1. INTRODUCTION

Wischmeier and Smith (1978) described rainfall erosivity as an interaction between kinetic energy of raindrops and soil surface. This interaction may result to a greater or lower degree of detachment and down-slope transport of soil particles according to the amount of energy and intensity of rain by considering the same type of soil, topographic conditions, soil cover and management. The rainfall erosivity factor, R, is one of the important parameters for the prediction of the erosive potential of raindrop impact. It represents the climatic influence on water related soil erosion (Hashim et. al, 2001) and can be used to quantify broad scale, climate driven soil erosion potential.

The most commonly used method for predicting soil loss from agriculture field due to water erosion is the universal soil loss equation (USLE) (Wischmeier and Smith, 1978), and its successor, the revised universal soil loss equation (RUSLE) (Renard et al., 1997). To determine the soil erodibility parameters for the USLE/RUSLE at a particular site for soil loss prediction, the numerical values of rainfall erosivity factor, R, or EI30 values are required. The original method to calculate the R factor needs long pluviographical records at short intervals (Wischmeier and Smith, 1978). Limited long term, continuous pluviograph data makes it difficult to determine the R factor in many parts of the world including Malaysia (Hashim et. al, 2001).

Besides, rainfall erosivity can be estimated using daily, monthly and annual rainfall averages by other equations. Hashim and Eusof (2001) developed a model to calculate the R factor for Peninsular Malaysia using monthly rainfall data. They came up with new values for the unknowns in the equation suggested by Yu and Rosewell (1996). Morgan (1986) developed a relation between rainfall erosivity and the annual precipitation. However, the use of annual precipitation ignores the regional seasonality which in some cases is necessary for two or more parallel analyses for specific seasons. Hui (1999) used this approach in Malaysia to produce soil erodibility nomograph. Loureiro and Coutinho (2001) developed a procedure to estimate the EI30 values based on monthly rainfall data and applied to the Algarve region in Portugal.

According to Alexandre (2003), many authors have found a good relationship between the Fournier index (Fi) and annual values of rainfall erosivity. The Fournier index is an equation relating to the monthly values of precipitation for a month and the annual values of precipitation. The Fournier index was developed and used for seasonal rainfall erosivity (Cohen et al., 2005; Countinho and Tomas, 1994).

The rainfall erosivity models can be used to show spatial distribution after the calculation of erosivity values for each station by interpolation using geographic information system (GIS). For example, rainfall erosivity map for Brazil was prepared using the

A. SHAMSHAD, W.M.A WAN HUSSIN, S.A. MOHD SANUSI, M.H. ISASchool of Civil EngineeringUniversity of Science Malaysia14300 Nibong Tebal, Pulau PinangMalaysia

KeyWords: RUSLE, Soil Loss, Rainfall Erosivity, Pluviographic Data, Spatial Distribution.

SPATIAL AND TEMPORAL DISTRIBUTIONS OF RAINFALL AND EI30 VALUES IN PULAU PINANG, PENINSULAR MALAYSIA

ABSTRACTSoil erosion is a serious problem for the agriculture land use in Malaysia and many other parts of the world. In most of the countries, Revised Universal Soil Loss Equation (RUSLE) is the popular model for predicting the soil losses in runoff from specific field areas in specific cropping and management system. Rainfall erosivity factor (R) is one of the important parameters of this model. As suggested in the past, the R factor or EI30 values should be computed from the long term, continuous pluviographic records. There is a lack of such kind of data in the world including Malaysia. In contrast daily, monthly and other long interval rainfall data are available at wider scale. In the present study, an attempt has been made to develop rainfall erosivity models using pluviographic data of six stations. The models can be used to compute rainfall erosivity using monthly rainfall data. Using the equations, the EI30 values were evaluated at other stations using monthly rainfall data. The result showed that the mean annual rainfall in the area varied from about 2200 mm to 3900 mm. The corresponding R-factor ranged from about 9,000 to14,000 MJ mm ha-1 hr-1 year-1. For these tropical sites, both rainfall and EI30 values are highly seasonal with two peaks, in general, in the months of April and October.

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PEER REVIEWED ARTICLE

Fournier models, linear model and exponential model developed by different authors for various regions of Brazil (Silva, 2004). Qi et al. (2000) generated rainfall erosivity map for the Republic of Korea.

The present study emphasizes on the spatial and temporal distributions of rainfall erosivity in the study area. One of the main objectives of this study is to develop reliable methods for estimating the EI30 values or the R factor for Malaysia and other similar tropical regions of the world using the limited pluviographic data.

2. STUDY AREA AND RAINFALL DATA

The present study has been carried out in the state of Pulau Pinang, Peninsular Malaysia. It consists of an island and a part of the mainland of the peninsular. Pulau Pinang is about 370 km from the capital city Kuala Lumpur. Pulau Pinang is divided into 5 divisions namely Central Seberang Perai, North Seberang Perai, South Seberang Perai, Northeast Seberang Perai and Southwest Seberang Perai. It consists of an area of about 1031 sq km with latitudes ranging from 58 to 535 and longitudes from 1008 to 10032. The mainland covers about 738 sq km of the state. The elevation in the area varies from 0 m to about 500 m. The main land use categories in the area are oil palm, coconut and paddies.

In Malaysia the rainfall data is recorded and maintained by the Malaysian Meteorological Services Department. The daily and monthly rainfall data can be easily obtained; however for such type of study rainfall, data at short intervals known as pluviographic data is required. Pluviometric records at 15 minutes interval of 6 weather stations of Peninsular Malaysia is available for the computation of EI30 values. The record lengths varies from 6 to 35 years, such as Simpang Ampat (1988-2004), Taliair Besar (1970-2004), Kompleks Perai and Ibu Bekalan (1971-1975), and Klinik Bukit Bendera and Kolam Bersih (1976-2004). In addition, the daily rainfall data of 16 more stations were obtained.

3. METHODOLOGY

All of the continuous 15 minutes rainfall data of 6 stations have been used to compute rainfall erosivity (EI) for each storm event and a relationship between EI30 and the Fournier index (Cc) is established for each station. Individual rainfall events of less than 10 mm were excluded from the computation of EI. The annual rainfall erosivity, R, is the sum of erosive storm EI30 values occurring during a mean year. Individual rainfall events of less than 10 mm which were separated from other events by more than 6 hours without rain were excluded from the computation of EI values unless the depth of rainfall in 15 minutes exceeds 6 mm (Wischmeier and Smith, 1978; Mannarerts and Gabriels, 2000).

Firstly, the kinetic energy of rainfall (Ej) is computed using the formula derived by Onaga et.al (1998) for eastern Asia:

Ei = 9.81 + 10.6 log10 lj

where, Ei = kinetic energy of rainfall (J/m2) and lj = rainfall intensity (mm/h).

The sum of the kinetic energy gives the kinetic energy of the whole rainfall event (E). The rainfall erosivity (EI30) is the kinetic energy of the whole rainfall event (E) multiplied by the maximum 30-minute rainfall intensity (I30).

EI30 = E x I30 x 1/100

EI30 = rainfall erosivity (kJ/m2.mm/hr)

By summing the EI values for each storm, the total erosivity for each month and year is computed.

4. RESULT AND DISCUSSION

4.1 Rainfall Distribution

To study the annual and monthly rainfall patterns, the time series plots of total yearly rainfall, monthly mean and maximum values of rainfall were plotted for all the stations. For examples, such plots for Kolam Bukit Berapit and Simpang Ampat are presented in Figures 1a and 2a. All the rainfall stations show similar patterns of rainfall for the years 1996 to 2004. For most stations, the rainfall is high in 1999 and 2003 but low in 2002. However, the annual rainfall curve is oscillating about the mean, thereby indicating that there is no significant trend in the data, and the mean annual rainfall is almost the same. Furthermore, the mean monthly rainfall values at all the stations fall in the range of 100 mm to 250 mm.

It is observed that the months of April and October are the raining seasons in this area, which is not the case throughout Malaysia. October brings the most rainfall to this region as compared to other months. Overall, the temporal patterns of rainfall distribution are the same for all stations. This is because these stations are situated in the same area.

The probability graphs of monthly rainfall for each station are studied to show the possibility of a certain amount of rainfall occurring in an area according to the location of the station. The graphs for Kolam Bukit Berapit and Simpang Ampat are plotted in Figures 1b and 2b. The figures reveal that the rainfall between 100 mm to 200 mm per month has the highest probability of occurrence. During the period of study, rainfalls of about 900 mm per month also occur.

To study the mean monthly and annual spatial distributions of the rainfall data, ArcView GIS was used to interpolate using the Inverse Distance Weighted (IDW) interpolator method in which input point has a local influence that diminishes with distance. It weights the points closer to the processing cell greater than those further away. The resulting maps of rainfall for the month of January, February, June, October and

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December and the annual rainfall were presented in Figure 3. The results show that the rainfall at all the stations is highly seasonal with two peaks in the months of April and October and ranges from about 80 mm to 250 mm except in the months of April and October.

The rainfall is observed to be highest at most of the stations in October, varying from 320 mm to 660 mm followed by April in between 150 mm and 300 mm. The annual rainfall is least (2130 mm) at Ladang Malakof and highest (3850 mm) at Bukit Bendera.

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Figure 1a. Total yearly, monthly mean and maximum values of rainfall at Kolam Bukit Berapit

Figure 2a. Total yearly, monthly mean and maximum values of rainfall at Simpang Ampat

Figure 2b. Probability distribution of monthly rainfall at Simpang Ampat

Figure 1b. Probability distribution of monthly rainfall at Kolam Bukit Berapit

Figure 3. Spatial distribution of monthly and annual rainfall

Further, to estimate the monthly and the annual erosivity for the Peninsular Malaysia or even for the whole country or other similar tropical regions of the nearby country, a common equation was derived in this study by combining the data of all the six stations (Figure 4). The model can be represented by the equation 2 as given below:

EI30 = 227.0 Cc0.548, R =0.90 (2)

Besides the seasonal (monthly) values of rainfall erosivity, the storm erosivity values are needed to study the non-point source pollution based on a particular storm event. One example of such study is the event based analysis in Agriculture Non-Point Source Pollution model (AGNPS) (Bhuyan, 2002). Using all the EI30 values based on erosive storms of all the six stations under study, a model was developed for rainfall erosivity of a single storm (EIstorm) as given by the following equation:

EIstorm = 0.537 P1.736, R = 0.77 (3)

Similar types of models were developed by several other authors to compute storm erosivity based on a single storm (Bhuyan, 2002; Mannaerts and Gabriels, 2000).

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4.2 Development of Erosivity Models

The monthly EI30 values were computed for each of the 6 stations (Table 1) using the pluviographic data. Fournier Index (Fi) for each month was determined using the following equation (Cohen et al., 2005):

(1)

where, Mi is the average monthly precipitation depth (mm) and P is the average annual precipitation (mm). Regression analysis was carried out between EI30 values and the Fournier Index. Three different types of equations were obtained for every station to compare and give us the best equation at the end. The three different types of derived equations are the linear, logarithmic and power. After all equations are derived, comparisons between all equations for all the stations were made. From the results obtained using the Microsoft Excel (and checked by using SPSS software), the equations with the highest correlation are the equations of power. A comparison carried out based on the error analysis also produced the best result with power equation at almost all the stations. These equations for EI30 values with Fournier index of all the six stations have been presented in Table 2 along with their coefficients of correlation (R). The values of R are mostly above 0.90 which are very good values. This showed that the accuracy of the unknowns found is high and the difference between the observed and the computed value is little. However, this is not true for one station which is located in Kompleks Perai whereby the R is 0.88. Though this value is low, it is still acceptable when compared to the values calculated by Banasik and Gorski (1998) in their study for East and Central Poland.

Station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual (R)

Simpang Empat 592.0 572.5 374.5 717.5 402.9 236.2 532.7 676.9 958.5 1122.6 1229.1 544.8 7960.2Taliair Besar 197.2 787.6 1084.4 1746.5 1517.4 1032.6 1326.5 1332.0 2102.9 2569.6 1535.5 579.9 15812.1

Kompleks Perai 199.3 631.9 448.7 1283.3 1318.4 2053.5 695.6 825.0 1263.7 2645.5 1349.3 644.0 13358.2Klinik Bukit Bendera 386.8 464.3 1219.2 1830.4 1392.7 1251.7 1683.6 1926.8 2248.8 2492.9 1543.7 575.7 17016.6

Kolam Barsih 311.4 368.7 830.8 1051.4 1258.3 877.1 1101.5 1151.9 1864.4 1538.4 1386.4 462.6 12202.9

Ibu Bekalan 229.7 581.6 861.1 1826.2 1737.3 404.9 1038.2 871.5 2389.3 2235.8 1452.5 1055.1 14683.2

Table 1 EI30 and R (MJ mm ha-1hr-1) values computed using pluviographic data at 15 minutes interval

Table 2 Fournier models developed using pluviographic data

Station Fournier Model R

Simpang Ampat EI30 = 119.0 Cc0.635 0.92Taliair Besar EI30 = 238.3 Cc0.579 0.98Kompleks Perai EI30 = 265.6 Cc0.535 0.88Klinik Bukit Bendera EI30 = 311.4 Cc0.487 0.98Kolam Bersih EI30 = 280.9 Cc0.458 0.99Ibu Bekalan EI30 = 204.6 Cc0.658 0.95

Figure 4. Relationship between erosive storm EI30 values and storm rainfall (P)

P

MF ii

2

=

5. CONCLUSION

Rainfall erosivity factor, R is the average annual summation of EI30 values in a normal year's rain or in other words, it is the quantitative expression of the erosivity of local average annual precipitation and runoff. This study addresses the problem of predicting the rainfall erosivity factor (R) of the Revised Universal Soil Loss Equation (RUSLE) with limited rainfall data.

In this study, equations were derived using two different ways for EI30 values while using the monthly and storm rainfall data. One of the ways related to the rainfall erosivity factor is through the Fournier Index. The best equations derived are the ones with power relationship. The values of coefficient of correlation (R) for all the stations using these power equations range from 0.88 to 0.99. A general equation was then derived in this study which can be used to calculate the monthly values of rainfall erosivity factor even if the available data is monthly and not pluviographic data. Another way of deriving the equations is to get a relationship between storm rainfall erosivity with storm rainfall. The value of coefficient of correlation (R) for this equation is only 0.77 which is acceptable for such type of studies. The rainfall erosivity (R) map derived using the model developed shows that the monthly rainfall erosivity EI30 values are particularly high during the months of April and October for the overall results of all stations which are 1270 MJ mm ha-1h-1 and 2100 MJ mm ha-1h-1, respectively. This is in accordance with the rainfall in these locations. A higher value of rainfall produces a higher value of rainfall erosivity factor. The soil erosivity maps developed in the study are useful soil for conservationists, agronomists and civil engineers. It is recommended that in further research, rainfall erosivity maps should be developed for the whole of Malaysia using pluviographic data covering a wider spectrum of the region.

ACKNOWLEDGEMENT

The Authors wish to thank the Ministry of Science, Technology and the Environment, Malaysia (MOSTE) and Universiti Sains Malaysia (USM) for the financial support under the IRPA grant for the project GIS Based Watershed Management System for Non-Point Source (NPS) Pollution Modelling to carry out this study.

REFERENCES

Banasik Kazimierz and Gorski Dariusz (1998). Estimating the Rainfall Erosivity for East and Central Poland. Warsaw Agricultural University, Department of Water Engineering, Sedimentation Laboratory, Warsaw, Poland.

Bhuyan, S.J., Prasanta, K.K., Janssen, K.A., Barnes, P.L., 2002. Soil loss predictions with three erosion simulation models. Environmental Modelling & software 17, 137-146.

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Using the model developed above (i.e. Equation 2), the monthly EI30 values were calculated at 16 other stations using the monthly rainfall data. The spatial and temporal variabilities of rainfall erosivity were studied in detail in the study area. It was observed that the rainfall erosivity is highly variable from one month to the other. This is mainly due to the climatic situation during a certain time. Malaysia is known for their hot and rainy seasons and these vary according to months. For this study area, the months of April and October show more rainfall than the other months. Hence, the values of the rainfall erosivity EI30 during these months are higher than the rest of the months. The EI30 values during the months of April and October for the overall results of all stations are 1270 MJ mm ha-1hr-1 and 2100 MJ mm ha-1h-1, respectively. Whereas, the months of January, February and December show the lower values of rainfall erosivity which are about 575 MJ mm ha-1hr-1. This shows that for these months, the total rainfall is much lesser. As mentioned earlier, EI30 is the amount of energy applied to the land surface due to rainfall energy from the falling rain drops. It can be said that if there is more rainfall in a certain area, the probability of erosivity is higher.

Figure 5 shows the annual rainfall erosivity, R, distribution in the study area. The erosivity values vary geographically. The annual rainfall erosivity calculated for all stations in the study area range from about 9,000 to 14,000 MJ mm ha-1hr-1. The station with the highest total annual rainfall erosivity is Bukit Bendera with R-factor of 14,000 MJ mm ha-1hr-1 year-1. This means that this area has a higher possibility to experience higher soil erosion compared to the other stations. Whereas the stations at Simpang Ampat and Ladang Malakof show the least annual rainfall erosivity factor which is 9,000 MJ mm ha-1hr-1 year-1. These stations have the least annual rainfall. Rainfall erosivity map created by GIS is an important map to be referred to by soil conservationists, landuse planners and civil engineers in studying the distribution of erosivity of a particular area.

Figure 5. Annual rainfall erosivity (R) map

Contours of Rainfall Erosivity (R)Rainfall Erosivity (R)

9000 - 1000010000 - 1100011000 - 1200012000 - 1300013000 - 14000No Data

Morgan, R.P.C., 1986. Soil Erosion and Conservation. Longman Group, Essex, UK

Qi, H., Gantzer, C.J., Jung, P.K. Lee, B.L. (2000). Rainfall erosivity in the Republic of Korea. J. Soil Water Conservatin 55, 115-120.

Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K., Yoder, D.C. (1997). Predicting soil loss by water: A guide to conservation planning with the revised soil loss equation (RSULE). Handbook, Vol 703. US Department of Agriculture, Washington, DC, USA.

Silva da, A.M. (2004). Rainfall erosivity map of Brazil. CATENA 57, 251-259.

Wischmeier, W.H., Smith, D.D. (1978). Predicting Rainfall Erosion Losses. Agric. Hbk 537.U.S.D.A. Sci. and Educ. Admin., Washington, DC.

Yu, B., Hashim, G.M. and Eusof, Z. (2001) Estimating R-factor with limited rainfall data: A case study from Peninsular Malaysia. Journal of Soil and Water Conservation 50 (2), 101-105.

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Cohen M.J., Shepherd, K.D., Walsh, M.G., 2005. Empirical formulation of the universal soil loss equation for erosion risk assessment in a tropical watershed. Geoderma 124, 235-252.

Countinho, M.A. Tomas, P.P., 1994. Comparison of Fournier with Wischmeier rainfall erosivity indices. In: Rickson, R.J. (Ed.). Conservation Soil Resources, European Perspectives. CAB International, Wallingford

Hashim B. Yu. G.M and Eusof Z. (2001). Estimating the r-factor with limited rainfall data: A case study from Peninsular Malaysia. Journal of Soil and Water Conservation, Vol 56, No.2.

Hui, T.K., 1999. Production of Malaysian soil erodibility nomograph in relation to soil erosion issues. Perputakan Negara Malaysia.

Loureiro, N.D.S., Coutinho, M.D.A., 2001. A new procedure to estimate the RUSLE EI30 index, based on monthly rainfall data and applied to the Algarve region, Portugal. Journal of Hydrology 250, 12-18

Mannaerts, C.M., Gabriels, D., 2000. Rainfall erosivity in Cape Verde. Soil & Tillage Research 55, 207-212.