Microphysics-based rainfall energy estimation using remote sensing and reanalysis data

Rainfall plays a critical role in soil erosion by detaching and transporting soil particles, making it a key factor in soil erosion models. Its potential effect on soil erosion is reflected by rainfall erosivity, which considers the interaction between raindrops and soil, primarily driven by the kinetic energy carried by raindrops. Traditional approaches rely on empirical relationships between rainfall rate and kinetic energy, but they often overlook regional differences in rainfall microphysical characteristics. This study aims to compare the performance of conventional fixed and disdrometer-derived local microphysics-based unit rainfall kinetic energy-rain rate (KE-I) relationships for estimating rainfall energy using the IMERG and ERA5-land precipitation datasets. The findings reveal that conventional method using the fixed KE-I relationship do not account for regional differences in rainfall microphysical characteristics and tend to overestimate KE values for high rainfall intensities in the UK. Disdrometer-derived microphysics-based KE-I relationships improve estimation correlations, with the fully fitting formula exhibiting the highest correlation coefficient. The findings highlight the need to consider local rainfall characteristics for improved estimation accuracy and demonstrate that ERA5-land exhibits higher correlation and lower error compared to IMERG for estimating rainfall energy, but it generally underestimates rainfall intensity. This study will provide valuable insights into the applicability of using gridded precipitation datasets for large-scale soil erosion estimation and emphasizes the significance of accounting for regional variations in rainfall characteristics.