Research paperConceptual hydrogeological and numerical groundwater flow modelling around the Moab Khutsong deep gold mine, South Africa.
Graphical abstract
Introduction
Proper management of groundwater in underground mining is one of the most critical aspects of mining economics (Lines, 1985; Rubio and Loca, 1993; Wolkersdorfer, 2008; Woldeyohannes et al., 2015 a and b). Failure to have a thorough understanding of groundwater flow dynamics could make or break the profitability of the overall mining operation due to exceedingly high human, operational and material costs (Rubio and Loca, 1993; Balasubramaniam and Panda, 2004). Wasteful and expensive dewatering schemes can be avoided with proper, economically sound as well as environmentally sustainable design of mine water management on the basis of clear and sound understanding of groundwater storage and flow direction as well as flow rate. In many occasions, the conventional groundwater flow modelling approaches may not be strictly applicable to design effective groundwater management in mining conditions owing to the complexity of groundwater flow system (Woldeyohannes and Webb, 2015).
Groundwater management in underground mines has another layer of difficulty because of the substantial infrastructure development within and in the vicinity of the mine and the considerable change of the groundwater flow regime relative to natural conditions (Hodgson et al., 2001). Conventional hydrological tools designed to characterize groundwater flow even in undisturbed conditions mostly fall short of representing real conditions in a satisfactory manner (Reilly and Habough, 2004; Ji-Chun and Xian-Xi, 2013). Mining activities including construction of tunnels and haulages, massive hauling of rock debris from underground, construction of tailings storage facilities, waste rock dumps, evaporation dams and installation of associated metallurgical plants on surface, causes significant change in groundwater recharge, flow and storage conditions. Mining operations further complicate conceptualization of groundwater and surface water flow regimes with conventional hydrologic tools (Rubio and Loca, 1993). It is almost unconceivable that any viable numerical modelling can be developed without a fact based and reliable conceptual model (Zhau and Li, 2011; Singhal and Goyal, 2011). It has been proven that the uncertainty caused by mining operations exceed all the known uncertainty reduction methods including parameter estimation, calibration, sensitivity analyses as well as invoking additional conceptual model recalibration tools (Stauffer, 2005; Fu and Gomez-Hernandez, 2009: Keating et al., 2010; Vrugt et al., 2014; Mengistu et al., 2015).
The application of environmental stable isotope finger printing method has been widely used in combination with other methods such as geochemical foot printing and tracer testing to evaluate various hydrological processes, which include identification of recharge area, qualitative groundwater pathway and mixing as well as water rock interaction (Abyie et al., 2011; Jaunat et al., 2012; Chesson et al., 2014; Mengistu et al., 2015). Data from environmental stable isotope composition of precipitation waters show close relationship with a number of environmental parameters, including source of moisture, surface air temperature, amount and seasonality of precipitation, and recharge altitude. The relationship between climate and mean annual stable isotope contents of precipitation (Craig, 1961; Clark and Fritz, 1997; Dotsika et al., 2010) provides significant insights into paleoclimatic conditions and the underlying pre-existing evaporation and condensation processes.
Environmental stable isotopes of water (δ18O and δ2H)) are commonly used to develop conceptual groundwater flow path, identify recharge/discharge areas, mixing process, salinization process of groundwater (Pulido-Bosch et al. 1997; Larsen et al., 2001; Huang and Chen, 2012; Schofielda and Jankowski, 2004). Various processes in the hydrologic cycle including evaporation, condensation, recharge, mixing and water-rock interaction cause fractionation of Hydrogen and Oxygen isotopes, thereby modifying the equilibrium of isotopic ratios from baseline condition (Aravena, 1995; Mazor, 1997). Thus, by systematically sampling, measuring and mapping the isotopic ratio of various water sources, it is possible to understand the process that the water was subjected (Dotsika et al., 2010). Successful quantification of evaporation process can also be done using the extent to which stable isotope ratios Deuterium and Oxygen-18 deviate from the defined local or global meteoric water line values on a catchment level provided that long-term, spatially representative and detail stable isotope and meteorological data are available (Gibson et al., 1993). In a scenario where considerable mixing of surface water from mining and metallurgical processes and fissure water from shaft dewatering activities is expected, qualitative extent of mixing of surface water – groundwater can be estimated from measured Environmental Stable Isotope (ESI) data (Mengistu et al., 2015).
Hydrochemical data of major ions and cations can be interpreted using mixing models and water rock interaction processes to classify water types. On the basis of these various classes of water chemistry, possible flow paths through which the water may have circulated could be identified (Woldeyohannes et al., 2015a, 2015b). The hydrochemical data is proven to be indicative of percent mixing thereby showing groundwater migration in a defined direction as well as vertical migration of water, if any.
Improving predictive model result using various data sets has shown increasing acceptance and has been advocated to be one way of limiting uncertainties (Xu et al., 2012; Mengistu et al., 2015). In this study, the use of hydrochemical, environmental stable isotope (ESI) and radioactive isotope data are used to refine the understanding of conceptual groundwater flow. Information on groundwater flow paths, groundwater recharge conditions, groundwater residence times and evaporative processes can be retrieved and integrated with the initial conceptual groundwater model for refinement, making it a novel approach.
Numerical modelling is a powerful tool in providing improved understanding of groundwater flow and information on relevant groundwater management parameters provided that scientifically sound and robust conceptual model is available even in the most stressed site conditions such as in mining areas (Konikow and Bredehoeft, 1974; Robson, 1974; Owen et al., 1996; Gvirtzman et al., 1997; Mills et al., 2002; Leake et al., 2005; Mengistu et al., 2015). The most important aspect of using numerical models as a tool for various management purposes lies on addressing the issue of how well the real site condition is captured as closely as possible (Voss, 2011a, 2011b; Merz, 2012; Woldeyohannes et al. 2015a, 2015b). One school of thought advocates, developing and implementing generalized groundwater guidelines as an overarching document and modify it to fit the specific site conditions (Johnson, 2010; Barnett et al., 2012). Others argue that the approach of using groundwater numerical modelling guidelines doesn't bring much traction because of the fact that the real world is too complex to be fairly represented with guidelines (Reilly and Habough, 2004). In some instances analytical and empirical models calibrated to fit the specific site are used in conjunction with water balance data (Woldeyohannes and Webb, 2015). The most common approach to the majority of the scientific community is that uncertainties associated with anisotropy and heterogeneity of aquifer parameters in real field conditions had to be minimized as much as possible through various model result validations (Swanson and Bahir, 2004; Ji-Chun and Xian-Xi, 2013; Sarkar et al., 2015). However, the use of various data sets to refine a conceptual model to reduce model result uncertainties has not been employed. Therefore, in this study, refining of conceptual model, a pre-requisite for improving numerical modelling through minimizing uncertainties is implemented by integrating hydrochemical and environmental isotope information.
Section snippets
Study area
The study area is situated about 200 km southwest of Johannesburg City, near the boundary of Northwest and the northern part of Free State provinces, South Africa. The study area covers approximately 96 km2 surrounding an active mining Shaft, located 3 Km South of the Vaal River with a waste rock dump very close to the shaft (Fig. 1). The mine started stoping from the shaft in 2003 reaching full production in 2010 reaching in depth to over 4 km below groundwater level (DWA, 2006). The main
Geological and hydrogeological setting
The geology of the area is mainly characterized by the Archaean Malmani Dolomite Group of the Transvaal Supergroup (3.2 Ga), composed of carbonates and arenacious rocks, exposed mainly in the northern half of the project area covering roughly 30% of the overall surface geology and referred as Transvaal dolomite the surface geology map (Fig. 2). There is a subordinate cover of the Karoo fluvio-lacustrine sequences (Paleozoic era), which becomes more important in the south quarter of the study
Methodology
Numerical groundwater modelling was executed by Groundwater Modelling System (GMS 9.2) based on MODFLOW 2005, a broad graphical user environment developed by Aquaveo, LLC in Provo, Utah. GMS is compatible with GIS based graphical pre-processing tools to automate and streamline the modelling process for conceptualizing and performing groundwater flow simulation. The entire GMS system consists of a graphical user interface (the GMS program) and a number of modelling codes (MODFLOW, MT3DMS, etc.),
Environmental stable isotope
Twelve samples were taken from the Vaal River, seepage water from a return water dam, shallow aquifer, deep aquifer and very deep shaft water intercepted around 900 m bgl during summer season (June–July 2012) (Fig. 4). Four boreholes are upstream of the shaft although the majority of boreholes cluster around the shaft whereas three boreholes are situated around the main tailings storage facility (Fig. 4).
The environmental stable isotope data of deuterium ranges from most depleted −49‰ in a
Conclusions
The conceptual groundwater flow model built on the basis of existing borehole logs and detail deep mine map has been verified using hydrochemical and environmental isotope data and Tritium data (δ18O, δ2H, 3H). All these datasets demonstrate that at the current time, shaft fissure water has a strong signature of Na–Cl type water presumably originating from surface mine infrastructure such as the local mine office and loading/unloading as well as parking areas. The shaft fissure water also shows
Acknowledgement
The Authors would like to acknowledge Mr. Robert White of Basic Bed Rock Strata, Mr. Charles Human and Mr. Joel Mallan of AngloGold Ashanti Mine, Mr. Ugo Nzota, Ms. Thato Kgari and Mr. Yazeed van Wyk of the Council for Geoscience as well as staff of the Northwest Regional office of the Department of Mines and Energy.
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