Rainfall erosivity: An historical review

Nearing, M. A.; Yin, Shuiqing; Borrelli, Pasquale; Polyakov, Viktor O.

doi:10.1016/j.catena.2017.06.004

Cited by 210 publications

(119 citation statements)

References 27 publications

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“…However, daily EI 30 estimated using RUSLE was underestimated by 8.20% in comparison to that from RUSLE2 (Figure e). This underestimation agrees with Nearing et al () and Foster et al (), who believed that the KE–I relationship from RUSLE underestimates the rainfall erosivity by approximately 10%. Despite changing the coefficient to 0.082 instead of the commonly applied 0.05 in RUSLE2 (Foster et al, ), the radar‐derived daily EI 30 was still underestimated by 11% (Figure f) compared with the gauge‐estimated EI 30 .…”

Section: Discussionsupporting

confidence: 91%

“…The difference of these two equations is that the revised exponent value (0.082) is slightly higher than the counterpart value (0.05) of Brown and Foster (). It is believed that this kinetic energy and intensity (KE–I) coefficient (Brown & Foster, ) underestimates the rainfall erosivity by about 10% (Nearing, Yin, Borrelli, & Polyakov, ; Renard & Freimund, ). Thus, in this study, we compared daily EI 30 computed from Brown and Foster (; RUSLE) with its revised version (Foster et al, ; RUSLE2).…”

Section: Methodsmentioning

confidence: 99%

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Estimation of event‐based rainfall erosivity from radar after wildfire

Zhu

Yang

et al. 2018

Land Degrad Dev

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Rainfall erosivity impacts all stages of hillslope erosion processes and is an important factor (the ‘R factor’) in the Revised Universal Soil Loss Equation. It is estimated as the average annual value of the sum of all erosive events (EI30) over a period of many years. For each storm event, the EI30 value is the product of storm energy, E in MJ ha−1, and peak 30‐min rainfall intensity (I30, mm hr−1). Previous studies often focused on estimation of the R factor for prediction of mean annual or long‐term soil losses. However, many applications require EI30 values at much higher temporal resolution, such as postfire soil erosion monitoring, which requires a time step at storm events or on a daily basis. In this study, we explored the use of radar rainfall data to estimate the storm event‐based EI30 after a severe wildfire in Warrumbungle National Park in eastern Australia. The radar‐derived rainfall data were calibrated against 12 tipping bucket rain gauges across an area of 239 km2 and subsequently used to produce a time series of rainfall erosivity maps at daily intervals since the wildfire in January 2013. The radar‐derived daily rainfall showed good agreement with the gauge measurements (R2 > 0.70, Ec = 0.66). This study reveals great variation in EI30 values ranging from near zero to 826.76 MJ·mm·ha−1·hr−1 for a single storm event. We conclude that weather radar rainfall data can be used to derive timely EI30 and erosion information for fire incident management and erosion control. The methodology developed in this study is generic and thus readily applicable to other areas where weather radar data are available.

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Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Estimation of event‐based rainfall erosivity from radar after wildfire

Zhu

Yang

et al. 2018

Land Degrad Dev

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“…The R factor represents the ability of rainfall to cause soil erosion [66]. In RUSLE, the rainfall erosivity is usually calculated as an average of the long-term mean individual storm erosivity index (EI) values measured over 20 years to accommodate apparent cyclical rainfall patterns [67,68].…”

Section: Rainfall Erosivity (R) Factormentioning

confidence: 99%

Land Use Change Impacts on Water Erosion in Rwanda

Nambajimana

Zhou

et al. 2019

Sustainability

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Rwanda has experienced accelerated soil erosion as a result of unsustainable human activities and changes in land use. Therefore, this study aimed at applying the RUSLE (Revised Universal Soil Loss Equation) model using GIS (Geographical Information System) and remote sensing to assess water erosion in Rwanda, focusing on the erosion-prone lands for the time span 2000 to 2015. The estimated mean annual soil losses were 48.6 t ha −1 y −1 and 39.2 t ha −1 y −1 in 2000 and 2015, respectively, resulting in total nationwide losses of approximately 110 and 89 million tons. Over the 15 years, 34.6% of the total area of evaluated LULC (land use/land cover) types have undergone changes. The highest mean soil loss of 91.6 t ha −1 y −1 occurred in the area changing from grassland to forestland (0.5%) while a mean soil loss of 10.0 t ha −1 y −1 was observed for grassland converting to cropland (4.4%). An attempt has been made to identify the embedded driving forces of soil erosion in Rwanda. As a result, we found that mean soil loss for Rwanda's districts in 2015 was significantly correlated with poverty (r = 0.45, p = 0.013), increased use of chemical fertilizers (r = 0.77, p = 0.005), and especially was related to extreme poverty (r = 0.77, p = 0.000). The soil conservation scenario analysis for Rwanda's cropland in 2015 revealed that terracing could reduce the soil loss by 24.8% (from 14.6 t ha −1 y −1 to 11.7 t ha −1 y −1 ). Most importantly, the study suggests that (1) terracing integrated with mulching and cover crops could effectively control water erosion while ameliorating soil quality and fertility, and (2) reforestation schemes targeting the rapid-growing tree species are therefore recommended as an important feature for erosion control in the study area.

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“…The deviation of the USLE from the RUSLE2 based on data in Italy varied from 0.2 to 15.6% with a mean of 5.7%, which is greater than that based on data in China (Tables 2 and 3). The reason for the lower computed erosivity values for the RUSLE compared with the RUSLE2 is evident in the graph of unit rainfall energy vs. rainfall intensity (Nearing et al, 2017). The underestimation depends on the local dynamics of rainfall intensities.…”

Section: Development Of the R Factor In New Versions Of The Uslementioning

confidence: 99%

Rainfall Erosivity: An Overview of Methodologies and Applications

Yin

Nearing

Borrelli

et al. 2017

Vadose Zone Journal

Self Cite

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The rainfall erosivity factor (R factor) is one of six erosion factors in the Universal Soil Loss Equation (USLE), which together reflect the combined effects that cause soil loss by rill and interrill erosion on hillslopes by precipitation. It is defined as the summation of event EI 30 (the product of kinetic energy and maximum 30-min intensity) over a year and calculated based on rainfall hyetograph data. The R factor was developed in the various versions of the USLE, including the definition of the individual event and the criterion for selecting events used in the calculation, the equation used to estimate the unit kinetic energy from the rainfall intensity, the estimation of erosivity from the snowmelt and thaw, and erosivity mapping. Most research on rainfall erosivity deals with any of three aspects: developing estimation methods for deriving erosivity from courser resolution rainfall data (such as daily, monthly, and annual) but with greater spatial and temporal coverages than those from hyetograph data; preparing erosivity maps including those for annual average, monthly, and 10-yr recurrence erosivity; and documenting temporal trends in erosivity. Rainfall erosivity research on these three aspects is summarized to provide a greater understanding of the R factor.

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Rainfall erosivity: An historical review

Cited by 210 publications

References 27 publications

Estimation of event‐based rainfall erosivity from radar after wildfire

Estimation of event‐based rainfall erosivity from radar after wildfire

Land Use Change Impacts on Water Erosion in Rwanda

Rainfall Erosivity: An Overview of Methodologies and Applications

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