Lake Superior is historically a nutrient poor lake that does not typically support significant cyanobacterial blooms. However, the lake has been experiencing an increase in blooms in the western portion of the basin recently. The largest blooms documented have occurred after recent major flooding events, indicating that nutrients transported to the lake during these events may be a source for the blooms. This study looks into the combination of streambed sediment-derived nutrient data during base flow conditions and suspended and settled sediment-derived nutrient data from storm events to provide information about nutrient transformation and storage in the river networks of the Bois Brule River and Siskiwit River watersheds, both tributaries of western Lake Superior.

The Maumee River transports huge loads of nitrogen (N) and phosphorus (P) to Lake Erie. The increased concentrations of N and P are causing eutrophication of the lake, creating hypoxic zones, and contributing to phytoplankton blooms. It is hypothesized that the P loads are a major contributor to harmful algal blooms that occur in the western basin of Lake Erie, particularly in summer. The Maumee River has been identified by the United States Environmental Protection Agency as a priority watershed where action needs to be taken to reduce nutrient loads. This study quantified rates of biogeochemical processes affecting downstream flux of N and P by 1) measuring indices of potential sediment P retention and 2) measuring nitrification and ambient and potential denitrification throughout the Maumee River Basin. Data generated from this project will inform models that estimate P retention and N removal potential in the basin and watershed models that simulate the effects of different conservation practices on the landscape.

The Maumee River transports huge loads of nitrogen (N) and phosphorus (P) to Lake Erie. The increased concentrations of N and P are causing eutrophication of the lake, creating hypoxic zones, and contributing to phytoplankton blooms. It is hypothesized that the P loads are a major contributor to harmful algal blooms that occur in the western basin of Lake Erie, particularly in summer. The Maumee River has been identified by the United States Environmental Protection Agency as a priority watershed where action needs to be taken to reduce nutrient loads. This study quantified rates of biogeochemical processes affecting downstream flux of N and P by 1) measuring indices of potential sediment P retention and 2) measuring nitrification and ambient and potential denitrification throughout the Maumee River Basin. Data generated from this project will inform models that estimate P retention and N removal potential in the basin and watershed models that simulate the effects of different conservation practices on the landscape.

The Maumee River transports huge loads of nitrogen (N) and phosphorus (P) to Lake Erie. The increased concentrations of N and P are causing eutrophication of the lake, creating hypoxic zones, and contributing to phytoplankton blooms. It is hypothesized that the P loads are a major contributor to harmful algal blooms that occur in the western basin of Lake Erie, particularly in summer. The Maumee River has been identified by the United States Environmental Protection Agency as a priority watershed where action needs to be taken to reduce nutrient loads. This study quantified rates of biogeochemical processes affecting downstream flux of N and P by 1) measuring indices of potential sediment P retention and 2) measuring nitrification and ambient and potential denitrification throughout the Maumee River Basin. Data generated from this project will inform models that estimate P retention and N removal potential in the basin and watershed models that simulate the effects of different conservation practices on the landscape.

Nutrient and soil runoff from agricultural fields in the Upper East River basin contribute to eutrophication in downstream waters like Lake Michigan. Phosphorus is often the limiting nutrient in freshwater ecosystems and the binding of dissolved phosphorus to suspended sediment can reduce its bioavailability. These data provide information about what size particles are binding phosphorus and how the proportion of total phosphorus that is dissolved changes as the phosphorus is transported from the field through the stream network during runoff events. Samples were collected for analysis in 2022 and 2023. Information about management of Wisconsin Waterway 3 was also collected from 2020-2023 and is included in this data release.

Phosphorus, nitrogen, and suspended-sediment loads, in 24 U.S. tributaries of the Great Lakes, were calculated using U.S. Geological Survey (USGS) data from the Great Lakes Restoration Initiative (GLRI) monitoring program for the period Oct 2010 through Sept 2020 (USGS water years 2011–2020). Total phosphorus, orthophosphate, particulate phosphorus, total nitrogen, nitrate plus nitrite, ammonium plus ammonia, and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). The load results represent the total mass of nitrogen, phosphorus, and suspended sediment exported downstream at each tributary site. Daily loads were estimated using the WRTDS method with Kalman filtering. To determine the change in loads between the first and last water year in record, the annual load results were flow-normalized to standardize among years with varying flow dynamics. Three primary data tables are provided that describe phosphorus, nitrogen, and suspended-sediment conditions at 24 tributaries across the U.S. Great Lakes watershed: (1) annual results, (2) daily results, and (3) changes results. The annual table is organized by USGS water year (defined as Oct 1–Sept 30). The annual and daily tables also include time-weighted mean concentrations, mean discharge, and yield estimates, the latter being calculated by dividing loads by watershed areas. Descriptions for data tables are provided in the DataDictionary.csv file. Results are also displayed in an interactive web application (https://rconnect.usgs.gov/glritrends). This data release also contains a model archive. The “model_code.zip” file contains the R code and input data to run the models, generate the results, and display the results on a shiny application. The Readme.md file includes instructions of how to run the model and descriptions of all inputs, model arguments, and outputs. Each model (saved as an eList) contains the input data (USGS station information (i.e., site information), daily discharge, and water sample data), model parameters, estimated values, and bootstrapped results. All eLists are archived in the “model_archive” folder, which is organized by sample dataset (i.e., allsamples and midsamples) and are named with the parameter and station number (e.g., NH4_04024000.RData). The RData files used in the shiny application are archived in the “shinydata” folder.

Phosphorus, nitrogen, and suspended-sediment loads, in 24 U.S. tributaries of the Great Lakes, were calculated using U.S. Geological Survey (USGS) data from the Great Lakes Restoration Initiative (GLRI) monitoring program for the period Oct 2010 through Sept 2020 (USGS water years 2011–2020). Total phosphorus, orthophosphate, particulate phosphorus, total nitrogen, nitrate plus nitrite, ammonium plus ammonia, and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). The load results represent the total mass of nitrogen, phosphorus, and suspended sediment exported downstream at each tributary site. Daily loads were estimated using the WRTDS method with Kalman filtering. To determine the change in loads between the first and last water year in record, the annual load results were flow-normalized to standardize among years with varying flow dynamics. Three primary data tables are provided that describe phosphorus, nitrogen, and suspended-sediment conditions at 24 tributaries across the U.S. Great Lakes watershed: (1) annual results, (2) daily results, and (3) changes results. The annual table is organized by USGS water year (defined as Oct 1–Sept 30). The annual and daily tables also include time-weighted mean concentrations, mean discharge, and yield estimates, the latter being calculated by dividing loads by watershed areas. Descriptions for data tables are provided in the DataDictionary.csv file. Results are also displayed in an interactive web application (https://rconnect.usgs.gov/glritrends). This data release also contains a model archive. The “model_code.zip” file contains the R code and input data to run the models, generate the results, and display the results on a shiny application. The Readme.md file includes instructions of how to run the model and descriptions of all inputs, model arguments, and outputs. Each model (saved as an eList) contains the input data (USGS station information (i.e., site information), daily discharge, and water sample data), model parameters, estimated values, and bootstrapped results. All eLists are archived in the “model_archive” folder, which is organized by sample dataset (i.e., allsamples and midsamples) and are named with the parameter and station number (e.g., NH4_04024000.RData). The RData files used in the shiny application are archived in the “shinydata” folder.

This data release includes estimates of annual and daily concentrations and fluxes for nitrate plus nitrite, total phosphorus, and suspended sediment for two sites in the Upper Macoupin Creek Basin watershed (UMCB) produced using the Weighted Regressions on Time, Discharge, and Season -- Kalman Filter (WRTDS-K; Zhang and Hirsch, 2019) model in the Exploration of and Graphics for RivEr Trends (EGRET) package (Hirsch and De Cicco, 2015). It also includes a model archive (R scripts in Scripts.zip) used to retrieve and format the model input data and run the model. Input data, for Macoupin Creek at Highway 111 near Summerville, Illinois, and Macoupin Creek at Highway 108 near Carlinville, Illinois (U.S. Geological Survey site numbers 05586745 and 05586647, respectively), including discrete concentrations and daily mean streamflow, were retrieved from the National Water Information System database (USGS, 2021). Annual and daily estimates range from water year 2018 through water year 2021 (although water year 2018 is protracted due to data collection starting on October 24, 2017). Reference: Hirsch, R.M., and De Cicco, L.A., 2015, User guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R packages for hydrologic data (version 2.0, February 2015): U.S. Geological Survey Techniques and Methods book 4, chap. A10, 93 p., https://dx.doi.org/10.3133/tm4A10. U.S. Geological Survey, 2021, USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed 12/15/2021, at https://doi.org/10.5066/F7P55KJN Zhang, Q., and Hirsch, R.M., 2019. River water‐quality concentration and flux estimation can be improved by accounting for serial correlation through an autoregressive model: Water Resources Research, v. 5, no. 11, p. 9705–9723, https://doi.org/10.1029/2019WR25338.

This data release includes estimates of annual and daily concentrations and fluxes for nitrate plus nitrite, total phosphorus, and suspended sediment for two sites in the Upper Macoupin Creek Basin watershed (UMCB) produced using the Weighted Regressions on Time, Discharge, and Season -- Kalman Filter (WRTDS-K; Zhang and Hirsch, 2019) model in the Exploration of and Graphics for RivEr Trends (EGRET) package (Hirsch and De Cicco, 2015). It also includes a model archive (R scripts in Scripts.zip) used to retrieve and format the model input data and run the model. Input data, for Macoupin Creek at Highway 111 near Summerville, Illinois, and Macoupin Creek at Highway 108 near Carlinville, Illinois (U.S. Geological Survey site numbers 05586745 and 05586647, respectively), including discrete concentrations and daily mean streamflow, were retrieved from the National Water Information System database (USGS, 2021). Annual and daily estimates range from water year 2018 through water year 2021 (although water year 2018 is protracted due to data collection starting on October 24, 2017). Reference: Hirsch, R.M., and De Cicco, L.A., 2015, User guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R packages for hydrologic data (version 2.0, February 2015): U.S. Geological Survey Techniques and Methods book 4, chap. A10, 93 p., https://dx.doi.org/10.3133/tm4A10. U.S. Geological Survey, 2021, USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed 12/15/2021, at https://doi.org/10.5066/F7P55KJN Zhang, Q., and Hirsch, R.M., 2019. River water‐quality concentration and flux estimation can be improved by accounting for serial correlation through an autoregressive model: Water Resources Research, v. 5, no. 11, p. 9705–9723, https://doi.org/10.1029/2019WR25338.

The National Lakes Assessment (NLA) is a statistical survey of the condition of our nation's lakes, ponds, and reservoirs. It is designed to provide information on the extent of lakes that support healthy biological condition and recreation, estimate how widespread major stressors are that impact lake quality, and provide insight into whether lake quality is improving or getting worse. This dataset is an archived (zipped) file comprised of chemical, physical and biological files used in developing the NLA 2022 report. Sampling was conducted in the summer of 2022 at approximately 1000 sites in the conterminous U.S. Sites were selected using a statistical survey (probabilistic) design. The files include water chemistry, profile data, benthic macroinvertebrates, physical habitat, landscape metrics, secchi depth, tropic status, zooplankton, etc. Users are encouraged to visit the NARS data webpage for updates to data files and data from other surveys. https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys Citation for the NLA 2022 archived data: U.S. Environmental Protection Agency. 2024. National Lakes Assessment: The fourth collaborative survey of lakes in the United States. EPA 841-R-24-006. U.S. Environmental Protection Agency, Office of Water and Office of Research and Development. https://nationallakesassessment.epa.gov/webreport EPA encourages users who are publishing subsets of the data (say as part of a journal article publication) to include the above citation. EPA also encourages users of the data to include the following acknowledgement: “The National Lakes Assessment 2022 data were a result of the collective efforts of dedicated field crews, laboratory staff, data management and quality control staff, analysts and many others from EPA, states, tribes, federal agencies, universities, and other organizations. Please contact nars-hq@epa.gov with any questions.”. Citation information for this dataset can be found in Data.gov's References section.