Modeling Nitrates and Phosphates in Agricultural
Watersheds with the Soil and Water Assessment Tool
Randolph B. Flay
Abstract
The application of nitrogen (N) and phosphorus (P) to agricultural land rose nearly 10fold from 1945 to 1985 in California (Alexander and Smith, 1990). Increased N and P
application on the land has enlarged N and P burdens to surface- and ground-waters through
runoff and leaching. The presence of these nutrients can lead to the eutrophication of surface
waters and have adverse health effects when occurring in drinking water supplies (Dubrovsky,
Kratzer et al., 1995). In order to better understand N and P dynamics in agricultural basins, we
examined the ability of the Soil and Water Assessment Tool (SWAT) to model the effect of
changing land use patterns and practices on N and P loads to surface- and ground-waters. This
article reviews the development of the SWAT model, discusses its applications to date, identifies
the limitations and assumptions of physically-based modeling, and demonstrates the applicability
of SWAT to model N and P transport processes under two hypothetical land management
scenarios.
The limitations of SWAT identified in the literature are those inherent in most physicallybased models. Several issues complicate the successful application of SWAT to N and P
modeling, including: (1) the sensitivity of streamflow to the runoff curve number (CN) (King,
Arnold et al., 1999), (2) determination of the baseflow component of streamflow (Arnold and
Allen, 1999), (3) nonlinear relationships between hydrologic response (outputs) and hydrologic
features (inputs), (4) scale-effects of aggregation (FitzHugh and Mackay, 2000), and (5) the
required calibration of numerous model parameters. Despite these obstacles, there have been
demonstrated successes of SWAT in modeling basin hydrologic budgets (Arnold and Allen,
1996), long-term streamflow, sediment yield (FitzHugh and Mackay, 2000), and pesticide
transport (Rekolainen, Gouy et al., 2000). Although the analysis performed in this article was
not exhaustive, initial results from our two land management scenarios showed significant
differences in N, P, and sediment transport between the two land management scenarios. With
the introduction of grazing and a 60 kg/ha-yr application of fertilizer (41 kg/ha-yr as N, 11
kg/ha-yr as P), NO3 and sediment yield in runoff increased by 0.09 kg/ha (26%) and 2.8 metric
tons/ha (67%), respectively. NO3 and P leached to the shallow aquifer increased 0.23 kg/ha
(46%) and 1.44 kg/ha (1400%), respectively. While these modeling exercises in SWAT should
include ample sensitivity analysis, their application to developing best management practices to
reduce non-point source pollution in watersheds under various land uses shows promise (U.S.
EPA, 1997).
1. Introduction
Nutrients are an important water quality concern in the U.S., leading to the eutrophication
of surface waters and posing a significant health threat to infants due to methemoglobinemia
(Battaglin and Goolsby, 1994; Puckett, 1994; Dubrovsky, Kratzer et al., 1995; Nolan and Ruddy,
1996; Smith, Alexander et al., 2000). Nitrogen (N) and phosphorus (P) are the two most
important nutrients, originating largely from inorganic fertilizers, animal manure, disinfectants,
-1-
and surfactants. Agricultural activities are the largest source of nutrients. In California, fertilizer
application rates of N are increasing and quite high in comparison to other states (Figure 1).
Similar trends are seen in P applications, stemming from fertilization and industrial discharges of
P-rich disinfectants and surfactants. These increases in application rates have translated directly
into increases in groundwater and surface water concentrations (Dubrovsky, Kratzer et al., 1995).
In California, section 303(d) of the federal Clean Water Act requires the establishment of
Total Maximum Daily Loads (TMDLs) for waters of the U.S. determined to be impaired (State
Water Resources Control Board, 1999). Nutrients such as N and P are regulated under the Act
and the California State Water Resources Control Board has identified dozens of rivers, streams,
estuaries, and lakes as affected by excessive nutrients. This will require TMDL establishment
and management measures to mitigate nutrient contamination. California’s list of impaired
waters was finalized in May of 1999 and a plan has been authored to present ways of meeting
U.S. EPA’s TMDL requirements. In the plan, the State Water Resources Control Board has
adopted several management measures for nutrients (State Water Resources Control Board,
Central Valley Regional Water Quality Control Board et al., 2000).
Trends in Nitrogen Fertilizer Application in the Top 5 California Counties (kg/yr)
90,000,000
80,000,000
Nitrogen Fertilizer Applied (kg/yr)
70,000,000
60,000,000
Fresno
Kern
Tulare
Kings
Imperial
50,000,000
40,000,000
30,000,000
20,000,000
10,000,000
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
Year
Figure 1: Trends in N Fertilizer Application in California
Physically-based models such as SWAT can help achieve several of these management
measures by: (1) determining crop nutrient budgets; (2) identifying the types, amounts, and
timing of nutrients; and (3) elucidating hazards to the site and adjacent environment, such as
nutrient quantity in runoff. The purpose of this paper is to highlight the development and major
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applications of SWAT in different settings for different purposes, to discuss the components of
SWAT, and to display the model’s input requirements, outputs, and application to N and P
modeling.
2. The Development of SWAT
SWAT is a physically-based watershed model that is integrated into ArcView geographic
information systems (GIS) software as an extension. It was developed by scientists at the
USDA’s Agricultural Research Service (ARS) in the early 1990s and is a combination of earlier
models such as the Erosion Productivity Impact Calculator (EPIC) and Groundwater Loading
Effects of Agricultural Management Systems (GLEAMS).
2.1. EPIC
In the early 1980's teams of USDA Agricultural Research Service (ARS), Soil
Conservation Service (SCS), and Economic Research Service (ERS) scientists developed EPIC
to quantify the costs of soil erosion and control in the U.S. (Williams, Dyke et al., 1983;
Williams, 1998). EPIC is designed to: (1) simulate relevant biophysical processes
simultaneously; (2) use available input data; (3) model cropping systems for long time periods;
(4) apply to a wide range of soils, climates and crops; and (5) determine the effects of
management on soil erosion and productivity. EPIC uses a daily time step to simulate weather,
hydrology, soil temperature, erosion-sedimentation, nutrient cycling, tillage, crop management
and growth, pesticide and nutrient movement with water and sediment, and field-scale costs and
returns. EPIC is a field-scale model and assumes homogeneous soil, cropping, irrigation, and
weather.
2.2. GLEAMS
GLEAMS is a continuous simulation, field-scale model, which was developed as an
extension of the Chemicals, Runoff and Erosion from Agricultural Management Systems
(CREAMS) model (Knisel and Davis, 1999). In GLEAMS, a field can be divided into segments
and each is assumed to have homogeneous land use, soils, and precipitation. It consists of four
major components: hydrology, erosion/sediment yield, pesticide transport, and nutrients.
GLEAMS was developed to evaluate the impact of management practices on potential pesticide
and nutrient leaching within, through, and below the root zone. It also estimates surface runoff
and sediment losses from the field. GLEAMS was not designed to be an absolute predictor of
pesticide or nutrient loading. It is a tool for comparative analysis of complex pesticide chemistry,
soil properties, and climate. GLEAMS can also be used to assess the effect of farm level
management decisions on water quality.
Some of the features of GLEAMS include: (1) automatic irrigation, manual irrigation,
and chemigation options; (2) a comprehensive erosion/sediment yield component based on field
topography; (3) channel erosion; and (4) evapotranspiration and canopy interception modules,
allowing for the simulation of management alternatives in forested areas.
2.3. SWAT
CREAMS (Knisel, 1980), GREAMS (Leonard, Knisel et al., 1987), and EPIC (Williams,
Dyke et al., 1983) contributed to the development of SWAT. SWAT differs from its
predecessors given the addition of: (1) a method of performing simultaneous computations on
several sub-basins to predict basin water yield; (2) a groundwater or return flow component; (3)
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a reservoir storage component to calculate the effect of reservoirs on water and sediment yield;
(4) a weather simulation model incorporating data for rainfall, solar radiation, and temperature to
facilitate long-term simulations and provide temporally and spatially representative weather; (5)
an improved method for predicting peak runoff rates; (6) an EPIC crop growth component to
account for annual variation in growth; (7) a simple flood routing component; (8) a sediment
transport component to simulate sediment movement through ponds, reservoirs, streams and
valleys; and (9) calculations of transmission losses (Neitsch, Arnold et al., 2001).
3. Overview of the SWAT Model Components
SWAT is a physically-based model, requiring extensive input data about weather, soil
properties, topography, vegetation, and land management practices occurring within the
watershed (Neitsch, Arnold et al., 2001). SWAT models water flow, sediment transport,
crop/vegetative growth, and nutrient cycling, thereby permitting users to: (1) model watersheds
with little monitoring data (water flows are modeled directly from climate files without the need
for gauging stations) and (2) assess predictive scenarios using alternative input data, such as
climate, land use practices, and land cover, on nutrient cycling, water movement, water quality,
and other outputs. Although data intensive, the integration of SWAT into GIS allows for the
acceptance of readily available datasets from governmental sources on climate, soil, topography,
and land use. Model results are aimed at assessing the long-term effects of land use decisions,
not of short-term events such as flooding.
Within the land phase of the hydrologic cycle of SWAT, climate conditions in the
watershed provide much of the moisture and energy inputs that control the important function of
water balance. Furthermore, they determine the relative importance of different components of
the hydrologic cycle within the watershed. SWAT requires several climatic variables such as
daily precipitation, maximum/minimum air temperature, solar radiation, wind speed, and relative
humidity. These data can be directly input into the model from daily observations or they can be
generated from average monthly values with the weather generator. The weather generator is a
program used to create weather for user-specified time periods from a core set of basic weather
properties. While the generated data is statistically similar to the weather station(s) from which
it is derived, it is does not represent actual storm events and other multi-year phenomena.
Precipitation, air temperature and solar radiation, wind speed, and relative humidity all have
separate methods for creating daily values from statistical data sets representative of observed
conditions.
Soil temperature has a significant effect on water movement (at temperatures
approaching 0 degrees C) and the decay rate of residues in the soil. Therefore, daily average soil
temperature needs to be calculated for each soil layer. The temperature at the soil surface is
typically a function of snow cover, plant and residue cover, the bare soil surface temperature, and
the previous day’s soil surface temperature.
Additional components such as canopy storage, infiltration, redistribution within the
snow profile, evapotranspiration, lateral subsurface flow, surface runoff, ponding effects,
tributary channels, and return flow are also integrated into the hydrological component.
Land cover and plant growth are simulated in the model with potential growth, potential
actual evapotranspiration, nutrient uptake, and growth constraints. Erosion and sediment yield
are estimated for each hydrologic response unit with a Modified Universal Soil Loss Equation
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(MUSLE, see Appendix 2 for an overview)(Williams and Berndt, 1977). SWAT monitors the
movement and transformation of several forms of nitrogen and phosphorus in the watershed (see
Appendix 3 for an overview). SWAT does not simulate stress on the plant from pests;
however, pesticides may be applied to a hydrologic response unit to study the transport and fate.
The approach used in SWAT to simulate a pesticide adsorbed, transported by runoff, movement
through the soil profile, and in solution is similar to that of the GLEAMS model. The movement
of the pesticide is controlled by its solubility, degradation half-life, and soil organic carbon
adsorption coefficient. Agricultural decision-makers can benefit from the ability of SWAT to
simulate the effects of various agricultural practices, such as: (1) the beginning and end of the
growing season; (2) the timing and amounts of fertilizer, pesticide and irrigation applications;
and (3) the timing of field tillage operations.
Within the routing phase of the hydrologic cycle issues such as flood routing, sediment
routing, nutrient routing, and channel pesticide routing become important. Routing in the
reservoir is a function of inflow, outflow, rainfall the surface, evaporation, and seepage from
reservoir bottoms. Important issues in reservoirs are outflow, sediment routing, reservoir
nutrients, and reservoir pesticides.
4. Limitations and Applications of the SWAT Model
SWAT has been applied to solve water management problems in a variety of settings.
Some applications include basin water balance calculations, sediment transport, and streamaquifer interactions in overdrafted basins.
These applications have largely focused
geographically on agricultural areas of United States such as Kansas, Nebraska, and California.
The following cases discuss the results, limitations, and areas of future work of several
applications of SWAT.
4.1. Water Balance
An early application of SWAT sought to compare the results of SWAT to historical
streamflow and groundwater flow on three Illinois watersheds (Arnold and Allen, 1996). The
purpose was to design a model (SWAT) that incorporated of the relevant processes
(precipitation, streamflow, groundwater flow, evapotranspiration, groundwater storage) and was
easy to calibrate. The authors found that SWAT was able to simulate all the components of the
hydrologic budget within acceptable limits on both an annual and monthly time step.
The common watershed hydrology problem of partitioning streamflow between runoff
and base flow was addressed by two later papers (Arnold and Allen, 1999; Arnold, Muttiah et al.,
2000). In these papers, the authors presented improved methods of estimating the base flow
component of streamflow by using a continuous water balance model and an automated
technique to separate base flow and recharge from daily stream flow. After calibration the model
had a tendency to over predict spring flows but the overall timing and magnitude of the peaks
and recessions matched.
4.2. Surface-water, groundwater, and stream-aquifer interactions
(Sophocleous, Koelliker et al., 1999; Sophecleous and Perkins, 2000) have developed a
model to combine SWAT with the Modular Three-dimensional Finite-Difference Ground-water
Flow Model (MODFLOW) for the purpose of developing and calibrating a comprehensive
computer model capable of simulating (1) surface-water, ground-water, and stream-aquifer
-5-
interactions for the Rattlesnake Creek basin, Kansas, and (2) the effects of management
decisions on water yields, such as the impact of water rights and agricultural and other land uses
on water resources (McDonald and Harbaugh, 1988; Arnold, Allen et al., 1993). The two
models were coupled together with the addition of two subroutines that are called by SWAT and
MODFLOW at the end of each time step to pass data back and forth.
(Sophocleous, Koelliker et al., 1999; Sophecleous and Perkins, 2000) are similar studies,
identifying the limitations, parameter uncertainties, and sensitivity of SWAT. SWAT model
breaks up a watershed into a number of sub-basins as determined by the user. Within any subbasin, several hydrologic features are assumed to be homogenous and are held constant, such as
channel width, roughness, and hydraulic conductivity. Most transport processes are modeled by
using a mean distance of overland flow to the stream. The effect of using a mean distance is to
geometrically distort the shape of the watershed. In watersheds with little topography, typical of
agricultural areas, many parts of the basin might not contribute to streamflow given appreciable
depression storage area. To represent these depressions, SWAT uses a pond function to trap
water, typically increasing groundwater infiltration and decreasing surface flow.
Limitations identified in MODFLOW include the assumption of a rectangular crosssection in streams; however, this is been tested by previous works of the author and found only
minimal effects on streams with flat or parabolic channel beds (Sophocleous, Koelliker et al.,
1999; Sophecleous and Perkins, 2000).
With SWAT and MODFLOW coupled, the impact of parameter uncertainty was tested
on pond parameters, the runoff curve number (CN), soil water storage characteristics, the type of
crop, and different water stress factors for irrigation applications (irrigation waters are usually
scheduled to apply given a certain osmotic stress factor for plants). (Sophocleous, Koelliker et
al., 1999) showed that varying pond coverage in the basin from 2.5 to 7.5 percent increased pond
seepage for the aquifer by only three percent. A second test examined the CN partitioning
between surface runoff and seepage under various land cover (Sophocleous, Koelliker et al.,
1999). Increasing the CN from 65 to 70 increased simulated runoff by 44 percent and decreased
recharge to the aquifer by one percent. Thus, changes in CN had a substantial effect on surface
yields but little effect on total recharge. Soils with greater water holding capacities resulted in
more runoff and less recharge. Total recharge was found most sensitive to the hydraulic
conductivity of the pond bottoms and to the storage characteristics of the soil type. CN had a
great effect on surface runoff and little on total recharge.
The predicted streamflow and aquifer recharge of SWAT/MODFLOW can be readily
calibrated with stream gauging stations and groundwater levels. The model was verified by
selecting data from 1981 to 1994, calibrating the model, and comparing the simulation results to
other years of observed data not used in calibration. Except for one flood cycle from 1973 to
1974, most of the streamflows were reported to be accurately estimated (Sophocleous, Koelliker
et al., 1999).
Several tests were conducted to evaluate the validity of the model for several land use
management scenarios of interest. The scenarios involved making hypothetical changes to the
system to determine their impact on water levels and stream flows. One example of this
approach involved taking initial land use and water use conditions in 1994 with observed
climatic conditions in the past 40 years and running the model 40 years into the future. This
produced future stream hydrographs and groundwater contour maps, which predicted declining
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groundwater levels and streamflow. The uniqueness of SWAT is demonstrated by its ability to
test management scenarios, such as changing appropriated irrigation amounts, water rights
administration, and the locations of irrigation wells.
In summary, the outcome model of (Sophocleous, Koelliker et al., 1999; Sophecleous and
Perkins, 2000) proved to be: (1) physically-based to represent the region and readily accept data;
(2) capable of operating on at watershed-scale, allowing for several sub-basins to be simulated;
(3) able to accommodate topographical, land use, and management differences; (3) continuous in
time to simulate several land management practices such as cropping patterns, tillage operations,
and irrigation scenarios; (4) able to simulate long periods of time; and (5) to be calibrated
through field testing and other data sources.
4.3. Pesticides
The underlying equations of pesticide transport and fate in SWAT are the same as those
of GLEAMS and the approach taken in one article revealed several interesting caveats of
physically-based modeling. The study on which the article was based supplied identical datasets
to three different groups and asked each to simulate the movement and fate of pesticides in a
watershed (Rekolainen, Gouy et al., 2000). Each of the groups had to calibrate the model
independently, revealing significant differences in how people approached calibration and also
having a large influence on the results.
The objective of the paper was to test the performance of the GLEAMS model in
predicting soil temperature, soil moisture, water percolation, and the mobility of a tracer in
selected pesticides under varying conditions. The GLEAMS model was tested against three
observed data sets. The GLEAMS model required several data series to be simulated each year
such as precipitation, daily values for maximum and minimum air temperature, solar radiation,
wind speed, and soil temperature. Other parameters required by the model included soil texture,
hydraulic conductivity, porosity, field capacity, wilting points, pesticide degradation half-life,
and the absorption coefficient. Estimation was carried out without calibration and with
calibration (i.e. first, with strictly empirical data and second, with field data for calibration).
The results of the study showed large differences in evapotranspiration, water
percolation, soil temperature, soil moisture, bromide concentration (the tracer), and pesticide
concentrations. It was noted for evapotranspiration that soil moisture is a limiting factor
resulting in much lower evapotranspiration estimates, particularly during the growing season and
summer. Even with calibration, water percolation as simulated was significantly lower in some
cases and higher and others. Although soil moisture was observed to be in agreement between
the simulated and observed readings, certain peak conditions were predicted more extreme by the
model than observed. Soil moisture as modeled by GLEAMS varied quite a bit between users.
During wet periods GLEAMS also underestimated soil moisture values in deeper soil layers.
Additionally, direct soil moisture measurements vary extensively at the sites. Calibration based
on less frequent and highly variable observations could be quite problematic. Modeling of
pesticide concentration revealed significant variations between users. Often two- or three-fold
differences were reported. It was further noted that laboratory degradation values for pesticides
did not accurately describe degradation in the field, or simply the model does not properly
account for all processes that affect degradation.
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4.4. Other Issues of Accuracy and Model Sensitivity
The effect of spatial aggregation on SWAT was examined by (FitzHugh and Mackay,
2000). Specifically, the article addressed how the size or number of subwatersheds used to
partition the watershed affect model output and identified the most important processes
responsible for model behavior. Hydrologic response units (HRUs) were also examined as a
way of representing soil and land-cover types within a delineated watershed.
In studying sediment yield in SWAT, the authors found that: (1) streamflow is not greatly
affected by decreasing subwatershed size, largely due to a constant mean CN parameter; (2)
sediment generation decreases significantly with decreasing subwatershed size, largely
attributable to the sensitivity of the runoff term in the MUSLE equation to HRU area and other
coefficients in the MUSLE equation; and (3) outlet sediment is not greatly affected by changes in
subwatershed size (FitzHugh and Mackay, 2000).
4.5. Summary of Progress and Limitations of SWAT
The
major
shortcomings of the SWAT
model and its predecessors are:
extensive
data
input
requirements, difficulties in
selecting
appropriate
parameters to calibrate the
model, the effects of lumping
parameters arbitrarily into subbasins, subjectivity of selecting
parameter
coefficients,
limitations in simulating shortterm events (e.g. flood
routing), and the overall
complexity of the model.
The major benefits of
the model are: its applicability
to decision-making in the area
of
land
management, Figure 2: South Fork Little Panoche Creek
including: cropping patterns,
fertilizer applications, pesticide
applications, the timing and amount of irrigation, and other management decisions that can have
substantial impacts on water quality and quantity within a watershed. Despite its complexity, it
is also used by a wide variety of decision-makers and with proper testing, could be very useful
for government agencies at the state and local level that have a role in protecting water quality.
5. Modeling N and P with SWAT
5.1. South Fork Little Panoche Creek
To demonstrate some of the features of SWAT, the South Fork of Little Panoche Creek
was chosen for an N and P modeling exercise. The basin is located on the eastern slope of the
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Coast Ranges and drains into the San Joaquin Valley (Figure 2). The 110 km2 watershed (as
delineated) is predominantly rangeland, with four major soil types. For our simulation we
simply created two land-use scenarios. The first scenario was simply a baseline condition where
no grazing or fertilization took place in the watershed. The second scenario involved adding
grazing and fertilization operations.
The following steps outline the general procedure to modeling N and P in SWAT,
showing how the addition of grazing and rangeland fertilization might affect N, P, and sediment
processes.
5.2. Steps in Modeling
1. In ArcView, we converted input data (e.g. DEM, land cover/land use, soil cover, river
reach) into projected (Albers Equal Area) GRIDs.
Figure 3: Sub-basin Delineation
2. After loading SWAT in ArcView and we began the watershed configuration process.
The first part required loading a Digital Elevation Model (DEM) and selecting river reach
files to “burn” into the watershed. Watershed sub-basins are delineated and watercourses
are determined by topography but, in flat watersheds, the river reach files can improve
the accuracy of watercourse location. Lastly, we selected a masking polygon to reduce
the amount of area under study in SWAT.
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3. Next we determined the threshold size of sub-basins to be delineated in the watershed.
This requires selecting a particular threshold hectareage for sub-basin size. All subbasins exceeding this threshold size will be defined as sub-basins. We also selected the
Figure 4: Land Use/Land Cover
main watershed outlet(s).
4. After running the delineation process, we obtained the sub-basin scheme shown in
Figure 3.
5. The next step required loading appropriate land use/land cover and soil cover GRIDs.
The USGS has a very general land use classification system at 1:100,000 scale for the
entire United States and the California Department of Water Resources (DWR) performs
much more detailed surveys at 1:24,000 scale every few years (USGS and DOI, 1986;
California Department of Water Resources, 1994). We preferred the accuracy of the
DWR data, and after loading and trimming the land use/land cover GRID, we were left
with the map in Figure 4. Next, we loaded the STATSGO soil classification scheme
(Natural Resources Conservation Service, 2001). Los Banos (CA365), Quinto (CA370),
Vaquero (CA376), and Garretson (CA573) soil series occur in our study area and are
shown in Figure 5.
6. Weather data was then supplied to SWAT. This can be done in two general ways: (1)
from statistical data about local weather conditions (e.g. monthly mean values,
maximum, minimum, etc, or from (2) daily observations. From the weather data SWAT
will estimate solar radiation, evapotranspiration (with land cover data), and other
parameters. We elected to use the first method to avoid the time-consuming process of
formatting and inputting daily observations.
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7. Land management data is the final information that must be supplied. SWAT is geared
towards agricultural land use, and has growth parameters for more than 100 crop types.
Grazing, irrigation, fertilization, and other operations can also be included.
8. With the data in the model, SWAT generate all the input files for the model (converts all
the ArcView based files to text files to run the program).
9. Last we determined the time period for which the model should be run. We chose to run
the model for ten years, to reduce the influence of the initial conditions on model results.
10. Once the run completes, we were able to read, map, and graph the results.
Figure 5: MUID Soil Code
5.3. Results and Discussion of the Little Panoche Creek Simulation
In our simulation we simply chose two land management patterns to examine the effect
of grazing and fertilization on N, P, and sediment loads to streams and groundwater in the
watershed. The first scenario was to assume rangeland vegetation with no grazing or fertilizer
inputs. The results of this run are in Appendix 1.A. The second scenario was to add cattle
grazing and to fertilize the rangeland with 60 kg/ha (41 kg N, 11 kg P). Results for the second
scenario can be found in Appendix 1.B. Although this is simply a hypothetical scenario to
demonstrate how the model functions, P and N in surface and sub-surface flow did increase
under the fertilization scenario, along with sediment loading. Table 1 summarizes the results for
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several output variables in SWAT. Studies such as this should examine the sensitivity of output
values to changes in model parameters and calibrate the model to available data.
Table 1: Results of the South-Fork, Little Panoche Creek Simulation
Scenario 1: No
Grazing or
Fertilization
Scenario 2:
Grazing and
Fertilization
Change
(%)
Units
Surface Runoff
35.3
34.8
-0.5
(-1.4%)
MM
Total Sediment
Loading
4.2
7.0
+2.8
(+67%)
T/HA
Organic N
3.0
5.3
+2.3
(+77%)
KG/HA
Organic P
0.36
0.77
+0.41
(+110%)
KG/HA
NO3 Yield in
Surface Flow
0.08
0.11
+0.03
(+38%)
KG/HA
NO3 Yield in
Subsurface Flow
0.27
0.33
+0.06
(+22%)
KG/HA
NO3 Leached to
Shallow Aquifer
0.50
0.73
+0.23
(+46%)
KG/HA
P Leached to
Shallow Aquifer
0.10
1.54
+1.44
(1400%)
KG/HA
N Fertilizer
Applied
0.00
41.01
+41.01
KG/HA
P Fertilizer
Applied
0.00
11.28
+11.28
KG/HA
6. Conclusions
The publications reviewed and the included SWAT example point to several issues that
should be considered when using physically-based computer models such as SWAT. First,
modeling is best undertaken when field data is available for model calibration (Philip, 1991).
SWAT provides easy means to calibrate base-flow and transport processes. Second, analysis of
parameter uncertainty and sensitivity is key to the successful use of SWAT and helps determine
which input data is most important to the outcome. To that end, SWAT and computer models
provide an efficient means to thoroughly test theoretical equations, given that computer
simulations are quite cheap in comparison to field studies. However, any study should reflect on
previous publications that have identified the most sensitive parameters, such as CN, and
ascertain the extent parameter sensitivity on the outcome. Lastly, lumped, physically-based
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models such as SWAT are more appropriate for assessing long-term trends, given sensitivity to
climate and difficulties with predicting or simulating extreme events.
Despite these obstacles, the ability of the SWAT model to accept readily available data
on weather, land use, soil type, reservoir activity, and other conditions makes it relatively easy to
calibrate for large watersheds. SWAT also makes a very important connection between land use
activities and watershed processes, an indispensable tool in land management planning for water
resource protection. As efforts continue under the Clean Water Act to protect and restore water
quality and reduce nutrients, physically-based models that have predictive capability will be
helpful in guiding decision-making.
References
Alexander, R. B. and R. A. Smith (1990). County-level Estimates of Nitrogen and Phosphorus
Fertilizer Use in the United States, 1945-1985. Reston, U.S. Geological Survey.
Arnold, J. G. and P. M. Allen (1996). “Estimating Hydrologic Budgets For Three Illinois
Watersheds.” Journal of Hydrology 176(1-4): 57-77.
Arnold, J. G. and P. M. Allen (1999). “Automated methods for estimating baseflow and ground
water recharge from streamflow records.” Journal of the American Water Resources
Association 35(2): 411-424.
Arnold, J. G., P. M. Allen, et al. (1993). “A comprehensive surface-groundwater flow model.”
Journal of Hydrology 142(1-4): 47-69.
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California Department of Water Resources (1994). Fresno County Land Use Survey Data,
California Department of Water Resources. 2001.
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methods on Goodwin Creek Watershed using SWAT.” Transactions of the American Society
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from A*gricultural M*anagement S*ystems. Washington, Dept. of Agriculture Science and
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U.S. Department of Agriculture. 2001.
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Neitsch, S. L., J. G. Arnold, et al. (2001). Soil and Water Assessment Tool Theoretical
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Service.
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States. Reston, U.S. Geological Survey.
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behaviour in soil with the GLEAMS model.” Agricultural Water Management 44(1-3): 201224.
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Managing Nutrients and Pathogens from Animal Agriculture, Camp Hill, Pennsylvania, U.S.
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and groundwater models in Kansas.” Hydrology 236: 185-201.
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W. K. Lauenroth, G. V. Skogerboe and M. Flug.
- 14 -
Appendix 1: Sample SWAT Output
A. Baseline Scenario with No Grazing or Fertilization (selected output)
Some values were zero because they were not activated (e.g. pond functions)
SWAT
Feb.'01 VERSION2000
General Input/Output section (file.cio):
Sun Nov 04 13:44:20 2001 AVSW
AVE ANNUAL BASIN VALUES
PRECIP =
424.0 MM
SNOW FALL =
3.42 MM
SNOW MELT =
3.42 MM
SUBLIMATION =
0.00 MM
SURFACE RUNOFF Q =
35.28 MM
LATERAL SOIL Q =
10.43 MM
TILE Q =
0.00 MM
GROUNDWATER (SHAL AQ) Q =
20.93 MM
REVAP (SHAL AQ => SOIL/PLANTS) =
2.47 MM
DEEP AQ RECHARGE =
0.94 MM
TOTAL AQ RECHARGE =
23.73 MM
TOTAL WATER YLD =
66.06 MM
PERCOLATION OUT OF SOIL =
23.85 MM
ET =
344.5 MM
PET =
1326.7MM
TRANSMISSION LOSSES =
0.58 MM
TOTAL SEDIMENT LOADING =
4.188 T/HA
POND BUDGET
EVAPORATION =
0.000 MM
SEEPAGE =
0.000 MM
RAINFALL ON POOL =
0.000 MM
INFLOW
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
OUTFLOW
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
RESERVOIR BUDGET
EVAPORATION =
0.000 MM
SEEPAGE =
0.000 MM
RAINFALL ON RESERVOIR =
0.000 MM
INFLOW
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
OUTFLOW
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
YIELD LOSS FROM PONDS
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
YIELD LOSS FROM RESERVOIRS
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
1
SWAT Feb.'01 VERSION2000
General Input/Output section (file.cio):
Sun Nov 04 13:44:20 2001 AVSW
AVE ANNUAL BASIN VALUES
NUTRIENTS
ORGANIC N =
2.969 (KG/HA)
ORGANIC P =
0.361 (KG/HA)
NO3 YIELD (SQ) =
0.083 (KG/HA)
NO3 YIELD (SSQ) =
0.269 (KG/HA)
SOL P YIELD =
0.006 (KG/HA)
NO3 LEACHED =
0.503 (KG/HA)
P LEACHED =
0.102 (KG/HA)
- 15 -
N UPTAKE =
6.567 (KG/HA)
P UPTAKE =
1.127 (KG/HA)
ACTIVE TO SOLUTION P FLOW =
1.153 (KG/HA)
ACTIVE TO STABLE P FLOW =
0.941 (KG/HA)
N FERTILIZER APPLIED =
0.000 (KG/HA)
P FERTILIZER APPLIED =
0.000 (KG/HA)
N FIXATION =
0.000 (KG/HA)
DENITRIFICATION =
15.749 (KG/HA)
HUMUS MIN ON ACTIVE ORG N =
14.087 (KG/HA)
ACTIVE TO STABLE ORG N =
-8.125 (KG/HA)
HUMUS MIN ON ACTIVE ORG P =
2.399 (KG/HA)
MIN FROM FRESH ORG N =
0.036 (KG/HA)
MIN FROM FRESH ORG P =
0.006 (KG/HA)
NO3 IN RAINFALL =
4.240 (KG/HA)
INITIAL NO3 IN SOIL =
50.809 (KG/HA)
FINAL NO3 IN SOIL =
2.625 (KG/HA)
INITIAL ORG N IN SOIL =
4153.972 (KG/HA)
FINAL ORG N IN SOIL =
3983.810 (KG/HA)
INITIAL MIN P IN SOIL =
912.573 (KG/HA)
FINAL MIN P IN SOIL =
923.216 (KG/HA)
INITIAL ORG P IN SOIL =
508.862 (KG/HA)
FINAL ORG P IN SOIL =
481.334 (KG/HA)
NO3 IN FERT =
0.000 (KG/HA)
AMMONIA IN FERT =
0.000 (KG/HA)
ORG N IN FERT =
0.000 (KG/HA)
MINERAL P IN FERT =
0.000 (KG/HA)
ORG P IN FERT =
0.000 (KG/HA)
N REMOVED IN YIELD =
6.495 (KG/HA)
P REMOVED IN YIELD =
1.114 (KG/HA)
AMMONIA VOLATILIZATION =
0.000 (KG/HA)
AMMONIA NITRIFICATION =
0.000 (KG/HA)
NO3 EVAP-LAYER 2 TO 1 =
4.798
B. Grazing and Fertilization Scenario (selected output)
SWAT
Feb.'01 VERSION2000
General Input/Output section (file.cio):
Sun Nov 04 14:41:24 2001 AVSW
AVE ANNUAL BASIN VALUES
PRECIP =
422.7 MM
SNOW FALL =
3.43 MM
SNOW MELT =
3.43 MM
SUBLIMATION =
0.00 MM
SURFACE RUNOFF Q =
34.79 MM
LATERAL SOIL Q =
10.36 MM
TILE Q =
0.00 MM
GROUNDWATER (SHAL AQ) Q =
20.61 MM
REVAP (SHAL AQ => SOIL/PLANTS) =
2.43 MM
DEEP AQ RECHARGE =
0.92 MM
TOTAL AQ RECHARGE =
23.37 MM
TOTAL WATER YLD =
65.20 MM
PERCOLATION OUT OF SOIL =
23.39 MM
ET =
345.4 MM
PET =
1327.3MM
TRANSMISSION LOSSES =
0.56 MM
TOTAL SEDIMENT LOADING =
6.986 T/HA
POND BUDGET
EVAPORATION =
0.000 MM
SEEPAGE =
0.000 MM
RAINFALL ON POOL =
0.000 MM
INFLOW
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
OUTFLOW
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
RESERVOIR BUDGET
EVAPORATION =
0.000 MM
- 16 -
SEEPAGE =
0.000 MM
RAINFALL ON RESERVOIR =
0.000 MM
INFLOW
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
OUTFLOW
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
YIELD LOSS FROM PONDS
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
YIELD LOSS FROM RESERVOIRS
WATER =
0.000 MM
SEDIMENT =
0.000 T/HA
1
SWAT
Feb.'01 VERSION2000
General Input/Output section (file.cio):
Sun Nov 04 14:41:24 2001 AVSW
AVE ANNUAL BASIN VALUES
NUTRIENTS
ORGANIC N =
5.342 (KG/HA)
ORGANIC P =
0.768 (KG/HA)
NO3 YIELD (SQ) =
0.105 (KG/HA)
NO3 YIELD (SSQ) =
0.328 (KG/HA)
SOL P YIELD =
0.064 (KG/HA)
NO3 LEACHED =
0.726 (KG/HA)
P LEACHED =
1.539 (KG/HA)
N UPTAKE =
16.265 (KG/HA)
P UPTAKE =
1.733 (KG/HA)
ACTIVE TO SOLUTION P FLOW =
10.346 (KG/HA)
ACTIVE TO STABLE P FLOW =
7.538 (KG/HA)
N FERTILIZER APPLIED =
41.014 (KG/HA)
P FERTILIZER APPLIED =
11.279 (KG/HA)
N FIXATION =
0.000 (KG/HA)
DENITRIFICATION =
37.715 (KG/HA)
HUMUS MIN ON ACTIVE ORG N =
18.100 (KG/HA)
ACTIVE TO STABLE ORG N =
-6.541 (KG/HA)
HUMUS MIN ON ACTIVE ORG P =
3.272 (KG/HA)
MIN FROM FRESH ORG N =
33.740 (KG/HA)
MIN FROM FRESH ORG P =
7.269 (KG/HA)
NO3 IN RAINFALL =
4.227 (KG/HA)
INITIAL NO3 IN SOIL =
50.809 (KG/HA)
FINAL NO3 IN SOIL =
2.835 (KG/HA)
INITIAL ORG N IN SOIL =
4153.972 (KG/HA)
FINAL ORG N IN SOIL =
3988.177 (KG/HA)
INITIAL MIN P IN SOIL =
912.573 (KG/HA)
FINAL MIN P IN SOIL =
996.006 (KG/HA)
INITIAL ORG P IN SOIL =
508.862 (KG/HA)
FINAL ORG P IN SOIL =
483.231 (KG/HA)
NO3 IN FERT =
0.103 (KG/HA)
AMMONIA IN FERT =
10.151 (KG/HA)
ORG N IN FERT =
30.761 (KG/HA)
MINERAL P IN FERT =
4.101 (KG/HA)
ORG P IN FERT =
7.178 (KG/HA)
N REMOVED IN YIELD =
10.080 (KG/HA)
P REMOVED IN YIELD =
1.376 (KG/HA)
AMMONIA VOLATILIZATION =
9.235 (KG/HA)
AMMONIA NITRIFICATION =
0.916 (KG/HA)
NO3 EVAP-LAYER 2 TO 1 =
10.634
- 17 -
Appendix 2: Sediment Transport and the Modified Universal Soil Loss
Equation (MUSLE) in SWAT
SWAT uses the Modified Universal Soil Loss Equation (MUSLE) (Williams and Berndt,
1977) to predict sediment generation (adopted from (FitzHugh and Mackay, 2000)).
SWAT calculates channel sediment transport using the following equation:
T=a×Vb
(
1)
where T, is the transport capacity (ton/m3); V, is flow velocity (m/s); and a and b, are
constants. Depending on whether the amount of sediment being carried is above or below
transport capacity, SWAT either deposits excess sediment or re-entrains sediment through
channel erosion. Flow velocity is computed as:
(
2)
where F, is the flow volume (m3/s); w, is channel width (m); and d, is depth of flow (m).
For flows below bankfull depth, depth of flow is calculated using Manning's equation, assuming
that channel width is much greater than depth:
(
3)
where n, is the Manning's roughness coefficient for the channel; and cs, is channel slope
(m/m). For flows above bankfull depth, depth of flow is equal to channel depth.
The MUSLE equation used to estimate sediment generation is as follows:
Y=11.8(Q×pr)0.56K×C×P×LS
(
4)
where Y, is the sediment generation (metric tons); Q, is volume of runoff (m3); pr, is peak
runoff rate (m3/s); K, is K-factor; C, is C-factor; P, is P-factor; and LS, is LS-factor. For each day
with rainfall and runoff, sediment generation is estimated by applying Eq. (4) for each HRU in
the watershed.
Peak runoff rate is calculated using a modified version of the Rational equation (USDASCS 1986):
(
5)
where pr, is the peak runoff rate (m3/s); q, is runoff (mm); A, is HRU area (ha); tc, is time
to concentration (h); and , is a dimensionless parameter that expresses the proportion of total
rainfall that occurs during tc. The value of is calculated as:
(
6)
- 18 -
where a1 is the fraction of rainfall that occurs during 0.5 h; tp6 and tp5 are the 10-year
frequencies of a 6 and 0.5 h rainfall, respectively, derived from Herschfield (1961); and a2 is a
constant equal to 0.242 for Dane County, Wisconsin.
SWAT computes time to concentration by summing channel time to concentration and
overland time to concentration for the HRU. Channel time is computed as:
(
7)
where ct, is the channel time to concentration (h); L, is channel length (km); n, is
Manning's roughness coefficient for the channel; A, is HRU area (km2); and cs, is channel slope
(m/m). Overland time is computed as:
(
8)
where ot, is the overland time to concentration (hours); sl, is average subwatershed slope
length (m); n, is Manning's overland roughness coefficient for the HRU; and s, is overland slope
(m/m).
- 19 -
Appendix 3: The Movement of Nutrients in SWAT
Adopted from (Neitsch, Arnold et al., 2001).
Nitrate Movement
  wmobile 
NO3ly * exp 

 (1   e * SATly 
Conc NO 3,mobile 
wmobile
wmobile  Qsurf  Qlat,ly  w perc,ly
wmobile  Qlat,ly  w perc,ly
NO3 surf   NO 3 * conc NO 3,mobile * Qsurf
NO3lat,ly   NO 3 * conc NO 3,mobile * Qlat,ly
NO3lat,ly  conc NO 3,mobile * Qlat.ly
NO3 perc,ly  conc NO 3,mobile * w perc,ly
Organic N in Surface Runoff
orgN surf  0.001 * concorgN *
conc orgN  100 *
sed
*  N :sed
area hru
(orgN frsh, surf  orgN sta, surf  orgN act , surf )
 b * depthsurf
 N:sed  0.78 * (concsed ,surq ) 0.2468
concsed ,surq 
sed
10 * area hru * Qsurf
Soluble Phosphorus Movement
Psurf 
Psolution, surf * Qsurf
 b * depthsurf * k d , surf
- 20 -
Organic and Mineral P Attached to Sediment in Surface Runoff
sedPsurf  0.001 * conc sedP *
conc sedP  100 *
sed
*  P:sed
area hru
(min Pact , surf  min Psta, surf  orgPhum, surf  orgPfrsh, surf )
 b * depthsurf
 P:sed  0.78 * (concsed ,surq ) 0.2468
concsed ,surq 
sed
10 * area hru * Qsurf
Nutrient Lag in Surface Runoff and Lateral Flow

  surlag  
NO3 surf  ( NO3surf  NO3 surstor,i 1 ) * 1  exp 
 
t
 conc  


 1 
NO3lat  ( NO3lat  NO3latstor,i 1 ) * 1  exp 
 
 TTlat  


  surlag  
  orgN stor,i 1 ) * 1  exp 
orgN surf  (orgN surf
 

t
conc




  surlag  
  Pstor,i 1 )1  exp 
Psurf  ( Psurf
 

t
 conc  


  surlag  
  sedPstor,i 1 ) * 1  exp 
sedPsurf  ( sedPsurf
 

t
 conc  

Definitions
NO3lat,ly
NO3lat
NO3latstor,i-1
NO3ly
NO3perc,ly
NO3surf
NO3surf
Nitrate removed in lateral flow from a layer (kg N/ha)
Amount of lateral flow nitrate generated in HRU on a given day (kg N/ha)
Lateral flow nitrate stored or lagged from the previous day (kg N/ha)
Amount of nitrate in the layer (kg N/ha)
Nitrate moved to the underlying layer by percolation (kg N/ha)
Nitrate removed in surface runoff (kg N/ha)
Amount of surface runoff nitrate generated in HRU on a given day (kg N/ha)
- 21 -
NO3surstor,i-1
Psolution,surf
Pstor,i-1
Psurf
Psurf  
Qlat
Qsurf
SATly
TTlag
areahru
concNO3,mobile
concorgN
concsed,surq
concsedP
depthsurf
kd,surf
minPact,ly
minPsta,ly
orgNact,ly
orgNfrsh,surf
orgNsta,ly
orgNstor,i-1
orgNsurf
orgNsurf
orgPfrsh,ly
orgPhum,ly
sed
sedPstor,i-1
sedPsurf
sedPsurf
surlag
tconc
wmobile
wperc,ly
NO3
e
N:sed
P:sed
b
Surface runoff nitrate stored or lagged from the previous day (kg N/ha)
Amount of phosphorus in solution in the top 10 mm (kg P/ha)
Solution P loading stored or lagged from the previous day (kg P/ha)
Amount of soluble phosphorus lost in surface runoff (kg P/ha)
Amount of solution P loading generated in HRU on a given day (kg P/ha)
Lateral flow from soil layer (mm H2O)
Accumulated runoff or rainfall excess (mm H2O)
Saturated water content of the soil layer (mm H2O)
Lateral flow travel time (days)
HRU area (ha)
Concentration of nitrate in the mobile water for a given layer (kg N/mm H2O)
Concentration of organic nitrogen in the soil surface top 10 mm (g N/ metric ton soil)
Concentration of sediment in surface runoff (Mg sed/m 3 H2O)
Concentration of phosphorus attached to sediment in the top 10 mm (g P/ metric ton soil)
Depth of the “surface” layer (10 mm)
Phosphorus soil partitioning coefficient (m 3 /Mg)
Amount of phosphorus in the active mineral pool (kg P/ha)
Amount of phosphorus in the stable mineral pool (kg P/ha)
Nitrogen in the active organic pool (mg/kg or kg N/ha)
Nitrogen in the fresh organic pool in the top 10mm (kg N/ha)
Nitrogen in the stable organic pool (mg/kg or kg N/ha)
Surface runoff organic N stored or lagged from the previous day (kg N/ha)
Amount of organic nitrogen transport to the main channel in surface runoff (kg N/ha)
Amount of surface runoff organic N generated in HRU on a given day (kg N/ha)
Phosphorus in the fresh organic pool in layer ly (kg P/ha)
Amount of phosphorus in humic organic pool in the layer (kg P/ha)
Sediment yield on a given day (metric tons)
Sediment-attached P stored or lagged from the previous day (kg P/ha)
Amount of phosphorus transported with sediment to the main channel in surface runoff (kg P/ha)
Amount of sediment-attached P loading generated in HRU on a given day (kg P/ha)
Surface runoff lag coefficient
Time of concentration for a subbasin (hr)
Amount of mobile water in the layer (mm H2O)
Amount of water percolating to the underlying soil layer on a given day (mmH2O)
Nitrate percolation coefficient
Fraction of porosity from which anions are excluded
Nitrogen enrichment ratio
Phosphorus enrichment ratio
Bulk density (Mg/m 3 )
- 22 -