Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000-2020)

In soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (RE), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (ED), which relates RE to precipitation. The RE 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 RE estimation. This study evaluates the performance of a new gridded dataset of RE and ED 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 RE obtained by the observed data. An average annual RE for Peru of 7840 MJ center dot mm center dot ha-1 center dot h-1 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 ED and rainfall. Both erosivity datasets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision.

Automated summary

This study evaluates the performance of the RE and ED datasets in soil erosion estimation models in Peru and finds that estimating with observed-corrected satellite data is more effective. The study also presents the average annual RE for Peru and identifies the spatial distribution of erosion risk areas.