TY  - JOUR
AU  - Zeng, Rongchang
AU  - Xu, Jintao
AU  - Chen, Tao
AU  - Lin, Dawei
AU  - Zhu, Xuchao
AU  - Teng, Hongfen
AU  - Yu, Wu
AU  - Fu, Zhiyi
AU  - Du, Yuanyuan
AU  - Chang, Jinfeng
AU  - Shi, Zhou
AU  - Chen, Songchao
PY  - 2026
DA  - 2026/08/17
TI  - China rainfall erosivity (2015–2023): A new annual dataset inferred from national hourly meteorological station records
JO  - Scientific Data
AB  - Rainfall erosivity (R-factor) is a critical parameter for soil loss prediction and ecological risks assessment, yet its quantification is often limited by the coarse temporal resolution of available precipitation data. While high-frequency observations provide a more accurate characterization of rainfall kinetic energy, a national-scale assessment of the uncertainties induced by these varying data resolutions in China has been lacking. This study presents a new, high-precision annual rainfall erosivity dataset for China (2015–2023) derived from hourly precipitation records across 57,740 meteorological stations (at least 5 years). Using the hourly-derived erosivity (Rhourly) as the benchmark, we systematically evaluated the estimation biases of widely used daily and monthly empirical models. Results show that Rhourly exhibits a decreasing gradient from southeastern to northwestern China, peaking at over 8,000 MJ mm ha−1 h−1 y−1 in the Southern Red Soil Hilly Region. Nationwide, daily-scale estimates show a mean absolute error of 20.7%, transitioning from systematic underestimation in the southeast to severe overestimation in the northwest. Monthly-scale estimations yield a higher mean absolute error of 25.8%, with intensified underestimation in southern regions and a reversal of error direction in the northwest. Across China, the interannual coefficient of variation averages 59.0%, with the highest variability (93.8%) observed in the Northern Wind-Sand Region. By providing station-based benchmark data and identifying spatial error patterns, this dataset offers a robust foundation for high-precision soil erosion mapping, regional conservation planning, and climate change impact assessments.
SN  - 2052-4463
UR  - https://doi.org/10.1038/s41597-026-07995-6
DO  - 10.1038/s41597-026-07995-6
ID  - Zeng2026
ER  -