At first, in this paper a general definition of the event rainfall-runoff erosivity factor for the USLE-based models, REFe = (QR)b1(EI30)b2, in which QR is the event runoff coefficient, EI30 is the single-storm erosion index and b1 and b2 are coefficients, was introduced. The rainfall-runoff erosivity factors of the USLE (b1 = 0, b2 = 1), USLE-M (b1 = b2 = 1), USLE-MB (b1 ≠ 1, b2 = 1), USLE-MR (b1 = 1, b2 ≠ 1), USLE-MM (b1 = b2 ≠ 1) and USLE-M2 (b1 ≠ b2 ≠ 1) can be defined using REFe. Then, the different expressions of REFe were simultaneously tested against a dataset of normalized bare plot soil losses, AeN, collected at the Sparacia (south Italy) site. As expected, the poorest AeN predictions were obtained with the USLE. A distinction was made among the four power-type models since the fitting to the data was poor with the USLE-MR as compared with the other three models. Estimating two distinct exponents (one for EI30 and another for QR, USLE-M2) instead of a single exponent (USLE-MB, USLE-MR, USLE-MM) did not appreciably improve soil loss prediction. The USLE-MB and the USLE-MM were the best performing models.

Bagarello V., Ferro V., Pampalone V. (2020). A Comprehensive Check of Usle-Based Soil Loss Prediction Models at the Sparacia (South Italy) Site. In A. Coppola, G.C. Di Renzo, G. Altieri, P. D'Antonio (a cura di), Innovative Biosystems Engineering for Sustainable Agriculture, Forestry and Food Production (pp. 3-11). Springer [10.1007/978-3-030-39299-4_1].

A Comprehensive Check of Usle-Based Soil Loss Prediction Models at the Sparacia (South Italy) Site

Bagarello V.
Membro del Collaboration Group
;
Ferro V.
Membro del Collaboration Group
;
Pampalone V.
Membro del Collaboration Group
2020-01-01

Abstract

At first, in this paper a general definition of the event rainfall-runoff erosivity factor for the USLE-based models, REFe = (QR)b1(EI30)b2, in which QR is the event runoff coefficient, EI30 is the single-storm erosion index and b1 and b2 are coefficients, was introduced. The rainfall-runoff erosivity factors of the USLE (b1 = 0, b2 = 1), USLE-M (b1 = b2 = 1), USLE-MB (b1 ≠ 1, b2 = 1), USLE-MR (b1 = 1, b2 ≠ 1), USLE-MM (b1 = b2 ≠ 1) and USLE-M2 (b1 ≠ b2 ≠ 1) can be defined using REFe. Then, the different expressions of REFe were simultaneously tested against a dataset of normalized bare plot soil losses, AeN, collected at the Sparacia (south Italy) site. As expected, the poorest AeN predictions were obtained with the USLE. A distinction was made among the four power-type models since the fitting to the data was poor with the USLE-MR as compared with the other three models. Estimating two distinct exponents (one for EI30 and another for QR, USLE-M2) instead of a single exponent (USLE-MB, USLE-MR, USLE-MM) did not appreciably improve soil loss prediction. The USLE-MB and the USLE-MM were the best performing models.
Settore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-Forestali
978-3-030-39298-7
978-3-030-39299-4
Bagarello V., Ferro V., Pampalone V. (2020). A Comprehensive Check of Usle-Based Soil Loss Prediction Models at the Sparacia (South Italy) Site. In A. Coppola, G.C. Di Renzo, G. Altieri, P. D'Antonio (a cura di), Innovative Biosystems Engineering for Sustainable Agriculture, Forestry and Food Production (pp. 3-11). Springer [10.1007/978-3-030-39299-4_1].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/416595
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