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OCR for page 237
8
Applications of the NRI Data to
Inventory, Monitor, and Appraise
Offsite Erosion Damage
Lee A. Christensen
There are two general impacts of soil erosion from
agricultural land. Onsite effects, those occurring at
the field or farm level, are primarily reflected in soil
productivity changes associated with erosion. Offsite
impacts occur primarily when soil and chemicals are
carried from fields and farms in runoff, causing water
pollution and deposition problems downstream and
groundwater infiltration. This paper addresses the
applicability and use of the U.S. Department of
Agriculture (USDA) National Resources Inventory (NRI) to
investigate such offsite damages.
Historically, public attention and funds have con-
centrated on reducing the adverse onsite impacts of soil
erosion. Increased concerns about water quality degrada-
tion and the associated cleanup costs, combined with
passage of clean water legislation during the 1970s,
focused attention on offsite impacts. Negative offsite
impacts have broadened public concern about soil erosion
to include more than soil productivity issues. Soil
leaving a field due to water erosion represents costs not
only to the farmer and consumer in terms of lost profits
and high food prices, but also to those downstream, for
associated cleanup costs. Solving offsite problems has
been complicated by the lack of information and
capabilities to explain the diffuse and complex nature of
the physical relationships. Recent studies in this area
have vastly improved the ability to explain and model the
physical processes (Bailey and Swank, 1983). This work
is being linked with economic data to develop more
complete assessments of the offsite damages and the
alternatives for their control and reduction.
The NRI conducted by the Soil Conservation Service
(SCS) in 1977 and 1982 is a data source that might help
237
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238
assess offsite damages associated with soil erosion
(USDA, 1984). This paper considers such a use of the NRI
data. It begins with a brief discussion of terms and the
nature of offsite pollution, followed by an examination
of some applications of the NRI data. Possible uses of
the data in conjunction with available water quality
models to address water quality questions are explored.
The final section focuses on potential uses of the NRI
and presents some suggestions for future inventories.
THE NATURE OF OFFSITE DAMAGES
Water bodies receive pollution loads from point
sources, such as municipalities and industrial plants,
and nonpoint sources, including agriculture. Nonpoint
source pollution originates from ill-defined and diffused
sources r such as urban areas, cultivated fields, forests,
and pastures. Most agricultural nonpoint source pollution
is caused by sediment and sediment-transported chemicals
(Bailey and Waddell, 1979). It does not include runoff
from urban areas, mining and construction activities,
highways, logging activities, or streambank erosion.
The offsite effects of agricultural nonpoint source
pollution are diverse and complex. Each type of pollutant
has unique characteristics, both with respect to the mode
of transport through the water course, and the fate of
the pollutant as it moves from field to stream to lake to
river or reservoir.
Heavy sediment loads can fill reser-
voirs and cause channel siltation, which raises the costs
of water treatment and channel dredging. Excess levels
of nitrogen or phosphorus in streams, lakes, or estuaries
can cause eutrophication. Sediment and chemicals can
have adverse effects on fish and wildlife, greatly
reducing the economic and recreational value of streams
and lakes.
It is important to understand the distinction between
problems with surface water quality and those related to
groundwater or contamination. Agriculturally related
groundwater questions arise primarily with regard to the
leaching of nitrates and to soluble persistent pesticides.
Solutions to surface water quality problems are not
necessarily answers to groundwater problems.
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239
APPLICABILITY OF NRI DATA TO WATER QUALITY ISSUES
Design Constraints
The NRI is a tremendously rich source of information,
but with serious limitations for addressing water quality
questions. It is designed to systematically develop
information on the condition of the nation's agricultural
land base every 5 years. Its area of primary application
is the onsite, or farm, level. The NRI data base can be
used to address some of the water quality issues
influenced by activities based on agricultural land,
primarily sediment loadings. However, it contains no
direct data for the analysis of water quality problems
attributable to nonagricultural sources.
Levels of Detail and Aggregation
Analysis of water quality problems due to agricultural
sediment is facilitated if data can be aggregated along
hydrologic boundaries, such as a watershed or river basin,
rather than Major Land Resource Area (MLRA) boundaries.
However, since the NRI is considered reliable at the BRA
level, information for small watersheds that are fractions
of MLRAs must be used with care. For the larger basins
that consist of one or more MLRAs, however, the accuracy
of the expanded NRI data should be adequate. This assump-
tion needs some further testing and examination.
Components Affecting Water Quality
Assessment of the water quality impacts of agricultural
activities requires land-based information and practices
that can be linked with hydrologic and toxicological
information. The NRI provides data that can be used to
estimate sediment movement, and stream loading by
inference, but it provides no time-sequenced hydrologi
data or direct information on either the amount of
fertilizers or pesticides applied to the fields or that
transported by sediment or carried in solution.
An assessment of the data and factors most applicable
for the assessment of offsite impacts is shown in Table
1. Information from these fields can be used to estimate
gross sediment movement, but not deposition. Estimating
movement beyond the edge of a field, although possible,
,c
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240
TABLE 1
Impacts
Data File Fields in 1982 NRI Related to Offsite
Field
Name
6
10
12
20-22
24-27
28-30
32-37
39
Hydrologic unit
T factor
Degree of erosion
Land use and cover
Cropping history
Conservation practice
USLE factors
Average annual tons of soil
40-41
Average annual tons of soil
loss due to sheet and rill
and wind erosion
SOURCE: USDA (1984).
requires the development and use of sediment delivery
ratios, a complex and difficult task. By comparing 1977
and 1982 NRI data for a particular area, it may be
feasible to determine some trends in sediment loading
rates, particularly in areas where there have been
significant changes in land use or cropping or tillage
practices. However, although the NRI can be used to
estimate partial sediment loads, it provides no
information on particle size distribution, which is very
important for assessing such offsite impacts as fish
reproduction. Soils-5 data contain particule size
information which can be combined with the NRI.
SOME WATER QUALITY APPLICATIONS OF THE DATA
The NRI data base can be used in conjunction with
pollution loading models and more complex water quality
models. This section examines some generalized loading
model considerations, some specific applications of NRI
data with other data bases, and some possible uses of the
NRI data with existing water quality models.
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241
Gross Load Estimation
Generalized Procedure
The quantity of a given pollutant passing through a
system at any time is the interaction of the process of
supply and transport. Several generalized procedures can
use NRI data to estimate pollutant loads, which can be
useful indicators of the water quality impacts of erosion.
One approach is to multiply the Universal Soil Loss
Equation (USLE) estimates of soil movement at the water
shed or subwatershed level by a sediment delivery ratio
and a potency factor, which yields an estimate of the
pounds/acre/year of a pollutant (such as sediment,
nitrogen, or phosphorus) moving into streams. The
potency factor measures the amount of pollutant associated
with each unit of sediment (Dean, 1983). The challenge
is devising the proper sediment delivery ratio and
potency factors for this procedure.
There are several ways to predict or simulate
agricultural pollutant loads. These range from simple
sediment loading functions to physical processes
requiring simulation of chemical reaction, transformation,
and dynamic transport.
A pollutant load is defined as a mass of pollutant
moving to a receiving water body in a given period of
time. If the pollutant is assumed to be linearly
correlated with the amount of sediment moving from the
watersheds, the pollutant load (or loading function) can
be estimated as the product of the amount of sediment
delivered to a receiving water body and a potency factor
P. which is a factor relating the load of pollutant
associated with each unit loading of sediment.
P is very complex and difficult to estimate. As an
empirical approximation, the potency factor can be
envisioned as the product of the average concentration of
a pollutant in the surface layer of the soil, the
enrichment ratio of the pollutant of interest, and the
ratio of the mean particle density of surface soil to the
mean particle density of the eroded sediment (Dean,
1983). The enrichment ratio represents the effect of
several processes that cause the ratio of the mass of
pollutant to sediment to be higher at stream edge rather
than at the source, back at the watershed.
Estimates of the sediment delivery ratios and potency
factors for various pollutants have been developed for
specific studies, but there are few generalized sets
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242
available to apply in large area studies (Gianessi et
al., 1981a). Loading rates are related indirectly to the
various tillage practices through the interactions of the
management components in the USLE calculations. If some
figures on changes in use of conservation tillage are
available for a given watershed, changes of likely
pollutant loadings can be estimated.
The gross loading information from the 1982 NRI
provides some information for estimates. However, the
data needed for state-of-the-art models to assess offsite
effects of agricultural activities are much greater and
more complex than those provided by the NRI.
Examples of Loading Models
Haith and Tubbs (1981) developed three loading models
to estimate nutrient and pesticide losses from cropland.
The models range from simple loading functions to detailed
computer simulation models for the soil environment.
They share several common attributes. All have a daily
time step and are based on the SCS Curve Number Runoff
Equations and the USLE. None requires calibration, and
each model was tested. Simple planning models or loading
functions can provide straightforward means of estimating
nonpoint source pollution.
Simple nonpoint source models (loading functions) have
several deficiencies. The USLE was not designed to
evaluate nonpoint source pollution. It can be used to
calculate average annual soil loss, but not loss from
single storm events. Although loading functions have
been used extensively for this purpose, their ability to
provide reasonable estimates of agricultural nonpoint
source pollution in large watersheds has not been
established. The problem often overlooked in the use of
simple loading models is that different categories of
potential pollutants are transported in different
fashions. For example, dissolved chemicals move with
runoff water while most phosphorus and some nitrogen and
hydrophobic chemicals are associated with sediment.
Loading functions for sediment-associated chemicals
should be based on soil loss estimates; dissolved
chemicals require runoff-based loading functions.
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243
Specific Applications
Resources for the Future
Resources for the Future (RFF) used the 1977 NRI data
base to analyze the relative importance of nonpoint source
pollution control options at the national level (Gianessi
et al., 1981a,b). This national network model linked
point and nonpoint sources of pollution to evaluate
agricultural sediment control policies in conjunction
with point source controls.
The model linked pollution-
y~erac~ng aches In each county to a detailed network
of rivers, lakes, and bays. It provides general estimates
of the impact of sediment and sediment-bound pollutants
on water quality in specified bodies of water. However,
it does not evaluate the transport and impact of soluble
pollutants.
RFF is updating its national model to incorporate the
1982 NRI data and refining it to include a sediment
transport component. This will help in revising
estimates for gross sediment and associated pollutants
reaching streams.
Economic Research Service
The Economic Research Service of the USDA is using NRI
data to estimate offsite benefits associated with soil
conservation (Ribaudo, 1984). ma_ -- ' ~
fine NHl Information is
being used in conjunction with other data sets to relate
the levels of pollution associated with erosion parameters
to specific impaired water uses. Water quality data from
the National Stream Quality Accounting Network (NASQUAN)
was used to estimate the ambient water quality levels,
total suspended solids, total phosphorus, and total
nitrate for each of the 99 watershed units defined by the
Water Resource Council as aggregated subareas (ASA).
These levels were then compared with standards reflecting
impacts on water use.
Pollutant loads in the various watercourses from all
sources were estimated using the National Water Discharge
Inventory developed by RFF. Sediment discharges from
cropland, pastureland, rangeland, and forestland were
based on erosion estimates provided by the 1977 NRI.
Estimates of streambank, gully, construction site, and
other erosions come from other sources. A sediment
delivery ratio was calculated for each ASA by RFF and
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244
used to estimate the amount of eroded soil reaching
waterways. The amounts of total suspended solids, total
phosphorus, and total nitrogen in the discharge were
estimated using coefficients based on the characteristics
of the major soil groups contained in each ASA.
The final step was to compare the pollutant loadings
from agriculture with the uses made of the streams in the
affected areas and to identify regions where agricultural
erosion has significant impact on offsite water uses.
Thirty-eight ASAs were identified as having a water
quality problem due to agriculture, but only 15 were
intensive use regions. The estimates are being updated
by incorporating the 1982 NRI and other information into
the RFF model.
Linkages with Water Quality Models
Several models have been developed to evaluate the
impacts of alternative management strategies on water
quality and the influence of specific management
practices on the levels of particular pollutants. Some
use NRI data, but since most models have gone beyond the
gross loading stage, they use the NRI data as one input
among several, not as the primary data set. A number of
these models may be able to take the erosion estimates as
input, but others need only the USLE coefficients in the
NRI. Selected examples of models that can use some of
the NRI data are described in this section.
A pesticide root zone model (PRZM) being developed by
the U.S. Environmental Protection Agency (EPA) at the
Athens Environmental Research Laboratory simulates the
vertical movement of pesticides in the unsaturated soil
within and below the plant root zones (Carsel et al.,
1984). The model consists of hydrology and chemical
. . . . . .
transport components that simulate runoff, erosion, plant
uptake, leaching, decay, or surface washoff and
vo~at~zation of a pesticide. The hydrology component
for calculating runoff and erosion is based on the SCS
Curve Number technique and the USLE.
PRZM can be used to estimate frequency distributions
of the mass of pesticide leaching from the plant root
zone to investigate the risks of pesticide use, par-
ticularly pertaining to groundwater pollution. The model
uses the Modified Universal Soil Loss Equation (Williams
and Berndt, 1977). This modification replaces the R
(rainfall erosivity) term with an energy term and allows
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245
estimation of the volume of event runoff and peak storm
runoff. The model requires all the other USLE factors.
A comprehensive basin-scale simulation model developed
to predict water quality arising from both point source
and agricultural nonpoint source pollution is the Hydro-
logical Simulation Program in For tran (HSPF) (Donigian et
al., 1983, 1984). The goal of this model is to go beyond
the prediction of the quantity and quality of runoff from
agricultural lands and to predict instream water quality
effects of the best management practices. However, runoff
models by themselves are not sufficient to do this, since
instream transport and transformations are usually not
represented. Using a model of this type requires far
more data than the NRI provides. Yet, applying it allows
simulation of the movement of pollutants and assessment
of the likely impacts of changes in management practices
on water quality through time. Models like this are
indicative of the state of the art in water quality
modeling.
Linkage with the Chemicals, Runoff, and Erosion from
Agricultural Management Systems (CREAMS) model developed
by USDA is another possible application of the NRI data
(Knisel, 1980; Knisel et al., 1983). A major use of
CREAMS is evaluation of alternative management practices
for control or minimization of runoff of sediment and
chemicals. It has three components (hydrology, erosion/
sedimentation, and chemistry) and describes the movement
of runoff, sediment, and plant nutrients and pesticides
from field-sized areas. It is a continuous simulation
model that operates efficiently to allow consideration of
long-term records (20 years). The model can be used to
evaluate the impact of management practices on the yield
of sediment and chemical pollutants from field-sized
areas at specific sites. It is also being expanded to
address questions at the watershed level.
The model's erosion/sedimentation component could use
some of the NRI data. It considers the basic processes
of soil detachment, transport, and deposition. Detachment
is described by a modification of the USLE for a single
storm event. The calculation of a rate of interrill
detachment and the rate of detachment by rill erosion
uses the USLE factors.
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246
Uses of the MRI Data for Offsite Analysis: A Summary
As noted, some inherent constraints in the NRI data
base influence its usefulness and applicability for
inventorying, monitoring, and appraising offsite erosion
damages and adverse impacts on water uses. It provides
information on land use and gross erosion estimates,
which are useful for loading estimates and models, but it
does not have complete data for water quality analysis.
Nevertheless, there are several opportunities for its use.
The NRI is a source of coefficients for estimating
sediment load from agricultural nonpoint sources, which
can be combined with other sources to estimate combined
point and nonpoint sediment stream-pollution loads. The
applications by RFF (Gianessi et al., 1981a,b) and the
water quality assessments by the Economic Research
Service (Ribaudo, 1984) are examples. As a screening
tool, the NRI data can be used as part of a system to
identify areas where sediment and associated pollutant
loads in streams really impair stream usage. Comparisons
between subbasin characteristics are useful for isolating
exceptional situations.
The USLE coefficients in the NRI can be used directly
in regional water quality modeling efforts. For example,
PRIM needs USLE coefficients to operate, and CREAMS uses
USLE factors as part of the input data.
Data from the 1977 and 1982 NRIs has limited use for
trend assessment. For particular areas, changes in land
use and conservation and tillage practices can be used to
estimate changes in gross pollution loads from agricul-
ture, provided sediment delivery ratios and pollution
loading coefficients are available. Using the same
system, projected changes such as significant shifts to
conservation tillage can be analyzed for impact on
pollution loads, provided the distinctions are maintained.
For example, studies have found a reduction in sediment-
transported nitrogen and phosphorus with fluted courter
conservation tillage, and control of both sediment-
transported and solution nitrogen and phosphorus with
in-row chisel tillage (Langdale and Leonard, 1983).
The NRI can also be part of the data base needed for
water quality assessments in specific regions, as done by
Ribaudo (1984). It can be coupled with the vast amounts
of information already developed in areas of intensive
study, such as the Chesapeake Bay (EPA, 1982; Northern
Virginia Planning District Commission, 1983). Loading
rates for the entire Bay have been estimated. The NRI
-
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247
can be used to estimate loadings from specific basins or
subbasins to aid in planning and analysis.
CONSIDERATIONS FOR FUTURE NRIs
Before advocating changes to remedy the NRI's inability
to address water quality questions, it is important to
remember that the main objective of inventory is to
assess the nation's soil and water resources. As such,
it was not designed or specifically charged with a
responsibility to assess offsite damages. Yet, those
charged with designing the next NRI might consider the
extent to which it should address offsite questions.
Although few data bases are intended to be universal in
scope and applicability, with some minor modifications to
the NRI, a more complete set of data for water quality
analysis could be assembled. Some suggestions for
consideration are offered in this section.
A critical missing link in the NRI as far as offside
damage assessment goes is its lack of linkages with
hydrologic, toxicological, or meteorological data bases.
To get good estimates of sediment load, nitrogen runoff
concentration, or phosphorus runoff concentration, the
timing of tillage practices and fertilizer applications
needs to be tied to meteorological data, particularly
rainfall. Time-series data on meterological and
hydrologic data are needed for water quality simulation
models such as the nonpoint source model (Donigian and
Crawford, 1976b, 1977), the agricultural runoff model
(Donigian and Crawford, 1976a), HSPF, and CREAMS.
Water quality problems are time-based, but the NRI
provides no time-variance loading information. It is
impossible to predict water quality accurately from an
average annual estimate provided by the USLE. It has
validity only for cases where the retention time is 1
year or greater. Linkages are needed on a storm-by-storm
basis to rainfall, runoff, soil loss, pollutant concen-
tration, utilization, infiltration, percolation, and
movement of soluble pollutants to groundwater. The
feasibility of collecting such information in the future
needs to be evaluated.
Better links between movement on the field and
deposition in the field and streams are also needed. The
USLE generates sediment load information, but the
sediment delivery ratios and potency factors needed to
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248
better assess the amount actually moving to streams are
generally inadequate.
Information on management practices and application
rates of pesticides and fertilizer would help assess
runoff problems as well as the enrichment of sediment.
Very little is known about the composition of runoff,
either in terms of quantity or quality. Data on
fertilizer and pesticide management practices and the
properties of the chemicals are particularly important in
assessments of whether potential pollutants are moving
overland to a stream in solution or bound to soil
particles, or whether they are moving down through the
soil profile in solution. Research has indicated greater
efficiencies in the use of nitrogen in corn and soybeans
with no-till than with conventional till (Hoyt et al.,
1983). In the Southeast, changes in tillage practices
have resulted in greater concentrations of phosphorus and
nitrogen, but in reductions of the transported mass
(Langdale and Leonard, 1983).
The NRI does not now address the question of ephemeral
gully erosion, in which there is considerable interest
(see Foster, this volume). Whether it should or could do
so needs to be assessed, first to explain the total
erosion process better, and second to assess impacts on
water quality that fall between rill and gully erosion.
Given the rapid adoption of conservation tillage and
no-till, future NRIs need to consider more explicit
measurement of these practices (as well as related
practices such as fertilizer, insecticide, and herbicide
use) and to ensure that they are properly accounted for
in the gross erosion calculations. Explicit linkages
between the type of tillage and the impact on a specific
pollutant and its pathway to stream or groundwater are
needed.
There are several excluded sources of erosion needing
consideration in efforts to a~ the imp of "rna;^n
~ ...= ~ ~ · ~
on water quality. Ephemeral gully erosion, which occurs
between rill and gully erosion, is one such source.
There is considerable interest in better explaining
ephemeral gully erosion as part of the total erosion
process; its inclusion in the NRI needs to be assessed.
Other erosion not measured in the NRI includes streambank
erosion, erosion trom federal lands, and erosion from
construction sites.
Lastly, assessment of the offsite water quality
impacts of soil erosion needs to involve major federal
and state agencies with capabilities and responsibilities
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249
in the area. Coordination with departments, such as the
EPA, is needed if water quality becomes a major emphasis
of NRI efforts.
SUMMARY
There are limits on the usefulness of NRI data for
assessing the offsite effects of soil erosion. Selected
data can be used to estimate pollutant loads, primarily
sediment, and thus help identify and inventory potential
sources of offsite damages.
Data from the 1977 and 1982
NRIs can provide points for assessing trends in changes
in the resource use, thus assisting in monitoring factors
that influence offsite erosion damages. Data and
coefficients from the NRI can be linked with other data
bases and water quality models to appraise offsite
impacts.
It must be remembered that the NRI was not designed
with water quality as its primary focus. Thus, it must
be viewed as an important source of information, but
useful for addressing water quality questions primarily
in conjunction with other models and data. The next NRI
could be modified to be more directly applicable to water
quality and offsite damage. Issues to consider include
improved linkages with hydrologic data bases, linkages
with time-based pollutant loads, field-to-stream
linkages, linkages to management and tillage practices,
the role of ephemeral gully erosion and streambank
erosion, and possible coordination with other water
quality agencies.
. . . . . . .
REFERENCES
Bailey, G. W., and R. R. Swank, Jr. 1983. Modeling
agricultural nonpoint source pollution: A research
perspective. Pp. 29-41 in Proc. of the National
Conference on Agricultural Management and Water
Quality. Ames: Iowa State University Press.
Bailey, G. W., and T. E. Waddell. 1979. Best management
practices for agriculture and silviculture: An
integrated overview. Pp. 33-36 in Best Management
Practices for Agriculture and Silviculture, Proc. of
the 1978 Cornell Agricultural Waste Management
Conference. Ann Arbor, Mich.: Ann Arbor Science
Publishers.
OCR for page 250
250
Carsel, R. F., C. N. Smith, L. A. Mulkey, D. Dean, and
P. Jowise. 1984. Users Manual for the Pesticide Root
Zone Model (PRZM)--Release 1. (Draft.) Athens, Gal:
U.S. Environmental Protection Agency.
Dean, J. D. 1983. Potency factors and loading functions
for predicting agricultural nonpoint source pollution.
Pp. 155-177 in Proc. of the National Conference on
Agricultural Management and Water Quality. Ames: Iowa
State University Press.
Donigian, A. S., and N. H. Crawford. 1976a. Modeling
Pesticides and Nutrients on Agricultural Lands. EPA
600/2-76-043. Athens, Gal: U.S. Environmental
Protection Agency.
Donigian' A. S., and N. H. Crawford. 1976b. Modeling
Nonpoint Pollution from the Land Surface. EPA
600/3-76-083. Athens, Gal: U.S. Environmental
Protection Agency.
Donigian, A. S., and N. H. Crawford. 1977. Simulation of
Nutrient Loadings in Surface Runoff with the NPS
Model. EPA 600/3-77-065. Athens, Gal: U.S.
Environmental Protection Agency.
Donigian, A. S., Jr., J. C. Imhoff, and B. Bicknell.
1983. Predicting water quality resulting from
agricultural nonpoint source pollution via
simulation--HSPF. Pp. 200-249 in Proc. of the National
Conference on Agricultural Management and Water
Quality. Ames: Iowa State University Press.
Donigian, A. S., Jr., J. C. Imhoff, B. R. Bicknell, and
J. L. Kittle, Jr. 1984. Application Guide for
Hydrological Simulation Program in For tran (HSPF). EPA
600/3-84-065. Athens, Gal: U.S. Environmental
Protection Agency.
EPA (Environmental Protection Agency). 1982. Chesapeake
Bay Program Technical Studies: A Synthesis.
Washington, D.C.: U.S. Environmental Protection Agency.
Gianessi, L. P., H. M. Peskin, and G. K. Young.
1981a. Analysis of national water pollution control
policies: 2. Agricultural sediment control. Water
Resources Res. 17:803-821.
Gianessi, L. P., H. M. Peskin, and G. K. Young. 1981b.
Analysis of national water pollution control policies:
1. A national network model. Water Resources Res.
17:796-801.
Haith, D. A., and L. J. Tubbs. 1981. Operational
Methods for Analysis of Agricultural Nonpoint Source
Pollution. Ithaca: Cornell Agricultural Experiment
Station, State University of New York at Cornell.
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251
Hoyt, G. D., R. L. Todd, R. A. Leonard, L. Asmussen, and
E. D. Threadgill. 1983. Effect of tillage on nutrient
cycling in Southeastern Coastal Plain agroecosystems.
In Nutrient Cycling in Agricultural Ecosystems.
Special Publication No 23. Athens: University of
Georgia College of Agriculture Experiment Stations.
Knisel, W. G., ed. 1980. CREAMS: A Field Scale Model
for Chemicals, Runoff, and Erosion from Agricultural
Management Systems. Conservation Research Report No.
26. Washington, D.C.: U.S. Department of Agriculture.
Knisel, W. G., G. R. Foster, and R. A. Leonard. 1983.
CREAMS: A system for evaluating best management
practices. In Nutrient Cycling in Agricultural
Ecosystems. Special Publication No. 23. Athens:
University of Georgia College of Agriculture
Experiment Stations.
Langdale, G. W., and R. A. Leonard. 1983. Nutrient and
sediment losses associated with conventional and
reduced-tillage agricultural practices. In Nutrient
Cycling in Agricultural Ecosystems. Special
Publication No. 23. Athens: University of Georgia
College of Agriculture Experiment Stations.
Northern Virginia Planning District Commission. 1983.
Chesapeake Bay Basin Model--A Final Report. (Draft.)
Annandale: Northern Virginia Planning District
Commlsslon.
Ribaudo, M. 1984. Water resource problems. Economic
Research Service (ERS) draft materials. Washington,
D.C. November 29. Photocopy.
USDA (U.S. Department of Agriculture). 1984. National
Resources Inventory--A Guide for Users of 1982 NRI
Data Files. (Draft.) Washington, D.C.: Soil
Conservation Service.
Williams, J. R., and H. D. Berndt. 1977. Sediment yield
reduction based on watershed hydrology. Trans. ASAE
20:1100-1104.
Discussion
Ronald B. Outen
The National Resources Inventory (NRI), as
Christensen's paper points out, is just a piece of the
puzzle. It does provide some useful information, but
alone it is not yet sufficient to make the critical
connection between land use practices and water quality
OCR for page 252
252
When water quality problems associated with nonpoint
source pollution were first raised years ago, the
discussion almost always turned to agriculture, and
almost immediately to soil erosion. Yet as Christensen
notes, sediment per se is not necessarily the biggest
problem in all or perhaps even most of the watersheds,
strictly in terms of water quality. Very often the
issues most discussed are nutrients from fertilizer and
animal waste runoff and, also, pesticides.
Most people make a brief reference to groundwater and
then move on to talk about surface water runoff, which is
understandable because over the last few years the Clean
Water Act has dealt almost exclusively with surface water.
There may be a bit of a contradiction in national policy
goals in terms of protecting groundwater and surface
water. On a given piece of land with an excessive amount
of nitrogen, phosphorus, other fertilizer materials, or
pesticides, measures to prevent runoff might exacerbate
the groundwater problem. More coordination is needed
between these programs.
Ultimately, groundwater, like nonpoint source pollution
of surface water, must be dealt with on an areawide basis
in terms of aquifer recharge areas, at least for those
pollutants that tend to be dispersed across the landscape.
Moreover, an integrated hydrologic regime that includes
both groundwater and surface water must be considered, as
well as an integrated land/water network. Furthermore,
management practices must be broadly defined.
There soon will be a federal law calling on states to
develop implementation programs to apply best management
practices in large areas of the country for purposes of
water quality. This new nonpoint source management
program, which will be added to the Clean Water Act, has
received strong support in both houses of Congress.
Representative terms from entire chapter:
nri data
