Sub-seasonal streamflow forecasts for hydropower dams in the Brazilian Eletrical Interconnected System

Previsões de vazões sub sazonais para barragens hidrelétricas no Sistema Interligado Elétrico Brasileiro

ABSTRACT

Inflow prediction on sub-seasonal timescale have the potential for important contributions to the management of water resources in hydroelectric dam operations. These forecasts challenge the limitations of the medium-term and extend it, bridging a long-standing technical-scientific gap in the forecasting field. In Brazil, the use of sub-seasonal hydrological predictions can boost the hydroelectric production of the National Interconnected System (SIN), since inflow forecast in reservoirs of up to 2 weeks are routinely used using a rain-flow model. This study aimed at the statistical evaluation of hydrological forecasts of up to 6 weeks using a hydrological-hydrodynamic model on a continental scale associated with ensemble precipitation forecasts generated by an atmospheric model, producing future streamflow in the continent basins, and consequently at the SIN’s hydroelectric dams. The statistical evaluation was based on deterministic scores typically used by the SIN operating agent, and additionally we assessed the skill of forecasts based on atmospheric models in relation to simpler forecasts based on the climatology of observed inflows. The performance of the forecasts varies according to the season and geographic location, that is, depending on different hydrological regimes. The best performances were obtained in dams located in the southwest and central-west regions, which have well-defined seasonality, while dams in the south showed greater sensitivity in metrics according to the season. The study presented serves as a technical-scientific contribution for agents and decision makers who seek to improve water resource management by incorporating extended forecasts into the operational chain.

Keywords:
Sub seasonal streamflow forecasting; S2S; Hydropower production; Brazilian Interconnected System