Ann Maest, James Kuipers, Connie Travers, and David Atkins

1. EVALUATION OF METHODS AND MODELS USED TO PREDICT WATER QUALITY AT HARDROCK MINE SITES: SOURCES OF UNCERTAINTY AND RECOMMENDATIONS FOR IMPROVEMENT Ann Maest, James Kuipers, Connie Travers, and David Atkins Buka Environmental; Kuipers and Associates; Stratus Consulting, Inc. WMAN Conference, Worley, ID October 1, 2005

2. Why Characterize and Predict? • Regulators use characterization and modeling information to determine if a mine will be protective of water resources during and after mining • Will mine generate acid and contaminants? • Future environmental liability – set bonds • Cost of remediating mine sites on the National Priorities List (NPL) ~$20 billion • Recent increases in the prices of precious and base metals have triggered increase in new mines around the world • ~170 large hardrock mines in US in various stages of permitting, operation, closure

3. This Study • Lays out framework for evaluating methods and models used to predict water quality at hardrock mine sites • Makes recommendations for improvement • Intended audience: regulators, citizens, mine operators and managers

4. Nature of Predictions • Forward modeling • Timeframe of impacts • Uncertainties • Regulatory authorities require predictions

5. Study Approach • Synthesize existing reviews • Develop “toolboxes” • Evaluate methods and models • Recommendations for improvement • Outside peer review (Logsdon, Nordstrom, Lapakko) • Case studies – NEPA/EIS Study

6. Characterization Methods • Method description • Method reference • Use in water quality predictions • Advantages • Limitations • Characterization during different phases of mining

7. Sources of Uncertainty - General • Extent/representativeness of environmental sampling • need more environmental sampling; let geologic/mineralogic variability dictate extent of sampling; define geochemical test units

8. Recommended Minimum # Samples

9. Sources of Uncertainty – Static • Effect of mineralogy on NP and APP • Rely on mineralogy more than on operationally defined lab tests • Interpretation of static testing results • only use as initial screening technique to estimate total amount of AGP/ANP

10. Sources of Uncertainty – Leach Tests • Water:rock ratio • never known definitively; 20:1 too dilute • Use of unweathered materials • must start with weathered materials • Interpretation of results • may have limited use as scoping tool if use weathered rock and evaluate applicability of results

11. Sources of Uncertainty - Kinetic • Particle size • minimize amount of size reduction for samples – field/lab discrepancies • Length of tests • 20 weeks is too short for kinetic tests, unless shown to be AG before then. NP≥APP. • Interpretation of results • analyze effluent for all COCs; use for short- and long-term AGP/leaching potential

12. Length of Kinetic Tests Source: Nicholson and Rinker, 2000 (ICARD).

15. Modeling Toolbox • Category/subcategory of code • Hydrogeologic, geochemical, unit-specific • Available codes • Special characteristics of codes • Inputs required • Modeled processes/outputs • Step-by-step procedures for modeling water quality at mine facilities

16. Modeling Opportunities

18. Sources

19. Pathways

20. Processes

21. Sources of Uncertainty - Modeling • Conceptual model • Conceptual models are not unique and can change over time • Revisit conceptual models and modify mining plans and predictive models based on new site-specific information • Use of proprietary codes • need testable, transparent models – difficult to evaluate, should be avoided. Need efforts to expand publicly available pit lake models (chemistry). • Modeling inputs • large variability in hydrologic parameters; seasonal variability in flow and chemistry; sensitivity analyses (ranges) rather than averages/medians • Estimation of uncertainty • Acknowledge and evaluate effect on model outputs; test multiple conceptual models • “…there is considerable uncertainty associated with long-term predictions of potential impacts to groundwater quality from infiltration through waste rock...for these reasons, predictions should be viewed as indicators of long-term trends rather than absolute values.”

24. Summary • Characterization methods need major re-evaluation, especially static and short-term leach tests • Increased use of mineralogy in characterization – make less expensive, easier to use/interpret • Modeling uncertainty needs to be stated and defined • Limits to reliability of modeling – use ranges rather than absolute values • Increased efforts on long-term studies and collection of site-specific data over modeling

25. Conclusion • Predictive modeling is an evolving science with inherent uncertainties • Using the approaches described in this report, predictive water quality modeling and site characterization information can be reliably used to design protective mitigation measures and to estimate the costs of future remediation of hardrock mine sites.