Rainfall Erosivity Dynamics in a Tropical Basin: Integration of Rain Gauge Data and Satellite-Based Precipitation

This study evaluated the spatial and temporal variability of rainfall erosivity (R factor) and its implications for soil loss in the Velhas River Basin, Minas Gerais, Brazil. Rainfall erosivity was estimated from 49 rain gauge stations and CHIRPS precipitation data using empirical equations-based on monthly and annual precipitation totals. Soil loss was estimated using the RUSLE model for the years of minimum and maximum erosivity. Between 2014 and 2024, annual R values ranged from approximately 3900 to more than 9000 MJ mm ha−1 h−1 yr−1, with the lowest values recorded in 2014 and the highest in 2022. Although 2020 had the highest annual rainfall, 2022 showed the highest erosivity, indicating that rainfall intensity and temporal concentration were more important than total rainfall volume. Furthermore, the comparison of erosivity was estimated from ANA stations and derived from CHIRPS agreement for paired station-year observations (r = 0.7196), although CHIRPS slightly underestimated erosivity values (mean bias −5.74%). Estimated soil loss ranged from 0.60 to 274.17 Mg ha−1 yr−1, with the highest values occurring mainly in exposed soil and agricultural areas. These findings highlight the importance of rainfall temporal distribution in erosion risk and support the use of satellite-derived precipitation products for regional-scale erosion assessments in data-scarce tropical basins.

Authors

Institutions

Publication Details

Journal
Climate
Published
2026-05-22
DOI
https://doi.org/10.3390/cli14060111
Primary Topic
Soil erosion and sediment transport
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rainfall Erosivity Dynamics in a Tropical Basin: Integration of Rain Gauge Data and Satellite-Based Precipitation

Mariana Raniero, Pedro F. R. Grande, Victor H. F. D. Silva, Guilherme D. S. Rios et al.
Climate
Soil erosion and sediment transport
article

Rainfall Erosivity Dynamics in a Tropical Basin: Integration of Rain Gauge Data and Satellite-Based Precipitation

Mariana Raniero, Pedro F. R. Grande, Victor H. F. D. Silva, Guilherme D. S. Rios, Derielsen B. Santana, Joaquim E. B. Ayer, Talyson D. M. Bolleli, Marcelo A. R. Pires, Fellipe S. Gomes, Velibor Spalevic, Felipe G. Rubira, Ronaldo L. Mincato
article en

Abstract

This study evaluated the spatial and temporal variability of rainfall erosivity (R factor) and its implications for soil loss in the Velhas River Basin, Minas Gerais, Brazil. Rainfall erosivity was estimated from 49 rain gauge stations and CHIRPS precipitation data using empirical equations-based on monthly and annual precipitation totals. Soil loss was estimated using the RUSLE model for the years of minimum and maximum erosivity. Between 2014 and 2024, annual R values ranged from approximately 3900 to more than 9000 MJ mm ha−1 h−1 yr−1, with the lowest values recorded in 2014 and the highest in 2022. Although 2020 had the highest annual rainfall, 2022 showed the highest erosivity, indicating that rainfall intensity and temporal concentration were more important than total rainfall volume. Furthermore, the comparison of erosivity was estimated from ANA stations and derived from CHIRPS agreement for paired station-year observations (r = 0.7196), although CHIRPS slightly underestimated erosivity values (mean bias −5.74%). Estimated soil loss ranged from 0.60 to 274.17 Mg ha−1 yr−1, with the highest values occurring mainly in exposed soil and agricultural areas. These findings highlight the importance of rainfall temporal distribution in erosion risk and support the use of satellite-derived precipitation products for regional-scale erosion assessments in data-scarce tropical basins.

ClimateVol. 14(6)
Universidade Federal de Alfenas (BR), Institute of Physics (PL), Centro Universitário Paulistano (BR), University of Montenegro (ME)
Openalex Percentile: Top 8%
Soil erosion and sediment transport
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.