<p>The release rules for large reservoirs are generally graded and judgment-based, allowing for some flexibility in managing uncertainty in forecast information. Ignoring this aspect can introduce bias in flood water resource utilization and flood risk analysis. This study proposes a dynamic control flood limit method for reservoirs, accounting for the uncertainty in forecast errors, to optimize the use of forecast data. Monte Carlo simulations are employed to model and assess uncertainty in the sample, and the error domain is systematically quantified to evaluate the probability that the observed inflow falls within the forecast error range. This determination is pivotal in ascertaining the utilization of forecast data, thereby facilitating the integration of multiple forecast lead times. The proposed method is applied to the Three Gorges Reservoir and compared with a traditional approach using single forecast data. Results show that: (1) the proposed method has been demonstrated to facilitate real-time dynamic decision-making, thereby enabling the selection of forecast data that exhibits both acceptable risk and optimal benefit. (2) during the rising flood stage of 20,190,724, this method has been shown to enhance power generation by 2.83 × 10⁸ kWh while concomitantly reducing flood risk in comparison with conventional methods. Flood risks upstream and downstream are reduced by 0.415 and 0.005, respectively, without compromising the benefits of power generation. This methodology provides a reference for flood water resource management in basin reservoirs, providing decision-makers with a strategy that best aligns with their preferences.</p>