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ABSTRACT In Brazil, energy production predominantly relies on hydropower generation, necessitating precise hydrological planning tools to manage the uncertainty inherent in river flows. While traditional hydrological models provide valuable deterministic forecasts, addressing the need for probabilistic information remains crucial. This paper introduces a novel approach, the Hybrid Generator of Synthetic Streamflow Scenarios (GHCen), which combines a conceptual SMAP/ONS model with stochastic simulation techniques to generate synthetic streamflow scenarios. The stochastic methodology employed in GHCen effectively reproduces the key characteristics of precipitation processes on daily to annual scales. Through a comprehensive case study, conducted for 2021, GHCen demonstrates its capability to accurately replicate the hydrological behaviors from historical data. The analysis reveals a strong alignment between the synthetic scenarios and observed Natural Energy Inflow for the National Interconnected System, both monthly and in accumulated terms.

ABSTRACT Synthetic streamflow scenario generation is particularly important in countries like Brazil, where hydroelectric power generation plays a key role and properly handling the uncertainty of future streamflow is crucial. This paper showcases a collaborative effort within the Brazilian electrical sector to enhance streamflow scenario models, focusing on horizons up to one year. Five institutions proposed diverse methodologies, and their effectiveness was evaluated using a comparative framework. The results reveal the strengths and areas for improvement in each model. GHCen emerged as the top performer, excelling in both short-term and moving average analyses, while the PARX model demonstrated superior performance in specific regions. The PAR(p)-A, which is the official methodology in Brazil, was the second-best model in the moving average analysis. This research offers valuable insights for countries facing similar hydrothermal scheduling and scenario generation challenges.