This data release contains data and rloadest models used to estimate sediment and nutrient loads in selected New York tributaries to eastern Lake Erie. Load estimates for suspended sediment and nutrients were calculated using rloadest (Lorenz and others, 2013; Runkel and De Cicco, 2017) models. Included are a zip file with input and output files for selected rloadest models for 13 U.S. Geological Survey sites in the eastern Lake Erie Basin. Also included are brief methods and the R-code used to create the models and generate model summaries. Models for total nitrogen, nitrate plus nitrite, total phosphorus, orthophosphate, and suspended sediment were evaluated at each of the 13 sites. Model results that had high bias or non-significant model variables were not considered. In total, 46 rloadest models were created for the 13 sites. Of the 13 sites, rloadest models were created for total phosphorus at 12 sites, total nitrogen at all 13 sites, nitrate plus nitrite at five sites, ammonium at five sites, and for suspended sediment concentrations at 11 of the 13 sites. Orthophosphate samples were highly censored and did not produce any viable models at the study sites. These models and load results were generated using data pulled from the U.S. Geological Survey National Water Information System (U.S. Geological Survey, 2020 ) in May of 2020. Results from rloadest models were used in a Soil and Water Assessment Tool (SWAT) (Arnold and others, 1998; Douglas-Mankin and others, 2010; Gassman and others, 2007) analysis for the sites. References Arnold, J. G., Srinivasan, R., Muttiah, R. S., Williams, J. R., 1998, Large area hydrologic modeling and assessment part I: model development. Journal of the American Water Resources Association. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x Douglas-Mankin, K. R., Srinivasan, R., Arnold, J. G, 2010, Soil and Water Assessment Tool (SWAT) Model: Current Developments and Applications. Transactions of the ASABE, 53(5), 1423–1431. https://doi.org/10.13031/2013.34915 Gassman, P. W., Reyes, M. R., Green, C. H., Arnold, J. G., 2007, The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions. Transactions of the ASABE, 50(4), 1211–1250. https://doi.org/10.13031/2013.23637 Lorenz, D., Runkel, R., and De Cicco, L., 2013, rloadest: U.S. Geological Survey water science R functions for LOAD ESTimation of constituents in rivers and streams, v 0.4.1: accessed September 12, 2018, at https://github.com/USGS-R/rloadest. Runkel, R.L., and De Cicco, L.A., 2017, Rloadest: River Load Estimation. R package version 0.4.5, accessed September 12, 2018, at https://github.com/USGS-R/rloadest. U.S. Geological Survey, 2020, U.S. Geological Survey water data for the Nation: U.S. Geological Survey National Water Information System database, accessed May 7, 2020, https://doi.org/10.5066/F7P55KJN.

Monthly average estimated loads by site (Great Lakes tributary) and constituent.

Yearly estimated loads by site (Great Lakes tributary) and constituent.

The primary data set consists of continuous water-quality data (temperature, specific conductance, pH, dissolved oxygen, turbidity, nitrate plus nitrite, and streamflow) from in-situ equipment, and discrete water-quality samples (total nitrogen, total phosphorus, suspended sediment concentration, and suspended sediment sieve diameter) collected during site visits at the USGS streamgage Iroquois River near Foresman, Indiana, April 7, 2015 to July 19, 2018. These continuous and discrete measurements were used to develop a regression model which may be used to compute concentrations and loads of suspended sediment. The secondary data set consists of daily streamflow and daily turbidity values collected continuously by in-situ monitors at Iroquois River near Foresman, Indiana March 20, 2015 to July 19, 2018 which serve as input explanatory variables for the developed regression model to compute suspended sediment at Iroquois River near Foresman. The tertiary data set for March 20, 2015 to July 19, 2018 is the output data set that was developed by application of the regression model and includes the computed daily mean suspended sediment concentration (concentration, upper 95-percent prediction interval, and lower 95-percent prediction interval) and daily mean suspended sediment load (load, upper 95-percent prediction interval, and lower 95-percent prediction interval) estimates.

This data release contains the calibration data set and R code used to create regression models for estimating daily loads of suspended sediment, total nitrogen, and total phosphorus at the Iroquois River near Foresman, Indiana streamgage (05524500). The USGS R software package, rloadest, was used to develop the regression models and estimate loads. The models were developed using discrete water-quality data and concurrent daily streamflow data measured/determined at the streamgage for the period April 2015 through July 2018. Also included in this data release is the input data set of daily streamflow data and the daily load output data sets that were produced by the regression models. Summary information for the model development for each constituent are included in this data release.

This U.S. Geological Survey (USGS) data release contains model scenario input and output files and nine hydrology and water-quality models developed using the Soil and Water Assessment Tool (SWAT). The models represent nine watersheds in eastern New York that drain to Lake Erie or the Niagara River and were created, calibrated, and validated for hydrology, sediment, and nutrients as baseline scenarios. Twenty-six additional scenarios were created to explore the effects of agricultural and urban best management practices, point source discharges, and green infrastructure on the water quality of tributaries to Lake Erie/Niagara River. Model documentation and scenario development are described in Merriman and others (2024).

Daily estimated loads by site (Great Lakes tributary) and constituent.

Regression diagnostics (including number of observations, residual variance, R squared, bias percentage, Akaike's information criterion (AIC), Nash-Sutcliffe efficiency) and coefficients (variable, estimate, standard error, Z score, p-value) for the tributary nutrient and sediment monitoring program on the Great Lakes, 2011-2013

In 1991, the U.S. Geological Survey (USGS) began a study of more than 50 major river basins across the Nation as part of the National Water-Quality Assessment (NAWQA) project of the National Water-Quality Program. One of the major goals of the NAWQA project is to determine how water-quality conditions change over time. To support that goal, long-term consistent and comparable monitoring has been conducted on streams and rivers throughout the Nation. Outside of the NAWQA project, the USGS and other Federal, State, and local agencies also have collected long-term water-quality data to support their own assessments of changing water-quality conditions. Data from these multiple sources have been combined to support one of the most comprehensive assessment conducted to date of water-quality trends in the United States. Ultimately, these data will provide insight into how natural features and human activities have contributed to water-quality changes over time in Nation’s streams and rivers. This USGS data release contains all of the input and output files necessary to reproduce the results from the Weighted Regressions on Time, Discharge, and Season (WRTDS) models described in the associated U.S. Geological Survey Scientific Investigations Report. Data preparation for input to the models is also fully described in the above-mentioned report.

In 1991, the U.S. Geological Survey (USGS) began a study of more than 50 major river basins across the Nation as part of the National Water-Quality Assessment (NAWQA) project of the National Water-Quality Program. One of the major goals of the NAWQA project is to determine how water-quality conditions change over time. To support that goal, long-term consistent and comparable monitoring has been conducted on streams and rivers throughout the Nation. Outside of the NAWQA project, the USGS and other Federal, State, and local agencies also have collected long-term water-quality data to support their own assessments of changing water-quality conditions. Data from these multiple sources have been combined to support one of the most comprehensive assessment conducted to date of water-quality trends in the United States. Ultimately, these data will provide insight into how natural features and human activities have contributed to water-quality changes over time in Nation’s streams and rivers. This USGS data release contains all of the input and output files necessary to reproduce the results from the Weighted Regressions on Time, Discharge, and Season (WRTDS) models described in the associated U.S. Geological Survey Scientific Investigations Report. Data preparation for input to the models is also fully described in the above-mentioned report.