Dynamic rainfall erosivity estimates derived from IMERG data

Abstract

Abstract. Soil degradation is a critical threat to agriculture and food security around the world. Understanding the processes that drive soil erosion is necessary to support sustainable management practices and to reduce eutrophication of water systems from fertilizer runoff. The erosivity of precipitation is a primary control on the rate of soil erosion, but to calculate erosivity high-frequency precipitation data are required. Prior global-scale analysis has almost exclusively used ground-based rainfall gauges to calculate erosivity, but the advent of high-frequency satellite rainfall data provides an opportunity to estimate erosivity using globally consistent gridded satellite rainfall. In this study, I have tested the use of IMERG (Integrated Multi-satellitE Retrievals for GPM, Global Precipitation Mission) rainfall data to calculate global rainfall erosivity. I have tested three different approaches to assess whether simplification of IMERG data allows for robust calculation of erosivity, finding that the highest-frequency 30 min data are needed to best replicate gauge-based estimates. I also find that in areas where ground-based gauges are sparse, there is more disparity between the IMERG-derived estimates and the ground-based results, suggesting that IMERG may allow for improved erosivity estimates in data-poor areas. The global extent and accessibility of IMERG data allow for regular calculation of erosivity in a month-to-month time frame, permitting improved dynamic characterization of rainfall erosivity across the world in near-real time. These results demonstrate the value of satellite data to assess the impact of rainfall on soil erosion and may benefit practitioners of sustainable land management planning.