The conventional point-source discharge permitting approach, referred to as the National Pollutant Discharge Elimination System (NPDES), is based on either a regulatory low flow (hydrologic, biological, or seasonal) criterion or on a hydrograph controlled release (HCR) approach. Regulatory low flows are often estimated using empirical equations because of the lack of historical flow data. Overestimated low flows may threaten water quality protection, while underestimated low flows can result in uneconomical wastewater treatment. Since uncertainty in low flow estimations is caused by climate variability, uncertainty in the permitting process can be reduced through explicit incorporation of climate variability. Therefore, the objective of this study was to demonstrate how the NPDES permitting process can be improved through the incorporation of climate information. A dissolved oxygen (DO) model was developed for the Chickasaw Creek watershed located in southeast Alabama using the Loading Simulation Program C++ (LSPC) linked with a hydrodynamic and water quality model (EPD-RIV1). Models were calibrated and validated for flow, stream temperature, DO, and other water quality variables. DO and stream temperature variations were examined for historic, climate variability-causing events of La Niña and El Niño using a number of statistical criteria to develop toxicity-based and DO-based criteria for ammonia. The analysis identified December to April for El Niño as periods of high assimilation (28% higher as compared to the current assimilation). Similarly, August to October for La Niña was identified as a period of high assimilation, but with a higher degree of variability. May to July for La Niña and August to October for El Niño were identified as periods of restrictive point-source discharge. Analysis suggested that El Niño Southern Oscillation (ENSO) predictions provide sufficient warning for the impending drought for reducing point-source discharges because low flow in summer months is a function of winter and spring sea surface temperature, precipitation, and streamflow due to its autocorrelation and cross-correlation characteristics. Overall, the research demonstrated the usefulness of integrating climate information with NPDES permitting.