This metadata record documents an ascii comma-delimited file representing estimated nitrogen in kilograms by NHDPlus v2.1 catchments for the conterminous United States. Nitrogen estimates were taken from Estimated nitrogen from septic for the conterminous United States, 2010 published by the U.S. Geological Survey. Nitrogen values by individual reach catchments (COMID) were determined by calculating a zonal mean in a GIS.

This metadata record documents an ascii comma-delimited file representing estimated nitrogen in kilograms by NHDPlus v2.1 catchments for the conterminous United States. Nitrogen estimates were taken from Estimated nitrogen from septic for the conterminous United States, 2010 published by the U.S. Geological Survey. Nitrogen values by individual reach catchments (COMID) were determined by calculating a zonal mean in a GIS.

This tabular data set represents the mean rate of nitrogen from fertilizer and manure applied on agricultural land in 1997 compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for mean rate of nitrogen from fertilizer and manure applied on agricultural land in 1997 was produced by Ruddy and others, (USGS, 2006). Units are kilogram per square kilometer. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

This tabular data set represents the mean rate of nitrogen from fertilizer and manure applied on agricultural land in 1997 compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for mean rate of nitrogen from fertilizer and manure applied on agricultural land in 1997 was produced by Ruddy and others, (USGS, 2006). Units are kilogram per square kilometer. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

This tabular data set represents the mean rate of nitrogen from fertilizer and manure applied on agricultural land in 1997 compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for mean rate of nitrogen from fertilizer and manure applied on agricultural land in 1997 was produced by Ruddy and others, (USGS, 2006). Units are kilogram per square kilometer. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

This metadata record describes three best management practice scenarios for Nitrogen, Phosphorous, and Sediment based on information from the National Resource Conservation Service. The potential benefits of agricultural management practices were developed by USDA using estimates of nutrient and sediment reductions from agricultural fields due to the implementation of a suite of Best Management Practices (BMP's) (USDA, 2012). Estimates of losses were developed by the USDA Conservation Effects Assessment Program (CEAP) using a sampling and modeling approach. Data collected by the National Resources Inventory (NRI) Program were utilized to characterize representative agricultural areas for features such as soils, climate, and topography. Additional information was collected from each of those representative areas through the Conservation Effects Assessment Project (CEAP) in order to characterize current farming practices. The effects of specific agricultural management practices were simulated using a model known as the Agricultural Policy Environmental Extender (APEX) implemented on a location by location basis. Those effects were then extrapolated to watershed scales using another model known as the Soil and Water Assessment Tool (SWAT). The output from this procedure provides estimates of loss under three scenarios including: 1) a baseline defined by the conditions observed by the 2003-2006 CEAP survey; 2) an estimate of the amount of loss if no BMP's had been implemented; and 3) an estimate of loss if enhanced nutrient management had been implemented. These estimates were available at a large watershed (HUC2) scale, but were further processed using geospatial techniques and land characteristics to extend the information to the NHDPlus version 2.1 catchment scale. For use in the SPARROW models, an index was calculated as the difference between the baseline and the "no implementation" scenarios. The index was compiled for two spatial components of the NHDPlus version 2.1 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. The index was intended to provide an indication of the expected aggregate benefits of the implementation of a suite of management practices.

This metadata record describes three best management practice scenarios for Nitrogen, Phosphorous, and Sediment based on information from the National Resource Conservation Service. The potential benefits of agricultural management practices were developed by USDA using estimates of nutrient and sediment reductions from agricultural fields due to the implementation of a suite of Best Management Practices (BMP's) (USDA, 2012). Estimates of losses were developed by the USDA Conservation Effects Assessment Program (CEAP) using a sampling and modeling approach. Data collected by the National Resources Inventory (NRI) Program were utilized to characterize representative agricultural areas for features such as soils, climate, and topography. Additional information was collected from each of those representative areas through the Conservation Effects Assessment Project (CEAP) in order to characterize current farming practices. The effects of specific agricultural management practices were simulated using a model known as the Agricultural Policy Environmental Extender (APEX) implemented on a location by location basis. Those effects were then extrapolated to watershed scales using another model known as the Soil and Water Assessment Tool (SWAT). The output from this procedure provides estimates of loss under three scenarios including: 1) a baseline defined by the conditions observed by the 2003-2006 CEAP survey; 2) an estimate of the amount of loss if no BMP's had been implemented; and 3) an estimate of loss if enhanced nutrient management had been implemented. These estimates were available at a large watershed (HUC2) scale, but were further processed using geospatial techniques and land characteristics to extend the information to the NHDPlus version 2.1 catchment scale. For use in the SPARROW models, an index was calculated as the difference between the baseline and the "no implementation" scenarios. The index was compiled for two spatial components of the NHDPlus version 2.1 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. The index was intended to provide an indication of the expected aggregate benefits of the implementation of a suite of management practices.

This tabular data set represents the estimated percent presence of the soil restrictive layer on agricultural land compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data is the "Estimates of the Soil Restrictive Layer in the Upper 25, 35, 45, and 55 centimeters of agricultural land in the conterminous United States" produced by the United States Geological Survey (Nakagaki and others, 2012). Units are percent. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

This tabular data set represents the estimated percent presence of the soil restrictive layer on agricultural land compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data is the "Estimates of the Soil Restrictive Layer in the Upper 25, 35, 45, and 55 centimeters of agricultural land in the conterminous United States" produced by the United States Geological Survey (Nakagaki and others, 2012). Units are percent. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

This tabular data set represents the estimated percent presence of the soil restrictive layer on agricultural land compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data is the "Estimates of the Soil Restrictive Layer in the Upper 25, 35, 45, and 55 centimeters of agricultural land in the conterminous United States" produced by the United States Geological Survey (Nakagaki and others, 2012). Units are percent. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).