This dataset provides details from stream metabolism models for 20 stream sites in the United States that were sampled as part of the National Water Quality Program's Regional Stream Quality Assessments (RSQA). Metabolism was estimated at each site using the streamMetabolizer package in R. For each of the 20 sites, three files are provided; (1) the input data, which includes continuous dissolved oxygen, water temperature, light, and stream depth, (2) the output data containing the daily metabolism estimates, and (3) a site-specific html 'guide' for running the streamMetabolizer package in R. The site 'short_name' is included in each file name to distinguish the files associated with each field site. Final metabolism models were run using a constant value for K600, the mean reaeration rate coefficient scaled to a Schmidt number of 600, which was determined for each site based on initial model calibration runs. Model input data were downloaded directly from the USGS National Water Information System (NWIS) (dissolved oxygen and temperature) or directly from data loggers deployed in the field (light and depth). In addition to the stream metabolism files, a comma delimited file containing basic site characteristics is included for reference. This site file includes the USGS site ID (SITE_NO), the RSQA study name, the state where the site is located, the site 'short name', the USGS site name, and the decimal degree latitude and longitude of the sampling reach.

This dataset provides details from stream metabolism models for 11 stream sites in Connecticut that were monitored during 2015-2018 by the U.S. Geological Survey. Metabolism was estimated at each site using the streamMetabolizer package in the R computing environment. When data were collected for multiple years at a site, stream metabolism was separately estimated for each year. For each site and for each year, three files are provided; (1) the input data file, which includes continuous dissolved oxygen, solar time, water temperature, light, and stream depth, (2) the output data file, containing the daily metabolism estimates, and (3) a site-specific html file that serves as a guide for running the streamMetabolizer package for each site. File names include an abbreviated name for the site (The site 'short_name') to distinguish the files associated with each field site. Final metabolism models were run using a fixed value for K600, which is the mean reaeration rate coefficient scaled to a Schmidt number of 600; this fixed value was determined for each site based on initial model calibration runs. In addition to the stream metabolism files, a comma-delimited file containing basic site characteristics is included for reference. This site file includes the USGS site identifier, the site 'short name', the complete USGS site name, and the latitude and longitude of the sampling reach.

This dataset provides details from stream metabolism models for 11 stream sites in Connecticut that were monitored during 2015-2018 by the U.S. Geological Survey. Metabolism was estimated at each site using the streamMetabolizer package in the R computing environment. When data were collected for multiple years at a site, stream metabolism was separately estimated for each year. For each site and for each year, three files are provided; (1) the input data file, which includes continuous dissolved oxygen, solar time, water temperature, light, and stream depth, (2) the output data file, containing the daily metabolism estimates, and (3) a site-specific html file that serves as a guide for running the streamMetabolizer package for each site. File names include an abbreviated name for the site (The site 'short_name') to distinguish the files associated with each field site. Final metabolism models were run using a fixed value for K600, which is the mean reaeration rate coefficient scaled to a Schmidt number of 600; this fixed value was determined for each site based on initial model calibration runs. In addition to the stream metabolism files, a comma-delimited file containing basic site characteristics is included for reference. This site file includes the USGS site identifier, the site 'short name', the complete USGS site name, and the latitude and longitude of the sampling reach.

Ecosystem metabolism is a measure of energy flow in terrestrial and aquatic environments that quantifies a balance between the rate of biomass production by photosynthesizing plants and the rate of biomass oxidation by respiring plants and animals to maintain and build living biomass. It is therefore a fundamental measure of ecosystem function that quantifies the balance between the rate of production, maintenance, and decay of organic matter. It also provides an understanding of energy flow to higher trophic levels that supports food webs with secondary and tertiary productivity. Furthermore, metabolism helps explain when aquatic ecosystems undergo out-of-balance behaviors such as hypoxia. Recent advances in sensor technology and modeling capabilities have enabled estimation of aquatic system metabolism and gas exchange over long time periods in rivers, streams, ponds, and wetlands where oxygen sensors have been deployed. For convenience, metabolism can be measured by tracking the rate of oxygen production and consumption in the aquatic system. Over time, the measurements of dissolved oxygen concentration can be analyzed to estimate both gross primary productivity (GPP) and ecosystem respiration (ER). GPP is defined as positive, adding oxygen and organic carbon to the system, and ER is defined as negative, subtracting oxygen by consuming organic carbon to fuel work. The sum of GPP and ER is the net ecosystem productivity, a net measure of whether oxygen and organic carbon are building up or being depleted in the system. In order to use the oxygen balance method to quantify GPP and ER in shallow waters it is also necessary to quantify the rate of gas exchange with the atmosphere by accounting for physical effects of surface renewal as well as the dissolved oxygen difference compared to the saturated concentration for a given temperature and atmospheric pressure. Here we present RiverMET for estimating river metabolism with provided workflows that streamline data preparation, run a metabolism model, assess the model performance, and flag and censor final output data. We tested RiverMET by calculating gross primary productivity (GPP), ecosystem respiration (ER) and the air-water gas exchange rate constant (K600) across seventeen (17) river sites in the Illinois River basin (ILRB) using water quality data and hydrologic data from National Water Information System (NWIS, https://waterdata.usgs.gov/nwis, data accessed September 2021) and pressure data from National Oceanic and Atmospheric Administration (NOAA, https://www.ncei.noaa.gov/maps/lcd/, data accessed September 2021). The workflows are specifically tailored to use streamMetabolizer (version 0.12.0; https://github.com/USGS-R/streamMetabolizer), a model for one-station calculations of stream metabolism that calculates daily average areal rates of GPP and ER, and the daily average volumetric air-water gas exchange rate constant, K600. We advise potential users of RiverMET to review core publications for the streamMetabolizer model to ensure best practices that produce the most useful results. We encourage feedback about our workflows, although issues regarding the streamMetabolizer model itself should be referred to the model authors. The zipped (.zip) folder "RiverMET_Outputs.zip" contains two (2) folders entitled "Outputs_from_script-2" and "Outputs_from_script-5". The folder "Outputs_from_script-2" contains four (4) folders. Each of the folders contains the prepared model input files for each unique USGS station as produced by running R script "2_Prepare-Model-InputFiles.R" from the provided "RiverMET_Scripts.zip" folder as described in the "RiverMET_readMe.txt" file. The resultant streamMetabolizer input files (.csv) are created for each unique USGS study station and stored within four (4) sub-folders titled according to the applied water depth estimation approaches with a common naming convention "bayesInput_[date]_depth-[estimation_approach]_[site_no].csv". The script

Ecosystem metabolism is a measure of energy flow in terrestrial and aquatic environments that quantifies a balance between the rate of biomass production by photosynthesizing plants and the rate of biomass oxidation by respiring plants and animals to maintain and build living biomass. It is therefore a fundamental measure of ecosystem function that quantifies the balance between the rate of production, maintenance, and decay of organic matter. It also provides an understanding of energy flow to higher trophic levels that supports food webs with secondary and tertiary productivity. Furthermore, metabolism helps explain when aquatic ecosystems undergo out-of-balance behaviors such as hypoxia. Recent advances in sensor technology and modeling capabilities have enabled estimation of aquatic system metabolism and gas exchange over long time periods in rivers, streams, ponds, and wetlands where oxygen sensors have been deployed. For convenience, metabolism can be measured by tracking the rate of oxygen production and consumption in the aquatic system. Over time, the measurements of dissolved oxygen concentration can be analyzed to estimate both gross primary productivity (GPP) and ecosystem respiration (ER). GPP is defined as positive, adding oxygen and organic carbon to the system, and ER is defined as negative, subtracting oxygen by consuming organic carbon to fuel work. The sum of GPP and ER is the net ecosystem productivity, a net measure of whether oxygen and organic carbon are building up or being depleted in the system. In order to use the oxygen balance method to quantify GPP and ER in shallow waters it is also necessary to quantify the rate of gas exchange with the atmosphere by accounting for physical effects of surface renewal as well as the dissolved oxygen difference compared to the saturated concentration for a given temperature and atmospheric pressure. Here we present RiverMET for estimating river metabolism with provided workflows that streamline data preparation, run a metabolism model, assess the model performance, and flag and censor final output data. We tested RiverMET by calculating gross primary productivity (GPP), ecosystem respiration (ER) and the air-water gas exchange rate constant (K600) across seventeen (17) river sites in the Illinois River basin (ILRB) using water quality data and hydrologic data from National Water Information System (NWIS, https://waterdata.usgs.gov/nwis, data accessed September 2021) and pressure data from National Oceanic and Atmospheric Administration (NOAA, https://www.ncei.noaa.gov/maps/lcd/, data accessed September 2021). The workflows are specifically tailored to use streamMetabolizer (version 0.12.0; https://github.com/USGS-R/streamMetabolizer), a model for one-station calculations of stream metabolism that calculates daily average areal rates of GPP and ER, and the daily average volumetric air-water gas exchange rate constant, K600. We advise potential users of RiverMET to review core publications for the streamMetabolizer model to ensure best practices that produce the most useful results. We encourage feedback about our workflows, although issues regarding the streamMetabolizer model itself should be referred to the model authors. The zipped (.zip) folder "RiverMET_Outputs.zip" contains two (2) folders entitled "Outputs_from_script-2" and "Outputs_from_script-5". The folder "Outputs_from_script-2" contains four (4) folders. Each of the folders contains the prepared model input files for each unique USGS station as produced by running R script "2_Prepare-Model-InputFiles.R" from the provided "RiverMET_Scripts.zip" folder as described in the "RiverMET_readMe.txt" file. The resultant streamMetabolizer input files (.csv) are created for each unique USGS study station and stored within four (4) sub-folders titled according to the applied water depth estimation approaches with a common naming convention "bayesInput_[date]_depth-[estimation_approach]_[site_no].csv". The script

The U.S. Geological Survey National Real-Time Water Quality (NRTWQ) Data Service (https://nrtwq.usgs.gov) provides computed concentrations and loads for sediment, nutrients, bacteria, and many additional constituents; uncertainty values and probabilities for exceeding drinking water or recreational criteria; frequency distribution curves; and all historical in-stream sensor measurements.

This dataset provides shapefile outlines of the catchments contributing to sites where metabolism was or could have been estimated. The format is a shapefile for all sites combined (.shp, .shx, .dbf, and .prj files). This dataset is part of a larger data release of metabolism model inputs and outputs for 356 streams and rivers across the United States (https://doi.org/10.5066/F70864KX). The complete release includes: modeled estimates of gross primary productivity, ecosystem respiration, and the gas exchange coefficient; model input data and alternative input data; model fit and diagnostic information; site catchment boundaries and site point locations; and potential predictors of metabolism such as discharge and light availability.

This dataset provides shapefile outlines of the catchments contributing to sites where metabolism was or could have been estimated. The format is a shapefile for all sites combined (.shp, .shx, .dbf, and .prj files). This dataset is part of a larger data release of metabolism model inputs and outputs for 356 streams and rivers across the United States (https://doi.org/10.5066/F70864KX). The complete release includes: modeled estimates of gross primary productivity, ecosystem respiration, and the gas exchange coefficient; model input data and alternative input data; model fit and diagnostic information; site catchment boundaries and site point locations; and potential predictors of metabolism such as discharge and light availability.

Nonstationary streamflow due to environmental and human-induced causes can affect water quality over time, yet these effects are poorly accounted for in water-quality trend models. This data release provides instream water-quality trends and estimates of two components of change, for sites across the Nation previously presented in Oelsner et al. (2017). We used previously calibrated Weighted Regressions on Time, Discharge, and Season (WRTDS) models published in De Cicco et al. (2017) to estimate instream water-quality trends and associated uncertainties with the generalized flow normalization procedure available in EGRET version 3.0 (Hirsch et al., 2018a) and EGRETci version 2.0 (Hirsch et al., 2018b). The procedure allows for nonstationarity in the flow regime, whereas previous versions of EGRET assumed streamflow stationarity. Water-quality trends of annual mean concentrations and loads (also referred to as fluxes) are provided as an annual series and the change between the start and end year for four trend periods (1972-2012, 1982-2012, 1992-2012, and 2002-2012). Information about the sites, including the collecting agency and associated streamflow gage, and information about site selection and the data screening process can be found in Oelsner et al. (2017). This data release includes results for 19 water-quality parameters including nutrients (ammonia, nitrate, filtered and unfiltered orthophosphate, total nitrogen, total phosphorus), major ions (calcium, chloride, magnesium, potassium, sodium, sulfate), salinity indicators (specific conductance, total dissolved solids), carbon (alkalinity, dissolved organic carbon, total organic carbon), and sediment (total suspended solids, suspended-sediment concentration) at over 1,200 sites. Note, the number of parameters with data varies by site with most sites having data for 1-4 parameters. Each water-quality trend was parsed into two components of change: (1) the streamflow trend component (QTC) and (2) the watershed management trend component (MTC). The QTC is an indicator of the amount of change in the water-quality trend attributed to changes in the streamflow regime, and the MTC is an indicator of the amount of change in the water-quality trend that may be attributed to human actions and changes in point and non-point sources in a watershed. Note, the MTC is referred to as the concentration-discharge trend component (CQTC) in the EGRET version 3.0 software. For our work, we chose to refer to this trend component as the MTC because it provides a more conceptual description (Murphy and Sprague, 2019). The trend results presented here expand upon the results in De Cicco et al. (2017) and Oelsner et al. (2017), which were analyzed using flow-normalization under the stationary streamflow assumption. The results presented in this data release are intended to complement these previously published results and support investigations into natural and human effects on water-quality trends across the United States. Data preparation information and WRTDS model specifications are described in Oelsner et al. (2017) and Murphy and Sprague (2019). This work was completed as part of the National Water-Quality Assessment (NAWQA) project of the National Water-Quality Program. De Cicco, L.A., Sprague, L.A., Murphy, J.C., Riskin, M.L., Falcone, J.A., Stets, E.G., Oelsner, G.P., and Johnson, H.M., 2017, Water-quality and streamflow datasets used in the Weighted Regressions on Time, Discharge, and Season (WRTDS) models to determine trends in the Nation’s rivers and streams, 1972-2012 (ver. 1.1 July 7, 2017): U.S. Geological Survey data release, https://doi.org/10.5066/F7KW5D4H. Hirsch, R., De Cicco, L., Watkins, D., Carr, L., and Murphy, J., 2018a, EGRET: Exploration and Graphics for RivEr Trends, version 3.0, https://CRAN.R-project.org/package=EGRET. Hirsch, R., De Cicco, L., and Murphy, J., 2018b, EGRETci: Exploration and Graphics for RivEr Trends (EGRET) Confidence Intervals, version 2.0.

Nonstationary streamflow due to environmental and human-induced causes can affect water quality over time, yet these effects are poorly accounted for in water-quality trend models. This data release provides instream water-quality trends and estimates of two components of change, for sites across the Nation previously presented in Oelsner et al. (2017). We used previously calibrated Weighted Regressions on Time, Discharge, and Season (WRTDS) models published in De Cicco et al. (2017) to estimate instream water-quality trends and associated uncertainties with the generalized flow normalization procedure available in EGRET version 3.0 (Hirsch et al., 2018a) and EGRETci version 2.0 (Hirsch et al., 2018b). The procedure allows for nonstationarity in the flow regime, whereas previous versions of EGRET assumed streamflow stationarity. Water-quality trends of annual mean concentrations and loads (also referred to as fluxes) are provided as an annual series and the change between the start and end year for four trend periods (1972-2012, 1982-2012, 1992-2012, and 2002-2012). Information about the sites, including the collecting agency and associated streamflow gage, and information about site selection and the data screening process can be found in Oelsner et al. (2017). This data release includes results for 19 water-quality parameters including nutrients (ammonia, nitrate, filtered and unfiltered orthophosphate, total nitrogen, total phosphorus), major ions (calcium, chloride, magnesium, potassium, sodium, sulfate), salinity indicators (specific conductance, total dissolved solids), carbon (alkalinity, dissolved organic carbon, total organic carbon), and sediment (total suspended solids, suspended-sediment concentration) at over 1,200 sites. Note, the number of parameters with data varies by site with most sites having data for 1-4 parameters. Each water-quality trend was parsed into two components of change: (1) the streamflow trend component (QTC) and (2) the watershed management trend component (MTC). The QTC is an indicator of the amount of change in the water-quality trend attributed to changes in the streamflow regime, and the MTC is an indicator of the amount of change in the water-quality trend that may be attributed to human actions and changes in point and non-point sources in a watershed. Note, the MTC is referred to as the concentration-discharge trend component (CQTC) in the EGRET version 3.0 software. For our work, we chose to refer to this trend component as the MTC because it provides a more conceptual description (Murphy and Sprague, 2019). The trend results presented here expand upon the results in De Cicco et al. (2017) and Oelsner et al. (2017), which were analyzed using flow-normalization under the stationary streamflow assumption. The results presented in this data release are intended to complement these previously published results and support investigations into natural and human effects on water-quality trends across the United States. Data preparation information and WRTDS model specifications are described in Oelsner et al. (2017) and Murphy and Sprague (2019). This work was completed as part of the National Water-Quality Assessment (NAWQA) project of the National Water-Quality Program. De Cicco, L.A., Sprague, L.A., Murphy, J.C., Riskin, M.L., Falcone, J.A., Stets, E.G., Oelsner, G.P., and Johnson, H.M., 2017, Water-quality and streamflow datasets used in the Weighted Regressions on Time, Discharge, and Season (WRTDS) models to determine trends in the Nation’s rivers and streams, 1972-2012 (ver. 1.1 July 7, 2017): U.S. Geological Survey data release, https://doi.org/10.5066/F7KW5D4H. Hirsch, R., De Cicco, L., Watkins, D., Carr, L., and Murphy, J., 2018a, EGRET: Exploration and Graphics for RivEr Trends, version 3.0, https://CRAN.R-project.org/package=EGRET. Hirsch, R., De Cicco, L., and Murphy, J., 2018b, EGRETci: Exploration and Graphics for RivEr Trends (EGRET) Confidence Intervals, version 2.0.