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Potential Priority Watersheds for Protection of Water Quality
from Nonpoint Sources Related to Agriculture*
Poster Presentation at the 52nd Annual SWCS Conference
Toronto, Ontario, Canada, July 22-25, 1997
(Revised October 7, 1997)
Robert L. Kellogg,
Susan Wallace, and Klaus Alt (retired)
Natural Resources Conservation Service
Don W. Goss, Texas Agricultural Experiment Station, Temple,
Texas
Objective
National maps were developed to assist decision-makers in identifying
priority watersheds for water quality protection from nonpoint sources related
to agriculture. The purpose of these maps is to systematically identify where
the greatest potential exists for water pollution based on factors known
to be important influences on soil and chemical loss from farm fields, such as
climate, soil characteristics, and pesticide and nitrogen loadings from
agricultural sources. The basis for the analysis is 2,105 8-digit hydrologic
units, or watersheds, in the 48 States (910,000 acres average size).
Summary
The potential for loss of pesticides, nutrients, and soil from farm fields in
each watershed was assessed using national-level databases on land use, soils,
climate, agricultural chemical use, and confined livestock populations. Maps of
four pollution sources are presented for leaching and runoff. The top 400
watersheds (about 20%) were selected from each of the contributing maps and
overlaid to identify which watersheds have the greatest potential for
combinations of water pollution sources. Watersheds identified as having a high
potential for more than one pollution source would have a high priority for
implementation of conservation programs to reduce externalities associated with
agricultural production.
NRI Modeling
Most of the maps shown here were produced using the National Resources
Inventory (NRI) as a modeling framework. The NRI is a national survey of private
land use conducted by the Natural Resources Conservation Service that is based
on about 800,000 sample points throughout the US, including cropland,
pastureland, rangeland, forest land, urban land, and other uses of private land.
At each NRI sample point, information is collected on nearly 200 attributes,
including land use and cover, cropping history, conservation practices,
potential cropland, highly eroding land, water and wind erosion estimates,
wetlands, wildlife habitat, vegetative cover conditions, and irrigation. The NRI
is linked to a national soils database (SOILS5). Data from other sources, such
as precipitation and agricultural chemical use, were imputed onto the NRI sample
points using geographic and land use links.
A simulation of potential loss of agricultural chemicals from farm fields was
conducted by treating each NRI sample point as a "representative
field." Pesticide loss and nitrogen vulnerability indexes were estimated at
individual NRI sample points using the site-specific information available from
the NRI and other sources. Environmental indicators at the watershed level were
constructed by aggregating measurements over the NRI sample points in each
watershed. Expansion factors for each sample point were previously determined
during the development of the NRI survey design. These expansion factors are the
number of acres each sample point represents, and are used as weights to
aggregate over the sample points.
NRI modeling was used here to create environmental indicators for cropland.
The potential exists to create similar environmental indicators in this manner
for carbon sequestration, chemical loss from forestland, and the contribution of
soil loss and chemical loss to air pollution from nonpoint sources.
The NRI is based on Primary Sampling Units (PSUs) that are selected using a
stratified random design. The standard PSU is 160 acres; smaller and larger PSUs
were used in some areas of the country. Within each PSU, typically three points
were selected for sampling. These points were used as the framework for
simulation modeling.
About the Maps
Each of our maps is also available as a zipped ( ) postscript file and ( )PDF file. These files can be used to produce high quality copies of our maps. For more infomation on postscript see our postscript help page. For PDF files Adobe Acrobat is required.
A variety of algorithms were used to create watershed values, generating a
variety of units (pounds per watershed, index scores per watershed, etc.). To
facilitate comparisons among the maps, the classes shown in each map were based
on a consistent set of watershed rankings. The 200 watersheds with the highest
scores are shown with the darkest color. The next highest 200 watersheds are
shown with a slightly lighter color, and so on.
Caveats
-
Analyses do not show which watersheds will have water quality
impairments related to agricultural production. The simulation models
estimate soil loss, chemical loss, or vulnerability indexes for conditions
at the edge of the field and the bottom of the root zone. Not included in
the indicators are dynamics of fate and transport from the farm field to a
water body. Dilution from runoff and recharge on noncropland areas in the
watershed will also reduce the potential for actual water quality
impairments.
-
Maps provide a relative ranking among watersheds. It is not known how the
class breaks relate to observed water quality problems.
-
Farm management practices are not included in the determination of the
indicators. Research has shown that soil, chemical, and water loss from farm
fields can be substantially reduced by management practices. Where these
practices are being used, the potential estimated by these indicators will
be over-estimated.
-
The land base varies among the maps--7 crops for commercial nitrogen
fertilizer, 13 crops for pesticides, cultivated cropland for climatic
factors, all cropland for soil erosion, and all land for manure nitrogen.
Where indicators are based only on the major crops, watersheds with large
acreage of other crops may be mis-represented.
Climatic Factors
Percolation and runoff factors were created for each NRI sample point for use
in converting nitrogen inputs to vulnerability indexes.
Percolation
Factor [ GIF | Postscript | PDF ].
The percolation factor represents average annual percolation of water through
the root zone in inches per year. It is based on precipitation and the
hydrologic group of the soil. The calculation weighs precipitation during
non-growing periods (fall and winter months) more than during growing periods to
account for plant uptake. Hydrologic group is an attribute of the NRI.
Precipitation data were obtained from a network of 1,473 climate stations
throughout the US. A database of average monthly precipitation for each climate
station was assembled using 25 years of daily precipitation data. Monthly
precipitation data were imputed to NRI sample points on the basis of the
proximity of the NRI sample points to the climate stations.
Runoff factors. Runoff factors represent runoff from a field in inches
per year. Daily precipitation data for 25 years were used with the NRCS curve
number method to calculate a monthly runoff factor for each of 1,473 climate
stations. At each climate station, monthly runoff was estimated for twelve curve
numbers ranging from 70 through 92. Monthly runoff values were imputed to NRI
sample points according to the proximity of the sample point to one of the
weather stations and according to curve number. Curve numbers were assigned to
NRI sample points using information on the soil hydrologic group, tillage,
conservation practice, and crop type. The annual
runoff factor [ GIF | Postscript | PDF ]
was obtained by summing over the 12 monthly runoff factors. The two-month
runoff factor [ GIF | Postscript | PDF ]
was obtained by summing over the two months following planting (determined from
average planting dates for 55 regions), when the potential for runoff losses of
soil and chemicals is often the greatest.
Soil Erosion
Tons of soil
loss from sheet and rill erosion [ GIF | Postscript | PDF ]
per watershed were obtained directly from the NRI. The NRI estimated sheet and
rill erosion (tons per acre) at each cropland sample point using the Universal
Soil Loss Equation (USLE). These per-acre estimates were multiplied by the
number of acres represented by the NRI sample point and aggregated over all the
cropland sample points in each watershed. Erosion estimates are for 1992.
Manure Nitrogen Fertilizer
Manure
Nitrogen Loadings from Confined Livestock [ GIF | Postscript | PDF ].
Pounds of manure nitrogen loadings per watershed were estimated using data on
livestock populations from the 1992 Agriculture Census. The average number of
livestock present on each farm and the average length of time they lived on the
farm were estimated for 16 types of livestock, adjusting the estimates to
reflect the number of livestock held in confinement. Assuming all manure from
confined operations was applied to the land, nitrogen loadings were estimated by
multiplying the livestock population times the average amount of manure produced
by each type of livestock, and then multiplying times an estimate of the average
nitrogen content of the manure for each type of livestock. An additional
adjustment was made for typical losses of nitrogen during storage and from
volatilization during application. County totals for manure nitrogen were
obtained by aggregating over the farms in the county. Watershed totals were
obtained by multiplying county estimates by county-watershed conversion factors
derived from a GIS calculation of the percentage of each county in each
watershed. (Weights were adjusted using NRI information on the presence or
absence of pastureland and cropland in each county-watershed polygon.)
Manure
Nitrogen Leaching Vulnerability Index [ GIF | Postscript | PDF ].
A watershed index score for the leaching potential for manure nitrogen was
calculated by multiplying the total manure nitrogen estimate for the watershed
times the average percolation factor for the watershed. The average percolation
factor was based on values for all non-Federal rural land.
Manure
Nitrogen Runoff Vulnerability Index [ GIF | Postscript | PDF ].
A watershed index score for the runoff potential for manure nitrogen was
calculated by multiplying the total manure nitrogen estimate for the watershed
times the average 12-month runoff factor for the watershed. The average runoff
factor was based on values for all non-Federal rural land.
Commercial Nitrogen Fertilizer
Nitrogen
Loadings from Commercial Fertilizer Applications, Adjusted for Crop Uptake
[ GIF | Postscript | PDF ].
Pounds per watershed of nitrogen from commercial fertilizer applications was
calculated based on production of 7 crops--corn, soybeans, wheat, cotton,
barley, rice, and sorghum--comprising 162,343 NRI sample points. The nitrogen
loading for each sample point was estimated as the difference between the amount
of commercial nitrogen fertilizer applied and the amount taken up by the crop
and removed at harvest. State data on nitrogen application rates and percent
acres treated for 1992 were obtained from farmer survey data published by the
Economic Research Service (Taylor). The amount of nutrients taken up by the crop
was estimated by multiplying the percent of nutrients in the harvested portion
times the average county per-acre yield, using county yield data published by
the National Agricultural Statistics Service for 1988-1992. A nitrogen credit
was estimated for legumes grown in the previous two years. The total pounds of
"excess" nitrogen for each sample point was estimated by multiplying
the per acre estimate by the percentage of acres treated in the state. Estimates
of pounds per watershed were obtained by aggregating over the NRI sample points
in each watershed.
Commercial
Nitrogen Fertilizer Leaching Vulnerability Index [ GIF | Postscript | PDF ]. The
estimate of commercial nitrogen fertilizer loadings was multiplied by the
percolation factor at each NRI sample point for each of the seven crops to
obtain a nitrogen leaching vulnerability index score. For irrigated sample
points, five inches was added to the percolation factor prior to the
calculation. Watershed scores were obtained by summing the vulnerability index
scores for the NRI sample points in the watershed for the seven crops.
Commercial
Nitrogen Fertilizer Runoff Vulnerability Index [ GIF | Postscript | PDF ].
The estimate of commercial nitrogen fertilizer loadings was multiplied by the
runoff factor for two months following planting at each NRI sample point for
each of the seven crops to obtain a nitrogen runoff vulnerability index score.
Watershed scores were obtained by summing the vulnerability index scores for the
NRI sample points in the watershed for the seven crops.
Pesticides
Pounds
of Pesticides Applied to Crops by Watershed [ GIF | Postscript | PDF ].
Pounds of pesticides per watershed were estimated using the NRI and the National
Pesticide Use Database created by Gianessi and Anderson. Gianessi and Anderson
organized data from publicly available reports and surveys from Federal and
State agencies into a national database of pesticide use in US crop production
for the period 1990-93. State average application rates and the percentage of
acres treated for 141 pesticides were imputed to NRI sample points by crop and
state for 13 crops: barley, corn, cotton, oats, peanuts, potatoes, rice,
sorghum, soybeans, sugar beets, sunflowers, tobacco, and wheat. (These are the
only crops specifically identified in the NRI.) A total of 170,219 sample points
were included. For each chemical used on a specific crop in a specific state,
the application rate (pounds per acre) was multiplied by the number of acres
represented by the NRI sample point and by the percentage of acres treated in
the state. Estimates of pounds per watershed were obtained by first aggregating
over all chemicals at each sample point, and then aggregating over all sample
points for the 13 crops in the watershed.
Potential Pesticide
Leaching [ GIF | Postscript | PDF ]
and Runoff
Loss [ GIF | Postscript | PDF ]
from Farm Fields. A National Pesticide Loss Database was created using the
chemical fate and transport model GLEAMS. GLEAMS is a process model that
estimates pesticide leaching and runoff loss using as inputs: soil properties,
field characteristics (such as slope and slope length), management practices,
pesticide properties, and climate. GLEAMS estimates were generated for 243
pesticides applied to 120 specific soils for 20 years of daily weather for each
of 55 climate stations distributed throughout the United States. Separate GLEAMS
estimates were made for irrigated and nonirrigated conditions. The maximum
percent loss over the 20-year period was used to construct the indicator.
Pesticide loss estimates were for movement beyond the edge of the field and
beyond the bottom of the root zone, measured in percent loss per acre per year.
Loss estimates were imputed to NRI sample points by soil group and proximity to
climate stations. A total of 141 pesticides were included. Estimates of percent
acres treated and average application rates from Gianessi and Anderson were
imputed to the NRI sample points by crop and state. Each NRI sample point where
corn was grown in Iowa, for example, included chemical use for 22 pesticides
Gianessi and Anderson reported were used on corn in Iowa. The total loss of
pesticides in pounds per watershed was estimated by 1) multiplying the estimate
of percent loss per acre times the application rate to obtain the mass loss per
acre for each pesticide at each NRI sample point, 2) calculating the number of
acres treated for each pesticide by multiplying the estimate of percent acres
treated by the number of acres associated with the sample point, 3) multiplying
the number of acres treated by the mass loss per acre to obtain the mass loss
for each pesticide at each NRI sample point, and 4) summing over the mass loss
estimates for all the pesticides and sample points in the watershed.
Watersheds with High Potential for Chemical and Soil Loss from Farm Fields
The three maps of leaching
indicators [ GIF | Postscript | PDF ]
and the four maps of runoff
indicators [ GIF | Postscript | PDF ]
were overlaid to identify which watersheds have the greatest potential for combinations
of water pollution sources. Watersheds identified as having a high potential for
more than one pollution source (pesticides, nitrogen, or soil) would have a high
priority for implementation of conservation programs. The top 400 watersheds
(about 20%) were selected from each of the seven contributing maps as watersheds
with a high potential for chemical or soil loss from farm fields.
Data Availability
Watershed estimates used in this analysis are available for downloading. The
variable names are matched to the maps in a documentation table. If questions
arise using the data, please contact Robert
Kellogg.
Documentation
Table
Watershed
Database (Pipe delimited ASCII file)
* Some of the maps and numbers have been revised since
the July presentation.
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