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ABSTRACT Reservoirs considerably affect river streamflow and need to be accurately represented in environmental impact studies. Modeling reservoir outflow represents a challenge to hydrological studies since reservoir operations vary with flood risk, economic and demand aspects. The Brazilian Interconnected Energy System (SIN) is an example of a unique and complex system of coordinated operation composed by more than 160 large reservoirs. We proposed and evaluated an integrated approach to simulate daily outflows from most of the SIN reservoirs (138) using an Artificial Neural Network (ANN) model, distinguishing run-of-the-river and storage reservoirs and testing cases whether outflow and level data were available as input. Also, we investigated the influence of the proposed input features (14) on the simulated outflow, related to reservoir water balance, seasonality, and demand. As a result, we verified that the outputs of the ANN model were mainly influenced by local water balance variables, such as the reservoir inflow of the present day and outflow of the day before. However, other features such as the water level of 4 large reservoirs that represent different regions of the country, which infers about hydropower demand through water availability, seemed to influence to some extent reservoirs outflow estimates. This result indicates advantages in using an integrated approach rather than looking at each reservoir individually. In terms of data availability, it was tested scenarios with (WITH_Qout) and without (NO_Qout and SIM_Qout) observed outflow and water level as input features to the ANN model. The NO_Qout model is trained without outflow and water level while the SIM_Qout model is trained with all input features, but it is fed with simulated outflows and water levels rather than observations. These 3 ANN models were compared with two simple benchmarks: outflow is equal to the outflow of the day before (STEADY) and the outflow is equal to the inflow of the same day (INFLOW). For run-of-the-river reservoirs, an ANN model is not necessary as outflow is virtually equal to inflow. For storage reservoirs, the ANN estimates reached median Nash-Sutcliffe efficiencies (NSE) of 0.91, 0.77 and 0.68 for WITH_, NO_ and SIM_Qout respectively, compared to a median NSE of 0.81 and 0.29 for the STEADY and INFLOW benchmarks respectively. In conclusion, the ANN models presented satisfactory performances: when outflow observations are available, WITH_Qout model outperforms STEADY; otherwise, NO_Qout and SIM_Qout models outperform INFLOW.

ABSTRACT Tens of thousands of dams were built around the world to reduce flood risks, produce energy, and maximize benefits of limited freshwater resources. In Brazil, the main and largest reservoirs are related to hydropower plants. Improving the understanding of reservoir dynamics is important not only to evaluate their impact in the flow regime of Brazilian rivers, but also to simulate the combined effect of constructing new dams and potential alterations under future climatic conditions. Here, we analyze how an ideal representation of reservoirs in terms of forced discharge would improve a previously calibrated hydrological model under the Brazilian domain. We forced the continental-scale version of the MGB model on observed reservoir outflows from 109 hydropower dams, which are part of the Brazilian National Interconnected System controlled by the National Electrical System Operator. Model simulated flows were replaced by the reservoir outflows in all dam locations and were compared to the original discharge in downstream gauges. The forced discharge simulation presented a mean improvement for Kling-Gupta Efficiency of 21%, when compared to the original model (naturalized flow). This analysis is a preliminary step towards an explicit representation of the reservoirs in the model, what will be conducted in a future study.