2. WATER QUALITY PREDICTIONS AT HARDROCK MINE SITES: METHODS, MODELS, AND CASE STUDY COMPARISON James Kuipers, Kuipers & Assoc Ann Maest, Buka Environmental

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

7. Specific Models Used Water Quantity only (18 mines) Water Quantity only (18 mines) –Near-surface processes: HEC-1, HELP –Sediment transport: SEDCAD, MUSLE, RUSLE, R1/R3SED –Storm hydrographs: WASHMO –Groundwater flow: MODFLOW, MINEDW –Vadose zone: HYDRUS; drawdown

8. Specific Models Used (cont.) Water quantity and quality (21 mines) Water quantity and quality (21 mines) –Water quantity + PHREEQE (3), WATEQ (1), MINTEQ (5) –Pyrite oxidation (PYROX) – 3 mines –Water balance/contam transport (LEACHM) – 1 mine –Pit water flow and quality (limited): CE-QUAL- W2 (3), CE-QUAL-R1 (1) –Mass balance/loading: unspecified (4 mines), FLOWPATH –Proprietary codes for pit lake water quality or groundwater quality downgradient of waste rock pile (4 mines)

9. Sources of Uncertainty - Modeling Use of proprietary codes 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 Modeling inputs –large variability in hydrologic parameters; seasonal variability in flow and chemistry; sensitivity analyses (ranges) rather than averages/medians Estimation of uncertainty 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.”; “…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.”;

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

12. Comparison of Predicted and Actual Water Quality

13. EIS Review Mines Mines –183 major, 137 NEPA, 71 NEPA reviewed Similar in location, commodities, extraction, processing, operational status Similar in location, commodities, extraction, processing, operational status 104 EISs reviewed for 71 mines 104 EISs reviewed for 71 mines –16 months to obtain all documents Compared EIS predictions to actual water quality for 25 case study mines Compared EIS predictions to actual water quality for 25 case study mines

14. EIS Years

15. EIS Approach to Impacts Potential Water Quality Geochemical Information Engineering Design Hydrologic/ Climatic Information Mitigation Measures Predicted Water Quality

16. Case Study Mines Selection based on Selection based on –ease of access to water quality data –variability in geographic location, commodity type, extraction and processing methods –variability in EIS elements related to water quality (climate, proximity to water, ADP, CLP) –Best professional judgment Similar to all NEPA mines, but Similar to all NEPA mines, but –more from CA and MT –fewer Cu mines –more with moderate ADP and CLP –more with shallower groundwater depths

17. Case Study Mines: General States States –AK: 1- MT: 6 –AZ: 2- NV: 7 –CA: 6- WI: 1 –ID: 2 Commodity Commodity –Au/Ag: 20 - Pb/Zn: 1 –Cu/Mo: 2 - PGM: 1 –Mo: 1 Extraction type Extraction type –open pit: 19 –underground: 4 –both: 2 Processing type Processing type –CN Heap: 12 –Vat: 4 –Flotation: 7 –Dump Leach: 2

18. Case Study Mines Selected

19. Comparison Results Mines with mining-related surface water exceedences: 60% Mines with mining-related surface water exceedences: 60% –% estimating low impacts pre-mitigation: 27% –% estimating low impacts with mitigation: 73% Mines with mining-related groundwater exceedences: 52% Mines with mining-related groundwater exceedences: 52% –% estimating low impacts pre-mitigation: 15% –% estimating low impacts with mitigation: 77% Mines with acid drainage on site: 36% Mines with acid drainage on site: 36% –% predicting low acid drainage potential: 89%

20. Inherent Factors ore type and association ore type and association climate climate proximity to water resources proximity to water resources pre-existing water quality pre-existing water quality constituents of concern constituents of concern acid generation and neutralization potentials acid generation and neutralization potentials contaminant leaching potential contaminant leaching potential

21. Surface Water Results

22. Groundwater Results

23. Mines without Inherent Factors California desert mines California desert mines –American Girl, Castle Mountain, Mesquite –No groundwater or surface water impacts or exceedences –Delayed impacts? Climate change (but less precip predicted) Stillwater, Montana Stillwater, Montana –Close to water, low ADP, moderate/high CLP –Unused surface water discharge permit –Increases in nitrate (predicted from LAD by modeling) in Stillwater River, but no exceedences –Mining-related exceedences in adit and groundwater under LAD, but related to previous owners? –Inherently lucky – ultramafic mineralogy

24. Predicted vs. Actual Water Quality Potential Water Quality Geochemical Information Engineering Design Hydrologic/ Climatic Information Mitigation Measures Predicted Water Quality Actual Water Quality Failure at 64% of sites

25. Implications Mines close to water with mod/high ADP/CLP need special attention from regulators Mines close to water with mod/high ADP/CLP need special attention from regulators Water quality impact predictions before mitigation in place more reliable Water quality impact predictions before mitigation in place more reliable These can be in error too – geochemical and hydrologic characterization need improvement These can be in error too – geochemical and hydrologic characterization need improvement Why do mitigations fail so often and what can be done about it? Why do mitigations fail so often and what can be done about it?

26. Characterization Methods Geology Geology Mineralogy Mineralogy Whole rock analysis Whole rock analysis Paste pH Paste pH Sulfur analysis Sulfur analysis Total inorganic carbon Total inorganic carbon Static testing Static testing Short-term leach testing Short-term leach testing Laboratory kinetic tests Laboratory kinetic tests Field testing of mined materials Field testing of mined materials

27. Modeling Opportunities

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

29. EIS Information Reviewed Geology/mineralogy Geology/mineralogy Climate Climate Hydrology Hydrology Field/lab tests Field/lab tests Predictive models used Predictive models used Water quality impact potential Water quality impact potential Mitigation measures Mitigation measures Predicted water quality impacts Predicted water quality impacts Discharge information Discharge information

30. Surface Water Examples Flambeau, WI had inherent factors but no impact or exceedence to date Flambeau, WI had inherent factors but no impact or exceedence to date Stillwater, MT had an impact (0.7 mg/l nitrate in Stillwater River) but no exceedence Stillwater, MT had an impact (0.7 mg/l nitrate in Stillwater River) but no exceedence McLaughlin, CA predicted exceedences in surface water and was correct McLaughlin, CA predicted exceedences in surface water and was correct

31. Groundwater Examples Lone Tree, NV had inherent factors but no mining-related impact or exceedence Lone Tree, NV had inherent factors but no mining-related impact or exceedence –baseline issue McLaughlin, CA had inherent factors and did have exceedences McLaughlin, CA had inherent factors and did have exceedences –regulatory exclusion for groundwater (poor quality, low K), so no violations –92% of rocks not acid generating –Tailings leachate flunked STLC – hazardous aquifer

32. McLaughlin Mine, CA: Waste Rock Monitoring Well

33. Case Study Mines: Water Quality Climate Climate –Dry/Semi-Arid: 12 –Marine West Coast: 1 –Humid subtropical: 3 –Boreal: 8 –Continental: 1 Perennial streams Perennial streams –No info: 1 –>1 mi: 6 –<1 mi: 7 –On site: 11 Groundwater depth Groundwater depth –No info: 1 –>200’: 3 –50-200’: 4 –0-50’/springs: 17 Acid drainage potential Acid drainage potential –No info: 2 –Low: 12 –Moderate: 8 –High: 3 Contaminant leaching Contaminant leaching –No info: 3 –Low: 8 –Moderate: 10 –High: 4

34. Failure Modes and Effects Analysis

35. Hydrological Characterization Failures: 7 of 22 mines exhibited inadequacies in hydrologic characterization 7 of 22 mines exhibited inadequacies in hydrologic characterization –At 2 mines dilution was overestimated –At 2 mines the presence of surface water from springs or lateral flow of near surface groundwater was not detected –At 3 mines the amount of water generated was underestimated

36. Failure Modes and Effects Analysis Geochemical Characterization Failures: 11 of 22 mines exhibited inadequacies in geochemical characterization 11 of 22 mines exhibited inadequacies in geochemical characterization –Geochemical failures resulted from: Assumptions made about geochemical nature of ore deposits and surrounding areas Assumptions made about geochemical nature of ore deposits and surrounding areas Site analogs inappropriately applied to new proposal Site analogs inappropriately applied to new proposal Inadequate sampling Inadequate sampling Failure to conduct and have results for long-term contaminant leaching and acid drainage testing procedures before mining begins. Failure to conduct and have results for long-term contaminant leaching and acid drainage testing procedures before mining begins. Failure to conduct the proper tests, or to improperly interpret test results, or to apply the proper models Failure to conduct the proper tests, or to improperly interpret test results, or to apply the proper models

37. Failure Modes and Effects Analysis Mitigation Failures: 18 of 22 mines exhibited failures in mitigation measures 18 of 22 mines exhibited failures in mitigation measures –At 9 of the mines mitigation was not identified, inadequate or not installed –At 3 of the mines waste rock mixing and segregation was not effective –At 11 of the mines liner leaks, embankment failures or tailings spills resulted in impacts to water resources

38. Failure Modes Root Causes Hydrologic Characterization Failures most often caused by: Failures most often caused by: –Over-estimation of dilution effects –Failure to recognize hydrological features –Underestimation of water production quantities Prediction of storm events or deficiencies in stormwater design criteria is the most typical root cause of hydrologic characterization failures Prediction of storm events or deficiencies in stormwater design criteria is the most typical root cause of hydrologic characterization failures

39. Failure Modes Root Causes Geochemical Characterization Root causes of Geochemical Prediction Failures include: Root causes of Geochemical Prediction Failures include: –Sample representation –Testing methods –Modeling/Interpretation Geochemical Characterization Failures can be addressed by: Geochemical Characterization Failures can be addressed by: –Ensuring sample representation –Adequate testing –Interpretation

40. Failure Modes Root Causes Mitigation Hydrologic and geochemical characterization failures are the most common root cause of mitigation not being identified, inadequate or not installed Hydrologic and geochemical characterization failures are the most common root cause of mitigation not being identified, inadequate or not installed –Most common assumption is that “oxide” will not result in acid generation –Mitigations are often based on what is common rather than on site specific characterization

41. Failure Modes Root Causes Mitigation Waste rock mixing and segregation not effective Waste rock mixing and segregation not effective –In most cases, no real data is available (e.g. tons of NAG versus tons of PAG and overall ABA accounting) –Failures typically caused by: Inadequate neutral material Inadequate neutral material Inability to effectively isolate acid generating material from nearby water resources Inability to effectively isolate acid generating material from nearby water resources

42. Failure Modes Root Causes Mitigation Liner leak, embankment failure or tailings spill Liner leak, embankment failure or tailings spill –Mitigation frequently fails to perform and can lead to groundwater and surface water quality impacts –Failures are typically caused by: Design mistakes Design mistakes Construction mistakes Construction mistakes Operational mistakes Operational mistakes

43. Failure Modes Root Causes Recommendations A more systematic and complete effort should be undertaken when collecting data A more systematic and complete effort should be undertaken when collecting data Recognize the importance of thorough hydrological and geochemical characterization Recognize the importance of thorough hydrological and geochemical characterization Utilize information in a conservative manner to identify and utilize mitigation measures Utilize information in a conservative manner to identify and utilize mitigation measures Consider the likelihood and consequences of mitigation failures Consider the likelihood and consequences of mitigation failures