Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil release_jqduiynrtjcy5lymkoa73coyxy

Abstract

ABSTRACT Rainfall erosivity (R factor) is one of the six factors of the Universal Soil Loss Equation, being calculated based on the product of rainfall kinetic energy multiplied by its 30-minute maximum intensity. However, the lack of detailed and reliable rainfall data in many parts of the world has driven the use of other methods to estimate rainfall erosivity based on daily, monthly or annual data. These methods still need to be assessed to determine if their estimates are consistent with the standard method for calculating rainfall erosivity. This study aimed to select a consistent method for such replacement in Brazilian conditions without access the rainfall intensity data. The tested methods included: modified Fournier, MF; modified Fournier by Zhang, MF-Z; modified Fournier by Men, MF-M; Rainfall Disaggregation, RD; TRMM Satellite with modified Fournier coefficient, TRMM-F; and TRMM Satellite with monthly rainfall, TRMM-M. The rainfall data were obtained from the USP Meteorological Station, referring to the period from 2009 to 2015. The analyses were performed according to the Additive Main effects and Multiplicative Interaction (AMMI) model and Scott-Knott statistical tests. Considering the 1:1 line, all methods had a good adjustment, presenting similar behavior in relation to the standard method. The methods behaved differently for monthly and annual periods. The MF method proved to be capable of consistently replacing the standard method in all aforementioned situations. Considering the driest period, any method can be used. For annual rainfall erosivity estimation, the RD, MF, TRMM-F and TRMM-M methods can be applied; highlighting that the TRMM-based methods are optimal for locations without on-site rain gauges. Additionally, it was computed that the modified Fournier by Men and the modified Fournier by Zhang underestimated and overestimated the rainfall erosivity, respectively.
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