Estimation of rainfall erosivity using 5- to 60-minute fixed-interval rainfall data from China

Yin, Shuiqing; Xie, Yonghong; Nearing, M. A.; Wang, C.

doi:10.1016/j.catena.2006.10.011

Cited by 103 publications

(76 citation statements)

References 13 publications

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“…By linear regression, we obtained the regression slope, i.e., the coefficient c as 1.793 (R 2 = 0.95). This slope value is close to the results obtained by Yin et al (2007) with breakpoint data taken from continuous rain gauge charts, e.g., the value was 1.811 at Zizhou station in the semi-arid northern Shaanxi of our study area. Adjusted with this c value, the storm's erosivity from 60-min rainfall data was greatly improved (Fig.…”

Section: Model Validation Resultssupporting

confidence: 91%

Spatiotemporal variation and trends in rainfall erosivity in China's dryland region during 1961–2012

Yang

2015

CATENA

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This study evaluated rainfall erosivity and its changes in China's dryland region during 1961-2012, using a daily rainfall erosivity model, ArcGIS based spatial interpolation tools, and the Mann-Kendall and Sen's methods. It was found that mean rainfall erosivity was 2-4098 MJ mm ha −1 h −1 a −1 in the region, with the regional average of 794 MJ mm ha −1 h −1 a −1 , showing a significant increase trend accordingly with an increase in rainfall from northwest to southeast. Rainfall erosivity varied annually with the CV of 32.4-491.3%, and varied seasonally with about three-quarter distributed in summer. During 1961-2012, a statistically significant increase/decrease trend in rainfall erosivity was detected only for 24/10 stations or about 4.6/0.4% of the region, and a nonsignificant increase/decrease trend was observed for 32/45 stations or 16.5/10.0% of the region at the 50 to 90% confidence level. No statistically confidential trend was found for other 126 stations or 68.5% of the region. At regional level, mean annual erosivity of the arid zone indicated an upward trend, while the sub-humid zone a downward, but the semi-arid zone generally had no evidential trend. Comparing the 26 year's averages during 1987-2012 and 1961-1986, mean erosivity of the arid zone increased by 17.4%, the sub-humid zone decreased by 6.1%, and the semi-arid zone reduced slightly by 0.45%. The results suggest that change in the rainfall characteristics during the past half century had no significant influence on water erosion in the majority of the region, but it could probably induce an increase of water erosion risk in parts of the drier west and a slight decrease in parts of the wetter east including the Loess Plateau.

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Section: Model Validation Resultssupporting

confidence: 91%

Spatiotemporal variation and trends in rainfall erosivity in China's dryland region during 1961–2012

Yang

2015

CATENA

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“…The results of the annual calibration showed that the calibration factors in Europe are closer to the smallest values obtained by Renard et al [4]. The estimated calibration factors match well with the findings in Sicily [17], Calabria [20], United States [6,21], and they are 10%-20% larger than the ones estimated in China [16]. However, not only the precipitation regimes of China (e.g., [22,23]) are remarkably different from the corresponding European ones [24], but also the general physical, climatic, and hydrological features are dissimilar compared to European ones ( [25,26]); thus, similar calibration factors are not to be expected.…”

Section: Monthly Calibration Factors For Different Temporal Resolutionsupporting

confidence: 82%

“…As expected, the calculated rainfall erosivity values decrease as the rainfall measurement interval increases. According to our analyses, the relationship between time resolution and conversion factors is exponential, different from past assessments that proposed linear functions [4,16]. The coefficients of different resolutions for monthly rainfall erosivity allow normalizing the monthly R-factor values to a common 30-min resolution for all the REDES The mean MED reaches the largest values (close to 3.67 during summer) in the Eastern Alps (Slovenia, North-East Italy) and Croatia; in Central European countries (Austria, Slovakia, and Hungary), the mean MED is larger than 1.65 during summer (Figure 8b).…”

Section: Discussioncontrasting

confidence: 69%

“…In the United States, Renard et al [4] developed a range of coefficients varying from 1.08 to 3.16 in order to "transfer" R-factor values calculated on an hourly basis to 30 min R-factor values based on measurements from 713 stations. Yin et al [16] calculated the conversion factors between different resolutions based on measurements of 5 stations in China. Agnese et al [17] also estimated the R-factor at 3 different resolutions (5-min, 15-min, and 60-min) for 7 stations in Sicily (Italy).…”

Section: Calibration Of Monthly R-factors Calculated From Different Tmentioning

confidence: 99%

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Monthly Rainfall Erosivity: Conversion Factors for Different Time Resolutions and Regional Assessments

Panagos

Borrelli

Spinoni

et al. 2016

Water

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As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall erosivity has been added to REDES. Rainfall erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory

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“…Yet there is a need for developing models for application in all areas of the world in order to produce erosivity maps that can be used for evaluating soil erosion rates (e.g., Sadeghi et al, 2011, Sadeghi andTavangar, 2015;Oliveira et al, 2012;Panagos et al, 2015;Zhang and Fu, 2003). For that reason many efforts have been undertaken to estimate rainfall erosivity by using daily (Richardson et al, 1983;Yu, 1998;Capolongo et al, 2008;Yin et al, 2007;Zhang et al, 2002a, b;Xie et al, 2001Xie et al, , 2015, monthly (Arnoldus, 1977;Renard and Freimund, 1994;Yu and Rosewell, 1996;Ferro et al, 1999;Wu, 1994;Zhou et al, 1995), or annual rainfall data (Lo et al, 1985;Renard and Freimund, 1994;Yu and Rosewell, 1996;Bonilla and Vidal, 2011;Zhang and Fu, 2003;Wang, 1987;Sun, 1990). Generally the technique has been to develop a simple empirical relationship between erosivity and coarse resolution rainfall based on limited finer resolution data and then to extend the analyses to wider areas and longer periods with coarser temporal resolution rainfall data (Angulo-Martinez and Begueria, 2012;Ma et al, 2014;Ramos and Duran, 2014;Sanchez-Moreno et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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

Yin

Xie

Liu

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Rainfall erosivity is the power of rainfall to cause soil erosion by water. The rainfall erosivity index for a rainfall event (energy-intensity values -EI 30 ) is calculated from the total kinetic energy and maximum 30 min intensity of individual events. However, these data are often unavailable in many areas of the world. The purpose of this study was to develop models based on commonly available rainfall data resolutions, such as daily or monthly totals, to calculate rainfall erosivity. Eleven stations with 1 min temporal resolution rainfall data collected from 1961 through 2000 in the eastern half of China were used to develop and calibrate 21 models. Seven independent stations, also with 1 min data, were utilized to validate those models, together with 20 previously published equations. The models in this study performed better or similar to models from previous research to estimate rainfall erosivity for these data. Using symmetric mean absolute percentage errors and Nash-Sutcliffe model efficiency coefficients, we can recommend 17 of the new models that had model efficiencies ≥ 0.59. The best prediction capabilities resulted from using the finest resolution rainfall data as inputs at a given erosivity timescale and by summing results from equations for finer erosivity timescales where possible. Results from this study provide a number of options for developing erosivity maps using coarse resolution rainfall data.

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Estimation of rainfall erosivity using 5- to 60-minute fixed-interval rainfall data from China

Cited by 103 publications

References 13 publications

Spatiotemporal variation and trends in rainfall erosivity in China's dryland region during 1961–2012

Spatiotemporal variation and trends in rainfall erosivity in China's dryland region during 1961–2012

Monthly Rainfall Erosivity: Conversion Factors for Different Time Resolutions and Regional Assessments

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

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