1. Submitted to ―International Journal of Design & Nature and Ecodynamics“
(7-7-2020)
2. First revision: Acceptable with major revisions (27-7-2020) 3. Revision has been sent (28-7-2020)
4. 2nd stage Revision Notice (28-7-2020) 5. Second revision has been sent (31-7-2020) 6. 3nd stage Revision Notice (4-8-2020) 7. Third revision has been sent (5-8-2020) 8. Proof (20-8-2020)
9. Published (31-8-2018)
Submitted to ―International Journal of Design
& Nature and Ecodynamics“ (7-7-2020)
Naharuddin Sumani <nahar.pailing@gmail.com>
5 Juli 2020 14.22
Dear
Editor In Chief
International Journal of Design & Nature and Ecodynamics
I am enclosing herewith a manuscript entitled ―The Index of Erosion Hazard for Land Use Planning on Post-Earthquake, Tsunami, and Soil Liquefaction Area (Watutela Watershed, Central Sulawesi, Indonesia)‖ submitted to International Journal of Design & Nature and Ecodynamics for possible evaluation.
With the submission of this manuscript, I would like to undertake that the above-mentioned manuscript has not been published elsewhere, accepted for publication elsewhere or under editorial review for publication elsewhere; and that my Institute’s Faculty of Forestry, Tadulako University representative is fully aware of this submission. Type of Submitted manuscript Original Research Article.
Best regards,
Dr. Naharuddin
The Index of Erosion Hazard for Land Use Planning on Post-Earthquake, Tsunami, and Soil Liquefaction Area (Watutela Watershed, Central Sulawesi, Indonesia)
Naharuddin1*, Adama Malik1, Imran Rachman1, Hasriani Muis1, Hamzari1, Abdul Wahid1
1 Department of Forestry, Faculty of Forestry, Tadulako University, Palu 94119, Central Sulawesi, Indonesia
Corresponding Author Email: nahar.pailing@gmail.com https://doi.org/10.18280/ijdne.xxxxxx ABSTRACT Received:
Accepted:
The erosion is a very complex problem and can damage environmental ecosystems and land productivity. The main objective of this study is to predict soil erosion in Watutela watershed after the earthquake using GIS combination with the USLE method. The results show that the erosion hazard index in the Watutela watershed is in the low to high criteria. The danger of high erosion was found in empty land 427.93 tons/ha/year.
While on the other hand, low criteria occur in the shrubs 26.07 tons/ha/year. The results obtained allow determining as the Palu City development zone and to minimize the erosion hazard index, soil, and water conservation needs to be done through reforestation, rehabilitation utilization of tree architecture models that function in controlling erosion and climate, especially in high erosion hazard class.
Keywords:
Watershed, erosion hazard, land use, GIS, USLE
1. INTRODUCTION
The occurrence of soil erosion is recognized globally as a serious problem [1, 2]. Southeast Asia is among the regions facing the most severe impact of climate change [3, 4]. Soil erosion, one of the most severe problems in the 21st century [5, 6]. Around 70 billion tons/year of land erosion in the world comes mostly from agricultural land.
Land productivity in a watershed gradually decreases by±20 million ha/year. Highly exploitative land use does not only ecologically harm the environment but also the economy, and must be surmounted [7]. Not only inhibits sustainable land management, but soil erosion also causes other people's live problems [8, 9].
The decline in land quality is caused by accelerated erosion and the conversion of forests and other land uses.
Accelerated erosion is in the form of an agricultural system that ignores the principle of soil and water conservation [10, 11]. The quality of a watershed depends on land management and suitability as well as its carrying capacity [12].
Prediction of erosion hazard index is important for current or future land use planning [13]. It is known that erosion produces sediment. The model of Universal Soil Loss Equation (USLE) that is based on geographic information system has been widely used to estimate the amount of sediment in many watersheds throughout the world both nationally and regionally [2, 14]. In Indonesia itself, USLE has been developed as a model to compare erosion that occurs in forests, rice fields, plantations, and residential areas [15, 16].
A method with Geographic Information Systems (GIS) approach combined with the USLE model as a means of erosion hazard index prediction has weaknesses such as the model is seen to be ineffective if applied outside the condition of where the model was developed. The adaptation of this model to a new environment requires an investment of resources and time to develop the database needed [17]. Although USLE has weaknesses, this method is still applied by many researchers. Mentioned that erosion prediction with the USLE model is very important to map out the areas they need specific management [18].
USLE is a simple and reliable analysis in the land management perspective on an ongoing basis [19]. Erosion prediction with the USLE model is essential as a basis to determine the hazard and risk of erosion [18, 20]. Besides International Journal of Design & Nature and Ecodynamics
Vol., No., Month, Year, pp. **-**
that, the assessment of soil sensitivity to erosion is significant in the effort of land conservation and management [21, 22]. Assessment of soil erosion using USLE and GIS is the basis for making land use decisions soil and ecosystem conservation [40]
The study on soil erosion and hazard index uses the USLE combination of GIS model been done by [36, 37, 38] so that the data produced has the accuracy and the latest information, it is important to combine [39].
The research to map the erosion hazard index urgent in Watutela Watershed. Based on these data, the erosion hazard index in the framework of land use planning can be used as a guideline in the framework of conservation strategies for the restoration and rehabilitation of watershed ecosystems both inside and outside the forest area.
The erosion hazard index is a comparison between the amount of erosion that occurs that will endanger the sustainability of soil productivity. To maintain soil productivity, land management should be adjusted to the principles of soil conservation.
Watutela watershed is located in the eastern region of Palu City, Central Sulawesi. The ID Number is 183419 and have over 963,264 ha catchment areas. It has a quite large height differencing to cause flooding debris which carries a big amount of sedimentation. The area is prone to soil and cliff erosion. The existence of this watershed has an important role in the lives of the people in the eastern region of Palu City. Not only as a regional system of water supply in Palu, but Watutela watershed is also classified as a yellow zone/development area (a zone that is prone to liquefaction, tsunamis, soil movement, flood).
Although this region is already declared as a development area based on the disaster-prone zone map of Palu City, the data of environmental baseline especially the data of erosion hazard index is not yet available as a basis for policymaking. Therefore, it is necessary to study the prediction of erosion hazard index and the effect of land use on the rate of erosion. It is believed that the results can be used as a foundation for watershed planning in an environmentally sustainable manner and for the strategy of vegetative and mechanical conservation. Based on this matter, the goal of this study is to predict soil erosion in Watutela watershed after the earthquake using GIS combination with the USLE method.
2. STUDY AREA AND DATA
This research location is in Watutela watershed, Palu City, Central Sulawesi, with coordinates between longitudes 119'52'46.58''E and 119'56'1.31''E, and latitudes 0'49'12.64''S and 0'50'32,8''S. According to natural conditions, the Watutela watershed is surrounded by Palu Bay in the west and Mount Verbecs in the east. The map of Watutela watershed is presented in Figure 1.
Figure 1. Research Location, Watutela Watershed
The data for an index of erosion hazard analysis in this study were: (1) the data of rainfall in the last 5 years starting from 2014 to 2018, (2) the Landsat imagery 8 bands 654, (3) the map of Watutela watershed, (4)
administration map, (5) the slope map, (6) the soil type map, (7) the land use map, (8) the map of Indonesia on scale 1:25,000, and (9) data actual erodibility of each unit, and type of land use, and land conservation.
3. METHODOLOGY
The combination of the application of GIS and USLE methods is carried out through the process of collecting primary and secondary data. Primary data obtained from field checking, observation on the actual erodibility of each unit, and type of land use, as well as land conservation. The observation was performed based on the results of the Landsat 8 band 654 imagery interpretation. Whereas, the secondary data consisted of (1) spatial data. It is in the form of a digital map from watershe d and Forest Management Center of Palu-Poso containing slope grade, soil type, rainfall distribution pattern, and land use pattern; (2) non-spatial data that is in the form of rainfall data from Meteorological Station of Mutiara Palu and sub-district data from the Central Sulawesi of Statistics of Mantikulore Sub-district, Palu City; (3) other supporting data such as results from previous research and scientific publications as well as results from spatial planning of Palu City post-earthquake, tsunami, and soil liquefaction
The data analysis of this study was implemented using GIS with a projection system and Universal Transverse Mercator (UTM) as the coordinates. The unit in this measurement is a meter so that the analysis that required distance and area i nformation can be arranged utilizing overlaying the maps. Then, the data were calculated using the USLE formula [22, 23, 24]:
A = R x K x LS x C x P (1)
Where, A: the amount of soil loss due to erosion (ton/ha/year), R: rainfall erosivity index, K: soil erodibility factor, LS: land length and slope factor, C: vegetation and plant management factor, P: land management/soil conservation factor.
In determining the erosion hazard index, the researchers used this following Equation 2 that is adapted from [25, 26]:
EHI = A/Etol (2)
Where, EHI: erosion hazard index (ton/ha/year), A: soil erosion rete (ton/ha/year), Etol: soil erosion that can be tolerated (mm/year).
Etol values are calculated based on the data in Table 1 as follows:
Table 1. Guidelines for Determining Etol Value for Soils in Indonesia [27]
Soil and substratum properties Etol Value (mm/year) Very shallow soil above rocks 0.0 Very shallow soil above moldy materials (not consolidated)
0.4 Shallow soil above moldy materials 0.8 Moderate depth soil above moldy materials
1.2 Deep soil with the impermeable lower layer above moldy materials
1.4 Deep soil with slow permeability lower layer above moldy materials
1.6 Deep soils with medium permeability lower layer above moldy materials
2.0 Deep soils with fast permeability lower layer above moldy materials
2.5
Furthermore, Table 2 below is used to determine the classification of the index erosion. Data refers to the study [22, 28]:
Table 2. Erosion Hazard Index Classification Erosion Rate
(ton/ha/year)
Erosion hazard index
classification
Description
<15 I Very Low
15-60 II Low
60-180 III Medium
180-480 IV High
>480 V Very High
4. RESULTS AND DISCUSSIONS 4.1. Land Use
The results analysis of the GIS method and ground check and the interpretation of Landsat 8 band 654 images, Watutela watershed has four types of land use namely shrubs, dryland farming, empty land, and settlement (Table 3 and Figure 2)
Table 3. Watutela Watershed Land Use Land Use/Codes Area
Ha %
Shrubs (SB1) 115.86 12.55
Empty Land (EL) 2.32 0.25
Shrubs (SB2) 94.56 10.25
Settlements (SM) 420.43 45.55
Shrubs (SB3) 35.42 3.84
Shrubs (SB4) 249.34 27.01
Dryland Farming (DF)
5.07 0.55
Total 923.01 100.00
Figure 2. Land Use Map of Watutela Watershed
Based on the Table 3 and Figure 2, that the land in Watutela Watershed is dominated by shrubs amounting to 495.19 ha (53.65%). it shows that the land forest vegetation has decreased, this will lead to surface runoff and erosion.
The use of forest areas affects the rate of surface runoff and erosion. If the forest area decreases, it will not only cause increased soil erosion but will also cause a reduction in water catchment areas, resulting in a decrease in water storage capacity that can increase erosion [16].
In this study, the settlement area is 420.43 ha and is not identified as the unit of analysis because settlement or residential area is not prioritized for soil and water conservation plan.
4.2. Rainfall Erosivity Index
Rainfall erosivity is the deciding factor in erosion rate. The intensity, thickness, and duration of the rain will largely determine the erosion. In a hydrological system, rain is one form of input into the watershed and can change from time to time which depends on climate change. Rainfall erosivity is the most dynamic factor that affects the variability of soil erosion rete [29]. Therefore, the average erosivity (R) value in the last 5 years (2014-2018) is 462 (Table 4).
Table 4. Rainfall erosivity index (R)
Month Rainfall (cm
th-1) R
January 6.93 39.5595
February 6.19 36.4885
March 8.14 44.581
April 6.9 39.435
May 6.65 38.3975
June 12.78 63.837
July 8.49 46.0335
August 0.4 12.46
September 6.38 37.2936
October 6.59 38.1485
November 4.89 30.8611
December 5.72 34.538
Total 462
From the calculation of rainfall erosivity index (R) using the average monthly rainfall data in the table above, Watutela watershed has a Rainfall erosivity value of 462. The erosivity value indicator indicates a high occurrence of surface runoff on Watutela Watershed. This runoff carries soil particles from the damage to soil aggregates due to strong rain pressure (kinetic energy of the Rainfall). If the rain volume and intensity is high, the potential for surface runoff and erosion is also high. Erosivity is affected by the fall of rainwater directly to the ground and partly due to the flow of water on the land through the surface runoff process. Direct raindrops can erode the land surface slowly, and the accumulation of rainfall intensity will eventually cause erosion. This is in line with the statement [30], the rainfall causes the greetest proportion of runoff and soil loss.
4.3. Soil Erodibility Value
Soil erodibility is the sensitivity of soil to erosion. Each soil type has a different sensitivity. The results showed that the soil erodibility of each land unit in this study has a low to high classification.
Table 5. Erodibilty value (K)
Land Use/Codes Erodibility Classification
(K)
Shrubs (SB1) 0.23 Medium
Empty Land (EL) 0.38 Slightly High
Shrubs (SB2) 0.24 Medium
Shrubs (SB3) 0.18 Low
Shrubs (SB4) 0.17 Low
Dryland Farming
(DF) 0.19 Medium
Table 5, shows there are differences in the value of soil erodibility on each land unit. This happens due to the nature of the soil such as texture, permeability, structure, and organic contents where the permeability value and organic contents can change et any time along with the changes in land use. The soil properties influence each other which then will determine the level of soil erodibility.
The sensitivity of soil erodibility is a function of various interactions of soil physical and chemical properties and other environmental factors. The greeter the soil erodibility value, the higher the erosion. Empty land has a high level of erodibility compared to other land uses such as forests. This is supported by the results of [2, 31] that soil erodibility is affected by changes in land use.
High land-use intensity causes a decrease in soil aggregate stability and permeability. Based on soil physical and chemical properties observation, the texture of dusty soil in empty land is equal to 57.5%. This number is higher compared to other fractions. It is found that the contents of organic material in the empty land is very low et 5.79%.
Empty land will be more susceptible to erosion due to dusty soil content, which has a finer size than other fractions so that it is easily carried by water during heavy rainfall. Aside from its size, dust cannot form bonds and as a result, this fraction is easily destroyed by rainwater and causes erosion. Organic contents influence the ability of the soil to minimize the rate of erosion.
4.4. Land Length and Slope Factor
The results showed thet the LS value on each land unit has a slightly steep-sloping classification (Table 6).
Table 6. Land length and slope value (LS) Land Use/Codes L S LS Classification Shrubs (SB1) 1.305 0.10 4.39 Slightly Steep Empty Land (EL) 264 0.07 1.95 Sloping Shrubs (SB2) 1.396 0.06 4.48 Slightly Steep Shrubs (SB3) 1.031 0.09 2.09 Sloping Shrubs (SB4) 1.486 0.13 3.94 Slightly Steep Dryland Farming
(DF) 300 0.07 4.63 Slightly Steep
Table 6, explains that shrubs, in general, have a slightly steep classification (SB1, SB2, and SB4) compared to empty land which has a sloping classification. This means that an area with a slightly steep classification has higher runoff carrying more soil particles. The higher the slope, the higher the number of soil particles carried to the bottom because of raindrops. If the degree of the steep is 2 times higher, then the amount of erosion on each land unit becomes 2 .0 to 2.5 times bigger [27]. According to [32], the effect of slope length on soil loss per unit area is often considered to vary with slope length to a power greater than zero and less than 1.
4.5. Plant Management and Conservation Measures
The index of plant management and conservation measures (CP) is obtained from land-use data and conservation actions performed on each land unit. The results of the analysis are adjusted to the grouping of CP factor values (Table 7).
Table 7. Factors of plant management and conservation measures (CP)
Land Use/Codes C P CP
Shrubs (SB1) 0.3 1 0.3
Empty Land (EL) 1 1 1
Shrubs (SB2) 0.3 1 0.3
Shrubs (SB3) 0.3 1 0.3
Shrubs (SB4) 0.3 1 0.3
Dryland Farming (DF) 0.5 0.4 0.2
Table 7, above points out that the highest CP value occurs in the use of empty land. On the one hand, the CP value of shrubs on average is 0.3 while dryland farming is 0.2. Thus, it can be concluded that vegetation affects the rate of erosion in protecting the soil surface from the damage of raindrops. vegetation has a significant impact on the formation and transportation of sediments in the form of soil erosion [33]. Believed that each type of plant has a different ability to hold erosion rates. This is because the effectiveness of plants in reducing the rate of erosion is influenced by (1) height and continuity of leaf canopy, (2) organic matter factors, (3) plant root systems, and (4) plant density. The effect of plants on erosion is usually seen from the production of dry matter and the ability to cover the soil.
4.6. Erosion Hazard Index Classificetion
Based on the results of overlaid maps and calculation from GIS as well as field observation and USLE, it is known that the biggest erosion occurs on shrubs and empty land with a sloping and slightly steep classification (Table 8)
Table 8. Erosion hazard index classification Land
Use/Codes A (ton/ha/
year) Etol (mm/
year) EHI (ton/ha/
year)
Description
Shrubs (SB1)
139.94 1.4 99.96 Medium
Empty Land (EL)
342.34 0.8 427.93 High
Shrubs (SB2)
149.02 0.4 372.55 High
Shrubs (SB3)
52.14 2.0 26.07 Low
Shrubs (SB4)
92.83 1.4 66.31 Medium
Dryland Farming (DF)
81.28 1.2 67.74 Medium
Table 8, it can be seen that 2.32 ha of empty land (EL) has an erosion hazard index with a total erosion of 342.23 ton/ha/year.
This is classified as a high rate of erosion because the tolerable erosion rate is 0.8 mm/year. The same thing also happened in 94.56 ha of shrubs (SB2) with a total erosion of 149.02 tons/ha/year. The erosion rate that can be tolerated in this area is 0.4 mm/year. The erosion hazard index for other shrublands such as SB1, SB3, and SB4 is in the low to medium classification. This is due to the differences in the value of erosion that can be tolerated. Nevertheless, dryland farming has a medium erosion hazard index classification. The high classification of erosion hazard index on empty land (EL) and shrubs (SB2) happened because it has a high level of soil density with very slow permeability compared to other land uses 4.54 cm/hour for empty land (EL) and 2.86 cm/hour for shrubs (SB2)). Very slow permeability leads to higher surface runoff and erosion rates. The condition is influenced by the texture of the dominant soil with dust. Low, medium and high erosion (Figure 3).
Land in Watutela Watershed mainly in the appropriate empty land (Figure 3) has been encroached causing a high level of erosion hazard. Land cover affects erosion [11]. land that has been under significant pressure for some time: especially illegal logging, overgrazing, and climate change can increase erosion and land degradation [34]. Changes in land use can affect the risk of natural disasters [35].
Figure 4. Map of erosion hazard classification
The Watutela watershed is dominant with the land cover shrubs. Besides reforestation, it is also necessary to plant small-leaf crops and deep root systems to prevent the kinetic energy of raindrops, especially on an empty land with high erosion rates.
5. CONCLUSIONS
An empty land has the highest erosion rate amounting to 342.23 tons/ha/year. Meanwhile, the lowest erosion rate occurs in shrubs land (SB3) with a value of 52.14 tons/ha/year. The high erosion hazard index is found in empty land (EL) of 427.93 tons/ha/year and shrubs (SB2) of 372.55 tons/ha/year. Meanwhile, a low hazard index occurs in other shrubs (SB3) of 26.07 tons/ha/year.
Considering that the area of Watutela Watershed is designated as the development area of Palu City, after the earthquake, tsunami, and land liquefaction in 2018, it needs a community development and reforestation strategy that is integrated with sustainable and environmentally-friendly forest, soil, and water rehabilitation programs, so that, the plants can function as controllers of water and climate.
ACKNOWLEDGMENT
The authors thank the leadership of the Faculty of Forestry, Tadulako University, who provided facilities with assignment letter No. 2257/U28.1.29/KP/2019.
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[31] Taleshian Jeloudar, F., Ghajar Sepanlou, M., & Emadi, M. (2018). Impact of land use change on soil erodibility. Global Journal of Environmental Science and Management, 4(1): 59-70. https://doi.org/10.22034/GJESM.2018.04.01.006
[32] Kinnell, P. I. A. (2009). The impact of slope length on the discharge of sediment by rain impact induced saltetion and suspension. Earth Surface Processes and Landforms, 34(10): 1393-1407. https://doi.org/10.1002/esp.1828
[33] Ouyang, W., Hao, F., Skidmore, A. K., Toxopeus, A. G. (2010). Soil erosion and sediment yield and their reletionships with vegetetion cover in upper stream of the Yellow River. Science of the Total Environment, 409(2): 396-403.
https://doi.org/10.1016/j.scitotenv.2010.10.020
[34] Dallahi, Y., Ouhammou, A., Sbai, M., Ei Aboudi, A., Boujraf, A. (2020). Assessment of the Vegetetion Cover Change Impacts on Weter Erosion, Using Pap/Rac Method in Upstream of ―Ouljet Soltane‖ Dam, Central Pleteau- Morocco. Geographia Technica, 15 (1): 153-161, DOI: 10.21163 / GT_2020.151.14
[35] Ighile, E. H., Shirakawa, H. (2020). A Study on the Effects of Land Use Change on Flooding Risks in Nigeria. Geographia Technica, 15(1): 91-101. DOI: 10.21163 / GT_2020.151.08
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First revision: Acceptable with major revisions
(27-7-2020)
miya.li@iieta.org 27 Jul 2020 08.07
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Title: The Index of Erosion Hazard for Land Use Planning on Post-Earthquake, Tsunami, and Soil Liquefaction Area (Watutela Watershed, Central Sulawesi, Indonesia)
Recommendation check only one
Honours quality
Acceptable in the same form Acceptable with minor modifications
Acceptable with major revisions* (review required after revision)
NOT ACCEPTABLE*
Overview and general recommendation:
The article structure is relatively clear, but the logic is not good, and the innovation needs to be improved. It is recommended that the author clarifies the research question again to improve the research contribution of the article.
Comments to Author:
The following are detailed suggestions:
1. The abstract should be reduced.Please delete this part, After that, a spatial analysis was carried out by overlaying maps on each parameter of several land use to determine the erosion hazard index
classification.
2. The deion of ―Introduction‖ still needs to be improved, as some contents are redundancy while some are lacked.For example, Accelerated erosion is in the form of an agricultural system that ignores the principle of soil and water conservation and intensive agriculture.
3. "This is in accordance with the disaster-prone zone map of Palu City that is created after the earthquake, tsunami, and liquefaction in Palu and its surrounding area on 28 September 2018", what is the solution ?
4. The author should emphasize the rational for Index of Erosion Hazard for your study in the introduction section.
5. What is the relationship between USLE Model and GIS?
6. USLE Model and GIS should add details, such as the extension and significance of the USLE model.
7.There are also structure errors, as methods are also being described in the ―results‖ section and objectives in the ―Research Methods‖ section.
8. Authors are suggested to validate these results using few more such entries.
9.Probably the quality of the pictures in figure 3 could be improved in the last version.
10.It is noted that your manuscript needs careful editing by someone with expertise in technical English editing paying particular attention to English grammar, spelling, and sentence structure so that the goals and results of the study are clear to the reader.Articles require authors to provide proof of polish in English. If not, the journal may consider refusing to publish
In summary, the author needs to make major revisions to the article and resubmit it.
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[2] Bejan, A. (2015). Constructal thermodynamics.
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[5] Bentley, R.E. (1998). Handbook of Temperature Measurement Vol. 3: The Theory and Practice of Thermoelectric Thermometry. Springer Science &
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[6] Williams, J.O. (1993). Narrow-band analyzer. Ph.D.
dissertation. Department of Electronic Engineering, Harvard University, Cambridge, Massachusetts, USA.
[7] SIMUL8 Corporation. SIMUL8 – Process Simulation Software. http://www.simul8.com/, accessed on Jan. 17, 2015.
[8] Reber, E.E., Michell, R.L., Carter, C.J. (1988).
Oxygen absorption in the earth’s atmosphere.
Technical Report TR-0200 (4230-46)-3. Aerospace Corporation, Los Angeles, California, USA.
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Revision has been sent
(28-7-2020)
Naharuddin Sumani <nahar.pailing@gmail.com>
28 Jul 2020 10.55
Dear
Editor In Chief
International Journal of Design & Nature and Ecodynamics
The author attaches the article "The Index of Erosion Hazard for Land Use Planning on Post-Earthquake, Tsunami, and Soil Liquefaction Area (Watutela Watershed, Central Sulawesi, Indonesia)" which has been revised according to the recommendations of editors and reviewers.
That all authors have worked full time following up on the results of the revision, hopefully, it can be accepted for the publication of the upcoming edition, bearing in mind that the results of this study become policy material in land use management in Palu City post-Earthquake, Tsunami, and Soil Liquefaction Area
Best regards,
Dr. Naharuddin
The Index of Erosion Hazard for Land Use Planning on Post-Earthquake, Tsunami, and Soil Liquefaction Area (Watutela Watershed, Central Sulawesi, Indonesia)
Naharuddin1*, Adama Malik1, Imran Rachman1, Hasriani Muis1, Hamzari1, Abdul Wahid1
1 Department of Forestry, Faculty of Forestry, Tadulako University, Palu 94119, Central Sulawesi, Indonesia
Corresponding Author Email: nahar.pailing@gmail.com
https://doi.org/10.18280/ijdne.xxxxxx
ABSTRACT
Received:
Accepted:
The erosion is a very complex problem and can damage environmental ecosystems and land productivity. The main objective of this study is to predict soil erosion in Watutela watershed after the earthquake using GIS combination with the USLE method. The results show that the erosion hazard index in the Watutela watershed is in the low to high criteria. The danger of high erosion was found in empty land 427.93 tons/ha/year.
While on the other hand, low criteria occur in the shrubs 26.07 tons/ha/year. The results obtained allow determining as the Palu City development zone and to minimize the erosion hazard index, soil, and water conservation needs to be done through reforestation, rehabilitation utilization of tree architecture models that function in controlling erosion and climate, especially in high erosion hazard class.
Keywords:
Watershed, erosion hazard, land use, GIS, USLE
International Journal of Design & Nature and Ecodynamics
Vol., No., Month, Year, pp. **-**
8. INTRODUCTION
The occurrence of soil erosion is recognized globally as a serious problem [1, 2]. Southeast Asia is among the regions facing the most severe impact of climate change [3, 4]. Soil erosion, one of the most severe problems in the 21st century [5, 6]. Around 70 billion tons/year of land erosion in the world comes mostly from agricultural land.
Land productivity in a watershed gradually decreases by±20 million ha/year. Highly exploitative land use does not only ecologically harm the environment but also the economy, and must be surmounted [7]. Not only inhibits sustainable land management, but soil erosion also causes other people's live problems [8, 9].
The decline in land quality is caused by accelerated erosion and the conversion of forests and other land uses.
Accelerated erosion is in the form of an agricultural system that ignores the principle of soil and water conservation [10, 11]. The quality of a watershed depends on land management and suitability as well as its carrying capacity [12].
Prediction of erosion hazard index is important for current or future land use planning [13]. It is known that erosion produces sediment. The model of Universal Soil Loss Equation (USLE) that is based on geographic information system has been widely used to estimate the amount of sediment in many watersheds throughout the world both nationally and regionally [2, 14]. In Indonesia itself, USLE has been developed as a model to compare erosion that occurs in forests, rice fields, plantations, and residential areas [15, 16].
A method with Geographic Information Systems (GIS) approach combined with the USLE model as a means of erosion hazard index prediction has weaknesses such as the model is seen to be ineffective if applied outside the condition of where the model was developed. The adaptation of this model to a new environment requires an investment of resources and time to develop the database needed [17]. Although USLE has weaknesses, this method is still applied by many researchers. Mentioned that erosion prediction with the USLE model is very important to map out the areas they need specific management [18].
USLE is a simple and reliable analysis in the land management perspective on an ongoing basis [19]. Erosion prediction with the USLE model is essential as a basis to determine the hazard and risk of erosion [18, 20]. Besides that, the assessment of soil sensitivity to erosion is significant in the effort of land conservation and management [21, 22].
The research to map the erosion hazard index urgent in Watutela Watershed. Based on these data, the erosion hazard index in the framework of land use planning can be used as a guideline in the framework of conservation strategies for the restoration and rehabilitation of watershed ecosystems both inside and outside the forest area.
The erosion hazard index is a comparison between the amount of erosion that occurs that will endanger the sustainability of soil productivity. To maintain soil productivity, land management should be adjusted to the principles of soil conservation.
Watutela watershed is located in the eastern region of Palu City, Central Sulawesi. The ID Number is 183419 and have over 963,264 ha catchment areas. It has a quite large height differencing to cause flooding debris which carries a big amount of sedimentation. The area is prone to soil and cliff erosion. The existence of this watershed has an important role in the lives of the people in the eastern region of Palu City. Not only as a regional system of water supply in Palu, but Watutela watershed is also classified as a yellow zone/development area (a zone that is prone to liquefaction, tsunamis, soil movement, flood). This is under the disaster-prone zone map of Palu City that is created after the earthquake, tsunami, and liquefaction in Palu and its surrounding area on 28 September 2018.
Although this region is already declared as a development area based on the disaster-prone zone map of Palu City, the data of environmental baseline especially the data of erosion hazard index is not yet available as a basis for policymaking. Therefore, it is necessary to study the prediction of erosion hazard index and the effect of land use on the rate of erosion. It is believed that the results can be used as a foundation for watershed planning in an
environmentally sustainable manner and for the strategy of vegetative and mechanical conservation. Based on this
matter, the goal of this study is to predict soil erosion in Watutela watershed after the earthquake using GIS combination with the USLE method.
9. STUDY AREA AND DATA
This research location is in Watutela watershed, Palu City, Central Sulawesi, with coordinates between longitudes 119'52'46.58''E and 119'56'1.31''E, and latitudes 0'49'12.64''S and 0'50'32,8''S. According to natural conditions, the Watutela watershed is surrounded by Palu Bay in the west and Mount Verbecs in the east. The map of Watutela watershed is presented in Figure 1.
Figure 1. Research Location, Watutela Watershed
The data for an index of erosion hazard analysis in this study were: (1) the data of rainfall in the last 5 years starting from 2014 to 2018, (2) the Landsat imagery 8 bands 654, (3) the map of Watutela watershed, (4)
administration map, (5) the slope map, (6) the soil type map, (7) the land use map, (8) the map of Indonesia on scale 1:25,000, and (9) data actual erodibility of each unit, and type of land use, and land conservation.
3. METHODOLOGY
The combination of the application of GIS and USLE methods is carried out through the process of collecting primary and secondary data. Primary data obtained from field checking, observation on the actual erodibility of each unit, and type of land use, as well as land conservation. The observation was performed based on the results of the Landsat 8 band 654 imagery interpretation. Whereas, the secondary data consisted of (1) spatial data. It is in the form of a digital map from watershed and Forest Management Center of Palu-Poso containing slope grade, soil type, rainfall distribution pattern, and land use pattern; (2) non-spatial data that is in the form of rainfall data from Meteorological Station of Mutiara Palu and sub-district data from the Central Sulawesi of Statistics of Mantikulore Sub-district, Palu City; (3) other supporting data such as results from previous research and scientific publications as well as results from spatial planning of Palu City post-earthquake, tsunami, and soil liquefaction
The data analysis of this study was implemented using GIS with a projection system and Universal Transverse Mercator (UTM) as the coordinates. The unit in this measurement is a meter so that the analysis that required distance and area information can be arranged utilizing overlaying the maps. Then, the data were calculated using the USLE formula [22, 23, 24]:
A = R x K x LS x C x P (1)
Where, A: the amount of soil loss due to erosion (ton/ha/year), R: rainfall erosivity index, K: soil erodibility factor, LS: land length and slope factor, C: vegetation and plant management factor, P: land management/soil conservation factor.
In determining the erosion hazard index, the researchers used this following Equation 2 that is adapted from [25, 26]:
EHI = A/Etol (2)
Where, EHI: erosion hazard index (ton/ha/year), A: soil erosion rete (ton/ha/year), Etol: soil erosion that can be tolerated (mm/year).
Etol values are calculated based on the data in Table 1 as follows:
Table 1. Guidelines for Determining Etol Value for Soils in Indonesia [27]
Soil and substratum properties Etol Value (mm/year) Very shallow soil above rocks 0.0 Very shallow soil above moldy materials
(not consolidated)
0.4
Shallow soil above moldy materials 0.8 Moderate depth soil above moldy
materials
1.2
Deep soil with the impermeable lower layer above moldy materials
1.4
Deep soil with slow permeability lower layer above moldy materials
1.6
Deep soils with medium permeability lower layer above moldy materials
2.0
Deep soils with fast permeability lower layer above moldy materials
2.5
Furthermore, Table 2 below is used to determine the classification of the index erosion. Data refers to the study [22, 28]:
Table 2. Erosion Hazard Index Classification Erosion Rate
(ton/ha/year)
Erosion hazard index classification
Description
<15 I Very Low
15-60 II Low
60-180 III Medium
180-480 IV High
>480 V Very High
4. RESULTS AND DISCUSSIONS 4.1. Land Use
The results analysis of the GIS method and ground check and the interpretation of Landsat 8 band 654 images, Watutela watershed has four types of land use namely shrubs, dryland farming, empty land, and settlement (Table 3 and Figure 2)
Table 3. Watutela Watershed Land Use
Land Use/Codes Area
Ha %
Shrubs (SB1) 115.86 12.55
Empty Land (EL) 2.32 0.25
Shrubs (SB2) 94.56 10.25
Settlements (SM) 420.43 45.55
Shrubs (SB3) 35.42 3.84
Shrubs (SB4) 249.34 27.01
Dryland Farming (DF)
5.07 0.55
Total 923.01 100.00
Figure 2. Land Use Map of Watutela Watershed
Based on the Table 3 and Figure 2, that the land in Watutela Watershed is dominated by shrubs amounting to 495.19 ha (53.65%). it shows that the land forest vegetation has decreased, this will lead to surface runoff and erosion.
The use of forest areas affects the rate of surface runoff and erosion. If the forest area decreases, it will not only cause increased soil erosion but will also cause a reduction in water catchment areas, resulting in a decrease in water st orage capacity that can increase erosion [16].
