Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_swus Nolan, B.T. Hitt, K.J. 2006 Environmental Science and Technology Volume 40, Number 24, pages 7834-7840 Reston, Virginia U.S. Geological Survey https://water.usgs.gov/nawqa/nutrients/pubs/est_v40_no24/ http://pubs3.acs.org/acs/journals/doilookup?in_doi=10.1021/es060911u This data set represents the amount of fresh surface water withdrawal for irrigation, in megaliters per day, in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water. 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 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 16 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 shallow > ground water (GWAVA-S) and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-s_ffer > 2 confined manure (kg/hectare) gwava-s_conf > 3 orchards/vineyards (percent) gwava-s_orvi > 4 population density (people/km2) gwava-s_popd > 5 cropland/pasture/fallow (percent) gwava-s_crpa > >Transport to Aquifer Factors > 6 water input (km2/cm) gwava-s_wtin > 7 carbonate rocks (yes/no) gwava-s_crox > 8 basalt and volcanic rocks (yes/no) gwava-s_vrox > 9 drainage ditch (km2) gwava-s_ddit > 10 slope (percent x 1000) gwava-s_slop > 11 glacial till (yes/no) gwava-s_gtil > 12 clay sediment (percent x 1000) gwava-s_clay > >Attenuation Factors > 13 fresh surface water withdrawal gwava-s_swus > for irrigation (megaliters/day) > 14 irrigation tailwater recovery (km2) gwava-s_twre > 15 histosol soil type (percent) gwava-s_hist > 16 wetlands (percent) gwava-s_wetl "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. "Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks. "Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Slope" is the soil surface slope, in percent times 1000. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Clay sediment" is the amount of clay sediment in the soil, in percent times 1000. "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. "Histosol soil type" is the amount of histosols soil taxonomic order, in percent. "Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications. 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. This particular data layer was created to help characterize nitrogen attenuation factors at a national level for input to a national model to predict nitrate concentration in shallow ground water. Nitrate is considered to be the most widespread contaminant in ground water. High nitrate concentration in ground water is a concern for human health, and protecting drinking water sources is a national priority. The U.S. Geological Survey's National Water-Quality Assessment (NAWQA) Program monitors the occurrence and distribution of nitrate and other contaminants in ground water and streams. However, because monitoring everywhere for the occurrence and distribution of nitrate in ground water is impractical, national water-quality models are used to address data gaps. The goal of the current study was to predict ground water vulnerability to nitrate at the national scale, to complement measured data. The data set is provided as native ESRI ArcInfo Workstation GRID and as ASCII text (plain text) format. The file "gridname".tgz file contains the GRID in a directory (folder) called arctar00000, where "gridname" is the name of the data set. For example, a GRID without a VAT (value attribute table) has the following files: >arctar00000/ >arctar00000/gridname/ >arctar00000/gridname/dblbnd.adf >arctar00000/gridname/hdr.adf >arctar00000/gridname/log >arctar00000/gridname/metadata.xml >arctar00000/gridname/prj.adf >arctar00000/gridname/sta.adf >arctar00000/gridname/w001001.adf >arctar00000/gridname/w001001x.adf >arctar00000/info/ >arctar00000/info/arc.dir >arctar00000/info/arc0000.dat >arctar00000/info/arc0000.nit >arctar00000/info/arc0001.dat >arctar00000/info/arc0001.nit >arctar00000/log A GRID with a VAT (value attribute table) (gwava-s_orvi, gwava-s_crpa, gwava-s_crox, gwava-s_vrox, gwava-s_slop, gwava-s_gtil, gwava-s_clay, gwava-s_hist, gwava-s_wetl) has these files: >arctar00000/ >arctar00000/gridname/ >arctar00000/gridname/dblbnd.adf >arctar00000/gridname/hdr.adf >arctar00000/gridname/log >arctar00000/gridname/metadata.xml >arctar00000/gridname/prj.adf >arctar00000/gridname/sta.adf >arctar00000/gridname/vat.adf >arctar00000/gridname/w001001.adf >arctar00000/gridname/w001001x.adf >arctar00000/gridname.aux >arctar00000/info/ >arctar00000/info/arc.dir >arctar00000/info/arc0000.dat >arctar00000/info/arc0000.nit >arctar00000/info/arc0001.dat >arctar00000/info/arc0001.nit >arctar00000/info/arc0002.dat >arctar00000/info/arc0002.nit >arctar00000/info/arc0002r.001 >arctar00000/log To extract the ArcInfo Workstation GRID from the "gridname".tgz archive file, use TARARC, WINZIP, or the following commands: >gunzip gridname.tgz >tar xvof gridname.tar The data set is provided in ASCII text format in addition to the native ESRI ArcInfo Workstation GRID format in case the user's software cannot access the data in ArcInfo Workstation GRID format. The ASCII file is compressed using gzip as "gridname".txt.gz, where "gridname" is the data set name. 1991 2003 Water-quality data used in this study were collected during 1991-2003 and represent the first full decade of sampling by the NAWQA program. The input data layers describe conditions in the mid 1990's, and so the predictions represent mid 1990's land-use and nitrogen-loading conditions. Complete None planned. -128.30785909 -65.14338696 51.857984 22.73659812 USGS Thesaurus Ground water Ground water contamination Ground water pollution Ground water susceptibility Nutrients Nitrate National Water-Quality Assessment Program NAWQA Nonlinear model Nitrate concentration inlandWaters Fresh surface water withdrawal Irrigation water use National Land Cover Data ISO 19115 Topic Category geoscientificInformation inlandWaters environment Geographic Names Information System Conterminous United States None None None None USGS Metadata Identifier USGS:7460b171-36b8-4e55-a98c-ab446834c6de None. This spatial data set is one of a group of data sets developed specifically for use in a national model of nitrate in ground water. The data set should be used at the national or regional level, not at the local level. Users should consider the various assumptions that went into generating each spatial data set and the limitations inherent in the source data materials in deciding whether the data set is appropriate for use in other national or regional applications. Please acknowledge the U.S. Geological Survey in products derived from these data. Kerri Hitt. U.S. Geological Survey Hydrologist mailing address

MS-413 National Center

12201 Sunrise Valley Drive

Reston VA 20192 USA 703 648-6854 khitt@usgs.gov Please contact thru email https://water.usgs.gov/GIS/browse/gwava-dw_out.jpg Map of nitrate concentration in U.S. ground water used for drinking, as predicted by the GWAVA-DW model. jpg Thanks to Nancy T. Baker who reviewed the metadata and provided useful comments. Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.0.0.535 Nolan, B.T. 2001 1 Ground Water Volume 39, Number 2, pages 290-299 https://water.usgs.gov/nawqa/nutrients/pubs/gw_v39_no2/ Nolan, B.T., Hitt, K.J., and Ruddy, B.C. 2002 1 Raster digital data Environmental Science & Technology Volume 36, Number 10, pages 2138-2145 https://water.usgs.gov/nawqa/nutrients/pubs/est_v36_no10/ https://water.usgs.gov/lookup/getspatial?gwrisk Nolan, B.T. Hitt, K.J. 2006 Environmental Science and Technology Volume 40, Number 24, pages 7834-7840 Denver, Colorado U.S. Geological Survey https://water.usgs.gov/nawqa/nutrients/pubs/est_v40_no24/ http://pubs3.acs.org/acs/journals/doilookup?in_doi=10.1021/es060911u Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_out Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_ffer Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_conf Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_orvi Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_popd Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_crpa Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_wtin Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_crox Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_vrox Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_ddit Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_slop Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_gtil Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_clay Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_twre Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_hist Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-s_wetl Hitt, K.J. 2007 1 Raster digital data Reston, Virginia, USA U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?gwava-dw_out Nakagaki, Naomi Wolock, David M. 2005 1 U.S. Geological Survey Open-File Report 2005-1188 https://pubs.usgs.gov/of/2005/1188/ The model results were checked using standard USGS review procedures. Not applicable for raster data. The data spans the conterminous United States. An output cell was assigned a value of "no data" if any of the corresponding input data cells at that location was "no data." Solley, W.B. Pierce, R.R. Perlman, H.A. 1998 Version 1.0 tables U.S. Geological Survey Circular 1200 Reston, Virginia U.S. Geological Survey https://water.usgs.gov/watuse/pdf1995/html/ On-line 1995 1995 water use report WU95 The data set is the source for tabular county estimates of fresh surface water withdrawal for 1995 irrigation in million gallons per day (Mgal/day). The county data was apportioned to mapped agricultural land use. [Retrieved September 2004.] Vogelmann, J.E. Howard, S.M. Yang, L. Larson, C.R. Wylie, B.K. Van Driel, N. 2001 raster digital data Photogrammetric Engineering and Remote Sensing Journal of the American Society for Photogrammetry and Remote Sensing v. 67, no. 6, p. 650-662 https://www.mrlc.gov/nlcd2k1_product_desc.asp http://www.asprs.org/publications/pers/2001journal/june/highlight.html On-line 1992 Source imagery NLCD 92 NLCD 92 (National Land Cover Dataset 1992) is a 21-category land cover classification scheme that has been applied consistently over the conterminous United States. It is based primarily on the unsupervised classification of Landsat TM (Thematic Mapper) 1992 imagery. Ancillary data sources included topography, census, agricultural statistics, soil characteristics, other land cover maps, and wetlands data. The NLCD 92 classification is provided as raster data with a spatial resolution of 30 meters. Price, Curtis Nakagaki, Naomi Hitt, Kerie Clawges, Rick 2007 Version 1.0 map U.S Geological Survey Data Series 240 Reston, Virginia U.S. Geological Survey https://pubs.usgs.gov/ds/2006/240/ On-line 1970 1985 ground condition USGS DS 240 A 30-m spatial resolution version of this land use and land cover (LULC) data set was used to develop an enhanced version of the NLCD 92 (called NLCDE 92) at 30-meter and 1-kilometer resolutions using methods described in: Nakagaki, N., and Wolock, D.M., 2005, Estimation of agricultural pesticide use in drainage basins using land cover maps and county pesticide data: U.S. Geological Survey Open-File Report 2005-1188, pp.4-10. The LULC data were derived from aerial photography, and NLCD 92 data were derived from satellite imagery. The NLCD was modified with selected land-use categories of LULC, as described in Nakagaki and Wolock (2005), because LULC data are a better source for some land categories that are difficult to distinguish using only satellite imagery (residential, orchards/vineyards/other, and tundra). The enhanced NLCD (NLCDE 92) includes 21 land-cover classifications from the original NLCD 92 plus an additional four categories from the LULC (LULC tundra, NLCD/LULC forested residential, LULC residential, and LULC orchards/vineyards/other) (Nakagaki and Wolock, 2005). The 30-m resolution NLCDE 92 was used to create a set of 25 1-km resolution national grids of "percentage" land cover (one grid for each land cover class) using methods described in Nakagaki and Wolock (2005), pages 10-13. In each 1-km resolution national grid, the value of a grid cell was the percentage of the 1 km by 1 km area specific to that land cover class. For example, the grid of "pasture/hay" indicated the percentage of pasture/hay in each cell, and the grid of "row crops" indicated the percentage of row crops in each cell. Spatial data sets representing nitrogen (N) loading and transport and attenuation factors were compiled for the conterminous United States. N source variables include farm fertilizer, manure from confined animal feeding operations, and loading surrogates that reflect additional sources of N. For example, population density is a surrogate for nonagricultural sources of N from septic tanks, sewers, and domestic animal waste in urban areas. Transport factors include water input, sediments, rock type, and selected management practices. "Water input" is an interaction term expressed as the ratio of the total area of irrigated land to precipitation. Attenuation factors include variables that are surrogates for dilution and/or denitrification. Each of the variables in the model was compiled within 1-km by 1-km grid cells for prediction of nitrate concentration at the national scale. >Table 2.-- Variables compiled for regression model GWAVA-S, > units and estimated coefficients > [kg, kilograms; km2, square kilometers.] > >Nitrogen Loading Units Estimated coefficient > 1 farm fertilizer kg/hectare 0.2265 > 2 confined manure kg/hectare 0.4049 > 3 orchards/vineyards percent 1.9600 > 4 population density people/km2 0.006658 > 5 cropland/pasture/fallow percent 0.1473 > >Transport Factors > 6 water input km2/cm 38.16 > 7 carbonate rocks presence/absence 0.5630 > 8 basalt and volcanic rocks presence/absence 0.5182 > 9 drainage ditch km2 -6.483 > 10 slope percent* -0.03861 > 11 glacial till presence/absence -0.8141 > 12 clay sediment percent* -0.04751 > >Attenuation Factors > 13 fresh surface water withdrawal megaliters/day -1.078 > 14 irrigation tailwater recovery km2 -8.327 > 15 histosol soil type percent -0.0185 > 16 wetlands percent -0.03213 > >*Values in the SLOPE and CLAY grids were multiplied by 1000 to convert > the grids from floating point to integer. 200512 A national grid of 1995 fresh surface water withdrawal for irrigation was generated by adding a spatial component to tabular county estimates of fresh surface water withdrawal for irrigation presented in Solley and others (1998). Mapped agricultural land cover from NLCDE 92 1-km resolution GRIDS was used to spatially refine the geographic location of irrigation water withdrawal from countywide tables to agricultural land within a county. In each NLCDE 92 1-km resolution national grid, the value of a grid cell was the percentage of the 1 km by 1 km area specific to that land cover class. If a 1-km2 cell contained 78 percent agricultural land, this translated to .78 km2 of agricultural land in the cell. Agricultural land where irrigation was applied was defined as the sum of these six percentage land cover classifications from NLCDE 92: >Code Land cover classification > 61 Orchards/vineyards/other > 62 LULC orchards/vineyards/other > 81 Pasture/hay > 82 Row crops > 83 Small grains > 84 Fallow The assumption was that surface water withdrawals were spread equally among these land cover categories. A 1-km resolution national grid of fresh surface water withdrawal for fresh surface water withdrawal was generated using steps similar to those used to generate a 1-km resolution pesticide use grid described in Nakagaki and Wolock (2005), pages 17-20. The steps were: 1. Compute the county area of NLCDE 92 agricultural land cover classifications in each county in the conterminous United States. a. The 1-km grid of percent values of agricultural land was converted to a grid of km2 of agricultural land by dividing the value of each cell (percent) by 100. b. The area of agricultural land was summed for each county by overlaying the cell values (km2) of the areal grid with a 1-km resolution county grid. 2. Compute the county "intensity" (or "rate") of surface water withdrawal for each county in the conterminous United States. The "intensity" was computed by dividing the surface water withdrawal of the county (km2) by the county area of agricultural land computed in step 1b (km2). The "intensity" data was linked to the 1-km national county grid using county FIPS codes. 3. Create a national grid of estimated surface water withdrawal. The cell values of the areal grid of km2 of agricultural land (step 1a) were multiplied by the cell values of the areal grid of county surface water withdrawal "intensity" (step 2). The cell values of the final grid represent the Mgal/day of surface water withdrawal in each 1 km by 1 km cell. Here is an example of how surface water withdrawal for one 1-km2 cell in Pierce County, Nebraska (FIPS 31119) was generated: >Step 1a. Percent of NLCDE 92 ag land in cell = 13, so the >amount of NLCDE 92 ag land in cell = .13 km2 > >Step 1b. Total area of NLCDE 92 ag land in county = 1,302.17 km2 > >Step 2. Total surface water withdrawal in county = 2.85 >Mgal/day (from Solley and others, 1998) > >County "intensity" = 2.85 Mgal/day / 1,302.17 km2 = .0022 >Mgal/day per km2 >Step 3. Amount of surface water withdrawal in cell = .13 >km2 * .0022 Mgal/day per km2 = .00028 Mgal/day > >The sum of surface water withdrawal calculated for all >cells in Pierce County, Nebraska (FIPS 31119) equals 2.85 >Mgal/day. These steps were repeated for all cells nationwide to generate a 1-km resolution national grid. Each cell value represented the amount of fresh surface water withdrawal in the cell in Mgal/day. The values were converted to megaliters per day (MLD) by multiplying each value by 3.785. 200512 To make the national map of predicted nitrate concentration, the values from the 1-km by 1-km grid cells for each of the input data layers were put in to the model equation to calculate a predicted concentration for each output cell. An output cell was assigned a value of "no data" if any of the corresponding input data cells at that location was "no data." The following ArcInfo Workstation GRID commands produced the predicted nitrate concentration grid: >/*Nonlinear regression model 1 for nitrate in shallow ground water >/*Dec 23, 2005 >setwindow -2380000.000 260000.000 2265000.000 3200000.000 gwava-s_orvi >setcell 1000 >/*Set directory housing the filtered input grids for fertilizer, manure, population, and water input. >&s fdir = d:/ancill/model2005/dec2005/filtered >/* >/*Use filtered N input grids in kg/hectare. >/* >gwava-s_out = ((0.226544 * %fdir%/gwava-s_ffer) + (0.404948 * %fdir%/gwava-s_conf) + (1.959994 * gwava-s_orvi) + (0.006658 * gwava-s_popd) + ~ >(0.147298 * gwava-s_crpa)) * ~ >exp ((0.562985 * gwava-s_crox) + (0.518172 * gwava-s_vrox) + (-6.48311 * gwava-s_ddit) + (-0.03861 * ((gwava-s_slop / 1000))) + ~ >(-0.81412 * gwava-s_gtil) + (-0.04751 * (gwava-s_clay / 1000)) + (38.16395 * %fdir%/gwava-s_wtin)) * ~ >exp ((-1.07756 * gwava-s_swus) + (-8.3267 * gwava-s_twre) + (-0.01846 * gwava-s_hist) + (-0.03213 * gwava-s_wetl)) GRID cells were randomly selected from this data set and were checked by hand to ensure the correct values were calculated using the model equation and the input data layers. Areas with high N load, low to moderate clay content, sufficient water input, and low denitrification potential have the highest predicted nitrate concentration in ground water and therefore may be vulnerable to nitrate contamination. The most extensive areas of predicted, severe contamination (nitrate greater than 10 mg/L) occur in the High Plains, and areas of predicted, moderate contamination (more than 5 to 10 mg/L nitrate) occur extensively in the northern Midwest. 200512 The input GRID was converted to ASCII (plain text) for distribution using ArcInfo Workstation command: >gridascii gwava-s_swus gwava-s_swus.txt 2007 Raster Grid Cell 2940 4645 1 Albers Conical Equal Area 29.5 45.5 -96 23 0.00000 0.00000 row and column 1000.0 1000.0 METERS North American Datum of 1983 GRS80 6378137.000000 294.257222 Each 1-km by 1-km grid cell stores the amount of fresh surface water withdrawal, in megaliters per day. Data Type: Floating Point Minimum Value = 0.000 Maximum Value = 11.115 Mean = 0.041 Standard Deviation = 0.263 The grid is floating point; a VAT is not present. In the ASCII text file, "no data" is indicated as -9999. None. U.S. Geological Survey Michael Ierardi IT Specialist mailing and physical

445 National Center

Reston Virginia 20192 USA 1-888-275-8747 (1-888-ASK-USGS) mierardi@usgs.gov Although this data set has been used by the U.S. Geological Survey, U.S. Department of the Interior, no warranty expressed or implied is made by the U.S. Geological Survey as to the accuracy of the data and related materials. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the U.S. Geological Survey in the use of this data, software, or related materials. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. ArcInfo Workstation GRID gzip -d; gunzip 8303844 bytes https://water.usgs.gov/GIS/dsdl/gwava-s/arctar/gwava-s_swus.tgz ASCII file gzip -d; gunzip 11051691 bytes https://water.usgs.gov/GIS/dsdl/gwava-s/gascii/gwava-s_swus.txt.gz Index to all files related to the GWAVA-S model to facilitate downloading all the GIS data sets Web page with links to all data sets HTML 5 bytes https://water.usgs.gov/GIS/dsdl/gwava-s/index.html None. This data set is provided by USGS as a public service. 20201117 U.S. Geological Survey Ask USGS -- Water Webserver Team mailing

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Reston VA 20192 1-888-275-8747 (1-888-ASK-USGS) mierardi@usgs.gov FGDC Content Standards for Digital Geospatial Metadata FGDC-STD-001-1998