The USLE‐M and the USLE‐MM estimate event plot soil loss. In both models, the erosivity term is given by the runoff coefficient, Q_R, times the single‐storm erosion index, EI₃₀. In the USLE‐MM, Q_REI₃₀ is raised to an exponent b₁ > 1 whereas b₁ = 1 is assumed in the USLE‐M. Simple linear regression analysis can be applied to parameterize both models, but logarithmically transformed data have to be used for USLE‐MM. Parameterizing the USLE‐MM with nonlinear regression of untransformed data could be a more appropriate procedure. A statistical check of the two suggested models (USLE‐M and USLE‐MM), considering two alternative parameterization procedures for the USLE‐MM, was carried out for the Masse and Sparacia experimental stations, in Italy. The analysis showed that the USLE‐MM with the linear regression parameterization procedure was the only correctly specified model, that is, with normally distributed and homoscedastic residuals. With this model, the normalized soil loss, A_e,N, prediction error did not exceed a factor of 5.7 for A_e,N > 17.3 Mg ha⁻¹ at Masse and of 3.5 for A_e,N > 27.5 Mg ha⁻¹ at Sparacia. Stable values of b₁ require inclusion of high A_e,N values in the calibration dataset. Using a common exponent b₁ for the two stations increases the practical interest for the model and did not imply a substantial worsening of the model performances, especially for the highest soil loss values. Development of a USLE‐MM‐type model having a wide applicability appears possible, and data from other experimental sites could make this conclusion more robust.

中文翻译：

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对USLE‐M和USLE‐MM进行统计检查以预测两个意大利环境中的裸地土壤流失

USLE‐M和USLE‐MM估计事件绘制土壤流失图。在这两个模型中，侵蚀性项均由径流系数Q _R乘以一次暴雨侵蚀指数EI ₃₀得出。在USLE‐MM中，Q _R EI ₃₀升高到指数b ₁  > 1而b ₁ 在USLE‐M中假定= 1。可以使用简单的线性回归分析来对两个模型进行参数化，但是对数转换后的数据必须用于USLE-MM。使用未转换数据的非线性回归对USLE-MM进行参数化可能是一个更合适的过程。在意大利的Masse和Sparacia实验站进行了对两个建议模型（USLE-M和USLE-MM）的统计检查，其中考虑了USLE-MM的两种备选参数化程序。分析表明，采用线性回归参数化程序的USLE-MM是唯一正确指定的模型，即具有正态分布和均方差的残差。利用该模型，归一化土壤流失量A _e，N，预测误差不超过5.7倍对甲_é，Ñ  > 17.3镁公顷^-1在Masse的和的3.5甲_é，Ñ  > 27.5镁公顷^-1在Sparacia。b _1的稳定值需要在校准数据集中包含较高的A _e，N值。使用共同指数b ₁这两个站的使用增加了该模型的实用价值，并不意味着该模型的性能会显着下降，尤其是对于最大的土壤流失值而言。具有广泛适用性的USLE-MM型模型的开发似乎是可能的，并且来自其他实验站点的数据可以使这一结论更可靠。