D.L. Nofziger1, H.D. Scott2, and T.H. Udouj2
1Oklahoma State University and 2University
The previous chapters of this bulletin explained many of the important
factors and processes regulating water and chemical movement in soils.
Experimental results of water movement and chemical transport studies in
landscapes found in the southeastern United States were also presented.
This chapter describes ways in which soil, chemical, weather, and management
practices can be used to predict the impact of these processes upon water
Soil Variability in the Landscape
Soils differ in size and shape of their areas, in degree of contrast
with adjacent soils, and in geographic relationships in landscapes of the
southeastern United States. As a result, soil properties vary from place
to place in the landscape, but this variation is not random. Natural soil
bodies are the result of climate and living organisms acting upon parent
material, with topography or local relief exerting a modifying influence
and with time required for soil-forming processes to act. For the most
part, soils are the same wherever all five factors of soil formation are
similar. This permits prediction of the location of many different soils
in the landscape. Regional patterns of climate, vegetation, and parent
material can be used to predict the soils in large areas. Local patterns
of topography or relief, parent material, time, and their relationships
to vegetation and microclimate can be used to predict soils in localized
areas. This document has presented examples of the extensive variability
of soils found in landscapes across the southern region of the United States.
The geographic distribution of individual soil properties in the landscape
can be extracted from soil maps and displayed cartographically for special
purposes. A soil map unit is a collection of areas or polygons defined
and named the same in terms of their soil components or miscellaneous areas
or both. A soil map unit is a collection of pedons, the smallest identifiable
unit of a soil, defined and named the same in terms of their soil components
or miscellaneous areas or both. The soils which compose each map unit generally
will have formed in similar kinds of parent materials and have a repeating
pattern in the landscape, but will vary in one or more physical or chemical
characteristic. Each map unit differs in some respect from all others in
a survey area and is uniquely identified on a soil map. The delineation
of a map unit generally contains the dominant components in the map unit
name, but it may not always contain a representative of each kind of inclusion.
The different soils used to name soil map units have sets of interrelated
properties that are characteristic of soil as a natural body. However,
the term map unit is intended to exclude maps showing the spatial distribution
of a single property such as texture, slope, permeability, drainage, shrink-swell
potential or depth, alone or in limited combinations (USDA-SCS, 1993).
Map units are designed to carry important information, called soil attribute
data, for the more common uses of soils. The attribute data associated
with each map unit can be used to develop information for estimating the
transport of chemicals in landscapes. For example, soil attribute data
for the leaching of chemicals toward the ground water may include soil
hydraulic conductivity, water retention, and/or drainage, which are functions
of texture and organic carbon contents (Leonard et al., 1991; Hornsby,
1992; Wilson et al. 1993).
In this chapter, we present two methods of using soils information to
predict the transport and impact of chemicals on water bodies. These methods
describe vertical movement of water and chemicals to the ground water and
as overland transport to streams.
Predicting Impact of Chemicals upon Groundwater Quality
It is generally much easier to prevent the degradation of groundwater
quality than to restore polluted waters. Therefore it is desirable to predict
the impact of management practices upon groundwater quality so practices
with a high risk of degrading groundwater can be avoided. The previous
sections of this publication explained important factors and processes
regulating water and chemical movement. Experimental results of water movement
and chemical transport studies in southeastern United States were also
presented. This section describes several ways in which soil, chemical,
weather, and management practices can be used to predict the impact of
these practices upon groundwater quality. Members of these regional projects
have developed useful research and management tools. Some of those tools
developed specifically for assisting in management decisions are presented
here. The tools are applied to a group of herbicides labeled for cotton
production in Oklahoma. Simplifications and limitations of the tools are
Groundwater quality can be characterized by the concentration of the
chemical of interest relative to the critical concentration of that chemical
(Hoag and Hornsby, 1992). This ratio, called the groundwater hazard index,
is given by
Many factors interact to determine the concentration of a chemical in
an aquifer or in water drawn from a well. In this chapter, we will separate
these factors into two groups. The first involves factors that determine
the amount of chemical entering the aquifer and the second with spatial
and temporal mixing of the chemical with water in the unsaturated soil
and in the underlying aquifer.
The quantity of chemical leaching through the surface soil and entering
the groundwater depends upon the quantities applied, biologically degraded,
chemically transformed, taken up by plants, lost to atmosphere, and lost
to runoff. Chemicals move downward through soils primarily with water in
the soil. Thus the amount of chemical leached increases as the amount of
water moving through the soil profile and entering the groundwater increases.
This amount depends upon the ability of the soil to take in water at the
surface, to store water in the root zone, and to conduct water through
the profile. Soils with low infiltration rates tend to have more water
runoff the soil surface and lower percolation rates. Sandy soils conduct
water rapidly when wet and store only small amounts of water so leaching
losses can be high. Critical values for these soil parameters depend upon
the distribution of rainfall and the irrigation practices used. If the
amount of water entering a soil is no more than the amount that can be
stored in the root zone, little water will be available to move the chemical
below the root zone. If large amounts of water enter the soil in a short
time, the soil near the surface will not be able to retain all of it so
leaching will occur. Finally, the amount of water taken up by plants and
lost to surface evaporation since the last infiltration event impacts the
capacity of the soil to store water in the root zone. Water balance models
are often used in management models to estimate the quantity of water moving
through the root zone and entering the aquifer.
The amount of a chemical leached also depends upon properties of the
chemical. The solubility of the chemical affects the maximum amount of
chemical that can be leached per unit of water leached. The soil-chemical
interaction is more complex because many chemicals are adsorbed on solid
surfaces of soils. The concentration of a chemical in soilwater is usually
directly related to the concentration adsorbed. High initial concentrations
lead to high concentrations on the soil solids. Adsorbed material is not
free to move with the soilwater. However, as the chemical in the liquid
phase moves downward in the soil, the remaining solution concentration
is reduced so some of the adsorbed material leaves the solid surfaces and
goes back into solution and moves with the soilwater. Chemicals that are
strongly adsorbed tend to stay near the soil surface. These products are
most likely to enter surface water by means of runoff and erosion of surface
soil. Chemicals that are not adsorbed to soil particles are free to move
with the soilwater and are more likely to be transported to groundwater.
Some chemicals are highly volatile so a significant portion of the applied
chemical can be lost to the atmosphere. In that way it is not available
for leaching to groundwater. The quantity lost in this way will be sensitive
to the manner in which the chemical is applied and to the soil water content.
Chemicals moving through soils may be degraded by biological organisms,
may undergo chemical transformations, or may be taken up by plants. All
of these tend to decrease the amount of applied chemical reaching groundwater.
Pesticides are biologically degraded in soils near the surface. Therefore
the longer these products reside in surface soil, the greater the degradation
and the smaller the amount leached. Pesticide degradation rates vary greatly.
Pesticides that degrade slowly or are persistent must reside in the surface
soils for a longer time to achieve the same amount of degradation as pesticides
with higher degradation rates. The timing of a chemical application in
relation to that of irrigation or large rainfall events will influence
the residence time of the chemical in the root zone. Large infiltration
events soon after a mobile chemical is applied have the potential to cause
large leaching losses.
Up to this point we have focused on factors influencing the amount of
chemical entering the aquifer. The concentration of chemical observed in
well water depends upon the initial concentration of the chemical in the
aquifer, the amount leached, and the volume of water into which the leached
chemical is mixed. As a first approximation, this volume of water can be
estimated from the porosity of the aquifer and the depth to which the chemical
is mixed. This approximation assumes that all of the water in the aquifer
percolates through the surface soil. Many aquifers are partially recharged
with water entering directly from rivers. This water will tend to dilute
the solution leached through the soil profile. So quantitative estimates
of concentration in a well will require an understanding of the sources
of water entering the aquifer and the flow processes within the aquifer.
Additional dilution occurs due to non-uniform flow in the unsaturated
soil above the aquifer. Soils contain pores of differing sizes. Water and
chemicals move at different speeds in different pores and even within a
pore. As a result, the time required for chemicals to reach the groundwater
depends upon the flow path. So the chemical does not reach the aquifer
in a single sharp pulse, but in a much more disperse pulse that continues
for days, weeks, or longer. This dispersion effect tends to reduce the
concentration of chemical entering the aquifer and increases the volume
of water into which the chemical is mixed.
Dilution can also occur due to non-uniform spatial application patterns.
Consider the situation in which the chemical of concern is applied to only
a portion of the land area recharging the aquifer for the well of interest.
Water leaching from areas in which the chemical was not applied will not
carry the chemical to the aquifer. That water will tend to reduce the concentration
observed at the well.
Spatial Variability and Parameter Uncertainty
From the discussion above, we see many factors influence groundwater
quality. Mathematical models have been developed to describe these processes.
Two additional items must be considered when predicting the impact of management
practices upon groundwater quality. Those are spatial variability and parameter
Results presented in the experimental sections of this document illustrate
that soil properties vary from site to site, even within a soil map unit.
This variability can be quite large for many hydraulic properties. As a
result, leaching losses can vary greatly from one location to another.
A small portion of a map unit or a field may contribute a large portion
of the chemical entering the aquifer. There is little that we can do to
reduce this soil variability. However, knowledge of the variability can
assist us in estimating leaching losses. The exact manner in which this
variability is used in the predictions will depend upon the model used
and the preferences of the modeler. The final result should enable the
user to observe the impact of this variability upon the predictions.
Some factors that govern chemical leaching are not known in advance.
This uncertainty presents extra challenges. An example of this is future
weather (see Haan et al., 1994). The amount and distribution of rainfall
at a site has a large effect upon the amount of chemical leached. Since
the future weather is unknown, it is impossible to predict leaching losses
with certainty. The use of daily or monthly average rainfall amounts results
in underestimating leaching since the primary leaching events are associated
with large rainfall events. A better approach is to perform simulations
for the chemical application in different years for which daily weather
records are available. A probability distribution for the predicted groundwater
hazard can be obtained and the probability of exceeding a critical concentration
or groundwater hazard can be determined. If that probability is low, the
practice may be deemed acceptable. If it is high, it may be unacceptable.
Weather is just one parameter that is uncertain when the simulation
is performed. Most model inputs have an associated uncertainty. Spatial
variability results in uncertainty of soil properties. Chemical properties
also have uncertainty. Rainfall patterns and irrigation amounts often vary
within a management unit. Scientists are still developing tools for incorporating
all of these sources of uncertainty into model outputs.
Predictive Tools for Management
Various tools have been developed to provide insight into hazards
associated with different soil - chemical - management systems. Each tool
is developed for a specific purpose and is a simplification of reality.
Some simplifications are made because of a lack of more detailed data.
Others reflect the current understanding of the process. Each is useful
for certain types of decision-making and inappropriate for others. Care
should be taken when selecting tools to assure that the simplifications
made are appropriate for the area of interest. Several tools are presented
in the following pages along with samples of available output. An overview
of the tools is presented in Table 1.
Maximum Groundwater Hazard: The groundwater hazard index was
introduced previously (see equation 1). It is sometimes useful to know
the absolute worst situation that might result from the use of a particular
chemical. The maximum groundwater hazard, GWHMax, is
the groundwater hazard that would result if all of the chemical applied
to a soil entered the groundwater (Yoder et al.,1995). That is,
The GWHMax is primarily useful as an initial screening
tool for chemicals that pose very little risk to groundwater. Clearly we
do not expect entire amount of a chemical applied to enter the aquifer.
If GWHMax for a chemical is less than 1, we do not need
to be highly concerned about polluting groundwater with that product. If
is large, we will need to examine it more carefully using tools that incorporates
more soil, chemical, and hydrologic parameters.
Retardation Factor: Jury et al. (1983, 1984a, 1984b) and Rao
et al., (1985) published several simple indices for ranking the likelihood
to leach past a particular depth and enter groundwater. The retardation
factor is the simplest one of these. The retardation factor, RF, for a
uniform soil is given by
where r is the soil bulk density, KD
is the partition coefficient of the chemical in the soil, f is the
porosity of the soil, JFC
is the field capacity of the soil, and Kh is the dimensionless
Henry's constant. Nofziger et al. (1988) extended this index for layered
soils. The retardation factor has a minimum value of 1 for non-adsorbed
chemicals with low volatility and increases for other chemicals. Chemicals
with small retardation factors are more likely to leach to groundwater
than chemicals with larger retardation factors.
For a particular soil, RF can also be viewed as an index for
ranking chemicals based on the amount of water needed to leach them to
a specified depth. Chemicals with high RF values require more water
to leach them to a particular depth than chemicals with low values.
The major assumptions and simplifications involved in this index are
summarized in Table 1. Note that this
index ignores differences in degradation rate, toxicity, and application
rate for the chemicals. Soil properties are incorporated into the index
by means of the bulk density, porosity, field capacity, and, indirectly,
the soil organic carbon content and other properties influencing KD.
(For many soils, KD is approximately KOC
* OC where KOC is the organic carbon partition
coefficient and OC is the organic carbon content of the soil(Hamaker
and Thompson ,1972; Karickhoff, 1981, 1984)). The RF index does
not incorporate any weather or water management characteristics of the
site. It is purely an index related to the rate at which the chemicals
move through soils and is useful for comparing one chemical to another.
provides one method for estimating the order at which chemicals will appear
at a particular depth. This may be useful in designing a monitoring scheme
for an area.
Table 3 presents retardation factors
for the chemicals in Table 2 for several different soils. Soil properties
used in the calculation are shown for each soil. A value of zero was used
for Kh for all chemicals. Chemicals in the table are
listed in increasing order of RF for Eufaula soil. Note that RF
values change with soil but the ranking of chemicals is the same across
soils in this example. Differences in RF values increase as the organic
carbon content, OC, increases. You may also note that paraquat, the chemical
which must be retained for nearly 11 years (according to Table 2) to allow
degradation to reduce the groundwater hazard to less than 1, is the chemical
with the largest retardation factor. This means it will leach more slowly
in these soils than any other chemical in the table.
Attenuation Factor: A second index introduced by Rao et al. (1985)
is called the attenuation factor. It provides an index for ranking chemicals
based on their relative amounts leaching past a specified depth. It incorporates
a simple estimate of travel time and first-order degradation. Ranking by
this scheme assumes that the larger the relative amount of chemical passing
a specified depth, the greater the risk of degrading water quality. The
attenuation factor, AF, is given by
The attenuation factor was developed for ranking chemicals. It was not
intended as an estimator of actual groundwater hazard. It may be an improvement
over the retardation factor in that it incorporates the degradation rate
of each chemical, but it does not incorporate the application rate. Table
4 presents values of the attenuation factor for chemicals listed in
Results in Table 4 illustrate that
rankings based on RF and AF are often different. Since AF incorporates
the degradation rate, it may provide a better estimate than RF. However,
AF does not change if the amount applied changes and it does not incorporate
the toxicity of the product, so it may not correctly rank the chemicals
for groundwater hazard.
Groundwater Hazard Based on CMLS: The groundwater hazard, GWH,
(equation 1) requires an estimate of the concentration of the chemical
in the groundwater. Any model capable of predicting this concentration
can be used to calculate the hazard associated with a soil-chemical-management
system. The CMLS model (Nofziger and Hornsby, 1986, 1994) is a management
model which has been used for this purpose (Hornsby et al, 1998; Nofziger
et al, 1998). Figure 1 illustrates the conceptual model used and the processes
incorporated into this approach.
The unsaturated zone above the groundwater is divided into two regions.
The upper region includes the root zone plus additional soil for which
soil properties are known and in which degradation takes place. The depth
of this region is usually 1 - 2 meters. The second region (called the vadose
zone in Fig. 1) stretches from the bottom of the upper zone to the water
table. Within the root zone, CMLS is used to model the movement and degradation
of the chemical. CMLS provides an estimated amount (per unit surface area)
of chemical entering the vadose zone. This amount is assumed to be transmitted
through the vadose zone and to ultimately enter the groundwater. The concentration
of the chemical in the groundwater is then estimated by mixing the amount
leached in an aquifer with a user-defined porosity and mixing depth.
Figure 1. Conceptual diagram of the model used to calculate groundwater
hazard using CMLS.
The upper region of the soil modeled by CMLS can be composed of up to
20 distinct layers with different soil and chemical properties in each
layer. CMLS uses a simple water balance model to determine daily water
fluxes. The chemical is moved downward in response to these water fluxes.
Sorption of the chemical on the soil solids is assumed to be described
by a linear isotherm. Sorption is assumed to occur very rapidly and to
be reversible. The amount of chemical remaining in the profile is calculated
assuming first-order degradation.
In the introductory section of this section, we presented an overview
of the factors affecting groundwater quality. It is informative to evaluate
the GWH based on CMLS in light of those factors. Table
5 provides such a comparison. Many of the factors are incorporated.
Some are ignored. In most cases including these additional processes would
decrease the estimates obtained with this model. Thus, these estimates
are likely to exceed those actually found in nature.
Figure 2 shows the distribution of computed groundwater hazard values
resulting from different weather patterns that might occur at the site.
Fifty equally likely weather patterns for this site were used ion the calculations.
The GWH values range from 0.01 to 2.3 based on the amount passing a depth
of 0.5 m. Two hundred-fold differences in estimated GWH values due to weather
are not uncommon. The probability of exceeding a GWH value of 1.0 is approximately
0.3 when the 0.5 m depth is used to estimate the amount leached. This probability
drops to only 0.02 when a depth of 0.6 m is used. This illustrates that
the estimated amount leached and the resulting groundwater hazard can be
quite sensitive to the soil depth at which degradation is assumed to cease.
This suggests a need for research that characterizes the degradation rate
as a function of soil depth so an appropriate value can be used in the
Table 6 provides a summary of estimated
travel times and GWH values for several herbicides. Travel times represent
the time required for the center of mass of the chemical to move from the
soil surface to a depth of 0.5 m. GWH values shown are estimates based
mass of chemical passing the 0.5 m depth. Values shown in the table summarize
those 50 predictions for each system.
Several interesting observations can be made from the results in Table
Prediction of Chemical Transport Impact upon Surface Water Quality
A portion of the rainfall that reaches the earthís surface infiltrates
the ground to replenish the vadose zone and ground water. However, a portion
also may flow over the land surface as runoff. Runoff occurs when the rate
of rainfall exceeds the rate of infiltration and surface storage. Runoff
is affected by several meteorological factors including the type, intensity,
and amount of rainfall as well as by temperature, evapotranspiration, relative
humidity and season of the year. High intensity rains are more effective
in sealing a bare soil surface than low intensity rains. In the absence
of ponded water or runoff the maximum infiltration rate is the lesser of
the rainfall rate or the soilís infiltration capacity. In the presence
of ponded water or runoff, the infiltration rate equals the infiltration
capacity until the surface supply of water is exhausted. The amount and
type of vegetation, slope, and soil properties such as antecedent water
content, texture, structure and hydraulic conductivity are also important
factors affecting infiltration, and therefore, runoff. Little or no runoff
will occur on soils having infiltration capacities as high or higher than
the rate of rainfall. Runoff can be reduced if the infiltration rate of
the soil can be increased or if the water can be retained on the soil surface
for longer periods of time. Both of these conditions permit more water
to enter the soil profile, and therefore, less water to runoff. A granular
soil structure, coverage of the soil surface by vegetation, and a rough
soil surface promote infiltration and reduce runoff. The presence of vegetation
and tillage practices affect infiltration, interception and detention of
water. Vegetation anchors the soil and also intercepts rainfall, provides
cover from high wind and water velocities with their residues, increases
infiltration and aggregation. On steep slopes, there is less surface storage
and rainfall tends to runoff more rapidly with less opportunity for infiltration
than on flatter slopes.
Runoff is a major pathway for the transport of sediment and contaminants
to surface waters, drinking water supplies and downstream ecosystems. Runoff
from agricultural landuses introduces sediment, bacteria, suspended solids,
pesticides, and nutrients into surface waters. The most common non-point
source pollutants are sediment and nutrients (EPA, 1996). Recent attention
has been given to the development and use of simulation models to predict
transport from runoff and and assess the impact of present and alternative
agricultural management practices on water quality (DeCoursey, 1985). One
of the more important environmental pollution topics is eutrophication
of surface waters. Eutrophication is the response of a water body to over
enrichment by nutrients. Phosphorus (P) often limits eutrophication, therefore,
proper management strategies must be implemented to minimize the effects
of accelerated eutrophication. We present an example where soils, landuse,
climate and topographic information were used to examine the transport
of P by runoff in a small watershed.
Simulation of P Transport to Streams--An Example Study
Hays (1995) and Udouj and Scott (1999) showed how computer simulation
modeling and geographical information systems (GIS) techniques could be
combined to simulate the transport of P to a stream within a watershed.
Their application was to examine the P loading after simulated applications
of poultry litter to two watersheds in northwest Arkansas ( MLRA 116A)
and to delineate the areas and soils most responsible for runoff of P.
The simulation model chosen is known as Spatially Integrated Model for
Phosphorus Loading and Erosion (SIMPLE). SIMPLE is a distributed parameter
model and employs a P transport model, a digital terrain model, and a data
base manager to evaluate sediment and P loading from a watershed to streams
(Sabbagh, et al., 1995). The model operates on a daily time step and requires
the development of four geo-referenced primary data layers: soils, landuse/landcover,
elevation and hydrography. The four primary data layers served as building
blocks to derive parameters required to run the model, which included the
hydrologic group for each soil mapping unit and factors necessary to calculate
sediment yield from the Universal Soil Loss Equation (USLE). Surface runoff
volume of a rainfall event was estimated with the NRCS curve number method.
These soil curve numbers are functions of landuse, condition of the vegetation,
hydrologic group and antecedent soil moisture at the soil surface. Soil
and landuse/landcover data were used to generate the following input files
for each soil series: erodability factor, soil pH, % organic carbon, %
clay, bulk density, hydrologic group, slope length and curve number (Table
7). The data were obtained from the Washington County, Arkansas soil
survey and were input as raster files, which supplied the geographic location
and associated attribute value for each parameter specified in the spatial
attribute databases within SIMPLE. Daily rainfall data were input from
an external file of 30 years of weather data recorded at Fayetteville Arkansas,
the nearest weather station. Simulations were conducted using the raster-based
scale where each pixel represented a 30 m by 30 m area in the watershed.
The Fish watershed was one of the watersheds examined for the transport
of P to the Illinois River (Udouj and Scott, 1999). This watershed occurs
in the headwaters region of the Illinois River Watershed in northwest Arkansas
and is dominantly agricultural (Fig. 3) with the spatial characteristics
given in Table 8. Deciduous forest and
tall fescue or bermudagrass pastures account for about 99% of the watershed.
The forested areas tend to occur on the steeper slopes whereas the pastures
generally occur on the lower slopes. There are 14 soil series containing
a total of 25 map units in the watershed (Fig. 4) with the five dominant
soil series accounting for 92% of the watershed area.
The model results showed that P loading increased with application rate
of poultry litter. The cumulative probability of P loadings to the Illinois
River as a function of application rate for the 30 years of weather is
shown in Fig. 5. These curves provide a means to assess long-term environmental
risks associated with variable application rates of poultry litter to the
watershed. As expected, the higher the application rate of poultry litter
the higher the probability of loading of P to the Illinois River. However,
at the higher probabilities the rate of application curves were not linear
indicating that there is an increased risk of P loading to the stream.
The spatial distribution of P loadings to the Illinois River was used
to locate the most sensitive areas of loading (Fig. 6). A simulated application
of 9000 kg/ha of poultry litter at a P concentration of 1.3% P in the litter
and 30 years of weather were used in the simulations. The simulated P loadings
to the river were arbitrarily divided into categories and the areal extent
in each category is given in Table 9.
The results showed that 81.2% and 99.8% of the watershed had loadings of
runoff and sediment P that were less than 2.0 kg/ha/yr, respectively. Thus,
less than 20% of the watershed contributed more than 2.0 kg/ha/yr, an area
similar to that of the pasture. Only 0.42% of the watershed (about 10 ha)
contributed more than 5 kg/ha/ yr. These most sensitive areas were located
near the Illinois River on the Johnsburg and Nixa soils. These soils have
relatively high CN values of 84 and 79, respectively and are classified
in hydrologic groups D and C, respectively. Soils in hydrologic groups
C and D tend to have low infiltration rates when thoroughly wetted and
high runoff potential.
This study shows that spatial information on soil characteristics, landuse/landcover,
and climate and topography can be used to locate the most sensitive or
source areas for P loading of streams. The major advantage of this approach
is that management strategies can be targeted to the high risk areas to
remediate transport problems associated with P losses from the landscape.
This could reduce the costs of remediation and prevention as compared with
application of these same strategies over a broad area. General management
practices that could be focused on these high risk areas include minimizing
erosion and runoff, avoiding P additions to high P testing soils where
crop response is unlikely, avoiding P applications when the soil is wet
and rainfall is imminent, and incorporation or injection of P inputs (fertilizer
and animal manures) below the soil surface.
Soil and chemical properties, site characteristics, and management
practices interact in complex manners to determine the amount of a chemical
transported to surface and ground waters and the impact of that chemical
upon water quality. Scientists have developed a wide range of models to
predict this impact. All of the models involve simplifications. The inherent
simplifications must be considered when utilizing each model. We must also
consider the uncertainty and variability in model parameters when making
predictions. The uncertainty in water hazard can be large due to unknown
future weather at a site and to spatial variability of soil properties.
The uncertainty in model output resulting from unknown input parameters
means that a single simulation or a single experiment is of limited value
for predicting ground water quality. However, models help us to determine
which landscape parameters and management paractices have the greatest
influence on water quality. They also can guide our future efforts to develop
environmental regulations that remediate or prevent further degradation
of our natural resources.
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