Assessment and environmental zoning of soil erosion potential using RUSLE model (Case study: Gharahsoo watershed)

Introduction
Soil erosion is one of the serious land degradation processes, which can be exacerbated by intensification of land utilization, land degradation and global climate change. Therefore, it is necessary to take actions such as management, conservation, and control in the watershed to restore the soil production potential and to prevent further damages. Generally, experimental methods and field observations are often time consuming and costly in developing countries. Therefore, use of alternative and less expensive methods such as various erosion risk models are more desirable to predict and assess of soil erosion rate. The zoning models of soil erosion potential identify critical areas to erosion. Awareness of erosion rate in watershed helps planners and managers to identify critical areas of the watershed as well as to select and prioritize appropriate practices and conservation strategies to control erosion and conservation of natural resources. A wide range of empirical models has been developed to quantify and assess the soil loss. Revised Universal Soil Loss Equation (RUSLE) is one of the most widely used erosion models to soil loss predictions that introduced by Wischmeier and Smith in 1965. The advantage of this model is its convenience in implementation and compatibility with GIS technique, which can be considered as an efficient approach for estimating the magnitude and spatial distribution of erosion. In conclusion, the study shows the application of the RUSLE model in estimating the total annual erosion rate in Gharahsoo watershed, north of Iran. By applying erosion models, we are able to identify the areas with high erosion potential in watershed, and then prioritize them for soil conservation schemes.
Material & Method
Soil erosion is one of the environmental problems that can be considered as a serious threat for natural resources, agriculture, and the environment. This study aimed to qualitative estimation of annual soil loss in the Gharahsoo watershed, northern Iran, using the RUSLE model in Geographic Information System (GIS) technique framework. The soil erosion parameters were evaluated for this model applying different methods. The parameters involved: soil erodibility factor (K), rainfall erosivity factor (R), land cover management (C), slope length and steepness factor (LS), and support practice (P). The R-factor map was obtained from the rainfall data, the K-factor map was obtained from the litological map, the C factor map was generated based on Landsat-ETM image, and the LS-factor was generated by using digital elevation model with a spatial resolution of 30m. Because the watershed doesnt has conservation practices, the P-factor map was assigned the value of 1.0 for the watershed. The spatial distribution of soil loss in the watershed was generated by overlaying and multiplying pixel-by-pixel soil erodibility factor, rainfall erosivity factor, land cover management, slope length and steepness factor, and support practice.
Discussion of Results
RUSLE is an empirical model that has the ability to estimate average annual rate of soil loss. Several data were used for the production of RUSLE factors that each factor is represented as a map layer in the GIS-based database to quantify, evaluate, and generate the map of soil erosion potential.
Soil erodibility factor (K)
The soil erodibility factor determines intrinsic susceptibility of soil particles to detachment and transport by runoff and raindrop impact according to soil texture, organic matter, and permeability. For the present study, the soil erodibility (K-factor) was generated with the use of the soil map provided by the Soil Geographic Data Base of Iran at the scale of 1:100,000. By considering the particle size, permeability class, and organic matter content, the K-value for the soil types were obtained from the USDA soil erodibility nomograph (Fig. 2). Soil erodibility values vary from 0.08 to 0.48 t ha MJ-1 mm-1.
Rainfall erosivity factor (R)
The rainfall factor determine the erosive power of rainfall to soil erosion that kinetic energy of rainfall (A storm’s maximum 30-min precipitation intensity) is used for indication of erosive power. If the values of these factors have not recorded at meteorological stations, researchers can use readily available rainfall values like annual rainfall that have correlated with R-values. The R-factor for Gharahsoo watershed was calculated according to available station data. After calculation of the MFI value, the rainfall erosivity was estimated by equation. The R-factor was in the range 3.9 to 274.2 MJ mm ha-1 h-1yr -1. The highest R-values prevail in the southern part of watershed and the lowest occurs in the upper of watershed.
Land cover and management factor (C)
The land cover management factor (C) reflects the effect of vegetation cover on soil erosion. Plant cover can protects the soil surface from runoff velocity. In order to determine the C factor coefficient, the NDVI layer is required. The NDVI layer was produced by Landsat–ETM satellite image. Then the C factor layer producted according to NDVI values. The C factor coefficient was in the range 0.002 to 1.0. As a result, the mean C values range inside the watershed from 0.002 for the forest class to 1.0 for the bare land and residential area categories.
Slope length and Steepness factor (LS)
The slope length and steepness factors (LS) are topographic factors that reflect the effect of topography on soil erosion. The LS calculation was performed using flow accumulation and slope steepness. The flow accumulation and slope steepness were computed from DEM layer using ArcGIS Spatial analysis plus and Arc hydro extensions. The LS factor values in the watershed vary from low 0 to high 32.6 %. High LS values are associated with steep slopes greater than 15°- 20° and 20°- 30° slope category in the middle and lower of the Gharahsoo watershed.
Support practice (P)
Because most regions in Gharahsoo watershed have no conservative practice management, so the P factor coefficient has been assigned as 1.0. When all factors required for the RUSLE were prepared, these data layers were overlaid and multiplied pixel-by-pixel for soil loss per year according to the RUSLE equation.
Conclusion
A quantitative assessment of average annual soil loss for Gharahsoo watershed was undertaken applying GIS based well-known RUSLE equation, which considers rainfall, soil, land cover pattern and topographic datasets. In the studied watershed, the land use pattern in potential areas to soil erosion indicates that areas with natural forest cover in the have minimum rate of soil erosion, while areas with human intervention have higher rates of soil erosion. By reviewing the value of parameter A and correlation coefficient of the study area, we noted that the cover management and slope length and steepness factors were more influential than the others. The highest amounts of erosion have occurred in the north and southern regions. In the central parts of the watershed, in spite of high values of LS factor (10– 30), the areas depict low to moderate erosion potential. This is due to the dense forest coverage in the region that decreases the energy of rain droplets. In the southern part of the watershed, the erosion rate increased by factors such as steep slopes and medium vegetation density. The predicted amount of soil loss and its spatial distribution can provide a basis for comprehensive management and sustainable land use for the watershed. Areas with high and severe soil erosion warrant special priority for the implementation of controlling practices.