This metadata record describes monthly estimates of natural baseflow for 15,866 stream reaches, defined by the National Hydrography Dataset Plus Version 2.0 (NHDPlusV2), in the Delaware River Basin for the period 1950-2015. A statistical machine learning technique - random forest modeling (Liaw and Wiener, 2018; R Core Team, 2020) - was applied to estimate natural flows using about 150 potential predictor variables (Miller and others, 2018). Calibration data used for the random forest model are available from (Foks and others, 2020). Each model was run twice, first using all potential predictor variables, which represents a "full" model run, and a second time using the top 20 predictors from the original run, which represents the "partial" model run. Model performance of the full and partial models were compared and identified to be similar. Therefore, predictions for all NHDPlusV2 stream reaches were made using the partial model. Methods used to calibrate the random forest models, and references to predictor data sources are detailed in (Miller and others, 2018). The R scripts used and directions to run the scripts are included in this data release. References cited: Liaw, A., and Wiener, M., 2018, Package 'randomForest': The Comprehensive R Archive Network, https://cran.r-project.org/web/packages/randomForest/randomForest.pdf. Miller, M.P., Carlisle, D.M., Wolock, D.M., and Wieczorek, M., 2018, A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States: Journal of the American Water Resources Association, v. 54, no. 6, p. 1258-1269, https://doi.org/10.1111/1752-1688.12685. Foks, S.S., Miller, M.P., and Hopple, J.A., 2020, Daily-timestep and monthly-timestep estimates of baseflow at 49 reference stream gages located within 25 miles of the Delaware River basin watershed boundary for the years 1950 through 2015: U.S. Geological Survey data release, https://doi.org/10.5066/P9XY70L4. R Core Team, 2020, R-A language and environment for statistical computing: R Foundation for Statistical Computing, https://www.eea.europa.eu/data-and-maps/indicators/oxygen-consuming-substances-in-rivers/r-development-core-team-2006.