*3.1.6.2 Techniques for evaluating RUSLE factors in a GIS environment*

The application of the Revised Universal Soil Loss Equation (RUSLE) has been extensively implemented in various settings, including tropical watersheds with mountainous terrain, expansive watersheds, those dominated by agricultural practices, locales exhibiting discernible wet and dry seasons, and areas undergoing dynamic transformations in terms of land coverage patterns, agricultural farmland utilization, and developmental activities. The RUSLE model comprises three primary databases, namely the climatic and survey database, the crop database, and the soil data. The climatic and survey database contains monthly temperature

**Figure 4.** *Flowchart for RUSLE-based estimation of soil erosion.*

and precipitation data, as well as contours essential for the computation of factors such as erosivity, slope length, and steepness (LS). On the other hand, the crop database contains crucial data required for the determination of the surface cover factor (C). Lastly, the soil data includes relevant information on soil survey and soil characterization. The RUSLE model incorporates the five variables enumerated in eq. 1 to compute the mean annual soil erosion loss [108]. Estimation of the various components of the model, which is rooted in a significant corpus of research, is a prevalent approach to employing the Revised Universal Soil Loss Equation (RUSLE). Prior scholars have employed diverse techniques to compute these variables such as utilizing meteorological data, geological and soil maps, satellite imagery obtained remotely, empirical formulas, and digital elevation models (DEM) sourced from multiple origins [109].
