Estimating rainfall erosivity in Guangdong Province using IMERG remote sensing precipitation products

Spatiotemporal resolution of remote sensing precipitation products has been continuously improved in recent years. New possibilities can be provided to estimate the spatially continuous rainfall erosivity at the regional scale. However, previous studies have often failed to consider the differences between these products and observation data, as well as the potential for bias correction. In this study, the remote sensing precipitation products were used to estimate the rainfall erosivity in Guangdong Province, China. The precipitation data was collected hourly from 86 meteorological stations from 2001 to 2020. Two IMERG remote sensing precipitation products were selected, including the GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree × 0.1 degree V06 (GPM_3IMERGHH, 0.1°, every 30-min) and GPM IMERG Final Precipitation L3 1 day 0.1 degree × 0.1 degree V06 (GPM_3IMERGDF, 0.1°, daily). The accuracy of two IMERG remote sensing precipitation products in estimating rainfall erosivity at semi-monthly, annual, multi-year average, and extreme event scales was then evaluated to quantify the bias. Fitting and bias correction were utilized to determine the optimal rainfall erosivity using remote sensing precipitation data. The EI₃₀ was an indicative measure of event rainfall erosivity derived from hourly rainfall data. Fitting revisions of the EI₃₀ were estimated from 5-minute rainfall data collected from three meteorological stations. The results showed that neither product was suitable for the direct estimation of rainfall erosivity with low accuracy at different time scales. Pearson correlation coefficients (CC) and Kling-Gupta efficiency (KGE) were less than 0.63 and 0.51 on a semi-monthly scale, indicating low estimation accuracy and small correlation. The KGE values were less than 0.5 on an annual scale, whereas, the CC values were higher than those on a semi-monthly scale, all exceeding 0.63. The KGE values were still less than 0.5 on the multi-year average and extreme event rainfall erosivity. But the CC was greater than or equal to 0.78. There was a strong correlation between the estimated and the observed values, indicating the potential for bias correction. Therefore, the linear models were used to correct the IMERG estimates. Among them, the correction models were y=2.0017x-1373 and y=1.6916x-4, respectively, for the multi-year average annual rainfall erosivity (R-factor) and 10-year storm EI₃₀ (EI₃₀ that occurs once in 10 years). Cross-validation results showed that the corrected GPM_3IMERGHH estimates of R-factor and 10-year storm EI₃₀ had the KGE values of 0.79 and 0.64, respectively, which were superior to the spatial interpolation using station daily rainfall data (KGE values of 0.60 and 0.59, respectively), and similar to the station hourly rainfall data (KGE values of 0.77 and 0.66, respectively). R-factor and 10-year storm EI₃₀ were necessary to require for the areas of Guangdong Province without observation stations, in order to map the spatial distribution of these factors. Specifically, IMERG remote sensing precipitation products were used to estimate the rainfall erosivity with the bias corrections. The accuracy of the corrected estimates was superior to that using spatial interpolation with the station daily rainfall data, and similar to that with the station hourly one. The findings can effectively demonstrate the substantial potential of the remote sensing precipitation products in the field of soil water erosion, indicating the broad range of future possibilities.