6533b830fe1ef96bd12965aa

RESEARCH PRODUCT

A comprehensive analysis of Universal Soil Loss Equation-based models at the Sparacia experimental area

subject

description

Improving Universal Soil Loss Equation (USLE)‐based models has large interest because simple and reliable analytical tools are necessary in the perspective of a sustainable land management. 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 and b₂ = 1), USLE‐M (b₁ = b₂ = 1), USLE‐MB (b₁ ≠ 1 and b₂ = 1), USLE‐MR (b₁ = 1 and 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 data set 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. The observed tendency of this model to overestimate small AₑN values and underestimate high AₑN values was reduced by introducing in the soil loss prediction model both QR and an exponent for the erosivity term. The fitting to the data was poor with the USLE‐MR as compared with the USLE‐MB and the USLE‐MM. Estimating two distinct exponents (USLE‐M2) instead of a single exponent (USLE‐MB, USLE‐MR, and USLE‐MM) did not appreciably improve soil loss prediction. The USLE‐MB and the USLE‐MM were recognized to be the best performing models among the possible alternatives, and they performed similarly with reference to both the complete data set and different sub‐data sets, only including small, intermediate, and severe erosion events. In conclusion, including the runoff coefficient in the soil loss prediction model is important to improve the quality of the predictions, but a great importance has to be paid to the mathematical structure of the model.