Analysis of Farmland Potency to Improve Community Economy in
Letbaun Village Semau Sub-district Kupang District East Nusa
Tenggara Province
Sutirto, Yunus Fallo and Amy Wadu
Civil Engineering Lecturer, Kupang State Polytechnic, Kupang City, East Nusa Tenggara, Indonesia
Keywords: Farm Potency, Improving Community Economy,Water Needs Supply.
Abstract: Semau Island is a small island with a minimum population of 8000 people who come from the Helong tribe,
one of the tribes in East Nusa Tenggara. The location of this island is in the waters west of Timor Island, west
of Kupang City. Kupang City is the administrative center of East Nusa Tenggara Province. Letbaun Village,
Semau District, Kupang Regency has productive land to be used as agricultural land. Constrained by the
limited availability of water, so the land is not utilized so that it becomes a shrub field. The results of the
analysis of the potential of agricultural land in this village are 900,126 hectares of the total area of 1,379,634
hectares or 65.24% of the total area. And to meet the water needs, a reservoir was built in Sub-watershed 10
at coordinates 123°24'23.41” East Longitude and 10°11'44.23” South Latitude with a water capacity of
442,230 m3/second every 10 years with a Chatment Area of 215,571 Ha. In this case, the provision of land
by optimizing non-productive land into productive land will overcome the problem of food supply shortages.
1 INTRODUCTION
At present, one of the goverment targets is making
Indonesia as food-self sufficiency country. Therefore
it is planned various goverment program to achieve
that goal, even in the remote regions is strived for
improving like East Nusa Tenggara province
(NTT).It has to be supported by some factors, among
other are natural resources and human resources.
Indonesia is very famous with the strategic location
so that it has farmland which becomes the primary
livelihood for the people. The dominant factor in
improving the agriculture is the water supply, NTT
region is one of the regions that always experiences
the drought and the lack of water. To answer it
therefore the water management is needed in order to
fulfill the people need.The goverment attempts to
build irrigation area in dry land or wet land which
have potency to be improved into farmland that can
fulfill the needs of the community. The limited water
supply is one of the obstacles in improving the
community welfare in Kupang District through
agricultural sector which is the biggest income
resource for the community in general in Kupang
District or Central Kupang Sub-District, but the
yields or agricultural product is still low.
Pesident Joko Widodo said that the problem in
East Nusa Tenggara is only the water, the
improvement of NTT depends on water supply. The
people in East Nusa Tenggara region have various
livelihoods, one of them is farming. The goverment
tries to build the irrigation area in dry land or wet land
that has potency to be improved into farmland that
can fulfill the people needs (Juditha, 2016).
Semau island is a small island that populated
about 8000 people who come from Helong tribe,one
of the tribes in East Nusa Tenggara Province. The
location of Semau island is in the Western waters of
Timor island,it is in the west of Kupang city, Kupang
city is the government centre of East Nusa Tenggara
province. In Letbaun Village Semau Sub-District,
there is productive land to be made into farmland.
According to the background and the problem
above, it is required the Analysis Study of Land
Usage as The Farmland to Improve The Community
Economy in Letbaun Village Semau Sub-District
Kupang District East Nusa Tenggara Province.
1158
Sutirto, ., Fallo, Y. and Wadu, A.
Analysis of Farmland Potency to Improve Community Economy in Letbaun Village Semau Sub-district Kupang District East Nusa Tenggara Province.
DOI: 10.5220/0010961300003260
In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 1158-1168
ISBN: 978-989-758-615-6; ISSN: 2975-8246
Copyright
c
 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

2 LITERATURE REVIEW
2.1 DAS (Watershed) Managemen
DAS management is a formulation process and
activity implementation or a program that
manipulated the natural resources and human
resources in watershed to obtain the benefit of
production and service without causing the damage of
water and land resources. Included in the DAS
management is linkages identification between land
use, land and water, and the linkages between
upstream areas and downstream areas of the DAS
(Asdak,2004:5).
2.1.1 Spatial System based on Land and
Water Conservation
Spatial system is a way to manage, to plan, to run and
to control the region. In the sustainable spatial
system, variables of economy, social and
conservation of water and land resources become the
unity.
Border requirement for spatial planning that based
on land and water conservation is using the border of
watershed (DAS).
2.1.2 Land Conservation
Land conservation is efforts to use, to maintain and to
protect the land resource, or an effort to improve and
to protect land resources. Generallyland conservation
is to protect the land from the rainfall directly, to
improve the capacity of land infiltration, to reduce the
surface runoff, to improve land aggregate stability
(Hardjowigeno, 1995).
Therefore, the thing that is very important in
utilizing the land resources is the analysis of that land
ability. Based on this land ability analysis, the
direction of land usage can be known so that the land
conservation can be one of the bases in spatial
arrangement.
2.1.3 Water Conservation
Water conservation is the efforts in utilizing and
protecting the water resources. Empowering water
conservation principle in spatial planning is an
effective way to maintain nature condition and
environment equilibrium.
From the description above, it can be interpreted
that water conservation is an effort to put the water
into the soil in order to fill groundwater, both natural
recharge and artificial recharge.
2.1.4 AGWA Model (Automated Geospatial
Watershed Assessment) Tool
Kineros method is a part of AGWA extension which
is a tool to analize the hidrology phenomenon for the
research about watershed. This model is designed to
stimulate the infiltration process, the depth of surface
runoff and erosion that occurs in a DAS with
relatively small scale that is ≤ 100 km2 (Agwa, 2000).
2.2 Philosophical Idea of Kineros
Method
The idea of Kineros method is if a land gets rain with
certain intensity, then the part of water that falls into
land surface will be infiltrated into the land until the
certain saturation limit, whereas the other part will
overrun on the land surface or flooded. This condition
depends on the land ability to absorb the water based
on various factors that influence it, such as the slope
of the land, the components of land structure and the
soil physical properties (
Bisri, 2017).
Figure 1: Basic Philosophy of Surface Water Runoff.
2.2.1 The Processing of Land Type Map and
Land Texture Definition
Each one area of land texture polygon contains some
different land components. These components are
noted on the table that named Comp.dbf. then for each
component has different land component in each
depth and it is noted in the table that named Layer.dbf.
The determination of land structure properties here is
based on the data of land texture that obtained by
using texture triangle as follows.
Analysis of Farmland Potency to Improve Community Economy in Letbaun Village Semau Sub-district Kupang District East Nusa Tenggara
Province
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Figure 2: Triangle diagram of Land texture class .
2.2.2 Research Result – Previous Research
Herawati, 2010. The research result showed that the
erosion danger level in Cisadane DAS covered very
light to very heft with the precentage of land area in a
row from very light to very heft55,85%;15,74%;
6,33%; 0,81%; and n 0,30%. The land with the very
heft erosion danger level covered the area of 316 ha
and the heft level covered the area of 851 ha.
Tamansari was the Sub-district that had land area
with the most serious erosion danger level,that was 87
ha. The others Sub-Districts which had serious
erosion danger level were Tenjolaya, Caringain,
Cijeruk, and Nanggung. This result research can be
used as the basic data to make the better planning of
DAS management.
Sulaiman et al, 2017. His research result showed
that Kupang city had high ground water potency, it
was proved from 49% of total area of Kupang City or
about 8070.74 ha had high ground water potency.
And only 561.85 Ha or about 3.42 % which had low
ground water potency. Most of spread of ground
water in Kupang city was influenced by the
topography condition in Kupang city. The wavy relief
made the spread of ground water in Kupang city was
randomly scattered from the flat topography area to
sloping area. Beside the topography condition, the
spread of ground water in Kupang city was also
influenced by the land usage in Kupang city, the kind
of land usage would influence in surface flow and
infiltration capacity that occured in some areas in
Kupang city which had high ground water potency.
2.3 Research Method
2.3.1 Research Location
This research location was in Uitiuh Tuan Village
South Semau Sub-District Kupang District. To reach
the research location, it was traveled through cruise
ship by speed boat or ferry then continued by
motorcycle or car from Semau Port to Letbaun
Village Semau Sub-District for at least 30 minutes
with the distance of 60 Km.
Figure 3: Research Location.
2.3.2 The Necessary Data and Data Sources
1. The data of daily rainfall in 2011 until 2020 that
sourced from Meteorology and Geophysics
Agency Kupang. the data of rainfall from the
Station observation result that located around
DAS Semau
2. Watershed (DAS) map and river network in
Timor island that sourced from BP DAS Benain
Noelmina as the comparing tool in making DAS
border.
3. RBI map with the scale of 1: 50.000 used to
know the nature condition, elevation, and flow
direction.
4. Remote sensing image of Lansat7 ETM
Satellite+ and Lansat 8 (OLI) location in
recording years of 2004, 2009, and 2020 that
sourced from USGS (U.S. Geological Survey)
used to identify the condition of land usage in
DAS Noel Amabi.
5. Land usage map in 2020 Kupang District as the
comparing tool, land usage map that obtained
from processing Remote sensing image.
6. Land type map used to know the land type in
DAS Semau and to determine land erodibility
value.
7. Rain station map used to know the spread of rain
gauge station. Beside that to know the area of
rain station influence.
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3 RESULT AND DISCUSSION
3.1 Hidrology Analysis
The research location located in DAS Uitao, there are
three nearest rainfall stations that influenced to DAS
Uitao region. Those stations are the nearest station in
DAS Uitao region. Rainfall data used in that analysis
consisted of daily rainfall with the observation period
from 2011 until 2020.
The location of coordinate and elevation for each
raifall station in research area are as follows
Table 1: Rainfall Stations of Research Area.
Whereas for spread map of rainfall station in DAS
Uitao research area can be seen in figure 4
Figure 4: Map of DAS Uitao Rainfall Station Influence
Source: Analysis Result, 2021.
Table 2: Yearly maximum rainfall of study area
(2011-
2020).
Source: BMG Stasiun Klimatologi Lasiana Kupang,2021
Table 3: Total rainfall and rain day of study area
2011-
2020.
Total Rainfall and Monthly Rain Day
Batakte Station Tenau Station Manulai Station
No Yea
r
Total Rainfall Total Rain Day Total Rainfall Total Rain Day Total Rainfall Total Rain
D
(mm) ( day) (mm) (dayi) (mm) (day)
1 2 3 4 5 6 7 8
1 2011 1,383.00 76.00 1,412.30 91.00 1,276.30 84.
2 2012 1,715.30 69.00 1,346.00 71.00 1,009.80 68.
3 2013 1,317.20 70.00 1,483.30 69.00 1,350.50 82.
4 2014 1,065.50 67.00 387.50 42.00 1,331.50 74.
5 2015 1,999.00 92.00 4,241.00 62.00 1,222.00 58.
6 2016 914.00 60.00 1,950.30 79.00 2,055.00 55.
7 2017 2,015.00 71.00 2,445.00 67.00 1,047.50 35.
8 2018 1,635.50 82.00 1,951.00 99.00 1,277.60 56.
9 2019 1,737.00 60.00 2,288.00 112.00 1,006.50 50.
10 2020 1,555.00 89.00 1,662.00 81.00 1,179.00 79.
Source : BMG Stasiun Klimatologi Lasiana Kupang, 2021.
3.2 Regional Average Rainfall
The determination of regional average rainfall uses
Polygon Thiessen method. The description of
Polygon Thiessen done by inserting each coordinate
into the table to obtain rain station spread map. Then
made the Polygon Thiessen by activating extension
spatial analyst with border of influence area is Rain
Station map of Uitao DAS by producing DAS Uitao
Polygon Theissen map (Figure.5). The Influence
area of Polygon Thiessen with Thiessen coefficient of
each Rain Station in research area of DAS Uitao is
presented in Table 4.
Table 4: The Area of Influence Spread to Rain Station in
DAS Uitao.
Elevation Coordinate(Geography)  RainStation  Thiessen 
No. Rain (m)  Tgeography  InfluenceArea  Coefficient
Name  (m)  EastLongitude
S
outhLatitude  (Ha)  (C) 
1 
TenauStation  418  1.233,912  ‐101,631 
526.057  0.381
2 
ManulaiStation 20  1.234,476  ‐101,855 
41.053  0.298
3 
BatakteStation  380  1,234,340  ‐102,038 
442.615  0.321
Total
1009.725  1.000
Source : Calculation Result, 2021
From the data of Yearly Maximum Rainfall of
Research Area (2010-2019) Table 6. Multiplied by
precentage of Thiessen coefficient. The calculation
result of Software ArcView GIS Software obtained
the value of Regional Average Maximum Rainfall,it
can be seen on the table below:
From the calculation result of Average Yearly
Maximum Rainfall of Research Area with the
influence of Thiessen Coefficient obtained the yearly
average rainfall and it has been sorted, it is presented
on the table below:
Name Elevatio
n
Coordinate( Geography)
Rain Station (m) East Longitude South
Latitude
Tenau 418 1.233,912 -101,631
Manulai 20 1.234,476 -101,855
Batakte Station 380 1,234,340 -102,038

Maximum Rainfall
No Year Batakte Station Tenau Station Manulai Station
(mm) (mm) (mm)
1 2011 180.00 161.00 53.50
2 2012 159.00 102.00 72.00
3 2013 94.00 108.00 125.00
4 2014 74.00 80.00 154.00
5 2015 289.00 310.00 63.00
6 2016 98.00 90.00 275.00
7 2017 145.00 190.00 204.00
8 2018 171.00 210.00 130.00
9 2019 195.00 201.00 75.00
10 2020 125.00 183.00 140.00
Analysis of Farmland Potency to Improve Community Economy in Letbaun Village Semau Sub-district Kupang District East Nusa Tenggara
Province
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Figure 5: Polygon Theissen Map Source: Analysis Result,
2021.
Table 5: Average Rainfall after sorted.

No Year Average Rainfall
 
1 2014 100.101
2 2013 108.567
3 2012 111.363
4 1011 135.099
5 2020 151.587
6 2019 161.570
7 2018 173.672
8 2016 147.634
9 2017 179.726
10 2015 229.740
Source : Calculation Result, 2021
3.3 Planning Rainfall Analysis
Planning rainfall is the biggest rainfall that is possible
to occur in an area with certain chance. The analysis
of planning rainfall is a procedure to predict the
frequency of raining in the past and in the future. With
the analysis of rainfall frequency, it can be known rain
distribution type that can represent the spread of daily
rain data so that it can be determined the planning rain
with various repeated period (Suripin, 2018).
3.4 Statistic Parameter
By calculating the statistic parameter such as average
value, deviation standard, variation coefficient, and
skewness coefficient from the available data and
followed by statistic test, then the suitable rain
probability distribution can be determined
Table 6: The Determination of Statistic Parameter of
Research Area.
No Year Xi ( mm) (Xi-X)
(Xi -?)
2
(Xi -?)
3
(Xi -?)
?
1 2014 100.10 -49.8 2,480.52 -123541.88 6,152,981.62
2 2013 108.57 -41.34 1,708.88 -70,642.66 2,920,267.91
3 2012 111.36 -38.54 1,485.56 -57,257.88 2,206,888.14
4 1011 135.10 -14.81 219.23 -3,245.96 48,060.75
5 2020 151.59 1.68 2.83 4.75 7.99
6 2019 161.57 11.66 136.05 1586.86 18,509.15
7 2018 173.67 23.77 564.81 13423.11 319,010.07
8 2016 147.63 -2.27 5.16 -11.73 26.65
9 2017 179.73 29.82 889.22 26516.53 790,719.26
10 2015 229.74 79.83 637.43 508814.02 40,620,621.80
Total 1,499.06 0.00 13,865.69 295.646,19 53.077.098,35
X 149,906
iation Standard 39,251

Cs 0,679
Ck 4,437
Source : Calculation Result, 2021
The calculation step on Table 11 to determine the type
of that probability distribution is as follows:
1. Determine Average Value
𝑋
∑
𝑋𝑖


𝑛
𝑥
1.499,06
10
X = 149,906
2. Determine Deviation Standard Value (Sd)
𝑆𝑑

∑
𝑋𝑖 𝑋




𝑛1
𝑆𝑑

13.865,69
10  1
Sd = 39,251
3. Determine Skewness Coefficient Value (Cs)
𝐶𝑠
𝑛 𝑥
∑
𝑋𝑖 𝑋³



𝑛1

𝑛2

𝑆𝑑³
𝐶𝑠
10 𝑥 295.646,19
9 𝑥 8 𝑥 39,251

𝐶𝑠0,679
4. Determine Kurtosis Coefficient Value (Ck)
𝐶𝑘
𝑛²
∑
𝑋𝑖 𝑋⁴



𝑛1

𝑛2

𝑛  3𝑆𝑑⁴
𝐶𝑘
10

𝑥 53.077.093,35
9 𝑥 8 𝑥 7 𝑥 32,036

𝐶𝑘4,437
3.5 Distribution Type Election
In Statistic knowledge, it is known some types of
frequency distribution and four types of distribution
that widely used in hidrology field that are Normal
Distribution, Log-Normal distribution, Gumbel
Distribution and Log-Person Type III Distribution
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(Suripin, 2018).
Table 7: Distribution Type Election of Research Area.
No Distribution Type Requirement Calculatio n Res ult Description

Cs = 0 Cs = 0,679
1 Normal Ck = 0 Ck = 4,437 Ineligible

Cs = 1,14 Cs = 0,679
2 Gumbel Ck = 5,4 Ck = 4,437 Ineligible

Cs = 0,688 Cs = 0,679
3 Log Normal Ck = 3,14 Ck = 4,437 Ineligible

4 Log Pearson Type III Besides the value Cs = 0,679 Eligible
Source : Analysis Result, 2021
Table 8: The calculation of Deviation Standard and
Skewness Coefficient (Cs) of Research Area.
No Year Rainfall Log Xi Log Xi - Log X
(Log Xi - Log X)
2
(Log Xi - Log X)
3

Xi ( mm)

1 2014 100.101 2.000 -0.162 0.026 -0.004
2 2013 108.567 2.036 -0.217 0.016 -0.002
3 2012 111.363 2.047 -0.136 0.013 -0.002
4 1011 135.099 2.131 -0.032 0.001 0.000
5 2020 151.587 2.181 0.018 0.000 0.000
6 2019 161.570 2.208 0.046 0.002 0.000
7 2018 173.672 2.240 0.077 0.006 0.000
8 2016 147.634 2.169 0.006 0.000 0.000
9 2017 179.726 2.255 0.092 0.008 -0.001
10 2015 229.740 2.361 0.199 0.099 -0.008
Total 1,499.059 21.628 0.000 0.113 0.001
Log X 2,163
S Log X 0.112
Cs 0.100
Source : Analysis Result, 2021
Table 9: The Calculation of Planning Rain with various
repetition.
No Tr DeviationStand Skewness Chance PlanningRainfall 
(Year) LogX (S.LogX) (Cs) (%) K LogX X( mm)
1 2 3 4 5 6 7 8
9
1 1.01 2.163 0.112 0.1 99 ‐2.252 1.91 81.319
2 2 2.163 0.112 0.1 50 ‐0.03 2.159 144.334
3 5 2.163 0.112 0.1 20 0.84 2.257
180.688
4 10 2.163 0.112 0.1 10 1.29 2.307 202.951
5 25 2.163 0.112 0.1 4 1.79 2.363 230.92
0
6 50 2.163 0.112 0.1 2 2.11 2.399 250.811
7 100 2.163 0.112 0.1 1 2.4 2.432
270.314
8 1000 2.163 0.112 0.1 0.1 3.24 2.526
335.788
Source : Analysis Result, 2021
Information
[1) = Number
[2] = Repeat
[3] = (S.Log Xi)/n
[4] = ((S.Log Xi)/n))//(n-1)
0,5
[5] = (n.S.(Log Xi-LogX)
3
/(n-1(n-2)(LogX))
3
[6] = ( 1/Tr)*100
[7] = Table of Pearson III. log distribution properties
factor table Based on Cs Value and Opportunity or
Repeat Time
[8] = LogX + K.S.Log X
[9] = Antilogue of X
3.6 Distribution Suitability Test
Distribution suitability test means to know whether
the selected distribution can be used or not, for the
available data series. In this study, to the requirement
of distribution suitability test analysis used two
statistic methods, that are Chi Square test and
Smirnov Kolmogorov test.
3.7 Chi Square Test
Chi Square test means to determine whether the
selected chance distribution equation can represent
analyzed sample data statistic distribution. The
decision of this test uses parameter.
Table 10: Interpolation of G Value.
No. Pr (%) G
1
80 -0,830
2
75 -0,697
3
50 -0,032
4
25 0,695
Source : Calculation Result, 2021.
Table 11: Recapitulation of Rainfall for each Research
Area.
No Pr Lo g X Deviation Standard Skewnes s G Rainfall
(%) (Log X) (Cs) Tabel ( mm)
1 25 2.153 0.112 0.100 0.695 170.15
2 50 2.153 0.112 0.100 -0.032 141.58
3 75 2.153 0.112 0.100 -0.697 118.84
4 80 2.153 0.112 0.100 -0.830 114.83
Source : Calculation Result, 2021
Table 12: Calculation of Chi-Square Test.
Source : Calculation Result, 2021
Table13: Calculation of Chi-Square Test.
Source : Calculation Result, 2021
No A
X
2
Teble
X
2
Count
1 1% 13.277 3
X
2
Count
< X
2
Teble
Distribution is acceptable
2 5% 14.860 3
X
2
Count
< X
2
Teble
Distribution is acceptable
Information
Frequency
No Class Class Theoretical Observation
(%) ( mm ) (Ea) (Oi)
1 I 0 - 25 0 - 118,62 2 3
2 II 25 - 50 118,62 - 122,13 2 0
3 III 50 - 75 122,13 - 141,58 2 2
4 IV 75 - 80 141,58 - 165,59 2 3
5 V 80 ~ 165,59 ~ 2 2
Total 10
Analysis of Farmland Potency to Improve Community Economy in Letbaun Village Semau Sub-district Kupang District East Nusa Tenggara
Province
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The calculation of K value is presented in the
Table completely as follows:
Table 14: Interpolation of Pr Value.

Pr (%)
No K Pr (Pr/100)
1 -1,606 94,462 0,945
2 -1,235 88,955 0.890
3 -1,119 80.000 0,800
4 -0.235 57,696 0,577
5 -0.291 37,955 0,380
6 0,583 28,871 0,289
7 0,913 17,485 0,175
8 0,170 43,091 0,431
9 1,070 14,898 0,149
10 2,192 -0.823 -0.008
Source : Calculation Result, 2021
Table 15: Calculation of Smirnov Kolmogorof Test.
No Year Planning Rainfall Log Xi Pe K Pr Pt (Pt-Pe)
1 2 3 4 5 6 7 8 9
1 2014 100.101 2.000 0,091
-1,606
0,945 0,055 -0.036
2 2013 108.567 2.036 0,182 -1,235 0.89 0,110 -0.071
3 2012 111.363 2.047 0,273 -1,119 0,800 0,200 -0.073
4
1011 135.099 2.131
0,364 -0.235
0,577
0,423 0.059
5 2020 151.587 2.181 0,455 -0.291 0,380 0,620 0.166
6 2019 161.570 2.208 0,545 0,583 0,289 0,711 0.166
7 2018 173.672 2.240 0,636 0,913 0,175 0,825 0.189
8
2016 147.634 2.169
0,727 0,170
0,431
0,569 0.158
9
2017 179.726 2.255
0,818 1,070
0,149
0,851 0.033
10 2015 229.740 2.361 0,909 2,192 -0.008 1,008 0.099
Jumlah 21.627
Log X 2.153
S Log X 0.095 D Max 0.1989
Cs 0.1
Source : Calculation Result, 2021
Table 16: The Decision of Smirnov Kolmogorof Test.
C Δcritical ΔMax Description
0,2 0,32 0,189 Accepted
0,1 0,37 0,189 Accepted
0,05 0,41 0,189 Accepted
0,01 0,49 0,189 Accepted
Source : Calculation Result, 2021
3.8 The Drawing of DAS Uitao Map
The drawing of DAS Uitao border map by using the
assistance of ArcView GIS software and ArcSWAT
extension.This drawing generates DEM (Digital
Elevation Model) which is taken from topography
map that has the shape of contour line that modified
into cell shape (grid). The use of digital surface model
in surface runoff analysis process which presents the
earth relief surface will give accuracy in identifying
the land slope, flow accumulation, flow line lenght
and flow area detemination.
The drawing of water catchment area is done after
generating DEM is over, in this case is DAS Uitao
drawing. This drawing aimed to find Sub DAS from
DAS Uitao and its attribute and generating synthetic
river network. The computer will translate the basins
or mound by using DEM. The result of DAS Uitao
drawing can be seen in Figure 6 and Sub DAS can be
seen in figure 7 whereas its attribute data is presented
on table 17.
Source: Analysis Result, 2021
Figure 6: Research Area Watershed Map.
Figure 7: Map of DAS Uitao Research Area Source:
Analysis Result, 2021.
Table 17: DATA of Sub-DAS Attribute, the Result of
Making DAS Border by using DEM.
Strem Reach Subbasin Stream Reach Stream Reach Stream Reach Elevation
Area Slope Lenght Slope Slope Width Depth Subbasin Controid
Subbasin Subbasin ( LEN1) (SL01) (SLL) (WID1) (DEP1) (ELEV)
( ha ) ( m) (%) (%) (m) (m) (m)
1 2 3 4 5 6 7 8
1 98.125 1.967,635 3.466 91,436 1.2754 0.1290 51.9484
2 234.438 3.132,412 3.209 91,436 2.1508 0.1828 64.0555
3 138.688 2.646,168 20.434 15,239 1.5697 0.1482 116.3659
4 157.000 3.117,767 8.183 60,957 1.6909 0.1557 91.9773
5 61.750 1.484,772 25.522 15,239 0.9660 0.1072 115.6407
6 63.500 2.098,681 17.675 18,287 0.9823 0.1084 85.5157
7 104.438 3.277,082 2.058 91,436 1.3240 0.1323 3.7135
8 83.000 1.489,949 1.317 121,914 1.1536 0.1207 29.6032
9 19.375 851,777 3.307 91,436 0.4819 0.0674 24.1000
10 215.571 400,000 1.494 121,914 0.0844 0.0211 12.0000
11 203.750 4.297,056 4.728 91,436 1.9772 0.1728 37.8644
Total 1379.634
Source : Analysis Result, 2021
Runoff Debit Analysis of Rational Method The
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
1164

Determination of Runoff Coefficient (C) The big
coeffricient value of runoff/flow (C) shows the
quantity of surface runoff that occurs in the land is
big, in other words the water management condition
and land usage in that land is damaged.
Table 18: Runoff Coefficient in Existing Land Usage in
DAS Uitao.
Sub Sub Land Area Runoff Average
DAS Usage (ha) Coefficient C ) Runoff Coefficient

Meadow 10.8929 0.200

1 Bush es 84.9673 0.010 0.034
Rainfed Rice field 1.4434 0.150
Farm 0.8213 0.020
Bush es 190.9824 0.010
Meadow 17.3568 0.200
2 Plantation/farm 8.8219 0.020 0.026
Field 15.6944 0.020
Rainfed Rice field 1.5819 0.150
Bush es 5.3534 0.010
3 Jungle 132.2407 0.030 0.031
Meadow 1.0934 0.200
Bush es 132.2926 0.010
4 Plantation 5.2532 0.020
Meadow 3.0872 0.200 0.021
Jungle 15.2375 0.030
Settlement 1.1295 0.700
5 Bush es 35.4504 0.010 0.019
Jungle 26.2996 0.030
6 Bush es 32.3945 0.010 0.020
Jungle 31.1055 0.030
Meadow 20.1485 0.200
7 Bush es 56.5264 0.010 0.095
Rainfed Rice field 25.7082 0.150
Settlement 2.0545 0.700
8 Meadow 17.615 0.200 0.050
Bush es 65.385 0.010
Bush es 182.3029 0.010
9 Settlement 1.0399 0.700 0.014
Meadow 0.1049 0.200
Bush es 181.5992 0.010
Settlement 9.7486 0.700
10 Meadow 16.0342 0.200 0.058
Field 3.4027 0.020
Rainfed rice field 2.7872 0.150
Bush es 136.1688 0.010
Meadow 50.8111 0.200
11 Settlement 6.1475 0.700 0.079
Rainfed Rice field 0.0544 0.150
Plantation 7.768 0.020
Lake 2.5736 0.000
Source : Analysis Result, 2021
Source : Analysis Result, 2021
Figure 8: Map of Land Management of DAS Uitao
Research Area.
3.9 Determination Concentration Time
(Tc) and Rain Intensity (I)
Below is the calculation example of Concentration
time (Tc) and Rain Intensity (I) in Sub-DAS 1
The data :
Land slope (Sloland) = 3,466 Slope lenght (L) =
1.967,61 m, River slope (Sloriver) = 0,100
River lenght (S) = 268,57 m. manning special number
(n) = 0,025
R
24
repetition 2 years = 144,34 mm
R
24
repetition 5 years = 180,688 mm
R
24
repetition 10 years = 202,951 mm
R
24
repetition 25 years = 230,920 mm
R
24
repetition 50 years = 250,811 mm
The Calculation Analysis is as folows:
1. Calculating To (Overland flow time)
𝑇𝑜
2
3
𝑥3,28𝑥𝐿𝑥
𝑛
√
𝑆
𝑥
1
60

𝑇𝑜
2
3
𝑥3,28𝑥1.967,61 𝑥
0,025
√
0,100
𝑥
1
60

To = 0,963 hours
2. Calculating v (flow speed)
v = 4,918(S)
1/2
v = 4,918 (0,100)
1/2
= 1,555 m/dt
3. Calculating Td (Drain flow time)
𝑇𝑑
1
3.600𝑣

1
3.600𝑥1,555
Td = 0,351 jam
4. Calculating Tc ( Concentration time)
Tc = To + Td
Tc = 0,963 + 0,351
Tc = 1,314 hours
Table 19: Concentration Time Calculation (Tc).
SubSub LandSlope  RiverSlope  Slope  River  SpecialNumber 
Overland  Flow  DrainFlow  Concentration
DAS  (Slo.Land)  (Slo.River)  Lenght(L)  Lenght(S)  Manning(n) 
FlowTime  Speed(V)  Time(Td)  Time(Tc) 
1  2  3  4  5  6  7  8  9  10 
1  3,466  0,100  1.967,61 268,57 0,025  0,963  1,555  0,351  1,314 
2  3,209  0,688  3.967,61 1.817,09 0,025  1,593  4,079  0,213  1,806 
3  20,434  1,916  2.646,17 130,50 0,025  0,533  6,807  0,108  0,806 
4  8,183  2,776  3.117,77 1.682,63 0,025  0,933  8,195  0,106  1,099 
5  25,522  0,100  1.484,77 138,39 0,025  0,268  1,555  0,265  0,533 
6  17,625  2,053  2.098,68 121,75 0,025  0,455  7,047  0,083  0,538 
7  2,058  0,514  3.277,08 1.996,02 0,025  2,081  3,524  0,258  2,340 
8  1,317  0,245  1.489,95 919,97 0,025  1,183  2,432  0,170  1,353 
9  3,307  2,929  851,78 426,78 0,025  0,427  8,417  0,028  0,455 
10  1,494  0,100  400,00 425,00 0,025  0,298  1,555  0,072  0,370 
11  4,728  0,646  4.297,06 387,03 0,025  1,801  3,953  0,302  2,103 
Source : Calculation result, 2021
Rain intensity calculation of Mononobe method
Analysis of Farmland Potency to Improve Community Economy in Letbaun Village Semau Sub-district Kupang District East Nusa Tenggara
Province
1165

a). I =
3/2
24
24
24






Tc
R
, with R
24
for repetition
2 th = 144,34 mm
I =
,



,

/
= 40,902 mm/hour
b). I =
3/2
24
24
24






Tc
R
,with R
24
for repetition
5 th = 180,688 mm
I =
,



,

/
= 49,472 mm/hour
c) I =
3/2
24
24
24






Tc
R
, with R
24
for repetition
10 th = 202,951 mm
I =
,



,

/
= 54,587 mm/hour
d) I =
3/2
24
24
24






Tc
R
, with R
24
for repetition
25 th = 230,920 mm
I =
,



,

/
= 60,893 mm/ hour
e). I =
3/2
24
24
24






Tc
R
, with R
24
for repetition
50 th = 250,811 mm
I =
,



,

/
= 65,307 mm/hour
The quantity of Rain intensity (I) added to
attribute data of Sub- DAS Uitao map. Then the
calculation result can be seen on Table 20 as follows:
Table 20: Rainfall Intensity Calculation (I).
Sub - Concentration R24 R24 R24 R24 R24 Rainfall Intensity
Sub Time (Tc) (2 Years) (5 Years) (10 Years) (25 Years) (50 Years) (2 Years) (5 Years) (10 Years (25 Years) (50 Years)
DAS ( Hour) (mm) (mm) (mm) (mm) (mm) (mm/jam) (mm/jam) (mm/jam) (mm/jam) (mm/jam)
1 2 3 4 5 6 7 8 9 10 11 12
1 0,351 141.427 171.06 188.747 210.553 225.813 40.902 49.472 54.587 60.893 65.307
2 0,213 141.427 171.06 188.747 210.553 225.813 33.085 40.017 44.155 49.256 52.826
3 0,108 141.427 171.06 188.747 210.553 225.813 66.008 79.838 88.003 98.271 105.393
4 0,106 141.427 171.06 188.747 210.553 225.813 46.095 55.754 61.518 68.625 73.599
5 0,265 141.427 171.06 188.747 210.553 225.813 74.681 90.329 99.668 111.183 119.241
6 0,083 141.427 171.06 188.747 210.553 225.813 74.198 89.744 99.023 110.463 118.46
7 0,258 141.427 171.06 188.747 210.553 225.813 27.198 33.674 37.156 41.449 44.453
8 0,170 141.427 171.06 188.747 210.553 225.813 40.116 48.521 53.538 59.723 64.052
9 0,028 141.427 171.06 188.747 210.553 225.813 83.008 100.401 110.782 123.58 132.537
10 0,072 141.427 171.06 188.747 210.553 225.813 95.332 115.306 127.229 141.928 152.214
11 0,302 141.427 171.06 188.747 210.553 225.813 29.898 36.163 39.902 44.512 47.738
Source : Calculation result, 2021
3.10 The Determination of Runoff Debit
and the Drawing of Runoff Debit
Spread Map
The formula used based on the equation of runoff
debit of Rational Method is as follows:
Q = 0,278.C.I.A
Its calculation example is as follows:
In Sub DAS 1 with the data:
1. Land area for Sub DAS 1= 98,125 Ha
2. Runoff Coefficient (C) = 0,034
3. Rainfall Intensity (I) :
2 years = 40,902 mm/hour
5 years = 49,472 mm/hour
10 years = 54,587 mm/hour
25 years = 60,893 mm/hour
50 years = 65,307 mm/hour
The calculation of surface runoff debit (Q
2,
Q
5,
Q
10,
Q
25,
Q
50
) in Sub DAS 1 location is as follows:
Q = 0,278.C.I.A
Q
2
= 0,278 x 0,034 x 40,902 x 98,125
= 37,395 m
3
/second
Q
5
= 0,278 x 0,034 x 49,472 x 98,125
=
45,230 m
3
/second
Q
10
= 0,278 x 0,034 x 54,587 x 98,125
=
49,907 m
3
/second
Q
25
= 0,278 x 0,034 x 60,893 x 98,125
=
55,673 m
3
/second
Q
50
= 0,278 x 0,034 x 65,307 x 98,125
=
59,708 m
3
/second
Its calculation recapitulation result can be seen on
Table 21 and Runoff Debit Graphic (Q) of DAS Uitao
is presented in Figure 9. And its drawing result for
Runoff Debit with 10 years repetition can be seen in
the figure below:
Table 21: Calculation of Runoff Debit of DAS Uitao.
Sub Sub Q Q Q Q Q
DAS (2 Years) (5 Years) (10 Years) (25 Years) (50 Years)
(m
3
/second) (m
3
/second) (m
3
/Second) (m
3
/Second) (m
3
/second)
1 2 3 4 5 6
1 37.395 45.230 49.907 55.673 59.708
2 56.186 67.959 74.985 83.649 89.711
3 77.795 94.095 103.824 115.819 124.213
4 42.201 51.043 56.321 62.827 67.381
5 24.358 29.462 32.508 36.364 38.892
6 26.196 31.685 34.961 39.000 41.827
7 76.791 92.880 102.484 114.324 122.610
8 46.282 55.979 61.767 68.903 73.897
9 6.259 7.571 8.354 9.319 9.994
10 331.361 400.790 442.23 493.321 529.075
11 133,787 161.819 178.551 199.179 213.614
Total 858.610 1.038.513 1.145.892 1.278.277 1.370.921
Source : Calculation result, 2021
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
1166

Figure 9: Map of Runoff Debit of 10 years repetition in
DAS Uitao.
3.11 The Factors of Slope Lenght (L)
and Slope Slant (S) and the
Drawing of Slope Slant Map of
DAS Uitao
The factors of Slope Lenght (L) and Slope slant (S)
influence the quantity of erosion that occurred. The
slant influences the speed and volume of surface
runoff. Basically if the slope is getting steeper, so the
precentage of slope slant is bigger, therefore the
surface runoff rate is getting faster. The average of
slope lenght quantity in DAS Uitao can be seen
through the measurement on digital contour map with
the assistance of ArcView software and measure
facilities. The slope slant can be known from Sub
DAS map attribute data that has been made by DEM
method.
L =

,


With :
L = value of Slope Lenght factor
Lo = Slope lenght (obtained from result attribute of
making DAS border /subbasin through software).
Data on Sub DAS 1 :
Lo = 1.967,615 m from Analysis measurement
then ;
L = 
.,
,

,
= 9,436%
The slant influences speed and volume of surface
runoff.
= 9,436%
Slope slant factor (S) can be calculated
𝑆
,,,


,
𝑆
,  ,  ,,  ,

,
= 0,295
3.12 The Potency of Land Usage as the
Farmland in Letbaun Village
In Letbaun Village Semau Sub-District, there is DAS
Uitao that influenced the Letbaun village itself and it
has productive land to be processed as farmland.
The usage of ideal farmland is in the lowland area
with the slope slant < 10%, the steep land is not ideal
to use as farmland. Based on calculation from each
Sub DAS Uitao with each slope slant, there are some
some Sub DAS with the slope slant < 10% which is
ideal for farmland, the area is= 900,126 Ha from total
area of DAS = 1379,634 Ha. The land potency for
farmland in accodance with Sub DAS is below :
Sub DAS 1 Slope Slant 2,831 %
Extensive Area = 98,125 Ha
Sub DAS 2 Slope Slant 3,301 %
Extensive Area = 234.438 Ha
Sub DAS 4 Slope Slant 8,183 %
Extensive Area = 157,000 Ha
Sub DAS 7 Slope Slant 2,058 %
Extensive Area = 104.438 Ha
Sub DAS 8 Slope Slant 1,317 %
Extensive Area = 83.000 Ha
Sub DAS 9 Slope Slant 3,307 %
Extensive Area = 19.375 Ha
Sub DAS 11 Slope Slant 4,728 %
Extensive Area = 203.750 Ha
3.13 Reservoir Location for Agricultural
Water Supply
To support the problem of limited water supply , it is
required the usage of water resourse in that location
to control the excess water in the rainy season and it
becomes the source of irrigation water in dry season.
In Sub- DAS 10 based on the calculation result of
fairly big surface runoff debit and Land slope > dari
10 % is planned to build Reservoir ( Retention basin)
to fulfill the water need supply in the land of 900,126
Ha. The covered land (catchment area) is 215,571 Ha.
The location of the Reservoir (Retention Basin)
building is at coordinates 123°24'23.41” East
Longitude and 10°11'44.23” South Latitude with a
water capacity of 442,230 m3/second every 10 years,
493,321 m3/second a Chatment Area of 215,571 Ha.
The map image is presented in Figure 10.
Analysis of Farmland Potency to Improve Community Economy in Letbaun Village Semau Sub-district Kupang District East Nusa Tenggara
Province
1167

Figure 10: The Map of Land Usage Potency and Location
and Coordinate of Reservoir.
4 CONCLUSIONS
The farmland potency in Letbaun Village Semau Sub-
District Kupang District of area = 900,126 Ha from
the total area of =1.379,634 Ha, it means 65,24 %
from total region. And to fulfill the need of water in
reservoir building(retention basin) in Sub DAS 10 in
coordinate of 123°24’23.41” East Longitude and
10°11’44.23” South Latitude with the covered area
(catchment area) of 215,571 Ha. In the DAS which
has land slope > of 10 % is made the construction of
water path that made parallel to the contour line with
the distance of 10 - 20 m and the width size of 0,50,
the depth of 0,50m to minimize surface runoff debit.
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