Employing Different Precipitation Products to Estimate Soil Loss and Sediment Yield: Application in the Zagros Mountains (Iraq–Iran)

Soil erosion is one of the most serious environmental concerns of our time, leading to land degradation, agricultural inefficiency, reservoir siltation, and ecological imbalance. Thus, a thorough assessment of soil erosion is crucial whilst developing and implementing any conservation strategies and measures. In this context, the present research aims to estimate Soil Loss (SL) and Sediment Yield (SY) in the Chami Qeshan Watershed (CQW) between Iraq and Iran using the integrated application of the Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Ratio (SDR) techniques. In addition to rain gauge observations, three satellite-derived precipitation datasets were applied for calculating the rainfall–runoff erosivity (R) variable of RUSLE, namely the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and the Tropical Rainfall Measuring Mission (TRMM). We also considered four other key factors for predicting soil erosion: soil erodibility (K), slope length and steepness (LS), cover management (C), and conservation practice (P). Ultimately, we used the Pearson correlation to statistically evaluate relationships between input variables and soil erosion outcomes. Results revealed substantial differences among the implemented products, where CHIRPS provided the best reproduction of annual precipitation. Likewise, the CHIRPS-based RUSLE-SDR model reproduced the closest mean soil loss of 12.18 t·ha−1·yr−1 and total outlet sediment yield of 51,826 t·yr−1 to the corresponding gauge-driven model estimates. The correlation analysis found moderate associations between modeled soil loss estimates and LS (r = 0.56–0.57), C (r = 0.50–0.61), and P (r = 0.48–0.63) variables. Given the direct influence of precipitation products on rainfall erosivity and RUSLE–SDR estimates, native-resolution sensitivity analyses and alternative interpolation schemes are recommended to assess result robustness.