Coupling meteorological forecasts with hydrologic and hydraulic models: The Itaipu Dam ensemble inflow forecasting system

This work presents the development of the inflow ensemble forecasting system for Itaipu Dam. The system is based on combination of twelve Quantitative Precipitation Forecast (QPF) with three hydrological models and one hydrodynamic one-dimensional model. The QPF are provided by different meteorological institutions based in the results of the global Numerical Weather Prediction (NWP) models GFS and ECMWF, as well as of the regional NWP models WRF and COSMO executed by the Brazilian and Paraguay meteorological services (SIMEPAR, INMET and DINAC). The semi-distributed model MGB – Large Basin Model, the lumped models SMAP and HEC-HMS are considered as the hydrological models. Furthermore, the computed flows are propagated in a HEC-RAS scheme designed with extensive field data from bathymetry of Parana River and tributaries. The daily results are presented as fifteen inflow scenarios that are considered for the definition of a unique flow forecast. Finally, that forecast is used for the electric generation scheduling of the power plant. Each of these fifteen methods performance was evaluated as well as the suitability of the system for it purposes. For a short-term forecast horizon (less than 4 days), the performances of the hydrological models forced by the different rain forecast are quite similar. However, it is remarkable the difference between the results of the three hydrological models for the same horizon. On the other hand, for medium-term horizon (more than 4 days) both hydrological and meteorological models have diverse behavior and contribute for a wide representation of the possible scenarios. Overall, it has been showed that the simulations are complementary and provides to the forecaster a general overview of the hydrologic situation. Nevertheless, at this moment, further analysis of accuracy and reliability of the prediction have not been realized, so the forecaster needs to appeal to its own expertise to assure the consistence of the scenarios for decision-making.