Censored and uncensored generalized additive models (GAMs) are developed from 955 U.S. Geological Survey streamflow-gaging stations (streamgages) to predict decadal statistics of streamflow. The streamgages are located on streams draining to the Gulf of Mexico. Decadal statistics include no-flow fractions and selected L-moments of nonzero streamflow for six decades (1950s—2000s). These statistics represent metrics of decadal flow-duration curves (dFDCs) derived from about 10 million daily mean streamflows. The L-moments include the mean, coefficient of L-variation, and the third through fifth L-moment ratios. The models are fit using watershed properties such as basin area and slope, decadal precipitation and temperature and decadal values of flood storage and urban development percentages. The GAMs then estimate decadal statistics for 8,988 prediction locations (stream reaches) coincident with outlets of level-12 hydrologic unit codes. Both the entire dataset (whole model) and leave-one-watershed-out model results are reported. No-flow fractions are censored data and Tobit extensions to GAMs are effective in estimation of ephemeral streamflow conditions. Uncensored GAMs conversely were used for estimation of the L-moments. The R language was used to pull and process the streamflow data, and the scripts can be found online at https://code.usgs.gov/water/restore/fdclmrpplo.

Using previously published (Robinson and others, 2019) no-flow fractions and L-moments of nonzero streamflow from decadal streamflow flow-duration analysis (daily mean streamflow), probability distributions were fit to provide 27 estimated quantiles of decadal flow-duration curves, and hence the probability distributions are a form of parametric modeling that ensures monotonicity of the quantiles by non-exceedance probability (NEP). For both U.S. Geological Survey streamflow-gaging stations (streamgages) and level-12 hydrologic unit code (HUC12) catchments, as defined by Crowley-Ornelas and others (2019), the 27 quantiles were estimated and tabulated in this data release. Three probability distributions were used and are summarized by Asquith and others (2017): the asymmetric exponential power (AEP4) (4-parameter), generalized normal (GNO) (3-parameter log-normal), and kappa (KAP) (4-parameter). A summary of the mathematics for these distributions is provided in the README files within this data release and close consultation of the mathematical discussion in Asquith and others (2017) also is suggested. The lmomco R package (Asquith, 2020) was used for distribution fitting and the technically-demanding implementation for a single location is archived in the RESTORE/fdclmrpplo software release within file fdclmrpplo/scripts/pred_fdc_ref/pred_fdc_ref.R (Asquith and others, 2020). The implementation for the streamgages is archived in the RESTORE/fdclmrpplo software release within file fdclmrpplo/scripts/pred_fdc_gage/pred_fdc_gage.R, and the implementation for the HUC12s is archived file fdclmrpplo/scripts/pred_fdc_huc12/pred_fdc_huc12.R and README files therein. For a given data set of no-flow fraction and L-moments, the three distributions will have similar results in the central parts of NEP and differences will be largest in the far left (low flow) and far right (flood flow) tails. No opinion that a particular distribution is more suitable than another is provided with exception that the GNO is fit to the first three L-moments and the AEP4 and KAP are fit to the first four L-moments. As a result, it is logical to state that more information on the distribution of streamflow is retained by the AEP4 and KAP distributions than the GNO. The availability of three distributions with the data release is considered a feature because a semi-quantitative assessment of model error (uncertainty attributed to choice of model) can be made. Asquith, W.H., 2020, lmomco—L-moments, censored L-moments, trimmed L-moments, L-comoments, and many distributions: R package version 2.3.6, https://CRAN.R-project.org/package=lmomco. Asquith, W.H., Kiang, J.E., and Cohn, T.A., 2017, Application of at-site peak-streamflow frequency analyses for very low annual exceedance probabilities: U.S. Geological Survey Scientific Investigation Report 2017–5038, 93 p., https://doi.org/10.3133/sir20175038. Asquith, W.H., Knight, R.R., and Crowley-Ornelas, E.R., 2020, RESTORE/fdclmrpplo—Source code for estimation of L-moments and percent no-flow conditions for decadal flow-duration curves and estimation at level-12 hydrologic unit codes along with other statistical computations: U.S. Geological Survey software release, Reston, Va., https://doi.org/10.5066/P93CKH92. Crowley-Ornelas, E.R., Worland, S.C., Wieczorek, M.E., Asquith, W.H., Knight, R.R., 2019, Summary of basin characteristics for National Hydrography Dataset, version 2 catchments in the Southeastern United States, 1950–2010: U.S. Geological Survey data release, https://doi.org/10.5066/P9KXTDU4. Robinson, A.L., Asquith, W.H., and Knight, R.R., 2019, Summary of decadal no-flow fractions and decadal L-moments of nonzero streamflow flow-duration curves for National Hydrography Dataset, version 2 catchments in the southeastern United States, 1950–2010: U.S. Geological Survey data release, https://doi.org/10.5066/P9Z4PM55.

Using previously published (Robinson and others, 2019) no-flow fractions and L-moments of nonzero streamflow from decadal streamflow flow-duration analysis (daily mean streamflow), probability distributions were fit to provide 27 estimated quantiles of decadal flow-duration curves, and hence the probability distributions are a form of parametric modeling that ensures monotonicity of the quantiles by non-exceedance probability (NEP). For both U.S. Geological Survey streamflow-gaging stations (streamgages) and level-12 hydrologic unit code (HUC12) catchments, as defined by Crowley-Ornelas and others (2019), the 27 quantiles were estimated and tabulated in this data release. Three probability distributions were used and are summarized by Asquith and others (2017): the asymmetric exponential power (AEP4) (4-parameter), generalized normal (GNO) (3-parameter log-normal), and kappa (KAP) (4-parameter). A summary of the mathematics for these distributions is provided in the README files within this data release and close consultation of the mathematical discussion in Asquith and others (2017) also is suggested. The lmomco R package (Asquith, 2020) was used for distribution fitting and the technically-demanding implementation for a single location is archived in the RESTORE/fdclmrpplo software release within file fdclmrpplo/scripts/pred_fdc_ref/pred_fdc_ref.R (Asquith and others, 2020). The implementation for the streamgages is archived in the RESTORE/fdclmrpplo software release within file fdclmrpplo/scripts/pred_fdc_gage/pred_fdc_gage.R, and the implementation for the HUC12s is archived file fdclmrpplo/scripts/pred_fdc_huc12/pred_fdc_huc12.R and README files therein. For a given data set of no-flow fraction and L-moments, the three distributions will have similar results in the central parts of NEP and differences will be largest in the far left (low flow) and far right (flood flow) tails. No opinion that a particular distribution is more suitable than another is provided with exception that the GNO is fit to the first three L-moments and the AEP4 and KAP are fit to the first four L-moments. As a result, it is logical to state that more information on the distribution of streamflow is retained by the AEP4 and KAP distributions than the GNO. The availability of three distributions with the data release is considered a feature because a semi-quantitative assessment of model error (uncertainty attributed to choice of model) can be made. Asquith, W.H., 2020, lmomco—L-moments, censored L-moments, trimmed L-moments, L-comoments, and many distributions: R package version 2.3.6, https://CRAN.R-project.org/package=lmomco. Asquith, W.H., Kiang, J.E., and Cohn, T.A., 2017, Application of at-site peak-streamflow frequency analyses for very low annual exceedance probabilities: U.S. Geological Survey Scientific Investigation Report 2017–5038, 93 p., https://doi.org/10.3133/sir20175038. Asquith, W.H., Knight, R.R., and Crowley-Ornelas, E.R., 2020, RESTORE/fdclmrpplo—Source code for estimation of L-moments and percent no-flow conditions for decadal flow-duration curves and estimation at level-12 hydrologic unit codes along with other statistical computations: U.S. Geological Survey software release, Reston, Va., https://doi.org/10.5066/P93CKH92. Crowley-Ornelas, E.R., Worland, S.C., Wieczorek, M.E., Asquith, W.H., Knight, R.R., 2019, Summary of basin characteristics for National Hydrography Dataset, version 2 catchments in the Southeastern United States, 1950–2010: U.S. Geological Survey data release, https://doi.org/10.5066/P9KXTDU4. Robinson, A.L., Asquith, W.H., and Knight, R.R., 2019, Summary of decadal no-flow fractions and decadal L-moments of nonzero streamflow flow-duration curves for National Hydrography Dataset, version 2 catchments in the southeastern United States, 1950–2010: U.S. Geological Survey data release, https://doi.org/10.5066/P9Z4PM55.

This dataset provides numerical and categorical descriptions of 48 basin characteristics for 956 basins with observed streamflow information at U.S. Geological Survey (USGS) streamflow-gaging stations. Characteristics are indexed by National Hydrography Dataset (NHD) version 2 COMID (integer that uniquely identifies each feature in the NHD) and USGS station number for streamflow-gaging station. The variables represent mutable and immutable basin characteristics and are organized by characteristic type: physical (5), hydrologic (6), categorical (12), climate (6), landscape alteration (7), and land cover (12). Mutable characteristics such as climate, land cover, and landscape alteration variables are reported in decadal increments (for example, average percent forest for the decade 1950-1959, 1960-1969, etc). The majority of basin characteristics in this dataset were calculated using divergence-routing methods and are often referred to as “network-accumulated”. This method uses a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the values derived from the reach catchment scale (Schwarz, G.E., and Wieczorek, M.E., 2018, Database of modified routing for NHDPlus version 2.1 flowlines: ENHDPlusV2_us: U.S. Geological Survey data release, https://doi.org/10.5066/P9PA63SM ). In four instances, values are also provided for the entire catchment above a site and area designated using the “CAT_” prefix.

This dataset provides numerical and categorical descriptions of 48 basin characteristics for 9,314 ungaged basins coinciding with 12-digit hydrologic unit code (HUC12) pour points that drain to the Gulf of Mexico. Characteristics are indexed by National Hydrography Dataset (NHD) version 2 COMID (integer that uniquely identifies each feature in the NHD) and HUC12 identifying number. The variables represent mutable and immutable basin characteristics and are organized by characteristic type: physical (5), hydrologic (6), categorical (12), climate (6), landscape alteration (7), and land cover (12). Mutable characteristics such as climate, land cover, and landscape alteration variables are reported in decadal increments (for example, average percent forest for the decade 1950-1959, 1960-1969, etc). The majority of basin characteristics in this dataset were calculated using divergence-routing methods and are often referred to as “network-accumulated”. This method uses a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the values derived from the reach catchment scale (Schwarz, G.E., and Wieczorek, M.E., 2018, Database of modified routing for NHDPlus version 2.1 flowlines: ENHDPlusV2_us: U.S. Geological Survey data release, https://doi.org/10.5066/P9PA63SM ). In four instances, values are also provided for the entire catchment above a site and area designated using the “CAT_” prefix.

This dataset contains statistical descriptions of observed daily-mean streamflow for 956 sites in the southeast United States. For each site, statistical descriptions are provided according to decade for up to six decades, beginning in 1950 (1950-59 calendar years) and ending with 2000 (2000 - 2009 calendar years) with no more than 7 missing values per year in total (continuous or noncontinuous). There are 40 statistical descriptions including 28 flow-duration curve values, 8 L-moments, and 4 describing the minimum, maximum, median flow for days not equal to zero, and number of zero-flow days. Site information is provided by decade - the number of rows per site varies from 1 to 6 depending on the number of decades with observed record available. This information was used as the response variable(s) for statistical models for estimating the same characteristics at nearly 10,000 ungaged locations throughout the southeast United States. The dataset has been provided as a shapefile and a comma-delimited file. The comma-delimited file is an exact copy of the attribute table of the shapefile.

Two methods of calculating hydrologic alteration were applied to modeled daily streamflow data for 9,201 12-digit hydrologic unit code (HUC12) pour points draining to the Gulf of Mexico (Robinson and others, 2020). The first method is a new modified method of calculating ecosurplus and ecodeficit called hydro change. For this project, ecosurplus and ecodeficit have been combined to assess overall hydrologic regime change. The second method is the confidence interval hypothesis test (Kroll and others, 2015). The first method is a means of quantifying hydrologic alteration while the second is a hypothesis test to simply determine if statistically significant alteration has occurred. Both methods are employed to determine which is best at analyzing alteration of the hydrologic regime in the Gulf Coast Ecosystem Restoration Council (RESTORE) study area. Statistical analysis was done in RStudio (2020). The data release includes four attached files: (1) metadata .xml file, (2) csv with the p-values for each HUC12, (3) csv with results from the hydrologic change analysis, and (4) the shapefile of the pour point locations for the HUC12s used in the analyses.