Abstract
Soil erosion caused by intense rainfall events is one of the major problems affecting agricultural and forest ecosystems. The Universal Soil Loss Equation (USLE) is probably the most adopted approach for rainfall erosivity estimation, but in order to be properly employed it needs high resolution rainfall data which are often unavailable. In this case, empirical formulas, employing aggregated rainfall data, are commonly used. In this work, we select 12 empirical formulas for the estimation of the USLE rainfall erosivity in order to assess their reliability. Moreover, we used a Stochastic Rainfall Generator (SRG) to simulate a long and high-resolution rainfall time series with the aim of assessing its application to rainfall erosivity estimations. From the analysis, performed in the Rieti province of Central Italy, we identified three equations which seem to provide better results. Moreover, the use of the selected SRG seems promising and it could help in solving the problem of hydrological data scarcity and consequently guarantee major accuracy in soil erosion estimation.
Highlights
Soil erosion caused by intense rainfall events is recognized as one of the major problems affecting agricultural and forest ecosystems [1]
One pivotal contribution can be represented by the studies of Wischmeier and Smith [5,6], which gave birth to the worldwide known Universal Soil Loss Equation (USLE), later rearranged in the revised USLE (RUSLE) [7,8]
The application of the Stochastic Rainfall Generator (SRG) allows the synthetic generation of a high-resolution time series that can be used for estimating the rainfall erosivity, employing the same empirical formulas of previous point 1), or using the original USLE formulation
Summary
Soil erosion caused by intense rainfall events is recognized as one of the major problems affecting agricultural and forest ecosystems [1]. The application of the SRG allows the synthetic generation of a high-resolution time series that can be used for estimating the rainfall erosivity, employing the same empirical formulas of previous point 1), or using the original USLE formulation. Such application will allow assessment if the proposed SRG can be a suitable alternative for the estimation of soil erosion in case of rainfall data scarcity. The present manuscript is organized as follows: in chapter 2, materials and methods are dTeshceripbreeds.eInntpmaratnicuuslcarri,ptthiespoarrgaagnriazpehddaessfcorlilboews tsh: einseSleeccttieodnc2a,sme sattuedriya,lsreapnodrtsmtehtehods ainrveedsetisgcartiebdedf.orInmpulaarst,icaunladr, itlhluestpraarteasgrtahpehpdreospcorsibedes SthReGs.eIlenctcehdapcatesre s3t,urdeys,urletspoarrtes the ipnrveesestnitgeadteadndfodrmiscuulsasse,da,nwdhilillue sintractheaspttheer 4prthope ocosendclSuRsiGon. sInarSeecretipoonrt3e,dr.esults are presented and discussed, while in Section 4 the conclusions are reported
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