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Consideration of Spatial Variability
in the Management of Non-Point
Source Pollution to Groundwater
W. Woldtl, F. Goderya2, M. Dahab3, and I. ~ogard?
Abstract. - Geostatistical simulation and unsaturated zone modeling are
combined to evaluate the impact of spatial variability of selected
parameters on groundwater nitrate contamination from agricultural
production. Three management scenarios involving spatially variable
application were investigated with consideration of spatial variability in
residual soil nitrate, yield, and hydraulic conductivity. The process is
applied to three conceivable situations, differing in the extent of spatial
variability. One decision input, the fertilizer amount, is distinct for
different scenarios as well as spatial location for one scenario. Modeling
results indicate that variable application of nitrogen, based on spatially
variable parameters, not only reduced the over-application of nitrogen,
but also reduced the overall groundwater contamination potential. The
modeling results indicate that spatial management techniques hold
promise for maintaining production while simultaneously protecting the
environment.
INTRODUCTION
The natural and management induced variabilities of field soils and hydrologic
formations are extensively acknowledged as dominant factors influencing fluid
and mass transport through the subsurface zone. The variations of soil and crop
properties have led to attempts to understand those variations and to manage
production accordingly. The management of production according to localized
conditions is variously known as spatially variable, site specific, soil specific,
precision, or prescription production. A consensus does not appear to have been
reached within the scientific community on the proper term or definition.
However, the methodology presented in this research complements production and
environmental concerns according to localized conditions and will be referred to
as spatial management technology.
Assistant Professor, Depamnent of Biological Systems Engineering. Universityof Nebraska - Lincoln, Lincoln NE.
Graduate Research Assistant, D e p a m n t of Civil Engineering, University of Nebraska-Lincoln, Lincoln, NE.
3Professor, Department of Civil Engineering, University of Nebraska-Lincoln, NE.
The key factor driving spatially-variable control is field variability. Spatial
variability may be random or auto-correlated, may be long distance or short
distance, and may be small or large. However, all affect the feasibility of
spatially-variable control and the design of a particular system to achieve such
control. In addition, variability may result from both natural processes and
management practices. If there were no variability, production through traditional
practice with proper adjustment for field conditioils would be adequate. For all
its importance, spatial variability and it's impact on crop production and the
environment has not been studied to a large extent. Furthermore, the procedures
for developing an understanding of the variability in a particular field and
effective utilization of that information to reduce NPS groundwater pollution,
while at the same time maintaining production are still not clearly defined. The
purpose of this research is to develop a framework for exploring the effect of
spatial variability related to different field scale parameters in managing crop
production and minimizing groundwater contamination.
METHODOLOGY
The methodology outlined in this research incorporates spatial variability of
various parameters on a field scale in estimating nitrate contamination to
groundwater. Unsaturated zone modeling in combination with conventional
measurements and simulation is utilized to quantify the effect of field spatial
variability on contaminant loading for quantifying environmental benefits of
spatial management technology. The approach involves gemtatistical simulation
to generate a number of realizations reflecting differing degrees of spatial
variation. The model is used simulate crop growth and maintain a nitrogen mass
balance in the system. Primary elements of the methodology are discussed
below.
Management Scenarios and Variability
Three application scenarios were developed in the methodology leading to the
spatial management technology. The spatial management scenario is based on
appropriate management of nitrogen to meet, but not to exceed production needs.
Phosphorus and other nutrients are not assumed to be limiting factors. The
scenarios include traditional practices and are modified to reflect possible
advancement through application of spatial management technology (Figure 1).
The first scenario is designed to consider uniform input of nitrogen based on
traditional practices with the input rate set at a "typical" level and held constant
through time. The second scenario assumes the same crop and uniform
application, but the amount of application is modified based on soil and yield
information from a single location in the field. Thus, the application is uniform
over the field, but is variable with respect to time.
Scenario 1
Scenario 2
UA-TP
UA-16
Scenario 3
VA-16-
Legend:
UAWT Uniform Applicstioil ova Time
VAWT Variable Appkafion over Time
Figure 1.
UA-TP Unifgm Application, Traditioaal Practice
UA-1B UniformApplicltion, 1 CoatFd Point
VA-16CP Vrtiablc Application, 16 b t r d Point
-- Graphical illustration of three application scenarios.
The third scenario uses variable application based on the spatial variation of
selected model and decision parameters. The selected parameters include residual
soil nitrate, crop yield and soil hydraulic conductivity. The field is divided into
sixteen sectors, and the application rate is based on a single hypothetical
measurement of the decision variables in each sector. Hence, the field will
receive an application with respect to position as well as time conforming to
decision variables within the sector.
The methodology is applied to three different cases which are distinguished by
the magnitude of field variability. They are referred to hereafter as; low,
medium, and high variability case. In the low variability case, available field
information for residual soil nitrate and yield was used to develop the lateral and
vertical parameter distributions and geostatistical relations (Goderya et al., 1996;
Goderya, 1996). Field conditions were then generated for medium and high
variability cases. Mean values for these fields were comparable to the observed
field data. However, each case is distinguished by differing coefficients of
variation. For this study, values obtained from the literature were used to define
these coefficients of variation (Goderya, 1996). Using the subjective prior values
of mean, standard deviation and correlation length values for selected parameters,
a number of equally likely unconditional realizations were generated. Employing
this approach, medium and high variability field conditions were generated for
residual soil nitrate and yield values. The general statistical properties for low,
medium and high variability fields are given in Table 1.
Geostatistical Simulation and Mass Balance
A total of 100 field realizations, each realization having 120 locations, were
simulated for each identified spatially variable parameter under each variability
case. Simulated fields were then used as an input to an unsaturated zone
transport model for predicting crop production and nitrate loading to groundwater.
Table 1.--Generated field properties for low, medium, high variability cases.
Residual NO3 (lcglha)
Mean
-
165.3
--
Std. Dev.
51.5
88.5
163.5
Coeff.Var.
0.31
0.60
1.06
Minimum
65.7
29.4
5.2
Maximum
362.5
518.8
861.0
Hence, the complete process of gwstatistical simulation and unsaturated zone
transport modeling was executed within each variability case for each spatial
management scenario. The spatial input data changed between each set of field
realizations, but remained constant between each spatial management scenario.
On the other hand, decision input changed between each spatial management
scenario, but remained identical for various field realizations. For example,
spatial inputs of residual soil nitrate, yield, and hydraulic conductivity were
different between realizations 1 and 2, but they were identical between realization
1 of scenario 1 and realization 1 of scenario 2 and 3.
A quasi-three-dimensional approach to modeling the important processes and
maintaining a mass balance in the system is used in this research. In this case,
a one-dimensional model is combined with geostatistical simulation to represent
the heterogeneity (spatial variability) in a typical field. The intention of this
formulation is to investigate contaminant loading and production from a
heterogeneous field within a spatial framework. The model, TDNIT, is used in
this research for simulating nitrate movement through root- and vadose- zone
(Goderya et al., 1995). However, the methodology is not dependent on the type
of model. The use of the selected transport model offers the advantage of short
computation time and reduced input data demands. The results from this model
also compared well with the program Erosion Productivity Impact Calculator
(EPIC) in terms of its predictive ability (Goderya et al., 1995).
A total of 48 simulation runs, each encompassing 100 field realizations, were
completed for this effort. The resulting decision input and output were analyzed
for each individual model node, and for each run over a five year period.
CASE STUDY
A case study is presented to demonstrate the methodology. A farm field in
Central Nebraska represents a typical crop production area with the potential for
nitrate contamination that may be found in the Midwest. Site selection was based
upon availability of data, shallow water table, highly permeable vadose zone,
rather uniform soil characteristics, and homogeneity of crops produced as well as
the general agricultural practices employed. Here, most of the land adjacent to
the site is cropland and nitrogen fertilizer is a major source of crop nutrients and
nitrate contamination to groundwater. Continuous corn is selected as the crop to
be simulated since it is the major crop grown in the case study area.
Site specific information for the model input variables was compiled from a
variety of sources. The transport program required meteorological input data in
the form of daily precipitation (including irrigation amounts), average monthly
temperature, average monthly solar radiation and albedo. Additional input data
included depth to groundwater, potential mineralization amounts, the number of
soil layers, soil data for each layer, plant data, and the initial moisture conditions
for each layer. The soil input data (porosity, field capacity, permanent wilting
point, sand content, organic matter content, saturated hydraulic conductivity, and
residual water content) and initial conditions (water content and nitrate content)
for the field were based on the vertical profile of the soil horizon and the
unsaturated zone. The plant data include crop type, four different nitrate uptake
coefficients, leaf area index values and root depth values as a function of time,
dry mattedyield ratio, and potential maximum yield. The nitrogen fertilizer data
include application amount, date and depth of application.
RESULTS AND DISCUSSION
The model data were evaluated in terms of area-wide leaching potential and
subsequently for leaching as a function of spatial management practice. For all
evaluations, output variables were used for relative comparisons. These results
represent short-term impacts of the spatial management technology and were
calculated for the following variables of interest: annual fertilizer input, annual
crop uptake of nitrogen, and nitrogen losses to groundwater.
Figure 2 summarizes the primary nitrogen input. In the low variability case,
nitrogen fertilizer input was set at 200 kg/ha/yr (178 bulaclyr) for the traditional
practice scenario, which compares favorably with the average corn fertilization
rate in Nebraska (Follet et al., 1991). Fertilizer nitrogen inputs were lower for
second scenario because of the information contained in the field sample which
was used to adjust application rate. The overall amount of nitrogen fertilizer
applied to the crop in the third scenario was reduced further due to the added
level of information and use of spatial application methods.
The simulated crop nitrogen uptake was used as a measure of yield potential.
The annual uptake was similar under management scenarios 1 and 2 for all three
cases of variability (Figure 3). However, there was a detectable decrease in
uptake for scenario 3 (ie., spatial management technology). Based on these
results, it appears as though production based on spatial management technology
compares reasonably well with the traditional baseline output. Hence, while the
spatial management scenario reduced the fertilizer requirements, it also resulted
in simulated reduction in the uptake.
The predicted amount of nitrate-nitrogen loading to the groundwater is
presented in Figure 4. Potential nitrate loading to groundwater is significantly
Scenarios
-
Figure 2. Annual nitrogen input for variability cases and management scenarios.
-
Figure 3. Simulated annual crop uptake for variability cases and management scenarios.
Scenarios
Figure 4. - Simulated nitrate leaching for variability cases and management scenarios.
reduced for all three scenarios. However, the reduction is especially evident in
the third scenario with high levels of field variability. The results of these
simulations are consistent with the magnitude of the responses reported from
experimental studies in the Midwest (Ferguson et al., 1994; Phillips et al., 1993).
These results suggest that increased information for different parameters and
conditions in a spatial management system may not necessarily result in a
significant decrease in nitrate loading to groundwater. In fact, it appears that the
success of spatial management technology is highly dependent on the degree of
spatial variability of the primary field parameters that influence nitrate fate and
transport. Thus, the effort associated with spatially variable application in a field
with inherent low variability of key parameters would most likely be too costly
to justify the benefits. However, as the field variability of the key process
dependent parameters increases, the apparent environmental benefits of spatial
management technology become clearer as depicted in Figure 4.
SUMMARY AND CONCLUSIONS
The methodology presented in this research incorporates spatial variability of
various parameters in estimating groundwater contamination potential. It employs
a combination of geostatistical simulation and unsaturated zone transport
modeling. The methodology outlined in this research was applied to three
conceivable variability cases, differing in extent of spatial variability. In all the
cases, management practices of spatially variable application were evaluated on
the basis of sustaining agricultural production and minimizing environmental
pollution. Three management scenarios, including one using spatial management
technology were developed and evaluated given the spatial distribution of residual
soil nitrates, yield, and hydraulic conductivity in the fields.
The first case was defined as one exhibiting low variability. Results of
scenario modeling for this case indicate that while there is a slight reduction in
nitrate leaching, the use of spatial management technology may not necessarily
result in substantial benefits. It appears that uniform application, given realistic
yield goals and composite soil samples, may achieve results comparable to spatial
management technology in fields exhibiting low spatial variability. However, use
of variable rate application not only reduced the over-application of nitrogen, but
also reduced the non-point source pollution to groundwater due to agricultural
practices for fields exhibiting both medium and high variability. The
methodology described in this study provides a framework to ascertain if the use
of spatially variable fertilizer application, or spatial management technology, is
environmentally friendly and also provides a basis to evaluate the economics of
the technology.
ACKNOWLEDGEMENTS
This paper is supported, in part, by the Center for Infrastructure Research, the
Water Center, and the Agricultural Research Division at the University of
Nebraska-Lincoln and, in part, by the Cooperative State Research Service (CSRS)
of the U.S. Department of Agriculture.
REFERENCES
Ferguson, R.B., G.W. Hergert, J.E. Cahoon, T.A. Peterson, C.A. Gotway, and
A. H. Hartford, 1994, "Managing spatial variability with furrow irrigation
to increase nitrogen use efficiency," Second International Conference on
Site-Specijic Management for Agricultural Systems, Bloomington 1
Minneapolis, Minnesota, March.
Follet, R.F., D.R. Keeney, and R.M. Cruse, 1991, "Managing nitrogen for
groundwater quality and farm profitability," Published by Soil Science
Society of America Znc. , Madison, Washington.
Goderya, F. S., 1996, "Evaluation and estimation of groundwater potential using
spatial parameters, " Ph. D. Dissertation, Department of Civil Engineering,
University of Nebraska, Lincoln, Nebraska.
Goderya, F.S., M.F. Dahab, W.E. Woldt, I. Bogardi, 1995, "Comparison of
Two Transport Models for Predicting Nitrates in Percolating Water",
Submitted for publication in Transaction of the ASAE.
Goderya, F.S., M . F . Dahab, W.E. Woldt, I. Bogardi, 1996, "Spatial Patterns
Analysis of Field Measured Residual Soil Nitrate", In Geostatistics for
Environmental and Geotechnical Applications, ASTM STP 1283, R .
Mohan Srivastava, Shahrokh Rouhani, Marc V. Cromer, A. Ivan Johnson,
Eds., American Society for Testing and Materials, Philadelphia (In press).
Phillips, D.L., P.D. Hardin, V.W. Benson, and J.V. Baglio, 1993, "Nonpoint
source pollution impacts of alternative agricultural management practices
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BIOGRAPHICAL SKETCH
Wayne E. Woldt is an assistant professor in the Department of Biological Systems
Engineering at the University of Nebraska-Lincoln. His interests include risk analysis,
spatial management technology, and environmental systems analysis.
Farida Goderya is a graduate research assistant in the Department of Civil Engineering
at the University of Nebraska-Lincoln. She is currently completing her Ph.D.
dissertation on spatial management technology.
Mohamed F. Dahab is a professor in the Departments of Civil Engineering and
Biological Systems Engineering at the University of Nebraska-Lincoln. His interests
include pollution prevention and nitrate treatment methods for municipal water supplies.
Istvan Bogardi is a professor in the Department of Civil Engineering at the University
of Nebraska-Lincoln. His interests include risk assessment, systems analysis for
environmental issues, and global climate change.