GHCen: a stochastic-conceptual approach for generating synthetic streamflow scenarios

Abstract

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.

Keywords

• Synthetic streamflow scenario generation
• Hybrid model
• Conceptual rainfall-runoff model