Yenigul, N.B., Elfeki, A.M.M. (2002). Influence of Subsurface Heterogeneity on Detection of Landfill Leakage. Published at 14th International Conference Computational Methods in Water Resources “CMWR2002”, Developments in Water Science No. 47, Elsevier Publisher, The Netherlands, pp. 1323-1330. Eds. SM Hassanizadeh, RJ Schotting, WG Gray, and GF Pinder.
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Influence of Subsurface Heterogeneity on Detection of Landfill Leakage
1. Optimum Design of Groundwater
Monitoring Networks at Landfill
Sites
Nusin Buket Yenigul
Prof. Dr. C. van den Akker
Dr. A.Elfeki
Dr. J.C.Gehrels
Faculty of Civil Engineering & Geosciences
Department of Hydrology and Ecology
2. Content
Research Outline
Influence Of Uncertainty In Leak Location On Detection
of Contaminant Plumes Released At Landfill Sites
Objectives
Hypothetical Test Cases
Results of the analysis
Motivation and Objectives
Influence Of Subsurface Heterogeneity On Detection of
Landfill Leakage
Objectives
Hypothetical Test Cases
Results of the analysis
Concluding Remarks
Future Plan
3. Formulation of a methodology for the design
of an optimum monitoring well network at a
landfill site.
Motivation and Objectives
Highest probability of
contaminant detection
Cost effectiveEarly detection
4. Research Outline
Effects due to spatial heterogeneity of the subsurface
GROUNDWATER FLOW AND TRANSPORT MODEL
STOCHASTIC CHARACTERIZATION & SENSITIVITY ANALYSIS
Influences related to the uncertainties in contaminant source location
Steady state uniform flow
Transient flow
Random walk transport model
Influence of number of wells, on the detection probability
Influence of dimension of the source & detection limit on the detection probability
Influence of dispersivity of medium on the detection probability
Influence of pumping & sampling frequency on the detection probability
OPTIMIZATION
trade-off among the maximum detection probability, early detection and minimum cost.
APPLICATION OF METHODOLOGY
Application to a real case study.
FORMULATION OF GUIDELINES
5. Cooperation With
TNO
GEODELFT
TAUW
TU DELFT MATHEMATICS DEPARTMENT
Publication
Influence of Uncertainty In leak Location On Detection of
Contaminant Plumes Released at Landfill Sites
Modelcare 2002, 4th International Conference on Calibration And Reliability In
Groundwater Modelling, Praque, Czech Republic, 17-20 June 2002”
Influence of Subsurface Heterogeneity on Detection of Landfill
Leakage
CMWR 2002, 14th International Conference on Computational Methods in Water
Resources, Delft, The Netherlands, 23-28 June 2002”
6. Influence Of Uncertainty In Leak Location
On Detection Of Contaminant Plumes
Released At Landfill Sites
“Presented in Modelcare 2002”
7. uncertainties due to subsurface heterogeneity
Objectives
To Analyze The Influence Of :
uncertainties due to contaminant leak location
dispersivity of medium
number of wells in monitoring system
the initial contaminant source size
9. Steady state groundwater flow
2000 particles with a total mass of 1000 gram
Zero flux and constant head
Hydraulic gradient is 0.001
Confined aquifer
Y= ln (K) is modeled as a Gausian stationary
distribution
2
Y is set to “0”, “1” and “2” and x= x =5 m
Monte Carlo method is used to generate leak locations
Hypothetical Test Model
10. Random leak locations follow a uniform distribution
Failure is modeled as a point and a small areal source
Detection limit corresponds the detection of the first
particle hits the well
L= 0 m, T= 0 m (advection); L= 0.5 m, T= 0.15 m;
L= 1.5 m T= 0.15 m
porosity = 0.25
contaminant are assumed to be conservative
Hypothetical Test Model
11. 0
5
10
15
20
25
30
35
40
0 1 2 3 4 5 6 7 8 9 10 11
number of the wells
detectionprobability(%)
0
1
2
L=0T=0
x=y= 5 m
2
Y=
Influence of 2
Y On Monitoring Systems of 3, 5
& 10 wells for Point Contaminant Source
12. 0
5
10
15
20
25
30
35
40
0 1 2 3 4 5 6 7 8 9 10 11
number of the wells
detectionprobability(%)
0
1
2
L=0T=0
x=y= 5 m
2
Y=
Influence of 2
Y On Monitoring Systems of 3, 5
& 10 wells for Areal Contaminant Source
13. 0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7 8 9 10 11
number of the wells
detectionprobability(%)
L=0,T=0
L=0.5,T=0.05
L=1.5,T=0.15
Influence of Dispersivity On Monitoring Systems of
3, 5 & 10 Wells for Areal Contaminant Source
(2
Y=0)
14. Subsurface heterogeneity detection probability
Number of wells detection probability
Dispersivity of medium detection probability
Current practice (3 wells) is not sufficient.
Initial size contamination source detection probability
Results of The Analysis
15. Influence Of Subsurface Heterogeneity On
Detection Of Landfill Leakage
“Presented in CMWR 2002”
16. To analyze the spatial variability of hydraulic
conductivity on contaminant plume detection
Purpose
To characterize the subsurface heterogeneity based on
Gaussian and Non-gaussian models
The comparison of the results of two approaches
17. Hydraulic conductivity is assumed to be the major contributor to
the uncertainty
Logarithm of hydraulic conductivity (ln K) is modeled;
1) as a Gaussian stationary distribution with mean, variance and a
correlation length,
2) as a non-Gaussian distribution using a coupled Markov chain
model (CMCM).
A Monte Carlo method is used to generate multiple random hydraulic
conductivity field.
Steady state groundwater flow model
random walk transport model
Contaminants are assumed to be conservative.
L=0 m, T=0 m; L=0.5 m, T=0.05 m; L=1.5 m, T=0.15 m.
4 geological units are considered in coupled CMCM
Hypothetical Test Model
19. Unit
Color in
Figure 1
Wi Low Contrast High contrast
1 yellow 0.24 80 m/day 100 m/day
2 blue 0.25 50 m/day 10 m/day
3 red 0.31 20 m/day 1 m/day
4 green 0.20 10 m/day 0.1 m/day
Parameter Low Contrast High Contrast
Km(m/day) 39.9 26.8
K 26.7 41.2
Y=lnK 3.5 2.68
Y 0.61 1.1
x 25.0 m 25.0 m
y 2.0 m 2.0 m
Hydraulic conductivity values of the units in non-Gaussian (Markovian) field.
Estimated simulation parameters for generation of statistically equivalent
Gaussian fields.
23. Results of The Analysis
Detection probabilities in non-Gaussian and Gaussian
cases are slightly different.
Less discrete variation Gaussian stationary distribution.
Complex geology with particular features Markov model
Dispersivity of medium detection probability
24. Concluding Remarks
Detection probability of contaminant plumes highly
depends on:
subsurface heterogeneity
size of the plume
number of the wells in a monitoring system
Efficiency of 3 well system particularly in medium with
relatively low dispersivity is quite dubious
in case of less discrete variation between the
geological units, subsurface heterogeneity can be
modeled based on a Gaussian stationary distribution.
25. Future Plan of Work (2003)
Continue Calculations for Stochastic Characterization
and Sensitivity Analysis
• To create test models representing hydrogeological conditions in
east and west part of The Netherlands
• Designing of various monitoring networks to be utilized in
formulation of guidelines
• Developing an analytical approach that can provide compatible
results with the simulation model
• Analyzing the detection probability of each network to be used
in optimization model in far steps of the research
Literature study
Publications