an efficient reliability-based approach to aquifer remediation design howard w. reeves u.s....
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An Efficient Reliability-Based Approach to Aquifer Remediation Design
Howard W. Reeves
U.S. Geological Survey
Michigan District, Water Resources Discipline
EPA Region 5 STAR SeminarJuly 14, 2004
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 2
Acknowledgements• U.S. Environmental Protection Agency (EPA) STAR
Program through Grant R 827126-01-0 • Department of Civil Engineering, Northwestern University• Co-PIs: C.H. Dowding (Northwestern University) and T.
Igusa (Johns Hopkins University)• Colleagues and students: A.J. Graettinger (University of
Alabama), J. Lee (University of Missouri-Kansas City), M.D. Fortney (Law School at Northwestern U.), D. Dethan (ERM Consulting)
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 3
Motivating Problem
• Design of remedial strategies for contaminated soil and groundwater– Uncertainties in site conditions– Variety remedial options– Desire to quantify design process
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 4
Challenges
Given a contaminated site and proposed remedial activities:
– Geology of subsurface may be complex– Small volume of soil at a site is sampled– Parameters of interest may vary over large ranges– Contaminants may have complex interactions with
soil and native ground water– Clean-up schemes impose different hydrologic,
chemical, or biological conditions or constraints
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 5
Example Cone Penetrometer(CPT) log
CPT has an area of10 cm2, but continuity of this layer across thesite is important
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 6
Heterogeneityat different scales
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 7
Reaction to Uncertainty
• Over design - leads to increased costs without
improving
performance
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 8
Reaction to Uncertainty
• Over design - leads to increased costs without improving performance
• Over sampling - increased cost without changing design
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 9
Site Characterization
• Are there sufficient data to base the design?
• What data are required and where should these data be collected to increase confidence in the design?
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 10
Approach• Combine design model and
geostatistical description of geologic setting to estimate design uncertainty
• Use design uncertainty to guide exploration
• Contrast with sampling based on budget or regulatory constraints
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 11
FieldInvestigation
Program
GeologicalUncertainty
Model
ParameterUncertainty
Model
DesignModel
EngineeringReliability
Model
DecisionModel
Hydrologic Decision Framework (Freeze et al., 1990)
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 12
FieldInvestigation
Program
GeologicalUncertainty
Model
ParameterUncertainty
Model
DesignModel
EngineeringReliability
Model
DecisionModel
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 13
Performance Model
m
Performance Evaluation
m
P
Goal
Design Proposal and
Data Acquisition
Design Model Performance
Performance
Uncertainty
Evaluation of Design
and Performance
Reliability
Bayesian Condition
al Calculation
MODFLOW- 2000
First-Order
Second Moment (FOSM) Method
Reliability
Index,
Analysis
Sensitivity
Analysis
Sensitivity- Equation
Sensitivities
Input Parameter Model
Finite Element/
Performance Uncertainty
Design & Data
Sampling
Input parameter model
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 14
Input Component
• Bayesian approach to condition input vector, u, to observation vector, v
• Variance of u is the diagonal of C(u|v) matrix
• Can reduce to kriging estimate of E[u|v] with appropriate priors for E[u] and Cov(u)
E[u|v] = E[u] + Cov(v,u) Cov(v)-1 (v - E[v])
Cov(u|v) = Cov(u) - Cov(v,u) Cov(v)-1 Cov(u,v)
Input parameter model
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 15
First-Order Second-Moment
E[C] g( E[u|v] )
Cov(C(t1),C(t2)) Ju(to,t1) Cov(u|v) JuT
(to,t2)
E[C] = expected value for concentrationg() = design model u = vector of uncertain input parametersJu = [CI/ uJ ] Cov(.,.) = covariance matrix describing uncertainty in input parameters
Performance Uncertainty
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 16
C
c
Ca
Probability of Success
Performance Evaluation
m
P
Goal Performance Evaluation
N(C,c)
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 17
• Point reliability may be determined
• c - the standard deviation of C = Square root of the variance of C
• Uncertainty in site input and model performance are combined in C
Reliability Index
= Ca - C
c
Performance Evaluation
m
P
Goal
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 18
Case 1
Case 2
2
1
E[C] 2 E[C] 1
Probability of Failure for Case 2
Probability of Failure for Case 1
Performance Goal
Performance Evaluation
m
P
Goal
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 19
0 1000-60
-50
-40
-30
-20
-10
0
0 10 20 30 40 (mg/L)
Kx=Ky=Kz=0.0004 n=0.66
Kx=Ky=Kz=4 n=0.33
Kx=Ky=Kz=0.0004 n=0.66
Clay
Clay
Sand
Z
X
K : conductivity (m/day), n : porosity
0 10000
1000
flow
No flow boundary
No flow boundary
h=50m
Y
X
h=40m
(UNIT=m)
Sampling location
Proposed pumping well
Compliance point
3-D Transport Simulation
Hypothetical Model
Date Sampling
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 20
3-D Transport Simulation
Steady state flow and transient transport
- Uncertain input parameter -Geologic interface elevations : 4 samplesFirst-order decay rate : 0.02 /day 0.005
- Design parameter - Design I : No pumping well (Natural
Attenuation) Design II : Single pumping well(Proposed pumping rate : 300 m3/day))
- Output parameter -Clean-up goal at compliance point : 10-3 mg/L
Model Conditions and parameter description
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 21
0.00.40.81.21.62.02.42.8
(m )
Dep
th, Z
(m
)
Input parameter model
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 22
Performance Model
m
0 500 1000
X (m )
-60
-40
-20
0
De
pth
, Z (
m)
0 500 1000
X (m )
-60
-40
-20
0
De
pth
, Z (
m)
01234567
0 500 1000
X (m )
-60
-40
-20
0
De
pth
, Z (
m)
0
10
20
30
40
(m g/L)
Aquifer boundaries
(m g/L)
Design INo Pumping Well
Design IISingle Pumping Well
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 23
0.00.20.40.60.81.01.21.4
0.00
0.04
0.08
0.12
0.16
0.20
0.00.20.40.60.81.01.2
0.00.40.81.21.62.02.42.83.2
0.00.40.81.21.62.02.42.83.2
0.000
0.002
0.004
0.006
0.008
0.010
(mg2/L2)
Design INo Pumping Well
Design IISingle Pumping Well
TotalVariance
Variance from Interface uncertainty
Variance from First-order Decay rate
uncertainty
Performance Uncertainty
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 24
Reliability index indicates which design is more reliable
0
10
20
30
40
50
60
70
0 50 100 150 200 250 300 350
Time(days)
Design I
Design II
Performance Evaluation
m
P
Goal
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 25
54.3061
100.000099.6825
99.9996 100.0000
65.4521
0
20
40
60
80
100
0 50 100 150 200 250 300 350Time (days)
x 1
00 (%
)
Design I
Design II
Performance Evaluation
m
P
GoalReliability index can be used to estimate probability of success
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 26
0 1000
X(m )
0
1000
Y(m
)
0.00.20.40.60.81.01.21.4
0 1000
X(m )
0
1000
Y(m
)
0.00.20.40.60.81.01.21.4
Directed Sampling Ad hoc Sampling
(mg2/L2)
Will directed sampling give more confidence to the remedial design?For Design I : No pumping well (Natural Attenuation)
Performance Uncertainty
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 27
0.10
0.11
0.12
0.13
3 4 5 6 7
Number of samples
directed sampling
ad hoc sampling
For Design I : No pumping well (Natural Attenuation)
Performance Evaluation
m
P
Goal
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 28
De
pth
, Z
(m
)
De
pth
, Z
(m
)
(m )-4.0 -2.0 0.0 2.0
Input parameter model
For Design II : Single pumping wellAdditional Sampling
4 Sample 6 Sample
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 29
4 Sample 6 Sample
Additional sampling reduces the concentration uncertainty
For Design II : Single pumping well
0 1000
X (m )
0
1000
Y (
m)
0 1000
X (m )
0
1000
Y (
m)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
S 5
S 6S 7
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
Performance Uncertainty
(mg2/L2)
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 30
68.3880
65.4521
60
65
70
3 4 5 6 7
Number of samples
x 1
00 (
%)
Performance Evaluation
m
P
Goal
For Design II : Single pumping well
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 31
Future Work• Approach incorporated with other design
models (Dowding - NU, Graettinger - UA)• Incorporate use of geophysical data for input
(Lee - UMKC)• Incorporate techniques into comprehensive
modeling approach that includes model calibration and other uncertainty issues (Reeves - USGS)
• Test with field data and designs (All)
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 32
Bibliography (STAR + Related)Dowding, C.H., Reeves, H.W., Graettinger, A.J., and Lee, J., 2000, Inclusion of the Performance Model to Direct and
Control Site Characterization, in Mayne, P.W. and Hyrciw, R.D., eds., Innovations and Applications in Geotechnical Site Characterization: Geo-Institute of the American Society of Civil Engineers, Geotechnical Special Publication Number 97, Reston, Virginia, ASCE, p. 130-141.
Reeves, H.W., Lee, J., Dowding, C.H., and Graettinger, A.J., 2000, Reliability-Based Evaluation of Groundwater Remediation Strategies, in Stauffer, F., Kinzelbach, W., Kovar, K., and Hoehn, E., eds., Calibration and Reliability in Groundwater Modelling–Coping with Uncertainty, Proceedings of the ModelCARE ’99 Conference, Z¨urich, September, 1999: IAHS Publication no. 265, Wallingford, Oxfordshire, UK, IAHS Press, p. 304-309.
Fortney, M.D., 2001, Reliability Analysis for Groundwater Modeling using MODFLOW-2000: M.S. Thesis, Northwestern University, Evanston, Illinois, 114 p.
Lee, J., 2001, Reliability-Based Approach for Groundwater Remediation Design: Ph.D. Dissertation, Northwestern University, Evanston, Illinois, 161 p.
Graettinger, A.J., Lee, J., and Reeves, H.W., 2002, Efficient Conditional Modeling for Geotechnical Uncertainty Evaluation: International Journal for Numerical and Analytical Methods in Geomechanics, v. 26, no. 2, p. 163-179.
Lee, J., Reeves, H.W., and Dowding, C.H., 2002, Integrating Site Characterization with Aquifer and Soil Remediation Design in Lipnick, R.L., Mason, R.P., Phillips, M.L., and Pittman, C.U., Jr., eds., Fate and Transport of Chemicals in the Environment: Impacts, Monitoring, and Remediation, ACS Symposium Series 806: Washington, D. C., American Chemical Society, p. 384-396.
Glasgow, H.S., Fortney, M.D., Lee, J., Graettinger, A.J., and Reeves, H.W., 2003, MODFLOW-2000 Head Uncertainty, A First-Order Second-Moment Method: Ground Water, v. 41, no. 3, p. 342-350.
Graettinger, A.J., Reeves, H.W., Lee, J., and Dethan, D., 2003, First-Order Second-Moment Site Exploration Approaches, Mishra, S., ed., Groundwater Quality Modeling and Management Under Uncertainty Proceedings of the Probabilistic Approaches & Groundwater Modeling Symposium held during the World Water and Environmental Resources Congress in Philadelphia, Pennsylvania, June 24-26, 2003: Washington, D.C., American Society of Civil Engineers, p. 215-225.
July 14, 2004 Reeves - EPA Region 5 STAR Seminar 33
R 827126-01-0
Thank you
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