International Journal of Engineering Advanced Research eISSN: 2710-7167 | Vol. 4 No. 4 [December 2022]
Journal website: http://myjms.mohe.gov.my/index.php/ijear
COMPARATIVE ANALYSIS ON RAINFALL EROSIVITY (R) FORMULA FOR SOIL EROSION PREDICTION IN
ENVIRONMENTAL IMPACT ASSESSMENT STUDY
Abdul Rahman Mahmud1*
1 Environmental Institute of Malaysia (EiMAS), Bangi, MALAYSIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 12 October 2022 Revised date : 20 December 2022 Accepted date : 26 December 2022 Published date : 30 December 2022 To cite this document:
Mahmud, A. R. (2022).
COMPARATIVE ANALYSIS ON RAINFALL EROSIVITY (R) FORMULA FOR SOIL EROSION PREDICTION IN ENVIRONMENTAL IMPACT ASSESSMENT STUDY.
International Journal of Engineering Advanced Research, 4(4), 76-91.
Abstract: Soil erosion is one of the serious problems that occur caused by improper control of land disturbing activities in development project. In every EIA report in Malaysia, study with regard soil erosion and sedimentation is compulsory. Revised Universal Soil Loss Equation (RUSLE) is the model that has been used to predict soil erosion rate. One of the most important factors that used to calculate soil erosion risk is rainfall erosivity (R). However, EIA consultants have used various formulas and methods in order to determine the R value. This study was conducted to achieve three main objectives. Firstly; to identify the formula that has been used to obtain R value in the literature study. Secondly to identify the formula and method that has been used in determining the R value in recent EIA studies. Thirdly; to compare the R value that obtain from various method used in EIA. This study begin with literature review study by reffering the recent journal articles and several guidelines with regard to emperical study on soil erosion in Malaysia. After that, quantitative content analysis has been used to identity the methods that used in EIA study. The results of the study found that there are two main method that has been used there are Wischmeier & Smith (1978) and Morgan (2005). R value that found in the EIA study, has been compared and analyse by using T-Test. This study found that there is significant difference (p<0.05) between both method. The value that obtain by using Morgan (2005) method is relatively lower than the Wischmier and Smith (1978). This study, suggest that the best formula to be used in determine
1. Introduction
One of the crucial problem during the land disturbing and earthwork projects is soil erosion and sedimentation. To determine the level of soil erosion that will occur, a thorough impact assessment study is required. Environmental impact assessments are studies that seek to predict, anticipate, and recommend appropriate control measures to minimize the impact on the environment from the development of a project. EIA is known as a usefull instruments in environmental management.
EIA has been applied to environmental management in Malaysia formally started in 1987, with the enforcement Section 34A, Environmental Quality Act 1974. EIA can be implemented effectively if the EIA study is conducted according appropriate methodology. Among the factors that can influence the effectiveness of EIA implementation is the quality of the studies provided from the consultant.
Soil erosion and sedimentation in Malaysia have become increasingly severe since Malaysia achieved independence. Widespread forest exploration and the extraction of mineral resources have led to the opening of thousands of hectares of land without any effective control, thus causing soil erosion and implications for water quality. Several researchers have discussed the aspect of soil erosion and sedimentation studies. They have debated a lot about the extent to which EIA can help reduce soil erosion and sedimentation. The Environmental Quality Report 2021 (DOE 2022) shows that the suspended solids parameter (SS) in river water quality is found higher in the area which have many land disturbing activities. This study was carried out to achieve three main objectives as follows:
the R value is Wischmier and Smith (1978), EI30. Because it has ability to be used for larger rainfall data that can be translated into R value. It is in line with ‘site specific’
concept in EIA that need specific and local data to be used in the prediction of the impact. Isoerodent map that established by the Department of Irrigation Malaysia (DID) is very usefull to be used in order to predict soil erosion. However, the need to improve the isoredant map is highly recommended by taking consideration of new rainfall data and climate changes factor. This study can be benefited to the EIA consultant in order to choose the right method in soil erosion assessment in EIA study.
.
Keywords: soil erosion, rainfall erosivity, environmental impact assessment.
i. To identify the formula that has been used to obtain rainfall erosivity (R) value in the literature study.
ii. To identify the formulas and methods used in determining the value of rainfall erosivity (R) factor in soil erosion studies in EIA reports.
iii. To identify the differences among the method used in determining rainfall erosivity (R) value.
2. Literature Review
Soil erosion is a natural process where soil particles become weak and separate physically.
Rainwater is one of the leading agents that cause soil erosion. Rainwater that acts on the soil surface will separate the soil particles and then transport the soil particles from one point of location to another (Meng et al., 2021). Wei et al. (2020) stated that soil erosion had become an important problem, especially in urban areas that experience rapid development and also agricultural areas. Soil erosion and sedimentation are problems that often occur from development projects in Malaysia. According to Mahmud and Sakawi (2015) soil erosion and sedimentation problems from EIA projects mainly occur at the earthwork and construction stages. The increases of congested development has approached environmentally sensitive areas. Furthermore, accelerating soil erosion, resulting in water pollution, sedimentation and subsequent flooding in downstream areas (Saint-Laurent et al., 2019). Guide (2002) stated that the soil erosion estimation method through impact asessement is an excellent method to ensure that the soil erosion impact of a project is assessed and adequate control measures can be suggested before the project being implement.
The enforcement of EIA approval conditions for projects at the earthwork stage shows a relatively low percentage of compliance. From 2011 to 2014, a total of 807 enforcements were implemented.
There are 34 per cent to 43 per cent of the enforcement visits conducted by the DOE found incompliance with the conditions of EIA approval (Abdul Rahman, Zaini, Khairul Nizam 2015).
Ageel et al. (2016) reported sediment yield (SY) values from logging areas in Pahang River basin from 2005 to 2010. The data that has been measured is rainfall, SY values and total suspended solids at five (5) sampling locations along the river. The study found that the SY range was from 172.6 tons/hectare/year to 622.8 tons/hectare/year, while the value of suspended solids was from 6.23 mg/l to 119.47 mg/l.
The study also found a strong relationship between SY and the number of suspended solids in the river. Ling et al. (2016) found a significant impact of logging activities on water quality and sediment produced in the Sarawak River. The study found that water quality was affected after heavy rain with an increase in suspended solids from 8.3mg/L to 104.1mg/L. This study found that logging activities significantly brought the impact to the water quality in the Sarawak River, especially during rainy season.Yusof et al. (2016) studied land use changes from 2010 to 2013 and the potential for soil erosion in 2013 in low-lying areas in the Perak River basin. The RUSLE model has been used to assess soil erosion. The study results found that the level of soil erosion is from low to high. As much as 18 per cent (536.19km) is at a high level of soil erosion which is more than 100 tons/hectare/year. Rizeei et al. (2016) found that the risk of soil erosion in the Semenyih River basin was between 143.35 tons/hectare/year in 2004 and increased by 151 tons/hectare/year in 2010, while the forecast in 2016 found that the rate of soil erosion that will
occur is as much as 162.24 tons/hectare/year. It shows that the Semenyih River basin tended to experience erosion in 2016 at a high level. From this study, it is concluded that the issue of soil erosion from development projects in Malaysia needs to be considered a serious matter.The first factor of soil erosion is rain erosion (R).
According to Mineo et al. (2019) the strength of rainfall erosivity can be estimated by kinetic energy of rain and its intensity. They also found a positive correlation between rainfall trends and soil erosion. In order to determine the value of R, rainfall data in the range of previous and recent periods are needed to ensure that the values used in the impact prediction will follow current situation according to the recent rainfall pattern. Rainfall data can usually be obtained from the nearest rain station. According to Wischmeier and Smith (1978), the rain erosivity index is calculated based on the total amount of kinetic energy and density within 30 minutes of a rain event. There are several formulas used to determine the R-value.
2.1 Problem Statement
EIA studies have been carried out for more than 30 years in Malaysia. However, the effectiveness of EIA in minimizing the pollution has been questioned. In terms of soil erosion assessment method there are various methods are used. One of the important factors for estimate soil erosion is rainfall erosivity or R factor. However, the extent to which method used to determine R-value has not been thoroughly studied. It is important to know, what formula has been used in EIA study in Malaysia. Thefore the best method can be selected to be used to ensure the impact can be predicted following the right methodology.
3. Method
This study was conducted using quantitative methods. There are two main method for this study, as shown in figure 1.0. The first stage is the literature review. A literature review was conducted to review the rainfall erosivity factor. The sudy started by literature searching in the google scholar database. Keyword “rainfall erosivity factor AND R value”, has been used to identify the relevant literature. The articles found, then will undergo screening by using mendeley desktop software.
Only the emperical studies that discussing rainfall erosivity will be analyses. After the relevant articles are selected the process of reviewing the articles is done by using scoping techniques which focus on the R formula used in literature.
The second stage is content analysis. This study has conducted a quantitative content analysis to identify the R-value method that used in EIA studies. A total of twelve EIA has been collected randomly from the EIA database from Department of Environment (DOE). A Checklist was developed to evaluate the content of EIA report. Basically the ‘Evaluation of Impact’ chapter in EIA report have explanation on the methodology of impact assessment. All the documents is in the pdf format. It then easily to be transfer in Mendeley database to be analysed.
Figure 1: Study Methodology
3.1 Samples
The samples of EIA report are obtained from the e-KAS database. Twelve EIA report that has been choosen for this study. The criteria of selection are the activities that have major earthwork at the earlier stages. It is more relevant in the context of this study. Random sampling for the EIA report is done for twelve report that received in 2021 and 2022.
3.2 Data Analysis
T-test has been used to compared two group of data. Unpaired t-test has been used by using SPSS 25.0 to analyse the data that retrieved from the content analysis. The hipotesis for this study as follow.
Ho: There is no significant difference between R value, obtain from difference formula.
Ha: There is significant difference between R value, obtain from difference formula.
4. Results and Discussion
The findings of the study are divided into three parts base on the objective of the study. The first part will discuss on the rainfall erosivity factor R, in the literature study. Second part will discuss on the rainfall erosivity formula used in EIA study. Third part is the discussion on comparison of the R value found in the EIA report.
4.1 Rainfall Erosivity Formula (R) for Soil Erosion Assessment Analysis
The USLE formula has been used to evaluate and predict the impact of soil erosion. The USLE model has been produced through empirical studies since 1978 and was modified into RUSLE by Renard et al. (1997). At the same time, the determination of the value of sediment yield was also modified from the USLE formula to MUSLE by Williams (1975). In soil erosion impact assessment studies, the RUSLE and MUSLE formulas are often used to determine the rate of soil erosion and sediment yield. This formula also suggested to be use in impact prediction for EIA study by DOE (2016). In evaluation of soil erosion using the RUSLE formula involves few factor as in formula 1:
A = R × K × L × S × C × P ... (1) where:
A - temporal and spatial average soil loss per unit area (tonne/ha/yr) R - rainfall erosivity factor, (MJ mm ha-1h-1 yr-1)
K - soil erodibility factor, (ton ha yr ha-1 MJ-1 mm-1), L - slope length factor
S - slope factor,
C- ground cover plants, P - soil practice factor .
Rainfall erosivity (R) is one of the main significant factor for soil erosion. The determination of the R-value can be divided according to the context of the study, whether the study is a time series or a single station. R factor in soil erosion calculation is the most highly correlated index with soil erosion rate. According to Brown and Foster (1987) the R factor is explained mathematically as the result of a combination of the rainfall event that produce kinetic energy and the rainfall maximum intensity in a 30-minutes period. Formula 2 shows the relationship between rain erosivity and the combination of rain kinetic energy and intensity.
Where:
R – soil erosion average annual rainfall, in units of MJmm ha−1 h−1 yr−1, n – number of years,
mj – number of rain events per year, and
EI30 – rain erosivity index for an event k
where:
er – rain energy unit (MJ ha−1 mm−1), vr – rainfall volume in r time period.
I30 – maximum rainfall intensity within 30 minutes of the event (mm h−1).
Rain energy units can be estimated for each time interval using formula 4.
i, is the intensity of rain in the time interval.
Criteria for identifying rain erosion events were defined by Renard et al. (1997) as follows:
i. Cumulative rainfall for an event exceeds 12.7 mm, or 0.5 inches
ii. Rainfall events have at least one peak exceeding 6.35 mm within 15 minutes iii. Rainfall periods of less than 1.27 mm in 6 hours were used to divide longer rainfall
periods into two rainfall events.
Due to the difficulty in obtaining precise rainfall data from meteorological stations, the R factor has been estimated as a function of monthly and average rainfall data using the average monthly and annual rainfall Renard and Freimund (1994). To determine the value of factor R, Renard et al.
(1997) proposed using monthly and annual rainfall average data. Arnoldus (1980) was the one who first proposed it, and it makes use of the same modified Fournier index, or F, as Formula 5.
where:
Pi – average amount of rainfall in mm for month i. According to Arnoldus (1980), the F-index is correlated with the R-factor. Even with seasonal variability in rainfall, Bagarello (1994) showed that the F-index is highly linearly correlated with average annual rainfall. Colotti (2004) reported that the Food and Agriculture Organization (FAO) uses the modified
Fournier index as an estimate of erosion according to formula 6.
𝑅 = 𝑎F + 𝑏 (6) (6) where:
R – rain erosion factor,
F – modified Fournier index (MFI), a and b are two regional fitting parameters.
The FF index in formula 7 is also linked to the R factor Ferro et al. (1991) taking into account the distribution of actual monthly precipitation in each year (Fa, j) for a period of N years (as defined in formula 7 .
Where Pij – amount of rain in a month (mm) in year j P – the total amount of rain in the same year.
Arnoldus has produced formula 8. Arnoldus (1980) developed this formula by relating F and R for regions of the United States and some African regions. However, some researchers relate R to total annual rainfall, MFI or summer rainfall. Morgan (1986), involving the Ivory Coast and Malaysia, confirmed the validity of using this approach. In the literature review, several other formulas are reported for estimating the R factor, such as the Fournier index (MFI), Sicilian, Arnoldus and Moroccan formulas, Wischmeier and Smith and others.
R – rainfall erosivity factor (MJ mmha-1 year-1) Pi – average annual rainfall (mm)
Wischmeier and Smith (1978) proposed formula 9 to determine the value of the R factor using monthly rainfall data. The Arnoldus approximation method known as the Modified Fournier Index introduces a gradient factor of 1.6881 mm-1 to determine the value of the R factor from monthly rainfall data in formula 10.
Where:
rainfall erosivity R (MJ mm ha-1 h-1) and 1.6881 is slope factor/
An empirical model to relate the factor R to rainfall P was created by a study by Bols (1978) in Indonesia. Soo (2011) utilised this approach to calculate the rate of soil erosion in Cameron Highlands, Pahang Malaysia. The fact that the climate is similar to Malaysia condition in justifies the adoption of this formula.
By correlating with local rainfall data, several other researchers have also generated R factors.
Numerous studies demonstrate that the R factor and annual rainfall are highly correlated in the majority of global regions Bonilla and Vidal (2011). Loureiro and Coutinho (2001) created a new model utilising multiple linear regression to calculate values from twenty-eight years of monthly rainfall data from thirty-two rain gauge stations in the Algarve region of Portugal. The model is displayed in formula 12. Due to its strong predictive capability, it has been utilised in numerous study.
where:
N – number of years of rain records, rain10 – monthly rainfall for days ≥10 mm, others are set to zero,
days10 – monthly number of days with precipitation ≥10 mm
The method of determining the value of R was once highlighted by Morgan (2005). He used three formulas Morgan (1974), Roose (1975) and Foster (1981) to get the rainfall erosivity (R). Morgans (1974), was produced using the average annual rainfall in peninsular Malaysia with limited data to corellate R value and the rainfall value. Formula 13 has been formed as follow.
R = 9.28P – 8838.15 x I30 (13) Where:
P = is the average annual rainfall I30 = 75 mmh-1
Roose (1975), in his study at Ivory Coast and Burkina Faso, the average value of R can be estimated by the average amount of annual rainfall (mm) multiplied by 0.5.
R = P x 0.5 (14) where
P = is the average annual rainfall
Foster et al.(1981) introduced formula 15 to show the relationship between the average amount of annual rainfall and the value of rainfall intensity ie
R = 0.276 P x I30 (15) where
P = is the average annual rainfall I30 = 75 mmh-1
In the determination of the R value, Morgan (2005) suggests to take the average reading of the two highest value among the three closest formulas 13, formula 14 and formula 15 for the determination of the R value.
In the EIA study in Malaysia, there are several guidelines that are used as a guide for EIA consultants to carry out soil erosion and sedimentation studies. Among the guidelines are as follows
1. Environmental Impact Assessment Guideline (EGIM), Appendix 3- Soil erosion impact assessement, (DOE, 2016).
2. Guidelines on Land Disturbing Pollution Prevention and Mitigation Measures (LD-P2M2), (DOE, 2017).
3. Manual Kawalan Hakisan dan Kelodakan (BMPs), (DOE, 2017).
4. Manual Saliran Mesra Alam (MSMA 2nd edition), (DID, 2012).
5. Guidelines of Soil Erosion and Sediment Control (DID, 2010)
Appendix 3, Soil erosion impact assessment in Environmental Impact Assessment Guideline Malaysia - EGIM (DOE, 2016) explains the method of predicting the impact of soil erosion and sedimentation. Among the important matters that are stressed in the guideline is the need to provide local data in predicting the soil erosion impact. Local data refers to the rainfall data and soil types data in calculating soil erosion. This guideline also stated that in EIA study, impact assessment must include three scenarios: existing soil erosion condition, with control and without control.
While the Guidelines on Land Disturbing Pollution Prevention and Mitigation Measures - LD- P2M2 (DOE, 2016), explain in detail the aspects of EIA studies and structured and unstructured control measures. These guidelines also suggest using EI30 as the basis for determining the value of rainfall erosivity, R-value. As for the Manual Kawalan Hakisan dan Kelodakan (BMPs) (DOE, 2015), it also explains the structured control measures and the appropriateness of their use onsite.
It is one of the comprehensive guidelines for erosion and sedimentation control in EIA projects.
In the Guideline of Soil Erosion and Sediment Control (DID 2010), it is recommended to use a standard R-value coordinated with the formation of an isoredant map that shows the value of rainfall erosivity, R, in peninsular Malaysia. The formula used in forming the R = EI30 value is by using the formula of Wischmeier and Smith (1978). The isoredant map is produced using GIS modelling. Rainfall data from 241 rain monitoring stations, from 1999 to 2008 throughout peninsular Malaysia has been analyzed. Figure 1.0 showing the steps in developing isoerodent map. There are two main stages that are carried out to produce isoerodent maps. The first step is to analyze the R factor values. Rainfall data was collected and analyzed for each location of the rainfall monitoring station. The data will be used for calculation using the EI30 formula. The calculation begins by evaluating the EI30 for individual storms, the second stage is to combine the annual values of EI30 and finally obtain then combine to get annual average value of R. The second step is to produce isoerodent maps. The use of GIS software such as ArcMap can be used to make kriging analysis. Mapping requires geospatial information of the location of rainfall monitoring stations. Tools in GIS can produce smooth contours for the production of isoerodent maps.
Figure 1: Flowchart Process Development of Isoredant Maps
Overall, based on previous studies, it is found that there are at least eleven main formulas used to estimate the value of R (Table 1.0). There are mainly produced using average annual rainfall data in a region or area. The determination of the R-value is closely related to the rainfall data in speific.
Therefore, the R formula should be given attention in impact assessments and predictions in EIA studies.
Table 1: Formula Used for Rainfall Erosivity, R.
No. Formula Location Reference
1. R= [∑N∑M(EI30)]/(100N)*1.735 United States of America
Wishmier and Smith (1978)
2. R = 0.5 (P)*1.735 West Afrika Roose(1975)
3. R = (9.28(P)-8838 x I30)*0.001 Malaysia Morgan (1974) 4. R = 0.276 P x I30 x 0.01 Amerika Syarikat Foster et al (1981)
5. R = (38.46+3.48P) x 0.1 Hawaii Lo et al (1985)
6. R = (0.264*(( ∑12 i=1 p i2 )/P)1.50) Morocco Arnoldus (1977) 7. R = (0.04830P1.610)*0.1 United States of
America
Renard and Freimund (1994) 8. R = (587.8-1.219P+0.004105P2 )*0.1 United States of
America
Renard and Freimund (1994) 9. R = (0.07397*(( ∑12i=1 pi2 )/P)1.847)*0.1 United States of
America
Renard and rFeimund (1994) 10. R= 2.5P2 / [100*(0.07P+0.73)] Indonesia Bol (1978)
11. Portugal Coutinho (2001
4.2 Rainfall Erosivity Formula in EIA Studies
The EIA report's content has been analysed based on 12 EIA reports received in 2021 and 2022.
Content analysis is done by analysing of the chapter ‘impact evaluation’. This chapter states the methodology for determining the rainfall erosivity value R. Based on the analysis that has been done, most of the EIA reports that are analysed using the RUSLE model to assess the soil loss in term of hectre ton per year. As overall, two main methods have been used to determine the R- value in the EIA study that has been analysed, i.e using the equation R= EI30 by Wischmeir and Smith (1978) and Morgan (2005). From 12 EIA reports that were analysed, it was found that eight EIA studies used the formula R = EI30 by Wischmeir and Smith (1978). Four reports use the method proposed by Morgan (2005). It done by taking the average R-value of formulas Morgan (1974), Roose (1977) and Foster et al. (1981). The value of R using, EI30 mostly ranging from 11500 - 18000 Mjmm ha-1 hr-1 yr-1 and the value for Morgan (2005) method is ranging from 1034.68 to 3800 Mjmm ha-1 hr-1 yr-1.
Table 2: Findings of the Content Analysis the Use of R Values in EIA Studies Report Project Type Area
hectares
Rainfall erosivity formula used
R value Mjmmha-1hr-
1yr-1
Erosion (Existing Condition) Ton/Ha/Yr
A1 Logging and
Agriculture
8,498.58 R=EI30,
Isoerodant Map (MASMA 2nd ed)
15,000 8.09
A2 Mining 80.94 R=EI30,
Isoerodant Map (MASMA 2nd ed)
19,000 296.43
A3 Mining 210 R=EI30,
Isoerodant Map (MASMA 2nd ed)
17,000 174.27
A4 Mining 174.16 R=EI30,
Isoerodant Map (MASMA 2nd ed)
15,000 1957.87
A5 Construction 3.08 Using the Method Suggested by Morgan (2005)
1034.68 4.56
A6 Agriculture 263.2 R=EI30,
Isoerodant Map (MASMA 2nd ed)
18000 26
A7 Industrial Area
Development
96 R=EI30,
Isoerodant Map (MASMA 2nd ed)
15000 1.608
A8 Electrical Power
Transmission Lines
60.335 Using the Method Suggested by Morgan (2005))
2400 10
A9 Agriculture 587 Using the Method
Suggested by Morgan (2005)
1200 264.82
A10 Forest
Plantation
323.76 R=EI30,
Isoerodant Map (MASMA 2nd ed)
11500 22.59
A11 Industrial Area Development
100 Using the Method Suggested by Morgan (2005)
3807.7 0.38
A12 Forest
Plantation
404 R=EI30,
Isoerodant Map (MASMA 2nd ed)
16,500 304.21
4.3 Comparison Analysis of R Value Base on Different Formula Use in EIA
Comparative analysis of R value, for both of these methods was carried out using unpaired T-Test.
The results of the analysis found a value of p<0.05, this shows that there is a significant difference for these two methods. Base on this finding the hipotesis of Ha, where there is significant difference R values, between difference method of rainfall erosivity formula i accepted. Table 3.0 and 4.0 shows the result of the statistical analysis. Average mean value for R value using morgan method (2005) is 2110.59, meanwhile R value using Wischmeier and Smith (1978), EI30, R value is 7.5 times more higher than Morgan (2005).
Table 3: Mean and Standard Deviation Analysis Group Statistics
Method N Mean Std. Deviation Std. Error Mean R_
Value
Wischmeier &
smith
8 15875.00 2310.685 816.95
Morgan 4 2110.59 1284.613 642.30
Table 4: T-Test Analysis
However Morgan (2005) method is not appropriate to be used in EIA study because:
i. The formula by Morgan (1974) is the result of using rainfall data in peninsular Malaysia in the early 1970s where the rainfall pattern is relatively difference than current condition.
ii. While the formulas of Foster et al. (1981) and Roose (1975) were each produced and used outside of Malaysia, where the rainfall distribution are different from Malaysia.
iii. The use of average value from the two highest values which is from this formulas is not suitable for impact assessment study.
The EIA study will differ and be inconsistent due to utilising two approaches to calculating the R- value. The interpretation of the resulting soil erosion level may be imprecise. It will have an impact on the suggested control methods. Therefore, it is necessary to create a standardisation of the usage of an acceptable approach to resolve this issue. It is intended to avoid ambiguities when assessing the EIA report. Therefore, it is imperative to incorporate the usage of EI30 in the EIA study. It is essential to apply the rainfall erosivity method EI30 as recommended in MSMA 2nd edition (DID 2012) and LD-P2M2 (DOE 2017).
Considering the potential for variations in Malaysia's rainfall pattern. It is crucial, therefore, to update the MSMA 2nd edition guidelines to incorporate the most recent rainfall data. To guarantee that the degree of soil erosion can be accurately estimated. Other variables, including climate change and future predictions, must be included in developing new equations. The proposed mitigation strategies must coincide with the predicted rate of soil erosion.
5. Conclusion
Environmental impact assessment is a comprehensive study aimed at identifying impacts, predicting impacts and proposing effective control measures to minimize pollution. Therefore the EIA study should be site-specific. Hence, the data used for forecasting should be taken within the project site zone. For the determination of the R-value, the use of the EI30 formula can be produced by obtaining data from the nearest train station. Using the isoerodent map produced by DID (2012) is one of the proper steps to be implemented in the EIA study. The quality of EIA report largely depends on the knowledge of the consultants and the review processs by the authority.
6. Acknowledgement
Congratulations to the Department of Environment Malaysia and the Environment Institute for making this research a success.
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