The performance of the Revised Universal Soil Loss Equation (RUSLE) as the most widely used soil erosion model is a challenging issue. Accordingly, the objective of this study is investigating the estimated sediment delivery by the RUSLE method and Sediment Delivery Distributed (SEDD) model. To this end, the Talar watershed in Iran was selected as the study area. Further, 700 paired sediment-discharge measurements at Valikbon and Shirgah-Talar hydrometric stations between the years 1991 and 2011 were collected and used in sediment rating curves. Nine procedures were investigated to produce the required RUSLE layers. The estimated soil erosion by RUSLE was evaluated using sediment rating curve data by two methods including least squares and quantile regression. The average annual suspended sediment load was calculated for each sub-watershed of the study area using the SEDD model. Afterwards, a sediment rating curve was estimated by least squares and quantile regression methods using paired discharge-sediment data. The average annual suspended sediment load values were calculated for two hydrometric stations and were further evaluated by the SEDD model. The results indicated that the first considered procedure, which utilized 15-min rainfall measurements for the rainfall factor (R), and the classification method of SENTINEL-2 MSI image for the cover management factor (C), offered the best results in producing RUSLE layers. Furthermore, the results revealed the advantages of utilizing satellite images in producing cover management layer, which is required in the RUSLE method.

中文翻译：

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通过修订的通用土壤流失方程（RUSLE）和沉积物分配（SEDD）评估伊朗塔拉尔流域的估计沉积物输送

经修订的通用土壤流失方程（RUSLE）作为使用最广泛的土壤侵蚀模型的性能是一个具有挑战性的问题。因此，本研究的目的是研究通过RUSLE方法和沉积物分配（SEDD）模型估算的泥沙输送量。为此，选择了伊朗的塔拉尔流域作为研究区域。此外，还收集了1991年至2011年之间在Valikbon和Shirgah-Talar水文测量站进行的700次成对沉积物排放量测量，并将其用于沉积物额定值曲线。研究了九种程序以产生所需的RUSLE层。通过泥沙等级曲线数据，通过最小二乘和分位数回归两种方法，评估了由RUSLE估算的土壤侵蚀。使用SEDD模型，为研究区域的每个子流域计算了平均年悬浮泥沙负荷。之后，使用成对的排放-泥沙数据，通过最小二乘和分位数回归方法估算泥沙等级曲线。计算了两个水文站的年平均悬浮泥沙负荷值，并通过SEDD模型进行了进一步评估。结果表明，首先考虑的程序是使用15分钟的降雨测量作为降雨因子（R），以及SENTINEL-2 MSI图像对覆盖率管理因子（C）的分类方法，在产生RUSLE层方面提供了最佳结果。此外，结果揭示了在RUSLE方法中需要在生产覆盖层管理层中利用卫星图像的优势。