LANDSCAPE MODELS FOR SIMULATING WATER QUALITY
AT POINT, FIELD, AND WATERSHED SCALES
P. Srivastava, K. W. Migliaccio, J. Šimůnek
ABSTRACT. In the last four decades, a plethora of models has been developed to simulate nonpoint‐source (NPS) pollutant
fate and transport at point, field, and watershed scales. Developed by experts in various disciplines, these models tend toreflect the needs of those disciplines. For example, the original intent of the solute transport models was to determine impact
of water, nutrient, and salts on plant growth. Later, these models were extended to examine solute transport in the vadose zone
to assess possible contamination of soil and groundwater. Similarly, a number of field‐ and watershed‐scale models have beendeveloped by linking together submodels of system components to quantify best management practice (BMP) effectivenessand watershed‐level impact. New model users are often unaware of the suite of models available and are often uncertain about
the appropriateness of models for their situation. The goals of this article are to discuss why NPS pollutant models weredeveloped at various spatial scales (i.e., scale issues), briefly review commonly used models, and reflect on the future oflandscape NPS models. Since the computational power of computers has significantly increased, automated data acquisition
systems that can capture and transmit data at high resolution are being used, software that can handle large volumes of data
has been developed, and improved chemical analysis capabilities are being developed, we conclude this article with the
projection that development of a scale‐independent model that can address complex issues of the next century by couplingstate‐of‐the‐art understanding of multiple hydrogeological, geochemical, and microbiological processes is possible. Future
improvement in these models will result in a more scientific and robust approach for managing NPS pollutants.
Keywords. Field, Hydrology, Model, Runoff, Scaling, TMDL, Water quality, Watershed.

M

odeling nonpoint‐source (NPS) pollutant fate
and transport processes across multiple scales
is fundamental to addressing a number of envi‐
ronmental and natural resource issues, includ‐ing degradation of soil and contamination of surface andground waters. Landscape NPS models are currently used fora variety of purposes, including registration of pesticides andother agrochemicals, designing soil conservation practices,
water table management, prevention of chemical pollution of
surface water bodies and groundwater, protection of aquaticbiota and development of Total Maximum Daily Loads(TMDLs). Virtually all federal agencies currently have theirown models or support models developed elsewhere. Ac‐
cording to Singh and Woolhiser (2002), “In the years ahead,the models will become even more common and will play anincreasing role in our day‐to‐day lives.”

Submitted for review in March 2007 as manuscript number SW 6915;
approved for publication by the Soil & Water Division of ASABE in June2007 as a contribution to the ASABE 100th Anniversary Soil and Water
Invited Review Series.
The authors are Puneet Srivastava, ASABE Member Engineer,
Assistant Professor, Department of Biosystems Engineering, Auburn
University, Auburn, Alabama; Kati W. Migliaccio, ASABE Member
Engineer, Assistant Professor, Extension Specialist, Institute of Food andAgricultural Sciences, Department of Agricultural and BiologicalEngineering, University of Florida, Homestead, Florida; and JiriŠimůnek, Professor, Department of Environmental Sciences, University ofCalifornia, Riverside, California. Corresponding author: Puneet
Srivastava, Department of Biosystems Engineering, Auburn University,
200 Corley Bldg., Auburn, AL 36849; phone: 334‐844‐7426; fax:
334‐844‐3530; e‐mail: srivapu@auburn.edu.

Since NPS pollutant transport is mainly driven by meteo‐
rological events, the early to mid‐20th century saw the devel‐
opment of mathematical descriptions of individualhydrologic components (e.g., infiltration, runoff, evapotran‐
spiration, and interception). The digital revolution of 1960s
witnessed the integration of individual hydrologic compo‐
nents (Singh and Woolhiser, 2002) into functional models
that can be applied at various spatial and temporal scales. The
development of NPS models in the U.S. began in response to
the Clean Water Act (Arnold and Fohrer, 2005). Examples ofthese models include AGNPS (Young et al., 1987), An‐
nAGNPS (Bingner and Theurer, 2003), ANSWERS (Bou‐raoui and Dillaha, 1996, 2000), APEX (Williams andIzaurralde, 2005), CREAMS (Knisel 1980), DRAINMOD(Skaggs, 2007), EPIC (Williams et al., 1984), GLEAMS(Leonard et al., 1987; Knisel and Davis, 1999), GWLF (Haith
and Shoemaker, 1987), HSPF (Bicknell et al., 2001), HY‐
DRUS (Šimůnek et al., 2005, 2006), KINEROS (Woolhiser
et al., 1990), LEACHM (Hutson and Wagenet, 1992), MAC‐
RO (Jarvis, 1994), NLEAP (Shaffer et al., 1991), RUSLE(Renard et al., 1991), RZWQM (Ahuja and Hebson, 1992) ,SWAT (Neitsch et al., 2002), and WEPP (Laflen et al., 1991).
These models tend to be scale dependent and are usuallysuited for profile/horizon/pedon (point scale), field/farm(field scale), or watershed scale; however, there are a few ex‐
ceptions.
The land phase of the hydrologic cycle is complicated andincludes a great deal of uncertainty (Singh and Woolhiser,
2002) because of the spatial and temporal heterogeneity ofsoil properties, land use, precipitation, etc. Currently, a mod‐el that simultaneously operates at multiple spatial scales does
not exist (the hierarchies of scales at which NPS models areTransactions of the ASABE
Vol. 50(5): 1683-1693

E 2007 American Society of Agricultural and Biological Engineers ISSN 0001-2351

1683

often developed and applied are highlighted in gray in fig. 1).
Because a scale‐independent NPS model currently does notexist, two or more models are required for simultaneous simula‐tions at different spatial scales. Use of a watershed‐scale model
for identifying hotspots of NPS pollution and a field‐scale mod‐el for evaluating the specified fields (i.e., the hotspots) to opti‐
mize best management practice (BMP) efforts provides anexample of multi‐scale modeling. A watershed model alone
cannot be used because either (1) the definition of fields does
not exist or has been replaced by subwatersheds (or hydrologic
response units (HRUs)) or regular‐sized grids (or cells), or (2)many of the field‐scale BMPs (e.g., riparian areas, grassed wa‐
terways) cannot be easily simulated. Current attempts to bringseveral models under the same framework are limited to sophis‐
ticated geographic information system (GIS) interfaces that pre‐and post‐process data for several models through the use of asingle graphical user interface (GUI). Examples of such at‐
tempts are the U.S. Environmental Protection Agency's (USE‐
PA) Better Assessment Science Integrating Point and Nonpoint
Sources (BASINS) (USEPA, 2007) modeling system and theWatershed Modeling System (WMS) developed by Brigham
Young University (EMSI, 2007).
The goals of this article are to discuss why NPS pollutant
models were developed at various spatial scales (i.e., scale is‐sues), briefly review commonly used models, and reflect on
the future of landscape NPS models.

SCALE ISSUES WITH LANDSCAPE NPSPOLLUTANT MODELS
Soil properties exhibit considerable spatial and temporalvariability (Corwin and Wagenet, 1996; Corwin et al., 2006).
Corwin et al. (1997) and Corwin et al. (2006) provide a num‐
ber of useful references on this subject. Because of the vari‐ability of soil properties, “the movement of water and solute
through the vadose zone is a complex, three‐dimensional pro‐cess” (Woods et al., 2006). At field and watershed scales, thetransport of water and pollutants is affected by surface andsubsurface boundary conditions (e.g., energy balance, pre‐cipitation, depth of water table, and depth of impermeable
layer) and state variables (e.g., soil hydraulic and transport
properties) that vary considerably in space and time (Woods
et al., 2006).
Since model development at a particular scale requires ob‐
served data at that scale, a model developed at one scale is notalways appropriate for application at a different scale (Cor‐win et al., 2006). Modeling NPS pollutant transport acrossmultiple spatiotemporal scales can only be achieved by un‐derstanding the interrelationship between scale and spatial
variability (Corwin et al., 2006). Issues of spatial and tempo‐
ral variability and hence spatial and temporal scaling have re‐ceived considerable attention since 1980s. Yet “scale andscaling continue to be significant issues” (Sivapalan et al.,2004). Both upscaling (transfer functions) and downscaling
techniques are being explored. Upscaling refers to deriving
effective parameters describing a system's behavior and av‐
erage fluxes at a higher scale (e.g., at a field scale) (Vander‐borght et al., 2006). Downscaling refers to constructing
subscale heterogeneity from information obtained at a partic‐
ular scale (Bierkens et al., 2000). There are a number of ex‐cellent discussions on scaling issues in hydrology and NPS
pollutant transport (e.g., Corwin et al., 2006; Wagenet andHutson, 1996; Singh and Woolhiser, 2002). Further, Corwin
et al. (2006) provide a number of important references on this
topic.
Regardless of the scale, a model must sufficiently accountfor the predominant processes at that scale. Further, the sam‐pling and measurement approaches should be consistent withthat scale (Wagenet and Hutson, 1996; Singh and Woolhiser,
2002; Corwin et al., 2006). Because complex local patternsof solute transport are attenuated and dominated by macros‐cale characteristics, mechanistic models are more common
at the point scale, whereas functional and stochastic models
are appropriate for field and watershed scales (Corwin et al.
2006).

Organizational
Hierarchy
Political Units, Regional
Interacting Watersheds
Qualitative
ModelsWatersheds
Farm/field(Polypedon)

Functional
ModelsPedon

Mechanistic
ModelsProfile, Horizon
Ped, Aggregates

Quantitative
ModelsMixtures (batch)
Molecular

Figure 1. Organizational hierarchy of spatial scales at which NPS pollutant models can be developed and applied (redrawn after Hoosbeek and Bryant,1992). The discussion in this article focuses on the scales (point, field, and watershed scales) highlighted in gray.

1684

TRANSACTIONS OF THE ASABE

MODELS APPLIED AT VARIOUS SCALES
Because of the variability of soil properties and meteoro‐
logical events, scaling issues, and the need to address NPS
pollution at several spatial and temporal scales, numerous
point‐, field‐, and watershed‐scale models have been devel‐
oped. Further, because scaling issues have not been resolved,
a variety of processes are being modeled at different scales.
POINT‐SCALE MODELS
Many point‐scale models have been developed to quantify
the basic physical and chemical processes in the unsaturated
zone. These models have a varying degree of complexity anddimensionality and are often focused on processes affecting
water flow, heat transport, and movement of different nutri‐ents, pollutants, and/or pathogens in the subsurface. While
analytical and semi‐analytical models (e.g., Šimůnek et al.,1999) of heat or solute transport can be applied under steady‐state flow conditions, more complex numerical codes permit
consideration of a large number of simultaneous nonlinear
processes.
Processes Simulated in Point‐Scale Models
Any quantitative analysis of the point‐scale transport ofenergy, nutrients, pollutants, and/or pathogens must firstevaluate water fluxes into and through the vadose zone. Wa‐
ter flow in variably saturated porous media is usually de‐scribed using the Richards equation (Richards, 1931). Since
the Richards equation is generally highly nonlinear, it usuallyrequires a numerical solution. Solute transport and heatmovement in the point‐scale models are usually based onconvection‐dispersion (CDE) type governing equations.
These equations can be extended to account for additional
processes, such as volatilization and diffusion of contami‐
nants in the gas phase, or storage and transfer of latent heat.
Commonly Used Point‐Scale Models
Only relatively simplified system‐independent boundary
conditions were implemented in the earlier models (e.g., vanGenuchten, 1987). Models developed recently can cope withmuch more complex system‐dependent boundary conditions
for which the actual boundary conditions depend on the statusof the system and are calculated by the model itself. The
boundary representing the soil‐air interface is one example ofa system‐dependent boundary. Point‐scale models have be‐come increasingly sophisticated in terms of handling this up‐
per boundary condition. They can commonly evaluate
surface flow and energy balances and account for the simulta‐
neous movement of water vapor and heat. Examples are
DAISY, TOUGH2, SHAW, SWAP, HYDRUS‐1D (Saito etal., 2006), UNSATH, and COUP (see table 1 for references).
Many environmental models, such as DAISY, LEACHM(Hutson and Wagenet, 1992), RZWQM, COUP, and HY‐
DRUS (2D/3D), provide, as a standard feature, options tosimulate carbon and nitrogen cycles. These models typically
distribute organic matter, carbon, and organic and mineral ni‐
trogen over multiple computational pools, while allowing or‐
ganic matter to be decomposed by multiple microbial
biomass populations.
The most dramatic improvements in the currently usedmodels likely occurred in the type and complexity of solutetransport processes that can be simulated. Current transportmodels can now consider a variety of nonlinear sorption andexchange processes, physical and chemical nonequilibrium

Vol. 50(5): 1683-1693

transport, volatilization, gas diffusion, colloid attachment/
detachment, decay chain reactions, and many other pro‐
cesses. For example, the general formulation of the transport
equations in the HYDRUS codes allows users to simulate avariety of contaminants, such as viruses (Schijven andŠimůnek, 2002), colloids (Bradford et al., 2002), trace ele‐ments (Seuntjens et al., 2001, Carrillo‐González et al., 2006),
hormones (Casey et al., 2003), explosives (Dontsova et al.,2006), or chemicals involved in the sequential biodegrada‐tion (Schaerlaekens et al., 1999; Casey and Šimůnek, 2002).
The transport of reactive contaminants in the subsurface
is generally affected by a large number of nonlinear and often
interactive physical, chemical, and biological processes.
Simulating these processes requires comprehensive reactive
transport codes that couple the physical processes of waterflow and advective‐dispersive transport with a range of bio‐
geochemical processes, such as surface complexation, pre‐cipitation/dissolution, cation exchange, and/or biologicalreactions (Šimůnek and Valocchi, 2002). Models that simu‐
late the transport of major ions, such as RZWQM, LEACHM,
and HYDRUS‐1D, and various reclamation models(Šimůnek and Valocchi, 2002) are typical examples of suchcoupled models. Models with more general chemistry wererecently reviewed by Šimůnek and Valocchi (2002) and Jac‐ques et al. (2006). Users of these models can select species
and reactions from large geochemical databases. Examples
of codes with general geochemistry are, for example,
PHREEQC (Parkhurst and Appelo, 1999), CRUNCH (Steef‐el, 2000), PHAST (Parkhurst et al., 2004), 3DHYDROGEO‐CHEM (Yeh and Cheng, 1999), and HP1 (Jacques andŠimůnek, 2005).
Existing models are often extended so that they can be ap‐
plied to situations where preferential flow occurs. MACRO
and HYDRUS‐1D are example of such models. Possible ap‐
proaches for simulating preferential flow range from rela‐
tively simple, physically based, dual‐porosity models tomore complex dual‐permeability and multi‐region models.
Dual‐porosity, mobile‐immobile water flow models consider
that flow occurs in preferential flow paths, while immobile
water is assumed to exist in the matrix or soil aggregates. On
the other hand, dual‐permeability models assume that water
is mobile in both the matrix and fracture domains.
Existing models vary widely in terms of their complexity,
sophistication, and ease of use. Table 1 summarizes the more
widely used numerical models for simulating variably satu‐
rated water flow and solute transport in soils.
FIELD‐SCALE MODELS
Numerous field‐scales models have been developed to de‐termine the effectiveness of BMPs on transport of sediment,
nutrients (nitrogen and phosphorus), and pesticides fromagricultural management systems. These models simulate
water quality constituents leaving the edge of the field
through surface runoff and leaving the vadose zone throughleaching, transport of dissolved constituents in tile drains,
and transport of dissolved constituents leaving the edge of the
field through lateral flow.
Processes Simulated in Field‐Scale Models
Contrary to point‐scale models that describe water flowusing the Richards equation and solute transport using CDE,
process‐based field‐scale models often link together a num‐
ber of empirically derived submodels to simulate: (1) various

1685Model (Reference)

Table 1. Some of the widely used numerical models for simulating variably saturated waterflow and solute transport in soils at the point scale (adapted from Šimůnek, 2005).
FeaturesInternet Address

COUP(Jansson andKarlberg, 2001)

Carbon/nitrogen cycle, thawing/freezing cycle, coupled water, vapor, andheat transport, meteorological input, crop growth.

DAISY(Hansen et al., 1990)

Carbon/nitrogen cycle, multi‐year crop growth cycles, management practices,pesticide processes.

www.dina.dk/~daisy/

HP1(Jacques andim nek, 2005)

Coupled HYDRUS‐1D with the PHREEQC geochemical code (Parkhurst andAppelo, 1999), transient water flow, transport of multiple components, mixed
equilibrium/kinetic biogeochemical reactions, and heat transport.

www.sckcen.be/hp1

HYDRUS‐1D( im nek et al., 2005)

Multiple soil hydraulic functions, neural network based pedotransfer
functions, nonlinear nonequilibrium solute transport, mobile‐immobile andtwo‐site sorption concepts, chain reactions, volatilization, major ion
chemistry, transport of colloids and bacteria, inverse option, intuitive
sophisticated graphical user interface.

www.hydrus2d.com

HYDRUS (2D/3D)( im nek et al., 2006;
the latest upgrade of
HYDRUS‐2D)

Two‐ and three‐dimensional, multiple soil hydraulic functions, neuralnetwork based pedotransfer functions, nonlinear nonequilibrium solutetransport, mobile‐immobile and two‐site sorption concepts, chain reactions,transport of colloids and bacteria, inverse option, unstructured triangular
finite element meshes, intuitive sophisticated graphical user interface (GUI).

www.hydrus2d.com

MACRO(Jarvis, 1994)

Preferential flow using kinematic wave equation, snow accumulation,
pesticide transport, compensated root water uptake.

www.mv.slu.se/BGF/Macrohtm/macro.htm

RZWQM(Ahuja and Hebson,
1992)Complex modular program, crop growth, chemical equilibrium module,
management practices, transport of nutrients, pesticide processes, input of
meteorological variables.

http://gpsr.ars.usda.gov/products/rzwqm.htm

SHAW
Thawing/freezing cycle, coupled water, vapor, and heat transport,(Flerchinger et al., 1996) multi‐species plant canopy, input of meteorological variables.

www.lwr.kth.se/Vara%20Datorprogram/
CoupModel/

www.nwrc.ars.usda.gov/models/shaw

SWAP(van Dam et al., 1997)

Three‐level drainage system at regional scale, crop growth, swelling soils,
input of meteorological variables.

www.swap.alterra.nl/

SWIM(Verburg et al., 1996)

Bypass flow, flexible description of hydraulic properties, hyperbolic sinetransformation of the pressure head.

www.clw.csiro.au/products/swim

TOUGH2(Pruess, 1991)

Multidimensional multiphase fluid and heat flow, multicomponent transport,
dual permeability flow and transport, very hard to use.

UNSATH(Fayer, 2000)

Coupled water, vapor, and heat transport, no solute transport, input of
meteorological variables.

http://hydrology.pnl.gov/resources/
unsath/unsath_download.asp

VS2DT(Healy, 1990)

Two‐dimensional water flow and solute transport, finite differences.

http://water.usgs.gov/software/vs2di.html

components of the land phase of the hydrologic cycle, and(2)the fate and transport of sediment, nutrients, and pesti‐
cides. Currently, these models are not designed to simulate
pathogen transport (table 2). Many of the currently usedfield‐scales models have their roots in CREAMS, GLEAMS,
and DRAINMOD.
CREAMS estimates surface runoff, percolation, soil ero‐sion and sediment yield, dissolved and sediment‐bound nutri‐ents, plant uptake of nutrients, and transport andtransformation of pesticides (Knisel, 1980). Hydrologic re‐sponse simulation includes models for infiltration, soil water
movement, and evapotranspiration between storms. Surface
runoff and infiltration are simulated using the SCS curve
number (CN) method or the Green‐Ampt equation. Water
movement through the soil profile is modeled using a simple
capacity approach, with flow occurring when a layer exceeds
field capacity. The evapotranspiration model is taken from
Ritchie (1972). Erosion is simulated using the USLE ap‐
proach, while sediment transport is modeled based on trans‐
port capacity of overland flow using Yalin's sedimenttransport equation (Foster and Meyer, 1972). The erosion/de‐position feature simulates concentrated flow within the field(e.g., terrace channels, diversions, major flow concentrations
where topography causes overland flow conversion, andgrassed waterways). The channel element does not describe
gully or stream channel erosion. The plant nutrient submodel

1686

www‐esd.lbl.gov/TOUGH2/

contains a nitrogen component that considers mineralization,
nitrification, and denitrification processes; a plant uptake
component; and a nitrate leaching component. The pesticidecomponent considers foliar interception, degradation, andwash‐off, as well as adsorption, desorption, and degradationin the soil. The nitrogen, phosphorus, and pesticide compo‐
nents use enrichment ratios to estimate the portions of these
constituents transported with sediment (Knisel, 1980).
GLEAMS was developed to remove weaknesses of
CREAMS (Knisel, 1993). GLEAMS provides a more de‐tailed simulation of water, sediment, nutrient, and pesticide
movement in surface runoff and subsurface flows. Priestly‐
Taylor and Penman‐Monteith methods are used for potential
evapotranspiration, while actual soil evaporation and planttranspiration are simulated after Ritchie (1972). The plant
nutrient component includes ammonification, nitrification,
denitrification, uptake, fixation, leaching, and runoff of ni‐
trogen, and mineralization, uptake, leaching, and runoff ofphosphorus. GLEAMS represents nitrogen fixation more ad‐equately than CREAMS. The GLEAMS model represents ni‐
trogen fixation by legume; fertigation; application of animal
waste as solid, slurry, or liquid through irrigation systems;
and tillage. In addition, GLEAMS considers movement ofphosphorus in subsurface flows and vertical flux of pesticides
into, within, and through the root zone (table 2). The major
limitation of both CREAMS and GLEAMS is that they can‐TRANSACTIONS OF THE ASABE

not be applied in areas influenced by urbanization and inlandscapes with substantially altered hydrology due to artifi‐
cial drainage.
DRAINMOD combines the original DRAINMOD hydrol‐ogy with DRAINMOD‐N (Brevé et al., 1997) and
DRAINMOD‐S (Kandil et al., 1995). The combined model
simulates the hydrology of poorly drained, high water tablesoils; predicts the effects of drainage (open ditches and sub‐surface tiles) and associated water management practices onwater table depths, soil water regime, crop yields (Skaggs,
2007); and models one‐dimensional transport of nitrogenusing
anadvective‐dispersive‐reactive
equation(DRAINMOD‐N). DRAINMOD‐S predicts salinity in semi‐arid and arid lands. The model can evaluate long‐term perfor‐
mance of drainage systems (using the Houghoudt andKirkham equations), subirrigation, controlled drainage, andwastewater application to artificially drained soils. Infiltra‐tion (Green‐Ampt equation), potential evapotranspiration(Thornthwaite method), and actual evapotranspiration are
also simulated. DRAINMOD is not generally applicable forareas that do not experience high water tables.
Other Commonly Used Field‐Scale Models
Other common field‐scale models include ADAPT,APEX, EPIC, and FHANTM. ADAPT, a water table manage‐
ment model, is an extension of DRAINMOD and GLEAMS
for pesticide transport in agricultural environments. APEX,
an extension of EPIC for whole farm and small watersheds,
evaluates land management strategies considering sustain‐
ability, erosion (wind, sheet, and channel), economics, watersupply and quality, soil quality, plant competition, weather,and pests. Management capabilities include irrigation, drain‐
age, furrow diking, buffer strips, terraces, waterways, fertil‐
ization, manure management, lagoons, reservoirs, crop
rotation and selection, pesticide application, grazing, andtillage. APEX can be used for evaluating the effects of global
climate/CO 2 changes, designing environmentally safe andeconomic landfill sites, designing biomass production sys‐
tems for energy, and other spin‐off applications (Williams
and Izaurralde, 2005). APEX is one of the few models thatroutes water, sediment, nutrients, and pesticides across com‐plex landscapes and channel systems to the outlet of small
watersheds. It has been applied to watersheds as large as
1000km 2. The model contains groundwater and reservoir
components that are usually missing from other field‐scalemodels. The submodels in APEX are based on EPIC,
CREAMS, and GLEAMS.
EPIC, which was originally developed to assess the effectof soil erosion on soil productivity, has been expanded to al‐
low simulation of hydrology, erosion‐sedimentation, nutrient
cycling, pesticide fate, plant growth, soil temperature, till‐age, economics, and plant environment control. The eco‐nomic component of EPIC estimates the cost of producing
and marketing crops. FHANTM is mainly designed for flat
fields with the high water table environment of Florida; thehydrology is based on DRAINMOD, and the nutrient trans‐
port is based on GLEAMS.
WATERSHED‐SCALE MODELS
Watershed‐scale models are a result of the combination of
two modeling concepts: landscape modeling and lotic/lentic
modeling. The coalescence of landscape and water‐routing
models has been timely due to the interest in addressing waterpollutant concerns that are characterized as having diverseand diffuse potential sources, namely NPS pollution. Thus,
watershed‐scale models are generally applied to understandor quantify a relationship between landscape activities anddownstream water quality or quantity. Watershed‐scale mod‐els vary in the processes they simulate (table 3).

Table 2. Some of the widely used field‐scale models for simulating runoff, sediment, nutrients,
and pesticides leaving the edge‐of‐the‐field and leaching through the root zone.
Processes Simulated
Nutrients[c]Hydrology [c]Sediment[f]Nitrogen Phosphorus Pesticides[c] Pathogens[c]Mass Balance[g]
Time
Time
S[d]
SS[e]
E
T
Y
S
SS
S
SS
S
SS
S
SS
W N
P Pest.
Model Scale[a] Step[b]ADAPT (Chung et al., 1992)
C
D
OF,CFF SM,AD,LF Y
Y
Y
Y
Y
Y
Y
Y
Y
--Y
Y
Y
Y
APEX (Williams and Izaurralde, 2005)
C
H,D
OF,CF SM,AD,LF Y
Y
Y
Y
Y
Y
Y
Y
Y
--Y
Y
Y
Y
CREAMS (Knisel 1980)
E,C
H,D
OF,CFF
SM,LF
Y
Y
Y
Y
Y
Y
-Y
---Y
Y
Y
Y
DRAINMOD (Skaggs, 2007)
C
H,D
OF,CFF
SM,AD-- -- -YY------Y
Y --EPIC (Williams et al., 1984)
C
D
OF,CF SM,AD,LF Y
-Y
Y
Y
Y
Y
Y
Y
--Y
Y
Y
Y
FHANTM (Tremwel, 1992)
C
H,D
OF,CFF SM,AD,LF -- -- -Y
Y
Y
Y
----Y
Y
Y
-GLEAMS (Leonard et al., 1987; Knisel and Davis, 1999)
C
D
OF,CFF
SM,LF
Y
Y
Y
Y
Y
Y
Y
Y
Y
--Y
Y
Y
Y
[a] C = continuous; E = event.
[b] H = hourly; D = daily. This is the computational and/or output time step at which the model operates. This does not mean that the outputs generated
are good for that time step. Quite often, model outputs are good for a time step that is larger than the computational time step. For example, if thecomputational time step is daily, then monthly predictions are more appropriate for analysis.
[c] S = surface; SS = subsurface.
[d] OF = overland flow; CF = channel flow; CFF = channel flow in field only.
[e] SM = soil moisture; AD = artificial drainage (e.g., subsurface tiles); LF = lateral flow.
[f] E = erosion; T = transport; Y = yield[g] W = water, N = nitrogen; P = phosphorus; Pest. = pesticide.

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Processes Simulated in Watershed‐Scale Models
Watershed‐scale models include hydrologic processes,
such as precipitation, infiltration, surface runoff, evapotran‐
spiration, subsurface (groundwater) flows, and stream flow.
The landscape portions of the hydrologic cycle are common‐ly represented in a watershed‐scale model using a water bal‐ance equation. Watershed‐scale models differ in how each of
the terms in the water balance equation is derived. For exam‐
ple, potential evapotranspiration (PET) may be estimated us‐
ing Penman, Penman‐Monteith, or some other algorithm. As
with landscape processes, in‐stream or reach water dynamics
often use another common relationship, the continuity equa‐tion. The terms in the continuity equation are solved using
numerical techniques that often include some form of theManning equation (Bicknell et al., 2001; Neitsch et al.,2002). These hydrologic processes are discussed in greater
detail by Migliaccio and Srivastava (2007).
In addition to hydrology, erosion and sediment transport
are simulated in watershed‐scale models. The predominant
method used to estimate erosion is some form of the USLE
equation, such as the MUSLE (Neitsch et al., 2002) or the
RUSLE and HUSLE (Theurer and Clarke, 1991). Nitrogenand phosphorus cycles are also included in most watershed‐scale models, including landscape and in‐stream processes.
Other constituents, such as pesticides, bacteria, and conser‐
vatives (or tracers) are likewise simulated in somewatershed‐scale models.
Simulation of agricultural crops and their subsequent
yield is less common in currently available watershed‐scalemodels, one exception to this is SWAT. However, there is
growing interest in using watershed‐scale models to not only
evaluate water quality concerns but also simulate the coupled
response of crop yield and water quality. As the TMDL pro‐
cess continues to evolve, the ability to predict both reach wa‐
ter quality and crop yields will be crucial for selecting
reasonable BMPs. A recent ASABE effort described by
Muñoz‐Carpena et al. (2006) resulted in a series of publica‐
tions addressing TMDLs. TMDLs often require watershed‐level modeling; hence, several TMDL‐related publications
include relevant watershed modeling discussions (e.g., Ben‐ham et al., 2006; Borah et al., 2006; Vellidis et al., 2006; Ya‐
gow et al., 2006).
Simulation of NPS pollution at the watershed scale often
requires inclusion of water bodies, such as reservoirs. Al‐
though watershed‐scale models provide some representationof these systems, they do not provide the same simulation
abilities as stand‐alone reservoir models. If reservoir systems
are fairly complex and influential on downstream waterflows, it may be prudent to link a watershed‐scale model toa more complex reservoir model (White et al., 2004; Debele
et al., 2006).
Commonly Used Watershed‐Scale Models
Watershed‐scale models are relatively young compared topoint‐scale and field‐scale models and are still evolving.
However, several watershed‐scale models have become
more common in their application and appearance in pub‐lished literature. These include AnnAGNPS (Yuan et al.,
2001; Suttles et al., 2003), ANSWERS‐2000 (Bouraoui and
Dillaha, 2000; Niu et al., 2001), HSPF (Paul et al., 2004; Sa‐leh and Du, 2004), SWAT (Saleh et al., 2000; White andChaubey, 2005), and WAM (Ouyang, 2003; Bottcher et al.,2005). Additional information on these and other watershed‐1688

scale models may also be found in Bora and Bera (2003,
2004).
Four of the selected models were specifically designed toevaluate NPS pollutants from agricultural land uses: An‐nAGNPS, ANSWERS‐2000, SWAT, and WAM. Each model
has unique features that should be considered in selection ofa watershed‐scale model for implementation. One difference
among the four models is groundwater simulation. SWAT or
WAM may be a preferred choice if stream hydrology is domi‐
nated by groundwater. The groundwater algorithms in
ANSWERS‐2000 currently simulate simplistic groundwater
storage such that groundwater is evenly released into streams
at a rate proportional to the volume of accumulated storage.
AnnAGNPS is also limited in representing groundwater, as
the model includes only interflow and subsurface/tile drain‐age.
Another watershed‐scale model is HSPF, which is ahydrologic and water quality model often used to simulate
stream bacteria (Paul et al., 2004; Benham et al., 2005). Al‐
though HSPF is one of the most comprehensive watershed‐scale models, its application may be more difficult for theuser than that of other watershed‐scale models. A comparisonof SWAT and HSPF by Van Liew et al. (2003) indicated thatthe HSPF manual was less intuitive, input data preparation
was cumbersome, and more parameters were required forcalibrating hydrologic response in HSPF.
Unique to the set of models selected for our article isWAM. WAM consists of a collection of models that are ap‐
plied at the cell level and managed by the BUCSHELL sub‐program. The collection of land cell source models includes:
GLEAMS, the Everglades Agricultural Area Model (EAAM‐OD), a wetland model, and an urban model. In other words,
WAM uses field‐scale models to simulate cell processes,
which are then linked using BLASRoute, which routes waters
and constituents through the watershed system.

FUTURE OF LANDSCAPE NPS MODELS
In past few decades, much of the research and model de‐velopment effort has focused on surface water flow, variably
saturated water flow in the vadose zone, and fate and trans‐
port processes of a few key pollutants (e.g., sediment, nitro‐gen, phosphorus, and pesticides). With the rapid
technological development of society, the need to understandhow manipulation of one component affects other parts of thesystem will continue to increase at a fast rate. The impact of
producing bioenergy from crops, understanding the effect ofclimate variability and climate change, and the impact of
emerging contaminants (e.g., nanoparticles, antimicrobials,
hormones, pathogens, and other pharmaceuticals) provide
just a few examples. Addressing the complex issues of thenext century will require a systems approach that is based ona thorough understanding and coupling of multiple hy‐
drogeological, geochemical, and microbiological processes.
Since the mid‐1990s, a number of sophisticated GIS‐
based GUIs have been developed that have substantially in‐creased the use of NPS pollutant models. Currently available
GIS‐based modeling systems (e.g., BASINS and WMS) pro‐vide a common GUI for various models. However, movingfrom one spatial scale to another is still cumbersome, if not
impossible. Further, the outputs provided by models applied
at different spatial scales might not be compatible because of

TRANSACTIONS OF THE ASABE

Process Simulated

Table 3. Some of the widely used watershed‐scale models for simulating hydrologic processes, nitrogen andphosphorus cycles, erosion, and pesticide transport from landscape environments into stream reaches.
AnnAGNPSANSWERS‐2000
HSPF
SWAT
WAM
(Bingner and(Bouraoui and Dillaha,(Bicknell et al., 2001)(Neitsch et al., 2002)(SWET, 2006)
Theurer, 2003)
1996; Bouraoui and
Dillaha, 2000)

Surface hydrology
Groundwater hydrology

Potential
evapotranspiration
Erosion

Modified SCS CN2

Green‐Ampt

Philip's equation

Modified SCS CN2
and Green Ampt

Modified SCS CN2Interflow andsubsurface/tile
drainage

Subsurface/tile
drainage, shallow
aquiferInterflow, shallow
aquifer, deep aquifer

Interflow, subsurface/tile drainage, shallow
aquifer, deep aquifer

Interflow, shallow
aquifer, deep
aquiferPenmanSolar radiation,
temperature, andalbedo equation

Input time series (notestimated by the model)

Penman‐Monteith,
Priestly‐Taylor,
Hargreaves

Priestly‐Taylor,
Penman‐Monteith

MUSLE

Varies dependingon the land cellsource model
selectedY

RUSLE and HUSLE

Relationships developed
Based on the ARM
by Meyer andand NPS models
Wischmeier (1969)(Donigian and Crawford,
and Yalin's modified
1976a, 1976b)
equation (Foster andMeyer, 1972)

P and N fertilization

Y

Y

Y

Y

P and N transport

Y

Y

Y

Y

Y

Pesticide application

Y

--

Y

Y

--

Pesticide transport

Y

--

Y

Y

--

Crop land management

Y

Y

--

Y

Y

Crop yield

--

--

--

Y

--[a]

Daily or subdaily

Daily for dry days, 30 s
for precipitation days

Hourly (user may selectnumber of hours)

Daily or subdaily

Daily

Stream reachand reservoir

Stream reachStream reachand reservoir

Stream reachand reservoir

Stream reach

Bacteria

--

--

Y

Y

--

Plankton and algae

--

--

Y

Y

--

Spatial representation

Cells or hydrologic
response units

Cells

Hydrologic
response units

Hydrologic
response units

Cells

Developer

USDA‐ARS,
USDA‐NRCS

Purdue University
and Virginia TechUSEPA, USGSUSDA‐ARS

Soil and Water
Engineering
Technology, Inc.

Web sitewww.ars.usda.gov/
Research/docs.htm?docid=5199--

water.usgs.gov/
software/hspf.html;
www.epa.gov/
ceampubl/swater/hspf/

www.brc.tamus.
edu/swat/www.swet.com/
SoftwareWAM.html

Output time step
Water bodies[a]

WAM is currently undergoing modification to link Decision Support System for Agrotechnology Transfer DSSAT (crop modeling suite) to WAM, which
will enable crop yield simulation.

the incompatibility of the physical basis of these models.
Many NPS models (especially those that operate at field andwatershed scales) also provide outputs at low temporal reso‐
lution (days, months, and years). For many toxic chemicals,a high enough concentration for a short period of time can sig‐
nificantly impact a surface water body. In addition to spatial
and temporal scaling issues, currently available models arestill relatively specialized, and a single model that can de‐scribe the multiple problems and chemicals mentioned above
is presently unavailable.
In the last decade or so, significant effort has been directed
towards developing technologies to capture data at high spa‐tial and temporal resolutions. However, relatively little effort
has been directed towards developing better understanding ofscaling issues and the fundamental fate and transport pro‐
cesses at different scales. In next few decades, there will be
a greater need to develop affordable sensors that can capture
data on surface and subsurface boundary conditions and statevariables at high spatial and temporal resolutions. The
technologies that can help develop such sensors (e.g., nano‐Vol. 50(5): 1683-1693

technology) and that can capture and transmit data (wirelessengineering, database management systems, and GIS) arecurrently underway. These technologies will help develop
better understanding of underlying fundamental processes
and scaling issues.
In the next few decades, we will likely see improved input
data resolution leading to better understanding of fundamen‐
tal processes, resolution of spatial and temporal scaling is‐sues, inclusion of state‐of‐the‐art process‐based understanding in models, and models that have high predictive ca‐pability and that can be applied at several spatial and tempo‐
ral scales simultaneously. We will also see great improvements in GUIs, which will allow increased utilization ofmodels for addressing a number of complex environmental
issues. Improvement in data resolution will also help reduce
input data uncertainty and develop true frequency distribu‐
tions of various input parameters, which will lead to reduced
output data uncertainty and increased utilization of uncer‐
tainty analysis. It is not unrealistic to envision a scale‐
independent modeling system in the future that operates in1689

real‐time and provides signals to water managers regarding
specific NPS pollution concerns, particularly those related to
public health (e.g., drinking water, swimability, and fishabil‐
ity).

SUMMARY
The number of models developed at various spatial scales
has exploded. Point‐scale models are based on the mechanis‐tic understanding developed at the point scale. However,
upscaling to field and watershed scales is not currently pos‐sible because of the vast spatial and temporal variability ofsurface and subsurface boundary conditions and state vari‐ables. Hence, field‐ and watershed‐scale models link togeth‐
er empirical submodels of system components. Further, thesemodels are relatively specialized and cannot describe multi‐
ple problems and chemicals simultaneously.
Scaling issues, underlying processes simulated at various
spatial scales, widely used models, and what the future holds
for the NPS pollution models are discussed in this article. The
need for a scale‐independent model that can operate at multi‐
ple spatial and temporal scales and that can address the com‐plex issues of the next century by coupling state‐of‐the‐art
understanding of multiple hydrogeological, geochemical,
and microbiological processes was emphasized. A case wasmade that a scale‐independent model was possible in the fu‐ture. This conclusion was based on the availability of newtechnologies that will provide improved chemical analysis
capabilities, affordable sensors, and hardware and softwaresystems for transmission and analysis of data. Because of
these technologies, better understanding of fundamental hy‐
drogeological, geochemical, and microbiological processes
will be developed, and spatial and temporal scaling issues
will be resolved. Increased computing power will result in in‐creased use of models through sophisticated graphical userinterfaces. These improvements will lead to greater trust andacceptance of models in society and resolution of current andfuture complex environmental issues.
ACKNOWLEDGEMENTS
The authors wish to thank Wes Wallender, ASABE Soiland Water Division Editor, for his invitation to write this cen‐tennial article.

REFERENCES
Ahuja, L. R., and C. Hebson. 1992. Root Zone Water Quality
Model. GPSR Tech. Report No. 2. Fort Collins, Colo.:
USDA‐ARS.
Arnold, J. G., and N. Fohrer. 2005. SWAT2000: Current capabilities
and research opportunities in applied watershed modeling.
Hydrol. Process. 19(3): 563‐572.
Benham, B. L., K. M. Brannan, G. Yagow, R. W. Zeckoski, T. A.
Dillaha, S. Mostaghimi, and J. W. Wynn. 2005. Development ofbacteria and benthic total maximum daily loads: A case study,
Linville Creek, Virginia. J. Environ. Qual. 34(5): 1860‐1872.
Benham, B. L., C. Baffaut, R. W. Zeckoski, K. R. Mankin, Y. A.
Pachepsky, A. M. Sadeghi, K. M. Brannan, M. L. Soupir, and
M. J. Habersack. 2006. Modeling bacteria fate and transport inwatersheds to support TMDLs. Trans. ASABE 49(4): 987‐1002.
Bicknell, B. R., J. C. Imhoff, J. L. Kittle, Jr., T. H. Jobes, and A. S.
Donigian, Jr. 2001. Hydrological Simulation Program ‐FORTRAN, Version 12: User's Manual. Mountain View, Cal.:
Aqua Terra Consultants.

1690

Bierkens, F. P., P. A. Finke, and P. de Willigen. 2000. Upscaling
and Downscaling Methods for Environmental Research.
Dordrecht, The Netherlands: Kluwer Academic.
Bingner, R. L., and F. D. Theurer. 2003. AnnAGNPS Technical
Processes Documentation, Version 3.2. Washington, D.C.:
USDA‐NRCS. A more recent version (2007) of this document isavailable at:www.wsi.nrcs.usda.gov/products/w2q/h&h/tools_models/agnps/
downloads.html.
Bouraoui, F., and T. Dillaha. 1996. ANSWERS‐2000: Runoff andsediment transport model. J. Environ. Eng. 122(6): 493‐502.
Bouraoui, F., and T. Dillaha. 2000. ANSWERS‐2000:
Nonpoint‐source nutrient planning model. J. Environ. Eng.
126(11): 1045‐1055.
Borah, D. K., and M. Bera. 2003. Watershed‐scale hydrologic andnonpoint‐source pollution models: Review of mathematicalbases. Trans. ASAE 46(6): 1553‐1566.
Borah, D. K., and M. Bera. 2004. Watershed‐scale hydrologic andnonpoint‐source pollution models: Review of applications.
Trans. ASAE 47(3): 789‐803.
Borah, D. K., G. Yagow, A. Saleh, P. L. Barnes, W. Rosenthal, E. C.
Krug, and L. M. Hauck. 2006. Sediment and nutrient modelingfor TMDL development and implementation. Trans. ASABE
49(4): 967‐986.
Bottcher, A. B., B. M. Jacobson, and J. G. Hiscock. 2005. TMDL
assessment using WAM/WASP: Hillsborough River case study.
In Proc. 3rd Conference Watershed Management to Meet Water
Quality Standards and Emerging TMDL. St. Joseph, Mich.:
ASAE.
Bradford, S. A., S. R. Yates, M. Bettehar, and J. Šimůnek. 2002.
Physical factors affecting the transport and fate of colloids insaturated porous media. Water Resour. Res. 38(12): 63.1‐63.12.
Brevé, M. A., R. W. Skaggs, J. E. Parsons, and J. W. William. 1997.
DRAINMOD‐N, A nitrogen model for artificially drained soils.
Trans. ASAE 40(4): 1067‐1075.
Carrillo‐González, R., J. Šimůnek, S. Sauve, and D. Adriano. 2006.
Mechanisms and pathways of trace element mobility in soils.
Advances in Agronomy 91: 111‐178.
Casey, F. X. M., and J. Šimůnek. 2002. Inverse analyses of thetransport of chlorinated hydrocarbons subject to sequential
transformation reactions. J. Environ. Qual. 30(4): 1354‐1360.
Casey, F. X. M., G. L. Larsen, H. Hakk, and J. Šimůnek. 2003. Fateand transport of 17 b‐estradiol in soil‐water systems. Environ.
Sci. Tech. 37(11): 2400‐2409.
Chung, S. O., A. D. Ward, and C. W. Schalk. 1992. Evaluation of
the hydrologic component of the ADAPT water table
management model. Trans. ASAE 35(2): 571‐579.
Corwin, D. L., and R. J. Wagenet. 1996. Application of GIS to themodeling of nonpoint‐source pollutants in the vadose zone: A
conference overview. J. Environ. Qual. 25(3): 403‐411.
Corwin, D. L., P. J. Vaughan, and K. Loague. 1997. Modelingnonpoint‐source pollutants in the vadose zone with GIS.
Environ. Sci. Tech. 31(8): 2157‐2175.
Corwin, D. L., J. Hopmans, and G. D. de Rooij. 2006. From field‐
to landscape‐scale vadose zone processes: Scale issues,modeling, and monitoring. Vadose Zone J. 5(1): 129‐139.
Debele, B., R. Srinivansan, and J.‐Y. Parlange. 2006. Coupling
upland watershed and downstream waterbody hydrodynamic
and water quality models (SWAT and CE‐QUAL‐W2) for better
water resources management in complex river basins. Environ.
Model. Assess. DOI 10.1007/s10666‐006‐9075‐1.
Donigian, A. S., Jr., and N. H. Crawford. 1976a. Modeling
pesticides and nutrients on agricultural lands. EPA
600/2‐7‐76‐043. Athens, Ga.: U.S. EPA Environmental
Research Laboratory.
Donigian, A. S., Jr., and N. H. Crawford. 1976b. Modelingnonpoint pollution from the land surface. EPA 600/3‐76‐083.
Athens, Ga.: U.S. EPA Environmental Research Laboratory.

TRANSACTIONS OF THE ASABE

Dontsova, K. M., S. L. Yost, J. Šimůnek, J. C. Pennington, and C.
Williford. 2006. Dissolution and transport of TNT, RDX, andcomposition B in saturated soil columns. J. Environ. Qual.
35(6): 2043‐2054.
EMSI. 2007. Watershed Modeling System (WMS). South Jordan,
Utah: Environmental Modeling Systems, Inc. Available at:www.ems‐i.com/. Accessed February 2007.
Fayer, M. J. 2000. UNSAT‐H Version 3.0: Unsaturated soil waterand heat flow model: Theory, user manual, and examples.
Report No. PNNL‐13249. Richland, Wash.: U.S. Department ofEnergy, Pacific Northwest National Laboratory.
Flerchinger, G. N., C. L. Hanson, and J. R. Wight. 1996. Modelingevapotranspiration and surface energy budgets across awatershed. Water Resour. Res. 32(8): 2539‐2548.
Foster, G. R., and L. D. Meyer. 1972. Transport of soil particles byshallow flow. Trans. ASAE 15(1): 99‐102.
Haith, D. A., and L. L. Shoemaker. 1987. Generalized watershedloading functions for stream flow nutrients. Water Resour.
Bulletin 23(3):471‐478.
Hansen, S., H. E. Jensen, N. E. Nielsen, and H. Svendsen. 1990.
DAISY: Soil plant atmosphere system model. NPO Report No.
A 10. Copenhagen, Denmark: The National Agency for
Environmental Protection.
Healy, R. W. 1990. Simulation of solute transport in variably
saturated porous media with supplemental information on
modifications to the U.S. Geological Survey's computer
program VS2D. USGS Water‐Resources Investigation Report
90‐4025. Washington, D.C.: U.S. Geological Survey.
Hoosbeek, M. R., and R. B. Bryant. 1992. Towards the quantitativemodeling of pedogenesis: A review. Geoderma 55(3‐4):
183‐210.
Hutson, J. L., and R. J. Wagenet. 1992. LEACHM: Leaching
estimation and chemistry model. Research Series No. 92‐3.
Ithaca, N.Y.: Cornell University.
Jacques, D., and J. Šimůnek. 2005 User manual of themulti‐component variably saturated flow and transport model
HP1: Description, verification, and examples. Version 1.0.
SCK‐CEN‐BLG‐998, Waste and Disposal. Mol, Belgium:
SCK‐CEN.
Jacques, D., J. Šimůnek, D. Mallants, and M. Th. van Genuchten.
2006. Operator‐splitting errors in coupled reactive transport
codes for transient variably saturated flow and contaminanttransport in layered soil profiles. J. Contam. Hydrology 88(3‐4):
197‐218.
Jansson, P.‐E., and L. Karlberg. 2001. Coupled Heat and Mass
Transfer Model for Soil‐Plant‐Atmosphere Systems. Stockholm,
Sweden: Royal Institute of Technology, Department of Civil andEnvironmental Engineering.
Jarvis, N. J. 1994. The MACRO model (Version 3.1): Technicaldescription and sample simulations. Reports and dissertations
19. Uppsala, Sweden: Swedish University of Agricultural
Science, Department of Soil Science.
Kandil, H. M., R. W. Skaggs, S. Abdel Dayem, and Y. Aiad. 1995.
DRAINMOD‐S: Water management model for irrigated arid
lands, crop yield, and applications. Irrig. Drain. Systems 9(3):239‐258.
Knisel, W. G. 1980. CREAMS: A field‐scale model for chemicals,
runoff, and erosion from agricultural management systems.
USDA Conservation Research Report No. 26. Washington,
D.C.: USDA‐ARS.
Knisel, W. G., ed. 1993. GLEAMS: Ground Water Loading Effects
of Agricultural Management Systems. Ver. 2.10.
VGA‐CPESBAED Publ. No. 5. Tifton, Ga.: University ofGeorgia, Coastal Plain Experiment Station.
Knisel, W. G., and F. M. Davis. 1999. GLEAMS: Groundwater
Loading Effects of Agricultural Management Systems. Version
3.0 Users Manual. SEWRL‐WGK/FMD‐050199. Tifton, Ga.:
USDA‐ARS Southeast Watershed Research Laboratory.

Vol. 50(5): 1683-1693

Laflen, J. M., L. J. Lane, and G. R. Foster. 1991. WEPP: A newgeneration of erosion prediction technology. J. Soil Water Cons.
46(1): 34‐38.
Leonard, R. A., W. G. Knisel, and D. A. Still. 1987. GLEAMS:
Groundwater Loading Effects of Agricultural Management
Systems. Trans. ASAE 30(5): 1403‐1428.
Meyer, L. D., and W. H. Wischmeier. 1969. Mathematicalsimulation of the process of soil erosion by water. Trans. ASAE
12(6): 754‐762.
Migliaccio, K. W., and P. Srivastava. 2007. Modeling hydrology at
the watershed scale. Trans. ASABE 50(5): 1695-1703.
Muñoz‐Carpena, R., R. G. Vellidis, A. Shirmohammadi, and W. W.
Wallender. 2006. Evaluation of modeling tools for TMDL
development and implementation. Trans. ASABE 49(4):961‐966.
Neitsch, S. L., J. G. Arnold, J. R. Kiniry, J. R. Williams, and K. W.
King. 2002. Soil and Water Assessment Tool: Theoretical
documentation, Version 2000. Temple, Tex.: Blackland ResearchCenter and USDA‐ARS Grassland Soil and Water ResearchLaboratory. Available at: www.brc.tamus.edu/
swat/downloads/doc/swat2000theory.pdf. Accessed May 2005.
Niu, Z., G. Sun, S. G. McNulty, M. Xie, and W. Byne. 2001.
Applying ANSWERS‐2000 to simulate BMP effects on
sediment and runoff from two watersheds in the Three Gorges
Area, southern China. In Soil Erosion Research for the 21stCentury, Proc. Intl. Symp., 653‐656. ASAE Pub. No. 701P0007.
J. C. Ascough II and D. C. Flanagan, eds. St. Joseph, Mich.:
ASAE.
Ouyang, Y. 2003. Simulating dynamic load of naturally occurring
TOC from watershed into a river. Water Res. 37(4): 823‐832.
Parkhurst, D. L., and C. A. J. Appelo. 1999. User's guide to
PHREEQC: A computer program for speciation, batch‐reaction,
one‐dimensional transport, and inverse geochemical
calculations. Version 2. USGS Water‐Resources Investigation
Report 99‐4259. Denver, Colo.: U.S. Geological Survey.
Parkhurst, D. L., K. L. Kipp, P. Engesgaard, and S. C. Charlton.
2004. PHAST: A program for simulating groundwater flow,
solute transport, and multi‐component geochemical reactions.
USGS Techniques and Methods 6‐A8.
Paul, S., P. K. Hann, M. D. Matlock, S. Mukhtar, S. D. Pillai. 2004.
Analysis of the HSPF water quality parameter uncertainty inpredicting peak in‐stream fecal coliform concentrations. Trans.
ASAE 47(1): 69‐78.
Pruess, K. 1991. TOUGH2: A general‐purpose numerical simulator
for multiphase fluid and heat flow. Report LBL‐29400.
Berkeley, Cal.: Lawrence Berkeley Laboratory.
Renard, K. G., G. R. Foster, G. A. Weesies, and J. P. Porter. 1991.
RUSLE: Revised Universal Soil Loss Equation. J. Soil Water
Cons. 46(1): 30‐33.
Richards, L. A. 1931. Capillary conduction of fluid through porous
mediums. Physics 1: 318‐333.
Ritchie, J. T. 1972. A model for simulating evaporation from a row
crop with incomplete cover. Water Resour. Res. 8(5):1204‐1213.
Saito, H., J., J. Šimůnek, and B. Mohanty. 2006. Numericalanalyses of coupled water, vapor, and heat transport in thevadose zone. Vadose Zone J. 5(2): 784‐800.
Saleh, A., and B. Du. 2004. Evaluation of SWAT and HSPF within
BASINS program for the Upper North Bosque River watershedin central Texas. Trans. ASAE 47(4): 1039‐1049.
Saleh, A., J. G. Arnold, P. W. Gassman, L. M. Hauck, W. D.
Rosenthal, J. R. Williams, and A. M. S. McFarland. 2000.
Application of SWAT for the Upper North Bosque River
watershed. Trans. ASAE 43(5): 1077‐1087.
Schaerlaekens, J., D. Mallants, J. Šimůnek, M. Th. van Genuchten,
and J. Feyen. 1999. Numerical simulation of transport andsequential biodegradation of chlorinated aliphatic hydrocarbons
using CHAIN_2D. Hydrol. Process. 13(17): 2847‐2859.

1691

Schijven, J., and J. Šimůnek. 2002. Kinetic modeling of virustransport at field scale. J. Contam. Hydrol. 55(1‐2): 113‐135.
Seuntjens, P., K. Tirez, J. Šimůnek, M. Th. van Genuchten, C.
Cornelis, and P. Geuzens. 2001. Aging effects on cadmiumtransport in undisturbed contaminated sandy soil columns. J.
Environ. Qual. 30(3): 1040‐1050.
Shaffer, M. J., A. D. Halverson, and F. J. Pierce. 1991. Nitrateleaching and economic analysis package (NLEAP): Modeldescription and application. In Managing Nitrogen for
Groundwater Quality and Farm Profitability, 285‐322. R. F.
Follett, D. R. Kenney, and R. M. Cruse, eds. Madison, Wisc.:
SSSA.
Šimůnek, J. 2005. Chapter 78: Models of water flow and solutetransport in the unsaturated zone. In Encyclopedia of
Hydrological Sciences, 1171‐1180. M. G. Anderson and J. J.
McDonnell, eds. Chichester, U.K.: John Wiley and Sons.
Šimůnek, J., and A. J. Valocchi. 2002. Chapter 6.9: Geochemicaltransport, Part 1: Physical methods. In Methods of Soil Analysis,
1511‐1536. 3rd ed. J. H. Dane and G. C. Topp, eds. Madison,
Wisc.: SSSA.
Šimůnek, J., M. Th. van Genuchten, M. Šejna, N. Toride, and F. J.
Leij. 1999. The STANMOD computer software for evaluating
solute transport in porous media using analytical solutions ofconvection‐dispersion equation. Versions 1.0 and 2.0.
IGWMC‐TPS‐71. Golden, Colo.: Colorado School of Mines,
International Ground Water Modeling Center.
Šimůnek, J., M. Th. van Genuchten, and M. Šejna. 2005. The
HYDRUS‐1D software package for simulating theone‐dimensional movement of water, heat, and multiple solutes
in variably saturated media. Version 3.0. HYDRUS Software
Series 1. Riverside, Cal.: University of California, Department ofEnvironmental Sciences.
Šimůnek, J., M. Th. van Genuchten, and M. Šejna. 2006. The
HYDRUS software package for simulating two‐ andthree‐dimensional movement of water, heat, and multiple solutes
in variably saturated media: Technical manual. Version 1.0.
Prague, Czech Republic: PC Progress.
Singh, V. P., and D. A. Woolhiser. 2002. Mathematical modeling ofwatershed hydrology. J. Hydrol. Eng. 7(4): 270‐292.
Sivapalan, M., R. Grayson, and R. Woods. 2004. Scale and scalingin hydrology. Hydrol. Process. 18(8): 1369‐1371.
Skaggs, W. F. 2007. DRAINMOD. Raleigh, N.C.: North Carolina
State University, Department of Biological and AgriculturalEngineering. Available at:www.bae.ncsu.edu/soil_water/drainmod.htm. Accessed
February 2007.
Steefel, C. I. 2000. New directions in hydrogeochemical transportmodeling: Incorporating multiple kinetic and equilibrium
reaction pathways. In Computational Methods in Water
Resources XIII, 331‐338. L. R. Bentley, J. F. Sykes, C. A.
Brebbia, W. G. Gray, and G. F. Pinder, eds. Rotterdam, The
Netherlands: A. A. Balkema.
Suttles, J. B., G. Vellidis, D. D. Bosch, R. Lowrance, J. M.
Sheridan, and E. L. Usery. 2003. Plain streams using theannualized AGNPS model. Trans. ASAE 46(5): 1325‐1335.
SWET. 2006. Watershed Assessment Model documentation anduser's manual. Gainesville, Fla.: Soil and Water Engineering
Technology, Inc. Available at:www.swet.com/SoftwareWAMUsersManual.html. Accessed
February 2007.
Theurer, F. D., and C. D. Clarke. 1991. Wash load component for
sediment yield modeling. In Proc. 5th Federal Interagency
Sedimentation Conf., 7‐1 to 7‐8. Washington, D.C.: Federal
Energy Regulatory Commission.
Tremwel, T. K. 1992. Field hydrologic and nutrient transportmodel, FHANTM. PhD diss. Gainesville, Fla.: University of
Florida.
USEPA. 2007. Better Assessment Science Integrating Point andNonpoint Sources (BASINS). Washington, D.C.: U.S. EPA.

1692

Available at: www.epa.gov/waterscience/basins/. Accessed
February 2007.
van Dam, J. C., J. Huygen, J. G. Wesseling, R. A. Feddes, P. Kabát,
P. E. V. van Valsum, P. Groenendijk, and C. A. van Diepen.
1997. Theory of SWAP, version 2.0: Simulation of water flow,
solute transport, and plant growth in the soil - water atmosphere - plant environment. Wageningen, The Netherlands:
Wageningen Agricultural University, Department of Water
Resources (Report 71), and DLO Winand Staring Centre(Technical Document 45).
Vanderborght, J., R. Kasteel, and H. Vereecken. 2006. Stochastic
continuum transport equations for field‐scale solute transport:
Overview of theoretical and experimental results. Vadose Zone J.
5(1): 184‐203.
van Genuchten, M. Th. 1987. A numerical model for water andsolute movement in and below the root zone. Research Report
No 121. Riverside, Cal.: USDA‐ARS U.S. Salinity laboratory.
Van Liew, M. W., J. G. Arnold, and J. D. Garbrecht. 2003.
Hydrologic simulation on agricultural watersheds: Choosing
between two models. Trans. ASAE 46(6): 1539‐1551.
Vellidis, G., P. Barnes, D. D. Bosch, and A. M. Cathey. 2006.
Mathematical simulation tools for developing dissolved oxygen
TMDLs. Trans. ASABE 49(4): 1003‐1022.
Verburg, K., P. J. Ross, and K. L. Bristow. 1996. SWIM v. 2.1 User
Manual. Divisional Report 130. Clayton South, Victoria,
Australia: CSIRO.
Wagenet, R. J., and J. L. Hutson. 1996. Scale‐dependency of solutetransport modeling/GIS applications. J. Environ. Qual. 25(3):499‐510.
White, K. L., and I. Chaubey. 2005. Sensitivity analysis, calibration,and validation for a multisite and multivariable SWAT model. J.
American Water Resources Assoc. 41(5): 1077‐1078.
White, K. L., I. Chaubey, and B. Haggard. 2004. Linking watershed
and reservoir models. In Proc. 2004 ASAE/CSAE Annual
International Meeting. St. Joseph, Mich.: ASAE.
Williams, J. R., and R. C. Izaurralde. 2005. The APEX model. BRC
Report 2005‐02. Temple, Tex.: Blackland Research Center.
Williams, J. R., C. A. Jones, and P. T. Dyke. 1984. A modelingapproach to determining the relationship between erosion andsoil productivity. Trans. ASAE. 27(1): 129‐144.
Woods, S. A., R. G. Kachanoski, and M. F. Dyck. 2006. Long‐term
solute transport under semi‐arid conditions: Pedon and field
scale. Vadose Zone J. 5(1): 365‐376.
Woolhiser, D. A., R. E. Smith, and D. C. Goodrich. 1990.
KINEROS, A kinematic runoff and erosion model:
Documentation and user manual. ARS‐77. Washington, D.C.:
USDA‐ARS.
Yagow, G., B. Wilson, P. Srivastava, and C. C. Obropta. 2006. Useof biological indicators in TMDL assessment andimplementation. Trans. ASABE 49(4): 1023‐1032.
Yeh, G.‐T., and H.‐P. Cheng. 1999. 3DHYDROGEOCHEM: A
3‐dimensional model of density‐dependent subsurface flow andthermal multispecies‐multicomponent hydrogeochemicaltransport. EPA/600/R‐98/159. Washington, D.C.: U.S. EPA.
Young, R. A., C. A. Onstad, D. D. Bosch, and W. P. Anderson.
1987. AGNPS, Agricultural Nonpoint‐Source Pollution model:
A large watershed analysis tool. Conservation Research Report
No. 35. Washington, D.C.: USDA.
Yuan, Y., R. L. Bingner, and R. A. Rebich. 2001. Evaluation of
AnnAGNPS on Mississippi Delta MSEA watersheds. Trans.
ASAE 44(5): 1183‐1190.

NOMENCLATURE
3DHYDROGEOCHEM

3‐dimensional model of flow
and thermal multispecies‐
multicomponent hydrogeochemical transport

TRANSACTIONS OF THE ASABE

ADAPTAGNPSAnnAGNPSANSWERSAPEX
BASINSCOUP
CREAMSCRUNCHDAISYDRAINMODDRAINMOD‐N
DRAINMOD‐S
EAAMODEPIC
FHANTM
GLEAMS

Vol. 50(5): 1683-1693

Agricultural Drainage andPesticide Transport
Agricultural Nonpoint Source
Annualized Agricultural Non
point Source
Areal Nonpoint Source
Watershed Environment
Response Simulation
Agricultural Policy/
Environmental Extender
Better Assessment Science
Integrating Point andNonpoint Sources
Coupled heat and masstransfer model for soil‐plant‐
atmosphere system
Chemicals, Runoff, Erosion
from Agricultural
Management Systems
Computer program for multicomponent reactive transportin porous media
Crop/soil simulation model
Drainage Model
Drainage Model for Nitrogen
Drainage Model for Salinity
Everglades Agricultural Area
Model
Erosion Productivity Impact
Calculator
Field Hydrologic and Nutrient
Transport Model
Groundwater Loading Effects
of Agricultural Management
Systems

HYDRUS
HSPF
HUSLE
LEACHM
MACRO
MUSLE
PHAST

PHREEQC
RUSLE
RZWQM
SHAW
SWAP
SWAT
SWIM
TOUGH2UNSATCHEMUNSATH
USLE
VS2DT
WAM
WMS

Variably saturated water flow
and solute transport model
Hydrologic Simulation
Program - FortranHydrogeomorphic USLE
Leaching Estimation andChemistry Model
Variably saturated water flow
model for macroporous soils
Modified USLE
Computer program forsimulating groundwater flow,
solute transport, and multicomponent geochemical
reactionsGeochemical model
Revised USLE
Root‐Zone Water Quality
Model
Simultaneous Heat and Water
Soil Water Atmosphere Plant
Soil and Water Assessment
Tool
Variably saturated water flow
model
Transport of Unsaturated
Groundwater and Heat
Unsaturated water flow modelwith major ion chemistry
Unsaturated water flow model
Universal Soil Loss Equation
Variably Saturated 2‐D Flowand Transport model
Watershed Assessment Model
Watershed Modeling System

1693

1694

TRANSACTIONS OF THE ASABE