CE5504 – Surface Water Quality Modeling

AQUATOX Assignment 1. TSS and Secchi Disk Transparency in Onondaga Lake

Objective. To develop an initial familiarization with the AQUATOX software while examining the relationship between external loads and lake water quality.

A. Study Setup

Bring up the AQUATOX software.

Select File and then New Simulation Wizard and then Next

Step 1. Simulation Type

Select Create Simulation from Scratch and then Next

Enter name, Onondaga TSS-SD

Select Lake and then Next

Step 2. Simulation Time Period

Enter Start Date 1-1-1996 [you need 1996, otherwise it makes it 2096]

Enter End Date 12-31-1996 and then Next

The year will eventually be student-specific.

Step 3. Nutrients

Enter “0” for all five chemicals and then Next.

Step 4. Detritus

Enter “0” for both categories and then Next.

Enter “0” for both categories and then Next.

Select Organic Matter, then “0”, “0” and “100”.

In order to eliminate detritus from the simulation.

Step 5. Plants

Leave the six Plants screens blank for this simulation.

Enter Next.

Step 6. Invertebrates

Leave the eight Invertebrates screens blank for this simulation.

Enter Next.

Step 7. Fish

Leave the two Fish screens blank for this simulation.

Enter Next.

Step 8. Site Characteristics

Enter Site Name, Onondaga Lake

Enter Site Length, 0.779 km. [we’ll discuss this in more detail in a later assignment]

Enter Surface Area, 12e+06 m2. [Effler, 1996, p. 26]

Enter Mean Depth, 10.9 m. [Effler, 1996, p. 26]

Enter Maximum Depth, 19.5 m. [Effler, 1996, p. 1]

Enter Mean Evaporation, 0 in. / year. [we’ll reconsider this in a later assignment]

Enter Latitude, 43 degrees. [we’ll discuss this in more detail in a later assignment]

Enter Next.

Step 9. Water Volume Data

Select Keep Volume Constant on first screen and then Next.

On second screen, left side, enter 1.308e+08 m3 and select Keep Constant at Initial Level.

On second screen, right side, enter 1.4e+06 m3 [based on 3.9 flushes/year; Effler 1996, p. 108] and select Keep Constant at Initial Level.

Discharge of Water is locked out for constant volume simulations.

Enter Next.

Step 10. Water Temperature

Select Use Mean and Annual Range and then Next.

Enter an Average Temperature of 20 °C and a range of 0 °C for both the epilimnion and hypolimnion. The system will not stratify. [The simulation is not sensitive to T]

Enter Next.

Step 11. Wind Loadings

Select Enter Constant Wind and then Next.

Enter an Initial Condition of 5 m/s.

Select Use Constant Loading and enter 5 m/s. [The simulation is not sensitive to light]

Enter Next.

Step 12. Light Loadings

Select Enter Constant Light and then Next.

Enter 250 ly/d. [The simulation is not sensitive to light]

Enter Next.

Step 13. ph of Water

Select Enter Constant pH and then Next.

Enter 7.5 pH. [The TSS simulation is not sensitive to pH]

Enter Next.

Step 14. Inorganic Solids

Select, No, Don’t Simulate TSS and then Next.

Step 15. Chemicals to Simulate

Leave the Chemicals screen blank for this simulation.

Enter Next.

Step 16. Inflow Loadings

Leave the Inflow Loadings screen blank for this simulation.

Enter Next.

Step 17. Direct Precipitation Loadings

Leave the Direct Precipitation Loadings screen blank for this simulation.

Enter Next.

Step 18. Point Source Loadings

Leave the Point Source Loadings screen blank for this simulation.

Enter Next.

Step 19. Nonpoint Source Loadings

Leave the Nonpoint Source Loadings screen blank for this simulation.

Enter Next.

This completes the Wizard.

B. Specifying the Simulation Parameters

Secchi Disk transparency, the depth (m) to which a white or black and white disk remains visible in the water provides a measure of light penetration or water clarity.

Light attenuates in water in the manner of a first order decay,

where I0 is incident light (Ly∙d-1), Iz is light at depth z (m) and kd is the vertical+ extinction or attenuation coefficient (m-1).

The overall extinction coefficient is made up of the extinction coefficient due to pure water (kw), the extinction coefficient due to (dissolved color, kcolor) and the partial extinction coefficients for phytoplankton (chlorophyll; aChl) and trypton (the non-algal portion of total suspended solids; atrypton),

AQUATOX, includes kw, treats color as a partial extinction coefficient based on dissolved organic matter (DOM, a state variable), accommodates species-specific partial extinction coefficients for phytoplankton, and splits trypton into organic and inorganic fractions. The organic fraction utilizes a partial extinction coefficient based on particulate detritus (POM, a state variable), and the inorganic fraction utilizes a partial extinction coefficient based on inorganic sediment (TSS, total suspended solids, less the algal and POM components).

We will simplify this treatment for the present assignment, where we do not simulate phytoplankton, DOM or POM, to include extinction due only to water, dissolved color and inorganic suspended solids.

We will incorporate attenuation due to color in the water extinction coefficient, kw, which may be user-specified in AQUATOX. Effler (1996) reports minimum values of kd of 0.42-0.51 m-1 for Onondaga Lake, suggesting a value of ~0.4 m-1 for a kw that includes extinction due both to water and dissolved color. This value can be entered by, selecting Site from the study screen and then Underlying Site Data on the Site: Onondaga Lake screen. The value 0.4 may then be entered for Extinct. Coeff Water on the Site Data screen.

The partial extinction for inorganic suspended sediment (= TSS – Chl – POM) is hard-wired in AQUATOX at 0.17 m-1∙g-1∙m3. Thus extinction due to inorganic suspended sediment increases with concentration (g∙m-3), e.g.

Finally, Secchi disk transparency (m) is computed as,

In this exercise, we will examine the behavior of SD in response to changes in the external loading of TSS. The AQUATOX software does not model TSS per se, i.e. a mass balance on loads, outflow and settling, but rather permits user-specification of in-lake concentration. We will circumvent this issue by ‘constructing’ a TSS state variable from one of the biological components.

On the AQUATOX: Study Information screen, select Add under State and Driving Variables in Study. Then scroll down and pick OtherAlg1 and then OK. On the Select Database Entry screen select Cryptomonas and then OK.

We have added a state variable and now need to configure it to behave like TSS. Double click on OtherAlg1 under State and Driving Variables in Study and then select Edit Underlying Data. Here, change the Plant name from Cryptomonas to TSS Surrogate. Then set the five coefficients from Max. Photosynthetic Rate through Exponential Mort. Coeff. to “0”, eliminating all growth and death kinetics … we have created an inanimate material. TSS will settle, however, so set the Sedimentation Rate to 1 m∙d-1 (Effler, 1996, p. 514) and the Exp. Sedimentation Coeff to “0”. The latter coefficient accounts for the effects of metabolic stress on phytoplankton settling velocities, a consideration which doesn’t apply here. Hit OK and then OK to return to the AQUATOX Study Information screen.

C. Evaluating Model Performance

Check 1. Under TSS Surrogate, set loads and initial conditions = 0. Under Site, set Extinct. Coeff Water = 0. This should yield a lake with TSS = 0 and a very large SD. On the AQUATOX Study Information screen, select Control to run the model and then Output to view the results. From the Output Window, select Control Graph, Change Variables and Use Two Axes. Then select TSS Surrogate for the Y1 axis and Secchi d for the Y2 axis and then OK. This shows the expected result. To confirm this finding, let’s look at some tabular data. Select Control Simulation, then Change Variables, add the TSS Surrogate and Secchi d to the list and select OK. This confirms our findings. Select Exit Output.

Check 2. Change the value for kw from 0 to 0.4 and re-run the control. Predict the impact on SD before examining the output. Does this make sense?

Check 3. Add some TSS now by specifying an initial condition of 1 mg/L and a sedimentation rate of ‘0’and re-running the control. The output will be easier to visualize here if you specify the plot ranges. Select Change Variables and Use Below Values for both parameters (with TSS = 0-2 and SD = 3-5). Explain, mechanistically, the behavior of TSS and SD. Change the TSS sedimentation rate to 1 m∙d-1 and run the model again. Is the result as expected?

Check 4. Add a TSS load of 1 mg∙L-1. How does this change the TSS response? Verify the SS TSS with a long hand calculation, i.e. solve,

for TSS at steady state and compare it with the model prediction.

Check 5. Examine model sensitivity to loads by doubling first the TSS load (note there is a special button to do this) then flow and determine that the model performs appropriately. If this doesn’t work out exactly right, check it with the SS solution.

Finally, let’s settle on an average in-lake TSS 5 mg/L as an initial condition and determine, by iteration, the TSS load required load to maintain that concentration. Is there another way (other than brute force iteration) to determine the required load? Check your result both ways and take one last look at the predicted Output for TSS and SD for constant loads.

D. Model Application

Our objective here is to develop an annual time series of loads for TSS and to apply those loads in examining changes in Secchi disk transparency over the annual cycle. To do this, we need to determine daily values for flow and TSS concentration for the input to Onondaga Lake.

A file is attached, detailing the sum of all tributary inputs for 1996. Import these data to the Inflow of Water screen, run the model and examine an Output plot of flow. Also take a look at the TSS and SD plots. Of course these are not complete, because the loading TSS varies with flow, so we must accommodate this.

Effler (1996) reports a relationship between TSS and flow of,

with TSS in mg∙L-1 and flow (Q) in m3∙s-1.

Use this to calculate the daily tributary TSS concentration and import it to AQUATOX. Run the model and examine Output for TSS and SD. Consider the implications and feedback effects associated with adding phytoplankton to the simulation.