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

• Autores: Gutierrez, Leonardo, Huerta, Adrian, Sabino, Evelin, Bourrel, Luc, Frappart, Frédéric, Lavado-Casimiro, W.
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Servicio Nacional de Meteorología e Hidrología del Perú
• Repositorio: SENAMHI-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.senamhi.gob.pe:20.500.12542/3068
• Enlace del recurso: https://hdl.handle.net/20.500.12542/3068; https://doi.org/10.3390/rs15225432
• Nivel de acceso: acceso abierto
• Materia: Rainfall Erosivity; Satellite Rainfall Product; Precipitación; https://purl.org/pe-repo/ocde/ford#1.05.11; precipitacion - Aire y Atmósfera

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