1. Probabilistic Modelling Golder Associates (UK) ltd Ruth Davison Attenborough House Browns Lane Stanton on the Wolds Nottingham NG12 5BL RDavison@Golder.com

2. Outline  Probabilistic modelling  What’s involved  Why model probabilistically  Examples of applications  ConSim  Locate the plume  BOS  Practical issues  Processing  Communication Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

3. 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, 50 th percentile, 95 th percentile, single site value, single literature value  Accounts for uncertainty  Because it’s there  Makes a real difference to the results  Should be an unbiased methodology  Helps in decisions Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

4. 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

5. Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS GoldSim Issues Summary The probabilistic approach

6. Difficulties of probabilistic simulation  Communication  No single answer!  Over uncertainty- is this an excuse for a poor site investigation?  What is the decision?  Calibration  Is it possible? Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

7. Outline  Probabilistic modelling  What’s involved  Why model probabilistically  Examples of applications  ConSim  Locate the plume  BOS  Practical issues  Processing  Communication Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

8. ConSim 2 Conceptual Model Introduction Migration Uncertainty PDFs Data Interpretation Black Box ConSim 2 Limitations Review Wrap up

9. Model Example

10. Correlation of Variables Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

11. Outline  Probabilistic modelling  What’s involved  Why model probabilistically  Examples of applications  ConSim  Locate the plume  BOS  Practical issues  Processing  Communication Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

12. Conceptual Model

13. 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

14. Plume overlay Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

15. Outline  Probabilistic modelling  What’s involved  Why model probabilistically  Examples of applications  ConSim  Locate the plume  BOS  Practical issues  Processing  Communication Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

16. Conceptual Model

17. 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

18. The catchment zone model Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

19. The catchment zone model output Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

20. The pollution risk model

21. The output Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

22. Outline  Probabilistic modelling  What’s involved  Why model probabilistically  Examples of applications  ConSim  Locate the plume  BOS  Practical issues  Processing  Communication Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

23. 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 analysis  Don’t worry about insensitive parameters  Retain calibration Introduction Probabilistic Modelling Why? ConSim Plume Locator BOS Issues Summary

24. 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

25.  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