Development of an appropriate procedure for estimation of RUSLE EI30 index and preparation of erosivity maps for Pulau Penang in Peninsular Malaysia

Shamshad, A.; Azhari, Mirza; Isa, Mohamed Hasnain; Hussin, W.M.A. Wan; Parida, B.P.

doi:10.1016/j.catena.2007.08.002

Cited by 90 publications

(54 citation statements)

References 16 publications

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“…Loureiro and Coutinho (2001) used 27 years of daily rainfall data from Portugal and the R/USLE method of calculating EI30 to construct an equation that uses the number of days that received over 10.0 mm of rainfall and the amount of rainfall per month when the day's rainfall exceeded 10.0 mm. The Loureiro and Coutinho (2001) equation was modified by Shamshad et al (2008) using long-term rainfall data in Malaysia and used to construct a regression equation relating monthly rainfall and annual rainfall with the R-factor. The 20 equation was modified because the original Loureiro and Coutinho (2001) equation was developed in Portugal, and the aim of Shamshad et al (2008) was to modify it to suit the climatic conditions of tropical Malaysia.…”

Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

“…The Loureiro and Coutinho (2001) equation was modified by Shamshad et al (2008) using long-term rainfall data in Malaysia and used to construct a regression equation relating monthly rainfall and annual rainfall with the R-factor. The 20 equation was modified because the original Loureiro and Coutinho (2001) equation was developed in Portugal, and the aim of Shamshad et al (2008) was to modify it to suit the climatic conditions of tropical Malaysia. Similarly, Sholagberu et al (2016) used 23 years of daily rainfall data to create a regression equation relating annual rainfall and the R-factor for the highlands of Malaysia.…”

Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

“…Diodato (2004) in Italy and Kavian et al (2011) used the R/USLE method to calculate storm energy and summed these up per month 10 and season to obtain R-factors. Other studies used daily and monthly rainfall to calculate monthly R-factors and combine them for seasonal R-factors (Alexandridis et al, 2015;Kavian et al, 2011;López-Vicente et al, 2008;Lu et al, 2003;Panagos et al, 2015d;Shamshad et al, 2008). The results of these studies focused on identifying high and low periods of the landscape's vulnerability to soil erosion, depending on combinations of rainfall intensity and land cover.…”

Section: Seasonal Erosion Vulnerabilitymentioning

confidence: 99%

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A review of the (Revised) Universal Soil Loss Equation (R/USLE): with a view to increasing its global applicability and improving soil loss estimates

Benavidez¹,

Jackson²,

Maxwell³

et al. 2018

Preprint

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Abstract. Soil erosion is a major problem around the world because of its effects on soil productivity, nutrient loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind soil erosion, we can more easily identify erosion-prone areas within a landscape and use land management and other strategies to effectively manage the problem. Soil erosion models have been used to assist in this task. One of the most commonly used soil erosion models is 10 the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviewed the different components of USLE and RUSLE etc., and analysed how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with R/USLE and related approaches. We investigate some of the limitations of R/USLE, such as issues 15 in data-sparse regions, its inability to account for soil loss from gully erosion or mass wasting events, and that it does not predict sediment pathways from hillslopes to water bodies. These limitations point to several future directions for R/USLE studies: incorporating soil loss from other types of soil erosion, estimating soil loss at sub-annual temporal scales, and using consistent units for future literature. These recommendations help to improve the applicability of the R/USLE in a range of geoclimatic regions with varying data availability, and at finer spatial and temporal scales for scenario analysis. 20

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Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

Section: Rainfall Erosivity Factor (R)mentioning

confidence: 99%

Section: Seasonal Erosion Vulnerabilitymentioning

confidence: 99%

See 1 more Smart Citation

A review of the (Revised) Universal Soil Loss Equation (R/USLE): with a view to increasing its global applicability and improving soil loss estimates

Benavidez¹,

Jackson²,

Maxwell³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…The rainfall erosivity factor (USLE R factor) is considered very important in the estimation of soil loss (Shamshad et al, 2008). The value of R corresponds to the average of the annual erosivity EI30 index, and is evaluated by using a long pluviographic data series (Cassol et al, 2008;Silva et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Erosive rainfall in the Rio do Peixe Valley: Part III - Risk of extreme events

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Pola

Ladwig

et al. 2018

Rev. bras. eng. agríc. ambient.

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Understanding the risks of extreme events related to soil erosion is important for adequate dimensioning of erosion and runoff control structures. The objective of this study was to determine the rainfall erosivity with different return periods for the Valley of the Rio do Peixe in Santa Catarina state, Brazil. Daily pluviographic data series from 1984 to 2014 from the Campos Novos, and Videira meteorological stations and from 1986 to 2014 from the Caçador station were used. The data series of maximum annual rainfall intensity in 30 min, maximum annual erosive rainfall, and total annual erosivity were analyzed for each station. The Gumbel-Chow distributions were adjusted and their adhesions were evaluated by the Kolmogorov-Smirnov test at a significance level of 5%. The Gumbel-Chow distribution was adequate for the estimation of all studied variables. The mean annual erosivity corresponds to the return period of 2.25 years. The data series of the annual maximum individual rainfall erosivity coefficients varied from 47 to 50%.

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“…Many authors (Qi et al 2000, Silva 2004, Moreira et al 2006, yin et al 2007, Men et al 2008, Shamsad et al 2008, Angulo-Martínez et al 2009, Silva et al 2010a, b, Alves Sobrinho et al 2011) used spatial interpolation techniques like "inverse distance weighted", "kriging" and "artificial neural networks" (ANNs) to create maps representing spatial distribution of R values. Since no single interpolation method among those available for spatial interpolation of R factor is optimal for all regions and all indices, it is very important to compare the results obtained through different methods, applied to each set of data (Goovaerts 1999, Beguería and VicenteSerrano 2006, Angulo-Martínez et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks

Cecílio

Moreira

Pezzopane

et al. 2013

An. Acad. Bras. Ciênc.

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The rainfall parameter that expresses the capacity to promote soil erosion is called rainfall erosivity (R), and is commonly represented by the indexes EI 30 and KE>25. The calculations of these indexes requires pluviographical records, that are difficult to obtain in Brazil. This paper describes the use of synthetic rainfall series to compute EI 30 and KE>25 in Espírito Santo State (Brazil). Artificial neural networks (ANNs) were also developed to spatially interpolate R values in Espírito Santo. EI 30 and KE>25 indexes values were close to those calculated on a homogeneous area according to the similarity of rainfall distribution; indicating the applicability of the use of synthetic rainfall series to estimate the R factor. ANNs had a better performance than Inverse Distance Weighted and Kriging to spatially interpolate rainfall erosivity values in the State of Espírito Santo.

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Development of an appropriate procedure for estimation of RUSLE EI30 index and preparation of erosivity maps for Pulau Penang in Peninsular Malaysia

Cited by 90 publications

References 16 publications

A review of the (Revised) Universal Soil Loss Equation (R/USLE): with a view to increasing its global applicability and improving soil loss estimates

A review of the (Revised) Universal Soil Loss Equation (R/USLE): with a view to increasing its global applicability and improving soil loss estimates

Erosive rainfall in the Rio do Peixe Valley: Part III - Risk of extreme events

Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks

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