE) research capacity, encourages regional
collaboration, and promotes EE relevance in its member countries (i.e., Cambodia, China, Indonesia, Lao PDR,
Malaysia, Myanmar, Papua New Guinea, the Philippines, Thailand, and Vietnam). It provides: a) research
grants; b) increased access to useful knowledge and information through regionally-known resource
persons and up-to-date literature; c) opportunities to attend relevant learning and knowledge events; and d)
opportunities for publication.
EEPSEA was founded by the International Development Research Centre (IDRC) with co-funding
from the Swedish International Development Cooperation Agency (Sida) and the Canadian International
Development Agency (CIDA). In November 2012, EEPSEA moved to WorldFish, a member of the
Consultative Group on International Agricultural Research (CGIAR) Consortium.
EEPSEA’s structure consists of a Sponsors Group comprising its donors (now consisting of IDRC and
Sida) and host organization (WorldFish), an Advisory Committee, and its secretariat.
EEPSEA publications are available online at http://www.eepsea.org.
ACKNOWLEDGMENTS
The authors would like to thank the Economy and Environment Program for Southeast Asia
(EEPSEA) and WorldFish for providing funding support to this project. We would especially like to thank
EEPSEA Director Dr. Herminia Francisco for her guidance, comments and suggestions in improving the
quality of the report; Dr. David James for his invaluable comments and suggestions; and numerous others
for their comments and suggestions throughout the 2013 EEPSEA Annual Conference. We are also grateful
for the administrative support provided by Ms. Julienne Bariuan-Elca and Ms. Rhona Coronado.
We likewise appreciate the support of the DKI Jakarta province, Public Work Ministry, and the
Department of Environmental Economics and Resources (ESL) at Bogor Agricultural University (IPB) in
conducting this project. We also would like to thank the four heads of the districts of Pesanggrahan,
Kebayoran Lama, Kebon Jeruk and Cengkareng, who allowed us to conduct this project in their areas. Finally,
our gratitude goes to the: survey enumerators for assisting us in conducting the household and business
surveys; and the household and business respondents and the selected community leaders for their time
and cooperation during the survey, the key informant interviews, and the focus group discussions.
TABLE OF CONTENTS
EXECUTIVE SUMMARY
1
1.0 RESEARCH PROBLEMS
1
1.1 Description of the Problem
1
1.2 Gender Issues
2
1.3 Policy Context
3
1.4 Review of Literature
4
1.5 Potential Policy Contribution
5
2.0 RESEARCH OBJECTIVES
6
3.0 RESEARCH METHOD
6
3.1 Study Areas
6
3.2 Analytical Methods
7
3.3 Data Collection Methods
10
3.4 Survey Instruments
10
4.0 RESULTS
11
4.1 Sample Characteristics
11
4.2 Estimation of Actual Flood Damage in the Residential Sector
11
4.3 Estimation of Actual Flood Damage in the Business Sector
14
4.4 Relationship between Flood Exposure Indicators, Vulnerability Indicators,
and the Flood Damage Model
16
4.5 Household and Business Responses to Flooding Based on Perceptions
21
4.6 Assessment of the Benefits of Women’s Roles in Relation to Flooding
24
5.0 CONCLUSIONS
27
6.0 POLICY RECCOMENDATIONS
28
REFERENCES
29
LIST OF TABLES
Table 1.
Description of study sites
7
Table 2.
Detailed scale of beliefs and perceptions
9
Table 3.
Average flood damage per house during the 17-19 January 2013 flood event
12
Table 4.
Actual flood damage function estimates for the residential sector
12
Table 5.
Coefficient estimates for actual flood damage in the residential sector across three
income groups
13
Table 6.
Actual flood damage in the residential sector by income level
13
Table 7.
Average flood damage per unit in the business sector during the 17-19 January 2013
flood event
14
Table 8.
Actual flood damage function estimates for the business sector
14
Table 9.
Coefficient estimates of actual flood damage, business sector, two turnover groups
15
Table 10.
Actual flood damage in the business sector by turnover
15
Table 11.
Estimation of flood damage reduction from the river normalization project
16
Table 12.
Correlation between the intention to mitigate and two components of risk perception
in the residential sector
22
Table 13.
Correlation of the intention to mitigate with two components of risk perception in the
business sector
23
Table 14.
Distribution of activities within families during the three phases of flooding
24
Table 15.
Correlation between women’s age, duration of involvement, family time contributions,
and society time contributions in relation to flooding
26
LIST OF FIGURES
Figure 1.
Inundated areas, 2007 floods
3
Figure 2.
Inundated areas, 2013 floods
3
Figure 3.
Indicators to be employed in flood vulnerability analysis
5
Figure 4.
The study areas
7
Figure 5.
SDC in the residential sector based on flood duration and distance from river
17
Figure 6.
SDC in the residential sector based on household income and house area
17
Figure 7.
SDC in the residential sector based on flood frequency
18
Figure 8.
Flood vulnerability curve in the residential sector
18
Figure 9.
SDC in the business sector based on flood duration and distance from river
19
Figure 10. SDC in the business sector based on total area and daily turnover
20
Figure 11. SDC in the business sector based on flood frequency
20
Figure 12. Flood vulnerability curve in the business sector
21
Figure 13. Intentions to undertake future flood mitigation measures in the residential sector
21
Figure 14. Demand for public flood reduction in the residential sector
22
Figure 15. Intention to undertake future flood mitigation measures in the business sector
23
Figure 16. Demand for public flood reduction measures in the business sector
24
Figure 17. Discussion with women in Ulujami, South Jakarta, and in Kedoya Selatan, West Jakarta
24
Figure 18.
Average time contribution of women in relation to flood mitigation, coping and
recovery activities (n = 347)
25
Figure 19.
Average time contribution of women in relation to flood mitigation, coping and
recovery activities (n = 47)
26
ESTIMATION OF RIVER FLOOD DAMAGE IN JAKARTA:
THE CASE OF PESANGGRAHAN RIVER
Pini Wijayanti, Tono, Hastuti, and Danang Pramudita
EXECUTIVE SUMMARY
Flooding is a major problem in Jakarta. It mainly occurs between December and February as a result
of both natural and human factors. Hence, the ability to assess flood damage is an important factor for the
region to consider when developing flood policy. It is particularly useful to do an economic analysis of flood
alleviation programs since such analyses can indicate the benefits and the financial costs of each program.
This study aims to estimate the damage caused by river flooding, using the Pesanggrahan River in
Jakarta as a case study. The study estimates the actual damage that occurred during the January 2013
flooding, focusing on direct damage to the residential and business sectors in six villages along the river. The
damage was calculated by employing stage damage function. It was formulated by considering actual flood
damage, flood characteristics, and socio-economic characteristics. Stage damage curves were constructed to
see how they varied under several different flood and socio-economic characteristics. Statistical descriptive
and correlation analysis was used to assess society’s ability to anticipate and respond to flooding. An
assessment of the benefits of women’s roles in reacting to flooding was conducted using descriptive
quantitative analysis.
The findings show that during the three-day flood from 17-19 January 2013, damage in the
residential sector amounted to USD 308 per household and USD 0.5 million in total. In the business sector,
damage was USD 837 per business unit and USD 0.7 million in total. Generally, buildings that were
inundated for more than three days and occupy a large area were more likely to experience severe
damage. The residential and business sector buildings located in the study area are aware that they are in a
high flood risk area. However, instead of setting up private mitigation measures, they depend more on
public systems. By making a monetary assessment of women’s roles in relation to the three phases of
flooding (i.e., before, during, and after), the individual benefit provided by each woman was estimated to be
about USD 26 for families and USD 27 for society. However, only 30% of women were actually able to
contribute to both flood adaptation at the family and society levels, and most of these women are elderly.
1.0 RESEARCH PROBLEMS
1.1
Description of the Problem
Jakarta, the capital city of Indonesia, is regarded as one of the most vulnerable cities in the world
with regard to climate change-related disasters. The city is susceptible to flooding, rising sea levels and other
natural calamities (Firman et al. 2011). In reality the city has been prone to flooding for a hundred years. In
the past three decades, three big floods have ravished Jakarta (i.e., in1996, 2002 and 2007), the last of which
was the most devastating. The 2007 flood was regarded as a national disaster, causing a total loss of USD 516
million 1, with the residential sector suffering 73.56% of the total losses (BAPPENAS 2007).
The most recent occurrence of excessive rain in Jakarta and the surrounding cities caused major
flooding between 17 and 19 January 2013. It inundated 50 villages, submerged 98,444 houses, affected
40,416 people, and killed 20 people. The flood caused a total damage of USD 755 million (BPBD 2013). It is
clear that flooding is a major threat to the city.
Flooding occurs every year in Jakarta, particularly between November and February when
precipitation is at its most prevalent. When intense rain occurs, some places, particularly roads and houses
1
USD 1 = IDR 9,735.5
1
Economy and Environment Program for Southeast Asia
along rivers, are immediately flooded due to ineffective drainage systems. The rain that occurs locally in
Jakarta is not the sole cause of flooding. In fact, it is the rain that occurs in the areas surrounding Jakarta that
often contributes to overflowing streams, resulting in the overflow of rivers. Flooding in Jakarta occurs when
water cannot easily flow to the Java Sea, owing to river clogging and narrowing, so overflowing river water
inundates residential and business areas.
The ability to assess flood damage is vital for a region to develop flood policy. In particular,
stakeholders should conduct flood damage assessments as part of the economic analysis of flood alleviation
programs. This kind of analysis reveals the benefits and the costs of each flood alleviation program. Benefits
include flood damage reduction, while costs include the initial investment cost, operation costs, and
maintenance costs required during the lifetime of the program. Since most flood alleviation programs span
many years, a program will be selected if its stream benefit exceeds its stream cost. Therefore, the
assessment of flood damage is important if stakeholders are to compare the costs and benefits of flood
alleviation measures (Smith 1994).
A number of theoretical and empirical studies have examined the issue of Jakarta’s flooding from
multiple perspectives (Steinberg 2007; Brinkman and Hartman 2008; Texier 2008; Hurford et al. 2010;
Akmalah and Grigg 2011; Ward et al. 2011). Most have studied the causes and the impacts of flooding. Their
results show that a combination of topography, natural factors and human factors cause flooding.
Jakarta is a delta city and 40% of its area is lowland. Thirteen rivers pass through it (BPS 2013) and
natural meteorological conditions include high levels of rainfall, high tides, and land subsidence (Brinkman
and Hartman 2008). Human factors create many problems, from upstream to downstream areas. In upstream
areas, land conversion reduces the land’s capacity to retain water. In middle stream and downstream areas,
rivers have narrowed due to sedimentation, the accumulation of garbage, and people illegally settling along
the riverbank – all of these factors exacerbate the effects of flooding. Human factors are considered the main
causes of flooding (Steinberg 2007; Texier 2008).
After the 2007 flood, estimating flood damage in Jakarta assumed a new importance. The
government conducted flood damage assessments after the 2007 and 2013 floods by using the economic
commission for Latin America and the Caribbean damage and loss assessment methodology. As well as the
assessment of post-flood events, several studies estimated the extent of damage that will be caused by
future coastal and river flooding in Jakarta (e.g., PU 2011; Ward et al. 2011). However, these previous studies
covered the whole Jakarta area and did not provide detailed information on specific flood areas. Such
information is important for stakeholders when presenting the local flood situation and in evaluating the
cost-effectiveness of the flood measure undertaken.
1.2
Gender Issues
Disaster reduction is an issue that affects the lives of both women and men. Fothergill (2004) found
that disastrous events, such as flooding, influenced women’s roles in their communities, their homes, and at
their places of work.
Many studies show that women are more vulnerable than men in the event of flooding. Firstly,
women’s lives are in danger because they are more likely to be caught in the path of a flood, and in many
cases they are directly injured during flooding (Norris et al. 2005). Secondly, women are more likely to suffer
symptoms of post-traumatic stress disorder in the long term (Bokszczanin 2007) so a focus on the mental
health of female flood victims should be prioritized.
In developing countries men and women play different roles when coping with flood hazards (Tu
and Nitivattananon 2011). The role of women in the event of flooding cannot be underestimated – they play
a significant part before, during, and after flooding. During a flood event women contribute not only to the
family but also to the community (Fothergill 2004). Women are vital in ensuring that the family is prepared,
can cope, and recovers. In addition, women’s physical and socio-emotional efforts on behalf of the family
significantly contribute to the process of mitigation and reconstruction (Enarson 2001). Women provide
resilience, which is much needed when dealing with flooding.
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
2
Research detailing women’s roles with regard to Jakarta’s floods is still limited as there is a lack of
qualitative and quantitative analysis. Such studies are important in shaping urban flood risk management
policy. Information on this subject would be useful so that the government can support women’s
contribution to family flood resilience.
1.3
Policy Context
Local and national governments work hard to reduce flood-prone areas by conducting flood
mitigation and adaptation programs, which consist of structural and non-structural programs from
upstream and downstream areas. Structural programs include rehabilitating lakes, replanting forests,
developing new flood canals, widening (the normalization of) rivers, building polder-systems, anticipating
high tides, and reducing land subsidence rates. Non-structural programs involve many activities such as
early warning systems, emergency response, and social awareness.
Flood mitigation and adaptation programs began intensively in 2007 after the January 2007 floods
hit Jakarta. All of the programs aim to reduce the number of flooded areas. During the 2007 floods, Jakarta
had 78 flooded areas (see green-shaded areas in Figure 1). In 2013 there were still 32 flooded areas (see
Figure 2). The areas of inundation were mostly located in West Jakarta, North Jakarta, and Central Jakarta. In
the latter, severe floods occurred because the revetments on the west flood canal, Jakarta’s oldest canal,
collapsed.
Figure 1. Inundated areas, 2007 floods
Figure 2. Inundated areas, 2013 floods
Of the six rivers (i.e., Krukut, Angke, Cipinang, Sunter, Pesanggrahan, and Ciliwung) in whose vicinity
flooding mainly occurs nowadays, the largest flooded area is located along the Pesanggrahan River, which
flows through the southern and western parts of Jakarta. Deltares (2012) found that flooding is caused by
the limited conveyance capacities of the main branches of the river due to a lack of maintenance. To reduce
flood damage from the Pesanggrahan, from 2013 until 2015, the government is conducting a flood
mitigation measure, called a “normalization program”, along a 26.7-kilometer stretch of the river. The aim is
to widen the river, thereby increasing water discharge. The river will be widened from 10-15 m to 30-50 m
and it is predicted that water flow will increase from 30 m3 per second to 220.3 m3 per second. Therefore, the
conveyance capacity of the Cengkareng floodway will increase, which means water should be able to flow
more easily to the sea (Deltares 2012), reducing the number of inundation areas along the river. The central
government is providing USD 60 million to meet the construction costs of the normalization program and
the local government is providing USD 40 million for land acquisition.
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Economy and Environment Program for Southeast Asia
1.4
Review of Literature
1.4.1
Flood damage
The effects of flood damage are classified into direct and indirect damage (Dutta et al. 2003; Merz et
al. 2010). Direct damage occurs as a result of floodwater coming into contact with people, property or any
other objects. Indirect damage is caused by the direct impacts of the flood event such as disruption to public
services and traffic jams (Merz et al. 2010). Based on the value of the damage, flood damage covers tangible
and intangible damages (Dutta et al. 2003; Merz et al. 2010). Tangible damage is defined as the damage that
can be counted in monetary terms. Intangible damage covers damage which is either not traded in a market
or is difficult to measure in monetary values.
Flood damage can be estimated based on actual and potential conditions. The actual damage is the
damage resulting from a specific flood (Smith 1994), while potential damage represents the maximum
possible amount of damage which may occur if the area becomes inundated without any flood-defense
measures (Messner and Meyer 2004). The difference between these types is called the “avoidable damage”
(Smith 1981).
Most studies estimate flood damage by developing flood damage functions that relate damage for
the particular element at risk to characteristics of the flood such as depth, duration, velocity and
contamination (Merz et al. 2010). “Element at risk” represents the amount of social, economic or ecological
units affected by flood hazards in a particular area, for example people, households, companies and
infrastructure (Messner and Meyer 2005).
To date, most studies develop flood damage functions by considering direct flood damage and
inundation depth (see Scawthorn et al. 2006; Jonkman et al. 2008; Kazama et al. 2010; Suriya et al. 2012). This
function is known as stage damage function or depth-damage function. Several studies use other
parameters such flood duration (e.g., Tang et al. 1992; Dutta et al. 2003), velocity (Thieken et al. 2005) and
contamination (Kreibich et al. 2010). Two steps are taken to analyze flood damage: determining the flood
characteristics and assessing the damage in monetary terms (Messner and Meyer 2005). Furthermore,
different techniques have been used to estimate flood damage and to relate flood damage to its factors, for
example regression (Tang et al. 1992; Thieken et al. 2005) and principal component analysis (Kreibich et al.
2010).
The flood damage function can be written as a flood damage model. Such a model can be
developed by using empirical and synthetic approaches. The empirical approach uses actual damage data
gathered after particular flooding events, while the synthetic approach uses data collected through “what
if?” questions (Merz et al. 2010). The synthetic approach represents a trade-off between time consumed and
accuracy because it does not rely on information from actual events (Smith 1994). In addition, flood damage
estimations can be conducted from three different approaches (i.e., micro, meso, and macro), which are
related to the spatial accuracy of damage analysis (Messner and Meyer 2005). The micro scale estimates
damage on a personal possession level, the meso scale assesses damage based on land use, and the macro
scale evaluates damage at the municipality level.
Many studies have attempted to estimate flood damage in both rural and urban areas. The latter
has been the main focus for the last three decades (e.g. Smith 1981; Appelbaum 1985; Smith 1994). Urban
flood damage can be very high because most urban areas are densely populated and contain a great deal of
infrastructure. Previous studies have developed stage damage functions for the residential, commercial and
industrial sectors. The results show that potential residential flood damage has increased drastically over the
last 30 years (Penning-Rowsell and Green 2000). This is because people have been investing more money in
property and its contents. As a result, shallow floods cause greater damage than anticipated.
1.4.2
Flood exposure indicators and flood vulnerability indicators
Messner and Meyer (2005) indicate two types of flood exposure indicators. Firstly, the exposure that
relates to different elements at risk, which informs us about elements of flood location (i.e., elevation,
distance to river, and return periods). Secondly, the exposure that focuses on general flood characteristics
(i.e., duration, depth, velocity and sedimentation load).
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
4
The actual flood damage depends on the vulnerability of the affected socio-economic and
ecological systems (Cutter 1996). Vulnerability indicators measure how sensitively an element at risk
performs when it is threatened with some kind of hazard (Messner and Meyer 2005). Flood vulnerability
analysis is represented by flood-damage functions, which relate the damage for the respective element at
risk to exposure indicators and vulnerability indicators. Information on flood damage, exposure indicators
and vulnerability indicators can be used to develop stage damage curves. Such curves present information
on the relationship of flood damage to flood depth (Smith 1994) and can be further analyzed under some
different flood characteristics and socio-economic characteristics. Figure 3 shows how these analyses are
related.
Exposures
indicators
Exposure
Flood characteristics
Elements at
risk
Affected units
Potential damage
Vulnerability
indicators
• Flood vulnerability analysis
• Stage damage curves
Socio-economic units
and systems
Figure 3. Indicators to be employed in flood vulnerability analysis
(Adapted from Messner and Meyer 2005)
1.4.3
Flood risk perception
Flood risk perception refers to people’s perceived risk of flooding in the context of limited and
uncertain information about flooding in their area. Perception motivates people to carry out flood risk
mitigation measures and also drives public demand for flood protection; therefore perceived flood risk
provides useful insights into flood risk management (Bubeck et al. 2012). Adger et al. (2009) stated that
perceived risk affects people’s responses and management of future risk.
Key to successful flood management is the way in which communities and local leaders respond to
flood risk and how these groups cooperate (Fatti and Patel 2013). However, different people may assess the
risk of flooding very differently because they do not have the same information about the probability of
flooding (Messner and Meyer 2005). Siegrist and Gutscher (2008) found that people with no experience of
flooding underestimated its negative effects because they visualize the flood consequences differently from
those who have actually experienced flooding.
Many potential factors influence people to take mitigation measures to prevent flood damage, such
as past experience of flooding (Siegrist and Gutscher 2008; Fatti and Patel 2013), a lack of reliance on public
flood protection, strong emotions, and fear (Grothmann and Reusswig 2006). Flood damage can be reduced
by adopting mitigation and adaptation measures both publicly and privately. However, Grothmann and
Reusswig (2006) found that self-motivation to mitigate the potential effects of flooding can be affected by
barriers such as a lack of resources (i.e., time, money, knowledge or social support).
1.5
Potential Policy Contribution
Most developing countries lack effective and efficient adaptation measures to combat climate
change-related risks, and face challenges in integration with, and adaptation to, policy processes (IPCC
2007). Adaptation processes cover three phases: a) determining and assessing vulnerability and adaptation
capacity; b) identifying adaptation options; and c) assessing adaptation strategies in order to develop
recommendations (Tu and Nitivattananon 2011).
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Economy and Environment Program for Southeast Asia
In the case of the Jakarta floods, this study aids the government by determining and assessing flood
vulnerability. Assessing flood damage from an economic perspective is important because it provides
information about how much damage can be reduced if a flood measure is provided. In addition,
information regarding flood damage and the costs of measures are important to develop a benefit and cost
analysis of flood measures.
Flood damage assessment contributes to the decision-making process. Social planners should focus
on the safety standards of flood measures and also on the valuables protected by flood measures. Only then
can they calculate the efficiency of flood measures. While the economic costs of alternative flood defense
options are considered, the benefits of flood measures in the form of prevented flood damage should be
taken into account (Messner and Meyer 2005). The study results highlight some positive contributions for
the government and social planners. These include:
1.
An estimation of the prevented flood damage as a result of the normalization program along
Pesanggrahan River;
2.
Useful input for flood risk mapping;
3.
A description of river flooding vulnerability in the Pesanggrahan area;
4.
Useful insight for the government into the demand for flood risk reduction;
5.
Data on flood damage and flood characteristics caused by the overflowing of the Pesanggrahan River,
which can be regularly evaluated in the future; and
6.
An estimation of the benefits of women’s roles in relation to flooding.
This report is organized as follows: the Section 2 indicates the research objectives of this study;
Section 3 describes the research method, including the study sites, the analytical method, the data collection
method, and the survey instruments; Section 4 presents the results,; Section 5 provides some concluding
observations; and Section 6 offers policy recommendations.
2.0 RESEARCH OBJECTIVES
This study aims to estimate the extent of river flood damage caused by the Pesanggrahan River in
Jakarta. In particular, it estimates the costs of flood damage by estimating actual flood damage cost
functions in terms of depth and duration. This assessment is essential to quantify the expected benefits of
flood protection measures. Damage calculation focuses on the direct damage caused to the residential and
business sectors. The research objectives are as follows:
1)
To estimate the actual flood damage in the residential sector;
2)
To estimate the actual flood damage in the business sector;
3)
To analyze the relationships between flood exposure indicators, vulnerability indicators and the actual
flood damage model;
4)
To assess the ability of households and businesses to anticipate and respond to flood events based on
their perceptions; and
5)
To assess the benefits of women’s roles in the family and society during the three phases of flooding.
3.0 RESEARCH METHOD
3.1
Study Areas
This study was mainly conducted in residential and business areas along the Pesanggrahan River,
which was selected for two reasons. Firstly, these areas are the largest river flood-prone areas in Jakarta.
Secondly, in 2012 the government adopted a river normalization program to reduce the amount of flooding
in flood-prone areas, and the residential and business areas along the Pesanggrahan River will benefit from
this program. In terms of the business sector, our survey also covers some parts of the Angke River and
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
6
Cengkareng Drain in West Jakarta. Figure 4 shows the study areas (in purple) from south to northwest
Jakarta, and the detailed survey locations are indicated by red circles.
Figure 4. The study areas
The study areas cover a total of 12.3km2, with the distance from central Jakarta approximately 13-20
km. These areas lie between 13m and 66m above sea level. In January 2013, 18 out of 65 villages in South
Jakarta flooded, as well as 30 out of 56 villages in West Jakarta (BPBD 2013). In South Jakarta, the survey was
conducted in the villages of Ulujami in Pesanggrahan district, and Kebayoran Lama Utara and Cipulir in
Kebayoran Lama district. In West Jakarta, the survey was conducted in the villages of Kedoya Selatan and
Sukabumi Selatan in Kebun Jeruk district, and Rawa Buaya in Cengkareng district. These areas were selected
because they are particularly prone to river flooding. In January 2013, 1,706 houses in these villages were
inundated with river water. Table 1 details the characteristics of each village.
Table 1. Description of study sites
Municipality
Village
South Jakarta
Cipulir
Kebayoran Lama Utara
Ulujami
Sukabumi Selatan
Kedoya Selatan
Rawa Buaya
West Jakarta
Area
(km2)
1.93
1.78
1.70
1.56
3.06
2.28
Population
(people)
40,907
42,422
42,455
42,152
37,695
71,231
Houses
(units)
10,778
10,563
9,798
10,073
9,963
19,579
Population density
(people/km2)
21,162
23,803
24,900
26,868
16,502
17,505
Source: BPS (2013)
These villages are typical of urban villages in developing countries in that they are densely
populated. Large numbers of people live in slum areas and work in informal sectors. In 2013 at least six flood
events of different scales occurred in these areas (17-19 January, 13-14 February, 4-5 April, 18-22 April, 20-21
July, and 8-9 August). In the aftermath of floods, people have become used to living in difficult and
unhealthy conditions such as muddy, dirty houses.
3.2
Analytical Methods
In response to the first and the second research questions, a flood damage model was formulated
by using stage damage function (SDF) applied to actual flood damage, areas of flood exposure (flood
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Economy and Environment Program for Southeast Asia
characteristics) and socio-economic characteristics. This study collected actual flood damage data based on
the 1719 January 2013 flood event. This data represents the ex-post costs incurred after the flood hit.
Furthermore, the study employed micro-scale approaches.
The flood damage analysis was conducted in two steps (Messner and Meyer 2004). Firstly the flood
hazard was determined through exposure indicators such as depth and duration. Secondly, direct and
indirect tangible damage was estimated in monetary terms. Direct damage includes building structure
damage, content damage, outside property damage and clean-up costs, while indirect damage includes loss
of income during flooding.
In assessing flood damage, replacement costs and productivity costs were considered. Replacement
costs represent the economic value of flood damage and are estimated by considering the cost to repair
flood damage. For the residential sector, replacement costs were used to estimate the physical damage to
structures, contents and exterior property damage. Content value assessment relates to the detailed
inventory of the interior and exterior contents of a house. As mentioned by Scawthorn et al. (2006), in the
flood area, it is important to estimate damages on the basis of depreciated value. Therefore, we used a
depreciation rate of 1% per year to calculate the depreciation values of durable goods. Productivity costs
were used for the business sector. These refer to economic loss as a result of flood damage, and were
estimated by measuring the loss of production of commercially marketed goods.
Flood damage function was estimated by the multiple regression analysis technique using the
ordinary least squares (OLS) method. The dependent variable was the actual flood damage, while the
independent variables were exposure indicators (flood characteristics) and socio-economic indicators. The
relationship between the dependent variables and independent variables was assumed to be linear. Those
data were subjected to parameter analysis using the Statistical Package for Social Sciences (SPSS) 20. Next,
stage damage curves were developed using actual flood damage and exposure and vulnerability indicators.
The actual flood damage was estimated for each sector. However the components of actual flood
damage in households differed from those in the business sector. Besides damage to structures and
contents, household damage included loss of income, evacuation costs and the cost of illness during
flooding. Business damage accounted for a loss of turnover during the flooding. The model used to estimate
flood damage in the residential sector was as follows:
RFD = a0 + a1 DEP + a2 DUR + a3 INC + a4 ARE + a5 DIS + e
(Equation 1)
Where:
RFD
DEP
DUR
INC
ARE
DIS
e
:
:
:
:
:
:
:
actual flood damage in the residential sector (monetary value in IDR)
maximum inundation depth (cm)
inundation duration (hrs)
household income (IDR/month)
house area (m2)
distance from house to the nearest river (m)
error term
The model used to estimate flood damage in the business sector was as follows:
BFD = b0 + b1 DEP + b2 DUR + b3 T UR + b4 ARE + a5 DIS + e
(Equation 2)
Where:
BFD
DEB
DUR
TUR
ARE
DIS
𝑒
:
:
:
:
:
:
:
actual flood damage in business sector (monetary value in IDR)
maximum inundation depth in business sector (cm)
inundation duration in business sector (hrs)
turnover per day (000 IDR)
total area of units (m2)
distance from business to the nearest river (m)
error term
The first model was used to analyze the actual flood damage of three different income groups in the
residential sector. The households were classified into three levels of monthly family income: low (<USD
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
8
226 2), middle (USD 226-514), and high (>USD 514). A USD 226 value (IDR 2.2 million) was used as the lowest
base since it is the minimum monthly wage rate for Jakarta workers based on the decree issued by the
Governor of Jakarta, No.182, 2012. The second model was used to analyze the actual flood damage for two
different business groups, represented by business turnover per year: micro (≤USD 30,000), and smallmedium (USD 30,000-500,000). This classification is based on Statute No. 20, 2008.
To satisfy the requirements of the third research question, flood vulnerability analysis was
performed to determine the relationship between damage, elements-at-risk and flood impacts (Messner and
Meyer 2004). For the first step, the elements at risk, exposure indicators and vulnerability indicators were
determined. Next, the analysis was conducted by constructing a stage damage curve (SDC), which showed
the relationship between the expected flood damage and inundation depth. Some SDCs were constructed
using different exposure indicators to see how the curves varied.
In order to tackle the fourth research question, respondents from the residential and business
sectors were asked about their beliefs and perceptions of future flooding events. We used the January 2013
floods as an example of a 30-year flood return period 3 and we adopted a method from Bubeck et al. (2012).
Respondents were requested to express their beliefs on the a) probability of occurrence and b) expected
damage. These beliefs were expressed on a scale of 1 to 7. For example, a score of 1 shows that a future flood
event will not occur at all and therefore has no consequences. A score of 7 indicates that a future flood event
will definitely occur so its consequences are considered to be enormously high.
The perceptions were articulated in two questions: a) the intention to adopt flood-defense
measures in the future, and b) the demand for flood risk reduction measures from the government. For the
first perception, a score of 1 indicates that a respondent certainly does not intend to carry out mitigation
measures, while a score of 7 indicates that a respondent absolutely intends to carry out mitigation measures.
For the second perception, a score of 1 demonstrates that a respondent considers it not at all important to
prevent the negative impacts of flooding, while a score of 7 shows that a respondent thinks that preventing
the negative impacts of flooding is extremely important. Table 2 shows how each scale represents each
belief and perception.
Table 2. Detailed scale of beliefs and perceptions
a) Belief in the probability of flooding in the future
in their area
[ 1 ] Definitely will not happen
[ 2 ] Very unlikely to happen
[ 3 ] Unlikely to happen
[ 4 ] Neutral
[ 5 ] Likely to happen
[ 6 ] Very likely to happen
[ 7 ] Definitely will happen
c) Intention to adopt flood-defense measures in
the future
[ 1 ] Definitely not
[ 2 ] Very unlikely
[ 3 ] Unlikely
[ 4 ] Neutral
[ 5 ] Likely
[ 6 ] Very likely
[ 7 ] Definitely
b) Perceived consequences of future flooding
events
[ 1 ] Definitely won’t have any consequences at all
[ 2 ] Very unlikely to have consequences
[ 3 ] Unlikely to have consequences
[ 4 ] Neutral
[ 5 ] Likely to have consequences
[ 6 ] Very likely to have consequences
[ 7 ] Consequences are extremely high
d) Perceptions of the demand for flood risk
reduction measures by the government
[ 1 ] It is not important at all
[ 2 ] It is very unlikely to be important
[ 3 ] It is somewhat important
[ 4 ] Neutral
[ 5 ] It is likely to be important
[ 6 ] It is important
[ 7 ] It is extremely important
The data that represents beliefs and perceptions was analyzed to assess the level of participants’
abilities to anticipate and to respond to flood events. The Spearman correlation was applied to determine
the relations between these beliefs and perceptions because of the non-parametric distributions of the
variables.
2
3
Values in USD as of January 2013, using an average exchange rate of 1 USD = 9,735.5 IDR.
Based on discussion with a staff member of the Indonesian Meteorological, Climatological and Geophysical Agency.
9
Economy and Environment Program for Southeast Asia
In addition to the beliefs and perceptions, we collected other information such as respondents’
attitudes towards efforts in dealing with flood events. The attitudes included: a) their actions to save their
lives and assets when the water reaches a high level, and b) the measures that they will take to reduce the
risk of flooding in the future, e.g., taking out flood insurance, adding an extra floor to their dwelling, or
increasing the height of the floor. We also asked whether or not respondents would move to a new, safer
location and the reasons behind their decision.
In order to answer the fifth research question, a number of steps were taken. Firstly, the roles of
women during the three phases of flooding were identified as preparedness, coping and recovery. These
roles were described not only at a family level but also at the societal level. Secondly, the benefits that
women provide via their roles were monetized. This estimation followed four steps: a) identifying the
number of hours that women contribute in each phase; b) expressing those hours into a person-day
equivalent, in which a person-day equals eight working hours; c) multiplying the number of person-day
equivalents by minimum wage rates per day, which were taken to be IDR 100,000 (USD10), and; d) adding
up all of the benefits that women contribute during each phase of flooding.
3.3
Data Collection Methods
This study used primary data gathered by surveying households and business units based in the
study areas. Structured face-to-face interviews were conducted by trained interviewers in order to collect a
cross-section of data. For gender issues, we specifically conducted four sessions of focus group discussions
(FGD) with women who actively contributed to helping society in times of flooding. Additional information
was collected from some government agencies such as the Regional Development Planning Agency
(BAPPEDA), the Provincial Statistical Agency (BPS), the Disaster Mitigation Agency (BPDB), and the Public
Work Ministry (PU). Key informant interviews and a final workshop were used to collect even more detailed
information.
Households and business units were eligible for the survey if they had been affected by the floods
economically, in health terms, or via physical damage, and if they agreed to participate. The contact person
in households was the head of household (husband) or his wife, while the contact person in business units
was the owner or the manager of the business. If the target household or a business unit was not eligible or
was not available at the time of the survey, the next closest household or business unit was approached.
Samples were selected by using a multistage sampling method. The population was quantified as
the number of households and business units located in flood-prone areas along the Pesanggrahan River.
The sampling method consisted of two stages. First, villages along the river were listed and the number of
flooded neighborhoods (rukun tetangga) within these villages was identified. Second, the sample
neighborhoods were selected using purposive sampling. After that, household samples were selected in the
sample neighborhoods by using simple random sampling. In total, 300 households and 150 business units
were sampled.
3.4
Survey Instruments
Each survey used a set questionnaire as an instrument. The household questionnaire covered seven
areas: personal information, condition of house, emergency response to flooding, the cost and damage of
floods, beliefs and perceptions regarding the risk of floods, willingness to move, and the layout of the house.
The business questionnaire covered six areas: information about the business, the condition of the
building, the emergency response to flooding, the costs associated with structural damage, beliefs and
perceptions regarding the risk of floods, and the layout of the building. During the survey, each respondent’s
location was marked using a Global Positioning System (GPS) device so that accurate locations are available
for further spatial analysis.
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
10
4.0 RESULTS
4.1
Sample Characteristics
4.1.1
Residential sector
In April 2013, 300 households were interviewed. The ages of the respondents ranged from 16 to 87
years, with an average of 43.7 years. At 63.6%, slightly more women participated in the survey than men,
probably because their husbands were working during the interview stage. Nonetheless, these women
could explain what had happened to their families during the January flood event and answered the
questions clearly.
Most respondents lived in highly-populated areas and come from low- or middle-income
households. They lived, on average, 84.8 m away from the river, and the distribution of the data was from 0
m to 284 m away from the river. Their houses were mostly permanent constructions and had a total area of
less than 70 m2. The proportion of households living in one-floor houses and two-floor houses were equal. A
second floor had often been deliberately built as a response to flooding. One third of the respondents were
migrants from outside Jakarta who had been living in Jakarta for less than 10 years. On average, their
monthly income was IDR 3,447,800 (USD 354) and 42% had monthly incomes below the standard regional
minimum income, which is USD 220. The majority of people in these villages mostly work in informal sectors
and 36% of them earn their living from running small businesses.
From April 2012 to April 2013, 53% of respondents experienced between 6 and 15 flood events.
However people are used to living with flooding and its aftermath, including mud and dirt in and around
their homes.
4.1.2
Business sector
From May to June 2013, 150 business owners were interviewed. Most run small or medium scale
enterprises such as grocery stores, clothes shops and small restaurants. About 81% employ between one
and four workers, and about 75% have a turnover of less than IDR 2 million (USD 205) per day. Some of these
business units are located in business areas and others are located in residential areas.
Most businesses occupy small buildings and, on average, each business occupies a total area of 38
m2 (including garages). None of the buildings have a basement and 87% are permanent structures. About
72% have an attic and 13% have a garage or carport. Premises are located, on average, 84.8 m away from the
river, ranging from the riverbank (minimum distance, 0 m) up to 123.7 m (maximum distance) away from the
river. About 51% of the business units run their businesses from their own buildings.
From May 2012 to May 2013, 45% of respondents experienced between 6 and 15 flood events and
51% of them said that the time between receiving flood information and flood arrival was less than three
hours. During the January floods, on average, the inundation depth was 74 cm, and the water stayed inside
buildings for 84 hours.
4.2
Estimation of Actual Flood Damage in the Residential Sector
The median total household damage amounted to about IDR 2.4 million (USD 247) for the 300
households included in the sample. The mean total damage to houses was IDR 3.1 million (USD 318). About
18% of households experienced flood losses of less than IDR 1 million (USD 102) and 10% of households
suffered flood damage in excess of IDR 6.6 million (USD 678). Just 2% of households had to cope with
damage amounting to over IDR 9 million (USD 924). Households with more property at risk, such as vehicles
and electronic goods (e.g. televisions, computers, refrigerators), experienced higher flood damage.
For most households, direct damage was greater than indirect damage. On average, per house,
direct losses were IDR 2.3 million (USD 236) and indirect losses were IDR 0.8 million (USD 82). Content
damage was the major component of direct losses (see Table 3).
11
Economy and Environment Program for Southeast Asia
Table 3. Average flood damage per house during the 17-19 January 2013 flood event
Damage
1. Direct
1.a. Structural damage
1.b. Content damage (inside and outside)
2. Indirect
2.a. Clean-up cost
2.b. Loss of income
2.c. Evacuation and temporary house
2.d. Cost of illnesses
Total
Value (IDR)
Value (USD)
Percentage
423,395
1,881,703
43
193
14
60
241,186
290,345
117,383
142,252
3,096,264
25
30
12
15
318
8
9
4
5
100
Content losses varied across households from zero to IDR 8.4 million (USD 863). Higher losses were
found in households with higher family incomes and larger houses. Households with two stories could move
their assets to a higher floor to minimize losses. After flood events, on average, households spent 24 hours or
three-person days 4 ridding their houses of mud and cleaning and tidying the contents of the home. We
calculated the loss of income by multiplying the number of days absent from work due to flooding. On
average, the number of days absent was four, and the loss of income per household during those days
varied from zero to IDR 3.5 million (USD 359).
The cost of evacuation and temporary accommodation in residential areas was a result of the
indirect effects of flooding (Penning-Rowsell and Green 2000). Because of evacuation or the need for house
repair, about 70% of respondents moved to temporary places such as family houses, mosques or evacuation
camps. On average, they stayed in these places for five or six days and spent about IDR 117,383 (USD 12) on
traveling, food and lodging. About 71% used their own money to finance their evacuation.
With regard to the cost of illness, the number of family members who fell ill during and after floods
varied from one person to three people per household, most of them being children. Of those who fell ill,
34% suffered from a fever, 21% from external irritation, and 18% from diarrhea. Even though 89% visited a
doctor, the average cost of illness was low because 45% of those affected visited doctors provided by the
government or social agencies free of charge.
We performed a multiple regression analysis to predict the actual flood damage in residential areas.
Table 4 shows that inundation depth and duration, household income, and house area are all good
predictors of the actual flood damage. However, distance from the river makes no significant contribution to
the model. Households living close to the river took precautionary action, such as increasing the height of
the floor and constructing concrete barricades in front of their houses. In some cases, elevated areas located
close to the river were higher than those farther from the river, making their upper floors more vulnerable.
Table 4. Actual flood damage function estimates for the residential sector
Constant
Inundation depth
Inundation duration
Household income
Total house area
Distance from river
Coefficients
-14.161
13.038
4.474
0.197
7.963
2.652
Std. Error
358.553
3.237
2.553
0.037
1.762
1.828
Sig.
0.969
0.000
0.081
0.000
0.000
0.148
VIF
1.349
1.303
1.065
1.078
1.052
Dependent variable: total damages (000 IDR), R2 = 0.256, adjusted R2 = 0.244, Durbin-Watson = 1.804, total n = 300.
The model is significant at 99% confidence level because the value of significance F is less than 0.01.
About 25.6% of the variation of flood damage is attributed to independent variables. A lower R2 is still
acceptable because this study employed cross-section data and four variables have positive values, as
4
One person day = eight hours.
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
12
expected in theory. Variable inundation depth, inundation duration, household income and total house area
appear to positively and significantly influence the dependent variable.
Based on the model, we estimated the actual flood damage per household by multiplying each
coefficient with the mean of each variable. An estimation of the total damage per household was calculated
as follows:
RFD = -14.161 + 13.038 (86.59) + 4.474 (97.62) + 0.197 (3447.79) + 7.963 (67.93) + 2.652 (84.80)
= 2,996.60 (IDR 000) = IDR 2,996,600
An estimation of total damage for the residential population is obtained by multiplying RFD with
the population, 1,706. Therefore, the total flood damage for the total population is IDR 5,112,301,960 (USD
0.5 million).
The flood model was further analyzed in detail based on the three levels of household income. The
respondents were classified into low-income (42%), middle-income (45.7%), and high-income (12.3%)
households. Table 5 shows that, among the models, inundation depth and total house area are positive and
linear in influencing flood damage at all levels of income. Inundation duration and household income
positively influence flood damage middle- and high-income households; these variables negatively
influence the damage and are even less significant for low-income households.
Based on Table 5, we estimated the total damage incurred by each income level by multiplying the
variable coefficient with the variable mean in each model. Table 6 indicates that high-income households
experience the highest level of flood damage per house. However, because the middle-income household
bracket contains the highest number of houses, the highest total flood damage occurs at this income level.
Table 5. Coefficient estimates for actual flood damage in the residential sector across three income groups
Constant
Inundation depth
Inundation duration
Household income
Total house area
Distance from river
R2
Adjusted R2
Durbin-Watson
n
Low-income
1,382.847
(903.128)
10.805 **
(5.203)
- 0.291
(3.901)
- 0.427
(0.426)
11.790 **
(4.784)
4.558
(2.920)
0.130
0.094
1.743
126
Middle-income
-1,073.035
(847.195)
11.724 ***
(4.321)
9.642 ***
(3.526)
0.422 **
(0.221)
6.323 ***
(2.385)
2.623
(2.482)
0.279
0.252
1.944
137
High-income
-1,408.069
(1, 413.337)
30.529**
(12.810)
7.121
(11.293)
0.216***
(0.077)
7.665**
(3.348)
- 1.411
(6.789)
0.462
0.375
1.653
37
*, **, *** indicates significance at the 90%, 95% and 99% levels, respectively.
Standard deviation is given in the bracket.
Table 6. Actual flood damage in the residential sector by income level
Income
group
Low
Middle
High
Total
13
Damage per house
(USD)
(1)
264
293
447
Economy and Environment Program for Southeast Asia
Percentage from
population (%)
(2)
42.0
45.7
12.3
100.0
House
(units)
(3) = (2) * 1,706
717
779
210
1,706
Total damage (USD)
(4) = (1)*(3)
189,211
228,564
93,803
511,578
4.3
Estimation of Actual Flood Damage in the Business Sector
The median total amount of damages was IDR 5.9 million (USD 606) for the 150 business units
included in the sample. The mean total damage per unit was IDR 8.1 million (832 USD). About 12% of
respondents experienced flood damage of less than IDR 1.5 million (USD 154), and 10% of respondents
suffered flood damage in excess of IDR 20 million (USD 2,054). Business units with more content experienced
higher losses.
The total damage per business unit, on average, was IDR 8.6 million (USD 883). For most business
units the total indirect damage was greater than the total direct damage. The total direct damage (i.e.,
structural and content damage) was valued at IDR 2,107,860 (USD 216), while the total indirect damage (i.e.,
including loss of turnover, the cost of temporary quarters, the cost of labor, and the cost of cleaning up)
amounted to IDR 6,479,004 (USD 665).
In terms of direct costs, structural damage was less costly than content damage because most
owners have small buildings. Content damage was quite high, suggesting that owners did not have enough
time to evacuate their contents. Table 7 shows that compared to other costs, the highest cost incurred was
loss in turnover. This accounted for about 61% of the total costs, depending on the number of days the
business was closed. About 93% of respondents closed their businesses as a result of flooding, with five days
being the average closure. Only 4% of businesses established temporary headquarters at another location.
Table 7. Average flood damage per unit in the business sector during the 17-19 January 2013 flood event
Damage
1. Direct
1.a. Structural damage
1.b. Content damage
2. Indirect
2.a. Loss of turnover
2.b. Temporary headquarters
2.c. Labor cost
2.d. Clean-up cost
Total
Value (IDR)
Value (USD)
Percentage
564,800
1,543,060
58
158
7
18
5,252,704
56,000
383,667
786,633
8,586,864
540
6
39
81
882
61
1
4
9
100
We performed a multiple regression analysis to predict the actual flood damage in the business
sector. The model is significant at 99% confidence level because the value of significance F is less than 0.01.
Table 8 shows that inundation depth and duration, turnover per day, and total area are sufficient predictors
of actual flood damage. About 44.4% of the variation in flood damage is attributed to independent variables.
However, it is important to remember that distance from the river makes no significant contribution to the
model.
Table 8. Actual flood damage function estimates for the business sector
Constant
Inundation depth
Inundation duration
Turnover per day
Total area
Distance to river
Coefficients
-1,698.855
30.816
30.310
2.171
28.775
2.084
Standard error
1,298.524
12.535
8.642
0.220
8.283
2.789
Significance
0.193
0.015
0.001
0.000
0.003
0.455
VIF
1.247
1.282
1.176
1.023
1.047
Dependent variable: total damages (IDR 000), R2 = 0.444, adjusted R2 = 0.425, Durbin-Watson = 2.030, total n = 150.
Based on the model, we estimated the actual flood damage per business unit by multiplying each
coefficient with the mean of each variable. An estimation of the total damage per business unit is as follows:
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
14
= -1,698.855 + 30.816 (74.15) + 30.310 (84.45) + 2.171 (1,787.66) + 28.775 (30) + 2.084 (129.69)
= 8,147.861 (IDR 000) = IDR 8,147,861
An estimation of the total damage attributed to the business population is found by multiplying the
business actual damage (BAD) with the population, 816. Hence, the total flood damage for the business
population is IDR 6,648,654,348 (USD 0.7 million).
We further analyzed the flood damage by classifying the business units based on their turnover per
year. The respondents included micro businesses (49%), small businesses (46%), and medium-sized
businesses (5%). We had previously planned to develop three models for each group but because the
medium-sized business group contained only seven business units, we could not analyze it separately.
Additionally, for further analysis, we divided all the respondents into two groups: micro businesses and
small- to medium-sized businesses. Table 9 shows that for both models inundation duration, inundation
depth, and the total area of the business’ building positively and significantly influences flood damage.
Moreover, turnover per day positively influences flood damage, but it is only significant in the small- to
medium-sized business group.
Table 9. Coefficient estimates of actual flood damage, business sector, two turnover groups
Micro businesses
-1,785.344
(1,154.075)
30.955 ***
(9.514)
19.155 ***
(6.473)
2.678
(1.676)
20.210 *
(11.256)
1.543
(2.109)
0.374
0.328
1.500
74
Constant
Inundation depth
Inundation duration
Turnover per day
Total building area
Distance to river
R2
Adjusted R2
Durbin-Watson
n
Small- to medium-sized businesses
-1,094.352
(847.195)
43.102
(23.570)
48.367**
(17.243)
1.684**
(0.351)
25.668*
(11.251)
3.760
(5.517)
0.328
0.280
1.916
76
*, **, *** indicates significance at the 90%, 95%, and 99% levels, respectively.
Standard deviation is given in brackets.
Based on the two models in Table 9, we estimated the total damage incurred in each group by
multiplying the variable coefficient with the variable mean in each model. Table 10 indicates that the flood
damage per business in the small- to medium-sized business group is 2.5 times higher than that of the micro
business group.
Table 10. Actual flood damage in the business sector by turnover
Business turnover
Micro
Small-medium
Total
Damage per units
(USD)
(1)
481
1,210
Percentage of
population (%)
(2)
49.0
51.7
100.0
Business
(units)
(3) = (2) * 816
400
416
816
Total damage
(USD)
(4) = (1)*(3)
192,286
503,362
695,648
Based on the previous flood damage models, we can roughly estimate two values: a) the total flood
damage inflicted on the residential and business sectors after a three-day flood event, and b) the benefits of
the normalization program. The total flood damage amounted to IDR 12 billion (USD 1.2 million) for six
15
Economy and Environment Program for Southeast Asia
villages for the one flood event in 2013. Compared to results from BPBD (2013), this study seems to have
underestimated the flood damage because it did not include intangible damage and damage to other
sectors such as large-scale business, public facilities and transportation.
It is predicted that the normalization program will reduce flood damage. The normalization
program aims to reduce the depth and duration of flood inundation in the most vulnerable areas as well as
the number of areas inundated by flood water (by increasing water flow or discharge). We do not have any
clear information about the reduction targets for either inundation depth or inundation duration as a result
of the normalization program. Therefore we have assumed that both inundation depth and inundation
duration will reduce by 50%.
Based on our data, in the residential sector the mean inundation depth was 86.6 cm and the mean
inundation duration was 97.6 hours. In the business sector, the mean inundation depth was 74.2 cm and the
mean inundation duration was 84.5 hours. We reduced these numbers by 50% and put the new figures into
equations (1) and (2), ceteris paribus. Since inundation depth and inundation duration have a positive
influence on the models, reduction in these variables results in a reduction in flood damage per unit in each
sector. Finally, after multiplying the damage per unit with the total number of units, we estimated that a
50% reduction in both variables would result in a flood damage reduction of about 26% in the residential
sector and 30% in the business sector. These values indicate the potential benefits of the normalization
project (see Table 11).
Table 11. Estimation of flood damage reduction from the river normalization project
Sector
Residential
Business
4.4
Number
of units
(1)
1,706
816
Before normalization
Total
Damage per
damages
unit (USD)
(USD)
(2)
(3) = (1) x (2)
300
511,230
815
664,869
After normalization
Total
Damage per
damages
unit (USD)
(USD)
(4)
(5) = (1) x (4)
222
373,333
572
466,962
Damage reduction
(%)
[(5)-(3)]/(3) x 100%
26
30
Relationships between Flood Exposure Indicators,
Vulnerability Indicators, and the Flood Damage Model
In this study, the elements at risk were households, people and business units along the
Pesanggrahan River that experienced flood damage during the 17-19 January 2013 flood event. The
exposure indicators were divided into two types. First, the indicators were focused on general flood
characteristics such as inundation depth, inundation duration, distance of buildings from the river, and flood
frequency. Second, we focused on the kind of exposure to different elements at risk. Household area and
household income were included in the exposure indicators for the household sector, while the business
area and turnover per day were the two items included for the business sector. The vulnerability indicator
focused on flood awareness and is indicated by the time lag between becoming aware that flooding might
reach the building and the moment when the water actually reaches the building.
Each exposure indicator was classified into several categories. Inundation duration was classified
into above and below 72 hours (three days). The distance between a building and the river was classified
into near (≤ 50 m), medium (51-100 m), and far (> 100 m). House area was classified into small (≤ 70 m2),
medium (71-120 m2), and large (120 m2). Household income was classified into low income (≤ IDR 2.2 million
or USD 225 per month), middle income (IDR 2.2-5 million or USD 225-514 per month), and high income (>
IDR 5 million or USD 514 per month). Flood frequency was classified into the rate of occurrence of flood
events per year: infrequent (< 5), often (6-15), and very often (> 15).
Since SDCs depict the relationship between inundation depth and flood damage, the correlation
has been predicted to be positive; hence, the higher the position of the SDC, the higher the damage at the
same level of inundation depth. Furthermore, a steeper SDC indicates a greater rate of flood damage when
inundation depth is increased. So far, inundation depth is the main flood characteristic used to estimate
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
16
expected flood damage by means of depth-damage curves (Messner and Meyer 2005) because other
variables are difficult to measure (Smith 1994).
4.4.1
Residential sector
All SDCs have a positive gradient, which shows that an increase in flood depth also increases flood
damage. Figure 5 shows that households that were inundated for more than 72 hours experienced more
flood damage than those that were inundated for less than 72 hours. The longer the duration of a flood, the
greater the total damage will be. This result is in line with Penning-Rowsell and Green (2000) and Messner
and Meyer (2005).
Households that were located close to the river suffered less damage than those further away from
the river. This result is in contrast with the hypothesis. Based on observation, households that were located
between 51 and 100 m from the river had a lower elevation than those located closer to the river.
Figure 5. SDC in the residential sector based on flood duration and distance from river
Figure 6 shows that high-income households suffered more damage than low-income households
and that large houses suffered more damage than small ones. Household income and house area are
related, since large houses are mostly owned by high-income households. Large houses are more vulnerable
to flooding because, generally speaking, they contain more furniture and electronic appliances. PenningRowsell and Green (2000) confirmed that potential flood damage increases significantly in houses that are
occupied by the higher socio-economic groups.
Figure 6. SDC in the residential sector based on household income and house area
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Economy and Environment Program for Southeast Asia
This study was conducted in six flood-prone villages where many people have become accustomed
to flooding. Figure 7 indicates that if the inundation depth is above 70 cm, rarely-flooded houses tend to
experience a higher loss than those that are often flooded. This demonstrates that the more experienced
households are in dealing with floods, the better prepared they are in keeping their belongings safe.
Households employed reactive adaptation measures during floods such as constructing concrete barriers in
front of their homes to act as flood retainers, raising the height of floors, or adding extra floors.
Figure 7. SDC in the residential sector based on flood frequency
When considering the vulnerability indicator, Figure 8 shows that the longer the time lag between
learning that the building might be flooded and the moment when the water actually reaches the building,
the greater the flood damage is. This result does not confirm the hypothesis. It was initially predicted that
time lag and flood damage would have a negative correlation. We further analyzed the relationship between
the proportion of salvaged contents, total damage and flood awareness. The results showed that these
variables have a positive correlation. It seems that flood awareness does not contribute significantly to flood
damage reduction.
Figure 8. Flood vulnerability curve in the residential sector
Households received flood information from many different sources, such as television, mosque
broadcasts, family, and neighbors, with mosque broadcasts being the main source. Normally only the local
leader has direct access to flood information. He receives information from Katulampa dam officers (a dam
located upstream of Jakarta that controls the water flow from Bogor to Jakarta), BPDB, or the community
radio that operates during heavy rain. The time lag between the receipt of information and the arrival of
flooding is quite short. About 52% of the sample received information less than three hours before the flood
arrived. The mean flood awareness time is 5.5 hours, while the mean preparation time is 1.4 hours.
Most households do not take any direct action when they first receive information about the
impending flood because they do not believe that the flood will actually occur until they see the water
heading towards their house. Consequently, they only start to evacuate their belongings when the water
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
18
reaches their home. In this time, 58% of people are able to save their belongings, worth around IDR 15
million (USD 1,540), which mostly comprises vehicles. Most households take several preventative measures
to reduce flood damage such as moving belongings to a second floor or higher ground, moving vehicles to
higher ground, and shutting down the electricity supply.
For households located on riverbanks, floods often come without any rain. For example, even on
sunny days, water can suddenly inundate houses. People call this condition banjir kiriman or “the sent flood”,
which means that the flood is caused by rain falling in surrounding areas. Consequently, households have
grown familiar with flooding and have become relatively unresponsive to flood information. This does not
mean that households do not take any action to save their belongings but that they have created their own
adaptation measures.
Jakarta implemented a Flood Early Warning System (FEWS) on the Pesanggrahan River after the
breach, and complete destruction, of Situ Gintung dam on 27 March 2009. After the Situ Gintung
catastrophe occurred, the Pesanggrahan riverbank in Jakarta became more prone to flooding because the
dam that once retained floodwater from the river was no longer in place. FEWS is an important flood
measure because it warns people about future flooding events. Penning-Rowsell and Green (2000) found
that an increase of more than two hours warning time can reduce flood damage by more than 10%. This tells
us that human effort and coping strategies during the warning lead-time of a flood have a clear impact on
the amount of flood damage suffered.
4.4.2
Business sector
As in the residential sector, the duration of floods in the business sector has a positive correlation
with greater flood damage. Figure 9 shows that business units that experienced more than three days of
flooding suffered more damage than those that endured less than three days of flooding. Also, business
units located close to the river suffered more flood damage than those more distant from the river. For
example, the first floor of Cipulir market, located less than 50 m from the river, is frequently flooded if there
is persistent rain that lasts for several hours. During the January floods, at least 666 kiosks located on that
floor were inundated, which resulted in interrupted business activities and financial losses.
Figure 9. SDC in the business sector based on flood duration and distance from river
A larger area and a greater turnover per day tend to increase flood damage of business units. Figure
10 shows that business units with an area greater than 60 m2 suffered more damage than those with an area
of less than 30 m2. Business units with a daily turnover of more than IDR 5 million (USD 514) have a higher
and steeper gradient than those with a daily turnover of below IDR 2 million (USD 205). Business area and
turnover per day seem to have a positive correlation with expected damage.
For the three categories of flood frequency, Figure 11 indicates that if a flood is more than 120 cm
deep, a frequently flooded business unit will tend to have more damage than that of an infrequently flooded
business unit. During the last year, Cipulir market experienced seven flood events that forced traders to stop
19
Economy and Environment Program for Southeast Asia
their activities. The market houses more than 650 clothes vendors, and each of these has an approximate
average turnover of IDR 4 million (USD 411). Despite the fact that flooding has caused damage to these
businesses and has reduced revenues (due to temporary closure), these business owners continue to trade
in the same place. This is most likely because their businesses are their main livelihoods and they have built a
clientele in the vicinity of Cipulir market and so may be reluctant to move.
Figure 10. SDC in the business sector based on total area and daily turnover
Figure 11. SDC in the business sector based on flood frequency
Business units located near residential areas receive most of their flood information from mosque
broadcasts. Business units located in Cipulir market receive flood information from friends and the market
security team. Several business owners check the water level in the river themselves. The mean flood
awareness time is 4.7 hours and the mean of preparedness time is 1.4 hours. Although business units have
sufficient time to prepare and evacuate their contents, they can’t save them as quickly as households
because they have more things to save. Business owners with units located near residential areas are able to
evacuate their belongings quickly as their business is located close to their home. Other business units have
difficulty evacuating their belongings, especially when flooding occurs at night (outside working hours).
The flood vulnerability curve (Figure 12) shows that flood awareness has a negative correlation with
expected flood damage. It also suggests that a longer time lag could provide an adequate amount of time to
save belongings and reduce flood damage. Business owners think in terms of their business – they don’t
take risks by delaying the evacuation process. As soon as flood information is received, business owners act
immediately to protect their goods and inventory.
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
20
Figure 12. Flood vulnerability curve in the business sector
4.5
Household and Business Responses to Flooding Based on Perceptions
4.5.1
Residential sector
Respondents’ perceived probability of a flooding event and their perception of there being
consequences should such an event happen were used to determine their risk perception. The mean rating
and standard deviation (SD) were found to be higher for the perceived probability (mean = 5.9 and SD = 1.4)
than for the perceived consequence (mean = 5.7 and SD = 1.3). These findings confirm the result achieved
by Bubeck et al. (2012), which shows that perceived probability has a higher standard deviation and
indicates a larger variability in responses.
The survey showed that risk perceptions were high, with mean scores above 5 points. Most
households viewed the perceived probability of flooding as “likely to happen” or “very likely to happen”. As
for perceived consequence, households viewed it as “likely to have consequences” or “very likely to have
consequences”. These results showed that households were aware that they were living in a flood-prone
area. Moreover, most households (89.4%) believed that future flood events would have consequences.
In response to flood events, more than 50% of the surveyed households were going to undertake
private flood mitigation measures (see Figure 13). This indicates that households are quick to take flood risk
mitigation measures. However, 25.7% of households stated that they would not undertake any mitigation
measures in the future because mitigation measures are already in place or because they lacked the financial
resources to undertake such measures. The results are in accordance with Grothmann and Reusswig (2006),
who also found that the risk perceptions of an individual who had implemented mitigation measures were
likely to decrease after the measures had been put in place.
12.0%
10.3%
34.0%
25.7%
11.0%
3.3%
Definitely not
Very unlikely
Unlikely
Neutral
Likely
Very likely
Definitely
3.7%
Figure 13. Intentions to undertake future
flood mitigation measures in the residential sector
Table 12 presents the results of correlation analysis of the intention to undertake mitigation
measures and the two dimensions of flood risk perception. Perceived probability has a small correlation with
the intention to undertake flood mitigation, while the perceived consequence is not statistically significant.
These results were confirmed by Bubeck et al. (2012) who found a rather weak relationship between the
21
Economy and Environment Program for Southeast Asia
intention to employ mitigation measures and risk perceptions (perceived probability). This situation can be
explained by the characteristics of the households in the study area. About 69.3% of households had been
living in the same house for more than 10 years, so most of them had experienced at least 10 annual flood
events and three five-year flood events (2002, 2007 and 2013). The worst flood, which happened in 2007,
had compelled most households to be more prepared. In addition, between April 2012 and April 2013
several areas had flooded more than five times and households had already taken precautionary actions
after the previous bout of flooding.
Table 12. Correlation between the intention to mitigate and two components of risk perception in the
residential sector
Perceived Probability
0.118*
-0.034
Intention to mitigate
Demand for public flood mitigation
Perceived Consequence
0.070
-0.038
* Correlation is significant at the 0.05 level (2-tailed).
Surprisingly, although it was expected that the correlation between public mitigation and risk
perception would be strong, the correlation between intention to mitigate and risk perception was weak
and as a result Table 12 shows that there is no statistically significant correlation. People living along the
Pesanggrahan River argued that if the government did not come up with any public flood mitigation
measures, then their private mitigation measures would be useless. Figure 14 indicates that 74% of
households considered it extremely important to make public flood reduction efforts. This opinion is in
accordance with Grothmann and Reusswig (2006), who stated that people often rely solely on public flood
mitigation measures and as a result people probably have less intention to perform mitigation activities
themselves. There is no clear correlation between demand for public mitigation measures and the two flood
risk perceptions.
0.3%
0.0%
0.3%
1.7%
3.7%
It is not important at all
It is very unlikely important
It is somewhat important
20.0%
74.0%
Neutral
It is likely important
It is important
It is extremely Important
important
Figure 14. Demand for public flood reduction in the residential sector
The correlation between flood risk perception and demand for public flood reduction efforts is
hardly detectable. In South Jakarta, although the government is conducting the normalization project,
households do not consider expanding the river to be effective enough. Households in Ulujami claimed that
their area had become more vulnerable to flooding since early 2012, when a new apartment project turned
a swamp area into an apartment block. People in neighborhood 3 (RW 3) Ulujami pointed out that flooding
was worsening and occurred more often, even on sunny days and during light rainfall. Although such
allegations need further verification, they make the case that the reduced swamp area has forced the water
to flow back to residential areas as a water catchment area no longer exists.
In Kedoya Selatan in West Jakarta, there is a particular conflict of interest within the community. The
river flows through two avenues, the main river and its creek. Previously the creek had been useful for
irrigation, but is now used to control water flows during heavy rainfall or when the main river is overflowing.
Each stream is controlled by a floodgate that plays an important role during flooding. If the creek floodgate
is closed, then lowland areas will be inundated. If the main river floodgate is closed, then upland areas will
be inundated. This condition worsens if both floodgates are open, because all neighborhoods will be
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
22
inundated. The government intends to implement the normalization project only in the creek, although
people hope that the project will also be implemented in the main river.
4.5.2
Business sector
In the business sector both the mean rating and standard deviation were found to be higher
compared to the residential sector. The mean perceived probability was 6.20 (SD = 1.19) and the mean
perceived consequence was 5.95 (SD = 1.07). In contrast with the residential sector, many business units
were not interested in undertaking future flood mitigation measures. As can be seen in Figure 15, only 47.4%
stated that they were “likely”, “very likely”, or would “definitely” undertake flood risk mitigation measures.
This is because several business units had already undertaken these measures. Furthermore, the perceived
consequences in the business sector were statistically more significant than perceived probability.
8.0%
6.7%
Definitely not
Very unlikely
29.3%
Unlikely
Neutral
32.7%
Likely
14.0%
2.0%
7.3%
Very likely
Definitely
Figure 15. Intention to undertake future
flood mitigation measures in the business sector
Table 13 presents the correlation analysis of the intention to undertake mitigation measures and
the two dimensions of flood risk perception. Although perceived consequence had a statistically significant
correlation with the intention to mitigate, the correlation was weak. Business units recognized the
consequences of flooding better than flood probability because flooding has an effect on their products and
inventory and reduces their profit. Unfortunately, no statistically significant correlation was found between
the demand for public mitigation and risk perception.
Table 13. Correlation of the intention to mitigate with two components of risk perception in the business
sector
Intention to mitigate
Demand for public flood mitigation
Perceived Probability
0.081
-0.071
Perceived Consequence
0.165*
0.023
* Correlation is significant at the 0.05 level (2-tailed).
In the business sector mitigation efforts tend to vary depending on a building’s ownership (whether
the premises are private or rented). If the premises are privately owned, then all flood damage is borne by
the owner. In rented premises, structural damage is borne by the building’s owner, and content losses are
borne by tenants. Building owners usually put in place better mitigation measures than their tenants
because tenants cannot make changes to the building’s structure. Although most tenants do not allocate
special budgets to reduce the risk of flooding, they do prepare temporary mitigation measures that are
dictated by limited space and regulations. Generally, business unit owners expressed the view that it was
useless to take mitigation actions if the government did not undertake public flood mitigation measures. As
can be seen in Figure 16, about 97.9% of respondents claimed that the demand for public flood reduction
efforts is important.
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Economy and Environment Program for Southeast Asia
0.0%
0.7%
0.7%
0.7%
3.3%
It is not important at all
It is very unlikely important
It is somewhat important
26.7%
Neutral
It is likely important
67.9%
It is important
It is extremely important
Important
Figure 16. Demand for public flood reduction measures in the business sector
4.6
Assessment of the Benefits of Women’s Roles in Relation to Flooding
From April to June 2013, 347 women from different households were interviewed (Figure 17).
Majority (90%) of them had experienced the January flood event and had been involved in mitigation,
coping and recovery activities before, during and after the flooding. The ages of the women respondents
ranged from 17 to 85 years old, with an average age of 42 years (SD = 11.3). Their educational backgrounds
varied widely, from elementary school level to bachelor’s degrees, although most had only graduated from
elementary school. Majority had no flood management training, with only 1.4% having participated in such
a training course as provided by the government.
Figure 17. Discussion with women in Ulujami, South Jakarta (left),
and in Kedoya Selatan, West Jakarta (right)
These women were asked to complete a table listing women’s and men’s specific roles during a
flood event. Table 14 shows that women contributed more than men during the three phases of flooding.
Men’s roles were mainly guarding the house and cleaning up. The information displayed in Table 14 may
contain some biases because the survey was only conducted amongst women.
Table 14. Distribution of activities within families during the three phases of flooding
Activities
1.
2.
3.
4.
5.
6.
7.
8.
9.
Ensuring food availability
Receiving the flood warning
Evacuating goods
Evacuating family and others
Caring for the children
Cooking
Going to the market
Guarding the house
Taking care of the sick
Before flood
Female
Male
√
√
√
√
√
√
√
√
√
√
√
√
During flood
Female
Male
√
√
√
√
√
√
√
√
√
After flood
Female
Male
√
√
√
√
√
√
√
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
24
Table 14 continued
Activities
10.
11.
12.
13.
14.
15.
Cleaning the house
Washing clothes
Repairing the house
Cleaning up the environment
Giving health education
Attending community meetings
Before flood
Female
Male
√
√
√
√
√
During flood
Female
Male
√
√
√
√
√
√
After flood
Female
Male
√
√
√
√
√
√
√
Time contribution (hrs)
Women play important roles in supporting the family before, during and after flood events. Based
on the survey responses, during three days of flooding, more time was spent on the family than on society.
On average, total time contribution to the family was 22.2 hours, while 3.2 hours were given over to society.
Figure 18 shows that the highest time contribution was made during the flood phase, and to the family.
Before the floods, women helped their families to pack belongings and evacuate family members, prepare
food supplies, evacuate the contents of homes and take care of family members. Most time was spent
evacuating belongings (38%). During flooding, women play important roles such as taking care of children,
cooking, and taking care of the house and its contents. Most of their time is spent taking care of belongings
at home (41%). After the flood, women help their families by cleaning the house and taking care of sick
family members, among others. Cleaning the house took up the largest amount of time (53%).
13.63
15
10
5
6.56
2.05
0.31
2.29
in family
0.61
0
Before
During
in society
After
Flood phases
Figure 18. Average time contribution of women in relation to
flood mitigation, coping and recovery activities (n = 347)
Most women’s activities in relation to flooding and society are organized by a group called “the
family welfare movement” (PKK). The members of this group are women from households located in the
community. Generally, each group consists of between 10 and 20 active members. These are voluntary
groups whose members meet monthly. PKK aims to help improve community welfare. It provides a
community kitchen to make food for flood victims. If inundation lasts for more than a day, the local leader or
head of village (Lurah) asks the PKK to start running the community kitchen.
The community kitchen’s activities include preparing food from scratch, cooking the food, and
distributing the food. All members work voluntarily. These women are also involved in many activities
related to flood countermeasures, such as arranging the evacuation camp, cleaning up their surroundings,
and helping the local government provide a counseling service. The group has two sources of finance: from
the government (via the Social Welfare Agency), and from non-governmental (company and society)
budgets.
In society, women engage in many roles before, during and after flooding. However, only 30% of
the respondents carry out both family and societal roles simultaneously. Some women experience conflict
between their family and community roles during and after flood events. This situation was also
encountered by Fothergill (2004), who stated that many women were not able to take on time-demanding
community roles due to their domestic obligations and responsibilities.
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Economy and Environment Program for Southeast Asia
In a bid to unearth more information, 47 women who actively took part in public activities during
the floods were interviewed. These women ranged from 23 to 73 years old, with an average age of 45.8 years
(SD = 9.2). Their educational backgrounds varied widely, from elementary school level to bachelor’s degrees;
high school graduates were dominant amongst the respondents.
Time contribution (hrs)
On average, these women spent 20.5 hours helping their families and 20.98 hours helping the
community during the three-day flood event of 17-19 January (Figure 19). Before the flood, the activities that
the women engaged in included cleaning the surroundings, preparing the equipment for the communal
kitchen, and coordinating with women serving in local government institutions. The biggest portion of time
was spent cleaning the surroundings (59.2%). During the flood, the activities that these women performed
included cooking in the community kitchen, distributing food and medicine, and helping people at the
evacuation camps. Most time was spent cooking in the communal kitchen (42.7%). After the flood common
activities included cleaning the surroundings, tidying up the communal kitchen, working as a health
volunteer and distributing medicine. The biggest portion of time was spent cleaning the surroundings
(45.6%).
20
16.88
15
9.2
10
2.89
5
8.44
2.3
in family
1.8
in society
0
Before
During
After
Flood phases
Figure 19. Average time contribution of women in relation to
flood mitigation, coping and recovery activities (n = 47)
Table 15 shows that age has a positive significant correlation with the duration of involvement in
community activities, but a negative significant correlation with family time contribution. These findings
indicate that the PKK is dominated by elderly women, whose time is not taken up with caring for young
families. In other words, the older the women, the more time they can give to society.
Table 15. Correlation between women’s age, duration of involvement, family time contributions, and
society time contributions in relation to flooding
Age
Age
Duration of involvement
Family time contribution
Society time contribution
0.600 **
-0.437 **
0.115
Duration of
involvement
-0.279
0.026
Family time
contribution
Society time
contribution
0.147
-
** Correlation is significant at the 0.01 level (2-tailed).
After identifying women’s roles in relation to flooding and their time contribution, the benefits
provided by women were monetized to find out the value of women’s contribution to their families’ and
their communities’ flood mitigation, coping and recovery efforts. An estimation of this value was made for
both family and society.
Monetary value of benefits of women’s roles in family:
= Total time contribution (hours) / 8 hours * standard minimum wage per day in Jakarta
= 20.52 / 8 x IDR 100,000 = IDR 256,500 (USD 26)
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
26
Monetary value of benefits of women’s roles in society:
= Total time contribution (hours) / 8 hours * standard minimum wage per day in Jakarta
= 20.98 / 8 x IDR 100,000 = IDR 262,250 (USD 27)
Total monetary value of benefits of women’s roles:
= IDR 256,500 + IDR 262,250 = IDR 518,750 (USD 53)
Based on the survey, only 30% of women took on both family and societal roles during flood
mitigation, coping and recovery activities. Using the same number of inundated households in the
Pesanggrahan area study sites (1,706 households) the total value of the benefits of women’s roles are as
follows:
= (70% x 1,706 x IDR 256,500) + (30% x 1,706 x IDR 518,750)
= IDR 306,312,300 + IDR 265,496,250 = IDR 571,808,550 (USD 57,180)
5.0 CONCLUSIONS
On 17-19 January 2013, about 1,706 households and 816 small- to medium-sized businesses were
inundated along the Pesanggrahan River in the six villages that served as study sites. In the residential
sector, the total damage per house was IDR 2,996,600 (USD 308) and the total damage to the population was
IDR 5,112,301,960 (USD 0.5 million). In the business sector, the total damage per business unit was IDR
8,147,861 (USD 837) and the total damage to the population was IDR 6,648,654,348 (USD 0.7 million). The
total damage in both sectors reached IDR 11,760,956,308 (USD 1.2 million) in the six villages. The total
damage per business unit was 2.7 times higher than per house. A reduction in the flood depth and duration
of up to 50% (via the government’s normalization program) could reduce flood damage by 26% in the
residential sector and by 30% in the business sector.
The houses that bore high levels of flood risk were those with the following characteristics:
inundated for more than 72 hours, located far from the river, has a large house area, has a high household
income, has few flooding experiences, and their flood awareness duration was long. The following
characteristics, on the other hand, were common to business units with high levels of flood risk: inundated
for more than 72 hours, close to the river, has a large business area, has a high daily turnover rate, has been
frequently flooded, and their flood awareness duration was short.
Flooding is not something new for the people living along the Pesanggrahan River; they are aware
that they live in high flood risk areas. However, there is a weak correlation between their perceived
probability of flooding and their perception that flooding will come with consequences (both of which
comprise flood risk perception) with their intention to engage in flood mitigation activities. The business
sector had a higher flood risk perception than the residential sector. Nevertheless, the business sector made
less effort to conduct mitigation measures than the residential sector. These results provide useful insights
into flood risk management and show that both sectors depend more on public mitigation measures than
they do on their own private mitigation measures.
Women play many roles in the family and in society during the three phases of flooding (before,
during and after). However, few women were able to perform both roles simultaneously. Women’s roles, in
relation to flooding, gave positive benefits to both families and communities. Although their roles were
voluntary, the benefits of their contributions can be considered in monetary terms by assessing their time
contributions and multiplying those numbers with the minimum wage income. During the three days of
flooding in January 2013 the value of women’s roles in the family was IDR 256,500 (USD 26) per person,
while the benefits of their roles in society were equal to IDR 262,250 (USD 27) per person. The total benefit of
a woman contributing to both family and society was IDR 518,750 (USD 53) during the three-day flooding.
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Economy and Environment Program for Southeast Asia
6. 0 POLICY RECOMMENDATIONS
People living in the Pesanggrahan River area are vulnerable to flooding and routinely experience
flood damage. In order to reduce the number of areas that are inundated, thus reducing the consequent
flood damage, the government should accelerate the river normalization program, including ongoing flood
measures, and combine this with other flood measures such as an early warning system.
To gain support from society, the government should provide clear information on the benefits and
costs of flood measures. With regard to the difficulty of relocating people from the riverbank, the
government should dialogue with local communities to understand what these communities consider to be
a successful outcome in terms of compensation and new housing.
Due to the complexity of this situation, there is a need for collaboration between individuals,
communities and the government in Jakarta to minimize the risks of future flooding. Individuals (households
or business units) could take proactive and reactive adaptation measures. Communities can contribute by
managing the local emergency response and encouraging good communication between communities to
effectively spread flood information.
The local government should provide sufficient flood mitigation and adaptation measures. In
addition, to motivate people to be proactive regarding flood prevention, it is important to communicate not
only the risk of flooding and its consequences, but also the options, effectiveness and costs of private
precautionary measures.
As for the role of women in relation to flooding, their capabilities could be increased by conducting
a number of capacity building programs, such as training for flood preparation, flood evacuation and postflood events. There is also a need to encourage and motivate younger women to join in community
activities in relation to flooding.
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
28
REFERENCES
Adger, W.N.; S. Dessai; M. Goulden; M. Hulme; I. Lorenzoni; D.R. Nelson; L.O. Naess; J. Wolf; and A. Wreford.
2009. Are there social limits to adaptation to climate change? Climatic Change. 93:335-354.
Akmalah, E. and N.S. Grigg. 2011. Jakarta flooding: systems study of socio-technical forces. Water
International. 36:733-747.
Appelbaum, S.J. 1985. Determination of urban flood damages. Journal of Water Resources Planning and
Management. 111:269-283.
BAPPENAS (Badan Perencanaan dan Pembangunan Nasional). 2007. Laporan Perkiraan Kerusakan dan
Kerugian Pasca Bencana Banjir Awal Februari 2007 di Wilayah Jabodetabek (Jakarta, Bogor, Depok,
Tangerang, dan Bekasi). Badan Perencanaan Pembangunan Nasional. Jakarta.
Bokszczanin, A. 2007. PTSD symptoms in children and adolescents 28 months after a flood: Age and
gender differences. Journal of Traumatic Stress. 20:347-351.
BPBD (Badan Penanggulangan Bencana Daerah). 2013. Kajian Kerusakan, Kerugian Dan Kebutuhan
Pemulihan Dampak Bencana Banjir Jakarta Januari 2013. BPBD DKI Jakarta. Jakarta.
BPS (Badan Pusat Statistik). 2013. Jakarta in Figures, 2012. Central Board of Statistics. Jakarta.
Brinkman, J.J. and M. Hartman. 2008. Jakarta flood hazard mapping framework. World Bank Report
(unpublished). http://www. hkv. nl/documenten/Jakarta_Flood_Hazard_Mapping_Framework_MH.
pdf (Retrieved 10 December, 2012).
Bubeck, P.; W.J.W. Botzen; L.T.T. Suu; and J.C.J.H. Aerts. 2012. Do flood risk perceptions provide useful
insights for flood risk management? Findings from central Vietnam. Journal of Flood Risk
Management. 5:295-302.
Cutter, S.L. 1996. Vulnerability to environmental hazards. Progress in Human Geography. 20:529-539.
Deltares. 2012. FMIS – Annex A: FHM Framework and Measures. Jakarta flood management information
system. Ministry of Public Works Indonesia. Directorate General of Water Resources. Directorate of
Rivers and Coastals. Jakarta.
Dutta, D.; S. Herath; and K. Musiake. 2003. A mathematical model for flood loss estimation. Journal of
Hydrology. 277:24-49.
Enarson, E. 2001. What women do: Gendered labor in the Red River Valley flood. Environmental Hazards.
3:1-18.
Fatti, C.E. and Z. Patel. 2013. Perceptions and responses to urban flood risk: Implications for climate
governance in the South. Applied Geography. 36:13-22.
Firman, T.; I.M. Surbakti; I.C. Idroes; and H.A. Simarmata. 2011. Potential climate-change related
vulnerabilities in Jakarta: Challenges and current status. Habitat International. 35:372-378.
Fothergill, A. 2004. Heads Above Water. Gender, Class, and Family in the Grand Forks Flood. State
University of New York Press. Albany.
Grothmann, T. and F. Reusswig. 2006. People at risk of flooding: Why some residents take precautionary
action while others do not. Natural Hazards. 38:101-120.
Hurford, A.P.; Č. Maksimović; and J.P. Leitão. 2010. Urban pluvial flooding in Jakarta: Applying state-of-theart technology in a data-scarce environment. Water Science and Technology. 62:2246-2255.
29
Economy and Environment Program for Southeast Asia
IPCC (Intergovernmental Panel on Climate Change). 2007. IPCC fourth assessment report: Synthesis report.
IPCC, [S.l.].
Jonkman, S.N.; M. Bočkarjova; M. Kok; and P. Bernardini. 2008. Integrated hydrodynamic and economic
modelling of flood damage in the Netherlands. Ecological Economics. 66:77-90.
Kazama, S.; A. Sato; and S. Kawagoe. 2010. Evaluating the cost of flood damage based on changes in
extreme rainfall in Japan. Adaptation and Mitigation Strategies for Climate Change. 3-17.
Kreibich, H.; I. Seifert; and B.H. Thieken. 2010. Development of FLEMOcs – a new model for the estimation
of flood losses in the commercial sector. Hydrological Sciences Journal. 55(8):1302-1314.
Merz, B.; H. Kreibich; R. Schwarze; and A. Thieken. 2010. Review article "Assessment of economic flood
damage". Natural Hazards Earth System Science. 10:1697-1724.
Messner, F.; and V. Meyer. 2004. Flood damage, vulnerability and risk perception – challanges for flood
damage research. Schance, J.; E. Zeman; and J. Marsalek (ed) In NATO advanced research workshop
on flood risk management: Hazards, vulnerability, and mitigation measures. Springer. Ostrov, Czech
Republic.
Messner, F.; and V. Meyer. 2005. Flood damage, vulnerability and risk perception – challenges for flood
damage research. UFZ-Diskussionspapiere. Helmholtz Centre for Environmental Research (UFZ).
Norris, F.H.; C.K. Baker; A.D. Murphy; and K. Kaniasty. 2005. Social support mobilization and deterioration
after Mexico's 1999 flood: Effects of context, gender, and time. American Journal of Community
Psychology. 36:15-28.
Penning-Rowsell, E.C.; and C. Green. 2000. New Insights into the appraisal of flood-alleviation benefits: (1)
Flood damage loss information. Journal of the Chartered Institution of Water and Environmental
Management. 14:347-353.
PU (Pekerjaan Umum). 2011. ATLAS: Pengamanan Pantai Jakarta. Kementerian Pekerjaan Umum (PU).
Jakarta, Indonesia.
Scawthorn, C.; P. Flores; N. Blais; H. Seligson; E. Tate; S. Chang; E. Mifflin; W. Thomas; J. Murphy; C. Jones;
and M. Lawrence. 2006. HAZUS-MH flood loss estimation methodology. II. Damage and loss
assessment. Natural Hazards Review. 7:72-81.
Siegrist, M.; and H. Gutscher. 2008. Natural hazards and motivation for mitigation behavior: People cannot
predict the affect evoked by a severe flood. Risk Analysis. 28:771-778.
Smith, D. 1994. Flood damage estimation – A review of urban stage-damage curves and loss functions.
Water South Africa. 20:231-238.
Smith, D.I. 1981. Actual and potential flood damage: a case study for urban Lismore, NSW, Australia.
Applied Geography. 1:31-39.
Steinberg, F. 2007. Jakarta: Environmental problems and sustainability. Habitat International. 31:354-365.
Suriya, S.; B.V. Mudgal; and P. Nelliyat. 2012. Flood damage assessment of an urban area in Chennai, India,
part I: Methodology. Natural Hazards. 62:149-167.
Tang, J.C.S.; S. Vongvisessomjai; and K. Sahasakmontri. 1992. Estimation of flood damage cost for Bangkok.
Water Resources Management. 6:47-56.
Texier, P. 2008. Floods in Jakarta: When the extreme reveals daily structural constraints and
mismanagement. Disaster Prevention and Management. 17:358-372.
Estimation of River Flood Damage in Jakarta: The Case of Pesanggrahan River
30
Thieken, A.H.; M. Müller; H. Kreibich; and B. Merz. 2005. Flood damage and influencing factors: New insights
from the August 2002 flood in Germany. Water Resources Research. 41:1-16.
Tu, T.T. and V. Nitivattananon. 2011. Adaptation to flood risk in Ho Chi Minh city, Vietnam. International
Journal of Climate Change Strategies and Management. 3:61-73.
Ward, P.J.; M.A. Marfai; F. Yulianto; D.R. Hizbaron; and J.C.J.H. Aerts. 2011. Coastal inundation and damage
exposure estimation: A case study for Jakarta. Natural Hazards. 56:899-916.
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Economy and Environment Program for Southeast Asia