Effects of atmospheric systems operating in the Northeast of Brazil on the volumes of artificial reservoirs.

This study aims to quantify the impact of rainfall patterns influenced by the seasonal movement of the Intertropical Convergence Zone (ITCZ), Frontal Systems (FS), and South Atlantic Convergence Zone (SACZ) during El Niño, La Niña, and neutral years on reservoirs water volumes across the Northeast of Brazil (NEB) from 1998 to 2018. Data sources include meridional and zonal components of the winds, outgoing longwave radiation, monthly rainfall, oceanic sea surface temperature, and reservoir volume from the Brazilian National Agency for Water and Basic Sanitation and state departments were used. ITCZ identification employed semi-objective techniques, while CPTEC/INPE reports aided in identifying FS and SACZ.

Wind power density in areas of Northeastern Brazil from Regional Climate Models for a recent past.

Investments in renewable energy sources are increasing in several countries, especially in wind energy, as a response to global climate change caused by the burning of fossil fuels for electricity generation. Thus, it is important to evaluate the Regional Climate Models that simulate wind speed and wind power density in promising areas for this type of energy generation with the least uncertainty in recent past, which is essential for the implementation of wind farms. Therefore, this research aims to calculate the wind power density from Regional Climate Models in areas at Northeast of Brazil from 1986 to 2005.

The dependence of hydropower planning in relation to the influence of climate in Northeast Brazil.

Water scarcity in Northeast Brazil has caused latent perturbations in hydropower generation, which is undesirable for Energy Planning. Thus, this study aims to identify the influence of climate on hydropower generation by Sobradinho Dam in Bahia by: (i) assessing the streamflow climatology (1964-2017) and rainfall (1964-2015) through time series analysis, hypothesis testing and cluster analysis; (ii) assessing hydropower generation (2000-2017) using climate and energy data, through principal component analysis and dynamic regression models. The results indicated reductions of up to 30% in the mean climatological streamflow patterns; and reductions in rainfall amounts of 22.