Mine closure and sustainable water management by prof carolyn oldham
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This document discusses sustainable water management strategies for mine closure. It outlines a conceptual model and protocol for assessing water quality and balances at mine sites over their lifecycle from exploration to closure. Key aspects include developing hydrological and lake conceptual models, quantifying water quality evolution, scenario testing, field data collection, water quality modelling, and validation. The goal is to predict pit lake water quality and identify options to achieve beneficial end uses or acceptable environmental impacts at minimum cost. A multi-disciplinary team applies several tools and considers factors like geochemistry, meteorology, and ecology.
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Mine closure and sustainable water management by prof carolyn oldham
- 1. Mine closure and sustainable water management Prof. Carolyn Oldham School of Environmental Systems Engineering
- 2. Mine Lake 116 Central German coal district
- 3. Photo: T. Fischer Island Copper Lake British Columbia, Canada
- 4. Lake Stockton Collie coal district
- 5. Lake Stockton Collie coal district
- 6. Gravel pit Central German coal district
- 7. Chicken Creek Pit, Collie Coal Basin
- 8. Sustainable water management • Optimisation: – Impact on downstream water resources – Impact on environment – Beneficial end uses – Social impact – Ongoing management costs – Mine operations • Unlikely to be a “walk-away” solution
- 9. Outline • Mine life stages • Conceptual model • Water balances • Water quality modelling • Validation • AMCER Protocol
- 10. Phases of mine life Island Copper Lake British Columbia, Canada Photo: T. Fischer
- 11. Exploration No solution: reassess? data, decision: Mine feasible 1. Collate any General existing site data 1. Mine plan, conceptual history model 2. Site-specific conceptual model WQ modelling 3. Quantify/predict likely tools WQ evolution Flow chart of mine 4. Assess potential environmental 4. Scenario testing: impact and end uses - prevention possible? WQ - remediation possible? water assessment “poor” - backfilling feasible? WQ good/ - downstream mitigation? impact acceptable 5. Design and begin data collection program 6. Document all data, calculations (+assumptions), sampling plan 7. As input data collected, update predictions Goal: pit lake with 8. As external and internal validation data beneficial end uses or collected, test prediction; acceptable impact for if necessary, improve minimum cost
- 12. Development of a hydrological conceptual model
- 13. Development of a lake conceptual model
- 14. Bathymetry Island Copper Lake British Columbia, Canada
- 15. Bathymetry - landscaping WO5B Collie, Australia
- 16. Meteorological data Photo: T. Fischer Island Copper Lake British Columbia, Canada
- 17. Mass balances - groundwater inflow Chicken Creek Collie, Australia
- 18. Mass balances - inputs from surface runoff
- 19. Exploration No solution: reassess? data, decision: Mine feasible 1. Collate any General existing site data 1. Mine plan, conceptual history model 2. Site-specific conceptual model WQ modelling 3. Quantify/predict likely tools WQ evolution Flow chart of mine 4. Assess potential environmental 4. Scenario testing: impact and end uses - prevention possible? WQ - remediation possible? water assessment “poor” - backfilling feasible? WQ good/ - downstream mitigation? impact acceptable 5. Design and begin data collection program 6. Document all data, calculations (+assumptions), sampling plan 7. As input data collected, update predictions Goal: pit lake with 8. As external and internal validation data beneficial end uses or collected, test prediction; acceptable impact for if necessary, improve minimum cost
- 20. Water balances - inputs
- 21. Water balance - outputs
- 22. Water balance
- 23. Meteorological data Huber et al. 2008
- 24. Impact of wind speeds on water balance
- 25. Impact of wind sheltering on water levels 100% surface wind speed 10% surface wind speed
- 26. Effect of wind on stratification Huber et al. 2008
- 27. Effect of wind sheltering on stratification DYRESM - 100% surface wind speed DYRESM - 10% surface wind speed
- 28. Exploration No solution: reassess? data, decision: Mine feasible 1. Collate any General existing site data 1. Mine plan, conceptual history model 2. Site-specific conceptual model WQ modelling 3. Quantify/predict likely tools WQ evolution Flow chart of mine 4. Assess potential environmental 4. Scenario testing: impact and end uses - prevention possible? WQ - remediation possible? water assessment “poor” - backfilling feasible? WQ good/ - downstream mitigation? impact acceptable 5. Design and begin data collection program 6. Document all data, calculations (+assumptions), sampling plan 7. As input data collected, update predictions Goal: pit lake with 8. As external and internal validation data beneficial end uses or collected, test prediction; acceptable impact for if necessary, improve minimum cost
- 29. Mass balances - AMD from walls Chicken Creek Collie, Australia
- 30. Mass balances - AMD from overburden runoff Chicken Creek Collie, Australia
- 31. Surface inflow assumptions CAEDYM - Fe(III) and Fe(II) – CAEDYM - Fe(III) and Fe(II) – assuming 100% seepage through assuming 10% seepage through black shale black shale
- 32. During Filling Monitoring • Geochemical characterisation of mine void and surrounds, to determine changes in contaminant release • Changing pit bathymetry • On-site meteorological forcing • Establish current and predicted mass balances • On-site water column sensor chains • Water quality sampling
- 33. Mass balances - inflows Island Copper Lake British Columbia, Canada Photo: T. Fischer
- 34. On-site water column sensor chains Photo: T. Fischer
- 35. On-site water column sensor chains WO5B Collie, Australia
- 36. Lake Kepwari Collie, Australia river diversion LDS data, Oct 2003-May 2005 2003-
- 37. Post-filling Monitoring • Geochemical characterisation of local mineralogy • Geochemical characterisation of source waters • On-site meteorological forcing • Establish current and predicted mass balances • On-site water column sensor chains • Water quality sampling
- 38. Water quality sampling temperature, salinity, dissolved oxygen pH turbidity
- 39. T salinity DO% sat
- 40. Water quality sampling LakeKepwari Collie coal istrict
- 41. Water quality sampling LakeKepwari Colliecoal district
- 42. Water quality sampling river diversion LDS data, Oct 2003-May 2005
- 43. Water Quality Modelling • Range of models – Lake stratification models – Geochemical models – Ecological models All require input data
- 44. Model validation Field DYRESM
- 45. Forward prediction - 10 year
- 46. Where to start Oldham et al., ACMER Protocol
- 47. Where to start Oldham et al., ACMER Protocol
- 48. Exploration No solution: reassess? data, decision: Mine feasible 1. Collate any General existing site data 1. Mine plan, conceptual history model 2. Site-specific conceptual model WQ modelling 3. Quantify/predict likely tools WQ evolution Flow chart of mine 4. Assess potential environmental 4. Scenario testing: impact and end uses - prevention possible? WQ - remediation possible? water assessment “poor” - backfilling feasible? WQ good/ - downstream mitigation? impact acceptable 5. Design and begin data collection program 6. Document all data, calculations (+assumptions), sampling plan 7. As input data collected, update predictions Goal: pit lake with 8. As external and internal validation data beneficial end uses or collected, test prediction; acceptable impact for if necessary, improve minimum cost
- 49. The team The funding Team leaders Carolyn Oldham Australian Research Greg Ivey Council Jason Plumb, CSIRO ACMER Research Assoc.BibhashNath Centre for Sustainable Ursula Salmon Mine Lakes Matt Hipsey, CWR State Government of Geoff Wake Western Australia PhD students Deborah Read Wesfarmers Premier Coal Huynh Pham Griffin Coal Masters students Anita Huber Sons of Gwalia Alisa Krasnostein Collie Shire Council Honours students Emma Craven University of Western Peter Chapman Australia Tung Nguyen ManuellaSusanto Alice Turnbull Tom Zdun Aaron Brunt
- 50. Thank you