Emulation modelling has been successfully applied in many environmental applications to reduce large, computationally demanding, process-based models to low order surrogates to be used in place of the original model in problems involving hundreds or thousands of model simulations. Typical examples include optimal planning and management, data assimilation, and sensitivity analysis. In this study, we describe the identification of a dynamic emulator of a 3D hydrodynamic reservoir model and its subsequent use within a real-time control framework for dam operation. In particular, we adopt a novel data-driven approach that combines the many advantages of data-driven modelling in representing complex, non-linear relationships, but preserves the state-space representation typical of process-based models, which is particularly effective in designing the controller. The approach is demonstrated on Marina Reservoir, Singapore, which was recently reclaimed to the sea and transformed into a freshwater storage by constructing a barrage. A dynamic emulator of the salinity evolution in a control point near the dam was identified and then used in combination with Model Predictive Control to design the real-time operation of the barrage. Results show that the salinity levels, due to saline intrusion through groundwater seepage, can be dropped to drinking water standards by embedding the emulator in the real-time controller.