MULTICRITERIA ANALYSIS AND REMOTE SENSING FOR FLOOD HAZARD DELINEATION IN PADDY FIELD LAND UTILIZATION (A CASE STUDY OF LOWER CITARUM WATERSHED, WEST JAVA)

The 1st International Conference of Indonesian Society for Remote Sensing 2015
” Ha essi g Ea th I fo atio f o Spa e”
At October 27-28th 2015
Geomatic Engineering, Institut Teknologi Sepuluh Nopember (ITS) Surabaya

PAPER ID – J.10
MULTICRITERIA ANALYSIS AND REMOTE SENSING FOR FLOOD HAZARD DELINEATION
IN PADDY FIELD LAND UTILIZATION
(A CASE STUDY OF LOWER CITARUM WATERSHED, WEST JAVA)
Widiatmaka1, Wiwin Ambarwulan2, Paulus B.K. Santosa3,4, Chandrasa E. Sjamsudin3
1

Dept. of Soil Science and Land Resource, Bogor Agricultural University, Indonesia
2
Geospatial Information Agency, Indonesia
3
Study Program of Environmental Management, Bogor Agricultural University, Indonesia
4
Ministry of Agriculture, Indonesia

Abstract

Indonesia is among one of the highest rank country where natural disasters very frequently occur. Such natural
calamities may be in the form of geological- as well as hydro meteorological disasters. Flood is one of the most
recurrent hydro meteorological disasters induced by wet tropical climate and other factors of natural resources. The
island of Java is currently still the pillar for national food sovereignty. Flood disasters that inundate paddy fields are
often the cause of crop failures. This research was conducted at the lower watershed of Citarum, West Java. The
research areas covered 3 (three) administrative regions, namely Karawang- (its western part), Purwakarta- (the
northern part) and Bekasi regency (at the eastern and northern parts of the regency). These regions are among the
numerous food production centres in Java, boasting a fairly wide area of paddy fields. The research objective is to
delineate flood hazard in lower Citarum watershed, West Java, Indonesia. The methodology used is a combination of
multi-criteria analysis and remote sensing. Parameters that influence flood hazard were weighed using analytical
hierarchy process, these include rainfall, elevation, slope, soil type, substrate, landform and land cover. Delineation of
the area according to the different levels of flood hazard was done through weighed spatial overlay in the geographic
i for atio s ste ’s odels. The e isti g la d utilizatio for padd field was obtai ed fro a IKONOS i ager
interpretation in 2012. Spatial overlay was done on paddy fields exposed to different levels of flood hazard. The result
of the analysis showed that there is a region with different levels of flood hazard, which can be spatially delineated.
Validation was performed using archival chronology of flood events. This research is important within the context of
maintaining national food sovereignty. National food supply is still dependent on paddy production centres in Java,
however failure to harvest the rice paddies often occur. The recommendations of this study based on the results are to
include the use of flood-resistant paddy seed varieties as well as other management required in high flood-hazard
areas.

Key words: disaster mitigation; food sovereignty; IKONOS imagery; multi-criteria land evaluation; spatial overlay

INTRODUCTION
Indonesia is among one of the highest rank
country where natural disasters very frequently
occur. Natural disasters can be broadly classified
into geological disasters such as earthquakes,
volcanic eruptions and tsunamis; and hydrometeorological disasters such as floods, droughts
and landslides. In terms of geological disasters,
the nature of the country as a meeting place of
three tectonic plates, the Eurasian, IndoAustralian and the Pacific plates, has caused
considerably frequent disaster incidences. The
meeting point of these 3 plates creates a volcanic
arc that stretches from Sumatra to Papua, which
is characterized by a volcanic mountain range
that is distributed along the plate meeting zone
(Katili, 1974; Verstappen, 2010).

The climate in Indonesia is wet tropical
climate with high rainfall since Indonesian

archipelago is located between the Asian and
Australian continents. This makes the country
vulnerable to hydro meteorological disasters.
Based on the data from the National Disaster
Management Agency (BNPB), there were 6,528
natural disasters, in which 250,000 people were
killed, within the period 1915-2010 (BNPB, 2015).
Hydro meteorological disasters are dominant in
the country, with 80% of occurrences.
In addition to the natural catastrophes
caused by the climate and geological condition,
high population growth has also led to an
increase of disasters, both in terms of frequency
and intensity. The human need for food, clothing
and housing has led to uncontrollable and
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The 1st International Conference of Indonesian Society for Remote Sensing 2015
” Ha essi g Ea th I fo atio f o Spa e”
At October 27-28th 2015

Geomatic Engineering, Institut Teknologi Sepuluh Nopember (ITS) Surabaya

destructive use of natural resources, offsetting
the balance of natural equilibrium. According to
the data, most flooding occurred because of
deforestation (Utomo & Widiatmaka, 2014).
Even in the world, flood disaster has a
special place in natural hazards because of its
losses. As an example, total economic losses
produced by major floods in Europe over the
1970-2006 period amounted to 140 billion expressed in 2006 US$ normalized values - with
an average annual flood loss of 3.8 billion
(Barredo, 2009; Ghizoni et al., 2010)
One way to delineate the flood hazard in an
area is using the multi-criteria analysis (MCA).
MCA has been widely used in natural resource
study, one of the reasons is because most of the
data resources are associated with each other
geographically (Malczewski, 1999; Yahaya, 2008).
With spatial geo-referenced data, the analysis of

geographic information systems can contribute
to decision making through an overlay process
which involves many criteria and so, it can
provide a compromise for decision making. The
concept is based on a multi-criteria decisionmaking that involves many factors. In this
analysis, various factors were weighed by their
role in the context of decision making. This
method has been widely used in determining the
use of resources, for example to model habitat
suitability (Store & Kangas, 2001; Widiatmaka et
al., 2015), determine waste disposal site (Effat
and Hegazy, 2012), delineate agricultural land
suitability (Bandyopadhyay, 2009; Mendas &
Delali, 2012; Akinci et al., 2013), determine
suitability area for industry (Rikalovic et al., 2014)
and urban aquaculture development (Hossain et
al., 2009) and many other cases involving the
need of decision-making. In case of natural
disasters, several studies use this method, either
to delineate flood hazard (Brivio et al., 2002;

Rohde et al., 2006; Kourgialas & Karatzas, 2011;
Abdalla et al., 2014), or determine the area
susceptible to land slide (Rawat & Joshi, 2012)
and earthquake (Parvaiz et al., 2012).
The objective of this study are: (i)
determining the weight of the parameters
influencing flood hazard, (ii) delineating the area
at different levels of flood hazards, (iii) setting
the existing extent of paddy field, and (iv)
demarcating paddy fields that are exposed to
flood hazards in lower Citarum watershed, West
Java, Indonesia. The Citarum watershed is one of
the watershed on the island of Java, which is

listed as a priority watersheds because of its high
vital land utilization for human being as well as its
high vulnerability. Paddy fields can be found in
abundance in this watershed environment,
making the region one of the food production
centres in Java.

METHODOLOGY
Research Area
The research was conducted at the lower
Citarum sub-watershed, West Java Province,
Indonesia (Figure 1). This sub-watershed is part
of Citarum watershed which is one of most
important watershed in West Java Province.
Geographically, the research area lies between
106o5 '5 ”E-107o27'23"E
and
5o54'34"6o33'52"S. This sub-watershed is part of 3 (three)
administrative regencies, namely Karawang (the
western part), Purwakarta (the northern part)
and Bekasi (eastern and northern parts).

Figure 1. Research Area of Lower Citarum Watershed

The lower Citarum sub-watershed is one of
the important centres of rice production in
Indonesia. As an illustration, Karawang regency,

which is part of this sub-watershed, in 2013
produced 1,153,830 tons of paddy, or 9.6% of the
total amount of rice production in West Java
(BPS, 2014). West Java Province itself is the
largest rice producing province in Indonesia. This
high production of rice is due to its high soil
fertility. The fertile soil comes from its parent
material, which is geologically formed from
sedimentary parent material, although volcanic
material appears locally. This region is a flat
region with a slope of 0-3%, which constitutes
more than 95% of the region. The area is of vast
plains, where more than 85% of its area is
located less than 50 m above sea level. The subwatersheds are in the areas that are distant-wise
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The 1st International Conference of Indonesian Society for Remote Sensing 2015
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Geomatic Engineering, Institut Teknologi Sepuluh Nopember (ITS) Surabaya


close to the state capital, Jakarta, so that changes
in land use, e.g. from agricultural land to nonagricultural use, are quite dynamic. This region
has a wet tropical climate, with an annual rainfall
that vary from 1,000 to 3,400 mm.year-1.

Multicriteria Analysis for Flood Hazard
Delineation
Basically, there are four (4) main steps to
produce flood hazard maps using the multi
criteria analysis technique. These steps were
conducted in this study: (1) defining the
parameters that determine flood hazard and
preparation of data sets of parameters (2)
determining the weight of these parameters, (3)
delineating and mapping of flood hazard, and (3)
determining the level of flood hazard in various
land utilization, with an emphasize on paddy field
land use.
Parameters Determining Flood Hazard and Data

Sets
Determinant parameters used for the
analysis of flood hazard are identified at the early
step through interviews with experts. A total of
five experts were interviewed about flooding and
its causes. Interview results are summarized in
the form of floods and determinant parameter
classification.
Rainfall. Rainfall is the main source of water
which causes floods. The impacts of rainfall on
subsequent floods are determined by how well
people can manage the water. Areas with high
rainfall have a greater possibility of flood
occurrence compared to the areas with lower
rainfall. In this research, the rainfall data used
was obtained from the Meteorology, Climatology
and Geophysics Agency (BMKG, 2014). The
rainfall data was obtained in spatial form, which
resulted based on treated data from 42 points of
BMKG station in Lower Citarum sub-watershed.

Elevation. There is a higher possibility of flooding
at low-elevation areas compared to areas located
at high elevation. Nonetheless, the area located
at a higher elevation still may experience
flooding, for example those that have flat slopes.
The elevation parameter for this study was
obtained from the elevation maps, created using
contour data of the Topographic Map scale at
1:25,000 from Geospatial Information Agency,

Indonesia. Elevation map-creation was done by
the module of 3-D Analyst in ArcGIS 10.2.
Slope. Water tends to accumulate in flat areas.
Water will automatically flow down on areas with
slopes; the possibility of any flooding is therefore
smaller. Similar to the elevation parameter, slope
parameters were created using contour data
from topographic maps of the Geospatial
Information Agency, Indonesia, through the use
of Spatial Analyst module on ArcGIS 10.2.
Soil Type. Different types of soil have different
rates of infiltration and water holding capacities.
When the infiltration capacity has exceeded,
overland flow results (Parawingira, 2008). Soil
with rough texture has a higher infiltration
capacity than the fine textured soils. Similarly, a
good soil structure will further facilitate water
absorption so that there will be a smaller
possibility of flooding. Various soil names are
generally characterized by different infiltration
characteristics. The soil data was obtained from
the Soil Map of 1:50,000 and 1:100,000 scale,
which was a result of a soil mapping activity
conducted jointly by the Research Centre for Soil
and Agro-climate, Bogor, and Faculty of
Agriculture, Bogor Agricultural University
(Faperta IPB, 1993; PPT, 1982; Puslitanak, 1993;
Puslitanak, 1995). In this study however, the soil
classification used was only at the Order category
(PPT, 1983).
Geology. Soils were derived from the various
rock types. Therefore, geology affects directly the
rate of infiltration of water in form of deep
percolation. Pervious rocks allow increased deep
percolation while the less pervious ones like
granite result in increased overland flow.
Geological data for this research was obtained
from a geological map at scale of 1:100,000 of
Karawang sheet (Djadja & Sudradjat, 1990) and
Cianjur sheet (Sudjatmiko, 1972).
Land use and land cover. Land use and land
ove has a i pa t o the soil’s i filt atio
capacity. Runoff is typically low in areas where
the percentage of vegetation cover is high, as
vegetated areas allow high infiltration until the
soil is saturated. Runoffs are spread over a long
period while increased overland is expected for
bare land. In this research, land use and land
cover were obtained from the analysis of Landsat
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TM-8 imagery of 2013. Image analysis was done
using a supervised classification of ERDAS
imagine software. The land cover classification
used the standard classification of SNI (SNI,
2010). Specifically for paddy field, the spatial data
used is paddy field spatial data measured by the
Ministry of Agriculture in 2012 using IKONOS
imagery. The land use and land cover data in this
research were used in the preparation of creating
a flood parameter map as a first step, and then in
the second step they were used to calculate
different land use and land cover affected by
flood hazard.
Landform. Some landforms are associated with
flood events. Alluvial landforms will be more
associated with the incidence of flooding
compared to the hilly or mountainous landforms.
In this research, landform was obtained from the
Land System Map from Soil Research Institute,
scale 1:250,000.

All data parameters obtained are drawn and
presented on the digital base map of the
Indonesian Topographic Map scale at 1:25,000
produced by Geospatial Information Agency,
Indonesia. The research area was covered by 19
sheets, which were sheets No. 1209243,
1209244, 1209444, 1209512, 1209514, 1209521,
1209522, 1209523, 1209532, 1209533, 1209534,
1209541, 1209542, 1209543, 1209544, 1210122,
1210211, 1210212 and 1210221. All spatial data
was geo-referenced and then drawn on the base
map. According to Law No. 4/2011 on Geospatial
Information, all spatial analysis conducted should
be based on a single reference that refers to this
base map. In the next process, all data is
converted into a vector format to be treated
using Geographic Information System. The
distribution of each parameters as well as its area
coverage are given in Figure 2 and Table 1. In this
table, as according to the description in this
paragraph, the results are presented in column 1,
3, 4 and 5.

Figure 2. Distribution of the main and sub-criteria in the study area of Lower Citarum Watershed
Table 1. Distributions of the main and sub-criteria parameters in the study area (column 1, 3, 4 and 5), weight and
score (column 2 and 6)
Main Criteria
1
Rainfall

Weight
2
0.362

Sub-Criteria
3
1,000 - 1,300 mm/year
1,300 - 1,600 mm/year
1,600 - 1,900 mm/year
1,900 - 2,200 mm/year
2,200 - 2,500 mm/year

Area
Ha
4
6,218.3
55,584.0
56,908.6
9,248.7
5,563.3

Score

%
5
4.4
38.9
39.8
6.5
3.9

6
1
1
3
8
10

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Main Criteria
1

Slope

2

0.238

Elevation

0.156

Geology

0.103

Landform

0.068

Landcover

0.044

Soil Type

3
2,500 - 2,800 mm/year

Area
Ha
4
4,468.0

2,800 - 3,100 mm/year
3,100 - 3,400 mm/year

Weight

Sub-Criteria

0-3%
3-8%
8 - 15 %
15 - 25 %
25 - 40 %
0 - 25 m
25 - 50 m
50 - 75 m
75 - 100 m
100 - 250 m
250 - 500 m
Surface Sediment
Sediment
Crest
Volcano
Alluvial Plains
Alluvial Valleys
Fans and Lahars
Hills
Mountains
Plains
Terraces
Tidal Swamps
Mixed Farm
Plantation
Settlement
Swamp
Rice Field
Shrub
Pond
Bared Land
Dryland agriculture
Water Body
Aluvial
Grumusol
Humus & Aluvial
Latosol
Podsolik
Regosol
Resina & Litosol

Determining Weights of Criteria using AHP
Approach
In the main procedure used, flood
determinants consist of main criteria, which are
further divided into sub-criteria. The weights
used in this study is the weight of the main
criteria, while score was given to the sub-criteria.
Both the weight of the main criteria as well as
scores of sub-criteria were classified according to
their contribution to flood as reviewed by the
experts.
Scoring was based on literature review and
autho ’s judg e t, e.g. heav ai fall that auses
flooding was given a higher score than low

Score

%
5
3.1

6
10

4,819.3

3.4

10

109.0

0.1

10

113,966.4
23,675.0
3,653.1
1,372.3

79.7
16.6
2.6
1.0

10
6
1
0

252.4
113,722.1
10,176.5
8,594.4
6,083.0
4,220.0
123.2
122,546.7
18,268.5
70.8
2,033.1
92,171.3
4,715.7
770.9
3,423.8
22,808.7
300.7
18,676.4
51.7
127.4
3,097.5
15,562.8
215.3
86,022.3
2,604.3
22,690.8
785.7
10,947.3
865.9
103,428.1
6,658.9
6,269.5
8,083.1
16,587.0
1,523.2
369.4

0.2
79.6
7.1
6.0
4.3
3.0
0.1
85.8
12.8
0.1
1.4
64.5
3.3
0.5
2.4
16.0
0.2
13.1
0.0
0.1
2.2
10.9
0.2
60.2
1.8
15.9
0.6
7.7
0.6
72.4
4.7
4.4
5.7
11.6
1.1
0.3

0
10
8
4
3
2
1
6
4
1
0
6
8
2
0
0
4
1
10
4
6
1
2
1
1
1
0
0
0
6
4
6
1
2
1
1

rainfall. Similarly, flood-prone landforms are
given higher score than less-prone ones. This
approach was used for all main-criteria: rainfall,
soil, geology, land use and land cover, and
landform. The scores for all sub-criteria are
presented in Table 1, column 6.
The weighing was performed for each main
criteria. This is the main procedure in MCE
(Ceballos-Silva & Lopez-Blanco, 2003, Akinci et
al., 2013; Widiatmaka et al., 2015). Weighing is
done by using the Analytical Hierarchy Process
(AHP). The basic concept used is the concept of
Saaty (1988). The comparison is done by
assessing the relative importance of two criteria
involved in determining suitability (Saaty, 1988;
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Widiatmaka et al., 2014). The ratings consist of 9
interests scale, from 9 to 1/9. If the rating shows
9, it would indicate that the line has a relatively
more important than the column. If the value is a
ninth, this shows that the line is significantly less
important than the column. If two variables are
equally important, they are given a value of 1. In
estimating the weights, a group of experts were
asked to compare the matrix in pairs. In practical,
the AHP analysis was performed using the
software of Expert-Choice.
Delineating and Mapping Flood Hazard
The main-criteria weights and sub-criteria
scores were appointed to the related layers in
the ArcGIS 10.2 environment. Raster maps of 7
main criteria were overlaid using the weighted
sum overlay analysis for generating map of flood
hazard. The weight of the main criteria were
multiplied with the score of the sub-criteria. This
multiplication was performed in raster format on
the map. The result was then reclassified using
equal distances as four classes of suitability:
highly suitable (S1), moderately suitable (S2),
marginally suitable (S3) and not suitable (N)
(Widiatmaka et al., 2014; 2015).

Determining flood hazard level at various
land utilizations with an emphasize on
paddy field
Land utilization used is land use and land
cover resulted from Landsat satellite image
interpretation as well as Ikonos imagery. Flood
hazard mapping results as presented before,
were overlaid in GIS environment. The
calculation of flood hazard area is performed
through tabulation. The discussion on paddy field
would be prioritized, in accordance with the main
theme of this paper.
RESULT AND DISCUSSION

related to the fact that flooding is caused by
precipitation. The next weights were respectively
slope, elevation and landform, followed by
geology, land cover and soil. In terms of the
flooding genesis, such a result makes sense. The
results of weighting each of these parameters
were multiplied by the scoring as presented in
Table 1 (column 6) to delineate spatial
distribution of flood.
Table 2. The weight of each parameter forming flood
S
oi
l

Land
Cover

Geol
ogy

Landf
orm

Eleva
tion

1

½

1/3

¼

1/5

2

1

1/2

1/3

¼

3

2

1

½

1/3

4

3

2

1

½

5

4

3

2

1

Slope

7

5

4

3

2

1

½

Rainf
all

9

7

5

4

3

2

1

Soil
Landc
over
Geolo
gy
Landf
orm
Eleva
tion

Slo
pe
1/
7
1/
5
1/
4
1/
3
1/
2

Rai
nfal
l
1/9
1/7
1/5
¼
1/3

Wei
ght
0.0
29
0.0
44
0.0
68
0.1
03
0.1
56
0.2
38
0.3
62

Ma eige value γ a = 7.203730872
n=7
Consistency index (Ci) = (γmax - n)/(n - 1) =
0.033955145
Random index (Ri) = 0.9
Consistency ratio (Cr) = Ci/Ri = 0.037727939
Flood Hazard Distribution. The results of flood
hazard analysis are presented in Figure 3a and
Table 3. The results indicate that the area of no
hazard occupies only a small area, 12.4% of the
total area. The largest area is the area associated
to medium hazard, reaching 72.1% of the total
area. The amount of medium hazard area
combined with high hazard area reach 89% of
total area analysed. These results confirm that
this region is a region with a high possibility of
flood hazard.

Weighed Result. The results of pairwise
comparison were given in Table 2 and was
summarized in Table 1 (column 2). In the table,
the results have a consistency ratio (CR) of 0.037.
As recommended by Saaty (1988), a CR equal to
or less than 0,10 was acceptable, and signifies a
small probability that the weights were
coincidentally developed (Widiatmaka et al.,
2014; 2015).
The overall results showed that the most
weighed criteria is rainfall. This result is highly
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Tabel 3. Hazard in Lower Citarum

(a)

(b)

Figure 3. Flood Hazard in Karawang Regency: (a)
Flood Hazard Classification; (b) Flood Hazard
Classification with Paddy Field Land cover

Nr

Hazard Classification

Value

1
2
3
4
5

No Hazard
Low Hazard
Medium Hazard
High Hazard
Very High Hazard
Total

2.376 - 3.709
3.709 - 5.042
5.042 - 6.374
6.374 - 7.707
7.707 - 9.040

Area
Ha
563.9
11,361.2
103,034.9
24,161.4
3,797.8
142,919.1

%
0.4
8.0
72.1
16.9
2.7
100.0

Flood Hazard in Variety of Land Use and Land
Cover. The results of flood hazard maps which
are overlaid with maps of land use and land cover
are presented in Table 4. This table is a
translation of the entire flood hazard as
presented in the previous table (Table 3), where
89% of the area have medium- to high level
hazard for flood. Table 4 shows that land use and
land cover which have a lower than average level
of flood hazard is broad enough. They are mixed
farm, plantation, settlement, shrub, bare land,
and dry land agriculture. They are land use for
dry land. Several other land use and land cover
are relatively prone to flooding. Land use and
land cover with heavier than the average level of
flood hazard are swamps, paddy fields and
ponds. They are the wetlands land utilization,
and lie with flat region.

Table 4. Flood Hazard in variety land uses of Karawang Regency
Nr
1
2
3
4
5
6
7
8
9
10

Landcover/
Landuse
Mixed Farm
%
Plantation
%
Settlement
%
Swamp
%
Paddy Field
Shrub
%
Pond
%
Bare Land
%
Dryland agriculture
%
Water Body
%
Total
% Total

No
Hazard
12.1
0.1
5.4
0.0
123.2
4.7
0
13.9
1.8
409.3
3.7
0
563.9
0.4

Low
Hazard
16.9
13.2
879.9
28.4
2,092.3
13.4
3.3
1.5
2,967.0
3.4
747.4
28.7
36.3
0.2
300.4
38.2
4,225.2
38.6
92.64
10.7
11,361.2
7.9

Medium
Hazard
71.0
55.7
893.3
28.8
11,803.8
75.8
39.0
18.1
75,920.8
88.3
1,493.5
57.4
8,706.9
38.4
364.7
46.4
3,161.4
28.9
580.61
67.1
103,034.9
72.1

High
Hazard
15.8
12.4
1,313.5
42.4
1,194.7
7.7
59.6
27.7
4,301.7
5.0
237.1
9.1
13,947.5
61.5
97.4
12.4
2,921.9
26.7
72.1
8.3
24,161.4
16.9

Very High
Hazard
23.7
18.6
10.7
0.3
460.1
3.0
113.4
52.7
2,827.3
3.3
3.2
0.1
0
9.34
1.2
229.6
2.1
120.5
13.9
3,797.8
2.7

Total
127.4
100
3,097.5
100.0
15,562.8
100.0
215.3
100.0
86,022.3
100.0
2,604.3
100
22,690.8
100
785.7
100
10,947.3
100
865.9
142,919.1
100

425

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Geomatic Engineering, Institut Teknologi Sepuluh Nopember (ITS) Surabaya

Comparison of model result with flood
occurrence. A comparison was done between
flood hazards that resulted from the model with
actual flood events. The actual flood events used
is the data of flood event recordings (Figure 4
and Table 5). The flood actual data include 28
points, in which most data derived from the
internet and BNPB (2015). It should be noted that
the original data present only the name of the
village, while in this paper, we put each point
spatially at the centre of the village. Such a
comparison is therefore somewhat of an

approach. From those 28 points, 1 point occurs in
the area of very high hazard, 1 point in high
hazard while the 26 point were located in
medium-level hazard area. The high number of
points in the medium-level hazard area was
caused by the large area it occupies, covering
72.1% of the study area. It also shows that even
in the medium-level hazard areas, flood events
occur a lot. There were no points found in the no
hazard area.

Table 5. Existing flood occurrence
Village
Baturaden
Rengasdengklok Utara
Tegallega
Tanjungpura
Tanjungmekar
Karawangkulon
Adiarsa Barat
Nagasari
Adiarsa Timur
Parungsari
Telukjambe
Sukaharja
Puseurjaya
Sukaluyu
Sirnabaya
Teluk Buyung
Karangligar
Mekarmulya
Mulyajaya
Wadas
Purwadana
Segaran
Kertajaya
Klari
Jatimulya
Karyamakmur
Karyabakti
Warungbambu

Sub-District
Batujaya
Rengasdengklok
Ciampel
Karawang Barat
Karawang Barat
Karawang Barat
Karawang Barat
Karawang Barat
Karawang Timur
Telukjambe Barat
Telukjambe Timur
Telukjambe Timur
Telukjambe Timur
Telukjambe Timur
Telukjambe Timur
Pakisjaya
Telukjambe Barat
Telukjambe Barat
Telukjambe Barat
Telukjambe Timur
Telukjambe Timur
Batujaya
Jayakerta
Klari
Pedes
Batujaya
Batujaya
Karawang Timur

Flood Level
Medium Hazard
Medium Hazard
Very High Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
High Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard
Medium Hazard

Occurrence
February 2008
February 2008
18 - 21 March 2010
18 - 21 March 2010
18 - 21 March 2010
18 - 21 March 2010
18 - 21 March 2010
18 - 21 March 2010
18 - 21 March 2010
18 - 21 March 2010
18 - 21 March 2010
18 - 21 March 2010
18 - 21 March 2010
18 - 21 March 2010
18 - 21 March 2010
16-17 January 2013
16-17 January 2013
16-17 January 2013
16-17 January 2013
16-17 January 2013
16-17 January 2013
18 January 2013
18 January 2013
18 January 2013
18 January 2013
19 January 2013
19 January 2013
19 January 2013

Source
http://berita.i-y-i.com
http://berita.i-y-i.com
http://papuapost.wordpress.com
http://regional.kompas.com
http://regional.kompas.com
http://regional.kompas.com
http://regional.kompas.com
http://regional.kompas.com
http://regional.kompas.com
http://papuapost.wordpress.com
http://papuapost.wordpress.com
http://papuapost.wordpress.com
http://papuapost.wordpress.com
http://papuapost.wordpress.com
http://papuapost.wordpress.com
http://papuapost.wordpress.com
http://papuapost.wordpress.com
http://papuapost.wordpress.com
http://papuapost.wordpress.com
http://papuapost.wordpress.com
http://papuapost.wordpress.com
http://www.karawanginfo.com
http://www.karawanginfo.com
http://www.karawanginfo.com
http://www.karawanginfo.com
http://m.inilah.com
http://m.inilah.com
http://www.karawanginfo.com

426

The 1st International Conference of Indonesian Society for Remote Sensing 2015
” Ha essi g Ea thth I fo atio f o Spa e”
At October 27-28 2015
Geomatic Engineering, Institut Teknologi Sepuluh Nopember (ITS) Surabaya

Measure necessary for paddy field land utilization. The analysis of this study shows that there are regions
which are vulnerable to natural disasters that however are crucial for the provision of food. As stated in the
introduction, Indonesia is a country with a high incidence of natural disasters. During meteorological
disasters, this is reinforced by an increasingly diverse catastrophic events due to climate change. In the dry
season in 2015, for example, many plants fail to be harvested or their plantings were delayed due to lack of
water. An analysis stated that the negative effects of drought in 2015 is the disruption of approximately
111,000 hectares of agricultural land, of which crop failures came to about 8,900 ha
(http://agroindonesia.co.id/). Conversely, in the rainy season, flooding often come causing huge losses
(Kompas, 2013). For this reason, actions are required. It is suggested to use flood-resistant rice varieties in
rice cultivation. Some flood-resistant rice varieties have been discovered. Japanese researchers have found
SNORKEL 1 and 2 (Fukai & Cooper, 1995). In Indonesia, the Agricultural Research Agency, Ministry of
Agriculture, has released a number of high-yielding varieties of flood-resistant rice, among others Tapus.
Tapus is developed on swamp land with a pool depth of up to 150 cm. Banyuasin, Batang, Dendang,
Indragiri, and Punggur are also rice varieties which are suitable to be developed on potential wetlands, peat
and acid sulphate. The varieties of Martapura and Margasari are well adapted to grow on tidal land. From
2008 to 2012, seven flood-resistant paddy varieties were released namely Inpara 1 to Inpara 7
(http://www.litbang.pertanian.go.id/). In addition, some other measures should also be taken, such as the
preparation of farmer human resources in anticipation of flooding. In this context, such a study that
provides direction on locations with different levels of flood hazard would be very important.
CONCLUSION
The research for delineating flood hazard was conducted at the lower Citarum sub-watershed areas,
which covered 3 (three) administrative regions, namely Karawang- (its western part), Purwakarta- (the
northern part) and Bekasi regency (at the eastern and northern parts of the regency). Delineating flood
hazard was performed using multicriteria analysis and remote sensing. The parameters used were rainfall,
elevation, slope, land use and land cover, landform, soil class and geology. An Analytical Hierarchy Process
was conducted to obtain the weight of parameter in the genesis of flood hazard. The result of the research
indicates that there is a region with different levels of flood hazard, which can be spatially delineated. From
a total area of 142,919 ha encompassing the Lower Citarum Sub-watershed, 0.4% of the area is classified
as a hazardless flood area, 8.0% defined as slightly flood hazard area, 72.1% considered as medium flood
hazard area, 16.9% high flood hazard and finally, 2.7% of the area is of very high hazard level. The map that
resulted from this study has also been compared with the field data. This research is important within the
context of maintaining national food sovereignty. In fact, national food supply is still dependent on paddy
production centers in Java. Unfortunately failure to harvest rice paddies often occur. The recommendations
of this study based on the results are to include the use of flood-resistant paddy seed varieties as well as
other management policies required in high flood-prone level areas.
ACKNOWLEDGEMENT
This research was financed by the Directorate General of Higher Education, Ministry of National
Education of the Republic of Indonesia, through Operational Support for State Universities (Bantuan
Operasional Perguruan Tinggi Negeri - BOPTN), Bogor Agricultural University, Indonesia.
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Geomatic Engineering, Institut Teknologi Sepuluh Nopember (ITS) Surabaya

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