Prediction of monthly flows for Três Marias reservoir (São Francisco river basin) using the CFS climate forecast model

• Autor(a) principal: De Paiva,Luana Ferreira Gomes
• Data de Publicação: 2020
• Outros Autores: Montenegro,Suzana Maria, Cataldi,Marcio
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: RBRH (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312020000100215
• topic: Flow forecast; São Francisco river; Neural Networks (NN); SMAP (Soil Moisture Accounting Procedure); Precipitation forecast; Hydrological simulation

ABSTRACT Despite the water crisis in 2016, 76% of the energy in Brazil was generated by hydroelectric plants, which shows that the Brazilian system is still strongly dependent on the hydrological conditions of basins. Therefore, the flow forecasts for these plants subsidize the decision making within the scope of the Electric Sector, since they allow the evaluation of the operational conditions of the hydroelectric and thermoelectric plants through the use of energy optimization models, providing gains in the operations of SIN (Sistema Interligado Nacional – the Brazilian National Interconnected System). The precipitation forecast is of fundamental importance for the elaboration of these hydroelectric flow forecasts. For energy evaluations, the DECOMP and NEWAVE models are used, with the GEVAZP model being applied to generate scenarios through an AR (p) (autoregressive) model. Accordingly, this study shows the impact of precipitation forecast on flow predictions in the climate horizon. For this, a statistical correction was made in the rain predicted by the CFS (Climate Forecast System) model, which tends to overestimate the predicted rain, with rainfall-flow models being calibrated. Tests were performed with this new modeling system and the results, in the form of scenarios, were compared with the scenarios generated by the GEVAZP model, showing the possibility of reducing the generated range by the latter, consequently causing the DECOMP model to not consider ranges with little or no probability of occurrence, which can improve the optimization of the SIN operation planning. This work also shows that the SMAP model exhibited better performance when compared to the Neural Networks model, in terms of the average flow range predicted in relation to the observed flow. There was a clear improvement in the flow predictions with the incorporation of the rain observed one month ahead in the simulations, mainly in the forecast of high flows. Finally, the climate indices had a good relationship with the flow and rain variables.