Predictability of extreme streamflow in Brazilian rivers through climate indices

Abstract

In the present study empirical modeling was used to estimate extreme monthly streamflow in 161 locations of hydroelectric plants in the main Brazilians basins through a climate indices set. Principal components analysis was applied to capture the combined influences of climate indices variability. The main predictors of the models were climate patterns of tropical Atlantic and Pacific (Tropical Southern Atlantic and El Niño Southern Oscillation (ENSO) Modoki), complemented by high latitudes patterns (Antarctic Oscillation and Pacific North American Pattern). The contribution of the ENSO/Pacific Decadal Oscillation mode occurs in preferential months, especially in the transition seasons. There is also a contribution of the lagged streamflow itself, mainly in basins located in the Southeast of Brazil. The accuracy of the model for most of the Brazilian basins is higher than 70%, with higher values than 90% for the estimate of very low streamflow in northeast and north-central Brazil, as well as for very high streamflow in south-central region, which performance decreases with the increase in the lag. The results showed that the climate indices have a highly predictive potential for extreme streamflow, with a higher predictability for more long-term forecasts in case of very low streamflow than very high streamflow.