Draft
U.S. Army Corps
of Engineers
Philadelphia District
Draft ASR Regional Study
Phase I - Groundwater Modeling
Prepared for
U.S. Army Corps of Engineers
Jacksonville District
and
South Florida Water Management District
Prepared by
U.S. Army Corps of Engineers
Philadelphia District
December 2006
Draft - Phase I Regional ASR Model Report
Draft ASR Regional Study
Phase I – Groundwater Modeling
December 2006
Table of Contents
1.0 INTRODUCTION....................................................................................................... 1
2.0 PHASE I REGIONAL MODELING APPROACH AND ASSUMPTIONS ......... 2
3.0 CONCEPTUAL MODEL .......................................................................................... 4
3.1 CONCEPTUAL GEOLOGIC FRAMEWORK........................................................ 5
3.2 DISCHARGE AND RECHARGE ........................................................................... 6
3.3 ANALYSIS OF MONITORING WELL DATA...................................................... 7
3.4 GROUNDWATER AGE ANALYSIS ..................................................................... 8
3.5 CONCEPTUAL MODEL SUMMARY ................................................................... 8
4.0 MODEL CONSTRUCTION...................................................................................... 9
4.1 MODEL CODES ...................................................................................................... 9
4.2 THREE DIMENSIONAL MESH/GRID DEVELOPMENT ................................. 10
4.3 MODEL INPUT PARAMETERS .......................................................................... 11
4.3.1 TRANSMISSIVITY AND HYDRAULIC CONDUCTIVITY....................... 12
4.3.2 STORAGE TERMS......................................................................................... 12
4.3.3 WATER TABLE HEAD (RECHARGE) ........................................................ 12
4.3.4 PERIMETER BOUNDARY CONDITIONS .................................................. 12
4.3.5 DISPERSIVITY............................................................................................... 13
4.3.6 UNSATURATED PARAMETERS................................................................. 14
4.3.7 INITIAL CONDITIONS HEADS ................................................................... 14
4.3.8 INITIAL CONDITIONS CONCENTRATIONS ............................................ 14
4.4 SIMULATION DURATION SELECTION ........................................................... 14
5.0 WASH123D PHASE 1 MODEL RESULTS........................................................... 15
5.1 WASH123D COARSE MODEL RESULTS.......................................................... 16
5.2 WASH123D SENSITIVITY ANALYSIS RESULTS ........................................... 20
5.2.1 HYDRAULIC CONDUCTIVITY – AQUIFERS ........................................... 20
5.2.2 HYDRAULIC CONDUCTIVITY – CONFINING UNITS ............................ 21
5.2.3 INITIAL CONCENTRATIONS...................................................................... 21
5.2.4 CONCENTRATION BOUNDARY CONDITIONS ...................................... 22
5.2.5 DISPERSIVITY............................................................................................... 22
5.2.6 TIME STEP SIZE ............................................................................................ 23
5.2.7 LIMITED TEMPERATURE EFFECTS IN THE BOULDER ZONE............ 24
5.3 WASH123D SALTWATER INTRUSION RESULTS.......................................... 24
6.0 SEAWAT PHASE I MODEL RESULTS ............................................................... 25
6.1 COMPARISON OF WASH123D AND SEAWAT RESULTS............................. 25
6.2 SEAWAT SENSITIVITY ANALYSIS RESULTS ............................................... 27
6.2.1 TIME STEP SIZE ............................................................................................ 27
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6.2.2 SOLVER .......................................................................................................... 28
6.2.3 DISPERSIVITY............................................................................................... 29
7.0 SOURCES OF ERROR............................................................................................ 30
8.0 PHASE I SUMMARY AND PHASE II RECOMMENDATIONS ...................... 32
8.1 PHASE I MODEL SUMMARY............................................................................. 33
8.2 RECOMMENDATIONS FOR PHASE II.............................................................. 34
9.0 FIGURES................................................................................................................... 37
10.0 REFERENCES...................................................................................................... 125
List of Figures
1 – Model Area
2 – Existing Model Locations – Data Collection Report
3 – Schematic Geologic Cross Section
4 – Hydrostratigraphic Surfaces
5 – Transmissivity - UF
6 – Transmissivity - MF
7 – Transmissivity - LF
8 – Hydraulic Conductivity – MC1
9 – Hydraulic Conductivity – MC2
10 – Hydraulic Conductivity - LC
11 – Hydraulic Conductivity - SAS
12 – Selected Head Data - SAS
13 – Selected Head Data - UF
14 – Selected Head Data - MF
15 – Selected Head Data - LF
16 – Elevation of 10,000 mg/l TDS
17 – Water Quality Data - UF
18 – Water Quality Data - MF
19 – Water Quality Data - LF
20 – Age of Groundwater - UF
21 – Age of Groundwater - MF
22 – Florida Peninsula Outcrop
23 – Model Boundary & Topography/Bathymetry
24 – Model Cross Section
25 – Horizontal Mesh & Grid Resolution
26 – Model Hydraulic Conductivity - SAS
27 – Model Hydraulic Conductivity – IC above IA
28 – Model Hydraulic Conductivity – IC/IA
29 – Model Hydraulic Conductivity – IC below IA
30 – Model Hydraulic Conductivity - UF
31 – Model Hydraulic Conductivity – MC1
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32 – Model Hydraulic Conductivity - MF
33 – Model Hydraulic Conductivity – MC2
34 – Model Hydraulic Conductivity - LF
35 – Model Hydraulic Conductivity - LC
36 – Model Hydraulic Conductivity – LC transition
37 – Model Hydraulic Conductivity - BZ
38 – Elements within the Ocean
39 – Specified Surface Heads
40 – Specified Observed Heads – Boundary Conditions
41 – Specified Equivalent Freshwater Heads – Boundary Conditions
42 – Salt Concentrations in mg/l TDS – Boundary Conditions
43 – Salt Concentrations in mg/l TDS – Initial Conditions
44 – UF Predevelopment vs. Computed Head Contours – Steady State Conditions
45 – UF Predevelopment vs. Computed Head Contours – Transient Conditions
46 – Northern Cross Section – Transient Conditions
47 – Central Cross Section – Transient Conditions
48 – Head Comparison in the IA and UF – Steady State
49 – Velocity Vectors in the IA and UF – Steady State
50 – Head Comparison in the MF and LF – Steady State
51 – Velocity Vectors in the MF and LF – Steady State
52 – ROMP-49 Head Data
53 – UF Salt Concentration Change
54 – MF Salt Concentration Change
55 – LF Salt Concentration Change
56 – Change in Elevation of 10,000 mg/l TDS
57 – Sensitivity – Base Run UF Results
58 – Sensitivity – 2 x K(Aquifer) UF Results
59 – Sensitivity – Half K(Aquifer) UF Results
60 – Sensitivity – 2 x K(Confining Unit) UF Results
61 – Sensitivity – Half K(Confining Unit) UF Results
62 – Sensitivity – Increase Initial Concentrations 25% - UF Results
63 – Sensitivity – Decrease Initial Concentrations 25% - UF Results
64 – Sensitivity – Specified Conc. Boundary Cond. – MF Results
65 – Sensitivity – Dispersivity Effects at Selected Wells
66 – Sensitivity – Time Step Effects at Selected Wells
67 – Sensitivity – Temperature Effects on BZ BC
68 – Sensitivity – BZ Temperature Effects on Resulting Heads
69 – Saltwater Intrusion Front at Ocean Boundary
70 – WASH123D to SEAWAT Head Comparison – SAS Steady State Solution
71 – WASH123D to SEAWAT Head Comparison – UF Steady State Solution
72 – WASH123D to SEAWAT Head Comparison – MF Steady State Solution
73 – WASH123D to SEAWAT Head Comparison – LF Steady State Solution
74 – WASH123D to SEAWAT Head Comparison – BZ Steady State Solution
75 – WASH123D to SEAWAT Head Comparison – BZ Transient Solution at 35,000
years
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76 – WASH123D to SEAWAT Concentration Comparison – Transient Solution at
35,000 years
77 – WASH123D (variable bottom BC) to SEAWAT Head Comparison – BZ Transient
Solution at 20,000 years
78 – WASH123D (variable bottom BC) to SEAWAT Head Comparison – LF Transient
Solution at 20,000 years
79 – WASH123D to SEAWAT Head Comparison – UF Transient Solution at 35,000
years
80 – WASH123D to SEAWAT Head Comparison – MF Transient Solution at 35,000
years
81 – WASH123D to SEAWAT Concentration Comparison – Transient Solution at
35,000 years
82 – Schematic Conductivity Distribution
83 – Sensitivity – Time Step Effects at Selected Wells
84 – Sensitivity – Solver Effects at Selected Wells
85 – Sensitivity – Dispersivity Effects at Selected Wells
86 – Head and Concentration Oscillations over time
87 – Head and Concentration Oscillations – Plan View
88 – Proposed Revised Boundary
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1.0 INTRODUCTION
The U.S. Army Corps of Engineers (USACE), Philadelphia District, has prepared this
report for the USACE, Jacksonville District, and the South Florida Water Management
District (SFWMD) in support of the Comprehensive Everglades Restoration Plan
(CERP). Aquifer Storage and Recovery (ASR) is one of the proposed alternatives
recommended by the CERP to help with water supply, storage, and distribution in South
Florida. The CERP recommends approximately 333 ASR wells distributed over a large
region with well field clusters proposed within the Floridan Aquifer System (FAS) near
Lake Okeechobee, near the proposed C-43 reservoir in Hendry County, and at several
locations along the Lower East Coast in Palm Beach and Broward Counties. The
proposed plan, with an injection and recovery pumping rate of approximately 1.65 billion
gallons per day, is larger than any currently operating ASR project. To evaluate the
numerous design considerations and the variation in aquifer response on regional, subregional, and local scales, density-dependent numerical modeling of the FAS is required
as discussed in the ASR Regional Study Project Management Plan developed in 2003.
The ASR Regional Study – Benchscale Modeling report (Brown et al, 2006) evaluated
several density-dependent flow and transport modeling codes. Two codes were selected
for use in the ASR Regional Modeling application: the finite element code, WASH123D
(Yeh et al., 1998) and the finite difference code, SEAWAT (Langevin et al, 2003, and
Guo and Langevin, 2002). The two models were selected as the best of those evaluated
to address the maximum number of questions regarding the ASR Regional Study and to
reduce technical uncertainties that might arise from the proposed CERP ASR plan. Some
of the issues to be addressed by the two models include potential regional changes in
aquifers heads, flows, and water quality, the potential for salt water intrusion caused by
ASR pumping, regional impacts to wells completed in the FAS, and ASR design, siting,
layout, and performance considerations. The WASH123D and SEAWAT models, under
development simultaneously, encompass a peninsula-wide area of approximately 39,000
square miles extending from Polk County to Florida Bay (Figure 1).
The regional modeling effort was divided into 2 Phases because of its extent and
complexity. The Phase I model is a coarse test bed for the more refined Phase II model.
Specific goals of the Phase I and Phase II models are:
Phase I
•
•
•
•
•
•
Identify model boundaries and test model boundary parameters
Identify regional flow and salt migration pathways
Identify the timing of salt water intrusion
Evaluate model run times and model sensitivity to time step sizes
Test hydraulic and transport parameter sensitivity
Compare WASH123D and SEAWAT results
Phase II
• Identify areas of the Phase I model for refinement
• Incorporate regional-scale transient groundwater withdrawal
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•
•
•
Calibrate density-dependent flow and transport results to observed
measurements in major geologic units
Select sites and determine refinement locations to incorporate ASR well field
clusters
Evaluate proposed ASR project alternatives effects on aquifer heads, flows,
and water quality; for pressure-induced changes; for increased potential for
salt-water intrusion; and on withdrawals for existing well users
The focus of this report is the Phase I Regional Modeling. This Phase I effort includes
regional conceptualization, estimates of hydraulic and transport parameters, densitydependent 3-D groundwater model construction, gross comparisons to head and
concentration data, evaluation of flow and transport patterns, and sensitivity analyses to
several model parameters. Each of these tasks and analyses provide a greater
understanding of the flow and transport system that makes up the majority of the Florida
subsurface. This increased understanding provides a platform on which to build in the
additional complexities of the Phase II model.
2.0 PHASE I REGIONAL MODELING APPROACH AND ASSUMPTIONS
The primary objective of the Phase I model is to identify the parameters and issues that
will be most important when building the more-detailed Phase II model. The Phase I
model is a coarse resolution regional-scale model constructed based on several
simplifying assumptions. The simplifying assumptions are intended to maintain the
model ability to represent the regional flow and transport patterns while facilitating
model construction and reducing model run times. This section introduces the most
significant model assumptions. Model conceptual and construction details are presented
in Sections 3 and 4.
For the Phase I modeling effort, it was assumed that porous media models adequately and
accurately represent the system modeled for both flow and transport. The impact of
lineament and/or fracture flow was not included in the Phase I model, but may be
significant as discussed in Section 5.
The boundary used for the Phase I model was approximately twenty miles offshore to
reduce the effects of boundary conditions on model results and to attempt to simplify
boundary condition assignments. The horizontal model resolution was coarse to decrease
the model run times and to allow long-term simulations to be investigated.
Geologic interpretations were based on previous ASR Regional Study reports discussed
in Section 3. The model focuses on the FAS, the geologic units where the ASR wells are
proposed. Geologic units that are shallower and deeper than the FAS are incorporated to
the Phase I model with geologic information that is greatly simplified and functions only
to provide flow and salinity interaction with the FAS. Unsaturated flow in the surface
aquifers is omitted as it is not pertinent to the model objectives. Therefore the top of the
model is assumed to be the water table surface rather than ground topography.
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Pumping wells are omitted from the Phase I model. The coarse resolution of the model
makes incorporating pumping wells difficult. Also, model parameter assignments can be
simplified without the inclusion of pumping wells. The omission of pumping has a
significant effect on the results; however, these “pre-development” results can provide an
overall picture of flow and transport patterns.
Flow boundary conditions around the model perimeter are specified as constant heads
that do not vary in time. This simplification was made to reduce the amount of data
collection required for the Phase I model. The heads specified at the boundaries are
considered as “pre-development” heads because there is no pumping in the model. Predevelopment boundary heads were assembled based on pre-development head contours
generated by USGS (Bush and Johnston, 1988) and by selecting the highest consistent
values at selected wells from online databases. Although the boundary heads were based
on available data, some interpretation was required for boundaries that either did not have
adequate water level data or that did not intersect the ocean, where head values are
known. As discussed later in this report, the heads assigned to these boundaries were
extensively tested until reasonable regional flow patterns were developed. The head data
in the surface aquifer were utilized to specify the model surface head boundary instead of
estimating recharge based on precipitation and evapo-transpiration. This assumption is
reasonable since only the surface aquifer flow interaction with the Floridan aquifers is
necessary to meet the ASR model objectives. No flow is assumed to enter or exit the
model from the bottom.
Boundary conditions for salt concentrations also were set around the perimeter of the
model. Water quality data from SFWMD and online databases were used to estimate the
salt concentration around the boundary. Much less water quality data were available than
head data so considerable interpolation and extrapolation was necessary to determine the
boundary concentrations. Due to this uncertainty, sensitivities were performed on the
type of boundary condition used, fixed or variable, as well as on concentrations specified.
These sensitivities were completed for both the aquifer perimeter and model bottom. The
concentration boundary conditions on the model surface were fixed as fresh water over
land and seawater over the ocean. These estimates of boundary concentrations are
sufficient to generate and test transport patterns for this investigation. Further discussion
of these sensitivity analyses is presented in Sections 5 and 6.
The effect of heat transfer was not considered for the Phase I model and is not currently
within the scope of the ASR Regional Study effort. Heat transfer may be important as a
primary mechanism for salt intrusion based on the convectional cell theory (Kohout
1965). The convectional cell theory states that salt water upwells from deep geologic
units based on the geothermal reduction of water density. For the Phase I model, it is
assumed that the temperature variation does not have a significant impact on the water
density. Although heat transfer is not considered, limited temperature effects within the
deepest model aquifer are examined as part of the sensitivity analyses (Section 5).
Variable density models must be run under transient conditions to allow the system to
equilibrate over time and because solute transport calculations always are performed
under transient conditions. The duration of the simulations was dictated by recent
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groundwater age research as discussed in Section 3.4 and 4.4. Steady state runs were
done first to provide initial conditions for the transient simulations to help the solution
converge on an answer more quickly. Once steady state results compared reasonably
with monitoring data, long-term transient simulations were completed. The transient
results were used as a basis for several sensitivity tests.
The Phase I modeling was performed using both WASH123D and SEAWAT codes. The
model parameters used to produce results comparable with monitoring well data were
determined using the WASH123D model runs. Subsequently, the WASH123D model
configuration and parameters were used, with conversion to the proper format, to
complete a comparable model run in SEAWAT. The differences in the results from the
two codes are compared and evaluated. In addition, sensitivities were performed for both
models to evaluate the effects of several parameters. This approach helped evaluate the
sensitivity of each model to variations in input data, as well as similarities and differences
between the two codes.
3.0 CONCEPTUAL MODEL
A conceptual model was developed for the system to identify all known hydrologic and
hydrogeologic factors that influence flow and transport.
The factors include
sources/sinks, the boundary of the model domain, and stratigraphy. The conceptual
model provides an understanding of observed groundwater flow directions and transport
pathways and helps determine the dimensions and important features of the numerical
model.
A wealth of geologic and hydrogeologic data exists across the region. As part of
previous ASR Regional Study tasks, certain information was collected and evaluated for
use in the Phase I model. Reese and Richardson (2004) compiled The Draft-Final Report
– Task 3.0 Define Preliminary Hydrogeologic Framework, (referred to herein as the
Preliminary Hydrogeologic Framework), which presents an evaluation of regionally
significant works and new data to generate a consistent hydrostratigraphic and
hydrogeologic visualization of the Florida peninsula subsurface from Orlando to Key
West. A further assessment of geologic and hydrogeologic data is presented by
CH2MHill (2006) in Groundwater Numerical Model Development Support and Data
Collection Report, (referred to herein as the Data Collection Report). The Data
Collection Report provides a review of the Preliminary Hydrogeologic Framework
together with geologic and hydrogeologic data used for eight existing numerical models
of various scales and resolutions across the peninsula (Figure 2). Also included in the
Data Collection Report is a dispersion research study that gathered and tabulated
dispersion data from technical reports of similar geologic environments around the world.
Additional information for the Phase I conceptual model was collected from other
sources listed in the References section and from online databases including SFWMD’s
DBHYDRO, USGS’s South Florida Information Access (SOFIA), and the National Park
Service’s South Florida Natural Resources Center (SFNRC).
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All geographic information associated with geologic and hydrogeologic data was
converted to the following coordinates:
• Horizontal geographic data – Florida East State Plane 1983 (FESP 83)
• Vertical geographic data – National Geodetic Vertical Datum 1929 (NGVD)
3.1 CONCEPTUAL GEOLOGIC FRAMEWORK
The conceptual geology is based predominantly on the findings documented in the
Preliminary Hydrogeologic Framework. For that report, key wells were reviewed to
correlate major aquifers and confining units of the FAS. Figure 3, taken from Table 1 of
the Preliminary Hydrogeologic Framework, shows the resulting Regional Study
schematic cross section representing the geologic units that are defined for the numerical
models. The Preliminary Hydrogeologic Framework also defined hydrostratigraphic
surfaces for the geologic units and hydraulic parameters for the FAS units.
Hydrostratigraphic surfaces define the depth and thickness of the geologic units over the
study area. (Figure 4). Hydraulic parameters were generated for the Upper Floridan
(UF), Middle Floridan (MF), Lower Floridan (LF), Upper Middle Confining Unit (MC1),
Lower Middle Confining Unit (MC2), and the Lower Confining Unit (LC) (Figures 5, 6,
7, 8, 9 and 10, respectively). The transmissivity of the Boulder Zone (BZ) was estimated
to be on the order of 106 to 107 ft2/d. Details regarding the correlation of the geologic
units and the definition of the hydrostratigraphic surfaces and hydrogeologic parameters
for the FAS can be found in the Preliminary Hydrogeologic Framework.
The Preliminary Hydrogeologic Framework defines the hydrostratigraphic surface
between the Intermediate Confining Unit/Intermediate Aquifer System (IC/IA) and the
Surficial Aquifer System (SAS) but does not define hydraulic parameters for those units.
These two units are both complex systems of alternating aquifers and confining units.
Part of the conceptualization of the IC/IA and SAS is to greatly simplify these units
because they are not the focus of this modeling effort. The primary function of these two
units in the Regional Model is to permit water to enter the FAS, where the ASR wells are
proposed.
Hydraulic parameters for the IC/IA were composited from information in the Data
Collection Report. All of the eight numerical models reviewed for the Data Collection
report provide ranges and distributions of hydraulic parameters for portions of the IC/IA.
The IC is represented as a thick semi-confining and confining unit that retards flow
between the SAS and IA or between the SAS and UF, where the IA is not present. The
IA is found only in west-central and southwest Florida. The lateral extent of the IA is
shown in Randazzo and Jones (1997), Figure 6.23.
The hydraulic conductivities for the SAS were based on a figure generated by SFWMD
(Figure 11). The lateral extent of the SAS is shown in Randazzo and Jones (1997),
Figure 6.6. In the northwest corner of the model area, the SAS pinches out and the IC/IA
is present at the surface. For this Phase I model, this simplified SAS layer acts primarily
as a boundary condition providing the driving flow into and out of the FAS.
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3.2 DISCHARGE AND RECHARGE
Discharge from the system is mainly to the Atlantic Ocean or Gulf of Mexico and also to
surface water features, such as rivers, lakes, streams, canals. Although the numerous
agricultural and municipal pumping wells are a significant discharge from the system, no
pumping is included in the Phase I model. Without pumping, all heads generated by the
Phase I model are considered to be “predevelopment heads”. Regional pumping
information, including locations, open hole depths, and transient pumping rates, will be
assembled and assessed for model input to the Phase II model.
Recharge to the system is mainly attributed to infiltration to ground water from
precipitation. Infiltration can be difficult to quantify, especially over such a large region,
as it depends on obtaining transient precipitation gage information and estimating evapotranspiration rates and land-use information. Correctly quantifying infiltration would
facilitate computing correct heads within the SAS that make up the driving head through
the IC/IA to the FAS. Because the SAS heads are important mainly as a surface
boundary condition, the head data in the SAS was set based on observations rather than
estimating several parameters to compute head data in the SAS layer. This simplifying
assumption facilitates model construction and should not have an adverse effect on the
computed results in the FAS, the primary area of concern for the Regional Study.
Head data from SAS monitoring wells was gathered and used to specify the heads over
the surface of the model domain. SAS monitoring well head data was gathered from a
number of sources, including online databases, a depth-to-water grid from the Florida
Geological Survey (FGS), Reese and Cunningham (2000), and model results from
Sepulveda, 2002. Not all SAS monitoring well head data available in online databases
were used. SAS wells that form well clusters with wells screened in lower geologic units
were favored. Additional SAS wells were selected to obtain an adequate coverage over
the model area. Also notable is that transient data sets available for SAS monitoring
wells were condensed into a constant head value that represents the highest consistent
value over the period of record. The highest consistent value for the period of record of
each well was chosen as a representation of the predevelopment condition for the Phase I
model. The highest consistent value for the SAS was selected from all the aquifers
present in a particular region, including artesian aquifers that exist in the SAS. As a
result, some of the SAS heads selected are actually above ground surface, such as those
beneath the Caloosahatchee River Basin west of Lake Okeechobee.
Figure 12 shows the surface head distribution and values over the peninsula. The FGS
depth-to-water grid data is not shown in the figure because the data density is too large
for clarity. The FGS data compares favorably with the data shown in Figure 12 except
where artesian values were selected.
Figure 12 shows the highest head values and the area of most significant recharge are in
the Polk County area with values as high as 175 ft NGVD. Much lower heads are found
across the large area south of Lake Okeechobee, where head values range from 0 to 20
feet NGVD.
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3.3 ANALYSIS OF MONITORING WELL DATA
Visualization of regional groundwater elevation and water quality data provides an
understanding of groundwater flow patterns and the location and movement of the salt
water concentrations over time. The visualization was performed using the Department
of Defense’s Groundwater Modeling System (GMS) version 6.0. Head monitoring data
for numerous wells across South Florida is contained within online databases, but not all
wells were used. Wells clusters were chosen to see the relative changes in different
geologic units, and additional wells were selected to allow for adequate coverage across
the unit. From the transient head data set available at a particular well, only the highest
consistent value was chosen to represent predevelopment conditions. These datasets
were also used to compare with the computed results of the Phase I model.
Figure 13 shows the predevelopment head distribution and values for the UF as collected
from selected wells in online databases, model results from Sepulveda (2002), and from
Development of a Density-Dependent Saltwater Intrusion Model for the Lower East
Coast Project Area, prepared by HydroGeoLogic, Inc., April 2006. Also shown in the
figure are contours of UF predevelopment heads generated by USGS (Bush and Johnston,
1988). This data gives the overall flow pattern of UF predevelopment heads. The
primary inflow to the UF is in the Polk County area. Groundwater flows radially from
the high point in Polk County toward the eastern and western coasts and to the south from
the central ridge of higher heads.
Figure 14 shows that less head information was evaluated in the MF. The data were
collected from online databases and HydroGeoLogic, Inc., April 2006. Generally the MF
heads seem similar to the UF heads with the highest heads in the Polk County area.
Lower heads are found in all directions surrounding the highest head in Polk County.
Head information for the LF was gathered from HydroGeoLogic, Inc., April 2006, from
online databases and from Hydrogeology and Water-Quality Characteristics of the Lower
Floridan Aquifer in East-Central Florida (O’Reilly et al., 2002) as shown in Figure 15.
These head values are generally lower than the UF and MF heads. Much lower heads in
the LF are found in the southern counties. In the northern counties, especially Polk
County, the heads are somewhat lower but more similar to those observed in the UF and
MF. A similar pattern appears to exist for each of the units with the highest heads in
central part of the Florida peninsula and lower heads to the south.
Similar visualizations were performed for selected water quality data. For this study,
total dissolved solids (TDS) are used as a measure of salinity. Seawater salinity is
assumed if TDS values are 35,000 mg/l or larger (Parker et al, 1955). An overall
depiction of the water quality pattern in South Florida is shown in Figure 16 which shows
elevation contours where the TDS concentrations are 10,000 mg/l as developed by
SFWMD. Fresh-to-brackish groundwater is present at greater depths below Polk County
than at the coasts. There is a secondary area of fresh-to-brackish groundwater south of
Lake Okeechobee. Visualizations of water quality data in the UF, MF and LF taken from
online databases and HydroGeoLogic, Inc., April 2006 (Figures 17, 18 and 19), show that
the majority of the UF has TDS values lower than 5,000 mg/l while MF concentrations
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are slightly higher and LF concentrations are much higher. These values are generally
consistent with the elevation of 10,000 mg/l TDS data depicted in Figure 16. However,
in some areas on the west coast, the selected data evaluated for Phase I shows fresher
water at greater depths than shown in Figure 16. Judgment was used in determining the
value used for model input.
3.4 GROUNDWATER AGE ANALYSIS
To better understand the timing of water flow through the FAS, additional investigations
related to groundwater age data for the UF and MF were reviewed. These investigations
included Kaufmann and Bennett (2005) and Mirecki (unpublished) (Figures 20 and 21).
The contours in these figures indicate the age of groundwater in “years before present”
and were computed based on the Pearson correction method. The general trend shows
that the groundwater age is younger near Polk County where rainfall infiltration is
believed to occur and along the central portion of the peninsula extending southward just
west of Lake Okeechobee. Groundwater becomes increasingly older as it moves to the
south and as it moves east and west of the central peninsula ridge. An area near the east
coast indicates some mixing with ocean water because the groundwater ages are younger
than those upgradient. Also, the MF groundwater ages are younger than the UF
groundwater ages indicating that groundwater moves faster in the MF. The maximum
age of groundwater in both aquifers is about 35,000 years.
3.5 CONCEPTUAL MODEL SUMMARY
The final conceptual model for Phase I was formed with the information and data
gathered, and provided the basis upon which the numerical models were developed.
Recharge through infiltration to the SAS provides driving heads that supply water to the
FAS through the IC/IA. The primary area of recharge to the FAS appears to be in the
Polk County area where flow infiltrates through the confining units to recharge the UF,
MF and the LF. This statement is supported by the fact that the highest observed heads
for the UF, MF and LF monitoring wells compiled for this study occur in Polk County
and lower salinity is found in the deeper units in the Polk County area. Based on the data
used for the Phase I model, flow appears to move through the aquifers in all directions
from Polk County. In the UF, fresh-to-brackish water exists through much of the aquifer
until it reaches an outcrop to ocean water on the east coast where some mixing occurs.
The MF has similar heads and slightly higher concentrations compared to the UF. In the
MF, a salinity mixing zone is also present on the east coast. Water flow is expected to
move faster through the MF as hydraulic conductivities are larger and groundwater ages
are younger than the UF. The LF appears to be influenced in the north by recharge from
the Polk County region with water elevations that are much higher than those observed in
the south. The southern LF water levels and salt concentrations are more consistent with
ocean water. Mixing is expected in areas of the LF where the less-saline infiltrated water
mixes with the ocean water. Discharge from the system is mainly to the Atlantic Ocean
or Gulf of Mexico and also to surface water features.
The conceptual model was translated into the two numerical models, but the process is an
iterative one. Numerical model results will be used to refine and revise the conceptual
model with an updated understanding of flow and salt transport processes for Phase II.
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Draft - Phase I Regional ASR Model Report
4.0 MODEL CONSTRUCTION
Two density-dependent flow and transport codes, WASH123D and SEAWAT, were used
in the Phase I modeling. The following sections chronicle model development from
mesh/grid development to model input selection. Mesh/grid development and model
input was completed for both models using GMS, version 6.0.
4.1 MODEL CODES
The WASH123D model code (Yeh et al., 2003), compiled in September 2006, was one of
the codes used for model construction. WASH123D is an unstructured finite element
code that simulates variable-density flow and reactive chemical and sediment transport in
1-D channel networks, 2-D overland regimes and 3-D subsurface media. For the Phase I
model, the capability of computing density-dependent coupled subsurface flow and
transport using WASH123D was utilized. With WASH123D, the variably saturated,
density-dependent water flow is described by the modified Richards’ equation and solved
with the Galerkin finite element method. The Lagrangian-Eulerian (LE) method is
employed to solve the subsurface transport equation, where particle tracking is used in
the Lagragian step to handle the advection term, and the other terms (such as sources,
sinks, diffusion, and dispersion) are calculated in the Eulerian step to determine the
spatial concentration distribution at the end of each time step. The use of this
methodology allows the numerical stability of WASH123D not to be restricted by the
Mesh Courant number. In addition, the Mesh Peclet number is restricted only by
computational accuracy, not numerical stability. Therefore, a sensitivity to time step
sizing was performed to ensure the numerical accuracy was adequate for the scale and
stated goals of the Phase I modeling effort. More detailed discussion on various types of
numerical dispersion and how the LE method deals with these types of numerical
dispersion are found elsewhere. (Cheng et al., 1996; Cheng et al., 1998; Yeh et al.,
2006).
The SEAWAT model code (Langevin, et al, 2003, and Guo and Langevin, 2002) also
was chosen for the Phase I modeling effort. SEAWAT is a finite difference code that
simulates variable-density flow in three dimensions by combining a version of
MODFLOW–2000 (Harbaugh et al, 2000) and MT3DMS (Zheng and Wang, 1999) in a
single program to solve the coupled flow and solute transport equations necessary to
model variable-density flow. MODFLOW—2000 is used to solve the flow part of the
variable-density flow equations and MT3DMS is used to solve the solute transport
equations. SEAWAT runs in one of four simulation modes: Constant-density flow
without solute transport; constant-density flow with solute transport; variable-density
flow without solute transport; and variable-density flow with solute transport. SEAWAT
uses a finite difference approximation for the variable-density form of Darcy’s law for
groundwater flow and simulates dispersive transport using Fick’s law. The program
contains several methods for solving the solute transport equation including an implicit
finite difference method with either upwinded or central-in-space weighting, the method
of characteristics, and a third order total-variation-diminishing (TVD) scheme. For the
Phase I study, SEAWAT modeling was performed with the variable-density flow with
solute transport mode to simulate the system.
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Draft - Phase I Regional ASR Model Report
4.2 THREE DIMENSIONAL MESH/GRID DEVELOPMENT
The 3-D nature of the study area must be considered during mesh/grid development. As
such, the 3-D mesh/grid must balance accurate depiction of topography, geology, and
groundwater sources/sinks with model goals and computational resources.
Horizontally, the ideal model boundary alignment would be around the Florida peninsula
boundary, where all the geologic units outcrop to the ocean (Figure 22). This would
ensure that boundary effects in the interior of the model would be limited because
boundary condition assignments would be greatly simplified: all of the boundary heads in
each geologic unit would be equal to sea level and all of the boundary concentrations
100% salt water. However, since the Florida peninsula extends 150 miles westward into
the Gulf of Mexico, modeling the entire peninsula was not feasible within the scope of
this study. The model boundary chosen for the Phase I model generally follows a path
just north of Polk County and extends around the peninsula approximately 20 miles
seaward from the coast (Figure 23). The northern boundary was chosen to ensure that the
entire recharge area, the driving force of the model, was incorporated. The remainder of
the model boundary was selected a distance offshore to reduce boundary effects, but
close enough to the coast to maintain a model size that would not result in unreasonable
computation times and to have some information on which to base the head and
concentration boundary condition assignments. The model encompasses an area of
approximately 39,000 square miles.
Vertically, the mesh/grid was based on the geologic interpretations discussed in the
Conceptual Geologic Framework section of this report. The 3-D mesh/grid represents
geology between the low water table and the Sub-Floridan Confining Unit. The top of
the model is the low water table rather than the land surface. The low water table was
generated by selecting the lowest value from the transient data set of SAS monitoring
well heads in online databases and interpolating over the model area. Low water table
was chosen for the top of model to eliminate computations within the unsaturated zone.
Computations within the unsaturated zone are not needed to reach the model goals and
would slow model run times. From the low water table, the model extends down to a
constant elevation of approximately -3250 feet NGVD. Figure 24 shows a cross section
of the geologic units as classified in the 3-D mesh and grid. The cross section shows the
distribution of the model’s 23 layers of nodes for the WASH123D model and 22 layers of
cells for the SEAWAT model. Each geologic unit is represented by a number of layers
depending on its importance to the model goals. The SAS is only represented by 1 layer
because variation in the SAS is not expected to impact the ASR plan objectives. The UF
and MF are each represented by three layers of elements as the results within these layers
will affect several ASR plan decisions. Between the LC and BZ is a transition layer that
represents a combination of non-continuous permeable layers and confining units. This
transition layer also allows for a transition of hydraulic parameters between the low
magnitude values assigned for the LC and the extremely high values for the BZ. The
cross section also shows that the shallower layers, SAS and IC/IA, are very thin in the
recharge area in the northern part of the model and become thicker toward the south.
Additional horizontal resolution in the recharge area, an area with large flow gradients,
may be necessary for stability because the vertical resolution is fine.
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Draft - Phase I Regional ASR Model Report
The WASH123D mesh and the SEAWAT grid were developed in GMS using triangular
irregular network (TIN) surfaces based on the hydrostratigraphic surfaces from the
Preliminary Hydrogeologic Framework. The same TIN surfaces were used to create the
mesh and the grid. For both models, a relatively coarse resolution is used for the Phase I
study to allow the long-term simulations to run quickly to test many model parameters.
Figure 25 shows the resolution and horizontal extent of the mesh and grid.
The WASH123D mesh used element sizes of approximately 25,000 feet along the model
perimeter and no additional refinement within the model boundary. The mesh is
comprised of 49,151 nodes and 90,376 elements.
The SEAWAT grid is comprised of 41 cells in the x-direction, 56 cells in the y-direction
and 22 vertical layers. The model contains 50,512 cells and 55,062 nodes with a cell size
of 25,000 feet by 25,000 feet across the entire model domain. The grid developed for the
SEAWAT model is oriented along the major direction of flow, 18 degrees southeast,
according to the predevelopment flow patterns in the region. The SEAWAT grid is
created with the Layer-Property Flow (LPF) package, a MODFLOW package that allows
for dipping beds.
4.3 MODEL INPUT PARAMETERS
WASH123D and SEAWAT groundwater flow and transport models are made up of three
basic input file types: a 3-D mesh or grid, head and concentration boundary conditions,
and initial total head and concentration files. For WASH123D, the 3-D mesh is
comprised of unstructured prismatic elements that represent the low water table and
geology of the study area. Computational nodes are located at the corner of each
element. WASH123D solves for flow and transport parameters, such as head, velocity,
and concentration at each node. For SEAWAT, a 3-D grid comprised of cells represents
the study area low water table and geology. Computational points for the grid are cellentered and represent the point at which flow and transport parameters are calculated.
For both codes, model boundary conditions are used by the model to define flow and
transport parameters within the system that are known with a reasonable degree of
certainty. Boundary conditions are generally defined for parameters such as heads along
the ocean or lakes and known concentrations such as salinity within the ocean. The
initial total head conditions and initial salt concentrations must be defined at every node
or grid cell within the model. The initial condition files are used as a starting point in the
iterative solution process. The important considerations for each of these input files are
described in the following sub-sections.
Both WASH123D and SEAWAT support the input of head data to the model as observed
heads rather than equivalent freshwater heads. Equivalent freshwater head is the head of
a column of water if the entire column’s density is that of fresh water. Observed head is
subject to the effects of the variation in density of the water column. During a
simulation, the observed heads are applied as boundary conditions and converted to
equivalent freshwater heads for the numerical model computations. Both model codes
use the equivalent freshwater heads to compute the flow fields and velocities. Once the
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Draft - Phase I Regional ASR Model Report
head results are computed, the WASH123D code outputs both equivalent freshwater
heads and observed heads, and the SEAWAT code outputs observed heads.
4.3.1 TRANSMISSIVITY AND HYDRAULIC CONDUCTIVITY
The transmissivity (T) and hydraulic conductivity (K) are both measures of the capability
of an aquifer to transmit water where T is the product of K and the saturated thickness of
the aquifer. Because WASH123D utilizes K rather than T as an input parameter, K
values were estimated from each aquifer unit using the range of T values from the
sources cited in the Conceptual Model. The saturated thickness of the aquifers was
estimated from the distance between the hydrostratigraphic surfaces. For the aquifers, it
is assumed that the vertical hydraulic conductivity (Kv) is 10 times less than the
horizontal hydraulic conductivity (Kh).
For the confining units, MC1, MC2, and LC, Kv values published in the Preliminary
Hydrogeologic Framework were input to the model. For the ICU, leakance and Kv
values published in the Data Collection Report were used to estimate Kv. Values for Kh
are assumed to be 2 times the Kv values.
Figures 26 through 37 present the Kh values and their distribution within each model
geologic unit for the WASH123D mesh and SEAWAT grid. Kh values of 1.0 ft/d or less
within an aquifer represents an area where the aquifer is not believed to be present based
on the Preliminary Hydrogeologic Framework. In the case of the SAS, the IC outcrops to
the surface in northwest corner of the model domain so the IC hydraulic conductivities
are used within the upper layer of the model. Any element that was found to be
completely within the ocean when compared to the bathymetry was assigned with an
ocean material type (Figure 38) and a high hydraulic conductivity of 10,000 ft/d. Where
the ocean abuts a confining unit hydraulic conductivity, a buffer hydraulic conductivity
of 100 ft/d was defined to make the simulation more computationally stable.
4.3.2 STORAGE TERMS
Transient simulations require storage parameters to make the transient calculations. For
WASH123D, the storage coefficient is made up of several terms including the porosity,
moisture content and modified compressibility of the soil matrix for each geologic unit
and the modified compressibility of water. For SEAWAT, the specific yield or specific
storage for each geologic unit is defined. The values for the storage terms were obtained
from the ASR Benchscale Modeling study (Brown et al, 2006).
4.3.3 WATER TABLE HEAD (RECHARGE)
To simplify the model input, recharge based on precipitation is not used. A specified
constant head is assigned to the top of the model that represents the predevelopment
water table, created as described in Section 3.2. This provides a constant source of water
and the driving force of the model. Figure 39 shows the heads assigned to the SAS layer
of the mesh and grid.
4.3.4 PERIMETER BOUNDARY CONDITIONS
The head boundary condition values are specified along the entire model perimeter for
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Draft - Phase I Regional ASR Model Report
each aquifer. The head data depicted in Figures 12, 13, 14, and 15 are used to help define
the specified heads. Along the northern model boundary, the data coverage is good for
the SAS and UF; however assumptions are required for the MF, LF, and BZ head
boundaries. For the remainder of the model boundary, the head is known for the SAS,
i.e. sea level, but the heads for the UF, MF, LF and BZ must be estimated. The UF
specified heads are extrapolated from the predevelopment head contours generated by
SFWMD in areas beyond the data shown in Figure 13. The MF specified heads along the
entire model perimeter are assumed to be equal to the UF specified heads. The LF
specified head boundary values are extrapolated from the data shown in Figure 15 along
the northern boundary and are assumed to be 0.0 feet on the remainder of the boundary.
Specified heads in the BZ are assumed to be 0.0 feet. For aquifer elements on the
boundary that are completely within the ocean, a head value of 0.0 feet is assigned.
Because many assumptions were made in assigning the specified boundary heads, these
values were varied until they compared favorable with the head data. The distribution of
assigned observed heads is shown in Figure 40. Figure 41 shows the same view after the
observed heads are converted to equivalent freshwater heads.
Boundary conditions for salt concentration were assigned around the entire model
perimeter using a variable concentration condition. A variable concentration is equal to
the concentration specified in the input file if the direction of flow is into the model. If
the direction of flow is out of the model, the concentration on the boundary is computed
by the model. For both WASH123D and SEAWAT codes, salinity values along the SAS
perimeter boundary are known and are specified as fresh water along the land boundary
in the north and 100% salt water along the ocean boundary. Concentrations are also
assigned for the SAS on the model top with fresh water specified over land and salt water
specified over the ocean. For the UF, an extrapolation of concentrations shown in Figure
17 is used to assign the variable boundary concentrations. Boundary concentrations in
the MF are assumed to be the same as for the UF. Figures 16 and 19 are used to help
define variable boundary concentrations in the LF. The BZ boundaries are assumed to be
fully salt water except for a small area along the northern boundary which is less than
10,000 mg/l based on Figure 16. For aquifer elements that are completely within the
ocean, a 100% salinity value of 35,000 mg/l is assigned. Salt concentrations are specified
as 35,000 mg/l on the model bottom. The distribution of assigned salt boundary
concentrations is shown in Figure 42.
4.3.5 DISPERSIVITY
Dispersion refers to the spreading of salt, or any solute, caused by variations in fluid
velocity about the mean velocity. Dispersion is taken into account in the model by
defining the longitudinal and transverse dispersivity coefficients that represent mixing.
As part of the Data Collection Report, a database of dispersion data for sandstone and
carbonate aquifers around the world was created. Few of the values are based on
physical test data and most values are based on estimates or groundwater model
calibrations. Longitudinal dispersivities range from 0.002 feet to 13,100 feet. Transverse
dispersivities are between 1% and 100% of the longitudinal values. Dispersivities are
found to be scale-dependent, i.e. for large models, like the Phase I model, the
dispersivities are typically higher than for well tracer tests. An analysis of the data
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Draft - Phase I Regional ASR Model Report
indicates that dispersivity is more of a model calibration parameter rather than a property
of an aquifer. While the dispersivity values do depend on the nature of the aquifer
materials, they also depend on the size of the flow field and model discretization. A
range of dispersivity values were tested for use in the Phase I model as discussed in the
sensitivity analyses. A value of 2.5 feet was chosen for use in all the geologic units while
a value of 25.0 feet was used for elements within the ocean. For simplicity, the
longitudinal and transverse coefficients used are equal. Further study of dispersivity will
be performed for the Phase II model including calibration to field values as discussed in
Section 8.0.
4.3.6 UNSATURATED PARAMETERS
Because the top of the model is the water table, no unsaturated zones occur within the
model. Although they are not used, the WASH123D model requires unsaturated
parameters in order to run, so simplified curves were adopted. For SEAWAT rewetting
of the cells was allowed in the model, however, none of the cells went dry, so rewetting
was not needed.
4.3.7 INITIAL CONDITIONS HEADS
The initial condition potentiometric heads were specified at every computational point in
the model. The initial condition is used as a starting point in the iterative solution
process. A constant total head was specified as elevation for the steady state model
simulation. The resulting heads from the steady state simulation are used to begin the
transient simulation. Initial head assumptions have no impact on final results because the
convergence criteria used for the steady state results is very small, 1x10-6 feet.
4.3.8 INITIAL CONDITIONS CONCENTRATIONS
The initial condition salinity concentrations were specified at every computational point
in the model based on the visualizations of monitoring well data in aquifers in Section
3.3. In addition, it is assumed that the SAS is 100% salt water in the ocean and 100%
fresh water over land. The BZ is 100% salt water except under a small portion of the
Polk County recharge area where Figure 16 shows that a value of 10,000 mg/l extends
into the BZ. The salt concentrations in the confining units were interpolated based on the
aquifer values. The initial condition is used as a starting point in the solution process.
The distribution of initial concentrations is shown in the fence diagram in Figure 43 for
the WASH123D model, but applies to SEAWAT as well.
4.4 SIMULATION DURATION SELECTION
Because the mesh and grid are coarse and simulations can run fairly quickly, geologic
time-scale simulations are possible. Initial simulations were performed starting from
120,000 years ago, the last time that the peninsula was partially inundated by ocean
water, and marching through time to the present. Initial concentrations for the
simulations were 100% salinity throughout the entire model with fresh water infiltrating
through the Polk County recharge area. These simulations were very unstable due to
sharp salt fronts moving through the thin vertical elements in that area of the model. A
shorter duration model with more variation in initial concentrations was chosen.
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Draft - Phase I Regional ASR Model Report
As shown in the groundwater age data for the UF and MF in Figures 20 and 21, the oldest
groundwater within the aquifers is about 35,000 years old. A total simulation time of
35,000 years was estimated to provide enough time for groundwater to move completely
through the system. The boundary heads and concentrations set as described throughout
Sections 3 and 4 do not vary over the 35,000 years. The assumption is that the
boundaries are in equilibrium over the course of the simulation time.
A time step of 10 years was used. This is the largest time step tested that provided results
as accurate as any smaller time step. Time step sensitivities are presented in Sections 5
and 6.
5.0 WASH123D PHASE 1 MODEL RESULTS
The Phase I Regional WASH123D model was developed to meet specific objectives
associated with the ASR Regional Study. Few density-dependent models of this size
have been completed so the main goal of the Phase I model is to construct a test to
determine what problems need to be addressed to build a successful Phase II model.
Using the Phase I model, several parameters were tested including code computation
parameters, site-specific flow and transport parameters, and code and platform run times.
The model results provide insight into how the WASH123D model represents the
behavior of the FAS, the impacts of changes in specific model parameters on that
behavior, potential missing elements from the conceptual model, and the importance of
collecting and understanding all available existing data for formulation of the Phase II
model.
When viewing the model results, it is essential to remember that many significant factors
are excluded from the Phase I model. The most notable omission is that of pumping
stresses. Without pumping, the resulting heads are higher than measured monitoring well
heads, the hydraulic conductivity values may not be reliable values and do not have as
great an impact on the model results, and computed transport patterns may have a lesser
variation. It is not advised that pumping be added to the Phase I model as it exists. The
resolution of the Phase I model is not sufficient to capture the behavior of pumping
stresses for any pump, let alone the numerous pumping wells that will need to be
incorporated to completely assess pumping effects in the FAS.
Another important caveat regarding the Phase I model is related to the model time period.
Although the model time period extends over 35,000 years, the model is not necessarily
representative of the Florida subsurface changes over that period. Over the last 35,000
years, sea level has varied by 100 feet or more. Wet and dry rainfall periods have
impacted the surface heads and recharge. Hydraulic conductivities have varied due to
dissolution and diagenesis. The model boundary conditions have not been fixed in time
as assumed in the model and salt concentrations 35,000 year ago did not have the pattern
used as the initial conditions. This type of data is sparsely, if at all, available. These
assumptions and simplifications were used to determine model behavior over a long time
period rather than to reconstruct an accurate history of flow and transport in the Florida
peninsula.
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Draft - Phase I Regional ASR Model Report
5.1 WASH123D COARSE MODEL RESULTS
To assess the WASH123D coarse model results, model input parameters were varied
within a reasonable range in order to match simulated output to observed conditions
within some acceptable error criteria. This comparison was completed for both steadystate or transient conditions. Steady-state model simulations provide a snap-shot of the
hydraulic conditions in a stable aquifer system. Steady-state simulations were performed
and results compared to observed data to improve model input parameters without
waiting for longer transient simulations to complete. However, a transient comparison is
a more reliable means of ensuring that a large scale regional model is accurately
responding to a variety of stresses. Once model input parameters were relatively close to
observed values, transient comparisons were performed. Comparisons of the computed
head data were made to the UF predevelopment head contours shown in Figure 13 and to
selected monitoring well head data in the IAS, UF, MF, and LF. Comparison to the
elevation of 10,000 mg/l TDS map is used to compare salt transport patterns. More
important than the actual comparisons of the computed values and monitoring data, this
analysis is concerned with reproducing observed flow and transport patterns to generate
starting parameters for the Phase II model. This section presents separate results for
heads, velocities, and transport leading to a summary of how these results represent the
entire system.
Because the monitoring head data are affected by pumping, the best comparison to the
simulated heads is the UF predevelopment head contours (Bush and Johnston, 1988).
Figure 44 shows the comparison for the steady state condition. Generally the contours
agree with the highest heads in the Polk County area and the lowest heads at the coasts.
The highest computed head is approximately 115 feet. The highest predevelopment head
contour, 120 feet, is in the same area. Agreement between the computed and
predevelopment head contours is very good down to the 70-foot contour. There is also
fair agreement between the computed and predevelopment contours from the 40-foot
contour down to the 10-foot contour. Between the 40-foot and 70-foot contours, the
computed heads are much lower than the predevelopment heads. The predevelopment
head contours show much higher head values into the southern part of the state.
Comparing the UF predevelopment heads to the UF computed heads after 35,000 years
shows that the computed heads in the southern part of the state continue to decrease over
time (Figure 45). The heads in the northern half of the model stay nearly the same as
those shown in the steady state conditions with some increase in the 50-foot contour
toward the west coast. A cross section showing flow vectors through the northern part of
the model shows water entering the model in the Polk County area and moving down
through the geologic units toward the coasts (Figure 46). Conversely, the flow vectors in
a cross section (Figure 47) just south Lake Okeechobee shows that water in the central
part of the UF moves vertically upward from the lower aquifers. The flow vectors show
this pattern of upward movement throughout the southern part of the state.
In order to see the spatial variation of heads based on monitoring well data, computed
water elevations were contoured along with calibration targets. The calibration targets
show the location of each monitoring well water level and the relative error between
16
Draft - Phase I Regional ASR Model Report
computed and monitoring well heads. Each target is color coded. Green means that the
computed head is within 10 foot of the observed head, yellow means that the computed
head is within 15 feet of the monitoring well head, and red means that the computed head
is more than 15 feet different than the monitoring well head. If the target is colored
above its centerline, the computed head is greater than the monitoring well head. If the
target is colored below its centerline, the computed head is less than the monitoring well
head.
Figures 48, 49, 50 and 51 show the computed heads and velocity vectors for the four
major aquifer units under steady state conditions. In all of the aquifers, the highest
computed heads are located in the Polk County and Highlands County areas with
computed head values decreasing and the velocity vector pointing out toward the east and
west coasts. The calibration targets comparing the computed heads to monitoring well
heads are also shown in the figures.
For the IA, most of the wells show green calibration targets meaning the computed heads
are within 10 feet of the monitoring wells heads. A few of the computed values are too
high. Some of these discrepancies are due to small variations that cannot be reproduced
with the coarse mesh resolution. For example, on the Sarasota and Charlotte County
border there are two monitoring wells near each other where the heads differ by 13 feet.
It is not possible to match both of these values with coarse resolution of this model.
Other discrepancies between the computed and monitoring well heads result from the
omission of pumping effects. At ROMP-49 near Tampa, the head at the well is largely
affected by pumping (Figure 52) so it would be difficult to simulate the heads in this well
with the Phase I model. In fact, because the Phase I model does not include pumping, the
computed head values are expected to be higher than the monitoring well head values.
Velocity vectors within the IA show flow from the Polk and Highlands County areas to
the Gulf of Mexico. Outside the limits of the IA, the vectors point more vertically
upward or downward transferring flow through the IC toward either the SAS or UF.
The computed head results compared to the monitoring data in the UF and MF show
similar patterns to each other. In the Polk County area, where flow enters the two
aquifers, the computed head values are generally higher than the monitoring data results.
In the southern half of the state, the computed values are lower than the monitoring data
results. The plots also show that the velocities are higher in the Polk County area for the
UF and MF than in other areas of those aquifers. In the south, the velocities are low,
except in the area along the east coast where the proximity to the ocean allows mixing of
salt water with the brackish water in the aquifers.
The computed head contours for the LF match reasonably with several monitoring data
points in the southern portion of the model; however velocity vectors for the LF appear to
show chaotic patterns. To understand these patterns and some of the results in the UF
and MF, it is necessary to review the initial salt concentrations and changes to those
concentrations over time. Transport of salt is the mechanism that acts to change the
model heads over time through solution of the variable density flow equations. Without
salt transport, there is no transient stress in the model. Consequently the heads in the IA
unit, which is close to the surface and mostly present in the northern part of the model
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Draft - Phase I Regional ASR Model Report
where water is freshest, do not vary greatly over the course of 35,000 years. Figures 53,
54, and 55 depict the change in salt concentrations from steady state conditions (Time 0.0
years) to the end of the 35,000 year simulation for the UF, MF, and LF, respectively. In
the northern part of the model, salt concentrations in the UF and MF change very little
since these areas start as fresh water and are constantly recharged by fresh water. In the
southern part of the model, salt concentrations increase significantly in the UF and MF as
flow from the coast and lower geologic units carry the salt upward and inward from the
model boundary through time. The LF starts with concentrations that are much higher
than the aquifers above it. In the Polk County area, the LF becomes fresher over time as
fresh water infiltrates downward from the UF and MF. Salt concentrations in the LF start
high in the south and remain high during the simulation as salt migrates with the upward
flow from the bottom of the model.
Another view of the change in concentrations over time is shown in Figure 56. The left
and right sides of the figure show elevation contours where salinity concentrations are
10,000 mg/l at time 0.0 years and time 35,000 years, respectively. At time 0.0 years, the
elevation contours appear very similar to those shown in Figure 16 in the northern portion
of the model, where fresher water is deepest. In the southwest at time 0.0 years, 10,000
mg/l salt concentrations are deeper in Figure 56 than in Figure 16 because the model
initial conditions are based on monitoring well data used for Phase I that show that
fresher water is located at greater depths. As time progresses to 35,000 years, the area
where fresher water is deepest shrinks and moves from Polk County to just northwest of
Lake Okeechobee. Also, the depth to fresher water decreases significantly in the south
half of the model, especially on the southwest coast where the elevation of 10,000 mg/l
TDS changes from -1,800 feet to -600 feet.
To summarize these results, water entering the model in the Polk County area moves
downward to provide a source of fresh water to all of the aquifers. The highest heads and
lowest salt concentrations occur in this area in all the aquifers. From Polk County, water
moves out in all directions. As the water moves south, there is a point in the vicinity of
Lake Okeechobee where the heads within the SAS are not high enough to maintain a
downward gradient. In this area and south, the gradients of all of the confining units are
upward. Freshwater in the UF moves to the surface. Saltier water from below and from
the continental shelf on the east infiltrates the MF and UF and acts to reduce the heads in
these units over time. As the heads in these units decrease in the southern part of the
model, the gradient between the higher heads in the north and lowering heads in the south
increases, resulting in a southern shift in the area where fresher water is deepest. The LF
is an area where significant mixing takes place between fresher water infiltrating from
above and the high salinity water that exists across most of the unit. The chaotic flow
patterns seen in the LF are indicative of this mixing.
Numerous model simulations have been conducted to determine if the model is an
accurate representation of the flow system or if something is missing in the conceptual
model that leads to the significant increase in saltwater in the south. When salt
concentrations are low, the southern part of the model is dominated by the boundary
conditions. Looking at the UF steady state head results, Figure 48, the computed
boundary heads all compare favorably with the monitoring well heads. The boundary
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condition values could be revised to match these boundary wells more closely. However,
during the transient simulation when salt water begins to enter the UF, the heads decrease
significantly across the entire southern portion of the model. Based on data presented in
the Conceptual Model section, these heads in the UF and MF are higher than the
computed results at the end of the simulation and likewise the actual salt concentrations
are lower.
The main problem is to identify the mechanism to allow more freshwater into the south
part of the UF and MF aquifers. Freshwater can only come from the surface or from the
upgradient Polk County area. Unless there is a shallow artesian aquifer south of Lake
Okeechobee that has higher heads than the UF, the gradient will be upward preventing
any surface freshwater from entering the UF. While artesian SAS aquifers do exist and
have heads higher than the UF heads in some locations (i.e. beneath the Caloosahatchee
River Basin), in areas further south their heads are not higher than the UF heads. If the
hydraulic conductivity in the UF or MF is higher than that selected in the conceptual
model, it is possible that more freshwater will travel to the south. Trials of several
hydraulic conductivities in these aquifers improve the model results only slightly. If the
hydraulic conductivity of the MC2 confining unit is lower or the IC is tighter, it is
possible that more freshwater will travel to the south part of the UF and MF aquifers.
Trials of several hydraulic conductivities in these confining units also showed only small
improvements in the model results. Decreasing the hydraulic conductivity in the MC2
also acts to reduce the amount of water that enters the UF and MF because the gradient is
upward. The hydraulic conductivity of the IC in the south is very small even in the
conceptual model and reductions do not cause significant model changes. Another
potential mechanism for freshwater flow south of Lake Okeechobee is preferential
flowpaths or fractures. The USGS predevelopment head contours and the monitoring
data indicate that the higher heads observed in the UF fall along a fairly narrow axis
along the center of the peninsula. It is possible that a preferential pathway exists that
leads flow from north to south along that axis. In fact, a recent unpublished study by the
USACE, Jacksonville District identifies several northeast trending surface lineaments on
the northwest side of Lake Okeechobee. Surface lineaments indicate possible fractures or
fault zones which could act as conduits for groundwater flow.
Other possible explanations for the discrepancy in model results and observed values are
related to model construction and assumptions. Many of the model parameters are
dependent on mesh resolution. It is possible that with the more refined mesh that will be
used for Phase II, some of the discrepancies in the results will be resolved. Also, a major
assumption in conceptualizing the Phase I model is that the boundary conditions do not
change over time and that the initial salt conditions, which represent salt conditions
today, are representative of salt conditions 35,000 years ago. Over the last 35,000 years,
there have been several major changes that are not coded into the model including
changes in sea level which lead to changes in head and salt levels, changes in
precipitation amounts, and changes to hydraulic conductivity in the geologic units due to
dissolution and diagenesis.
A complete explanation of the salt migration processes over 35,000 years may be beyond
the scope of the ASR Regional Model effort. To meet the Regional Study goals, the
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Phase II model has to adequately capture the migration patterns over the calibration and
validation periods and over the selected duration of the predictive simulations. For the
calibration and validation periods, the simulation duration will be approximately 1 year.
For the predictive simulations, the duration will be on the order of 1 to 50 years. Using
the Phase I model run times as a guide, a simulation with a 5-day time step and a duration
of 50 years shows a salt concentration change of less than 1 percent inland from the
boundaries in the FAS. It may be that for the Phase II model runs, regional salt migration
will not have a significant impact on the computed results relating to the specific ASRrelated goals for the study.
5.2 WASH123D SENSITIVITY ANALYSIS RESULTS
Sensitivity analyses were performed to determine how changes in input values affected
the model results compared to the base run results described in Section 5.1. Several
parameters were varied as part of the Phase I model to determine what variables have the
greatest impact. Those parameters include hydraulic conductivity, initial concentrations,
concentration boundary conditions, dispersivity, time step size and temperature effects on
boundary conditions in the BZ. The sensitivity analysis duration was chosen at 10,000
years to provide enough simulation time for significant changes in results to be observed
without unnecessarily extending run times. The results of the Phase I sensitivity analysis
provide some basis for how Phase II model parameters will behave however, most of
these parameters are heavily dependent on mesh resolution. Additional sensitivity
analyses will be performed as part of the Phase II modeling.
5.2.1 HYDRAULIC CONDUCTIVITY – AQUIFERS
Hydraulic conductivity was varied in all of the aquifers in the model by doubling and
halving the values used for the base run. Head and concentration results compared in the
Upper Floridan are typical of the results shown in the Middle and Lower Floridan. The
base run head and concentration results at time 10,000 years across the model are shown
in Figure 57 for the UF. Figure 58 presents the head and contour results for the UF for
doubling the hydraulic conductivities in all the aquifers. When the aquifer K values are
doubled, the resulting heads in the UF are lower than the base run along the central ridge
of the peninsula. Between the central ridge and the coasts, the UF head values are higher
than the base run head values except near the area where the UF abuts the ocean. The
largest head differences are in the Polk County area with a maximum of approximately 6
feet. The concentrations decrease across the model when the aquifer K values are
doubled except near the east coast where the UF abuts the ocean. In other words, in the
Polk County region, fresh water enters the aquifer and the higher K values allow the fresh
water to move quickly through the region resulting in lower heads and concentrations.
The higher conductivities also allow fresh water to move more quickly out from the
central ridge extending the area of fresher water and lowering concentrations. The
increase in heads between the central ridge and the coast for the higher K run is a
combination of the decrease in concentrations and the milder gradient between the lower
maximum heads in Polk County and the constant boundary conditions. Near the ocean,
the higher hydraulic conductivities allow salt intrusion to occur more quickly increasing
the concentrations and decreasing the heads. Figure 59 shows the UF head and
concentration results when the aquifer hydraulic conductivities are halved compared to
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the base run. With the aquifer conductivities halved, the results are the opposite of
doubling the conductivities.
Neither doubling nor halving the hydraulic conductivities in the aquifers has a significant
impact on the flow or concentration patterns for the Phase I model.
5.2.2 HYDRAULIC CONDUCTIVITY – CONFINING UNITS
Hydraulic conductivity was varied in all the confining units in the model by doubling and
halving the values used for the base run. Head and concentration results compared in the
Upper Floridan are typical of the results shown in the Middle and Lower Floridan. The
base run head and concentration results at time 10,000 years across the model are shown
in Figure 57 for the UF. Figure 60 presents the head and contour results for the UF for
doubling the hydraulic conductivities in all the confining units. When the confining units
are more permeable, the resulting UF heads are higher than the base run along the central
ridge of the peninsula because more fresh water is able to infiltrate from the surface. The
UF heads are lower near the coasts and in the southern part of the peninsula when the
confining units are more permeable compared to the base run due to saltwater intrusion
from the coasts and from deeper units. Figure 61 shows the UF head and salt
concentration results when the confining unit hydraulic conductivities are half as
permeable. The results of halving the confining unit hydraulic conductivities are the
opposite of doubling the conductivities.
Doubling the K values in the confining units has a larger impact on the model results than
halving the confining unit K values, especially in the southern part of the model. For
example, at the location where Dade, Broward and Collier Counties meet, the head value
is 6 feet lower when the confining unit K values are doubled than the head values for the
base run. When the K values are halved, the UF head at that location is only
approximately 2 feet higher than the UF head for the base run. This result is directly
related to the upward direction of the velocity vectors in the southern part of the model.
When the confining units are more permeable, more salt water upwells to shallow
aquifers. When the confining units are less permeable, the upwelling occurs but at a
slower rate.
5.2.3 INITIAL CONCENTRATIONS
Initial concentrations were increased and decreased by 25% compared to the base run.
The minimum and maximum concentrations were maintained at 0 mg/l and 35,000 mg/l,
respectively. Head and concentration results compared in the Upper Floridan are typical
of the results shown in the Middle and Lower Floridan. The base run head and
concentration results at time 10,000 years across the model are shown in Figure 57 for the
UF. Figures 62 and 63 show the heads and concentrations within the UF after 10,000
years when the initial concentrations are increased and decreased by 25%, respectively.
The UF heads and concentrations along the central ridge of the model after 10,000 years
are almost exactly the same for the three different initial conditions as shown in Figures
57, 62, and 63. The UF initial concentrations in the base run are very low in that area so
varying them by +- 25% does not change the values significantly. In addition, fresh
water enters the model in that area keeping the concentrations low. Beyond the central
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ridge area, the UF heads are lower and concentration are higher where the initial
concentrations were increased by 25% and the UF heads are higher and concentrations
are lower where the initial concentrations were decreased by 25% compared to the base
run. Note that the concentrations at 10,000 years are significantly impacted by the initial
concentrations. When the concentrations start higher they remain higher for the duration
of the simulation and vice versa.
5.2.4 CONCENTRATION BOUNDARY CONDITIONS
For the base run, the boundary salt concentrations are assigned as a variable boundary
condition. This boundary condition allows the concentrations to be calculated by the
model when the flow direction is out of the model. When the flow direction is into the
model, the concentrations are defined based on a specified concentration that varies
within each aquifer. A sensitivity analysis was performed to observe the results if the salt
concentrations on the boundary were held constant using the observed data for each
aquifer. The resulting heads and concentrations for the UF and LF are not very different
compared to the base run. Generally, the UF heads are slightly lower and the LF heads
are slightly higher than the base run. The opposite is true for the salt concentrations. In
the MF, the head results can vary up to 5 feet. Also, the MF salt concentrations near the
boundary increase as a result of salt intrusion from below. This creates a strange pattern
where the concentration on the boundary is fixed at a lower value but just inside the
boundary the concentrations are 20,000 mg/l higher (Figure 64). Therefore, it appears
that the specified concentration boundary conditions do not provide an accurate picture of
flow and transport patterns. Although the variable concentration boundary condition
appears to provide the model more freedom to compute accurate flow and transport
patterns, some oscillation occurs on the boundary which may result in propagation of
error through the model. This is discussed further in Section 7.0.
5.2.5 DISPERSIVITY
Several values of dispersivity, 0.0, 25.0 and 250.0 feet, were tested and the results were
compared to the base run results. The dispersivity of the base run is 2.5 feet for all
materials except the ocean and the ocean buffer material types where dispersivities of
25.0 feet and 10 feet, respectively, were used. The dispersivities of the ocean and ocean
buffer materials for the base run were the lower limits used for those material types in the
sensitivity runs. As dispersivity increases, the rate of mixing of higher salinity water with
lower salinity water increases. Through the upper part of the model, from the surface
down to the MF, the salt concentrations are initially low, so a higher dispersivity acts to
increase the concentrations and lower the heads in those areas over time. In some areas
of the LF and deeper units where salt concentrations are initially high, large dispersivities
cause a salt concentration reduction and an increase in head.
A further investigation was completed to determine the impact of dispersivity variation
on head and concentration at specific wells within the UF. ROMP-45 is located in Polk
County where water enters the UF from the surface geologic units. ENP-100 is located in
Dade County where water enters the UF from the deeper geologic units. Locations for
both wells are shown in Figure 57. Figure 65 shows the head and concentrations results
over time for dispersivities of 0.0, 2.5, 25.0, and 250.0 feet. Note that for a dispersivity
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of 0.0 feet, the code was unable to converge after 4,000 years as small instabilities can
not be smoothed out without any mixing coefficient. At ROMP-45, the resulting
concentrations vary through time from the initial concentration until a particular time
when the concentration becomes constant until the end of the run. For a dispersivity of
250.0 feet, the concentration increases to a constant value of almost 6,000 mg/l. This
result is unlikely to be correct since ROMP-45 is within the Polk County area where
concentrations are expected to be low. The results from the runs with dispersivity values
of 2.5 feet and 25.0 feet seem more reasonable as the concentrations are less than 1,000
mg/l. The same pattern is true for the head results at ROMP-45 where the heads for a
dispersivity of 250.0 feet are much less than the values for both 2.5 feet and 25.0 feet.
Also notable at ROMP-45 is that the heads decrease initially and then increase until
approximately 4,000 years when a constant value is reached. This dip in the graph
indicates an initial period of instability until the model reaches an equilibrium. In the
case where dispersivity is 250.0 feet, the instability is so large that the long-term results
are impacted.
In the southern part of the model at ENP-100 (Figure 65), the salt concentrations for the
variety of dispersivities increase through the simulation as salt water intrusion occurs
from the east coast and from the deeper geologic units. No constant value is reached as
the salt continues to increase, and therefore the heads decrease, through time. The largest
magnitude of increase in salt concentrations and decrease in head is shown for the
dispersivity of 250.0 feet. The smallest changes in concentration and head are shown for
dispersivities of 0.0 and 2.5 feet.
Based on these results, it is reasonable to remove a dispersivity of 250.0 feet from
consideration as it results in too much mixing. However, it is difficult to determine
which is better between a value in the magnitude of 2.5 or 25.0 feet. For the Phase I base
run, a dispersivity of 2.5 feet was chosen in an attempt to limit the mixing and salt
intrusion in the southern part of the model. For the Phase II model, additional sensitivity
analysis will be completed to provide more insight in selection of this parameter. In
Phase II, the spatial and temporal discretizations of the model will be more refined which
will have a significant impact on dispersivity. In addition, whereas the Phase I model has
a constant dispersivity across all the geologic units, the Phase II model will take account
potential variation of dispersivity through and between geologic units. Calibration of the
Phase II dispersivities will be dependent on data, where it’s available, as discussed in
Section 8.0.
5.2.6 TIME STEP SIZE
Several time steps were evaluated to determine the largest time step (i.e. shortest run
time) that provides accurate results. These values include 0.2 year, 1 year, 5 years, 10
years and 100 years. Plots were created depicting the head and concentration variation
over time at two specific wells within the UF (Figure 66). Locations for the two wells are
shown in Figure 57. ROMP-45 is located in Polk County where water enters the UF
from the surface geologic units. ENP-100 is located in Dade County where water enters
the UF from the deeper geologic units. At ROMP-45, the heads are almost identical for
each of the simulations tested except for the 100 year time step. Even for the 100-year
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time step, the resulting heads are only approximately 0.5 foot higher than the head results
using the other time steps. The concentration results at ROMP-45 using the different
time steps are all very similar and decrease to a constant value of almost 0.0 mg/l at 5,000
years. Note that only 1,000 years are plotted in Figure 66 for ROMP-45 so the variation
between the simulations is visible. At ENP-100, the head values decrease and
concentrations increase throughout the simulation time for all the different time steps
evaluated. The 100-year time step yields results that have the largest deviation from the
results of the other time step simulations. The 100-year time step results are only plotted
out to 3,000 years on Figure 66 because the simulation failed to converge due to
boundary instabilities. The 0.2-year time step results are only plotted out to 1,000 years
for the same reason.
Based on the results of this sensitivity, the 10-year time step was selected as the most
computational efficient time step size because it provides the shortest run time and results
that are very similar to those produced by the smaller time steps.
5.2.7 LIMITED TEMPERATURE EFFECTS IN THE BOULDER ZONE
The effect of temperature variation in the Boulder Zone was coded into the head
boundary condition of the model for that geologic unit to observe any impact it may have
on the aquifers above. Figure 67 shows the head used for the BZ boundary that
incorporates the temperature effect on density added to the assumed head for the BZ
boundary condition of 0.0 feet around the perimeter of the model. Note that heat transfer,
temperature effects on salt concentrations on the boundary of the BZ, and temperature
effects on head and salt concentrations within the model boundary and on other geologic
units are not considered. Revising the boundary heads in the BZ does have an effect on
the heads in the geologic units above the BZ as shown in Figure 68. The figure shows
two maps of change in heads resulting from the revised BZ boundary condition as
compared to the base run for Time 0.0 years. The impact on the heads in the overlying
units is largest at Time 0.0 years and decreases as the model simulation proceeds. The
left map shows the head change in the UF and MF layers where the magnitude varies
from -1.0 foot on the southeast coast to 5.0 feet on the southwest coast. The map on the
right shows the head change in the LF where the magnitude varies from -2.0 feet on the
southeast coast to 18 feet beyond the southwest coast.
These results show that the temperature effects can have a significant impact on the
computed model results. Consideration should be given to including temperature effects
in the Phase II model boundary conditions or the possibility of including full heat transfer
computations, if feasible.
5.3 WASH123D SALTWATER INTRUSION RESULTS
Where the model boundary extends to the Atlantic Ocean, it is possible to observe how
the model behaves due to the effects of saltwater intrusion. Figure 69 shows the salt
fronts at three time steps over the 35,000 year simulation at a cross section through the
mixing zone. Comparing the three snap shots reveals the salt fronts in the UF, MF and
LF moving westward from the coast. For the UF, the concentrations increase from a
range up to 3,500 mg/l to a range up to 10,500 mg/l with the 10,500 mg/l contour moving
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westward by approximately 30 miles over 35,000 years. In the MF, the concentrations
increase from a range up to 7,000 mg/l to a range up to 14,000 mg/l with the 14,000 mg/l
contour moving westward by more than 40 miles over 35,000 years. The concentrations
in the LF increase from a range up to 28,000 mg/l to up to 31,500 mg/l and the 31,500
mg/l contours moves westward by approximately 15 miles. Even at the coarse resolution
of the Phase I mesh, the wedge shape of the salt water front in each aquifer is clearly
visible with the denser, saltier water extending further westward along the bottom of the
aquifer and the less dense, less salty water extending further eastward along the top of the
aquifer. This demonstrates that WASH123D is capturing the processes that occur in the
saltwater intrusion mixing zone which will be important for the Phase II model.
6.0 SEAWAT PHASE I MODEL RESULTS
As stated previously, the Phase I modeling effort included the construction of a fully
density-dependent SEAWAT model. This model had essentially the same grid resolution
and utilized the same hydraulic parameters developed for the Phase I WASH123D model.
The purpose of this SEAWAT model was to provide a comparison of the WASH123D
model results, identify similarities and differences in the flow and concentration fields
computed by the two models, and help identify issues to be addressed in the Phase II
modeling effort.
Using the Phase I SEAWAT model, several parameters were tested including code
computation parameters, site-specific flow and transport parameters, and code and
platform run times. The model results provide insight into how the SEAWAT model
represents the behavior of the FAS, the impacts of changes in specific model parameters
on that behavior, potential missing elements from the conceptual model, and the
importance of collecting and understanding all available existing data for formulation of
the Phase II model.
Since the SEAWAT model was constructed in a manner consistent with the WASH123D
model, this SEAWAT model will have the same abilities and limitations discussed for the
WASH123D model. Another potential limitation of the Phase I model is the relatively
coarse vertical discretization. The model contains 22 vertical layers. The UF and the MF
are represented by three layers each; the LF has two model layers. Guo and Langevin
(2002) discuss the importance of grid resolution for variable density flow models in
particular vertical resolution. In areas of complex flow patterns and high concentration
gradients, additional vertical discretization often is needed to adequately represent the
flow system. These authors have found that vertical grid resolution often needs to be
much finer than horizontal resolution and recommend up to 10 model layers per aquifer.
The grid will be refined in Phase II of the ASR modeling study.
6.1 COMPARISON OF WASH123D AND SEAWAT RESULTS
The SEAWAT model was run as a base case to 35,000 years with two stress periods
starting with a steady state stress period for one day. Those steady state results were used
as the starting point for the transient stress period. Most parameters were kept the same
between the WASH123D and SEAWAT models; however, minor variations were
required due to the inherent differences between the model codes. The SEAWAT model
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was not compared to monitoring well data to determine input parameters, but rather used
the input parameters generated from the WASH123D results for the same period. When
the results of the WASH123D and SEAWAT models were compared, several interesting
similarities and differences were observed.
The steady state results computed for both the SEAWAT and WASH123D models were
similar. Figures 70, 71, 72, 73 and 74 show the computed heads in both models for the
SAS, UF, MF, LF, and BZ, respectively. The computed SEAWAT heads are shown as
dashed lines while the computed WASH123D heads are shown as solid lines. Although
some minor discrepancies exist, the regional steady state flow patterns simulated for both
models are reasonably consistent.
The results of the transient simulations revealed that several differences exist between the
WASH123D and SEAWAT model results. The magnitude of these differences increased
with time. One of the most notable differences occurs in the BZ in the Polk County area.
Figure 75 shows that after 35,000 years the WASH123D simulation computes heads in
Polk County of between 30 and 40 feet, while SEAWAT computes higher heads of
approximately 60 to 70 feet in the same area. The differences in heads between the two
models are directly related to the way in which salt concentrations are computed in each
model. In both models the salt concentrations in the northern portion of the BZ start at
10,000 mg/L TDS. However, in the WASH123D model, the concentration quickly
increases to a fully saline concentration of 35,000 mg/L TDS. Conversely, the
concentrations computed in the SEAWAT model gradually decrease during the
simulation to a concentration of less than 100 mg/L TDS at 35,000 years. Figure 76
shows a cross sectional view of the concentration distribution in each model after 35,000
years. The primary reason for this discrepancy is due to differences in the boundary
conditions applied to the bottom of each model. In the WASH123D model, the BZ is
represented by one element layer with computational nodes above and below this layer.
The conceptual model for the BZ assumed that the bottom of the BZ is a no-flow
boundary with a constant saline concentration. As such, a constant salt concentration
boundary condition of 35,000 mg/L was applied to the bottom layer of nodes in the
WASH123D model. In the SEAWAT model, one element/cell was again used to
simulate the BZ. However, since the computations are cell centered in SEAWAT, a
constant concentration boundary condition could not be reasonably applied without
overly constraining the salt transport in the bottom layer of the model. Instead a variable
boundary condition was applied that allowed the model to compute the concentration in
the BZ based on the flow and concentration of the surrounding cells, unless flow was into
the model from the boundary. Along the model boundary, if the flow was into the model,
the assigned salt concentration of the fluid entering the model was assumed to be 35,000
mg/L TDS. After comparing the computed results in both models, the WASH123D
model was modified to simulate a variable boundary condition similar to that used in the
SEAWAT model. Figure 77 shows the computed heads in the BZ at 20,000 years in the
SEAWAT model and the revised WASH123D model. This figure illustrates that once
the WASH123D boundary condition was revised to a variable concentration type
boundary, the computed results are similar in WASH123D and SEAWAT. Figure 78
shows that this revision also improved the correlation between the two models in the Polk
County area in the LF.
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In addition to the differences noted in the heads of the BZ and LF, the heads in the
southern portion of the UF and MF (Figures 79 and 80, respectively) appear to be lower
in the SEAWAT model. This again is a direct result of the salt migration patterns. In
these areas of lower head, the SEAWAT model computed higher salt concentrations than
those in the WASH123D model. Figure 81 shows a cross section view of the
concentration distribution in each model after 35,000 years. These differences in
computed salt migration may be caused by the resolution of the mesh and grid used in the
models and how each model calculates hydraulic conductivity for a particular element or
cell. Figure 82 shows a simplified representation of the way in which vertical hydraulic
conductivities are treated in each model. For the numerical computations, WASH123D
determines the inter-nodal conductivity based on the conductivity assigned to each of the
elements in the model. This inter-nodal conductivity is used in the matrix equations to
resolve the flow fields and corresponding heads at each finite element node. SEAWAT
treats the conductivity slightly differently. The vertical conductivity between each cell is
described as the equivalent conductance between the cells, when a fully three
dimensional approach is used. This difference in the treatment of vertical hydraulic
conductivity is compounded by the configuration of the finite element computational
nodes and the finite difference computational cells. As shown in Figure 82, the finite
element nodes are not located at the same horizontal and vertical location as the center of
the finite difference cells. Some of the issues with vertical conductance may be resolved
by adding increased vertical discretization in the Phase II SEAWAT model.
The differences in computational methodologies and treatment of hydraulic parameters
between WASH12D and SEAWAT may require actual calibration to observed heads and
salt concentrations for both models in Phase II of the project. The WASH123D model
parameters were determined as discussed in previous sections of this report; however, the
SEAWAT model used hydraulic and transport parameters from the WASH123D model
and the best parameters for the SEAWAT model were not developed independently.
Hydraulic parameters used to calibrate the finite difference based model may be slightly
different than those required to calibrate the finite element model. A detailed
examination of this phenomenon was not within the scope of the Phase I ASR work.
6.2 SEAWAT SENSITIVITY ANALYSIS RESULTS
In addition to the comparisons between the SEAWAT and WASH123D results, several
sensitivity analyses were performed on the SEAWAT simulation. These sensitivity
analyses varied parameters such as time step size, flow and transport solvers, dispersivity
and initial salt concentrations. The results of these sensitivity simulations were then
compared to the base run described in the previous section. As discussed for the
WASH123D model, the results of the Phase I sensitivity analysis provide some basis for
how Phase II model parameters will behave. However, since most of these parameters are
heavily dependent on mesh resolution, additional sensitivity analyses will be performed
as part of the Phase II modeling.
6.2.1 TIME STEP SIZE
Time step size sensitivity SEAWAT model runs were made using the Strongly Implicit
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Procedure (SIP) flow solver and the implicit finite-difference solver with upwinded
weighting to solve the solute transport equations. This transport solver allows the user to
select the length of the transport time step, however long transport time steps with this
method can lead to problems with numerical dispersion that can result in sharp
concentration fronts and large concentration gradients.
Model simulations using a 0.1 year, 1 year, 10 years and 100 years time step sizes were
evaluated to determine the largest time step (i.e. shortest run time) that provides accurate
results. Plots showing head and concentration variation over time at two specific wells
within the UF (ROMP-45 and ENP-100) are shown in Figure 83. At both wells, the base
run time step size of 10 years, shows oscillations in the computed heads for the first 100
year period. These oscillations are substantially greater in the ROMP-45 well. After this
first 100 year period, the computed heads using the 10 year time step are in relatively
synchronous agreement with the heads computed using the 0.1 and 1 year time step sizes.
Results at both wells also show noticeable deviations in the computed heads when the
time step size is increased to 100 years.
6.2.2 SOLVER
SEAWAT uses one set of solvers for the flow portion of the variable density flow
equation and another set of solvers for the solute transport portion of the equations.
Osiensky and Williams (1996) discuss the accuracies and problems associated with
different flow solvers. The solvers that had the best results were the Strongly Implicit
Procedure (SIP) and Preconditioned Conjugate-Gradient (PCG2) solvers. The main flow
solver used for the Phase I modeling was the SIP solver, although the base run was tested
with the PCG solver as a test of solver sensitivity. An advantage of the PCG2 solver is
that one of its convergence criteria depends on the water budget, so errors related to water
budget are reduced. In addition to the PCG2 solver, a sensitivity simulation was also
performed using the Geometric Multigrid Solver (GMG), based on recommendations
from the USGS. The computed heads using each of these flow solvers at groundwater
wells, ENP-100 and ROMP-45, are shown in Figure 84. The heads computed using each
of these flow solvers are reasonably correlated. All SEAWAT runs had solutions that
converged, except where noted, and mass balance errors that were less than 1.0%.
In addition to flow solvers, different numerical methods and corresponding solvers exist
to solve the advection-dispersion-reaction equations. Details of the transport solvers are
described by Zheng and Wang, 1999. One solution method is the Eulerian approach
which uses a fixed grid, in this case a finite difference grid, to solve the transport
equation. The Eulerian approach is better in advection-dominated groundwater systems,
but may be subject to numerical dispersion and/or artificial oscillation in the results. Two
options within the Eulerian approach finite difference solution are available to calculate
advection; upwinded weighting and central-in-space weighting. Upwinded weighting can
result in fewer oscillations, but numerical dispersion can be increased because upwinding
provides only a first order solution. Central-in-space weighting solves the advection term
with an accuracy to the second order and problems related to numerical dispersion are
minimized. However, if the transport in the system is advection-dominated, this scheme
can cause excessive oscillation in the model results. Because of this oscillation issue,
28
Draft - Phase I Regional ASR Model Report
Phase I simulations using the central-in-space weighting methodology were very unstable
and consequently are not evaluated as part of this sensitivity analysis. The standard finite
difference method is somewhat limited in systems that are advection-dominated,
however, when the grid Peclet number is smaller than four (Zheng and Bennett, 1995),
this method is thought to be reasonably accurate. Another advantage of the finite
difference method is faster run times.
Another class of transport solution techniques for advection are the total-variationdiminishing (TVD) methods. MT3DMS contains a third-order TVD solver with a
universal flux limiter called the third-order ULTIMATE scheme (Zheng and Wang,
1999). This scheme conserves mass, has little numerical dispersion, and is fairly free of
numerical oscillation. TVD solutions are generally more accurate for advectiondominated flow, but have much longer run times than the finite difference methods.
The SEAWAT model for Phase I was run initially with the finite-difference solution for
the transport equations with upwinded weighting. Sensitivity runs were made to test the
central-in-space weighting, and also the third-order TVD solver. The TVD runs were
slow in comparison to the standard finite difference solver simulations. The TVD solver
selects the best time step that meets convergence criteria. For SEAWAT runs with the
TVD solver, the Courant number was specified to be 0.75. For these SEAWAT TVD
runs, the time step size was on the order of 0.2 days. By comparison, the upwinded
simulations used time step sizes of 10 years. This resulted in significantly longer model
run times for the TVD simulations, as shown in Section 8.2 of this report. The computed
heads for each of the transport solver simulations at the ENP-100 and ROMP-45
groundwater wells are shown in Figure 84. This figure shows that although more
oscillation occurred in the finite difference solver simulations, the results of the TVD
solver are higher than those computed using the upwinded methodology. This slightly
higher head in the UF, is the result of slower vertical salt migration in the TVD solution.
However, it is important to note that because of the extended run times for the TVD
solution, only 1,000 years of each simulation is shown on Figure 84. Since the ASR
injection and extraction is expected to be advection-dominant, the computed differences
between the two transport solvers are expected to be significant once pumping stresses
are incorporated into the model. Additional methodologies for increasing the speed of
the simulations using the TVD solver are being examined. The goal of this effort is to be
able to decrease the run time of the TVD simulations, without compromising numerical
accuracy in the Phase II modeling effort.
6.2.3 DISPERSIVITY
The dispersivity sensitivity analysis performed for the SEAWAT simulation was similar
to that performed for WASH123D. The baseline SEAWAT simulation used a
dispersivity value of 2.5 feet for longitudinal and transverse dispersivity for the aquifer
and confining unit materials, which was the same as used in the baseline WASH123D
simulation. The ocean and the ocean buffer used dispersivities of 25.0 feet and 10 feet,
respectively. For the SEAWAT sensitivity analysis, dispersivity values of 0.0, 25.0 and
250.0 feet were simulated and compared to the base run. The dispersivities of the ocean
and ocean buffer materials for the base run were the lower limits used for those materials
in the sensitivity runs.
29
Draft - Phase I Regional ASR Model Report
As seen in the WASH123D dispersivity sensitivity analysis, increases in dispersivity
generally resulted in higher concentrations with time. This increase in concentration
resulted in a corresponding decrease in heads, especially in the southern portion of the
model. An example of this can be seen in the vicinity of ENP-100. Figure 85 shows the
head and concentration profiles for the dispersivity simulations at ENP-100 and ROMP45. The charts for ENP-100 show an increase in concentration and decrease in head as
dispersivity is increased. This indicates that as dispersivity increases, the rate of mixing
of higher salinity water with lower salinity water also increases.
A similar trend, but with a smaller magnitude, is observed in the Polk County area. In
this area of the model, the concentration generally drops with time. However, increases
in dispersivity tend to keep the concentrations higher for a slightly longer period of time.
This results in slightly lower computed heads in the high dispersivity simulations, as
shown ROMP-45 charts in Figure 85. Since dispersivity is scale dependent parameter,
further evaluations of its affect on computed heads will be performed for the more refined
Phase II model.
7.0 SOURCES OF ERROR
Many potential sources of error exist for the Phase I model. For such a large model, it is
impossible not to condense available data and make several simplifying assumptions. In
addition, the Phase I model is a test bed model so some simplifications are built-in to the
conceptual model with the knowledge that more details will be incorporated and more
data will be available for Phase II.
Model Resolution
The horizontal distance between computational points for the WASH123D and
SEAWAT models is 5 miles. The vertical distance between computational points varies,
with some computational points only a few feet from each other as found in the northern
part of the SAS and others up to 500 feet apart as found in parts of the MC2. This
resolution contributes to several potential sources of error. The surface heads are set as
fixed boundary conditions across the top of the model. Because the computation points
are 5 miles apart, the driving head of the model is very dependent on the interpolation of
the data applied to the surface. For example, if the computational points fall on either
side of the central ridge through Polk and Highlands Counties, the highest value of head
that forces water into the top of the model may be missed therefore underestimating the
head in geologic units below. Also the location where the majority of water enters the
model from the top is the same area where the model elements are very thin vertically
compared to their large horizontal scale. Large gradients occurring through these
horizontally coarse and large aspect ratio elements/cells may result in increased
instabilities which can propagate through the model. Generally, the large distances
between the computational points leads to the inability to resolve small variations that
may be important to fully understand the flow and transport results.
Boundary heads and salt concentrations
An exhaustive data search was not completed for the Phase I model. Particular data that
was considered representative was selected for use in determining head and concentration
boundary conditions. No attempt was made to collect data to vary the head and
30
Draft - Phase I Regional ASR Model Report
concentration boundaries through time. This strategy was implemented in order to
shorten the time needed for data collection and model construction so that boundary
conditions and other parameters could be tested. Additional data collection for Phase II
will provide more information on which to base boundary conditions, allow transient
boundary condition assignments, and therefore reduce the error associated with
estimating the boundary values.
It will not be possible to completely eliminate the error regarding the boundary
conditions. One problem is the availability of data for use along the boundaries. For the
UF, a large number of monitoring wells exist to provide head and water quality data over
time. In the LF and BZ, this is not the case and some estimation will still be required and
potential errors will exist. In addition, because the model boundaries are set 20 miles into
the ocean, the head and concentration boundary conditions for the UF, MF, and LF are
extrapolated from the selected data at the Florida coast. This extrapolation introduces
uncertainties that are a potential source of error for the model. This is especially true in
the southern portion of the model where boundary conditions have a greater impact on
the head and concentration results. As discussed in Section 8.2, the Phase II model
boundary may be moved inland along the western coast of Florida to minimize the
amount of extrapolation necessary to generate the boundary condition values.
The model boundaries were set out from the coast to reduce boundary effects in the areas
of the proposed ASR wells. Because a variable concentration boundary condition is
used, oscillations of concentrations and heads are possible due to minor changes in flow
direction at computational points on the model boundary. If the direction of flow varies
over consecutive time steps such that flow is into the model for one time step but out of
the model for the next time step, then the salt concentration is computed differently for
the two time steps. Also, when heads are specified, it is the effective freshwater head that
is specified rather than the observed head. Therefore a change in concentration over two
consecutive time steps results in a change in observed heads when the constant equivalent
freshwater head is converted to observed head. Figure 86 shows a graph of head and salt
concentration computed using the WASH123D model over the simulation duration for a
node on the boundary in the MF. The observed head specified at the node is 35 feet and
the concentration is variable with a value of 5,200 mg/l specified when flow is moving
into the model. The observed head oscillates around the specified value of 35 feet and
the salt concentration oscillates around the model computed value of approximately
11,000 mg/l. The southern boundary of the MF is the model location where the
oscillations are the most pronounced. A plan view of the south boundary of the MF
(Figure 87) shows that the oscillating head and concentrations do not propagate to the
interior of the model and so do not have a critical impact on the results. However, for
this reason, it is appropriate to maintain the model boundaries far away from the areas of
interest.
More investigation of this boundary oscillation issue will be conducted for the Phase II
model. For the Phase II model, maintaining the boundaries at 20 miles offshore with a
finer spatial discretization may result in too many model computational points. With a
finer spatial and temporal discretization and shorter simulation duration that will be used
for Phase II, the oscillations observed in the Phase I model may not occur.
31
Draft - Phase I Regional ASR Model Report
Pumping
As mentioned several times through this report, pumping is not included in the Phase I
model which constitutes a significant source of error for the head and concentration
results and the determination of other parameters such as hydraulic conductivity.
Pumping was excluded to shorten the data collection and model set-up time and because
the model resolution was not sufficient to support pumping. Pumping will be included in
the Phase II model and will also be considered a source of error for that model. Errors
associated with the availability and accuracy of transient pumping data and the likelihood
that several pumps will need to be combined on a computational point will result in
sources of error for Phase II.
Dispersivity
For this study, the value of dispersivity used in the model is an estimate. From the
sensitivity results, it appears that the value is in the range of 1 to 50 feet, however no site
specific data has been reviewed at this time. Site specific data will be reviewed in
conjunction with the Phase II model (Section 8.2) to attempt to obtain values that are
more representative. However, because dispersivity is difficult to measure and depends
on mesh/grid resolution as well as geologic material, some variation in this parameter
will remain to be a potential source of error for Phase II.
Time Step and Duration
The use of such a large time step, 10 years, creates some error in the model results.
However the time step sensitivity plots indicate that this error is small enough to be
acceptable for this Phase I test model. Use of smaller time steps does produce less
instability on the boundaries. For Phase II, a much smaller time step will be required
because the spatial resolution will be refined. A time step sensitivity will also be
performed for all Phase II models.
The Phase I model duration is 35,000 years. Data is not available for this time period to
determine the accuracy of the initial condition concentrations or to understand how the
head and concentration values have varied over that period. The Phase I model is not an
accurate representation of actual changes in head and concentration over 35,000 years as
discussed in Section 5.0. For the Phase II model calibration, simulation durations will
not exceed 1 years which will allow for a better comparison of model result variation
with variation observed in monitoring wells.
Despite all the assumptions and potential sources of error, the model fulfills its intended
purpose. That is, to test several parameters to determine their relative importance to the
numerical model, to determine starting parameters for the Phase II model, and to identify
some of the problems that may develop during the Phase II model.
8.0 PHASE I SUMMARY AND PHASE II RECOMMENDATIONS
A regional groundwater model was developed for southern Florida using both the
WASH123D and SEAWAT density-dependent codes. This coarse-resolution model was
developed to test model boundaries, hydraulic and transport parameters and run times; to
generate preliminary flow and transport results to compare against monitoring well data;
32
Draft - Phase I Regional ASR Model Report
and to compare the WASH123D and SEAWAT results as Phase I of the ASR Regional
Modeling Study. Lessons learned in the Phase I study will be incorporated into the
development of the more-detailed Phase II regional model.
8.1 PHASE I MODEL SUMMARY
The Phase I model conceptualization was completed using information from existing
ASR Regional Study task reports and limited review of existing monitoring well data.
Based on the information, the primary area of freshwater recharge for the FAS is the
highland ridge area of Polk County. In this area, groundwater elevations are highest and
salt concentrations lowest in all of the Floridan aquifers. As groundwater moves out in
all directions from this area towards the locations where the geologic units outcrop to the
ocean, heads decrease and salinity increases. Groundwater in the UF is fresh-to-brackish
through most of the unit with an increase in salinity near the southeast coast adjacent to
the ocean. The MF has slightly higher salt concentration and the LF has significantly
higher salt concentrations.
Using WASH123D and SEAWAT, the Phase I model was constructed to include the
Surficial Aquifer System, the Intermediate Aquifer System, the Floridan Aquifer System
and the Boulder Zone but the focus of the model is the Floridan Aquifer System. Three
vertical layers of elements were used to define the most important units for the ASR
study, the UF and MF, with most of the other units represented with two or less vertical
layers. The horizontal model boundaries were defined just north of the recharge area and
20 mile beyond the coast with 5 miles between computational points. The model was run
for 35,000 years assuming that the oldest age of groundwater found in recent
groundwater age research is the length of time for water to traverse the system. The
major assumptions used in construction of the Phase I model are:
• Coarse resolution is able to resolve flow and transport behavior
• No pumping stresses
• Top of the model is the water table (no unsaturated zones)
• Bottom of the model is a no-flow boundary
• Recharge from precipitation is represented using fixed surface heads generated
from existing surface aquifer monitoring well data
• Head boundary conditions do not vary over time
• Dispersivity is constant throughout the model for all materials
• No temperature effects on groundwater density
The results of the model replicate flow and transport behavior in the Polk County
recharge area. Model heads are highest and salt concentrations are lowest in that area for
all the aquifers. Model heads are too low and salt concentrations too high compared to
monitoring well data in the southern part of the model. In that area, the direction of flow
is vertically upward so salt intrudes from the model bottom as well as the ocean in the
southeast resulting in an over-estimation of the salinity. The timing of this upward salt
migration is faster in the SEAWAT simulations than in the WASH123D simulations.
This results in significantly lower computed heads in the UF and MF in the SEAWAT
model at the end of the 35,000 year simulation. Despite varying several parameters
including surface heads, hydraulic conductivities, and boundary heads and
33
Draft - Phase I Regional ASR Model Report
concentrations, the mechanism for allowing more fresh water into the southern part of the
model is unclear. It is possible that the mechanism is missing from the conceptual model,
such as a preferential flowpath or lineament/fracture zone, or that the Phase I model
construction assumptions limit the ability to reproduce the long-term flow and transport
patterns. Additional work will be completed for the Phase II model to determine if higher
spatial and temporal resolution and a shorter transient duration exhibit similar results.
Sensitivity analysis results show that the Phase I model is very sensitive to the
concentration boundary condition method selected and the effect of temperature on
density. The trials for confining unit hydraulic conductivity, initial concentration
conditions and dispersivity show the model is somewhat sensitive to those parameters.
The smallest effects were demonstrated by changes in aquifer hydraulic conductivity.
These results provide some basis for how Phase II model parameters will behave
however, most of these parameters are heavily dependent on mesh resolution. Additional
sensitivity analyses will be required for the Phase II model.
8.2 RECOMMENDATIONS FOR PHASE II
Construction and execution of the Phase I model provide valuable information to aid in
the approach and construction of the Phase II model. The Phase II model will consist of
several parts. First, a comprehensive data review will be completed to ensure that all
available head, concentration, and groundwater withdrawal data is incorporated. Phase II
model construction will accompany the data review since the model construction will
depend heavily on data availability and data requirements. Once the Phase II model is
constructed, a flow only calibration will be performed. For the flow-only transient
calibration, concentration values and transport parameters will be held constant. Flow
parameters will be adjusted until the computed heads reasonably match observed data.
Once a satisfactory flow-only calibration is obtained, full transient calibrations, with
variable flow and concentration data, will be completed. In conjunction with the regional
model, smaller scale transient stress-test “inset” models will be developed in the vicinity
of operational ASR wells. Data collected during the on-going operations or cycle testing
will be used to facilitate the calibration of transport parameters for the regional model.
Regional model calibration data will encompass flow and water quality data in
approximately 200 monitoring wells in multiple geologic units. Regional model
calibration will be performed where computed and observed data are correlated over a
duration of approximately 1 year. Flow and water quality data for a different 1-year time
period, representing a different hydraulic condition (e.g. drier or wetter year), will be
used for model validation. The goal of this effort is a fully-calibrated Phase II Regional
Model. The calibrated Phase II model will ultimately be used in the evaluation of
proposed ASR project alternatives.
During the data review and model construction portion of Phase II, several decisions will
be required. The location of the model boundary is a trade-off between keeping the
boundary conditions as far away from the areas of interest as possible and limiting the
number of computational points required. The areas of interest for this modeling study
are the proposed locations of the ASR wells. Most of the ASR wells are proposed near
Lake Okeechobee and on the east coast (e.g. Hillsborough and Palm Beach County).
34
Draft - Phase I Regional ASR Model Report
Only one proposed ASR location, near proposed reservoir C-43, is a west of Lake
Okeechobee. It is located approximately 40 miles away from the western coast so
moving the model boundary closer to the western coast should not impact it. It is
recommended that the model boundary on the west and south side of the state be moved
in to the Florida coastline as shown in Figure 88. In addition to reducing the amount of
model computational points, moving the model boundary to the western coast has the
benefit of eliminating uncertainties associated with extrapolating head and concentration
boundary condition values along a portion of the model perimeter. As discussed in
Section 7.0, testing will be required to determine if potential oscillations on the boundary
will limit how close to the coast the boundary can be moved.
Once the model boundary is defined, the resolution will be selected. The Phase II model
resolution will depend on how many calibration wells are chosen, how many pumping
wells are incorporated for the time period selected, the potential for combining several
pumping wells in the model and how the proposed ASR locations are simulated. This
selection is critical because the number of computational points selected will dictate the
model run times. The Phase I model contains approximately 50,000 computational points
for both WASH123D and SEAWAT. To run 50,000 points with a 10-year time step for
35,000 years takes approximately 110 hours for WASH123D, 3.25 hours for SEAWAT
(upwinded) and 1,247 hours (estimated) for SEAWAT (TVD). For Phase II, the mesh
size will increase by approximately 10-fold yielding 500,000 computational points.
Because the mesh resolution will be finer, the time step size will be smaller. Even
though, more computations will be made at more frequent intervals, the run times are still
expected to be reasonable because the total simulation duration will decrease to
approximately 1 year for the separate calibration and validation runs. Table 1 shows the
estimated run times for a 1 year simulation for a 500,000 node discretization with a 0.5day, 1-day and 5-day time step size, assuming a direct relationship between the variables.
This estimate indicates that run times should be reasonable as long as the mesh resolution
does not increase beyond 500,000 computational points. Note that the additional model
stresses resulting from incorporating pumping into the Phase II model could alter these
calculations.
Table 1. Phase II Run Time Estimate
WASH123D
Nodes
Time step
Duration (yrs)
Phase I – 50,000
10 year
35,000
Phase II – 500,000
5 day
1
Phase II – 500,000
1 day
1
Phase II – 500,000
0.5 day
1
35
Run time (hrs)
110
23
115
230
Draft - Phase I Regional ASR Model Report
Table 1. Phase II Run Time Estimate (cont’d)
SEAWAT
Cells
Time step
Duration (yrs)
Phase I (upwinded) – 50,000
10 year
35,000
Phase II (upwinded) – 500,000
5 day
1
Phase II (upwinded) – 500,000
1 day
1
Phase II (upwinded) – 500,000
0.5 day
1
Phase I (TVD) – 50,000
10 year
35,000
Phase II (TVD)– 500,000
5 day
1
Phase II (TVD) – 500,000
1 day
1
Phase II (TVD) – 500,000
0.5 day
1
Run time (hrs)
3.25
0.7
3.4
6.8
1,247 (est.)
260
1,300
2,600
Another model construction decision that will impact the resolution and run time is the
vertical discretization. Near the ASR wells, complex flow patterns and high
concentration gradients will exist that may not be adequately resolved with the three
layers defined in both the UF and MF for Phase I. ASR wells will be located in the both
UF and MF so these layers will require additional vertical discretization.
Additional vertical discretization may also be necessary in the confining units. As part of
Phase II, several test models will be constructed to further examine the differences
between WASH123D and SEAWAT noted in Section 6.1. Mesh/grid resolution issues,
differences in treatment of vertical hydraulic conductivity and the configuration of the
finite element computational nodes and the finite difference computational cells may
affect the computed rate of vertical salt migration. A better understanding of these
differences is required to ensure that the proper vertical salt migration is simulated in
both the SEAWAT and WASH123D models.
Boundary heads will be set in all aquifers for Phase II based on transient data collected
for the stress periods selected. The head and salt concentration within the surface aquifer
will also be set based on transient data collected for the stress period. Variable
concentration boundary conditions will be used in both models for the salinity boundaries
in all aquifers. When the flow direction on the boundary is into the model, the
concentration will be based on transient data collected for the stress period selected.
When the flow direction is out of the model, the concentration will be model-computed.
For the flow-only calibration, a good concentration initial condition dataset will be
selected based on available data. The initial condition concentrations will be smoothed
by running the model for a few time steps and using the resulting concentration variation
as the initial conditions for the calibration runs. This method should reduce any sharp
fronts or “blockiness” in the concentration initial condition from data interpolation.
Based on the results of the sensitivity to temperature effects for Phase I, it is also
recommended that temperature be included in some capacity in the Phase II model. At a
minimum, the boundary head for the BZ should incorporate temperature effects.
Additional Phase II sensitivity runs may determine whether temperature effects should be
coded to other head boundaries or if full heat transfer computations are warranted.
36
Draft - Phase I Regional ASR Model Report
9.0 FIGURES
Report Figures are submitted in a separate file “ASRDraftReportFigures.pdf
37
Model Area
Lake
Okeechobee
Florida Bay
Figure 1
Model Area
ASR Regional Model – Phase I
December 2006
From CH2MHill (2006)
Figure 2
Existing Model Locations – Data Collection Report
ASR Regional Model – Phase I
December 2006
SAS
IC / IA
UF
MS / MC1
MF
Surficial Aquifer System
Intermediate Confining and/or Intermediate Aquifer System
Upper Floridan Aquifer
Upper middle semi and/or confining unit
Middle Floridan aquifer
MC2
Lower middle confining unit (SFCU is sub Floridan confining unit)
LF1
Lower Floridan Aquifer - first permeable zone.
LC
Lower confining unit
LF2 (LF3, etc)
Confining Unit VIII
BZ
Confining Units from Miller, 1986 - not always continuous within region. LF2, LF3, etc. are deeper
permeable zones within the Lower Floridan Aquifer.
Boulder Zone - not continuous across study area
From Reese and Richardson (2004)
Figure 3
Schematic Geologic Cross Section
ASR Regional Model – Phase I
December 2006
Approximate
Vertical Scale
(feet – NGVD)
1000
SU
IC/IA
UF
MC1
MF
MC2
LF
LC
BZ
0
-1000
-2000
-2850
Vertical scale exaggerated by 200:1
Graphic of Florida at elevation 1000’ for reference
Figure 4
Hydrostratigraphic Surfaces
ASR Regional Model – Phase I
December 2006
From Reese and Richardson (2004)
Figure 5
UF - Transmissivity
ASR Regional Model – Phase I
December 2006
From Reese and Richardson (2004)
Figure 6
MF - Transmissivity
ASR Regional Model – Phase I
December 2006
From Reese and Richardson (2004)
Figure 7
LF - Transmissivity
ASR Regional Model – Phase I
December 2006
g
0.0257
0.0961
1.8045
0.5
1.
5
1500000
1.25
5
0.2
1.4605
25
1.
1
1
75
0.
0.6915
0.5
0.4522
0.2983
0.2
5
0.2
5
0.0206
0.0125
0.003
5
0.7
0.5
0.0062
0.2561
0.0012
0.1942
0.0501
0.1094
0.1641
0.0564 0.02
0.0072
0.0395
0.0107
0.0061
0.0751
25
0.
0.0725
1250000
0.0306
0.39
0.25
0.0042
0.0016
0.3715
0.4053
0.0038
0.0073
0.1018
0.2
5
0.0194
0.0079
0.0008
0.0267
0.124
0.0071
0.0181
0.1051
0.0289
0.0218
0.25
0.0727
0.0075
0.3137
1.5059
0.7163
0.5
0.3748
1
1.2
5
0.1005
5
0.2
0.2796
5
0.7
0.
25
0.3718
0.2899
Northing
750000
0.5
1.7498
1
0.0142
0.25
0.0206
0.0237
0.
75
0.152
0.5
0.1024
25
0.
1000000
0.75
0.0009
0.
25
0.0081
0.5523
0.0073
0.0043
0.0219
0.0785
0.2
5
0.5
0.0781
0.5
0.1409
0.
25
0.575
0.0958
0.0186
0.25
0.1211
0.0087
0.2289
0.1057
0.087
0.0601
0.1728
0.2454
0.0841
0.0268
0.2 0.0029
50.3776
0.0087
0.0177
0.6228
25
0.
500000
0.0436
0.299
0.0011
250000
Kv MC1- leakance only
Kv MC1- core only
Kv MC1- APT only
Kv MC1 - Leakance & Core
Contour Line Start: 0.01 Step: 0.25 Stop: 2
0.01
0.5
0
250000
1
500000
Easting
From Reese and Richardson (2004)
2
2
1.5
750000
1000000
Figure 8
MC1 - Hydraulic Conductivity
ASR Regional Model – Phase I
December 2006
1500000
g
1.30E-02
4.48E-02
1.51E-01
1.64E-02
1250000
4.25E-01
2.40E-03
4.12E-01
0.25
0.2
5
8.88E-02
1.27E-01
1000000
5
0.2
0.2
6.24E-01
2.22E-02
5
0.5
Northing
750000
0.25
0.5
5
0.2
4.61E-04
9.90E-01
0.5
0.75
1.56E-03
0.75
0.
25
0.5
5.85E-02
0.25
0.75
3.27E-02
1
250000
1.25
1
1.5
500000
0.7
5
1.2
5
1.83E+00
Kv MC2 - Leakance
Kv MC2 - Core
Contour Line Start: 0.001 Step: 0.25 Stop: 2
0.001
0
0.5
250000
500000 1
Easting
From Reese and Richardson (2004)
750000 1.5
1000000
2
2
Figure 9
MC2 - Hydraulic Conductivity
ASR Regional Model – Phase I
December 2006
g
5
1500000
0.
7
0.5
0.25
1.14E-01
1.18E+00
0.7
5
0.75
9.05E-01
1250000
5
0.
0.75
3.07E-02
1.86E-01
0.75
0.
25
0.5
0.25
2.48E-01
7.30E-02
5.93E-01
1000000
0.5
0.75
4.71E-01
1.06E+00
0.5
0.5
0.2
5
1.04E-01
7.40E-02
0
0.5
Northing
750000
.2
5
2.23E-02
5.63E-01
3.12E-01
4.31E-02
7.23E-02
500000
0.2
5
2.71E-02
250000
25
0.
Kv LC - Core
Contour Line Start: 0.001 Step: 0.25 Stop: 2
0.001
0.5
0
250000
1
500000
Easting
From Reese and Richardson (2004)
2
2
1.5
750000
1000000
Figure 10
LC - Hydraulic Conductivity
ASR Regional Model – Phase I
December 2006
From SFWMD
Figure 11
Hydraulic Conductivity - SAS
ASR Regional Model – Phase I
December 2006
Head from several online databases
Head from Reese and Cunningham, 2000
Head from USGS online database
x
Head from Sepulveda, 2002 model results
(small x points appear as shaded area)
Figure 12
Selected Head Data - SAS
ASR Regional Model – Phase I
December 2006
10
20
30
40
120
110
100
10
20
90
80
70
60
30
50
40
40
50
60
Head chosen from online databases
Head from Table 4-1, HydroGeoLogic, 2006
X
Head from Sepulveda, 2002 model results
(small x points appear as shaded area)
Predevelopment head contours from USGS (1988)
(feet NGVD – 10 ft interval)
Figure 13
Selected Head Data - UF
ASR Regional Model – Phase I
December 2006
Head chosen from online databases
Head from Table 4-1, HydroGeoLogic, 2006
Figure 14
Selected Head Data - MF
ASR Regional Model – Phase I
December 2006
Head selected from O’Reilly et al., 2002
Head from Table 4-1, HydroGeoLogic, 2006
Head chosen from online databases
Figure 15
Selected Head Data - LF
ASR Regional Model – Phase I
December 2006
Prepared by SFWMD
Figure 16
Elevation of 10,000 mg/l TDS
ASR Regional Model – Phase I
December 2006
Concentration chosen from online databases
Concentration from Table 4-4, HydroGeoLogic 2006
Figure 17
Water Quality Data - UF
ASR Regional Model – Phase I
December 2006
Concentration from Table 4-4, HydroGeoLogic 2006
Figure 18
Water Quality Data - MF
ASR Regional Model – Phase I
December 2006
Concentration chosen from online databases
Concentration from Table 4-4, HydroGeoLogic 2006
Figure 19
Water Quality Data - LF
ASR Regional Model – Phase I
December 2006
Figure 20
Age of Groundwater - UF
ASR Regional Model – Phase I
December 2006
Figure 21
Age of Groundwater - MF
ASR Regional Model – Phase I
December 2006
Prepared by SFWMD
Figure 22
Florida Peninsula Outcrop
ASR Regional Model – Phase I
December 2006
Model Boundary
Topography/Bathymetry prepared by Jacksonville District
Figure 23
Model Boundary &
Topography/Bathymetry
ASR Regional Model – Phase I
December 2006
A’
A
WASH123D Mesh
SAS
UFAS
MC1
MFAS
MC2
IAS
ICU
LFAS
LC
LC trans
BZ
SEAWAT Grid
SAS
UFAS
MC1
MFAS
MC2
IAS
ICU
LFAS
LC
LC trans
BZ
A
A’
23 layers of WASH123D nodes and 22 layers of SEAWAT cells
1 Surficial Aquifer System (SAS)
5 Hawthorne Group or Intermediate Confining Unit (HG or ICU)
Middle layer of 5 contains Intermediate Aquifer System (IAS)
3 Upper Floridan Aquifer (UFAS)
2 Middle Confining Unit 1 (MC1)
3 Middle Floridan Aquifer (MFAS)
2 Middle Confining Unit 2 (MC2)
2 Lower Floridan Aquifer (LFAS)
2 Lower Confining Unit (LC)
1 Lower Confining Unit transition zone (LC Transition)
1 Boulder Zone (BZ)
Figure 24
Model Cross Section
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
’
SEAWAT Grid
Figure 25
Horizontal Mesh & Grid Resolution
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
SEAWAT Grid
SAS
Kh (ft/d)
25
50
100
1000
5000
10000 - ocean
Kh=10Kv
’
IC
Kh (ft/d)
0.2
0.02
0.002
Kh=2Kv
Figure 26
Model Hydraulic Conductivity - SAS
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
SEAWAT Grid
IC
Kh (ft/d)
Ocean
Kh (ft/d)
0.2
0.02
0.002
0.0002
Kh=2Kv
10000
100
’
Kh=10Kv
Figure 27
Model Hydraulic Conductivity
IC above IA
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
SEAWAT Grid
IA
Kh (ft/d)
100
Kh=10Kv
IC
Kh (ft/d)
Ocean
Kh (ft/d)
0.2
0.02
0.002
0.0002
Kh=2Kv
10000
100
’
Kh=10Kv
Figure 28
Model Hydraulic Conductivity – IC/IA
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
SEAWAT Grid
IC
Kh (ft/d)
0.2
0.02
0.002
0.0002
Kh=2Kv
Ocean
Kh (ft/d)
10000
100
Kh=10Kv
Figure 29
Model Hydraulic Conductivity
IC below IA
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
SEAWAT Grid
UF
Kh (ft/d)
Ocean
Kh (ft/d)
50
100
200
Kh=10Kv
10000
’
Kh=10Kv
Figure 30
Model Hydraulic Conductivity - UF
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
MC1
Kh (ft/d)
1.0
1.0
2.0
Kh=2Kv
SEAWAT Grid
Ocean
Kh (ft/d)
10000
100
’
Kh=10Kv
Figure 31
Model Hydraulic Conductivity – MC1
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
MF
Kh (ft/d)
1.0
500
1000
3000
Kh=10Kv
Where K=1.0,
aquifer is not’ present and Kh=2Kv
SEAWAT Grid
Ocean
Kh (ft/d)
10000
Kh=10Kv
Figure 32
Model Hydraulic Conductivity - MF
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
SEAWAT Grid
MC2
Kh (ft/d)
0.01
0.02
0.1
0.2
Kh=2Kv
Ocean
Kh (ft/d)
10000
100
Kh=10Kv
Figure 33
Model Hydraulic Conductivity – MC2
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
SEAWAT Grid
LF
Kh (ft/d)
1.0
100
500
1000
Kh=10Kv
Where K=1.0,
aquifer is not’ present and Kh=2Kv
Figure 34
Model Hydraulic Conductivity - LF
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
LC
Kh (ft/d)
1.0
1.0
Kh=2Kv
SEAWAT Grid
Figure 35
Model Hydraulic Conductivity - LC
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
SEAWAT Grid
LC trans
Kh (ft/d)
500
Kh=10Kv
’
Figure 36
Model Hydraulic Conductivity – LC trans
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
SEAWAT Grid
BZ
Kh (ft/d)
1.0
10000
Kh=10Kv
Where K=1.0,
aquifer is not’ present and Kh=2Kv
Figure 37
Model Hydraulic Conductivity - BZ
ASR Regional Model – Phase I
December 2006
Land surface
Ocean
Kh (ft/d)
10000
Figure 38
Elements within the Ocean
ASR Regional Model – Phase I
December 2006
WASH123D Mesh
SEAWAT Grid
SAS Head
NGVD29 ft
Figure 39
Specified Surface Heads
ASR Regional Model – Phase I
December 2006
Observed Head
NGVD29 ft
Northern Boundary
’
Figure 40
Specified Observed Heads
Boundary Conditions
ASR Regional Model – Phase I
December 2006
Equivalent Freshwater Head
NGVD29 ft
Northern Boundary
’
Figure 41
Specified Equivalent Freshwater Heads
Boundary Conditions
ASR Regional Model – Phase I
December 2006
Concentration (mg/L)
35000
Northern Boundary
’
Figure 42
Salt Concentrations in mg/l TDS
Boundary Conditions
ASR Regional Model – Phase I
December 2006
A’
Concentration (mg/L)
35000
D
Salt water in ocean
B’
Fresher water in UF/MF
A
C’
B
D
C
A’
B’
A
B
C’
C
D’
D’
Figure 43
Salt Concentrations in mg/l TDS
Initial Conditions
ASR Regional Model – Phase I
December 2006
10
20
110 ft Computed Head
30
40
120
50 ft Computed Head
110
100
90
10
20
40 ft Computed Head
80
70
30
10 ft Computed Head
60
Observed Head
NGVD29 ft
40
Contour interval = 10 feet
50
50
60
40
Figure 44
UF Predevelopment vs. Computed
Head Contours
Steady State Conditions
ASR Regional Model – Phase I
December 2006
10
20
110 ft Computed Head
30
40
120
50 ft Computed Head
110
100
90
10
20
40 ft Computed Head
80
70
10 ft Computed Head
60
50
30
40
Observed Head
NGVD29 ft
40
Contour interval = 10 feet
50
60
Figure 45
UF Predevelopment vs. Computed
Head Contours
Transient Conditions
ASR Regional Model – Phase I
December 2006
SAS
IC/IA
A
A’
UF
MC1
MF
MC2
LF
LC
BZ
A’
A
Figure 46
Northern Cross Section
Transient Conditions
ASR Regional Model – Phase I
December 2006
SAS
B
B’
IC/IA
UF
MC1
MF
MC2
LF
LC
BZ
B’
B
Figure 47
Central Cross Section
Transient Conditions
ASR Regional Model – Phase I
December 2006
IA
UF
60
130
110
20
60
60
20
40
40
Observed Head
NGVD29 ft
20
Contour interval = 10 feet
40
Figure 48
Head Comparison in the IA and UF
Steady State
ASR Regional Model – Phase I
December 2006
IA
UF
Velocity (ft/s)
Figure 49
Velocity Vectors in the IA and UF
Steady State
ASR Regional Model – Phase I
December 2006
MF
LF
60
20
110
20
40
60
20
20
40
Observed Head
NGVD29 ft
40
Contour interval = 10 feet
Figure 50
Head Comparison in the MF and LF
Steady State
ASR Regional Model – Phase I
December 2006
MF
LF
Velocity (ft/s)
Figure 51
Velocity Vectors in the MF and LF
Steady State
ASR Regional Model – Phase I
December 2006
-
ROMP-49
01/01/95
01/01/00
01/01/05
01/01/10
Figure 52
ROMP-49 Head Data
ASR Regional Model – Phase I
December 2006
Time 0.0 years
Time 35,000 years
35000
Figure 53
UF Salt Concentration Change
ASR Regional Model – Phase I
December 2006
Time 0.0 years
Time 35,000 years
35000
Figure 54
MF Salt Concentration Change
ASR Regional Model – Phase I
December 2006
Time 0.0 years
Time 35,000 years
35000
Figure 55
LF Salt Concentration Change
ASR Regional Model – Phase I
December 2006
Time 0.0 years
-2100
Time 35,000 years
-1800
-2100
-1800
-600
Elevation (ft)
Contour interval = 300 feet
Figure 56
Change in Elevation of 10,000 mg/l TDS
ASR Regional Model – Phase I
December 2006
Time 10,000 years
Head contours
ROMP-45
Salt concentrations
60
110
40
20
60
20
40
Observed Head
NGVD29 ft
Concentration
(mg/L)
35000
ENP-100
Contour interval = 10 feet
Figure 57
Sensitivity – Base Run UF Results
ASR Regional Model – Phase I
December 2006
Time 10,000 years
Head contours
Salt concentrations
60
110
40
20
20
60
40
Observed Head
NGVD29 ft
Contour interval = 10 feet
Concentration
(mg/L)
35000
Figure 58
Sensitivity – 2 x K(Aquifer) UF Results
ASR Regional Model – Phase I
December 2006
Time 10,000 years
Head contours
Salt concentrations
60
120
40
20
20
60
40
Observed Head
NGVD29 ft
Contour interval = 10 feet
Concentration
(mg/L)
35000
Figure 59
Sensitivity – Half K(Aquifer) UF Results
ASR Regional Model – Phase I
December 2006
Time 10,000 years
Head contours
Salt concentrations
60
120
40
20
20
60
40
Observed Head
NGVD29 ft
Contour interval = 10 feet
Concentration
(mg/L)
35000
Figure 60
Sensitivity – 2 x K(Confining Unit) UF Results
ASR Regional Model – Phase I
December 2006
Time 10,000 years
Head contours
Salt concentrations
60
110
40
20
20
60
Observed Head
NGVD29 ft
40
Contour interval = 10 feet
Concentration
(mg/L)
35000
Figure 61
Sensitivity – Half K(Confining Unit) UF Results
ASR Regional Model – Phase I
December 2006
Head contours
Time 10,000 years
Salt concentrations
60
110
40
20
60
20
40
Observed Head
NGVD29 ft
Contour interval = 10 feet
Concentration
(mg/L)
35000
Figure 62
Sensitivity – Increase Initial Concentrations 25%
UF Results
ASR Regional Model – Phase I
December 2006
Head contours
Time 10,000 years
Salt concentrations
60
110
40
20
20
60
40
Observed Head
NGVD29 ft
Contour interval = 10 feet
Concentration
(mg/L)
35000
Figure 63
Sensitivity – Decrease Initial Concentrations 25%
UF Results
ASR Regional Model – Phase I
December 2006
35000
Boundary concentrations ~ 5,000 mg/l
Interior concentrations > 25,000 mg/l
Inset
enlarged
at right
Figure 64
Sensitivity – Specified Conc. Boundary Cond. - MF Results
ASR Regional Model – Phase I
December 2006
ROMP-45
ENP-100
25000
7000
6000
20000
Concentration (mg/l)
Concentration (mg/l)
5000
4000
3000
2000
15000
10000
5000
1000
0
0
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0
10000
2000
4000
8000
10000
12000
45
92
90
40
Total Head (ft)
88
Total Head (ft)
6000
Simulation Time (years)
Simulation Time (years)
86
35
30
84
25
82
20
80
0
2000
4000
6000
8000
10000
0.0
2000
2.5
25.0
0
2000
4000
6000
8000
10000
12000
Simulation Time (years)
Simulation Time (years)
0
12000
4000
250.0
Figure 65
Sensitivity – Dispersivity Effects at Selected Wells
ASR Regional Model – Phase I
December 2006
ENP-100
2000
6000
1800
5900
1600
5800
1400
5700
Concentration (mg/l)
Concentration (mg/l)
ROMP-45
1200
1000
800
600
5600
5500
5400
5300
400
5200
200
5100
0
5000
0
200
400
600
800
1000
0
500
1000
1500
Simulation Time (years)
2000
2500
3000
3500
4000
4500
5000
Simulation Time (years)
91
44
90.5
43.5
90
Total Head (ft)
Total Head (ft)
43
89.5
89
88.5
42.5
42
88
41.5
87.5
87
41
0
100
200
300
400
500
600
700
800
900
1000
Simulation Time (years)
0.2 yr
1 yr
5 yr
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Simulation Time (years)
10 yr
100 yr
Figure 66
Sensitivity – Time Step Effects at Selected Wells
ASR Regional Model – Phase I
December 2006
Prepared by SFWMD
Figure 67
Sensitivity – Temperature Effects on BZ BC
ASR Regional Model – Phase I
December 2006
UF and MF
Time 0.0 years
LF
Head difference (ft)
Figure 68
Sensitivity – BZ Temperature Effects on Resulting Heads
ASR Regional Model – Phase I
December 2006
A
A’
35000
UF
Time
1,000 years
MF
LF
UF
Time
10,000 years
MF
LF
A
Time
35,000 years
A’
UF
MF
LF
Figure 69
Saltwater Intrusion Front at Ocean Boundary
ASR Regional Model – Phase I
December 2006
Model Boundary
WASH123D Total Head
SEAWAT Total Head
50
30
Observed Head
NGVD29 ft
10
Contour interval = 10 feet
Figure 70
WASH123D to SEAWAT Head Comparison
SAS Steady State Solution
ASR Regional Model – Phase I
December 2006
Model Boundary
80
WASH123D Total Head
80
SEAWAT Total Head
60
60
40
40
Observed Head
NGVD29 ft
Contour interval = 10 feet
Figure 71
WASH123D to SEAWAT Head Comparison
UF Steady State Solution
ASR Regional Model – Phase I
December 2006
Model Boundary
80
80
WASH123D Total Head
SEAWAT Total Head
60
60
40
40
Observed Head
NGVD29 ft
Contour interval = 10 feet
Figure 72
WASH123D to SEAWAT Head Comparison
MF Steady State Solution
ASR Regional Model – Phase I
December 2006
Model Boundary
WASH123D Total Head
50
SEAWAT Total Head
50
10
10
30
30
Observed Head
NGVD29 ft
Contour interval = 10 feet
Figure 73
WASH123D to SEAWAT Head Comparison
LF Steady State Solution
ASR Regional Model – Phase I
December 2006
50
50
Model Boundary
10
10
WASH123D Total Head
SEAWAT Total Head
Observed Head
NGVD29 ft
Contour interval = 10 feet
Figure 74
WASH123D to SEAWAT Head Comparison
BZ Steady State Solution
ASR Regional Model – Phase I
December 2006
30
70
Model Boundary
WASH123D Total Head
SEAWAT Total Head
10
10
Observed Head
NGVD29 ft
Contour interval = 10 feet
Figure 75
WASH123D to SEAWAT Head Comparison
BZ Transient Solution at 35,000 years
ASR Regional Model – Phase I
December 2006
35000
A
WASH123D Concentration at 35,000 years
A’
A’
A
SEAWAT Concentration at 35,000 years
Figure 76
WASH123D to SEAWAT Concentration Comparison
Transient Solution at 35,000 years
ASR Regional Model – Phase I
December 2006
Model Boundary
WASH123D Total Head
SEAWAT Total Head
60 60
10
10
Observed Head
NGVD29 ft
Contour interval = 10 feet
Figure 77
WASH123D (variable bottom BC)
to SEAWAT Head Comparison
BZ Transient Solution at 20,000 years
ASR Regional Model – Phase I
December 2006
Model Boundary
WASH123D Total Head
70
70
50
SEAWAT Total Head
50
30
30
10
Observed Head
NGVD29 ft
Contour interval = 10 feet
Figure 78
WASH123D (variable bottom BC)
to SEAWAT Head Comparison
LF Transient Solution at 20,000 years
ASR Regional Model – Phase I
December 2006
Model Boundary
80
80
WASH123D Total Head
SEAWAT Total Head
60
60
40
40
Observed Head
NGVD29 ft
20
Contour interval = 10 feet
Figure 79
WASH123D to SEAWAT Head Comparison
UF Transient Solution at 35,000 years
ASR Regional Model – Phase I
December 2006
Model Boundary
80
WASH123D Total Head
80
SEAWAT Total Head
60
60
40
40
Observed Head
NGVD29 ft
20
20
Contour interval = 10 feet
Figure 80
WASH123D to SEAWAT Head Comparison
MF Transient Solution at 35,000 years
ASR Regional Model – Phase I
December 2006
35000
B
B’
B
WASH123D Concentration at 35,000 years
B’
SEAWAT Concentration at 35,000 years
Figure 81
WASH123D to SEAWAT Concentration Comparison
Transient Solution at 35,000 years
ASR Regional Model – Phase I
December 2006
SEAWAT Hydraulic Conductivity
WASH123D Hydraulic Conductivity
Effective Vertical K
Between SEAWAT
Computational Points
SEAWAT Layer
1
Effective Vertical K
Between WASH123D
Computational Points
Cell
Vertical K
WASH123D Layer
Aquifer 1
100
Element
Vertical K
100
1
100
1
2
1
1
3
1
100
4
100
50.5
2
1
1
3
Confining Unit 1
1
50.5
4
100
Computational Locations
Aquifer 2
Figure 82
Schematic Conductivity Distribution
ASR Regional Model – Phase I
December 2006
ENP-100
2000
6000
1500
5500
Concentration (mg/l)
Concentration (mg/l)
ROMP-45
1000
500
5000
4500
0
4000
0
100
200
300
400
500
600
700
800
900
1000
0
100
200
300
Simulation time (years)
400
500
600
700
800
900
1000
Simulation time (years)
47.5
90.5
90
47
89.5
46.5
Total Head (ft.)
Total Head (ft.)
89
88.5
88
87.5
87
46
45.5
45
86.5
44.5
86
44
85.5
0
100
200
300
400
500
600
700
800
900
1000
SEAWAT_BASE_TS=10yrs
10 yrs
SEAWAT_TS=0.1yr
0.1 yr
0
100
200
300
400
500
600
700
800
900
1000
Simulation time (years)
Simulation time (years)
SEAWAT_TS=1yr
1 yr
SEAWAT_TS=100yr
100 yrs
Figure 83
Sensitivity – Time Step Effects at Selected Wells
ASR Regional Model – Phase I
December 2006
ENP-100
2000
6000
1500
5500
Concentration (mg/l)
Concentration (mg/l)
ROMP-45
1000
500
5000
4500
0
4000
0
100
200
300
400
500
600
700
800
900
1000
0
100
200
300
Simulation time (years)
400
500
600
700
800
900
1000
Simulation time (years)
47.5
90.5
90
47
89.5
46.5
Total Head (ft.)
Total Head (ft.)
89
88.5
88
87.5
46
45.5
87
45
86.5
44.5
86
44
85.5
0
100
200
300
400
500
600
700
800
900
1000
SEAWAT_BASE_SIP/upw
ind
SIP Upwinded
SEAWAT_SIP/TVD
SIP TVD
SEAWAT_GMG/upw
ind
GMG Upwinded
0
100
200
300
400
500
600
700
800
900
1000
Simulation time (years)
Simulation time (years)
SEAWAT_PCG/upw
ind
PCG Upwinded
Figure 84
Sensitivity – Solver Effects at Selected Wells
ASR Regional Model – Phase I
December 2006
ROMP-45
ENP-100
25000
2000
1800
20000
1600
Concentration (mg/L)
Concentration (mg/L)
1400
1200
1000
800
600
15000
10000
5000
400
200
0
0
0
5000
10000
15000
20000
25000
30000
35000
0
5000
10000
15000
20000
25000
30000
35000
30000
35000
Simulation time (years)
90.5
50
90
45
89.5
40
89
35
Total Head (ft.)
Total Head (ft.)
Simulation time (years)
88.5
88
87.5
30
25
20
87
15
86.5
10
86
5
85.5
0
0
5000
10000
15000
20000
25000
30000
35000
Simulation time (years)
SEAWAT_BASE_2.5dispersivity
2.5 ft
SEAWAT_0dispersivity
0.0 ft
0
5000
10000
15000
20000
25000
Simulation time (years)
SEAWAT_25dispersivity
25.0 ft
SEAWAT_250dispersivity
250.0 ft
Figure 85
Sensitivity – Dispersivity Effects at Selected Wells
ASR Regional Model – Phase I
December 2006
Middle Floridan Head and Concentration at a South Boundary Node
50
25000
head
concentration
Head (ft)
45
20000
40
15000
35
10000
30
5000
25
0
5000
10000
15000
20000
25000
30000
0
35000
Time (yr)
Figure 86
Head and Concentration Oscillations over Time
WASH123D Model
ASR Regional Model – Phase I
December 2006
Head contours
Salt concentrations
Concentration (mg/l)
Head (ft)
35000
Oscillation locations
Figure 87
Head and Concentration Oscillations – Plan View
WASH123D Model
ASR Regional Model – Phase I
December 2006
Land surface
Model Boundary
Figure 88
Proposed Revised Boundary
ASR Regional Model – Phase I
December 2006
Draft - Phase I Regional ASR Model Report
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