Complex three-dimensional (3-D) numerical models are often used for simulation and forecasting of salinity for estuarine water resources management. However, the effort which goes into these models is significant, partly due to the difficulty associated with model development and the prolonged computation time required for model runs. Modern water resources management sometimes requires a quick turnaround time for long-term simulations or short-term forecasts of estuarine salinity conditions. This paper presents an innovative approach for the development of an alternative salinity model based on time series analyses of salinity data. The structure of the model consists of an autoregressive term representing the system persistence and an exogenous term accounting for physical drivers including freshwater inflow, rainfall, and tidal water surface elevation that cause salinity to vary. An analogy to 1-D physical models reveals that major physical processes of salt transport are implicitly embedded in the time series model. Model calibration and validation using up to 20 years of measured data collected in the Caloosahatchee River Estuary, Florida indicate that the time series model offers comparable or superior performance compared with its 3-D counterpart. This model has been used as a tool for water resources management projects relating to ecosystem restoration and water control in south Florida. A special case of model application is included to demonstrate how the model has been used for salinity forecasting to support weekly operation of water control infrastructure and water resources management decision making. Similar modeling tools can be developed using this approach for other estuaries.