This data release contains the results from a study of the transport and transformation of nitrate in groundwater and streams across diverse glacial terranes within the Western Lake Michigan (WMIC) Drainages and Eastern Iowa (EIWA) Basins of the United States. The primary focus of the study was on how varying redox conditions influence the timing and delivery of nitrate to streams in agricultural watersheds. The data represents dissolved gas modeling results that were obtained from the Dissolved Gas Modeling and Environmental Tracer Analysis (DGMETA) software program. DGMETA was used to produce calibrated dissolved gas models that provide optimal recharge temperature, excess air, and excess nitrogen gas concentrations (mainly from denitrification) based on measured dissolved gases.

This data release documents nine Microsoft Excel tables that contain data for understanding groundwater ages in the Glacial aquifer system. Results for the four sample networks (PAS, principal aquifer study; MSS, modeling support study; FPS, flow path study) are described by three tables each: dissolved gas modeling results, environmental tracer concentrations (tritium, tritiogenic helium-3, sulfur hexafluoride, carbon-14, and radiogenic helium-4), and results for the mean age and age distribution. Tables are labeled by network and data type (as described below) separated by an underscore (_). For example, dissolved gas modeling results from the PAS network is label ‘PAS_NGmodel’. Dissolved gas modeling results (NGmodel) contains detailed information on the calibration of dissolved gas models to dissolved gas concentrations (neon, argon, krypton, xenon, and nitrogen). Calibration was done using methods described by Aeschbach-Hertig and others (1999) with modifications to include nitrogen gas (Weiss 1970). In most cases, a single set of noble gas data (neon, argon, krypton, and xenon) were used to determine recharge conditions (recharge temperature, excess air or entrapped air, fractionation). In cases where noble gas data were not available, multiple analyses of nitrogen and argon (collected sequentially on the same sample date) were used to determine recharge conditions. Environmental tracer results (Tracers) contain detailed information on calculations of environmental tracer data. Dissolved gas models were paired with sulfur hexafluoride and helium isotopes (3He/4He) and helium to determine concentrations of tritiogenic helium-3 (from decay of tritium; Solomon and Cook, 2000) and radiogenic helium-4 (from decay of uranium and thorium in aquifer materials; Solomon, 2000). Multiple tracer concentrations were computed when sites had multiple dissolved gas model results and analyses for sulfur hexafluoride or helium isotopes. Mean age and age distribution results (LPMModOut) contain final models of groundwater age by calibration of lumped parameter models to tracer concentrations (Jurgens and others, 2012). One additional table describes LPM results from a previous sampling of the FPS network in 2004. Tracer concentrations from 2004 FPS sampling are described in previous publication (Tesoriero et al., 2007; Saad, 2008). Dissolved gas modeling and environmental tracer results were averaged when multiple dissolved gas models and tracer concentrations were computed. In cases where age was modeled with a binary lumped parameter model (BMM), the mean age was computed from the mean age and fraction of the two components in the mixture. Please see the processing steps below and the main manuscript for additional details on the results presented in this table.

Groundwater age distributions and susceptibility to natural and anthropogenic contaminants were assessed for selected wells across Minnesota. The data provide information to understand how long it will take to observe groundwater quality improvements from best management practices implemented at land surface to reduce losses of nitrate (and other chemicals) from agricultural practices. A total of 13 age estimates were done on samples collected from 8 wells between July 1996 and May 1997. Groundwater ages were estimated from dissolved gas (argon and nitrogen) and environmental tracer data (tritium, chlorofluorocarbons, and tritiogenic helium-3) from field samples using the equations available in TracerLPM (an Excel® workbook for interpreting groundwater age distributions from environmental tracer data) and DGMETA (an Excel® workbook for dissolved gas modeling and environmental tracer analysis); groundwater age estimates are reported in Table_1_Age_Information.txt. DGMETA was used to compute optimal water temperature and excess air that explain the measured dissolved gases (argon and nitrogen) in a sample; condensed results are reported in Table_1_Age_Information.txt and these results are reported in detail in Table_2_Dissolved_Gases.txt. These values were then used to convert the raw measured concentrations of chlorofluorocarbons into a form appropriate for age dating analysis, the dry air mixing ratio of chlorofluorocarbons; these results are reported in Table_3_Computed_Tracer_Concentrations.txt. Table_4_Additional_Helium.txt reports calculated concentrations of tritiogenic helium that were used in groundwater age calculations and the measured concentrations used in those calculations. Table_5_Site_And_Background_Information.txt reports additional site information and field parameters. In addition to these five tables, two ancillary tables are included to provide more detailed information about the fields and the abbreviations used in tables 1-5. A readme file is provided that describes each table in more detail and processes to use the data in this data release to view age distributions in TracerLPM and to set up TracerLPM to run scenarios for other chemicals of interest.

Groundwater age distributions and susceptibility to natural and anthropogenic contaminants were assessed for selected wells, streambed piezometers, and springs in southeastern Minnesota. The data provide information to understand how long it will take to observe groundwater quality improvements from best management practices implemented at land surface to reduce losses of nitrate (and other chemicals) from agricultural practices. Nineteen water samples were collected from ten wells, three streambed piezometers, and four springs between August 2020 and September 2022. Two of these samples are field replicate samples collected from a spring site and a well site. A child item contains historical data from 15 water samples from 10 wells between July 1996 to May 1997. Groundwater ages were estimated from dissolved gas (neon, argon, krypton, and xenon) and environmental tracer data (tritium, sulfur hexafluoride, chlorofluorocarbons, and tritiogenic helium-3) from field samples using the equations available in TracerLPM (an Excel® workbook for interpreting groundwater age distributions from environmental tracer data) and DGMETA (an Excel® workbook for dissolved gas modeling and environmental tracer analysis); groundwater age estimates are reported in Table_1_Age_Information.txt. DGMETA was used to compute the optimal water temperature, excess air, entrapped air, fractionation of gases, and excess nitrogen gas (mainly from denitrification) for the measured dissolved gases in a sample; condensed results are reported in Table_1_Age_Information.txt and these results are reported in detail in Table_2_Dissolved_Gases.txt. These values were then used to convert the raw measured concentrations of environmental tracers into a form appropriate for age dating analysis; these results are reported in Table_3_Computed_Tracer_Concentrations.txt. Calculated concentrations of environmental tracers that were used in groundwater age calculations are the dry air mixing ratio of sulfur hexafluoride or chlorofluorocarbons, and tritiogenic helium-3, which is the concentration of helium-3 from the decay of tritium. Table_4_Site_And_Background_Information.txt reports additional site information and field parameters. In addition to these four tables, two ancillary tables are included to provide more detailed information about the fields and the abbreviations used in tables 1-4. A readme file is provided that describes each table in more detail and processes to use the data in this data release to view age distributions in TracerLPM and to set up TracerLPM to run scenarios for other chemicals of interest.

Groundwater age distributions and susceptibility to natural and anthropogenic contaminants were assessed for selected wells, streambed piezometers, and springs in southeastern Minnesota. The data provide information to understand how long it will take to observe groundwater quality improvements from best management practices implemented at land surface to reduce losses of nitrate (and other chemicals) from agricultural practices. Nineteen water samples were collected from ten wells, three streambed piezometers, and four springs between August 2020 and September 2022. Two of these samples are field replicate samples collected from a spring site and a well site. A child item contains historical data from 15 water samples from 10 wells between July 1996 to May 1997. Groundwater ages were estimated from dissolved gas (neon, argon, krypton, and xenon) and environmental tracer data (tritium, sulfur hexafluoride, chlorofluorocarbons, and tritiogenic helium-3) from field samples using the equations available in TracerLPM (an Excel® workbook for interpreting groundwater age distributions from environmental tracer data) and DGMETA (an Excel® workbook for dissolved gas modeling and environmental tracer analysis); groundwater age estimates are reported in Table_1_Age_Information.txt. DGMETA was used to compute the optimal water temperature, excess air, entrapped air, fractionation of gases, and excess nitrogen gas (mainly from denitrification) for the measured dissolved gases in a sample; condensed results are reported in Table_1_Age_Information.txt and these results are reported in detail in Table_2_Dissolved_Gases.txt. These values were then used to convert the raw measured concentrations of environmental tracers into a form appropriate for age dating analysis; these results are reported in Table_3_Computed_Tracer_Concentrations.txt. Calculated concentrations of environmental tracers that were used in groundwater age calculations are the dry air mixing ratio of sulfur hexafluoride or chlorofluorocarbons, and tritiogenic helium-3, which is the concentration of helium-3 from the decay of tritium. Table_4_Site_And_Background_Information.txt reports additional site information and field parameters. In addition to these four tables, two ancillary tables are included to provide more detailed information about the fields and the abbreviations used in tables 1-4. A readme file is provided that describes each table in more detail and processes to use the data in this data release to view age distributions in TracerLPM and to set up TracerLPM to run scenarios for other chemicals of interest.

Understanding and projecting the downstream benefits of terrestrial surface water storage (volumetric water stored in lakes and wetlands, SWstorage) requires watershed hydrologic models. Use of external datasets to calibrate and validate modeled SWstorage dynamics remains uncommon, particularly across major river basins. Here, we: (1) develop and assess the utility of a novel remote sensing-based (RS) SWstorage approach for verifying watershed-model SWstorage estimates, (2) compare average modeled and RS SWstorage volume across the landscape, and (3) compare variability in modeled and RS SWstorage through time. We used SWstorage informed by Sentinel-1 and -2 (RS SWstorage), with Soil and Water Assessment Tool (SWAT) model simulations (SWAT SWstorage) across the ~450,000 km2 Upper Mississippi River Basin. We found that RS SWstorage was, on average, lower than SWAT SWstorage in tile-drained agricultural regions where static Digital Elevation Model (DEM)-generated depressions used in the SWAT model often did not contain RS surface water. Conversely, RS SWstorage was higher than SWAT SWstorage in wetland-rich regions where surface water was shallower than DEM vertical accuracy. In modeled subbasins where DEM-generated maximum SWstorage capacity was low relative to SWAT SWstorage volumes, SWAT SWstorage was effectively capped and unable to vary through time, whereas RS SWstorage in the same subbasins continued to vary. Thus, RS SWstorage allows for a more accurate representation of where, when, and how much water is on the landscape. This finding is useful for informing watershed model initial conditions and highlights the potential for RS to be used in SWstorage calibration or data assimilation.

Understanding and projecting the downstream benefits of terrestrial surface water storage (volumetric water stored in lakes and wetlands, SWstorage) requires watershed hydrologic models. Use of external datasets to calibrate and validate modeled SWstorage dynamics remains uncommon, particularly across major river basins. Here, we: (1) develop and assess the utility of a novel remote sensing-based (RS) SWstorage approach for verifying watershed-model SWstorage estimates, (2) compare average modeled and RS SWstorage volume across the landscape, and (3) compare variability in modeled and RS SWstorage through time. We used SWstorage informed by Sentinel-1 and -2 (RS SWstorage), with Soil and Water Assessment Tool (SWAT) model simulations (SWAT SWstorage) across the ~450,000 km2 Upper Mississippi River Basin. We found that RS SWstorage was, on average, lower than SWAT SWstorage in tile-drained agricultural regions where static Digital Elevation Model (DEM)-generated depressions used in the SWAT model often did not contain RS surface water. Conversely, RS SWstorage was higher than SWAT SWstorage in wetland-rich regions where surface water was shallower than DEM vertical accuracy. In modeled subbasins where DEM-generated maximum SWstorage capacity was low relative to SWAT SWstorage volumes, SWAT SWstorage was effectively capped and unable to vary through time, whereas RS SWstorage in the same subbasins continued to vary. Thus, RS SWstorage allows for a more accurate representation of where, when, and how much water is on the landscape. This finding is useful for informing watershed model initial conditions and highlights the potential for RS to be used in SWstorage calibration or data assimilation.

Data provided in this release support the findings in Choquette et al. (2019), utilizing methods for evaluating water-quality and daily-streamflow trends described also in Hirsch and DeCicco (2015 and 2018a) and Hirsch (2018). The trend results and model-input data focus on 10 locations in the Lake Erie watershed that have long-term (20 or more years) water-quality and streamflow monitoring records. The trend results include the years 1987 through 2016 or specified sub-periods during this time frame. The model-input data records spanned the time period 1974 through 2016 although record lengths varied by site, data type, and trend analysis. The water-quality records were provided by the National Center for Water Quality Research (NCWQR; Heidelberg University, Tiffin, Ohio) and the Indiana Department of Environmental Management (IDEM), and streamflow records were provided by the U.S. Geological Survey (USGS). The 10 water-quality trend sites were identified using abbreviated names of the nearby USGS streamgage that provided streamflow data for determining nutrient fluxes at these sites (see Site_map.pdf and Site-summary_table.csv). Trends in flow-normalized nutrient fluxes were determined using the method Weighted Regression on Time, Discharge, and Season (WRTDS) method (Hirsch and DeCicco, 2015, 2018a, and 2018b) and streamflow (discharge) trends were determined using the graphical-statistical method of Quantile-Kendall plots (Hirsch, 2018). The nutrient trend analyses focus on the parameters total phosphorus (TP, as P), soluble reactive phosphorus (SRP, as P), total nitrogen (TN, as N), nitrate plus nitrite (NO23, as N) filtered at NCWQR sites or unfiltered at IDEM sites, and total Kjeldahl nitrogen (TKN, as N). TN was calculated as TKN plus NO23. SRP was monitored at only 6 of the 10 trend sites. Additional information on field and laboratory methods appears in Choquette et al. (2019). The dataset is presented in two parts: 1. Nutrient and Streamflow Model-Input Data 2. Nutrient and Streamflow Trend Results References: Choquette, A.F., Hirsch, R.M., Murphy, J.C., Johnson, L.T., and Confesor, R.B. Jr., 2019, Tracking changes in nutrient delivery to western Lake Erie: approaches to compensate for variability and trends in streamflow: J. of Great Lakes Research, v. 45, no. 1, p. 21-39, https://doi.org/10.1016/j.jglr.2018.11.012. Hirsch, R.M., 2018, Daily streamflow trend analysis: U.S. Geological Survey Office of Water Information Blog, 38 p., at: https://owi.usgs.gov/blog/Quantile-Kendall/. Hirsch, R.M., and De Cicco, L.A., 2015 (revised). User Guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R Packages for Hydrologic Data, Version 2.0, U.S. Geological Survey Techniques Methods, 4-A10. U.S. Geological Survey, Reston, VA., 93 p. (at: http://dx.doi.org/10.3133/tm4A10). Hirsch, R.M., and De Cicco, L.A., 2018a, Guide to EGRET 3.0 Enhancements: at https://cran.r-project.org/web/packages/EGRET/vignettes/Enhancements.html. Hirsch, R.M., and De Cicco, L.A., 2018b, EGRET release 3.0, and EGRETci release 2.0, at: https://cran.r-project.org/ .

This data release contains a data compilation and analysis of the hydrogeology in the U.S. portion of the Lake Superior watershed, for the purpose of providing background data for future study and modeling of groundwater and contaminant movement in the watershed. The data support an analysis of groundwater contributions to the water budget of Lake Superior and provide hydrogeologic context for future modeling and sampling of groundwater in the region. The data release contains three child items: Baseflow analysis for tributaries to Lake Superior from 1946 to 2020; Geohydrology data for groundwater analysis in the Lake Superior Watershed; and Groundwater wells from Minnesota, Wisconsin, and Michigan state databases and the U.S. Geological (USGS) National Water Information System (NWIS) database with static water level data within 10km of the Lake Superior watershed.

This data release (DR) is the update of the U.S. Geological Survey - ScienceBase DR (Bera, 2024b), with the processed data for the period October 1, 2022, through September 30, 2023. This DR describes the watershed data management (WDM) database file SC23.WDM. The precipitation data are collected from a tipping-bucket rain-gage network and the hydrologic data (stage and discharge) are collected at USGS streamflow-gaging stations in and around Salt Creek watershed in DuPage County, Illinois, as described in Bera (2014). Hourly precipitation and hydrologic data for the period October 1, 2022, through September 30, 2023, are processed following the guidelines described in Bera (2014) and Murphy and Ishii (2006) and appended to SC22.WDM (Bera, 2024b) and renamed as SC23.WDM. Meteorological data (wind speed, solar radiation, air temperature, dewpoint temperature, and potential evapotranspiration) from October 1, 2022, through September 30, 2023, are copied from ARGN23.WDM (Bera, 2024a) and uploaded to SC23.WDM. Data in dataset number (DSN) 107 and 801–810 are used in comparisons of precipitation data. DSN 107 contains hourly precipitation data collected at Argonne National Laboratory at Argonne, Illinois. DSN 801-810 contains the processed Next Generation Weather Radar (NEXRAD)-multisensor precipitation estimates (MPE) data from 10 NEXRAD–MPE subbasins in the Salt Creek watershed (Bera and Ortel, 2018). Data in these DSNs are not quality-assured and quality-controlled. The data are downloaded and uploaded daily into a WDM database that is used for the near-real-time streamflow simulation system. Data from DSN 107 and 801-810 are copied from this WDM and stored in the SC23.WDM. DSN 107 and 801-810 in the SC23.WDM are updated with the data through September 30, 2023. Data in DSN 5400 (water-surface elevation at the Elmhurst Quarry at Elmhurst, Illinois) and 5700 (water surface elevation at Thorndale Avenue in Wood Dale, Illinois) are copied and updated through September 30, 2023, similarly (Murphy and Ishii, 2006). The Gage at Thorndale Avenue in Wood Dale, Illinois is maintained by DuPage County Stormwater Department. The complete list of missing precipitation data periods and the nearby stations used to fill in those missing periods from October 1, 2022, through September 30, 2023, is given in Table1.csv. This file is in the comma separated values (CSV) file format and can be downloaded from this landing page. The list of snow affected days of precipitation data and the missing and estimated period of the stage and flow data in SC23.WDM database during the period October 1, 2022, through September 30, 2023, are given in the USGS annual Water Data Report at https://waterdata.usgs.gov/nwis. To open the WDM database SC23.WDM the user may use the Sara Timeseries Utility executable file attached in this page. Table1.csv can be opened with any text editor or Microsoft Excel. References Cited: Bera, M., 2024a, Meteorological Database, Argonne National Laboratory, Illinois: U.S. Geological Survey data release, https://doi.org/10.5066/P146RBHK. Bera, M., 2024b, Watershed Data Management (WDM) Database (SC22.WDM) for Salt Creek Streamflow Simulation, DuPage County, Illinois, January 1, 1997, through September 30, 2022: U.S. Geological Survey data release, https://doi.org/10.5066/P14D6FRA. Bera, M., and Ortel, T.W., 2018, Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system: U.S. Geological Survey Open-File Report 2017–1159, 16 p., https://doi.org/10.3133/ofr20171159. Bera, M., 2014, Watershed Data Management (WDM) database for Salt Creek streamflow simulation, DuPage County, Illinois, water years 2005–11: U.S. Geological Survey Data Series 870, 18 p., https://doi.org/10.3133/ds870. Murphy, E.A., and Ishii, A.L., 2006, Watershed Data Management (WDM) Database for Salt Creek Streamflow Simulation, DuPage County, Illinois: