The main objective of this research is to devise a risk-based methodology for developing emergency operation schedules (EOS). EOS are decision tools that provide guidance to reservoir operators in charge of making real-time release decisions during major flood events. A computer program named REOS was created to perform the computations to develop risk-based EOS. The computational algorithm in REOS is divided in three major components: (1) synthetic streamflow generation, (2) mass balance computations, and (3) frequency analysis. The methodology computes the required releases to limit storage to the capacity available based on the probabilistic properties of future flows, conditional to current streamflow conditions. The final product is a series of alternative risk-based EOS in which releases, specified as a function of reservoir storage level, current and past inflows, and time of year, are associated with a certain risk of failing to attain the emergency operations objectives. The assumption is that once emergency operations are triggered by a flood event, the risk associated with a particular EOS reflects the probability of exceeding a pre-established critical storage level given that the same EOS is followed throughout the event. This provides reservoir operators with a mechanism for evaluating the tradeoffs and potential consequences of release decisions. The methodology was applied and tested using the Addicks and Barker Reservoir system in Houston, TX as a case study. Upstream flooding is also a major concern for these reservoirs. Modifications to the current emergency policies that would allow emergency releases based on the probability of upstream flooding are evaluated. Riskbased EOS were tested through a series of flood control simulations. The simulations were performed using the HEC-ResSim reservoir simulation model. Rainfall data recorded from Tropical Storm Allison was transposed over the Addicks and Barker watersheds to compute hypothetical hydrographs using HEC-HMS. Repeated runs of the HEC-ResSim model were made using different flooding and residual storage scenarios to compare regulation of the floods under alternative operating policies. An alternative application of the risk-based EOS in which their associated risk was used to help quantify the actual probability of upstream flooding in Addicks and Barker was also presented.