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Hydrological classification of mine pit lakes using modelling experiments.

Benedictor Kemanga1, Neil McIntyre2, Nevenka Bulovic1

  • 1Centre for Water in the Minerals Industry, Sustainable Minerals Institute, The University of Queensland, Brisbane, Australia.

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Summary
This summary is machine-generated.

Sustainable mine closure requires understanding pit lake hydrology. This study classifies pit lakes based on climate and conductivity, simplifying risk assessment for mine voids.

Keywords:
GroundwaterLake classificationOpen pit minePit lakeWater balance model

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Geotechnical Engineering

Background:

  • Global open-pit mining necessitates sustainable mine closure and pit lake management.
  • Pit lake interactions with groundwater and surface water are crucial for risk assessment.
  • Existing studies lack a general hydrological classification of pit lakes based on geographical context.

Purpose of the Study:

  • To develop a general understanding and hydrological classification of pit lakes.
  • To examine the influence of geographical factors on pit lake behavior post-closure.
  • To assess the long-term interactions between pit lakes, groundwater, and surface water.

Main Methods:

  • Utilized an integrated generic pit lake water balance model.
  • Simulated 243 scenarios considering climate, hydraulic conductivity, groundwater level, catchment area, and pit slope.
  • Assessed pit lake attributes (water level, time to equilibrium, recharge/overflow fractions) up to 6000 years post-closure.

Main Results:

  • Developed a five-class hydrological classification for pit lakes.
  • Climate and hydraulic conductivity reliably predict long-term pit lake hydrology in many contexts.
  • Classification accuracy requires refinement in non-arid climates with high rainfall variability.

Conclusions:

  • A generalized pit lake classification is feasible using broad geographical data.
  • This classification aids initial risk assessments, guiding the need for site-specific modeling.
  • The findings support more sustainable mine life cycle planning and closure strategies.