This PhD thesis explores simulation-based reservoir operation models by maximizing the use of available data (from in-situ to global) and available modeling techniques (process-driven and data-driven). The research includes model developments, performance examinations and applications. The (process-driven) wflow_sbm model was used to investigate the model realism in representing reservoir operations and was applied to quantify effects of reservoir operations on the daily streamflows. The (data-driven) machine learning models was used to investigate the roles of available reservoir-related data as their inputs to estimate the daily reservoir outflow, and thus the model accuracy. It was also applied to improve the simulation and multi-step reforecast of the daily reservoir outflow using global forecast data. This thesis focuses on major, multi-purpose and over-year storage reservoirs in the Greater Chao Phraya River Basin in Thailand. Its findings can enhance the understanding of reservoir operations, their effects and their modeling to support reservoir operators.