Effect of time resolution of rainfall measurements on the erosivity factor in the USLE in China

Yue, Tianyu; Xie, Yonghong; Yin, Shuiqing; Yu, Bofu; Miao, Chiyuan; Wang, Wenting

doi:10.1016/j.iswcr.2020.06.001

Cited by 30 publications

(19 citation statements)

References 27 publications

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“…Data at 1 min intervals were collected from 62 stations in mainland China (Fig. 2; and were used in Yue et al, 2020a) of 2005-2016. The missing data in the effective years were assumed to be zero.…”

Section: Rainfall Datamentioning

confidence: 99%

“…Since hourly data aggregation of 1 min data and temporal variation in rainfall intensity is reduced, erosivity factor values computed with hourly data would be underestimated. Therefore, the R-factor and 1-in-10-year EI 30 values computed with hourly data need to be adjusted by multiplying conversion factors of 1.871 and 1.489 (SI units), which were fitted by 1 min rainfall data from 62 stations over mainland China (Yue et al, 2020a). Erosivity factors from daily data were also obtained as described in Sect.…”

Section: Calculation Of Rainfall Erosivity Using 1 Min Hourly and Dai...mentioning

confidence: 99%

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Rainfall erosivity mapping over mainland China based on high-density hourly rainfall records

Yue

Yin

Xie

et al. 2022

Earth Syst. Sci. Data

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Abstract. Rainfall erosivity quantifies the effect of rainfall and runoff on the rate of soil loss. Maps of rainfall erosivity are needed for erosion assessment using the Universal Soil Loss Equation (USLE) and its successors. To improve erosivity maps that are currently available, hourly and daily rainfall data from 2381 stations for the period 1951–2018 were used to generate new R-factor and 1-in-10-year event EI30 maps for mainland China (available at https://doi.org/10.12275/bnu.clicia.rainfallerosivity.CN.001; Yue et al., 2020b). One-minute rainfall data from 62 stations, of which 18 had a record length > 29 years, were used to compute the “true” rainfall erosivity against which the new R-factor and 1-in-10-year EI30 maps were assessed to quantify the improvement over the existing maps through cross-validation. The results showed that (1) existing maps underestimated erosivity for most of the south-eastern part of China and overestimated for most of the western region; (2) the new R-factor map generated in this study had a median absolute relative error of 16 % for the western region, compared to 162 % for the existing map, and 18 % for the rest of China. The new 1-in-10-year EI30 map had a median absolute relative error of 14 % for the central and eastern regions of China, compared to 21 % for the existing map (map accuracy was not evaluated for the western region where the 1 min data were limited); (3) the R-factor map was improved mainly for the western region, because of an increase in the number of stations from 87 to 150 and temporal resolution from daily to hourly; (4) the benefit of increased station density for erosivity mapping is limited once the station density reached about 1 station per 10 000 km2.

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Section: Rainfall Datamentioning

confidence: 99%

Section: Calculation Of Rainfall Erosivity Using 1 Min Hourly and Dai...mentioning

confidence: 99%

Rainfall erosivity mapping over mainland China based on high-density hourly rainfall records

Yue

Yin

Xie

et al. 2022

Earth Syst. Sci. Data

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“…6). To solve the discontinuity near the boundary area between the eastern and western part of China caused by the difference in correction factors, a buffer was used for each region following the method outlined in Yue et al (2020a).…”

Section: Correction Factors For Rainfall Erosivity Values Estimated Umentioning

confidence: 99%

Rainfall erosivity estimation using gridded daily precipitation datasets

Wang

Yin

Yue

et al. 2020

Preprint

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Abstract. Rainfall erosivity is one of the most important factors incorporated into the empirical soil erosion models USLE (Universal Soil Loss Equation) and RUSLE (Revised Universal Soil Loss Equation). Gridded precipitation datasets have been widely used in the estimation of rainfall erosivity, whereas biases due to scale differences between gridded data and gauge data have been ignored. Based on daily precipitation observations from over 2000 stations in China, as well as four widely used gauge-based gridded daily precipitation datasets, CPC, GPCC, CN05.1 and NMIC, this study compared the probability density functions (PDFs) of the gridded and gauge datasets using the skill score method, quantified the bias of rainfall erosivity (including the R-factor and 1-in-10-year event rainfall erosivity) estimated using the gridded daily precipitation datasets from that estimated using the gauge daily precipitation dataset based on the area reduction factor (ARF) method, and established correction factors for rainfall erosivity maps generated from gridded datasets. The results showed that the gridded daily data reduced the frequency of no-rain days and the intensity of heavy precipitation. In the eastern part of China, the grid-estimated R-factor values were underestimated by 15–40 % compared with the gauge-estimated values, and the grid-estimated 1-in-10-year event rainfall erosivity values were underestimated by 25–50 %, whereas in the western part of China, noticeable random errors were introduced. The lower probability and intensity of the daily precipitation larger than the 90th percentile in the gridded datasets were mainly responsible for the underestimation. CN05.1 was the most-recommended among the four datasets, as it had the lowest mean relative error (MRE), and the accuracy was higher for the eastern part of China than for the western part of China. The MREs were 16.1 % and 25.1 % for the R-factor after applying correction factors of 1.708 and 1.010, respectively, for the eastern and western part of China. The 1-in-10-year event erosivity had larger correction factors and MREs than did the R-factor, with the MREs being 22.1 % and 27.2 % after applying correction factors of 1.959 and 1.880, respectively, for the eastern and western part of China. This study pointed out that in the applications of gridded precipitation datasets, the empirical models established based on gauge precipitation data should not be used directly for the gridded data, or a bias correction process needed to be considered for the model outputs.

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“…Rainfall erosivity mapping for Africa utilizing Tropical Rainfall Measurement Mission (TRMM), based on TMPA 3B43 satellite data (precipitation data) coupled with the modified Fournier index, proved to be a reliable methodology (Vrieling et al, 2010). Although regions use available databases and automated data collection systems -automatic rain gauges, and real-time data transfer-provide reliable hourly or daily rainfall records, even some with 5-, 10-, or 15-minute resolution (Angulo-Martínez and Beguería, 2009;Porto, 2016;Diodato et al, 2017;Todisco et al, 2019;Yue et al, 2020), this is not the case for less technologically advanced countries (Waltrick et al, 2015;Di Raimo et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil

Cardoso¹,

Avanzi²,

Ferreira³

et al. 2022

Revista Brasileira De Ciência Do Solo

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Rainfall erosivity (R factor) is one of the six factors of the Universal Soil Loss Equation, being calculated based on the product of rainfall kinetic energy multiplied by its 30-minute maximum intensity. However, the lack of detailed and reliable rainfall data in many parts of the world has driven the use of other methods to estimate rainfall erosivity based on daily, monthly or annual data. These methods still need to be assessed to determine if their estimates are consistent with the standard method for calculating rainfall erosivity. This study aimed to select a consistent method for such replacement in Brazilian conditions without access the rainfall intensity data. The tested methods included: modified Fournier, MF; modified Fournier by Zhang, MF-Z; modified Fournier by Men, MF-M; Rainfall Disaggregation, RD; TRMM Satellite with modified Fournier coefficient, TRMM-F; and TRMM Satellite with monthly rainfall, TRMM-M. The rainfall data were obtained from the USP Meteorological Station, referring to the period from 2009 to 2015. The analyses were performed according to the Additive Main effects and Multiplicative Interaction (AMMI) model and Scott-Knott statistical tests. Considering the 1:1 line, all methods had a good adjustment, presenting similar behavior in relation to the standard method. The methods behaved differently for monthly and annual periods. The MF method proved to be capable of consistently replacing the standard method in all aforementioned situations. Considering the driest period, any method can be used. For annual rainfall erosivity estimation, the RD, MF, TRMM-F and TRMM-M methods can be applied; highlighting that the TRMM-based methods are optimal for locations without on-site rain gauges. Additionally, it was computed that the modified Fournier by Men and the modified Fournier by Zhang underestimated and overestimated the rainfall erosivity, respectively.

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Effect of time resolution of rainfall measurements on the erosivity factor in the USLE in China

Cited by 30 publications

References 27 publications

Rainfall erosivity mapping over mainland China based on high-density hourly rainfall records

Rainfall erosivity mapping over mainland China based on high-density hourly rainfall records

Rainfall erosivity estimation using gridded daily precipitation datasets

Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil

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