Assessment of subseasonal streamflow predictions in a tropical basin

Brazil is a country highly dependent on hydroelectricity. Because electricity generation is programmed monthly, one of the most critical information is the inflow of the main hydropower plants of the country. In this context, this study evaluates the predictability and accuracy of raw and bias-corrected ECMWF S2S forecasts as input of a hydrological model, focusing on high and low-flows within the Tocantins-Araguaia Basin in Brazil. Rainfall forecasts were also evaluated against observations across 22 sub-basins of the Tocantins-Araguaia River, considering lead times ranging from 1 to 28 days. Results indicated that the predictability of S2S forecasts vary with the sub-basin area and through seasons (wet and dry periods). Bias corrections were able to improve the accuracy of the forecasts for all lead-times and across sub-basins of different sizes. In the case of low-flows, using raw ECMWF S2S rainfall forecasts as input of the hydrological model resulted in streamflow forecast errors that increase with the scale of the sub-basin. For high-flows results are the opposite, errors are larger in small sub-basins and smaller at larger scales. This is due to the overestimation of dry season rainfall in the ECMWF S2S forecasts. When using bias-corrected rainfall forecasts as input, river flow forecasts are useful up to 28 days of lead-time. The results of this study revealed the potential of using subseasonal forecasts for decision-making in the Tocantins-Araguaia Basin, and encouraging further developments.