United States Office of Water EPA-823-R-04-001 

Environmental Protection (4305) January 2004 

Agency

AQUATOX (RELEASE 2)

MODELING ENVIRONMENTAL FATE 

AND ECOLOGICAL EFFECTS IN 

AQUATIC ECOSYSTEMS 

VOLUME 1: USER’S MANUAL

AQUATOX (RELEASE 2)

MODELING ENVIRONMENTAL FATE 

AND ECOLOGICAL EFFECTS 

IN AQUATIC ECOSYSTEMS 

VOLUME 1: User’s Manual 

Richard A. Park, 

Jonathan S. Clough, 

and 

Marjorie Coombs Wellman

JANUARY 2004 

U.S. ENVIRONMENTAL PROTECTION AGENCY 

OFFICE OF WATER 

OFFICE OF SCIENCE AND TECHNOLOGY (MAIL CODE 4305T) 

WASHINGTON DC 20460
ii 

DISCLAIMER 

This document has been approved for publication by the Office of Science and Technology, Office 

of Water, U.S. Environmental Protection Agency. Mention of trade names, commercial products or 

organizations does not imply endorsement or recommendation for use. 

This document describes an aquatic ecosystem simulation model. It is not intended to serve as 

guidance or regulation, nor is the use of this model in any way required. This document cannot 

impose legally binding requirements on EPA, States, Tribes, or the regulated community. 

ACKNOWLEDGMENTS 

 

 This model has been developed and documented by Dr. Richard A. Park of Eco Modeling 

and Jonathan S. Clough of Warren Pinnacle Consulting, Inc. under subcontract to Eco Modeling. 

The EPA work assignment manager, Marjorie Coombs Wellman of the Health Protection and 

Modeling Branch, Office of Science and Technology, tested the model and helped prepare this 

report. Model documentation was funded originally with Federal funds from the U.S. 

Environmental Protection Agency, Office of Science and Technology under contract number 68-C4-

0051 to The Cadmus Group, Inc. Significant enhancements to the model and revision of the 

documentation has been performed under subcontract to AQUA TERRA Consultants, Anthony 

Donigian, Work Assignment Manager, under EPA Contracts 68-C-98-010 and 68-C-01-0037. 

 Additional Federal funding for program development has come from the U.S. Environmental 

Protection Agency, Office of Pollution Prevention and Toxics, through Purchase Orders 7W-0227-

NASA and 7W-4330-NALX to Eco Modeling and a Work Assignment to AQUA TERRA 

Consultants. 

 Further technical and financial support from Donald Rodier of the Office of Pollution 

Prevention and Toxics and from David A. Mauriello and Rufus Morison, formerly of that Office, is 

gratefully acknowledged. Marietta Echeverría, Office of Pesticide Program, contributed to the 

integrity of the model through her careful analysis and comparison with EXAMS. The model 

underwent independent peer review by Donald DeAngelis, Robert Pastorok, and Frieda Taub, whose 

diligence is greatly appreciated. 
iii 

TABLE OF CONTENTS 

DISCLAIMER...................................................................................................................................... ii 

ACKNOWLEDGMENTS .................................................................................................................... ii 

TABLE OF CONTENTS.....................................................................................................................iii 

PREFACE............................................................................................................................................. v 

1 QUICK START ................................................................................................................................. 1 

1.1 System Requirements............................................................................................................ 1 

1.2 Installation............................................................................................................................. 1 

1.3 Starting.................................................................................................................................. 2 

1.4 Loading a Study .................................................................................................................... 2 

1.5 Loading a Library ................................................................................................................. 5 

1.6 Running and Saving a Simulation ........................................................................................ 7 

1.7 Overview of AQUATOX Release 2 Enhancements............................................................. 9 

Enhanced Scientific Capabilities................................................................................................ 10 

Additional User Interfaces.......................................................................................................... 11 

Corrected Errors.......................................................................................................................... 11 

2 MODEL COMPONENTS ............................................................................................................... 13 

2.1 State Variables.................................................................................................................... 13 

Selection...................................................................................................................................... 13 

2.2 Initial Conditions, Loadings, and Parameters..................................................................... 15 

Introduction................................................................................................................................. 15 

Dissolved Organic Toxicant ....................................................................................................... 16 

Oxygen, Nutrients....................................................................................................................... 19 

Detritus........................................................................................................................................ 21 

Plants........................................................................................................................................... 23 

Animals....................................................................................................................................... 24 

Temperature ................................................................................................................................ 33 

Light............................................................................................................................................ 34 

Water Volume............................................................................................................................. 34

pH................................................................................................................................................ 35 

Setup ........................................................................................................................................... 35 

Control Setup .............................................................................................................................. 37 

Uncertainty Setup........................................................................................................................ 39 

Output Setup ............................................................................................................................... 40 

2.3 Displaying Output............................................................................................................... 41 

Graphs......................................................................................................................................... 41 

Tables.......................................................................................................................................... 47 

2.4 Exporting Results................................................................................................................ 47 

2.5 Site Information .................................................................................................................. 49 

2.6 Using the Toolbar ............................................................................................................... 53 

2.7 Running Batch Mode .......................................................................................................... 54 

2.8 The Wizard.......................................................................................................................... 56 

Step 1: Simulation Type............................................................................................................. 56 

Step 2: Simulation Time Period.................................................................................................. 57 

Step 3: Nutrients ......................................................................................................................... 58 

Step 4: Detritus ........................................................................................................................... 58 

Step 5: Plants to Simulate ........................................................................................................... 60 
iv 

Step 6: Invertebrates to Simulate ................................................................................................ 61 

Step 7: Fish Species.................................................................................................................... 61 

Step 9: Water Volume Data ........................................................................................................ 64 

Step 10: Water Temperature ....................................................................................................... 65 

Step 11: Wind Loadings.............................................................................................................. 66 

Step 12: Light Loading ............................................................................................................... 67 

Step 13: pH of Water .................................................................................................................. 68 

Step 14: Inorganic Solids............................................................................................................ 69 

Step 15: Chemicals to Simulate .................................................................................................. 70 

Step 16: Inflow Loadings............................................................................................................ 71 

Step 17: Direct Precipitation Loadings....................................................................................... 72 

Step 18: Point-source Loadings.................................................................................................. 72 

Step 19: Nonpoint-source Loadings............................................................................................ 73 

Wizard Completion..................................................................................................................... 73 

3 DATA CONSIDERATIONS........................................................................................................... 75 

3.1 Toxicant .............................................................................................................................. 75 

3.2 Nutrients and Remineralization .......................................................................................... 75 

3.3 Plants................................................................................................................................... 76 

3.4 Animals............................................................................................................................... 76 

3.5 Inorganic Sediments............................................................................................................ 77 

4 CALIBRATION FOR A CONTAMINATED STREAM ............................................................... 79 

4.1 Introduction......................................................................................................................... 79 

4.2 Review Initial Conditions and Driving Variable Data........................................................ 79 

4.3 Ecosystem Calibration ........................................................................................................ 94 

Hydrodynamics........................................................................................................................... 96 

Biomass....................................................................................................................................... 97 

4.4 PCB Calibration................................................................................................................ 110 

5 APPLICATIONS ........................................................................................................................... 117 

5.1 Recovery Following PCB Remediation............................................................................ 117 

5.2 Possible Response to Invasive Snail Species.................................................................... 119 

5.3 Nutrient Enrichment.......................................................................................................... 124 

5.4 Pesticides in a Pond Mesocosm........................................................................................ 133 

5.5 Multiple Stressors Due To Agricultural Runoff ............................................................... 139 

Controlling Nutrients and Sediments........................................................................................ 139 

Controlling Pesticides............................................................................................................... 147 

Controlling All Pollutants......................................................................................................... 150 

6 UNCERTAINTY ANALYSIS ...................................................................................................... 152 

7 QUALITY ASSURANCE............................................................................................................. 165 

REFERENCES ................................................................................................................................. 169 

References Cited ........................................................................................................................... 169 

Bibliography for Biotic Parameters.............................................................................................. 169 

Bibliography for Chemical Parameters......................................................................................... 172 
v 

PREFACE 

 The Clean Water Act— formally the Federal Water Pollution Control Act Amendments of 

1972 (Public Law 92-50), and subsequent amendments in 1977, 1979, 1980, 1981, 1983, and 1987— 

calls for the identification, control, and prevention of pollution of the nation's waters. In the 

National Water Quality Inventory: 2000 Report (US EPA, 2002), 40 percent of assessed river 

lengths and 45 percent of assessed lake areas were impaired for one or more of their designated uses. 

The most commonly reported causes of impairment in rivers and streams were pathogens, siltation, 

habitat alterations, oxygen-depleting substances, nutrients, thermal modifications, metals (primarily 

mercury), and flow alterations; in lakes and reservoirs the primary causes included nutrients, metals, 

siltation, total dissolved solids, oxygen-depleting substances, excess algal growth and pesticides. 

The most commonly reported sources of impairment were agriculture, hydrologic modifications, 

habitat modification, urban runoff/storm sewers, forestry, nonpoint sources, municipal point sources, 

atmospheric deposition, resource extraction and land disposal. There were 2838 fish consumption 

advisories, which may include outright bans, in 48 States, the District of Columbia and American 

Samoa. Of these 2838 advisories, 2242 were due to mercury, with the rest due to PCBs, 

chlordane, dioxin, and DDT (US EPA, 2002). States are not required to report fish kills for the 

National Inventory; however, available information for 1992 indicated 1620 incidents in 43 States, 

of which 930 were attributed to pollution, particularly oxygen-depleting substances, pesticides, 

manure, oil and gas, chlorine, and ammonia. 

 New approaches and tools, including appropriate technical guidance documents, are needed 

to facilitate ecosystem analyses of watersheds as required by the Clean Water Act. In particular, 

there is a pressing need for refinement and release of an ecological risk methodology that addresses 

the direct, indirect, and synergistic effects of nutrients, metals, toxic organic chemicals, and nonchemical stressors on aquatic ecosystems, including streams, rivers, lakes, and estuaries. 

 The ecosystem model AQUATOX is one of the few general ecological risk models that 

represents the combined environmental fate and effects of toxic chemicals. The model also 

represents conventional pollutants, such as nutrients and sediments, and considers several trophic 

levels, including attached and planktonic algae, submerged aquatic vegetation, several types of 

invertebrates, and several types of fish. It has been implemented for streams, small rivers, ponds, 

lakes, and reservoirs. 

 AQUATOX Release 2 is described in these documents. Volume 1: User’s Manual

describes the usage of the model. Because the model is menu-driven and runs under Microsoft 

Windows on microcomputers, it is user-friendly and little guidance is required. Volume 2: 

Technical Documentation provides detailed documentation of the concepts and constructs of the 

model so that its suitability for given applications can be determined. Volume 3: User’s Manual 

for the BASINS Extension to AQUATOX describes how AQUATOX can be run with site 

characteristics and loadings input directly from the BASINS data layers or from the HSPF and 

SWAT watershed models. 
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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

1 QUICK START 

1.1 System Requirements 

Minimum Requirements 

• PC Compatible, Pentium 133 MHz 

• Microsoft Windows 95

• 64 MB RAM 

• 30 MB free disk space 

Recommended 

• Pentium PC, 600 MHz or higher 

• Microsoft Windows NT, 2000, or XP 

• 128 MB RAM 

• 75 MB free disk space 

1.2 Installation 

To install AQUATOX, run AQTXSetup.exe, the files will unzip, and InstallShield will 

lead you through the straightforward installation. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

1.3 Starting 

Double-click on the AQUATOX icon in Windows to open the program. Alternatively, 

you can double-click on a study file (with the suffix “aps”) listed in Windows Explorer.

Then a “splash” window will open briefly, indicating that the model is still subject to

modification and that, while the model is in the public domain, there are parts of the model 

interface that are proprietary. 

1.4 Loading a Study

The study is the basic unit in AQUATOX; it contains site data, loadings, and parameter

values used in a simulation; and it may contain results from a prior simulation. Usually we

model one study at a time; however, a batch mode is described later. Click on File in the menu 

bar to get the pull-down file menu, and click on Open. You will then be given a choice of

AQUATOX study files to load. For this example, we will choose EsfenPond.aps. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

The main window will appear with the name of the study, the list of state variables used, 

and buttons from which to choose various operations. The Study Name can be edited; it is 

separate from the name of the file, which you loaded and which is displayed at the top of the 

screen. The study name is used as a title in graphical output, so is best capitalized. The Status

window tells when the perturbed and control runs were made, and warns if they are incomplete. 

The Initial Conditions button brings up a screen with all the state variable values at the

beginning of a simulation. The Chemical button brings up a list of organic chemicals in the 

study; selecting a particular chemical brings up the loading screen for that chemical, from which 

you can access the chemical parameter and toxicity screens. Double clicking on Dissolved org. 

toxicant at the top of the list of state variables and driving variables has the same effect. The 

Site button loads the site characteristic screen. Setup allows the user to set the dates of the

simulation, and to specify various options such as the control setup, uncertainty analysis, and

saving biologic rates. Notes provides a window for writing comments on the study. Perturbed

starts the simulation with changed conditions, such as with a toxicant. Control starts a 

simulation without the stressor; the user can use Control Setup as mentioned above to specify 

what is changed and what is held constant. Output presents the results as a series of charts and

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

graphs. The output can be exported as database files by clicking on Export Results or Export

Control. There also are Help and Wizard buttons. 

To save a file, click on File then Save or Save As on the menu bar; you will also be given 

an opportunity to save an altered file before exiting or loading another file. Study files range in 

size from 25 KB to over 2 MB. If you wish to minimize the size of a study—for example, to

transmit to someone else—you can strip out the results by clicking on Study and choosing Clear 

Results from the menu bar. The study files distributed with AQUATOX have been minimized

in this way. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

1.5 Loading a Library 

There is a basic dichotomy in working with AQUATOX. You have a choice of editing

database files in the general library or of opening a particular study. Studies are self-contained 

files with all the information on a particular simulation, including initial conditions, loadings,

parameter values, first and last dates for the simulation, and simulation results. Parameter values

can be edited, but changes apply only to that study. The intent is to be able to archive a model 

application so that all assumptions and results are saved for future reference. This is especially 

important for regulatory applications that are subject to later review. (Of course, you also should 

archive the version of AQUATOX that was used.) 

Parameter and site records that will be used repeatedly should be saved in the appropriate 

library. Each library is a database in Paradox format with records for each organism, chemical, 

or site. Generally, editing of parameters should be done in the library mode to maintain

consistency among studies. In contrast, if a site record is only going to be used for a single 

study, it may be desirable to create it within the study. Study records can be copied into the

library; so the choice of where to edit parameters is up to the user. It is the user’s responsibility,

though, to synchronize parameter values among studies. This can be done by saving a record to

a library and then loading that record to each study. 

To create or edit a record for general use, click on Library in the menu bar. You can 

then click on the specific library from the pull-down menu. Alternatively, you can click on the 

appropriate library icon on the task bar (Animals, Chemicals, Plants, Sites, or Remineralization). 

In this example we will choose Chemicals and Default in sequence. The first record is for 

2,4-D Acid. We can click on the arrows in the upper left or can search for a particular name to 

move through the database. Use the arrow to move to Esfenvalerate. When you leave a database 

you will be asked whether you want to save it or lose any changes you might have made. The 

frequent requests for confirmation may be irritating, but they are for your protection. Any time

you leave a record you may back out of a change by not saving it. There is no undo capability,

so if you save a change, you are stuck with it, except by re-editing the entire record. It is easy to 

print a record, and you are encouraged to make a hard copy before you make extensive changes.

Some variables are not used at this time and are so indicated by being grayed out. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

You can examine the toxicity data for the chemical, by clicking on the Toxicity Data

button at the top right. Toxicity can be estimated for several organisms, given data for others 

indicated in bold type. For example, change the LC50 for trout from 1.3177 to 1.4 (or any other 

value). You will then get a window presenting you with other organisms for which the LC50 can 

be estimated. If any have zero values, they will be checked automatically. The estimation 

procedures were developed with pesticide databases (Mayer and Ellersieck, 1986, Suter et al.,

1986), so they should be applied with caution to industrial chemicals. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

 

1.6 Running and Saving a Simulation 

You can run both Perturbed and Control simulations to see the impacts of various

stressors. They can be run concurrently by clicking on first one and then the other. The results 

can be exported in Excel, dBase, Paradox, or text formats. When you click on Export you will

be given a list of output variables to choose among, or you may choose to export all to GenScn, 

the post-processor in BASINS (see Volume 3). The Output subdirectory is the default for saving 

the results, but you may choose some other directory. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

1.7 Overview of AQUATOX Release 2 Enhancements 

Release 2 has been designed to be as user-friendly as possible, while providing greatly 

expanded analytical capability over Release 1. It follows MS Windows conventions in providing 

buttons on the Task Bar to give the user direct access to most functions. It supports multiple 

windows so that a user can edit parameters, plot previous results, and even run multiple 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

simulations simultaneously (although resources may be limited in earlier versions of MS 

Windows). It has a powerful Wizard and context-sensitive Help files to guide the user through 

an implementation. 

The changes fall in three categories.

Enhanced Scientific Capabilities 

The model is much more powerful and can better represent a variety of environments, 

a large increase in the number of biotic state variables, with two r

a special type of macrophyte; 

e-dependent 

! maximum of twenty, with the 

!

Task bar with Help

Multiple windows

Export

Add/delete variables

Wizard

especially streams and rivers compared to Release 1. Specific enhancements include: 

! epresentatives for each 

taxonomic group or ecologic guild; 

! the addition of bryophytes as

! a multi-age fish category with up to fifteen age classes for ag

bioaccumulation and limited population modeling;

an increase in the number of toxicants from one to a

capability for modeling daughter products due to biotransformations; 

disaggregation of stream habitats into riffle, run, and pool; 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

! mechanistic current- and stress-induced sloughing, light extinction, and accumulation of 

detritus in periphyton; 

! macrophyte breakage due to currents; 

! computation of chlorophyll a for periphyton and bryophytes, as well as for 

phytoplankton; 

! fish biomass is entered and tracked in g/m2

; 

! entrainment and washout of animals, including fish, can occur during high flow; 

! the options of computing respiration and maximum consumption in fish as functions of 

mean individual weight using allometric parameters from the Wisconsin Bioenergetics 

Model; 

! respiration in fish is density-dependent; 

! fish spawning can occur on user-specified dates as an alternative to temperature-cued 

spawning; 

! elimination of toxicants is more robust; 

! settling and erosional velocities for inorganic sediments are user-supplied parameters; 

! uncertainty analysis now covers all parameters and loadings; 

! biotic risk graphs are provided as an alternative means of portraying probabilistic results; 

! limitation factors for photosynthesis are output along with the biotic rates; and 

! AQUATOX is now an extension to BASINS, providing linkages to geographic 

information system data, and HSPF and SWAT simulations. 

Additional User Interfaces 

The model is even more user-friendly, taking full advantage of current Windows 

capabilities on modern high-speed personal computers. Capabilities include: 

! a Wizard to guide the user through the setup for a new study; 

! context-sensitive Help screens; 

! multiple windows for simultaneous simulations and input and output screens; 

! a task bar that can be customized by the user; 

! enhanced graphics, including secondary Y axes; and 

! a hierarchical tree structure for choosing variables for uncertainty analysis. 

Corrected Errors 

Several errors were discovered and corrected during the course of continuing model 

evaluation. Some of these may require recalibration of studies. The example studies provided 

with the software have been recalibrated, but users may wish to check their own calibrations in 

upgrading from various versions. The corrections include: 

! a change in the bathymetric computations affecting the areas of the thermocline and 

littoral zone (Release 1); 

! removal of an unnecessary conversion from phosphate and nitrate, assuming that all 

nutrient input is in terms of N and P; this could affect nutrient limitations (all versions); 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 1

! inclusion of an oxygen to organic matter conversion factor (a factor of 1.5) and inclusion 

of specific dynamic action in the allometric computation of fish respiration (Release 2 

Beta Test only); 

! adding a second-to-day conversion factor for inorganic sediment deposition; previously, 

deposition of suspended sediments was much slower than expected (all versions); 

! adding a conversion factor for wind measured at 10 m height to wind occurring at 10 cm

above the water surface in the volatilization computations; for some compounds this 

could result in a two-fold reduction in volatilization (all versions); 

! nitrification is formulated to occur only at the sediment-water interface (Release 1); and 

! bioaccumulation, and hence toxicity, are constrained by the life span of an animal (all 

versions). 

At this point you may experiment with the various buttons and screens. You cannot hurt 

anything; just don’t save the edited data or the study when you exit the screens and AQUATOX 

unless you Save As a different name. On the other hand, if you are more comfortable following 

directions, read on, doing the operations as you go.

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

2 MODEL COMPONENTS 

2.1 State Variables 

Selection

State variables are those ecosystem components that are being simulated. These include

organism and detrital compartments and their associated toxicants (which are not listed in the 

Study Information window), nutrients, dissolved oxygen, and other variables traditionally 

considered driving variables, such as water inflow, temperature, pH, light, and wind. 

AQUATOX is very powerful because you can add or delete state variables. It is even

possible to remove all biotic components in order to model a tank or other sterile system. In

general, the fewer state variables, the better. In particular, unnecessary state variables slow down

the simulation and create additional requirements for verification. This is especially true for

streams, which tend to be more dynamic and therefore slower to simulate. Nevertheless, often it 

is desirable to model a food web rather than a food chain, for example to examine the possibility

of less tolerant organisms being replaced by more tolerant organisms as environmental

perturbations occur. The choice of which state variables to model depends to a large extent on 

the purpose of the modeling application. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Open the file Esfenpond.aps, if it is not already open. We will remove the macrophyte 

compartment by highlighting it in the list, clicking on the Delete button, and confirming the 

deletion. When the change is made, you will see a small warning in the upper left corner of the 

main screen that the study has been modified. 

Likewise, state variables can be added by clicking on the Add button and choosing from 

the list. Let’s add macrophytes back to the list of state variables. 

Note that the names of the taxonomic groups and ecologic guilds on the main study 

screen are followed by the names of the specific groups in brackets. We have to specify the type 

of macrophyte by highlighting Macrophytes and clicking on Edit, or by double-clicking on 

Macrophytes. That will give us a double screen representing both the macrophytes and the 

associated toxicant. Click on Load Data to load a specific plant record for macrophytes. In this 

example, Chara and Myriophyllum are the only macrophytes listed; we highlight Myriophyllum

and click on OK. If there is no selection made you will receive an error message indicating that 

there is no data associated with the state variable Macrophyte. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

2.2 Initial Conditions, Loadings, and Parameters 

Introduction 

Initial values and loadings are needed for all the state variables or compartments 

simulated. These are input on the loadings screen. If one or more toxicants are modeled, then 

initial concentrations associated with the biota can also be specified. The initial condition will 

depend on when the simulation starts (which is specified in Setup). Initial conditions are often

unknown. A useful procedure is to run a simulation for several repetitive years and then use the 

ending values as initial conditions, assuming steady state. 

Constant loadings for plants and invertebrates can be considered as “seed” values, 

although care should be taken to use small values or the loadings can dominate the simulation. 

Even periphyton and zoobenthos may be maintained through drift from upstream, and a constant, 

very small loading is appropriate. Likewise, macrophytes may die back in winter and sprout 

from rhizomes; because rhizomes are not explicitly modeled, a very small loading is the 

mechanism for reestablishing the population in the simulation. 

Of course, upstream loadings may be significant inputs to a reach or lake. These may be 

represented by constant or dynamic (time-varying) loadings. AQUATOX has a very flexible 

interpolation routine to obtain daily values from irregular data points and even time series 

occurring or extending outside the simulation period. Dynamic loadings can be entered directly 

on the loadings screen, or they can be composed or obtained offline and imported into the model. 

Imported data can be in a variety of formats, which are evident when the Import button is used.

Loadings can be altered by means of a multiplier; this is especially useful for analyzing various

loading scenarios. It also is a way to correct or convert data series. However, ordinarily the 

multipliers are set to 1 for the Control simulation, so use for other than perturbations is

discouraged.

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Initial conditions can be displayed for all state variables in a summary screen, obtained 

by clicking on the Initial Conds. button on the main screen. In order to avoid conflicts with 

other windows, you cannot edit the initial conditions in this screen; that is reserved for the 

loading screens. 

Parameters provide values for coefficients in the process equations. Although default 

values are given, the user has great flexibility in specifying values to represent site-specific 

species or groups. Parameters are available for editing and downloading from the general 

libraries; they also may be site-specific and associated with particular studies. 

The following sections discuss loadings and parameters according to type of state 

variable. 

Dissolved Organic Toxicant

Organic toxicant initial conditions and loadings are relatively straightforward. New to

Release 2 is the capability to indicate biotransformation to one or more daughter products,

accessed by clicking on the Biotransformation button. To use this capability the daughter

products have to be included as state variables. The biotransformation rate from one toxicant to 

another is specified by the user for each type of organism and is entered on the toxicity screen. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Clicking on the Edit Underlying Data takes one to the Chemical Properties and Fata

Data screen. The only new feature on this screen is that the sediment/detritus and water partition

coefficient can be calculated dynamically or entered manually by the user. The dynamic

calculation is based on the octanol-water partition coefficient and the degree of ionization using

Equation 212 as described by US EPA (2000b). 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Clicking on the Toxicity Data button on the upper right portion of the screen takes the 

user to a large screen, requiring sideways scrolling, that provides entry and comment fields for 

animal and plant acute and chronic toxic effects. Minimal LC50 data are required; with a value

for Daphnia one can click on Perform invertebrate regressions to obtain estimated LC50s for 

several groups. Likewise, with a value for minnows, bluegills, or rainbow trout one can click on 

Perform fish regressions to estimate LC50s for several fish species. Depuration or elimination

rates are difficult to obtain, so usually one would want to click on Estimate elimination rate 

constants using octanol water coefficient. Chronic toxicity values may be available for only a 

subset of organisms; to extend those to other groups click on Estimate plant LC50s using EC50

to LC50 ratio, and Estimate animal EC50s using LC50 to EC50 ratio. Drift pertains only to 

zoobenthos; in the absence of good data, a fraction of the EC50 for growth or some other

toxicological benchmark can be used. The lipid fraction flows from the organism parameter 

screen, but the mean weight (used in computing the elimination rate) is independent of the mean 

weight given in the parameter screen because it also can be parameterized in library mode when 

the animal screen cannot be accessed. The comment columns are skipped when the animal 

toxicity screen is printed in order to keep the font a legible size; the plant comment columns are 

printed because there are fewer columns. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Oxygen, Nutrients

A simple input screen is used for oxygen; don’t forget that oxygen dissolved in inflow 

water can be a significant loading. Likewise, 0 mg/L in a high flow loading can drive a stream

reach anoxic. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

More complex input screens are used for nutrients. Loadings can be in concentrations 

(mg/L) in inflow water, in g/d from point sources (PS) and from non-point sources (NPS), and in 

g/m2

 d from direct precipitation (DP), including dry fall. A button at the bottom of the loading

screen toggles between NPS and PS/DP. An additional button on the Phosphate loading screen

enables the user to adjust the fraction available for each loading source; the default is 1. 

Nutrient Conversions

 Following limnological convention, nutrients are expressed in 

elemental form (such as phosphate as phosphorus and ammonia as

nitrogen). Phosphate is treated as available or orthophosphate; if part 

of the phosphate loading is not available, the fraction available should 

be adjusted. Nitrate and nitrite are combined because nitrite levels are 

usually negligible. Organic nutrient loadings are considered part of the

detrital loadings, with the simplifying assumption of constant

stoichiometry, and are not considered separately.

If you have nutrient data in molecular form, use the following

conversions: 

Phosphorus = phosphate ⋅ 0.33

Nitrogen = ammonia ⋅ 0.78

Nitrogen = nitrate ⋅ 0.23

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Detritus 

A complex loading screen is necessary for suspended and dissolved detritus. 

AQUATOX simulates Organic Matter (dry weight); however, the user can input data as 

Organic Carbon or Biochemical Oxygen Demand (BOD), and the model will make the 

necessary conversions. Suspended and dissolved detritus initial conditions and loadings are 

divided into four compartments: particulate refractory and labile detritus and dissolved refractory

and labile organic matter. Initial conditions and loadings are parsed by specifying % 

Particulate and % Refractory. Loadings can be constant or dynamic (time series) for 

concentrations in inflowing water (mg/L), and for mass from point sources and non-point sources 

(g/d). Toxicants associated with detritus also can be specified (:g/kg). 

Separate screens are provided for refractory and labile organic sediments. The initial 

conditions are given as g/m2

, and the loadings are given as mg/L. Associated toxicants are given 

as :g/kg (ppm). 

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Plants 

The plant initial condition and loading screens are sensitive to the types of plants.

Phytoplankton units are mg/L, and periphyton and macrophytes are given as g/m2

. Toxicants

associated with biota are given as :g/kg (ppm). Related parameter files can be accessed by 

clicking on Edit Underlying Data. Some parameters are not used; those will appear grayed out 

on the screen. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Animals 

The animal initial condition and loadings screens are sensitive to animal type. 

Zooplankton are given in units of mg/L; zoobenthos and fish are given in units of g/m2

. As

always, toxicants associated with biota are given as :g/kg (ppm); loadings of associated 

toxicants can be thought of as body-burden sources due to immigration.

By clicking on Trophic Interactions one can access a separate screen with feeding

preferences and egestion coefficients. The entire trophic interaction record can be displayed or 

just the organisms simulated in the present study. The program normalizes preferences during 

execution so that they sum to 1.0, facilitating the addition or deletion of state variables without 

the user having to recalculate the preference values. The matrix can be imported or exported, 

providing considerable flexibility in defining organisms. However, the format is internal to 

AQUATOX, so the matrices should be prepared in the context of the parameter screen for an 

organism in either a study or library. The actual values used at the beginning of a simulation can 

be determined by clicking on Study, then Export Trophic Interactions; it will create a text file 

with the matrix. 

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Clicking on Edit Underlying Data can access related parameter files. Release 2 can 

model two size classes for each fish species and up to fifteen age classes for one species.

Records for different size classes are linked by clicking on Species Data and choosing the 

correct record from the list given. In the example, “Green Sunfish, Adult” is linked with “Green 

Sunfish, YOY.” Two different species can be modeled instead of two size classes, in which case

“No other state variable” should be chosen in the Species Data window. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Scrolling down through the window, we find several parameters that are new to Version 

2. VelMax is the maximum velocity that the organism can experience without being swept 

downstream. Preferences for stream habitats can be specified; the default is 100% for the run; if

the site is not a stream, the habitat preferences are ignored. Fish spawning dates can be specified 

as an alternative to being cued by temperature preferences. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Allometric Parameters

The allometric parameters permit computation of maximum consumption rates 

(CMax) and respiration rates based on size using parameter values available from 

documentation of the Wisconsin Fish Bioenergetics Model (Hewett and Johnson, 1992); we 

do not recommend using these without reference to that document. To facilitate use of this 

source of parameters, the parameter names are the same. CA is the intercept of the allometric 

consumption function, and CB is the weight exponent: 

 CB CMax = CA ⋅Wt

The maximum respiration rate (RMax) is computed from the intercept of the 

allometric respiration function (RA), the weight exponent (RB), and an activity factor 

(Activity):

RMax RA Wt Activity RB = ⋅ ⋅

Computation of the activity factor can be a complex function of swimming speed with 

several parameters (Set 1). Briefly, these are the Q10 (the increase per 10E C, RQ), the 

optimum temperature (RO), the maximum temperature (RM), the temperature above which 

swimming speed changes (RTL), the intercept for swimming speed above RTL (RK1), the

weight exponent for swimming (RK4), the intercept for swimming speed below RTL (ACT), 

and the temperature dependence of swimming speed below RTL (BACT). Otherwise, the Set 

2 activity parameter (ACT) is used as a multiplier.

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As mentioned previously, Release 2 also can model one species as having multiple age

classes. Tabs indicate the different input screens, including General (fish name and age at which 

sexually mature), Initial Conditions, Inflow Loadings (from upstream), Toxicant I.C. (initial 

conditions), Toxicant Loadings, Lipid Fractions, Mortality coefficients, and Mean Weights. For 

each, the user can enter values for each age class or can choose one of several distributions, 

characterized by user-supplied statistics. The values can be graphed as well. Parameter screens

and trophic interaction screens can be accessed for young-of-the-year (YOY) and older fish. 

Entering values for each category can be tedious. Initial biomass in each age class can be 

entered using a normal distribution. The “Y Scale” value is the total biomass, and the mean is 

the most important age class in terms of biomass. This is best seen by selecting View Graph; 

values can be seen by selecting View/Edit Values—but, contrary to the label, you cannot edit 

them in that mode. By selecting Use User Defined Values, you can make any changes you wish 

to individual age class values. Distributions are not as useful for other categories; one might use 

a uniform distribution to populate the age classes with a constant value for lipid fraction or a log 

normal distribution to obtain an initial distribution of values for mortality. You could then

modify individual values. Caution: if you switch to Use Distribution from Use User Defined 

Values, the manually entered values will be lost! Mean weights are very important because they 

determine the maximum consumption and respiration rates, using the allometric equations.

There is no way to use a distribution to get meaningful mean weights. 

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Temperature 

The annual mean and range in temperature from the site file can be used, or time-series 

can be entered—in which case make sure that the time period being simulated is covered. If the

system stratifies then temperatures must be given for both epilimnion and hypolimnion. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Light 

Annual mean and range values for light are input in the site screen and may be used to 

estimate daily incident solar radiation at the surface of the water in Langleys/day (Ly/d). 

Dynamic loadings are especially useful for areas where light intensity is not a simple sinusoidal 

relationship, such as areas with seasonal coastal fog and streams where there is seasonal riparian

shading. Ordinarily the photoperiod is computed from the latitude; but occasionally, such as 

with indoor experimental streams, it may be desirable to set a constant photoperiod. 

Light Conversions 

Approximate conversions are: 

1 Ly/d = 10 Kcal/m2

 = 5.2 Einsteins = 2764.46 Joules/m2 d. 

Water Volume

Considerable flexibility exists to compute or specify water volume. Depending on the 

method chosen, inflow or discharge values may be required. The Manning’s equation can be 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

used to compute changing volumes in a stream. The simplest procedure is hold volume constant 

at the initial condition. Volume can be computed dynamically using both inflow and discharge,

which are input on this screen; however, it also uses the annual evaporation rate, which is input 

on the site screen. When available, a known time series can be entered or imported. Flow data 

can be imported in several formats, including USGS tab-delimited; however, recent changes in 

the USGS format, including variable header material, makes this prone to problems. If the data 

do not appear in the preview window then the flow data will have to be converted in a

spreadsheet from cfs to m3

/d, and the date column and flow column then exported as a tabdelimited or comma-separated file suitable for importing into AQUATOX. 

pH 

AQUATOX does not compute pH as a state variable at present. Therefore, it is important 

to enter a reasonable constant value or provide an observed time series. This variable is 

especially important in computing hydrolysis of toxic chemicals. Extreme values (below 5 and 

above 9) can affect microbial degradation. 

Setup 

 By clicking on Setup in the Main screen, the user can set some simulation variables and 

several important procedures. At the top of the setup screen, you can modify the first and last 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

days of the simulation. Use a 4-digit year designation to avoid any confusion between the years 

1900 and 2000; the model will interpret “/00" as “/2000.” The Data Storage Step defines how 

often the results are saved; it is usually one day, but can be varied to save space or show high 

frequency results. AQUATOX interpolates variable-step output to obtain the desired interval. 

The Relative Error is the acceptable error in the simulation; if it is not achieved in a particular 

time step, the variable Runge-Kutta routine decreases the step size and tries again. If the relative 

error is too large, the results may be erroneous; if it is too small, the run time may be too long. 

Usually a value between 0.005 and 0.0005 is appropriate, but you may wish to experiment for a 

particular application. 

What follows are three choices for computing bioaccumulation factors (BAFs) and a 

choice for saving output. If you are computing steady-state BAFs, you may wish to hold the 

freely dissolved contaminant constant. AQUATOX calculates time-varying lipid fractions in 

fish, but those calculations can be disabled and default or user-supplied initial values can be 

used. The older literature often did not distinguish between freely dissolved contaminants and 

those complexed with dissolved organic matter. You may choose to include the complexed 

contaminant in computing BAFs so that the results are directly comparable with the older 

literature values. In plotting output for stratified systems, it is usually more pleasing to plot 

continuous values for the hypolimnion, even when the system is not stratified. This is done by 

duplicating epilimnion values for the hypolimnion when the system is well mixed; however, that 

takes additional storage, so you may choose not to duplicate those data points. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Equilibrium Fugacity is grayed out because the model only represents kinetic partitioning 

now. If you click on Show Integration Info., you will be able to see what time steps are used in 

solving the differential equations and what rates and associated relative errors are causing the 

integration to slow down while the model is running. The progress screen also will show when 

periphyton sloughing is occurring in a stream simulation. 

You may save biologic (and chemical) rates for examination with a spreadsheet program. 

Choose Save Biologic Rates and click on Rate Specifications to designate those state variables 

for which you want the additional output. Don’t save rates for all state variables or the output 

will be voluminous! Usually you would save rates for each output step, by choosing “When 

Writing Results.” However, you can save rates for each step in the solution of the differential 

equations, that is, “Each Attempted Step”. You also can choose to save just the errors associated 

with each state variable. These latter choices are useful only if you are concerned with the 

details of the numerical analysis. Unless you have Paradox or Quattro, choose to save as an 

Excel File (*.xls). 

Uncertainty, Control and Output setups are complex and are covered below. 

Control Setup 

A powerful feature of AQUATOX is that it can run paired simulations for perturbed and 

control conditions. The default is for the control simulation to have all organic toxicants zeroed 

out or omitted. However, there is considerable flexibility in setting up the control run. For 

example, toxicants can be kept and point-source nutrients can be omitted in the control run. In 

fact, it is possible with a few judicial choices to set up a factorial analysis to determine the 

effects of various combinations of pollution control scenarios. Click on Control Setup to check 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

which options should be used in the Control simulation. Most are self-explanatory; however, Set 

Multiply-Loadings Factors to 1 deserves some explanation. Each loading screen has a field 

labeled “Multiply loading by” with a default value of 1. A loading can be perturbed easily by 

changing the factor to a fraction less than 1 (decreasing the loading) or to a value greater than 1 

(increasing the loading). These perturbations can be negated in the control screen by setting 

them to 1. Also, note that “Direct Precipitation” includes both wet and dry fall. 

Two examples are given: the first zeroes out all organic toxicants in the control. In the

second, there are no organic toxicants (indicated by the grayed out box), and the only change in 

the control simulation is that the point sources for nutrients and detritus are omitted. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Uncertainty Setup 

Another powerful feature of AQUATOX is that it can perform uncertainty or sensitivity 

analysis to provide probabilistic results. Latin hypercube sampling is performed, ensuring that 

all parts of the chosen distribution are sampled. Therefore, the number of iterations can be kept 

to a minimum, which is important because each iteration is a complete simulation. Twenty 

iterations is the default, meaning that the distribution is divided into 20 segments for purposes of 

sampling, and that often may be more than sufficient to obtain an adequate sample. 

Any and all parameters and loadings can be chosen, either singly or in combination 

(although linked distributions or covariances are not modeled). By double-clicking on a variable

one can access the corresponding distribution information. The default is a normal distribution 

with a mean of the point or parameter value and a standard deviation of 60% that value. Often

loadings are well represented by lognormal distributions. If less is known about the distribution,

but minimum and maximum values and some central tendency can be defined, then a triangular 

distribution may be appropriate. If only minimum and maximum values are known, then a

uniform distribution may be sufficient, and the number of iterations can be decreased. A 

separate section will discuss Uncertainty Analysis in more detail. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Output Setup 

Because Release 2 has the potential for modeling so many state variables, the user has the 

option of not saving given state variables, thus decreasing the memory requirements. Click on 

Output Setup to access the Results Tracking window. By removing the check mark from Save

PPB Data any toxicant concentration results can be discarded; this is useful, for example, if an 

ecosystem calibration is being performed prior to calibrating the toxicants.

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

2.3 Displaying Output 

Graphs 

AQUATOX has extensive capabilities for graphical and tabular output. By clicking on

Output on the main screen one can access the Output Window. There are six tabs, each 

representing a different form of output. Graphical output has a title that is input on the main 

screen, and a time and date stamp to facilitate record keeping, especially if the graph is printed 

out. Two different sets of variables can be plotted with separate scales on the Y1 and Y2 axes. 

Variables are chosen by clicking on Change Variables to access the Graph Setup screen. The 

model will only allow variables with the same units to be plotted together. One or two Y axes

with separate scales can be specified. It is often useful to Copy Setup From Perturbed or to 

Copy Setup From Control so that results can be compared easily. Either automatic scaling or

user-specified scaling can be used. By clicking on Graph Setup the user can access a screen

that controls the appearance of the graph. 

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One of the most useful forms of output is the Difference Graph, which plots the results

of the perturbed simulation minus the control simulation as percent differences. The default 

minimum and maximum percentages are 400% and -100% (complete loss). However, these can 

be changed by accessing the Change Variables screen. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Another powerful form of graphical output is the Uncertainty Graph. If an uncertainty 

analysis has been performed the results can be potted as a series of lines representing the mean, 

minimum, maximum, mean - 1 standard deviation, mean + 1 standard deviation, and 

deterministic results. Only one variable can be viewed at a time, so click on View a Different 

Variable to view another. With a large number of variables, it may be necessary to split the 

output into two files. If you do not see all the results that you wish to plot, such as 

bioaccumulation factors (BAFs), click on View a Different Database and load another file with

the same name but with the suffix “2.” 

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Of particular interest to risk assessors is the Risk Graph, which plots the probabilistic

results as percent probabilities of percent declines by the end of the simulation. Any number of 

organisms can be plotted simultaneously on the Risk Graph, so that the responses of both tolerant 

and intolerant organisms can be analyzed. If an organism increases, such as through release of 

herbivory (for plants) or predation (for animals), then the “ percent declines” are shown as 

negative values. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

 By clicking on Copy a graph can be saved to the Windows clipboard as a bit map (bmp)

or Windows enhanced meta file (emf) for insertion in a document or graphical presentation. Bit 

maps are raster files with every pixel being saved; meta files are usually vector files with 

instructions being saved to replicate the image. A typical AQUATOX graph saved as a bit map

is 2,716 kb compared to 46 kb for an enhanced meta file, and the results are virtually identical! 

The only reason for saving a graph as a bit map is that not all programs support enhanced meta

files. 

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Because Release 2 can support multiple windows, output from another study can be 

loaded (by clicking on Load Results from File). This can be useful for comparing results from 

two or more simulations saved as separate studies. 

Tables

Tables can be obtained for both perturbed and control runs. The user can specify what 

variables are to be tabulated. The results can be saved to an Excel file. 

2.4 Exporting Results 

All results can be exported in Paradox, dBase, Excel, or comma-separated values 

by clicking on Export Results (perturbed) or Export Control. Results also can be exported to 

GenScn if it has been installed as part of the BASINS implementation (see Volume 3).

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Two Ways to Export Results 

Results also can be exported from a table in Output. However, at this time, 

exporting from the main screen as described here is the faster procedure. For a 

long simulation with numerous state variables, the difference is appreciable! 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

2.5 Site Information 

 Click on Site on the main screen to access site information. Five site types are available: 

pond, lake, stream (creek or river), reservoir, and limnocorral (an experimental enclosure). 

Site Types

There are five types of sites. Lakes, reservoirs, and ponds are treated similarly 

in AQUATOX; only the default extinction coefficients for water differ. However, 

their implementation is often quite different. Limnocorrals differ because the area of 

the enclosure wall is specified and enters into the computation of area available for 

colonization by periphyton. Streams differ even more in that they have an additional

screen for entry of data on slope, roughness (Manning’s coefficient), and percent 

habitat distribution. Implementation considerations are given below. 

Type Discharge Plants Invertebrates Detritus Stratified 

Pond and unimportant periphyton, zoobenthos, autochtho- no 

Limnocorral macrophytes zooplankton nous 

Lake site-specific phytoplankton zooplankton autochth. often 

 +/- allochth.

Reservoir important phytoplankton zooplankton allochtho- often 

 nous 

Stream/river v. important periphyton zoobenthos allochtho- no 

 bryophytes nous 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

A limited number of sites can be loaded from the database, but ordinarily one would Edit 

Underlying Site Data, to modify a default site to represent a new site. The site data screen is for

entry of site characteristics. If a site does not stratify, enter the same temperature values for

“hypolimnion” as for “epilimnion.” Several characteristics such as alkalinity are not used at this

time, so they are grayed out. 

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If the site is a stream, one can click on Stream Data to edit site characteristics such as 

the channel slope, the Manning’s roughness coefficient, and percent habitats. If inorganic 

sediments are being simulated, the user also can enter site-specific parameters for critical shear 

stresses and fall velocities. 

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The Remineralization parameter screen can be accessed; but these are global parameters

that usually would not be changed for a particular site. 

2.6 Using the Toolbar 

Virtually every function in AQUATOX can be accessed by clicking on the applicable 

icon on the toolbar. For the experienced user they provide a quick way to bring up a particular 

screen or to perform a function, such as saving a simulation, without going through several 

layers of options in the menu bar or the “big buttons.” (The big buttons can be suppressed 

entirely through the View menu option.) The icons can be added, deleted, or moved by clicking 

on Edit Toolbar under the View menu option. This is also a good way to learn the functions of 

the icons. There are 32 icons that are listed and can be used, compared to the 24 icons shown on

the default toolbar. One can also use dividers to visually group icons representing similar 

functions. 

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2.7 Running Batch Mode 

There are applications where it is desirable to run a series of studies automatically. This 

can be done by creating a Batch subdirectory under the Study directory and placing studies with 

appropriately chosen options in it. The subdirectory should also contain a text file labeled 

“batch.txt” that lists the names of the studies to be run, one to a line. On the menu bar you should 

click on Run Batch on the pull-down File window. That will open a window that allows you to 

Run in Batch Mode. You also can save the BAFs and organic-matter partition coefficients 

(KOMs) to a comma-separated text file batchout.csv. (See the Technical Documentation for 

discussion of bioaccumulation of organic toxicants, BAFs and KOMs.) The Help button will 

give you context-sensitive help. 

Important Specifications for Batch Mode: 

• To run a program in batch mode, you must have a subdirectory under your Studies 

Subdirectory named "batch." 

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• In that "batch" subdirectory must be all studies you wish to run along with a file named 

"batch.txt." 

• The batch.txt file must include each study name that you wish to run on a separate line. No 

blank lines may be included. 

• The program will then execute each of the specified studies one by one and save them along 

with their results. 

• To output the last BAF datapoint for each organism in each of the batch files, select the 

Output button. This will open each study and write all the BAF data from each study file 

into a CSV file named batchout.csv. 

 

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2.8 The Wizard 

The Wizard is intended to guide the user through the 19 steps necessary to set up a new

simulation. Even experienced users may find it to be a convenient checklist, providing a 

measure of quality assurance not usually found in models. Most steps consist of several parts, 

and one can move systematically through these by making choices or entering values, then 

clicking on Next. 

 The Progress Screen lists the steps and shows which step is currently active. It also

provides a means of skipping from one step to another by double-clicking on any step in order to 

move there.

 The Summary Screen lists the state and loading variables as they are changed while 

going through the Wizard. 

Step 1: Simulation Type 

The name for the simulation will appear on the main screen and will be used as a heading 

in the output. Five site types are available: pond, lake, stream (creek or river), reservoir, and 

limnocorral (an experimental enclosure). 

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Step 2: Simulation Time Period 

The time period for the simulation may be a few days, corresponding to an experiment, or 

a year, or even several decades. The time period does not have to correspond to the loadings,

because the loadings can be interpolated automatically. However, it is advisable to consider the 

correspondence between the start date and the initial conditions; if the initial conditions are 

poorly known then a start date in the middle of winter may allow the simulation to “spin up”

before going into the growing season. Years have to be entered as four digits. 

 

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Step 3: Nutrients 

Initial conditions for dissolved nutrients must be entered. Phosphate can be considered as 

soluble reactive phosphate; by going into the phosphate loading screen phosphate can be

adjusted for availability. Because of interchange with the atmosphere, the model is not very 

sensitive to the initial conditions for carbon dioxide and dissolved oxygen. 

 

Step 4: Detritus 

Labile detritus is readily decomposed and assimilated, refractory is resistant to break 

down. 

 

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Initial conditions and loadings of detritus in the water column can be input as Organic 

Matter (dry weight), Organic Carbon, or Biochemical Oxygen Demand (BOD) and the model

will make the necessary conversions. Suspended and dissolved detritus initial conditions and

loadings are divided into four compartments: particulate refractory and labile detritus and 

dissolved refractory and labile organic matter. Initial conditions and loadings are parsed by 

specifying % particulate and % refractory. 

 

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Step 5: Plants to Simulate

The user is provided with a list of plants for each taxonomic group from which to choose. 

These reflect the organisms that are in the current data libraries. 

 

 

Initial conditions should be entered for each plant group; as with any biotic group, a value 

of “0" coupled with 0 loadings will keep the group from being simulated. Note that the units are

sensitive to whether the plant is planktonic or benthic. 

 

 

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Step 6: Invertebrates to Simulate 

The user is presented with a list of invertebrates for each ecological guild from which to 

choose. As with the plants, this list reflects the organisms currently in the data libraries. Some

are general taxonomic groups and some are genera and species. The initial conditions are either 

mg/L or g/m2

, depending on the mode of life (pelagic or benthic). 

 

Step 7: Fish Species 

Altogether, it is possible to model 13 fish species. Nominally, fish are classified as

forage fish, bottom fish, and game fish, with two species for each general class. Usually two size 

classes are modeled for each species, but separate species can be modeled instead. Furthermore, 

one species can be modeled as multiple year classes. For each guild, the user can choose from a

list of species in the database. The guild designations do not determine the feeding preferences–

those are specified by the trophic interaction matrix, which is accessed by editing the state 

variable from the main screen; therefore, one could ignore the guild designations and 

parameterize the fish whatever way is appropriate. Initial conditions are given as g/m2

 because 

it is easiest to express biomass on an areal basis, and field data are usually available in that form. 

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Step 8: Site Characteristics 

The most important morphometric characteristic is mean depth because that controls light

penetration, volatilization, and attached plant distribution. Mean annual evaporation is used for 

computing the water balance. Latitude is used to compute photoperiod for photosynthesis. The 

wall area is important for a limnocorral because it represents additional area for attachment of 

periphyton. 

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The site characteristics screen is sensitive to the type of site, so that a stream has an 

additional screen. 

Step 9: Water Volume Data 

Depending on the method chosen, inflow or discharge values may be required. The 

Manning’s equation can be used to compute changing volumes in a stream. The simplest 

procedure is to hold volume constant at the initial condition. Volume can be computed 

dynamically, given the inflow and outflow (and factoring in evaporation). Finally, time series of

known values can be entered, as was done for this closely monitored limnocorral. 

 

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If the site is a stream then there is an additional option, using the Manning’s equation (see 

Volume 2) to compute the volume. 

Step 10: Water Temperature 

Temperature is a driving variable and is not computed as a state variable based on a heat 

budget. A constant temperature, annual mean and range in temperature, or time-series can be 

entered. Except for stratified lakes and reservoirs, the model is not very sensitive to temperature, 

so a sinusoidal function based on the annual mean and range is often sufficient. Even a stratified 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

system usually can be modeled with sinusoidal functions for the epilimnion and hypolimnion

temperatures. If stratification is not desired, enter the same temperature values for “epilimnion” 

and “hypolimnion.” 

Step 11: Wind Loadings 

Wind loadings can be constant, a time series can be entered, or a default time series can

be used. The default is a 140-day record from Columbia, Missouri, represented by a Fourier 

series, with a mean value that can be specified by the user (the default is 3 m/s). Near-surface 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

blue-green algae are represented as being sensitive to wind, so a realistically varying sequence of

synthetic wind values provides a better simulation than a constant value. 

Step 12: Light Loading 

Constant, time series, and annual mean and range may be given for light in Langleys/day.

The photoperiod is usually computed from the latitude, which is a site characteristic; however, 

the user can specify a constant photoperiod–useful in modeling some experimental facilities. 

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Step 13: pH of Water 

At present pH must be specified by the user, either as a constant or as a time series. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Step 14: Inorganic Solids 

AQUATOX can simulate sand, silt, and clay, but only in streams. For standing water an 

alternative is to use total suspended solids as a measure of inorganic solids. The model subtracts 

phytoplankton and detritus from the TSS to estimate the inorganic solids; therefore, care should 

be taken to use contemporaneous TSS and nutrient time series. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Step 15: Chemicals to Simulate 

AQUATOX can simulate as many as 20 different organic chemicals simultaneously. The 

assumption is that the toxic effects are additive. Initial concentrations for each toxicant are 

required for all associated state variables; as a check, the model calculates the total mass. 

70 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Step 16: Inflow Loadings 

The Wizard compiles a list of all variables that may be loaded as concentrations in 

inflowing water. The units are sensitive to each given variable. Note that choosing any dynamic

loading without entering at least one value will be interpreted as loadings of zero. Occasionally 

a user may wish to ignore all loadings for a state variable, such as when performing complex 

alternate simulations; this capability exists through the Wizard and each of the loading screens. 

71 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

Step 17: Direct Precipitation Loadings 

Another screen lists loadings from atmospheric deposition (“direct precipitation” and dryfall). The units are on an areal basis because deposition is on the surface of the water.

Step 18: Point-source Loadings 

Point-source loadings are entered as mass per day (g/d) to the water body. 

72 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

 Step 19: Nonpoint-source Loadings 

Nonpoint-source loadings are also entered as mass per day (g/d) to the water body. 

Wizard Completion 

When you have completed the 19 steps in the Wizard, you should review the parameter

files for the individual state variables because they are not subject to editing through the Wizard. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 2

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 3

3 DATA CONSIDERATIONS 

AQUATOX has many possible loading variables and process-level parameters. What 

input data are most critical for the simulations? The answer depends on the goals of the

simulation and the site-specific requirements. By using the principles outlined later in 

Uncertainty Analysis, one can perform sensitivity analysis to identify the more sensitive 

parameters and loadings for a particular simulation. Sensitive parameters may require sitespecific determination or careful calibration. Some sensitive variables have been identified in 

other studies and are listed below. 

3.1 Toxicant 

• The octanol-water partition coefficient is critical to bioaccumulation in organisms and

partitioning in detritus. It often can be estimated better than it can be measured. 

• Henry’s law constant is important for volatilization and yet is often difficult to measure. 

• Chemical and microbial degradation parameters determine the persistence in an 

ecosystem. If only half-lives are reported, they should be represented as uncatalyzed

hydrolysis rates, which are not affected by seasonal conditions as are microbial rates. 

• The thickness of the active layer, represented as the mass of sediment detritus, is 

important because of the simplifying conceptualization in this version that treats 

sediment-water interaction of contaminants as very efficient but restricted to the active 

layer. 

• Some toxicants, such as parathion, may bind more tightly to sediments than indicated by

organic partitioning. Estimation of the sediment partition coefficient may need to be 

overridden with observed values. 

3.2 Nutrients and Remineralization 

• The fraction of phosphate that is available depends on the nature of the phosphate 

loadings. The model distinguishes between detrital loadings, with implicit phosphorus 

content that is more or less available depending on whether the material is refractory or 

labile. Phosphate loadings may be in the dissolved phase or may be bound tightly in 

mineral particles; the user accounts for these by varying the fraction available: 1.0 if the 

phosphate is readily available and a small fraction if it is tightly bound. 

• Release of phosphate from anaerobic sediments is not modeled at this time. Previously, it 

was a constant (during periods of anoxia) and was set in the Remineralization screen. It 

will be reinstated in the near future. 

• Co-precipitation of phosphate with calcium carbonate is not modeled. In sites where that 

is important the best work-around is probably to decrease the loading accordingly. 

• Chemical oxygen demand is not modeled explicitly because of its site-specific nature; a 

work-around would be to decrease oxygen loadings. 

• Constant stoichiometry for nutrients in organic matter is a simplifying assumption. One

can change the value of the ratio of a given nutrient to organic matter in the

Remineralization screen. The Redfield et al. (1963) ratio is used as the default. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 3

• The proportions of refractory and labile and dissolved and particulate organic matter in a 

system control the rate of remineralization. Inappropriate initial conditions will cause a 

transient response, but poor characterization of loadings may affect the long-term nutrient 

budget and bioavailability of organic contaminants. If possible, obtain seasonal values 

for total organic carbon (TOC), dissolved organic carbon (DOC), and biological oxygen 

demand (BOD, which is labile); these can be used to obtain the necessary proportions.

Otherwise, consider the source of detritus loadings (forests, treatment plant, etc.) when

deciding how much may be refractory and particulate. 

3.3 Plants 

• Half-saturation constants for nutrients control how responsive phytoplankton and 

periphyton are to eutrophication; parameter values may depend on trophic status. 

• Maximum photosynthetic rates determine the competitiveness and resilience of algae; 

observed rates vary greatly and composite rates, such as for a diatom community, are 

most appropriate for most applications. 

• The model assumes that blue-green algae float unless the wind exceeds 3 m/s; this makes

the model sensitive to the mean wind loading. 

• Most macrophytes are sensitive to fall dieback; cold-tolerant groups, such as charaphytes,

should be so characterized with appropriately low optimal temperatures. Stream 

bryophytes or moss are parameterized to be tolerant of low-light, cold conditions and are 

subject to nutrient limitations; half-saturation constants for nutrients may require 

calibration. 

3.4 Animals 

• Consumption of refractory detrital sediments by zoobenthos increases the degradation 

rates of those sediments, increasing the simulated sediment oxygen demand and 

remineralization. The user should assume that most zoobenthos selectively feed on labile 

detritus, which includes freshly sedimented algae.

• The minimum biomass for feeding (BMin) is seldom measured, yet the model can be very

sensitive to this. The BMin value protects prey from being totally consumed, but if it is 

set too high the predators may starve to death. It may require site calibration. 

• Half-saturation for feeding is very seldom measured, but it can significantly reduce 

predicted feeding rates. Therefore, it should be set low in the absence of data. 

• Consumption and respiration rates in fish are functions of body size. As a default, the 

model uses the allometric equations presented by Hewett and Johnson (1992). Selection 

of representative mean weights for use in the equations is important. 

• Mortality rates may vary greatly from one site to another. This often becomes a 

calibration parameter, especially since fishing pressure is not modeled and death due to

predation is separate in the model. 

• If modeling an aquatic insect that emerges, be sure to select “Benthic Insect” in the dropdown list in the parameter screen because otherwise emergence will not be simulated.

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 3

3.5 Inorganic Sediments 

• Inorganic sediments are not explicitly modeled for standing water. They can be 

simulated in streams, following the approach used in the HSPF model. However, if

sediment transport, burial, and scour are important, the model should be coupled to a 

hydrodynamic model. 

• Total suspended solids are used to back-calculate suspended silts and clays in the model. 

This loading is compared with phytoplankton biomass in the computation of Secchi depth 

and light extinction; therefore, it should be provided for the entire period of the 

simulation (most loadings can be repeated automatically if the simulation period is longer 

than the available data). 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 3

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

4 CALIBRATION FOR A CONTAMINATED STREAM 

4.1 Introduction 

Users often ask for guidance on the best way to calibrate such a complex model. This

section is intended to provide guidance for a typical ecosystem and bioaccumulation calibration

for a site given a minimum amount of data. The goal for any calibration should be to obtain a 

good fit to observed data by varying as few parameters as possible and only within reported 

ranges. The above section, Data Considerations, provides a list of sensitive parameters, and 

applicable ones are considered in the following example. 

Criteria for acceptance of a calibration depend in large part on the amount of available 

data. In general, a weight-of-evidence approach should be taken with increasingly rigorous tests. 

An example of calibration of AQUATOX using an extensive dataset was given by EPA (2001). 

With minimal or sparse data the criteria may be restricted to less rigorous tests, including: 

• Observation of long-term reasonable behavior based on general experience with similar 

ecosystems; 

• Stability of simulations of key compartments compared to observed initial conditions (does a

species or group maintain roughly the observed biomass used as a starting point?); 

• General concordance based on visual inspection of data points compared to plots of model

results; 

• Bracketing of observations by predicted bounds obtained from uncertainty analysis; 

• Bracketing of predictions within ranges observed for replicated variables. 

The Wizard provides a useful checklist for setting up a simulation, and it may provide the 

structure for reviewing and changing initial conditions and loadings. It does not provide a means

for changing parameter values; and, therefore, it is not very useful for a typical calibration 

involving the iterative modification of parameter values. 

The example given below involves both ecosystem calibration and bioaccumulation 

calibration for a stream contaminated with PCBs in central Tennessee. It is provided only as an 

example and has no regulatory implications. 

4.2 Review Initial Conditions and Driving Variable Data 

Several study files with “EFPC” and “EForkPoplarCr” in their names are in the Studies 

directory. These are provided because you may not wish to perform all the changes described in 

this lengthy example. For purposes of illustration, we will begin with a prepared study labeled

EFPC3Const.aps (East Fork Poplar Creek, station 3, constant discharge). Load the study into 

AQUATOX. However, before proceeding, let’s save the study under a different name so that we

can preserve the original study file; we’ll name it EForkPoplarCr.aps. Now click on Edit 

With Wizard. Click on Next until Step 2. The two-year simulation period encompasses the 

period for which we have PCB data. However, stream simulations tend to be slow because of 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

the dynamics of changing flow, and so for purposes of the tutorial we will reduce the end date

from 1993 to 1992. It would be desirable to use a longer simulation period to avoid transient 

conditions, but we will do that only when checking the stability of the calibration and calibrating 

the bioaccumulation. 

In Step 3 observed values are given for nitrogen and phosphorus initial conditions; 

carbon dioxide and oxygen values are based on approximate saturation levels. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

The initial condition for organic detritus in the sediment bed is important because 

AQUATOX assumes steady-state conditions for sediment burial. That is, as a simplifying 

assumption, any organic detritus in excess of the initial condition is transferred to buried detritus

at the end of a year’s simulation. The proportion of labile to refractory is not critical for initial 

conditions, so we will guess that it is 1:2. 

The detritus in the water column can be expressed as organic matter, organic carbon, or

BOD; we have an observation of 5 mg/L organic carbon, so we will use that form and value. 

Seldom do we know the proportion of labile and refractory detritus. In this example, we know 

that the stream is fed by outflow from a holding pond with high algal biomass, so we will use our 

professional judgment that only 50% is refractory (it is often much higher) and that 30% is 

particulate (it is often only 10% in lakes and reservoirs). 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

In Step 5, which spans several screens, we choose the plants to simulate. Because East 

Fork Poplar Creek is shallow in the reach we are simulating, we will assume that most of the 

algae are periphyton. Therefore, we have chosen periphytic diatoms. 

For green algae, we have a choice between periphytic greens and Stigeoclonium, which is 

a particular genus of periphytic green algae. We have chosen Stigeoclonium because it was 

calibrated and validated for a stream on the other side of the ridge (see US EPA, 2001). 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

We will skip blue-green algae in the interest of using a minimal number of groups, 

although often we would include it because of different growth characteristics and susceptibility

to grazing. For macrophytes, we will choose Fontinalis because this moss is well adapted to

shallow streams. 

Initial conditions for plants are usually set at 0.5 or 1.0 g/m2

. These and other initial 

biomass values for which we have no site observations will be used as estimates, and better 

initial conditions will be obtained from the ending values after a “spin-up” period of one to three

years to reach steady state. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

In Step 6 we have the opportunity to choose invertebrates representing several feeding 

guilds and taxonomic groups. We will choose amphipods to represent the invertebrates that feed 

on detritus in the sediments. We will choose mayfly (Baetis) larvae as a grazer, and we will 

choose gastropods as another grazer (although they are a separate taxonomic group). We could

have chosen several other functional groups and we could have chosen two genera to represent

each group, but our goal is to model bioaccumulation in the food web with as few groups as 

necessary to capture the ecosystem dynamics. 

In the absence of data, we will set the initial conditions to 1.0 g/m2

 for each of the 

invertebrate groups. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

We will model three fish species, based on what we know for this reach. Other species

can be added, and age classes could be simulated for bass, but we have insufficient data to 

support such an approach. We will use two size classes for bass. 

We do have some data on abundance of fish, although upstream of this site and of 

unknown reliability; we convert those to biomass estimates and enter as initial conditions. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

In Step 8 we can review and change site characteristics. The length is arbitrary, but the 

surface area is important for computing depth as a function of changing discharge; it is good 

practice to document the assumed channel width in the comment field for the surface area (which

is available in the site screen). One consideration in specifying the reach length: the shorter the 

length and smaller the volume, the slower the simulation because retention time decreases,

causing the solution time step to be decreased. An arbitrary length of 1.0 km is chosen so that 

the surface area and volume will be sufficient to avoid an unduly short retention time. 

The computed velocity, which controls periphyton scour and macrophyte breakage, also 

is a function of channel slope, so the model is sensitive to slope. When entering a value try to 

represent a typical channel, not a regional slope that, for a small stream, may include rapids and 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

even waterfalls. Release 2 can model varying stream habitats. Rapid bioassessment data can 

provide percent distribution of riffle, run, and pool habitats. In the absence of such information, 

we will guess at a reasonable distribution for this valley and ridge stream and change it to 40% 

riffle and 10% pool. 

There are several ways to model water volume. If an experimental channel or pond, the 

volume may be held constant; computing based on inflow and outflow is also an option, but it 

can lead to incremental errors if the hydrodynamics are not well known. In some cases, such as 

managed reservoirs, the time-varying volume may be known and can be entered. Generally, for 

a stream with known time-varying discharge, it is preferable to use the Manning’s equation to 

estimate the changing volume. We will change it from Keep Volume Constant to Use Mannings 

Equation. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Having chosen to use the Manning’s equation, it is necessary to enter dynamic outflow 

data. Click on the Import button for the Use Dynamic Outflow entry. 

We will choose the file type as USGS Flow Data, and pick EFPCDischarge, which was

downloaded earlier from the http://waterdata.usgs.gov/nwis/discharge site. Unfortunately, the

USGS data can now be retrieved in several different formats, so you may have to copy and 

convert the pertinent data in a spreadsheet program rather than using the automatic conversion 

utility in AQUATOX. However, you should try to import it directly first. Click on Import to

load the data and click on Use Dynamic Outflow to activate the dataset.

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

In Step 10 we have chosen to enter an average annual temperature and a range for a 

typical year. In order to maintain maximum flexibility, the model has separate fields for

epilimnetic and hypolimnetic temperatures. Of course, these seldom apply to streams, so enter 

the same values in the corresponding fields. 

In Step 11 we can specify a constant wind, a time series of observed wind values, or a 

default time series. The model is insensitive to wind when simulating a small stream, although it 

can be important for standing water bodies. We have chosen to use a default time series. For 

many lakes and reservoirs a mean value of 3 m/s is appropriate, but for a sheltered stream a mean 

89 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

value of 0 represents the damping out of wind effects–note that with a mean of 0 there will still 

be wind roughly half the time. 

In Step 12 we will use an annual mean and range for light. These can be obtained from

the Internet for any major town in the U.S. The user will have to decide if these are appropriate 

for the site being modeled or if they need to be adjusted, especially for shading from riparian 

vegetation. The adjustment can be made in the values entered here, or the user can enter a

reduction factor in the light-loading screen. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

The model is not sensitive to pH, so in this simulation we will use a constant pH of 8. In

addition, because it is a small stream and we are lacking data, we will ignore inorganic sediments 

in the simulation. We have chosen to simulate PCB 1254, and observed values are entered in 

Step 15. 

In Step 16, loadings associated with inflow can be reviewed and modified. Observed 

values are entered for nutrients, and constant values are used for carbon dioxide, oxygen, and 

detritus. For now, we’ll leave the loading of the organic chemical as 0. Ammonia has Ignore 

ALL Loadings for this State Variable checked. That option is usually used for 

experimentation, such as sensitivity analysis, so uncheck it and check Use Constant Loading of

(with a value of 0.4). 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Loadings for periphyton and macrophyte groups can be considered as “seeds” to

reestablish the group following scour, emergence, and toxic- and seasonal-induced mortality. 

The user is cautioned, though, to use a value of 0.00001 (1e-5) or less as a seed for periphyton, 

invertebrates, and fish; otherwise, the “loading” will dominate the rates during high-flow 

conditions. A value of 0.0001 is often appropriate for macrophytes because AQUATOX 

simulates (and most studies measure) above-ground biomass, which can be an order of

magnitude greater than that of other organisms and can be replenished from rhizomes. However,

this doesn’t apply to moss, so we’ll use a value of 0.00001 for Fontinalis as well. 

In this example, there are no organic chemical loadings associated with any of the biota. 

Furthermore, there should be no loadings from direct precipitation, point- or nonpoint-sources. 

You should check them to make sure; and, if there are, change them to 0. (However, there are

organic chemical loadings associated with detritus.) When you are finished with the review and 

modifications using the Wizard, click on Finish to save your modifications. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

At any time, one can return to the Wizard; however, more editing functions are available 

by going directly to the state variables. By double-clicking on any variable listed on the right 

side of the main screen, a dialog screen can be pulled up. For example, one should review the 

loadings for phosphate and consider if the entire loading is available for utilization by algae.

Click on Fraction Available to change the availability. In this example, we chose to accept the

phosphate loadings as being entirely available. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

 Double-clicking on Light, we adjust the multiplicative loading factor to 0.9 to account 

for nominal shading by riparian vegetation. 

4.3 Ecosystem Calibration 

Before modeling bioaccumulation, one should ensure that the ecosystem model is 

providing reasonable results; calibration may be necessary. (By using old parameter files, we

will ensure that calibration is necessary for purposes of the tutorial.) First, let’s save the study,

keeping the name we gave it at the beginning: EForkPoplarCr.aps. Rate files are named after

the study file name, so it is advisable to save the study under the desired name before running a 

simulation. To save the rates, click on Setup and Save Biologic Rates. Click on Rate 

Specifications to choose which variables to save (you may wish to choose all biota); also, click 

on Excel File (*.xls) unless you really want to use Paradox or Quattro. 

94 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Clicking on Control we start the simulation. With this study, a series of warning screens

will appear, cautioning that no LC50 exposure time has been set for the algae; click on OK to 

accept a default time of 24 h. (It’s a good idea, after the simulation has finished to go to the 

toxicity screen and specify LC50 exposure times so the messages won’t continue to appear.)

Having chosen Show Integration Info in the Setup screen, we can see what process is

responsible for slowing down the simulation. We also can see when periphyton sloughing is 

occurring. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Hydrodynamics

First, the predicted volumes and velocities should be checked against known or expected 

values. If the values seem to be erroneous then the site characteristics and discharge driving 

variables should be examined. In this example, velocities range from 14 to 170 cm/s and 

volumes from 1000 to 6500 m3

, which are acceptable in the absence of actual site data. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Water Vol (cu.m)

 

Run Velocity (cm/s)

Pool Velocity (cm/s)

E. F POPLAR CREEK TN (CONTROL) 12/18/2003 2:46:05 PM

(Epilimnion Segment)

1/11/1992 4/10/1992 7/9/1992 10/7/1992 1/5/1993

cu.

m

6500.0

6000.0

5500.0

5000.0

4500.0

4000.0

3500.0

3000.0

2500.0

2000.0

1500.0

1000.0

cm/s

170.0

160.0

150.0

140.0

130.0

120.0

110.0

100.0

90.0

80.0

70.0

60.0

50.0

40.0

30.0

20.0

Biomass 

Examining the predicted plant biomass, the combined periphyton vary between 0.1 and 6 

g/m2

 and are dominated by periphytic diatoms; these values are reasonable for a shallow-water 

stream. The moss Fontinalis fluctuates from an initial condition of 1 to 3 to 0.8 g/m2

, which also 

is reasonable. 

Periphyton, Di (g/sq.m)

Stigeoclonium, (g/sq.m)

 

Fontinalis (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/23/2003 11:12:40 AM

(Epilimnion Segment)

1/11/1992 4/10/1992 7/9/1992 10/7/1992 1/5/1993

g/sq.m 7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

g/sq.m

3.0

2.0

1.0

Having saved the rates, we will find the Excel file in the Output directory, and it is 

labeled with the study name preceded by “C_” because it is the control run. The rates are saved

as percentages of biomass on each day, and we have chosen to plot them as areagraphs. We can 

see that the periphytic diatom rates indicate seven sloughing events, corresponding to the sudden 

drop in biomass in the previous plot. The sloughing removes the light limitation on the 

Fontinalis (shown by plotting the limitations to photosynthesis) and leads to a rapid increase in 

biomass of that group. The decline in Fontinalis in the summer is seen to be caused by the 

severe limitation by high temperatures. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Periphytic Diatoms

0.00%

200.00%

400.00%

600.00%

800.00%

1000.00%

1200.00%

1/2/19922/2/19923/2/19924/2/19925/2/19926/2/19927/2/19928/2/19929/2/1992

10/2/199211/2/199212/2/1992

Percent of Biomass

Washout1

Predation1

Non Tox Mort1

T1 Poisoned1

Excret1

Respir1

Photosyn1

Fontinalis Limits to Photosynthesis

0

0.2

0.4

0.6

0.8

1

1.2

1/2/1992

1/16/19921/30/19922/13/19922/27/19923/12/19923/26/1992

4/9/1992

4/23/1992

5/7/1992

5/21/1992

6/4/1992

6/18/1992

7/2/1992

7/16/19927/30/19928/13/19928/27/19929/10/19929/24/199210/8/1992

10/22/1992

11/5/1992

11/19/1992

12/3/1992

12/17/199212/31/1992

Lt_LIM3

Nutr_LIM3

Temp_LIM3

When we plot the invertebrates and fish we find that gastropods maintain a biomass of 

about 1 g/m2

 while the other invertebrates decline rapidly in biomass after the initial conditions. 

Stonerollers, which feed on periphyton and Fontinalis, decline to 0.75 g/m2

. The adult bass 

(“Largemouth Ba2”) decline to 0.3 g/m2

; and shiners decline almost to 0. Clearly, the animal 

initial conditions and dynamics should be calibrated, and we can expect that the periphyton 

dynamics will be affected. We will set the initial condition for stonerollers to 0.75 and for adult 

bass to 0.3 g/m2

.

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Amphipod (g/sq.m)

Mayfly (Baetis (g/sq.m)

Gastropod g_m2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/23/2003 3:26:06 PM

(Epilimnion Segment)

1/10/1992 4/9/1992 7/8/1992 10/6/1992 1/4/1993

g/sq.m 1.0

0.0

Shiner (g/sq.m)

Stoneroller (g/sq.m)

Largemouth Bas (g/sq.m)

Largemouth Ba2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/23/2003 3:31:25 PM

(Epilimnion Segment)

1/10/1992 4/9/1992 7/8/1992 10/6/1992 1/4/1993

g/sq.m 4.0

3.0

2.0

1.0

0.0

Looking at the trophic interactions matrix for adult bass, we see that they have a 0 

preference factor for stonerollers. This is incorrect, and should be changed to a value 

comparable to that for shiners (0.05). In addition, there are no sunfish in the simulation, so we 

will set the preference by bass for gastropods to 0.05 to account for predation pressure on 

gastropods. At the same time, we should set the egestion parameter for gastropods to 0.158 and 

correct the egestion parameters for shiners and bass to 0.05 (based on literature values).

Furthermore, we will adjust the initial conditions, using the previously noted ending values,

except for shiners. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Now we find that the bass are stable at 0.35 g/m2

, with a continued decrease in other fish.

Given the increased predation, gastropods decline to 0.75 g/m2

. Shiners continue to decline 

precipitously, although observed data indicate that they are common. Examining their 

preference matrix, we find that there is no preference for periphytic diatoms or labile detritus and 

that egestion is set at 0.6. The values are changed as pictured. The other problematic pattern is

that of mayflies, which decline exponentially and do not recover; let’s return to them after

calibrating the fish. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Shiner (g/sq.m)

Stoneroller (g/sq.m)

Largemouth Bas (g/sq.m)

Largemouth Ba2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/23/2003 4:24:33 PM

(Epilimnion Segment)

1/10/1992 4/9/1992 7/8/1992 10/6/1992 1/4/1993

g/sq.m

0.8

0.75

0.7

0.65

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

Amphipod (g/sq.m)

Mayfly (Baetis (g/sq.m)

Gastropod g_m2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/23/2003 4:28:48 PM

(Epilimnion Segment)

1/10/1992 4/9/1992 7/8/1992 10/6/1992 1/4/1993

g/sq.m

1.0

0.0

101 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

With the changes, shiners now exhibit a realistic seasonal fluctuation in biomass—in part, 

at the expense of the gastropods, which decline to 0.64 g/m

2

. Grazing by shiners also removes 

competition and favors Stigeoclonum for part of the year. Turning our attention to mayflies, we 

see from a rate plot that defecation is almost equal to consumption, and drift is a continuous loss 

of 4%. 

Shiner (g/sq.m)

Stoneroller (g/sq.m)

Largemouth Bas (g/sq.m)

Largemouth Ba2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/23/2003 4:59:41 PM

(Epilimnion Segment)

1/10/1992 4/9/1992 7/8/1992 10/6/1992 1/4/1993

g/sq.m

0.8

0.75

0.7

0.65

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

Amphipod (g/sq.m)

Mayfly (Baetis (g/sq.m)

Gastropod g_m2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/23/2003 4:58:33 PM

(Epilimnion Segment)

1/10/1992 4/9/1992 7/8/1992 10/6/1992 1/4/1993

g/sq.m

1.0

0.0

102 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Periphyton, Di (g/sq.m)

Stigeoclonium, (g/sq.m)

Fontinalis (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/23/2003 5:01:47 PM

(Epilimnion Segment)

1/10/1992 4/9/1992 7/8/1992 10/6/1992 1/4/1993

g/sq.m

6.0

5.0

4.0

3.0

2.0

1.0

0.0

The bass young-of-the year (YOY) should not decline, considering that the adults 

are stable. Examining the trophic interaction matrix, we see that they are not parameterized to

feed on mayflies and the egestion factors are too high. We change the YOY preference and 

egestion parameters to the values shown in the figure below. 

Turning our attention to mayflies, we suspect that, here again, the preference matrix is 

responsible for the poor simulation. The preference of mayflies for periphytic diatoms should be 

the same as for Stigeoclonum, there should be no preference for refractory detritus, and the 

egestion factor for periphyton should be set to 0.3 as in the other consumers. 

103 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Interestingly, the mayflies still decline to almost to 0. When we plot the rates, we find 

that respiration accounts for much of the loss. (The rates have spikes during storm events 

because the loading or “seed” is set too high.) Examination of the parameter file reveals that 

specific dynamic action (which is a fraction of the consumed food) is set at 0.8, although the 

literature value is 0.15. We set it to the proper value and re-run the simulation. 

Amphipod (g/sq.m)

Mayfly (Baetis (g/sq.m)

Gastropod g_m2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/23/2003 9:17:40 PM

(Epilimnion Segment)

1/10/1992 4/9/1992 7/8/1992 10/6/1992 1/4/1993

g/sq.m 1.0

0.0

104 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Mayflies

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

1/2/1992

1/16/19921/30/19922/13/19922/27/19923/12/19923/26/1992

4/9/1992

4/23/1992

5/7/1992

5/21/1992

6/4/1992

6/18/1992

7/2/1992

7/16/19927/30/19928/13/19928/27/19929/10/19929/24/199210/8/1992

10/22/1992

11/5/1992

11/19/1992

12/3/1992

12/17/199212/31/1992

Percent of Biomass

EmergeI5

Drift5

Non Tox Mort5

T1 Poisoned5

Predation5

Excretion5

Respiration5

Defecation5

Consumption5

Load5

Mayflies are now an important component in the simulation—perhaps too important. 

Furthermore, bass increase significantly, probably because of the increased secondary 

productivity through the mayflies. 

Amphipod (g/sq.m)

Mayfly (Baetis (g/sq.m)

Gastropod g_m2 (g/sq.m)

 

Shiner (g/sq.m)

Stoneroller (g/sq.m)

Largemouth Bas (g/sq.m)

Largemouth Ba2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/24/2003 10:47:07 AM

(Epilimnion Segment)

1/11/1992 4/10/1992 7/9/1992 10/7/1992 1/5/1993

g/sq.m

35.0

30.0

25.0

20.0

15.0

10.0

5.0

0.0

g/sq.m

1.0

With shiners parameterized to eat mayflies, we would expect mayflies to decline and

shiners to increase, but that is not the case. Examining the mayfly parameter file, we will cut the

maximum consumption rate from 0.5 to 0.25 g/g d, set the minimum prey for feeding to 0.1 

g/m2

, set respiration rate to 0.05 /d, change the mortality coefficient from 0.01 to 0.05 /d, and

specify that there is a 75% preference for riffles. (These were obtained by calibration at another 

site, so we will use them here to speed up the process.) We will ignore the steep decline in the 

stonerollers because it may reflect competition from the mayflies. The decline in shiners is

puzzling, but we will wait to address it further. 

105 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Plotting the results, we see that mayflies now slowly decline and disappear. On the other 

hand, shiners exhibit a stable fluctuation and stonerollers do not decline as much as previously,

indicating that, indeed, competition from mayflies is important. However, we know that 

mayflies are important at the site, so we will set the maximum consumption rate for mayflies

back to 0.5 g/g d. 

Amphipod (g/sq.m)

Mayfly (Baetis (g/sq.m)

Gastropod g_m2 (g/sq.m)

 

Shiner (g/sq.m)

Stoneroller (g/sq.m)

Largemouth Bas (g/sq.m)

Largemouth Ba2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/24/2003 12:14:41 PM

(Epilimnion Segment)

1/11/1992 4/10/1992 7/9/1992 10/7/1992 1/5/1993

g/sq.m 1.0

0.0

g/sq.m

0.8

0.75

0.7

0.65

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

With the change in the maximum consumption rate (CMax), mayflies reach a predicted 

maximum biomass of 15 g/m2

, and the shiners reach a slightly lower equilibrium. Obviously, 

CMax is a sensitive parameter. We could run sensitivity analysis to suggest an optimal value 

that would constrain the mayflies and permit the other grazers to coexist. However, in the 

interest of time, we will split the difference in the values, changing CMax for mayflies from 0.5 

to 0.45 g/g d. Bass reach an end value of 0.54 g/m2

, which seems high. Intrinsic mortality often 

is site-specific because of factors that are not simulated adequately, such as fishing pressure. To 

hold the bass at approximately the initial condition, we calculate that we need to increase the 

mortality rate by 1.6e-3 /d; we will increase the mortality factor by that amount (from 0.0004 to 

0.002 /d). 

Calculating Rate of Change 

To calculate the increased mortality required to offset the increase in biomass of bass, we 

observe that the initial biomass is 0.3 g/m2

 and the end value is 0.54 365 days later: 

rate of increase / 365 1.6e 3/ d

0.3

0.54 ln ⎟ = − ⎠

⎞ ⎜

⎝

⎛ =

With the change in maximum consumption in mayflies, the animals now exhibit a 

reasonable pattern, except for stonerollers, which decline to 0.13 g/m2

. However, stonerollers 

are usually not an important component of stream ecosystems, so we will reserve judgment until 

obtaining a longer simulation.

106 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Amphipod (g/sq.m)

Mayfly (Baetis (g/sq.m)

Gastropod g_m2 (g/sq.m)

 

Shiner (g/sq.m)

Stoneroller (g/sq.m)

Largemouth Bas (g/sq.m)

Largemouth Ba2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/24/2003 2:57:43 PM

(Epilimnion Segment)

1/11/1992 4/10/1992 7/9/1992 10/7/1992 1/5/1993

g/sq.m 11.0

10.0

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

g/sq.m

0.8

0.75

0.7

0.65

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

A plot of the plants shows that they are still providing a reasonable predicted pattern of

algal succession although the moss is increasing because of increased grazing on periphyton and 

removal of light limitation. The problem with Fontinalis is that the biomass will continue to 

build from one year to the next. There is predicted mortality during the summer months when 

the temperature is too high, but the moss is not being set back to some nominal level. One way 

to reset the biomass is to parameterize breakage to occur during the maximum discharge event 

each year. Plotting velocity, we see that the maximum riffle velocity is about 176 cm/s, so we

will use 170 as the VelMax value instead of 500 cm/s. This is a site-specific solution, but both

breakage and scour can be expected to vary from one site to another. 

Periphyton, Di (g/sq.m)

Stigeoclonium, (g/sq.m)

Fontinalis (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/24/2003 3:21:54 PM

(Epilimnion Segment)

1/10/1992 4/9/1992 7/8/1992 10/6/1992 1/4/1993

g/sq.m

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

107 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Riffle Velocity (cm/s)

E. F POPLAR CREEK TN (CONTROL) 12/24/2003 3:25:55 PM

(Epilimnion Segment)

1/10/1992 4/9/1992 7/8/1992 10/6/1992 1/4/1993

cm/s 170.0

160.0

150.0

140.0

130.0

120.0

110.0

100.0

90.0

80.0

70.0

At this time, we need to run the simulation for a longer period to confirm that there is a 

stable, repeating pattern of biomass for the various groups. We will want to run the model for at

least three years to simulate PCB accumulation, so let’s use that period for the control as well.

We enter Setup and change the end date to 12/31/1994. 

A three-year simulation indicates that we have an issue with transient conditions for both 

animals and plants in the first year. That is almost certainly because the initial conditions are

inappropriate. The remedy is to set the initial conditions to the end conditions predicted after 

three years. The best way to obtain the end conditions is to click on the Control Simulation tab 

in Output, click on Change Variables, and select the biotic state variables. Then you can scroll

to the bottom of the table to see the end values. The quickest way to enter initial conditions is

through the Wizard. Double-click on Step 5 and click on Next until the initial condition screen 

for plants appears. Change the values and then click on Next again until you get the initial 

condition screen for invertebrates. Repeat the process for fish. Finally, click on Finish to leave 

the Wizard and save the changes. As a check, you can click on the Initial Conds. button to

display all the initial conditions.

Amphipod (g/sq.m)

Mayfly (Baetis (g/sq.m)

Gastropod g_m2 (g/sq.m)

 

Shiner (g/sq.m)

Stoneroller (g/sq.m)

Largemouth Bas (g/sq.m)

Largemouth Ba2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/24/2003 4:06:15 PM

(Epilimnion Segment)

1/15/1992 1/14/1993 1/14/1994 1/14/1995

g/sq.m

5.0

4.0

3.0

2.0

1.0

0.0

g/sq.m

0.8

0.75

0.7

0.65

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

108 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Periphyton, Di (g/sq.m)

Stigeoclonium, (g/sq.m)

Fontinalis (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/24/2003 4:09:06 PM

(Epilimnion Segment)

1/13/1992 1/12/1993 1/12/1994 1/12/1995

g/sq.m

6.0

5.0

4.0

3.0

2.0

1.0

0.0

With the changes in initial conditions, we have a reasonably stable simulation with 

seasonal fluctuations of all state variables over the course of three years. The biomass patterns 

are repeated because constant loadings of nutrients and only 11⁄2 years of discharge data are used. 

We will consider that to be a calibrated ecosystem simulation, and save it as

EForkPoplarCr3.aps. (You may wish to use the file provided instead--your results may differ.) 

109 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Amphipod (g/sq.m)

Mayfly (Baetis (g/sq.m)

Gastropod g_m2 (g/sq.m)

 

Shiner (g/sq.m)

Stoneroller (g/sq.m)

Largemouth Bas (g/sq.m)

Largemouth Ba2 (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/24/2003 5:18:56 PM

(Epilimnion Segment)

1/15/1992 1/14/1993 1/14/1994 1/14/1995

g/sq.m 2.0

1.0

0.0

g/sq.m

0.36

0.34

0.32

0.3

0.28

0.26

0.24

0.22

0.2

0.18

0.16

0.14

0.12

0.1

0.08

0.06

0.04

Periphyton, Di (g/sq.m)

Stigeoclonium, (g/sq.m)

Fontinalis (g/sq.m)

E. F POPLAR CREEK TN (CONTROL) 12/24/2003 5:21:48 PM

(Epilimnion Segment)

1/13/1992 1/12/1993 1/12/1994 1/12/1995

g/sq.m

5.0

4.0

3.0

2.0

1.0

0.0

4.4 PCB Calibration 

Now we need to run the perturbed simulation to represent the bioaccumulation of PCBs at

Station 3 on East Fork Poplar Creek (EForkPoplarCr3.aps). Unfortunately, we have sparse 

data for comparison to the simulations. Based on conversions of data given in Moore et al. 

(1999), we expect the concentration of PCBs in periphyton to be about 40 ug/kg (wet). 

Unpublished data of SAIC (1998) indicate that the concentrations of PCB 1254 in fish are 66 +/- 

36 ug/kg (wet); if those are based on fillets, as Moore et al. (1999) suggest, then those convert to

about 150 +/- 84 ug/kg (wet). The concentrations of PCB 1254 in whole sediments are based on 

observed data (SAIC, 1998, unpublished data), but there is no information on what fraction was 

organic matter, so the data are not useful. The concentrations in dissolved phase in the water are

below detection limits (1 to 1.3 ug/L). We will use the value of 0.0006 ug/L estimated by Moore 

et al. (1999) as a starting point; make sure that value is entered as both an initial condition and as 

an inflow loading. 

110 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Given the available data, our objective is to calibrate the water concentration of PCB 1254

so that the simulations are consistent with the observed concentrations in biota. If we can obtain

consistent results, then we could decrease the loadings to predict long-term recovery.

Looking at the toxicity records, we find that there are no toxicity values for PCB Aroclor 

1254. 

A quick check of US EPA’s ECOTOX database shows that there are EC50 values for 

algal mats. We will use an EC50 of 50 ug/L for the two groups of periphytic algal. 

111 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

In the toxicity screen click on Add a Plant Toxicity Record and enter the two algal 

groups as shown below, keeping in mind that you probably added an LC50 exposure time earlier 

for the other groups to avoid the repetitive warning message. You have to provide the EC50, an 

estimate of the LC50, and the lipid fraction; leave the elimination constants blank: the program

will provide estimates for them. 

Now click on Estimate elimination rate constants .... The rate coefficients will be 

estimated using the octanol water partition coefficient and the plant’s lipid content, which is why 

it is important to completely fill in each record. After saving the toxicity and chemical screens, 

double-click on each of the plant state variables, open the parameter screen, and associate the 

plant with the appropriate toxicity record. Save the study (as EForkPoplarCr3 PCB), so that

your changes will not be lost in the event of a problem—although we have not mentioned it 

before, it’s a good practice to save the study periodically. (If you are just following along in the 

text, a copy of the file is provided in the InstallShield and will be installed on your system.) 

112 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Having run the perturbed simulation, we should first examine the difference graph to see 

if there were any predicted toxic effects of PCBs at the concentrations used. In one run there 

was less than a 4% difference for any variable, well within the integration differences for the two

simulations. However, another run exhibited a peculiar set of spikes, initiated by the diatoms. 

Switching back and forth between the control and perturbed simulations (not shown), it 

eventually becomes clear that a minor sloughing event for the diatoms occurred in the control 

simulation and not in the perturbed simulation. The fact that it wasn’t consistent suggests it

could be just a numerical glitch (although the relative error is set quite low), or it may indicate a 

very small toxic effect on photosynthesis that kept the diatom biomass from reaching a threshold 

value at which sloughing would occur. The effect is short-lived so we will ignore it.

We then plot the concentrations in the biota, and compare them with the observed data. 

An easy way to do this is to set up a spreadsheet as a template with the observed data and a 

default graph. Click on the Perturbed Simulation tab for Output, then click on Change

Variables and choose the T1 H2O (ug/L) and ppb output. These will be displayed in tabular 

form and can be saved to an Excel file. We’ll label the file “1-EFPC3 PCB” so that it will 

appear near the top of the list of files in the Output directory. We also prepared a template with 

the observed data and labeled it “2-EFPC3 PCB.” We then cut and paste the predicted data 

from 1-EFPC3 PCB.xls into 2-EFPC3 PCB.xls and inspect the fit between the observed and 

predicted concentrations. 

Periphyton, Di

Stigeoclonium,

Fontinalis

Amphipod

Mayfly (Baetis

Gastropod g_m2

Shiner

Stoneroller

Largemouth Bas

Largemouth Ba2

E. F POPLAR CREEK TN (Difference) 1/21/2004 2:56:42 PM

(Epilimnion Segment)

1/15/1992 7/15/1992 1/13/1993 7/14/1993 1/12/1994 7/13/1994 1/11/1995

% DIFFERENCE

450.0

400.0

350.0

300.0

250.0

200.0

150.0

100.0

50.0

0.0

-50.0

-100.0

113 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

114 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

 As seen in the following graph, the results are off by a factor of at least 20. 

EFPC PCB

0

50

100

150

200

250

1/1/19922/1/19923/1/19924/1/19925/1/19926/1/19927/1/19928/1/19929/1/1992

10/1/199211/1/199212/1/1992

1/1/19932/1/19933/1/19934/1/19935/1/19936/1/19937/1/19938/1/19939/1/1993

10/1/199311/1/199312/1/1993

ug/kg wet

T1Stigeoclonium,(ppb)

T1Fontinalis(ppb)

T1Stoneroller(ppb)

T1Largemouth Bas(ppb)

T1Largemouth Ba2(ppb)

Obs Periphyton (ppb)

Obs Fish (ppb, fillets?)

Whole Fish (ppb)

The concentration 0f 0.0006 ug/L estimated by Moore et al. (1999) was based on the 

assumption of equilibrium between periphyton and the water column using a single observed

BCF for PCBs and Anabaena. This was sufficient for their risk assessment, but AQUATOX is

more sensitive to the dissolved concentrations and, with its nonlinear representation of 

bioavailability and fate, can be used to obtain a more robust estimate through calibration.

115 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 4

Therefore, we will increase the concentration in water by a factor of 20 to 0.012. The study will 

be saved as “EForkPoplarCr3 X20 PCB.aps.” The results are closer to what we would expect,

although they are still too low. 

EFPC X20 PCB

0

50

100

150

200

250

1/1/19922/1/19923/1/19924/1/19925/1/19926/1/19927/1/19928/1/19929/1/1992

10/1/199211/1/199212/1/1992

1/1/19932/1/19933/1/19934/1/19935/1/19936/1/19937/1/19938/1/19939/1/1993

10/1/199311/1/199312/1/1993

ug/kg wet

T1Stigeoclonium,(ppb)

T1Fontinalis(ppb)

T1Stoneroller(ppb)

T1Largemouth Bas(ppb)

T1Largemouth Ba2(ppb)

Obs Periphyton (ppb)

Obs Fish (ppb, fillets?)

Whole Fish (ppb)

Next, we will increase the dissolved concentration by a factor of 60 to 0.036 ug/L, and

save the study as “EForkPoplarCr3 X60 PCB.aps.” The results look good: the observed 

periphyton concentration is on the predicted periphyton line, and the observed range of fish

concentrations encompasses both the predicted stoneroller and adult bass concentrations. The

young-of-the-year bass are predicted to bioconcentrate more PCB 1254 because of their small

size and high respiratory rates; however, they are not an important component of the fish 

community, probably were not sampled, and were virtually ignored in our ecosystem calibration. 

Given the data limitations, this is the best calibration to the PCB data that we can expect. 

EFPC X60 PCB

0

50

100

150

200

250

300

350

1/1/19922/1/19923/1/19924/1/19925/1/19926/1/19927/1/19928/1/19929/1/1992

10/1/199211/1/199212/1/1992

1/1/19932/1/19933/1/19934/1/19935/1/19936/1/19937/1/19938/1/19939/1/1993

10/1/199311/1/199312/1/1993

ug/kg wet

T1Stigeoclonium,(ppb)

T1Fontinalis(ppb)

T1Stoneroller(ppb)

T1Largemouth Bas(ppb)

T1Largemouth Ba2(ppb)

Obs Periphyton (ppb)

Obs Fish (ppb, fillets?)

Whole Fish (ppb)

116 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

5 APPLICATIONS 

The following examples are intended as illustrations of potential applications. AQUATOX 

has been validated with several data sets from diverse sites and applications; however, like any 

complex model, it should be evaluated for the intended use. More detailed reports on model

validation, including analysis of model predictions as compared to observed data, are found in 

(U.S. Environmental Protection Agency 2000). No warranty, either expressed or implied, is

made.

5.1 Recovery Following PCB Remediation 

With a calibrated model of East Fork Poplar Creek, we can now forecast the consequences

of planned and unplanned modifications to the stream. Two examples are given, this and the one 

following. Recently, PCBs were removed from East Fork Poplar Creek. As part of a remedial 

investigation, it is prudent to estimate the time to recovery once the contaminants are removed. 

That can be done by running the model for a sufficiently long period and noting when pollutants 

drop below the level of concern. We will take a concentration of 1 ppb PCB 1254 in bass as our 

endpoint. 

Let’s start with EForkPoplarCr3 X60 PCB.aps. We will keep the loadings for the first 

two years as a spin-up for the recovery simulation, then drop the loadings of dissolved and 

detrital PCBs to 0 on 1/1/1994. In our initial simulation, we set the end date to 12/31/2002; 

however, for purposes of the tutorial and if you have a slow computer, you may wish to set the 

end date to 12/31/1994. We’ll save it as “EForkPoplarCr3 PCB recovery.aps.” 

117 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

At the end of the 4-year simulation, and two years after the “cleanup,” the predicted 

concentration in bass was 8.5, in stonerollers was 0.03, and in buried refractory detritus was 6.7

ppb. The target value of 1 ppb in bass was predicted to be reached on 6/19/1997, three and a half 

years after the PCB loadings were removed. Unfortunately, we don’t know what the actual 

recovery time was after cleanup. According to the longer simulation, buried detritus still had 4.7 

ppb nine years after the simulated cleanup; however, the model was not run with the more 

realistic scour and deposition algorithms of the inorganic sediment submodel. 

EFPC 2-yr Recovery PCB

0.001

0.01

0.1

1

10

100

1000

1/1/924/1/927/1/92

10/1/92

1/1/934/1/937/1/93

10/1/93

1/1/944/1/947/1/94

10/1/94

1/1/954/1/957/1/95

10/1/95

ug/kg wet

T1Stigeoclonium,(ppb)

T1Fontinalis(ppb)

T1Stoneroller(ppb)

T1Largemouth Bas(ppb)

T1Largemouth Ba2(ppb)

Obs Periphyton (ppb)

Obs Fish (ppb, fillets?)

T1R detr sed(ppb)

Whole Fish (ppb)

T1BuryRDetr(ppb)

EFPC 9-yr Recovery PCB

0.001

0.01

0.1

1

10

100

1000

1/1/19927/1/19921/1/19937/1/19931/1/19947/1/19941/1/19957/1/19951/1/19967/1/19961/1/19977/1/19971/1/19987/1/19981/1/19997/1/19991/1/20007/1/20001/1/20017/1/20011/1/20027/1/2002

ug/kg wet

T1Stigeoclonium,(ppb)

T1Fontinalis(ppb)

T1Stoneroller(ppb)

T1Largemouth Bas(ppb)

T1Largemouth Ba2(ppb)

Obs Periphyton (ppb)

Obs Fish (ppb, fillets?)

T1R detr sed(ppb)

Whole Fish (ppb)

T1BuryRDetr(ppb)

118 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

5.2 Possible Response to Invasive Snail Species 

Another possible impact on the stream ecosystem could come from invasion by the New 

Zealand mud snail Potamopyrgus antipodarum. This exotic species has invaded the rivers in 

Yellowstone National Park (Hall et al. 2003) and is spreading through the Missouri River 

watershed. Based on data presented by (Hall et al. 2003), the species was added to the

AQUATOX animal library. 

Let’s add the species to the baseline East Fork Poplar Creek study (EForkPoplarCr3.aps) 

and rename that file “EForkPoplarCr invasion.aps.” Click on Add below the list of state 

variables, and then choose Snail and Potamopyrgus. Double-click Snail2 to open the initial 

condition and loading window. We will set the initial condition to 1 g/m2

. Next, double-click 

on LgGamefish1, then Trophic Interactions and set the preference and egestion for 

Potamopyrgus to the same values as Gastropod so that bass will serve as a predator (we have no

reason to believe that bass, as a surrogate for sunfish, would treat the two types of gastropods 

differently). Finally, select Dissolved org. tox 1:[PCB 1254] in the state variable list and click 

on Delete. Since we are not interested in bioaccumulation in this application, simulating PCB 

fate would only slow down the simulation. 

The results are striking. Within two years, Potamopyrgus goes from 1 to 6 g/m2

. A 5-

year simulation (EForkPoplarCr 5yr invasion.aps) shows that a seasonal maximum of 6 g/m2 

is maintained. At the same time, the native gastropod increases slightly in biomass, perhaps 

because competition for food is offset by decreased predation pressure due to an alternate food

source for predators. Inspection of the parameter files for these two gastropods shows that the 

119 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

consumption rate for Potamopyrgus is three times that of the native gastropod and the egestion 

coefficient is half again as great. 

Periphyton, Di (g/sq.m)

Stigeoclonium, (g/sq.m)

Fontinalis (g/sq.m)

 

Shiner (g/sq.m)

Stoneroller (g/sq.m)

Largemouth Bas (g/sq.m)

Largemouth Ba2 (g/sq.m)

Amphipod (g/sq.m)

Mayfly (Baetis (g/sq.m)

Gastropod g_m2 (g/sq.m)

Potamopyrgus (g/sq.m)

E. F POPLAR CREEK TN (PERTURBED) 1/20/2004 11:43:26 AM

(Epilimnion Segment)

1/15/1992 1/14/1993 1/14/1994 1/14/1995

g/sq.m 2.0

1.0

g/sq.m

6.0

5.0

4.0

3.0

2.0

1.0

0.0

Periphyton, Di (g/sq.m)

Stigeoclonium, (g/sq.m)

Fontinalis (g/sq.m)

 

Amphipod (g/sq.m)

Mayfly (Baetis (g/sq.m)

Gastropod g_m2 (g/sq.m)

Potamopyrgus (g/sq.m)

Shiner (g/sq.m)

Stoneroller (g/sq.m)

Largemouth Bas (g/sq.m)

Largemouth Ba2 (g/sq.m)

E. F POPLAR CREEK TN (PERTURBED) 1/20/2004 12:39:24 PM

(Epilimnion Segment)

1/25/1992 1/24/1993 1/24/1994 1/24/1995 1/24/1996 1/23/1997

g/sq.m

2.0

1.0

g/sq.m

6.0

5.0

4.0

3.0

2.0

1.0

0.0

The other changes are subtler and are best depicted with the difference graph, which 

shows that mayflies and stonerollers also decline. Amphipods increase, probably through 

increased production of detritus by Potamopyrgus due to their high defecation rate. Shiners, 

which feed on amphipods, increase even more—demonstrating an interesting cascade of 

productivity. Surprisingly, the periphytic greens (Stigeoclonum) actually increase, although at 

the expense of the diatoms. Fontinalis also increases significantly due to decreased shading by 

periphyton. A difference graph shows that periphytic chlorophyll is about 50% less except for 

brief periods of accelerated growth of diatoms. Thirty percent more ammonia is predicted during

the growing season with Potamopyrgus. This is quite similar to what was found in the field 

study by (Hall et al. 2003). A detailed comparison of plant rates and limitations for the

simulations, with and without Potamopyrgus, shows that diatoms have a higher overall 

productivity, which is offset by the higher grazing rates of Potamopyrgus. The higher 

120 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

productivity with Potamopyrgus is seen in the limitation plots to be a result of less light 

limitation; nutrient limitation is unchanged. 

Periphyton, Di

Stigeoclonium,

Fontinalis

Amphipod

Mayfly (Baetis

Gastropod g_m2

Shiner

Stoneroller

E. F POPLAR CREEK TN (Difference) 1/21/2004 8:51:47 AM

(Epilimnion Segment)

1/15/1992 7/15/1992 1/13/1993 7/14/1993 1/12/1994 7/13/1994 1/11/1995

% DIFFERENCE

400.0

350.0

300.0

250.0

200.0

150.0

100.0

50.0

0.0

-50.0

-100.0

Periphyton Chla

NH4

PO4

E. F POPLAR CREEK TN (Difference) 1/21/2004 9:00:11 AM

(Epilimnion Segment)

1/15/1992 7/15/1992 1/13/1993 7/14/1993 1/12/1994 7/13/1994 1/11/1995

% DIFFERENCE

400.0

350.0

300.0

250.0

200.0

150.0

100.0

50.0

0.0

-50.0

-100.0

121 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Periphytic Diatoms with Potamopyrgus

0.00%

50.00%

100.00%

150.00%

200.00%

250.00%

1/2/19923/2/19925/2/19927/2/19929/2/1992

11/2/1992

1/2/19933/2/19935/2/19937/2/19939/2/1993

11/2/1993

Percent of Biomass

Washout2

Predation2

Non Tox Mort2

Excret2

Respir2

Photosyn2

Load2

Periphytic Diatoms without Potamopyrgus

0.00%

50.00%

100.00%

150.00%

200.00%

250.00%

1/2/19923/2/19925/2/19927/2/19929/2/1992

11/2/1992

1/2/19933/2/19935/2/19937/2/19939/2/1993

11/2/1993

Percent of biomass

Washout2

Predation2

Non Tox Mort2

Excret2

Respir2

Photosyn2

Load2

122 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Periphytic Diatom Limitations with Potamopyrgus

0

0.2

0.4

0.6

0.8

1

1.2

1/2/19923/2/19925/2/19927/2/19929/2/1992

11/2/1992

1/2/19933/2/19935/2/19937/2/19939/2/1993

11/2/1993

Lt_LIM2

N_LIM2

PO4_LIM2

CO2_LIM2

Temp_LIM2

Periphytic Diatom Limitations without Potamopyrgus

0

0.2

0.4

0.6

0.8

1

1.2

1/2/19923/2/19925/2/19927/2/19929/2/1992

11/2/1992

1/2/19933/2/19935/2/19937/2/19939/2/1993

11/2/1993

Lt_LIM2

N_LIM2

PO4_LIM2

CO2_LIM2

Temp_LIM2

123 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

5.3 Nutrient Enrichment 

AQUATOX has its roots in what was basically a eutrophication model, and it provides a 

reasonable representation of the effects of nutrient enrichment. It can be configured to depict a 

complex food web that is both phytoplankton- and detritus-based, with both game fish and 

bottom fish—often considered regulatory endpoints. It also reports phytoplankton both as 

biomass and as chlorophyll a, which is an important index of water quality. Dissolved oxygen is 

another important index that is computed. The Secchi depth, an indicator of clarity, also is 

estimated. 

For this example, we will use data from Onondaga Lake, New York (Onondaga.aps). 

The lake has been described very well in a book edited by Effler (1996). It has received 

municipal and industrial wastes for many years, and effluent from the municipal wastewater 

treatment plant accounts for nearly 20% of the annual inflow to the lake (Effler et al. 1996). Of 

particular concern are the combined sewer overflows (CSOs) that carry storm water and raw

sewage into tributary creeks about 50 times a year. In 1991 there were 45 CSOs discharging into 

Onondaga Creek, 19 into Harbor Brook, and 2 into Ley Creek. In a separate report, Park (U.S.

Environmental Protection Agency 2000) described three levels of analyses in validating Version 

1.66 with Onondaga Lake data. For purposes of this example, we will use the third-level 

implementation with detailed loadings for nutrients, a site-specific mixing depth, and 

compartments parameterized for cryptomonads and rotifers. 

Discharge data from the four gauged streams in the watershed (Onondaga Creek, 

Ninemile Creek, Ley Creek, and Harbor Brook, listed in order of importance) were downloaded 

from the U.S. Geological Survey Web site (see Table 1). Discharge from four ungauged streams 

was estimated, assuming that they had an aggregate flow rate that was 94% of the discharge of 

Ley Creek and Harbor Brook based on data in Effler (1996, p. 102). 

124 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Table 9-1. Input Data for Onondaga Lake Simulation 

Variable Source Format

Inflow www.waterdata.usgs.gov

(note: URLs may change) 

daily values for 4 gauged streams; 

extrapolated to ungauged streams 

Phosphorus, NPS 

METRO 

Effler 1996, calc. from p. 162 

Effler 1996, calc. from p. 159 

Effler 1996, p. 162 

mean annual conc., 7 tributaries, 1989-

1990; mult. by respective inflow 

mean loads, April-September, 1990 

NOx & NH3, NPS 

METRO 

Effler 1996, calc. from p. 138 

Effler 1996, calc. from p. 128 

Effler 1996, calc. from p. 138 

mean annual concentrations for 1989 

for 4 tributaries 

mean annual loads for 1989 

Org. matter, NPS 

METRO 

Effler 1996, calc. from p. 138 

Effler 1996, calc. from p. 128 

Effler 1996, calc. from p. 138 

back-calculated from organic-N 

mean annual loads for 1989 

Epilimnion 

temperature 

Effler 1996, p. 207 monthly interpolation from figure 

Hypolimnion

temperature 

Effler 1996, p. 247 monthly interpolation from figure 

Wind Effler 1996, p. 248 mean value est. from figure for 30 

years 

Solar radiation unpub. data, Lake George, 

N.Y. 

observed annual mean and range 

Initial conditions Effler 1996 obs. data and professional judgment 

The loadings were then computed using average concentrations for the respective 

streams, assuming a constant relationship between concentration and discharge. Different

average phosphate values were used for 1989 and 1990 for Onondaga and Ninemile Creeks,

which varied considerably between the two years due to combined sewer overflows. Also, the

concentration of ammonia in Ninemile Creek, which flows through soda ash waste beds, exhibits

an inverse relationship to flow rate according to Effler (1996, p. 131); therefore, his Equation

3.12 was used to compute the ammonia concentrations: 

Flow

T NH 0.73 [ − 3 ] = 0.20 +

where: 

 [T-NH3] = concentration of total ammonia (mgN/L), 

Flow = flow rate (m3

/s). 

125 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

The computations were performed in a spreadsheet by first converting the discharge data 

from cfs to m3

/d and m3/s then, for the nutrients, multiplying by the given concentrations to 

obtain mass per day (g/d) in successive columns. The loadings were imported into AQUATOX 

by clicking on Import in the Edit State Variable screen and choosing the appropriate commadelimited (csv) or database file. 

Given the readily available hydrologic data, both 1989 and 1990 were simulated with 

daily loadings. Examination of the loading plots confirms that the streams draining into 

Onondaga Lake are indeed “flashy” or subject to fast runoff with distinct peaks; the nutrient and 

organic matter loadings vary accordingly, except the ammonia loadings, which vary slightly 

from the other loadings due to the inverse flow relationship cited above. The data files and plots 

were prepared using Quattro Pro and Excel. 

126 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

 

 Onondaga Lake inflow Onondaga Lake ammonia loadings 

127 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Results from preliminary model runs indicated that some of the model assumptions and 

defaults were inappropriate for this application, and therefore needed to be modified. For 

instance, the model computes the depth of the well-mixed layer (epilimnion) using a robust 

regression equation with the fetch (distance across which the wind can blow) as the independent 

variable; this equation is based on a dataset for 167 lakes. In Onondaga Lake the computed

mixing depth of 15 m is twice as deep as observed (Effler, 1996). It appears that salinity from

industrial pollution in the lake is restricting the mixing depth. By back-calculating from the 

regression equation, a fetch (Length) of 0.779 km was found to give the observed well mixed 

depth (MaxZMix) of 7.75 m:

Length m

Length

MaxZMix Length

779

0.245

0.336

log(7.75) log( )

0.569 0.336

=

= +

= ⋅

The maximum length was then changed in the Site Characteristics screen.

128 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

A second modification was necessary because the observed spring algal bloom was not

predicted in initial runs. The spring bloom was reported to be due to cryptomonads, a flagellated 

algal group that was not in the default data set. Using values from Collins and Wlosinski (1983), 

a cryptomonad compartment was parameterized. The present version of AQUATOX can 

simulate four algal groups: diatoms, green algae, blue-greens, and “others.” Cryptomonads were 

129 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

classified as “others.” Rotifers are important grazers on cryptomonads, so they were 

parameterized based on the literature, especially one paper (Walz 1995).

130 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

In order to conduct “what if” exercises with the model, we will set the control options to

remove point source loadings for nutrients and detritus (click on Setup then Control Setup). 

This effectively turns off the contributions of the metropolitan sewage treatment plant. (Another 

option would be to turn off the non-point source loadings.) Save the study as “Onondaga no 

effluent.aps.” 

 Using chlorophyll a as an indication of water quality, and plotting the Exported results

with observed values, we can see the normal predicted responses and those predicted if sewage 

effluent were diverted. A cryptomonad bloom in spring of 1990 is not supported by the observed 

131 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

data. As expected, diversion is predicted to result in substantially lower chlorophyll and thus 

better water quality, especially after the first year.

Lake Onondaga Algae

0

20

40

60

80

100

120

140

160

1/1/19892/1/19893/1/19894/1/19895/1/19896/1/19897/1/19898/1/19899/1/1989

10/1/198911/1/198912/1/1989

1/1/19902/1/19903/1/19904/1/19905/1/19906/1/19907/1/19908/1/19909/1/1990

10/1/199011/1/199012/1/1990

Chloroph (ug/L)

Obs. Chl

Lake Onondaga Algae without Waste Water Treatment Plant Effluent

0

10

20

30

40

50

60

70

80

90

100

1/1/19892/1/19893/1/19894/1/19895/1/19896/1/19897/1/19898/1/19899/1/1989

10/1/198911/1/198912/1/1989

1/1/19902/1/19903/1/19904/1/19905/1/19906/1/19907/1/19908/1/19909/1/1990

10/1/199011/1/199012/1/1990

Chloroph (ug/L)

Obs. Chl

Other water quality parameters related to eutrophication and nutrients are also computed,

such as dissolved oxygen, Secchi depth, nitrate, ammonia, and phosphate. The user could 

perform similar analyses with these parameters as was just shown with chlorophyll a. This 

would give a more complete picture of the lake’s responses to proposed nutrient control 

scenarios, and whether water quality standards would be met. For example, the anoxia that 

occurs in the hypolimnion is predicted to go away with diversion of effluent. See an earlier 

validation document (U.S. Environmental Protection Agency 2000) for a more detailed 

discussion of the application to this highly eutrophied lake. 

132 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Chloroph (ug/L)

 

Cyclotella nan (mg/L)

Greens (mg/L)

Cryptomonad (mg/L)

Blue-greens (mg/L)

Oxygen (mg/L)

ONONDAGA LAKE, NY (PERTURBED) 1/4/2004 11:40:21 AM

(Hypolimnion Segment)

1/11/1989 7/12/1989 1/10/1990 7/11/1990 1/9/1991

ug/

L

50.0

45.0

40.0

35.0

30.0

25.0

20.0

15.0

10.0

5.0

0.0

mg/L

13.0

12.0

11.0

10.0

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

Chloroph (ug/L)

 

Cyclotella nan (mg/L)

Greens (mg/L)

Cryptomonad (mg/L)

Blue-greens (mg/L)

Oxygen (mg/L)

ONONDAGA LAKE, NY (CONTROL) 1/4/2004 11:39:32 AM

(Hypolimnion Segment)

1/11/1989 7/12/1989 1/10/1990 7/11/1990 1/9/1991

ug/

L

55.0

50.0

45.0

40.0

35.0

30.0

25.0

20.0

15.0

10.0

5.0

0.0

mg/L

14.0

13.0

12.0

11.0

10.0

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

5.4 Pesticides in a Pond Mesocosm 

As the only general fate and effects model of potentially toxic chemicals in aquatic

ecosystems, AQUATOX is well suited for risk assessment of organic toxicants. An earlier 

133 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

version was used in a comparative risk assessment of twenty-five pesticides. As an example, 

let’s consider the ecological risk assessment of the pesticide chlorpyrifos in an experimental 

pond enclosure. Load the study ChlorMed.aps and click on Chemical. You will see the Edit 

State Variable window. First, check to be sure that Gas-phase conc. is set to 0 and that the 

initial condition is 6.3 :g/L (we will start the simulation with an initial concentration and no

loadings). Then click on Edit Underlying Data to get the chemical parameters. Click on 

Toxicity Data or page down to see the ecotoxicology parameters. Click on Estimate

Elimination Rate Constants to be sure that estimates are up to date. See Volume 2: Technical

Documentation for a discussion of elimination rate constants (K2s). Then save and go back to

main menu to run the perturbed and control simulations. If you wish to evaluate biologic rates,

that should be specified in the Setup screen prior to running the simulations. 

The impacts are substantial when the Perturbed simulation is contrasted with the Control 

simulation. The difference graph shows the direct and indirect effects of the chlorpyrifos. It is 

obvious that a significant fraction of the invertebrates are killed immediately, only to recover 

later. Algae benefit immediately from decreased grazing pressure. Some of the effects are 

subtler, and interpretation requires additional information. Examination of the plots suggests that

134 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

the study is not well calibrated; for example, there are transient conditions in the Control 

simulation, such as the rapid buildup and decline of chironomids and its effect on the sunfish, 

that may reflect a poor choice of initial conditions. However, the comparison of Perturbed and 

Control results is still valid, and the user is able to isolate the effects of the pesticide because of

the use of the baseline simulation. 

Diatoms

Stigeoclonium,

Blue-greens

Chara

Chironomid

Daphnia

Shiner

Green Sunfish2

Green Sunfish,

Periphyton, Di

CHLORPYRIFOS 6 ug/L (Difference) 1/7/2004 10:47:51 AM

(Epilimnion Segment)

6/22/1986 7/22/1986 8/21/1986 9/20/1986

% DIFFERENCE

400.0

350.0

300.0

250.0

200.0

150.0

100.0

50.0

0.0

-50.0

-100.0

Periphyton, Di (g/sq.m)

Stigeoclonium, (g/sq.m)

Green Sunfish2 (g/sq.m)

Chironomid (g/sq.m)

Green Sunfish, (g/sq.m)

Shiner (g/sq.m)

 

Diatoms (mg/L)

Blue-greens (mg/L)

Daphnia (mg/L)

CHLORPYRIFOS 6 ug/L (PERTURBED) 1/7/2004 10:50:02 AM

(Epilimnion Segment)

6/23/1986 7/23/1986 8/22/1986 9/21/1986

g/sq.m

15.0

14.0

13.0

12.0

11.0

10.0

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

mg/L

5.0

4.0

3.0

2.0

1.0

0.0

135 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Periphyton, Di (g/sq.m)

Stigeoclonium, (g/sq.m)

Green Sunfish2 (g/sq.m)

Chironomid (g/sq.m)

Green Sunfish, (g/sq.m)

Shiner (g/sq.m)

 

Diatoms (mg/L)

Blue-greens (mg/L)

Daphnia (mg/L)

CHLORPYRIFOS 6 ug/L (CONTROL) 1/7/2004 10:47:51 AM

(Epilimnion Segment)

6/23/1986 7/23/1986 8/22/1986 9/21/1986

g/sq.m 14.0

13.0

12.0

11.0

10.0

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

mg/L

5.0

4.0

3.0

2.0

1.0

0.0

The rates were saved by choosing Setup from the main screen, Save Biologic Rates, and 

then Rate Specifications. The state variables and file type were chosen in the following screen.

The plots were produced using Excel, but any spreadsheet or graphing program could be used. 

With chlorpyrifos, the sunfish immediately suffer loss of food base; there is a slight 

increase in defecation that is paralleled by decreased consumption in the simulation, indicating 

initial chronic toxicity; but, more important, there is acute toxicity as the fish bioaccumulate 

more chlorpyrifos. Examination of the chemical record shows that sunfish (bluegill) have a 

laboratory LC50 of 2.4 :g/L As the bluegill begin to recover, they are still affected by chronic 

and acute toxicity. In particular, compare the percent defecation in the perturbed graph with the

percent in the control graph that follows. 

136 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Sunfish with Chlorpyrifos

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

6/17/1986

6/24/1986

7/1/1986

7/8/1986

7/15/1986

7/22/1986

7/29/1986

8/5/1986

8/12/1986

8/19/1986

8/26/1986

9/2/1986

9/9/1986

9/16/1986

Percent of Biomass

GameteLoss11

Non Tox Mort11

T1 Poisoned11

Predation11

Excretion11

Respiration11

Defecation11

Consumption11

Sunfish without Chlorpyrifos

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

6/17/1986

6/24/1986

7/1/1986

7/8/1986

7/15/1986

7/22/1986

7/29/1986

8/5/1986

8/12/1986

8/19/1986

8/26/1986

9/2/1986

9/9/1986

9/16/1986

Percent of Biomass

GameteLoss11

Non Tox Mort11

Predation11

Excretion11

Respiration11

Defecation11

Consumption11

Daphnia exhibit a similar response with almost complete and immediate mortality. Their 

LC50 is very low (0.17); however, there is not complete mortality, so they rebound with 

decreased predation pressure. 

137 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Daphnia with Chlorpyrifos

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

140.00%

160.00%

180.00%

6/17/1986

6/24/1986

7/1/1986

7/8/1986

7/15/1986

7/22/1986

7/29/1986

8/5/1986

8/12/1986

8/19/1986

8/26/1986

9/2/1986

9/9/1986

9/16/1986

Percent of Biomass

GameteLoss8

Non Tox Mort8

T1 Poisoned8

Predation8

Excretion8

Respiration8

Defecation8

Consumption8

The shiners suffer a short-lived loss of food base and low-level chronic effects, but 

there is no acute toxicity (the minnow LC50 is 203 ug/L), and they increase in the absence 

of predation pressure. 

Shiners with Chlorpyrifos

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

6/17/1986

6/24/1986

7/1/1986

7/8/1986

7/15/1986

7/22/1986

7/29/1986

8/5/1986

8/12/1986

8/19/1986

8/26/1986

9/2/1986

9/9/1986

9/16/1986

Percent of Biomass

GameteLoss10

Non Tox Mort10

T1 Poisoned10

Predation10

Excretion10

Respiration10

Defecation10

Consumption10

Chlorpyrifos is a bioaccumulative chemical. A plot of bioaccumulation factors indicates

that there is biomagnification up the food chain, from Daphnia to sunfish, and that steady state 

has not been achieved for the fish in the three-month simulation. 

138 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

BAF T1Diatoms

BAF T1Periphyton, Di

BAF T1Stigeoclonium,

BAF T1Blue-greens

BAF T1Chara

BAF T1Chironomid

BAF T1Daphnia

BAF T1Green Sunfish,

BAF T1Shiner

BAF T1Green Sunfish2

CHLORPYRIFOS 6 ug/L (PERTURBED) 1/7/2004 10:47:50 AM

(Epilimnion Segment)

6/22/1986 7/22/1986 8/21/1986 9/20/1986

logBAF 7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

5.5 Multiple Stressors Due To Agricultural Runoff 

In our example, we will model a run-of-the-river reservoir receiving extensive 

agricultural runoff and minimal municipal and industrial effluents (Park, 1999a). In the 1970s 

approximately 90% of the watershed of Coralville Lake, Iowa, was in agricultural land

(MacDonald and MacDonald, 1976). Water quality was so poor that the lake was referred to

locally as the “Dead Sea.” We will use the reservoir study Coralville.aps as a starting point. 

Open the file, then click on File and Save As, and name it AgricRes.aps so we don’t write over

the default reservoir study by mistake. Also, change the Study Name to “CORALVILLE LAKE, 

IA” (this will be the heading for the graphs). 

Controlling Nutrients and Sediments 

Because this reservoir receives a large quantity of suspended sediments, we need to load 

observed total suspended solids (TSS). Clay, silt, and sand are only available if the site is a 

stream. Suspended algae and detritus are subtracted from the observed TSS and the difference is 

considered suspended inorganic sediments. These are used in calculating the extinction 

coefficient and the Secchi depth. 

 Click on Add at the bottom of the state variable list and choose Tot. Susp. Solids. In the 

main screen we then see this as an additional state variable. 

139 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Double-click on Tot. Susp. Solids obtain the loadings screen. Then click on Use 

Dynamic Valuation and Import to load the file TSSCoral.csv. Change the initial condition of 

TSS to 20. Save the file (under the same name, AgricRes.aps). 

140 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

 Click on Initial Conditions to see the initial values for all the state variables. Dieldrin is 

0 because we will let the model compute the concentration in the reservoir. 

141 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

 Click on Setup and make sure that the simulation dates correspond to, or are less than,

the range of dates for TSS, 10/1/1973 to 9/30/1978. Be careful, if you enter “10/1/73" it will be 

interpreted as “10/01/2073.” Because observed TSS values are being used in lieu of dynamically 

simulated inorganic sediments, the capability of the model to repeat a time-series loading should 

not be used for TSS unless all other loadings, particularly inflow, are restricted to the same

range. Otherwise, the model will extrapolate the TSS beyond the observed dates and obtain

unacceptable estimates of suspended sediments. Note that the 5-year simulation may be quite 

lengthy on a slow machine; you may wish to decrease the period. 

First, we will investigate the impact of nutrient reduction, most likely through best

management practices, without any change in loadings of dieldrin or inorganic sediments. Click 

on Control Setup and uncheck all the Organic Toxicant controls, and check Set Multiply 

Loadings Factors to 1.0 for Nutrients and Detritus. Then, going back to the main screen,

double-click on ammonia, nitrate, phosphate, and suspended and dissolved detritus and enter a 

multiplicative loading of 0.5 on the Edit State Variable Data screen for each. In doing so, we 

have set the model so that nutrients and detritus will be halved in the perturbed run and kept 

unchanged for the control run. Dieldrin will be present in both simulations. This demonstrates

142 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

the power of the control settings to set up various pollution control scenarios. Save as

“AgricRes less nutr detr.aps.” 

Run the simulation without any additional changes, clicking on Perturbed and Control. 

Select Output, and view the Control graph. Note that detritivorous invertebrates, especially

Tubifex, have a high biomass, reflecting the large influx of detritus from upstream. Algal blooms

occur periodically, with maximum biomass of about 35 mg/L. 

Bluegill (g/sq.m)

Shad (g/sq.m)

Buffalofish22 (g/sq.m)

Tubifex tubife (g/sq.m)

Largemouth Ba2 (g/sq.m)

Walleye (g/sq.m)

Chironomid (g/sq.m)

 

Cyclotella nan (mg/L)

Greens (mg/L)

Anabaena (mg/L)

Dinoflagellate (mg/L)

CORALVILLE LAKE, IA (CONTROL) 1/7/2004 4:23:14 PM

(Epilimnion Segment)

10/15/1974 10/15/1975 10/14/1976 10/14/1977

g/sq.m

70.0

65.0

60.0

55.0

50.0

45.0

40.0

35.0

30.0

25.0

20.0

15.0

10.0

5.0

0.0

mg/L

34.0

32.0

30.0

28.0

26.0

24.0

22.0

20.0

18.0

16.0

14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

If we plot Secchi depth, we see that it varies considerably. By tabulating and exporting to 

Excel (click on the Control Simulation tab in Output, display Secchi d, then Save Table to 

143 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Excel) we can obtain statistics. In the simulation, Secchi depth has a maximum of 1.5, minimum 

of 0.08, and mean of 0.32 m. 

Secchi d (m)

CORALVILLE LAKE, IA (CONTROL) 1/7/2004 5:31:36 PM

(Epilimnion Segment)

10/14/1974 4/14/1975 10/13/1975 4/12/1976 10/11/1976 4/11/1977 10/10/1977

m 1.0

Now view the Perturbed graph. There is an overall similarity with the Control graph,

but several algal blooms are do not occur. Furthermore, there is a decline in detritivore biomass. 

Bluegill (g/sq.m)

Shad (g/sq.m)

Buffalofish22 (g/sq.m)

Tubifex tubife (g/sq.m)

Largemouth Ba2 (g/sq.m)

Walleye (g/sq.m)

Chironomid (g/sq.m)

 

Cyclotella nan (mg/L)

Greens (mg/L)

Anabaena (mg/L)

Dinoflagellate (mg/L)

Daphnia (mg/L)

Rotifer, Brach (mg/L)

CORALVILLE LAKE, IA (PERTURBED) 1/7/2004 5:30:40 PM

(Epilimnion Segment)

10/15/1974 10/15/1975 10/14/1976 10/14/1977

g/sq.m

50.0

45.0

40.0

35.0

30.0

25.0

20.0

15.0

10.0

5.0

0.0

mg/L

34.0

32.0

30.0

28.0

26.0

24.0

22.0

20.0

18.0

16.0

14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

A better way to portray the changes is by plotting a Difference graph. Because we have 

set the nutrient and organic loadings in the perturbed simulation to half the normal values, a 

positive percent difference means an increase in biomass with decreasing nutrient and organic 

loadings. (Remember that the Difference graph plots the percent difference of Perturbed minus

Control.) We also will plot bottom fish (buffalofish), which were so abundant in Coralville 

Reservoir that they supported a commercial fishery in the early 1970s. Based on this graph and 

examination of predicted rates for the invertebrates and fish, which were saved and plotted in 

Excel (not shown), we observe that invertebrate detritivores (Tubifex) declined due to decreased 

detritus loadings; this caused a decline in buffalofish. The blue-green Anabaena, another 

indicator of poor water quality, also declined. Caution should be exercised in interpreting 

difference graphs; these are plotted as percent changes, and small absolute differences are

144 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

magnified. For example, due to the toxicity of dieldrin, bass exhibit very low biomass values 

(<0.1 g/m2

), even in the control. This can be seen by plotting just the fish in the control 

simulation. 

Cyclotella nan

Greens

Anabaena

Dinoflagellate

Myriophyllum

Chironomid

Tubifex tubife

Daphnia

Rotifer, Brach

Chaoborus

Bluegill

Shad

Buffalofish

Buffalofish22

Largemouth Bas

Largemouth Ba2

Walleye

CORALVILLE LAKE, IA (Difference) 1/7/2004 5:30:40 PM

(Epilimnion Segment)

10/15/1974 4/15/1975 10/14/1975 4/13/1976 10/12/1976 4/12/1977 10/11/1977

% DI

F

F

E

R

ENC

E

400.0

350.0

300.0

250.0

200.0

150.0

100.0

50.0

0.0

-50.0

-100.0

Bluegill (g/sq.m)

Shad (g/sq.m)

Buffalofish (g/sq.m)

Buffalofish22 (g/sq.m)

Largemouth Bas (g/sq.m)

Largemouth Ba2 (g/sq.m)

Walleye (g/sq.m)

CORALVILLE LAKE, IA (CONTROL) 1/7/2004 5:58:10 PM

(Epilimnion Segment)

10/13/1974 10/13/1975 10/12/1976 10/12/1977

g/sq.

m

28.0

26.0

24.0

22.0

20.0

18.0

16.0

14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

If we plot the difference graph for the key environmental indicators, oxygen, Secchi 

depth, and chlorophyll, we see that halving the nutrient and detrital loadings improves the water 

quality as indicated by decreased chlorophyll levels and increased Secchi depths during times of 

algal blooms. Because the reservoir is shallow and seldom stratified, oxygen levels are not good

indicators of water quality in this system; actually, supersaturation of oxygen is predicted during 

blooms. 

145 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Oxygen

Secchi d

Chloroph

CORALVILLE LAKE, IA (Difference) 1/7/2004 6:04:07 PM

(Epilimnion Segment)

10/14/1974 4/14/1975 10/13/1975 4/12/1976 10/11/1976 4/11/1977 10/10/1977

% DIFFERENCE

100.0

80.0

60.0

40.0

20.0

0.0

-20.0

-40.0

-60.0

-80.0

-100.0

We have seen the effects of halving the nutrient and detritus loadings; let us now 

investigate the effects of suspended sediments. In this run-of-the-river reservoir, most of the 

suspended solids are silt and clay, and most are from upstream. In the event that best

management practices (BMPs) were to halve the TSS as well as the other pollutants, what would 

be the impacts on the Coralville ecosystem? This is easily analyzed with AQUATOX. In the

main window, double-click on Tot. Susp. Solids and set the Multiply loading to 0.5. Save the 

modified study as “AgricRes less nutr detr TSS.aps.” Then click Perturbed (but do not run 

Control) to obtain a simulation in which all the loadings are halved. 

 Click Output and plot Secchi depth, chlorophyll a, and oxygen in the Difference graph. 

By decreasing TSS, and hence inorganic sediments, turbidity decreases, and Secchi depth 

increases considerably. Baseline chlorophyll a increases marginally; blooms appear to be 

short-lived, followed by even greater declines in chlorophyll. Phytoplankton are not as severely 

light limited in the simulation. In turn, during blooms phosphate is quickly depleted—almost 

certainly becoming limiting for the phytoplankton. 

Oxygen

Secchi d

Chloroph

CORALVILLE LAKE, IA (Difference) 1/7/2004 9:08:02 PM

(Epilimnion Segment)

10/14/1974 4/14/1975 10/13/1975 4/12/1976 10/11/1976 4/11/1977 10/10/1977

% DIFFERENCE

200.0

180.0

160.0

140.0

120.0

100.0

80.0

60.0

40.0

20.0

0.0

-20.0

-40.0

-60.0

-80.0

-100.0

146 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

The perturbed graph demonstrates the changes in biomass; Tubifex reaches a maximum 

of 14 g/m

2

, and Anabaena reaches a maximum of 17 g/m

2

. Compare those with values of 34 and 

70 g/m2

 in the control. 

Bluegill (g/sq.m)

Shad (g/sq.m)

Buffalofish22 (g/sq.m)

Tubifex tubife (g/sq.m)

Largemouth Ba2 (g/sq.m)

Walleye (g/sq.m)

Chironomid (g/sq.m)

 

Cyclotella nan (mg/L)

Greens (mg/L)

Anabaena (mg/L)

Dinoflagellate (mg/L)

Daphnia (mg/L)

Rotifer, Brach (mg/L)

CORALVILLE LAKE, IA (PERTURBED) 1/7/2004 9:07:37 PM

(Epilimnion Segment)

10/15/1974 10/15/1975 10/14/1976 10/14/1977

g/sq.m

15.0

14.0

13.0

12.0

11.0

10.0

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

mg/L

22.0

20.0

18.0

16.0

14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

Controlling Pesticides 

Next, we will examine the effects of the dieldrin independent of the nutrients, detritus, and 

TSS. Similar to the example of esfenvalerate in the pond, we will use the perturbed run to

simulate the toxicant and the control run without the toxicant. Therefore, open AgricRes.aps, 

open the Setup window, and choose Control Setup. Now set the remaining options back to 

their original state, with all the Organic Toxicant choices checked, and the Nutrient and

Detritus choices unchecked. 

147 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

 Click Perturbed and Control to run the simulations. The perturbed graph is the

equivalent of the control graph seen earlier with the effects of dieldrin on selected state variables.

The control graph shows the seasonal patterns in biomass in this highly productive reservoir 

without dieldrin. Note that forage fish (shad) reach a maximum of 42 g/m2

, and bass slowly 

increase to a maximum of 7.8 g/m

2

, while buffalofish gradually decline. 

Bluegill (g/sq.m)

Shad (g/sq.m)

Buffalofish22 (g/sq.m)

Tubifex tubife (g/sq.m)

Largemouth Ba2 (g/sq.m)

Walleye (g/sq.m)

Chironomid (g/sq.m)

 

Cyclotella nan (mg/L)

Greens (mg/L)

Anabaena (mg/L)

Dinoflagellate (mg/L)

CORALVILLE LAKE, IA (CONTROL) 1/7/2004 10:34:11 PM

(Epilimnion Segment)

10/15/1974 10/15/1975 10/14/1976 10/14/1977

g/sq.m

40.0

35.0

30.0

25.0

20.0

15.0

10.0

5.0

0.0

mg/L

32.0

30.0

28.0

26.0

24.0

22.0

20.0

18.0

16.0

14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

The differences between the perturbed and control graphs are emphasized in the

difference graph. Negative values indicate relatively low biomass values in the perturbed 

simulation (in other words, in the presence of dieldrin). The decline of all fish except the hardy

buffalofish is easily seen; first shad, then YOY bass, walleye, and adult bass decline in that

order. The tolerant chironomids and tubificids benefit from the decreased predation and exhibit 

positive values. 

148 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Cyclotella nan

Greens

Anabaena

Dinoflagellate

Myriophyllum

Chironomid

Tubifex tubife

Daphnia

Rotifer, Brach

Chaoborus

Bluegill

Shad

Buffalofish

Buffalofish22

Largemouth Bas

Largemouth Ba2

Walleye

CORALVILLE LAKE, IA (Difference) 1/7/2004 10:34:11 PM

(Epilimnion Segment)

10/15/1974 4/15/1975 10/14/1975 4/13/1976 10/12/1976 4/12/1977 10/11/1977

% DIFFERENCE

400.0

350.0

300.0

250.0

200.0

150.0

100.0

50.0

0.0

-50.0

-100.0

From these results, we postulate that the decline in fish is a combination of direct and 

indirect effects of dieldrin. We can examine the rates for largemouth bass by clicking on Setup

in the main screen and Save Biologic Rates and Rate Specifications, then choosing Lg g fish

prior to running the model. The rates will be saved, with Excel format as the default, in the

Output subdirectory. The rates then can be plotted. In this example, consumption is very low 

due to chronic toxicity and loss of forage base. Acute toxicity does not seem to be a factor; the 

LC50 for bass is 3.5 ug/L, and the maximum concentration of dieldrin in the dissolved phase is

0.016 ug/L during the period of the simulation. Defecation increases due to the modeled effect 

of chronic toxicity on assimilation. This illustrates the use of biologic rates for analyzing cause 

and effect relationships. 

Bass with Dieldrin

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

10/16/197412/16/1974

2/16/19754/16/19756/16/19758/16/1975

10/16/197512/16/1975

2/16/19764/16/19766/16/19768/16/1976

10/16/197612/16/1976

2/16/19774/16/19776/16/19778/16/1977

Percent of Biomass

GameteLoss16

Non Tox Mort16

T1 Poisoned16

Predation16

Excretion16

Respiration16

Defecation16

Consumption16

149 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

T1 H2O (ug/L)

 

Largemouth Ba2 (g/sq.m)

CORALVILLE LAKE, IA (PERTURBED) 1/7/2004 11:00:12 PM

(Epilimnion Segment)

10/16/1974 10/16/1975 10/15/1976 10/15/1977

ug/L 1.60E-02

1.50E-02

1.40E-02

1.30E-02

1.20E-02

1.10E-02

1.00E-02

9.00E-03

8.00E-03

7.00E-03

6.00E-03

5.00E-03

4.00E-03

3.00E-03

2.00E-03

1.00E-03

-6.94E-18

g/sq.m

0.8

0.75

0.7

0.65

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

Controlling All Pollutants 

Finally, we can examine the effects of decreasing all pollutants from agricultural runoff 

simultaneously in the perturbed simulation. Dieldrin was set to a minimal value (loading

multiplier of 0.001), and the nutrient, organic matter, and TSS multiplicative loading factors 

were set to one-half. As we have seen from the above applications, there are many complex 

interactions, and comparing the perturbed and control graphs is difficult. 

Bluegill (g/sq.m)

Shad (g/sq.m)

Buffalofish22 (g/sq.m)

Tubifex tubife (g/sq.m)

Largemouth Ba2 (g/sq.m)

Walleye (g/sq.m)

Chironomid (g/sq.m)

 

Cyclotella nan (mg/L)

Greens (mg/L)

Anabaena (mg/L)

Dinoflagellate (mg/L)

Daphnia (mg/L)

Rotifer, Brach (mg/L)

CORALVILLE LAKE, IA (PERTURBED) 1/8/2004 7:43:01 AM

(Epilimnion Segment)

10/15/1974 10/15/1975 10/14/1976 10/14/1977

g/sq.m

14.0

13.0

12.0

11.0

10.0

9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

mg/L

22.0

20.0

18.0

16.0

14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

However, the difference graph provides a direct comparison. Bluegill (diamonds) and 

bass (heavy light-green line) increase significantly in the absence of dieldrin. Tubificids (heavy 

orange line) and chironomids decline due to increased predation pressure. The buffalofish

(heavy dark-green line) exhibit a long-term decline due to loss of forage because of increased 

competition. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 5

Cyclotella nan

Greens

Anabaena

Dinoflagellate

Myriophyllum

Chironomid

Tubifex tubife

Daphnia

Rotifer, Brach

Chaoborus

Bluegill

Shad

Buffalofish

Buffalofish22

Largemouth Bas

Largemouth Ba2

Walleye

CORALVILLE LAKE, IA (Difference) 1/8/2004 7:43:01 AM

(Epilimnion Segment)

10/15/1974 4/15/1975 10/14/1975 4/13/1976 10/12/1976 4/12/1977 10/11/1977

% DIFFERENCE

400.0

350.0

300.0

250.0

200.0

150.0

100.0

50.0

0.0

-50.0

-100.0

To better determine the effects of decreasing all pollutants on water quality, we will again

plot several environmental indices in a difference graph. We see that chlorophyll a is generally 

lower, oxygen does not supersaturate during blooms, and Secchi depth is improved. 

Oxygen

Secchi d

Chloroph

CORALVILLE LAKE, IA (Difference) 1/8/2004 7:56:57 AM

(Epilimnion Segment)

10/14/1974 4/14/1975 10/13/1975 4/12/1976 10/11/1976 4/11/1977 10/10/1977

% DIFFERENCE

400.0

350.0

300.0

250.0

200.0

150.0

100.0

50.0

0.0

-50.0

-100.0

We could have performed a full factorial analysis to isolate the effects of individual 

pollutants. For example, we could have decreased phosphate loadings while holding all other 

pollutant loadings at the observed levels. However, that is beyond the scope of this tutorial. 

In conclusion, AQUATOX can be used to analyze complex relationships in impaired 

ecosystems and to suggest the relative importance of various causes of impairment. In this

example, dieldrin was shown to be a very important stressor, completely changing the fish 

community, even at sub-acute concentrations. The simulations suggest that external loadings of 

nutrients and organic matter are also important; and, based on the model, halving the loadings

could improve water quality significantly. Therefore, this ecosystem model has the potential not 

only to help identify stressors, but to assess possible environmental management scenarios as 

well. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

6 UNCERTAINTY ANALYSIS 

Until now we have dealt with deterministic simulations. However, there are numerous

sources of uncertainty and variability in natural and polluted aquatic systems. These can be

represented easily in AQUATOX (see Technical Documentation), although access to the 

additional analytical power is not obvious to the casual user. We will go back to

ESFenPond.aps for this example. Open and rename the study “ESFenPond U.aps.” Click on 

the Setup button and choose Uncertainty Setup. 

That will open a large window with several choices at the top. Because AQUATOX 

uses a Latin hypercube sampling algorithm, it requires far fewer iterations than brute-force 

Monte Carlo sampling. Therefore, the default number of iterations is 20. This is probably

adequate for an analysis involving a single variable; however, it should be increased as more 

variables are chosen for analysis (and could be decreased if you just wish to become familiar 

with the procedure). To replicate the sampled values in successive analyses, you should choose 

a non-random seed for the number generator and keep it the same. 

A tree structure allows the user to navigate easily through all variables subject to 

uncertainty analysis and to list those variables already chosen for analysis. We will choose 

Distributions by State Variable, Dissolved org. tox 1:[Esfenvalerate], Chemical Parameters, 

and T1: Mult. Point Source Load by. The latter will open a separate interactive window to aid 

in picking a distribution and choosing the parameters. Choose “Use a Distribution” to activate 

the window. 

A normal distribution is the default, with a standard deviation of 60% of the mean. The 

user can accept the default distribution parameters or change them. The graph will show the 

results of any changes. The mean values are derived from the underlying parameter sets, but 

altering them in the uncertainty screen will not change them in the database and the deterministic 

simulation. We will keep the normal distribution, but change the standard deviation to 0.5 to 

avoid significant truncation at 0 (which has the effect of biasing the distribution toward higher 

values). We can also plot the approximate cumulative distribution that is used in the Latin

hypercube sampling. Let’s use that distribution to vary the multiplicative factor for point-source 

loadings of esfenvalerate in water. For each iterative simulation, the model will sample one 

value from the distribution and use it as a multiplicative factor for all dynamic point-source 

loading values. 

 Click on OK, which will take you back to the tree structure. You should collapse the tree 

structure and choose Selected Distributions for Uncertainty Run to make sure that you are 

using only the distributions that you intend to use. Also, be sure that the button in the upper left

is checked to Run Uncertainty Analysis (that button is a convenient way to toggle between the

deterministic and uncertainty options without disturbing the individual distributions). Back on 

the main screen, we see that there is now a message in red in the upper right indicating the 

number of iterations chosen. That message only appears when the uncertainty analysis is

enabled. 

152 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

When you click on Perturbed or Control you will be asked to give the output file name

and location. The folder to Save in will be given as the Output folder, which is the default; you 

might wish to change that to the Studies folder or some other special folder. 

 

154 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

Because of the voluminous output, it will be split into two or more separate files using

the root name that you provide. Ordinarily, you will not have to concern yourself with the

supplemental files, which will be listed in subsequent Save operations. However, you can load a 

different database file to view a previous uncertainty analysis.

The model will perform a deterministic simulation first to provide a baseline. Then it 

will cycle through the uncertainty iterations, with a window to keep you informed of the

progress. The specifications for each successive simulation are saved in a text file so that one

can determine exactly what parameter values were used. A file labeled with a suffix of 

“_decline.CSV” saves the results for the biomass risk graph, discussed below. 

155 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

156 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

The results of the uncertainty analysis can be viewed by scrolling to the far right tab in

the Output, and clicking on Uncertainty Graph. If you have not run a simulation or if you 

wish to see the results of a different simulation, you may choose to View a Different Database; 

furthermore, with a large number of state variables in a study, you may have to load a different 

database (it will have the same name you gave the uncertainty output followed by “2”). Only 

one state variable is plotted at a time, with separate curves for mean, minimum, maximum, mean

- one standard deviation, mean + one standard deviation, and deterministic results. The

deterministic simulation will use the value(s) entered for the uncertainty simulations. In this

example, the concentration of esfenvalerate in the water is the default plot. You may choose to 

View a Different Variable, such as the mass of toxicant associated with shiners or the biomass 

of bass. These are the distributions of the results for a particular state variable and are not 

necessarily a reflection of the distribution of the sampled input variable. For example, the 

maximum loading of esfenvalerate would almost certainly result in the minimum biomass of the 

bass (the mean – standard deviation is negative in this example, and the minimum is 0). 

Plotting catfish, we see that they are much more tolerant of esfenvalerate, although their biomass 

in the pond declines under all scenarios. Unless you specify a seed value for the random number

generator and use the same number of iterations, you will get different results for these 

probabilistic simulations.

Mean

Minimum

Maximum

Mean - StDev

Mean + StDev

Deterministic

T1 H2O (ug/L)

1/8/2004 12:03:55 PM

5/9/1994 7/8/1994 9/6/1994 11/5/1994 1/4/1995 3/5/1995 5/4/1995

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

Mean

Minimum

Maximum

Mean - StDev

Mean + StDev

Deterministic

T1Shiner (ug/L)

1/8/2004 1:05:55 PM

5/9/1994 7/8/1994 9/6/1994 11/5/1994 1/4/1995 3/5/1995 5/4/1995

0.11

0.1

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.0

157 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

Mean

Minimum

Maximum

Mean - StDev

Mean + StDev

Deterministic

Largemouth Ba2 (g/sq.

1/8/2004 12:51:07 PM

5/9/1994 7/8/1994 9/6/1994 11/5/1994 1/4/1995 3/5/1995 5/4/1995

3.0

2.0

1.0

0.0

Mean

Minimum

Maximum

Mean - StDev

Mean + StDev

Deterministic

Catfish (g/sq.m)

1/8/2004 12:55:10 PM

5/9/1994 7/8/1994 9/6/1994 11/5/1994 1/4/1995 3/5/1995 5/4/1995

0.85

0.8

0.75

0.7

0.65

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

A better way to compare the relative responses of organisms is to View Biomass Risk 

Graph and designate the organisms you wish to compare. In this example, catfish and shiners

are the most tolerant to esfenvalerate; and, under at least one exposure scenario, they actually 

increase (a “negative decline”). Note that each point on the graph corresponds to an iteration; if 

the curves are not smooth, you may wish to increase the number of iterations to provide more 

control. See Volume 2: Technical Documentation for additional information. 

158 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

Bluegill

Shiner

Catfish

Largemouth Bas

Biomass Risk Graph

1/8/2004 1:14:19 PM

Percent Decline at Sim ulation End

-100 -50 0 50 100

Percent Probability 100.0

90.0

80.0

70.0

60.0

50.0

40.0

30.0

20.0

10.0

0.0

Similarly, we can vary the input values for other variables by sampling from the

appropriate distributions. For example, we have two values for the Henry’s Law constant for 

esfenvalerate: a measured value of 6.1E-8 and a calculated value of 3.0E-6 (ARS Pesticide

Property Database). Why not just use the measured value? Unfortunately, the constant is not 

easily measured, so the calculated value may have as much validity as the measured value.

Therefore, we can use a uniform distribution defined by the two values, with equal probability of 

any value over that range being chosen. Henry’s Law constant helps control the bioavailability

of organic toxicants, so the sensitivity to a range of possible values is of interest. Before running 

the simulation, though, let’s change the multiplicative loading for point sources back to 0.2, and 

click on Use a Point Estimate to remove that variable from the uncertainty analysis.

Furthermore, it is not necessary to use 20 iterations for a single uniform distribution, so change it 

to 10. 

The results of varying just the Henry’s Law constant for esfenvalerate are shown in the

Uncertainty Graph for bass. The spread of values, although very small, is due to the differences 

in bioavailability and therefore differences in amount of toxicity. Likewise, the spread of 

159 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

predicted esfenvalerate mass associated with shiners is small, suggesting that the model is not 

sensitive to the uncertain Henry’s Law constant values. 

Mean

Minimum

Maximum

Mean - StDev

Mean + StDev

Deterministic

Largemouth Ba2 (g/sq.

1/8/2004 1:21:39 PM

5/9/1994 7/8/1994 9/6/1994 11/5/1994 1/4/1995 3/5/1995 5/4/1995

0.08

0.075

0.07

0.065

0.06

0.055

0.05

0.045

0.04

0.035

0.03

0.025

0.02

0.015

0.01

Mean

Minimum

Maximum

Mean - StDev

Mean + StDev

Deterministic

T1Shiner (ug/L)

1/8/2004 1:09:02 PM

5/9/1994 7/8/1994 9/6/1994 11/5/1994 1/4/1995 3/5/1995 5/4/1995

0.06

0.055

0.05

0.045

0.04

0.035

0.03

0.025

0.02

0.015

0.01

0.005

0.0

In another example, we will vary a critical parameter for the periphytic greens (nominally

Stigeoclonum) to see how it affects the response of this group. The most likely maximum

photosynthetic rate is set at 2 g/g-d; however, there is considerable variation reported in the

literature (Collins and Wlosinski 1983). The extreme values reported are 0.56 and 4.1. We

could take these as the constraints for a triangular distribution, but that would mean throwing out 

the lowest and highest observed values because the constraints have zero probability. Therefore, 

we will extend the constraints by 10% of the observed values. 

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

The results of varying this one photosynthetic parameter indicate that the model is

sensitive to it. It has a large effect on the biomass of the dominant periphyton; and, evidently,

that in turn affects the fate of esfenvalerate to some small extent in the latter part of the

simulation. 

Mean

Minimum

Maximum

Mean - StDev

Mean + StDev

Deterministic

Stigeoclonium, (g/sq.

1/8/2004 2:09:01 PM

5/9/1994 7/8/1994 9/6/1994 11/5/1994 1/4/1995 3/5/1995 5/4/1995

35.0

30.0

25.0

20.0

15.0

10.0

5.0

0.0

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

Mean

Minimum

Maximum

Mean - StDev

Mean + StDev

Deterministic

T1 H2O (ug/L)

1/8/2004 2:11:22 PM

5/9/1994 7/8/1994 9/6/1994 11/5/1994 1/4/1995 3/5/1995 5/4/1995

4.0

3.0

2.0

1.0

0.0

In the final analysis, we will examine the effects of varying mean depth of water in the 

pond. A normal distribution is used with a mean of 1.2 m and a standard deviation of 0.4. Based 

on the log of the uncertainty analysis (ESFenPond U Depth.TXT), the minimum depth simulated 

was 0.018 m, and the maximum depth was 1.859 m. 

The periphyton biomass is sensitive to water depth. Periphyton are well adapted to 

shallow water; the maximum biomass is at the minimum depth. However, they are light-limited 

at the greater depths simulated in this turbid pond. 

Because there is an abrupt decline in Stigeoclonum biomass that affects the deterministic

simulation and one tail of the distribution but not the other, we should examine the saved rates to 

determine the cause of the decline. We see that on 1/1/1995 there was a sloughing event. A plot 

of the limitations to photosynthesis suggests that there wasn’t any sudden change in conditions 

that caused the sloughing; rather, it was probably the combination of changing temperature 

limitation coupled with severe light limitation (predicted ice-on occurred on 1/1/). 

Mean

Minimum

Maximum

Mean - StDev

Mean + StDev

Deterministic

Stigeoclonium, (g/sq.

1/9/2004 8:19:18 AM

5/9/1994 7/8/1994 9/6/1994 11/5/1994 1/4/1995 3/5/1995 5/4/1995

30.0

25.0

20.0

15.0

10.0

5.0

0.0

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AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 6

Stigoeclonum with Esfenvalerate

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

140.00%

160.00%

5/2/1994

5/16/19945/30/19946/13/19946/27/19947/11/19947/25/1994

8/8/1994

8/22/1994

9/5/1994

9/19/199410/3/1994

10/17/199410/31/199411/14/199411/28/199412/12/199412/26/1994

1/9/1995

1/23/1995

2/6/1995

2/20/1995

3/6/1995

3/20/1995

4/3/1995

4/17/1995

5/1/1995

Percent of Biomass

Washout2

Predation2

Non Tox Mort2

T1 Poisoned2

Excret2

Respir2

Photosyn2

Stigeoclonum Limitations to Photosynthesis

0

0.2

0.4

0.6

0.8

1

1.2

5/2/1994

5/16/19945/30/19946/13/19946/27/19947/11/19947/25/1994

8/8/1994

8/22/1994

9/5/1994

9/19/199410/3/1994

10/17/199410/31/199411/14/199411/28/199412/12/199412/26/1994

1/9/1995

1/23/1995

2/6/1995

2/20/1995

3/6/1995

3/20/1995

4/3/1995

4/17/1995

5/1/1995

Lt_LIM2

N_LIM2

PO4_LIM2

CO2_LIM2

Temp_LIM2

Chem_LIM2

sloughing

163 

AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 7

7 QUALITY ASSURANCE 

 

AQUATOX is designed to facilitate documentation of assumptions and data sources 

for specific applications and to archive results. Note fields are provided for the study and for 

each of the state-variable loading screens. These are intended to provide the user with a way 

to record an overview of the study and to describe sources and salient features of the loading 

data. Furthermore, almost every parameter has an associated comment field to document the 

source of the value used. These fields are not fully utilized in the example sets; but, as

additional data are incorporated, comments should be used liberally. 

A study, with all associated data and output, can be archived in a study file. Good

practice dictates that the version of AQUATOX used for the application should be saved as

well. In that way the study can be opened and results examined at any time; and, if 

necessary, the model can be re-run. The main screen indicates the dates and times that the 

perturbed and control simulations were run. If you make a change to a study, you may 

choose not to save the changed file. To minimize file size, do not save the output; this can be 

done by clicking on Study on the menu bar in the main screen and choosing Clear Results. 

The file will usually be much smaller, but you will have to re-run the simulation to see the 

results. 

AQUATOX versions are upwardly compatible within reason—but not necessarily

years later—so if you open an old study with a newer version of the model, the data structure 

will be updated. Usually this is automatic, but often the user will be informed of critical

steps in the upgrade, as shown in the sequence of information windows below, which take a 

study from Release 1 (File Version 1.69) to Release 2. Some of these steps may indicate 

changes that should be examined and perhaps changed in the updated study, such as 

initializing the fractions available for phosphate loadings. 

165 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 7

166 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 7

Of course, studies are not backward compatible. If an earlier version of the program

attempts to access a later version of a study, an error message will be displayed and the study 

will not be loaded. 

167 
AQUATOX (RELEASE 2) USER’S MANUAL CHAPTER 7

168 
AQUATOX (RELEASE 2) USER’S MANUAL REFERENCES

REFERENCES

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Hall, R. O., Jr., J. L. Tank, and M. F. Dybdahl. 2003. Exotic Snails Dominate Nitrogen and 

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Heidtke. 1996. Mechanistic Modeling of Water Quality in Onondaga Lake. Pages 

667-788 in S. W. Effler, ed. Limnological and Engineering Analysis of a Polluted 

Urban Lake. Springer, New York, NY. 

Hall, R. O., Jr., J. L. Tank, and M. F. Dybdahl. 2003. Exotic Snails Dominate Nitrogen and 

Carbon Cycling in a Highly Productive Stream. Frontiers in Ecology and the 

Environment 1: 407-411. 

U.S. Environmental Protection Agency. 2000. AQUATOX for Windows: A Modular Fate 

and Effects Model for Aquatic Ecosystems-Volume 3: Model Validation Reports, 

Washington, DC. 

Walz, N. 1995. Rotifer Populations in Plankton Communities: Energetics and Life History 

Strategies. Experientia 51: 437-453. 

172