influence of subsurface heterogeneity on detection of landfill leakage

Optimum Design of Groundwater Monitoring Networks at Landfill Sites

Nusin Buket YenigulProf. Dr. C. van den Akker

Dr. A.Elfeki Dr. J.C.Gehrels

Faculty of Civil Engineering & GeosciencesDepartment of Hydrology and Ecology

ContentResearch Outline Influence Of Uncertainty In Leak Location

On Detection of Contaminant Plumes Released At Landfill SitesObjectivesHypothetical Test CasesResults of the analysis

Motivation and Objectives

Influence Of Subsurface Heterogeneity On Detection of Landfill Leakage ObjectivesHypothetical Test CasesResults of the analysis

Concluding Remarks Future Plan

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

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 flowTransient flowRandom 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 probabilityOPTIMIZATION trade-off among the maximum detection probability, early

detection and minimum cost.APPLICATION OF METHODOLOGYApplication to a real case study.

FORMULATION OF GUIDELINES

Cooperation WithTNOGEODELFTTAUWTU DELFT MATHEMATICS DEPARTMENT

PublicationInfluence 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”

Influence Of Uncertainty In Leak Location On Detection Of

Contaminant Plumes Released At Landfill Sites

“Presented in Modelcare 2002”

uncertainties due to subsurface heterogeneity

ObjectivesTo Analyze The Influence Of :

uncertainties due to contaminant leak location dispersivity of medium

number of wells in monitoring system

the initial contaminant source size

0 20 40 60 80 100 120 140 160 180 200-200

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

M -1M -2

M -3M -4

M -5M -6

M -7M -8

M -9M -1 0

L a n d fill

F lo w d ir ec tio n

Plan View of Model Domain

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

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


0

5

10

15

20

25

30

35

40

0 1 2 3 4 5 6 7 8 9 10 11number of the wells

dete

ctio

n pr

obab

ility

(%)

0 1 2

L=0T=0

x=

y= 5 m2

Y=

Influence of 2Y On Monitoring

Systems of 3, 5 & 10 wells for Point Contaminant Source

0

5

10

15

20

25

30

35

40


dete

ctio

n pr

obab

ility

(%)

0 1 2

L=0T=0

x=

y= 5 m2

Y=

Influence of 2Y On Monitoring

Systems of 3, 5 & 10 wells for Areal Contaminant Source

0

10

20

30

40

50

60

70

80

90

100


dete

ctio

n pr

obab

ility

(%)

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 (2Y=0)

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

Influence Of Subsurface Heterogeneity On

Detection Of Landfill Leakage

“Presented in CMWR 2002”

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

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


0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0-2 0 0

-1 8 0

-1 6 0

-1 4 0

-1 2 0

-1 0 0

-8 0

-6 0

-4 0

-2 0

0

1

2

3

4U n itsG eo lo g ica l

F low d irection

L a n d fill

L eak a ge

M W 1M W 2M W 3M W 4M W 5

Plan View of Geological Sample

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 ContrastKm(m/day) 39.9 26.8

K 26.7 41.2Y=lnK 3.5 2.68

Y 0.61 1.1x 25.0 m 25.0 my 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.

0 2 0 4 0 6 0 80 10 0 1 2 0 1 4 0 1 6 0 18 0 2 0 0-2 0 0

-1 8 0

-1 6 0

-1 4 0

-1 2 0

-1 0 0

-8 0

-6 0

-4 0

-2 0

0

01 02 05 08 01 0 02 0 03 0 04 0 0

K(m /d ay )

Gaussian conductivity field with low contrast.

Non-Gaussian conductivity field with low contrast.

0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0-2 0 0

-1 8 0

-1 6 0

-1 4 0

-1 2 0

-1 0 0

-8 0

-6 0

-4 0

-2 0

0

1 0

2 0

5 0

8 0

K(m /d ay )

0

10

20

30

40

50

60

70

80

90

100

advection dispersivity=0.5 dispersivity=1.5

mw1mw2mw3mw4mw5

dete

ctio

n pr

obab

ility

(%)

Detection Probabilities of Monitoring Wells in Low Contrast

Non-gaussian (Markovian) Case

Detection Probabilities of Monitoring Wells in Low Contrast Gaussian Case.

0

10

20

30

40

50

60

70

80

90

100

advection dispersivity=0.5 dispersivity=1.5

mw1mw2mw3mw4mw5

dete

ctio

n pr

obab

ility

(%)

Results of The AnalysisDetection 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

Concluding RemarksDetection probability of contaminant

plumes highly depends on:subsurface heterogeneitysize of the plumenumber of the wells in a monitoring systemEfficiency of 3 well system particularly in

medium with relatively low dispersivity is quite dubiousin case of less discrete variation between the geological units, subsurface heterogeneity can be modeled based on a Gaussian stationary distribution.

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 studyPublications