Sustainable agriculture and eco-environment conservation face constant threats from soil erosion in mountainous regions. This study aimed to predict soil loss in the mountainous watershed by applying the Revise Universal Equation Soil Loss (RUSLE) and Sediment Delivery Ratio (SDR) models and to evaluate the efficacy of the RUSLE model through the observed data in a hydrometric station. Comparing a polygonal, and a digital soil map (prepared by the Kriging method) for deriving the factor to estimate soil loss was another objective of the recent research. In this study, two scenarios were examined for the calculation of spatial variation of the -factor; scenario I comprised soil data derived from a legacy soil map with eight soil units and the data from their representative soil profiles (i.e., traditional soil survey), and scenario II comprised -factor derived from 100 studied sites using kriging technique (i.e., digital soil survey). The results of RUSLE modeling showed that there was no significant difference between scenario I (7.97 t/(ha. yr)) and scenario II (7.93 t/(ha. yr)) for predicting soil loss with the RUSLE model. This finding confirms that the RUSLE model with limited and legacy data can provide a reliable prediction of soil loss in the given watershed. Moreover, long and short-term periods were used to estimate soil loss, while in the long-term period, estimated soil loss had higher accordance with actual soil loss. Sediment delivery ratio was calculated using models of Vanoni, Boyce, USDA-SCS, and Slope-based models. The results indicated that the Slope-based model had the highest fitness (R=0.946, ME=0.27, and RMSE=0.275 for scenario I and R=0.9443, ME=0.26 and RMSE=0.40 for scenario II), with observed sedimentation rate at the hydrometric station at the outlet of the watershed. Overall, predicted soil loss severity map using the traditional soil map by RUSLE model could provide trustworthy information for decision makers and governors at the given and similar watersheds in semiarid regions.

中文翻译：

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使用 RUSLE 模型估算土壤流失：伊朗中部流域尺度传统土壤数据与数字土壤数据的比较


山区的可持续农业和生态环境保护面临着水土流失的持续威胁。本研究旨在通过应用修订通用方程土壤流失（RUSLE）和输沙比（SDR）模型来预测山区流域的土壤流失，并通过水文站的观测数据评估RUSLE模型的有效性。最近研究的另一个目标是比较多边形和数字土壤图（通过克里金法制备）以得出估计土壤流失的因子。在本研究中，检查了两种情景来计算 - 因子的空间变化；情景 I 包括从具有八个土壤单元的遗留土壤图导出的土壤数据以及来自其代表性土壤剖面的数据（即传统土壤调查），情景 II 包括使用克里格技术从 100 个研究地点导出的因子（即数字土壤）民意调查）。 RUSLE模型结果表明，情景一（7.97吨/（公顷·年））和情景二（7.93吨/（公顷·年））在RUSLE模型预测土壤流失方面没有显着差异。这一发现证实，具有有限和遗留数据的 RUSLE 模型可以对给定流域的土壤流失提供可靠的预测。而且，土壤流失量分别采用长期和短期估算，而长期估算的土壤流失量与实际土壤流失量的吻合程度较高。使用 Vanoni、Boyce、USDA-SCS 模型和基于坡度的模型计算泥沙输送率。结果表明，基于斜率的模型具有最高的适应度（场景 I 的 R=0.946，ME=0.27，RMSE=0.275，R=0.9443，ME=0.26，RMSE=0。情景 II 为 40），在流域出口处的水文站观测到的沉积速率。总体而言，使用 RUSLE 模型的传统土壤图预测土壤流失严重程度图可以为半干旱地区给定和类似流域的决策者和管理者提供可靠的信息。