This data set represents the presence or absence of semiconsolidated sand aquifers in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in ground water used for drinking. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 14 data sets (1 output data set and 13 input data sets) associated with the GWAVA-DW model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 13 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for > nitrate in ground water used for drinking (GWAVA-DW) > and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-dw_ffer > 2 confined manure (kg/hectare) gwava-dw_conf > 3 orchards/vineyards (percent) gwava-dw_orvi > 4 population density (people/km2) gwava-dw_popd > >Transport to Aquifer Factors > 5 water input (km2/cm) gwava-dw_wtin > 6 glacial till (yes/no) gwava-dw_gtil > 7 semiconsolidated sand aquifers gwava-dw_semc > (yes/no) > 8 sandstone and carbonate rocks gwava-dw_sscb > (yes/no) > 9 drainage ditch (km2) gwava-dw_ddit > 10 Hortonian overland flow gwava-dw_hor > (percent of streamflow) > >Attenuation Factors > 11 fresh surface water withdrawal gwava-dw_swus > for irrigation (megaliters/day) > 12 irrigation tailwater recovery (km2) gwava-dw_twre > 13 Dunne overland flow gwava-dw_dun > (percent of streamflow) > 14 well depth (meters) - "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Semiconsolidated sand aquifers" is the presence or absence of semiconsolidated sand aquifers. "Sandstone and carbonate rocks" is the presence or absence of sandstone and carbonate rock aquifers. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Hortonian overland flow" is infiltration excess overland flow estimated by TOPMODEL, in percent of streamflow. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Dunne overland flow" is saturation overland flow estimated by TOPMODEL, in percent of streamflow. "Well depth" is the depth of the well, in meters. Well depth was not compiled as a spatial data set. Well depth equals 50 meters for the model simulation being presented. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.