WATER QUALITY PREDICTIONS AT HARDROCK MINE SITES: METHODS, MODELS, AND CASE STUDY COMPARISON

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

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

6. Specific Models Used • 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

7. Specific Models Used (cont.) • 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)

8. Sources of Uncertainty - Modeling • 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.”;

10. 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 case studies

11. Comparison of Predicted and Actual Water Quality

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

13. EIS Years

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

15. Case Study Mines • 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 • more from CA and MT • fewer Cu mines • more with moderate ADP and CLP • more with shallower groundwater depths

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

17. Case Study Mines Selected

18. Comparison Results • 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% • % estimating low impacts pre-mitigation: 15% • % estimating low impacts with mitigation: 77% • Mines with acid drainage on site: 36% • % predicting low acid drainage potential: 89%

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

20. Percent (%) Percent (%) with Percent (%) with with Impact to Exceede nces of Exceede nces that # Mines Surface Water Standards in Predicted no Surface Water Exceeden ces Mines close to 92 85 91 surface water with 13 (12/13) (11/13) (10/11) mod/high ADP or CLP All case study mines 64 60 73 25 (16/25) (15/25) (11/15) Surface Water Results

21. Percent (%) Percent (%) with Percent (%) with with Impact to Exceede nces of Exceede nces that # Mines Groundw ater Standards in Predicted no G roundw ater Exceedences Mines close to 93 93 86 g round water with 15 (14/15 ) (14/15 ) (12/14 ) mod/high ADP or CLP All case study mines 68 68 77 25 (17 /25) (17 /25) (13 / 17 ) Groundwater Results

22. Mines without Inherent Factors • 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 • 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

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

24. Implications • Mines close to water with mod/high ADP/CLP need special attention from regulators • Water quality impact predictions before mitigation in place more reliable • 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?

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

26. Modeling Opportunities

27. 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

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

29. Surface Water Examples • 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 • McLaughlin, CA predicted exceedences in surface water and was correct

30. Groundwater Examples • Lone Tree, NV had inherent factors but no mining-related impact or exceedence • baseline issue • 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

31. McLaughlin Mine, CA: Waste Rock Monitoring Well

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

33. Failure Modes and Effects Analysis

34. Failure Modes and Effects Analysis Hydrological Characterization Failures: • 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

35. Failure Modes and Effects Analysis Geochemical Characterization Failures: • 11 of 22 mines exhibited inadequacies in geochemical characterization • Geochemical failures resulted from: • Assumptions made about geochemical nature of ore deposits and surrounding areas • Site analogs inappropriately applied to new proposal • Inadequate sampling • 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

36. Failure Modes and Effects Analysis Mitigation Failures: • 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

37. Failure Modes Root Causes Hydrologic Characterization • 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

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

39. Failure Modes Root CausesMitigation • 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

40. Failure Modes Root CausesMitigation • 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 • Inability to effectively isolate acid generating material from nearby water resources

41. Failure Modes Root CausesMitigation • 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 • Construction mistakes • Operational mistakes

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