Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020) - Leonardo Gutierrez; Adrian Huerta; Evelin Sabino; Luc Bourrel; Frédéric Frappart; Waldo Lavado-Casimiro (Remote Sensing, 2023)
In soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (<semantics>RE</semantics>), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (<semantics>ED</semantics>), which relates <semantics>RE</semantics> to precipitation. The <semantics>RE</semantics> requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for <semantics>RE</semantics> estimation. This study evaluates the performance of a new gridded dataset of <semantics>RE</semantics> and <semantics>ED</semantics> in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000–2020. By using this method, a correlation of 0.94 was found between PISCO_reed and <semantics>RE</semantics> obtained by the observed data. An average annual <semantics>RE</semantics> for Peru of 7840 MJ · mm · ha<semantics>−1</semantics>· h<semantics>−1</semantics> was estimated with a general increase towards the lowland Amazon regions, and high values were found on the North Pacific Coast area of Peru. The spatial identification of the most at risk areas of erosion was evaluated through a relationship between the <semantics>ED</semantics> and rainfall. Both erosivity datasets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision.