Shawn Naylor Amy A. Story Danielle D. Follette 20260325 Excel workbook Reston, VA U.S. Geological Survey https://doi.org/10.5066/P1S4GDK4 Shawn Naylor Matthew P. Miller Christopher T. Green Myles T. Moore Amy A. Story Matthew J. Hardebeck Edward G. Dobrowolski 20261231 publication n/a n/a A linear regression model was developed to estimate continuous nitrate concentration in groundwater at a shallow groundwater monitoring site in the Black Creek Watershed, Indiana. A Microsoft Excel workbook was used to perform the regression analysis and estimate nitrate concentration at a USGS monitoring well (411229084541701). The regression independent variable was based on continuous (15-minute) groundwater specific conductance data that were collected at the site. The regression input dependent variable was based on nitrate concentrations in monthly discrete water quality samples that were collected between April 2022 and March 2024. Specific conductance data were averaged for each sampling day to establish matched pairs for the regression analysis. The analysis was constrained between specific conductance values of 450 and 850 uS/cm with one matched pair corresponding with a low specific conductance (<300 uS/cm) removed from the regression analysis. The regression output consists of daily groundwater nitrate concentrations during the specific conductance monitoring period between December 2021 and April 2024. The dataset and linear regression analysis were created to estimate daily shallow groundwater nitrate concentrations in the Black Creek Watershed, Indiana. 20220403 20240329 ground condition Complete None planned Black Creek, Allen County, Indiana -84.91367 -84.89256 41.22024 41.20462 ISO 19115 Topic Category inlandWaters environment geoscientificInformation USGS Thesaurus nitrate groundwater specific conductance USGS Metadata Identifier USGS:68bb2272d4be0209bab07d31 Geographic Names Information System Black Creek No access constraints. Please see 'Distribution Information' for details. These data are marked with a Creative Commons Zero v1.0 Universal (CC0-1.0) public domain dedication and do not have any use constraints. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Shawn Naylor U.S. Geological Survey, Ohio-Kentucky-Indiana Water Science Center Hydrologist mailing and physical

5957 Lakeside Blvd

Indianapolis IN 46278 US 317-290-3333 snaylor@usgs.gov No formal attribute accuracy tests were conducted No formal logical accuracy tests were conducted Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. No formal positional accuracy tests were conducted No formal positional accuracy tests were conducted Discrete water-quality samples were collected following USGS protocols found in the USGS National Field Manual for the Collection of Water-Quality Data (USGS, 2006). Approved data were obtained from the USGS National Water Information System (USGS, 2025). Discrete data were collected at a specific date and time. Continuous specific conductance data were collected and calibrated using approved USGS methods. Continuous specific conductance data were obtained with 15-minute data intervals and averaged over daily time steps to obtain average daily values. U.S. Geological Survey, 2006, Collection of water samples (ver. 2.0): U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chap. A4, September 2006. U.S. Geological Survey, 2025, USGS Water Data for the Nation: U.S. Geological Survey National Water Information System database, accessed July 29, 2025 at https://waterdata.usgs.gov/nwis/. 20240329 Amy A Story U.S. Geological Survey, Ohio-Kentucky-Indiana Water Science Center Hydrologic Technician mailing and physical

5957 Lakeside Blvd

Indianapolis IN 46278 317-601-6888 astory@usgs.gov Discrete nitrate data from multiple sampling dates and daily-averaged specific conductance data for corresponding dates were used to create a linear regression model for estimating daily nitrate concentrations in shallow groundwater (USGS monitoring well: 411229084541701). Discrete nitrate data for two sampling dates had censored data (non-detect) results and nitrate values for these dates were set to one half the laboratory detection limit (0.05 mg/L) for the regression analysis. The regression was executed using the ordinary least squares method in an R script and diagnostic output are provided in the spreadsheet labeled "ReadMe" in the "SC_Nitrate_Regression.xlsx" Excel workbook. 20250903 Shawn Naylor U.S. Geological Survey, Ohio-Kentucky-Indiana Water Science Center Hydrologist mailing and physical

5957 Lakeside Blvd

Indianapolis IN 46278 317-619-5845 snaylor@usgs.gov The linear regression equation obtained in step 2 was used with daily specific conductance data as input to obtain estimated daily nitrate concentrations. The regression model was executed in the spreadsheet labeled "EstimatedNitrate" in the "SC_Nitrate_Regression.xlsx" Excel workbook. 20250903 Shawn Naylor U.S. Geological Survey, Ohio-Kentucky-Indiana Water Science Center Hydrologist mailing and physical

5957 Lakeside Blvd

Indianapolis IN 46278 317-619-5845 snaylor@usgs.gov RegressionInputAndResiduals The worksheet contains 6 columns including regression input, residuals, and quantiles. Producer Defined Date Day corresponding to the date of discrete nitrate sample collection. Producer Defined 4/3/2022 3/29/2024 GroundwaterSpecificConductance_uScm Daily averaged specific conductance for shallow groundwater at USGS monitoring well (411229084541701). Producer Defined 501.17 839.05 microsiemens per centimeter GroundwaterNitrate_mgL Discrete sampled nitrate concentration in shallow groundwater at USGS monitoring well (411229084541701). Producer Defined 0.05 7.3 milligrams per liter RegressionResiduals Residuals from linear regression Producer Defined -3.772792421 2.121150936 RegressionQuantiles Quantiles from linear regression Producer Defined -1.959963985 1.959963985 Comment Notes describing how censored nitrate data are represented. Producer Defined nitrate set to 0.5 detection limit (0.1 mg/L) nitrate set to 0.5 detection limit (0.1 mg/L) Producer defined EstimatedNitrate The worksheet contains 4 columns. Producer Defined Date Day during time period when continuous specific conductance data were monitored at USGS monitoring well (411229084541701). Producer Defined 12/4/2021 4/8/2024 GroundwaterSpecificConductance_uScm Average daily specific conductance at USGS monitoring well (411229084541701). Producer Defined 458.61 839.85 microsiemens per centimeter RegressionEstimatedGroundwaterNitrate_mgL Daily estimated nitrate concentration at USGS monitoring well (411229084541701) based on the linear regression equation. Producer Defined -1.534617 6.204555 milligrams per liter RegressionEstimatedGroundwaterNitratePositiveValues_mgL Daily estimated nitrate concentration at USGS monitoring well (411229084541701) based on the linear regression equation. Negative estimated values have been set at zero in this column. Producer Defined 0.0 6.204555 milligrams per liter ReadMe This worksheet contains summary information and diagnostic statistics for the surrogate regression model developed for this study. The dataset includes the full model specification, sample size, coefficient estimates for each predictor term, associated standard errors, test statistics, and p‑values. Model‑level performance metrics are provided, including the residual standard error, multiple and adjusted R‑squared values, F‑statistic, and model p‑value. The worksheet also summarizes the distribution of model residuals through minimum, quartile, median, and maximum values. A PRESS (Prediction Residual Sum of Squares) statistic is included to evaluate predictive performance using leave‑one‑out cross‑validation. Together, these attributes document the fitted model structure, statistical significance of predictors, overall goodness‑of‑fit, and predictive capability. Producer Defined Regression model method: statistical method used in determining regression Producer Defined Ordinary Least Squares OLS method estimates the linear relationship between a dependent variable and one or more independent variables by finding the line that minimizes the sum of the squared differences between observed and predicted values. Producer defined GW_NO3: groundwater nitrate concentration (discrete samples) Producer Defined groundwater nitrate concentration (discrete samples) GW_SC: groundwater specific conductance (daily average of 15-minute data) Producer Defined groundwater specific conductance (daily average of 15-minute data) Regression model formula: Sas a function of predictors and any transformations or interactionsymbolic specification of the fitted model: response Producer Defined Sas a function of predictors and any transformations or interactionsymbolic specification of the fitted model: response Number of observations: Total number of data points used to fit the model after all filtering and missing‑value handling. Producer Defined 20.0 20.0 NA Coefficients Name of the model term Producer Defined Intercept The estimated value of the response variable when all predictor variables equal zero (on the model’s scale). Represents the baseline level of the modeled constituent or parameter before accounting for effects of predictors. Producer defined GW_SC Regression coefficient for specific conductance measured in groundwater. Represents the change in the modeled response associated with a one‑unit increase in specific conductance, holding all other predictors constant. Producer defined Estimate Point estimate of the regression coefficient for the term. Producer Defined -10.84442 0.020304 Units of the response per units of the predictor (subject to transformation) Std. error Standard error of the coefficient estimate, reflecting its sampling variability. Producer Defined 0.002968 2.098498 Units of the response per units of the predictor (subject to transformation) t value Test statistic for the null hypothesis that the coefficient equals zero: estimate divided by its standard error. Producer Defined -5.168 6.842 NA Pr(>|t|) Two‑sided p‑value for the t‑test of the coefficient being zero. Producer Defined 2.11e-06 6.467e-05 Unitless in [0,1] Residual standard error: Estimate of the residual standard deviation (σ̂), i.e., the typical size of residuals on the model’s scale. Producer Defined Estimate of the residual standard deviation (σ̂), i.e., the typical size of residuals on the model’s scale. Multiple R-squared: Proportion of variance in the response explained by the model. Producer Defined 0.7223 0.7223 NA Adjusted R-squared: R‑squared adjusted for the number of predictors; penalizes overfitting. Producer Defined 0.7068 0.7068 NA F-statistic: Overall test statistic for whether the model explains variance beyond the intercept‑only model. Producer Defined Overall test statistic for whether the model explains variance beyond the intercept‑only model. p-value: Two‑sided p‑value associated with the F‑statistic (global test for model significance). Producer Defined 2.106e-06 2.106e-06 Probability in [0,1] Residual min: Minimum residual value. Producer Defined -3.7728 -3.7728 Units of response (model scale) Residual 1Q: First quartile (25th percentile) of residuals. Producer Defined -0.5191 -0.5191 Units of response (model scale) Residual median: Median (50th percentile) of residuals. Producer Defined 0.35 0.35 Units of response (model scale) Residual 3Q: Third quartile (75th percentile) of residuals. Producer Defined 0.8635 0.8635 Units of response (model scale) Residual max: Maximum residual value. Producer Defined 2.1212 2.1212 Units of response (model scale) Press statistic: Prediction Residual Sum of Squares: sum of squared leave‑one‑out prediction errors. Producer Defined 48.64114 48.64114 squared response units U.S. Geological Survey - ScienceBase mailing address

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Denver CO 80225 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P1S4GDK4 None 20260325 Shawn C Naylor USGS - MIDCONTINENT REGION Hydrologist mailing and physical

5957 Lakeside Boulevard, Lakeside Phase II

Indianapolis IN 46278 317-619-5845 snaylor@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999