Dados de radar meteorológico aplicados a uma modelagem chuva-vazão

Abstract

This dissertation aimed to utilize data from the Bauru meteorological radar to estimate precipitation and validate these data based on selected rain gauge stations in the Piracicaba River basin. Initially, raw radar data were processed and converted into precipitation estimates using correction and filtering techniques to reduce uncertainties. These estimates were then validated through comparisons with observational data from rain gauge stations, employing statistical indices such as the Pearson correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and percent bias (PBIAS). The results indicated a strong correlation between radar data and rain gauge measurements, with r values close to 0.85, RMSE of 12 mm/day, and PBIAS below 10%, demonstrating the reliability of radar-derived precipitation. With the validated data, a hydrological model for the Piracicaba River basin was calibrated, using the radar-based precipitation estimates as input. The model was adjusted to simulate the hydrological behavior of the basin, considering variables such as flow rate and surface runoff. Finally, the model's results were compared with the radar precipitation data, evaluating the consistency and effectiveness of integrating radar data into hydrological studies. The results demonstrated that the use of radar data can significantly improve the accuracy of precipitation estimates and, consequently, the quality of hydrological simulations. Furthermore, the developed methodology proved promising for applications in monitoring and predicting extreme events, such as floods and droughts, contributing to the sustainable management of water resources. This work reinforces the importance of integrating remote sensing technologies, such as meteorological radars, into hydrological models, highlighting their potential to enhance predictability and planning in river basins.