Flat lakes with a large catchment area are especially affected by sediment inputs. The Kolleru Lake catchment (south-eastern India) with a surface area of approximately 6121 km2 belongs to such types of lake basins. The study’s main objective was the assessment of both soil erosion and sediment yield concentration rate of the Kolleru catchment. The study was conducted using the revised universal soil loss equation (RUSLE) model due to its simple and good applicability for soil erosion estimation. Data such as rainfall, soil texture, topography, crop cover management, and support practice factor were integrated into the modeling using RUSLE and ArcGIS. Field data were used both to analyze the soil texture and the slope length factor. The results showed that average annual soil loss was estimated with 13.6 t/ha/year, classifying the Kolleru Lake Basin under a very high erosion rate category. About 38% of the catchment area has encountered slight soil loss. Areas covered with moderate, strong, severe, very severe erosion potential zones are 29%, 17%, 9%, and 5.5%, respectively. This study identified that upland areas with less vegetation cover exported high potential erosion rates. Unlike the soil loss, the sediment delivery ratio values for the catchment were not affected by land use, while it showed a strong relationship with the catchment drainage system. Whereas, the average annual sediment yield was determined with 7.61 t/ha/year, had identified with the same pattern of the soil erosion. Catchment topography, vegetation, drainage system, soil properties, and land use cover played a major role in exporting the highest sedimentation. The outcome of these studies can be used among others to identify critical erosion areas on a pixel basis for the planning of erosion management practices.
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Introduction
Soil erosion is a severe problem in agriculture and where it has the foremost economic aspect in developing countries (Erkossa et al. 2015; Mekonnen et al. 2016). However, soil erosion by runoff is a global land degradation problem (Oldeman 1992; Seutloali and Beckedahl 2015; Novara et al. 2016; Restrepo and Escobar 2018). The runoff is a major driving factor that accelerates the erosion rates from mountain to low-land regions (Civeira et al. 2016a, b; Dutta et al. 2017; Oliveira et al. 2019). Besides, human-induced erosion is a typical phenomenon of regions with intensive agricultural production, construction activities, mining, deforestation, high-density population, and a lack of integrated approaches (Amsalu et al. 2007; Mekonnen and Melesse 2011; Nyssen et al. 2015). This has been developed by significant changes in land use/land cover patterns. However, frequent land-use changes triggered surface runoff flows, reduce soil fertility, nutrient loss, and land degradation (Setegn et al. 2010; Gebremicael et al. 2013; Kim et al. 2013; Buendia et al. 2016; Hassen et al. 2016; Ang and Oeurng 2018). Many studies demonstrated that land-use changes alter the surface runoff and sediment yields at different spatial and temporal scales (Lin et al. 2015; Welde and Gebremariam 2017; Guzha et al. 2018). Therefore, sustainable development and strategic methods are needed to prevent more soil erosion and massive sediment loads.
Several methods for estimating the surface soil losses are known (Auerswald and Chesworth 2008; Brenner 2013). The RUSLE (Revised Universal Soil Loss Equation) is the most spread method and widely acceptable all over the world (Wishmeier and Smith 1978; Brown and Foster 1987; Sheikh et al. 2011; Addis and Klik 2015; Borelli et al. 2017) from the last few decades. Despite its disadvantages, the RUSLE model is best adapted to the developing countries where the application of other complex models could be limited to the lack of input data (Pervić et al. 2011). It has been used worldwide for a range of small scale (Fistikoglu and Harmancioglu 2002; Lee 2004; Pandey et al. 2009; Sheikh et al. 2011; Farhan et al. 2013) to large scale watersheds (Irvem et al. 2007; Chen et al. 2011; Kumar et al. 2012) to predict the longterm average annual rate of erosion. The RUSLE equation is a function of rainfall erosivity, soil erodability, slope length, crop management factor, and conservation practice (Sheikh et al. 2011). However, each factor has its characteristic influence on the erosion risk depending on the topography, vegetation cover, and soil type (Kumar et al. 2012; Farhan et al. 2013). Remote sensing and GIS applications are the best way to digitize the land cover maps (Rao 2003), including topography features.
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The management of soil erosion and sedimentation losses is extremely complex due to large catchments spatial distribution (Mati and Veihe 2011). Therefore, it is essential to identify the critical erosion-prone areas for applying best management practices (BMPs). It is cost-effective to control soil erosion, mainly for an agricultural dominant land-use class (Shen et al. 2014; Sorando et al. 2019). However, there is a requirement of knowledge to prevent soil erosion in Kolleru lake basin studies are imperative (Jayanthi et al. 2006). At present, in the Kolleru Lake catchment, there is no gauge station to estimate soil loss; however, conventional methods are time consuming and cost-effective. Therefore, the USLE method was developed by (Wischmeier and Smith 1978) can be used to model the erosion rates in the Kolleru Lake catchment.
This study aims to ascertain both the spatial distribution of soil erosion and the sediment yield concentrations and to identify the highly polluted variability ranges. The objectives were achieved by the following tasks: (1) application of the RUSLE model to the entire Kolleru lake basin, (2) identification of the spatial distribution of soil erosion and sediment delivery ratio factors, (3) estimation of the sediment yield concentration across the Kolleru lake basin. However, due to the insufficient data, stream erosion and sediment deposition were not included in this study. Remote sensing data, such as land use classification, NDVI, elevation profile (DEM), and topography models, were used. At the same time, precipitation and soil texture data were acquired from the Indian Meteorological Department (IMD) and the Department of Agricultural Development, Andhra Pradesh, respectively.
Study area
The Kolleru Lake (16° 32′–16° 46′ N, 81° 04′–81° 24′ E) is the largest freshwater lake in India (Fig. 1). It is also famous for a hospitable environment for aquatic life. Two large basins of Krishna and Godavari rivers formed its catchment. They act as natural flood reservoirs between these basins. The landform of the Kolleru lake catchment is mainly composed of upland (77.8%) and deltaic (22.1%) ecosystems (Azeez et al. 2011). The catchment of the lake expands up to 6,121km2. It consists of mountains, streams, agricultural lands, industrial and built-up areas. The major soil groups in this area are Black Cotton Soils (57.6%), Sandy Clay Loams (22.3%), Red Soils (19.4%), and minor soil group types are Coastal Sands (3%), and Alkaline Soils (1%) (Raju 2012). The mean annual temperature of the catchment area is 29 °C (Patil 2005), with the minimum temperature ranges from 14 to 22 °C, and the maximum temperature ranges from 35 to 46 °C. The mean annual precipitation is 1094 mm. 70% of the precipitation acquires from June to September (Azeez et al. 2011). Additionally, the Kolleru lake receives water from the seasonal tributaries Tammileru and Budameru, and it has only one outlet channel: Upputeru, which connects the lake with the Bay of Bengal. Due to the recent advancement in man-made activities in this region, land-use changes have been accelerated erosion processes.
Fig. 1
Study area (Kolleru Lake) location map, a top-left (India map shapefile, NRSC-Hyderabad), b location of Kolleru Lake basin, c top-right (Land use map of Kolleru Lake basin,
source: NRSC, Bhuvan)
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Materials and methods
The long-term analysis of soil erosion is carried out by the RUSLE method for this study (Wishmeier and Smith 1978). Five parameters are influencing the USLE equation. They are used in this study as raster formats such as rainfall erosivity (R), soil erodability (K), steepness factor (LS), crop factor (C), and support practice factor (P) (Auerswald 1992). The USLE equation can be expressed as:
$$A \, = \, R \, \times \, K \, \times {\text{ LS }} \times \, C \, \times \, P,$$
(1)
where A is the average annual soil loss in (t/ha/year).
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An overview of the used data and their contribution to the USLE shown in Figs. 2 and 3, reflecting the topographic and hydrographic conditions of the Kolleru Lake catchment, including the rain gauge stations. Figure 4 highlights the stream network in dependence on the scale of used pixels.
Fig. 2
Methodology flowchart of the study
Fig. 3
Digitization of Kolleru Lake catchment along with tributaries using DEM (Digital Elevation Model), Cartosat-1, USGS Earth Explorer
Fig. 4
Stream network of the Kolleru Lake catchment inferred from DEM
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Rainfall erosivity factor (R)
The rainfall erosivity factor (R) expresses the erosion rate, which was caused by the rainfall intensity, duration, and volume. In addition, velocity, the shape of raindrops, and the rain’s kinetic energy to promote a high impact on soil erosion (Renard et al. 1997). There is a direct proportion between rainfall intensity and erosion; a high rate of rainfall intensity affects more runoff and subsequently results in more soil erosion. The R-factor varies between regions due to the precipitation patterns and slope conditions (Farhan et al. 2013). Plain areas have low erosivity R values because of low inclination, respectively low slope degree, whereas larger R-values indicate higher erosion amounts. Based on the climate data, the R-value represents the product of the rainfall energy (E) and the maximum 30-min intensity (I30) (Brown and Foster 1987). On the other method, the R-value can be estimated by the Fourier index (F), proposed by (Arnoldus 1977), to establish the erosion risk.
where ri is the precipitation for 1 month, and P is the annual precipitation.
The estimation of the R-value from F can be determined by regression analysis. There are several regression equations for obtaining the R-factor. For this study, R = 0.3598 F1.9462 was used, because it was derived from climatic conditions similar to the study area proposed by (Zhang and Fu 2003).
Slope length and steepness factor (LS)
The LS factor in USLE represents the combined effect of the length (L) and slope (S) factors, merely depending on an area’s topography. The LS-factor, which depends on flow direction, flow accumulation, and topography of the soil, was also considered while calculating the LS-factor. Slope length was defined by (Wischmeier and Smith 1978) as the distance from the point of origin of overland flow to the point where runoff becomes concentrated in a defined channel. Many equations have been developed to estimate the LS factor by (Wischmeier and Smith 1978), (Moore and Burch 1986a, b, c), (Griffin et al. 1988), (McCool et al. 1989), (Moore and Wilson 1992) and (Desmet and Govers 1996); however, they are confined to the distinctive characteristics of the topography of an area. Most of the algorithms are already implemented to calculate LS factor within GIS platforms, such as ArcGIS, SAGA GIS, GRASS, IDRISI, etc. However, according to (Desmet and Govers 1996), the LS factor derived from such methods gives higher values than the manual method. In another case study, LS values are derived from the GIS method were lower by 10–30% than obtained from the manual method (Hrabalikova and Janecek 2017), while 22% higher values obtained by Griffin’s method. Therefore, a comparison between different approaches determining the LS factor, based on manual and GIS methods, Hrabalikova and Janecek (2017) extensively documented that the best results obtained from Wischmeier’s method, where a specific catchment area replaced slope length, and Moore’s method.
The LS-factor was calculated for the catchment area of Kolleru Lake by following Eq. (3) proposed by (Moore and Burch 1986a, b).
where LS is the combined slope length and steepness factor, flow accumulation determines the upslope contributing area for a given cell, size of the cell was considered by the resolution of the DEM (for this study, a 30 m resolution DEM was available), and sin slope is slope degree value in sin (Moore and Burch 1986a). The stream network was derived from the DEM by changing the threshold value (Fig. 4). A detailed flow network can be observed from the smaller chosen value. For the calculation of the LS factor, 100 pixels threshold value has been used in this study.
Support practice factor (P)
P-factor indicates the effect of support practices on the average annual erosion rate. It represents the soil loss ratio with contouring and strip cropping to that with a straight row farming up-and-down slope (Renard et al. 1997). On the other hand, it indicates the rate of soil loss according to the various cultivated lands on the earth (Sheikh et al. 2011). For satisfactory results, the P-factor depends on contour, strip cropping, and terraces, which control erosion. According to the cultivating methods and slope conditions, the support practice factor value is shown in Table 1 (Shin 1999). The value of the P-factor ranges from 0 to 1. Some agricultural support practices, such as contour farmland and surrounded by fish farmlands, occur within the Kolleru Lake catchment.
Table 1
Support practice factor according to the slope (after Shin 1999)
Slope (%)
Contouring
Strip cropping
Terracing
0.0–7.0
0.55
0.27
0.10
7.0–11.3
0.60
0.30
0.12
11.3–17.6
0.80
0.40
0.16
17.6–26.8
0.90
0.45
0.18
26.8 >
1.0
0.50
0.20
Soil erodibility factor (K)
The soil erodibility factor (K) expresses the disintegration of soil particles from parent rock material respectively the soil body under the action of rainfall intensity, wind, and natural or human activities results in sediment transport and runoff for a specific condition (Bancy et al. 2000; Pandey et al. 2007; Addis and Klik 2015). A wide range of soil eroded particles is known to belong predominantly to the silt and clay texture classes (Kim and Julien 2006). But also, sand particles are very less resistive to erosion (Buttafuoco et al. 2012; Karydas et al. 2013) because of their cohesive behavior towards the origin. Measurements can select the K-factor from field unit plots, for instance of 72.6 ft long with 9% slope, continuously maintained fallow, tilled up and down the hill slope (Weesies 1998; Bagarello et al. 2009; Addis and Klik 2015). Direct measurements of runoff plots can also determine k values. However, field measurements are more accurate than other studies. For this study, the K factor was determined based on soil texture classes and organic matter contents proposed by Williams (1995). In this study, the soil samples (Fig. 5) were collected from different locations around the lake (c.f. Fig. 6), where the inflow of water debouches into the lake and the outflow of the water discharges into the sea (Figs. 5, 6). The corresponding K-values for the soil types were identified based on the particle size distribution and organic matter content in addition to the soil texture data from the Department of Agriculture Development, Andhra Pradesh, in India.
Fig. 5
Field data processing based on the sieve analysis method of Kolleru Lake
Fig. 6
Texture classification of soil samples from Kolleru Lake (actual area shown as in DEM format) area based on the sieve analysis method
Fig. 7
NDVI classification of Kolleru Lake catchment
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The field samples further soil texture classification was done based on the particle size distribution system followed by the sieve analysis method, in which the size of sieves range between 4.75 and 0.002 mm (IS 383 2020). Based on the degree of fineness, the soil particles were classified as sand, silt, and clay (in percentages) (Fig. 6). The organic matter content was determined based on the loss-on-ignition (LOI), which is the percentage weight loss of the soil sample due to ignition at a certain temperature. The erodibility values range from 0 to 1, where 0 reflects soil with less water erosion, and 1 reflects soils with high water erosion. In this study, the K factor was derived from the following equation (Williams 1995) across the tributaries (Fig. 7).
where fcsand is a factor that lowers the K indicator in soils with high coarse-sand content and increases it for soils with low sand content; fcl-si gives low soil erodibility factors for soils with high clay-to-silt ratios; forgc reduces K values in soils with high organic carbon content, while fhisand lowers K values for soils with extremely high sand content:
where, \(f_{{\text{m}}_{\text{s}} }\) is the sand fraction content (0.05–2.00 mm diameter %); msilt is the silt fraction content (0.002–0.05 mm diameter %); mc is the clay fraction content (< 0.002 mm diameter %); and orgC is the soil organic carbon (SOC) content (%). Besides the soil classification, organic matter content was calculated by the loss of weight on ignition (LOI) method as follows:
The estimation of organic matter from LOI is done by regression analysis. Using the Walkley–Black method is used to convert LOI to total carbon (Gelman et al. 2011). The equation as follows:
The crop management factor (C) was determined from the existing land use and land cover patterns and NDVI (Kumar et al. 2012). It is expressed as the ratio of soil loss from land covered by the crop patterns under specific conditions to the corresponding loss from the plain area such as clean-tilled, waterbody and fallow land (Wischmeier and Smith 1978). The C-factor values range from 1 to 0, where higher values represent no cover effect and more prone to erosion. In comparison, lower values represent the dense cover effect, results in less erosion (Erencin 2000). To find the effective crop factor based on the spatial and temporal variations, satellite remote sensing data were used (Prasannakumar et al. 2012). It depends on the type of crop cover and soil over the area of concern, and it considers the second major factor controlling erosion (Farhan et al. 2013). Despite the availability of land cover patterns, the values were assigned to each cover based on the type of land used.
Predicting sediment yield
Sediment yield prediction in a catchment embraces the determination of the amount of soil eroded and transferred from the point of interest at a given period. The statistical product between the surface soil erosion (A) and the sediment delivery ratio (DR) is called sediment yield (Sy). The degree extent of DR values for an area is indiscriminately affected by catchment topography, vegetation cover, sediment sources, slope, soil texture, etc. (Richard et al. 1993; Lin et al. 2002; Fu et al. 2006). However, parameters such as land cover, slope, and catchment area have been mainly used in empirical equations for DR (Roehl 1962; Hadley et al. 1985; Kothyari and Jain 1997; Jain and Kothyari 2000; Fu et al. 2006).
DR in grid cells is the main function of the travel time of surface flow within the cell, which was extensively documented by (Ferro and Minacapilli 1995) and (Ferro 1997). A conformable grid with 30 m × 30 m (in this study area, resolution of DEM, 30 m) of cell size and a travel time of a surface flow is strongly dependent on land cover and topography characteristics of a catchment. It had been justified the relationship with DR (Jain and Kothyari 2000). Based on their studies, the land cover and topography has mainly considered in this study, and also assumed that grid cell existing in an overland region of a catchment. The empirical equation as follows:
where \(\gamma\) is a constant coefficient for a given catchment and \(t_i\) is the travel time (h) of surface flow from the ith grid to the nearest channel grid. Each grid's travel time in a channel can be estimated with the flow path if one knows lengths and velocities. In GIS (ESRI 1994) analysis, the eight-direction (D8) flow model can be achieved with the flow direction from one cell to a neighboring cell. There are eight valid output directions in a given grid cell relating to the eight adjacent cells into which the flow could take place. The flow direction is determined by the direction of steepest descent, or maximum drop, from each cell.
where \(S_y\) is the sediment yield concentration, \(A_i\) is the soil erosion over the catchment, and \(D_{R_i }\) is the sediment delivery ratio.
Results and discussion
Maps for values of the RUSLE parameters, such as rainfall erosivity factor (R), soil erodability factor (K), length and slope inclination factor (LS), crop factor (C), and support practice factor (P), were overlaid to form a composite map of soil erosion (A). The results discussed here based on the complete analysis of field and remote sensing data to estimate the soil erosion (A), sediment delivery ratio (\(D_{R_i }\)), and sediment yield concentration \((S_y )\) in the Kolleru Lake catchment. The results provide a general understanding of erosion risk levels and the rate of sediment yield concentrations in the catchment.
Soil loss
Figure 8 illustrates the spatial distribution of potential soil erosion risk map of Kolleru lake catchment. The annual average soil loss of the whole catchment was grouped into different classes, as Singh et al. (1992) suggested. About 38% of the area is encountered with slight soil loss (< 5 t/ha/year). Whereas 29% of the area was prone to moderate soil loss (5–10 t/ha/year), 17% of the area was identified as strong soil loss (10–20 t/ha/year), while 9% of the area severe soil loss (20–40 t/ha/year), and 5.5% area was under very severe soil loss (> 40 t/ha/year) respectively. The observation of extreme soil loss areas is more than 5.5% was already gone severe erosion due to its higher elevation slopes. The maximum soil loss areas are mainly concentrated in higher LS factor and conservation practice factors, where a large percent of the area still not covered by conservation measures. Meanwhile, the catchment area consists of less than 12% of vegetation cover that caused high erosion risk where forestland has the maximum water-retaining capacity while reducing the potential soil loss up to a certain extent. In addition, the studied soils are clayey in texture and non-porous in nature, which is more prone to runoff leading to more soil loss. In the Kolleru Lake catchment, topographic characteristics play a significant role in soil exports. In contrast, the proportion of the deltaic part is comprised of less than 22%, relatively balanced in the downstream region.
Fig. 8
Soil erosion risk map of Kolleru Lake catchment using RUSLE method
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Moreover, the study area belongs to the regions of maximum degradation of vegetation cover and changing weather parameters in Andhra Pradesh. However, frequent vegetation changes can promote erosion rates, especially in semi-arid regions (Kumar et al. 2012). According to (Patil 2005), about 35.6 million tonnes of surface soil was eroded everyyear in Andhra Pradesh. Similar data were reported in other parts of the world (Civeira et al. 2016a, b; Rodriguez-Iruretagoiena et al. 2016; Sanchís et al. 2015; Arenas-Lago et al. 2014; Silva et al. 2013), demonstrating the importance of this study.
Sediment delivery ratio
The sediment delivery ratio (DR) showed a strong relationship between cell size and the nearest channel (Fig. 9). DR assumes that it has an inverse relationship with travel time, a function of travel length and velocity (Eq. 11). Because of the surface roughness and slope features, the same distance does not imply to all the cell values have the same DR with different travel times (Fu et al. 2006). The spatial distribution of DR is essential for identifying the potential soil loss areas and their respective sediment delivery sources (Jain and Kothyari 2000; Fu et al. 2006).
Fig. 9
Spatial distribution of sediment delivery ratio at Kolleru Lake catchment
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Unlike soil loss, the DR values obtained for the Kolleru lake catchment did not show a strong relation with land uses (Fig. 9), while it more likely tends to be affected by the catchment drainage system (Richard et al. 1993). It can be explained in Fig. 9, and those large DR values are associated with the steep headwater areas, which are likely to be stream network channels, while smaller DR values are found in overland regions, the channel networks surrounded that. The steep channel areas had the highest DR between 0.85 and 1, whereas the low DR values are far from the stream channels. The average DR for the Kolleru lake catchment founded is 0.75.
Sediment yield
The sediment yield concentrations mainly from surface runoff, erosion, vegetation type, soil and water conservation practices, and topographic factor. Figure 10 depicts the sediment yield distribution was classified into five categories according to their proportion of distribution. The average annual sediment yield concentration in the Kolleru Lake catchment is found to be 7.61 t/ha/year. Whereas 27% of the area has encountered with slight sediment yield (< 1 t/ha/year), 32% of the area has prone to moderate sediment yield (1–7 t/ha/year), 22% of the area has identified as strong sediment yield (7–19 t/ha/year), while 13% of the area severe sediment yield (19–40 t/ha/year), and 4.5% of the area very severe sediment yield (> 40 t/ha/year). The sediment yield distribution from the entire catchment was followed by the same pattern of soil erosion (Fig. 8); thereby, it was modified with the stream patterns similar to that of DR (Fig. 9). The sediment yield concentration was relatively high in croplands, particularly from paddy fields. Meanwhile, the cultivation on higher elevation slopes caused great soil erosion in the Kolleru catchment. Besides, the areas with high sediment yields are also concentrated in wastelands, and there the erosion rate is higher in uncovered areas. In the catchment area, red soils and sandy clay soils exported most sediment. This is referenced to the soil properties while indicating the depth, slope, texture pattern, erosion, and drainage.
Fig. 10
Sediment yield (t/ha/year) at Kolleru Lake catchment
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Furthermore, agricultural and industrial activities are the major sources of surface erosion and, consequently, sedimentation and siltation within the Kolleru lake catchment (Azeez et al. 2011). According to (Narender 1993), eight major industries were located nearby the lake contributing about 7.2 million liters of industrial effluents containing suspended solids, organic acids, colloids, etc. led into the lake. Besides, Krishna and Godavari's perennial rivers drift down to the lake, about 68,000 tons of sediments (Azeez et al. 2011). As mentioned by own on-site observations, a substantial proportion of suspended sediments to the Kolleru lake comes from the river banks. Moreover, fishponds cultivation around the lake contains a high organic load, fertilizers, and pesticides (Narender 1993). The catchment confronts several other threats: siltation, eutrophication, and water circulation significantly affect the lake ecosystem (Rao and Pillala 2001; Sekhar et al. 2004; Rao et al. 2006). This study presents compiled RUSLE and GIS data and its analyses in different world areas previously reported (Cerqueir et al. 2011; Ribeiro et al. 2010; Quispe et al. 2012; Sánchiz et al. 2013).
Conclusions
This model helps identify the susceptible erosion-prone areas where the data’s uncertainty is available that should be targeted for the agricultural management plans. The coupling of GIS and RUSLE models was used in an efficient procedure to estimate soil erosion and sediment yield concentration using and collecting the existing data of the Kolleru Lake basin with remote sensing images. The overall results are presented on a pixel-wise spatial distribution of the soil erosion and sediment yield rate in the Kolleru Lake basin. Upland areas exhibited much greater erosion rates to the stream channels than plain areas. Tributaries and streamlines of the catchment carry high sediment loads to the Kolleru lake and its outlet. The average annual soil loss was estimated at 13.6 t/ha/year classifying the basin into a very high soil erosion category. Combined with a detailed investigation of the vegetation maps, soil texture, rainfall intensity, and topographic features and using remote sensing and GIS techniques, it becomes possible to estimate the erosion losses accurately. Meanwhile, the average annual sediment yield of the basin was found to be 7.61 t/ha/year. Agricultural and industrial activities were found as major sources of soil erosion and sedimentation. It was identified that soil loss and sediment yield patterns are spatially connected, and the sediment yield was highly modified with the sediment delivery ratio. In this area, priority attention must be given to the adjacent streamlines, either application of buffer strips has been suggested to prevent more soil losses. The areas under the potential erosion soil losses need immediate attention from the agriculture management point of view, in combination with both detailed investigations of the vegetation maps, soil texture, rainfall intensity, and topographic features and using remote sensing and GIS techniques the estimation of soil erosion losses and sediment loads accurately.
Acknowledgements
This research was carried out at the Philipps-Universität Marburg (Germany) (Grant number: Marburg International Doctorate), supported by the DAAD. The authors would like to thank anonymous reviewers, and constructive comments improved the presentation of this paper.
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