Journal of Great Lakes Research 37 (2011) 263–271

Contents lists available at ScienceDirect

Journal of Great Lakes Research
j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / j g l r

Application of the Soil and Water Assessment Tool for six watersheds of Lake Erie:
Model parameterization and calibration
Nathan S. Bosch a,⁎, J. David Allan b,1, David M. Dolan c,2, Haejin Han d,3, R. Peter Richards e,4
a

Department of Science and Mathematics, Grace College, Winona Lake, IN 46590, USA
School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI 48109, USA
Natural and Applied Sciences, University of Wisconsin — Green Bay, Green Bay, WI 54311, USA
d
Korea Adaptation Center for Climate Change, Korea Environmental Institute, Seoul 122-706, Republic of Korea
e
National Center for Water Quality Research, Heidelberg University, Tiffin, OH 44883, USA
b
c

a r t i c l e

i n f o

Article history:
Received 30 June 2010
Accepted 15 February 2011
Available online 9 April 2011
Communicated by Joseph DePinto

Index words:
SWAT
Great Lakes
Nutrients
Sediments
Catchment
Model confirmation

a b s t r a c t
The Soil and Water Assessment Tool (SWAT), a physically-based watershed-scale model, holds promise as a
means to predict tributary sediment and nutrient loads to the Laurentian Great Lakes. In the present study,
model performance is compared across six watersheds draining into Lake Erie to determine the applicability
of SWAT to watersheds of differing characteristics. After initial model parameterization, the Huron, Raisin,
Maumee, Sandusky, Cuyahoga, and Grand SWAT models were calibrated (1998–2001) and confirmed, or
validated (2002–2005), individually for stream water discharge, sediment loads, and nutrient loads (total P,
soluble reactive P, total N, and nitrate) based on available datasets. SWAT effectively predicted hydrology and
sediments across a range of watershed characteristics. SWAT estimation of nutrient loads was weaker
although still satisfactory at least two-thirds of the time across all nutrient parameters and watersheds. SWAT
model performance was most satisfactory in agricultural and forested watersheds, and was less so in
urbanized settings. Model performance was influenced by the availability of observational data with high
sampling frequency and long duration for calibration and confirmation evaluation. In some instances, it
appeared that parameter adjustments that improved calibration of hydrology negatively affected subsequent
sediment and nutrient calibration, suggesting trade-offs in calibrating for hydrologic vs. water quality model
performance. Despite these considerations, SWAT accurately predicted average stream discharge, sediment
loads, and nutrient loads for the Raisin, Maumee, Sandusky, and Grand watersheds such that future use of
these SWAT models for various scenario testing is reasonable and warranted.
© 2011 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Introduction
Nutrient delivery to the Laurentian Great Lakes through tributary

loading has long been identified as a major contributor to the trophic
status of the lakes. Eutrophication in the Great Lakes first attracted
attention in the 1960s and 70s as Lake Erie experienced benthic
anoxia and other water quality problems associated with nutrient
enrichment (Boyce et al., 1987; Rosa and Burns, 1987). The 1972 Great
Lakes Water Quality Agreement brought about reductions in point

⁎ Corresponding author. Tel.: + 1 574 372 5100x6447.
E-mail addresses: (N.S. Bosch), (J.D. Allan),
(D.M. Dolan), (H. Han),
(R.P. Richards).
1
Tel.: + 1 734 764 6553.
2
Tel.: + 1 920 465 2986.
3
Tel.: + 82 2 6922 7803.
4
Tel.: + 1 419 448 2240.

source loadings of phosphorus (P) that dramatically lowered annual P
loads from tributaries and reversed many of the effects of cultural
eutrophication in the lakes (DePinto et al., 1986). Despite this major
reduction, point-source control programs alone were not adequate to
reduce tributary nutrient loading, and attention shifted to nonpoint
sources (DePinto et al., 1986; Dolan, 1993; Richards, 1985).
Much research in the Great Lakes basin has provided insight into the
relationship between riverine nutrient export and characteristics of
watersheds, including land use and nutrients inputs (Baker and
Richards, 2002; Bosch and Allan, 2008; Dolan and McGunagle, 2005;

Han and Allan, 2008; Han et al., 2011; Robertson, 1997). Dolan and
McGunagle (2005) estimated that nonpoint sources often account for
more than 70% of total tributary loadings in Lake Erie. Watershed-scale
nutrient budgets have confirmed this observation and refined it to show
specifically that fertilizer application is the largest input of nitrogen (N)
and P in agricultural watersheds, while atmospheric deposition of N is
an important input to forested watersheds (Baker and Richards, 2002;
Bosch and Allan, 2008; Han and Allan, 2008; Han et al., 2011). In urban
watersheds, by contrast, point source P inputs may continue to be
significant (Nemery et al., 2005).

0380-1330/$ – see front matter © 2011 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.jglr.2011.03.004


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Watershed characteristics and source inputs determine riverine
nutrient export. For 18 well monitored tributaries of Lake Michigan
and Lake Superior, topography and surficial deposits were top
predictors of P and sediment yields, followed by land use (Robertson,
1997). Cropland has larger P losses per unit area than pasture or
forest (Alvarez-Cobelas et al., 2009; Castillo et al., 2000; Cooke and
Prepas, 1998; Pieterse et al., 2003). Crop land use is associated with P
fertilization and increased soil erosion caused by tillage practices
(Sharpley, 1999). Mass balance approaches have demonstrated
strong relationships between source inputs to the watershed's
landscape and river export for both N and P (Boyer et al., 2002;

Han and Allan, 2008; Han et al., 2011). Clearly, it is important to
incorporate point and nonpoint sources of N and P into models as
well as other watershed characteristics such as surficial geology and
stream gradients, in order to accurately predict tributary export of
nutrients to the Great Lakes.
The Soil and Water Assessment Tool (SWAT), a semi-empirical
hydrologic and water quality model that is calibrated to the
conditions of individual watersheds, may be used to predict changes
in tributary nutrient loads based on nutrient application and
management choices. SWAT is a continuous-time, watershed-scale
model that runs at a daily time step (Gassman et al., 2007). SWAT
models are internally organized in a nested spatial hierarchy,
including Hydrologic Response Units (HRUs) within subwatersheds
within watersheds. Watersheds are defined by a main outlet point
for the drainage area of interest. A variable number of subwatersheds
are delineated in SWAT based on interior outlet points located on the
stream channel and spatially linked to each other through the
adjoining stream channel network. Within each subwatershed a
variable number of HRUs are defined as areas with unique
combinations of land cover, soil type, and slope. It is important to
note that these HRUs are not spatially referenced except that they
are within a specific subwatershed. Processes modeled within each
HRU are aggregated up to the subwatershed scale by a weighted
average based on land area. Thus as a mechanistic model, SWAT
includes spatially explicit parameterization at the subwatershed
spatial scale and partially lumped parameterization within each
subwatershed.
SWAT hydrology, sediment, and nutrient processes are modeled in
both upland and water-routing phases (Neitsch et al., 2005). Various
forms of N and P, including mineral P (soluble reactive P (SRP)), other

P (total P - SRP), organic N, nitrate, nitrite, and ammonia are modeled
explicitly with a thorough representation of transport, uptake, loss,
and transformation mechanisms (more detail on SWAT is provided in
Appendix A).
The SWAT model was applied to six watersheds draining into
Lake Erie to model scenarios of riverine nutrient export as part of a
larger investigation of the recent resurgence of anoxic waters in Lake
Erie (Burns et al., 2005) and the contributions of tributary runoff. We
include six of the largest U.S. watersheds of Lake Erie, which receive
some of the highest nutrient inputs of any in the Great Lakes due
largely to intensive agriculture in the region. Previous application of
SWAT for two of the watersheds utilized some similarities in
methodology (Bosch, 2008), but the approach has been improved
and a newer version of the SWAT model has been employed. In the
present study, model performance was compared across these
diverse watersheds to determine the performance of SWAT within
watersheds of differing characteristics. Care was taken to apply
SWAT similarly to all six watersheds to allow comparison of model
performance. There is growing interest in restoring the Great Lakes,
in which nonpoint source nutrient loads are widely considered one
of the most important threats. It is our hope that this study will
inform future studies of the suitability of SWAT to predict tributary
sediment and nutrient loads under scenarios of agricultural best
management practices, nutrient source reductions, and future
climates.

Methods
Study area
The Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand
watersheds drain into the western and central basins of Lake Erie

(Fig. 1) from southeastern Michigan, northeastern Indiana, and
northern Ohio. Primary differences among watersheds are reflected
in land cover (Table 1), with the Raisin, Maumee, and Sandusky being
predominantly agricultural. The Huron and Cuyahoga are the most
urbanized watersheds. The Grand watershed is mostly forested with
relatively little agriculture and urban land. Average precipitation
increases slightly from west to east due to more lake-effect
precipitation in the Cuyahoga and Grand watersheds.
Model input data sources
The Geographic Information System (GIS) interface created for
SWAT, called ArcSWAT (version 2.1.5), was used to develop inputs for
the six watershed models. Elevation, stream network, land cover, soil
type, weather, point source discharges, impoundment (reservoir, lake,
or pond) characteristics, atmospheric N deposition, and land
management practices were included. Specific data descriptions,
sources, and scales are included in Appendix B.
Model setup and parameterization
For each of the six watershed models we first delineated
subwatersheds and distributed HRUs within subwatersheds (Appendix
C). The delineation process resulted in 31, 32, 203, 39, 23, and 22
subwatersheds for the Huron, Raisin, Maumee, Sandusky, Cuyahoga,
and Grand, respectively, which was the desired level of spatial detail for
the study. This was an average subwatershed size of 85 km2 across all
six watersheds. There were 441, 468, 2341, 567, 302, and 297 HRUs
distributed in the Huron, Raisin, Maumee, Sandusky, Cuyahoga, and
Grand watershed models, respectively, based on unique combinations
of land cover and soil type and leading to an average HRU size of 7 km2.
Measured data were used for all weather parameters including
daily rainfall, minimum and maximum air temperature, windspeed,
relative humidity, and solar radiation. Weather data were collected for

the time period January 1, 1995 through December 31, 2005. Data
from the nearest station were used to fill in missing data whenever
data records were incomplete.
Loading data for point source dischargers were entered into the
models as constant average daily loadings. For parameters that were
not measured by individual municipal wastewater treatments plants,
we used average estimated values (Table 2) obtained from measurements during 1995–2005 at eight Midwestern U.S. wastewater
treatment plants where all parameters were measured. For larger
wastewater treatment plants, parameters in addition to discharge and
TP were often reported, such that directly measured data could be
used. Discharge loading information was entered for 36, 23, 83, 13, 25,
and 7 dischargers in the Huron, Raisin, Maumee, Sandusky, Cuyahoga,
and Grand, respectively.
Tile drainage was implemented in the six watershed models
following the approach of Green et al. (2006; Appendix C). Average
denitrification rates in upland crop land over various soil types and N
application rates are near 15 kg N ha− 1 y− 1 (Hofstra and Bouwman,
2005), while rates in well-drained, clay loam, forested soils average
18 kg N ha− 1 y− 1 (Groffman et al., 1992). Since these six watersheds
are dominated with agriculture and forest land cover (Table 1), model
parameters were adjusted to approximate these denitrification rates
(Appendix C).
Agricultural land management practices were generalized for each
of the six watersheds based on most common management practices
in each watershed, and used to define operation schedules in SWAT


N.S. Bosch et al. / Journal of Great Lakes Research 37 (2011) 263–271

265


Fig. 1. The Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand watersheds draining into western and central Lake Erie as delineated in SWAT models.

for both row-crop and hay agricultural lands. Most land classified as
hay in these watersheds was managed for hay production or used for
grazing and was modeled by SWAT accordingly. Row-crop agriculture
in these six watersheds consisted of various rotational schedules and
combinations utilizing mostly corn, soybean, and wheat crop
production. SWAT operation schedules were defined according to
these rotation patterns for each watershed individually as well as for
corresponding practices such as tillage, fertilizer application, crop
planting, manure application, and crop harvesting. The operation
schedules were adjusted to accurately represent fertilizer and manure
application rates based on county estimates (Ruddy et al., 2006). In
addition, each of the multi-year operation schedules differed based on
the starting crop in the rotation pattern. This ensures that the single
year of wheat production in the 6-year rotation of the Maumee
watershed occurs in a staggered manner across the watershed rather

Table 1
Characteristics of the Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand
watersheds for the modeled areas, determined by the watershed outlet location.

Huron
Raisin
Maumee
Sandusky
Cuyahoga
Grand


Watershed
Size (km2)

Precipitation
(mm/y)

Landcover (%)
Agriculture
Urban

Forested

2379
2784
17030
3455
2100
1896

896
861
934
962
1039
1093

27
72
81
83

17
37

37
16
8
8
35
52

34
11
11
9
47
10

than all subwatersheds producing wheat in the same year. These land
management schedules were distributed among subwatersheds such
that each version was equally represented and applied uniformly
across the watershed.
Other land cover types (residential, industrial, range, forest,
wetlands, and water) were given general operation schedules based
on most common management practices across the six watersheds as
a whole. High, medium, and low density residential as well as

Table 2
Point source discharge input data by parameter. Average estimated concentrations
were based on measured data during 1995–2005 at eight Midwestern WWTPs. CBOD
refers to chemical/biological oxygen demand.

Parameter

Units Notes

Water flow
Sediment

m3
Mg

Organic
nitrogen
Organic
phosphorus
Nitrate
Ammonia
Nitrite
Mineral
phosphorus
CBOD
Dissolved
oxygen

kg
kg
kg
kg
kg
kg
kg

kg

Directly measured data used
Based on average total suspended solid concentration of
7.2 mg/L
Based on average organic nitrogen concentration of
2.2 mg N/L
Assumed to be 30% of directly measured total phosphorus
concentration
Based on average nitrate concentration of 11.3 mg N/L
Based on average ammonia concentration of 3.2 mg N/L
Based on average nitrite concentration of 0.6 mg N/L
Assumed to be 70% of directly measured total
phosphorus concentration
Based on average CBOD concentration of 4.9 mg/L
Based on average dissolved oxygen concentration of
6.8 mg/L


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N.S. Bosch et al. / Journal of Great Lakes Research 37 (2011) 263–271

industrial land covers were set to a plant type of Kentucky Bluegrass
and scheduled to grow from May 1 to September 29. Fertilizer
application equal to the watershed-specific, non-farm fertilizer
estimates (Ruddy et al., 2006) was applied to low, medium, and
high density residential in four equal applications scheduled on April
1, June 1, August 15, and September 30. Most land classified as range
in these watersheds is left fallow as conservation reserve areas, so the

plant type was set to Tall Fescue with a growing season from April 1 to
October 31 with no fertilizer or manure application operations.
Operations for forest and wetland land covers were scheduled to
correspond with leaf-out in the spring and leaf senescence in the fall,
with the growing season beginning on May 1 and ending on October
10 of each year. The water land cover was kept at its default settings.
Model calibration and confirmation
The six SWAT models were calibrated and confirmed individually for
stream flow discharge, sediment loads, and nutrient loads (total P (TP),
SRP, total N (TN), and nitrate) based on available datasets. Daily mean
stream discharge measurements were available for 1995–2005 from
USGS gage stations near the river mouth of each watershed, including
the Huron River at Ann Arbor, MI (04174500), the River Raisin at
Monroe, MI (04176500), the Maumee River at Waterville, OH
(04193500), the Sandusky River at Fremont, OH (04198000), the
Cuyahoga River at Independence, OH (04208000), and the Grand River
at Painesville, OH (04212100). For the Raisin, Maumee, Sandusky,
Cuyahoga, and Grand rivers, near-daily sediment, TP, SRP, TN, and
nitrate loads were available for sampling sites located near the USGS
gage stations provided by the National Center for Water Quality
Research at Heidelberg College for the entire time period of 1995–
2005. In order to produce observed monthly loads for later calibration
and confirmation tests, missing values for daily loads were determined
using the USGS Estimator protocol (Richards, 1998). For the Huron
River, fewer sediment and nutrient data were available. Approximately
biweekly TP, SRP, TN, and nitrate concentration data were collected
during 2003–2005 near the mouth of the river (Bosch, 2007). No
sediment data were available for the Huron watershed. As with the
other five watersheds, missing daily values for TP, SRP, TN, and nitrate
loads were generated for the Huron River using the USGS Estimator

protocol in order to produce observed monthly loads.
Observed data were then compared to simulated SWAT output. The
first 3 years (1995–1997) were used for model spin-up, in order to
minimize the impact of initial model parameter values which may be
suspect, and this model output was not used in calibration or
confirmation. The next 4 years of observed data were used for
calibration (1998–2001), and the remaining 4 years (2002–2005) for
model confirmation. Model confirmation (using the terminology of
Reckhow and Chapra, 1983 and Oreskes et al., 1994; others have used
the term “validation”) consisted of comparing model predictions with
observations using a data set for years and conditions distinct from those
represented by the calibration data. For the Huron watershed only, 2003
and 2004 observed loads were used for calibration, and 2005 loads were
used for confirmation. To evaluate model performance during calibration and confirmation, statistical measures at the monthly time-step
were used as well as visual graphical comparison at the daily time-step.
The monthly statistical measures used for calibration and
confirmation evaluation for stream discharge, sediment, TP, SRP, TN,
and nitrate included: coefficient of determination (R2), Nash–Sutcliffe
simulation efficiency (NSE), percent bias (PBIAS), and the ratio of the
root mean square error to the standard deviation of the observations
(RSR). Moriasi et al. (2007) thoroughly explain the utility of each of
these 4 evaluation statistics, how they are calculated, and what range
of values are satisfactory for watershed hydrology and water quality
modeling with models such as SWAT (Appendix D).
Model calibration included several sequential stages for each
individual watershed, including hydrology sensitivity analysis, hydrology

manual calibration, hydrology autocalibration, sediment manual calibration, and nutrient manual calibration. First, an automated sensitivity
analysis (Van Griensven et al., 2006) is carried out through the ArcSWAT
interface with hydrologic model parameters in order to identify the

parameters to be adjusted during the autocalibration procedure as well as
to give some insight into parameters to adjust during manual hydrology
calibration. The sensitivity analysis procedure uses the Latin hypercube
one factor at a time design, and identifies the top 15 most sensitive
parameters. Next, the hydrology was roughly calibrated manually by
changing sensitive hydrologic parameters as described in Santhi et al.
(2001). Once simulated stream discharge roughly fit the observed
discharge for the calibration time period, a second sensitivity analysis
was performed. The top 15 parameters of both the pre- and post-manual
calibration sensitivity analysis runs were then used for the hydrology
autocalibration. The hydrology autocalibration employed the PARASOL
calibration procedure included in the ArcSWAT interface (Van Griensven
and Meixner, 2007). The PARASOL method applied a shuffled complex
evolution optimization scheme to select the optimal parameter value set
for the 15 sensitive hydrologic parameters after several thousand model
runs. The calibration was based on monthly observed USGS daily mean
stream discharge data (1998–2001) and SWAT simulated stream
discharge. Hydrologic model parameter values were then adjusted to
reflect the optimal value set chosen by the autocalibration process.
After model hydrology calibration was completed, manual calibration of sediments and nutrients was completed, followed by model
confirmation. SWAT sediment parameters were calibrated following
the procedure of Santhi et al. (2001) based on monthly observed
sediment loads from 1998 to 2001. Since no observed sediment data
were available for the Huron River, the optimized sediment parameter
values from the adjacent Raisin watershed were used for the Huron
SWAT model as well. After sediment calibration was completed, TP,
SRP, TN, and nitrate calibration were done based on monthly observed
nutrient data (Santhi et al., 2001). SWAT output included mineral P
and other P, so SWAT mineral P was compared to observed SRP data
and observed TP was compared to the sum of SWAT mineral P and

other P. After model nutrient parameters were optimized for all six
watershed models, calibration was complete and model confirmation
was initiated. During model confirmation, evaluation statistics were
calculated for stream discharge, sediments, TP, SRP, TN, and nitrate
and for each of the six SWAT models, but no further model parameter
changes were made.
Results and discussion
Stream discharge
SWAT predicted measured stream discharge well for all six
watersheds during both the calibration and confirmation time periods
(Table 3). In fact, NSE, PBIAS, and RSR statistic values were mostly
“very good” with a few categorized as “good” according to Moriasi et
al. (2007). Somewhat surprisingly, SWAT prediction of discharge was
slightly more accurate during the confirmation period than the
calibration time period overall. Though all SWAT models performed
well, across the six watersheds, stream discharge was best predicted
for the Maumee, and least well predicted for the Huron.
Successful modeling of stream discharge is expected given the
extensive calibration data sets available from stations located near the
mouth of each watershed, and the effectiveness of the PARASOL
autocalibration method. This success indicates that the hydrologic
mechanisms included in the model are fit uniquely and well to each of
these watersheds and are reliable for future model simulations.
Stream discharge calibration is critical for subsequent water quality
calibration success.
The less satisfactory hydrologic performance of the Huron model
likely is due to specific characteristics of the Huron watershed. First,
the baseflow fraction of the Huron River is higher (0.74) than all other



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N.S. Bosch et al. / Journal of Great Lakes Research 37 (2011) 263–271
Table 3
Calibration and confirmation results for monthly stream discharge (m3/s) for the six
modeled watersheds. Coefficient of determination (R2), Nash–Sutcliffe simulation
efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to
observations standard deviation (RSR) are used as evaluators of model performance.
Statistics in bold type are categorized as satisfactory or better (Moriasi et al., 2007).
Observed mean
(m3/s)

Simulated mean
(m3/s)

R2

NSE

PBIAS
(%)

RSR

(a) Calibration
Huron
14
Raisin
23
Maumee

151
Sandusky
25
Cuyahoga
21
Grand
19

14
23
162
26
21
18

0.73
0.87
0.93
0.88
0.89
0.89

0.70
0.87
0.92
0.87
0.88
0.85

1

1
−7
−2
3
9

0.54
0.36
0.28
0.35
0.34
0.38

(b) Confirmation
Huron
11
Raisin
19
Maumee
174
Sandusky
39
Cuyahoga
34
Grand
32

10
21
176

38
30
30

0.75
0.88
0.95
0.90
0.90
0.83

0.71
0.87
0.95
0.90
0.87
0.82

9
− 11
−1
2
11
5

0.54
0.36
0.22
0.32
0.36

0.42

watersheds (0.39–0.60) included in this study (Arnold and Allen, 1999).
Second, the Huron River has far more impoundments than the other five
rivers combined, storing large volumes of water in its middle and lower
sections. Third, and related to the previous two characteristics, the
Huron River has a much less flashy hydrograph than other rivers of this
study and a slow return of discharge to baseflow conditions (Fig. 2).
While the relatively high urban land cover of the Huron (Table 1) might
be expected to result in flashy stream discharges due to impervious
surfaces, it seems that impoundments effectively dampen peak flows by
temporarily retaining excess water volumes in the river system.

Sediment
Sediment calibration was acceptable for the Raisin, Maumee,
Sandusky, and Grand (Table 4). In fact, most statistic values for the
confirmation time period were “very good” for these four rivers
according to the ranges given by Moriasi et al. (2007). For the Huron
River, no observed sediment data suitable for calibration or
confirmation was available, so sediment load prediction for the
Huron could not be evaluated. Sediment model output for the
Cuyahoga River, despite a strong data set for observed sediments,
was marginal to poor. Three of the evaluation statistics had
satisfactory values, while the remaining five were unsatisfactory.
This weak model performance for sediment loads for the Cuyahoga
watershed may be related to its hydrology calibration process as well
as the watershed's landcover. This was the only model for which the
observed-simulated sediment R2 decreased as a result of the stream

Table 4

Calibration and confirmation results for monthly sediment loads (Mg) for the six
modeled watersheds. Coefficient of determination (R2), Nash–Sutcliffe simulation
efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to
observations standard deviation (RSR) are used as evaluators of model performance.
Statistics in bold type are categorized as satisfactory or better (Moriasi et al., 2007). No
sediment evaluation was completed for the Huron River due to the lack of observed
sediment data.
Observed mean
(g)

Simulated mean
(Mg)

R2

NSE

PBIAS
(%)

RSR

–
5203
63475
9572
10087
5739

–

5008
63332
10490
10110
5718

–
0.65
0.68
0.74
0.46
0.85

–
0.63
0.67
0.73
0.25
0.83

–
4
0
− 10
0
0

–
0.62
0.58

0.53
0.87
0.42

(b) Confirmation
Huron
–
Raisin
4104
Maumee
77095
Sandusky 20082
Cuyahoga 29832
Grand
12789

–
4865
68423
17579
15745
9693

–
0.80
0.92
0.91
0.76
0.75


–
0.79
0.89
0.85
0.45
0.64

–
− 19
11
12
47
24

–
0.47
0.34
0.39
0.75
0.61

(a) Calibration
Huron
Raisin
Maumee
Sandusky
Cuyahoga
Grand

discharge calibration process. In the other four models for which

sediment calibration was possible, hydrology calibration improved
sediment prediction prior to undertaking sediment calibration.
Parameter adjustments that improved calibration of hydrology in
the Cuyahoga model apparently had a negative influence on
subsequent sediment and nutrient calibration, suggesting trade-offs
in calibrating for hydrologic vs. water quality model performance. In
addition, the landcover of the Cuyahoga watershed is the least
agricultural and most urban of the six study systems. Despite the low
extent of agriculture, the sediment load near the mouth of the
Cuyahoga River is second only to the much larger and more
agricultural Maumee River. In fact, the Cuyahoga River had the
highest single observed daily sediment load of all the watersheds,
including the Maumee River. This unique characteristic of the
Cuyahoga was not adequately simulated within the sediment
transport mechanisms included in SWAT.
Total phosphorus
Model performance for TP ranged from unsatisfactory to satisfactory
across the six study watersheds (Table 5). The Maumee and Sandusky
models both showed satisfactory to strong performance across multiple
parameters. Fig. 3 depicts the daily TP load for the Maumee, which is the
largest individual TP input to Lake Erie. Simulated TP loads for the Raisin
and Grand watersheds ranged from unsatisfactory to satisfactory,
indicating marginal model performance. Simulated TP for the Huron
and Cuyahoga was mostly unsatisfactory for calibration and

Fig. 2. Daily plot of observed and simulated mean stream discharge (m3/s) for the Huron River over the calibration (1998–2001) and confirmation (2002–2005) time periods.


268


N.S. Bosch et al. / Journal of Great Lakes Research 37 (2011) 263–271

Table 5
Calibration and confirmation results for monthly total phosphorus loads (Mg P) for the
six modeled watersheds. Coefficient of determination (R2), Nash–Sutcliffe simulation
efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to
observations standard deviation (RSR) are used as evaluators of model performance.
Statistics in bold type are categorized as satisfactory or better (Moriasi et al., 2007).
Simulated mean
(Mg P)

R2

(a) Calibration
Huron
1
Raisin
11
Maumee 149
Sandusky
23
Cuyahoga
14
Grand
6

1
8
149
24

14
7

0.53
0.52
0.72
0.66
0.41
0.71

− 2.55
0.47
0.72
0.64
0.11
0.68

−2
29
0
−1
1
−7

1.93
0.74
0.53
0.60
0.95
0.57


(b) Confirmation
Huron
1
Raisin
9
Maumee 182
Sandusky
43
Cuyahoga
29
Grand
13

3
8
137
30
16
9

0.16
0.50
0.86
0.87
0.68
0.30

− 46.12
0.49

0.74
0.77
0.39
0.28

− 220
11
25
29
44
25

56.51
0.72
0.51
0.49
0.79
0.86

Observed mean
(Mg P)

NSE

PBIAS
(%)

RSR

confirmation time periods, with particularly poor NSE values for the

Huron model.
Poor performance of modeled TP for the Huron and Cuyahoga may
be related to calibration methodology, land cover, and point source
nutrient inputs. Relatively sparse observed data are likely to blame for
the particularly poor TP prediction for the Huron. TP loads depend
largely on sediment transport, and so the lack of sediment data and
sediment calibration likely contributed to the poor prediction of TP
loads. In addition, only 3 years of biweekly TP loads were available for
Huron TP calibration and confirmation, compared to near daily TP
loads for 8 years for the other watersheds, making it difficult to fully
parameterize or evaluate model performance for the Huron model. A
different explanation is necessary for the poor performance of the
Cuyahoga model, since ample sediment data were available. As noted
previously with respect to sediment prediction, calibration to improve
hydrologic performance of the Cuyahoga model also decreased R2
values between observed and simulated TP loads for both the Huron
and Cuyahoga SWAT models. Furthermore, these two watersheds
have the most urban land use and least agriculture of the six study
watersheds, indicating SWAT may better predict TP loads in
agricultural than urban watersheds. Both the Huron and Cuyahoga
Rivers receive relatively high point source TP loads relative to total
observed river loads. For the Huron River, point source inputs of TP to
the river are estimated (and thus modeled by SWAT) as 29 kg P/d
while the observed average stream load near the Huron River mouth
is 36 kg P/d. Similarly, estimated point source inputs for the Cuyahoga

River are 233 kg P/d, roughly one-third of the total stream load of
703 kg P/d.
SWAT estimates of TP loads for the Grand and Raisin models can be
described as marginally satisfactory, likely due to characteristics of these

two watersheds. Compared to the Maumee and Sandusky watersheds,
which produced strong models, the Grand watershed is much less
agricultural and more forested (Table 1), which likely hampers SWAT's
ability to predict TP loads. Apparently, the high percentage of urban land
in the Huron and Cuyahoga, and correspondingly high point source
inputs, account for even poorer model performance for those two
watersheds. In the case of the Raisin, model TP estimation barely missed
the satisfactory threshold for NSE and RSR, possibly due to the presence
of impoundments in the upper Raisin watershed. Prior work (Bosch,
2008; Bosch et al., 2009) has shown that impoundments alter the timing
and magnitude of TP loads, and this may not have been adequately
portrayed in the current Raisin SWAT model.
Despite these mixed performance evaluations, it is important to
note that PBIAS values were largely categorized as “very good” across
all watersheds, indicating that model predictions of TP loads were not
generally higher or lower than observed data. Thus, SWAT predicted
TP loads accurately on average.

Soluble reactive phosphorus
SRP estimation was the weakest output from SWAT models for all six
watersheds (Table 6). This was apparent during both calibration and
confirmation time periods, and evaluation statistics were unsatisfactory
just over half of the time across all watersheds and for all statistical
measures for SRP (Moriasi et al., 2007). Only the Raisin model
consistently performed well in calibration and confirmation. Model
performance for the Maumee, Sandusky, and Grand was marginal, and
more satisfactory during the calibration than the confirmation time
period, as expected. PBIAS was satisfactory for the Raisin, Maumee,
Sandusky, and Grand, once again indicating strong model performance
for these four watersheds. As observed for TP, the Huron and Cuyahoga

models were largely unsatisfactory in their estimation of SRP over time
and as a monthly average.
Weak prediction of SRP loads for the Maumee and Sandusky is
unexplained. Inspection of observed and simulated means (Table 6)
shows that observed SRP loads increased dramatically from the
calibration time period (1998–2001) to the confirmation time period
(2002–2005). This same increase is not mirrored in simulated SRP
loads. Although observed stream discharge increases as well (Table 3),
this is insufficient to explain the SRP increase as SWAT should capture
this effect. Monitoring data show an increase in SRP as a fraction of TP
over recent years in these two watersheds (Richards, 2006; Richards,
2007), with no confirmed explanation for this trend. It is apparent

Fig. 3. Daily plot of observed and simulated total phosphorus (TP) loads (kg P/d) for the Maumee River over the calibration (1998–2001) and confirmation (2002–2005) time
periods.


269

N.S. Bosch et al. / Journal of Great Lakes Research 37 (2011) 263–271
Table 6
Calibration and confirmation results for monthly soluble reactive phosphorus loads (Mg P)
for the six modeled watersheds. Coefficient of determination (R2), Nash–Sutcliffe
simulation efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square
error to observations standard deviation (RSR) are used as evaluators of model
performance. Statistics in bold type are categorized as satisfactory or better (Moriasi
et al., 2007).
Observed mean Simulated mean R2
(Mg P)
(Mg P)

(a) Calibration
Huron
0.2
Raisin
2.3
Maumee 31.5
Sandusky
5.2
Cuyahoga
2.8
Grand
0.6
(b) Confirmation
Huron
0.1
Raisin
2.1
Maumee 56.9
Sandusky 11.2
Cuyahoga
3.9
Grand
1.4

0.2
2.2
32.2
5.1
8.0
0.6


0.9
2.1
29.8
7.1
8.6
0.8

0.37
0.81
0.65
0.43
0.05
0.53

NSE

PBIAS
(%)

− 1.12
− 17
0.79
3
0.61
−2
0.44
1
− 38.26 − 182
0.50

10

Table 7
Calibration and confirmation results for monthly total nitrogen loads (Mg N) for the six
modeled watersheds. Coefficient of determination (R2), Nash–Sutcliffe simulation
efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to
observations standard deviation (RSR) are used as evaluators of model performance.
Statistics in bold type are categorized as satisfactory or better (Moriasi et al., 2007).
Simulated mean
(Mg N)

R2

NSE

PBIAS
(%)

(a) Calibration
Huron
43
Raisin
380
Maumee
3199
Sandusky
578
Cuyahoga
172
Grand

76

42
383
3189
604
190
77

0.71
0.77
0.86
0.70
0.40
0.66

0.68
0.77
0.81
0.66
0.24
0.66

1
−1
0
−5
− 11
−2


0.58
0.49
0.44
0.59
0.88
0.59

(b) Confirmation
Huron
57
Raisin
332
Maumee
3806
Sandusky
867
Cuyahoga
243
Grand
125

59
338
3186
810
196
119

0.30
0.81

0.80
0.80
0.43
0.49

0.36
0.80
0.67
0.76
0.27
0.49

−3
−2
16
6
19
5

16.74
0.45
0.58
0.50
0.86
0.72

Observed mean
(Mg N)

RSR


1.49
0.47
0.63
0.75
6.33
0.72

0.75 − 182.45 − 709 41.87
0.70
0.70
2
0.55
0.69
0.29
48
0.85
0.45
0.35
37
0.82
0.11
− 34.31 − 119
6.00
0.63
0.40
42
0.78

that SWAT is not adequately representing the increase in SRP export

regardless of the mechanism causing this shift.

Total nitrogen
Model evaluation statistics indicate mixed success in estimating
TN across the six study watersheds (Table 7). However, PBIAS for all
models was less than 20% for the calibration and confirmation stages,
indicating “very good” average estimation of monthly loads (Moriasi
et al., 2007). Model performance for the three agricultural watersheds
received all “good” and “very good” evaluation statistics for
calibration and confirmation. Fig. 4 illustrates daily TN loads for the
Maumee, the largest single tributary TN load to Lake Erie. TN
estimation for the forested Grand watershed was satisfactory or
nearly so for all evaluation statistics. Models for the Huron and
Cuyahoga were once again the weakest, with several unsatisfactory
evaluation statistics for both calibration and confirmation time
periods.
Similar to TP model results, TN prediction by SWAT was strongest
for agricultural watersheds and weakest for highly urban watersheds.
The urbanized Huron and Cuyahoga once again had much higher
point source inputs relative to observed stream loads, in comparison
with other study watersheds. For the Cuyahoga, point source inputs
modeled by SWAT were 5851 kg N/d, while observed stream TN loads
were only 6814 kg N/d. In the Huron watershed, point source inputs
were 1460 kg N/d and the river's observed load was 1638 kg N/d.

RSR

Assuming additional nitrogen inputs from terrestrial lands from
fertilizer, manure, soil, and atmospheric deposition, a great amount of
N must be removed in the stream channels of these two watersheds.

SWAT only includes N settling as a removal mechanism in stream
channels, despite strong evidence that denitrification in stream
channels is an important N removal mechanism (Inwood et al.,
2005; Smith et al., 2006). Due to this shortcoming, SWAT is unable to
strongly predict the timing of TN loading, though it does predict
average loads with reasonable accuracy as seen by the PBIAS statistic
(Table 7).

Nitrate
Nitrate is the largest fraction of TN in river export from these
watersheds and thus is modeled by SWAT with similar accuracy as
was observed for TN (Table 8). Models for the Raisin, Maumee, and
Sandusky performed acceptably across all four evaluation statistics
during both the calibration and confirmation times periods. The
Huron, Cuyahoga, and Grand models performed inconsistently. All six
SWAT watershed models, however, had PBIAS statistic values
considered to be “very good”.
It is evident that SWAT model performance is consistently better
for the three agricultural watersheds than it is for the more urban and
forested watersheds. SWAT was developed and optimized for
agricultural watersheds (Gassman et al., 2007), and thus incorporates
mechanisms best suited for highly agricultural systems. In the case of

Fig. 4. Daily plot of observed and simulated total nitrogen (TN) loads (kg N/d) for the Maumee River over the calibration (1998–2001) and confirmation (2002–2005) time periods.


270

N.S. Bosch et al. / Journal of Great Lakes Research 37 (2011) 263–271


Table 8
Calibration and confirmation results for monthly nitrate loads (Mg N) for the six
modeled watersheds. Coefficient of determination (R2), Nash–Sutcliffe simulation
efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to
observations standard deviation (RSR) are used as evaluators of model performance.
Statistics in bold type are categorized as satisfactory or better (Moriasi et al., 2007).
Simulated mean
(Mg N)

R2

NSE

PBIAS
(%)

RSR

(a) Calibration
Huron
33
Raisin
306
Maumee
2613
Sandusky
515
Cuyahoga
115
Grand

36

34
303
2632
530
130
41

0.57
0.61
0.65
0.58
0.10
0.14

0.47
0.61
0.62
0.53
− 0.21
0.02

−2
1
−1
−3
− 13
− 15


0.75
0.63
0.62
0.69
1.11
1.00

(b) Confirmation
Huron
51
Raisin
277
Maumee
2958
Sandusky
728
Cuyahoga
135
Grand
53

39
259
2659
717
130
66

0.71
0.72

0.69
0.59
0.16
0.23

0.49
0.69
0.61
0.59
0.11
0.19

23
6
10
2
4
− 24

6.00
0.57
0.63
0.65
0.95
0.91

Observed mean
(Mg N)

nitrate and denitrification in terrestrial soils, SWAT potentially

predicts this removal mechanism more appropriately in agricultural
soils compared to forest soils. In the case of dominant nitrate sources
in the watersheds, SWAT may more accurately simulate nitrate
fertilizer application on agricultural lands than nitrate deposition on
forested lands.
Related to nitrate dynamics and land cover is the urban influence
seen in the Cuyahoga watershed where Cuyahoga N point source
inputs are over twice as high as the stream loads. Since the majority of
the Cuyahoga River stream TN load is nitrate, a plot showing daily
nitrate loads illustrates these effects (Fig. 5). The most striking
observation is the relatively high baseline of plotted nitrate daily loads
(provided by the high point source inputs) with numerous peaks in
both positive and negative directions. These peaks stretching below
the baseline are likely periods when the nitrate concentrations in the
stream were diluted by moderately increased stream discharge. SWAT
is not able to adequately predict these downward peaks, which offers
another explanation for weaker SWAT performance in predicting the
Cuyahoga nitrate load.
Implications
Model performance statistics and graphical plots reveal that SWAT
was effective in capturing system dynamics and estimating nutrient
and sediment export for the three agricultural watersheds. SWAT also
performed reasonably well for the highly forested Grand watershed.
Sediment and nutrient results for the more urban Cuyahoga and
Huron watersheds, however, suggest that SWAT should be applied

with caution when agriculture is not the dominant land use. Although
SWAT accurately simulated the hydrology of these urbanized
watersheds, water quality simulation was disappointing, evidently
because SWAT does not incorporate adequate mechanisms to remove

high point source inputs from the channels.
This research also indicates that trade-offs may exist in calibrating
SWAT parameters to best represent hydrologic response variables
versus water quality variables. This appears to be the case for the
Cuyahoga sediment and TP calibration as well as Huron TP calibration;
in each case, flow calibration resulted in a decreased fit as measured
by the R2 between observed and simulated water quality data — 0.44
to 0.40, 0.52 to 0.30, and 0.44 to 0.27, respectively. Usually calibration
of hydrology results in better sediment calibration which, in turn,
leads to better P and N calibration success. In the two urban
watersheds, however, it appears that hydrology calibration resulted
in some parameters and processes important for sediment and TP
simulation being altered negatively. It is uncertain which parameters
were responsible for this effect, but two hydrologic parameter values
were notably different for the Huron and Cuyahoga compared to the
other four watersheds. Groundwater delay (GW_DELAY) was set to
less than 1 day for both the Huron and Cuyahoga watersheds, versus
30 to 31 days for the other four watersheds, and the snowpack
temperature lag factor (TIMP) was higher for the Huron (0.74) and
Cuyahoga (0.23) compared to other watersheds (0.06–0.13). This may
be further cause for caution when applying SWAT to watersheds with
substantial urban area.
It is apparent that extensive empirical data of high quality are critical
for SWAT model applications. In the case of the Huron watershed, the
limited availability of water quality data for calibration and confirmation
was a likely contributor to weak model performance. Three years of
biweekly water quality data for the Huron model was insufficient to
separate the data set into meaningful calibration and confirmation time
periods. The 8 years of water quality data that were available for the
other five watersheds clearly enhanced model performance. Even

longer time series might be useful, but typically the land cover data
comes from one time period (2001 in this study), so lack of
correspondence between the time periods of hydrologic and water
quality data with the land cover data may then limit the realism of
scenarios. Sampling frequency also is important, as biweekly sampling
does not allow meaningful calibration with the model's daily time step,
and even modeling at the monthly time step requires filling in many
gaps using some estimation approach (such as the Beale Ratio Estimator
or USGS Estimator). In such cases, the monthly calibration is done based
on another largely simulated data set rather than observed data.
The final implication of this research is that despite weak
performance results for some watersheds and certain water quality
parameters, SWAT predicted loads, on average, that were not
seriously biased. This is evidenced by the relatively low PBIAS
statistics calculated throughout the calibration and confirmation
process. While SWAT predictions of the timing or magnitude of

Fig. 5. Daily plot of observed and simulated nitrate loads (kg N/d) for the Cuyahoga River over the calibration (1998–2001) and confirmation (2002–2005) time periods.


N.S. Bosch et al. / Journal of Great Lakes Research 37 (2011) 263–271

certain loading events or durations cannot be assumed to be accurate
and should be evaluated using appropriate statistics, SWAT accurately
predicted overall average loads for nearly all parameters for all six
watersheds during both the calibration and confirmation time
periods. Thus, when accompanied by acknowledgment of model
uncertainty, we have confidence that these six models will be readily
useful for future studies in this region to predict average sediment or
nutrient load changes in response to land use and climate alteration

scenarios.
Conclusion
The present study demonstrates the applicability of SWAT in the
Great Lakes basin and also identifies certain considerations for future
SWAT application in general. SWAT effectively predicted hydrology and
sediments at daily time steps across a range of watershed characteristics.
SWAT estimation of daily nutrient loads was weaker, although still
satisfactory at least two-thirds of the time across all nutrient parameters
and watersheds. Furthermore, the PBIAS statistic consistently showed
monthly average loads to be estimated without serious bias. Agricultural
and forested watersheds lend themselves particularly well to SWAT
modeling of hydrology, sediments, and nutrients. This study also
emphasizes the importance of the availability of observed data with
high sampling frequency and long duration for calibration and
confirmation evaluation and effectiveness. Despite these considerations,
SWAT accurately predicted average stream discharge, sediment loads,
and nutrient loads for the Raisin, Maumee, Sandusky, and Grand
watersheds such that future use of these SWAT models for various
scenario testing is reasonable and warranted.
Supplementary materials related to this article can be found online
at doi:10.1016/j.jglr.2011.03.004.
Acknowledgments
We are grateful to the following people for their insight related to
this work: Steve Davis, Tim Hunter, Les Ober, Jim Selegean, Michael
Winchell, and Tom VanWagner. Comments by reviewers on an earlier
draft substantially improved the manuscript. This is publication 10-003
of the EcoFore Lake Erie project, funded by the NOAA Center for
Sponsored Coastal Ocean Research under award NA07OAR4320006.
This work was performed under the authority of Section 516(e) of the
Water Resources Development Act of 1996 through additional funding

by the United States Army Corps of Engineers.
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