This page contains 15 estimated quantiles for 9,203 level-12 Hydrologic Unit Code in the Southeastern United States for the decades 1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, and 2000-2009. A multi-output neural network was used to generate the estimated quantiles (Worland and others, 2019). The R scripts that generated the predictions are also included along with a README file. The 15 quantiles are associated with the following 15 non-exceedance probabilities (NEPs): 0.0003, 0.0050, 0.0500, 0.1000, 0.2000, 0.3000, 0.4000, 0.5000, 0.6000, 0.7000, 0.8000, 0.9000, 0.9500, 0.9950, and 0.9997. The quantiles were calculated using the Weibull plotting position (more details can be found in the accompanying manuscript). In addition to the median estimate of the quantiles, 68th, 95th, and 99.7th percentile intervals are also included in .csv file. The percentile intervals were estimated using Monte-Carlo dropout for 500 forward passes of the neural network. The intervals are represented in the .csv file as p0.0015, p0.0250, p0.1600, p0.5000, p0.8400, p0.975, and p0.9985 which indicates the 68th, 95th, and 99.7th percentile intervals. The median (p0.5000) and the mean estimate should be used if only a single realization of the estimated quantiles is needed. The neural network was trained using streamflow data at sites with records that contained only non-zero streamflow values. However, the model was used to make predictions for every HUC12 pour point. Some of these predictions are likely for sites that have streamflow values equal to zero. Worland, S. C., Steinschneider, S., Asquith, W., Knight, R. and Wieczorek, M., 2019, Prediction and inference of flow-duration curves using multi-output neural networks, Water Resources Research , submitted.