In order to achieve a win-win situation for both power generation and ecological benefits of cascade reservoirs, a dynamic simulation chain of “forecasting-scheduling-risk analysis” was constructed. The forecast error evolution process was revealed by the martingale model of forecast evolution (MMFE). The rolling inflow forecast scenarios were generated by the Monte Carlo simulation (MCS) based on Latin hypercube sampling (LHS). Using cascade reservoirs in the Qingjiang River Basin as an example, on the basis of regional hydrological information with uncertainty, the minimum, suitable, and ideal ecological flow rates of the downstream control sections of the Shuibuya and Geheyan reservoirs were calculated, and a synergistic regulation model of power generation and ecological benefit of cascade reservoirs was constructed and solved using the multi-objective shuffling frog leaping algorithm (MOSFLA). The potential risk of water regulation under the influence of runoff forecast uncertainty was analyzed. The results demonstrate that the proposed model can dynamically identify the uncertain information propagated from the inflow forecast to reservoir scheduling, helping to reduce potential water regulation risk and enhance the robustness of water resources regulation for cascade reservoirs.