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

Abstract

Abstract. Rainfall erosivity represents the effect of rainfall and runoff on the average rate of soil erosion. Maps of rainfall erosivity are indispensable for soil erosion assessment using the Universal Soil Loss Equation (USLE) and its successors. To improve current erosivity maps based on daily rainfall data for mainland China, hourly rainfall data from 2381 stations for the period 1951–2018 were collected to generate the R factor and the 1-in-10-year EI30 maps (available at https://dx.doi.org/10.12275/bnu.clicia.rainfallerosivity.CN.001; Yue et al., 2020). Rainfall data at 1-min intervals from 62 stations (18 stations) were collected to calculate rainfall erosivities as true values to evaluate the improvement of the new R factor map (1-in-10-year EI30 map) from the current maps. Both the R factor and 1-in-10-year EI30 decreased from the southeastern to the northwestern, ranging from 0 to 25300 MJ mm ha−1 h−1 a−1 for the R factor and 0 to 11246 MJ mm ha−1 h−1 for the 1-in-10-year EI30. New maps indicated current maps existed an underestimation for most of the southeastern areas and an overestimation for most of the middle and western areas. Comparing with the current maps, the R factor map generated in this study improved the accuracy from 19.4 % to 15.9 % in the mid-western and eastern regions, from 45.2 % to 21.6 % in the western region, and the 1-in-10-year EI30 map in the mid-western and eastern regions improved the accuracy from 21.7 % to 13.0 %. The improvement of the new R factor map can be mainly contributed to the increase of data resolution from daily data to hourly data, whereas that of new 1-in-10-year EI30 map to the increase of the number of stations from 744 to 2381. The effect of increasing the number of stations to improve the interpolation seems to be not very obvious when the station density was denser than about 10 · 103 km2 1 station.

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

“…The rainfall erosivity maps (R-factor and 1-in-10-year EI 30 ) are available at: https://doi.org/10.12275/bnu.clicia.rainfallerosivity.CN.001 (Yue et al, 2020b).…”

“…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 EI 30 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 EI 30 maps were assessed to quantify the improvement over the existing maps through cross-validation.…”

New gridded dataset of rainfall erosivity (1950–2020) on the Tibetan Plateau

“…Previous studies of the TP have used in-situ precipitation observations with <50 stations and coarse temporal resolution, e.g., hourly (Yue et al, 2021), daily (Wang et al, 2017), or half-monthly (Teng et al, 2018;Gu et al, 2020;Liu et al, 2020). By contrast, this study estimated the rainfall erosivity on the 100 TP using precipitation observations at 1-min intervals in 2013-2020 at 1787 weather stations obtained from the National Meteorology Information Center of the China Meteorological Administration [Figure 1(a)].…”

“…The classical algorithm for rainfall erosivity requires a continuous precipitation data series with <15-min temporal resolution (Angulo-Martínez and Beguería, 2009). As networks of weather stations and observation platforms have matured considerably in the past two decades, rainfall erosivity has been calculated using the classical algorithm at the local scale (Agnese et al, 2006;Ma et al, 2014;40 Wang et al, 2017), and the application of the algorithm has been gradually extended to the national (Panagos et al, 2015;Kim et al, 2020;Yue et al, 2021) and global scale (Panagos et al, 2017;Liu et al, 2020). Despite substantial progress, it is still notable that the relative error of the estimated rainfall erosivity increases rapidly with increasing time interval of the precipitation data.…”

“…For example, the relative error based on hourly data was more than 80%, compared with the results based on 1-min data 45 (Lobo and Bonilla, 2015;Yin et al, 2015;Shin et al, 2019). In addition, the accuracy of the rainfall erosivity is greatly reduced by inadequate weather station coverage, especially in areas with complex climates and terrains (Yue et al, 2021). Therefore, the accuracy of rainfall erosivity estimation depends strongly on the temporal and spatial resolution of the precipitation observations (Panagos et al, 2017;Kim et al, 2020).…”