A web-based open-source decision support system (DSS) was developed to facilitate real-world engagement with dam-operating agencies in the decision-making process involving atmospheric modeling, hydrologic modeling, and web technology. The development process was decoupled into the container (frontend) and the modeling framework for the content (backend), to arrive at an intelligent system that improves the productivity and independent reuse of each component. The backend framework uses the weather forecasts from Numerical Weather Prediction models, downscales to a finer resolution, and simulates hydrologic and data-based artificial neural network models to optimize operations. The frontend architecture disseminates the forecasted meteorological variables, reservoir inflow, optimized operations, and retrospective weekly assessment of forecasts and hydropower benefits. The framework is automated and operationalized over the Detroit dam (Oregon) to generate the daily optimized release decisions. However, backend scripts and frontend elements are flexible and customizable enough that the DSS can be reproduced for other dams. The optimization of reservoir operations based on weather forecasts results in significant additional hydropower benefit without compromising other objectives when compared to the conventional operations. More importantly, the platform helps visualize for the dam operator how much more ‘smarter’ operations can be if weather forecasts and open-source technology are used.