This paper proposes an annual inflow forecasting model in an open-loop feedback control operational policy for long-term hydropower scheduling. A deterministic optimization model precisely represents hydropower generation by taking into consideration water head as a nonlinear function of storage, discharge and spillage. The inflow is made available by a forecasting model based on a fuzzy inference system that captures the nonlinear correlation of consecutive inflows on an annual basis, with disaggregation of the results on a monthly basis. The performance of the proposed approach is evaluated by simulation for a multi-reservoir system, based on historical inflow records and compared to the same approach on monthly basis. The results show that the proposed approach leads to an operational performance closer to that of the perfect foresight solution, providing lower spillages and higher average hydropower efficiency and generation.