United States
Environmental Protection
Agency
Office Of Water
(4503F)
EPA841-F-94-004
April 1994
Number 12
&EPA TMDL Case Study
Modeling the
Appoquinimink River
Key Feature:
Project Name:
Location:
Scope/Size:
Land Type:
Type of Activity:
Pollutants:
TMDL Development:
Data Sources:
Data Mechanisms:
Monitoring Plan:
Control Measures:
Application of WASP4 to support
TMDL development
Appoquinimink River
EPA Region Hi/New Castle County,
Delaware
River, watershed 30,200 acres
Flat plains
Agriculture, urban
Phosphorus (algae)
Phased, PS/NPS
Local, STORET
WASP4 model, DYNHYD5
submodel
Yes
NPDES permit, BMPs
* TMDL Site
FIGURE 1. Location of the Appoquinimink River
watershed
Summary: The Appoquinimink River watershed is located in
eastern Delaware (Figure 1). TMDL Case Study:
Appoquinimink River, Delaware (USEPA, 1993, Case Study
Number 9) describes the TMDL for phosphorus developed by
the Delaware Department of Natural Resources and
Environmental Control (DNREC) for the Appoquinimink
River using the phased approach to TMDL development. The
objectives of the TMDL included characterization of the nonpoint source nutrient loads and their impact on water quality
and description of further modeling studies necessary to refine the TMDL. DNREC used available ambient water quality
data and existing point and nonpoint source loading data to conduct the initial assessment and characterize the
Appoquinimink's water quality problems. In addition, the EUTRO4 version of EPA's Water Quality Analysis Simulation
Program (WASP4), a water quality model, was used to analyze the dissolved oxygen (DO) and nutrient economy of the
river. Phosphorus overenrichment was determined to be the ultimate cause of excursions of applicable DO criteria A
phosphorus TMDL of 18,947 lb/yr was calculated as the sum of the point source allocation (6 862 Ib/yr) and the
background/nonpoint source allocation (12,085 lb/yr). These allocations reflect a reasonable margin of safety and will
prevent further water quality degradation.
This case study describes the specific modeling efforts in more detail. The WASP4 model was used to predict the water
quality impacts of various point and nonpoint source loading scenarios. With the additional information about nonpoint
source loads collected as part of the initial phase of TMDL development, the modeling study found that even the most
aggressive pollution control scenariowhich consisted of total removal of point source loads, 50 percent removal of
nonpoint source phosphorus and nitrogen loads, and 50 percent removal of the oxygen demand (SOD), ammonia and
phosphorus flux of sediments-provided only a marginal difference in DO levels. These results indicated that the system
is driven by SOD. The TMDL has included a schedule for continued monitoring and modeling to address the SOD issue
Contacts Michael Morton, Tetra Tech, Inc., 10306 Eaton Place, Suite 340, Fairfax, VA 22030, Phone (7O3) 385-6000
Recycled/Recyclable
Printed with Soy/Canola Ink on paper that
contains at least 50% recycled fiber
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BACKGROUND
Editor's note: Tltis case study presents details about the
teclwical modeling aspects of TMDL Case Study:
Appoquinimink River, Delaware (USEPA, 1993, Case
Study Number 9) to provide an example of dynamic
water quality modeling for a river system in which
dissolved oxygen (DO) deficit is the primary water
quality problem.
The Resource
The Appoquinimink River watershed is located in the
flat coastal plain of eastern Delaware. The river's
headwaters and major tributaries drain agricultural lands
to feed Shallcross Lake, Silver Lake, Noxontown Pond,
and Wiggins Mill Pond (Figure 2). Below these lakes is
the tidal freshwater segment of the Appoquinimink,
which is bounded by the head of tide at Noxontown
Pond and Silver Lake (river mile 10.2) at the upstream
end and by Drawyer Creek's confluence with the
Appoquinimink River (river mile 5.0) at the downstream
end. The only point source within this reach, the
Middletown-Odessa-Townsend wastewater treatment
plant (MOT WWTP), discharges 0.5 million gallons per
day (mgd) at river mile 6.7. Salinity within the 5-mile
reach generally remains below 5 parts per thousand,
which, according to Delaware's water quality standards,
classifies the reach as freshwater (DNREC, 1990). For
freshwater systems, section 11.1 of the Standards
requkes a representative daily (24-hour) average DO
concentration of 5.5 milligrams per liter (mg/L) from
June through September and an instantaneous minimum
DO concentration of 4.0 mg/L.
The designated uses of the tidal freshwater portion of the
Appoquinimink are primary contact recreation;
secondary contact recreation; fish, aquatic life, and
wildlife;! industrial water supply; and agricultural water
supply. Recreational uses such as swimming have been
sharply curtailed because of water quality constraints,
especially the excessive algal growth and DO deficit that
have resulted from phosphorus loadings (Water
Resources Agency, 1986).
j
Although there are no numerical standards for nutrient
concentrations, the water quality standards do recognize
that nutrient overenrichment is a significant problem in
some of Delaware's surface'waters. For this reason, it
is DNREC's policy to minimize nutrient input to surface
waters from any controllable source, establishing the
types of, and need for, nutrient controls on a site-
specific basis.
DNREC chose the Appoquinimink River as the site of
this TMDL because it was identified as water quality-
limited and requiring a TMDL; a wastewater
management decision was pending at the MOT WWTP;
a single- point source made the TMDL relatively simple;
and information on nonpoint source loadings was
available.
Modeling Strategy
.
'
DNREC chose to use WASP4 because it is the most
complete EPA-supported water quality model in terms of
kinetics and processes. WASP4 is a detailed receiving
water quality model that allows users to interpret and
predict water quality responses to natural phenomena and
Kilometers
01234
..1M13I = Monitoring Station
FIGURE 2. Appoquinimink River WASP4 segmentation! and location of monitoring stations
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loan-made stresses for various pollution management
decisions. The main advantage of using WASP4 is its
ability to portray the relative spatial influences of each
major pollutant source on the receiving waters, as well
as a representation of the transport and water quality
kinetics phenomena (i.e., mixing and diel processes).
Because DNREC suspected that a nutrient and algal
problem was contributing to depressed DO levels, the
EUTRO4/WASP4 submodel for nutrient and
eutrophication analyses was applied. Initially, only
enough data were available to run steady-state
simulations of existing conditions in the Appoquinimink
for characterization purposes. Ultimately, DNREC
wanted to predict the effects of pollution reduction
controls on water quality, especially DO levels, to
facilitate TMDL development.
Data Issues
Ambient water quality data and existing point and
nonpoint source loading data were available to conduct
an initial assessment and characterization of water
quality problems in the Appoquinimink River. Most of
the ambient data came from intensive water quality
surveys that were conducted for New Castle County
from September through October 1990 to assess the
human health and environmental impacts that might be
caused by increasing the MOT WWTP discharge.
Ambient water quality monitoring program data from
1985 through 1990 were also available from EPA's
STORET data base to supplement the intensive survey
data. Table 1 of TMDL Case Study Number 9
summarizes the available water quality data. Monitored
parameters included DO, temperature, 5-day biological
oxygen demand (BOD5), ammonia nitrogen (NH3-N),
total Kjeldahl nitrogen (TKN), total phosphorus (TP),
soluble orthophosphorus (SOP), salinity, and pH. No
data on chlorophyll-a concentrations were available.
A diel DO profile was also developed at the river mile
6.4 station, just above the treatment plant discharge.
The diel DO data collected during October 1990
indicated violations of the daily average criterion of
5.5 mg/L. Periodic grab samples collected from 1985 to
1990 also indicated several violations of the minimum
criterion of 4.0 mg/L. To more completely characterize
the factors contributing to .these violations of the DO
standard using the WASP4 water quality model, DNREC
needed to monitor the Appoquinimink and its tributaries.
Monitoring began in November 1991 and is still under
way. The monitoring has included synoptic water
quality surveys of the tidal river and major tributaries;
measurement of tributary flows and nutrient
concentrations to estimate nutrient loads; analyses of
sediment nutrient content; and diel monitoring of DO,
temperature, and salinity at selected stations in the tidal
river for periods of several consecutive days. The
additional data have allowed DNREC to calibrate the
WASP4 model to a higher order of complexity, making
it more predictive. Previous modeling efforts that were
conducted at lower levels of complexity only mimicked
river responses.
The previous effort used the Level 3 order of complexity
within the EUTRO4/WASP4 model. This level
simulates DO processes using full linear DO balance
equations that include nitrification and the effects of
photosynthesis and respiration from given phytoplankton
levels. The variables simulated are ammonia nitrogen,
nitrate nitrogen, carbonaceous biochemical oxygen
demand (CBOD), dissolved oxygen, and organic
nitrogen. With the more sophisticated model, the effects
of combinations of various point and nonpoint source
reductions were predicted, providing valuable
information to decision makers who must ultimately
decide how the DO problem will be solved. Specifically,
intermediate eutrophication processes and benthic
reactions (nutrient enrichment, eutrophication, and DO
depletion) were simulated using the Level 6 order of
complexity. In addition to the system variables simu-
lated in Level 3, inorganic phosphorus, phytoplankton
carbon, and organic phosphorus are also simulated.
TMDL DEVELOPMENT
The Initial Modeling Effort
The EUTRO4 version of EPA's WASP4 water quality
model was used for the initial analysis of the DO and
nutrient economy of the Appoquinimink River to
determine the cause of the DO criteria violations. The
model was run to simulate DO and nutrient
concentrations in the river. The WASP4 model runs
were steady-state, tidally averaged simulations of a one-
dimensional channel to represent the tidal freshwater
portion of the Appoquinimink. Model simulations were
run using the Full Linear DO Balance (Level 3 order of
complexity), as defined in the WASP4 user's manual.
Key processes modeled included CBOD, nitrification,
reaeration, and sediment oxygen demand (SOD).
Although they were considered important, algal
photosynthesis and respiration rates were not modeled as
part of this initial effort because there were no
chlorophyll-a data. Instead, algal photosynthesis and
respiration rates were estimated using screening-level
analyses that involved evaluating available STORET
data.
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Point Sources
The dicl variation of DO concentrations that was noted
during an intensive water quality survey suggested that
phytoplankton productivity and respiration were
occurring at significant rates. The 24-hour average DO
concentration was 5.2 mg/L, with a maximum level of
6,1 mg/L and a minimum level of 4.2 mg/L (Ritter and
Levan, 1992). It was also suspected that the excessive
algal biomass production and subsequent die-off and
sedimentation of organic matter were contributing to
higher-than-normal SOD.
Phosphorus was determined to be the limiting factor in
phytoplankton growth. Because most of the phosphorus
from the wastewater treatment plant would be
bioavailable as SOP, likely causing eutrophic conditions
throughout the reach and possibly in downstream waters,
discharge limits were established. To take action and
prevent more frequent violations of the DO standard,
DNREC decided to use the phased approach to TMDL
development, establishing a TMDL that capped existing
phosphorus loads and called for additional monitoring
and modeling to determine whether the TMDL would
have to be refined.
The point source load limit was established by statistical
analysis of the effluent phosphorus load measurements.
The data were analyzed to define a monthly average load
limit at a 95 percent confidence level. The monthly
average phosphorus load was determined to be 14.57
pounds per day (Ib/day), of which the 95th percentile
value of a normal distribution is 18.8 Ib/day. This
translates into equivalent loads of 572 pounds per month
and 6,862 pounds per year (lb/yr). This allocation was
incorporated into the NPDES permit as final effluent
limits for the MOT WWTP. The permit goes into effect
May 9,1994.
Nonpoint Sources
Rural Clean Water Program studies conducted from
1980 through 1986 measured nonpoint source loading
rates of phosphorus and nitrogen hi the Appoquinimink's
Wiggins Mill subwatershed. Using a log-transformed
distribution of the seasonal data, a Monte Carlo
simulation program, PC-MC, was run to generate annual
loads by repeated random sampling of the seasonal
distributions. Monte Carlo simulation is a stochastic
modeling technique that involves the random selection of
sets of input data for use in repetitive model runs.
Monte Carlo simulation allows the modeler to obtain
statistical data without running the model continuously.
A total of 2,000 annual load simulations were run to
develop an entire distribution of annual loads based on
random sampling of the seasonal load distribution. The
median annual phosphorus load for the Wiggins Mill
sub-basin was determined to be 1,760 lb/yr. This value
was extrapolated to the watershed area upstream from
the tidal freshwater segment of the Appoquinimink by
multiplying by the ratio of watershed area (14,900 acres
upstream from the river reach/2,170 acres in the
Wiggins Mill sub-basin) to yield an annual nonpoint
source phosphorus load limit of 12,085 lb/yr.
DNREC decided that although these readily available
estimates were adequate to use for the first phase of the
Appoquiiiimink TMDL, they were not appropriate to use
for subsequent refinements of the TMDL. Land use
patterns and the widespread implementation of BMPs
since the last studies were conducted in 1986 have
certainly altered nonpoint source loading rates. The
validity of extrapolating the Wiggins Mill loading rates
to the rest of the Appoquinimink watershed is also
questionable because of differences among the
subwatersheds. Additional studies to characterize
nonpoint source nutrient loads to the reach and to assess
the effect of Noxontown Pond, Silver Lake, and
Shallcross Lake on the nonpoint source loads actually
delivered; to the reach were therefore proposed as part of
the initial TMDL analyses.
The studies to estimate existing nonpoint source loads
for the satire Appoquinimink watershed were completed
by the erid of 1992. DNREC monitored the outflows of
Silver Lsike and Noxontown Lake to determine actual
nonpoint source loads to the upper boundary of the tidal
river. These studies are documented in Nutrient Budgets
for the Appoquinimink Watershed (Ritter and Levan,
1992), which outlines the nonpoint source nitrogen and
phosphorus budgets that were developed using the unit
loading rate method and also details land uses that were
determined from 1989 aerial photographs, national
wetlands inventory maps, and parcel base maps.
I
Ritter ani Levan estimated nitrogen and phosphorus
loading rates for different land uses in pounds per acre
per year (Ib/ac/yr) for dry, normal, and wet conditions.
These loading rates were then multiplied by the acreage
in the six subwatersheds of the Appoquinimink River
basin to obtain annual loads in pounds. A summary of
nitrogen and phosphorus loads from each of the
subwatersheds for a normal year is presented in Table 1.
The loading rates determined by this study were applied
in the water quality modeling study of the
Appoquinimink River to better define the impact of
nonpoint source nutrient loads on the river's water
quality and to provide a basis for refining the TMDL.
The Second Modeling Effort
DNREC used the preliminary WASP4 model as the basis
for developing a more complex model of the
Appoquinirnink River that includes tidal hydrodynamics
(circulation patterns of water in the estuary caused by
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TABLE 1. Nitrogen and phosphorus loads during a
normal year from subwatersheds of the
Appoquinimink River (Ritter and Levan, 1992)
Subwatershed
Appoquinimink I
Appoquinimink n
Drawyer Creek
Silver Lake
Noxontown Pond
Hangman's Run
Nitrogen Load
0b/yr)
22,987
78,456
219,489
102,386
23,487
49,342
Phosphorus
Load flb/yr)
2,005
786
5,316
2,541
3,104
1,226
tides), phytoplankton dynamics (changes in
phytoplankton population and phytoplankton interaction
with nutrients), and benthic nutrient fluxes (the exchange
of nutrients between bottom sediments and the water
column). Additional field data collected during the
period from March 1992 to January 1993 allowed for
better determination of river geometry, boundary
conditions, nonpoint source loads, and various kinetic
rates than in previous models.
Calibrating and Validating the Model
Model calibration is the first stage of testing and tuning
a model to a set of field data not used in the original
model construction (Thomann and Mueller, 1987). This
tuning should include a consistent and rational set of
theoretically defensible parameters and inputs.
Calibration allows the modeler to better estimate
appropriate transport and reaction rate coefficients in the
model. Model calibration is not a curve-fitting exercise;
it should reflect wherever possible more fundamental
theoretical constructs and parameters. Models that have
widely varying coefficients to merely "fit" the observed
data are not considered to be calibrated.
Once the model is calibrated to one set of data, another
set of external data is tested to further examine model
validity. This subsequent testing is commonly known as
model validation. As in this case, a validated model is
often used to forecast expected water quality for a
variety of potential scenarios.
The Hydrodynamic Model
The Appoquinimink River was segmented into 27 nodes
and 26 connecting channels (Figure 2). For each
segment the surface area and average depth at mean sea
level were determined for input to the DYNHYD
hydrodynamic submodel. For each channel, the channel
depth, channel length, cross-sectional area, downstream
(positive flow) direction, and Mannings n roughness
coefficient were estimated. The channel geometries
(depth and width) were estimated from data measured by
the USGS at 10 stations along the Appoquinimink River.
The geometries for segments between the measured
cross-sections were estimated by interpolation.
Boundary tides at the mouth of the Appoquinimink River
were estimated from the National Oceanic and
Atmospheric Administration (NOAA) tide predictions
using Reedy Point as the primary station. The times and
heights of the high and low tides were then corrected to
Liston Point, which is about 3 miles south of the mouth
of the Appoquinimink River. The high and low tides
over the period August 11 to October 19, 1991, were
used as the boundary-forcing conditions in the model.
Tributary flows in the model were set to constant values
for the following locations for the August-October
period:
Noxontown Pond 4.0 cfs
Silver Lake 4.0 cfs
Drawyer Creek 13.5 cfs
model segment 26
model segment 27
model segment 11
These flows were estimated based on the drainage area
of each subwatershed and flows measured by a,nearby
USGS gage on Morgan Creek near Kennedyville,
Maryland. Hydrodynamic transport was calibrated to
chloride concentrations measured in the river. Because
chlorides are nonreactive chemicals, they can act as a
tracer. Thus, chloride concentration values plotted along
the river are longitudinally correct. The model
dispersion coefficients, roughness coefficients, and
upstream inflow rates were varied until the model results
matched the measured chlorides. Good agreement-for
the August-October period was attained using the above
stream flows and dispersion coefficients of 15.0 nrVsec
at the mouth (segment 1), 10.0 m2/sec for segments 2 to
13, 5.0 nvYsec for segments 14 to 20, and 1.0 nrVsec for
segments 21 to 27 (Figure 3). To eliminate excessive
tide-induced oscillation of chlorides at the boundary
segment (segment 1), it was necessary to make this
segment very large.
The transport and dispersion were validated to the May-
July 1991 chloride data set, and good agreement was
also obtained using the same dispersion coefficients used
for the August-October 1991 period. The upstream
boundary flows and the flow at Drawyer Creek were the
same as for the calibration period shown above. The
validation results show very good model agreement with
the observed chloride data (Figure 4).
The Water Quality Model
WASP4/EUTRO4 was linked to DYNHYD to provide a
dynamic water quality and hydrodynamic model of the
Appoquinimink River. Dynamic simulations provide a
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Lite Sttastr Chlorides Concentrations (Aug - Oct 1991)
FIGURE 3. Appoquinimink River WASP4 chlorides calibration
(August-October 1991)
Fitter and Levan (1992). For the previous model
application, nonpoint source loads were
extrapolated from the 1986 Rural Clean Water
Program report for the Wiggins Mill watershed;
the nonpoint source loads from Ritter and Levan
(1992) are lower by a factor of about 8 (Table 3).
The upstream and downstream boundary
conditions for the eight system variables were
teiken from DNREC's preliminary version of the
VfASP4 model. These variables represent those
simulated by the model. The boundary conditions
are presented in Table 4. These concentrations
must be specified for 'each water quality
constituent at each boundary. A boundary is a
tributary inflow, a downstream outflow, or an
open water end of the model network across
which dispersive mixing can occur. The boundary
conditions presented in Table 4 are the average of
thle August, September, and October 1991 data.
more realistic representation of the waterbody than
steady-state simulations where flows and loadings remain
constant over time. WASP4 allowed the user to model
the dynamic tidal action in the river. One point source
(the MOT WWTP) was located hi model segment 16.
WASP4 was able to account for discharge from the
MOT WWTP that traveled upstream, as weU as
downstream. The loadings for the MOT WWTP are
presented in Table 2. After the WASP4 source code
was modified to accommodate additional nonpoint source
segments, the nonpoint source loads were distributed
over the 27 model segments based on the average annual
loading rates for dry weather conditions determined by
TABLE 2. Point source loadings from the
Middletown-Odessa-Townsend wastewater treatment
plant
Parameter
Flow
CBOD
Organic nitrogen
Ammonia nitrogen
Nitrate nitrogen
Total phosphorus
Loading, mg/L (kg/day)
O.SOO.mgd
19.50 (36.9)
5:00 (9.5)
10.00 (18.9)
0.00 (0.0)
3.38 (6.4)
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nrlr Suier Chlorides concentrations ttlay - Jul 1991)
FIGURE 4. Appoquinimink River WASP4 chlorides validation
(May-July 1991)
No field measurements of SOD were made in the
Appoquinimink River. SOD rates were set to
vailues between 0.500 and 1.000 gO2/m2/day for
segments upstream of the MOT WWTP and 1.50
g()2/m2/day for segments downstream of the plant
based on typical ranges for estuarine muds found
in the literature (USEPA, 1985). The SOD rates
were adjusted within this range until the model
wiis calibrated. .Some field data on the chemical
composition (i.e., carbon, phosphorus, and
niitrogen) of the river sediment were available, but
no information was available on benthic flux of
ammonia nitrogen and phosphorus, so they were
set based on stoichiometry and the classical ratios
of O:C:N:P (109:41:7.2:1) developed by A.C.
Resdfield (Stumm and Morgan, 1981).
Oxygen-deficient waters are replenished via
atmospheric reaeration. The reaeration rate
coefficient is a function of the average water
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TABLE 3. Nonpoint source loading rates (kg/day)
used in the first and second WASP4 model
applications on the Appoquinimink River
Parameter
Total phosphorus
Orthophosphate
Total nitrogen
Initial effort
(RCWP, 1986)
16.12
6.48
615.3
Second effort
(Ritter and
Levan, 1992)
2.234
0.893
82.29
velocity, depth, wind, and temperature. In the
EUTRO4/WASP4 model, the user may specify a single
reaeration rate constant or spatially variable reaeration
rate constants, or may allow the model to calculate
variable reaeration rates based on flow or wind. In this
application, the reaeration rate was calculated by the
model. EUTRO4/WASP4 will use either the flow-
induced rate or the wind-induced rate, whichever is
larger. EUTRO4/WASP4 calculates flow-induced
reaeration based on the Covar method and wind-induced
reaeration based on O'Connor (Ambrose et al, 1993).
Photosynthetically active radiation (PAR) measurements
were made at a number of locations in the river from
May 1991 to June 1992. The PAR data, along with
measured chlorophyll-a concentrations, were analyzed to
determine the background light extinction coefficients for
the model segments. Regression analysis on the PAR
versus depth data yields the total extinction rate (IQ.
The portion of the total extinction attributed to
chlorophyll-a can be determined by the following
formula (Thomann and Mueller, 1987):
K^o = 0.0088P + 0.054P20
where P is the chlorophyll-a concentration in figJL. The
background extinction coefficient (K^) can then be
determined by:
These background extinction coefficients were
incorporated into the WASP4 model.
The model calibration results, averaged over the period
from August to October 1991, are presented in Figure 5.
The daily average DO is slightly under predicted by the
model, although most of the values fall within the range
of the standard deviations of the observed data. The
model chlorophyll-a is consistently lower than the
observed chlorophyll-a data. Two possible explanations
for this discrepancy are (1) the model chlorophyll-a is
depth-averaged over the water column while the
chlorophyll-a samples were collected near the surface
where the concentration tends to be higher or (2) there
may be a consistent bias in the laboratory analyses of the
chlorophyll-a samples. Nitrogen and phosphorus
concentrations predicted by the model were within the
range of the observed data.
The model was validated using data for the period May
to July 1991. Boundary conditions used for the
validation period are shown in Table 5. Model
validation results are shown in Figure 6. The observed
chlorophyll-a concentrations were in the range 10-35 ,
/*g/L, and the model did a better job of predicting these
lower levels of chlorophyll-a.
Overall, the WASP4 model of the Appoquinimink
appears to adequately simulate the events measured in
the field. The only difference in kinetic coefficients
TABLE 4. August to October 1991 calibration boundary conditions (mg/L) for an application of WASP4 to the
Appoquinimink River
System Variable
Ammonia
Nitr ate + Nitrite
Orthophosphate
Chlorophyll-a
CBOD
Dissolved oxygen
Organic nitrogen
Organic phosphorus
Location
Downstream Boundary
0.10
1.0
0.10
0.016
4.0
7.25
0.40
0.02
Silver Lake
0.20
1.0
0.05
0.064
7.0
8.60
1.67
0.05
Noxontown Pond
0.20
1.0
0.05
0.064
7.0
8.30
0.44
0.08
Drawyer Creek
0.20
1.0
0.05
0.032
7.0
8.0
0.40
0.05
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FIGURE S. Appoquinimink River WASP4 dissolved oxygen and
chlorophyll-a calibration (August-October 1991)
between the calibration and validation run was the
phytoplankton growth rate (K1C), which was set
to 2.200/day in the calibration run and 2.000/day
in the validation run. All other kinetic coefficients
were identical.
Uncertainties
Uncertainties in modeling are encountered when
illata required by the application are insufficient or
unknown. Often such uncertainties are accounted
for by conservative estimates based on best
professional judgment and data available from
!5imilar or nearby watersheds and from the
literature.
The most significant unknown in the
Appoquinimink model is the magnitude and the
spatial and temporal variation of SOD and other
nutrient fluxes from the benthos in the river.
ipther unknown or poorly defined parameters that
added uncertainty to the calibration and validation
were as follows:
I
The discharges from Noxontown Pond and
Silver Lake were not measured during the
calibration and validation periods.
The concentrations of chlorophyll-a from
Noxontown Pond and Silver Lake were not
known.
The concentrations of all system parameters at
the Delaware River boundary were not
measured. Values measured at the sampling
stations furthest downstream (109101 and
109121) were used to estimate the Delaware
River boundary conditions.
TABLE S. May to July 1991 validation boundary conditions (mg/L) for an application of WASP4 to the
Appoquinimink River
System Variable
Ammonia
Nitrate +Nitrite
Orthophosphate
Chlorophyll-a
CBOD
Dissolved oxygen
Organic nitrogen
Organic phosphorus
Location
Downstream Boundary
0.05
1.40
0.05
0.008
4.0
6.87
0.30
0.05
Silver Lake
0.17
3.47
0.04
0.032
7.0
8.00
. 0.59
0.05
Noxontown Pond
0.11
0.47
0.02
0.032
7.0
7.90
1.01
0.04
Drawyer Creek
0.05
0.50
0.05
0.004
7.0
6.0
0.30
0.05
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Appoquinimink River Transect
/
-
*-
E
- f
^N
MM', Be
JHK: UC
&
>.
Nt
>
la Run
r sn>
)0, NH4,
"rT
4
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7-4
f f
>S TP at
sedlmen
$
d TN loo
flux rod
i
_ _ _
Is reduci
iced by
^
d by 50!
KJSJ ever
^
where
4/
1 10 98765432 1 0
Distance from Mouth (miles)
FIGURE 7. WASP4 dissolved oxygen results for Alternative
Run 14 on the Appoquinimink River
the majority of oxygen demand is coming from the
sediment.
Given this information, the next phase of TMDL activity
on the Appoquinmiink will:
Define the phosphorus load reductions necessary to
meet DO criteria;
Further characterize nonpoint source nutrient loads;
* Monitor and model SOD; and
Specify how the TMDL will be implemented.
REFERENCES
Ambrose, R.B., Jr., T.A. Wool, J.P. Connolly, and
R.W. Schanz. 1988. WASP4, a hydrodynamic and
water quality model Model theory, user's manual, and
programmer's guide, EPA/600/3-87/039. U.S.
Environmental Protection Agency, Environmental
Research Laboratory, Athens, GA.
Ambrose, R.B., T.A. Wool, and J.L. Martin. 1993.
Tltc water quality analysis simulation program, WASPS,
version 5.10. U.S. Environmental Protection Agency,
Environmental Research Laboratory, Athens, GA.
DNREC. 1988. Clean water strategy. State of
Delaware, Department of Natural Resources and
Environmental Control, Division of Water Resources.
March 30, 1988.
DNREC. 1990. State of Delaware Surface Water
Quality Standards, as amended February 2, 1990. State
of Delaware, Department of Natural Resources and
Environmental Control, Division of Water Resources.
DNREC. 1992. Development of a phase I total
maximum daily load (TMDL) for the
Appoquinimink watershed. Delaware Department
of Natural Resources and Environmental Control.
Mills, W.B., D.B. Porcella, M.J. Ungs, S.A.
Gherini, K.V. Summers, Lingfung Mok, G.L.
Rupp, G.L. Bowie, and D.A. Haith. Water
quality assessment: A screening procedure for
toxic and conventional pollutants in surface and
^ground waters, parts I and II. EPA/600/6-
85/002a. U.S. Environmental Protection Agency,
Washington, DC.
Morton, M.R. 1993. Letter to Rick Green, 7
June. Appoquinimink River TMDL model update.
\
Omernik, J.M. 1987. Ecoregions of the
[conterminous United States. Annals of the
Association of American Geographers 77(1): 118-
125.
Ritter, W.F., and M.A. Levan. 1992. Nutrient budgets
for the Appoquinimink river watershed. Delaware
Department of Natural Resources and Environmental
Control.
Stumm, W., and JJ. Morgan. 1981. Aquatic
chemistry: An introduction emphasizing chemical
equilibria in natural waters, 2nd ed. John Wiley &
Sons, New York.
Tetra Tech, Inc. 1993. TMDL model study for
Appoquinimink River, Delaware. Prepared for Delaware
Department of Natural Resources and Environmental
Control. May 21, 1993.
Thorminn, R.V., and J.A. Mueller. 1987. Principles of
surface water quality modeling and control. Harper and
Row, Publishers, New York.
USEPA. 1985. Rates, constants, and kinetics
formulations in surface water quality modeling, 2nd ed.
EPA/600/3-85/040. U.S. Environmental Protection
Agency, Environ. Research Laboratory, Athens, GA.
USEPA. 1993. TMDL Case Study Appoquinimink River
Delaware. Case Study Number 9. EPA #841-F-93-7.
Office of Water, U.S. Environmental Protection Agency,
Washington, DC.
Water Resources Agency for New Castle County. 1986.
Appoquinimink River basin project, Rural Clean Water
Program, final report.
This
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