Testing the Universal Soil Loss Equation‐MB equation in plots in Central and South Italy

Abstract Planning soil conservation strategies requires predictive techniques at event scale because a large percentage of soil loss over a long‐time period is due to relatively few large storms. Considering runoff is expected to improve soil loss predictions and allows relation of the process‐oriented approach with the empirical one, furthermore, the effects of detachment and transport on soil erosion processes can be distinguished by a runoff component. In this paper, the empirical model USLE‐MB (USLE‐M based), including a rainfall‐runoff erosivity factor in which the event rainfall erosivity index EI 30 of the Universal Soil Loss Equation (USLE) multiplies the runoff coefficient Q R raised to an exponent b 1 > 1 is tested by the measurements carried out for the Masse (10 plots) and Sparacia (22 plots) experimental stations in Italy. For the Masse experimental station, an exponent b 1 > 1 was also estimated by tests carried out by a nozzle‐type rainfall simulator. For each experimental site in fallow conditions, the effect of the sample size of the plot soil loss measurements on the estimate of the b 1 coefficient was also studied by the extraction of a fixed number N of randomly obtained pairs of the normalized soil loss and runoff coefficient. The analysis showed that the variability of b 1 with N is low and that 350 pairs are sufficient to obtain a stable estimate of b 1 . A total of 1,262 soil loss data were used to parameterize the model both locally and considering the two sites simultaneously. The b 1 exponent varied between the two sites (1.298–1.520), but using a common exponent (1.386) was possible. Using a common b 1 exponent for the two experimental areas increases the practical interest for the model and allows the estimation of a baseline component of the soil erodibility factor, which is representative of the at‐site soil intrinsic and quasi‐static properties. Development of a single USLE‐MB model appears possible, and sampling other sites is advisable to develop a single USLE‐MB model for general use.