Published June 23, 2014
Journal of Environmental Quality
Special Section
Applications of the SWAT Model
Applications of the SWAT Model Special Section:
Overview and Insights
Philip W. Gassman,* Ali M. Sadeghi, and Raghavan Srinivasan
H
ydrological and water quality simulation models
are being used increasingly to address an extensive
array of water resource problems across the globe,
including the effects of alternative best management practices
(BMPs) and future climate change on streamflow and water quality. A plethora of such simulation models have been developed in
recent decades to address various water quantity and water quality problems. These models are designed to operate across a range
of scales (e.g., field versus watershed) and environmental conditions, and with varying levels of input data and model structure
complexity. Dozens of review studies have been compiled that
provide comparisons of either specific components or complete
modeling packages for various subsets of existing water quality
models (e.g., Borah et al., 2006; Breuer et al., 2008; Refsgaard et
al., 2010; Daniel et al., 2011).
The Soil and Water Assessment Tool (SWAT) model
(Arnold et al., 1998; Williams et al., 2008) has emerged as
one of the most widely used water quality watershed- and river
basin–scale models worldwide, representing multiple decades
of model development (Gassman et al., 2007; Williams et al.,
2008; Arnold et al., 2012b). The model is supported by online
documentation (Neitsch et al., 2011; Arnold et al., 2012a),
multiple geographic information system (GIS) interface tools
(e.g., Di Luzio et al., 2004; Olivera et al., 2006), other supporting
software (e.g., White et al., 2014a, 2014c), and online resources
(SWAT, 2013). SWAT has also proved to be highly flexible in
addressing a wide range of water resource problems, as a result of
the comprehensive nature of the model, strong model support,
and open access status of the source code. The publication,
citation, and search engine analysis reported by Refsgaard et al.
(2010) confirm the widespread use of SWAT relative to several
other leading hydrologic and water quality models.
The primary goal for this special collection of papers is to
highlight important trends, successes, and problems related to
The Soil and Water Assessment Tool (SWAT) model has emerged
as one of the most widely used water quality watershed- and river
basin–scale models worldwide, applied extensively for a broad range
of hydrologic and/or environmental problems. The international
use of SWAT can be attributed to its flexibility in addressing water
resource problems, extensive networking via dozens of training
workshops and the several international conferences that have been
held during the past decade, comprehensive online documentation
and supporting software, and an open source code that can be
adapted by model users for specific application needs. The catalyst
for this special collection of papers was the 2011 International
SWAT Conference & Workshops held in Toledo, Spain, which
featured over 160 scientific presentations representing SWAT
applications in 37 countries. This special collection presents 22
specific SWAT-related studies, most of which were presented at the
2011 SWAT Conference; it represents SWAT applications on five
different continents, with the majority of studies being conducted
in Europe and North America. The papers cover a variety of topics,
including hydrologic testing at a wide range of watershed scales,
transport of pollutants in northern European lowland watersheds,
data input and routing method effects on sediment transport,
development and testing of potential new model algorithms, and
description and testing of supporting software. In this introduction
to the special section, we provide a synthesis of these studies within
four main categories: (i) hydrologic foundations, (ii) sediment
transport and routing analyses, (iii) nutrient and pesticide transport,
and (iv) scenario analyses. We conclude with a brief summary of key
SWAT research and development needs.
P.W. Gassman, Center for Agricultural and Rural Development, Iowa State Univ.,
Ames, IA, 50010-1070; A.M. Sadeghi, USDA–ARS, Hydrology and Remote Sensing
Laboratory Bldg. 007, Rm. 104, BARC-West Beltsville, MD 20705-2350; R. Srinivasan,
Spatial Sciences Laboratory, 1500 Research Plaza, Office 221E, Texas A&M
University, College Station, TX 77845. Assigned to Associate Editor Colleen Rossi.
Copyright © American Society of Agronomy, Crop Science Society of America,
and Soil Science Society of America. 5585 Guilford Rd., Madison, WI 53711 USA.
All rights reserved. No part of this periodical may be reproduced or transmitted
in any form or by any means, electronic or mechanical, including photocopying,
recording, or any information storage and retrieval system, without permission in
writing from the publisher.
Abbreviations: BMP, best management practice; DEM, Digital Elevation
Model; EWFD, European Water Framework Directive; GCM, general circulation
model; GIS, geographic information system; LTC, landscape transport capacity;
MUSLE, Modified Universal Soil Loss Equation; NSE, Nash-Sutcliffe modeling
efficiency; PBIAS, percent bias; RCN, runoff curve number; SWAT, Soil and Water
Assessment Tool.
J. Environ. Qual. 43:1–8 (2014)
doi:10.2134/jeq2013.11.0466
Received 22 Nov. 2013.
*Corresponding author ([email protected]).
1
the growing adoption of SWAT across a wide range of watershed
scales and environmental conditions. The specific catalyst
for this effort was the 2011 International SWAT Conference
held in Toledo, Spain, which included over 160 presentations
representing a wide variety of innovative investigations with
SWAT in 37 countries. Most of the 22 papers included in this
special section were originally presented at the 2011 conference
and feature many of the key global application trends occurring
with the model (Table 1) via studies performed on five different
continents. Virtually all of the studies contained herein report
some level of hydrologic testing. The majority also describe
testing of SWAT output for one or more environmental
indicators. Several additional themes are woven across these
SWAT applications, as described in Table 1.
The purpose of this introductory paper is to present a concise
overview of the studies included in the special collection,
including innovative applications and adaptations of the SWAT
code, as well as strengths and weaknesses of the simulation
Table 1. Summary of studies included in this special collection of papers on the Soil and Water Assessment Tool (SWAT).
Study
Almendinger et al.
(2014)
Study region
Western Minnesota and
eastern Wisconsin
SWAT version
SWAT2000
Northern Tunisia
Watershed(s)
717-km2 Willow
(MN) and 991-km2
Sunrise (WI)
418-km2 Joumine
Aouissi et al. (2014)
Piniewski et al. (2014)
Northeast Poland
28,000-km2 Narew
SWAT2005
Roebeling et al.
(2014)
Santhi et al. (2014)
Central Portugal
3685.5-km2 Vouga
East-central U.S.
525,770-km2 Ohio
ArcSWAT
version 2009.93.7‡
SWAT2005‡
Beeson et al. (2014)
Bieger et al. (2014)
Boithias et al. (2014)
Bonumá et al. (2014)
Cerro et al. (2014)
Fohrer et al. (2014)
Hoang et al. (2014)
Jeong et al. (2014)
Lu et al. (2014)
Molina-Navarro et al.
(2014)
Ostojski et al. (2014)
Pagliero et al. (2014)
Watson and Putz
(2014)
Description of SWAT application†
Use of pond, wetland, and USLE P functions to depict
sediment trapping in landscape depressions
SWAT2009
Autocalibration of daily flow and nitrate output; evaluation
of alternative nitrogen-related BMPs
North-central Iowa
788-km2 S. Fork Iowa
SWAT2009
Effects of DEM resolution on slope estimation and resulting
River
(revision 510)
streamflow and pollutant estimates
Central China
3200-km2 Xiangi
SWAT2009
Streamflow and sediment testing as function of limited
(revision 477)
input and monitoring data constraints
Southwestern France
1110-km2 Save
ArcSWAT
Estimation of daily exceedance of EU nitrate standard at
version 2009.93.7a
watershed outlet
SWAT2009
Impacts on sediment transport due to introduction of
Southern Brazil
4.8-km2 Arroio Lino
landscape units in SWAT
Northern Spain
53-km2 Alegra
ArcSWAT
Evaluation of alternative nitrogen-related BMPs and likely
subwatershed
version 2009.93.5‡
exceedance of EU nitrate standards
Northern Germany
50-km2 Kielstau
ArcSWAT
Evaluation and sensitivity of herbicide fate and transport for
version 93.6
lowland region watershed
Funen Island in Denmark 622-km2 Odense
SWAT2005
Streamflow and nitrate transport in tile-drained watershed;
comparisons with DMS model output
North-central Texas
369-km2 Upper
SWAT2009‡
Effects of extreme urbanization and climate change on
White Rock
streamflow and associated stream health
Funen Island in Denmark 4.28-km2 Lillebaek
SWAT2009
Autocalibration of daily flow and sediment output;
comparison of two sediment routing methods
Central Spain
87.8-km2 Ompólveda
SWAT2005
Evaluation of the utility of the model for assessing
effectiveness of limno-reservoir
Northwest Poland
593.7-km2 Gąsawka,
Not reported
Comparison of simulated vs. measured nutrient
2766.8-km2 Rega,
concentrations for three different sized watersheds
and 54,500-km2
Warta
Eastern Europe
803,000-km2 Danube
SWAT2009
Autocalibration/validation approach for flow using nested
watersheds for the Danube River basin
West-central Alberta
Climate change impacts on stream health; includes
comparisons with WaterGAP model
Evaluation of nitrogen application rate BMP scenarios;
economic impacts also reported
BMP impact evaluation using a combined APEX-SWAT
modeling system for the Ohio River basin
Evaluation of different snowmelt algorithms in SWATBF, a
modified SWAT model applied to Boreal Forest conditions
3.0-km2 Mosquito SWAT Boreal Forest
5.2-km2 1A, 9.5-km2
(SWATBF)
Thistle, 15.0-km2
Willow, and 13.3-km2
Groat Creek
White et al. (2014a) Three example watersheds 260-km2 case I;
Not applicable§
Description of SWAT Check screening tool software,
3500-km2 case II, and
including three example applications
240,000-km2 case III
White et al. (2014b) Northeast Oklahoma and 4600-km2 Illinois
SWAT2005‡
Description, testing, and application of proposed modified
northwest Arkansas
in-stream P cycling submodel
White et al. (2014c) Four southern U.S. states Field sites ranging
SWAT2005‡
Description of PPM Plus interface tool (based on SWAT);
from 0.4 to 17.9 ha
validation with field data presented
Zabaleta et al. (2014)
Northern Spain
4.8-km2 Aixola
ArcSWAT
Impacts of climate change on flow and sediment loss,
version 2009.93.6
including sediment transport to reservoir
† BMP, best management practice; DEM, Digital Elevation Model; DMS, DAISY-MIKE SHE; USLE P, Universal Soil Loss Equation practice.
‡ Based on personal communication with authors (not reported in respective papers).
§ The SWAT Check software is compatible with SWAT versions 2005, 2009, and 2012 (2012 supersedes version 2011 stated in White et al. [2014a]).
2
Journal of Environmental Quality
results. The specific objectives are to provide (i) a synthesis of
the key results of this set of studies categorized by foundational
hydrologic results, pollutant transport and routing, and scenario
applications and (ii) a summary of future SWAT research and
development needs.
quality model output should be ≥0.5 or 0.75 for monthly timestep comparisons in order for the model results to be considered
satisfactory or good, respectively. Moriasi et al. (2007) further
suggested “appropriate adjustments” in their criteria for annual
or daily time-step evaluations. Table 2 lists the annual, monthly,
and daily hydrologic R2 and NSE statistics by frequency range
reported within the present collection of papers. Nearly all of
these statistics exceed Moriasi et al.’s (2007) criteria of 0.5 or
greater for evaluation of monthly NSE comparisons, assuming an
extension of the criteria to the R2 statistics and other time steps.
The strongest results are reported for the aggregated annual and
monthly time steps, which is consistent with previous statistical
compilations reported by Gassman et al. (2007), DouglasMankin et al. (2010), and Tuppad et al. (2011). However, over
half of the studies report relatively strong daily R2 and NSE
hydrologic statistics, which mirrors trends of increasing numbers
of SWAT studies reporting successful testing at a daily time step
(e.g., Douglas-Mankin et al., 2010; Tuppad et al., 2011).
Synthesis of Studies in the Special Collection
The synthesis of the 22 papers that form this special collection
(Table 1) is organized per the following four main categories:
hydrological foundations, sediment transport and routing
analyses, nutrient and pesticide transport, and scenario analyses.
The specific watersheds simulated and SWAT versions used in
each study are listed in Table 1.
Hydrological Foundations
Some level of hydrologic testing was performed in the
majority of the studies presented here, including one or more of
the phases described by Arnold et al. (2012b): sensitivity and/
or uncertainty analyses, manual and/or automatic calibration,
subsequent validation, and the use of graphical and/or statistical
measures to judge model results. Three of the studies (Cerro et
al., 2014; Santhi et al., 2014; Zabaleta et al., 2014) also report
using the SWAT Check software described by White et al.
(2014a) within their respective calibration analyses to confirm
the appropriateness of water balance and other results.
Hydrograph and Flow Path Representation
Graphical comparisons of simulated versus measured
streamflows presented in several of the studies reveal that SWAT
accurately or satisfactorily replicate observed total streamflow
hydrographs. However, several studies explicitly state, or show
via hydrograph comparisons, that daily and/or monthly peak
flows were typically underestimated (Bieger et al., 2014; Boithias
et al., 2014; Cerro et al., 2014; Fohrer et al., 2014; Hoang et al.,
2014; Molina-Navarro et al., 2014; Pagliero et al., 2014; Santhi
et al., 2014; Watson and Putz, 2014; Zabaleta et al., 2014). In
contrast, some studies report overprediction of peak flows
(Bieger et al., 2014; Hoang et al., 2014; Piniewski et al., 2014),
weaknesses in seasonal representation of streamflows (Piniewski
et al., 2014), or underprediction of low flow periods (Hoang et
al., 2014). Hoang et al. (2014) also note that SWAT accurately
replicated measured daily hydrographs but overpredicted surface
runoff and conversely underpredicted subsurface drainage flow.
Statistical Testing Results
The most commonly reported statistics are the coefficient
of determination (R2) and Nash-Sutcliffe modeling efficiency
(NSE), described in detail by Krause et al. (2005). Many of the
studies also report the percent bias (PBIAS), which measures the
average tendency of modeled output to be larger or smaller than
corresponding measured data (Moriasi et al., 2007). The majority
of the studies also cite Moriasi et al. (2007) in regards to judging
the success of SWAT testing results, especially their suggested
criteria that NSE statistics computed for hydrologic and water
Table 2. Summary of Soil and Water Assessment Tool (SWAT) hydrologic calibration and validation statistic frequency analyses for the present
studies.†‡
Frequency ranges
Annual
Calibration
Validation
R2
NSE
R2
NSE
Monthly
Calibration
Validation
R2
NSE
R2
NSE
Calibration
R2
NSE
Validation
R2
NSE
Number of studies
1
3
1
2
6
12
10
6
12
4
10
0.90–1.00
0.80–0.89
0.70–0.79
0.60–0.69
0.50–0.59
0.40–0.49
0.30–0.39
0.20–0.29
0.10–0.19
0.00–0.09
<0.00
3
1
3
1
1
3
2
2
1
5
4
1
1
12
8
3
2
7
7
3
2
3
2
1
3
1
1
3
6
9
5
1
2
1
5
8
7
1
1
1
5
6
2
1
1
1
Daily
† Almendinger et al. (2014) report daily Nash-Sutcliffe modeling efficiency (NSE) validation statistics of 0.51 or 0.63 for the Willow River watershed in
northwest Wisconsin, depending on whether a large precipitation event in October 2005 was included in the validation (i.e., the daily NSE improved to
0.63 when the event was excluded).
‡ Watson and Putz (2014) report six different sets of monthly and daily NSE calibration and validation statistics, corresponding to the six different
snowmelt algorithms they investigated, for the five watersheds they simulated with SWATBF; only the standard SWAT snowmelt algorithm results are
reported here (the results of the six snowmelt submodels were very similar).
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3
Likewise, Fohrer et al. (2014) report good or very good overall
hydrograph results but weaknesses in SWAT’s replication of
hydrograph falling limbs and representation of groundwater
storage depletion.
Watson and Putz (2014) report that simulation of snowmelt
runoff with SWATBF during multiple snowmelt seasons ranged
from good to very good across the six different snowmelt
submodels they tested. However, years with lower snowfall
amounts and thus lower snowmelt runoff were not as accurately
predicted. They also state that the use of more detailed snowmelt
algorithms did not provide improved accuracy in estimating
snowmelt runoff.
Input Data and Model Structure Limitations
Some weaknesses in the studies’ reported results can be
attributed to problems resulting from input data. Two extreme
examples are seen in Bieger et al. (2014), who report that only
three climate stations were available for their simulation of the
3200-km2 Xiangi watershed in central China, and Lu et al.
(2014), who used 10-km gridded precipitation data to simulate
the 4.28-km2 Lillebaek watershed on Funen Island in Denmark.
It is virtually certain that these inaccurate spatial representations
of precipitation data negatively affected their SWAT hydrologic
simulations. Zabaleta et al. (2014) and Watson and Putz (2014)
also discuss problems with precipitation data inaccuracies.
Almendinger et al. (2014) modified SWAT to allow seepage
from ponds, wetlands, and reservoirs to be added to lateral and
groundwater flow paths, rather than be lost from the system, for
the watersheds they simulated in Minnesota and Wisconsin.
Beeson et al. (2014) modified SWAT to better represent pothole
systems (described further in Beeson et al., 2011). They also
discuss making larger-than-typical reductions in the runoff curve
number (RCN; Williams et al., 2012) in their calibration process
to more accurately simulate tile drains and pothole depression
effects for their study watershed in north-central Iowa. Fohrer et
al. (2014) note that the incorporation of tile drains and wetlands
in their SWAT simulation of lowland conditions in northern
Germany greatly improved the simulated hydrologic results.
Sediment Transport and Routing Analyses
Eight of the special collection papers report sediment yield
and/or transport results (Almendinger et al., 2014; Beeson et al.,
2014; Bieger et al., 2014; Bonumá et al., 2014; Lu et al., 2014;
Santhi et al., 2014; White et al., 2014c; Zabaleta et al., 2014).
These studies describe a wide range of factors that impacted
sediment yield and transport results, including watershed
delineation and input data resolution, variations in landscape
features, and sediment routing methods.
Baseline Sediment Yield Testing Results
Zabaleta et al. (2014), Almendinger et al. (2014), and Beeson
et al. (2014) report mostly successful calibration and validation
of sediment yield results, with R2 and NSE values ranging from
0.47 to 0.94 for daily and/or monthly time-step comparisons.
Bonumá et al. (2014), Bieger et al. (2014), and Lu et al. (2014),
on the other hand, report weaker statistical results, especially
NSE values, in their respective validation periods, which ranged
between -12.1 and 0.38. However, all three of these studies note
important problems related to available measured sediment
4
yield data. These include the need for more extensive datasets to
perform in-depth model testing (Bonumá et al., 2014), lack of
data reliability (Bieger et al., 2014), and difficulty in obtaining
high statistical simulation accuracy due to the small magnitude
of measured sediment yields (Lu et al., 2014).
Input Data Effects and Issues
Problems with temporal and/or spatial precipitation data
accuracy were a probable source of inaccuracies in sediment
yield results for some studies (e.g., Zabaleta et al., 2014; Bieger
et al., 2014; Lu et al., 2014). Bieger et al. (2014) also note that
the coarse resolution of the Digital Elevation Model (DEM),
soil, and land use data they used, as well as problems in the
transferability of the Modified Universal Soil Loss Equation
(MUSLE) approach (Williams and Berndt, 1977), may have
introduced further error in their sediment yield results. In
contrast, Zabaleta et al. (2014) demonstrate the need to
capture sensitive combinations of extreme slopes and land use,
underscoring the importance of detailed watershed delineation
schemes for some SWAT applications.
Beeson et al. (2014) evaluated the impact of slope values
calculated from DEMs with different resolutions (90, 30, 10,
and 3 m) on sediment load. Their assessment reveals that a 2.5fold increase in slope occurred when using a high-resolution
3-m DEM instead of a low-resolution 90-m DEM, resulting in
a predicted sediment loss increase of 130%. The authors suggest
that care should be taken when using high-resolution DEMs to
parameterize SWAT because this could result in significantly
higher slopes, which can considerably alter modeled sediment
loss. They also argue that the low-resolution DEMs are not
consistent with older MUSLE technology.
Several of the studies (Almendinger et al., 2014; Bieger et al.,
2014; Lu et al., 2014; Bonumá et al., 2014) report reducing the
USLE Practice (P) factor (Wischmeier and Smith, 1978), which
is normally set to 1.0 when no conservation practices are present
in a field or watershed, to more accurately simulate soil erosion
with the MUSLE equation in SWAT. These P factor adjustments
reveal simplistic depictions of surface feature effects in the
respective watersheds and show the need for more mechanistic
methods to capture these processes.
Sediment Routing Approaches and Issues
Lu et al.’s (2014) study demonstrates improvement in channel
erosion estimates by calculating the erosion on the basis of
excess sheer stress compared with the existing channel sediment
routing method used in SWAT (Neitsch et al., 2011) for lowland
watershed conditions. Specifically, the proposed methods divide
channel erosion into river bank erosion and river bed erosion,
which is a more appropriate approach for modeling lowland
areas, where bank erosion contributes more than surface erosion
to sediment loads. The authors conclude that the modified
approach more accurately simulated peak and low values of
weekly flow-weighted suspended concentration and thus was
more appropriate for the simulated lowland conditions.
Bonumá et al.’s (2014) work introduces a landscape sediment
routing method into SWAT as follows: (i) determination of
landscape units using a landscape delineation routine (Volk
et al., 2007), (ii) computation of the landscape transport
capacity (LTC) of sediment, and (iii) computation of sediment
Journal of Environmental Quality
deposition by comparing the eroded sediment with the LTC
to limit the sediment delivery from the hydrolic response units
to the stream channels. The authors tested the new routing
method versus the standard SWAT model for the Arroio Lino
watershed in southern Brazil, concluding that the modified
approach more accurately simulates sediment delivery for the
steep and depositional topography that characterizes the study
watershed, as evidenced by an improvement in the sediment
yield PBIAS from -73 to -18%. The LTC method also
indicated that 60% of the mobilized soil was deposited before
it reached the stream channels, a phenomenon not captured by
the standard SWAT model.
The study by Almendinger et al. (2014) demonstrates that
SWAT was able to replicate the relatively low sediment yields
measured at the watershed outlets the authors simulated in
Minnesota and Wisconsin, primarily by accounting for the
effects of depressional features. However, the authors note that
“excessive parameterization” was required to perform the analysis.
They further indicate the need to modify the SWAT code to
better account for the surface water–groundwater interactions
of depressional features in lowland landscapes as well as adopting
the landscape position approach described by Volk et al. (2007)
and Bonumá et al. (2014).
Nutrient and Pesticide Transport
Eight of the present studies report testing of nutrient
transport simulations in SWAT (Aouissi et al., 2014; Boithias
et al., 2014; Cerro et al., 2014; Hoang et al., 2014; Ostojski et
al., 2014; Santhi et al., 2014; White et al., 2014b, 2014c), while
Fohrer et al.’s (2014) study describes testing SWAT pesticide fate
and transport. These authors provide further insights regarding
input data uncertainty and/or structural limitations of SWAT
for their respective analyses.
Baseline Nutrient and Pesticide Transport Testing Results
Cerro et al. (2014), Hoang et al. (2014), White et al. (2014b),
and Fohrer et al. (2014) report mostly strong daily and monthly
nutrient or pesticide baseline NSE statistics for their respective
studies, the majority of which ranged from 0.37 to 0.88. Hoang
et al. (2014) note a weaker daily nitrate NSE of 0.37 for reasons
discussed below. Fohrer et al. (2014) report stronger daily
statistics for the pesticide Metazachlor (NSE = 0.68; R2 =
0.62) compared with the pesticide Flufenacet (NSE = 0.13; R2
= 0.51), but graphical comparisons indicate that simulation of
both pesticides replicated overall measured trends. Boithias et
al. (2014) computed satisfactory PBIAS results of 10 to 27%
for daily and annual nitrate loads for the Save River in France.
Ostojski et al. (2014) also report satisfactory PBIAS results for
their simulation of total nitrogen and total phosphorus for three
different watersheds in Poland, although their overall results
suggest considerable uncertainty in their simulated output.
Aouissi et al. (2014) state that simulated nitrate concentrations
varied in the same range as a limited set of corresponding
measured concentrations for their SWAT analysis in Tunisia.
Input Data and Modeling Structure Limitations
Several studies in this special section describe uncertainty in
determining accurate rates and timing of nutrient or pesticide
applications due to several factors. These include having to base
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application assumptions on provincial-level data (Ostojski et al.,
2014), deriving rate and timing from surveys of farmers in the
study watersheds (Fohrer et al., 2014; Aouissi et al., 2014), or
not taking into account permutations in crop rotations (Hoang
et al., 2014). From a sensitivity analysis they performed, Fohrer
et al. (2014) indicate that their SWAT watershed-scale pesticide
transport results were highly sensitive to pesticide application
rate and timing. Other input data issues include uncertainties
related to assumed half-life and other key pesticide properties
(Fohrer et al., 2014), as well as a lack of accounting for septic
tank effects (Aouissi et al., 2014).
Hoang et al. (2014) note that SWAT simulated overall
accurate nitrate loads but that the model simulated twice as
much nitrate transport via groundwater flow compared with
tile drain flow, which is inconsistent with other studies in the
region. They also state that inaccurate partitioning among flow
components likely contributed to the weaker replication of daily
nitrate fluxes. Fohrer et al. (2014) also point to flow partitioning
problems in their SWAT pesticide transport study and note that
they were not able to directly simulate pesticide transport via
tile drains or shallow aquifers due to structural weaknesses in
the SWAT code. Boithias et al. (2014) report that 55% of the
simulated nitrate transport in their study occurred in surface
runoff, which is inconsistent with previous research showing
lateral and groundwater as the main observed nitrate pathways.
White et al. (2014c) modified SWAT to more accurately
simulate in-stream phosphorous (P) cycling for stream systems
dominated by attached algae or strongly affected by point
sources. The modified in-stream P cycling submodel features
an equilibrium P concentration approach that is coupled
to a particulate scour and deposition module. White et al.
(2014c) applied the modified SWAT model to the Illinois
River, which drains portions of Arkansas and Oklahoma, and
which is greatly impacted by P discharge from point sources
and widespread application of poultry manure to pasture areas.
The modified approach captured the spatial and temporal
trends of highly variable P concentrations and equilibrium P
concentrations more accurately than did the approach used in
existing SWAT codes.
Scenario Analyses
Several studies in this special section report scenario analyses
results, including the evaluation of BMP impacts on water
quality (Aouissi et al., 2014; Boithias et al., 2014; Cerro et al.,
2014; Roebeling et al., 2014; Santhi et al., 2014) and climate
change impacts on sediment yields or stream health ( Jeong et
al., 2014; Piniewski et al., 2014; Zabaleta et al., 2014). These
analyses provide numerous useful insights, including effective
approaches that can be used to meet European Water Framework
Directive (EWFD) and other governmental agency water quality
standard goals.
Climate Change Impacts
Zabaleta et al. (2014) present an analysis of climate
change impacts on runoff and sediment yield for the Axiola
watershed by inputting climate projections from four climate
change projections, representing combinations of two general
circulation models (GCMs) and two scenarios, for 2011 to 2100,
in SWAT. Three of the GCM-scenario combinations suggested
5
that runoff and sediments would decrease every year from 2011,
but the other combination resulted in a predicted increase in
runoff and sediments every year from 2011. These variations
in annual sediment yield can be attributed to differences in the
precipitation estimated between the GCMs.
Piniewski et al. (2014) and Jeong et al. (2014) used SWAT
to study the impact of climate change on stream health for
watersheds in northeastern Poland and north-central Texas,
respectively. Piniewski et al. (2014) evaluated stream health on
the basis of environmental flows (the quantity of water needed to
maintain a healthy river ecosystem), while Jeong et al. (2014) used
a set of 67 parameters developed for an EWFD methodology to
assess stream health. Piniewski et al. (2014) conclude that future
climate change, based on two GCM projections for the period
2040 to 2069, will have a considerable impact on the majority of
the Narew River aquatic ecosystems and that there is significant
variability (both in terms of magnitude and spatial) in the
projected impacts of climate change on the environmental flow
indicators. In contrast, Jeong et al. (2014) split historical climate
data into dry, wet, and average conditions, and historical flow
data into pre-urbanized and post-urbanized periods, to evaluate
alteration in stream health due to all three climate conditions.
They also analyzed the effect of urbanization on stream health
for a specific subwatershed where dramatic urbanization
occurred during the 1980s and 1990s. Their results indicate that
increasing urbanization had negative impacts on stream health
and that dry weather had more impact on stream health than did
wet weather.
BMP Assessment Studies
Boithias et al. (2014), Cerro et al. (2014), and Aouissi et al.
(2014) each evaluated a 20% nitrogen application rate reduction
scenario with SWAT for their respective study regions in France,
Spain, and Tunisia. Relatively minor crop yield reductions
were predicted: 5 to 9% (Boithias et al., 2014), 3% (Cerro et
al. (2014), and 5% (Aouissi et al., 2014). Boithias et al. (2014)
and Cerro et al. (2014) report that the number of predicted days
exceeding the EWFD nitrate concentration standard of 50 mg
L-1 declined by 62 and 50%, respectively, while Aouissi et al.
(2014) indicate a 20% decline in average nitrate concentrations.
Cerro et al. (2014) and Aouissi et al. (2014) also report nitrate
reduction results for other application rate and/or filter strip
scenarios. Roebeling et al. (2014) assessed various N-fertilizer
applications as best agricultural practice (BAP) scenarios, as
a function of percent reductions of the baseline N application
rates, for the Vouga watershed in central Portugal. They found no
“win-win” results; i.e., reductions in N losses to the Vouga stream
system were not beneficial enough to overcome agricultural
production losses.
Santhi et al. (2014) interfaced SWAT with the farm-scale
Agricultural Policy Environmental Extender (APEX) model
(Williams et al., 2008), which they used to estimate cropland
sediment and nutrient pollutant loads with more detailed
representation of an extensive set of conservation practices. These
practices include contouring, strip cropping, contour buffer
strips, terraces, conservation tillage, filter strips, field borders,
cover crops, and modifications in nutrient application rates
and timing. Santhi et al. (2014) found that using conservation
practices on cropland in the Ohio River basin reduced sediment,
6
nitrogen, and phosphorus loads delivered to the Ohio River by
15, 16, and 23%, respectively.
Conclusions and Future Research Needs
In summary, the findings from the 22 studies reported in
this special section provide solid evidence that SWAT is an
effective tool for many different types of water resource and
land management problems. The ongoing support of SWAT
by government and private educational institutions together
with its flexibility has resulted in increasing numbers of
innovative applications and adaptations, helping to explain
its growing adoption worldwide. But specific weaknesses
encountered in some of the studies presented here clearly
show that expanded testing and/or specific improvements are
needed, as discussed below.
Hydrologic Interface
Several of the present studies report RCN-related calibrations
(e.g., Aouissi et al., 2014; Beeson et al., 2014; Boithias et al.,
2014; Cerro et al., 2014; Zabaleta et al., 2014), which is common
in many SWAT studies due to the empirical nature of the method
(Gassman et al., 2007; Arnold et al., 2012b). These results stress
the need for further RCN-related research, as well as more
investigation of the Green-Ampt method (Green and Ampt,
1911), which has been tested in very few SWAT studies (e.g.,
Ficklin and Zhang, 2013; King et al., 1999). Alternative RCN
approaches have also been used in modified SWAT applications
(e.g., Easton et al., 2008; Kim and Lee, 2008), which warrant
further research and possible incorporation into standard
versions of SWAT.
Erosion Estimation and Sediment Routing
The studies performed by Almendinger et al. (2014), Beeson
et al. (2014), Bieger et al. (2014), Bonumá et al. (2014), and Lu
et al. (2014) indicate that continued testing and development
is required for the sediment delivery and sediment routing
algorithms used in SWAT, including (i) expanded testing of
sensitive inputs to the MUSLE sediment delivery routine,
possible modifications to the MUSLE algorithms, and/
or possible incorporation of alternative sediment delivery
methods (e.g., Lenhart et al., 2005); and (ii) improved routing
methods for different types of landscapes, which could also
account for depressions, riparian areas, and other landscapespecific features. The LTC approach introduced by Bonumá et
al. (2014) is particularly promising, building on the “landscape
unit” approach described by Volk et al. (2007) and Arnold et
al. (2010).
Subsurface Tile Drainage
SWAT has been used successfully in previous watershed
applications that included subsurface tile drainage (e.g.,
Beeson et al., 2011). However, the results presented by Hoang
et al. (2014) indicate that SWAT apparently underpredicted
subsurface tile drainage flows. Fohrer et al. (2014) also comment
on the need to decouple SWAT tile drainage pesticide transport
from subsurface lateral transport. These findings reveal the need
for further testing of both the original SWAT subsurface tile
drainage algorithms and new tile drainage algorithms developed
Journal of Environmental Quality
by Moriasi et al. (2012; 2013); the latter have been incorporated
in recently released SWAT versions.
Nutrient Cycling and Transport
Modifications in nutrient routines reported by White
et al. (2014b) reveal the need for improved in-stream P
cycling algorithms in SWAT. These results confirm previous
conclusions by Borah et al. (2006) and Breuer et al. (2008),
who stated that extensive future research will be required to
improve various SWAT soil, impoundment, and in-stream
nutrient cycling components to better replicate actual physical
nutrient cycling processes.
Uncertainty and Sensitivity Analyses
Several of the studies herein report performing sensitivity
analyses (e.g., Aouissi et al., 2014; Fohrer et al., 2014; Hoang et
al., 2014; Zabaleta et al., 2014) or autocalibration (e.g., Beeson
et al., 2014; Ostojski et al., 2014; Piniewski et al., 2014; Watson
and Putz, 2014) of selected subsets of SWAT inputs. However,
these studies report no explicit uncertainty analysis results,
despite extensive uncertainty evident in many of the studies
(e.g., Bieger et al., 2014; Ostojski et al., 2014). These results
underscore the need for increased uncertainty analyses in future
SWAT applications per the protocols outlined by Arnold et al.
(2012b).
Model Validation
Hoang et al. (2014) note that users of SWAT need to bear
in mind that the calibrated modeling results should reasonably
reflect the actual hydrologic and pollutant transport processes,
similar to some of the issues discussed by van Griensven et al.
(2012). Refsgaard et al. (2010) also cited several other important
calibration and validation issues relevant to SWAT applications,
including unrealistic calibration efforts due to calibrating too
many model parameters. In addition, further research is needed
that builds on the influential statistical evaluation criteria
suggested by Moriasi et al. (2007).
Acknowledgments
We sincerely thank all of the authors and coauthors for their timeless
efforts to produce this special collection of papers on SWAT. Next, we
extend our great appreciation to all of the reviewers for their valuable
time and effort that resulted in significant improvements to all of
the papers in this special collection. Finally, we express our heartfelt
appreciation to our colleague Dr. Jeffrey G. Arnold, the lead developer
of SWAT, who along with other members of the “SWAT Team” (at
Temple, TX, College Station, TX, and elsewhere) has led the worldwide
use and improvement of SWAT among a vast number of research teams,
government agencies, and other users. Lastly, we appreciate the JEQ
Editorial Board for their utmost help and patience throughout the
review process, as well as the logistical aspects of this special collection
of papers.
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