Erosion 3D

Distribution 3D soil erosion surrounding in Sumani Watershed, West Sumatera –Indonesia

Aflizar1) and Tsugiyuki Masunaga2)

1) National Polytechnic of Agriculture Payakumbuh, Indonesia
2) Faculty of Life and Environmental Science, Shimane University, Japan;

Abstract

The Universal Soil Loss Equation (USLE) and Erosion Three Dimension (E3D) in surfer were used to identify characteristic of dominant erosion factor in Sumani Watershed in west Sumatra, Indonesia using data soil survey and monitoring sediment yield in outlet watershed. Climatology data from three stations were used to calculate Rainfall erosivity(R) factor. 81 sampling sites were used to investigate soil erodibility (K) factor with physico-chemical laboratory analysis. Digital elevation model (DEM) of Sumani Watershed was used to calculate slope length and steepness (LS) factor. Landsat TM imagery and field survey were used to determine crop management (C) factor and conservation practices, (P) factor. Calculating soil loss and map of USLE factor were determined by kriging method in surfer. Sumani watershed has erosion hazard in criteria as: severe to extreme severe (26.23%), moderate (24.59%) and very low to low (49.18%). Annual average soil loss for Sumani watershed was 48.38 ton ha/ yr. Upland area was designated as having a severe to extreme severe erosion hazard compared to lowland which was designated as having very less to moderate. on the other land, soil eroded from upland were deposited in lowland. These results were verified by comparing one year’s sediment yield observation on the outlet of the watershed. Land use (C factor), rainfall erosivity (R factor), soil erodibility (K factor), slope length and steepness (LS factor) were dominant factors that affected soil erosion. Traditional soil conservation practices were applied by farmer for a long time such as terrace in Sawah. The USLE model in Surfer was used to identity specific regions susceptible to soil erosion by water and was also applied to identify suitable sites to conduct soil conservation planning in Sumani watershed.
Key words: Soil erosion, Sumani watershed, USLE and E3D, Surfer.
1. Introduction
Soil erosion is the process of detachment and transport of soil particles caused by water and wind (Morgon, 1985). Soil erosion in Indonesia is one of that nation’s most serious environmental degradation problems (Ambar et al, 1997). The deleterious effect of erosion on production is not well defined. Some data from a soil scalping experiment have shown yield losses of 48% following removal of 150 mm of soil in Sumatra, Indonesia (Sudirman et al, 1986); 23% following removal of for 299 mm, 46% for 457 mm soil removed and 63% for removal of 686 mm of soil in Australia (Harmswarth and Barreth, 1972; Naik Sinukaban, 1999). In Kenya on a high input agriculture 75% for 250 mm, 150% for 500 mm soil eroded and 225% for 750 mm removal of soil has also been observed (Kassam et al, 1992; Bhattacharyya et al, 2007). In Java average erosion of 6 – 12 ton/ha/year on volcanic soils and much higher loses on agricultural land has been reported to have caused economic loss US$ 340-406 million in 1989 (Margareth and Arent, 1989). Of this nearly 80% is due to decline in the productivity of agricultural land and the other is due to off-site cost such as siltation of irrigation systems and the loss of reservoir capacity (Margareth and Arens, 1989; World Bank, 1994).
Sumani watershed soil is under a serious risk of soil fertility and crop productivity decline due to hilly topography mainly exacerbated by soil erosion conditions by water because of high rainfall (2201 mm/yr) (ICRAF, 2005). Agricultural practices such as excessive soil tillage and cultivation on steep slopes has also increased the risk.
Typical erosion rate monthly by Sediment Delivery Ratio (SDR)method was 49 ton/ha/yr (Saidi. A, 1992). So far, this research can not show where main area of soil loss is located and dominant affect on erosion and erosion hazard for determining suitable land uses and soil conservation measures for watershed.
Evaluation of current situation of erosion is very important for improvement of endangered areas, and determining the type of conservation measures to be applied for the purpose of estimating a 3D distribution of erosion is required for sustainable management and conservation of the agricultural areas (Ahmet et al, 2007). Process-based methodologies for soil erosion prediction are: SEMMED (de Jong et al, 1999), WEPP (Elena et al., 2004), EUROSEM (Folly et al, 1999; Morgon et al, 1998), GUEST (Ciesiolka et al., 1995), ANSWERS ( Seyed et al, 2006), FUERO (Matternicht et al, 2005), AGNPS (Walling et al, 2003), LISEM (Takken et al, 1999), MMF (Morgon, 2001) and Erosion 3D (Schob et al, 2006; Schmidt et al, 1999). Some models, in spite of their strong theoretical base, may not be very suitable for Indonesia as it is a developing country. Situations such as those in Indonesia are prohibitive since detailed rainfall, topographic and other input data required to run them are often either not available or difficult to collect due to resource constraints. However, at present the most commonly used methods of predicting the average water erosion rate from agricultural lands are the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978) and the Revised Universal soil Loss Equation (RUSLE) (Renard et al, 1994).
Soil erosion models, such as the USLE estimates gross soil erosion rate at plot-scale. Erosion rates estimated by USLE are, therefore, higher than those measured at watershed outlet (Hua Lu, 2006). Sediment delivery ratio (SDR) was used to correct this reduction effect. SDR is the average annual sediment yield per unit area divided by the average annual erosion over that same area (Walling, 2003; Simon et al, 2007). For instance , actual values of SDR derived from experimental data from eastern central USA (Roehl, 1962). Erosion 3D (E3D), which is a raster-based physical soil erosion model that predict the spatiotemporal distribution of erosion and estimate where to locate the main area of soil loses on a watershed scale (Schmidt et al., 1996; Annekatrin, S., 2006) were combined with USLE and SDR models. The resultant model was used to evaluate the present situation and assess further activity and passivity of dominant erosion factor in order to control soil loss more efficiently with the aim of finding out suitable conservation methods in relation to agriculture sustainability.

2. Material and methods

2.1. Study area in Sumani watershed

The Sumani Watershed, covering 58,330 ha (Saidi, A. 1995) and located in Solok regency and city (latitude 00 42’17” to 10 2’2” S, longitude 1000 32’ 41”- 1000 40” E), West Sumatra (Figure 1). The outlet of the watershed is Lake Singkarak. The average annual rainfall for the watershed varies with altitude from 300 m to 2500 m a.s.l varies from 1669.4 mm to 3230 mm, respectively. Average temperature is 19.19 to 30.19 0C varying from hight to low altitude. average Humidity is 78.1 to 89.4%. Average wind flow varying from 2.1 to 3.8 m/second (Bambang I, 2005)
The Sumani watershed was chosen because it is one of the priority watersheds in Indonesia where water captured in Sumani Watershed inflows into Lake Singkarak. Hydroelectric power plant with capacity of 4x43 MW at Lake Singkarak used to fulfill the electric demand of the resident population 4.4 million in both West Sumatra and Riau Province before 2004 due to instability of water dynamic and extent soil erosion in Sumani Watershed power generation has been excessively affected to an extent of sudden power cuts. Besides soil loss has also affected the rice yields in West Sumatra. The third reason for choice of this site was because it provides flexibility to conduct comparison experiment since these exist various land uses such as forestry, mixed farming andd upland farming (Agroforestry) and paddy rice production(Sawah) , Shrub, Grass and settlement, mostly around Solok and mainly under rice production.
The relative flat areas (<10%)>500 m asl) mainly under Vegetable production is on slopes of 10 – 30%. And covers 40% of area. The slopes mostly occur in foothills in the south (of Mt Talang).Agricultural land like mixed gardens, vegetables gardens are still found in this class slope i.e. below 1000 m asl. In the higher elevation in Barisan hill (>1000 m asl) forest dominate this slope class. Combination of steep slopes (30% - 100%) appears as dissected plateau in the west side of the basin. These various steep areas are covered by natural vegetation like forest, shrubs, grass and patches of less intensive agricultures i.e. mixed gardens (ICRAF, 2005). The watershed has soil family namely Aeric Tropaquept, Typic Kandiudult, Typic Distropept, Oxic Hapludand and Typic Eutropept with developed three geology, whose type is Tufa volkan, alluvial and alluvial fan. Five soil texture type found are Silt, silt loam, silty clay loam, light clay and heavy clay with four soil structure whose type is granular, angular, sub angular blocky and blocky. A network of five major rivers, viz., Lembang river, Sumani river, Bagawan river, Ujung karang river and Barus Rivers feel drain into the Lake Singkarak.

2.2. USLE model and SDR Method to Predict Erosion in Sumani Watershed

In the USLE model the annual soil loss is expressed as a function of six erosion factors:
A = R K L S C P (1)
Where : A is the estimated soil loss in ton/ha/yr ; R is the erosivity of rainfall in MJmm/ha/yr ; K is inherent soil erodibility in t.ha.h/MJ mm; L is length of the slope factor, dimensionless; S is slope factor, dimensionless; C is crop cover factor, dimensionless; and P is a factor that accounts for the effects of soil conservation practices, dimensionless.
Sediment delivery ratio (SDR) was used to correct for USLE model. It is a dimensionless scalar and conventionally expressed as :
Y= E (SDR) A (6)
Where Y is the average annual sediment yield per unit area and E is the average annual erosion over that same area (Walling, 1993; Richards, 1993), SDR = α Aß , where A is watershed area (in km2) , α and ß are empirical parameters ( Lane et al 2007; Roehl, 1962). This study used instant, actual values of SDR derived from experimental data from Roehl (1962).
2.3. Erosion Tree Dimension (E3D) modeling approach

The study is based on EROSION 3D, which is a raster-based physical soil erosion model that predicts the spatiotemporal distribution of erosion and deposition as well as the delivery of suspended soil material to surface water course on a watershed scale (Schmidt et al., 1996; A. Schob, 2006). Erosion 3D model requires at least the following data (based on Von Werner, 2003). 1. Relief parameter: digital elevation model (e.g. interpolated grid from a digitized topographical map, topographic data was used to construct a surface map of the landslide and surrounding Sumani watershed. A block diagram showing geomorphic feature and sampling location in watershed was generated by kriging topographic data using Surver from Golden Software; Golden, CO (Lee et al, 2001). 2. Standard soil parameter: particle size distribution of the top soil (four main texture classes) and organic carbon content (%)(Schmidt et al., 1996; A. Schob, 2006). 3. Specific soil parameter: bulk density (kg/m3), soil permeability (cm/hour), soil structure, effective soil depth. 4. Percentage land slope: digitize map was generated by grid data using Surver program. 5. Soil sampling polygon, 6) Land use : digital maps e.g. digital topographical maps combined with orthophotos and field mapping with land use boundaries and land use-related information(A. Schob, 2006). 7). Meteorology parameters polygon: Data recording from tree station in Sumani watershed and polygon map was generated using SURFER Ver. 8. Since 1996, the Erosion 3D model has been integrated into the official agricultural soil conservation programs. Further validation of the Erosion 3D model has been done internationally (Schmidt et al., 1999, A. Schob, 2006).

2.4. Fields sampling methods

Eighty sampling sites ( 0 – 20 cm and 20 – 40 cm depth) and occupying a variety of geomorphic positions were sampled by auger, core sampling to analyze bulk density and field observers to identify soil structure. Natural profile was observed to identify effective soil depth in the Landscape of Sumani watershed which then were delineated based on vegetation, topography, soil and landscape position. GPS was used to record sampling positions and picture was taken to record evident of erosion in the field.

2.5. Laboratory methods

All soil samples were air dried and sieved to obtain the fine fraction particles < 2 mm) for physico-chemical analyses (ASA-SSSA, 1982, 1986;Soil Survey Staff, 1993a). C organic were determined by Walkley-Black type method, soil texture (pipette method), permeability soil by De boot method, Bulk density, Soil structure (Soil survey staff, 1993b).
The overall methodology involved use of a soil erosion model, USLE in a Surfer, with data obtained from the weather stations during 1996 – 2007 (BMG Sicincin, 2007), field survey and taken 42 samplies in 2002 and 39 samplies in 2006, river sediment was taken monthly between August 1992 until July 1993 (Saidi. A, 1995). Land use cover interpreted by Landsat ETM satellite image taken in July 2002 ( Farida et al, 2005) and topographic map at a scale of 1: 50.000 (TNI-AD, 1985). Individual Surfer files were built for each factor in the USLE. They were combined and connected by cell-grid with similar point of coordinate. in Surfer, to predict R factor and K factor distribution in the first method, The Nearest Neighbor gridding method which assigns the value of the nearest point to each grid node was used. This method is useful when data are already evenly spaced, but need to be converted to a Surfer grid file. Alternatively, in cases where the data are nearly on a grid with only a few missing values, this method is effective for filling in the holes in the data. The Second method used was a prediction of soil loss in the spatial distribution. In this method, the grid –cells were set to 125 m by 125 m (2.5 mm x 2.5 mm at map scale of 1:50000), which was resolution possible with the available data and computer facilities. Although this cell size is recommended for detail map, they were used in this study to give detail map. The cell sizes used in this study were considered to give adequate detail because this study was of reconnaissance scale (Goodchild, 1993; Mati et al, 2000).Each pixel was viewed as a single slope plane for which the USLE could be applied individually. Each Grid cell was labeled according coordinate UTM WGS 84 southern hemisphere for each single grid data in the sub watershed to make possible estimated average soil loss within a given sub watershed

2.6. Statistical analysis

Coefficient of correlation was used to statistically examine the effect of USLE Factor as RKLSCP on the soil erosion in the watershed and to examine the relationship among the soil physico-chemical and erosion factor.

3. Result and discussion

3.1. Rainfall erosivity (R) Factor

Rainfall erosivity values were calculated using eq (2). Observed degradation due to rainfall from period 1992-1993 was compared to period 1996-2007, probably because of change of global climate and differences in agroclimatic zones. O’Neal et al (2005) reported that increased precipitation and decreasing cover were increasing erosion. Obi et al (1995) reported that the magnitude of rainfall erosivity caused the catastrophic erosion problem. Sumani watershed was grouped into 3 rain erosivity class pursuant to distribution of 3 climatology stations which still exist hitherto. Sumani watershed almost each month in a year was happened rainfall. R values assigned for rainfall stations ranged from 1288 to 2458 with average of 1803 Mj mm/ ha/hour in the period June 1996 to April 2007. In the period August 1992 to July 1993, R values range from 1569 to 2190 with average 1904 Mj mm/ ha/hour (Table 1). Using calculated and estimated R values for each station, input maps of R were generated with Surfer (Fig. 2). This map shows distribution of R values over Sumani Watershed using combined method as , Nearest Neighbor gridding method. R values increased from lowland to upland watershed depending on precipitation characteristics. Vector rain water flow on the surface of Sumani watershed flows quickly from Upland area (> 500 a.s.l.m from long vector) going to lowland (< 500 asl m). similar Short vector indicated water flow slowly. R values of any place for USLE can be obtained from the map (Figure 2).
Table 1
Figure 2

3.2. Soil Erodibility (K) Factor

Table 2. shows description of 81 sampling sites and show that subwatershed such as Lembang-SW, Sumani-SW, Aripan-SW and Gawan-SW and Imang-SW have different characteristic. The result, suggest that there is need to conduct soil survey to investigate real conditions of soil erodibility (K) factor whose result are shown in Table 3. The traditional approach assume that one soil erodibility value represent the entire area of soil series. Therefore, The traditional approach for estimating soil erodibility does not account for spatial variability of individual soil properties or spatial correlation among those properties, including soil erodibility (Parysow et al, 2003).
K factor values for different family soil groups, land use, geology, slope, altitude are given in Table 3. The same soil group , land use, geology and topography have different K values in the lowland and upland of Sumani Watershed. K values range from 0.001 to 0.486 . Distribution of K values are shown in figure 3. K values were grouped into ten classes. K values in Lowland dominated high values where as in upland it was found that high and low values of K factor were dominant. Distributions of K factor in Sumani Watershed were dependent on natural soil characteristic. K value map was generated to show spatial distribution of erodibility according to 81 soil sampling (Fig 3). The Nearest Neighbor gridding method assigns the value of the nearest point to each grid node.
Table 2
Table 3
Figure 3

3.3. Topography (L and S) factors

Digital topographic data for Sumani Watershed were obtained by digitizing 3 sheets of topographic maps of scale 1: 50000. The contours and the drainage system were digitized separately and used to build up the DEM (Digital Elevation Model) of the Sumani watershed. The contour interval used was 25 m. A grid cell of 125 m was used in building the DEM, as this was considered to be less than the maximum slope length, based on reconnaissance surveys. A maximum length of 100 m for Forest and arable land uses while for settlementthe length of 10 m to 7.5 m which was set in order to get realistic L and S factor values in Sumani Watershed. The LS factor distribution was consequently determined by kriging method in Surfer .Table 4. shows LS factor in 81 sampling sites in Sumani Watershed. The LS factor was calculated using equation (4) and eq(5) depending on slope smaller than 20% or more.
Figure 4 shows 10 classes of LS factor from upland as compared to values from lowland areas. In general values from upland were higher than from lowland since they were dominated by sharp slopes of > 20%. Topography maps were used to develop a map of the slope length and slope steepness factor (LS). Fox et al (1999) reported that rain-impacted erosion increased roughly with the square root of slope gradient. Van Remortel et al (2001) reported that in USLE and RUSLE model are used to predict soil erosion at regional landscape scale, there are difficulties in obtaining an LS factor. To solve the problem DEM elevation data can be used to compute LS factor base on LS factor grid using DEM. Using the physically based topographical factor LS equation and DEMs led to a higher correlation of predicted LS values with topographical features, compared to a spatial simulation method based on LS empirical models and sample data (Wang et al, 2001). Slope lengths as generated by the DEM were based on the assumption that each slope plane consist of homogeneous soil and vegetation cover (Desmet and Govers, 1996).
Table 4
Figure 4
Table 4

3.4. Crop and Management (C) factor.

To determining the C-factor values for the Sumani Watershed, it was first necessary to prepare a land cover map of the watershed. This was achieved satellite by the satellite image and field survey (Mati et al, 2000). Landsat TM June 2002 was obtained to interpret ate land cover of Sumani Watershed, as well as topographic maps of scale 1: 50000. Ten major land cover types were identified: Forest, Pine, Mix (Agroforestry) garden, Mix garden (coconut), Agriculture field(Agroforestry)), Rice field (Sawah), Shrub, Grass, Settlement, Water Body (Farida et al, 2005).The C factor of USLE in Sumani Watershed corresponding to each vegetation/crop condition were estimated from USLE guide tables (Morgon, 1985; Abdurachman et al, 1984). Crop C values in Table 4 for 81 original sampling site where C values ranged from 0.001 to 0.95. Distribution C factor in upland and lowland of Sumani watershed was in Figure 5. Alejandro et al (2007) reported that using landsat ETM to produce maps the C factor for use in the modeling soil erosion provided a more detailed spatial variability and validation.
Table 4
Figure 5

3.5. Determining conservation practices (P) factor.

To determine the areas covered by soil conservation activities, maps of the cover crop from interpretation of Landsat TM June 2002 were used. These maps were redigitized and used in field survey to obtain the type of conservation practices on each land cover surrounding Sumani Watershed. The commonly used traditional conservation were found to be traditional terrace in sawah, moderate cover crop in mixed garden and vegetables field or agriculture field, and no conservation in forest, grass, brush. Settlement commonly lies around the sawah. The type of conservation for settlement was similar to sawah. The P factor values corresponding to each cover crop was estimated from USLE guide table (Morgon, 1985; Abdurachman et al, 1984). P factor for sampling site at Table 4. Figure 6 shows that upland P values range from 0.4 to 1 and dominated by sawah terrace and mixed garden and vegetable field,s however lowland P values range 0.4 ,0.5 and 1 and are dominated by sawah, mix garden , settlement and were not found in forests.
Table 4
Figure 6

3.6. Determining Erosion Hazard

Soil erosion hazard was determined by multiplying the respective R, K,LS,C and P factors interactively in Surfer kriging Method using Equation (1). Soil erosion was grouped in six classes (Odura Afriye, 1996). 3D erosion hazard map is shown in Figure 7 and original 81 sampling site data are outlined in Table 4. Annual average soil erosion for the Sumani watershed was estimated as 48.38 t /ha/yr. Table 5. shows that estimated average soil erosion for Sumani sub Watershed (Gawan-SW and Imang-SW) was found higher than Sumani Watershed but Lower for Lembang-SW, Sumani-SW, Aripan-SW . Similar trends were found in Figure 7 where erosion in Sumani watershed in upland is found to have dominated by moderate to extreme severe classes of erosion hazard while lowland is dominated by low to very low erosion hazard. Both trends for sub watershed and upland or lowland were affected by caused land use pattern, slope length and slope steepness.

3.7. Validation the USLE models with Sediment Yield

The validation of the USLE for the Sumani Watershed was performed with Sediment Yield in five outlet rivers in subwatershed of Lembang-SW, Sumani-SW, Aripan-SW, Gawan-SW and Aripan-SW and one outlet river in Sumani Watershed which has data with one year observation (Table 5). It was found that, in August 1992 to July 1993, average erosion investigated in Sumani Watershed using SDR method compared to a combination of USLE and E3D in Surfer predicted an erosion almost similar (49.25 – 46.64 t/ha/yr). This result indicated that USLE factors used were suitable in Sumani watershed to investigate dominant USLE factor which affect erosion and determine appropriate conservation practice for agriculture.
Table 5. shows for five subwatershed Lembang-SW, Sumani-SW, Aripan-SW, Gawan-SW and Imang-SW where a combined method of USLE and E3D in Surfer were used to estimate soil erosion and obtained higher values than SDR Method. Figure 7 show the reason that trend where is each subwatershed (Lembang-SW, Sumani-SW, Aripan-SW, Gawan-SW and Imang-SW) used USLE to predict soil loss from agriculture lands due to rill and sheet erosion (Wischmeier and Smith, 1969, 1978) while it is not all the erosion product flow to the outlet of river as sediment yield but some part erosion from upland is deposited in lowland at subwatershed at sawah area because sawah have traditional terrace. The Sawah area in Sumani watershed have traditional terrace which was make erosion product be accumulation. Because that there is not all soil loss drain into the river and when was measured sediment delivery in outlet the Sumani watershed that it was quantity low. Ni et al (2007) reported that Terrace stop the downslope transport of soil, so the soil accumulates upslope of boundary, and erodes downslope of the boundary. Terracing, an effective method of soil conservation on steep slopes, has been used extensively to control water erosion in hilly area. Farmer dissected the entire hillslope into a number of slope segment, i.e. terracing, for the sake of minimizing soil loss and for the convenience of field management operation (Zhang et al. 2004b).
This evidence was found in Figure 7 that identified erosion minus 50 t/ha/yr were deposited in lowland area in distribution in subwatershed (Lembang-SW, Sumani-SW, Aripan-SW, Gawan-SW and Imang-SW). other research reported that observations show that sediment yield from watershed are often about an order of magnitude lower than the soil erosion rates measured from hillslope plots (Edwards, 1993; Wasson et al, 1996; Lu et al, 2006) and is deposited (Hua lu et al, 2006). Lim et al (2005) reported that A sediment reduction ratio of 50%, indicating that half of the sediment retention basin and the rest of the sediment leaves the sediment retention basin to downstream areas. Estimated erosion in 1996 to 2007 was higher than August 1992 to July 1993 because of rainfall change (Table 1). Nearing (1998) reported that evaluation of various soil erosion models with large data sets have consistently shown that these models trend to over-predict soil erosion for small measured values, and under-predict soil erosion for larger measured values. The USLE was designed only to predict long-term, average annual soil loss.
3.8. Dominant USLE factor to affect soil erosion in Sumani Watershed

Table 6. shown that latitude was affected with significant positive correlation C –org, OM and LS factor s are significantly correlating with rainfall erosivity (R) factor. Thus, soil physico-chemical silt particles were significantly in positive correlation with soil erodibility (K) factor while C org and OM were significantly in negative correlation with K factor. Topography as percentage slope and slope length was in significant positive correlation with LS factor, thus slope length were significantly negative correlating negatively with Crop and management (C) factor. Basing on 81 sampling sites, It was found that crop and management (C) factors were significant by in positive correlation with erosion .Data input in USLE and E3D model in surfer were used to make clear dominant USLE factor to affect erosion in Sumani Watershed (Table 7). Erosion in Sumani Watershed was affected dominantly by K, L, S and C factor according statistics that indicated significant positive correlation however R factor affects K factor directly. Only soil conservation (P) factor was not significantly affecting soil loss because in general traditional conservation has been practiced by farmer in Sumani watershed (Field survey data). This result bears testimony to the fact that erosion in Sumani Watershed generally is caused first by natural factor which can not be modified like R, K and S factors, second factor can be modified by humans that is C and L factors. Kusumandari et al (1997) reported that from six USLE factor, two groups can be identified: factor that (1) can and (2) can not readily be modified by human action. First group are slope length (L), Cover/ vegetation C and soil conservation practices P and second group are rainfall erosivity R , soil erodibility K and slope steepness S.
Planning a soil conservation method for Sumani Watershed focused on reducing Crop C factor and soil conservation P factor or slope length L can be achieved by computing single numerical values as a cover and management factor CP or construct terrace. Sang-Arun et al (2006) reported that Bench terrace had much less soil erosion and nutrient losses compared bare soil. Reduced C factor values or change in land use can alter the soil erosion rate. Cebecauer et al (2007) reported that land cover (C factor) and crop rotation change had a significant influence on soil erosion pattern predominately in the hilly and mountainous areas. Ozhan et al (2005) reported, that appropriate conservation can be estimated from single numerical values as cover and management factor (CP). CP = Tolerable erosion(T)/ RxKxLxS.

Conclusion

This research was conducted ny use of collected soil survey representative data. The data were entered in USLE and E3D in surfer and were applied to determine watershed scale soil loss quantitatively and spatially and identified major factors affecting soil loss. Thematic useful 3D maps were yielded for Sumani Watershed that had not been previously available, such as R, K, LS, C and P factor of 3D thematic map, as well as the 3D erosion hazard map of Sumani watershed. Dominant USLE factors were affected by soil in Sumani Watershed that were C, K, LS and R factor and this factor were identified to provide result that can be used for preparation of soil conservation master plans. The USLE and E3D in Surfer were found to predict soil loss quite well for large watershed and over estimated for subwatershed and can help predict deposited area. After comparison with sediment yield from a major river in the watershed and reconnaissance survey that USLE model in surfer were considered realistic. Sumani Watershed is predicted erosion hazard was category of 26.23% (severe – extreme severe), 24.59% (moderate) and 49.18% (very low-low).The highest erosion hazard was predicted in upland where associated with mixed farming and agriculture fields and some erosion from upland of deposited in lowland. Forest and Sawah gave the lowest erosion hazard rates of less than 1 and 5 ton/ha/yr. As the problem of soil erosion in Sumani Watershed is land use change or crop (C) factor change and natural condition of watershed as high rainfall erososivity (R), soil erodibility (K) factor and Topography (LS) factor .Traditional soil conservation were applied by farmer in Sumani watershed but there are need research to determine appropriate land use pattern to minimize erosion in the area.

Acknowledgements

My deepest acknowledgements should go to Ministry of Education, Science, Sport and Culture of Japan for the financial assistance in this study.More appreciation goes to Polytechnic of Agriculture Payakumbuh and Soil department of Andalas University in Indonesia for providing the necessary support during soils sampling.

References

Annekatarin Schob, Jurgen Schmidt and Rolf Tenholtern 2006: Derivation of site-related measures to minimize soil erosion on the watershed scale in the Saxonian loess belt using the model EROSION 3D. Catena, 38, 153-160.
Abdurachman A, Abuyamin S, and Kurnia U 1990: Pengelolaan Tanah and Tanaman untuk Usaha Konservasi (Soil and Crop Management to Conservation), PPT Bogor (in Indonesian).
Ahmet Irvem, Fatih Topaglu and Veli Uygur 2007: Estimating spatial distribution of soil loss over Seyhan River Basin in Turkey. Journal of Hydrology, 336, 30-37.
Alejandro M. de Asis and Kenji Omasa 2007: Estimating of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data. CISPRS Journal of Photogrammetry & Remote Sensing, 62, 309-324.
Ambar Kusumandari and Bruce Mitchell 1997: Soil erosion and sediment yield in forest and agroforestry areas in West Java, Indonesia. J. Soil and Water Cons, 52(4), 376-380
Amrizal Saidi 1995: The Affecting Factors of Runoff, Sedimentation and their Impacts on Land Degradation in Sumani Watershed Solok West Sumatera.Disertasi Doctoral in Padjadjaran University, Bandung (in Indonesian).
Badan Meteorologi and Geofisika Sicincin 2007: Data curah hujan di Stasiun Klimatologi Kelas II Sicincin (Rainfall data in Climatology Class II Sicincin), Padang (in Indonesian).
Bambang Istijono 2006: Konservasi Daerah Aliran Sungai (DAS) dan Pendapatan Petani: Studi tetang Integrasi Pengelolaan Daerah Aliran Sungai -Studi Kasus: DAS Sumani Kabupaten Solok/Kota Solok, Sumatera Barat (Watershed conservation and Farmers Income) In Dissertation at Andalas University , Padang (in Indonesian).
Bancy M Mati, Royston PC Morgon, Francis N Gichuki, Jhon N Quinton, Tim R Brewer and Hans P Liniger 2000: Assessment of erosion hazard with the USLE and GIS: A case study of the Upper Ewaso Ng’iro North basin of Kenya. JAG, 2, 78-86.
Bhattacharyya T, Ram Babu, Sarkar D, Mandal C, Dyani BL and Nagar AP 2007: Soil loss and crop productivity model in humid subtropical India. Current Science, 93(10), 1397-1403.
Bols P C 2000: The Iso-Erodent Map of Java and Madura. Bogor, Indonesia, Belgian Technical Assistant Project ATA 105, Soil Research Institute, Bogor, Indoesia.
Chris SR and Hon Harbor 2002: Soil erosion assessment tools from point to regional scales-the role of geomorphologists in land management research and implication. Geomorphology, 47, 189-209.
Ciesiolka C A, Coughlan KJ, Rose CW, Escalante MC, Mohd. Hashim G, Paningbatan Jr EP and Sombatpanit S 1995: Methodology for a multi-country study of soil erosion management. Soil Technology, 8, 179-192.
De Boodt M 1967 : water saturated permeability determination on undisturbed soil samples, eoropean methods for Soil Structure determination, V106-V107.
De Jong SM, Paracchini ML, Bertolo F, Folving S, Megier J and De Roo APJ 1999:Regional assessment of soil erosion using the distributed model SEMMED and remotely sensed data. Catena, 37, 291-308.
Dennis M. Fox, Rorke B. Bryan 1999: The Relationship of soil loss by interrill erosion to slope gradient. Catena, 38, 211-222.
Departemen Pertanian Badan Penelitian dan Pengembangan Pertanian 1979: Penuntun Analisa Fisika Tanah (Soil Physic analysis Manual), Lembaga Penelitian Tanah, Bogor (in Indonesian).
Edwards K 1993: Soil erosion and conservation in Australia In:Pimentel D (eds), World Soil Erosion and Conservation, Cambridge, pp 147-169.
Elena Amore, Carlo Modica, Mark A. Nearing and Vincenza C. Santoro 2004: Scale effect in USLE and WEPP application for soil erosion computation from three Sicilian basins. Journal of Hydrology, 293, 100-114.
Farida, Kevin Jeanes, Dian Kurniasari, Atiek Widayati, Andree Ekadinata, Danan prasetyo Hadi, Laxman Joshi, Deshi Suyamto and Meine van Noordwijk 2005 : Rapid hydrological appraisal (RHA) of Singkarak Lake in the context of Rewarding Upland Poor for Environmental services (RUPES).ICRAF South Asia. Bogor, Indonesia, Working Paper 1-76.
Guangxing Wang, George gertner, Pablo Parysow and Alan Anderson 1995: Spatial prediction and uncertainty assessment of topographic factor for revised universal soil loss equation using digital elevation models. CISPRS Journal of Photogrammetry & Remote Sensing, 56, 65-80.
Harmswart LJ and Barret DS 1992 : The effect of removing various depth of soil on a subsequent potato crop. Australian Potato Agronomy Conference. Burnie, Tasmania, Working Paper 4 (c) 33-40.
Hua Lu, Moran CJ and ian P. Prosser 2006: Modelling sediment delivery ratio over the Murray Darling Basin. Environmental Modelling & Software, 21, 1297-1308.
Julian Gorman, Diane Pearson and Peter Whitehead 2008: Assisting Australian indigenous resources management and sustainable utilization of species through the use of GIS and environmental modeling techniques. Journal of Environmental Management, 86, 104-113.
Kassam AH, van Velthuizen GW, Fisher GW and Shal MM 1947: Agro-ecological land resources assessment for agricultural development planning. A case study of Kenya resources data base and land productivity. Land and Water Development Division, Food and Agriculture Organisation of the United Nations and International Institute for Applied Systems Analysis, Rome.
Kyonung Jae Lim, Myung Sagong, Berbard A. Engel, Zhennxu Tang, Joongdae Choi and Ki-Sung Kim 2005: GIS-based sediment assessment tool. Catena, 64, 61-80.
Lane SN, Reid SC, Tayefi V, Yu D and Hardy RJ 2004: Reconceptualising coarse sediment delivery problems in rivers as catchments-scale and diffuse . Geomorphology, xx, xxx-xxx.
Lee BD, Graham RC, Lauren TE, Amrhen C and Creasy RM 2001: Spatial Distribution of Soil Chemical condition in a serpentinitic Wetland and Surrounding Landscape. Soil Sci. Soc. Am. J, 65, 1183-1196.
Liu BY, Nearing MA, SHI PJ and Jia ZW 2000: Slope Length Effects on Soil Loss for Steep Slopes. Soil Sci. Soc. Am. J, 64, 1759-1763.
Metternicht G, Gonzalez S 2005: FUERO: foundations of a fuzzy exploratory model for soil erosion hazard prediction. Environmental Modeling & Software, 20, 715-728.
Moehansyah H, Maheswari BL and Armstrong J 2004: Field Evaluation of Select Erosion for Catchment Management in Indonesia. Biosystems Engineering, 88(4), 491-506.
Monte R. O’Neal, Nearing MA, Roel C. Vining, Jane Southworth and Rebecca A. Preifer 2005: Climate change impact on soil erosion in Midwest United States with change in crop management. Catena, 61, 165-184.
Morgon RPC 1999:A simple approach to soil loss prediction : a revised Morgon-Morgon-Finney model. Catena, 44, 305-322.
Morgon RPC 1985: Soil Erosion and Conservation. Longman, London, pp, 127.
Morgon RPC, Quinton JN, Smith RE, Govers G, Poesen JWA, Auerswald K, Chisci G, Torri D and Styczen ME 1998: The European Soil Erosion model (EUROSEM)” A Dynamic Approach for Predicting Sediment Transport from fields and Small Catchments. Earth Surf. Process. Landform, 23, 527-544.
Naik Sinukaban 1999: Impact of upland Agriculture and Conservation Project (UACP) on Sustainable Agriculture Development in Serang Watershed, Indonesia. In: Stott DE, Mohtar RH and Steinhardt GC (eds) 2001. Sustaining the Global Farm. Selected papers from the 10 th International Soil Conservation Organization Meeting held May 24-29, 1999 at Purdue University and the USDA-ARS National Soil Erosion Research Laboratory, pp. 186-190.
Nearing MA 1998 : Why soil erosion models over-predict small soil losses and under-predict large soil losses. Catena, 32, 15-22.
Obi ME and Salako FK 1995: Rainfall parameters influencing erosivity in southeastern Nigeria. Catena, 24, 275-287.
Oduro-Afriyie K 1996: Rainfall Erosivity map for Ghana. Geoderma, 74, 161-166.
Pablo Parysow, Guangxing Wang, George Gerter and Alan B. Anderson 1995: Spatial uncertainty analysis for mapping soil erodibility on joint sequential simulation. Catena, 53, 65-78.
Qiang Wu and Mingyu Wang 2007: A framework for risk assessment on soil erosion by water using an integrated and systematic approach. Journal of Hydrology, 337, 11-21.
Renard G. Kenneth and Jeremy R. Freimund 1994: Using monthly precipitation data to estimate the R-factor in the revised USLE. Journal of Hydrology, 157, 287-306.
Roehl JE 1962: Sediment sources areas, and delivery ratios influencing morphological factors. International Association of Hydrological Science, 59, 202-213.
Sang-Arun J, Mihara M, Horaguchi Y and Yamaji E 2006: Soil erosion and participatory remediation strategy for bench terrace in northern Thailand. Catena, 65, 258-264.
Schmidt J, Werner Mv and Michael A 1999: Application of the EROSION 3D model to the CATSOP watershed, The Netherlands. Catena, 37, 449-456.
Seyed Hamid ahmadi, Seifollah Amin, Ali Reza Keshavarzi and Naser Mirzamostafa 2006: Simulating Watershed outlet sediment Concentration using the ANSWERS Model by applying Two Sediment Transport Capacity Equations. Biosystems Engineering, 94(4), 615-626.
Simon CR, Stuart NL, David RM and Christopher JB 2007: Does hydrological connectivity improve modeling of coarse sediment delivery in upland environment. Geomorphology, 90, 263-282.
Soil Survey staff 1993a: National Soil Survey Handbook.Title 430 VI. (Washington, DC:USDA)
Soil Survey staff 1993b: Soil Survey Manual. Handbook no 18. (Washington, DC:USDA)
Sudirman, Sinukaban N, Suwardjo and Arsyad S 1986: The effect of soil erosion on soybean yield. Bull Soil Reg. Center 5, 15-19.
Suleyman Ozhan, Nihat Balci A, Necdet Ozyuvaci, Ahmet Hizal, Ferhat Gokbulak and Yusuf Serengil 2005: Cover and management factor for the Universal Soil-Loss Equation for forest ecosystems in the Marmara region, Turkey. Forest Ecology and Management, 214, 118-123.
Takken I, Beuselinck L, Nachtergaele J, Govers G, Poesen J and Degraer G 1999:Spatial evaluation of physically-based distributed erosion model (LISEM). Catena, 37, 431-447.
Thomas Cebecauer and Jaroslav Hofierka 2007: The consequences of land-cover change on soil erosion distribution in Slovakia. Geomorphology, xx, xxx-xxx.
Van Remortel RM, Hamilton M and Hickey R 2001: Estimating the LS factor for RUSLE through iterative slope length processing of digital elevation . Cartography, 30, 27-35.
Walling DE, He Q and Whelan PA 2003: Using 137 Cs measurement to validate the application of the AGNPS and ANSWERS erosion and sediment yield models in two small Devon catchments. Soil & Tillage Research, 69, 27-43.
Wischmeier WH and Smith DD 1978: Predicting rainfall erosion losses: a guide to conservation farming, USDA Handbook: No. 537 US DEpartement of Agriculture, Washingon, DC pp 1-58.
World Bank 1994: Indonesian Environment and Development: Challenges for the Future, Report No. 12083-Indonesia, Washington, DC” The World Bank, 21 March.