Validation of the CHIRPS precipitation estimate in a Brazilian Cerrado area

Abstract

Measuring and understanding precipitation over space and time is essential for several human activities. Satellite remote sensing products are presented as an alternative to the low-density network of pluviometric stations. Thus, the objective of the present study was to evaluate precipitation estimates obtained by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) product, from 1981 to 2020, in the Rio Grande basin, Bahia state, Brazil. This watershed has about 75,000 km², is inserted in one of the most active agricultural frontiers in the world and has undergone significant changes in land use and occupation and changes in rainfall patterns. We compared data from 11 series of conventional (Hidroweb) and CHIRPS-derived surface stations on monthly and seasonal scales, using statistical metrics – relative bias (BIAS), correlation coefficient (R²), mean error (ME), and mean squared error (RMSE) – and categorical – correct proportion (PC), probability of detection (POD), frequency bias index (FBI), false alarm (FAR). Results showed that the CHIRPS precipitation estimates provided good responses compared to the data observed in conventional surface meteorological stations. Furthermore, CHIRPS products accurately detected rain with an excellent capacity to represent the space-time precipitation variation.