SCSB# 395

Relationships between Soil Properties and Chemical Transport in Landscapes of the Southeastern United States

D.L. Nofziger1, H.D. Scott2, and T.H. Udouj2
1Oklahoma State University and 2University of Arkansas

Chapter Outline



Introduction
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 quality.

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 also presented.

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, GWH, is given by

where C is the concentration of the chemical in the aquifer and CCriticalis the critical concentration. Groundwater hazard values less than 1 correspond to chemical concentrations less than the critical concentration. Thus small GWH values are desirable. GWH is helpful in that it incorporates the toxicity of the chemical rather than considering only the absolute concentration. If a chemical is highly toxic, CCritical will be small so low concentrations will be considered hazardous while larger concentrations will be permitted for chemicals with lower toxicities and higher critical concentrations.

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 uncertainty.

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,

where CMax is the concentration which would result if all of the applied chemical entered the groundwater at one time and CCritical is the critical concentration. The maximum concentration is given by where r is the application rate of the chemical in kg/ha, JS is the saturated water content or porosity of the aquifer, and d is the mixing depth in meters. Table 2 shows GWHMax values for selected labeled herbicides using maximum labeled application rates, an aquifer porosity of 0.25 and a mixing depth of 1 meter. The critical concentration used for each chemical is the USEPA lifetime health advisory level or its equivalent. The table also shows the number of days the product must reside in the soil to reduce the maximum hazard to 1 or less and the maximum amount of the product which could enter the aquifer without exceeding the critical concentration.. Note that two of the products have GWHMax values less than or equal to 1.0.

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 GWHMax 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. RF 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

where m is the estimated amount passing the depth of interest, m0 is the amount applied at the surface, k is the degradation rate constant, t is the time required to move from the surface to the depth of interest, and e is the base of the natural logarithms . The degradation constant, k is equal to 0.693/half-life. For a uniform soil, the time t is estimated using the equation where d is the depth of interest, RF is the retardation factor (eqn 4), JFC is the field capacity of the soil, and q is the average recharge rate or average rate at which water moves through the soil.

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 Table 2.

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. Probability of exceeding different calculated groundwater hazards for diuron applied to Teller loam assuming all chemical passing the 0.5 m or 0.6 m depth enters the groundwater and mixes to a depth of 1 m.

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 model.

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 6.

  1. Ranking the chemicals by mean travel time estimated by CMLS provides the same order as that provided by the RF factor. Chemicals with lower RF values correspond to smaller travel times.
  2. The range in mean travel times across chemicals is greatest for the Mclain soil and least for the Eufaula soil. This range decreases as the organic carbon content of the soil decreases.
  3. Ranking of chemicals by mean GWH values produces a different order for the three Eufaula sites than for the Teller and Mclain soils. The Eufaula soils have very low organic carbon values so none of the chemicals are highly adsorbed. All of the chemicals move quite quickly through the Eufaula soils. As a result, residence times are much lower so less degradation takes place in the Eufaula soils and the final ranks depend more upon the application amounts.
  4. Large differences in calculated travel times and GWH values result from differences in weather at the site. In some cases, estimated GWH values do not exceed 1 for any weather records. In other cases, some values exceed 1.
  5. Results for the three sites of the Eufaula soil illustrate differences that can be expected within a single map unit. In this case, sites 1 and 3 are similar while site 2 is quite different. These differences within a map unit result in different GWH estimates and in different rankings of the chemicals based on GWH.
The values reported in Table 6 represent estimated losses below a depth of 0.5 m. This depth was selected for illustration only. A depth of 1 m is more commonly used. The GWH values are much less for that depth since more time for degradation occurs.
 



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.

Fig. 3. Landuse and landcover of Fish watershed.

Fig. 4. Soils in the Fish watershed.
 

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.
 


Fig. 5 . Probability of P loading to the Illinois River as a function of application rate of poultry litter.
 
 
 


Concluding Comments
 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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Email address: david.nofziger@okstate.edu