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

Abstract

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.

The Trade-Offs and Synergies of Ecosystem Services in Jiulianshan National Nature Reserve in Jiangxi Province, China

“…Meteorological data, including precipitation, temperature, and radiation, were obtained from the Jiulianshan National Nature Reserve Administration and the National Meteorological Administration of China (http://data.cma.cn, accessed on accessed on 15 January). The soil erodibility factor (K) was provided by the Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University (https://gda.bnu.edu.cn/, accessed on 15 January 2022) [33]. For model parameterization, all data were resampled to a 30 m grid.…”

Long-time Series Dataset of Soil Conservation Capacity Preventing Water Erosion in China (1992–2019)

“…Large-scale studies generally use monthly or annual rainfall data to calculate the R-factor due to the feasibility. A recent study uses hourly rainfall data to estimate the R-factor (Yue et al, 2022), which improve the accuracy of the erosivity map in China. However, it is difficult to obtain such high-precision and complete rainfall records in the long-term estimations.…”

“…1a, respectively. Annual values of the R-factor were firstly calculated for each station, then the values were interpolated to erosivity maps employing the method of Universal Kriging, which has proven to be an effective method for the spatial interpolation of the rainfall erosivity in China (Li et al, 2011;Yue et al, 2022).…”

Global assessment of storm disaster-prone areas

“…This critical threshold value was popularised in storm geomorphology by Dabney et al [ 58 ] as the runoff increases when RED exceeds 3 MJ hm -2 h -1 , thus leading to an increasing erosive hazard as storm erosivity represents a large proportion of the rainfall amount in an intense event. Mostly set regionally on a monthly basis [ 44 ], this hydrological threshold (alert) could help detecting areas prone to erosion- and overland flow [ 59 , 60 ]. Abstractions such as thresholds of change and strength are all essential as landscapes may be able to counter or assimilate pulses of change as a form of sensitivity or stability [ 61 ].…”

“…With a large volume of data and uneven distribution of stations, geostatistical methods provide reasonable estimates of what the variable of interest would be at intermediate locations. Geostatistics offers different approaches to deal with this issue and provides attracting results when experimentally determined rainfall erosivity data are available at both regional [ 41 , 42 ], continental [ 43 , 44 ], or even global scales [ 5 ]. However, geostatistical estimation of RED [ 17 , 45 , 46 ], and its spatial patterns above given thresholds [ 47 ], have generally received limited attention [ 48 ].…”