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This paper presents the application of a methodology for daily reservoir inflow forecasting in Brazilian hydroelectric plants. The methodology is based on Fuzzy Inference Systems (FIS) and the technique used for adjusting of the model parameters is an offline version of the Expectation Maximization (EM) algorithm. In order to automate the application of the methodology and facilitate the analysis of the results, a tool that allows managing streamflow forecasting studies and visualizing their information in graphical form was developed. A case study was applied to the data from three Brazilian hydroelectric plants whose operation is under the coordination of the Electric System National Operator. They are located in the Grande basin, a part of the Parana basin with two main rivers: the Grande and the Pardo. The benefits of the model are analyzed using statistics calculations, such as: root mean square error, mean absolute percentage error, mean absolute error and mass curve coefficient. Besides that, graphics that compare the registered and predicted streamflow are presented. The results show an adequate performance of the model, leading to a promising alternative for daily streamflow forecasting.

Knowledge on the effects of climate change in a system can contribute to the better management of its water and energy resources. This study evaluates the consequences of alterations in the rainfall and temperature patterns for a hydroelectric plant. The methodology adopted consists of four steps. First, a hydrological model is developed for the chosen basin following a semi-distributed and conceptual approach. The hydrological model is calibrated utilizing the optimization algorithm Shuffled Complex Evolution-University of Arizona (SCE-UA) and then validated. Secondly, a hydropower model is developed for a hydroelectric plant of the chosen basin. The hydropower model is adjusted to the physical characteristics of the plant. Thirdly, future climate scenarios are extracted from the literature for the studied area. These scenarios include quantitative and seasonal climate variations, as well as different initial reservoir levels. Fourth, the hydrological-hydropower model is simulated for 52 scenarios and the impact of changes in the rainfall and temperature patterns for hydropower generation is evaluated. For each scenario, the water storage in the reservoir and energy produced by the plant are analyzed. The financial impact for extreme scenarios is presented. The methodology is applied to the Três Marias hydroelectric plant at the upper São Francisco river basin (Brazil) and it can be replicated to any other hydropower system. The results show that extreme positive values predicted for rainfall will likely not cause issues to the plant, considering a moderate rise in temperature. However, negative predictions for rainfall, regardless of changes in temperature, should be an alert to the authorities responsible for water and energy resources management.

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