Transcript SFWMD Calibration of a Density-Dependent Groundwater Flow Model of the Lower West Coast Floridan Aquifer System Jorge I.

SFWMD
Calibration of a Density-Dependent Groundwater
Flow Model of the Lower West Coast Floridan
Aquifer System
Jorge I. Restrepo1, David Garces1,
Angela Montoya2, and Laura Kuebler2
1
FAU
2
SFWMD
1
SFWMD
Background
•
The ever-increasing population of south Florida
has put a strain on water supplies which are
mostly taken from the surficial and intermediate
aquifer systems
•
Alternative water sources sought to curb the
pressures exerted on present water supplies
•
Cooperative effort between SFWMD and FAU to
develop model to improve understanding of the
Floridan aquifer system and build on in future
2
SFWMD
Purpose
•
Developed a tool to handle density
variations in space and/or time due to
brackish nature of the FAS
•
Used a modified version of SEAWAT
(Langevin et al. 2003) to produce a density
dependent groundwater model for the
Lower West Coast Floridan aquifer system
3
SFWMD
Study Area
•
Active zone defined, when possible, as larger
than Lower West Coast (LWC) Planning Area,
such that a buffer zone is created
•
Three major aquifer systems:
•
Surficial aquifer system (SAS)
•
Intermediate aquifer system (IAS)
•
Floridan aquifer system (FAS)
•
Model focuses on the IAS and FAS
•
Hydrostratigraphy follows Reese & Richardson
•
Only stress is groundwater extraction and/or
injection
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SFWMD
Study Area
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SFWMD
Lithology, Geologic Units and Hydrogeologic
Units (Reese & Richardson)
E'
2000
E e
l va to
i n
1000
0
E
0
100000
200000
300000
400000
500000
S ec toi n D si tance
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SFWMD
Conceptual Model
 Data Collection (limited data)
• Potentiometric heads
• Salinity (even sparser)
• Pumping FAS-at the time of calibration little stress
• Pumping IAS-estimated
 Complexity
• Initial conditions for heads and salinity
• Boundaries
• Aquifer properties
• Heterogeneity (ky, kx) , very limited information
• Vertical conductivity of the confining units
• Current research in structural geology
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SFWMD
Groundwater Divide and Flow Lines of
FAS (modified-Meyer)
LAKE
OKEECHOBEE
Model Grid Boundary
Constant Head
LWC Boundary Planning Region
No Flow
«
0
Miles
5
Lower West Coast
Study Area
Prepared by : FAU
Date : 12 / 20 /07
Map Doc. : boundaries1.mxd
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SFWMD
Potentiometric Heads Based on Temperature
for the Boulder Zone (Richardson)
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SFWMD
Hypothetical Gulf Boundary Cross Section
(modified-Meyer) with Submarine Topography
-10
0
-100
-500
-100
0
0
-50 -5
-10
AT
-50
-2000
IC
NT
LA EAN
OC
-50
-750
00
-1000
-40
-50
-5000
LAKE
OKEECHOBEE
A'
-1000
0
A
0
-5
0
-10
-50
0
-300
-100
00
-30
00
-30
-3
00
0
-200
-2
00
0
-750
0
Bathymetry
-2999 - -2000
Coastal Relief Elevation Model
CONTOUR Depth Below MSL (Feet)
-1999 - -1000 Feet
-400 to-600
10 20 30 40
Miles
-600 to -800
-10000
-999 - -750
Greater than 0
-800 - 1000
-9999 - -5000
-749 - -500
0 to -200
< -1000
-4999 - -4000
-499 - -100
-200 to -400
Model Boundary
-3999 - -3000
-99 - -50
/
Lower West Coast
Model Area
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Prepared by : RESM
Date: 4/15/08
Map Doc.: NOAA.mxd
SFWMD
Model Design
•
Conceptual limitations affected modeling
framework:
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Layering
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Spatial discretization
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Temporal discretization
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Model stability
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Calibration approach and computer time
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Resulted in
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12,000 ft2 grid for calibration approach
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3,000 ft2 grid
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Running time
–
How to best use available data
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Density changing in space but not in time
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SFWMD
Model Design
•
Modified version of SEAWAT to handle data
efficiently and interpolate boundary conditions
•
•
A combination of boundaries conditions
•
•
UGEN and HBXY (Restrepo et al., 2001, 2006)
Constant head boundaries
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Western boundary coincides with Gulf of Mexico
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Top and bottom model layers
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Western-most active cells in coastal regions
•
Eastern boundary (followed a groundwater divide)
•
Northern boundary set with GHB for Layers 2 and 3 and
no-flow boundaries for Layers 4 to 11
SAS simulated mainly to represent vertical
confining effect
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SFWMD
Model Calibration
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Calibration period
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January 1997 to December 2001
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First trial and error with equivalent heads
•
•
Vertical hydraulic conductivity of confining units
was the main sensitive parameter and the most
unknown
Parameter estimation programs evaluated
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UCODE
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MODFLOWP
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GWVistas algorithm
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PEST
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SFWMD
Model Calibration
• Automated calibration procedure PEST
(Doherty, 2004)
• Pilot points
• Regularization parameterization scheme
• SVD-assist
• Stepwise approach starting with least known
parameter
• Vertical hydraulic conductivity of confining units
• Specific storage
• Horizontal hydraulic conductivity
• More weight to the focus area-FAS
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SFWMD
Model Calibration
•
Three versions of LWCFAS Model from a series
of steps
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A quasi-steady-state coupled model with a 3,000
ft2 resolution to generate only initial conditions
and boundary conditions for transient models.
•
A transient model for semi-automatic calibration
with a coarser resolution (12,000 ft2 grid),
•
A transient model with a finer resolution (3,000 ft2
grid) for predictions at subregional level
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SFWMD
Calibration Methodology
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Quasi-steady-state coupled model (3,000 ft2
grid)
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Gulf boundary refined during several manual,
trial-and-error calibration runs
–
•
Smooth surface around the boundary
Hypothetical depiction of the Gulf boundary
–
Unknown western boundary conditions
–
Located 60,000-80,000 ft off-shore, away from the
main study area.
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SFWMD
Calibration Methodology
•
Transient uncoupled model for semi-automatic
calibration (12,000 ft2 grid)
•
12 layers, 72 rows, 37 columns, 260 weekly stress
periods
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Transient potentiometric heads selected as
calibration targets
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Stepwise approach starting with the least known
parameter
–
Based upon sequential optimization steps for
physical basis and to reduce number of parameters
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SFWMD
Calibration Methodology
•
Sequential optimization steps:
–
–
Step 1:
»
PPs located every 8 cells (27 per layer)
»
Optimal solution for three decision variables found
using PEST
»
Several rounds of Step 1 carried out until solution did
not change substantially
Step 2:
»
Estimated parameters from Step 1
»
Increased PPs to every 7 cells (36 per layer)
»
Optimal solution for three decision variables found
»
One round of Step 2 carried out. Improved only for Ss
and Kz
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SFWMD
Calibration Methodology
•
Sequential optimization steps:
–
–
•
Step 3:
»
Started with the solution from Step 2
»
PPs located every 6 cells (49 per layer). Improved only
for Kz.
»
Additional round of runs made to allow for refinement
of lower-layer parameters
»
The total number of PPs in this simulation was from
294 to 490
Step 4:
»
Started with the solution from Step 3
»
Vertical conductivity using a 6,000 ft2 grid
»
Several PPs added in areas with concentrations of
monitoring data (10). No improvement.
Number of superparameters set between 12
and 16 percent of total number of parameters
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SFWMD
Locations of Sites with Aquifer Properties
C-
25
EX
C2
T
C-25
8
L12
L-26
L-21
L7
L-6
L35
A
67
C
C-11S
SR 29 CANAL
PRAIRIE CANAL
FAKA UNION CANAL
MILLER CANAL
L-28
R
N
IV E R
E
R
TU
C6
TAM IAMI
L-30
R
C-11
L-33
MERRITT C AN AL
I-7 5 C ANAL
A
35
L-
C-11 S
L-68A
L-6
L-68A
7C
IABroward
M
IC
AN
AL
L-37
7A
L-6
W
38
L-
L
L-6
8
NA
CA
TU
R
R N
IV ER
ER
39
L-
1
L-
ER
SR 29 CANAL
9
23
PT
CANAL
C-4
L-29
L-31E
C-102
Miami-Dade
C-1
03N
C-103
E
L-31W
11
C1
C-111
L-31W
E
C-111
C-113
C-1
00
A
C-100
C1
W
C- 1
1
C-2
MONROE
A
L-31N
L-67 EXT
0
C-100
C-113
C-1
CE
PRAIRIE CANAL
15
L-
1
L-
ER
L-28
I-7 5 C ANA L
COLLIER
W
C-
L-35B
M
C-3
C-2
10
C-1
L-67 EXT
L-31N
C-
C-102
Miami-Dade
C-1
0 3N
C-103
8
L-30
C-5
L-4
0
C-60
C-7
6
C-4
L-29
L-2
L-33
L-28
C-8
INT
C-11
C-
L-5
L-4
GULF
OF MEXICO
C-42
AL
L-37
N
CA
IABroward
M
IC
AN
A
L
CANAL
MONROE
L-23
L-3
W
L-36
38
L-
ER
M
L-24
L-2
39
L-
8
23
C-1
PT
CE
L-28
8
L-2
ER
INT
MERRITT CANAL
L-16
WEST
FEE DE
R
CAN AL
L-35B
I-75 CANAL
TAM IAMI
DEERFENCE
CAN AL
O CANAL
C- 60
COLLIER
L-13
PALM BEACH
L7
L-1E
LEE
L-1
L-3
FAKA UNION CANAL
MILLER CANAL
L-20
L-2W
HILLSBOR
L-5
L-4
WEST
FEE DE
R
CA NAL
I-75 CANAL
L12
L-1
4
HENDRY
9
L-23
DEERFENCE
CANAL
GULF
OF MEXICO
1
C-51
L-4
0
15
L-
L-1
L-24
L-2
LEE
L-1 6
C-2
L-1
L-21
L-13
PALM BEACH
L-20
L-1
L-1E
L-26
L8
L10
LD-1
3
L-31E
L-1
4
L-2W
C-4
L67
A
L-
L59
L-6
GLADES
C-2
1
HENDRY
4
C-4
LAKE
OKEECHOBEE
L306
CHARLOTTE
C-19
C-19
LD-3
L8
L10
LD-1
3
C-4
MARTIN
0
L-49
L-50
LAKE
OKEECHOBEE
L306
L59
L-6
1
L-6
1
4
L-50
4
48
L-
5
7
L-6 L-4
C-4
L64
4
0
L-49
LD-3
GLADES
0
MARTIN
5
7
L-6 L-4
L-6
C4
DE SOTO
Sarasota
ST. LUCIE
N
L-62
LD
C-41
-4
LD
C-41
L64
48
L-
L-6
3
C-4
1A
2
C-
HIGHLANDS
C-23
24
ST. LUCIE
N
L-62
40
DE SOTO
CHARLOTTE
T
C-25
C-
1A
C-23
L-6
3
C-4
Sarasota
EX
OKEECHOBEE
38
HIGHLANDS
C-
5
3
C-
C-
OKEECHOBEE
C-1
11
Aquifer Test Information
AQUIFER
Floridan System
Intermediate System
Lower Tamiami
Middle Confining Unit
Middle Floridan
Upper Floridan
«
«
0
Miles
5
Lower West Coast
Study Area
Prepared by : FAU
Date : 12 / 20 /07
Map Doc. : pumpage.mxd
Legend
Pilot Points
0
Miles
5
Lower West Coast
Study Area
Prepared by : FAU
Date : 12 / 20 /07
Map Doc. : boundaries6.mxd
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SFWMD
Calibration Methodology
•
3,000 ft2 model generated based on
estimated parameters from calibrated
12,000 ft2 model
•
Node values interpolated using kriging
•
3,000 ft2 model not further calibration
•
Run times for density dependent models
•
12,000 ft2  f(space): 156 sec
•
3,000 ft2  f(space): 4,700 sec
•
3,000 ft2  f(space,time): 134,000 sec
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SFWMD
Calibration Results (Scatter Plots)
Graphic and Analysis for Historical data vs Model data
70
70
60
60
50
50
40
40
Layer
Layer
Layer
Layer
Layer
30
20
2
3
5
7
10
Historical Data Value
Historical Data Value
Graphic and Analysis for Historical data vs Model data
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10
10
0
0
-40
-30
-20
-10
Layer
Layer
Layer
Layer
Layer
30
0
10
20
30
40
50
60
-30
-20
-10
0
10
20
30
40
50
60
-10
-10
-20
-20
Model Data Value
Model Data Value
Layer
R (12,000)
R (3,000)
3
0.75
0.76
5
0.95
0.91
7
0.97
0.91
10
0.40
0.32
22
2
3
5
7
10
SFWMD
Conclusions
•
Allows for manageable computer run times while
allowing the decision variables to improve in each step
•
A finer resolution model that meets calibration targets is
achieved after calibrating a coarser model with PEST
•
Model can be useful for evaluating regional groundwater
issues where concentration is not expected to change
significantly over long periods of time
•
With current knowledge-base and available data from
calibration period, this model’s strength lies in its
interpretive ability to
•
•
Understand system
•
Identify data gaps
Process of collecting data and developing the model
showed need to improve data quantity and quality
23