Document Type : Original Article

Subjects

• Geomorphology, natural geography

Extended Abstract

1. Introduction

Land degradation has become one of the most critical environmental crises of the present century. Human activities in the past century, particularly industrial agriculture and uncontrolled development have accelerated land degradation and surface soil loss. Soil erosion is rapidly expanding on a global scale, turning into a significant environmental challenge. Natural factors such as land slope, rainfall, and soil type directly influence soil erosion. However, human land management practices can modify these factors, playing a crucial role in either increasing or reducing soil erosion. Adopting appropriate agricultural methods and implementing soil conservation techniques can significantly prevent erosion and enhance soil stability.

2. Materials and methods

In this study, the Revised Universal Soil Loss Equation (RUSLE) was used to estimate soil erosion in the Meshkin-Chay watershed. This model is widely applied due to its semi-empirical structure, ease of implementation, and limited data requirements. RUSLE operates based on five key factors as rainfall, soil type, slope, vegetation cover, and land management, allowing for adaptation and calibration in different climatic and geographical conditions. Compared to more complex models like WEPP or SWAT, RUSLE requires fewer data and lower costs while providing a faster assessment of erosion on a large scale, making it a suitable option for both scientific and practical studies. Next, to integrate vegetation and morphometric indices, several parameters, including NDVI, SAVI, MSAVI, Topographic Wetness Index (TWI), Stream Power Index (SPI), Curvature, Profile Curvature, Plan Curvature, Slope, and Length-Slope Factor (LSF) were examined. After generating the soil erosion map using the RUSLE model, we aimed to analyze the impact of various factors on erosion rates in the study area. Pearson’s correlation analysis was employed to assess the relationship between model factors (such as land slope, soil type, and vegetation cover) and soil erosion rates. This analysis helps identify the key factors influencing soil erosion and propose appropriate mitigation strategies.

3. Results and Discussion

To create the final soil erosion map for the study area, the RUSLE model layers were overlaid in ArcMap using the Raster Calculator tool. The resulting map showed that annual soil erosion ranged from 0 to 108.61 tons per hectare per year. The average annual soil erosion for the Meshkin-Chay watershed was estimated at 50.89 tons per hectare per year. A regression analysis was conducted to determine the contribution of each RUSLE factor to soil loss, where soil loss was considered the dependent variable, and rainfall erosivity, soil erodibility, vegetation cover, topography, and soil conservation were the independent variables. The results indicated that the topographic factor had the highest impact on annual soil loss, with a coefficient of determination (R²) of 0.94. These findings align with those of Abedini et al. (2022), who also reported that topography had the most significant influence on annual soil erosion in RUSLE-based assessments, with an R² of 0.95. After generating spatial distribution maps for the indices (TWI, SPI, Slope, Curvature, Plan Curvature, Profile Curvature, NDVI, SAVI, MSAVI, EVI, and LSF), the correlation between these indices and annual soil erosion in the Meshkin-Chay watershed was analyzed. Regression analysis, a statistical method for estimating a quantitative variable based on its relationship with one or more quantitative variables, was utilized. This relationship, ranging from 0 to 1, indicates the model’s explanatory power, where 0 means the model does not explain any variation, and 1 indicates full explanatory power. For the regression analysis, the required layers were imported into SPSS software, and Pearson’s correlation coefficient was used. In this model, annual soil erosion was the dependent variable (Y), while the selected indices were independent variables (X). According to the results, Slope (R² = 0.74), NDVI (R² = 0.48), LSF (R² = 0.44), and Plan Curvature (R² = 0.49) exhibited the highest correlation and significance in explaining soil erosion in the Meshkin-Chay watershed.

4. Conclusion

This study utilized the RUSLE model to estimate soil erosion in the Meshkin-Chay watershed. Rainfall data, soil texture maps, Landsat 9 satellite imagery, a digital elevation model (DEM), and other environmental data were incorporated. The results showed that soil erosion varied between 0 and 108.61 tons per hectare per year, with an average annual rate of 50.89 tons per hectare. The rainfall erosivity (R) map indicated values ranging from 127.77 to 162.92 MJ·mm. The highest soil erodibility (K) was observed in northern regions, while the topographic factor (LS) had the greatest impact in areas with steep slopes.Vegetation cover (C) varied between 0 and 0.59 and played a significant role in reducing erosion. Among the studied indices, Slope, NDVI, LSF, and Plan Curvature had the highest impact on soil erosion. These findings can be applied to soil management and erosion mitigation in similar watersheds.

5. Acknowledgement & Funding

· The manuscript did not receive a grant from any organization.

6. Conflict of Interest

· The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.