Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Model of predicted nitrate concentration in U.S. ground water used for drinking (simulation depth 50 meters) -- Input data set for water input (gwava-dw_wtin)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_wtin
Nolan, B.T.
Hitt, K.J.
2006
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States
Environmental Science and Technology
Volume 40, Number 24, pages 7834-7840
http://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 "water input," the ratio of the total
area of irrigated land to precipitation, in square kilometers
per centimeter, 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.
This particular data layer was created to help characterize
nitrogen transport factors at a national level for input to a
national model to predict nitrate concentration in ground water
used for drinking.
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-dw_orvi,
gwava-dw_hor, gwava-dw_gtil, gwava-dw_semc, gwava-dw_sscb,
gwava-dw_dun) 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.
None planned.
-128.30785909
-65.14338696
51.857984
22.73659812
None
Ground water
Ground water contamination
Ground water pollution
Ground water susceptibility
Nutrients
Nitrate
National Water-Quality Assessment Program
NAWQA
Nonlinear model
Nitrate concentration
Drinking water
inlandWaters
Precipitation
Irrigated land
National Land Cover Data
None
Conterminous United States
None
None
None
None
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.
Thanks to Michael E. Wieczorek 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
Relating nitrogen sources and aquifer susceptibility to
nitrate in shallow ground waters of the United States
1
Ground Water
Volume 39, Number 2, pages 290-299
http://water.usgs.gov/nawqa/nutrients/pubs/gw_v39_no2/
Nolan, B.T., Hitt, K.J., and Ruddy, B.C.
2002
Probability of nitrate contamination of recently recharged groundwaters
in the conterminous United States
1
Raster digital data
Environmental Science & Technology
Volume 36, Number 10, pages 2138-2145
http://water.usgs.gov/nawqa/nutrients/pubs/est_v36_no10/
http://water.usgs.gov/lookup/getspatial?gwrisk
Nolan, B.T.
Hitt, K.J.
2006
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States
Environmental Science and Technology
Volume 40, Number 24, pages 7834-7840
Denver, Colorado
U.S. Geological Survey
http://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
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate
concentration in U.S. ground water used for drinking
(simulation depth 50 meters) -- Model output data set (gwava-dw_out)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_out
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for farm fertilizer (gwava-dw_ffer)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_ffer
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for confined manure (gwava-dw_conf)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_conf
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for orchards/vineyards (gwava-dw_orvi)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_orvi
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for population density (gwava-dw_popd)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_popd
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for glacial till (gwava-dw_gtil)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_gtil
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for semiconsolidated sand aquifers (gwava-dw_semc)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_semc
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for sandstone and carbonate rocks (gwava-dw_sscb)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_sscb
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for drainage ditch (gwava-dw_ddit)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_ddit
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for Hortonian overland flow (gwava-dw_hor)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_hor
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for fresh surface water withdrawal (gwava-dw_swus)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_swus
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for irrigation tailwater recovery (gwava-dw_twre)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_twre
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in U.S. ground water used for drinking -- Input data set for Dunne overland flow (gwava-dw_dun)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-dw_dun
Hitt, K.J.
2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Model output data set (gwava-s_out)
1
Raster digital data
Reston, Virginia, USA
U.S. Geological Survey
http://water.usgs.gov/lookup/getspatial?gwava-s_out
Nakagaki, Naomi
Wolock, David M.
2005
Estimation of agricultural pesticide use in drainage basins
using land cover maps and county pesticide data
1
U.S. Geological Survey Open-File Report
2005-1188
http://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."
Thornton, P.E.
Running, S.W.
1999
An improved algorithm for estimating incident daily solar radiation
from measurements of temperature, humidity, and precipitation
Version 1.0
map
Agricultural and Forest Meteorology
Vol. 93, issue 4, March 22, 1999
http://www.daymet.org/
http://www.daymet.org/daymetus_userguide.pdf
On-line
1980
1997
1999
DAYMET
DAYMET is a climate model that is the source for mean annual
precipitation (1980-1997), in centimeters. [Retrieved February
2004.]
Total precipitation (18-year mean annual) data was downloaded
as a binary image from http://www.daymet.org/ and was converted
to a 1-kilometer national floating point grid following the
Daymet User's Guide Appendix A, Using Daymet Summary Data in a
GIS - ESRI Imports. The grids were projected first from
Lambert to Geographic and then from Geographic to Albers.
Solley, W.B.
Pierce, R.R.
Perlman, H.A.
1998
Estimated use of water in the United States in 1995
Version 1.0
tables
U.S. Geological Survey Circular
1200
http://water.usgs.gov/watuse/pdf1995/html/
On-line
1995
1995 water use report
WU95
The data set is the source for county estimates of 1995
irrigated acres. The units were converted to square kilometers
(km2). [Retrieved September 2004.] The county data was
apportioned to mapped agricultural land use.
Vogelmann, J.E.
Howard, S.M.
Yang, L.
Larson, C.R.
Wylie, B.K.
Van Driel, N.
2001
Completion of the 1990s National Land Cover Dataset for the
conterminous United States from Landsat Thematic Mapper data and
ancillary data sources
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
http://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
Enhanced historical land-use and land-cover data sets
of the U.S. Geological Survey
Version 1.0
map
U.S Geological Survey Data Series
240
http://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-DW,
> units and estimated coefficients
> [kg, kilograms; km2, square kilometers.]
>
>Nitrogen Loading Units Estimated coefficient
> 1 farm fertilizer kg/hectare 0.1068
> 2 confined manure kg/hectare 0.1416
> 3 orchards/vineyards percent 0.2999
> 4 population density people/km2 0.0021
>
>Transport Factors
> 5 water input km2/cm 86.55
> 6 glacial till presence/absence -0.8658
> 7 semiconsolidated sand aquifers presence/absence 0.5057
> 8 sandstone and carbonate rocks presence/absence 0.3641
> 9 drainage ditch km2 -5.080
> 10 Hortonian overland flow percent of streamflow -0.0330
>
>Attenuation Factors
> 11 fresh surface water withdrawal megaliters/day -1.334
> 12 irrigation tailwater recovery km2 -13.84
> 13 Dunne overland flow percent of streamflow -0.1443
> 14 well depth meters -0.00163
200512
"Water input" is an interaction term expressed as the ratio of
area of irrigated land to precipitation:
>water input = irrigated land (km2) / precipitation (cm)
The national precipitation grid came from DAYMET (Thornton and
Running, 1999).
A national grid of 1995 irrigated land was generated by adding
a spatial component to tabular county estimates of total
irrigated area 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
irrigated area 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 irrigated area 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 irrigated acres were spread equally
among these land cover classes.
A 1-km resolution national grid of irrigated acres 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 irrigated
areas for each county in the conterminous United States.
The "intensity" was computed by dividing the irrigated area
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 irrigated area.
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 irrigated area "intensity" (step 2).
The cell values of the final grid represent the km2 of
irrigated area in each 1 km by 1 km cell.
Here is an example of how irrigated area 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 irrigated area in county = 445.114 km2 (from Solley and
>others, 1998)
>
>County "intensity" = 445.114 km2 / 1,302.17 km2 = .3418
>
>Step 3. Amount of irrigated land in cell = .13 km2 * .3418 = .0444 km2
>
>The sum of irrigated area calculated for all cells in Pierce
>County, Nebraska (FIPS 31119) equals 445.114 km2.
These steps were repeated for all cells nationwide to generate
a 1-km resolution national grid. Each cell value represented
the amount (km2) of irrigated land in the cell.
Finally, using the precipitation and irrigated land grids, a
preliminary grid of water input was calculated as:
>water input = irtkm2_useg (km2)/ dmprecip (cm)
Preliminary runs of the model resulted in unrealistically high
nitrate predictions (more than 100 mg/L) in some areas. To
prevent the model from overestimating nitrate concentration,
sensitive input variables (population density, farm
fertilizer, confined manure, and water input) were examined to
establish criteria for removing input cells with values above
certain thresholds. The filtered input values were then put
into the model. Because the model was based on network
averages of inputs, all of the grid cells could have been
averaged, but to preserve as much resolution as possible, only
"problem" cells were averaged.
The threshold established for water input was .05 km2/cm.
The "raw" water input numbers were filtered as follows:
a) If the original water input was less than or equal to .05
km2/cm, the original water input value was written to the
water input output grid.
b) If the original water input was greater than .05 km2/cm, a
mean was calculated for that cell using the GRID function
FOCALMEAN. FOCALMEAN computed the mean of selected cells
surrounding the input cell using a 99 by 99 rectangular
neighborhood of cells. Each cell was weighted equally. The
MEAN of the surrounding cells was assigned to the output grid
for water input.
>water-infm = selectmask((focalmean(rawwatin,rectangle,99,99)),rawwatin)
The filtered grid of "water input" then was used as input to
the GWAVA-S model.
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 2 for nitrate in drinking water wells. Depth 50 meters.
>/*Fertilizer and manure units are kg/ha (usegha)
>/*Feb 01 2006
>/*Remove filter on fertilizer, manure, population. Revise filter on precip to be 99x99 rectangle,
>/* instead of 9x9 kernel.
>setwindow -2380000.000 260000.000 2265000.000 3200000.000 orchvin
>setcell 1000
>/*Set directory housing the filtered input grid for precip.
>&s fdir = d:/ancill/model2005/dec2005/filtered
>/*
>gwava-dw_out = ((0.106751 * gwava-dw_ffer) + (0.141597 * gwava-dw_conf) + (0.299863 * gwava-dw_orvi) + (0.0021 * gwava-dw_popd)) * ~
>exp ((-5.08005 * gwava-dw_ddit) + (-0.03299 * gwava-dw_hor) + (-0.86585 * gwava-dw_gtil) + (0.505704 * gwava-dw_semc) + (0.364065 * gwava-dw_sscb) + ~
> (86.54621 * %fdir%/gwava-dw_wtin)) * ~
>exp ((-1.33354 * gwava-dw_swus) + (-13.8388 * gwava-dw_twre) + (-0.14432 * gwava-dw_dun) + (-0.00163 * 50))
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 GRID was converted to ASCII (plain text) for distribution
using ArcInfo Workstation command:
>gridascii gwava-dw_wtin gwava-dw_wtin.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 water input, in units of
square kilometers per centimeter (filtered).
Data Type: Floating Point
Minimum Value = 0.000
Maximum Value = 0.050
Mean = 0.001
Standard Deviation = 0.003
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
Ask USGS -- Water Webserver Team
mailing
445 National Center
Reston
VA
20192
1-888-275-8747 (1-888-ASK-USGS)
http://answers.usgs.gov/cgi-bin/gsanswers?pemail=h2oteam&subject=GIS+Dataset+gwava-dw_wtin
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
15609309 bytes
http://water.usgs.gov/GIS/dsdl/gwava-dw/arctar/gwava-dw_wtin.tgz
ASCII file
gzip -d; gunzip
19643485 bytes
http://water.usgs.gov/GIS/dsdl/gwava-dw/gascii/gwava-dw_wtin.txt.gz
Index to all files related to the GWAVA-DW model to facilitate downloading
all the GIS data sets
Web page with links to all data sets
HTML
5 bytes
http://water.usgs.gov/GIS/dsdl/gwava-dw/index.html
None. This data set is provided by USGS as a public service.
200704
U.S. Geological Survey
Ask USGS -- Water Webserver Team
mailing
445 National Center
Reston
VA
20192
1-888-275-8747 (1-888-ASK-USGS)
http://water.usgs.gov/user_feedback_form.html
FGDC Content Standards for Digital Geospatial Metadata
FGDC-STD-001-1998