Abstract. Soil erosion can cause various ecological problems, such as land
degradation, soil fertility loss, and river siltation. Rainfall is the
primary water-driven force for soil erosion, and its potential effect on
soil erosion is reflected by rainfall erosivity that relates to the raindrop
kinetic energy. As it is difficult to observe large-scale dynamic
characteristics of raindrops, all the current rainfall erosivity models use
the function based on rainfall amount to represent the raindrops' kinetic
energy. With the development of global atmospheric re-analysis data,
numerical weather prediction techniques become a promising way to estimate
rainfall kinetic energy directly at regional and global scales with high
spatial and temporal resolutions. This study proposed a novel method for
large-scale and long-term rainfall erosivity investigations based on the
Weather Research and Forecasting (WRF) model, avoiding errors caused by
inappropriate rainfall–energy relationships and large-scale interpolation.
We adopted three microphysical parameterizations schemes (Morrison, WDM6,
and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall
kinetic energy, and rainfall erosivity, with validation by two disdrometers
and 304 rain gauges around the United Kingdom. Among the three WRF schemes,
Thompson aerosol-aware had the best performance compared with the
disdrometers at a monthly scale. The results revealed that high rainfall
erosivity occurred in the west coast area at the whole country scale during
2013–2017. The proposed methodology makes a significant contribution to
improving large-scale soil erosion estimation and for better understanding
microphysical rainfall–soil interactions to support the rational
formulation of soil and water conservation planning.