Impact of Land-use Change on Surface Runoff

in Manikin Basin et Kupang Regency

Arnoldus Nama, Yacob Victor Hayer and Fabianus Jawal S. Nope

Department of Civil Engineering, State Polytechnic of Kupang, Jl. Adisucipto, Penfui-Kupang, Indonesia

Keywords: Manikin Basin, Land Use Change, Image Classification, Soil and Water Assessment Tool, Runoff.

Abstract: The study assessed the impacts of land-use change on runoff in the Manikin Basin, using the Soil and Water

Assessment Tool (SWAT) model. The model use to predict runoff for two scenarios. Both scenarios using

different land-use data. Land-use for the SWAT model is obtained from remote sensing images. Two remote

sensing images of Landsat 8 OLI from the years of 2014 and 2019 were used for land-use classification using

the supervised classification method. The classification results show that there is a change in land-use. Area

of land-use with the highest increase was shrubs with an increase of 20.25% (19.98 km

), while the land-use

area with the highest decrease was forest with a decrease of 14.1% (13.92 km

). The average annual runoff of

the first scenario is 134.7404, while the average annual runoff of the second scenario is 140.0596 mm, there

is an increase of 3.98% (5.19 mm). This study shows that, the increase in the area of shrubs, and the reduction

in forest area have an impact on increasing surface runoff in the Manikin watershed

1 INTRODUCTION

Watershed response to rainfall is determined by the

characteristics of the watershed, including

topography, soil moisture and type, land cover, and

drainage density of the watershed. The topography

and drainage density of the watershed are the physical

characteristics of the watershed that do not change. In

contrast to physical characteristics which tend to be

static, biophysical characteristics such as land cover

and soil interact dynamically. When these biophysical

factors change, the surface runoff and flood discharge

will also change as a watershed response to rainfall.

Research related to the impact of land-use

changes on surface runoff has been conducted by

several researchers. Patil, N. S. et. al. (2020), used the

hydrological model (SWAT), to find out the effect of

land-use change on surface runoff in the Hiranyakeshi

watershed, India. Astuti I. S. et al. (2019), assessed

the effect of land change on surface runoff using the

SWAT model in the Upper Brantas watershed,

Indonesia. Pertiwi, P. C. et. Al. (2020), analyzed the

effect of land-use change on runoff discharge using

the Nakayasu synthetic unit hydrograph (HSS)

method in the Pompong watershed, Indonesia..

Research related to the impact of land-use change

on surface runoff generally concludes that there is a

relationship between both factors. However, it should

be noted that the watershed response to rainfall differs

from one watershed to another. Therefore, an analysis

was carried out to understand the impact of land-use

change on runoff in the Manikin watershed. The Soil

and Water Assessment Tool (SWAT) hydrological

model was used to predict surface runoff. The

simulation of surface runoff in the Manikin watershed

consists of two scenarios that are simulated with two

different land-use data.

2 STUDY AREA

The study area (Fig. 1) is the Manikin River Basin in

West Timor, East Nusa Tenggara Province,

Indonesia. Manikin Basin has a catchment area of

about 98.69 km2, with the longest channel reach from

the upper basin of approximately 32 km to its outfall

into Kupang bay.

According to the Schmidth-Fergudon climate

classification, West Timor is dominated by climate

type E, which is a slightly dry area with savanna

forest vegetation. The rainy season in most of these

areas is short of only 3-4 months, starting in

December and ending in March or April. Annual

rainfall varies from 848 mm on Panite, on the south

526

Nama, A., Hayer, Y. and Nope, F.

Impact of Land-use Change on Surface Runoff in Manikin Basin et Kupang Regency.

DOI: 10.5220/0010948700003260

In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 526-533

ISBN: 978-989-758-615-6; ISSN: 2975-8246

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Figure 1: The Manikin Basin.

coast of Timor to 2,890 mm on Lahurus at the peak

of Mount Lakaan, but most of West Timor receives

900-1,500 mm of rainfall per year, and Kupang (the

provincial capital) and surrounding areas have an

average rainfall of 900-1,500 mm. average rainfall is

1,420 mm per year.

The land cover in the manikin watershed is

dominated by shrubs and savanna, while in the upper

watershed there is little forest area. The settlement

area is generally located in the downstream part of the

watershed close to the Kupang City, slightly in the

middle and upstream of the watershed.

3 MATERIALS AND METHODS

3.1 SWAT Model Description

The hydrological SWAT model used in this study is

a predictive model for watershed-scale developed by

Dr. Jeff Arnold for USDA Agricultural Research

Service (ARS). SWAT was developed to predict the

impact of land management practices on water,

sediment, and agricultural chemicals that enter rivers

or water bodies in a complex watershed with varying

soil, land use, and management over a long period of

time. SWAT models belong to a class of

ecohydrological process-based watershed models,

which can be defined as continuous dynamic models

based on mathematical descriptions of physical,

biogeochemical, and hydrochemical processes,

incorporating elements of both physical and semi-

empirical properties. The basic process model is not

completely distributed in three dimensions, but

usually includes a reasonable spatial disaggregation

scheme, apply into sub-watersheds and hydrological

response units (HRUs).

The basic SWAT model inputs are rainfall,

maximum and minimum temperature, radiation, wind

speed, relative humidity, land cover, soil, and

elevation. The watershed is subdivided into sub-

basins that are spatially related to one another, and,

further, into hydrological response units (HRUs),

which are homogenous units that possess unique

land-use/land-cover and soil attributes and account

for the complexity of the landscape within the sub-

basins. The subbasin watershed components can be

categorized as follows: hydrology, weather, erosion

and sedimentation, soil temperature, plant growth,

nutrients, pesticides, and land management. In the

land phase of the hydrological cycle, runoff is

predicted separately for each HRU. and routed to

obtain the total runoff for the watershed.

In SWAT, surface runoff is most commonly

predicted using the SCS curve number (SCS-CN)

method, which was developed by the Soil

Impact of Land-use Change on Surface Runoff in Manikin Basin et Kupang Regency

527

Conservation Service (now known as Natural

Resource Conservation Service/NRCS). It calculates

surface runoff according to the equation:

𝑄





𝑅



𝐼







𝑅



𝐼



𝑆

where Q

surf

is the surface runoff (mm), R

day

is the

rainfall for the day (mm), I

is the initial abstraction

including surface storage, interception, and

infiltration prior to runoff (mm), which is commonly

approximated as 0.2S, and S is a retention parameter.

The results of the SWAT model are calibrated

with monthly discharge data of 2012 measurements,

at locations near outlets. In the Manikin, river there is

no discharge measurement station, and the existing

discharge data is taken from manual measurements.

Nash-Sutcliffe Efficiency (NSE) was used to test the

reliability of the model. The NSE value of the test

results is 0.776. Referring to Moriasi et. al, in Nama

(2016), this model is very suitable to be applied in the

Manikin Basin.

3.2 Data

The data used in this study include: (a) daily rainfall

and temperature data – from Indonesian Meteorology

and Geophysics Agency; (b) streamflow data – from

Directorate General for Water Resources, Indonesian

Ministry of Public Works (c) land cover – generated

from Landsat 8 OLI satellite imagery (2014 and

2019); (d) soil map– from Indonesian Ministry of

Agriculture; and (e) digital contour map in Esri

format file (scale 1: 12500) from Indonesian

geospatial information agency, used to generate

digital elevation model (DEM).

3.3 Land-cover Assessment

Satellite imagery from Landsat 8 OLI (Operational

Land Imager), were obtained from U.S. Geological

Survey (USGS, http://earthexplorer.usgs.gov). There

are two images, the first is a 2014 image, the date of

recording (Date Acquired) 25 April 2014, and the

second is the 2019 image, the recording date is 23

April 2019, with a path-row of 111-067 (Fig.2). This

image covers the entire area of Kupang Regency,

Rote Ndao Regency, and part of South Central Timor

Regency, East Nusa Tenggara Province. This data is

a free download from the USGS website

(http://eartheexplorer.usgs.gov).

To obtain land use as input into the model, the

software ENvironment for Visualizing Images (ENVI

5.1) and ArcGIS 10.5, was used to perform image

analysis and classification. The ENVI software is

used for image pre-processing, such as radiometric

correction, noise reduction, and image sharpening.

Image sharpening using the Gram-Schmidt Pan

Sharpening method. To classify satellite images into

land use maps, ArcGIS 10.5 software is used.

Classification using the supervised technique with the

maximum likelihood classification method. Seven

land use classes were considered, namely: Grassland,

Shrub, Water Body, Cornfield, Forest-Mixed, Paddy

fields, and Residential-Medium Density.

Figure 2: The Landsat 8 OLI (Operational Land Imager) image path-row 111-067, True color.

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Figure 3: The comparison of land classes in 2014 and 2019.

Table 1: Land use in study area.

Land-use

2014 2019

Land area (Km

) % area Land area (Km

) % area

Grassland 9.16 9.28 3.24 3.29

Shrub 46.68 47.3 66.67 67.55

Water Body 1.46 1.48 0.48 0.49

Corn field 5.56 5.63 3.42 3.47

Forest-Mixed 25.86 26.2 11.94 12.1

Paddy fields 1.99 2.02 1.44 1.46

Residential 7.98 8.09 11.49 11.64

Total area

98.69

100

98.69

100

4 RESULTS AND DISCUSSION

4.1 Image Analysis

Landsat Images were classified to land-use map

using the Maximum Likelihood supervised

classification method. Classification results are

compared with observations. Verification was

carried out at 34 locations in and around the

watershed. Comparison of classification results and

field conditions is used to calculate the level of

accuracy in supervised interpretation, which is

85.29%.

The results of the classified images for the years

2014 and 2019 are shown in Table 1. The

distribution of land classes in the watershed, for

2014 and 2019 respectively, is shown in Figure 3.

As shown in Table 1, the residential land class

experienced a significant increase from 8.09% to

11.64%. Meanwhile, the land classes that decreased

in the area were grasslands and forests with changes

of 9.2% to 3.29% and 26.2% to 12.1% respectively.

Grassland Shrub Water Body Corn field Forest Paddy fields Residential

Percentage area

2014 2019

Impact of Land-use Change on Surface Runoff in Manikin Basin et Kupang Regency

529

Figure 4: Land use class distribution on watershed for the year of 2014 and 2019.

4.2 Preparing Precipitation Data

Before the precipitation data is used, the consistency

test is carried out using the multiple mass curve

method. There are 4 precipitation stations in and

around the watershed used for SWAT model input,

namely Oeletsala, Baun, Tarus, and Penfui

precipitation stations. The results of the consistency

test of the four stations did not show any data

inconsistency with the respective correlation

coefficients above 0,99.

The next step is to do statistical calculations to

arrange rain data in a format that can be read by the

SWAT model. The statistical calculation of rain data

includes (1) average amount of precipitation falling in

month (PCPMM). (2) standard deviation for daily

precipitation in month (PCPSTD), (3) skew

coefficient for daily precipitation in month

(PCPSKW), (4) probability of a wet day following a

dry day in month (PR_W1), (5) probability of a wet

day following a wet day in month (PR_W2), (6)

average number of days of precipitation in month

(PCPD), and (7) extreme half-hour rainfall for month

(RAINHHMX).

WGN Parameters Estimation Tool software

(downloaded for free at https://swat.tamu.edu) can be

used to calculating Statistics of precipitation for swat

model input. Overall results of the calculation of

precipitation statistics are arranged according to a

format that can be read by the SWAT model (SWAT

version 2012), then inputted into the SWAT database

(WGEN_user). An example of calculating rain

statistics can be seen in Table 1.

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Table 2: Statistical precipitation parameter of Tarus Rain gauge.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

PCPMM 309.0 444.9 267.9 87.6 18.0 5.9 4.8 1.4 1.9 17.8 138.8 245.2

PCPSTD 14.5 21.6 15.4 9.1 3.0 1.5 1.2 0.7 1.0 2.6 12.2 14.9

PCPSKW 1.9 1.7 2.6 4.9 6.9 12.5 11.1 16.1 16.3 5.5 3.6 3.5

PR_W1 0.5 0.5 0.3 0.2 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.4

PR_W2 0.7 0.9 0.8 0.5 0.4 0.5 0.5 0.3 0.3 0.2 0.5 0.7

PCPD 20.2 21.9 19.4 8.4 2.4 1.4 1.2 0.2 0.2 2.7 10.2 19.5

RAINHHMX 73.0 95.5 83.0 83.0 28.0 25.0 18.0 11.0 18.0 20.0 87.0 109.0

Figure 5: Graph of the relationship between the measurement discharge and the calibrated model discharge.

Table 3: The model evaluation statistics and performance.

Observed mean St. dev Simulated mean St. dev C

MAE NSE R

2.175 3.141 3.227 2.729 0.256 1.052 0.776 0.951

Table 4: Monthly run off.

Jan Feb Ma

Jun Jul Au

Oct Nov Dec

2014 18622 42078 39337 8274 4455 0.001 0.0 0.0 0.0 49 2093 18070

2019 19318 43593 40950 8583 4678 0.001 0.0 0.0 0.0 51 2171 18883

4.3 Hydrological Modelling

The output of a model after being calibrated must be

evaluated statistically for the field data, although the

graph of the output of the model after calibration and

field measurements shows that the difference is not

too significant. This is done to determine the

suitability of the model with field data and to avoid

too large a deviation between the model results and

the field data. To test the reliability of the model, in

this study several statistical indicators were used,

namely the Correlation Coefficient (R2), Mean

Absolute Error (MEA), Coefficient Performance

(Cp), and Nash-Sutcliffe Efficiency (NSE). Figure 3

shows a graph of the relationship between the

measurement discharge and the calibrated model

discharge, while the results of the model reliability

test presented in Table 2.

4.4 Runoff Depths

The output of the SWAT model simulation is

discharge, runoff, total sediment, etc., however,

the only runoff will be discussed here. The

impact of land change on runoff can be seen in

table 4 and table 5.

Impact of Land-use Change on Surface Runoff in Manikin Basin et Kupang Regency

531

Figure 6: Total monthly run of.

Table 5: Annual run off.

Total (mm) Average (mm)

2014 132977 134.7404

2019 138227 140.0596

As presented in Tables 4 and 5, there was an

increase in runoff from the first scenario to the second

scenario, both monthly and annual runoff. The largest

total runoff for the two scenarios occurred in

February, which was 43,593 mm, the lowest occurred

in June, which was 0.001 mm. From July to

September are period that are no rainfall in this area

(dry season), therefore there is no runoff in this

period. The average annual runoff for the first

scenario is 134,7404 mm or about 8,279% of the total

annual rainfall (mean annual rainfall is 1627,511

mm). For the second scenario, the average surface

runoff is 140.0596 mm, or about 8.606% of the total

annual average rainfall. From this description, the

average runoff for the two scenarios is very small

compared to the total average rainfall. This condition

occurs because only storm rainfall cause runoff. The

deep of rain that causes runoff requires further

analysis.

There is an increase in annual runoff depth from

the first scenario to the second scenario. Average

annual runoff increased by 3.948% (5.19 mm). Area

of land-use with the highest increase was shrubs with

an increase of 20.25% (19.98 km

), while the land-

use area with the highest decrease was forest with a

decrease of 14.1% (13.92 km

). Shrubs and forest

land-use classes have different runoff coefficients.

The forest land-use class has a runoff coefficient that

is lower than the shrubs land-use class. Increasing the

area of shrubs and reducing the area of forest can

increase runoff. Another factor that causes the

increase in runoff is the increase in the area of

residential areas.

5 CONCLUSION

This study shows that in five years, there has been a

change in land use in the manikin basin. There are

five land-use classes that decried in size, they were

grasslands, water bodies, rice fields, and forests .The

land classes that increased were residential land

classes and shrubs. The average annual runoff of the

first scenario is 134.7404, while the average annual

runoff of the second scenario is 140.0596 mm. There

is an increase in runoff depth from the first scenario

to the second scenario, which is 3.98% (5.19 mm).

The Area of land-use with the highest increase

was shrubs with an increase of 20.25% (19.98 km

while the land-use area with the highest decrease was

forest with a decrease of 14.1% (13.92 km

). The

forest land-use class has a runoff coefficient that is

lower than the shrubs land-use class, So, increase in

the area of shrubs, and the reduction in forest area

have an impact on increasing surface runoff in the

Manikin watershed. Another factor that causes the

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Skenario 1

18621,5 42077,7 39336,8 8273,59 4455,28 0,001 0,000 0,000 0,000 49,204 2093,03 18069,6

Skenario 2

19317,9 43592,5 40949,6 8583,02 4678,05 0,001 0,000 0,000 0,000 51,123 2171,31 18882,8

5000

10000

15000

20000

25000

30000

35000

40000

45000

Run of (mm)

iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science

532

increase in runoff is the increase in the area of

residential areas. In general, this study shows that

land-use change in the manikin basin from the year of

2014 to 2015 has effect on increase in the total runoff

in the Manikin basin.

ACKNOWLEDGEMENTS

The leading author of this paper would like to thank

to State Polytechnic of Kupang as a sponsor of the

author's research through the State Polytechnic of

Kupang DIPA fund.

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