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Statistical check of USLE‐M and USLE‐MM to predict bare plot soil loss in two Italian environments
Author(s) -
Bagarello Vincenzo,
Ferro Vito,
Giordano Giuseppe,
Mannocchi Francesco,
Todisco Francesca,
Vergni Lorenzo
Publication year - 2018
Publication title -
land degradation and development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 81
eISSN - 1099-145X
pISSN - 1085-3278
DOI - 10.1002/ldr.3072
Subject(s) - universal soil loss equation , mathematics , regression analysis , linear regression , surface runoff , erosion , plot (graphics) , statistics , soil loss , soil science , hydrology (agriculture) , environmental science , geology , geotechnical engineering , ecology , paleontology , biology
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, EI 30 . In the USLE‐MM, Q R EI 30 is raised to an exponent b 1 > 1 whereas b 1 = 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 b 1 require inclusion of high A e , N values in the calibration dataset. Using a common exponent b 1 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.