Enhancing precision in sediment yield Estimation: The synergistic effects of runoff and vegetation dynamics

Severe floods and high sediment production have caused numerous problems in watersheds. More insight studies are yet to be conducted to disclose existing ambiguities in fluvial phenomena. Therefore, refining and updating the parameters and factors of sediment yield estimation models, such as MUSLE, is essential for providing more precise results, leading to more effective watershed management. To achieve this, the role of more dynamic factors, including runoff and vegetation cover, was optimized in sediment yield estimation using 38 rainfall-runoff events recorded in the Galazchai Watershed, Northwestern Iran. Accordingly, the role of runoff, the parameter “m” of the MUSLE model, was optimized, and subsequent modeling was performed using linear, logarithmic, power, inverse, and multivariate inverse regression models. Furthermore, five vegetation cover-based methods were assessed for calculating the C-factor. The results indicated that the parameter “m” was successfully estimated using multivariate inverse regression based on runoff volume and peak discharge. The C-factor was also calculated using NDVI according to Method 1 as outlined in the original MUSLE model. The adapted MUSLE model provided the best sediment yield estimate for the Galazchai Watershed with determination coefficients (R²) of 0.87 and 0.56 for the calibration and validation stages, respectively. If the present adapted approach proves effective in other watersheds, it could enable the accurate and storm-based estimation of parameter “m” and precise sediment yield predictions using only runoff volume and peak discharge data. Therefore, the findings from the current study are highly valuable for enhancing erosion and sedimentation research and optimizing watershed management programs.