Modeling Nitrogen Loading to the
Groundwater in Response to
Land Use Change
By
By Dibyajyoti (Diby) Tripathy
ABE 527 Final Project
Spring’ 04)
1
Summary
Land use change has the potential to impact both surface and groundwater. However, so
far very few studies have been focused on the effects of land use change on groundwater.
This project is aimed at filling that gap. It provides insight into estimation of nitrogen
loading to groundwater as a result of land use change and a possible measure to reduce
nitrogen contamination to groundwater. Two models, L-THIA and GRASIM, are used in
this project. The results showed that routing storm runoff over a strip of orchard grass (or
meadow) detention base can be very effective for minimizing nitrogen contamination to
groundwater at a low cost and environment friendly way.
Introduction
Groundwater is the world’s largest and most important source of potable water (Howard,
1997), and is important in sustaining our environment and ecosystem (Alley et al., 2002).
About one-half of the total US population and 97 percent of residents in rural areas use
groundwater for drinking purposes (USEPA, 1987). There are numerous manifestations
of land use change which have the potential to affect both surface and groundwater.
Although groundwater is an important part of terrestrial waters and a crucial long-term
resource for humans and global environment, it has received little attention compared to
the impacts of land use change on surface water (Ni et al., 2001).
Groundwater pollution by point source and non point source (NPS) contaminants is a major
impact of landuse change (Goudie, 1990). Among the pollutants, Nitrate (NO3) is the most
common one found in shallow aquifers – the store house of groundwater. Many studies have
shown that agricultural activities are major sources of Nitrate contamination in groundwater
(Fletcher, 1991; Hudak, 2000; Harter et al., 2002). Recent research has also demonstrated
that there are significant non-agricultural activities which contribute nitrate to groundwater,
especially in urban areas (Lerner & Wakida, 1999). Thus, in order to achieve our objective of
conserving groundwater for human use and maintaining a healthy environment and
ecosystem, and for obtaining necessary knowledge to minimize the disruptive effects of land
use, it is important for us to understand the linkage between land use change and nutrient
(e.g., Nitrogen) loading to groundwater.
This project is an attempt towards estimating how land use affects Nitrogen loadings to the
groundwater at a local scale, and how to minimize such impact in an effective and low cost
way. Although geologic units can contribute nitrate to groundwater (Boyce et al., 1976),
sources of nitrogen generally occur at the land surface. If nitrate in the soil system is not lost
in runoff, used by the plants, or transformed to nitrogen gas or to organic nitrogen by
bacteria, then nitrate is available for leaching (Keeney, 1986). It is this leached nitrate that
contaminates the groundwater. In this project an area having different land use categories in
the Centre County, PA, is considered. The NPS Nitrogen loading to the surface runoff is
estimated. Then the runoff is diverted over a strip of orchard grass. Finally, the amount of
nitrogen leaching to groundwater is estimated. This project also served as a test to determine
possibility of using meadows/orchard grass as medium to reduce Nitrate contamination to
groundwater.
2
Objective
The objectives of the project are two fold:

Model Nitrogen loadings to surface water (or, runoff) in response to land use

Model how much of Nitrogen in the runoff is likely to contaminate groundwater if an
orchard grass strip is used as a detention base for the run off
Study Area
A 10 acre land with a hypothetical land use scenario in the state of Pennsylvania (PA) was
considered in this project. The land use in the study area consists of the following three
categories: 85% agricultural, 5% commercial, and 10% high density residential. The above
land use categories are deliberately chosen to generate a high amount of nitrogen in the
runoff (these land uses are known to produce high amounts of nitrogen loading, highest being
from agriculture). Since the Nitrogen loading to the runoff is in terms of kg/ha, the actual (or,
absolute) area (or, areal extent) of land uses are not important. However, the relative percent
of different categories of land use is important. Thus, a 10 acre land with 85% agricultural,
5% commercial, and 10% high density residential should produce the same amount (in kg/ha)
of Nitrogen loading to runoff as a 100 acre land with the same proportions of land use.
A 10 acre orchard grass strip in the State College area, Centre County, PA, is then considered
to be used a detention base for the Nitrogen laden runoff produced from the above study area.
Nitrogen leached below the root zone in the orchard grass is then computed for the year 1994
because of availability of precipitation and some field parameter data for that year.
Model Selection
Two models are used in this project: Long-Term Hydrologic Impact Assessment (L-THIA)
model, and Grazing Simulation Model (GRASIM).
Purdue University developed L-THIA model formed the basis to estimate Nitrogen loading
to runoff in this project. It is an empirical model that estimates annual runoff and pollutant
loadings (Bhaduri et al., 2000; Pandey et al., 2001). I modified it to estimate nitrogen loading
on a daily basis. It’s a curve numbers based model. The equations are explained in detail later
in the “methods” section. A simple schematic diagram of the model is shown below in figure
1.
GRASIM is a mechanistic grazing simulation model that links al components of the pasture
system. It has crop simulation, nitrogen and water budgeting, animal grazing, and pasture
management components (Mohtar et. al., 1997a, 1997b). It predicts standing biomass, soil
moisture, drainage, and Nitrogen leaching under pasture. It has four major interrelated
modules: grass growth, water balance, nutrient cycling, and harvest management. A
schematic diagram of the model is shown below in figure 2:
3
Figure 1. A schematic of the L-THIA model
Figure 2. A schematic diagram of the GRASIM model
My project is concerned mainly with the nutrient module. The nutrient module uses a simple
soil nitrogen transformation scheme. A simple schematic diagram of the nutrient module is
shown below in figure 3. It accounts for two nitrogen forms, nitrate and ammonia. The
source and sink pools include plant and soil storage, soil organic matter, and crop residues.
4
Transformation processes include: leaching, nitrification, mineralization, plant uptake,
volatilization, and denitrification (Mohtar et. al., 1997a).
Nitrogen available for leaching (NAL) (kg/ha) is calculated as:
NAL = Nfertilizer + Nrain + Nresidual + Nnitrification – Nplant – Ndenitrification – Nother(i.e, runoff and erosion)
Nitrogen leached below root zone (NL) (kg/ha) is calculated as:
NL = NAL[1 – e-{(K * WAL)/(n)}]
Where, K is leaching coefficient (~1.2), n is porosity (cm), and
WAL is the water available for leaching (cm) = Peff. – ET – AWHC – St
(where, Peff. Is the effective precipitation, ET is Evapotranspiration, AWHC is
available water holding capacity in a layer, and St is the available water in a
soil layer.)
Figure 3. A schematic diagram of the Nutrient module of GRASIM model
Methods
The first part of the project dealt with estimating Nitrogen loading to the runoff based on LTHIA model. I have used my modified version of L-THIA model for this purpose. The steps
are as follows: three hypothetical land use categories and soil hydrological groups (for each
land use category) was considered as shown in table 1 below. As explained earlier, the land
use and soil hydrologic group choices are made specifically to get a high amount of Nitrogen
loading to runoff.
5
Table 1. Land use in the study area
Land use Categories
Agricultural
Commercial
High density Residential
Area (acres)
8.5
0.5
1.0
Soil Hydrologic Group Curve Number
D
85
C
94
D
92
Curve numbers for each land use and soil hydrologic group was then determined (SCS, 1986)
as shown in table 1 above. Then daily rainfall data for the State College area for year 1994
was collected (Since there is a mismatch between the daily rainfall data provided by L-THIA
and GRASIM, in this project the GRASIM rainfall data was used for consistency).
Daily direct runoff for 1994 was then calculated using the following CN formulas:
Q = (P – Ia)2 / (P – Ia) + S
Where, Q = runoff (in), P = rainfall (in), S = Potential maximum runoff after runoff
begins (in), and Ia = initial abstractions (in).
Since, S = (1000/ CN) – 10, and if we assume Ia = 0.2 S (an empirical estimation based on
observations from a number small watersheds), then the formula for calculating direct runoff
can be reduced to following:
Q = (P – 0.2S)2 / (P + 0.8S)
When P – 0.2S <= 0, no runoff was computed (i.e., runoff = 0)
Then, depth of runoff in each land use categories multiplied by their respective area gave
daily runoff volumes.
Next, multiplying runoff volumes with Event Mean Concentration (EMC) value for
Nitrogen for each land use type gave the daily Nitrogen loadings (in kg/ha) to runoff (after
converting the units and then area weighting for the three land use categories). EMC (Baird
and Jennings, 1996) values of Nitrogen for different land use are shown in table 2 below:
Table 2. EMC values for each land use type
Land use Categories
Agricultural
Commercial
High density Residential
EMC (ppm)
4.4
1.34
1.82
After determining the nitrogen loading to runoff, the next step was to route the Nitrogen
laden runoff over an orchard grass detention base and estimate how much of nitrogen may
leach to groundwater below it. In other words, to test the performance of orchard grass (or,
meadows) in minimizing nitrogen loading to groundwater. GRASIM was used for that
purpose.
6
Daily N-loadings in the study area for 1994 (i.e., output from L-THIA) were then
incorporated into GRASIM as NO3, such that it mimicked application of manure over a
meadow of 10 acre (or, 40468.6 m2) area. Soil and nutrient parameters were adjusted
accordingly to reflect the conditions of the study area. Considering the trend of daily
Nitrogen loading to runoff, the total Nitrogen in the runoff (in the form of NO3) was then
applied in three phases to reflect the following three seasons: winter (December to February),
summer & spring (March to July), and fall (August to November). The application dates, in
Julian days, were entered as the middle days for each season e.g., application dates as: 45,
136, and 274 which corresponds to 14th February, 16th March, and 1st October respectively.
Then, simulation was run for 365 days in the year 1994. Finally, total Nitrate leaching to the
groundwater below the root zone (second layer: 30cm to 100cm) was calculated. A
sensitivity analysis was also conducted to gain insight into how nitrate leaching below root
zone may be impacted by different parameters.
Results and Discussion
For the study area, total Nitrogen loading (in 1994) to surface runoff, as estimated from my
L-THIA based model, was 5.23 kg/ha. Such a high value of nitrogen loading to surface
runoff was due to the specific land uses within the study area. Specifically, 85% of the study
area is covered with agricultural land which is considered to be one of the highest producers
of Nitrogen. As such high density residential areas (10% of the study area) are also known to
produce high amount of nitrogen loading due to anthropogenic activities and byproducts.
The plots for daily Nitrogen loading and monthly nitrogen loading are shown in figure 4 and
5 below. As can be seen in the figure 5, majority of the days in the year was without any
nitrogen loading (in fact, 295 days in the year had no nitrogen loading to runoff). This is
quite expected as nitrogen loading depends upon the direct runoff (or runoff volume), which
in turn depends upon rainfall. Besides as explained earlier even if there is rainfall in any day,
if P – 0.2S <= 0, then there won’t be any runoff. As shown in figure 5, the monthly nitrogen
loading is highest in the month of August; this again is consistent with the highest amount of
direct runoff generated in that month (the direct runoff is due to highest amount of rainfall in
that month. The plots for rainfall for the year is shown in figure 8)
Amount of Nitrate leaching from the top layer (top 30cm of soil) and lower layer (30 cm
to bottom of rooting depth) of soil in the orchard grass strip is estimated to be 57.5 kg/ha
and 2.0 kg/ha respectively. The output plots of GRASIM for those two layers are shown
in figure 6 and figure 7.
Figure 6 shows higher amount of Nitrate leaching from the top layer as compared to the
lower layer (Figure 7). This is the layer where most nitrogen additions, uptake and
transformations, and water evapotranspiration takes place (Mohtar et. al., 1997a).
Leaching from this layer is captured by the lower layer. Thus, even though it has a higher
amount of leaching, still it doesn’t contribute directly towards groundwater
contamination.
7
Daily Nitrogen Loading (1994), Centre County, PA
2
Nitrogen Loading (kg/ha)
Total N loading:
5.23 kg/ha
1.5
1
0.5
0
0
30
60
90
120
150
180
210
240
270
300
330
360
Julian Days
Figure 4. Daily nitrogen loadings in the study area
Monthly Nitrogen Loading (1994), Centre County, PA
Nitrogen Loading (kg/ha)
2.5
Total N loading:
5.23 kg/ha
2
1.5
1
0.5
0
1
2
3
4
5
6
7
8
Months (1:January, 12:December)
Figure 5. Monthly nitrogen loadings in the study area
9
10
11
12
8
Figure 6. Daily Nitrate leaching from the top layer
Figure 7. Daily Nitrate leaching from the bottom layer
Daily Rainfall (1994), Centre County, PA
3.5
3
Rainfall (in)
2.5
2
1.5
1
0.5
0
0
50
100
150
200
250
Julian days
Figure 8. Daily rainfall in the study area
300
350
9
For groundwater contamination problems the lower layer is of vital importance. Any
Nitrate leaching from this layer is not only lost from soil but also contaminate the
groundwater. In this layer mostly water and nitrogen intake occurs. The surplus nitrogen
(not taken up by plants or not lost in other forms) is leached below by the excess water.
Figure 8 shows the daily rainfall in the study area. A comparison between figures 6, 7,
and 8, clearly shows that nitrate leaching in both the layers more or less follows the
rainfall events. This is quite expected as infiltrated water after rainfall is vital for nitrate
leaching.
A close look at figures 6, and 7 shows that during the last week on January (near Julian
day #25) both the top layer and bottom layer had a small amount leaching (about
1.6kg/ha and 0.1 kg/ha for top layer and bottom layer respectively). Usually, during that
time of the year, such amount of leaching was not expected (due to frozen ground, lower
rate of release of nitrate from break down of organic matter etc.). However, for this
particular year, coincidentally there were also higher than usual rainfall events during the
same time in that area (refer to figure 8). Thus, it might be a reason for that nitrate
leaching event during the last week of January.
As shown in figures, 9 to 14. A strong positive correlation was found between rainfall, N
loading to runoff, and Nitrate leaching to groundwater. This is not surprising as, amount
of water available for infiltration which in turn facilitates leaching is directly dependent
upon rainfall events.
It was encouraging to find the substantially low amount (2.0 kg/ha) of nitrate leaching
below the root zone of the orchard grass strip. A plot of total available Nitrogen and total
nitrate leaching to groundwater in the orchard grass is shown in figure 15. Out of a total
available nitrogen of 59.5 kg/ha, only 2.0 kg/ha of nitrogen is able to leach below the root
zone of orchard grass. Thus it clearly shows the effectiveness of orchard grass (or.
meadows) in reducing nitrogen leaching to groundwater. Such a finding is one of the
important outcomes of this project. Thus, routing runoff through orchard grass (or
meadow) buffers can help minimizing nitrogen pollution to groundwater and also can be
valuable as an effective, low cost, and environment friendly measure.
Sensitivity Analysis
In order to understand the effects of different parameters on the amount of N leaching
beyond root zone (in GRASIM) a linear sensitivity analysis was done. The relative
sensitivity index was calculated as:
S = [{B2 – B1}/{(B2 + B1)/2}]/ [{P2 – P1}/{(P2 + P1)/2}]
Where, P2 and P1 are the input parameters representing high and low values
respectively; B2 and B1 are the corresponding output parameters.
S value close to zero can be considered as insensitive, and values close to 1 or
more are very sensitive. Negative S values indicate that there is an inverse relationship
between the parameter and the output.
10
Monthly Rainfall (1994), Centre County, PA
Rainfall Distribution (monthly) in 1994, Center Cnty, PA
9
8
8
7
7
Rainfall (in)
Rainfall (in)
6
5
4
3
6
5
4
3
2
2
1
1
0
1
2
3
4
5
6
7
8
9
10
11
0
12
1
Months (1:January, 12:December)
2
3
4
5
6
7
8
9
10
11
12
Months (1: January; 12:December)
Figure 9. Monthly rainfall in the study area
Figure 10. Monthly rainfall in the study area*
Monthly Nitrogen Loading (1994), Centre County, PA
(from L-THIA)
N Loading (kg/ha) from L-THIA: 1994, Center county, PA
3
2.5
N Loading:kg/ha
Nitrogen Loading (kg/ha)
2.5
2
1.5
1
0.5
2
1.5
1
0.5
0
1
2
3
4
5
6
7
8
9
10
11
0
12
1
Months (1:January, 12:December)
2
3
4
5
6
7
8
9
10
11
12
Months (1: January; 12:December))
Figure 11. Monthly N-loading
Figure 12. Monthly N-loading*
Nitrate Leaching (monthly) Below Root Zone in 1994, Center Cnty,
PA (from GRASIM)
Monthly Nitrogen Leaching (1994), Centre County, PA
(from GRASIM)
1
1
0.8
0.8
Nitrate:kg/ha
Nitrogen Loading (kg/ha)
1.2
0.6
0.4
0.2
0.6
0.4
0.2
0
1
2
3
4
5
6
7
8
9
Months (1:January, 12:December)
10
11
12
0
1
2
3
4
5
6
7
8
9
Months (1: January; 12:December)
Figure 13. Monthly N-leaching
Figure 14. Monthly N-leaching*
*These continuous plots are presented here only to aid in the visual analysis and
correlation. It should not be interpreted as interpolated or continuous values.
10
11
12
11
Total Available N Vs. Toal N Leaching to Groundwater
14
13
12
11
Nitrogen (kg/ha)
10
9
8
Total N Availabe
Total N Leaching
7
6
5
4
3
2
1
0
0
50
100
150
200
250
300
350
Julian Days
Figure 15. Plot of total available N vs. total N (as nitrate) leached to groundwater
Table 3. Sensitivity analysis for 16 parameters
12
For this project 16 different parameters were considered for the sensitivity analysis. Data
ranges for each of the parameters are collected from pertinent literature (Mohtar et. al.,
1997b, Zhai et. al., 1999). The results are reported in the table 3 above.
As can be seen in table 3, nitrate leaching beyond root zone is highly sensitive to the
following five parameters: leaching coefficient, soil bulk density(bottom layer), soil water
content after gravitational water drained from bottom layer, soil water content at 15 bar
of bottom layer, initial organic matter; And sensitive to following two parameters at the
higher range of input values: mineralization rate of soil organic matter, initial nitrate in
the bottom layer, and soil bulk density of top layer. The model is not sensitive (or,
insensitive) to other 8 parameters. A discussion on sensitive parameters is presented
below.
The model out put was found to be very sensitive to the leaching coefficient. A plot of
input ranges of leaching coeff. vs. corresponding output values for the nitrate leaching
beyond the root zone is shown in the figure 16. It is not uncommon for models (like
GRASIM) to have high sensitivity to leaching coeff. (infact, in their work, Pierce et al.,
1991, had also found that measured N percolation is sensitive to leaching coeff.). Another
reason for the high sensitivity for this parameter may be due to its inherent spatial
heterogeneity and interaction with other parameters.
Output: Leaching from Bottom
Layer (kg/ha)
Plot of Input Vs. Output values
5.3
4.3
3.3
2.3
1.3
0.3
0.3
0.8
1.3
1.8
2.3
Input: Leaching Coeff.
Figure 16. Plot of total Nitrate leaching as a function of leaching coeff.
Soil bulk density (especially for the bottom layer) was also found to be a highly sensitive
parameter. Also, soil bulk density for the top layer is found to be sensitive at higher end
of values. A plot of leaching vs. soil bulk density of bottom layer is shown in the figure
17. Bulk density refers to the weight (mass) of soil per unit volume. A compact soil
would have a higher bulk density and open friable soil with good organic matter content
will have lower bulk density. Presence of organic matter (or lack of it) according to the
bulk density, thus, can have profound effect on leaching (mineralization of organic
matter, or denitrification releases nitrate for leaching). Moreover, infiltration is
influenced by bulk density (e.g., soil with high bilk density will have less infiltration and
13
more runoff flowing over them) and can directly affect leaching. Thus, it is quite
expected that soil bulk density is highly sensitive for leaching.
Output: Leaching from Bottom
Layer (kg/ha)
Plot of Input Vs. Output values
16.6
12.6
8.6
4.6
0.6
0.9
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2
2.1
Input: Soil Bulk Density-Bottom Layer (g cm^3)
Figure 17. Plot of total Nitrate leaching as a function of soil bulk density(bottom layer)
The model out put was also found to be very sensitive to the soil water content after
gravitational water drained from bottom layer (i.e., the field capacity of bottom layer). A
plot of input ranges of the field capacity of bottom layer leaching vs. corresponding
leaching amount is shown in the figure 18. A negative sensitivity index shows that
leaching is inversely proportional to this parameter. It happens because precipitation that
falls after field capacity is either goes as runs off or percolates beyond the rooting zone.
Thus for a given precipitation event and same soil type a high field capacity means less
water available to carry on leaching (i.e., lesser leaching amount) and vice versa. And
since bottom layer of soil is the main controlling factor for leaching, field capacity is
expected to be a highly sensitive parameter.
Output: Leaching from Bottom
Layer (kg/ha)
Plot of Input Vs. Output values
2.9
2.2
1.5
0.8
0.1
0.3
0.4
0.5
0.6
0.7
0.8
Input: Soil Water Content after Gravitational Water - Bottom
Layer (%)
Figure 18. Plot of total Nitrate leaching as a function of field capacity (bottom layer)
14
Soil water content at 15 bar of bottom Layer (i.e, at wilting point of bottom layer) was
also found to be a highly sensitive parameter. A plot of leaching vs. available soil water
at wilting point of bottom layer is shown in the figure 19. This relation is quite expected
sbecause if there is more amount of water available at wilting point at which plants can
no longer remove sufficient water, then it is more likely that rest of the water will aid in
leaching.
Output: Leaching from Bottom
Layer (kg/ha)
Plot of Input Vs. Output values
3.1
2.8
2.5
2.2
1.9
0.05
0.15
0.25
0.35
0.45
Input: Soil Water Content at 15 Bar - Bottom Layer (%)
Figure 19. Plot of total Nitrate leaching as a function of water at wilting point(bottom
layer)
The model out put was also found to be very sensitive to the initial organic matter. A plot
of input ranges of the organic matter vs. corresponding leaching amount is shown in the
figure 20. Breakdown of soil organic matter releases N along with other nutrients. Thus
the higher the initial organic matter present in the soil, the higher will be available nitrate
for leaching. Thus the high sensitivity of this parameter is quite expected. Similarly
higher amount of initial nitrate in the bottom layer provides more nitrate for leaching.
Thus, it is not surprising that this is also a sensitive parameter (figure 21).
Output: Leaching from Bottom
Layer (kg/ha)
Plot of Input Vs. Output values
2.5
1.7
0.9
0.1
11000
21000
31000
41000
51000
61000
71000
81000
Input: Initial Organic Matter (kg ha^-1)
Figure 20. Plot of total Nitrate leaching as a function of initial organic matter
15
Output: Leaching from Bottom
Layer (kg/ha)
Plot of Input Vs. Output values
81.5
61.5
41.5
21.5
1.5
0
25
50
75
100
125
150
175
200
Input: Initial Nitrate - Bottom Layer (kg ha^-1)
Figure 21. Plot of total Nitrate leaching as a function of initial organic matter
Mineralization rate of soil organic matter (or, the rate coefficient) was also found to be a
sensitive parameter at the higher range of values. A plot of leaching vs. mineralization
rate of soil organic matter is shown in the figure 22. Mineralization of soil organic matter
releases N. The higher the rate, more nitrate will be available in the soil, which in turn,
can facilitate more nitrate leaching. Thus, sensitivity of this parameter is quite justifiable.
However, care must be taken while reading the sensitivity of this parameter as
mineralization rates are controlled by many factors such as: soil moisture, aeration,
temperature, and soil pH. The amount of N that becomes available from mineralisation of
organic matter and crop residues depends on previous cropping and soil type. Also,
mineralisation is quicker on sandy soils than on clay soils.
Output: Leaching from Bottom
Layer (kg/ha)
Plot of Input Vs. Output values
2.0
1.5
1.0
0.5
0.0
0.00001
Input: Mineralization Rate of Soil Organic Matter
Figure 22. Plot of total Nitrate leaching as a function of mineralization rate of soil
organic matter
16
Calibration and Error Analysis
A calibration of the GRASIM model for leaching was not possible because of
lack of actual field data (for many parameters) and because the different way in which
GRASIM was used in this project. For example, I have applied all Nitrogen in the runoff
as Nitrate and not a combination of Nitrate and Ammonia, which is the way model is
actually set up for (and also for which some field data was available for the year 1994).
However, to have some confidence on the model outputs, I utilized all the data
that was available for the study area for 1994 and used my judgment for values of other
parameters for which actual data was missing, and tried to do a calibration on the total N
leaching beyond the root zone. As can be seen in figures 23 and 24, my calibrated results
matched well with the original calibration, at least for the amount of total nitrate leaching
(Mohtar et. al., 1997b). 59.1
Figure 23. Original calibration (Mohtar et. al., 1997b) (total nitrate is about 52 kg/ha)
17
Predicted Nitrogen Leaching
100
90
Nitrate (kg No3/ha)
80
70
60
50
40
30
20
10
0
120
140
160
180
200
220
240
260
280
300
320
Julian Day
Figure 24. My limited calibration (total nitrate is 59.1 kg/ha)
Since an actual calibration couldn’t be done, it was not possible to find the errors, if any,
between the actual and model estimated leaching values. Assuming that there is no
human and equipment error in data collection, some of possible source of error in model
can be identified as follows. Leaching of Nitrate in soil is a complex process. Prediction
of nitrate movement below the root zone and into groundwater is difficult even though
physical and biological processes controlling N cycling in soil are well defined (Pierce et.
al., 1991). The difficulty arises because of the highly variable nature of soils and
processes that determine the overall fate of N in soils and also because of limited or
inaccurate site-specific data. The model is found to be highly sensitive to 5 parameters
and sensitive to another 3. The model uses typical values for most of the parameters
assuming that there is no spatial heterogeneity involved. However, parameters such as
soil bulk density and biomass (grazing) can vary a lot within a field. That results in error
in model output. Another possible source of error is the incompatibility of scale issue
between L-THIA and GRASIM. L-THIA is an annual scale empirical model where as
GRASIM is a process based daily model. Thus, when both the models are combined to
estimate leaching, it is natural to expect that there will be some discrepancy between
model results and real data. Other errors might be due to many numerical assumptions
that goes into model (such as, twocomponents of biomass strage and structure are
uniformly distributed through canopy, etc.).
Conclusion and Recommendations
Effect of nutrient loading to ground water, specifically Nitrogen loading, due to land use
change is a complex but understudied phenomena. Nitrate contamination of the world's
18
groundwater supply poses a serious human health threat. The result from this project
provides insight into how land use change can affect nitrate contamination to
groundwater and also proposes an environment friendly alternative to reduce nitrogen
contamination.
One of the significant outcomes of this project is that it made possible to combine two very
different models - L-THIA and GRASIM - to estimate possible N loading to groundwater
due to land use change. L-THIA is an empirical model that estimates nitrogen loading to
runoff at an annual scale, where as GRASIM is mechanistic grazing simulation model that
can estimate Nitrate leaching to groundwater on a daily basis.
The results showed that from a total amount of 59.5 kg/ha of Nitrogen supplied to the study
area, only 2.0 kg/ha leached beyond the root zone to contaminate groundwater. Thus, in this
study using orchard grass as a detention base for storm water runoff reduced the amount of
nitrogen leaching to groundwater by as much as 96.6% (assuming that all of the nitrogen on
the runoff was able to leach below to groundwater). Due to its ability to substantially reduce
N leaching, orchard grass strips (or, meadows) can be used as effective, low cost, and
environment friendly option for preventing groundwater contamination due to Nitrate.
Currently the importance of grass and forest buffers for reducing nutrient contamination to
both surface water (streams, lakes) and groundwater is increasingly being realized. Even gulf
courses are being proved to be helpful for reducing nutrient leaching, including Nitrates, to
groundwater. As per my findings, I would strongly recommend to route storm waters from
urban or rural areas over a strip of meadows or grass so that nitrogen contamination to
groundwater can be minimized.
The amount of Nitrogen leaching estimated by GRASIM varies with change in input
parameter values. The results found are realistic. However, caution must be maintained
for using right parameter values. As shown in this project the amount of leaching
estimated is sensitive to 8 parameters. Thus accurate and field specific values must be
used to get more realistic N leaching estimations.
To improve the accuracy of the results, I will do a calibration of the model. Since actual
field data is indispensable for calibrate and validate the results, care should be taken to
conduct field measurements of desired outputs. Next, I effort must be given to use a more
process based, mechanistic, daily time step model for determining N loading to runoff.
This improved estimation of Nitrogen loading in runoff, when used subsequently as input
to GRASIM, can enhance the final estimation of N leaching to groundwater. That will
also help in resolving issues related to incompatibility in scales between both the models.
Also to improve predictions related to N leaching, a spatially variable component of
biomass and grazing should be incorporated into GRASIM. Also, if possible, using soil
temperature for nitrogen transformation equations, instead of air temperature, may
enhance the predictions. Pastures (or, meadows) are often a mixture of different plant
species rather than a single as assumed in this modeling effort. Since GRASIM nutrient
module is intricately related to pasture (grass) module, ability to account for a natural
multi-species meadow may subsequently lead to better nitrogen leaching predictions.
19
As a further work, leaching of nitrate in grass and forested buffers should be carried out,
both individually and in combination. This will enable us to evaluate the effectiveness
each of the options to minimize N leaching beyond root zone. In the next step, impact of
land use change on other NPS pollutant loading to groundwater (such as phosphorous,
zinc etc.) should be studied to develop a comprehensive prediction and management tool
for protecting groundwater from pollutions resulting from land use.
References
Alley, W. M., Healy, R. W., LaBaugh, J. W., and Reilly, T.E. 2002. Flow and storage in
groundwater systems, Science, 296(5575), pp. 1985-1990.
Baird, C., and M. Jennings, 1996. Characterization of Non Point Sources and Loadings
to the Corpus Christi Bay National Estuary Program Study Area, Texas Natural
Resource Conservation Commission (TNRCC), Auston Texas, USA.
Bhaduri, B., Harbor, J., Engel, B., and Grove, M. 2000. Assessing watershed-scale, longterm hydrologic impacts of land-use change using a GIS-NPS model.
Environmental Management, 26(6), pp. 643-658.
Boyce, J. S., Muir, J., Edwards, A. P., Seim, E. C., and Olson, R. A. 1976. Geologic
nitrogen in Pleistocene loess of Nebraska. Journal of Environmental Quality, v. 5,
no. 1, pp. 93–96
Fletcher, D.A. 1991. A National Perspective. In: Follet, R.F., Keeny, D.R., and Cruse,
R.M. (eds.), Managing Nitrogen for Groundwater Quality and Farm Profitability.
Soil Science Society of America, Inc., Madison, WI. pp. 9-18.
Goudie, A. 1990. The Human Impact on Natural Environment, 3rd Ed. The MIT Press,
Cambridge, New Jersey.
Hallberg, G. R. 1986. From hoes to herbicides—agriculture and groundwater quality:
Journal Soil and Water Conservation, v. 41, pp. 357–364
Harter, T., Davis, H., and Mayer, R. 2002. Shallow groundwater quality on dairy farms
with irrigated forage crops. Journal of Contaminant Hydrology, 55, pp. 287-315.
Howard, K.W.F. 1997. Impacts of urban development on groundwater, in E. Eyles, (eds.)
Environmental Geology of Urban Areas, Special Publication of the Geological
Association of Canada, Geotext No. 3: pp. 93-104
Hudak, P. F. 2000. Regional Trends in nitrate content of Texas groundwater. Journal of
Contaminant Hydrology, 228, pp. 37-47.
Keeney, D. 1986. Sources of nitrate to ground water: CRC Critical Review
Environmental Control, v. 16, pp. 257–304
20
Lerner, D. N., and Wakida, F.T. 1999. Identifying, quantifying and managing sources of
non-agricultural nitrates in UK groundwater. Proc. IAWQ International
Conference on Diffuse Pollution, Perth, Australia, May 1999. Published by
CSIRO, Perth, ISBN 0643063544, 384-394.
Mohtar, R.H., Buckmaster, D.R., and Fales, S.L. 1997a. A grazing simulation model:
GRASIM, A: Model development. Transactions of the ASAE, v. 40(5), pp. 1483–
1493.
Mohtar, R.H., Jabro, J.D., and Buckmaster, D.R. 1997b. A grazing simulation model:
GRASIM, B: Field testing. Transactions of the ASAE, v. 40(5), pp. 1495–1500.
Ni, G., Jia, Y., Kinouchi, T., Tojima, K., Yoshitani, J., and Tanaka T. 2001. Monitoring
and modeling of urban impacts on groundwater in the Yata river watershed. IAHR
2000-Manuscript, pp.1-6.
Pandey, S., Lim, K.J., Harbor, J., and Engel, B. 2001. Assessing the long-term hydrologic
impact of land use change – A practical Geographic Information System (GIS)
based approach . In: Singh, R. (ed.), Urban Sustainability in the Context of Global
Change. Science Publishers, Inc., Enfield, New Hampshire. pp. 247-259.
Pierce, F.J., Shaffer, M.J., and Halvorson, A.D. 1991. Screening procedure for
estimating potentially leachable Nitrate-Nitrogen below root zone. In: Follet, R.F.,
Keeny, D.R., and Cruse, R.M. (eds.), Managing Nitrogen for Groundwater
Quality and Farm Profitability. Soil Science Society of America, Inc., Madison,
WI. pp. 259-283.
U.S. Environmental Protection Agency. 1987. Improved protection of water resources
from long-term and cumulative pollution: Prevention of ground-water
contamination in the United States. Office of Ground-water Protection,
Washington, DC.
U.S. Soil Conservation Service (SCS). 1986. Technical Release 55: Urban Hydrology for
Small Watersheds. Aavailable on the web at:
ftp://ftp.wcc.nrcs.usda.gov/downloads/hydrology_hydraulics/tr55/tr55.pdf
Zhai, T., Mohtar, R.H., Chen, X., and Engel. B. A. 1999. Optimization of pasture system
with grazing simulation model (GRASIM). ASAE Meeting Presentation, Toronto,
Canada, Paper No. 99-3091.