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An empirical framework for assisting with water quality management is proposed that relies on open-source hydrologic data. Such data are measured periodically at fixed water stations and commonly available in time-series form. To fully exploit the data, we suggest that observations from multiple stations should be combined into a single long-panel data set, and an econometric model developed to estimate upstream management effects on downstream water quality. Selection of the model's functional form and explanatory variables would be informed by rating curves, and idiosyncrasies across and within stations handled in an error term by testing contemporary correlation, serial correlation, and heteroskedasticity. Our proposed approach is illustrated with an application to the Nakdong River basin in South Korea. Three alternative policies to achieve downstream BOD level targets are evaluated: upstream water treatment, greater dam discharge, and development of a new water source. Upstream water treatment directly cuts off incoming pollutants, thereby presenting the smallest variation in its downstream effects on BOD levels. Treatment is advantageous when reliability of water quality is a primary concern. Dam discharge is a flexible tool, and may be used strategically during a low-flow season. We consider development of a new water corridor from an extant dam as our third policy option. This turns out to be the most cost-effective way for securing lower BOD levels in the downstream target city. Even though we consider a relatively simple watershed to illustrate the usefulness of our approach, it can be adapted easily to analyze more complex upstream-downstream issues.