ALBERT

Description: Rainfall erosivity (R-factor) represents the potential erosive power of rainfall to erode soil surfaces. The R-factor is generally estimated using the method adopted in the Universal Soil Loss Equation (USLE) or Revised-USLE. The R-factor estimation method requires high-temporal resolution data from a dense network of gauged rainfall stations for reliable mapping of the R-factor. However, the unavailability of a dense network of high temporal resolution gauge rainfall datasets hinders the R-factor mapping over a large spatial scale. In recent decades the availability of satellite rainfall datasets at a higher spatiotemporal resolution provided an opportunity to estimate rainfall erosivity at a large spatial scale. However, many studies have shown that satellite datasets underestimate erosivity values, and improvement is required to make them more representative before further use. This study aimed to improve the R-factor estimated from GPM-IMERG and CMORPH satellite precipitation datasets over India using gauge-based rainfall erosivity data. The high-resolution hourly gauge rainfall records from 1969 to 2021 of more than 250 stations across the country were used to improve the satellite-based rainfall erosivity estimates. The gauge-based rainfall erosivity data was merged with the satellite-based rainfall erosivity products. The result demonstrates that the merging satellite and gauge rainfall erosivity can accurately estimate rainfall erosivity over a broad spatial scale. The merged products are spatially effective for many regions where a dense network of high-resolution gauge rainfall datasets is limitedly available. The approach can be applied on the continental and global scale lacking ground observations and/or satellite records.