SOIL EROSION RISK MAP BASED ON GEOGRAPHIC INFORMATION SYSTEM AND UNIVERSAL SOIL LOSS EQUATION (CASE STUDY: TERENGGANU, MALAYSIA)

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1 SOIL EROSION RISK MAP BASED ON GEOGRAPHIC INFORMATION SYSTEM AND UNIVERSAL SOIL LOSS EQUATION (CASE STUDY: TERENGGANU, MALAYSIA) *Ranya Fadlalla Abdalla Elsheikh 1, 2, Sarra Ouerghi 2, 3 and Abdel Rahim Elhag 1 1 GIS Department, School of Survey, Sudan University of Science and Technology, Sudan 2 Department of Geographic and GIS, King Abdul Aziz University, Abdullah Sulayman, Jeddah 22254, Saudi Arabia 3 Laboratory 3E, Water-Energy-Environment, (LRAD-10-02), National School of Engineers of Sfax, Tunisia *Author for Correspondence ABSTRACT In Malaysia the potential problems associated with excessive soil erosion has long been recognized. The Universal Soil Loss Equation (USLE) is the most widely used erosion model. On other hand Geographical Information System (GIS) is a fatal tool for spatial data manipulation and management. In this paper, Geographical Information System (GIS) is integrated with the Universal Soil Loss Equation (USLE) model to analyze the soil erosion risk areas. Terengganu was selected as case study for erosion risk analysis. All parameters of the equation used were provided from Department of Agriculture in Terengganu, and the calculation was done under their supervision. The result indicated that the risky erosional locations are distributed in the southwestern areas where the slope is so steep. Mapping soil erosion using GIS would identify areas that are at potential risk of extensive soil erosion, provide information on the estimated value of soil loss at the logging areas. Key Words: Geographic Information System, Universal Soil Loss Equation, Erosion and Spatial INTRODUCTION Soil erosion leads to a reduction in soil quality and productivity and hence, crop yield. Erosion often results in a decrease of the soil supply functions by three ways: The removal of organic matter, the change in depth to a possible root-barrier and the loss of structure and increased compaction (Bakker et al., 2004; Wang and Cui, 2005; Rabia, 2012). The Universal Soil Loss Equation (USLE) is the most widely used erosion model. Originally developed in USA to predict long term average annual erosion under various types of crop management system, it has been widely used elsewhere (Farhan et al., 2013). The USLE is an empirical model developed from analysis of more than 10,000 plotyears of runoff and soil loss data from small plots scattered throughout the USA (Wischmeier and Smith, 1978). The USLE model is a statistical model and is a relatively simple erosion model, easy to parameterize and requires less data to operate with. Integrating the model with GIS facilitates data manipulation, data input and output display. Most importantly, GIS spatial display and analysis utilities allow the USLE model to be applied to individual raster cells (Dabral et al., 2008). Another advantage of the GIS USLE approach is its ability to predict soil loss over large areas due to the interpolation capabilities of GIS (Lufafa et al., 2003). In a GIS environment, the USLE can be applied to determine field-scale soil loss both quantitatively and spatially, and to predict erosion hazard over large watershed (Lufafa et al., 2003). A study by Fistikoglu and Harmancioglu (2002) concluded that GIS permits more effective and accurate application of the USLE model for small watersheds provided that sufficient spatial data are available. In this study, USLE was amalgamated with GIS to be used in the assessment of the erosion hazard in the study area. All parameters of the equation used were provided from Department of Agriculture in Terengganu, and the calculation was done under their supervision. MATERIALS AND METHODS Study Area This study was conducted in the State of Terengganu; West Malaysia Terengganu is located at the east coast of 38

2 Peninsular Malaysia, neighboring the State of Kelantan to the East, and the State of Pahang to the South (Fig. 1). It is located between latitudes N and N and longitudes E and E. Terengganu today covers 12,995 square kilometers and comprises seven districts. Figure 1: Location of the study area Universal Soil Loss Equation The procedures and steps undertaken to calculate and obtain the annual soil loss were fundamentally based on the Nomograph Method for Universal Soil Loss Equation (USLE) developed by Wischmeier and Smith (1978): A = R x K x L x S x C x P Equation 1 Where A = Average Annual Soil Loss in the project or farm area. R = Rainfall Erosivity Index K = Soil Erodibility Index L = Topographic Factor, L for Slope Length S = Topographic Factor, S for Percent Slope C = Cropping (C) Factor P = Conservation Practice (P) Factor Rainfall Erosivity(R) 39

3 This factor is the most important factor in the USLE compared to the other input parameters (Jebari, 2009).The kinetic energy of the rain can be defined as the potential rainfall energy available to be transformed into erosion.for Peninsular Malaysia, Morgan (1974) developed the Rainfall Erosivity (R) index, as used in the USLE by the following equation: R = (9.28 P 8,838.15)/100 Equation 2 in which P is the mean annual rainfall. The above equation is in SI units - Joules/m 2 or megajoules/m 2. Soil Erodibility(K) Index This factor depends on soil properties. The nomograph for the computation of K index of the soil series can be represented by the following equation relating to soil properties and soil erodibility: K = 2.1 M (12 OM)+3.25 (S 2)+ 2.5 (P 3) Equation 3 Where K is the Soil Erodibility Index M is the product of (% Silt + % Very Fine Sand) (% Fine Sand + % Medium Sand + % Coarse Sand) OM is the percent of Organic Matter S is the Soil Structure Code P is the Soil Hydraulic Permeability Class The equivalent soil structure classes used in the USLE in accordance to the reported Malaysian soil by the Department of Agriculture, Malaysia are given in Table 1. Table 1: Soil structure classes used in the USLE (DOA) Soil Definition of Soil Soil Structure Classes Structure Structure (Soil Report - DOA) Code (Nomograph) 1 Very Fine Granular Very Fine Granular, Crumb 2 Fine Granular Fine Granular / Crumb 3 Medium or Coarse Medium Granular/Crumb or Very Fine to Fine Subangular Blocky or Very Granular 4 Blocky, Platy or Massive Fine to Fine Angular Blocky Medium to Coarse Subangular Blocky, Angular Blocky, Prismatic, Columnar or Massive The drainage classes used in mapping Malaysian soils were used as equivalent to the soil hydraulic permeability that was used in the USLE Nomograph recommended by Department of Agriculture, Malaysia and shown in Table 2. From this table, Soil Hydraulic Permeability Parameter was readily determined. Table 2: Soil hydraulic permeability class recommended by Department Of Agriculture (DOA) Soil Permeability Soil Profile Hydraulic Soil Profile Drainage Classes Value Permeability (Soil Report Depart. Of Agriculture (USLE Nomograph) Malaysia) 1 Rapid Class 8 9 : Excessive to Very Excessive Drained 2 Moderate To Rapid Class 5 7 : Somewhat Imperfect to Well Drained 3 Moderate Class 4 : Imperfectly Drained 4 Slow To Moderate Class 3 : Somewhat Poorly Drained 5 Slow Class 2 : Poorly Drained 6 Very Slow Class 0 1 : Very Poorly to Somewhat Poorly Calculation of the Topographic Factors (LS) The factors of slope steepness (S) and length (L) can be calculated separately or they can be merged into a single index (LS). This factor can be calculated in various ways depending on unit preferences and other conditions like 40

4 available data. Different empirical relations are used to determine this factor and many recent studies (Jebari, 2009; Onyando et al, 2004) In this paper, the LS Factor can be calculated from the slope steepness and slope length in each soil polygon(s) derived from the map. For this application, the following equation was used: L LS= ( ) 0.5 (65.4sin2 θ sin θ )Equation 4 where L = Slope Length in meter θ = Field Slope in degrees. Slope length for each soil polygon was estimated by measuring the line from the point where surface flow originates or from the highest point to the outlet channel, or to a point down the slope where erosion stops and deposition takes place, or to the lowest point in the polygon. Each slope length measurement line within each polygon must transect the contour lines perpendicularly. The slope steepness was measured as an average steepness value from the point where surface flow originates to the outlet channel, or to a point down the slope where erosion stops and deposition takes place. In this case, the field boundaries follow quite closely to the mapping units boundaries. Determination of Cropping (C) Factor The cropping factor C is an indication of the positive effects of vegetative cover and crop residue in reducing soil erosion. The Department of Agriculture has summarized a list of C factor values for the various crops and conditions found in Malaysia (see table 3) Table 3: Summary of some Common Conservation Practices (C) Values Type of Fruit Crop C- Values Young Stage Mature Stage Mangoes Durian Papaya Guava Pineapple Pomelo Sweet Lime / Langat Lime Mangoesteen Determination of Conservation Practices (P) Factor In general, whenever sloping land is to be cultivated and exposed to erosive soil loss rain, those conservation measures, which can slow down the runoff water and reduce the amount of soil transported by the flowing water, would contribute to a reduction of soil loss. The Department of Agriculture has summarized a list of common P values for some conservation practices in Malaysia (see Table 4) Table 4: Summary of some Common Conservation Practices (P) Values No. Conservation Structure / Practices P- Values 1 Terraces - Continous Terraces Discontinous (Orchard) Traditional Terraces Hillside Trench (Silt Trap) Contour Ditches Mulching IndividualBasin Contour Planting Planting Beds along Contour Planting Beds Against/Perpendicular to Contour

5 RESULT AND DISCUSSION The values for the K and LS factors used in the computation were based on the soil polygons, while the values for the C and P factors were based on fields or blocks in the plantation. The basis for the computation of soil loss is the soil polygon. In this case, overlay the soil map on the field layout plan. Theoretically, the C, P-factors to be used should be weighted average values (based on percentage of acreages) of the various crops within each soil polygon. In this case, the field boundaries follow quite closely to the mapping unit boundaries. To simplify the calculation procedures, it was assumed that the C, P factors were equal to one. All the parameters values were added as information on attribute table in ArcGIS and the classes of erosion for each polygon were determined (see Figure 2). Figure 2: Erosion risk parameters for the study area, constructed as attribute table in ArcGIS Erosion Risk Map Figure 3 shows the erosion risk map in the study area calculated using the Universal Soil Loss Equation (USLE) in GIS to determine the average annual soil loss. From the map, The study area (presented with the darkest blue as shown in fig. 3) was the high erosion area it is noticed that the risky erosional locations are distributed in the southwestern areas where the slope is so steep. 42

6 Figure 3: Erosion risk map of the study area CONCLUSION Geographic Information Systems (GIS), coupled with the USLE model, can identify and assess soil erosion potential and estimate the value of soil loss. The Soil erosion risk map was produced based on USLE model and classified into four classes. As the results documented, most of the study area showed a relatively low erosion risk while high erosion risk was located at the southwestern part covering a much smaller area. The result can be useful to operators who take decisions about the management of land resources at the catchment scale. REFERENCES Bakker MM, Govers G & Rounsevell MDA (2004). The crop productivity-erosion relationship: an analysis based on experimental work. CATENA, Dabral PP, Baithuri N & Pandey A (2008). Soil Erosion Assessment in a Hilly Catchment of North Eastern India Using USLE, GIS and Remote Sensing. Water Resources Management, 22(12) Farhan Y, Zregat D & Farhan I (2013). Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A of Kufranja Watershed, Northern Jordan. Journal of Water Resource and Protection, 5(12) Fistikoglu O & Harmancioglu N (2002). Integration of GIS with USLE in Assessment of Soil Erosion. Water Resources Management, 16(6) Jebari S (2009). Water erosion modeling using fractal rainfall disaggregation A study in semiarid Tunisia. Water resources engineering, Lund University, Sweden. Lufafa A, Tenywa MM, Isabirye M, Majaliwa MJG & Woomer PL (2003). Prediction of soil erosion in a Lake Victoria basin catchment using a GIS-based Universal Soil Loss model. Agricultural Systems, 76(3) Morgan RPC (1974). Estimating regional variations in soil erosion hazard in Peninsular Malaysia. Malayan Nature Journal, Onyando JO, Kisoyan P & Chemelil MC (2004). Estimation of Potential Soil Erosion for River Perkerra Catchment in Kenya, Department of Agricultural Engineering, Egerton University, Njoro Kenya. Rabia AH (2012). Mapping Soil Erosion Risk Using Rusle, Gis and Remote Sensing. The 4th International Congress of ECSSS, EUROSOIL 2012 soil science for the benefit of mankind and environment Wang X & Cui P (2005). Support Soil Conservation Practices by Identifying Critical Erosion Areas within an American Watershed Using the GIS-AGNPS Model. Retrieved: March 28, 2006 from spatialhydrology.com/journal/paper/ soil_conservation/agnps.pdf Wischmeier WH & Smith DD (1978). Predicting rainfall erosion losses. USDA, Washington DC