A framework to evaluate and compare synthetic streamflow scenario generation models

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.

Generation of synthetic flow scenarios by means of multivariate sampling of contemporaneous ARMA model outputs

“…Therefore, an adaptation to the modeling was necessary to calculate and provide these synthetic incremental scenarios for comparison with other models. Consequently, the results to be presented in Treistman et al (2023) pertain to incremental flows, and their interpretation should consider this characteristic.…”

“…That being said, it should be emphasized that for the purposes of the activity proposed by the Hydrological Scenario Representation Working Group (GT-CH) within the Technical Committee of PMO/PLD (CT PMO/PLD), the module for identifying and correcting non-stationarity has been disabled because it alters historical data. If it were kept active, the model would lose a common basis for comparing results as shown in Treistman et al (2023).…”