Comparing theoretically supported rainfall‐runoff erosivity factors at the Sparacia (South Italy) experimental site

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8 Citazioni (Scopus)

Abstract

Interpreting rainfall‐runoff erosivity by a process‐oriented scheme allows to conjugate thephysical approach to soil loss estimate with the empirical one. Including the effect of runoff inthe model permits to distinguish between detachment and transport in the soil erosion process.In this paper, at first, a general definition of the rainfall‐runoff erosivity factor REFe includingthe power of both event runoff coefficient QR and event rainfall erosivity index EI30 of theUniversal Soil Loss Equation (USLE) is proposed. The REFe factor is applicable to all USLE‐basedmodels (USLE, Modified USLE [USLE‐M] and Modified USLE‐M [USLE‐MM]) and it allows todistinguish between purely empirical models (e.g., Modified USLE‐M [USLE‐MM]) and thosesupported by applying theoretical dimensional analysis and self‐similarity to Wischmeierand Smith scheme. This last model category includes USLE, USLE‐M, and a new model, namedUSLE‐M based (USLE‐MB), that uses a rainfall‐runoff erosivity factor in which a power of runoffcoefficient multiplies EI30. Using the database of Sparacia experimental site, the USLE‐MB isparameterized and a comparison with soil loss data is carried out. The developed analysis showsthat USLE‐MB (characterized by a Nash–Sutcliffe Efficiency Index NSEI equal to 0.73 and a rootmean square error RMSE = 11.7 Mg ha−1) has very similar soil loss estimate performances ascompared with the USLE‐M (NSEI = 0.72 and RMSE = 12.0 Mg ha−1). However, the USLE‐MByields a maximum discrepancy factor between predicted and measured soil loss values (176) thatis much lower than that of USLE‐M (291). In conclusion, the USLE‐MB should be preferred in thecontext of theoretically supported USLE type models.
Lingua originaleEnglish
pagine (da-a)507-515
Numero di pagine9
RivistaHydrological Processes
Volume32
Stato di pubblicazionePublished - 2018

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

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