Comparative analysis of rainfall data from remote sensing and surface stations in the Paracatu river basin, Brazilian Cerrado - Maycow Douglas de Oliveira Silva; Paulo Sérgio Cardoso Batista; Sharrine Omari D. de Oliveira Marra; Marinaldo Loures Ferreira (Investigaciones Geográficas)
Accurate precipitation estimation is essential for water resource management, especially in tropical regions where the spatial and temporal variability of rainfall presents significant challenges. In areas with limited terrestrial station coverage, satellite-based products such as the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) have become particularly valuable. This study compares CHIRPS data with surface station records in the Paracatu River Basin in the Brazilian Cerrado from 2010 to 2023, employing a “point-to-pixel” approach. Among the statistical analyses, we have used metrics commonly applied in precipitation studies, such as Pearson’s correlation coefficient (CC), relative bias (BIAS), coefficient of determination (R²), and root mean square error (RMSE). Monthly coefficients of determination (R²) ranged from 0.77 to 0.89, reflecting CHIRPS’ capability to capture rainfall patterns on this scale. However, its accuracy decreased when estimating total annual precipitation, highlighting some limitations. Despite these challenges, CHIRPS is a valuable tool for supplementing surface data on a monthly basis, although discrepancies are more pronounced during periods of intense rainfall and in annual aggregates. Future research should investigate the integration of additional remote sensing products and assess how changing climate conditions affect precipitation estimates.