This data release is comprised of data tables of input variables for seawaveQ and surrogate models used to predict concentrations of select pesticides at six U.S. Geological Survey National Water Quality Network (NWQN) river sites (Fanno Creek at Durham, Oregon; White River at Hazleton, Indiana; Kansas River at DeSoto, Kansas; Little Arkansas River near Sedgwick, Kansas; Missouri River at Hermann, Missouri; Red River of the North at Grand Forks, North Dakota). Each data table includes discrete concentrations of one select pesticide (Atrazine, Azoxystrobin, Bentazon, Bromacil, Imidacloprid, Simazine, or Triclopyr) at one of the NWQN sites; daily mean streamflow; 30-day and 1-day flow anomalies; daily median values of pH and turbidity; daily mean values of dissolved oxygen, specific conductance, and water temperature; and 30-day and 1-day anomalies for pH, turbidity, dissolved oxygen, specific conductance, and water temperature. Two pesticides were modeled at each site with three types of regression models. Also included is a zip file with outputs from seawaveQ model summary. The processes for retrieving and preparing data for regression models followed those outlined in the SEAWAVE-Q R package documentation (Ryberg and Vecchia, 2013; Ryberg and York, 2020). The R package waterData (Ryberg and Vecchia, 2012) was used to import daily mean values for discharge and either daily mean or daily median values for continuous water-quality constituents directly into R depending on what data were available at each site. Pesticide concentration, streamflow, and surrogate data (continuously measured field parameters) were imported from and are available online from the USGS National Water Information System database (USGS, 2020). The waterData package was used to screen for missing daily mean discharge values (no missing values were found for the sites) and to calculate short-term (1 day) and mid-term (30 day) anomalies for flow and short-term anomalies (1 day) for each water-quality variable. A mid-term streamflow anomaly, for instance, is the deviation of concurrent daily streamflow from average conditions for the previous 30 days (Vecchia and others, 2008). Anomalies were calculated as additional potential model variables. Pesticide concentrations for select constituents from each site were pulled into R using the dataRetrieval package (De Cicco and others, 2018). Three of the six sites (Kansas River at DeSoto, Kansas; Missouri River at Hermann, Missouri; and White River at Hazleton, Indiana) pulled pesticide data for WY 2013–17 whereas the other three sites (Fanno Creek at Durham, Oregon; Little Arkansas River near Sedgwick, Kansas; and Red River of the North at Grand Forks, North Dakota) pulled pesticide data for WY 2013–18. Discrete pesticide data were matched with daily mean discharge and daily mean or median water-quality constituents and the associated calculated short-term (1-day) and mid-term (30-day) anomalies from the date of sampling. Pesticide concentrations were estimated using the SEAWAVE-Q (with surrogates) model using 19 combinations of surrogate variables (table 2 in the associated SIR, "Comparison of Surrogate Models to Estimate Pesticide Concentrations at Six U.S. Geological Survey National Water Quality Network Sites During Water Years 2013–18.") at each of 12 site-pesticide combinations (table 3 in the associated SIR). Three measures of model performance—the generalized coefficient of determination (R2), Akaike’s Information Criteria (AIC), and scale—were included in the output and used to select best-fit models (Table 4 of the associated SIR). The three to four best-fit SEAWAVE-Q (with surrogates) models with sample sizes at least five times the number of variables were selected for each site-pesticide combination based on generalized R2 values—the higher, the better. If generalized R2 values were the same, the model with the lower AIC value was used. The standard surrogate regression and base SEAWAVE-Q models were then applied using the same samples that were used for each of the best-fit SEAWAVE-Q (with surrogates) models so that direct comparisons could be made for each site-pesticide-surrogate instance. The input data used to estimate daily pesticide concentrations for each of the best fit models have been included in this data release. An example of one output file for each model type is included in a .zip file named "output_examples.zip". Each of the output files shows the three measures of model performance. (1) The output file for the standard regression model named "HAZ8_Atrazine_Standard_Regression_Output.txt" includes: Pseudo R-square (Allison) of 0.631, Model AIC of 174.0232, and a Scale of 0.961. (2) The output file for the base SEAWAVE-Q model named "HAZ8_Atrazine_Base_Seawave-Q_Output.txt" includes: Generalized r-squared of 0.82, AIC (Akaike's An Information Criterion) of 36.38, and a Scale of 0.288. (3) The output file for the SEAWAVE-Q w/Surrogates model named "HAZ8_Atrazine_Seawave-Q_w_Surrogates_Output.txt" includes: Generalized r-squared of 0.85, AIC (Akaike's An Information Criterion) of 33.76, and a Scale of 0.268. These values match those for Site ID = HAZ, Pesticide = Atrazine, and Surrogate variable group 8 for each model type in Table 4 of the associated SIR.