Optimal hydrograph separation (OHS) is a two-component, hydrograph separation method that uses a two-parameter, recursive digital filter (RDF) constrained via chemical mass balance to estimate the base flow contribution to a stream or river (Rimmer and Hartman, 2014; Raffensperger et al., 2017). A recursive digital filter distinguishes between high-frequency and low-frequency discharge data within a hydrograph, where high-frequency data corresponds to quick flow or storms and low-frequency data corresponds to base flow. The two parameters within the RDF are alpha and beta, both are unitless. Alpha is defined as the recession constant and typically found through recession analysis. For the purposes of this data release and study, we derived alpha from a groundwater flow coefficient (gwflow_coef) defined in the National Hydrologic Model Infrastructure run with the Precipitation-Runoff Modeling System (NHM-PRMS) (Regan et al., 2018). The second parameter, beta, is defined as the maximum value of the base flow index (Eckhardt, 2005). Beta is optimized using specific conductance and mass balance techniques, where a hydrograph is split into quick flow and base flow and specific conductance values are proposed for these streamflow components. OHS uses two model types to estimate base flow specific conductance from stream specific conductance, referred to as 'SCfit' and 'sin-cos' model types. The 'SCfit' model type uses a peak-fitting algorithm to define time periods where the stream is entirely comprised of base flow, whereas the 'sin-cos' model type emulates seasonal variation in streamflow specific conductance with a sine-cosine function to pinpoint when base flow contributes to streamflow. For more information and equations regarding model type and OHS methods, please see the associated publication (Foks et al., 2019). OHS was applied to 1076 stream gages within the conterminous United States (CONUS) where daily streamflow and daily or discrete measurements of specific conductance were collected. Gages were selected for this method if they were of "reference quality" as defined by the Geospatial Attributes of Gages for Evaluating Streamflow (GAGES-II) dataset (Falcone, 2011). Of these 1076 sites, 825 had "successful" OHS models - implying good agreement between observed and simulated stream specific conductance. This data release contains the results of applying OHS to hundreds of stream gages of varying watershed characteristics, summary of watershed and hydro-climatological characteristics for each site (Falcone, 2011; USGS, 2003), and a comparison of OHS-defined base flow to base flow -analogous flow components within the NHM-PRMS (gwres_flow and slow_flow) (Regan et al., 2018; Regan et al., 2019). For this data release and study, comparisons of OHS-defined base flow were made to the "by HRU" calibration of the NHM-PRMS (Hay, 2019).

Optimal hydrograph separation (OHS) is a two-component, hydrograph separation method that uses a two-parameter, recursive digital filter (RDF) constrained via chemical mass balance to estimate the base flow contribution to a stream or river (Rimmer and Hartman, 2014; Raffensperger et al., 2017). A recursive digital filter distinguishes between high-frequency and low-frequency discharge data within a hydrograph, where high-frequency data corresponds to quick flow or storms and low-frequency data corresponds to base flow. The two parameters within the RDF are alpha and beta, both are unitless. Alpha is defined as the recession constant and typically found through recession analysis. For the purposes of this data release and study, we derived alpha from a groundwater flow coefficient (gwflow_coef) defined in the National Hydrologic Model Infrastructure run with the Precipitation-Runoff Modeling System (NHM-PRMS) (Regan et al., 2018). The second parameter, beta, is defined as the maximum value of the base flow index (Eckhardt, 2005). Beta is optimized using specific conductance and mass balance techniques, where a hydrograph is split into quick flow and base flow and specific conductance values are proposed for these streamflow components. OHS uses two model types to estimate base flow specific conductance from stream specific conductance, referred to as 'SCfit' and 'sin-cos' model types. The 'SCfit' model type uses a peak-fitting algorithm to define time periods where the stream is entirely comprised of base flow, whereas the 'sin-cos' model type emulates seasonal variation in streamflow specific conductance with a sine-cosine function to pinpoint when base flow contributes to streamflow. For more information and equations regarding model type and OHS methods, please see the associated publication (Foks et al., 2019). OHS was applied to 1076 stream gages within the conterminous United States (CONUS) where daily streamflow and daily or discrete measurements of specific conductance were collected. Gages were selected for this method if they were of "reference quality" as defined by the Geospatial Attributes of Gages for Evaluating Streamflow (GAGES-II) dataset (Falcone, 2011). Of these 1076 sites, 825 had "successful" OHS models - implying good agreement between observed and simulated stream specific conductance. This data release contains the results of applying OHS to hundreds of stream gages of varying watershed characteristics, summary of watershed and hydro-climatological characteristics for each site (Falcone, 2011; USGS, 2003), and a comparison of OHS-defined base flow to base flow -analogous flow components within the NHM-PRMS (gwres_flow and slow_flow) (Regan et al., 2018; Regan et al., 2019). For this data release and study, comparisons of OHS-defined base flow were made to the "by HRU" calibration of the NHM-PRMS (Hay, 2019).

Optimal hydrograph separation (OHS) is a two-component, hydrograph separation method that uses a two-parameter, recursive digital filter (RDF) constrained via chemical mass balance to estimate the base flow contribution to a stream or river (Rimmer and Hartman, 2014; Raffensperger et al., 2017). A recursive digital filter distinguishes between high-frequency and low-frequency discharge data within a hydrograph, where high-frequency data corresponds to quick flow or storms and low-frequency data corresponds to base flow. The two parameters within the RDF are alpha and beta, both are unitless. Alpha is defined as the recession constant and typically found through recession analysis. For the purposes of this data release and study, we derived alpha from a groundwater flow coefficient (gwflow_coef) defined in the National Hydrologic Model Infrastructure run with the Precipitation-Runoff Modeling System (NHM-PRMS) (Regan et al., 2018). The second parameter, beta, is defined as the maximum value of the base flow index (Eckhardt, 2005). Beta is optimized using specific conductance and mass balance techniques, where a hydrograph is split into quick flow and base flow and specific conductance values are proposed for these streamflow components. OHS uses two model types to estimate base flow specific conductance from stream specific conductance, referred to as 'SCfit' and 'sin-cos' model types. The 'SCfit' model type uses a peak-fitting algorithm to define time periods where the stream is entirely comprised of base flow, whereas the 'sin-cos' model type emulates seasonal variation in streamflow specific conductance with a sine-cosine function to pinpoint when base flow contributes to streamflow. For more information and equations regarding model type and OHS methods, please see the associated publication (Foks et al., 2019). OHS was applied to 1076 stream gages within the conterminous United States (CONUS) where daily streamflow and daily or discrete measurements of specific conductance were collected. Gages were selected for this method if they were of "reference quality" as defined by the Geospatial Attributes of Gages for Evaluating Streamflow (GAGES-II) dataset (Falcone, 2011). Of these 1076 sites, 825 had "successful" OHS models - implying good agreement between observed and simulated stream specific conductance. This data release contains the results of applying OHS to hundreds of stream gages of varying watershed characteristics, summary of watershed and hydro-climatological characteristics for each site (Falcone, 2011; USGS, 2003), and a comparison of OHS-defined base flow to base flow -analogous flow components within the NHM-PRMS (gwres_flow and slow_flow) (Regan et al., 2018; Regan et al., 2019). For this data release and study, comparisons of OHS-defined base flow were made to the "by HRU" calibration of the NHM-PRMS (Hay, 2019).

Optimal hydrograph separation (OHS) is a two-component, hydrograph separation method that uses a two-parameter, recursive digital filter (RDF) constrained via chemical mass balance to estimate the base flow contribution to a stream or river (Rimmer and Hartman, 2014; Raffensperger et al., 2017). A recursive digital filter distinguishes between high-frequency and low-frequency discharge data within a hydrograph, where high-frequency data corresponds to quick flow or storms and low-frequency data corresponds to base flow. The two parameters within the RDF are alpha and beta, both are unitless. Alpha is defined as the recession constant and typically found through recession analysis. For the purposes of this data release and study, we derived alpha from a groundwater flow coefficient (gwflow_coef) defined in the National Hydrologic Model Infrastructure run with the Precipitation-Runoff Modeling System (NHM-PRMS) (Regan et al., 2018). The second parameter, beta, is defined as the maximum value of the base flow index (Eckhardt, 2005). Beta is optimized using specific conductance and mass balance techniques, where a hydrograph is split into quick flow and base flow and specific conductance values are proposed for these streamflow components. OHS uses two model types to estimate base flow specific conductance from stream specific conductance, referred to as 'SCfit' and 'sin-cos' model types. The 'SCfit' model type uses a peak-fitting algorithm to define time periods where the stream is entirely comprised of base flow, whereas the 'sin-cos' model type emulates seasonal variation in streamflow specific conductance with a sine-cosine function to pinpoint when base flow contributes to streamflow. For more information and equations regarding model type and OHS methods, please see the associated publication (Foks et al., 2019). OHS was applied to 1076 stream gages within the conterminous United States (CONUS) where daily streamflow and daily or discrete measurements of specific conductance were collected. Gages were selected for this method if they were of "reference quality" as defined by the Geospatial Attributes of Gages for Evaluating Streamflow (GAGES-II) dataset (Falcone, 2011). Of these 1076 sites, 825 had "successful" OHS models - implying good agreement between observed and simulated stream specific conductance. This data release contains the results of applying OHS to hundreds of stream gages of varying watershed characteristics, summary of watershed and hydro-climatological characteristics for each site (Falcone, 2011; USGS, 2003), and a comparison of OHS-defined base flow to base flow -analogous flow components within the NHM-PRMS (gwres_flow and slow_flow) (Regan et al., 2018; Regan et al., 2019). For this data release and study, comparisons of OHS-defined base flow were made to the "by HRU" calibration of the NHM-PRMS (Hay, 2019).

This data set provides estimated and measured streamflow data and hydrograph-separation results for five sites located in northwest Mississippi. Streamflow data were collected by the U.S. Geological Survey (USGS) and the U.S. Army Corps of Engineers. Hydrograph-separation results provide runoff and baseflow estimates at each site that were calculated using four methods: PART, HYSEP Fixed, HYSEP Local Minimum, and BFI Standard, as well as an average base flow index (BFI) for all four methods.

This data set provides estimated and measured streamflow data and hydrograph-separation results for five sites located in northwest Mississippi. Streamflow data were collected by the U.S. Geological Survey (USGS) and the U.S. Army Corps of Engineers. Hydrograph-separation results provide runoff and baseflow estimates at each site that were calculated using four methods: PART, HYSEP Fixed, HYSEP Local Minimum, and BFI Standard, as well as an average base flow index (BFI) for all four methods.

This data release contains output of the initial calibration of the conterminous United States (CONUS) application of the Precipitation-Runoff Modeling System (PRMS) as implemented in the National Hydrologic Model (NHM) infrastructure (Regan et al, 2018). The PRMS version 5.0.0 hydrologic simulation code was used with the accompanying parameter files in the NHM infrastructure to produce the attached output files. Model input climate drivers include climate data derived from the Daymet gridded data set version 2 (Thornton et al., 2014) with values spatially-distributed to the HRUs using the USGS Geo Data Portal (https://cida.usgs.gov/gdp/; Blodgett et al., 2011). The parameter values are maintained in the National Hydrologic Model Parameter Database (NhmParamDb; Driscoll et al., 2017. The parameter file used to produce these model results is attached (NHM-PRMS.zip). CONUS-scale parameter calibration for the USGS National Hydrologic Model (NHM) infrastructure application of the Precipitation-Runoff Modeling System (NHM-PRMS) was conducted using five baseline data sets derived from multiple sources for each of the NHM’s 109,951 hydrologic response units (HRU) on time scales from annual to daily. A multiple-objective, step-wise, automated calibration procedure was used to identify the ‘optimal’ set of parameters for each HRU. This produced spatially distributed parameters for the CONUS using a calibration procedure termed ‘byHRU’ calibration. The NHM-PRMS simulations, with the byHRU calibrated parameters, were conducted with the same configuration for a time period from October 1, 1980 to December 31, 2016. The first three years of the simulations are should be considered ‘model spin up’ and not included in any subsequent analysis. Table 1 (attached) lists the output variables included in this data release. The individual *.csv files follow a naming convention of using an abbreviation for the spatial dimension of the model output (nhru or nsegment) _variable name.csv. The variable names included are defined further in Table 1. The structure of each output file includes a header line which labels the columns by the spatial unit (hru or stream segment) identification number and each row has the date followed by either 109951 or 56460 values, depending the output variable dimension nhru or nsegment, respectively. See table 1-10 in Regan and LaFontaine (2017) for a description of the variables included in this file. To distribute parameter and output values spatially, a join can be completed by using the nhru_ID or nseg_ID from the variable and the hru_ID from the national-extent Geospatial Fabric. This research used resources provided by the Core Science Analytics, Synthesis, & Libraries (CSASL) Advanced Research Computing (ARC) group at the U.S. Geological Survey.

This data release contains output of the initial calibration of the conterminous United States (CONUS) application of the Precipitation-Runoff Modeling System (PRMS) as implemented in the National Hydrologic Model (NHM) infrastructure (Regan et al, 2018). The PRMS version 5.0.0 hydrologic simulation code was used with the accompanying parameter files in the NHM infrastructure to produce the attached output files. Model input climate drivers include climate data derived from the Daymet gridded data set version 2 (Thornton et al., 2014) with values spatially-distributed to the HRUs using the USGS Geo Data Portal (https://cida.usgs.gov/gdp/; Blodgett et al., 2011). The parameter values are maintained in the National Hydrologic Model Parameter Database (NhmParamDb; Driscoll et al., 2017. The parameter file used to produce these model results is attached (NHM-PRMS.zip). CONUS-scale parameter calibration for the USGS National Hydrologic Model (NHM) infrastructure application of the Precipitation-Runoff Modeling System (NHM-PRMS) was conducted using five baseline data sets derived from multiple sources for each of the NHM’s 109,951 hydrologic response units (HRU) on time scales from annual to daily. A multiple-objective, step-wise, automated calibration procedure was used to identify the ‘optimal’ set of parameters for each HRU. This produced spatially distributed parameters for the CONUS using a calibration procedure termed ‘byHRU’ calibration. The NHM-PRMS simulations, with the byHRU calibrated parameters, were conducted with the same configuration for a time period from October 1, 1980 to December 31, 2016. The first three years of the simulations are should be considered ‘model spin up’ and not included in any subsequent analysis. Table 1 (attached) lists the output variables included in this data release. The individual *.csv files follow a naming convention of using an abbreviation for the spatial dimension of the model output (nhru or nsegment) _variable name.csv. The variable names included are defined further in Table 1. The structure of each output file includes a header line which labels the columns by the spatial unit (hru or stream segment) identification number and each row has the date followed by either 109951 or 56460 values, depending the output variable dimension nhru or nsegment, respectively. See table 1-10 in Regan and LaFontaine (2017) for a description of the variables included in this file. To distribute parameter and output values spatially, a join can be completed by using the nhru_ID or nseg_ID from the variable and the hru_ID from the national-extent Geospatial Fabric. This research used resources provided by the Core Science Analytics, Synthesis, & Libraries (CSASL) Advanced Research Computing (ARC) group at the U.S. Geological Survey.

This data release contains inputs and outputs for hydrologic simulations of the conterminous United States (CONUS) using the National Hydrologic Model (NHM) application of the Precipitation Runoff Modeling System (PRMS) in ASCII and binary format and explanatory graphics in pdf format. These simulations were developed to provide estimates of water availability for historical conditions for the period October 1, 1980 to September 30, 2016 for five different calibration configurations; the first three years of the simulation should be considered the initialization period and should not be used for subsequent analysis. The five versions of model parameters and associated model output included in this data release are described in table 1 and in the Supplemental Information section of this metadata record. Table 2 provides information about the baseline datasets used for model calibration for each of the five parameter configurations. Figure 1 shows a schematic of the multi-step calibration procedure used to develop the model parameters. Table 3 describes the 36 model output variables that are included in the five attached folders. Five .tar folders are named according to the simulation configuration in table 1 and include the 36-model output variable files. Table 4 provides information about the 8,274 streamgage locations that are included in the NHM-PRMS. The NHM-PRMS parameter and control files for each of the five simulations are located on the child pages associated with this data release. The PRMS climate forcing input files for the simulations are in the DAYMET_CBH.zip folder. Summary files by streamgage of measured and simulated streamflow for the byHRU, byHRU_musk, and byHRU_musk_obs simulations are in the Streamgage_location_simulations_5999.zip folder. Any time series data in the model output files prior to the October 1, 1983 start date should be considered part of the model initialization period and should not be used. Please refer to the Supplemental Information element of this metadata record for more information about the model calibration, inputs, outputs, and summaries included in this data release.

This data release contains inputs for and outputs from hydrologic simulations for the conterminous United States (CONUS) using the Precipitation Runoff Modeling System (PRMS) version 5.1.0 and the USGS National Hydrologic Model infrastructure (NHM, Regan and others, 2018). These simulations were developed to provide estimates of the water budget for the period 1950 to 2010. Specific file types include: 1) input atmospheric forcings of minimum air temperature, maximum air temperature, and daily precipitation accumulation derived from a gridded observation-based dataset developed by Maurer and others (2002), 2) input parameter files for static and dynamic land cover conditions, and 3) output files of simulated water budget components for each hydrologic response unit and stream segment. Figure 1 shows the calibration methodology that was used for the model application (see LaFontaine and others, 2019 for additional information). Table 1 lists the streamgages that are included in the model application. Table 2 lists the calibration datasets that were used in addition to USGS measured streamflow. The first three years of the simulations are considered 'model initialization' and should not be included in any subsequent analsysis.