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         Geoplanning 
Vol 4, No. 2, 2018, 263-272                                                                                                                                                            Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning 

doi: 10.14710/geoplanning.4.2.263-272 

MODEL OF SOIL AND WATER CONSERVATION MEASURES APPLICATION 
BASED ON DISTRICT SPATIAL PLANNING IN MAMASA WATERSHED, 
SOUTH SULAWESI 

K. Murtilaksonoa, S.M. Yusufa, R.K. Astutia, S. Arifina  

a Bogor Agricultural Institute, Bogor, Indonesia 

 

Abstract: Depletion of watershed carrying capacity cannot be omitted from mismanagement 

of the watershed. The integration between SWAT model and remote sensing data are able to 
identify, assess, and evaluate watershed problem as well as a tool to apply the mitigation of the 
problem. The aim of this study was to arrange the scenario of watershed management, and 
decide the best recommendation of sustainable watershed management of Mamasa Sub 
Watershed. The best recommendation was decided by hydrology parameters, e.i. surface runoff, 
sediment, and runoff coefficient. Hydrology characteristics of Mamasa Sub Watershed was 
analyzed based on land use data of year 2012 and climate data for period of 2010-2012. The 
scenarios were application of bunch and mulch in slope 1-15%; bunch terrace (scenario 1), mulch 
and strip grass in slope 15-25% (scenario 2), alley cropping in slope 25-40% (scenario 3), and 
combination scenario 1, 2, 3 with agroforestry in slope > 40% (scenario4). Surface runoff value of 
Mamasa Sub Watershed is 581.35 mm, while lateral flow, groundwater flow, runoff coefficient, 
and sediment yield of 640.72 mm, 228.17 mm, 0.29, and 187.213 ton/ha respectively. Based on 
the scenario’s simulation, the fourth scenario was able to reduce surface runoff and sediment 
yield of 33.441% and of 51.213%, while the runoff coefficient declined to 0.194. Thereby, the 
fourth scenario is recommended to be applied in Mamasa Sub Watershed so that the 
sustainability in the watershed can be achieved.  

 Copyright © 2017 GJGP-UNDIP  

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 

Murtilaksono, K., Yusuf, S.M., Astuti, R.K., & Arifin, S. (2017). Model of Soil and Water Conservation Measures Application Based on District Spatial 

Planning in Mamasa Watershed, South Sulawesi. Geoplanning: Journal of Geomatics and Planning, 4(2), 263-372. doi: 
10.14710/geoplanning.4.2.263-272 

1. INTRODUCTION 

Mamasa watershed potentially degraded due to the topography of its area which was dominated by 
slopes >40% (Department of Forestry, 2010). Cultivation activities, encroachment, illegal logging, over-
harvesting, tillage without conservation practices, and the other activities of the community in the forest 
area were also to be the factors that contributed to land degradation (Department of Forestry, 2010). As 
66.17% (76,497 hectares) area of Mamasa watershed is categorized as degraded land. Previous research 
showed that amount of erosion in the Mamasa watershed in 2010 was amounted to 19,561,011 tons/year. 
The amount of erosion indicated the amount of sediment that occurs in the Mamasa watershed. The 
volume of water in reservoirs of Bakaru hydroelectric power is decreasing in average of 301,707 m3/year. 

Mamasa watershed management underscore the need for planning, monitoring and evaluation in 
order to ensure the preservation of water distribution throughout the year and to minimize the increase in 
surface runoff or sediment. The assessment can be done by using a model such as described in previous 
review studies (Srinivasan et al., 2010; Cibin et al., 2013; Daniel, 2011; QIU et al., 2012; Himanshu et al., 
2016; Shi et al., 2017). From the literatures, it is known that SWAT hydrologic models are simplification 
representations of actual soil, land use, topographic, climate, and other interactions that occur within 
natural hydrologic and environmental systems that can be used to analyze runoff, sediment, and water 
balance. The model also can be effectively and efficiently used for simulating a various of conditions that 
could not be possible to measure in such complex conditions. Model also can be chosen base on watershed 
representation and spatial scale (Daniel, 2011). 

OPEN ACCESS 

Article Info: 
Received: 13 January 2017 
in revised form: 30 April 2017 
Accepted: 5 August 2017 
Available Online:  30 Oct 2017 
 

Keywords:  
SWAT, remote sensing data, soil 
and water conservation, 
sustainable watershed 
management. 
 

Corresponding Author: 
Sri Malahayati Yusuf 

Bogor Agricultural Institute, 

Bogor, Indonesia  

Email: malahayati10@gmail.com  

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mailto:malahayati10@gmail.com


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One model that can be used is SWAT hydrological model (Arnold et al., 2012; Neitsch et al., 2011; 
Suryavanshi et al., 2017) it is widely used by researchers, government agency and other users. SWAT was 
developed to predict the impact of land management practices on water, sediment, agricultural chemical 
yields that enter the river in a complex watershed with varying soil, land use and management conditions 
over a long time period. Several studies (Moriasi et al., 2011; Sunandar & Suhendang, 2014; Yusuf et al., 
2016) showed that SWAT was able to described the impacts of land management on hydrological 
characteristics of watershed and successfully used in scenario analysis of variety conditions of climatic and 
environmental worldwide (Douglas-Mankin et al., 2010; Gassman et al., 2014; Krysanova & White, 2015). 
The model also able to analyze and to create some scenarios including best management practices (soil and 
water conservation technique), climate change, and land use change in a watershed. Applying scenario of 
best management practices such as nutrient management, constructed wetland, and filter strip in Big Ditch 
Watershed, indicate that  the average nitrate-N load reduce of 15.8%, 17.1%, 9.9%, respectively (Getahun & 
Keefer, 2016). Application of conservation crop rotation and no-till is the best scenario in reducing sediment 
load in St. Joseph River watershed, while the conservation crop rotation and cover crop reduced the big 
amount of nutrients (Her et al., 2016). Jang et al., (2017) research showed that application of BMP in Haean 
highland agricultural catchment of South Korea such as vegetation filter strip, fertilizer control, and rice 
straw mulching could reduce the sediment load around 16-34.8%, 4.9-16.4%, and 3-14.1%, respectively. The 
other research by Liu et al., (2016) indicated that application of nutrient management, buffer strip, cover 
crop, and wetland restoration in Grand River watershed, Southern Ontario, reduce the sediment at the 
watershed outlet ranges between 0 – 5.54%, Total Phosphorus from 6.28 up to 41.32%, and Total Nitrogen 
between 1.97 – 18.54%. 

The aim of this study was to (1) estimate the hydrological characteristics including surface flow, lateral 
flow, base flow, and sediment yield in ungauged watershed of Mamasa using SWAT model, (2) arrange 
several scenarios for the best management practices in Mamasa watershed. This research is important for 
the stakeholders in Mamasa Watershed because the result will be used to evaluate the condition of 
Mamasa Watershed.  

 

2. DATA AND METHODS 
Mamasa watershed is one of multifunctional watershed on the island of Sulawesi, Indonesia.  The 

watershed is source of irrigation water, raw water for the people who live around it and hydroelectric 

power (Bakaru Power Plant) (Department of Forestry, 2010). Geographically, Mamasa watershed is located 

between 119o13’-120o21’ E and 2o43’-3o46’ S and administratively between Tana Toraja, Pinrang, Enrekang, 

Polewali Mandar, and Mamasa regency, which is located at West Sulawesi (upstream of the watershed) and 

South Sulawesi Province (downstream of the watershed), Indonesia. The total area of Mamasa watershed is 

115,607 ha. The study location is presented in Figure 1. 

2.1. Data 

Materials and data required in this study are: a) DEM (Digital Elevation Model, 30 x 30 m) map, b) land 

use map in 2014, c) soil map, d) daily climate data (rainfall, solar radiation, relative humidity, wind speed 

and maximum and minimum air temperature). Primary data of soil characteristics was obtained from the 

laboratory analysis of soil samples for chemical and physical parameters of the soil (soil texture, bulk 

density, available water content, permeability, soil erodibility value and soil organic matter content). Field 

observations included the measurement of effective depth, thickness of the horizon, soil structure, the 

pattern of crop management, conservation techniques, and river characteristic (roughness of the river and 

channel cover).  

 

 

 

 

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Figure 1. The Study Location  

2.2.  Method  

Analysis of the hydrological characteristics of Mamasa watershed was performed by using SWAT model 

version 2012 (SWAT2012) with the ArcGIS interface (ArcGIS 10.1). There are four (4) steps in SWAT model:  

delineate watershed, sub watershed, and stream network using Digital Elevation Model (DEM) data (1), 

generate Hydrology Response Unit/HRU in watershed using the information both map and characteristics of 

soil, land use, and slope (2), linking the HRU with climate data, and write the table input of model (3), and 

running the simulation for the certain year (4). The model analyzed the hydrology by using some equations 

consisted of SCS Curve Number, kinematic storage model, and the steady-state response of groundwater 

flow to recharge equation. The formula of SCS Curve Number is (equation 1): 

Qsurf =  

2

)(

)(

SIRday

IRday

a

a

+−

−
……………………………………………………………………………………….  (1) 

where Qsurf is amount of surface runoff volume in day i (mm), Rday is amount of precipitation in day i 

(mm), Ia is initial loss of surface storage, interception, and infiltration (mm), and S is retention parameter 

(mm). The kinematic storage model to calculate the lateral flow is (equation 2): 

Qlat = 0.024 










 hilld

satexcessly

L

slpKSW

.

...2 ,
…………………………………………………………………………… (2) 

Qlat is lateral volume that flow into the main channel in day i (mm), SWly,excess is excessive water in 

soil profile (mm), Ksat is saturated hydraulic conductivity (mm/hour), slp is slope (mm), Φd is soil porosity 

(mm/mm), and Lhill is length of slope (m). 

The steady-state response of groundwater flow to recharge equation is (equation 3): 

Qgw = wtbl

gw

sat xh
L

K
2

.8000
……………………………………………………………………………………………….  (3) 

where Qgw is ground water volume (mm), Ksat is saturated hydraulic conductivity (mm/hour), L2gw is 
distance between sub watershed to the main channel (m) and hwtbl is height of water table (m). 

The contribution of total runoff (amount of Qsurf, Qlat, and Qgw) and sediment to the sub watershed 

were used to identify the degraded sub watershed in Mamasa watershed. And then, the scenarios of soil 

and water conservation techniques were applied in the sub watershed to improve its condition. The 

scenarios are presented in Table 1. 

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Table 1. Scenario of land management and soil and water conservation technique 

No. Land Use Type  Conservation Technique Scenario 

Slope (%)       Technique 1 2 3 4 

1 Upland agriculture, plantation/unirrigated 
agricultural field mixed with bushes  

0-15 Bunch and mulch  √  √  √  √  

2 Upland agriculture, plantation/unirrigated 
agricultural field mixed with bushes 

15-25 Bunch terrace, mulch, 
and strip grass  

 √  √  √  

3 Upland agriculture, plantation/unirrigated 
agricultural field mixed with bushes 

25-40 Alley cropping and silt 
pilt 

  √  √  

4 Upland agriculture, plantation/unirrigated 
agricultural field mixed with bushes 

>40 Agroforestry    √  

 

3. RESULTS AND DISCUSSION 

3.1. Stream Network, Sub Watershed, and HRU 

The results from the first step in running the SWAT model are stream network, sub watershed, and 

watershed boundary. There are 16 sub watersheds in Mamasa watershed, namely sub watershed 1 until 

sub watershed 16. Sub watershed 1 is located in upstream, and sub watershed 16 is located in the 

downstream. The establishment of sub watershed was depended on area of threshold that used to 

delineate the stream network. The threshold is 2,500 ha, which is generates only the main channel in 

Mamasa watershed. River network and sub watershed map is presented in Figure 2. 

 

Figure 2. River Network and Sub Watershed Map 

The results from the second step are HRUs. HRUs are small analysis unit in SWAT model that is derived 
from overlay the soil, land use and slope map and its characteristics. So, each HRU has uniquely information 
about combination of soil, land use, and slope. The total amount of HRUs in a watershed is depends on 
threshold which is used for each map. The HRUs in Mamasa watershed were performed using threshold by 
percentage method (0%) so that generated 626 HRUs in Mamasa watershed. The spatial distribution of 
HRUs is presented in Figure 3. 

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Figure 3. HRUs Distribution in Mamasa Watershed 

3.2. Effect of Land Use to Hydrological Characteristics in Mamasa Watershed 

Hydrological characteristics of Mamasa watershed is presented in Table 2. The result showed that the 
highest surface runoff in Mamasa watershed was occur in December (304.92 mm) as 38.16% from the 
rainfall in the same month, while the lower was occur in October as 4.47% from the rainfall. The highest 
contribution of the rainfall to lateral flow was occurred in October as 37.74% and the highest base flow 
occurred in August as 28.65%. Flow discharge of Mamasa watershed is presented in Figure 4. Even this 
model was not calibrated because Mamasa watershed is the ungauged watershed, but the result can be 
used to analyze the condition of watershed due to the input detail field data of soil characteristics into the 
model and adjusted sensitivity parameter base on watershed characteristics so the uncertainty from model 
can be reduced. The same study in ungagged basin of Central Vietnam showed that surface runoff was 
occurred in October with approximately 764 mm and the highest sediment yield as 580 ton per hectare 
(Emam et al., 2016). But this study still calibrated using the regionalization river discharge and showed the 
Nash Suttcliffe efficiency range between 0.67-0.73.    

From result in Table 2, then flow regime and runoff coefficient were calculated for Mamasa watershed. 
The flow regime coefficient was 40.26 which categorized as lower rank (Regulation of Ministry of forestry RI 
No. P.61/Menhut-II/2014), while the runoff coefficient was 0.29 (lower category). This value indicates that 
Mamasa watershed is still in good condition. But, the dominant steep slope in this watershed remains as 
the cause of the damage in the watershed. 

 
Tabel 2.  Hydrological Characteristics in Mamasa Watershed 

Month  Average Rainfall Surface Flow Lateral Flow Base Flow Sediment Yield 

mm -------------------- mm ----------------- tons/ha 

January 112.33 26.48 36.85 4.1 15.19 

February 233.51 71.28 84.26 17.51 35.18 

March 181.54 53.76 63.9 39.34 28.8 

April 315.35 111.04 99.92 35.72 72.71 

May 216.71 73.86 61.77 41.66 14.13 

June 118.28 22.21 37.04 25.35 2.14 

July 186.36 59.74 44.18 15.8 9.02 

Augustus 17.42 0 4.69 4.99 0 

September 45.18 2.89 13.09 0.74 1.13 

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October 80.7 3.61 30.46 0.23 0.11 

November 180.5 40.13 66.75 5.34 1.66 

December 304.92 116.35 97.81 37.39 7.54 

Total 1,992.8 581.35 640.72 228.17 187.61 

 

 

 
Figure 4. Daily Discharge of Mamasa Watershed in 2012 

3.3. Degraded of Sub Watershed in Mamasa 

Degraded Sub watershed was determined based on contribution of total runoff and total sediment to 

the Sub watershed. The total runoff and total sediment in Mamasa watershed is presented in Table 3 and 

spatially presented in Figure 5 and Figure 6. Based on the data, Sub watershed 15 was the sub watershed 

that most degraded with the contribution of total runoff and total sediment to its Sub watershed as 46% 

and 59.75%, respectively. The second degraded sub watershed was sub watershed 16, followed by sub 

watershed 6. If the Mamasa watershed is not well-managed, it is possible that the degradation in Mamasa 

watershed will increase. In order to decrease the degradation in Mamasa watershed, the management 

planning especially related to soil and water conservation is arranged for this watershed. 

Table 3. Total Runoff and Sediment from SWAT Model in Sub watershed, Mamasa watershed 

Sub 
watershed 

Area 
(ha) 

Total runoff 
(mm) 

% total runoff to 
Sub watershed 

Total sediment  
(tons/ha) 

% sediment to Sub 
watershed 

1 2,842.50 2,233.84 4.24 2,110.90 0.66 

2 3,085.40 2,229.12 4.59 1,690.02 0.58 

3 3,082.00 2,245.47 4.62 1,776.87 0.61 

4 3,790.00 2,208.13 5.58 1,732.80 0.73 

5 3,858.10 2,215.19 5.70 1,881.14 0.80 

6 9,798.50 2,231.12 14.59 1,812.57 1.96 

7 7,379.00 220.79 1.09 55.88 0.05 

8 4,035.00 233.12 0.63 47.72 0.02 

9 10,568.00 163.30 1.15 94.56 0.11 

10 6,985.80 195.57 0.91 173.69 0.13 

11 4.09 42.28 0.00 4.59 0.00 

12 5,524.00 236.64 0.87 142.25 0.09 

13 3,842.70 212.61 0.55 119.41 0.05 

14 1,373.50 153.91 0.14 247.079 0.04 

15 38,940.00 1,770.17 46.00 13,880.14 59.76 

16 10,935.00 1,280.19 9.34 28,462.72 34.41 

Total 116,043.59 17,871.45 100.00  54,232.34 100.00  

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Figure 5. Total Runoff Map from Sub Watershed in Mamasa Waterhsed 

 
Figure 6. Sediment Yield from Sub Watershed in Mamasa Watershed 

3.4. Simulation of Land Management and Soil and Water  

The arrangement of scenario of land use is done to achieve the best management practices that can 
minimize the runoff and sediment which occur in Mamasa watershed. Scenario of land management and 
soil and water conservation were applied based on land use 2014 and in agriculture land because the 
highest contribution from it to the erosion process in a watershed. The goal of conservation technique 

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application is to increase soil capability in infiltrate the rain water, thus it cannot contribute to surface 
runoff, immediately. The scenarios were also arranged to ensure the preservation of water distribution in 
wet and dry season. The scenarios are presented spatially in Figure 7. 

 

Figure 7. Map of Scenario Application in Mamasa Watershed 

The results of scenarios simulation to characteristics hydrology in Mamasa watershed is presented in 

Table 4. The conservation technique application in SWAT simulation is able to decrease surface runoff in 

Mamasa watershed and also decrease the runoff coefficient. Decreasing surface runoff, the increasing 

lateral flow and base flow from the scenario. Surface runoff was decrease approximately between 2.48 – 

33.44%, while the lateral flow and base flow were increase as much as 0.19 – 20.38%, and 1.96 - 2.58%. The 

highest decreasing of surface runoff was generated from scenario 4, which was the combination of whole 

scenario so the effect of conservation techniques to be maximum. 

Table 4. Hydrological Characteristics from Scenario Application 

Scenario Rainfall Surface flow Lateral flow Base flow C value Sediment yield 

--------------------mm------------------- tons/ha 

Existing 1992.8 581.35 640.72 228.17 0.292 332.96 

Scenario 1 1992.8 566.95 641.91 234.06 0.284 330.28 

Scenario 2 1992.8 550.55 654.75 233.4 0.276 321.77 

Scenario 3 1992.8 480.62 702.71 232.65 0.241 290.13 

Scenario 4 1992.8 386.94 771.33 227.45 0.194 212.97 

 

Runoff coefficient of existing condition in Mamasa watershed as 0.29 was decrease about 2.74 – 33.56% 
after the watershed management scenario was applied. The highest decreasing of runoff coefficient was 
achieved by applying scenario 4. The combinations of whole scenario are able to increase the effectiveness 
of applied conservation technique. Mulch can decrease the rainfall energy so that it cannot destroy the soil 
structures, decrease the speed and amount of surface runoff thus can reduce surface runoff energy. Mulch 
also reduces the evaporation and keeps the water content in soil.      

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The function of strip grass application to preserve the river sustainability is achieved by holding and 
capturing soil eroded/mud as well as nutrient and chemicals including pesticide from agricultural land. The 
hedge plant on alley cropping system functioned to hold water, fertilize the soil, minimizing the erosion and 
landslide, and increasing the microorganism activity. Silt pilt was constructed to catch the runoff and soil 
eroded so that the water can infiltrate to the deep soil and reduce erosion.  

The simulation results also indicate a decrease in sedimentation. Simulation of scenario 4 was able to 
reduce sediment up to > 40%. Decreasing in sediment will be higher if the agricultural land is mostly applied 
by the conservation techniques. Thus, it can be said that the implementation of appropriate conservation 
techniques and appropriate to the circumstances in the field are effective in reducing runoff and sediment. 
This result is linier with other studies about best management practices. Application of vegetation filter 
strip (VFS) in Haean highland agricultural catchment of South Korea is the best way to reduce the sediment 
load, The VFS was applied in 3 different ways are vegetative filter strip with 1m, 3m, and 5m. The VFS5m is 
the best technique in reducing the sediment load in watershed outlet approximately 34.8%, while the 
VFS1m and VFS3m reduce sediment as 16.0 and 22.1%, respectively. However, the techniques fertilizer 
control, and rice straw mulching only reduce the sediment load around 4.9-16.4%, and 3-14.1%, 
respectively (Jang et al., 2016). The decreasing of sediment load came from the discharge load contribution 
in upland crop areas. Another research by Liu et al. (2016) indicated that application of buffer strip and 
cover crop in upland fields are the best techniques in reducing the sediment load approximately 5 – 13% 
compare to the initial condition, while the whole scenario including nutrient management, buffer strip, 
cover crop, and wetland restoration only reduce sediment as much as 0 – 5.54% in the watershed outlet. 

 

4. CONCLUSION 

The accuracy of SWAT model in estimating hydrological characteristics in Ungagged Mamasa watershed 
is based on detail soil data from field study and sensitivity parameters which developed by the watershed 
characteristics.  The estimate indicates that Mamasa watershed is in good condition.  

Four soil and water conservation scenarios including bunch and mulch scenario, bunch terrace, mulch, 
and strip grass scenario, alley cropping and silt pilt scenario, and agroforestry scenario plus combination all 
scenario were developed and evaluated. The agroforestry scenario plus combination all scenario has the 
highest reduction of surface flow and sediment yield compared to other scenario.  The alley cropping and 
silt pilt scenario have the second highest reduction of surface flow and sediment yield, followed by bunch 
terrace, mulch, and strip grass scenario, and bunch and mulch scenario. The combination scenario from all 
scenario showed that overall impacts is higher than individual scenario because each technique is 
interacted with other, so the accumulative result will be highest than individual scenario. 

 

5. ACKNOWLEDGMENTS 

The authors would like to thank to Watershed Management Agency of Saddang, Ministry of Forestry for 
funding this activity. And also, to Center for Environmental Research, Bogor Agricultural University for 
funding research publication.  

6. REFERENCES 

Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., Jha, M. K. (2012). 

SWAT: Model Use, Calibration, and Validation. Transactions of the ASABE, 55(4), 1491–1508. 

[Crossref] 

Cibin, R., Athira, P., Sudheer, K. P., & Chaubey, I. (2013). Application of distributed hydrological models for 

predictions in ungauged basins: a method to quantify predictive uncertainty. Hydrological Processes, 

28(4), 2033–2045. [Crossref] 

Daniel, E. B. (2011). Watershed Modeling and its Applications: A State-of-the-Art Review. The Open 

Hydrology Journal, 5(1), 26–50. [Crossref] 

Department of Forestry. (2010). Arrangement of integrated watershed management planning of Saddang 

Watershed. 

https://doi.org/10.14710/geoplanning.4.2.263-272
https://doi.org/10.13031/2013.42256
https://doi.org/10.1002/hyp.9721
https://doi.org/10.2174/1874378101105010026


Murtilaksono et al./ Geoplanning: Journal of Geomatics and Planning, Vol 4, No 2, 2017, 263-272 
doi: 10.14710/geoplanning.4.2.263-272 

 

272 | 
 

Department of Forestry. (2010). Characteristics of Saddang Watershed. 

Douglas-Mankin, K. R., Srinivasan, R., & Arnold, J. G. (2010). Soil and Water Assessment Tool (SWAT) Model: 

Current Developments and Applications. Transactions of the ASABE, 53(5), 1423–1431. [Crossref] 

Emam, A. R., Kappas, M., Nguyen, L. H. K., & Renchin, T. (2016). Hydrological Modeling in an Ungauged 

Basin of Central Vietnam Using SWAT Model. Hydrology and Earth System Sciences Discussions, 1–33. 

[Crossref] 

Gassman, P. W., Sadeghi, A. M., & Srinivasan, R. (2014). Applications of the SWAT Model Special Section: 

Overview and Insights. Journal of Environment Quality, 43(1), 1. [Crossref] 

Getahun, E., & Keefer, L. (2016). Integrated modeling system for evaluating water quality benefits of 

agricultural watershed management practices: Case study in the Midwest. Sustainability of Water 

Quality and Ecology, 8, 14–29. [Crossref] 

Her, Y., Chaubey, I., Frankenberger, J., & Smith, D. (2016). Effect of conservation practices implemented by 

USDA programs at field and watershed scales. Journal of Soil and Water Conservation, 71(3), 249–266. 

[Crossref] 

Himanshu, S. K., Pandey, A., & Shrestha, P. (2016). Application of SWAT in an Indian river basin for modeling 

runoff, sediment and water balance. Environmental Earth Sciences, 76(1). [Crossref] 

Jang, S. S., Ahn, S. R., & Kim, S. J. (2017). Evaluation of executable best management practices in Haean 

highland agricultural catchment of South Korea using SWAT. Agricultural Water Management, 180, 

224–234. [Crossref] 

Krysanova, V., & White, M. (2015). Advances in water resources assessment with SWAT an overview. 

Hydrological Sciences Journal, 1–13. https://doi.org/10.1080/02626667.2015.1029482 [Crossref] 

Liu, Y., Yang, W., Leon, L., Wong, I., McCrimmon, C., Dove, A., & Fong, P. (2016). Hydrologic modeling and 

evaluation of Best Management Practice scenarios for the Grand River watershed in Southern Ontario. 

Journal of Great Lakes Research, 42(6), 1289–1301. [Crossref] 

Moriasi, D. N., Steiner, J. L., & Arnold, J. G. (2011). Sediment Measurement and Transport Modeling: Impact 

of Riparian and Filter Strip Buffers. Journal of Environment Quality, 40(3), 807. [Crossref] 

Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and water assessment tool theoretical 

documentation version 2009. 

QIU, L., ZHENG, F., & YIN, R. (2012). SWAT-based runoff and sediment simulation in a small watershed, the 

loessial hilly-gullied region of China: capabilities and challenges. International Journal of Sediment 

Research, 27(2), 226–234. [Crossref] 

Shi, Y., Xu, G., Wang, Y., Engel, B. A., Peng, H., Zhang, W., Dai, M. (2017). Modelling hydrology and water 

quality processes in the Pengxi River basin of the Three Gorges Reservoir using the soil and water 

assessment tool. Agricultural Water Management, 182, 24–38. [Crossref] 

Srinivasan, R., Zhang, X., & Arnold, J. (2010). SWAT Ungauged: Hydrological Budget and Crop Yield 

Predictions in the Upper Mississippi River Basin. Transactions of the {ASABE}, 53(5), 1533–1546. 

[Crossref] 

Sunandar, A. D., & Suhendang, E. (2014). Hendrayanto; Jaya, INS; Marimin. Land use optimization in Asahan 

watershed with linear programming and SWAT model. Int. J. Sci. Basic Appl. Res, 18, 63–78. 

Suryavanshi, S., Pandey, A., & Chaube, U. C. (2017). Hydrological simulation of the Betwa River basin (India) 

using the SWAT model. Hydrological Sciences Journal, 62(6), 960–978. [Crossref] 

Yusuf, S. M., Murtilaksono, K., Harjianto, M., & Herlina, E. (2016). The Utilization of Satellite Imagery Data to 

Predict Hydrology Characteristics in Dodokan Watershed. Procedia Environmental Sciences, 33, 36–43. 

[Crossref] 

 

https://doi.org/10.14710/geoplanning.4.2.263-272
https://doi.org/10.14710/geoplanning.4.2.263-272
https://doi.org/10.13031/2013.34915
https://doi.org/10.5194/hess-2016-44
https://doi.org/10.2134/jeq2013.11.0466
https://doi.org/10.1016/j.swaqe.2016.06.002
https://doi.org/10.2489/jswc.71.3.249
https://doi.org/10.1007/s12665-016-6316-8
https://doi.org/10.1016/j.agwat.2016.06.008
https://doi.org/10.1080/02626667.2015.1029482
https://doi.org/10.1016/j.jglr.2016.02.008
https://doi.org/10.2134/jeq2010.0066
https://doi.org/10.1016/s1001-6279(12)60030-4
https://doi.org/10.1016/j.agwat.2016.12.007
https://doi.org/10.13031/2013.34903
https://doi.org/10.1080/02626667.2016.1271420
https://doi.org/10.1016/j.proenv.2016.03.054

