Rainfall From Brazilian Flying Rivers: Evaluating the Effectiveness of Precipitation Gridded Databases

Abstract

The uneven global distribution of rainfall significantly impacts water resources and environmental sustainability, emphasising the need for reliable climate prediction models. Accurate predictions are vital for sectors such as food security, urban planning and disaster management. Data from ground stations, radars and satellites are essential, despite challenges like instrumental errors. Satellites, with their comprehensive sensors, are crucial for atmospheric observations, aiding in the prediction of large-scale climatic events. Climate models such as CHIRPS, GLDAS, TerraClimate, and PERSIANN use different approaches to analyse precipitation data, which is key to understanding its spatial and temporal variability. This study evaluated (rainfall data) from these four climate models over 20 years (within the Brazilian territory), focusing on the spatiotemporal behaviour of rainfall using statistical metrics such as R ², RMSE, and MAPE. The findings showed that CHIRPS had the best performance (R ² = 0.843; RMSE = 42.83; MAPE = 0.09%), excelling in both overall database and extreme event analyses. TerraClimate, initially the lowest-performing model (R ² = 0.413; RMSE = 91.56; MAPE = 0.23%), improved significantly when combined with elevation through multiple linear regression (MLR), achieving R ² of 0.718, RMSE of 31.14, and MAPE of 9.56%. This made TerraClimate a viable model for studying the Flying Rivers. The study highlights that model selection should align with the specific characteristics of the area under consideration, with CHIRPS being particularly suitable for the studied region. This research enhances the understanding of the effectiveness of these models in estimating rainfall compared to in situ measurements, which is crucial for various applications. The authors advocate for further studies to advance research on the Flying Rivers, their significance, and the impacts of climate change on them.