United States        Office of Water          EPA-822-R-08-023
Environmental Protection  Office of Science and Technology December 2008
Agency           Washington, DC 20460      www.epa.gov
                         #19 Nutrient  Loading

United States          Office of Water             EPA-822-R-08-023
Environmental Protection   Office of Science and Technology December 2008
Agency              Washington, DC 20460        www.epa.gov
                                #19  Nutrient  Loading
                            Major Contributors

                             Iowa State University
                        U. Sunday Tim and William Crumpton
                            Prepared jointly by:

                       The U.S. Environmental Protection Agency
              Health and Ecological Criteria Division (Office of Science and Technology)


                  Wetlands Division (Office of Wetlands, Oceans, and Watersheds)


The material in this document has been subjected to U.S. Environmental Protection
Agency (EPA) technical review and has been approved for publication as an EPA document.
The information contained herein is offered to the reader as a review of the "state of the
science" concerning wetland bioassessment and nutrient enrichment and is not intended to
be prescriptive guidance or firm advice. Mention of trade names, products or services does
not convey, and should not be interpreted as conveying official EPA approval, endorsement,
or recommendation.


U. S. EPA.  2008. Methods for Evaluating Wetland Condition: Nutrient Loading.
Office of Water, U.S. Environmental Protection Agency, Washington, DC.


EPA acknowledges the contributions of the following people in the writing of this module:
U. Sunday Tim and William Crumpton both of Iowa Sate University.

This entire document can be downloaded from the following U.S. EPA websites:














                          LIST OF TABLES





                          LIST OF FIGURES


In 1999, the U.S. Environmental Protection Agency (EPA) began work on this series of reports
entitled Methods for Evaluating Wetland Condition. The purpose of these reports is to help
States and Tribes develop methods to evaluate (1) the overall ecological condition of wetlands
using biological assessments and (2) nutrient enrichment of wetlands, which is one of the pri-
mary stressors damaging wetlands in many parts of the country. This information is intended
to serve as a starting point for States and Tribes to eventually establish biological and nutrient
water quality criteria specifically refined for wetland waterbodies.

This purpose was to be accomplished by providing a series of "state of the science" modules
concerning wetland bioassessment as well as the nutrient enrichment of wetlands. The individual
module format was used instead of one large publication to facilitate the addition of other
reports as wetland science progresses and wetlands are further incorporated into water quality
programs. Also, this modular approach allows EPA to revise reports without having to reprint
them all. A list of the inaugural set of 20 modules can be found at the end of this section.

This last set of reports is the product of a collaborative effort between EPAs Health and
Ecological Criteria Division of the Office of Science and Technology (OST) and the Wetlands
Division of the Office of Wetlands, Oceans and Watersheds (OWOW). The reports were
initiated with the support and oversight of Thomas J. Danielson then of OWOW, Amanda K.
Parker and Susan K. Jackson (OST), and seen to completion by Ifeyinwa F. Davis (OST). EPA
relied on the input and expertise of the  contributing authors to publish the remaining modules.
More information about biological and nutrient criteria is available at the following
EPA website:
                            http://www. epa.gov/ost/standards
More information about wetland biological assessments is available at the following
EPA website:

                          CONDITION" MODULES









              IN WETLANDS











    Jhe purpose of this module is to describe
    and discuss the general hydrologic prop-
erties that make wetlands unique, and to pro-
vide an overview of the processes that control
wetland hydrologic behavior.  The intent is to
provide a general discussion of wetland hy-
drologic processes and methods in the hope
of fostering an understanding of the impor-
tant attributes  of wetland hydrology relevant
to the monitoring and  assessment  of these
systems. As such, it is not intended to address
the narrower definition of wetland hydrology
for jurisdictional  or classification purposes.
Also,  this module should  not replace more
advanced wetland texts. If the need arises to
obtain more specific information, the  reader
is advised to refer to wetland  books or ar-
ticles, including those referenced within this


 A7\itrient  loading  to  wetlands  is  deter-
L \ mined primarily by surface and subsur-
face transport from  the contributing  land-
scape, and varies significantly as a function
of weather and landscape characteristics such
as soils, topography, and land use. In the ab-
sence of sufficient measurements, nutrient
loading can only be estimated using an appro-
priate loading model. This module  provides
an overview of hydrologic and contaminant
transport models that can be used to estimate
nutrient loads to wetlands.
    Jhe purpose of this module is to provide
    an overview of hydrologic and contami-
nant transport models that can be used to es-
timate nutrient loads to wetlands.


      r the past three decades, considerable
    effort has  been expended in developing
models to simulate watershed hydrology and
nutrient transport, particularly the estima-
tion of cumulative field/watershed contribu-
tions of flow, sediment, nutrients, and other
contaminants of interest. Appropriately used,
existing models may apply when in evaluat-
ing wetland reference conditions or establish-
ing nutrient criteria for wetlands or guiding
management decisions once nutrient criteria
are established.

  Several reviews have summarized the char-
acteristics, features, strengths, and limitations
of models that are used  for estimating  wa-
tershed hydrology and water quality (Doni-
gian, et al., et al. 1991b, 1995b; DeVries and
Hromadka 1993; Novotny and Olem 1994;
Tim 1996a, 1996b). These models vary wide-
ly in  structure and in spatial and temporal
scale, and can be classified as (i) empirical or
semi-empirical loading function models and
ii) process-oriented simulation models.


 T oading function models are based on em-
M^ pirical or semi-empirical relationships that
provide estimates of pollutant loads based on
long-term measurements of flow and contam-
inant concentration. They provide for rapid
estimation  of critical pollutant loads with
minimal effort and data requirements. Load-
ing function models are widely used to esti-
mate pollutant loads in areas  where limited
data sets are available for process-based mod-
eling. A major advantage of loading function
models is their simplicity. Generally, loading
function models contain procedures for esti-
mating pollutant load based on either heuris-
tics or on the empirical relationships between
landscape physiographic characteristics and
phenomena that control pollutant export.

  McElroy et al. (1976) and Mills (1985) de-
scribed components of several screening models
developed by EPA's Environmental Research
Laboratory at Athens, Georgia to facilitate
estimation of nutrient loads from  point and
nonpoint sources and to enhance preliminary
assessment of water quality. The model con-
tains simple empirical expressions that relate
the magnitude of nonpoint pollutant load to
readily available or measurable input param-
eters such as soils, land use and land cover,
land management practices, and topography.
This model  is attractive because  it can be
applied to very large watersheds often with
minimal effort and little or no calibration is

  Regression modeling, an approach based on
statistical descriptions of historic flow and
pollutant concentration data, is an alternative
to the screening model Regression models are
used to obtain preliminary estimates of pollut-
ant load under limiting and incomplete data.
These models require primary input param-
eters such as drainage area, percent impervi-
ousness, mean annual precipitation, land use
pattern, and ambient temperature. Regression
models can determine storm-event mean pol-
lutant load with confidence intervals for the
estimated loads.

  In addition to regression modeling, sever-
al less complex, process-based models have
been used to  estimate flow and contaminant
transport  in  terrestrial environments.  Ex-
amples of process-based models include the
Generalized  Watershed Loading Function
(GWLF), the Spatially Referenced Regres-
sions on  Watersheds (SPARROW), and the
Pollutant Load model (PLOAD).


  The Generalized Watershed Loading Func-
tion Model (GWLF), developed at Cornell
University, estimates stream  flow, nutrient
load  and sediment  load  from  watersheds
management areas. The model allows simu-
lation of point and nonpoint loadings of nu-
trients and pesticides from urban and agricul-
tural watersheds,  including septic  systems.
The model also provides data to  evaluate the
effectiveness of certain land use management
practices. The GWLF is a temporally-contin-
uous simulation model with daily time steps,
but it is not spatially distributed. It simulates
overland flow and channel flow using a water
balance approach  based on measurements of
daily precipitation and  average temperature.
Precipitation is partitioned into direct surface
runoff and infiltration using the SCS Curve

Number technique. Here, the Curve Number
determines the amount of precipitation that
runs off directly, adjusted for antecedent soil
moisture based  on total precipitation dur-
ing the previous five days. A separate Curve
Number is specified for each combination of
land use types and soil hydrologic groups.
The amount of water available to the shallow
groundwater zone is  influenced by  evapo-
transpiration. This is estimated in GWLF us-
ing the available moisture in the unsaturated
zone, the evapo-transpiration potential, and a
cover coefficient. Potential evapo-transpira-
tion is estimated from a relationship to mean
daily temperature and the number of daylight
hours. GWLF calculates the groundwater dis-
charge  by performing  a lumped parameter
water balance on the  saturated and shallow
saturated zones.

  Soil erosion is modeled by the Revised Uni-
versal Soil-loss Equation (RULSE). Nutrient
fluxes in GWLF are  estimated empirically
using daily nutrient fluxes from surface run-
off from pervious and impervious surfaces,
sediment  erosion,  groundwater  base-flow,
and septic runoff. The monthly  nutrient load
is  calculated by totaling the daily nutrient
fluxes. In GWLF, the nitrogen and phospho-
rus loads from surface runoff are estimated by
multiplying excess runoff by their flow-weight-
ed average concentrations, respectively.

  The model assumes that each specific land-
cover type has unique event-mean-concen-
tration processes that affect  transport  and
storage, and are unique to the land use. The
nutrient-loading model for urban land use is
based on an accumulation/wash off model.
Nutrient  fluxes  from  impervious surfaces
and urban lands are estimated using chemical
build-up and wash-off parameterization. Both
nitrogen  and phosphorus from eroded  sedi-
ments are estimated using the sediment load,
enrichment ratio, and  the  concentration of
nitrogen  and phosphorus in the top layer of
the soil. As with many mid-range terrestrial
models, GWLF calculates concentrations of
dissolved and sediment-bound nitrogen and
phosphorus in stream flow as the sum total
of base flow, stream flow (overland flow) and
point sources. Groundwater only contributes
dissolved nitrogen and phosphorus values re-
flecting the effects of local land use. Nutri-
ent losses in urban runoff are assumed to be
entirely in the solid-phase, while point source
losses are assumed to be dissolved.

  The  GWLF requires three categories of
input parameters: meteorological; hydrology
and landscape; and chemical and biophysical
(see Table  1). The model requires daily pre-
cipitation and temperature.  The GWLF also
requires information related to land use, land
cover, soil,  and parameters that govern run-
off, erosion, and nutrient load generation. The
strength of  GWLF model is that data required
by this model are readily available from most
resource  management agency databases.

  In general, GWLF is  an empirically de-
rived,  statistically  based process that uses
daily inputs of precipitation  and temperatures
to compute nutrient fluxes.  A major strength
of GWLF is its simplicity in estimating pol-
lutant load. Because of this,  the model has
been used  for screening  landscapes accord-
ing to their  pollutant delivery potentials or for
identifying critical areas of nonpoint pollu-
tion. However, it does not account for rainfall
intensity  or storage along channels. Because
it uses a  simplified technique for estimating
base flow,  the model cannot reproduce the
precise history of overland flow and fluxes as

                    Basin/watershed size
                    Land use and land cover distribution
                    Curve Number by source area
                    USLE factors by source area
                    ET cover coefficient
                    Erosivity coefficients
                    Daylight hours by month
                    Growing season months
                    Initial saturated storage
                    Initial unsaturated storage
                    Recession coefficient
                    Seepage coefficient
                    Initial snow amount
                    Sediment delivery ratio
                    Soil water ava ilable cap acity
             3.Chemical and Biophysical:
                    Dissolved N and P in runoff by land cover type
                    N and P concentrations in manure runoff
                    N and P buildup in urban areas
                    N and P from point sources
                    Background N and P in groundwater
                    Background N and P in top soil layer
                    Duration of manure spreading
                    Population on septic systems
                    Per capita septic system loads for N and P
do event-based models. It can, however, repro-
duce the frequency and magnitude of monthly
nutrient fluxes from undisturbed watersheds.
The GWLF model does not have a sufficient-
ly long history of application and may not be
applicable to land areas with a high degree of
altered hydrology.


  As described in Preston and Brakebill (1999)
the Spatially Referenced Regressions on Wa-
tersheds (SPARROW) model was developed
to relate the water quality conditions within
a watershed to sources of nutrients as well as
those factors that influence transport of the nu-
trients. Developed specifically for conditions
within the Chesapeake Bay watershed,  the
SPARROW methodology utilizes statistical
techniques and spatially distributed landscape
data to estimate nutrient loads. Specifically,
the SPARROW methodology was designed
to provide  statistically based relationships
between  water quality  and  anthropogenic
factors (e.g., sources of contamination with-
in the watershed), land surface characteris-
tics that influence delivery of pollutants to
the stream, and in-stream transformation of
pollutants through  chemical and biological

pathways. The general form of the statistical
regression model for SPARROW is (Preston
and Brakebill 1999):
    L, =
in which L^ = nutrient load in stream reach i; n,
N = pollutant source index; N = total number
of sources; J(i) = number of upstream stream
reaches; Rn = estimated source parameter; Sn,j =
contaminant mass from source n in drainage to
reach j; a = estimated vector of land-to-water
delivery parameters; z} = land surface charac-
teristics associated with drainage reach j (e.g.,
temperature, slope,  stream density, irrigated
land, precipitation, and wetland); 8 = estimated
vector  of in-stream loss parameter; and T13J =
channel transport characteristics. The source
parameter (3 consists of point sources, nutrient
applications in  the form of  animal manure,
commercial fertilizer,  and atmospheric depo-
sition of pollutants.  The parameter, a, deter-
mines the relative influence of different types
of land-surface characteristics on the deliv-
ery of nutrients from  land surfaces to stream

  The literature reports a number of applica-
tions of SPARROW model, primarily applied
to the Chesapeake Bay ecosystem.  These ar-
ticles document water quality conditions and
assess  the effectiveness  of best management
practices in controlling nonpoint  pollution.
The  results  provided  the  basis for not only
delineating watershed areas that  are  most
critical to the export of nutrients, but also for
targeting and prioritizing remedial  control
strategies and conservation programs (Smith,
et al. 1997).

  In addition to the GWLF and SPARROW
models described above, other modeling ap-
proaches utilize a compromise between em-
piricism and more complex mechanistic ap-
proaches.  Typical examples  of such models
include the  Storm water Intercept and Treat-
ment Evaluation  Model for Analysis  and
Planning (SITEMAP) (Omnicron Associates
1990) and Pollutant Load model or PLOAD.
These models use daily time steps. Both can
be used to examine seasonal variability and
the load response to landscape characteristics
of specific watersheds. Due to their complex-
ity, they may have greater data requirements
and may require more site-specific data.

  SITEMAP is a dynamic simulation model
developed to assist with simulating stream
segment  waste-load allocations from point
and non-point sources. This model calculates
daily runoff and pollutant loading and can be
used for storm-event or continuous  simula-
tions (including probability distributions) of
runoff, pollutant loads, infiltration, soil mois-
ture, and evapo-transpiration. SITEMAP can
be used in either  single or mixed land uses,
and for event-based or continuous simulation
of surface runoff and pollutant load. Users of
the model are able to assess the effectiveness
of alternative management strategies and to
estimate load and waste-load from point and
nonpoint sources, respectively.  The primary
outputs  from the model include  probabilis-
tic estimates of runoff volume and nutrient
loadings. A typical  example application of
SITEMAP involved the assessment of pol-
lutant load and  surface runoff in the Tualatin
River Basin  and Fairview Creek watershed in

  PLOAD is a simplified GIS-based water-
shed-loading model. It can model combined
point and non-point source  loads in  either
small urban areas or in rural watersheds of
any size. As a loading model, PLOAD pro-
vides annualized estimates of pollutant ex-
port to waterbodies.  Pollutants  most com-
monly analyzed include sediments (TSS and
IDS), oxygen demand (BOD and COD), nu-
trients (nitrogen,  nitrate plus nitrite,  TKN,
ammonia, phosphorous), metals  (lead, zinc)
and bacteria (fecal  coliform),  or any other
user-specified pollutant. The model addresses
pollutant loading by land use categories and
sub-watersheds, but does not as certain indi-
vidual non-point sources or at actual pollut-
ant fate and transport processes. Additional
features of the model include: (i) the ability to
estimate average annual pollutant load, (ii) a
user-friendly interface that enhances manipu-
lation of input parameters and the assessment
of alternative pollution control  strategies,
(iii) tools to facilitate  evaluation  of land use
change impacts, and (iv) the ability to gener-
ate outputs at user-defined formats.

  To use the PLOAD  model,  users are re-
quired to provide  reasonably  accurate val-
ues  of input parameters describing   wa-
tershed land use and land cover, pollutant
loading  functions—based on  land  cover
types,  location of point source inputs, land
areas with specified BMPs,  and other gen-
eral  watershed characteristics.  When sup-
plied  with these input variables, PLOAD
generates  outputs that include average an-
nual loads,  aggregated  by  sub-watershed,
and reported in tables and maps  of loads by
watershed. In addition, users of PLOAD can
view and compare multiple loading scenarios
  The PLOAD is a part of the comprehensive
modeling tools in the EPAs Better Assess-
ment Science Integrating Point and Nonpoint
Sources (BASINS). The literature  also re-
ports the applications of the PLOAD model
for assessing the  effects of land use change
and BMPs for watersheds in North Carolina
and Maryland.


  In summary, the many and diverse loading
function models developed to  allow estima-
tion of point and non-point source pollution
loads are based on simplistic, functional and
empirical expressions that integrate flow and
pollutant concentration. Attractive  features
of these models are that they: (i) require very
limited data and computer modeling experi-
ence; (ii) contain relatively simple procedures
for estimating pollutant load; and, (iii) pro-
vide tools for rapid assessment of point and
non-point contributions to the watershed pol-
lutant load. However, these advantages come
at some expense regarding accuracy, nature of
environmental process and conceptualization
of the physical system.   In particular,  most
loading function  models fail to incorporate
the complex, nonlinear biogeochemical and
physical processes that influence the physical
system. Furthermore, loading function mod-
els are limited in how spatial  and temporal
processes  are handled and  how  landscape
variability  is  characterized.  Despite  these
limitations, there are situations in which these
models are logical and legitimate.


   /n contrast to the empirical and simplified
   loading function models described above,
process-oriented simulation models integrate
knowledge of physical, chemical, and biologi-
cal processes with empirical data, and allow
users to evaluate interactions among human,
economic  and  societal factors.  This section
provides an overview of some of the process-
oriented simulation  models that have  been
used to predict  watershed hydrology  and
water quality,  and that could provide mod-
eling tools for  predicting nutrient loading to
wetlands. These models include AGNPS and

          SOURCE MODEL

  The Agricultural Non-Point Source Pollu-
tion Model (AGNPS) is event-based, as well
as a continuous or annualized AGNPS (An-
nAGNPS) simulation model. These models
predict surface runoff, sediment yield,  and
nutrient transport primarily from agricultur-
al watersheds.  The two main nutrients simu-
lated are nitrogen and phosphorus, which are
essential plant nutrients and are major con-
tributors to eutrophication and surface water
pollution. The basic model components include
hydrology, erosion, sediment, and  chemical
transport (primarily nutrients and pesticides).
The model also considers point sources of
water, sediment, nutrients, and chemical oxy-
gen demand (COD from various sources in-
cluding feedlots). Water impoundments are
also considered as deposit!onal areas for sed-
iment-associated nutrients. The model also
has the ability to output water quality charac-
teristics at intermediate or user-defined points
throughout the watershed stream network.

  The AGNPS model uses a grid-cell-based
subdivision of the watershed, in which each
cell is considered homogeneous. The  cells
are linked together through the aspect or flow
direction,  and all watershed characteristics
and primary biophysical inputs are expressed
at the grid-cell level. The components of the
model use equations and methodologies that
have been well established in the water quality
modeling literature and are extensively used
by resource management agencies. For exam-
ple, the runoff volume is estimated using the
SCS curve number technique. The peak run-
off rate for each grid-cell is estimated using an
empirical relationship in the CREAMS mod-
el (Knisel 1980). Soil  erosion and sediment
yield are computed by  using the USLE and a
bedload equation, a relationship—developed
by Foster et al. (1981) based on the continuity
equation. In the model,  feedlots are treated
as point sources and pollutant  contributions
from these sources are  estimated by using the
feedlot pollution model developed by Young
(1982). Other point sources are accounted for
by incorporating incoming flow rates and
concentrations of nutrients to the cells where
they occur.

  In the  AGNPS model, the  resolution for
the individual  grid cells  can range from 2.5
acres  to  greater than  40 acres (or 1 ha to
more than 10 ha) depending on the problem
being addressed, the size and complexity of
the watershed, and the technical expertise of
the modeler. Smaller grid-cell sizes such as
10 acres (4 ha) are recommended for water-
shed less  than 2000 acres (800 ha). However,

                Watershed-level Input Parameters:
                       Watershed identification
                       Cell area (Acres)
                       Total number of grid cells
                       Precipitation (inches)
                       Energy-In tensity
                       Storm type
                Cell-level Input Parameters:
                      Cell number
                      SCS Curve Number
                      Average land slope (°o)
                      Slope shape factor (uniform, convex, COHC£IVC)
                      Average field slope length
                      Manning roughness coefficient
                      Soil erodibility factor (K-U SLE)
                      Cropping factor (C-USLE)
                      Practice (P-USLE)
                      Surface condition constant
                      Soil texture (sand, silt, clay, peat)
                      Fertilization level
                      Fertilization availability factor
                      Point source indication
                      Gully source level
                      Chemical oxygen demand factor
for watershed and catchments that are larger
than 2000 acres (800 ha), grid-cell sizes of 40
acres (16 ha) are normally used. The calcula-
tion  of flow  and transport processes in AG-
NPS occurs in three stages based on a set of
twenty or more parameters for each grid cell,
with the initial calculations for all cells in the
watershed made in  the  first stage.  The sec-
ond  stage  calculates the runoff volume and
sediment yield for each of the cells contain-
ing impoundments  and the sediment  yields
for primary cells.  A primary cell is one into
which no other cell drains.

  The non-point source  pollution  component
of the model estimates  transport and  trans-
formation  of nitrogen,  phosphorus, chemi-
cal oxygen demand, and pesticides. Pollutant
transport is  subdivided into soluble or dis-
solved phase and the  sediment-attached or
sediment-bound phase. Soluble nitrogen and
phosphorus compounds are calculated using
a relationship  adapted from the CREAMS
model (Knisel  1980);  along with  sediment
yield equations taken from the CREAMS and
the WEPP models. The input parameters for
the AGNPS  model include: cell number, re-
ceiving cell number, SCS curve number, land
slope, field slope length, channel slope, chan-
nel side-slope, soil  erodibility  factor,  cover
and management factor, support practice fac-
tor, surface  condition  constant, aspect, and
many other parameters related to land cover,
land topography, management practices, and
climate.  The watershed-level parameters re-
quired include:  area, area of each  grid-cell,
characteristics of storm  precipitation, and

storm energy-intensity. Table 2  summarizes
the major input parameters required by the
AGNPS model.

  The AGNPS model is, by far one of the
more widely used water quality models for
estimating the relative effects of agricultural
management practices in small  to large wa-
tersheds. However, the model has many limi-
tations,  including: lack  of process-level de-
scription of nutrient transformation processes
or the biochemical cycling of major plant ele-
ments to document the  biochemical cycling
during transport; inability to  characterize
the transport and transformation of nutrients
and pesticides in stream channels or similar
waterbodies; inability to handle sub-surface
flow and transport processes, as well as sub-
surface interactions; the lack of a process to
route flow or pollutants from individual grid-
cells to the watershed outlet; and the model is

          ANNUAL AGNPS

  To eliminate some of these limiting factors,
the AGNPS model has undergone numerous
refinements. The term "AGNPS" now refers
to the system of modeling components instead
of the single-event AGNPS  described above.
These enhancements made to the event-based
AGNPS of the 1980s  and early 1990s are in-
tended to improve the capability of the pro-
gram and to automate many of the input data
preparation steps needed for use with large
watershed systems. The current version of the
model is called AnnAGNPS, which is virtu-
ally the same computer  program as  AGNPS
5.x except that it allows for continuous simu-
lations of surface runoff, peak flow rate, and
pollutant transport for longer time periods
and on a daily basis. AnnAGNPS is designed
to handle watershed areas of up to 300,000
ha, and it divides the watershed area into sub-
divisions of homogenous cells with respect to
soil type, land use, and land management.

  In contrast to the event-based  model,
AnnAGNPS  operates  on a daily time  step.
It  simulates water, sediment, nutrients, and
pesticide transport at the cell and watershed
levels.  Special components are included  to
handle  concentrated  sources  of nutrients
from feedlots and point  sources, concentrat-
ed sediment sources with attached chemicals
from gullies, and irrigation (water with dis-
solved chemicals and sediment with attached
chemicals). Each day  the applied water and
resulting runoff are routed through the wa-
tershed system before the next day is consid-
ered. The model partitions soluble nutrients
and pesticides between  surface  runoff and
infiltration.  Sediment-transported nutrients
and pesticides are estimated and equilibrated
within the stream system, with the sediment
assumed to consist of five particle size class-
es (clay, silt, sand, small  aggregate, and large

  The soil profile is divided into two layers.
For estimating surface  runoff,  infiltration
and soil water storage. The top 200 mm are
used as a tillage layer whose properties can
change; the second layer's  properties remain
static. A daily soil moisture water budget con-
siders applied water (rainfall, irrigation, and
snowmelt), runoff, evapo-transpiration, and
percolation.  Surface runoff is estimated by
using the SCS Runoff Curve Number equa-
tion where the Curve Number can be modi-
fied daily, based  on tillage  operations, soil
moisture, and crop stage. Evapotranspiration
is estimated as a function of potential evapo-
transpiration by using the  Penman equation

(Penman  1948)  and soil  moisture content.
Erosion and sediment transport is predicted
within a watershed landscape according to
RUSLE (Renard, et al. 1997).

  For each day and each grid cell, the model
calculates mass  balances  of nutrients  (pri-
marily nitrogen, phosphorous), and organic
carbon. The model considers plant uptake of
nitrogen and phosphorus,  fertilization, resi-
due  decomposition, and nutrient transport.
Soluble and sediment-adsorbed nutrients are
estimated, and  they are further partitioned
into  organic  and mineral  phases.  Each nu-
trient component is decayed based upon the
reach travel  time,  water  temperature,  and
appropriate decay-constant.  The soluble nu-
trients are decreased further by infiltration.
Attached nutrients are adjusted for deposition
of clay particles Based on a first-order rela-
tionship, equilibrium concentrations are cal-
culated at both the upstream and downstream
points of reach. Plant uptake of nutrients is
modeled through a simple  crop growth stage
index. A daily  mass balance adapted from
GLEAMS (Leonard, et al. 1987) is estimated
for each pesticide. The pesticides have unique
chemodynamic properties, including half-life
and  organic matter partitioning coefficient.
Major components of the pesticide model in-
clude foliage wash-off, vertical transport in
the soil profile, and degradation.  Soluble and
sediment adsorbed fractions are calculated
for each grid cell on a daily basis.

  AnnAGNPS also contains simplified meth-
ods to route  sediment, nutrients, and pesti-
cides through the  watershed. Peak flow for
each reach is calculated using an extension of
the TR-55 graphical peak-discharge method.
Sediment routing is calculated based upon
transport  capacity  relationships  using  the
Bagnold stream power equation.  Sediments
are routed by particle size class, where each
particular size class can be deposited, more
entrained,  or  transported  unchanged;  de-
pending upon the amount entering the reach,
the availability of that size class in the chan-
nel and banks, and the transport capacity of
each size class.  If the sum of  all incoming
sediment is greater than the sediment trans-
port capacity, then the sediment is deposited.
If that sum is less than the sediment trans-
port capacity, the sediment discharge at the
downstream end of the reach will include bed
and bank material (if it is an erodible reach).
Nutrients and pesticides are subdivided into
soluble  and sediment attached components
for routing. Attached phosphorus is further
subdivided into organic and inorganic. Each
nutrient component is decayed based upon
the reach travel time, water temperature, and
appropriate decay constant. Soluble nutrients
are further reduced by infiltration. Attached
nutrients are adjusted for deposition of clay
particles. Based on a first-order relationship,
equilibrium concentrations are  calculated at
both the upstream and downstream points of
the reach.

  AnnAGNPS  includes 34 different input
data categories,  which can be grouped into
climate, landscape characterization, agricul-
tural management, chemical characteristics,
and feedlot operations. The climatic data con-
sist of precipitation, maximum and minimum
air temperature, relative humidity, sky cover,
and wind speed.  Land characterization data
include soil characterization, curve  number,
RUSLE parameters, and watershed drainage
characterization.  Agricultural   management
relates to data  on tillage,  planting,  harvest,
rotation, chemical operations, and irrigation
schedules.  Feedlot operations  include daily

manure production rates, times of manure re-
moval, and residual amount from previous op-
erations. Indeed, there are over 400 separate
input parameters necessary for model execu-
tion. Some of these parameters are repeated
for each cell, soil type, land use, feedlot, and/
or channel reach.  Separate  parameters are
necessary for the model verification section.
Default values are  available for some of the
input parameters. The daily climate data in-
put set includes twenty-two parameters, eight
of which are  repeated for each day simulat-
ed. A climate generator, GEM, can be  used
to generate the precipitation and minimum/
maximum air temperatures for AnnAGNPS.
The development of other input data can be
simplified because of duplication over a given
watershed. Some of the geographical inputs
including  cell boundaries, land  slope, slope
direction,  and land use, can be generated by
GIS and digital elevation models. Model in-
put is facilitated by an input editor, which is
currently available with the model. The input
editor  interface provides a page format for
data input, with each of the 34 major data cat-
egories on a  separate input page.  Input and
output can be in either all English or all  met-
ric units.  Separate input files for watershed
and climate data allow for quickly changing
climatic input.

  Extensive data checks (with appropriate er-
ror messages) are performed as data are en-
tered and, to a lesser extent, after all data are
read. Output  is expressed on an event basis
for selected stream channel  reaches  and  as
source accounting  from land or reach com-
ponents over  the simulation period. Primary
outputs parameters  generated by the model
relate to  soluble and attached sediment-nu-
trients and pesticides, surface runoff volume
and peak flow, and  sediment yield based on
particle size classes. Each output parameters
can be selected by the user for the desired wa-
tershed source locations (specific cells, reach-
es,  feedlots, point sources,  and gullies) and
for any simulation period. Source accounting
indicates the fraction of a pollutant load pass-
ing through any reach in the stream network
that came from the user-identified watershed
source location. In addition, event quantities
for user-selected parameters can be extracted
at desired stream reach locations.

  A major limitation of the AnnAGNPS is that
it does not estimate transport of pesticide me-
tabolites or daughter products. Other limita-
tions of AnnAGNPS models include: (1) they
lack a nutrient transformation component for
both nutrient, and pesticides; (2) they lack a
subsurface  or near-surface water flow com-
ponents; (3) they  lack flow  and contaminant
routing component; (4) all runoff and associ-
ated pollutant (sediment, nutrient, and pesti-
cide) loads  for a single day  are routed to the
watershed outlet before the  next day simula-
tion begins  (regardless of how many days this
may actually take); (5) there are no mass bal-
ance calculations tracking inflow and outflow
of water; (6) there is no tracking of sediment-
bound pollutants  in the stream reaches; (7)
point sources are  limited to constant loading
rates (water and nutrients); and, (8) there is no
provision for using spatially  variable rainfall
inputs. Detailed information on AGNPS and
AnnAGNPS can be found at http://www.sed-


  The Hydrologic Engineering Computation -
Hydrologic Modeling  System or HEC-HMS,

developed by the U.S. Army Corps of Engi-
neers, is a physically-based model designed to
simulate precipitation runoff processes of den-
dritic watersheds. The model was developed
to allow the simulation of large river basins
and flood hydrology, as well as small  urban
watersheds. HEC-HMS is the  latest version
of the HEC-1  model and exhibits a number of
similar options for  simulating  precipitation-
runoff processes.  In addition to unit hydro-
graphic and hydrologic routing  functions, ca-
pabilities available with HEC-HMS include a
linear-distributed  runoff transformation that
can be applied with gridded rainfall data,  a
simple "moisture-depletion" option that can
be used for simulations  over extended time
periods, and a versatile parameter optimiza-
tion option.

  HEC-HMS also provides the  capability for
continuous soil moisture accounting and res-
ervoir routing operations. Several options are
included in HEC-HMS to compute overland
flow and infiltration. These include the SCS
Curve Number equation, gridded SCS Curve
Number equation, and the Green-Ampt equa-
tion. In addition  to unit hydrographic and
hydrologic routing options, other capabilities
of the model include: linear quasi-distributed
runoff transformation for use  with  gridded
precipitation and terrain data such as DEM;
continuous simulation with either one layer or
a more complex five layer soil moisture meth-
od; and, a versatile parameter estimation op-
tion. The modified Clark method, ModClark,
is a linear quasi-distributed unit hydrograph
method that can be applied with gridded pre-
cipitation. A variety of flow routing schemes
are included in the model. Hydrographs pro-
duced by the model can be used directly or in
conjunction with other model  for  studies of
water quality, urban drainage, flow forecasting,
reservoir spillway  design, flood mitigation,
and flood management.

  The HEC-HMS modeling environment has
been enhanced by  geospatial  technologies.
For  example,  the  GEOspatial  Hydrologic
Modeling Extension or HEC-GEOHMS is a
software package that integrates HEC-HMS
with ArcView GIS. GEOHMS  also incor-
porates  ArcView Spatial Analyst Extension
to allow users to generate model inputs for
HEC-HMS. Using  the  digital  terrain  data
from GIS databases, HEC-GEOHMS trans-
forms  the  drainage  paths  and  watershed
boundaries into a hydrologic data structure
that represents watershed response to precipi-
tation. It provides an integrated, spatially-ex-
plicit simulation environment with data man-
agement and customized toolkit capabilities.
Other interactive capabilities allow users to
construct a hydrologic schematic of the wa-
tershed at stream gages, hydraulic structures,
and control points within the waterbody.

  HEC-HMS also features a Windows-based
graphical user interface (GUI), integrated hy-
drological analysis  components, data storage
and management capabilities,  and graphics
and reporting tools. The data storage and ma-
nipulation component is used for the storage
and retrieval of time series, paired functions,
and gridded data, in a manner that is largely
transparent to the user. The HEC-HMS GUI
provides a  means for specifying watershed
components, inputting data for each compo-
nent, and examining the results interactively.
It also contains global editors for entering or
examining  data for all applicable landscape

  Both  HEC-HMS  and  HEC-GEOHMS
have long history of application as a quasi-

dynamic hydrologic model. They are both in
the public domain and their technical refer-
ence manuals contain useful information on
how to model hydrological processes in gen-
eral, and the implementation of HEC-HMS
or HEC-GEOHMS in particular. In addition,
technical and users  supports are adequate.
However, several factors limit the use of the
model in many  situations, particularly when
assessing wetland hydrology. First, the model
was developed to predict the hydrologic re-
sponses of rural landscapes due to precipita-
tion and no water quality component is in-
cluded.  Second, the model is unsuitable for
landscapes  with significantly altered surface
hydrology due to, for example, tiling or other
landscape modification strategies. Finally, the
model does not have  an  explicit subsurface
modeling capability.


  The  Hydrologic  Engineering  Computa-
tion-5 Quality or HEC-5Q is a water  quality
model for use with U. S. Army Corps of En-
gineers' hydraulic model, HEC-5.  The water
flow simulation module, HEC-5, was devel-
oped to assist in planning studies for evalu-
ating proposed reservoirs in a system and to
assist in sizing the flood control and conser-
vation storage requirements for each  project
recommended for the system. It can also be
useful for selecting proper reservoir opera-
tional releases for hydropower, water  supply,
and flood control.

  The water quality simulation module, HEC-
5Q, is  used to  simulate  concentrations  of
various  combinations  of the following water
quality  constituents: temperature, dissolved
oxygen, nitrate (NO3) - nitrogen,  phosphate
(PO4) - phosphorus, ammonia (NH3) - nitro-
gen, phytoplankton,  C-biochemical oxygen
demand,  benthic oxygen  demand, benthic
source for nitrogen, benthic source for phos-
phorus, chloride,  alkalinity,  pH,  coliform
bacteria,  three user-specified conservative
constituents,  three user-specified  non-con-
servative  constituents, water column  and
sediment dissolved organic chemicals, water
column and sediment heavy metals,  water
column and sediment dioxins and furans, or-
ganic and inorganic particulate matter, sulfur,
iron and manganese.

  Using estimates of system flows generated
by HEC-5, the HEC-5Q model computes the
distribution of temperature and other water
quality constituents in the reservoir  and in
the associated downstream reaches. For those
constituents modeled, the water quality mod-
ule can be used in conjunction with the  flow
simulation  module to determine concentra-
tions resulting from operation of the reservoir
system for flow and  storage considerations,
or alternately, for determination of flow rates
necessary to meet water quality objectives.

  HEC-5Q can be used to evaluate options for
coordinating  reservoir  releases among proj-
ects to examine the effects on flow and water
quality at a specified location in the system.
Examples of applications of the flow simula-
tion model include examination of reservoir
capacities for flood control, hydropower, and
reservoir release requirements to meet water
supply and irrigation diversions. The model
may be used in  applications including the
evaluation  of in-stream  temperatures  and
constituent concentrations at  critical loca-
tions in the system, examination of the poten-
tial effects of changing reservoir operations
on temperature, or water quality constituent

  Reservoirs  equipped with selective  with-
drawal structures may be simulated to  de-
termine operations necessary to meet down-
stream water quality objectives. With  these
capabilities, planners could evaluate the ef-
fects on water quality of proposed reservoir-
stream system modifications and determine
how a reservoir intake structure could be op-
erated to achieve desired water quality objec-
tives within the system.

  The  1997 version  of HEC-5Q, modified
by Resource Management Associates, Inc.,
under contract to  the  HEC, provides flex-
ibility when applying it to  systems  consist-
ing of multiple branches of streams  flowing
into or out of reservoirs, which may be placed
in tandem or in parallel configurations. The
user can specify the number of streams and
reservoirs  that can be modeled, and program
dimensions can be  increased to meet project


  The Hydrologic  Simulation Program-For-
tran or HSPF (Johansen, et al. 1984; Bicknell, et
al. 1993; Donigian, et al. 1995a) is a physically
based, semi-distributed and deterministic mod-
el developed during the mid-1970's to predict
watershed hydrology and water quality  for both
conventional and toxic organic pollutants. It
provides an analytical tool for: (i) planning,
design  and operation of water  resource sys-
tems; (ii) watershed, water-quality manage-
ment and planning; (iii) point and non-point
source pollution analyses; (iv) fate, transport
exposure assessment and control of  conven-
tional and toxic pollutants; and,  (v) evaluation
of urban and rural  agricultural management
practices. HSPF combines three process-ori-
ented models: the Agricultural Runoff Man-
agement Model or ARM (Donigian and Davis
1978); the Non-point Source Runoff Model or
NPM (Donigian  and  Crawford 1979);  and,
the Hydrologic  Simulation Program or HSP
and its water quality component (Hydrocomp
1977). All of these components were seam-
lessly combined into a basin-scale framework
for simulating water quantity and water qual-
ity conditions of terrestrial  and aquatic sys-
tems (Bicknell, et al. 1993) and for integrated
analysis of in-stream hydraulic process.

  HSPF provides  continuous simulations of
hydrological water balance,  chemical trans-
port and fate in the terrestrial environment.
It  also includes an  in-stream  water quality
component for  evaluating nutrient fate and
transport, biochemical  oxygen demand, dis-
solved oxygen, phytoplankton, zooplankton,
and benthic algae. In general, the model con-
sists of three primary application modules: (1)
PERLND, which simulates water budget and
runoff processes, snowmelt and accumula-
tion, sedimentation, nutrients (e.g., nitrogen,
phosphorous) and  pesticide fate and transport
in runoff, and movement of a chemical tracer
(e.g.,  bromide);  (2)  IMPLND, which  simu-
lates impervious land area runoff and water
quality; and (3)  RCHRES,  which predicts
movement of runoff water and water quality
constituents in  stream channels and mixed

  The PERLND  module includes process-
based functions for predicting: (1) Ambient
temperature as  a function of elevation dif-
ferences between  land  segment and weather
station (ATEMP); (2) Water budget resulting
from precipitation on each previous land seg-
ment (PWATER);  (3) Sediment deposition and
detachment from the land areas (SEDMNT);

                           Land-surface elevation (DEM)
                           Land use and land cover
                           Hydrography/natural drainage network
                           Artificial drainage network
                           Drainage basin delineation
                  Innut Time
                             •earn How
                           Precipitation (daily/breakpoint)
                           Air Te mperatures (Maximum/Minimum)
                           Water use
                  Auxiliarv Data for Hvdrolop-ic Modeling
                           Channel geometry, roughness and gradient
                           Discrete-sample data foe water quality modeling
                           Nutrient concentrations
                           Sediment concentrations (total suspended sediment)
                           Sediment size distribution
                           Field parameters (e.g., dissolved oxygen, pH, etc.)
                  GIS and Auxiliarv data for Water Oualitv Modeling
                           Fertilizer application rates
                           Manure application rates
                           Atmospheric deposition
                           We tlands
                           Point Sources
(4)  Soil temperature for surface and subsur-
face layers and its impact on flow and contam-
inant transport,  (PSTEMP); (5) Surface run-
off water temperature and  dissolved oxygen
and carbon dioxide concentrations in over-
land flow (PWTGAS); (6) Water quality con-
stituents in the surface and subsurface flows
from  each previous land segment (PQUAL);
(7)  Storage and moisture fluxes  and solute
transport in each  soil layer or compartment
(INSTLAY); and,  (8) (Movement and behav-
ior  of pesticides  (PEST),  nitrogen (NITR),
phosphorus (PHOS) and tracers (TRACER)
through the top surface soil profile.

  The EVIPLND  module of HSPF predicts rele-
vant flow and transport processes in the imper-
vious land segments. It contains compartment
equations for simulating air temperature at
different locations within the watershed or
basin (ATEMP) as in the PERLND module,
snow accumulation and snowmelt (SNOW),
hydrologic water budget that includes infiltra-
tion and other interactions (IWATER), solids
accumulation and removal (SOLIDS), surface
runoff water temperature and gas concentra-
tions (IWTGAS), and generalized water quality
constituents. These modeling compartments
are similar  to the  PERLND module except
that little or no infiltration and other surface-
subsurface interactions occur.

  In the RCHRES module, constitutive equa-
tions  are used  to  route runoff and  water
quaity constituents predicted by  the PER-
LND and IMPLND modules through stream

channel  networks   and  reservoirs.   The
RCHRES module also  simulates those pro-
cesses  that  occur in open channels, such as
sediment detachment and deposition; chemi-
cal  phase  partitioning and  transformation
(e.g., oxygen and biochemical  oxygen de-
mand);  plankton population; nitrogen  and
phosphorus mass balances; and total carbon
and carbon dioxide  concentrations. Embed-
ded within RCHRES module are compart-
ment equations for describing channel flow
hydrodynamics (HYDR), sediment transport
(SEDTRN) advection of water quality con-
stituents (ADCALC), transport of conserva-
tive chemicals and water quality constituents
(CONS and EQUAL) and including synthetic
organic chemicals and pesticides.

  The HSPF modeling environment also con-
tains five utility models that enhance access,
manipulation and analysis  of time-series of
model parameters, including hourly precipita-
tion, daily evaporation and  daily stream flow
(Table  3). These utility  modules include the
following: (i) COPY, which copies data resid-
ing in the time series store or watershed man-
agement titles to another file; (ii) PLTGEN,
which  creates an ASCII file for display on
a plotter or for input to other programs; (iii)
DISPLAY, which generates summary data in
tabular form, (iv) DU RANL, a utility pro-
gram for frequency,  duration and statistical
analyses; and, (v) GENER,  which transforms
one or more  time series to produce a new
or different time series. In addition to these
utility programs, ancillary programs such as
ANNIE (Lumb, et  al.  1990) and HSPEXP
(Lumb and Kittle 1993) are used with HSPF
to interactively manipulate,  store,  retrieve,
list, plot, and update spatial,  parametric and
time-series  data. ANNIE and other similar
interactive pre and post-processing software
programs  greatly reduce the massive data
size  and intensive data  demands  of HSPF.
HSPEXP is a stand-alone land-surface hydro-
logic computation module that incorporates
an expert system component for model cali-
bration and for other modeling support.

  Since its debut in the early 1980s, HSPF has
undergone a number of enhancements. Some
of these improvements were in direct response
to changes in computer  operating systems
(e.g., shift from DOS to Windows), comput-
ing environment  (e.g., from mainframe to
minicomputer), human-computer interaction
(e.g., paradigm shift from command line inter-
faces to GUIs), and user  requirements (e.g.,
the need to predict hydrology and water qual-
ity of mixed land-use watersheds.) Today,
HSPF can be implemented on most computer
platforms, from laptops to the largest super-
computers using DOS, Windows, UNIX, or
other platforms. Depending on the size of the
watershed or basin, an HSPF simulation can
be efficiently executed on a 486-based mi-
crocomputer or a Pentium III (or greater) mi-
crocomputer with/without extended memory.
Overall, the HSPF modeling code accommo-
dates a wide range of operating environments
and user competencies. However, for water-
sheds and basins with complex land-use and
significant  spatial  heterogeneity,  powerful
computing resources and high levels of mod-
eling competency are required.

  The capabilities, strengths, and weaknesses
of HSPF have been demonstrated by its many
applications to urban and rural watersheds
(e.g., Donigian,  et al. 1990; Moore, et al.
1992; and Ball, etal. 1993). Some applications
have featured more comprehensive and innova-
tive uses of the model, particularly its ability
to handle complex landscapes and environmental

conditions. For  example, Donigian  et al.
(1990, 1991a) and Donigian and Patwardhan
(1992) describe the application of HSPF with-
in the framework of the Chesapeake Bay pro-
gram to determine total contributions of flow,
sediment, and other water quality constituents
(e.g., dissolved oxygen and nutrients) to the tidal
region of the  Chesapeake Bay estuary. They
use HSPF to estimate total loads of nitrogen
and phosphorus entering the Chesapeake Bay
from contributing sub-basins under a range
of land management scenarios and to  evalu-
ate  the feasibility of the 40% reduction in
non-point polluted loads  to the Bay.

  In another  application of the model, the
Maryland  Department   of the  Environ-
ment use HSPF to quantify nonpoint  source
contributions to the water quality impairment
in the Patuxent River and to evaluate alterna-
tive strategies for improving downstream wa-
ter quality in the Patuxent River Estuary. In
this application, the HSPF provides estimates
of non-point  pollution loads from  complex
mixed land-use areas of the drainage basin,
and the in-stream water quality throughout
the river system.

  As part of the EPA's Better Assessment Sci-
ence Integrating point and Nonpoint Sources
(BASINS) tool, HSPF is being applied to wa-
tersheds and basins for watersheds and wa-
ter-quality based  assessment for developing
the  Clean Water Act Total Maximum Daily
Loads. Linked to Windows-based user inter-
face, HSPF constitutes the major component
of BASINS' nonpoint source model (NPSM)
that  estimates  land-use-specific  nonpoint
source loadings for selected pollutants  within
the watershed.

  The Storage Treatment Overflow Runoff
Model or STORM is a model designed by the
Hydrologic Engineering Center of the U.S.
Army Corps of Engineers to simulate run-
off from urbanized landscapes. This model
consist of components that facilitate rainfall-
runoff assessment, water quality simulation,
and statistical and sensitivity analysis of the
modeling results.  In  general,  STORM's ad-
vantage over  other  continuous  simulation
models because of its relatively simple struc-
ture and moderate data requirements. It par-
ticularly addresses combined sewer outflows,
although it may be used to simulate storm-
water runoff quality and quantity. The hydro-
logic modeling procedures in STORM adopt
a modified rational formula with a simplified
runoff coefficient and depressive storage. Wa-
ter quality  constituents are  estimated based
on buildup or wash-off functions, and include
total suspended and settled solids, BOD, total
coliform, ortho-phosphate, and total nitrogen.
The model does have capability of continuous
and diffuse source release and uses the USLE
to estimate soil erosion by water. Limitations
of the STORM include minimal flexibility in
parameters with which  to calibrate model to
observed hydrographs, lack of a desktop ver-
sion that operates  in desktop environment,
and the  large amount of input data required
for its application.

          SOIL AND WATER

  The Soil and Water  Assessment  Tool or
SWAT (Arnold, et al. 1995) was  developed
by the USDA,Agricultural Research Services

by combining the  modeling components of
SWRRB-WQ, EPIC, and ROTO,  with  a
weather generator.  SWAT provides continu-
ous,  long-term simulation of the impact of
land management  practices  on water, sedi-
ment, and agricultural  chemical yields in
large complex watersheds. The SWAT model
assists resource  planners  in assessing non-
point source pollution impacts on watersheds
and large river basins. According to Arnold et
al. (1998), the model: (i) is based on physical
processes—associated with water flow, sedi-
ment detachment and transport, crop growth,
nutrient cycling,  and pesticide fate and trans-
port; (ii) uses readily available input param-
eters and standard  environmental databases;
(iii) is computationally efficient and supports
simulation of large basins or a variety of man-
agement scenarios  and practices; and,  (iv)
enables  users to  examine long-term implica-
tions of current and alternatives agricultural
management practices that can be juxtaposed
on the rural landscape.

  In the development of the SWAT model, em-
phasis was placed on: (i) reasonably accurate
depiction and characterization of the agricul-
tural land management and spatial variability;
(ii) accurate prediction of pollutant load;  (iii)
flexibility in discretization of the watershed
into  homogeneous, manageable  sub-basins;
and, (iv) continuous, long-term  simulations
as opposed to discrete storm-event simula-
tions of most quasi-distributed models.

  The SWAT modeling code consists of eight
major components: hydrology, weather, sedi-
mentation, soil  temperature,  crop  growth,
nutrients,  pesticides, and  agricultural man-
agement. Hydrologic processes simulated by
the model include  surface runoff, estimated
using the curve number methodology with an
option to simulate infiltration on the basis of
the Green-Ampt equation; percolation mod-
eled with a layered storage routing technique
combined with a crack flow model; lateral
subsurface flow; groundwater flow to streams
from  shallow  aquifers;  potential  evapora-
tion by  the Hargraves, Priestley-Taylor,  and
Penman-Montheith  techniques; snow melt;
transmission losses  from stream; and, water
storage losses from pond and reservoirs. Me-
teorological variables that drive the hydrologic
modeling component of SWAT include: daily
precipitation, daily minimum and maximum
temperatures, solar radiation, relative humid-
ity, and wind speed. For watersheds without
historical or current measurements  of these
climatic data variables, a weather generator
can be used to synthetically  simulate all or
some variables based on monthly histori-
cal statistics. Different climatic data can be
associated with specific sections of the  wa-

  Sediment yield from individual sub-basins
and hydrologic response units is computed by
using the modified Universal Soil Loss Equa-
tion. Crop growth is predicted by using algo-
rithms from the EPIC  model that character-
izes plant phenological developments based
on daily accumulation of heat units, harvest
index for partitioning grain yield, Montheit's
approach for potential biomass, and adjust-
ments for temperature  and  water stress.
Nitrate-N losses in runoff, deep percolation,
and lateral subsurface flow are simulated us-
ing methodologies in CREAMS and SWRRB-
WQ models. The transformation processes of
nitrogen (N) considered in SWAT include
mineralization (residue and humus), nitrifica-
tion, denitrification, volatilization, and plant
uptake.  For phosphorus (P),  the transforma-
tion processes include mineralization, soluble

                      Reach and main channels
                      Hydrologic respons e units
                      Groundwater aquifer data
                      Channel characteristics
                      General water quality information
                      Stream and lake water quality
                      Point sources
                      Ponds/wetlands/ reservoir days
                      Tributary channels
                      Precipitation (daily)
                      Solar radiation
                      Min/max temperatures
                      Solar radiation and wind speed
                      Relative humidity
                      Potential evapo-transpi ration
                      Soils and soil properties
                      Management practices
                      Fertilizer application
                      Manure application
                      Pesticide application
                      Urban data
P in runoff, sediment-bound P, P fixation by
soil particles, and crop uptake. Pesticide trans-
port and transformation follow algorithms in
the GLEAMS model and include equations
for describing interception by crop canopy,
volatilization, soil degradation, losses in run-
off and sediment, and leaching. Agricultural
management  practices  in the SWAT model
include tillage effects on soil and residue mix-
ing, bulk density and residue decomposition,
irrigation, and chemical management.
water balance). Algorithms are included to
characterize in-stream  parameters  such as
chlorophyll,  dissolved  oxygen, organic  N,
ammonia-N, and biological oxygen demand.
Within  stream and reservoirs,  the model
facilitates the simulation of major processes
including outflow, nutrient and pesticide load-
ing,  nutrient and pesticide transformations,
volatilization, diffusive transport of chemical
constituents, and chemical/sediment resus-
  In the SWAT  model, the stream  channel
processes include channel routing (flood, sed-
iment, nutrients, and pesticides) and reservoir
routing (sediment, nutrients, pesticides,  and
  Because of its semi-distributed  parameter
nature, coupled with its extensive climatic,
soil, and management databases, the SWAT
model is  probably one  of the most widely

used hydrologic and water quality model for
large watersheds and basins. To enhance the
use of the model, several interfaces that link
the modeling code with geographic informa-
tion systems (GIS) have been developed. For
example, Srinivasan  and Arnold (1994) de-
scribe an interface that links the SWAT mod-
el  to the GRASS (Geographical Resources
Analysis Support System), a raster-based GIS
software package. This  interface supports
watershed  delineation  into  hydrologically
homogeneous units and enhances the extrac-
tion of appropriate soil, topographic, climate,
agricultural management, and land use  data
for modeling and the display of the results in
the results  in the form of maps and  graphs.
Building on the popularity  and the look and
feel of the ArcView GIS (Environmental Sys-
tems Research Institute, Redlands, CA), an-
other interface was developed for the SWAT

  The SWAT-ArcView user  interface con-
tains  appropriately structured components
and functions for generating sub-basin topo-
graphic  attributes and model parameters, ed-
iting of input coverages and data, running the
SWAT model, and displaying model  outputs
in a user-defined format. With more than 500,
000 copies in use worldwide ArcView GIS is
probably the most versatile  desktop software
for the manipulation, analysis, modeling, and
visualization  of  geographically referenced
data. The interface uses  the many capabili-
ties of ArcView GIS to offer users desirable
housekeeping functions  such as creating a
new SWAT project (wherein a project refers
to a set of model parameters and model ap-
plication),  editing of the modeling database,
and opening, copying,  and  deleting of a SWAT
project.  In  general, the interface consists of
customized menus and dialog boxes  that fa-
cilitate interactive manipulation of watershed
and modeling database and for interrogating
the modeling code.

  As a quasi-distributed model, one of the
many limitations of SWAT is that it is input
data intensiveness andit requires the specifi-
cation of an appropriate data format that en-
sures error-free simulation (see Table 4 for a
partial list). The primary input parameters in-
cludes those that describe the watershed (e.g.,
area), the watershed landscape (e.g., number
of hydrologic response units, number of sub-
basins, average  sub-basin slope, etc.),  agri-
cultural management  (e.g.,  date of planting,
chemical application, tillage, and harvesting),
and the climatic conditions within the wa-
tershed. These input data categories are ar-
ranged in different hierarchically structured
data files with definable extensions. For ex-
ample, parameters that describe the different
hydrologic response units within a sub-basin
are constituted  under the  *.sub  input file.
They include tributary channels, amount of
topographic relief and its influence on climatic
conditions within a sub-basin, parameters af-
fecting surface and subsurface water flow and
contaminant transport. Likewise, the param-
eters describing soil physical  and chemical
properties within each  hydrologic response
unit are arranged as input files with *.sol and
*.chm extension, respectively, while the 14
different  types of agricultural management
operations simulated by SWAT are defined in
the *.mgt input file extension.

  To further assist users in creating and or-
ganizing  input  data for modeling, a digital
database  and customized menus are provid-
ed with the  modeling code. Users of SWAT
can select and  use  the  following data sets:
(i) USDA-NRCS STATSGO soil-association

database- consisting of soil map unit polygons
and attribute data; (ii) digital elevation model
(DEM) for the  contiguous United States as
derived  from 1:250,000  scale USGS topo-
graphic data; (iii) Anderson Level III classi-
fied land use/land cover data created by using
the l:250,000-scale USGS LUDA; and, (iv)
historical climatic database for 1130 weather
stations located  across the U.S.

  The SWAT model  and the  ArcView  GIS
modeling  interface are  available  to users
worldwide  through the  model's  Web  site
html) or by sending an  e-mail request to the
principals at the Blackland Research Center,
Temple, TX.  The SWAT models runs on a
number of operating environments including
Windows (95, 98, NT, and 2000) as well as
Unix workstations. Version 99.2 of the SWAT
model, for example, requires about 16 MB of
RAM, a 486 or  Pentium processor, and 10 to
15 MB of disk storage.

  The SWAT model  has found widespread
application in many  modeling  studies  that
involve systemic evaluation of the impact of
agricultural  management on  water quality.
Several case studies are available in the lit-
erature that demonstrate the reliability of the
model. For example,  as part of the national
Coastal  Pollutant Discharge Inventory, the
National Oceanic and Atmospheric Adminis-
tration utilized the SWAT model to estimate
nonpoint source loading into all U.S. coastal
areas. Srinivasan et al. (1998) describe the
application of SWAT to selected watersheds
in the Upper Trinity River Basin in Texas.
Manguerra and  Engel (1998) report the use of
SWAT model to evaluate runoff from two ag-
ricultural watersheds in west central Indiana.
More recent applications of the SWAT model
include watershed assessments and nonpoint
source pollution control in Texas (Rosenthal,
et al.  1995), Mississippi (Bingner 1996), and
Indiana (Engel and Arnold 1991). The U.S.
Environmental Protection Agency is consid-
ering adopting SWAT as a nonpoint source
modeling component of its BASINS (Better
Assessment  Science Integrating Point  and
Nonpoint  Sources)  modeling environment.
The  current version of BASINS uses the
HSPF model to assist in delineating impaired
and  critical  watersheds and  for  analyzing
baseline nonpoint source loadings and for ex-
amining total maximum daily load allocation
scenarios and TMDL compliance assessment
within watersheds.

           STORM WATER

  The Storm Water Management Model or
SWMM is a comprehensive computer model
used for the analysis of water quantity and
quality of runoff. The model has been widely
used to perform either single event or contin-
uous simulation (i.e. long-term) of hydrologic
and hydraulic problems of both combined and
separate sewer systems, as well as for assess-
ing urban nonpoint pollution problems.  The
model predicts  flows, stages, and pollution
concentrations.  SWMM also simulates  all
components  of the hydrologic cycle includ-
ing,  rainfall, snowmelt, surface runoff and
subsurface flow, flow/flood routing through
drainage networks, storage, and treatment.

  SWMM can be used both for planning and
designing sewers and for evaluating the hy-
drology of urban watersheds including those
with wetlands. In planning mode, the model
can be used as an overall assessment of the
urban runoff problems and potential pollutant

abatement options. This mode is realized by
continuous simulation of hydrology and hydro-
logic conditions using long-term precipitation
data. Users can perform frequency analysis of
predicted hydrographs and pollutographs, and
examine hydrological events of specific inter-
est. In design mode, event simulation may
also be performed using a detailed watershed
schematization and shorter time steps for the
precipitation  input.  SWMM  is structured
around six different, but related, modules or
blocks including: (i) RAIN, which processes
precipitation data for input into the RUNOFF
block; (ii) RUNOFF, which generates runoff
volume and quality from precipitation on the
watershed; (iii) TEMP, which processes are
temperature data for snowmelt computations;
(iv) TRANSPORT, which is based on kine-
matic wave routing of flow  and quality, base
flow generation, and infiltration; (v) STOR-
AGE  and TREATMENT, which handles de-
tention;  and,  (vi) EXTRAN,  which handles
dynamic flow routing equations (Saint Venant's
equations) for accurate  simulation of back-
water, looped connections,  surcharging, and
pressure flow. Within the EXTRAN block,
users  can perform  sophisticated  hydrau-
lic analysis of urban drainage networks us-
ing either the Saint Venant's hydrodynamic
equations  or  the kinematic wave equations.
The RAIN block facilitates  the processing of
hourly and 15-minute (breakpoint) precipita-
tion time series for input to  continuous simu-
lation. It also includes the statistical analysis
procedures of the EPA SYNOP model used
to characterize storm events. By using these
blocks, users can simulate all aspects of the
urban hydrologic and quality cycles, includ-
ing rainfall, snowmelt,  surface and subsur-
face runoff,  flow routing through the drain-
age network,  storage and treatment.
  In SWMM, the watershed/basin is divided
into basic spatial units called sub-watersheds
or subbasins.  Each sub-watershed requires
specification of a number of parameters char-
acterizing its landscape. Data requirements
for hydraulic and hydrologic simulation in-
clude area,  imperviousness,  slope, surface
roughness,  and depression storage and infil-
tration parameters. Land use information is
used to determine type of ground cover for
each model sub-area. Depression storage can
be estimated from rainfall  and stream flow
data or from published literature values. Soil
infiltration parameters are calculated from ei-
ther the Horton equation or the Green-Ampt
equation. Manning roughness values for pre-
vious and  impervious  areas are estimated
form  published values  for  each land cover
type. Water quality processes in SWMM are
simulated by a variety of options, including
constant concentrations, regression relation-
ships (load vs. flow), buildup and wash-off
Other water quality processes in SWMM are
those associated with precipitation, land sur-
face, erosion, sedimentation, soil, deposition
and treatment. SWMM can predict up to ten
different pollutants during  a single simula-
tion session. Pollutants that can be simulated
include total nitrogen,  total phosphorus, or-
tho-phosphate, copper and zinc. Ten different
land uses can be simulated and land uses are
grouped as appropriate. The event-mean con-
centration can be calculated for each pollut-
ant and each land use.

  Depending on the objective of the  model
application, the input data  requirements by
SWMM can  be minimal or  extensive. The
data collection and data preparation activities
for simulation  modeling can be intensive,
particularly for large watersheds and  drain-
age networks. For example,  the simulation

of sewer hydraulics requires  expensive and
time-consuming field verification of sewer
invert elevations. Extensive data is also need-
ed for the model calibration and validation.

  The outputs generated from SWMM consist
of hydrographs and pollutographs (concentra-
tion vs. time) at any desired point within the
drainage system. Users can output depths and
velocities of flow as well as summary statistics
defining surcharging, volumes, continuity, and
other water quality parameters. The statistics
block can be used to separate the hydrographs
and pollutographs  into storm events and to
compute summary statistics  on  parameters
such as volume, duration, inter-event time,
load, average concentration, and peak  con-
centration.  Model outputs can be in tabular
or geographical format.  There are  options
for dynamic plots of the hydraulic grade line
produced by the EXTRAN module. Linkages
have also been developed between SWMM
and GIS.

  SWMM is perhaps one of the most widely
used models developed by the EPA for urban
runoff simulations. Originally developed be-
tween 1969 and 1971, SWMM has withstood
many verification  tests. It continues to  be
used in countries throughout the world in-
cluding the United States as well as in Aus-
tralia, Canada and Europe. A large body of
literature exists describing the applicability of
the model. Within the United States, applica-
tions of SWMM are many and varied Span-
ning states as varied as California, Florida,
and Virginia.  The U.S. Geological Survey
has used the model to predict hydrology of a
watershed in Rolla, Missouri. The model was
applied to the Winter Haven  chain of lakes
and its watersheds to predict  pollutant load-
ing to the lake and to examine the effects of
human activities on lake water quality.

  One  of the major strengths of SWMM is
its ability to predict hydraulic systems such
as drains, detention  basins, wetlands,  sew-
ers, and related flow controls. The SWMM,
however, does have a number of limitations
including: (i) the lack  of component equa-
tions and functions to route subsurface flow
and water quality;  (ii)  limited interactions
between the relevant biophysical and chemi-
cal processes; (iii) the reliance on first-order
rate kinetics to describe pollutant transfor-
mation in the TRANSPORT block; and, (- iv)
the lack of explicit functional  components to
predict biogeochemical cycling in receiving
waterbodies and control structures.

  One  drawback, when using of earlier ver-
sions of SWMM, is the lack of an appropriate
user interface. Over the past decade develop-
ers have  worked to enhance  the "look-and-
feel" of the model's interface using interfaces
such as  MIKE-SWMM, PC_SWMM,  and
XP-SWMM.  In response to EPAs clients'
need for improved computational  tools for
managing urban runoff and wet weather wa-
ter quality problems, the agency has support-
ed development of a new version of SWMM
that  incorporates  recent  advancements  in
software  engineering methods and updated
computational  techniques. In this new ver-
sion, the architecture of SWMM's compu-
tational scheme has  been revised  by using
object-oriented   programming  techniques.
This revision of SWMM resulted from a col-
laborative effort  between EPA-NRMRL's
Water Supply and Water Resources Division
and Camp  Dresser McKee, Inc.  New fea-
tures include:  improved prediction of infil-
tration, soil  moisture accounting, functions

for estimating groundwater flow and energy
balance, and  techniques for routing surface
water flow. They also incorporated features
such as—Lagrangian water quality transport
model, bed/suspended load sediment trans-
port  model, and interactive real-time control
of sewer flow routing.


  The Simulation of Water Resources in Ru-
ral   Basins-Water  Quality (SWRRB-WQ)
(Arnold,  et al. 1990) adapts the CREAMS
(Knisel  1980) model to provide predictions
of hydrologic, sedimentation,  nutrient and
pesticide transport in large, complex  rural
watersheds and basins. The primary objec-
tive  of the model is to predict the effects of
alternative management decisions on water
flow, sediment yields,  and chemical trans-
port  with an acceptable level of accuracy for
un-gauged rural basins and watersheds. The
major modifications to the CREAMS model
which resulted in the SWRRB-WQ are:  (i)
the modeling code  now allows  simultaneous
computation of several  sub basins to predict
water and sediment yields and chemical load-
ing,  and each sub-basin was  considered a
homogeneous entity; (ii) a return flow  com-
ponent appropriately simulates the soil water
balances; (iii) reservoir storage routing com-
ponent provides estimates of effects of ponds
and  reservoirs on water flow and sediment
yield; (iv) a weather simulation model pro-
vides statistical, daily estimates of weather
inputs such as precipitation, solar radiation,
and  minimum and maximum temperatures;
(v) plant growth model provides predictions
of management and natural and anthropogenic
inputs on variation in crop growth; and, (vi)
components are incorporated to enable simu-
lation of sediment movement in ponds, reser-
voirs and streams. In general, the SWRRB-
WQ handles the major biophysical processes
including surface runoff, percolation, return
flow, evapo-transpiration, transmission losses,
pond  and reservoir storage, sedimentation,
nutrient cycling, pesticides fate and transport,
and plant growth.

  In the SWRRB-WQ model, the water bal-
ance in the soil-plant-water atmosphere sys-
tem is represented by the hydrologic model-
ing component. Thus, the hydrological cycle,
particularly  the  soil water balance,  is de-
scribed by the equation:
SW.-SW =
                  i-Qi- ETi- Pi-
in which SW = soil-water content less 15-bar
water content; t = time in days; R, Q, ET, P
and QR = daily amount of precipitation, run-
off, evapo-transpiration,  percolation,  and re-
turn flow, respectively surface runoff, Q is es-
timated by using modified form of the runoff
curve number technique and sediment yield is
predicted by using modified USLE (Williams
and Berndt 1977).

  Nutrient  yield  and  nutrient  cycling in
SWRRB-WQ adopts the expressions developed
in the EPIC model (Williams,  et al.  1989)
and the quantities calculated  for each  sub-
watershed is routed to watershed outlet. The
nutrient load is distributed between  the sol-
uble and sediment-bound phases. Pesticides
fate and transport modeling in SWRRB-WQ
adopts the methodology and  equations in
GLEAMS model  (Leonard, et al. 1987). As
with nutrients, the pesticides are distributed
between the soluble  and adsorbed phases
according to the organic matter content of the

  Inputs  parameters  required for SWRRB-
WQ model simulations are related to process-
es such as hydrology, sediment yield, chemical
fate and transport, and channel routing. The
basic inputs include time history of precipi-
tation, meteorological data, characteristics of
land surface including management practices,
vegetation cover,  and terrain, conversations
and structural management practices within
sub-basins, chemical characteristics of pol-
lutants,  stream channel characteristics, and
point source impacts such as reservoirs and
ponds.  The SWRRB-WQ also requires input
parameters that describe the entire drainage
basin (e.g., total drainage area,  basin slope,
and field capacity), pesticide parameters (e.g.,
soil partition coefficient, wash-off fraction,
soil biological  half-life,  and  water solubility),
and sub-basin characteristics (e.g., slope, area,
curve number, and type of vegetation cover).

  The hardware and software requirements
for implementing the SWRRB-WQ model are
fairly standard. Depending on the area of the
watershed and the degree of variability in hy-
drologic  (e.g., ponds, gullies, and reservoirs)
and landscape features, the model can be ex-
pected to run efficiently on standard desktop
computers operating under the Windows en-

  Several applications of SWRRB-WQ eval-
uate the hydrology and water quality of com-
plex, large rural watersheds and basins and
are  reported in the literature. For example, the
National Oceanic and Atmospheric Adminis-
tration used SWRRB-WQ to estimate loading
of nonpoint pollutants from rural basins in all
coastal counties in the United States (Sing-
er, et al.  1988). In this application,  disparate
data from the National Weather Service sta-
tions, Natural Resource Conservation Service
(NRCS) Soils 5 database, the U.S. Geological
Survey's digital land use land cover data, and
other watershed parameters were used with the
model to provide simulations  of water qual-
ity variables for  cropland, forest, and range-
land in  about 770 watersheds that comprise
the Gulf Coast, eastern, and western coastal
zones of the United States. In another appli-
cation, Arnold et al. (1987)  predicted the ef-
fects of urbanization on watershed water yield
and reservoir sedimentation. As a component
of the HUMUS  (Hydrologic Unit Model of
the United States) project, the  SWRRB-WQ
model was integrated with EPIC and ROTO
(Arnold, et al.  1995) to provide a tool for the
1997 Resource Conservation Assessment of
the NRCS. Lastly, a Windows interface to
enhance the use  of the model was developed
by the Office of Science and  Technology of
the U.S. EPA, to assist regional planning ju-
risdictions in developing the total  maximum
daily loads for agricultural watersheds.  This
can  be found  at http://www.epa.gov/docs/
SWRRB WINDOWS/metadata.txt.html.

     Mathematical  models of ecological sys-
     tems provide a simplified, approximate
representation of real-world processes and
phenomena.  Indeed,  researchers  describe
models as "metaphors for reality" or "delib-
erately simplified  construct of nature erected
for purposes of understanding a  system  or
phenomena"  (Batchelor 1994). Bear (1979)
defines a model as: "a simplified version of
a real investigated system that approximately
simulates the latter's excitation-response relations
that are relevant to the considered problems."
Application of models to ecological problems

mod eling
Water quality

purpose exi
requires well-designed protocols  for model
reliability  assessment and quality  assurance,
including model validation.

  Mathematical models are  routinely used in
most disciplines and fields related to earth and
environmental sciences. Their use in problem
solving and decision-making is increasing.
Examples  abound in many  application areas
on the potential benefits  of modeling. How-
ever, there is an area of concern to developers
and users of these models as well as the deci-
sion makers using information derived from
the output of the models. Indeed,  the ability
of models to replicate real-world  processes
and system  responses is greatly  influenced
by (i) errors in the underlying  theory upon
which  the model is based, (ii) uncertainty
in the input parameters,  and  (iii)  unpredict-
ability of the  system's  phenomena.  These
factors not only affect the integrity of model
outputs, but also the decisions that these out-
puts support. Because mathematical models
are increasingly relied upon in environmental
decision-making, it has become imperative to
document their reliability. In addition, mod-
els used to describe earth system processes
are becoming increasingly  complex, often
involving multiple media, multiple pathways
and widely varying endpoints. This complex-
ity could lead to errors and uncertainty in the
predicted  endpoints  and outcomes,  making
it  increasingly necessary to develop  meth-
odologies to convey critical  uncertainties in
environmental models.  Techniques  and ap-
proaches to  convey errors and uncertainties
in mathematical models fall under the do-
main of quality assurance and quality control
(QA/QC). In modeling, components of a QA/
QC protocol often include pre- and  post-au-
dit analyses that involve model verification,
sensitivity analysis,  model  calibration  and
validation, and the assessment of model un-
certainty (Figure 1).

  In hydrologic and water quality modeling, a
wide range of techniques are used to establish
the veracity and reliability of environmental
models. These include model verification, mod-
el sensitivity analysis, model calibration, model
validation, and  model uncertainty analysis.


  Model verification constitutes the process
of assessing the reliability of the  modeling
computer  code in generating both accurate
and "numerically  stable" outputs  that rep-
resent  the  conceptualized  physical system.
Often compared to or confused with  model
validation, verification of a model  generally
involves comparing the results of the numeri-
cal solution to those obtained using  analytical
or "closed-form" techniques. Through  model
verification, illogical statements in the com-
puter code or  incorrect assumptions that re-
quire significant model modifications can be
identified and  corrected. In the use of hydro-
dynamic models, for example, it is desirable
that the computational scheme (e.g., numeri-
cal finite difference or finite element) be free
of numerical dispersion due to the choice of
input parameters for the advection component
of flow. A model verification process assures
that the numerical results are reasonably  cor-
rect and matches prior specifications and as-


  For mathematical models, sensitivity anal-
ysis is required to help  identify key input
parameters and predictions errors. The  aim of
sensitivity analysis, in general is to estimate
the rate of  change in  the predicted  model
output  with respect to changes in the  model
inputs. Such information  is important for: (i)
assessing the range and limits of applicability
of the model, (ii) determining parameters for
which it is important to have highly accurate
values, and, (iii) understanding the behavior
of the physical system being modeled.  The
choice of method of sensitivity analysis de-
pends largely on the sensitivity measure em-
ployed, the desired accuracy in the estimates of
the sensitivity measure, and the computational
demands and costs involved.

  Methods of  sensitivity analysis can  be
broadly  divided into three main categories:
(i) variations of parameters or model formula-
tions in which the models is run for different
combinations of input parameters of concern,
or a straightforward change is made to the
model structure; (ii) domain-wide sensitivity
analysis involving the evaluation of the  sys-
tem behavior response over the entire range
of parameter variations; and (iii) local sen-
sitivity analysis which focuses on  estimates
of model sensitivity to input and parameter
variation in the vicinity of a point. One wide-
ly used method of sensitivity analysis is the
normalized gradient technique. For a math-
ematical model of the form:

                F(u,k) = 0

where k is a set of m parameters, and u is a
vector of n output variables. Thus the normal-
ized gradient  sensitivity  analysis  takes the
  Other techniques include the normalized
response and the local gradient approxima-
tion represented mathematically as:
              D  =du /u(k)
               i     i   iv '
         du = [S;j] 5k; S;j = du/dk

in which S and D  are sensitivity coefficients.

  This process of model QA involves adjust-
ing model input parameters until the system
output and the model output (predicted values)
show an acceptable level of agreement. Typi-
cally, this level of agreement is measured
using an objective  function (or some aggre-
gation function of the model residuals), usu-
ally  supported  by  visual  inspection of the
computed or predicted time series. Thus, the
modeling structure and parameter combina-
tion producing the best performance is com-
monly assumed to  represent the conceptual-
ized physical system.

  Fundamentally, model calibration is an in-
teractive process  involving: (i) simulations
using parameter sets from the search space
to document model performance; (ii) deter-
mination of parameter sets that are likely to
perform better than those  used in the previ-
ous simulations, and  model simulation us-
ing the new or revised parameter sets; and,
(iii) repetition of step  (ii) until a satisfactory
measure  of performance is obtained or until
further improvements are negligible. During
calibration, model  performance is quantified
by an objective function and coefficients. Some
commonly used coefficients include the coef-
ficient of determination, modeling error or
bias, and the root mean square error. Graphi-
cal plots such as hydrographs (in  hydrody-
namic models) and scatter  plots can be used.
The three steps for model calibration could
be undertaken manually or automatically us-
ing some form of optimization.
  An inherent issue in many modeling appli-
cations is what constitutes an acceptable bias
or difference between model predictions and
corresponding observations in the real-world.
Model reliability and quality assurance can
also be assessed through a validation process.
Model validation is probably one technique
of model performance assessment that has re-
ceived the most attention in the modeling lit-
erature. Differing opinions exist as to the def-
inition of model validation or what constitutes
a model validation  process. For example, the
U.S. Department of Energy defines validation
as the determination "that the model indeed
reflects  the behavior of the real world." The
International Atomic Energy Agency (IAEA)
defines a validated model as one that provides
"a good representation of the actual process
occurring in a real (physical) system". Fur-
thermore, the IAEA, in its Radioactive Waste
Management Glossary provides yet another
definition  of model validation as "a process
carried  out by comparison  of model predic-
tions  with  independent field observations
and  experimental  measurements". Wigman
(1972) defines validation as "the process of
discriminating between sets of postulates by
reference to fresh data not used in setting up,
fitting, and a calibration process". From these
definitions, the purposes of model validation
are to: (i) objectively assess the performance
and trustworthiness of the model, (ii) charac-
terize the effects of parameter variability and
parameter uncertainty on model outputs, and,
(iii) evaluate the results of model simulations
without human bias and interpretation.

  In general, model validation is the process
which determines the accuracy of a model by
comparing model outputs to data measured
from the natural world that the model is simu-
lating. The initial conditions for the model are
matched to those at the time of collection of
the field (observed) data. From a collection of
those comparisons, the overall model perfor-
mance is analyzed, evaluated, and documented.
Furthermore, model validation involves iden-
tifying those factors that contribute to differ-
ences between  model predictions and  field

  In model validation,  numerous  attempts
have been made to  develop practical and
quantitative performance  measures  to es-
tablish whether to accept,  modify, or refute
a model. For example, Whitmore (1991) sug-
gests a combination of graphical and statisti-
cal techniques for assessing model reliability.
The discrepancy between model predictions
and field  observations, whether random  or
systematic, can be classified as  space-time-
independent residuals. The sum of the squares
of the residual error  is partitioned into two
other sums of squares: one  derived from ran-
dom variations and the other due to systemat-
ic variation or mismatch between predictions
and confirming real-world observations. The
performance criteria for assessing model reli-
ability based on replicated field experiments, as
summarized by Whitman (1991) are as follows:
       LOFIT = lad/ = Ifl. (y. - x/

in which RSS is the residual sum of squares;
SSE is the sum of squares of the error; LOFIT
(or lack of fit) is sum of squares attributed to
the  lack of fit,  an indication of model bias;
d  = deviation or residual error (y  - x); d =
 1J                           VJjj    j75  j
the mean deviation (y -x); y = mean of the
                  \Jj j75 Jj
measurements in they'th experiment; and, x
= mean of the predictions of the y'th experi-
ment. League and Green  (1991)  and Green
and  Stephenson (1986) propose a combina-
tion of approaches for assessing model valid-
ity. They suggested the use of goodness-of-fit
tests that include: maximum error (ME), not
mean square error (RMSE), modeling  effi-
ciency (EF), coefficient of determination (CD)
and coefficient of residual mass (CRM). The
expressions for three performance measures
are as follows:
              = max_xi-yiJoralll

      RMSE = 100/y[ £(* - y^/N]0-5

  EF = [ Ify. - y)2 - Z(Xi - y/] / QXy. - y)2}

          = E(yi-y)2]/(I(xi-y)2}
where N is the number of pair of model-pre-
dicted (x ) and field observed (y ) values, and y
is the mean value of the observations. For the
models to be considered fully validated and
representative of real-world physical system,
values of ME, RMSE, EF, CD, and CRM must
be equal to 0, 0, 1.0,  1.0, and 0, respectively.


  Analysis  of model errors and uncertainty
is  rapidly  becoming an acceptable practice
in environmental modeling. It  is  essential
for making reliable predictions of complex
phenomena. Well informed and  technically
defensible  environmental  policy decisions
based on model simulations demand that we
identify and document: the significance of the

inherent variability of the physical system, the
impact of the approximations and simplifica-
tions made in formulating the model problem,
the consequences  of simulation errors, the
sensitivity of the predictions to limited un-
derstanding  of governing processes and sys-
tem dynamics, and, the probabilistic implica-
tions of inherent stochastic effects that exist
in most physical systems. A systematic anal-
ysis of model uncertainty provides valuable
insights into the level of confidence in model
predictions and assists in assessing how the
model predictions should be weighed in any
decision making process. Furthermore, mod-
el uncertainty analysis can  suggest to model
users reasons for strengthening or weakening
their belief in the model results.

  Increasingly, the reliability of mathematical
models requires that we gain a better under-
standing of  the  simplifying assumptions in
the model, the influence of potential  model-
ing error and uncertainties on the response of
the model, and the sources of the modeling
uncertainty.  A number of sources of model
uncertainty have been reported in the litera-
ture, including uncertainties due to  model
structure, model comprehensibility, choice of
boundary conditions, and model spatial and
temporal resolution.

  Uncertainty from modeling structure arises
when there are alternative sets of scientific or
technical assumptions for developing the mod-
el. Thus, when a competing model is used and
the results are compared; similar conclusion
could provide some level of confidence with
the model. If, however, an alternate model
formulation  provides different conclusions,
then further evaluation of model  structure
may be necessary.
  In the development of mathematical mod-
els, processes that describe the dynamics of
the physical  system are simplified for pur-
poses of tractability. Examples of model un-
certainty due to comprehensibility include
assumptions  of nonlinearity,  compressibil-
ity, unidirectional flow,  or the conversion of
nonlinear process to linear processes to allow
simplified analytical solutions to be obtained.
Uncertainty of predictions from simplified
models can be characterized by comparing
predictions to those obtained from more in-
clusive and detailed models.

  Mathematical models that are validated for
a section of the input space could be com-
pletely inappropriate when used for decisions
in other regions of the parameter space. For
example, in predicting components of the hy-
drologic cycle, models that are calibrated for
certain precipitation events may not be appro-
priately verified if similar events are applied
during the validation process.

  Model  uncertainty can arise from the se-
lection of the spatial and temporal resolution.
There is a trade-off between model prediction
accuracy and the computation time. Trade-off
also exists  between the  choice of the spatial
resolution (e.g., lumped or distributed) and
the validity of the governing equations. Quite
often, coarse spatial resolution introduces ap-
proximations and uncertainties in the model
results due to aggregation. However, a finer
resolution, in some situations, does not necessarily
result in predictions that are more accurate.

  A number of techniques have been utilized
to attempt to represent and/or  reduce  un-
certainty in mathematical modeling.  Some
widely used techniques  involve:  (i) classical
set theory, in which uncertainty is expressed

by sets of mutually exclusive alternatives in
situation where one alternative is desired, (ii)
probability theory, where model uncertainty,
notwithstanding  its  origin,  is expressed in
forms of a measure or subsets of a universal
set of alternatives; and, (iii) fuzzy set theory,
which unlike the classical set theory, is capa-
ble of incorporating vagueness that emerges
from imprecision of definitions  rather than
from  non-specificity.  Modeling  uncertain-
ty using fuzzy set theory is expressed as a
degree rather than an affirmation.

           CHOOSING A


     Models are increasingly used in many as-
     pects of environmental management and
planning, ranging from  evaluating changes
in watershed management to extending data-
sets to areas with little or no measurement,
and to assessing impact of external influ-
ences such as climate change. While there
are many mathematical models of hydrology
and water quality in use, the skill in selecting
the right model for an application and balanc-
ing the data requirements against the cost of
model implementation is an art as well as sci-
ence. For a critical and rigorous assessment of
model suitability,  users need to ask the follow-
ing questions:

(a) Hydrology:
•  Does the model have a built-in stochastic
   climate generator for constituting syn-
   thetic climate data if measurements are
   not available?

•  Does the model compute overland flow
   (runoff) using a processes-oriented ap-
   proach or physically-based approach
   (e.g.  SCS Curve Number technique)?

•  Does the model compute flow in a stream
   channel and route this downstream?

•  What method of flow routing is used?

•  Does the model account for flow into and
   out of artificial impoundments (e.g. lakes
   and wetlands)?

•  Does the model explicitly incorporate
   flow into and out of marshes and ponds?

•  Does the model contain specialized
   functions to deal with outflow or outfalls
   into estuaries, tidal flows, and saltwater

•  How does the model deal with irrigation
(b) Sedimentation:
•  What technique is used in the model
   to estimate soil erosion by water? Is
   it USLE, MUSLE, or RUSLE?

•  How is ephemeral gully erosion

•  How is streambed and bank erosion

   What physically based or process-oriented
   approach is adopted by the model to pre-
   dict sediment detachment, transport, and

   In estimating sediment yield, is an
   expression for the delivery ratio stated
   explicitly? If so, how does the model
   route sediment to the domain outlet
   Are there any provisions to handle other
   nutrient sources (e.g. organic wastes
   from municipal sludge and food process-
   ing residues or atmospheric inputs)?

   Are nitrogen and phosphorus predicted
   as total amounts or concentrations?
(c) Nutrient export
•  How does the model handle the fate and
   transport of nutrients in the landscape?

•  What forms of nutrients does the model
   handle? Nitrogen or phosphorus?

•  Does the model contain components that
   predict the fate and movement of nitro-
   gen in surface runoff?

•  Does the model handle inorganic forms
   of nutrients?

•  What forms of nitrogen does the model

•  Does the model include manure manage-
   ment and nitrogen transformation?

•  How does the model handle the fate and
   transport of phosphorus in runoff?

•  Are there component equations to differ-
   entiate between dissolved and particulate

•  What forms of phosphorus does the
   model predict?

•  Does the model handle subsurface leaching
   losses of both nitrogen and phosphorus?
(a) Meteorological:
•  Does the model require breakpoint,
   hourly, daily, or monthly values of

•  Does the model include a climate generator
   for constituting climate data where mea-
   surements are unavailable or inadequate?

•  Does the model require air temperature
   for each time-step of the modeled period?

•  Does the model require wind speed,
   relative humidity, and solar radiation
   data for each time-step?

•  Is precipitation data considered spatially
   distributed or lumped?

•  Does the model require information on
   percent cloud cover, sunshine hours, or
   other related surface air data?
(b) Landscape:
•  What topographic information is re-
   quired by the model (e.g. elevation, slope,
   aspect, drainage network)?

•  What soil properties and characteristics
   does the model require?

   Does the model require users to specify
   the type of land cover, land-use and land

   Are the landscape-related parameters re-
   quired by the model spatially distributed?

   What management factors (agricultural,
   urban, and forest) are considered in the

   In terms of tillage practices, are there
   any component of the model that incor-
   porates the different effects of these
   practices in hydrology and water quality?

   Does the model handle crop rotation or
   changes in crop growth parameters with
   respect to time and location?

   Are irrigation practices (application rate,
   type of irrigation system) and chemigation
   handled by the model?

   Does the model accept data on artificial
   drainage of the subsurface soil and the
   associated effects on hydrology and wa-
   ter quality?

   What conservation practices can be ad-
   equately incorporated into the model?

   Does the model incorporate information
   on nutrient and pesticide management?
(c) Model Output Parameters
•  At what time-step does the model
   produce flow and water quality results?

•  Does the model incorporate information
   on nutrient and pesticide management?
   Does the model lump output results with
   respect to watershed area?

   In what time-space format does the
   model generate the outputs?

   For the spatially distributed models,
   can users examine outputs at specified
   locations within the landscape?

   For water quantity and quality variables,
   can users track the source of the

   Can users evaluate or assess the effects
   of model and parameter uncertainty on
   the predicted outputs?

   Is the output generated in a tabular
   or graphical format?

   What output data format is provided
   in the model?
(d) Space and Time Scale
•  Does the model simulate discrete events
   or can it utilize long-term continuous

•  Is the model designed for the plot,
   field, whole-farm, or watershed-scale?

•  Does the model allow the use of
   GIS in extending its spatial scale?

•  Was the model developed for a specific
   geographical area?

•  Is the modeling technology applicable
   on a national basis?

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