Leila Goli Mokhtari; Najme Hshafiei; Abolalfazl Rahmani
Volume 5, Issue 17 , March 2019, , Pages 1-21
Abstract
Introduction
Soil erosion is a phenomenon that typically occurs in a large part of the earth and the exacerbation of this process, as a limiting factor, can be an obstacle to the management of the land. It reduces the soil fertility and results in the desertification of the fields and the sediment deposited ...
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Introduction
Soil erosion is a phenomenon that typically occurs in a large part of the earth and the exacerbation of this process, as a limiting factor, can be an obstacle to the management of the land. It reduces the soil fertility and results in the desertification of the fields and the sediment deposited in the drains and reservoirs of sediment droplets reduces their intake capacity. Soil contamination is one of the environmental problems that threatens natural resources, agriculture, and the environment. Soil erosion's time and space data plays an important role in management measures, erosion control, and management of catchment areas. Therefore, in order to protect effectively and prevent undesirable effects of erosion, it is necessary to identify the factors involved in erosion to provide an appropriate estimation of the amount of erosion in the area. So far, several methods have been proposed to estimate erosion in areas with different characteristics. The models presented in three categories are empirical, conceptual, and physical models. The empirical models have always been considered for ease of use and availability of the data, and there have been significant advances in their development. The Global Soil Erosion Equation (USLE) is one of the experimental models that has been proposed to predict mortality on grazed lands, but the modified Global Soil Deterioration Equation (RUSLE) has expanded for various uses, including forest, pasture, crop, and bayer lands. Similar to the USLE, the RUSLE model has six factors, but more accurate estimates of rainfall erosion, soil erosion, vegetation, and conservation operations are used to predict soil losses in wider areas and in different conditions such as crops, forests, grassland, and damaged forests. This model estimates soil erosion as a combination of six factors that indicate the rainfall erosivity, soil erodibility, length and gradient, cropping system, and management operations.
Methodology
In this research, the erosion of the Nourabad Mamassani basin using the RUSLE model was studied. The method was descriptive. To prepare the studied basin maps, the topographic map of 1: 50000, the geological map of 1: 100000, Google Earth images, Landsat 8 satellite images, soil layers, monthly and annual precipitation data of synoptic stations were used. In addition, Kriging zoning method was used to prepare the rainfall erosion layer. A regression analysis was used to determine the relationship between the dependent and the independent variables as well as the effect of the most important factor on the annual waste of soil. The annual regression model of the soil was the dependent variable. The rainfall erosivity factors, soil erosion, topography and vegetation were considered as independent variables. As previously mentioned, the model used in this study was the RUSLE global erosion model. It consists of 6 factors as follows
Relationship (1) R.K.L.S.C.P = A
A: soil erosion per unit area. R: rainfall erosion factor. K: soil erodibility factor. L: slope factor. S: slope factor. C: covering agent. P: a protective operation
Results
The annual mean erosion of the soil was determined using the coefficient of erosivity (R), soil erodibility factor (K), topographic factor (LS), vegetation cover factor (C) and conservation factor (P), and the ArcGIS software. The map obtained from this equation is shown in Fig. 7. The erosion values in the studied basin vary from 6 to 75 tons per hectare per year at the pixel level. According to table (3), about 48% of the area is a low erosion class, which mainly includes a large part of the basin. About 28% of the range is in average erosion, and about 23% of the basin is under severe erosion, which is located in the southern part of the basin.
Discussion and Conclusion
Investigating the rainfall erosivity map at the basin level showed that the values of this factor varied from 11 to 31. The erosivity values from the central parts of the basin to the northern part of the trend were decreasing and in the southern parts where rainfall was higher, erosivity has increased. Soil erosion rate varied from 0.25 to 0.48. The results of the vegetation analysis showed that the values of this factor varied from 0.7 to 1.35. The major part of the role of destructive factors on soil erosion was in rain and pasture lands related to the human factors. The study of the soil erosion risk map, which was produced from the combination of erosivity layers, soil erosion, topography, and vegetation, showed that the soil erosion risk level in the basin was variable from 8 to 75 per hectare per year. According to the map of the soil erosion risk, areas with high erosion risk were mainly in uneven areas of the region. Also, the effect of the rainfall erosion on the increase of erosion in the southern parts of the basin had a medium to high erosion risk. Also, areas with a high erosion risk included areas that had a rugged area. The results of this study showed the high capability of GIS and remote sensing to generate the data needed to generate RUSLE factors, resulting in Output data high quality. Therefore, GIS and RS can be effectively used to develop managerial solutions and provide selected choices for managers to solve the erosion problem.
