Enhancing monthly streamflow forecasting for Brazilian hydropower plants through climate index integration with stochastic methods

Abstract

ABSTRACT This study demonstrates the potential for enhancing monthly streamflow forecasting in Brazil through the incorporation of climatic indices. It extends the conventional periodic autoregressive model (PAR) for streamflow forecasts by integrating climate information, represented by three key climate indices reflecting sea surface temperatures in the Pacific and Atlantic Oceans, as well as zonal wind patterns in southeastern Brazil. Using the Kling-Gupta Efficiency (KGE) skill metric, our findings reveal that the inclusion of climate data consistently outperforms existing PAR models in numerous scenarios. Notably, during May, the proposed model enhances forecasts for 79% of the reservoirs (124 out of 157), while in January, it reduces forecast variance for up to 90% of the reservoirs (141 out of 157).