1. Goal Oriented Hydrogeological Site Characterization: A Framework and Case Study in Contaminant Arrival Time Bradley Harken 1,2 Uwe Schneidewind 3 Thomas Kalbacher 2 Peter Dietrich 2 Yoram Rubin 1 1 University of California, Berkeley, USA 2 Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany 3 RWTH Aachen University, Aachen, Germany

2. Groundwater Contamination Prevention, Regulation, Risk Assessment, Remediation – Will maximum concentration exceed Maximum Contaminant Levels? – Will a plume reach water supplies before it degrades? – Is a waste disposal site safe? Use hydrogeological models to answer these questions – How to cope with uncertainty? http://www.huffingtonpost.com/2013/01/12/tap-water-catches-fire-methane- debby-jason-kline_n_2462981.html

3. Uncertainty in Hydrogeological Models Conceptual model uncertainty Uncertainty in parameters Difficulty in characterization – Determination of necessary parameters (e.g. Hydraulic Conductivity) – Description of spatial variability of parameters (mean, drift, covariance structure, …) – Costs and logistics of field campaigns – Measurement Errors How to account for this uncertainty while answering questions relevant to remediation, regulation, risk assessment, etc.? – Decisions often made by non-hydrologists

4. Hypothesis Testing Framework

5. Role of Field Data More field data  less uncertainty Different field campaign designs result in different levels of uncertainty – Field campaign design: specifies quantity, type, and spatial location of field measurements Which design will best meet uncertainty requirements, subject to other constraints? – Cost – Field Logistics

6. Characterization Forward Modeling Decision Making Prior Information Field Data Parameter Estimates (e.g. K) Inverse Modeling Modeling Predictions Water resources management, policy, or regulation decision Find new water source? Remediate contaminated site? UNCERTAINTY Hypothesis Testing: allows us to account for all sources of uncertainty in a simple, easy to communicate manner Enables us to examine the link between field data and uncertainty in decisions

7. Hypothesis Testing: Summary Allows us to make risk-based, defensible decisions in face of uncertainty – Easily communicate uncertainty to decision-makers (not hydrologists) Next: use HT framework to “optimize” field campaign designs in order to best support decision-making

8. Use HT Framework to Assess Field Campaign Design Simulate Baseline Field Simulate Field Campaign Conditional Simulations Simulate Baseline Field

9. Synthetic Case Study Budget allows for 8 measurements of hydraulic conductivity – Measurements used for: Estimation of geostatistical parameters Conditioning values in forward model What is the best spatial configuration of measurements? Issue alert if contaminant will arrive at target before a critical amount of time passes

10. Measurements: Possible Spatial Configurations Option 1: Spread measurements throughout domain for improved estimate of geostatistical parameters and global trends Alternatively, focus on travel path for stronger conditioning Option 2: Spread along whole path Option 3: Clustered close to source Option 4: Clustered close to target

11. Results Conclusion: Best spatial configuration of measurements depends on “how early” are the early arrivals we’re trying to predict

12. Summary Hypothesis Testing enables risk-based decision making in face of uncertainty – Better communicate relationship between uncertainty in data, parameters, models, etc. and uncertainty in questions we ultimately want to answer – Improve link between hydrologists and managers/regulators Hypothesis Testing allows us to “optimize” our data collection – Uncertainty in final prediction as quantitative measure of data effectiveness

13. THANK YOU! References: Nowak, W., Y. Rubin, and F. P. J. de Barros (2012), A hypothesis-driven approach to optimize field campaigns, Water Resour. Res., 48, W06509, doi:10.1029/2011WR011016. Nowak, W., F. P. J. de Barros, and Y. Rubin (2010), Bayesian geostatistical design: Task‐driven optimal site investigation when the geostatistical model is uncertain, Water Resour. Res., 46, W03535, doi:10.1029/2009WR008312.