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

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

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

This data release provides computed rainfall (rain total, duration, intensity, erosivity and antecedent rainfall) and flow (flow volume, flow-weighted mean concentrations, total loads, and total yields) metrics from monitored precipitation, discharge, and water quality (nutrients and sediment concentrations) data collected at U.S. Geological Survey edge-of-field (EOF) monitoring sites located in five Great Lakes States (Wisconsin, Michigan, Ohio, Indiana, and New York). EOF monitoring sites are installed at the edge of agricultural fields, either on the field surface or using subsurface tiles, where runoff can be intercepted and channeled through monitoring equipment before it enters the natural stream system. The methods used to collect this data followed USGS EOF monitoring methods (https://pubs.usgs.gov/of/20081015/). These EOF monitoring sites are located at private farms under a variety of farming systems, landscape settings, drainage areas, soil types, and climates. Site information is provided in the ‘EOF_Site_Table.csv’ data table. Rainfall metrics were computed for EOF site locations and are provided in the ‘All_EOF_RainEvents.csv’ data table. Rainfall was directly monitored at many, but not every EOF monitoring site. EOF monitoring sites without on-site rainfall data were associated to rainfall data measured at a nearby EOF monitoring site or meteorological site. Rainfall was combined into a single event if it occurred within 2 hours of the previous rainfall. Flow data were computed for each flow event at each EOF monitoring site and are available in the ‘All_EOF_StormEventLoadsFormatted.csv’ data table. A flow event was defined as any period of flow at a site that was classified as a storm and represents flow that was related to rainfall or snowmelt. There were occurrences of continuous flow between rain events, which were not associated with a period of rainfall or snowmelt, likely due to excessive soil saturation or shallow groundwater discharge. These periods of intermittent tile discharge were not classified as a storm. Multiple precipitation and flow events were combined if they occurred within two hours of each other to account for similar rainfall/runoff characteristics. Rainfall metrics and flow data were then calculated for these combined events at each EOF monitoring site and available in the ‘All_EOF_StormEventLoadsRainCalculated.csv’ data table.

As part of the Great Lakes Restoration Initiative, the U.S. Department of Agriculture, Natural Resources Conservation Service; U.S. Environmental Protection Agency; and the U.S. Geological Survey (USGS) have partnered to evaluate agricultural conservation practices focused on nutrient management. Monitoring methods allow for rapid assessment of water-quality changes in response to conservation efforts by focusing on subsurface-tile drainage and direct surface runoff from fields. Estimated daily loads presented within this dataset are from five surface-runoff monitoring stations (USGS station identification number 441624088045601, approximated drainage area of 4.17 hectare; USGS station identification number 441546088082001, approximated drainage area of 11.0 hectare; USGS station identification number 441520880845001, approximated drainage area of 3.20 hectare; USGS station identification number 442119088085501, approximated drainage area of 1.94 hectare; USGS station identification number 442114088085701, approximated drainage area of 10.4 hectare) and one tile-runoff monitoring station (USGS station identification number 441520088045002; approximated drainage area of 2.02 hectare). The monitored fields are row-crop parcels planted in a biennial corn-soybean crop rotation. Best-management practices were applied during part of the monitoring period. Note: Daily load computations are presented directly as they were output from the GCLAS program and do not represent the degree of accuracy of the estimates.

As part of the Great Lakes Restoration Initiative, the U.S. Department of Agriculture, Natural Resources Conservation Service; U.S. Environmental Protection Agency; and the U.S. Geological Survey (USGS) have partnered to evaluate agricultural conservation practices focused on nutrient management. Monitoring methods allow for rapid assessment of water-quality changes in response to conservation efforts by focusing on subsurface-tile drainage and direct surface runoff from fields. Estimated daily loads presented within this dataset are from five surface-runoff monitoring stations (USGS station identification number 441624088045601, approximated drainage area of 4.17 hectare; USGS station identification number 441546088082001, approximated drainage area of 11.0 hectare; USGS station identification number 441520880845001, approximated drainage area of 3.20 hectare; USGS station identification number 442119088085501, approximated drainage area of 1.94 hectare; USGS station identification number 442114088085701, approximated drainage area of 10.4 hectare) and one tile-runoff monitoring station (USGS station identification number 441520088045002; approximated drainage area of 2.02 hectare). The monitored fields are row-crop parcels planted in a biennial corn-soybean crop rotation. Best-management practices were applied during part of the monitoring period. Note: Daily load computations are presented directly as they were output from the GCLAS program and do not represent the degree of accuracy of the estimates.

The United States Geological Survey’s (USGS) SPAtially Referenced Regressions On Watershed attributes (SPARROW) model was developed to aid in the interpretation of monitoring data and simulate water-quality conditions in streams across large spatial scales. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based streamflow and water-quality load estimates. Streamflow and load estimates considered for use in regional SPARROW model applications (2012 base year) are described in Saad and others, 2019 (https://dx.doi.org/10.3133/sir20195069). Load estimation methods described in this report include the Beale Ratio Estimator and Fluxmaster models. This USGS data release contains all of the input and output files necessary to reproduce the load estimates considered for inclusion in the 2012 regional SPARROW models. Data preparation for input to the load estimation models is also fully described in the above-mentioned report.

The United States Geological Survey’s (USGS) SPAtially Referenced Regressions On Watershed attributes (SPARROW) model was developed to aid in the interpretation of monitoring data and simulate water-quality conditions in streams across large spatial scales. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based streamflow and water-quality load estimates. Streamflow and load estimates considered for use in regional SPARROW model applications (2012 base year) are described in Saad and others, 2019 (https://dx.doi.org/10.3133/sir20195069). Load estimation methods described in this report include the Beale Ratio Estimator and Fluxmaster models. This USGS data release contains all of the input and output files necessary to reproduce the load estimates considered for inclusion in the 2012 regional SPARROW models. Data preparation for input to the load estimation models is also fully described in the above-mentioned report.

Phosphorus, nitrogen, chloride, and suspended-sediment loads, in 24 U.S. tributaries of the Great Lakes, were estimated using U.S. Geological Survey (USGS) data from the Great Lakes Restoration Initiative (GLRI) monitoring program for the period Oct 2010 through Sept 2023 (USGS water years 2011-23). Specific water-quality constituents include total phosphorus, orthophosphate, particulate phosphorus, total nitrogen, nitrate plus nitrite, ammonium plus ammonia, chloride, and suspended sediment. Concentrations and loads, including actual and flow-normalized estimates, were estimated with Weighted Regression on Time, Discharge, and Season (WRTDS). Results are reported at daily and annual time steps in this data release and via an interactive web application (https://rconnect.usgs.gov/glritrends). This data release is an update of a previous version for water years 2011-20. Major changes include the addition of data from water years 2021-23 and the addition of chloride to the constituents.