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Probabilistic ModellingGolder Associates (UK) ltd
Ruth Davison
Attenborough House
Browns Lane
Stanton on the Wolds
Nottingham
NG12 5BL
RDavison@Golder.com
Outline
Probabilistic modellingWhat’s involvedWhy model probabilistically
Examples of applicationsConSimLocate the plumeBOS
Practical issuesProcessingCommunication
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Why Are Risk Models Probabilistic?
Uncertainty in the inputs and outputs What would you like the answer to
be? Without probability we can choose!Which would you use:
• Mean, mode, median, 50th percentile, 95th percentile, single site value, single literature value
Accounts for uncertainty Because it’s thereMakes a real difference to the resultsShould be an unbiased methodologyHelps in decisions
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
What Type of Uncertainty
Conceptual Uncertainty River aquifer interactions LNAPL or DNAPL Dual or single porosity
Model Uncertainty Is it the right equation Limits on application
Parameter Uncertainty Spatial variability Measurement error Dependence on literature The unknown
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
GoldSim
Issues
Summary
The probabilistic approach
Difficulties of probabilistic simulation
CommunicationNo single answer!
Over uncertainty- is this an excuse for a poor site investigation?
What is the decision?
CalibrationIs it possible?
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Outline
Probabilistic modellingWhat’s involvedWhy model probabilistically
Examples of applicationsConSimLocate the plumeBOS
Practical issuesProcessingCommunication
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
ConSim 2 Conceptual Model
Introduction
Migration
Uncertainty
PDFs
Data
Interpretation
Black Box
ConSim 2
Limitations
Review
Wrap up
Model Example
Correlation of Variables
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Outline
Probabilistic modellingWhat’s involvedWhy model probabilistically
Examples of applicationsConSimLocate the plumeBOS
Practical issuesProcessingCommunication
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Conceptual Model
Simulation examples
Influence of flow model on plume centre position
Influence of electron acceptor inputs on plume concentrations
Influence of retardation on plume position
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Plume overlay
0 500 1000 1500 2000 2500 3000 3500 4000500
1000
1500
2000
2500
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Outline
Probabilistic modellingWhat’s involvedWhy model probabilistically
Examples of applicationsConSimLocate the plumeBOS
Practical issuesProcessingCommunication
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Conceptual Model
The model components
Catchment zone model
Landuse model
Pollution risk model
Groundwater flow model
Databases
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
The catchment zone model
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
The catchment zone model output
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
The pollution risk model
The output
Cumulative Chart
microgram/liter
Mean = 0.40.000
.250
.500
.750
1.000
0
250
500
750
1000
0.00 0.90 1.80 2.69 3.59
1,000 Trials 24 Outliers
Forecast: 2011-2022
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Outline
Probabilistic modellingWhat’s involvedWhy model probabilistically
Examples of applicationsConSimLocate the plumeBOS
Practical issuesProcessingCommunication
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Things to Consider
Large numerical flow and transport model can be very slow
Distributed processing may be only way to go
Will using stochastic approach affect the conclusion or just the results
Sensitivity analysisDon’t worry about insensitive
parameters
Retain calibration
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Summary of Techniques
Monte Carlo sampling
Probabilistic risk models
Superposition of plumes
Probabilistic capture zone analysis
Correlation of variables
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
Summary
Sensitivity analysis Is probabilistic modelling necessary Determine key parameters
What decision are you trying to make What type of model How to display your results
Distributed processing
Introduction
Probabilistic
Modelling
Why?
ConSim
Plume
Locator
BOS
Issues
Summary
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