Search results for "plot soil loss"
showing 3 items of 3 documents
Predicting soil loss in central and south Italy with a single USLE-MM model
2018
Purpose: The USLE-MM estimates event normalized plot soil loss, Ae,N, by an erosivity term given by the runoff coefficient, QR, times the single-storm erosion index, EI30, raised to an exponent b1> 1. This modeling scheme is based on an expected power relationship, with an exponent greater than one, between event sediment concentration, Ce, and the EI30/Pe(Pe= rainfall depth) term. In this investigation, carried out at the three experimental sites of Bagnara, Masse, and Sparacia, in Italy; the soundness of the USLE-MM scheme was tested. Materials and methods: A total of 1192 (Ae,N, QREI30) data pairs were used to parameterize the model both locally and considering all sites simultaneously. …
Predicting soil loss on moderate slopes using an empirical model for sediment concentration
2011
Summary The objective of this investigation was to estimate event soil loss per unit area from bare plots in central and southern Italy using an empirical model for sediment concentration. The analysis was developed using data collected on bare plots differing in length (11–44 m) and slope (10–26%) at three Italian stations (Masse, Umbria; Caratozzolo, Calabria; Sparacia, Sicily). At first, an analysis was carried out, using the experimental data collected at Sparacia, to establish a relationship between sediment concentration and hydrological variables, such as runoff, rainfall amount and single storm erosion index. Then, an empirical model to estimate plot soil loss as a function of rainf…
Statistical check of USLE-M and USLE-MM to predict bare plot soil loss in two Italian environments
2018
The USLE-M and the USLE-MM estimate event plot soil loss. In both models, the erosivity term is given by the runoff coefficient, QR, times the single-storm erosion index, EI30. In the USLE-MM, QREI30is 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 Mass…