Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

Yin, Shuiqing; Xie, Yonghong; Liu, B.; Nearing, M. A.

doi:10.5194/hess-19-4113-2015

Cited by 79 publications

(58 citation statements)

References 30 publications

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“…High consistency with other studies indicates that the method used in this study (based on daily rainfall series) is reliable. This is further validated by the works Xie et al [47] and Yin et al [48], who calibrated and compared models suitable for estimating erosivity from daily rainfall data. The changing climate could be a strong factor influencing the trends and variability of rainfall erosivity and erosivity density in time and space.…”

Section: Discussionmentioning

confidence: 87%

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

2018

Atmosphere

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Soil erosion is one of the most critical environmental hazards in the world. Understanding the changes in rainfall erosivity (RE) and erosivity density (ED), as well as their affecting factors, at local and catchment scales in the context of climate warming is an important prerequisite of soil erosion prevention and soil loss risk assessment. The present study identified the variability and trends of RE and ED in terms of both time and space in the Ganjiang River catchment over the period of 1960-2012, and also analyzed and discussed the impact of climate change. The results show that RE and ED in the catchment had great monthly variations and high year-to-year variability. Both presented long-term increasing trends over the entire study period. The highest RE and ED were observed in June and in the eastern and northeast parts of the catchment, which indicated that June was the most susceptible month for soil erosion in this area and the lower reaches of the Ganjiang River was the riskiest area for soil erosion. Finally, the East Asian summer monsoon and climate change were highly correlated with changes in RE and ED.

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Section: Discussionmentioning

confidence: 87%

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

2018

Atmosphere

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“…Analysis from 18 weather stations with 1‐min precipitation data distributed across the central and eastern regions of China (the same data set used by Yin et al, 2015) showed that the behavior of the USLE was very similar to that of the RUSLE2 (the average deviation of the R factor for 18 stations is 0.4%). The RUSLE underestimated R factor values by about 9.3% (Table 2).…”

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

confidence: 95%

Rainfall Erosivity: An Overview of Methodologies and Applications

Yin

Nearing

Borrelli

et al. 2017

Vadose Zone Journal

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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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“…However, this kind of precipitation record is typically lacking for yearly global mapping in Asia and Africa. Some alternative methods that used monthly precipitation were applied as substitutes for the lack of 30-min rainfall intensity (Diodato & Bellocchi, 2007;Yin, Xie, Liu, & Nearing, 2015). As we did not intend to precisely estimate the absolute amount of erosion, the basic formula of Fournier index was used as follows (Cerretelli et al, 2018;Fenta et al, 2017;Markose & Jayappa, 2016; Parras-Alcantara, Lozano-Garcia, Keesstra, Cerda, & Brevik, 2016;Wu, Liu, & Ma, 2016).…”

Section: The Estimation Of the Components In Rusle Modelmentioning

confidence: 99%

Vulnerability assessment of the global water erosion tendency: Vegetation greening can partly offset increasing rainfall stress

Liu

et al. 2019

Land Degrad Dev

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Global water erosion is a grave threat to the sustainability of agriculture. Regional climate change may aggravate the threat of erosion, whereas global vegetation greening (an increasing trend in vegetation cover) may act as a mitigation to the threat. On the basis of the Revised Universal Soil Loss Equation, we assessed the vulnerability of the global water erosion during 1982–2015. A contribution index was built to describe the influences of rainfall erosivity and cover management on global water erosion vulnerability changes during 1982–2015. The research objective was to explore the spatial pattern of global water erosion vulnerability change in recent decades and to identify the factor that has contributed most to the vulnerability change, and three main results were obtained. (a) The global water erosion vulnerability increased over 51% of the surface during 1982–2015, and the surface with significant decreasing trends only accounted for 12% of the middle and highly sensitive areas. (b) Rainfall erosivity contributed more than cover management for absolute value except in the Amazon, Congo Basin, and Southeast Asia, whereas the contribution of cover management was increasing. (c) Vegetation greening can partly offset the stress caused by climate change. The global water erosion vulnerability decreased more than increased in sensitive area. Consequently, enhancing the vegetation growth in the highly sensitive water erosion region could reduce the erosion threat at large scale.

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Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

Cited by 79 publications

References 30 publications

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

Rainfall Erosivity: An Overview of Methodologies and Applications

Vulnerability assessment of the global water erosion tendency: Vegetation greening can partly offset increasing rainfall stress

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