Empirical models were developed at South Carolina beaches and estuaries to create daily forecasts of bacterial water quality for use as decision support tools. These tools predict exceedance of bacteria criteria using integrated monitoring data, remote sensing, and meteorology information. The models developed for beach areas used precipitation data from a rain gauge network, tide data, and qualitative weather information to predict criterion exceedance. Current efforts on these tools include integrating data from ocean observing systems and precipitation data from remote sensing products to create near-real time prediction updates presented in a web-based GIS. Similar predictive models for fecal coliform bacteria concentration were developed using integrated data from monitoring programs, meteorology, and remote sensing. These two related modeling efforts highlight the utility and feasibility of integrating data from observing systems and remote sensing to create empirically-based decision support tools.