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Water balance of metal mining tailings management facilities : influence of climate conditions and tailings… Solgi, Narjes 2017

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 WATER BALANCE OF METAL MINING TAILINGS MANAGEMENT FACILITIES: INFLUENCE OF CLIMATE CONDITIONS AND TAILINGS MANAGEMENT OPTIONS   by Narjes Solgi M.Sc., The Tarbiat Modares University, 2006  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Mining Engineering)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2017  © Narjes Solgi, 2017 ii  Abstract The objective of this research was done to review and compare available methods for Tailings Management Facilities (TMFs) water balance; to develop deterministic and probabilistic models; and to compare the impacts of different tailings management options and climate conditions. The developed models were spreadsheet based. Mount Polley operational data were used. Water balance models were created for lined and unlined impoundments in both wet and dry climates. Wet condition climate data were extracted from ClimateBC (a University of British Columbia Software Program) using the location of the Kerr-Sulphurets-Mitchell (KSM) project in British Columbia. Climatic data from the Cerro Negro mine site in Argentina were used to simulate the dry condition.  After developing a deterministic model, Monte Carlo simulation computational algorithm was used to develop the probabilistic evaluations. Simulations were carried out using the Oracle Crystal Ball Excel add-in. Evaluations were done on four management options (slurry, thickened, paste, and filtered tailings) by changing the tailings solids content.  Results confirmed that entrainment and evaporation were the biggest water losses in TMF. For slurry tailings, entrainment loss was more than 80% of the total water loss in the wet condition and more than 50% of the total water loss in the dry condition. The reported average mine water consumption for slurry tailings in arid climate is between 0.4 and 0.7 m3/tonne. The estimated mean required make up water from the developed model in this reaserch was 0.70 m3/tonne. Water withdrawal in dry climate conditions can decrease to 0.18m3/tonne when a filtered tailings option is implemented.  iii  The average water surplus in wet climate conditions for an unlined impoundment varied between 0.83 and 1.12 m3/tonne for solids contents between 45% (slurry tailings) and 80% (filtered tailings). The corresponding values for a lined impoundment were 0.86 and 1.16 m3/tonne. Implementing dewatered tailings is not recommended in wet climates. In contrast, paste tailings and filtered tailings are good options in arid areas for proper-size operations. TMFs are site-specific complex systems. Results presented here are only examples to outline how the mining industry can work toward reducing water losses by using dewatering and tailings management technologies. iv  Preface As the author of this dissertation, I am fully responsible for the design, data gathering, statistical analyses, research outcomes, and printed results. v  Table of Contents  Abstract ......................................................................................................................................................... ii Preface ......................................................................................................................................................... iv Table of Contents .......................................................................................................................................... v List of Tables ............................................................................................................................................. viii List of Figures .............................................................................................................................................. xi List of Abbreviations ................................................................................................................................. xiv Acknowledgements ..................................................................................................................................... xv Dedication .................................................................................................................................................. xvi Chapter 1: Introduction ................................................................................................................................. 1 1.1 Background ...................................................................................................................................... 1 1.2 Thesis objectives .............................................................................................................................. 3 1.3 Thesis outline ................................................................................................................................... 4 Chapter 2: Literature review ......................................................................................................................... 5 2.1 Tailings water balance ..................................................................................................................... 5 2.1.1 Water inflow .......................................................................................................................... 10 2.1.1.1 Water with tailings ....................................................................................................... 10 2.1.1.2 Surface runoff ............................................................................................................... 11 2.1.2 Water outflow ........................................................................................................................ 12 2.1.2.1 Evaporation................................................................................................................... 12 2.1.2.2 Entrained water ............................................................................................................. 17 2.1.2.3 Seepage ......................................................................................................................... 18 2.1.2.4 Reclaimed water ........................................................................................................... 20 2.1.3 Water recovery (RGC “Make-up” water model) .................................................................. 20 2.1.4 Consolidation/seepage model ................................................................................................ 22 2.2 Tailings management options ........................................................................................................ 28 2.3 TMF Water savings ....................................................................................................................... 33 2.4 Role of climate and water data ....................................................................................................... 38 2.4.1 Water management in storm events ...................................................................................... 40 2.5 Probabilistic analyses ..................................................................................................................... 41 2.5.1 Water balance uncertainties .................................................................................................. 42 vi  2.5.2 Stochastic rainfall and evaporation ....................................................................................... 43 2.5.3 Oracle Crystal Ball and Monte Carlo simulation .................................................................. 43 2.6 Water balance case studies ............................................................................................................. 44 2.7 Water balance calibration and validation ....................................................................................... 45 2.8 Summary of literature review ........................................................................................................ 48 Chapter 3: Methodology- data input ........................................................................................................... 50 3.1 Water balance data selection .......................................................................................................... 50 3.1.1 Climate data .......................................................................................................................... 50 3.1.1.1 Climate data selection ................................................................................................... 50 3.1.1.2 Statistical analysis of climate data ................................................................................ 53 3.1.2 Operational data .................................................................................................................... 57 3.1.3 Tailings material characteristics ............................................................................................ 59 3.2 Water balance models .................................................................................................................... 59 3.2.1 Deterministic water balance .................................................................................................. 62 3.2.1.1 Lined impoundment ...................................................................................................... 62 3.2.1.2 Unlined impoundment .................................................................................................. 68 3.2.2 Probabilistic water balance.................................................................................................... 69 3.2.2.1 Lined impoundment ...................................................................................................... 72 3.2.2.2 Unlined impoundment .................................................................................................. 74 Chapter 4: Deterministic water balance results ........................................................................................... 75 4.1 Lined impoundment ....................................................................................................................... 75 4.1.1 Wet climate ........................................................................................................................... 75 4.1.2 Dry climate ............................................................................................................................ 80 4.2 Unlined impoundment ................................................................................................................... 85 4.2.1 Wet climate ........................................................................................................................... 85 4.2.2 Dry climate ............................................................................................................................ 91 Chapter 5: Probabilistic water balance results ............................................................................................ 98 5.1 Lined impoundment ....................................................................................................................... 98 5.1.1 Wet climate ........................................................................................................................... 98 5.1.2 Dry climate .......................................................................................................................... 101 5.2 Unlined impoundment ................................................................................................................. 104 5.2.1 Wet climate ......................................................................................................................... 104 5.2.2 Dry climate .......................................................................................................................... 107 vii  Chapter 6: Discussion ............................................................................................................................... 111 Chapter 7: Conclusion and recommendations .......................................................................................... 123 Bibliography ............................................................................................................................................. 126 Appendices ................................................................................................................................................ 130 Appendix A Data input and methodology ............................................................................................ 130 Appendix B Results of wet climate simulation .................................................................................... 139 Appendix C Results of dry climate simulation .................................................................................... 153   viii  List of Tables Table 1.1 Water use reduction scenarios (after Gunson, 2013) .................................................................... 2 Table 2.1 Water consumption at different mines (after Gunson, 2013) ........................................................ 6 Table 2.2 Water inflows and outflows at a TMF (after Wade, 2014) ........................................................... 8 Table 2.3 Evaporation phases following wetting (after Aydin et al., 2005) ............................................... 14 Table 2.4 Features of tailings management options .................................................................................... 30 (after Davies and Rice, 2001 & Martin et al., 2002 & www.tailings.info, UBC MINE 480 Course Notes) .................................................................................................................................................................... 30 Table 2.5 Water use reduction scenarios (after Gunson, 2013) .................................................................. 36 Table 2.6 Details of water consumption scenarios (after Gunson, 2013) ................................................ 37 Table 2.7 Modelling attributes of the projects studied by Naghibi (2015) ................................................. 47 Table 2.8 Modelling attributes of simplified water balance and models with refinements ........................ 48 Table 2.9 Modelling attributes suggested to be adopted to water models .................................................. 49 Table 3.1 Snowpack accumulation and melt times ..................................................................................... 52 Table 3.2 Mean and standard deviation of monthly climate data over from 1970 to 2013 ........................ 54 Table 3.3 Rainfall (mm/month) from 1978 to 2012 recorded in  Project Site Weather Station (EMA BN) (after Wade, 2014) ...................................................................................................................................... 55 Table 3.4 Dry climate monthly pan evaporation based on the average evaporation in the dry condition and temporal distribution of evaporation in the wet condition .......................................................................... 56 Table 3.5 The pattern for switching the values of monthly rainfall in the model ....................................... 57 Table 3.6 Operating data inputs and assumptions ....................................................................................... 58 Table 3.7 Proportions of pond and beaches areas relative to the total TMF area ....................................... 59 Table 3.8 Tailings material characteristics input data and assumptions ..................................................... 59 Table 3.9 The Excel spreadsheet table used to develop the water balance model. ..................................... 62 Table 3.10 Calculation methods and assumptions used to determine water balance parameters for wet condition lined impoundment ..................................................................................................................... 65 Table 3.11 The relationship between dry density and solids content of tailings ........................................ 70 Table 3.12 Triangular distribution parameters for wet climate data from 1970 to 2013 ............................ 73 Table 3.13 The properties of triangular distribution for the dry climate ..................................................... 73 Table 4.1 Deterministic water balance for solids content of 45% in the wet condition for a lined impoundment .............................................................................................................................................. 76 ix  Table 4.2 Deterministic water balance for solids content of 45% in the dry condition for a lined impoundment .............................................................................................................................................. 81 Table 4.3 Deterministic water balance for solids content of 45% in the wet condition for an unlined impoundment .............................................................................................................................................. 87 Table 4.4 Deterministic water balance for solids content of 45% in the dry condition for an unlined impoundment .............................................................................................................................................. 93 Table 6.1 Water loss proportion in different scenarios ............................................................................. 114 Table 6.2 Water deficit/water surplus for the month with highest precipitation and the ,onth with highest evaporation ................................................................................................................................................ 115 Table 6.3 Comparison of Gunson’s study (2012) with this study: Evaporation and entrainment losses in different scenarios ..................................................................................................................................... 116 Table 6.4 Cumulative water deficit/surplus averaged over the last 3 years of mine life .......................... 117 Table 6.5 Water consumption (m3/t solids) for a platinum project on the eastern limb of the Bushveld Complex (Moolman and Vietti, 2012) ...................................................................................................... 118 Table 6.6 Effect of changing seepage losses on water surplus for different solids contents in wet condition .................................................................................................................................................................. 119 Table A.1 Average monthly rainfall obtained through ClimateBC from 1970 to 2013 (mm) .................. 130 Table A.2 Average monthly snowfall obtained through ClimateBC over the period of 1970-2013 (mm) .................................................................................................................................................................. 132 Table A.3 Average monthly pan evaporation obtained through ClimateBC over the period of 1970-2013  .................................................................................................................................................................. 134 Table A.4 Rainfall (mm/month) over the period of 1978-2012 recorded in Project Site Weather Station (EMA BN) (after Wade, 2014) ................................................................................................................. 136 Table B.1 Deterministic model for solids content of 60% in the wet condition for a lined impoundment .................................................................................................................................................................. 140 Table B.2 Deterministic model for solids content of 70% in the wet condition for a lined impoundment .................................................................................................................................................................. 142 Table B.3 Deterministic model for solids content of 80% in the wet condition for a lined impoundment .................................................................................................................................................................. 144 Table B.4 Deterministic model for solids content of 60% in the wet condition for an unlined impoundment ............................................................................................................................................ 147 Table B.5 Deterministic model for solids content of 70% in the wet condition for an unlined impoundment ............................................................................................................................................ 149 x  Table B.6 Deterministic model for solids content of 80% in the wet condition for an unlined impoundment ............................................................................................................................................ 151 Table C.1 Deterministic model for solids content of 60% in the dry condition for a lined impoundment154 Table C.2 Deterministic model for solids content of 70% in the dry condition for a lined impoundment156 Table C.3 Deterministic model for solids content of 80% in the dry condition for a lined impoundment158 Table C.4 Deterministic model for solids content of 60% in the dry condition for an unlined impoundment .................................................................................................................................................................. 161 Table C.5 Deterministic model for solids content of 70% in the dry condition for an unlined impoundment .................................................................................................................................................................. 163 Table C.6 Deterministic model for solids content of 80% in the dry condition for an unlined impoundment .................................................................................................................................................................. 165  xi  List of Figures Figure 2.1 Inflow and outflow parameters in a water balance ...................................................................... 8 Figure 2.2 Relationship between the actual evaporation to potential evaporation (Aydin et al., 2005) .. 16 Figure 2.3 Physical processes occurring in the tailings during the life of a TMF  (Lopes and van Zyl, 2006a). ........................................................................................................................................................ 23 Figure 2.4 Soil shrinkage phases (Lopes and van Zyl, 2006a) ................................................................... 24 Figure 2.5 Relationship of Bingham yield stress versus solids content for a system transporting 300 dry tonnes per hour tailings (after Paterson, 2015) ........................................................................................... 31 Figure 2.6 Tailings management options based on degree of dewatering (Davies & Rice, 2001) ............. 32 Figure 2.7 Changes in water loss components of water balance over time (excluding entrainment losses)  (Wels and Robertson, 2003) ....................................................................................................................... 34 Figure 2.8 ClimateBC Desktop Version 5.21 interface .............................................................................. 40 Figure 2.9 Water levels required to be considered in designing of a TMF ................................................. 41 Figure 2.10 Ranges of water level at a TMF simulated with a water balance model and the spillway elevation proposed by the model (Nalecki and Gowan, 2008). .................................................................. 44 Figure 2.11 Graphical and object- oriented simulation environment of GoldSim (www.goldsim.com) .... 45 Figure 2.12 Comparison between simulated results of a probabilistic water balance and field measured data (McPhail, 2005) ................................................................................................................................... 46 Figure 3.1 Approximate location of KSM Project used for the wet climate location (Google Earth, retrieved Feb 2017) ..................................................................................................................................... 51 Figure 3.2 Approximate location of Cerro Negro Project used for the dry climate location (Google Earth, retrieved Feb 2017) ..................................................................................................................................... 53 Figure 3.3 Correlation between Rainfall and Evaporation data .................................................................. 54 Figure 3.4 Cumulative Frequency Diagram of the rainfall data of table 3.3 (the red and blue lines show the best normal and log normal distributions fitted to the data) ................................................................. 56 Figure 3.5 Conditions considered in developing the water balance models in this reserch ........................ 60 Figure 3.6 Simplified schematic of a TMF water balance used in the research ......................................... 61 Figure 3.7 Water circulation at TMF for wet climate condition ................................................................. 64 Figure 3.8 Water circulation at TMF for dry climate condition ................................................................. 67 Figure 3.9 Schematic of water circulation for the unlined impoundment in the wet climate condition ..... 68 Figure 3.10 Schematic of water circulation for the unlined impoundment in the dry climate condition .... 69 Figure 3.11 Most likely Dry density assumption for different solids contents ........................................... 71 xii  Figure 4.1 Schematics of water balance for different solids contents in the wet condition for a lined impoundment .............................................................................................................................................. 78 Figure 4.2 Cumulative water surplus for different solids contents in the wet condition for a lined impoundment .............................................................................................................................................. 79 Figure 4.3 Cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for a lined impoundment ............................................................................................................. 80 Figure 4.4 Schematics of water balance for different solids contents in the dry condition for a lined impoundment .............................................................................................................................................. 83 Figure 4.5 Cumulative water deficit for different solids contents in the dry condition for a lined impoundment .............................................................................................................................................. 84 Figure 4.6 Cumulative water deficit per tonne of mill throughput for different solids contents in the dry condition for a lined impoundment ............................................................................................................. 85 Figure 4.7 Schematics of water balance for different solids contents in the wet condition for an unlined impoundment .............................................................................................................................................. 89 Figure 4.8 Cumulative water surplus for different solids contents in the wet condition for an unlined impoundment .............................................................................................................................................. 90 Figure 4.9 Cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for an unlined impoundment ....................................................................................................... 91 Figure 4.10 Schematics of water balance for different solids contents in the dry condition for an unlined impoundment .............................................................................................................................................. 95 Figure 4.11 Cumulative water deficit for different solids contents in the dry condition for an unlined impoundment .............................................................................................................................................. 96 Figure 4.12 Cumulative water deficit per tonne of mill throughput for different solids contents in the dry condition for an unlined impoundment ....................................................................................................... 97 Figure 5.1 Mean cumulative water surplus for different solids contents in the wet climate condition for a lined impoundment ..................................................................................................................................... 99 Figure 5.2 The 5th percentile of cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for a lined impoundment ............................................................................ 100 Figure 5.3 The 95th percentile of cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for a lined impoundment ............................................................................ 101 Figure 5.4 Mean cumulative water deficit for different solids contents in the dry climate condition for a lined impoundment ................................................................................................................................... 102 xiii  Figure 5.5 The 5th percentile of cumulative water deficit per tonne of mill throughput for different solids contents in the dry condition for a lined impoundment ............................................................................ 103 Figure 5.6 The 95th percentile of cumulative water surplus/deficit per tonne of mill throughput for different solids contents in the dry condition for a lined impoundment ................................................... 104 Figure 5.7 Mean cumulative water surplus for different solids contents in the wet climate condition for an unlined impoundment ............................................................................................................................... 105 Figure 5.8 The 5th percentile of cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for an unlined impoundment ...................................................................... 106 Figure 5.9 The 95th percentile of cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for an unlined impoundment ...................................................................... 107 Figure 5.10 Mean cumulative water deficit for different solids contents in the dry climate condition for an unlined impoundment ............................................................................................................................... 108 Figure 5.11 The 5th percentile of cumulative water deficit per tonne of mill throughput for different solids contents in the dry condition for an unlined impoundment ...................................................................... 109 Figure 5.12 The 95th percentile of cumulative water deficit per tonne of mill throughput for different solids contents in the dry condition for an unlined impoundment ............................................................ 110 Figure 6.1 Cumulative water balance parameters in the arid condition for a lined impoundment ........... 111 Figure 6.2 Cumulative water balance parameters in wet condition for a lined impoundment .................. 112 Figure 6.3 Water balance parameters for hydraulic fill TMF (Blight, 2010) ............................................ 113 Figure 6.4 Cumulative water surplus percentiles for 45% solids content in wet climate for a lined impoundment ............................................................................................................................................ 120 Figure 6.5 The entrainment loss realative to the water with tailings at the end of year 4 ......................... 121 Figure 6.6 Range of water deficits and water surplus for different conditions at the end of year 4 ......... 122 Figure A.1 Distribution of average monthly rainfall obtained through ClimateBC over the period of 1970-2013 (mm) ................................................................................................................................................. 131 Figure A.2 Distribution of average monthly snowfall obtained through ClimateBC over the period of 1970-2013 (mm) ....................................................................................................................................... 133 Figure A.3 Distribution of average monthly evaporation obtained through ClimateBC over the period of 1970-2013 (mm) ....................................................................................................................................... 135 Figure A.4 Distribution of average monthly rainfall over the period of 1978-2012 recorded in Project Site Weather Station (EMA BN) (mm) ............................................................................................................ 137 Figure A.5 Flow chart of water balance simulation in the wet condition ................................................. 138  xiv  List of Abbreviations AEP Annual Exceedance Probability ARD Acid Rock Drainage CDA Canadian Dam Association EDF Environmental Design Flood IDF Inflow Design Flood NOWL Normal Operating Water Level PDF Probability Density Function PMF Probable Maximum Flood RD Relative Density TMF Tailings Management Facility xv  Acknowledgements I am grateful to all who helped me in my master’s program journey at UBC. I would like to express my sincere gratitude to my supervisor, Dr. Dirk Van Zyl for all his supports. The Mine Waste Management course he has been teaching expanded my vision about mine water balance and provided insight for my research. His valuable advice, guidance and feedback always brightened my path and helped in research and writing.  I would also like to thank my committee members, Dr. Scott Dunbar and Dr. Marek Pawlik for their support and comments on my work. I offer sincere appreciation to the faculty, staff, and my fellow students at the NBK Institute of Mining Engineering who provided me with a good time at UBC. I am grateful to my family and friends. I have been lucky to have their constant support and encouragement.         xvi  Dedication     gÉ Åç YtÅ|Äç‹  1  Chapter 1: Introduction 1.1 Background Water management at Tailings Management Facilities (TMF)1 has always been a challenging issue for the mining industry. Insufficient and excessive water cause problems for operations and the environment. Insufficient free pond water could result in water withdrawal from the water resources. Although mine water consumption has been a small portion of overall global water use, mine water requirements are still considered very high. In 2006, water use for the copper mining industry alone accounted for 1.3 billion m3 (Gunson et al., 2012).  Excess water in the pond can result in overtopping and uncontrolled release into the environment. The International Commission on Large Dams (ICOLD) and United Nations Environmental Programme (UNEP) (2001) listed 221 total historical tailings incidents and failures. Most reported failure cases were induced by, “overtopping, slope instability, seepage and erosion; all caused by a lack of control of the water balance within the impoundments” (ICOLD and UNEP, 2001, p. 31).  Water balance modeling should be used in the design stage of TMFs to evaluate the water inflows and outflows.  In a dry climate, zero surface discharge can be achieved. However, water conservation remains an issue. Conversely, in a wet climate the main challenge is to attain the most efficient containment in order to minimize environmental effects. Environmental impacts can be reduced by controlling the inflow from runoff, by expanding the ponds, by increasing exposure to                                                  1 In much of literature, the term Tailings Storage Facility (TMF) is used. However, in this thesis, Tailings Management Facility (TMF) is used to signify the importance of managing the tailings versus storing the tailings. 2  evaporation, by decreasing the use of fresh make-up water through recycling water from the TMF, and by decreasing the tailings water retention through fines generation reduction during grinding. Another method is to thicken slurry before deposition to reduce water reporting to the tailings impoundment.  Table 1.1 shows the influence of practising different tailings management options on water use in a hypothetical mine studied by Gunson (2013). This mine could save water by reducing evaporation losses, by thickening the slurry, using ore pre-sorting, or by combining the above mentioned methods. Gunson’s study only focuses on water losses. Table 1.1 Water use reduction scenarios (after Gunson, 2013) # Scenario Description Water consumption Water withdrawal m3/t of ore processed (considering water with ROM of 1,020 m3/d) 1 Base case Conventional concentrator 100 % (38,955 m3/d) 0.76 2 Base case with water conservation Conventional concentrator and reduction in evaporation losses 87% (33,822 m3/d) 0.66 3 Paste tailings case Conventional concentrator and paste tailings disposal 79% (30,682 m3/d) 0.59 4 Filtered tailings case Conventional concentrator and filtered tailings disposal 42% (16,491 m3/d) 0.31 5 Ore pre-sorting case Conventional concentrator and 20% ore rejection 82% (32,096 m3/d) 0.62 6 Combined water reduction case Conventional concentrator and reduction in evaporation losses, as well as 20% ore rejection and filtered tailings disposal 28% (10,878 m3/d) 0.20  Studies of the influence of different management practices on water balance are not conducted in a consistent way specifically for wet and dry climates. This research not only compares the 3  influence of various tailings management options on water balance, but also studies the effects of two different climate conditions. In the design stage, the initial water balance models are usually deterministic, meaning they are developed on the average values of input data. Although these models provide fairly good prediction of water balance, they do not include the uncertainty related to the stochastic parameters of water balance. To account for the uncertainties, probabilistic water balance models are introduced. This research implements probabilistic modelling by defining the variable parameters in the model. Spreadsheet models for water balances made it is easy to vary the input data to evaluate the changes in the system. Transforming the deterministic spreadsheet models to stochastic models is possible by using add-in packages such as Oracle Crystal Ball or @Risk. The water balance models in this research was developed in Microsoft Excel spreadsheets with the Oracle Crystal Ball add-in. Multivariate stochastic systems programs such as GoldSim can also be utilized to develop dynamic probabilistic water balance models. 1.2 Thesis objectives This research presents a probabilistic water balance model for metal mines incorporating the uncertainty of variables: climate data and tailings management options. It aims to answer the question: How do different tailings management options in different climates affect mine water requirements and surpluses? The objectives of this research are as follows: - Review and compare available methods for tailings water balance. 4  - Extract and analyze climate data for wet and dry conditions. Statistical analyses of climate data are used to reflect the uncertainty of the input data. - Develop both a deterministic and probabilistic water balances. - Conduct analyses on different tailings management options. 1.3 Thesis outline The introduction in Chapter 1 included an overview of the study and the thesis objectives. Chapter2 is a literature review that includes water management options and different methods of water balance. Chapter 2 also discusses the associated advantages and disadvantages of different tailings management options. Chapter 3 describes the methodology of the research. The structure and components of water balance models with data input and climate parameters are addressed in this chapter. Results of deterministic and probabilistic water balance models are presented in Chapters 4 and 5 respectively. The Discussion in Chapter 6 compares the results of this research with previous works. Chapter 7 presents the conclusions. Chapter 8, provides suggestions for future research. Graphs and tables of the developed models are provided in the Appendices. 5  Chapter 2: Literature review Davies (2001) categorizes tailings impoundments among the largest man-made structures which do not produce any income for the mining companies and only impose costs. How can the cost of tailings disposal be reduced while also environmental considerations are taken into account? In arid climates where water is scarce, water management is necessary to reduce fresh water removal. In wet climates, managing excess water can lower the cost of closure and reduce environmental risks.  The most critical component of water management is a mine water balance model which estimates the water quantity at each operational unit. A comprehensive water balance inspects all mining operations and develops flow diagrams of both impacted water and clean water. Mine site inflow networks include natural rainfall/runoff cycles and water contained in the ore (Meggyes et al., 2003).  Water balance models are developed on different spatial and temporal scales. These scales depend on the objectives of modeling. Periods of balance differ for each project, but it is usually annual, which can summarize one complete hydrological cycle (Naghibi, 2015; Welch, 2000). The focus of this research is around TMF. This chapter focuses on tailings water balance studies and modelling. 2.1 Tailings water balance TMF water balance is used for design purposes, as well as operational considerations such as the capacity of the impoundment. To store high precipitation events, water balance calculations are used to evaluate the minimum freeboard required. It is also used to estimate mine water requirements. The required water is site specific and influenced by many factors such as the type 6  of commodity being processed, processing method, climate, waste management option, etc. Over the years, multiple publications have provided various estimations of mine water requirements. Table 2.1 summarizes the water consumption statistics reported over time. The studies do not indicate whether the water consumption is the balance between water storage and water discharge or if it is only the water withdrawals (Gunson, 2013). Table 2.1 Water consumption at different mines (after Gunson, 2013) Year of publication Author Water Consumption (m3/tonne of ore) Note 1920 Callow 0.6-30 (including recycle water) Survey from 25 major mining operations. 1932 Gaudin 1.8-4.8 With flotation pulp density of 18-30%. 1939 Gaudin 0.07-1.5 For mill water consumption. 1985 Yezzi 0.4-20 (of ore processed)  2003 Brown 0.4-1.0 (of ore processed)  2003 Atmaca and Kuyumcu 0.13-0.71 (of product for coal) 18.5-23.9  (for copper porphyry) 4-10 (for chrome) 2-29 (for Cu-Pb-Zn) 0.4-5 (for potash and boron salts).   Welch (2000, p. 391) suggests water balances should have the following specifications: - simple and easy to use with identifiable input data 7  - easy to understand and validate - easy to vary the input data to apply the changes in the system and to conduct sensitivity analyses - easy to be used by the mine personnel and regulators Models can become complicated even when basic physics and hydrogeological equations are involved. Modeling is not only the use of equations, but it is a combination of skills, experience and knowledge to distinguish to what extent the model can become complex and if the complexity is necessary (Aydin et al., 2005). The simplest widely used traditional model is “merely a mathematical tool that adds inflows and subtracts losses in a system” based on average values of water cycle components over months or years (Nalecki and Gowan, 2008; Welch, 2000, p. 391; Wels and Caldwell, 2013). It simplifies or ignores complex and transient processes such as consolidation, settlement or evaporation (Wels and Caldwell, 2013). Equation [2.1] and Figure 2.1 show a typical diagram of water inflows and outflows of a TMF, and the mathematical presentation of the basic water balance model respectively. ܥ݄ܽ݊݃݁	݅݊	ݏݐ݋ݎܽ݃݁ ൌ෍ܹܽݐ݁ݎ ݂݈݅݊݋ݓ െ෍ܹܽݐ݁ݎ ݋ݑݐ݂݈݋ݓ [2.1] In this research, the terms “water out”, “water outflow”, and “water output” are equivalent. “Water in” refers to “water inflow”, or also “water input”.  8   Figure 2.1 Inflow and outflow parameters in a water balance for: a) conventional TMF, b) dewatered tailings (Blight, 2010) Table 2.2 shows the water inputs and outputs for a TMF water balance. Table 2.2 Water inflows and outflows at a TMF (after Wade, 2014) Water inflow Water outflow Precipitation Evaporation Runoff Seepage Water with tailings Reclaim water Other water streams Entrainment  The amount of water inflow with the deposited tailings depends on the tonnage of the throughput and the tailings solids content. This water turns into the water trapped in the pores after tailings 9  settles which usually cannot be recovered, and the supernatant water which is collected in the pool.  Precipitation can be in the form of rainfall or snowfall that falls directly on the free water at the TMF. Whereas runoff forms from the precipitation on the “undisturbed” regions above the TMF, inactive beaches and mine areas including pit, mine rock piles, stock piles, etc., if all impacted water is stored in TMF. Water with tailings is also a water inflow component. Considering the components in Table 2.1, Equation [2.1] expands to Equation [2.2].  ܥ݄ܽ݊݃݁	݅݊	ݏݐ݋ݎܽ݃݁	ሺݓܽݐ݁ݎ	ܾ݈ܽܽ݊ܿ݁ሻൌ෍ሾܹܽݐ݁ݎ	ݓ݅ݐ݄	ݐ݈ܽ݅݅݊݃ݏ ൅ ܲݎ݁ܿ݅݌݅ݐܽݐ݅݋݊ ൅ ܵݑݎ݂ܽܿ݁	ݎݑ݊݋݂݂ሿെ෍ሾܧݒܽ݌݋ݎܽݐ݅݋݊ ൅ ܧ݊ݐݎܽ݅݊݉݁݊ݐ ൅ ܴ݈݁ܿܽ݅݉ ݓܽݐ݁ݎ ൅ ܵ݁݁݌ܽ݃݁ሿ [2.2] According to Blight (2010), “The integral of the changes in the storage may be used to predict the water level at any time, both on the top of the tailings storage and in the return water reservoir” (Blight, 2010, p. 385). The details of water balance components and calculations methods are described in the following sections. The extra simplification in the basic water balance approach can result in inaccurate outcomes. Therefore refinements are introduced to the basic water balance. Models of water recovery, consolidation/seepage and probabilistic water balance are developed based on the refinements. The extent of water balance refinements depends on the project. Physical processes such as consolidation, dessication and desaturation are not usually studied or considered in water balance modelling. Therefore estimations of entrainment losses and the time-dependent discharges are usually not accurate (Lopes and van Zyl, 2006a). 10  2.1.1 Water inflow The inflow parameters are described in this section. 2.1.1.1 Water with tailings Water with tailings includes the water from the ore moisture content and the water which is added to the throughput in the process plant. The water addition to the process plant can be either fresh water or reclaimed water from the TMF. In theory, the required water for the mill is equal to the water with tailings minus the ore moisture water. However, in practise, the ore moisture water is not considred when the water is added to the process system. The amount of water leaving the process plant with the tailings depends on the density of the tailings, ܵ௧. The volume of water deposited with slurry is defined by the following equation ௪ܸ ൌ ൤൬ ௦ܹܵ௧ ൰ െ ௦ܹ൨ ൊ ߩ௪	ݓ݄݁ݎ݁, ܵ௧ ൌ ሺ௦ܹ௦ܹ ൅ ௪ܹሻ100 [2.3] Where: ௦ܹ:	 weight of dry solids fed to the process plant (tonne/day) ௪ܹ:	 weight of the water with tailings (tonne/day) ௪ܸ:	 volume of water leaving the process plant (e.g. m3/day)  ܵ௧:	 solids content of tailings (%) ߩ௪: density of water (tonne/m3) (it changes with temperature and altitude) Moisture content in the ore  Assuming targeted dry throughput of ௦ܹ (tonne/day), with a moisture content of x, total throughput including the moisture content reported to the TMF equals to ௐೞଵି௫ tonne per day. Therefore the amount of water (tonne/day) from the moisture content can be calculated from the following equations: 11  ܹܽݐ݁ݎ	݂ݎ݋݉	݉݋݅ݏݐݑݎ݁	ܿ݋݊ݐ݁݊ݐ ൌ ௦ܹ1 െ ݔ െ ௦ܹ [2.4] ܸ݋݈ݑ݉݁	݋݂	ݓܽݐ݁ݎ	݂ݎ݋݉	݉݋݅ݏݐݑݎ݁ ܿ݋݊ݐ݁݊ݐ ൌ ሺ ௦ܹ1 െ ݔ െ ௦ܹሻ/ߩ௪ [2.5] 2.1.1.2 Surface runoff Surface runoff into a TMF can be from natural terrain and/or from the tailings surface. There are different methods to estimate surface runoff from natural terrain. A simple method to calculate the surface runoff is the “Rational Method” showed in the following equation. ܴݑ݊݋݂݂ ൌ 	ܴݑ݊݋݂݂	ܿ݋݂݂݁݅ܿ݅݁݊ݐ	 ൈ ܴ݂݈݈ܽ݅݊ܽ ݅݊ݐ݁݊ݏ݅ݐݕ ൈ ܦݎܽ݅݊ܽ݃݁ ܽݎ݁ܽ [2.6] In a large watershed, the runoff coefficient is assumed to be 50%. In smaller watersheds like tailings impoundments, it is considered to be larger, up to 75%, because no vegetation cover exists in TMFs. In dry conditions, this number decreases to 10 to 20% (or even to zero) depending on the amount of infiltration into the watershed (Welch, 2000). Another method for calculating runoff is the NRCS (Natural Resources Conservation Services) method which uses a curve number to calculate surface runoff (See Equations [2.7] to [2.9]) (National Resources Conservation Service, 2004). This method was initially developed to calculate runoff on agricultural fields. It is now broadly used including estimating the peak runoff rates for urban hydrology. ܳ ൌ ሺܲ െ ܫ௔ሻଶሺܲ െ ܫ௔ሻ ൅ ܵ	, ݂݅	ܲ ൐ ܫ௔	, ݋ݎ ܳ ൌ 0	, ݂݅	ܲ ൑ ܫ௔	 [2.7] Where, ܳ:	 depth of runoff (inches) ܲ: depth of rainfall (inches) 12  ܫ௔:	 initial abstraction (inches) ܵ:	 maximum potential retention (inches) In Equation [2.7], S is determined by the curve number (CN) from the following equation. The curve number is site specific. It is critical to use the curve number that matches the hydrologic condition of the site and the type of the ground cover. ܵ ൌ 100ܥܰ െ 10 [2.8] ܫ௔ ൌ 0.2ܵ [2.9] In arid regions, “surface run-on and precipitation are negligible” (Wels and  Robertson, 2003, p. 88). 2.1.2 Water outflow What happens to water remaining after sedimentation depends on the water management plans. Any of the following might occur (Oliveira-Filho and van Zyl, 2006): - Reclaimed - Left to seep through the tailings into the TMF foundation or collected into the drainage system. - Left to evaporate  2.1.2.1 Evaporation Evaporation loss is calculated using the following formula. ܮா௏஺ ൌ ܲܧ ൈ ௣݂௔௡ ൈ ܣݎ݁ܽ [2.10] ܮா௏஺: evaporation losses from the area of study ܲܧ: pan evaporation ௣݂௔௡: pan factor (%) 13  It is diffucult to measure the evaporation rate from a lake. Therefore, as an alternative, evaporation is commonly measured from a pan. Then pan evaporation is multiplied by a factor which is less than one. That implies that the evaporation form the small area of the pan is greater than the evaporation from a lake. Because the pan’s side takes heat through. Pan factor is different for each part of the tailings embankment. Wels and Robertson (2003) used the same pan factor for pond areas, and for flooded areas. Penman (1948 & 1963) develped equations for estimating the potential of evaporation or evapotranspiration. The classical form of Penman equation is as follows (Shuttleworth, 1993): ܧ௣ ൌ ∆୼ାఊ .ሺோ೙ሻఒ .ఊ୼ାఊ .଺.ସଷሺ௙ೠሻఋఒ , where ௨݂ ൌ ܽ௨ ൅ ܾ௨ݑ and ߜ ൌ ሺ݁௦ െ ݁௔ሻ [2.11] Where, ܧ௣ is potential open water evaporation from the open water, or evapotranspiration  (kg m-2 d-1≈ mm d−1); ܴ௡, net radiation (MJ m−2 d−1); Δ, slope of saturated vapor pressure- temperature curve (kPa ◦C−1); ߛ, psychrometric constant (kPa ◦C−1); ߣ, latent heat of vaporization (MJ kg−1);. ௨݂, wind function; ܽ௨ and ܾ௨, wind function coefficients; ݑ, wind speed at 2m height (m s-1); ߜ, vapor pressure deficit (kPa); ݁௦, saturation vapor pressure (kPa); ݁௔, actual vapor pressure (kPa). Monteith (1965) modified Penman equation (refer to Equestion [2.12]. The Penman-Monteith equation can be used to evaluate the daily potential evaporation from soil  using the following equation for zero surface resistance (Aydin et al., 2005): ܧ௣ ൌ ∆ሺܴ௡ െ ܩ௦ሻ ൅ 86.4	ߩܿ௣ߜ/ݎ௔ߣሺΔ ൅ ߛሻ  [2.12] 14  “Where, ܩ௦ is soil heat flux (MJ m−2 d−1); ߩ, air density; ܿ௣, specific heat of air (kJ kg−1 ◦C−1=1.013); ݎ௔, aerodynamic resistance (s m-1). In Equation [2.12], the factor of 86.4 is for converting kJ s−1 to MJ d−1” (Aydin et al., 2005, p. 93). Aydin et al. (2005) mentioned that a large proportion of total water loss is due to evaporation from the soil surface. Temperature, atmospheric condition of the region, and soil properties have an important influence on evaporation rates. If there is no shallow water table, the “evaporation following wetting” happens in three stages. Table 2.3 lists these stages and the influencing factors on each stage. Table 2.3 Evaporation phases following wetting (after Aydin et al., 2005) Stage Influencing factors on the evaporation process Constant rate Climate and atmospheric conditions Falling rate Hydraulic properties of the soil and time Low rate The movement of vapor through the dry soil  According to the study of Aydin et al. (2005), when water dries out from the top layer of the soil after the rainfall, the dried surface acts as a barrier. It does not allow the water or vapor to come to the surface. However, depending on the type of the soil and moisture content, there is still some upward liquid movement due to capillary forces. Surface water loss in the soil decreases the moisture content and the hydraulic conductivity of the soil, as well as the water potential. On the other hand, it increases the hydraulic gradient. Decrease in soil hydraulic conductivity and water potential causes a decrease in soil evaporation. In normal field conditions, the values of moisture content at the surface of the soil, evaporative flux, and soil-water potential are unknown. Given that the water potential at the surface of the 15  dry soil is at equilibrium with the atmosphere, the Kelvin equation (Equation [2.13]) can be used to evaluate the mininmum water potential. ߰௔ௗ ൌ ܴ௚ܶ݉݃ lnܪ௥ [2.13] Where: ߰௔ௗ: water potential for air-dry conditions (cm of water) ܶ: absolute temperature (K) ݃: gravitational acceleration (981 cm s-2) ݉: molecular weight of water (0.01802 kg mol-1) ܪ௥: relative humidity of the air (fraction) ܴ௚: univeral gas constant (8.3143 ൈ 104 kg cm2 s-2 mol-1 K-1) Soil evaporates at a constant rate until it reaches the threshold of water potential. At the next stage, the evaporation drops progressively below the potential evaporation or so called threshold potential (߮௧௣). This stage is called the falling rate. When the top layer of the soil dries out to the air-dry wetness, the water potential reaches the minimum value and the top layer acts as a barrier. This value of the water potential (matric suction) is called air-dryness (߮௔ௗ). After this point, the evaporation and water loss are due to vapor diffusion. The loss is negligible. The model which Aydin et al. (2005) presented is showed in Figure 2.2. The ratio of actual evaporation to the potential evaporation can be calculated using the following equation. ݂ ൌܧ௔ ܧ௣൘log|߰| െ log|߰௔ௗ| ൌ1logห߰௧௣ห െ log|߰௔ௗ| [2.14] Where: ݂: slope of the solid line ܧ௔: actual evaporation rate 16  ܧ௣: potential evaporation rate ߰௧௣: “absolute values of soil- water potential (matric potential) at which actual evaporation starts to drop below potential one” ߰௔ௗ: water potential for air-dry conditions ߰: absolute value of soil- water potential (cm of water) to be determined in situe between ߰௧௣ and ߰௔ௗ  Figure 2.2 Relationship between the actual evaporation to potential evaporation (Aydin et al., 2005) The Equation [2.13] estimates the actual evaporation using the water potential (߮) near the surface of the soil. Estimating the surface soil-water potential, also known as surface matric potential, is difficult. If it is not practical to measure the surface water potential, the observed water potential at a deeper section of the top layer can be used for calculations. If the potential of soil at the deeper part of the top layer is used, it leads to overestimation of the soil evaporation during the drying period, because the soil is wetter in subsurface. Use of water potential in 17  deeper sections on the other hand, leads to underestimation of the soil evaporation in rewetting cycles (Aydin et al., 2005). Over a drying soil, some modifications such as “relative humidity” and “suction adjustment factor” are proposed to adjust the overestimation issue. The properties of the soil (e.g. swelling, cracking and shrinkage) can influence the ratio of the actual evaporation to the potential evaporation. There is a point in the soil evaporation process where the flow of water to the surface does not happen at a rate where the actual evaporation can reach the potential value. It is due to the decrease in hydraulic conductivity of the soil. At this point, the soil surface is desiccated and water is not exposed to the atmosphere.  Salt concentrations also affect the evpoartion rate. The rate of evaporation from a body of salty water is reduced according to salt concentration. This is because the saturation vapour pressure over salin water is less than that over fresh water. In saturated salt solutions, evaporation ceases.  2.1.2.2 Entrained water Entrainment is the water remaining in the tailings pores of the saturated tailings remaining during sedimentation. This water can be released during the subsequent flow processes of consolidation, desiccation, and desaturation. Although this water is not physically lost like evaporated water, it is not available for reclaiming to the mill. The proportion of water trapped in the pores of tails depends on their final dry density after sedimentation. Tailings grading affects entrainment, fines cause more entrainment losses. The mill circuit controls the grading and the percent of fines content (Blight, 2010; Wels et al., 2004). Therefore, “there is little, if any, opportunity to minimize entrainment losses as part of tailings management” (Wels et al., 2004, p. 5).  18  The equations used in the basic water balance to calculate the entrainment quantity for the fully saturated tailings is as follows: Wels and Robertson (2003) used the following equation to estimate the entrainment losses: ܮாே் ൌ ݁଴ ൈ ሺܶ݋݊݊݁ݏ	݋݂	ݐ݈ܽ݅݅݊݃ݏሻ/ܩௌ, where ݁଴ ൌ ቀீఊೢఊ೏ ቁ െ 1 [2.15] Where: ܮாே்: entrainment losses during the initial sedimentation ݁଴: void ratio after completion of initial sedimentation (dimensionless) ܩ:	 specific gravity of the solids (dimensionless) ߛ௪:	density of water (mass volume-1) ߛௗ: density of solids (mass volume-1) As noted before, the entrainment loss is controlled by void ratio. Accurate measurements of initial void ratio, hydraulic segregation and the required sedimentation time are important.  Sedimentation studies in the laboratory and on the site can assist in estimating these properties (Wels and Caldwell, 2013, p. 15). 2.1.2.3  Seepage Seepage is a complicated phenomenon and difficult to model. Useful seepage modelling requires a comprehensive study of tailings behaviour after deposition. Seepage loss represents a small portion of loss compared to evaporation and entrainment losses. Darcy’s Law gives a good estimate of seepage through the tailings impoundment (Ausenco, 2014). If the foundation hydraulic condcuctivity is much higher than the hydraulic conductivity at the TMF underlying the reclaim pond, ܭ௣௢௡ௗ, the seepage loss from the pond can be estimated by the following equation. ܲ݋݊݀	ݏ݁݁݌ܽ݃݁ ൌ 	ܭ௣௢௡ௗ ൈ ݅ ൈ ܲ݋݊݀ ܽݎ݁ܽ [2.16] 19  Where ݅ is the vertical hydraulic conductivity at the TMF underlying the reclaim pond. Seepage depends on the hydraulic properties of the tailings, permeability specifications of the liner and the phreatic surface. As the embankment rises, the phreatic surface goes up and as a result the head on the liner increases. Seepage flow rate also depends on layering of the tailings. The process of liquid migration through tailings and liners can be very complicated (Wade, 2014). Giroud (1997) has developed “equations for calculating the rate of liquid migration through composite liners due to geomembrane defects”. He developed equations for different boundary conditions as well as shapes of liner defects including circular, square, rectangular and infinitely long defects. Although seepage flow models such as Darcy’s law and more complex models have been used to estimate the seepage loss. Seepage estimation is always a site specific issue. In this research, it is assumed that there is no seepage loss through the liners. Rewetting Active discharge points form a fan-shaped wet beach that continues to grow as the discharge happens. Rewetting is the beach seepage into “deeper, previously deposited tailings layers and ultimately into the foundation soils/bedrock” (Wels and Robertson, 2003, p. 3). Once the discharge point is inactive, the wetted area stops growing.  Rewetting losses from the pond and beaches are defined by the following equations (Wels and Robertson, 2003). ܮோாௐ ൌ 	ܫ݊݅ݐ݈݅ܽ	ݎ݁ݓ݁ݐݐ݅݊݃	݈݋ݏݏ݁ݏ ൅ ܴ݁݌݁ܽݐ݁݀ ݓ݁ݐݐ݅݊݃ ݈݋ݏݏ݁ݏ [2.17] ܫ݊݅ݐ݈݅ܽ	ݎ݁ݓ݁ݐݐ݅݊݃	݈݋ݏݏ݁ݏ ൌ ܦܴܹ ൈ ሺ1 െ ܵௗ௥௬ሻ ൈ ௙݁1 ൅ ௙݁ [2.18] ܮோாௐ: rewetting losses from the flooded area ܦܴܹ: average effective depth of rewetting 20  ܵௗ௥௬: average degree of saturation of active beach ௙݁: final void ratio after completion of stage 1 and stage 2 ܴ݁݌݁ܽݐ݁݀	ݎ݁ݓ݁ݐݐ݅݊݃	ܮ݋ݏݏ ൌ ܯܦ ൈ ܨ݈݋݋݀݁݀ ܣݎ݁ܽ [2.19] ܯܦ: moisture deficit (“a function of the time the recently deposited tailings have been exposed to air-drying”) ܴ݁ݐݑݎ݊	ݐ݅݉݁ ൌ ሺ ௙ܴ െ 1ሻ ൈ ஽ܶ௘௣ [2.20] ௙ܴ: ratio of active beach to flooded area ஽ܶ௘௣: average time of deposition of a single layer ஽ܶ௘௣ ൌ ܦ௟௔௬௘௥ ൈ ܨ݈݋݋݀݁݀	ܽݎ݁ܽ ൈ ߜௗܳௗ [2.21] ܦ௟௔௬௘௥: thickness of individual layer ߜௗ: dry bulk density The repeated rewetting losses (second term in Equation [2.17] above), are subtle and can be considered negligible (Wels et al., 2004). Rewetting loss is not considered in in this study in order to simplify the process of modelling. 2.1.2.4 Reclaimed water Reclaimed water (also known as decanted, or water recovery) is the supernatant water that is taken from the pool on top of the TMF to be re-used, evaporated or treated and released. This is typically done using barge pump systems or penstocks. It can be stored in the return water reservoir to be reused in the processing plant. 2.1.3 Water recovery (RGC “Make-up” water model) A water recovery model developed by Wels and Robertson (2003) was based on recovery of water which is deposited with tailings. Water recovery is a function of tailings sedimentation, 21  evaporation, seepage and rewetting (Wels and Caldwell, 2013; Wels and Robertson, 2003). One of the advantages of this method over the tradition approach is that it “allows modeling of highly transient near-surface processes” (Wels and Caldwell, 2013, p. 15). Water Recovery is the difference between the total water from the tailings slurry feed delivery line and the total losses. Water losses include evaporation from the pond, active and inactive beaches, seepage and the water which is reclaimed back to the process plant.  The water recovery model only includes the active beach areas and water recovery right after the settlement begins. In other words, it does not take into account all components of water balance “(e.g. basal seepage, evaporation from dry beaches)”. It is not easy to determine the area of the wet beaches in a large TMF which is not contained by berms, as well as to estimate the size of flooded area involved in water recovery. Satellite images can be used to estimate sizes of active and flooded regions (Wels and Caldwell, 2013, p. 15). When flooded area moves to another part of the TMF, the evaporation and seepage rates decline. In the pond, however, these rates remain almost constant if the water level does not vary remarkably. In the pond, the evaporation rate is close to the flooded area, but the seepage is less because of the low permeability of the tailings deposited which mainly consist of clays. Change in the discharge locations as well as movement of the flooded areas which happens naturally influence water losses.  Water recovery model was used for the Pampa de Pabellon TMF at Minea Doña Ines de Collahuasi in northeastern Chile to determine water losses and water recovery. The make-up water required for the processing plant was estimated to vary between 0.61 and 0.65m3/ton of 22  processed ore. The input parameters of the model were based on the collected data over 9 months (Wels et al., 2004). 2.1.4 Consolidation/seepage model This approach focuses on estimating consolidation and seepage properties of TMF. It is used to estimate the capacity of the impoundment by implementing the consolidation theory (Wels et al., 2000). Typical one-dimensional models are used to delineate the consolidation and seepage development at TMF over time. This is an appropriate approach in the wet conditions or where the TMF is rising at a high rate (Wels and Caldwell, 2013, p. 15).  Oliveira-Filho and van Zyl (2006a) described the physical processes of tailings deposition in three stages to model the volume changes of tailings: - Sedimentation: Separation of solids particles from water with no interparticle forces. - Consolidation due to self-weight: Particles accumulation, creation of interparticle forces and deformation due to self weight. - Dessication: Drying of the surface due to decrease in water level. Dessication happens at the upper layers of the tailings until the shrinkage limit is reached. During the desaturation phase the water is drained down until the moisture content of the tailings reaches the residual water content. The timelines for the physical processes that happens in the tailings after deposition are showed in Figure 2.3. The most imporatant factor which affects these processes is the tailings particle sizes. In tailings with a high percentage of fine materials like clay, consolidation is the dominating phenomenon which takes longer and involves more volume change. However desatuartion is the dominant process of water discharge for coarser grained materials with a 23  higher percentage of sands. In silt tailings, it seems that all three processes are evenly involved (Lopes and van Zyl, 2006a).  Figure 2.3 Physical processes occurring in the tailings during the life of a TMF  (Lopes and van Zyl, 2006a). In saturated tailings, during consolidation, mechanical forces such as tailings weight, “seepage forces, surcharge or the combination of those” reduces the void ratio. The total change in volume is equal to the volume of water discharged, therefore volume change can be calculated using void ratio. This may also apply to early stages of dessication (Lopes and van Zyl, 2006a).  24  In unsaturated tailings, complication happens during the residual shrinkage phase when the water removed is bigger than the total change in volume (Figure 2.4). In this case, change in void ratio cannot be used to calculate water entrainment which adds to variable parameters and uncertainity of the modelling for unsaturated tailings. In unsaturated soils, in addition to mechanical forces, negative pore water pressure in fine grained soil may cause volume change (Lopes and van Zyl, 2006a).    Figure 2.4 Soil shrinkage phases (Lopes and van Zyl, 2006a) Negative pore water pressure and capillary forces cause volume changes in fine grained soils during the shrinkage stage. Net normal stress ሺߪ െ ݑ௔ሻ and matric suction ሺݑ௔ െ ݑ௪ሻ define the volume change in unsaturated soils. These stress state variables can also be used to define the volume changes in saturated soils. In unsaturated soils, the effective stress and pore pressure 25  replace respectively the net normal stress and matric suction. For unsaturated soil, uw is a negative value or is less than the atmospheric pressure.  It is difficult to separate sedimentation and consolidation processes during the operation, but “it is useful to separate them for modelling purposes” (Lopes and van Zyl, 2006a).  Tests such as “Constant Rate Deformation (CRD) and Seepage Induced Consolidation” can be used to study consolidation process and to model “self-weight, seepage forces, and surcharge” due to consolidation. In the consolidation stage, saturated soil has positive pore water pressure which is equal to atmospheric pressure, therefore matric suction is equal to zero (Lopes and van Zyl, 2006a). In the desiccation process, suction caused by negative pore pressure forces shrinkage. As soil starts to desiccate, it also desaturates while shrinking. Thus the void ratio changes are not good measures to estimate the changes in discharged water volume. A solution to this problem is using the gravimetric water content to track the changes in water volume. Another important factor to consider is the “net surface flows” which is influenced by actual evaporation rates. When tailings are wet, the evaporation is affected by meteorological consolidations. But when the tailings surface is dry due to desiccation, soil conditions become the influential factors on evaporation rate (Lopes and van Zyl, 2006a). For unsaturated soil, relations between unsaturated hydraulic conductivity and suction is usually analyzed with a soil water characteristics curve (or water retention curve) (Lopes and van Zyl, 2006a). The model developed by Oliveira-Filho and van Zyl (2006) has limited application in describing the desaturation process. Desaturation analysis is complex because the water flow is influenced by both climate and soil condition. In addition, “the void ratio (or volumetric water 26  content”) profile is not uniform due to the desiccation and consolidation effects. Therefore using the average properties of soil is inevitable (Lopes and van Zyl, 2006b). Sedimentation analysis is usually done through the sedimentation column. After the sedimentation is completed, for clay-like tailings the top layer void ratio is calculated, but for silt-like tailings, the average void ratio of the whole column is calculated (Lopes and van Zyl, 2006a). Sedimentation is a process with zero effective stress. During the sedimentation, change in interstitial water volume (∆ ௪ܸ) is directly related to change in void ratio (∆݁) (Lopes and van Zyl, 2006a).  ∆ ௪ܸ ൌ ܪ௦∆݁ ൌ ܪ௦ሺ݁௦௟௨௥௥௬ െ ݁଴଴ሻ [2.22] ݁௦௟௨௥௥௬: void ratio of slurry ݁଴଴: void ratio after sedimentation at zero effective stress ܪ௦: height of solids Height of solids can be estimated using the following equation: ܪ௦ ൌ ܪ௦௟௨௥௥௬1 ൅ ݁௦௟௨௥௥௬ ൌܪ௦௘ௗ1 ൅ ݁଴଴ [2.23] ܪ௦௟௨௥௥௬: “height of the whole tailings in the slurry state” ܪ௦௘ௗ: “height of the sedimented tailings (nominal heights)” Consolidation starts right after sedimentation and may continue after closure. A few meters of tailings deposited at the top show sharp transition in void ratio relative to the depth. But for the greater depths, void ratio remains almost constant. In desiccation process, the volume change only happens at the top of the tailings. Discharge from the top equals evaporation rate, and discharge from the bottom equals seepage rate (Lopes and van Zyl, 2006b). 27  During desaturation, evaporation decreases noticeably, thus no flow at the top boundary is considered in Oliveira-Filho and van Zyl’s (2006b) analysis. “In terms of water reclamation strategies, the sedimentation and consolidation events are far more important than the other phenomena” (Lopes and van Zyl, 2006a, p. 221). The desiccation process is short. Wels et al. (2000) applied the consolidation model to Culmitzsch, a large uranium tailings impoundment. They estimated the consolidation properties of coarse, intermediate and fine materials settled in different regions of the impoundment in order to study the degree of settlement under the self weight and cover placement. The air photos were used to identify zones with different geotechnical properties. The model input included “filling rate, slurry density, and functions of “void ratios- effective stress”, “permeability coefficients- void ratios”. Wels et al. (2000) calibrated their model against “observed void ratio profiles, measured rates of settlement (after filling was completed) and observed pore pressures using a trial-and-error approach”. These properties were collected through field characterization, in-situ monitoring and lab testing (Wels et al., 2000). They found out that during the first phase of discharge, sandy tailings deposited near the discharge points show less void ratio than the fine slimes which are “settled in the water covered pond area distant from discharge points”. Good underdrainage and lateral drainage in the impoundment cause decrease in excess pore water pressure. However the results show that the fine tailings in this impoundment were not fully consolidated. The void ratios in highly plastic clay with low shear strength ranging from 1.5 to 4.8 in the shallow depth, while the coarser tailings with a low, if any, plasticity and higher shear strength had void ratios between 0.5 and 1.1. Different zones of the impoundment show different degree of consolidation and 28  therefore different degrees of settlement due to self weight consolidation and cover placement. The degree of consolidation depends on the drainage system and segragation of the fine tailings, as well as the slurry discharge pattern. The sensitivity analysis that Wels et al. (2000) conducted indicated that the non-linear material functions (“void ratios- effective stress”, “permeability coefficients- void ratios”) have the most influences on the degree of consolidation. Evaporation, tailings “anisotropy”, and “heterogeneity” may significantly change the consolidation and seepage rates because of their effects on permeability. As a result, this model is difficult to calibrate. Comprehensive detailed monitoring that consists of “depth profiling” is required for calibration (Wels and Caldwell, 2013, p. 15).  In this research, sedimented tailings densities are used to calculate the entrained water volume. The volume change due to consolidation and desiccation processes are not considered. 2.2 Tailings management options New innovations in the dewatering systems have brought many advantages to mining industry including (Davis, 2001; Marques and Pérez, 2013): - To enable companies to save energy and water by reducing the size of pond area and the amount of water loss through evaporation - To reduce the expenses related to transportation, storage and management of tailings slurries and excess water.  - To reduce the Oxygen ingress in tailings with potential of Acid Rock Drainage (ARD). - To decrease the seepage rate, and downsize the storage facility area.  - To increase the stability of the storage facility due to the reduction or elimination of erosion and seepage in the storage. 29  The most common technologies of waste management in mining industry are listed below (Davies and Rice, 2001; Salfate, 2011): - Slurry tailings: Tailings with lower densities and solid contents of 35-55% which are pumpable and can become segregated. - Thickened tailings: Tailings dewatered to 45-65% solids content which are still pumpable. Chemical additives can be used to increase the thickening rate. With this degree of dewatering, tailings exhibits a yield stress which allows tailings to deposit in a self-supporting trend with mild slopes. - Paste tailings: When thickened tailings are mixed with chemical additive such as Portland cement, another tailings with higher density can be formed. Solids content of paste tailings ranges from 65% to 75%. Paste tailings are non-segregating and show small settlement after deposition. - Filtered tailings or dry stack: When tailings become unsaturated to more than 70% solids content, a non-pumpable product is formed. These tailings are not completely dry but the moisture content is way below saturation making the deposited tailings trafficable. Transportation can be done by conveyors or trucks. Retention dams are not required because the filtered tailings are not saturated and are compacted after placement. This method requires an expensive technology, but the economic benefits of this water recovery can offset the capital and operating expenditures particularly in arid regions where the cost of water is high. It is also beneficial in areas where there is a high seismic risk, and in cold areas with freezing temperature, where water transportation and handling is difficult. Present experience indicates that filtered tailings limits of application are throughputs of less than 15,000 t/d and they are most suitable for operations under 2,000 t/d. This method is not attractive when the TMF has dual purpose (i.e. as a storage for tailings and also as a storage for annual snow melt run off for year round water supply to the operation) (Davies and Rice, 2001). Table 2.4 summarizes the characteristics of different tailings management systems.    30 Table 2.4 Features of tailings management options  (after Davies and Rice, 2001 & Martin et al., 2002 & www.tailings.info, UBC MINE 480 Course Notes)  Tailings management option  Tailings Slurry Thickened Tailings Paste Tailings Filtered Tailings % Solid for typical  hard rock tailings 35-55 45-65 65-70 >70 Saturation >100% Dewatered- >100% Sat. Dewatered- additive to thickened tailings >100% Sat.  Unsaturated Typical tailings (i.e. Gs~2.7) can be dewatered to <20% moisture content Particle Segregation during deposition  Segregating Non- Segregating Non- Segregating Non- Segregating Conveyance system Pumpable Slurry pipeline and appropriate pumps can be used  Centrifugal Pumps Positive displacement pumps Non-pumpable- Conveyor or truck used for transport of tailings Placement by conveyor radial stacker system or trucks wopt~ 60-80% saturation Water Management - Considerable water - Surface water management - Using diversions to limit inflow   - Groundwater discharge must be collected in a drain  - Considerable water: - Bleeding water & surface water collected in a pond at the lowest point of the facility - Diversion channels if required - Little to no water to manage - Minimal water bleeding - The only available water is the water freed due to further consolidation - Groundwater and runoff should be diverted by perimeter ditches, bunds and/or drains; groundwater cut-off and drainage system (ditches and/or cut-off wall, depending on site conditions) - Precipitation on the dry stack should be collected (by armoured channels; slope lengths and gradients should be designed to prevent/reduce erosion) - Impacted groundwater and seepage from dry stack should be collected  Beach slope 0.5-1 % Slightly steeper than conventional slurry  Slightly steeper than conventional slurry (2-10%) No beaches Material can be treated as an earth fill Containment of tailings - Containment constructed using borrow materials or tailings - Three embankment configurations - Embankment construction using cyclones - Self-supporting on very low slope angles - Minor retention structures may be required - Self-supporting on low slope angles - Minor retention structures may be required - Possible desiccation and cracking after deposition, increase rate of evaporation and consolidation. - Improved stability through interlocking between new overlying flow and old cracked tailings surface   - Self- supporting at steep slopes - No dam for retention Dewatering method - Compression thickeners or by combining thickeners and filter presses  High rate and deep cone thickeners. Vacuum or pressure filtration Filtration configurations: horizontally or vertically stacked plates  Deposition   Self- supporting conical shape Similar to thickened tailings (conical pile), with often steeper slopes.    Operating Cost  Closure and reclamation Higher operating cost than conventional slurry due to dewatering cost Closure and reclamation High dewatering Cost Closure and reclamation Affecting items on unit cost: Dewatering equipment Haul distance Placement strategy Compacting effort Closure and reclamation  Capital Cost   - Dewatering plants (thickeners) - Conveyance system (pumps and pipelines) - Tailings containment dams - Water management structures (e.g. diversion ditches, liners) - Dewatering plants (thickeners, flocculants and additives included) - Conveyance system (pumps and pipelines), higher transportation costs compared to other methods - Tailings containment dams - Water management structures (e.g. diversion ditches, liners) - Booster stations may be required (increasing in capital costs)  Depends on: - Size of operation More attractive for operations <10,000 tpd - Costs related to closure,  site development costs - Dewatering plant (filters) - Conveyance system (trucks/conveyors/spreaders/dozers) - Water management structures (e.g. diversion ditches, liners) Closure and reclamation Large footprint Large footprint. - Smaller footprint. - Reduced seepage  - Reduced risk of tailings transport by water if embankment fails (less environmental problems)  - Reduced footprint - Less long-term risk and liability  - Easier to reclaim and close     31 “One of the main issues in balancing power cost and associated water saving is the effect of the exponential increase in yield stress as solids concentration increases” (Paterson, 2015, p. 188). Paterson (2015) compared the pumping energy as a function of solids concentration. Figure 2.5 shows a graph of yield stress vs solids contents. Result of Paterson study showed that an increase in the solids content from 40 to 60% reduces the water consumption by 0.206 m3/t, but it increses the energy requirement by 0.245 kW/t/km. Increasing the solids concentration to 70% reduces the water consumption by only 0.042 m3/t less than the water consumption when implementing solids concentration of 60%. In this case, the additional energy of 0.673 kW/t/km is required. It is important to consider the energy consumption and the related costs when determining the most appropriate solids content for a dewatering system.  Figure 2.5 Relationship of Bingham yield stress versus solids content for a system transporting 300 dry tonnes per hour tailings (after Paterson, 2015) Figure 2.6 shows a schematic of different tailings management technology and water content of the final products.  010020030035 40 45 50 55 60 65 70 75Bingham Yield Stress (Pa)Solids Content (% by mass)Bingham Yield Stress vs. Tailings Solids Content 32  Figure 2.6 Tailings management options based on degree of dewatering (Davies and Rice, 2001) Marques and Pérez (2013) reviewed the application of non-conventional tailings management in Brazil. They listed the studies performed in Brazil related to nine TMFs designed to use the non-conventional disposal methods from 2007 to 2013. The statistics show that eight of the cases use “non- segregating” method of disposal (thickened tailings) and one uses paste tailings.  33 2.3 TMF Water savings In slurry TMFs, limited water saving methods are available. The following approaches can be practised (Blight, 2010; Gunson et al., 2012): - Maintain the minimum size of the pool so that it is only enough to allow the solids to “settle out and the decant water to clarify”. - Reduce seepage and evaporation from the return water reservoir by having deep reservoirs with minimum surface areas. The reservoirs can be lined to reduce seepage and they can be covered to reduce evaporation.  - Increase the rate of embankment rise. It increases the ratio of water decanted to water evaporated. However, increasing the rising rate causes embankment instability due to the rise in phreatic surface and it causes the decline in the shear strength. - Selective mining reduces the tonnage of tailings deposited at the TMF and also water consumption. - To reduce clay generation during grinding. - To select tailings size classification during tailings deposition. Figure 2.7 shows the process of seepage and evaporation losses over time with duration of discharge. “Initially, seepage losses are high, because tailings are discharged onto older, desiccated tailings”. Once tailings have been resaturated, rewetting losses decrease substantially. “While the flooded areas, where active deposition of tailings occurs, approach a maximum size, there is a continuous increase in the size of the total (wetted) deposition area, as more and more tailings are deposited. Over time, the active tailings stream will occupy a smaller and smaller percentage of the total deposition area, i.e. the ratio of flooded area to wetted area will decrease. This natural shift of flooded areas across an active deposition fan will incur additional seepage losses because the freshly deposited tailings will desaturate (mainly due to evaporation) during the time between the deposition of successive lifts of tailings. When the active tailings stream  34 returns to this area, the recently deposited tailings will berewetted to saturation. Note that the magnitude of the variable water losses illustrated in Figure 2.5 will vary depending on the tailings properties. For example, a shift from active deposition in the coarse beach area to a fines area near the pond is likely to reduce seepage losses but increase evaporation losses. Such a shift may either increase or decrease the total water losses, depending on whether the variable system losses were seepage-controlled or evaporation-controlled at the time. While the time trends of water losses shown in Figure 2.5 are qualitative, they provide first clues on how to best manage tailings discharge. In general, both very short (days) and very long (months) periods of discharge from a single discharge point should be avoided. The best management practice is likely that of a regular rotation of discharge points to avoid the development of very large active deposition areas” (Wels and Robertson, 2003, pp. 23–24)  Figure 2.7 Changes in water loss components of water balance over time (excluding entrainment losses)  (Wels and Robertson, 2003) A way to reduce the water consumption is to use an alternative tailings management option by dewatering the slurry. “In the case of thickened tailings or paste storages, the intention is to save water by thickening and thus eliminating decant water” (Blight, 2010). However, water entrainment, seepage and evaporation losses still occur and the overall water consumption is not  35 lower unless the void ratio or the moisture content of the deposited dewatered tailings is less than the corresponding values in deposited slurry tailings. The slurry is not thickened to a “non-draining RD” by drainage alone and has to be dewatered and thickened to reach this RD or beyond it (Blight, 2010). Change in relative density (RD) of slurry tailings after deposition at the TMF is difficult to estimate. Gunson (2013) developed a model to study alternative ways to reduce water consumption based on Wels and Robertson’s recovery model for a hypothetical mine in an arid region with the throughput of 50,000 tonnes per day. Gunson’s study includes six scenarios as showed in Table2.5. The mine water consumption includes the water used in the processing plant, maintenance shop, office, and for dust suppression on the haul roads. The maintenance shop water use is considered to be negligible. Detail of water consumption in the mine for different scenarios is listed in Table2.6. Reduction in water consumption in this model is the result of different water management plans: The first scenario is the base case. The estimated water withdrawal in this scenario is 0.76 m3/t of ore processed. In the second scenario, the goal is: to reduce the water loss due to evaporation by covering the tanks and cells; to cut down the water required for dust suppression by applying “organic binders” to the roads; and to lower office water consumption by using low use shower heads and toilets. Water consumption for primary crusher dust suppression can decrease by installing a fog dust suppression system.  In the third scenario, the application of thickened tailings use (thickened to 70% solids content by mass) allows for reduction in water loss due to evaporation and rewetting. It is assumed that  36 paste tailings do not change the water entrainment loss. There is no free water available to form a pond and to be reclaimed. Therefore the pond evaporation becomes zero. Although the thickener area is added to the open area which is subjected to evaporation, the total evaporation is smaller than evaporation in the first scenario. Table 2.5 Water use reduction scenarios (after Gunson, 2013) # Scenario Description Water consumption Water withdrawal m3/t of ore processed (considering water with ROM of 1,020 m3/d) 1 Base case Conventional concentrator 100 % (38,955 m3/d) 0.76 2 Base case with water conservation Conventional concentrator and reduction in evaporation losses 87% (33,822 m3/d)) 0.66 3 Paste tailings case Conventional concentrator and paste tailings disposal 79% (30,682 m3/d) 0.59 4 Filtered tailings case Conventional concentrator and filtered tailings disposal 42% (16,491 m3/d) 0.31 5 Ore pre-sorting case Conventional concentrator and 20% ore rejection 82% (32,096 m3/d) 0.62 6 Combined water reduction case Conventional concentrator and reduction in evaporation losses, as well as 20% ore rejection and filtered tailings disposal 28% (10,878 m3/d) 0.20  In scenario 4, filtered tailings (thickened to 80% solids content by mass) eliminates rewetting loss, beach evaporation, as well as evaporation from the pond and the tailings. Water retained in the tailings also decreases. In Scenario 5, rejecting low grade ore after the primary crusher by using a pre-sorting system happens. The decrease in the mill feed not only downsizes the equipment and as a result the total area exposed to evaporation, but also it reduces the tailings production and entrainment, rewetting and evaporation losses.  Scenario 6 is a combination of Scenarios 2, 4 and 5.  37 Table 2.6 Details of water consumption scenarios (after Gunson, 2013) Water loss Scenario 1 (m3/day) Scenario 2 (m3/day) Scenario 3 (m3/day) Scenario 4 (m3/day) Scenario 5 (m3/day) Scenario 6 (m3/day) Road-Dust Suppression 3,520 757 3,520 3,520 3,520 757 Human Consumption 58 3.3 58 58 58 3 Raw Water Tank- Evap. 2.2 0 2.2 2.2 2.2 0 Process Water Tank- Evap. 3.4 0 3.4 3.4 3.4 0 Primary Crusher-Dust Supp. 360 50 360 360 360 50 Stockpile-Dust Supp. 120 0 120 120 120 0 Flotation Cell- Evap. 6.7 0 6.7 6.7 6.7 0 Conc. Thickener- Evap. 1.2 0.0 1.2 1.2 1.2 0 Final Concentrate 89 60 89 89 88 59 Tailings Thickener- Evap. 0 0 30.9 30.9 0 1.5 Tailings Retained, LENT 20,792 20,792 20,792 12,299 16,573 9,803 Beach Evaporation, LEVAP 9312 9312 5,698 9312 7,422 0 Pond Evaporation, LPOND 1,890 47 0 0 1,506 0 Beach Rewetting, LREW 2,800 2,800 0 0 2,232 0 Pre-Sorting Rejects 0 0 0 0 204 204 Total 38,955 33,822 30,682 16,491 32,096 10,878  Gunson (2013) concluded that the major reduction in water consumption is related to the combined scenario which can reduce the water withdrawal from 0.76 m3/t of ore processed to 0.20 m3/t of ore processed. However, the high operating and capital cost of dry stack technology, limitation of pre-sorting to some ores, negative process impacts of high water reuse and recycle, and costly and high risk evaporation saving methods are the drawbacks and constraints of the proposed scenarios. Gunson (2013) noted that hydrogeological properties, water flow in the mine, tailings impoundment, and climate data influence on the water balance. He emphasized that his developed model may not be realistic for mines and was an outline toward more water savings in the mining industry.  38 Similarly, Obermeyer et al. (2013) developed a water balance model for Cerro Verde Copper Mine in Peru at the design stage. The model was calibrated and updated during the operation phase of TMF. Although the mine is located in a very arid region with an evaporation to precipitation ratio of 60, the Enlozada centreline constructed TMF was recognized as the most water-efficient TMF in the world. The mine owes this title to its efficient water management plan. While the range of required make-up water of similar mines is between 0.4 and 0.7m3/tonne, average make-up water at the Enlozada was less than 0.38m3/tonne. The low water consumption was due to several water management design factors such as: - the method of embankment construction allows to deposit the tailings more on the embankment with low impermeability. “The ratio of impoundment to embankment volume is relatively low (about 3.3). The high density and low moisture content of the embankment result in much higher water recovery from the embankment than from the impounded finer tailing which remain saturated and at a lower density”. - the efficient drain network with a low permeable foundation for the impoundment; to collect the seeped water - the decrease in size of the reclaim pond surface area - the limiting of the location of the reclaim pond by practising an optimized deposition from outer perimeter of the embankment - the thickening of the tailings from 27% to about 55% solids content by weight (because of the high rate of production, application of dry stack was not practical) - the regular updating and calibrating of the water balance 2.4 Role of climate and water data Climate data that are used in water balance are usually gathered from the national databases. The data for a certain location is extracted by interpolation of the climate data of nearby stations. The National Oceanic and Atmospheric Adminitration (NOAA)'s Climate Divisional Data Centre  39 (NCEI) is an Operating Unit of the U.S. Department of Commerce which “is responsible for preserving, monitoring, assessing, and providing public access to the Nation's treasure of climate and historical weather data and information”. The National Weather Service has 122 Weather Forecast Offices in six regions in the USA. The official records of climate data for many climatological stations in Canada can be obtained at the National Climate Data and Information Archive.  In 2015, the Centre for Forest Conservation Genetics at UBC developed a series of programs (ClimateBC, ClimateWNA and ClimateNA) which are able to generate “high-resolution” climate data in British Columbia, Western North America, and North America as a whole. The programs (available from: http://cfcg.forestry.ubc.ca/projects/climate-data/climatebcwna/) generate monthly data based on the historical weather station data. They predict the future climate data for a specific geological location using global circulation models. The baseline climate data is based on 1961-1990 normals. The ClimateBC database includes data from 1901 to 2013. This software program is used to generate wet climate data for this research. Figure 2.8 shows the graphical user interface of ClimateBC.  40  Figure 2.8 ClimateBC Desktop Version 5.21 interface 2.4.1 Water management in storm events According to Canadian Dam Association (2014), mine water management requires to address the following “functions”: - “Temporary storage of seasonal flows and sufficient water to allow settling of fines - Temporary storage of the Environmental Design Flood (EDF) - Storage and/or safe passage of Inflow Design Flood (IDF) runoff to ensure the integrity of the containment dams” EDF is the design limit to ensure that if storm events with return periods of 50 to 200 years happen, unscheduled discharge of water to the environment is preventable. The EDF level is between the Normal Operating Water Level (NOWL) and the emergency spillway level (refer to Figure 2.9) (CDA, 2014).   41  Figure 2.9 Water levels required to be considered in designing of a TMF When the flood exceeds EDF, the excess water is allowed to be dischrged from the spillway. Since overtopping and release of tailings to the environment is not acceptable. Therefore TMF should have an emergency spillway. During unexpected events, water managemnt would rather release water to the environment through spillways than to let the dam overtop. If a TMF does not have spillways, the impoundment must have the capacity to store the Probable Maximum Flood (PMF). If an unscheduled release of water to the environment is not acceptable, EDF is at same level as IDF (Welch, 2000). 2.5 Probabilistic analyses Water management is site specific and a lot of uncertainities are involved in defining and estimating of water balance components. Deterministic water balance models usually deal with inherent uncertainty through sensitivity analysis on parameters such as runoff coefficients, annual precipitation, hydraulic properties of tailings, etc. In contrast, probabilistic water balance models do not analyse single data points and simulate inherent uncertainties and variabilities through histograms of data points. Simulations determine probability and likelihood of the  42 events. Stochastic scenarios are commonly used for risk-management purposes (Naghibi, 2015; Wade, 2014). Probability Density Function (PDF) of the outputs defines the uncertainty and confidence level, as well as variability of the outputs (McPhail, 2005) Application of probabilistic modeling requires that the owners and operators have a good understanding of statistics and the mathematics of probability. Probabilistic water balance is usually performed and simplified by external consultants other than the onsite professionals. When changes in the water balance are required, the external specialists have to apply the associated changes to the model due to the high complexity of the logic of the model. External specialists submit a report consisting all the necessary evaluations to the owner (McPhail, 2005). 2.5.1 Water balance uncertainties Plate and Duckstein (1987) lists two uncertainties for water resource issues; data uncertainty and parameter uncertainty. Data uncertainty is due to the errors in measuring and sampling hydrological variables which depend on the climate and/or runoff charactersitics. Parameter uncertainty is due to uncertain physical parameters in hydraulic phenomena (“such as, for a levee, roughness changes, wave and wind effects”) (Plate and Duckstein, 1987, p. 43). The most significant variabilities and uncertainties associated with the TMF water balance are related to components: rainfall, runoff factors, evaporation, and seepage into the foundation (McPhail, 2005).  To conduct a simple probabilistic water balance, required information for uncertainty analysis includes (Estergaard, 1999): - average and standard deviation of monthly rainfall - average and standard deviation of monthly snowfall  43 - average and standard deviation of monthly evaporation - number of simulated periods (e.g. months) of operations  - periods and intervals of snowpack accumulation  - snow melt period and temporal distribution 2.5.2 Stochastic rainfall and evaporation As previously noted, precipitation and evaporation have large influence on water balance. Therefore, using average climatic parameters can only provide a crude estimate of water balance of a tailings impoundment. Generation of stochastic rainfall and evaporation data enables development of “climate sequences” based on historical data. The same statistical parameters of historical data are utilized for climate sequences (i.e. the sequences have the same distribution as historical data: same mean, same coefficient of variation, same skewness, and same wet and dry months).  2.5.3 Oracle Crystal Ball and Monte Carlo simulation For the purpose of conducting a probabilistic analysis in this research, Oracle Crystal Ball was implemented to develop the water balance model. Crystal Ball is an Excel add-in which performs Monte Carlo simulations. The default number of iterations is set to 100 runs, but this number can be customized. It varies according to the level of reliability that one can expect from the model. The Oracle Crystal Ball was used to run the simulations in this model, because the visual presentation of the results and the ability to extract the probabilities and distribution percentiles help analyse the risk of water deficit or water surplus in the mine for different climate conditions. One of the advantages of using spreadsheet and Oracle Crystal Ball for simulation is that the developed model can be reviewed and checked.   44 2.6 Water balance case studies Nalecki and Gowan (2008) presented a probabilistic approach to mine water management using GoldSim. The variables of the proposed model are based on statistical distribution of possible values. A fictious example of application of their model was created based on a real-life problem. Figure 2.10 shows the forecast of changes in water level over time and proposed spillway changes. It shows the minimum volume of water required to be stored in order to provide the continuous supply of water during the operational life of the mine under different climate and operational conditions (Nalecki and Gowan, 2008). Using a classic water balance cannot predict a good estimation of the water level over time.  Figure 2.10 Ranges of water level at a TMF simulated with a water balance model and the spillway elevation proposed by the model (Nalecki and Gowan, 2008). Wade (2014) carried out a probabilistic water balance to forecast the water budget of a mine in South America. The water budget depends on the size of TMF and the capacity of the reclaim pump. GoldSim was used to conduct 100-realization simulations. GoldSim runs “what if” scenarios and accordingly predicts the results using Monte Carlo simulations. Figure 2.11 shows an example of GoldSim interface used for tailings management facility water balance.  45  Figure 2.11 Graphical and object- oriented simulation environment of GoldSim (www.goldsim.com) The precipitation data Wade (2014) used in her study were gathered from the closest meteorological stations to the mine site and one station on the site. The probabilistic model as a product of Wade’s research showed that the water available at the TMF seasonally declines under the water level where the pumps can function. It means pumps cannot work constantly. She predicted a 1 in 100 year 24 hour storm event and showed that the designed water management did not account for the storm event. She showed that this event would cause water to exceed the embankment elevation. 2.7 Water balance calibration and validation Coefficient of correlation between the simulated output and measured data can help calibrate the model (McPhail, 2005). Figure 2.12 shows the comparison between simulated results of a  46 probabilistic water balance and measured data. Calibration of the probabilistic models can be done by calculating the correlation coefficient between two sets of simulated and measured data. Histograms of simulated and measured data can be compared and gaps can be analysed.   Figure 2.12 Comparison between simulated results of a probabilistic water balance and field measured data (McPhail, 2005) Ongoing monitoring is required after water balance is modeled. Rainfall, streamflow, and groundwater data should continuely be updated. Water balance models have to be calibrated and validated in accordance with the mine site. Naghibi (2015) performed a study to review different water balance models in British Columbia. He studied the water balance models of five mining projects and compared the temporal and spatial scales of the models, as well as the modelling tool and the uncertain parameters that were considered in each project.  47 The model in the Project A (Table 2.7) did not consider uncertainty in parameter values and was calibrated only for the runoff coefficient for average annual precipitation. This model was not validated.  Table 2.7 Modelling attributes of the projects studied by Naghibi (2015) Project Temporal scale Spatial scale Runoff modelling Uncertain parameters considered Modelling tool A Annual Mine site Empirical (average annual runoff coefficient); external numerical modelling for groundwater dewatering estimates n/a Spreadsheet B Monthly Reginal watershed Empirical (monthly effective precipitation and runoff coefficient); external numerical modelling for groundwater dewatering estimates Precipitation, diversion efficiency GoldSimTM  C Monthly Mine site Lumped conceptual; external numerical modelling for groundwater dewatering estimates Precipitation, snowmelt, hydraulic conductivity Spreadsheet D Monthly Regional watershed Semi-distributed conceptual Precipitation Spreadsheet  The pre-mined water balance of Project B was calibrated using the data of flows observed in the TMF and mine site. The results showed that the model was reliable. In contrast, calibration of the model in Project C against the on site collected data showed that the results were not reliable due to the unreliable precipitation data. Therefore the model was modified based on regional precipitation data. The calibration of the model in Project D compared the data of the model which were based on streamflow assessment points and the time series created based on the observed site and reginal flows. The calibration showed reliable monthly flows except for one streamflow assessment point. The dry and wet climate conditions were reviewed using sensitivity analysis scenarios.  48 2.8 Summary of literature review From a review of the literature on water balance models, models can be devided into two major categories: simplified water balance models and the water balance models with refinements. Table 2.8 shows the simplified water balance vs the refinements to the model for lined and unlined facilities. Refinements on the water balance model are usually applied to seepage loss estimation. In simplified water balance, Darcy’s law is used to estimate the seepage. Beach seepage loss (or rewetting losse), and consolidation related discharges are not considered. When the tailings consolidation begins the water is discharged through the top and bottom of the tailings. The discharge water through the top is subsequently evaporated. The water that is released from the bottom is either collected in the drainage system or seeped into the foundation.  Table 2.8 Modelling attributes of simplified water balance and models with refinements Model/ Facilities Lined  Unlined Simplified No seepage  Seepage Refinements Rewetting (beach seepage) Consolidation related discharge through the top and subsequent evaporation of discharged water  Rewetting (beach seepage) Seepage loss through the bottom and consolidation related discharge through the top with subsequent evaporation of discharged water  Table 2.9 summerizes the refinements that Wels and Robertson (2003), Wels et al. (2000), and Oliveira-Filho and van Zyl (2006) have applied in their water balance models. More refinements are also suggested in this table that can take into account the unceratnities of evaporation.     49 Table 2.9 Modelling attributes suggested to be adopted to water models  Parameter Simplified Suggested adoption Water inflow Surface runoff Rational method (Eq. [2.6]) NRC Method (Eq. [2.7] to [2.9] (National Resources Conservation Service, 2004) Water outflow Seepage loss Darcy’s Law  Eq. [2.15] (Wels and Robertson, 2003) Eq. [2.22] & [2.23] (Lopes and van Zyl, 2006a) Evaporation Eq. [2.25] Penman-Monteith method (Eq. [2.12]) Eq’s. [2.13] & [2.14] (Aydin et al., 2005)    50 Chapter 3: Methodology- data input Chapter 3 discusses the data selection and the methodology used in this research to develop the water balance models. 3.1 Water balance data selection Reliable water balance evaluations require site specific information that is as accurate as possible, e.g. short monitoring intervals may not provide data that are sufficiently accurate. Assumed parameters and a generic understanding of the properties of tailings impoundments are not sufficient. The properties and characteristics of tailings may change hourly, daily or monthly. Good water management tracks the changes, makes adjustments to account for the changes, and updates the water balance. In this research, a generic model has been developed to study different scenarios using deterministic and stochastic approaches. Some values of the parameters are assumptions based on professional experience and based on reported data. 3.1.1 Climate data This section includes the methodology used to select the data for modelling and the statistical analysis conducted on climate data. 3.1.1.1 Climate data selection Wet climate and dry climate data that were used to develop the water balance models are described here. Wet climate Wet climate is defined as a climate in which extra annually accumulated water has to be released into the environment to maintain the annual water balance in the tailings impoundment (Welch, 2000).  51 To represent a mine in the wet condition, the location of Kerr-Sulphurets-Mitchell (KSM) project, of Seabridge Gold in North Western British Columbia on the Pacific Coast, was used. As shown in Figure 3.1, the KSM mine is located at latitude 56°30'00'', longitude -130°00'00'', and at an average elevation of 880m above sea level (Klohn Crippen Berger, 2013). The climate data were generated by ClimateBC program over the period of 1970-2013. Tables A.1, A.2, and A.3 in Appendix A show monthly rainfall, snowfall and evaporation respectively in this location. This information is not based on a weather station with a long history located at the site, it was effectively “estimated” on the basis of information from a database of weather stations in the vicinity.   Figure 3.1 Approximate location of KSM Project used for the wet climate location (Google Earth, retrieved in Feb 2017) The frost free days begin between the 120th and 175th days of the year. Some day between the 245th and 280th days of the year, the frost free days end. For simplification, the medians of these  52 days were used as the start date and end date of freezing. It is assumed that the mine site is frost free between June 1st and October 1st. Snow accumulation and thaw follows the pattern shown in  Table 3.1. Table 3.1 Snowpack accumulation and melt times   Snowpack Proportion of total snowpack melts Jan Accumulates 0% Feb Accumulates 0% Mar Accumulates 0% Apr Melts 10% May Melts 30% Jun Melts 50% Jul Melts 10% Aug No snowpack 0% Sep No snowpack 0% Oct Accumulates 0% Nov Accumulates 0% Dec Accumulates 0% Total  100%  Dry climate To simulate the dry condition, the climate data at the Cerro Negro mine site were used. The Cerro Negro site is located in Argentina, province of Santa Cruz. Figure 3.2 shows the approximate location of the mine site. The climate data were extracted from Wade (2014), refer to Table A.4 in Appendix A. For simplification of water balance modelling, it is assumed that the hypothetical mine site does not receive any snowfall.   53  Figure 3.2 Approximate location of Cerro Negro Project used for the dry climate location (Google Earth, retrieved in Feb 2017) 3.1.1.2 Statistical analysis of climate data Wet climate The average annual rainfall for the selected site was approximately 2571 mm/year from1970 to 2013. Table 3.2 presents the mean and standard deviation of the monthly climate data from Tables A.1 to A.3 in Appendix A.    54 Table 3.2 Mean and standard deviation of monthly climate data over from 1970 to 2013   Rainfall (mm) Snowfall (mm) Pan evaporation (mm) Mean (ࣆ) Standard deviation (࣌) Mean (ࣆ) Standard deviation (࣌) Mean (ࣆ) Standard deviation (࣌) Jan 373.1 145.7 287.4 144.2 0 0 Feb 197.7 73.4 166.6 70.3 0 0 Mar 167.2 47.8 154.3 46.7 0 0 Apr 117.8 57.5 76.8 42.3 30.2 16.1 May 98.5 34.8 27.4 19.2 61.7 7.9 Jun 98.3 35.1 4.5 3.6 80.1 10.1 Jul 102.8 40.8 2.6 1.7 79.2 8.4 Aug 163.6 64.5 4.5 2.1 68.3 7 Sep 246.2 74.2 26.9 13.5 34.7 3 Oct 354.8 100.7 122.1 69.1 13.5 2.5 Nov 337.9 121.3 248.6 89.8 0 0 Dec 313 120 207.9 95.2 0 0 Annual 2570.8 276.1 1329.6 227.2 367.8 33.2  Analysis of the rainfal and evaporation data showed that no correlation existed between rainfall and evaporation (see Figure 3.3). The coefficient of correlation of 0.01 indicated that the two variables were independent.  Figure 3.3 Correlation between Rainfall and Evaporation data Dry climate “The average monthly evaporation [at the Cerro Negro mine] is 136.5mm and when compared to the average monthly precipitation of 16.3 mm […], it can be seen that evaporation is eight times  55 higher than precipitation on a monthly average basis” (Wade, 2014, p. 34). The mean and standard deviation of annual dry climate rainfall data are respectively 198mm and 109.2mm. Hence, the annual evaporation is also considered to be 8 times greater than rainfall (1584mm). It is assumed that the standard deviation of evaporation is also 8 times greater than the rainfall standard deviation i.e. 873.6mm. Table 3.3 shows the mean and standard deviation of the monthly rainfall. The data of monthly evaporation for the dry climate was not available, therefore the temporal distribution of evaporation data in the wet cliamate condition was used to extract the mean and standard deviation of monthly pan evaporation using the mean and standard deviation of the annual evaporation the in the dry climate condition (Table 3.4).  Table 3.3 Rainfall (mm/month) from 1978 to 2012 recorded in  Project Site Weather Station (EMA BN) (after Wade, 2014)  Rainfall (mm) Mean (ࣆ) Standard deviation (࣌) Jan 8.9 18.5 Feb 8 13.8 Mar 12.5 17 Apr 18.3 21.6 May 30.4 26.3 Jun 30.8 31.6 Jul 17.6 23 Aug 13.8 16 Sep 17.4 16.6 Oct 16.8 17.9 Nov 15.9 39.4 Dec 7.8 15.2 Annual 198 109.2      56 Table 3.4 Dry climate monthly pan evaporation based on the average evaporation in the dry condition and temporal distribution of evaporation in the wet condition  Proportion of total evaporation (from wet climate data) Mean Evaporation (mm) Standard deviation (mm) (ૡ ൈ ࢇ࢔࢔࢛ࢇ࢒ ࢙࢚ࢊ ࢊࢋ࢜ ൈ ࢖࢘࢕࢖࢕࢚࢘࢏࢕࢔	࢕ࢌ	࢚࢕࢚ࢇ࢒	ࢋ࢜ࢇ࢖࢕࢘ࢇ࢚࢏࢕࢔) Jan 0% 0.0 0.0 Feb 0% 0.0 0.0 Mar 0% 0.0 0.0 Apr 8% 130.0 71.7 May 17% 265.9 146.6 Jun 22% 345.1 190.3 Jul 22% 341.2 188.2 Aug 19% 294.3 162.3 Sep 9% 149.6 82.5 Oct 4% 58.1 32.1 Nov 0% 0.0 0.0 Dec 0% 0.0 0.0 Annual 100% 1,584 874   Figure 3.4 Cumulative Frequency Diagram of the rainfall data of table 3.3 (the red and blue lines show the best normal and log normal distributions fitted to the data)  57 It is assumed that the mine is located in the northern hemisphere to be able to compare the results of wet and dry climate water balance models. Therefore when applying in the model, the values of monthly rainfall and evaporation were shiftted so that the seasons (summer: winter) matched the northern hemispher seasons i.e. first of September in Argentina became equivalent to the first of March in the Northern Hemisphere (see Table 3.5). Table 3.5 The pattern for switching the values of monthly rainfall in the model Month in original data set Switched to month July January August February September March October April November May December June January July February August March September April October May November June December  3.1.2 Operational data Daily total throughput, open pit area and open pit run off coefficient, areas of tailings impoundment pond and beaches, and solids content of the tailings reporting to the TMF are derived from the technical reports of Mount Polley Tailings Facilities. Table 3.6 presents the required assumptions used in the water balance model. The water balance models in this research are developed for metal mines tailings management facilities where the tonnage of mill throughput is almost same as the tonnage of ore being processed. For mines other than metal mines (such as industrial mineral mines), the amount of  58 tailings discharge from the mill is dependent on the amount of economical product produced, which can be 20% or higher. Table 3.6 Operating data inputs and assumptions Parameter Value Data source Nature of data Density of water 1000 kg/m3 Assumption Constant Daily ore throughput (dry solid) 13,425 tonne Mount Polley Mine reports (Knight Piesold, 1995a)* Constant Solids content in mill circuit 35% Assumption Constant Tailings solids content 45-80% Assumption Variable Impoundment area 230 ha Mount Polley Mine reports (Knight Piesold, 1995a)* Constant Pond, wet beach, drying beach, and dry beach areas Proportion of impoundment area Assumption Constant Pond evaporation pan factor 0.8 Assumption Constant Wet beach evaporation pan factor 0.8 Assumption Constant Drying beach evaporation pan factor 0.4 Assumption Constant Dry beach evaporation pan factor 0.1 Assumption Constant Pit area 18 ha Mount Polley Mine reports (Knight Piesold, 1995b)*  Constant Pit runoff coefficient 0.5 Mount Polley Mine reports (Knight Piesold, 1995b)* Constant Minimum storage required for wet climate and solids content less than 70%  1,000,000 Assumption Constant Snow density 310 kg/m3 Assumption Constant * Available from: https://www.mountpolleyreviewpanel.ca/     For considering the evaporation surface area, the total area of the TMF is divided into four areas: pond, wet beach, drying beach and dry beach. These areas for different tailings solids contents are shown in Table 3.7. For simplification, the area of the tailings impoundment, open pit, and beaches are assumed to be constant. However, these areas are changing over time.  59 Table 3.7 Proportions of poond and beaches areas relative to the total TMF area  Tailings solids content (%) Pond area (% of TMF area) Wet beach (% of TMF area) Drying beach (% of TMF area) Dry beach (% of TMF area) Wet condition 45 25 50 25 0 60 25 50 25 0 70 0 50 50 0 80 0 50 50 0 Dry condition 45 25 25 25 25 60 15 25 30 30 70 0 25 35 40 80 0 0 40 60 3.1.3 Tailings material characteristics Table 3.8 summarizes the characteristics of tailings material. The specific gravity and moisture content of tailings and seepage rate of the impoundment come from the Mount Polley Mine technical reports.  Table 3.8 Tailings material characteristics input data and assumptions Parameter Value Data source Nature of data Tailings specific gravity 2.78 Mount Polley Mine reports*  Constant Initial dry density of settled tailings 1.1 to 1.8 tonne/m3 Assumption (refer to Table 3.10) Variable Moisture content of ore 0.04 Mount Polley Mine reports*  Constant Average hydraulic conductivity of tailings 1 ൈ 10ି଺ cm/s Assumption Constant * Available from: https://www.mountpolleyreviewpanel.ca/     3.2 Water balance models The water balance developed in this study is only for the operating tailings management facilities. It does not include closure and post-closure water balances. The proposed water balance model is Excel spreadsheet based. It is possible to extend and use the model for more complex water managements by adding extra features to the model. Figure 3.5 shows the conditions that are considered in developing the water balance models in this research. 60   Figure 3.5 Conditions considered in developing the water balance models in this reserch   61 A simplified schematic of the water balance components, in Figure 3.6, summarizes the structure of the water balance model. The diagram is later quantified for different scenarios in Chapters 4 and 5. Solids content of the mill circuit is 35% solids by mass. It is assumed that the tailings solids content changes in dewatering systems to reach 45% to 80% by mass. Solids contents of 45%, 60%, 70%, and 80% are used to develop the models for different tailings management options: respectively slurry tailings, thickened tailings, paste tailings, and filtered tailings. These solids contents are typical in hard rock tailings.   Figure 3.6 Simplified schematic of a TMF water balance used in the research Evaporation Precipitation TMF Processing Plant Seepage Entrainment Water with slurry Open pit Mine impacted water Reservoir Ore  62 3.2.1 Deterministic water balance A deterministic water balance is developed based on Equation [2.2] and the average values of the parameters are used in an Excel Spreadsheet (Table 3.9). The ore moisture water is usually negligible and is not considered in the water balance calculations. Table 3.9 The Excel spreadsheet table used to develop the water balance model. No.  Category  Parameter 1   Time  Month 2     Number of days in month 3  Precipitation water (mm/month)  Rainfall 4  Snow water equivalent from snow melt 5  Water in (m3)  Water with tailings 6  Precipitation onto impoundment 7  Total Water Input 8  Water out (m3)  Evaporation 9  Entrained water 10  Seepage 11  Total Water Output 12  Mill required water (m3)  Water with tailings 13  Total mill required water 14  Water on the pond (m3)  Monthly change in storage 15  Water moved to reservoir 16  Cumulative change in storage 17  Water remained in TMF 18  Mine impacted water runoff    19  Reservoir storage (m3)  Monthly reservoir storage 20  Cumulative reservoir storage 21     Returned to TMF from reservoir 22     Water in TMF+ return to TMF from reservoir 23  Cumulative water deficit/ surplus (m3/tonne)  This model is the water balance over the mine life (60 months). The equations used to calculate the parameters for different scenarios are presented in the following sections. Water balance is developed for lined and unlined impoundments. 3.2.1.1 Lined impoundment The model was compiled with the assumption that there is no seepage loss in the lined impoundment. However, it has to be noted that even with a complex liner systems, zero discharge of seepage from TMF is not possible (www.tailings.info).  63 Wet climate Figure 3.7 shows the schematic flowchart of water circulation considered in the wet climate model. In wet climate, since there is a lot of water surpluses, It was assumed that the extra water is stored in the reservoir. The water balance was estimated based on the water that remained in the reservoir. So the water balance was done for the water remained in the TMF. Table 3.10 shows the calculation used to determine the parameters in Table 3.9 for a lined impoundment in the wet condition. The flow chart related to this model is provided in Appendix A, Figure A.5.  64   Figure 3.7 Water circulation at TMF for wet climate conditionWet Climate Assumptions    Parameter    Nature in this model ValueMean annual rainfall (mm)  Constant 2570.77Standard deviation of annual rainfall (mm) Constant 276.08Mean annual snowfall water equivalent (mm) Constant 412.16Standard deviation of annual snowfall (mm) Constant 70.42Mean annual pan evaporation (mm) Constant 367.82Standard deviation of annual pan evaporation (mm) Constant 33.15Evaporation pan factor for pond Constant 0.8 Evaporation pan factor for wet beach   Constant 0.8 Evaporation pan factor for drying beach   Constant 0.4 Evaporation pan factor for dry beach   Constant 0.1 Snow density (tonne/m3) Constant 0.31 Solids content in mill circuit (%) Constant 35 Daily ore throughput (tonne) Constant 13,425 45‐80% solidsSolids FeedPrecipitationRemaining in TSF35% solidsEvaporation Entrained in solidsRemaining in reservoirProcessing DewateringTMFTank Fresh waterReservoirMine 65  Table 3.10 Calculation methods and assumptions used to determine water balance parameters for wet condition lined impoundment Description Data Source Note/Calculation Method Rainfall (mm/month) ClimateBC Average	monthly rainfall extracted from ClimateBC rainfall data over the period of 1970 െ 2013. Snowpack accumulation water equivalent (mm/month) Calculated ൌ 	Monthly accumulated snow water equivalent Snow water equivalent of snow melt (mm/month)  Calculated ൌ 	Monthly snow water equivalent as the result of snowpack melting in the frost free	period  Evaporation (mm/month) ClimateBC Average	monthly evaporation extracted from ClimateBC rainfall  data	over	the period of 1970 െ 2013.    WATER IN (m3)   Volume of water with tailings Calculated ൌ 	Daily	ore throughput ൈ Days in a month ൈ ሺ1 െ tailings % solidሻtailings % solid ሻ Precipitation onto impoundment Calculated ൌ	Rainfallሺmmሻ ൅ Snow water equivalent of snowmelt ሺmmሻ1000 ሺmmm ሻൈ Pond areaሺhaሻ ൈ 10,000ሺmhaሻ Total water input Calculated ൌ 	water	with tailings ൅ Precipitation onto impoundment ൅ Mine impacted water runoff    WATER OUT (m3)   Evaporation  Calculated ൌ	 ሺPond	areaሺhaሻ ൈ Evaporation factor for pond ൅ Area of wet beachሺhaሻ ൈ Evaporation	factor	for	wet	beach ൅ Drying beachሺhaሻൈ Evaporation factor for drying beach ൅ Dry beachሺhaሻ ൈ Evaporation	factor	for dry beachሻ ∗ 10,000ൈ Evaporationሺmmሻ/1000ሺmmm ሻ Entrainment Calculated ൌ Daily	ore throughput ൈ Days in month ൈ ሺ Tails	S. G.Final	dry	density െ 1ሻ/tails S. G. Total water output Calculated ൌ 	Evaporation from pondand beaches ൅ Seepage losses ൅ Entrainment Monthly change in storage Calculated ൌ 	Total	Water Input െ Total Water Output    Total water required in the mill Calculated ൌ 	Water	with tailings Water moved to reservoir*  Calculated ൌ If	ሺcumulative storage ൏ ܯ݅݊݅݉ݑ݉ ݎ݁ݍݑ݅ݎ݁݀ ݏݐ݋ݎܽ݃݁, 0, Cumulative storage െ Minimum	required	storageሻ Mine impacted water runoff* Calculated ൌ Total	pit	areaሺhaሻ ൈ 10000 ൈ Pit area runoff coefficient ൈ Rainfallሺmmሻ ൅ Snow water	equivalent	of	snowmelt ሺmmሻ1000	ሺmmm ሻ Water remained in TMF Calculated ൌ IFሺCumulative storage ൐ ܯ݅݊݅݉ݑ݉ ݎ݁ݍݑ݅ݎ݁݀ ݏݐ݋ݎܽ݃݁,ܯ݅݊݅݉ݑ݉ ݎ݁ݍݑ݅ݎ݁݀ ݏݐ݋ݎܽ݃݁, ܥݑ݉ݑ݈ܽݐ݅ݒ݁	ݏݐ݋ݎܽ݃݁ሻ Water return to TMF*  Calculated If	the	water in the pond is less than the minimum required storage, the required 	water	is	returned	to	the	pond	from	reservoir Monthly reservoir storage*  Calculated ൌ Water	moved to reservoir ൅ Mine impacted water െWater returned to the pond െWater	required	in	the	mill Water surplus/deficit** Calculated ൌ Cumulative water remained in reservoirCumulative ore throughput  * Reservoir is only needed in the wet condition  ** Water surplus/deficit in the dry condition is the difference between the cumulative storage at the TMF and the water required in the mill  66  Rainfall-runoff from the pit reports to the reservoir. A minimum of 1,000,000 m3 water storage at the TMF is required. The minimum storage ensures that solids are not pumped as that can impact the quality of the water being returned to the mill for re-use. When the storage drops below the minimum storage volume the pumps must stop running. For solids contents greater than 65%, there is no need to maintain a minimum storage. It is assumed that for paste tailings and dry stack tailings, the precipitation and runoff water are diverted and collected. Dry climate Figure 3.8 provides a water cycle schematic and the monthly rainfall data at the hypothetical TMF located in an arid region. Because of the water deficit, having a reservoir was not required in the dry climate condition. The same Excel Spreadsheet which was used for the wet condition (Table 3.7) can be used to develop the model for the arid climate except that in the model for dry climate, no snow water input and no mine impacted water runoff exist. The calculations of water balance in the dry condition is same as the wet condition with a minor difference in precipitation water calculations. In the dry condition, precipitation water is only rainfall and the monthly value follows the temporal distribution from the historical data.  67 Dry Climate Assumptions        Parameter     Nature in this model Value Mean annual rainfall (mm)     Constant 198.03 Standard deviation of annual rainfall (mm)    Constant 109.25 Mean annual snowfall water equivalent (mm)    Constant 0 Standard deviation of annual snowfall (mm)    Constant 0 Mean annual evaporation (after applying pan factor)* (mm)   Constant 1584.2 Standard deviation of annual evaporation (after applying pan factor)* (mm) Constant 874 Evaporation pan factor for pond     Constant 0.8 Evaporation pan factor for wet beach     Constant 0.8 Evaporation pan factor for drying beach    Constant 0.4 Evaporation pan factor for dry beach     Constant 0.1 Solids content in mill circuit (%)     Constant 35 Daily ore throughput (tonne)     Constant 13,425  Tails solids content (%)      Variable 35-80 Tailings Specific gravity     Constant 2.78 Water content of ore     Constant 0.04 Final dry density (tonne/m3)     Variable 1.1-1.5 Pond area (ha)            Constant 230 Temporal Distribution     Rainfall Evaporation JAN       22% 0% FEB       19% 0% MAR       9% 0% APR       4% 8% MAY       0% 17% JUN       0% 22% JUL       0% 22% AUG       0% 19% SEP       0% 9% OCT       8% 4% NOV       17% 0% DEC       22% 0% * Evaporation is 8 times greater than rainfall.       Figure 3.8 Water circulation at TMF for dry climate condition45‐80% solidsSolids FeedPrecipitationRemaining in TSF35% solidsEvaporation Entrained in solidsProcessing DewateringTMFTank Fresh water 68 3.2.1.2 Unlined impoundment Wet climate Given the average hydraulic conductivity of 1 ൈ 10ି଼ m/s, and the hydraulic gradient of 1, the average seepage rate according to the Equation [2.16] is equal to 59,616 (m3/month) down through the 230-hectare area of the impoundment. It is assumed that 75% of the seepage is collected in a drainage system and returned to the reservoir. Therefore the seepage loss equals to 14,904m3/month. The water outflow is the sum of seepage, evaporation, and entrained water. Figure 3.9 shows the schematic of the water circulation for the unlined impoundment in the wet condition.  Figure 3.9 Schematic of water circulation for the unlined impoundment in the wet climate condition 45‐80% solidsSolids FeedPrecipitationRemaining in TSF35% solidsEvaporation Entrained in solidsRemaining in reservoirProcessing DewateringTMFTank Fresh waterReservoirMine 69 Dry climate It is assumed that the unsaturated seepage in the dry condition is only one-third of the seepage rate in the wet condition. It means the average seepage loss of 4,968 m3/month has been considered for the dry condition. Figure 3.10 shows the schematic for water circulation in the unlined impoundment.  Figure 3.10 Schematic of water circulation for the unlined impoundment in the dry climate condition 3.2.2 Probabilistic water balance The variable parameters of precipitation, evaporation and dry density have been changed using their distributions to reflect the uncertainity of the model. Precipitation and evaporation are variables with the highest degree of uncertainties.  45‐80% solidsSolids FeedPrecipitationRemaining in TSF35% solidsEvaporation Entrained in solidsProcessing DewateringTMFTank Fresh water 70 The water entrainment depends on the dry density of the deposited tailings. One of the challenges to estimate the volume of the entrained water is that the dry density of deposited tailings is not readily available during the design phase. Dry density is a dynamic value during the operational life of the TMF. It is a function of tailings particle size. It is assumed that the relationship between tailings solids content and dry density follows an S-shape curve. Triangle distributions have been used to simulate dry densities during the initial depositions of tailings (van Zyl, 2015). The values of dry density for different solids contents vary according to Table 3.11 and Figure 3.11. Table 3.11 The relationship between dry density and solids content of tailings  Solids content (%) Most likely dry density (tonne/m3) Minimum dry density (tonne/m3) Maximum dry density (tonne/m3) 35 1.1 0.90 1.3 40 1.14 0.94 1.34 45 1.2 1.00 1.4 50 1.29 1.09 1.49 55 1.42 1.22 1.62 60 1.55 1.35 1.75 65 1.64 1.44 1.84 70 1.7 1.50 1.9 75 1.74 1.54 1.94 80 1.8 1.60 2.0   71  Figure 3.11 Most likely Dry density assumption for different solids contents The distributions of climate parameters and dry density of the tailings after sedimentation are defined as inputs in Crystal Ball. Monte Carlo Simulation with previously defined realizations of 10000 is run to generate stochastic climate data over the 5-year (60 months) life of the mine which equals to 50000 years (600,000 months) of modelled mine life. Samples of precipitation, evaporation, and dry density are picked from their previously determined distributions. The model is run for these sampled values of the parameters and the output is saved in Oracle Crystal Ball. After completion of the simulation, histograms of outputs (in this case monthly water surpluses/deficits) are provided by Crystal Ball. Histograms can be used to predict the probability of expected events, such as:  - the probability of exceeding a given critical threshold of water surplus which might result in the low freeboard and/or overtopping - the probability of water level dropping to a critical point than the minimum required to maintain pumping and the full capacity of the reclaim pumps cannot be met 00.20.40.60.811.21.41.61.8235 40 45 50 55 60 65 70 75 80 85Likeliest dry density (tonne/m3 )Solids content by weight (%)Dry Density 72 - the probability of water dropping to a certain critical value of water level that requires adding specific amount of make-up water to the system. 3.2.2.1 Lined impoundment Wet climate Different distributions were tested to the climate data but they did not pass the goodness of fitness test, therefore triangular distribution with the properties in Table 3.12 was used. This distribution truncates the outlined values of the Probability Density Function. The minimum value in the triangular distribution was the mean minus 3 standard deviations, the peak value was equal to the mean and the maximum value equaled mean plus 3 standard deviations. This assumption ensured that 99% of data were considered in simulations. There is no seepage loss considered for lined impoundments.   73 Table 3.12 Triangular distribution parameters for wet climate data from 1970 to 2013  Rainfall (mm) Snowfall (mm) Pan evaporation (mm)   Minimum Value* =ࣆ െ ૜࣌ Peak Value = (ࣆ) Maximum Value =ࣆ ൅ ૜࣌ Minimum Value* =ࣆ െ ૜࣌ Peak Value = (ࣆ) Maximum Value =ࣆ ൅ ૜࣌ Minimum Value* =ࣆ െ ૜࣌ Peak Value = (ࣆ) Maximum Value =ࣆ ൅ ૜࣌ Jan 0 373.1 810.2 0 287.4 720 0 0 0 Feb 0 197.7 417.9 0 166.6 377.5 0 0 0 Mar 23.8 167.2 310.6 14.2 154.3 294.4 0 0 0 Apr 0 117.8 290.3 0 76.8 203.7 0 30.2 78.5 May 0 98.5 202.9 0 27.4 85 38 61.7 85.4 Jun 0 98.3 203.6 0 4.5 15.3 49.8 80.1 110.4 Jul 0 102.8 225.2 0 2.6 7.7 54 79.2 104.4 Aug 0 163.6 357.1 0 4.5 10.8 47.3 68.3 89.3 Sep 23.6 246.2 468.8 0 26.9 67.4 25.7 34.7 43.7 Oct 52.7 354.8 656.9 0 122.1 329.4 6 13.5 21 Nov 0 337.9 701.8 0 248.6 518 0 0 0 Dec 0 313 673 0 207.9 493.5 0 0 0 *If the minimum value in the triangular distribution was negative value we would substitute the negative value with zero.  Dry climate The triangular distribution of rainfall for the dry climate is according to Table 3.13.  Table 3.13 The properties of triangular distribution for the dry climate   Rainfall (mm) Minimum Value* =ࣆ െ ૜࣌ Peak Value = ࣆ Maximum Value =ࣆ ൅ ૜࣌ Jan 0 8.9 64.4 Feb 0 8 49.4 Mar 0 12.5 63.5 Apr 0 18.3 83.1 May 0 30.4 109.3 Jun 0 30.8 125.6 Jul 0 17.6 86.6 Aug 0 13.8 61.8 Sep 0 17.4 67.2 Oct 0 16.8 70.5 Nov 0 15.9 134.1 Dec 0 7.8 53.4 *If the minimum value in the triangular distribution was negative value we would substitute the negative value with zero.    74 3.2.2.2 Unlined impoundment Wet climate The seepage rate of 14,904 m3/month is applied to the wet condition water balance in the previous section to model the seepage loss. Dry climate The dry condition water balance in previous section has been re-run for unlined impoundment by adding the seepage loss rate of 4,968 m3/month to the model.  75 Chapter 4: Deterministic water balance results This chapter summarizes the results of deterministic water balance for lined and unlined impoundments for two climate conditions.  4.1 Lined impoundment The water balance parameters are determined using the equations and input data presented in Chapter 3. It is assumed that there is no seepage loss. 4.1.1 Wet climate Table 4.1 shows the model results for solids content of 45% for wet climate. This model is developed based on the average values of climate parameters and dry density of 1.2 tonne/m3. The water balance models for solids contents of 60, 70, and 80 percent are provided in Appendix B, Tables B.1 to B.3. The schematic flowchart of water circulations for the four tailings solids contents are illustrated in Figure 4.1. The volume of water flowing between each unit is calculated based on the average of water flows over 60 months of TMF operating life. Only some make up water is required for the first month of operation for 45 and 60 percent solids contents.   76 Table 4.1 Deterministic water balance for solids content of 45% in the wet condition for a lined impoundment       Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3)   Month Number of days in month Rainfall  Snow water equivalent from snow melt  Water with tailings  Precipitation onto impoundment Total Water Input Evaporation Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water moved  to reservoir Cumulative change in storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Water returned to TMF from reservoir Water remained in reservoir Water remained in TMF+ Water returned to TMF from reservoir     Year 1 JAN 31.0 373.1 0.0 508,658.3 858,107.0 1,366,765.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 1,169,656.0 169,656.0 1,169,656.0 1,000,000.0 33,578.1 203,234.1 203,234.1 0.0 0.0 1,000,000.0 FEB 28.0 197.7 0.0 459,433.3 454,618.0 914,051.3 0.0 178,034.2 178,034.2 459,433.3 459,433.3 736,017.1 736,017.1 1,736,017.1 1,000,000.0 17,789.4 753,806.5 753,806.5 0.0 294,373.2 1,000,000.0 MAR 31.0 167.2 0.0 508,658.3 384,468.0 893,126.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 696,017.0 696,017.0 1,696,017.0 1,000,000.0 15,044.4 711,061.4 1,005,434.6 0.0 496,776.3 1,000,000.0 APR 30.0 117.8 60.6 492,250.0 410,297.0 902,547.0 48,622.0 190,750.9 239,372.9 492,250.0 492,250.0 663,174.1 663,174.1 1,663,174.1 1,000,000.0 16,055.1 679,229.2 1,176,005.5 0.0 683,755.5 1,000,000.0 MAY 31.0 98.5 118.9 508,658.3 499,905.0 1,008,563.3 99,385.3 197,109.3 296,494.6 508,658.3 508,658.3 712,068.7 712,068.7 1,712,068.7 1,000,000.0 19,561.5 731,630.2 1,415,385.7 0.0 906,727.4 1,000,000.0 JUN 30.0 98.3 185.4 492,250.0 652,326.0 1,144,576.0 128,977.1 190,750.9 319,728.0 492,250.0 492,250.0 824,848.0 824,848.0 1,824,848.0 1,000,000.0 25,525.8 850,373.8 1,757,101.2 0.0 1,264,851.2 1,000,000.0 JUL 31.0 102.8 37.6 508,658.3 322,943.0 831,601.3 127,560.3 197,109.3 324,669.6 508,658.3 508,658.3 506,931.7 506,931.7 1,506,931.7 1,000,000.0 12,636.9 519,568.6 1,784,419.8 0.0 1,275,761.5 1,000,000.0 AUG 31.0 163.6 1.4 508,658.3 379,385.0 888,043.3 109,995.2 197,109.3 307,104.5 508,658.3 508,658.3 580,938.8 580,938.8 1,580,938.8 1,000,000.0 14,845.5 595,784.3 1,871,545.8 0.0 1,362,887.5 1,000,000.0 SEP 30.0 246.2 8.3 492,250.0 585,304.0 1,077,554.0 55,915.3 190,750.9 246,666.2 492,250.0 492,250.0 830,887.8 830,887.8 1,830,887.8 1,000,000.0 22,903.2 853,791.0 2,216,678.5 0.0 1,724,428.5 1,000,000.0 OCT 31.0 354.8 0.0 508,658.3 816,132.0 1,324,790.3 21,735.0 197,109.3 218,844.3 508,658.3 508,658.3 1,105,946.0 1,105,946.0 2,105,946.0 1,000,000.0 31,935.6 1,137,881.6 2,862,310.1 0.0 2,353,651.8 1,000,000.0 NOV 30.0 337.9 0.0 492,250.0 777,239.0 1,269,489.0 0.0 190,750.9 190,750.9 492,250.0 492,250.0 1,078,738.1 1,078,738.1 2,078,738.1 1,000,000.0 30,413.7 1,109,151.8 3,462,803.6 0.0 2,970,553.6 1,000,000.0 DEC 31.0 313.0 0.0 508,658.3 719,900.0 1,228,558.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 1,031,449.0 1,031,449.0 2,031,449.0 1,000,000.0 28,170.0 1,059,619.0 4,030,172.6 0.0 3,521,514.3 1,000,000.0 Year 2 JAN 31.0 373.1 0.0 508,658.3 858,107.0 1,366,765.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 1,169,656.0 1,169,656.0 2,169,656.0 1,000,000.0 33,578.1 1,203,234.1 4,724,748.4 0.0 4,216,090.1 1,000,000.0 FEB 28.0 197.7 0.0 459,433.3 454,618.0 914,051.3 0.0 178,034.2 178,034.2 459,433.3 459,433.3 736,017.1 736,017.1 1,736,017.1 1,000,000.0 17,789.4 753,806.5 4,969,896.6 0.0 4,510,463.3 1,000,000.0 MAR 31.0 167.2 0.0 508,658.3 384,468.0 893,126.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 696,017.0 696,017.0 1,696,017.0 1,000,000.0 15,044.4 711,061.4 5,221,524.7 0.0 4,712,866.4 1,000,000.0 APR 30.0 117.8 60.6 492,250.0 410,297.0 902,547.0 48,622.0 190,750.9 239,372.9 492,250.0 492,250.0 663,174.1 663,174.1 1,663,174.1 1,000,000.0 16,055.1 679,229.2 5,392,095.6 0.0 4,899,845.6 1,000,000.0 MAY 31.0 98.5 118.9 508,658.3 499,905.0 1,008,563.3 99,385.3 197,109.3 296,494.6 508,658.3 508,658.3 712,068.7 712,068.7 1,712,068.7 1,000,000.0 19,561.5 731,630.2 5,631,475.8 0.0 5,122,817.5 1,000,000.0 JUN 30.0 98.3 185.4 492,250.0 652,326.0 1,144,576.0 128,977.1 190,750.9 319,728.0 492,250.0 492,250.0 824,848.0 824,848.0 1,824,848.0 1,000,000.0 25,525.8 850,373.8 5,973,191.3 0.0 5,480,941.3 1,000,000.0 JUL 31.0 102.8 37.6 508,658.3 322,943.0 831,601.3 127,560.3 197,109.3 324,669.6 508,658.3 508,658.3 506,931.7 506,931.7 1,506,931.7 1,000,000.0 12,636.9 519,568.6 6,000,509.9 0.0 5,491,851.6 1,000,000.0 AUG 31.0 163.6 1.4 508,658.3 379,385.0 888,043.3 109,995.2 197,109.3 307,104.5 508,658.3 508,658.3 580,938.8 580,938.8 1,580,938.8 1,000,000.0 14,845.5 595,784.3 6,087,635.9 0.0 5,578,977.6 1,000,000.0 SEP 30.0 246.2 8.3 492,250.0 585,304.0 1,077,554.0 55,915.3 190,750.9 246,666.2 492,250.0 492,250.0 830,887.8 830,887.8 1,830,887.8 1,000,000.0 22,903.2 853,791.0 6,432,768.6 0.0 5,940,518.6 1,000,000.0 OCT 31.0 354.8 0.0 508,658.3 816,132.0 1,324,790.3 21,735.0 197,109.3 218,844.3 508,658.3 508,658.3 1,105,946.0 1,105,946.0 2,105,946.0 1,000,000.0 31,935.6 1,137,881.6 7,078,400.2 0.0 6,569,741.9 1,000,000.0 NOV 30.0 337.9 0.0 492,250.0 777,239.0 1,269,489.0 0.0 190,750.9 190,750.9 492,250.0 492,250.0 1,078,738.1 1,078,738.1 2,078,738.1 1,000,000.0 30,413.7 1,109,151.8 7,678,893.7 0.0 7,186,643.7 1,000,000.0 DEC 31.0 313.0 0.0 508,658.3 719,900.0 1,228,558.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 1,031,449.0 1,031,449.0 2,031,449.0 1,000,000.0 28,170.0 1,059,619.0 8,246,262.7 0.0 7,737,604.4 1,000,000.0 Year 3 JAN 31.0 373.1 0.0 508,658.3 858,107.0 1,366,765.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 1,169,656.0 1,169,656.0 2,169,656.0 1,000,000.0 33,578.1 1,203,234.1 8,940,838.5 0.0 8,432,180.2 1,000,000.0 FEB 28.0 197.7 0.0 459,433.3 454,618.0 914,051.3 0.0 178,034.2 178,034.2 459,433.3 459,433.3 736,017.1 736,017.1 1,736,017.1 1,000,000.0 17,789.4 753,806.5 9,185,986.7 0.0 8,726,553.4 1,000,000.0 MAR 31.0 167.2 0.0 508,658.3 384,468.0 893,126.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 696,017.0 696,017.0 1,696,017.0 1,000,000.0 15,044.4 711,061.4 9,437,614.8 0.0 8,928,956.5 1,000,000.0 APR 30.0 117.8 60.6 492,250.0 410,297.0 902,547.0 48,622.0 190,750.9 239,372.9 492,250.0 492,250.0 663,174.1 663,174.1 1,663,174.1 1,000,000.0 16,055.1 679,229.2 9,608,185.7 0.0 9,115,935.7 1,000,000.0 MAY 31.0 98.5 118.9 508,658.3 499,905.0 1,008,563.3 99,385.3 197,109.3 296,494.6 508,658.3 508,658.3 712,068.7 712,068.7 1,712,068.7 1,000,000.0 19,561.5 731,630.2 9,847,565.9 0.0 9,338,907.6 1,000,000.0 JUN 30.0 98.3 185.4 492,250.0 652,326.0 1,144,576.0 128,977.1 190,750.9 319,728.0 492,250.0 492,250.0 824,848.0 824,848.0 1,824,848.0 1,000,000.0 25,525.8 850,373.8 10,189,281.4 0.0 9,697,031.4 1,000,000.0 JUL 31.0 102.8 37.6 508,658.3 322,943.0 831,601.3 127,560.3 197,109.3 324,669.6 508,658.3 508,658.3 506,931.7 506,931.7 1,506,931.7 1,000,000.0 12,636.9 519,568.6 10,216,600.0 0.0 9,707,941.7 1,000,000.0 AUG 31.0 163.6 1.4 508,658.3 379,385.0 888,043.3 109,995.2 197,109.3 307,104.5 508,658.3 508,658.3 580,938.8 580,938.8 1,580,938.8 1,000,000.0 14,845.5 595,784.3 10,303,726.0 0.0 9,795,067.7 1,000,000.0 SEP 30.0 246.2 8.3 492,250.0 585,304.0 1,077,554.0 55,915.3 190,750.9 246,666.2 492,250.0 492,250.0 830,887.8 830,887.8 1,830,887.8 1,000,000.0 22,903.2 853,791.0 10,648,858.7 0.0 10,156,608.7 1,000,000.0 OCT 31.0 354.8 0.0 508,658.3 816,132.0 1,324,790.3 21,735.0 197,109.3 218,844.3 508,658.3 508,658.3 1,105,946.0 1,105,946.0 2,105,946.0 1,000,000.0 31,935.6 1,137,881.6 11,294,490.3 0.0 10,785,832.0 1,000,000.0  77       Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3)   Month Number of days in month Rainfall  Snow water equivalent from snow melt  Water with tailings  Precipitation onto impoundment Total Water Input Evaporation Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water moved  to reservoir Cumulative change in storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Water returned to TMF from reservoir Water remained in reservoir Water remained in TMF+ Water returned to TMF from reservoir     NOV 30.0 337.9 0.0 492,250.0 777,239.0 1,269,489.0 0.0 190,750.9 190,750.9 492,250.0 492,250.0 1,078,738.1 1,078,738.1 2,078,738.1 1,000,000.0 30,413.7 1,109,151.8 11,894,983.8 0.0 11,402,733.8 1,000,000.0 DEC 31.0 313.0 0.0 508,658.3 719,900.0 1,228,558.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 1,031,449.0 1,031,449.0 2,031,449.0 1,000,000.0 28,170.0 1,059,619.0 12,462,352.8 0.0 11,953,694.5 1,000,000.0 Year 4 JAN 31.0 373.1 0.0 508,658.3 858,107.0 1,366,765.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 1,169,656.0 1,169,656.0 2,169,656.0 1,000,000.0 33,578.1 1,203,234.1 13,156,928.6 0.0 12,648,270.3 1,000,000.0 FEB 28.0 197.7 0.0 459,433.3 454,618.0 914,051.3 0.0 178,034.2 178,034.2 459,433.3 459,433.3 736,017.1 736,017.1 1,736,017.1 1,000,000.0 17,789.4 753,806.5 13,402,076.8 0.0 12,942,643.5 1,000,000.0 MAR 31.0 167.2 0.0 508,658.3 384,468.0 893,126.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 696,017.0 696,017.0 1,696,017.0 1,000,000.0 15,044.4 711,061.4 13,653,704.9 0.0 13,145,046.6 1,000,000.0 APR 30.0 117.8 60.6 492,250.0 410,297.0 902,547.0 48,622.0 190,750.9 239,372.9 492,250.0 492,250.0 663,174.1 663,174.1 1,663,174.1 1,000,000.0 16,055.1 679,229.2 13,824,275.8 0.0 13,332,025.8 1,000,000.0 MAY 31.0 98.5 118.9 508,658.3 499,905.0 1,008,563.3 99,385.3 197,109.3 296,494.6 508,658.3 508,658.3 712,068.7 712,068.7 1,712,068.7 1,000,000.0 19,561.5 731,630.2 14,063,656.0 0.0 13,554,997.7 1,000,000.0 JUN 30.0 98.3 185.4 492,250.0 652,326.0 1,144,576.0 128,977.1 190,750.9 319,728.0 492,250.0 492,250.0 824,848.0 824,848.0 1,824,848.0 1,000,000.0 25,525.8 850,373.8 14,405,371.5 0.0 13,913,121.5 1,000,000.0 JUL 31.0 102.8 37.6 508,658.3 322,943.0 831,601.3 127,560.3 197,109.3 324,669.6 508,658.3 508,658.3 506,931.7 506,931.7 1,506,931.7 1,000,000.0 12,636.9 519,568.6 14,432,690.1 0.0 13,924,031.8 1,000,000.0 AUG 31.0 163.6 1.4 508,658.3 379,385.0 888,043.3 109,995.2 197,109.3 307,104.5 508,658.3 508,658.3 580,938.8 580,938.8 1,580,938.8 1,000,000.0 14,845.5 595,784.3 14,519,816.1 0.0 14,011,157.8 1,000,000.0 SEP 30.0 246.2 8.3 492,250.0 585,304.0 1,077,554.0 55,915.3 190,750.9 246,666.2 492,250.0 492,250.0 830,887.8 830,887.8 1,830,887.8 1,000,000.0 22,903.2 853,791.0 14,864,948.8 0.0 14,372,698.8 1,000,000.0 OCT 31.0 354.8 0.0 508,658.3 816,132.0 1,324,790.3 21,735.0 197,109.3 218,844.3 508,658.3 508,658.3 1,105,946.0 1,105,946.0 2,105,946.0 1,000,000.0 31,935.6 1,137,881.6 15,510,580.4 0.0 15,001,922.1 1,000,000.0 NOV 30.0 337.9 0.0 492,250.0 777,239.0 1,269,489.0 0.0 190,750.9 190,750.9 492,250.0 492,250.0 1,078,738.1 1,078,738.1 2,078,738.1 1,000,000.0 30,413.7 1,109,151.8 16,111,073.9 0.0 15,618,823.9 1,000,000.0 DEC 31.0 313.0 0.0 508,658.3 719,900.0 1,228,558.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 1,031,449.0 1,031,449.0 2,031,449.0 1,000,000.0 28,170.0 1,059,619.0 16,678,442.9 0.0 16,169,784.6 1,000,000.0 Year 5 JAN 31.0 373.1 0.0 508,658.3 858,107.0 1,366,765.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 1,169,656.0 1,169,656.0 2,169,656.0 1,000,000.0 33,578.1 1,203,234.1 17,373,018.7 0.0 16,864,360.4 1,000,000.0 FEB 28.0 197.7 0.0 459,433.3 454,618.0 914,051.3 0.0 178,034.2 178,034.2 459,433.3 459,433.3 736,017.1 736,017.1 1,736,017.1 1,000,000.0 17,789.4 753,806.5 17,618,166.9 0.0 17,158,733.6 1,000,000.0 MAR 31.0 167.2 0.0 508,658.3 384,468.0 893,126.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 696,017.0 696,017.0 1,696,017.0 1,000,000.0 15,044.4 711,061.4 17,869,795.0 0.0 17,361,136.7 1,000,000.0 APR 30.0 117.8 60.6 492,250.0 410,297.0 902,547.0 48,622.0 190,750.9 239,372.9 492,250.0 492,250.0 663,174.1 663,174.1 1,663,174.1 1,000,000.0 16,055.1 679,229.2 18,040,365.9 0.0 17,548,115.9 1,000,000.0 MAY 31.0 98.5 118.9 508,658.3 499,905.0 1,008,563.3 99,385.3 197,109.3 296,494.6 508,658.3 508,658.3 712,068.7 712,068.7 1,712,068.7 1,000,000.0 19,561.5 731,630.2 18,279,746.1 0.0 17,771,087.8 1,000,000.0 JUN 30.0 98.3 185.4 492,250.0 652,326.0 1,144,576.0 128,977.1 190,750.9 319,728.0 492,250.0 492,250.0 824,848.0 824,848.0 1,824,848.0 1,000,000.0 25,525.8 850,373.8 18,621,461.6 0.0 18,129,211.6 1,000,000.0 JUL 31.0 102.8 37.6 508,658.3 322,943.0 831,601.3 127,560.3 197,109.3 324,669.6 508,658.3 508,658.3 506,931.7 506,931.7 1,506,931.7 1,000,000.0 12,636.9 519,568.6 18,648,780.2 0.0 18,140,121.9 1,000,000.0 AUG 31.0 163.6 1.4 508,658.3 379,385.0 888,043.3 109,995.2 197,109.3 307,104.5 508,658.3 508,658.3 580,938.8 580,938.8 1,580,938.8 1,000,000.0 14,845.5 595,784.3 18,735,906.2 0.0 18,227,247.9 1,000,000.0 SEP 30.0 246.2 8.3 492,250.0 585,304.0 1,077,554.0 55,915.3 190,750.9 246,666.2 492,250.0 492,250.0 830,887.8 830,887.8 1,830,887.8 1,000,000.0 22,903.2 853,791.0 19,081,038.9 0.0 18,588,788.9 1,000,000.0 OCT 31.0 354.8 0.0 508,658.3 816,132.0 1,324,790.3 21,735.0 197,109.3 218,844.3 508,658.3 508,658.3 1,105,946.0 1,105,946.0 2,105,946.0 1,000,000.0 31,935.6 1,137,881.6 19,726,670.5 0.0 19,218,012.2 1,000,000.0 NOV 30.0 337.9 0.0 492,250.0 777,239.0 1,269,489.0 0.0 190,750.9 190,750.9 492,250.0 492,250.0 1,078,738.1 1,078,738.1 2,078,738.1 1,000,000.0 30,413.7 1,109,151.8 20,327,164.0 0.0 19,834,914.0 1,000,000.0 DEC 31.0 313.0 0.0 508,658.3 719,900.0 1,228,558.3 0.0 197,109.3 197,109.3 508,658.3 508,658.3 1,031,449.0 1,031,449.0 2,031,449.0 1,000,000.0 28,170.0 1,059,619.0 20,894,533.0 0.0 20,385,874.7 1,000,000.0    78  Figure 4.1 Schematics of water balance for different solids contents in the wet condition for a lined impoundment35% solids 35% solids1038.84 m3/h1038.84 m3/h45% solids 60% solids683.68 m3/h 372.92 m3/h458.46 m3/h 563.71 m3/h35% solids 35% solids1038.84 m3/h1038.84 m3/h70% solids 80% solids239.73 m3/h 139.84 m3/h628.05 m3/h 641.24 m3/h0 m3Evaporation Entrained in solids67.6 m3/h 264.93 m3/h45% SolidsPrecipitation783.18 m3/h1111.49 m3/hRemaining in TSFRemaining in reservoir22.83 m3/hSolids Feed22.38 m3/h1038.84 m3/hOnly for the first month 305424.2 m3355.16 m3/h 683.68 m3/h30.65 m3/h60% Solids70% Solids 80% SolidsSolids FeedSolids Feed Solids Feed0 m31038.84 m3/h665.92 m3/h 372.92 m3/h783.18 m3/h22.38 m3/h22.38 m3/h 22.38 m3/h1038.84 m3/hOnly for the first month 227111.7 m3PrecipitationPrecipitation30.65 m3/h30.65 m3/h 30.45 m3/hEvaporation Entrained in solidsRemaining in TSFRemaining in reservoir22.83 m3/h0 m3/h905.98 m3/h837.13 m3/h 750.63 m3/hRemaining in TSFRemaining in reservoir67.6 m3/h 159.67 m3/h57.94 m3/h 127.83 m3/h 57.94 m3/h 109.55 m3/h139.84 m3/hEvaporation Entrained in solids Evaporation Entrained in solids0 m3/hRemaining in TSFRemaining in reservoirPrecipitation783.18 m3/h 778.28 m3/h799.11 m3/h 239.73 m3/h 899 m3/h1038.84 m3/hProcessing DewateringTSF ReservoirTank Fresh waterMineProcessing ConditionerTSF ReservoirTank Fresh waterMineProcessing PlantConditionerTSF ReservoirTank Fresh waterMineProcessing PlantConditionerTSF ReservoirTank Fresh waterMineDewa eringDewa eringDewa ering 79 The cumulative water surplus and cumulative water surplus per tonne of mill throughput for solids contents of 45, 60, 70 and 80% are compared in Figures 4.2 and 4.3 The water surplus per tonne of mill throughput using the average values of parameters varies between 0.8 and 1.1 m3/tonne. Cumulative water surplus is the water retained in reservoir at the end of each month. Graphs in Figure 4.3 shows that water surplus reaches a relative steady state after end of the second year. Minimum storage in TMF was not required for 70 and 80% solids contents. This affected the water surplus graphs in Figure 4.3. The water surplus are significantly higher in the first 6 month of TMF operation.  Figure 4.2 Cumulative water surplus for different solids contents in the wet condition for a lined impoundment 0.05,000,000.010,000,000.015,000,000.020,000,000.025,000,000.030,000,000.0JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3)TimeCumulative water surplus45% solids60% solids70% solids80% solids 80  Figure 4.3 Cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for a lined impoundment 4.1.2 Dry climate The model outputs for solids content of 45% for dry climate is showed in Table 4.2. This model is based on the average climate values and dry density of 1.2 tonne/m3. The water balance models for solids contents of 60, 70, and 80 are provided in Appendix C, Tables C.1 to C.3. Figure 4.4 presents a schematic flowchart of the water circulations for four solids contents. The volume of water flowing between each unit is calculated based on the average of water flows over 60 months of TMF operating life. To supply the required water for the mill, the make up water required to add to the system for solids contents of 45, 60, 70, and 80% equals 683.68, 372.92, 239.73, and 139.84 m3/hour respectively. 0.00.10.20.30.40.50.60.70.80.91.01.11.21.31.41.51.61.71.81.92.02.1JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3/tonne)TimeCumulative water surplus45% solids60% solids70% solids80% solids 81 Table 4.2 Deterministic water balance for solids content of 45% in the dry condition for a lined impoundment Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from TMF Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   Year 1 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 56,412.7 104,043.8 -47,631.0 -0.1 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 79,584.2 93,975.0 93,975.0 46,130.8 93,975.0 -95,475.2 -0.1 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 55,952.7 104,043.8 -143,566.3 -0.1 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 151,099.4 100,687.5 100,687.5 0.0 100,687.5 -244,253.8 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 222,605.8 104,043.8 104,043.8 0.0 104,043.8 -348,297.5 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 259,838.8 100,687.5 100,687.5 0.0 100,687.5 -448,985.0 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 260,758.2 104,043.8 104,043.8 0.0 104,043.8 -553,028.8 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 236,976.2 104,043.8 104,043.8 0.0 104,043.8 -657,072.5 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 160,966.4 100,687.5 100,687.5 0.0 100,687.5 -757,760.0 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 117,509.6 104,043.8 104,043.8 28,624.1 104,043.8 -833,179.7 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 85,268.8 100,687.5 100,687.5 85,338.7 100,687.5 -848,528.4 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 86,772.7 104,043.8 -865,799.5 -0.2 Year 2 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 56,412.7 104,043.8 -913,430.5 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 79,584.2 93,975.0 93,975.0 46,130.8 93,975.0 -961,274.7 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 55,952.7 104,043.8 -1,009,365.7 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 151,099.4 100,687.5 100,687.5 0.0 100,687.5 -1,110,053.2 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 222,605.8 104,043.8 104,043.8 0.0 104,043.8 -1,214,097.0 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 259,838.8 100,687.5 100,687.5 0.0 100,687.5 -1,314,784.5 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 260,758.2 104,043.8 104,043.8 0.0 104,043.8 -1,418,828.2 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 236,976.2 104,043.8 104,043.8 0.0 104,043.8 -1,522,872.0 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 160,966.4 100,687.5 100,687.5 0.0 100,687.5 -1,623,559.5 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 117,509.6 104,043.8 104,043.8 28,624.1 104,043.8 -1,698,979.1 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 85,268.8 100,687.5 100,687.5 85,338.7 100,687.5 -1,714,327.9 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 86,772.7 104,043.8 -1,731,598.9 -0.2 Year 3 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 56,412.7 104,043.8 -1,779,230.0 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 79,584.2 93,975.0 93,975.0 46,130.8 93,975.0 -1,827,074.2 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 55,952.7 104,043.8 -1,875,165.2 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 151,099.4 100,687.5 100,687.5 0.0 100,687.5 -1,975,852.7 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 222,605.8 104,043.8 104,043.8 0.0 104,043.8 -2,079,896.5 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 259,838.8 100,687.5 100,687.5 0.0 100,687.5 -2,180,584.0 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 260,758.2 104,043.8 104,043.8 0.0 104,043.8 -2,284,627.7 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 236,976.2 104,043.8 104,043.8 0.0 104,043.8 -2,388,671.5 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 160,966.4 100,687.5 100,687.5 0.0 100,687.5 -2,489,359.0 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 117,509.6 104,043.8 104,043.8 28,624.1 104,043.8 -2,564,778.6 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 85,268.8 100,687.5 100,687.5 85,338.7 100,687.5 -2,580,127.4 -0.2  82 Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from TMF Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 86,772.7 104,043.8 -2,597,398.4 -0.2 Year 4 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 56,412.7 104,043.8 -2,645,029.5 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 79,584.2 93,975.0 93,975.0 46,130.8 93,975.0 -2,692,873.6 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 55,952.7 104,043.8 -2,740,964.7 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 151,099.4 100,687.5 100,687.5 0.0 100,687.5 -2,841,652.2 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 222,605.8 104,043.8 104,043.8 0.0 104,043.8 -2,945,695.9 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 259,838.8 100,687.5 100,687.5 0.0 100,687.5 -3,046,383.4 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 260,758.2 104,043.8 104,043.8 0.0 104,043.8 -3,150,427.2 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 236,976.2 104,043.8 104,043.8 0.0 104,043.8 -3,254,470.9 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 160,966.4 100,687.5 100,687.5 0.0 100,687.5 -3,355,158.4 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 117,509.6 104,043.8 104,043.8 28,624.1 104,043.8 -3,430,578.1 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 85,268.8 100,687.5 100,687.5 85,338.7 100,687.5 -3,445,926.8 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 86,772.7 104,043.8 -3,463,197.9 -0.2 Year 5 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 56,412.7 104,043.8 -3,510,828.9 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 79,584.2 93,975.0 93,975.0 46,130.8 93,975.0 -3,558,673.1 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 55,952.7 104,043.8 -3,606,764.2 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 151,099.4 100,687.5 100,687.5 0.0 100,687.5 -3,707,451.7 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 222,605.8 104,043.8 104,043.8 0.0 104,043.8 -3,811,495.4 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 259,838.8 100,687.5 100,687.5 0.0 100,687.5 -3,912,182.9 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 260,758.2 104,043.8 104,043.8 0.0 104,043.8 -4,016,226.7 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 236,976.2 104,043.8 104,043.8 0.0 104,043.8 -4,120,270.4 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 160,966.4 100,687.5 100,687.5 0.0 100,687.5 -4,220,957.9 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 117,509.6 104,043.8 104,043.8 28,624.1 104,043.8 -4,296,377.6 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 85,268.8 100,687.5 100,687.5 85,338.7 100,687.5 -4,311,726.3 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 86,772.7 104,043.8 -4,328,997.4 -0.2     83  Figure 4.4 Schematics of water balance for different solids contents in the dry condition for a lined impoundment 35% solids 35% solids1038.84 m3/h 1038.84 m3/h45% solids 60% solids683.68 m3/h 372.92 m3/h35% solids 35% solids1038.84 m3/h 1038.84 m3/h70% solids 80% solids239.73 m3/h 139.84 m3/h0 m3/h80% SolidsSolids Feed22.38 m3/h1038.84 m3/h 139.84 m3/h899 m3/h 0 m3/h0 m3/hPrecipitation52.04 m3/hRemaining in TSFEvaporation Entrained in solids91.5 m3/h 118.43 m3/hEvaporation Entrained in solids158.04 m3/h 127.83 m3/hPrecipitation52.04 m3/hRemaining in TSF70% SolidsSolids Feed22.38 m3/h1038.84 m3/h 239.73 m3/hEvaporation Entrained in solids195.47 m3/h 159.67 m3/hEvaporation Entrained in solids218.34 m3/h 264.93 m3/h0 m3/h0 m3/h799.11 m3/h665.92 m3/h 0 m3/hPrecipitation52.04 m3/hRemaining in TSF60% SolidsSolids Feed22.38 m3/h1038.84 m3/h 372.92 m3/h45% SolidsPrecipitation52.04 m3/hRemaining in TSF0 m3/hSolids Feed22.38 m3/h1038.84 m3/h 683.68 m3/h355.16 m3/h 0 m3/hProcessing PlantDewateringTSFTank Fresh water Processing PlantDewateringTSFTank Fresh waterProcessing PlantDewateringTSFTank Fresh water Processing PlantDewateringTSFTank Fresh water 84 The cumulative water deficit and cumulative water deficit per tonne of mill throughput for solids contents of 45, 60, 70 and 80% are compared in Figures 4.5 and 4.6. The water deficit per tonne of mill throughput using the average values of parameters varies between 0.17 and 0.72 m3/month. The cumulative water deficit is the cumulative change in storage.  Figure 4.5 Cumulative water deficit for different solids contents in the dry condition for a lined impoundment ‐20,000,000.0‐18,000,000.0‐16,000,000.0‐14,000,000.0‐12,000,000.0‐10,000,000.0‐8,000,000.0‐6,000,000.0‐4,000,000.0‐2,000,000.00.0JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEPYear 1 Year 2 Year 3 Year 4 Year 5Water deficit(m3)TimeCumulative water deficit45% solids60% solids70% solids80% solids 85  Figure 4.6 Cumulative water deficit per tonne of mill throughput for different solids contents in the dry condition for a lined impoundment 4.2 Unlined impoundment In this section, results of water balances with consideration of the monthly seepage losses for wet and dry climates are provided.  4.2.1 Wet climate Table 4.3 shows the model for solids content of 45% for wet climate. The model is adopted from the model illustrated in Table 4.1 with a monthly seepage loss of 14,904 m3/month. The water balance models for solids contents of 60, 70, and 80 percent are provided in Appendix B, tables B.4 to B.6. ‐0.90‐0.85‐0.80‐0.75‐0.70‐0.65‐0.60‐0.55‐0.50‐0.45‐0.40‐0.35‐0.30‐0.25‐0.20‐0.15‐0.10‐0.050.00JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEPYear 1 Year 2 Year 3 Year 4 Year 5Water deficit(m3/tonne)TimeCumulative water deficit45% solids60% solids70% solids80% solids 86 The schematic flowcharts of the water circulations for four scenarios of tailings solids contents are plotted in Figure 4.7. Similar to the model for unlined impoundment, make up water addition is only required for the fist month of operation for 45 and 60% solids contents. The difference of the water which was required to be added to the system for the first month of the mine operation for lined and unlined impoundments for solids contents of 45 and 60 % equals the monthly seepage loss (14,904 m3/month).    87 Table 4.3 Deterministic water balance for solids content of 45% in the wet condition for an unlined impoundment Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3) Month Number of days in month Rainfall Snow water equivalent from snow melt Water with slurry (m3) Precipitation onto impoundment Total Water Input Evaporation Water retained in tailings Seepage loss Total Water Output Water with slurry Total mill required water Monthly storage Water move  to reservoir Cumulative storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Returned to pond Water remained in reservoir Water remained on TMF+ return from reservoir   Year 1 JAN 31.0 373.1 0.0 508,658.3 858,107.0 1,366,765.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 1,154,752.0 154,752.0 1,154,752.0 1,000,000.0 33,578.1 188,330.1 188,330.1 0.0 0.0 1,000,000.0 FEB 28.0 197.7 0.0 459,433.3 454,618.0 914,051.3 0.0 178,034.2 14,904.0 192,938.2 459,433.3 459,433.3 721,113.1 721,113.1 1,721,113.1 1,000,000.0 17,789.4 738,902.5 738,902.5 0.0 279,469.2 1,000,000.0 MAR 31.0 167.2 0.0 508,658.3 384,468.0 893,126.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 681,113.0 681,113.0 1,681,113.0 1,000,000.0 15,044.4 696,157.4 975,626.6 0.0 466,968.3 1,000,000.0 APR 30.0 117.8 60.6 492,250.0 410,297.0 902,547.0 48,622.0 190,750.9 14,904.0 254,276.9 492,250.0 492,250.0 648,270.1 648,270.1 1,648,270.1 1,000,000.0 16,055.1 664,325.2 1,131,293.5 0.0 639,043.5 1,000,000.0 MAY 31.0 98.5 118.9 508,658.3 499,905.0 1,008,563.3 99,385.3 197,109.3 14,904.0 311,398.6 508,658.3 508,658.3 697,164.7 697,164.7 1,697,164.7 1,000,000.0 19,561.5 716,726.2 1,355,769.7 0.0 847,111.4 1,000,000.0 JUN 30.0 98.3 185.4 492,250.0 652,326.0 1,144,576.0 128,977.1 190,750.9 14,904.0 334,632.0 492,250.0 492,250.0 809,944.0 809,944.0 1,809,944.0 1,000,000.0 25,525.8 835,469.8 1,682,581.2 0.0 1,190,331.2 1,000,000.0 JUL 31.0 102.8 37.6 508,658.3 322,943.0 831,601.3 127,560.3 197,109.3 14,904.0 339,573.6 508,658.3 508,658.3 492,027.7 492,027.7 1,492,027.7 1,000,000.0 12,636.9 504,664.6 1,694,995.8 0.0 1,186,337.5 1,000,000.0 AUG 31.0 163.6 1.4 508,658.3 379,385.0 888,043.3 109,995.2 197,109.3 14,904.0 322,008.5 508,658.3 508,658.3 566,034.8 566,034.8 1,566,034.8 1,000,000.0 14,845.5 580,880.3 1,767,217.8 0.0 1,258,559.5 1,000,000.0 SEP 30.0 246.2 8.3 492,250.0 585,304.0 1,077,554.0 55,915.3 190,750.9 14,904.0 261,570.2 492,250.0 492,250.0 815,983.8 815,983.8 1,815,983.8 1,000,000.0 22,903.2 838,887.0 2,097,446.5 0.0 1,605,196.5 1,000,000.0 OCT 31.0 354.8 0.0 508,658.3 816,132.0 1,324,790.3 21,735.0 197,109.3 14,904.0 233,748.3 508,658.3 508,658.3 1,091,042.0 1,091,042.0 2,091,042.0 1,000,000.0 31,935.6 1,122,977.6 2,728,174.1 0.0 2,219,515.8 1,000,000.0 NOV 30.0 337.9 0.0 492,250.0 777,239.0 1,269,489.0 0.0 190,750.9 14,904.0 205,654.9 492,250.0 492,250.0 1,063,834.1 1,063,834.1 2,063,834.1 1,000,000.0 30,413.7 1,094,247.8 3,313,763.6 0.0 2,821,513.6 1,000,000.0 DEC 31.0 313.0 0.0 508,658.3 719,900.0 1,228,558.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 1,016,545.0 1,016,545.0 2,016,545.0 1,000,000.0 28,170.0 1,044,715.0 3,866,228.6 0.0 3,357,570.3 1,000,000.0 Year 2 JAN 31.0 373.1 0.0 508,658.3 858,107.0 1,366,765.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 1,154,752.0 1,154,752.0 2,154,752.0 1,000,000.0 33,578.1 1,188,330.1 4,545,900.4 0.0 4,037,242.1 1,000,000.0 FEB 28.0 197.7 0.0 459,433.3 454,618.0 914,051.3 0.0 178,034.2 14,904.0 192,938.2 459,433.3 459,433.3 721,113.1 721,113.1 1,721,113.1 1,000,000.0 17,789.4 738,902.5 4,776,144.6 0.0 4,316,711.3 1,000,000.0 MAR 31.0 167.2 0.0 508,658.3 384,468.0 893,126.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 681,113.0 681,113.0 1,681,113.0 1,000,000.0 15,044.4 696,157.4 5,012,868.7 0.0 4,504,210.4 1,000,000.0 APR 30.0 117.8 60.6 492,250.0 410,297.0 902,547.0 48,622.0 190,750.9 14,904.0 254,276.9 492,250.0 492,250.0 648,270.1 648,270.1 1,648,270.1 1,000,000.0 16,055.1 664,325.2 5,168,535.6 0.0 4,676,285.6 1,000,000.0 MAY 31.0 98.5 118.9 508,658.3 499,905.0 1,008,563.3 99,385.3 197,109.3 14,904.0 311,398.6 508,658.3 508,658.3 697,164.7 697,164.7 1,697,164.7 1,000,000.0 19,561.5 716,726.2 5,393,011.8 0.0 4,884,353.5 1,000,000.0 JUN 30.0 98.3 185.4 492,250.0 652,326.0 1,144,576.0 128,977.1 190,750.9 14,904.0 334,632.0 492,250.0 492,250.0 809,944.0 809,944.0 1,809,944.0 1,000,000.0 25,525.8 835,469.8 5,719,823.3 0.0 5,227,573.3 1,000,000.0 JUL 31.0 102.8 37.6 508,658.3 322,943.0 831,601.3 127,560.3 197,109.3 14,904.0 339,573.6 508,658.3 508,658.3 492,027.7 492,027.7 1,492,027.7 1,000,000.0 12,636.9 504,664.6 5,732,237.9 0.0 5,223,579.6 1,000,000.0 AUG 31.0 163.6 1.4 508,658.3 379,385.0 888,043.3 109,995.2 197,109.3 14,904.0 322,008.5 508,658.3 508,658.3 566,034.8 566,034.8 1,566,034.8 1,000,000.0 14,845.5 580,880.3 5,804,459.9 0.0 5,295,801.6 1,000,000.0 SEP 30.0 246.2 8.3 492,250.0 585,304.0 1,077,554.0 55,915.3 190,750.9 14,904.0 261,570.2 492,250.0 492,250.0 815,983.8 815,983.8 1,815,983.8 1,000,000.0 22,903.2 838,887.0 6,134,688.6 0.0 5,642,438.6 1,000,000.0 OCT 31.0 354.8 0.0 508,658.3 816,132.0 1,324,790.3 21,735.0 197,109.3 14,904.0 233,748.3 508,658.3 508,658.3 1,091,042.0 1,091,042.0 2,091,042.0 1,000,000.0 31,935.6 1,122,977.6 6,765,416.2 0.0 6,256,757.9 1,000,000.0 NOV 30.0 337.9 0.0 492,250.0 777,239.0 1,269,489.0 0.0 190,750.9 14,904.0 205,654.9 492,250.0 492,250.0 1,063,834.1 1,063,834.1 2,063,834.1 1,000,000.0 30,413.7 1,094,247.8 7,351,005.7 0.0 6,858,755.7 1,000,000.0 DEC 31.0 313.0 0.0 508,658.3 719,900.0 1,228,558.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 1,016,545.0 1,016,545.0 2,016,545.0 1,000,000.0 28,170.0 1,044,715.0 7,903,470.7 0.0 7,394,812.4 1,000,000.0 Year 3 JAN 31.0 373.1 0.0 508,658.3 858,107.0 1,366,765.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 1,154,752.0 1,154,752.0 2,154,752.0 1,000,000.0 33,578.1 1,188,330.1 8,583,142.5 0.0 8,074,484.2 1,000,000.0 FEB 28.0 197.7 0.0 459,433.3 454,618.0 914,051.3 0.0 178,034.2 14,904.0 192,938.2 459,433.3 459,433.3 721,113.1 721,113.1 1,721,113.1 1,000,000.0 17,789.4 738,902.5 8,813,386.7 0.0 8,353,953.4 1,000,000.0 MAR 31.0 167.2 0.0 508,658.3 384,468.0 893,126.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 681,113.0 681,113.0 1,681,113.0 1,000,000.0 15,044.4 696,157.4 9,050,110.8 0.0 8,541,452.5 1,000,000.0 APR 30.0 117.8 60.6 492,250.0 410,297.0 902,547.0 48,622.0 190,750.9 14,904.0 254,276.9 492,250.0 492,250.0 648,270.1 648,270.1 1,648,270.1 1,000,000.0 16,055.1 664,325.2 9,205,777.7 0.0 8,713,527.7 1,000,000.0 MAY 31.0 98.5 118.9 508,658.3 499,905.0 1,008,563.3 99,385.3 197,109.3 14,904.0 311,398.6 508,658.3 508,658.3 697,164.7 697,164.7 1,697,164.7 1,000,000.0 19,561.5 716,726.2 9,430,253.9 0.0 8,921,595.6 1,000,000.0 JUN 30.0 98.3 185.4 492,250.0 652,326.0 1,144,576.0 128,977.1 190,750.9 14,904.0 334,632.0 492,250.0 492,250.0 809,944.0 809,944.0 1,809,944.0 1,000,000.0 25,525.8 835,469.8 9,757,065.4 0.0 9,264,815.4 1,000,000.0 JUL 31.0 102.8 37.6 508,658.3 322,943.0 831,601.3 127,560.3 197,109.3 14,904.0 339,573.6 508,658.3 508,658.3 492,027.7 492,027.7 1,492,027.7 1,000,000.0 12,636.9 504,664.6 9,769,480.0 0.0 9,260,821.7 1,000,000.0 AUG 31.0 163.6 1.4 508,658.3 379,385.0 888,043.3 109,995.2 197,109.3 14,904.0 322,008.5 508,658.3 508,658.3 566,034.8 566,034.8 1,566,034.8 1,000,000.0 14,845.5 580,880.3 9,841,702.0 0.0 9,333,043.7 1,000,000.0 SEP 30.0 246.2 8.3 492,250.0 585,304.0 1,077,554.0 55,915.3 190,750.9 14,904.0 261,570.2 492,250.0 492,250.0 815,983.8 815,983.8 1,815,983.8 1,000,000.0 22,903.2 838,887.0 10,171,930.7 0.0 9,679,680.7 1,000,000.0 OCT 31.0 354.8 0.0 508,658.3 816,132.0 1,324,790.3 21,735.0 197,109.3 14,904.0 233,748.3 508,658.3 508,658.3 1,091,042.0 1,091,042.0 2,091,042.0 1,000,000.0 31,935.6 1,122,977.6 10,802,658.3 0.0 10,294,000.0 1,000,000.0 NOV 30.0 337.9 0.0 492,250.0 777,239.0 1,269,489.0 0.0 190,750.9 14,904.0 205,654.9 492,250.0 492,250.0 1,063,834.1 1,063,834.1 2,063,834.1 1,000,000.0 30,413.7 1,094,247.8 11,388,247.8 0.0 10,895,997.8 1,000,000.0  88 Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3) Month Number of days in month Rainfall Snow water equivalent from snow melt Water with slurry (m3) Precipitation onto impoundment Total Water Input Evaporation Water retained in tailings Seepage loss Total Water Output Water with slurry Total mill required water Monthly storage Water move  to reservoir Cumulative storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Returned to pond Water remained in reservoir Water remained on TMF+ return from reservoir   DEC 31.0 313.0 0.0 508,658.3 719,900.0 1,228,558.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 1,016,545.0 1,016,545.0 2,016,545.0 1,000,000.0 28,170.0 1,044,715.0 11,940,712.8 0.0 11,432,054.5 1,000,000.0 Year 4 JAN 31.0 373.1 0.0 508,658.3 858,107.0 1,366,765.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 1,154,752.0 1,154,752.0 2,154,752.0 1,000,000.0 33,578.1 1,188,330.1 12,620,384.6 0.0 12,111,726.3 1,000,000.0 FEB 28.0 197.7 0.0 459,433.3 454,618.0 914,051.3 0.0 178,034.2 14,904.0 192,938.2 459,433.3 459,433.3 721,113.1 721,113.1 1,721,113.1 1,000,000.0 17,789.4 738,902.5 12,850,628.8 0.0 12,391,195.5 1,000,000.0 MAR 31.0 167.2 0.0 508,658.3 384,468.0 893,126.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 681,113.0 681,113.0 1,681,113.0 1,000,000.0 15,044.4 696,157.4 13,087,352.9 0.0 12,578,694.6 1,000,000.0 APR 30.0 117.8 60.6 492,250.0 410,297.0 902,547.0 48,622.0 190,750.9 14,904.0 254,276.9 492,250.0 492,250.0 648,270.1 648,270.1 1,648,270.1 1,000,000.0 16,055.1 664,325.2 13,243,019.8 0.0 12,750,769.8 1,000,000.0 MAY 31.0 98.5 118.9 508,658.3 499,905.0 1,008,563.3 99,385.3 197,109.3 14,904.0 311,398.6 508,658.3 508,658.3 697,164.7 697,164.7 1,697,164.7 1,000,000.0 19,561.5 716,726.2 13,467,496.0 0.0 12,958,837.7 1,000,000.0 JUN 30.0 98.3 185.4 492,250.0 652,326.0 1,144,576.0 128,977.1 190,750.9 14,904.0 334,632.0 492,250.0 492,250.0 809,944.0 809,944.0 1,809,944.0 1,000,000.0 25,525.8 835,469.8 13,794,307.5 0.0 13,302,057.5 1,000,000.0 JUL 31.0 102.8 37.6 508,658.3 322,943.0 831,601.3 127,560.3 197,109.3 14,904.0 339,573.6 508,658.3 508,658.3 492,027.7 492,027.7 1,492,027.7 1,000,000.0 12,636.9 504,664.6 13,806,722.1 0.0 13,298,063.8 1,000,000.0 AUG 31.0 163.6 1.4 508,658.3 379,385.0 888,043.3 109,995.2 197,109.3 14,904.0 322,008.5 508,658.3 508,658.3 566,034.8 566,034.8 1,566,034.8 1,000,000.0 14,845.5 580,880.3 13,878,944.1 0.0 13,370,285.8 1,000,000.0 SEP 30.0 246.2 8.3 492,250.0 585,304.0 1,077,554.0 55,915.3 190,750.9 14,904.0 261,570.2 492,250.0 492,250.0 815,983.8 815,983.8 1,815,983.8 1,000,000.0 22,903.2 838,887.0 14,209,172.8 0.0 13,716,922.8 1,000,000.0 OCT 31.0 354.8 0.0 508,658.3 816,132.0 1,324,790.3 21,735.0 197,109.3 14,904.0 233,748.3 508,658.3 508,658.3 1,091,042.0 1,091,042.0 2,091,042.0 1,000,000.0 31,935.6 1,122,977.6 14,839,900.4 0.0 14,331,242.1 1,000,000.0 NOV 30.0 337.9 0.0 492,250.0 777,239.0 1,269,489.0 0.0 190,750.9 14,904.0 205,654.9 492,250.0 492,250.0 1,063,834.1 1,063,834.1 2,063,834.1 1,000,000.0 30,413.7 1,094,247.8 15,425,489.9 0.0 14,933,239.9 1,000,000.0 DEC 31.0 313.0 0.0 508,658.3 719,900.0 1,228,558.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 1,016,545.0 1,016,545.0 2,016,545.0 1,000,000.0 28,170.0 1,044,715.0 15,977,954.9 0.0 15,469,296.6 1,000,000.0 Year 5 JAN 31.0 373.1 0.0 508,658.3 858,107.0 1,366,765.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 1,154,752.0 1,154,752.0 2,154,752.0 1,000,000.0 33,578.1 1,188,330.1 16,657,626.7 0.0 16,148,968.4 1,000,000.0 FEB 28.0 197.7 0.0 459,433.3 454,618.0 914,051.3 0.0 178,034.2 14,904.0 192,938.2 459,433.3 459,433.3 721,113.1 721,113.1 1,721,113.1 1,000,000.0 17,789.4 738,902.5 16,887,870.9 0.0 16,428,437.6 1,000,000.0 MAR 31.0 167.2 0.0 508,658.3 384,468.0 893,126.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 681,113.0 681,113.0 1,681,113.0 1,000,000.0 15,044.4 696,157.4 17,124,595.0 0.0 16,615,936.7 1,000,000.0 APR 30.0 117.8 60.6 492,250.0 410,297.0 902,547.0 48,622.0 190,750.9 14,904.0 254,276.9 492,250.0 492,250.0 648,270.1 648,270.1 1,648,270.1 1,000,000.0 16,055.1 664,325.2 17,280,261.9 0.0 16,788,011.9 1,000,000.0 MAY 31.0 98.5 118.9 508,658.3 499,905.0 1,008,563.3 99,385.3 197,109.3 14,904.0 311,398.6 508,658.3 508,658.3 697,164.7 697,164.7 1,697,164.7 1,000,000.0 19,561.5 716,726.2 17,504,738.1 0.0 16,996,079.8 1,000,000.0 JUN 30.0 98.3 185.4 492,250.0 652,326.0 1,144,576.0 128,977.1 190,750.9 14,904.0 334,632.0 492,250.0 492,250.0 809,944.0 809,944.0 1,809,944.0 1,000,000.0 25,525.8 835,469.8 17,831,549.6 0.0 17,339,299.6 1,000,000.0 JUL 31.0 102.8 37.6 508,658.3 322,943.0 831,601.3 127,560.3 197,109.3 14,904.0 339,573.6 508,658.3 508,658.3 492,027.7 492,027.7 1,492,027.7 1,000,000.0 12,636.9 504,664.6 17,843,964.2 0.0 17,335,305.9 1,000,000.0 AUG 31.0 163.6 1.4 508,658.3 379,385.0 888,043.3 109,995.2 197,109.3 14,904.0 322,008.5 508,658.3 508,658.3 566,034.8 566,034.8 1,566,034.8 1,000,000.0 14,845.5 580,880.3 17,916,186.2 0.0 17,407,527.9 1,000,000.0 SEP 30.0 246.2 8.3 492,250.0 585,304.0 1,077,554.0 55,915.3 190,750.9 14,904.0 261,570.2 492,250.0 492,250.0 815,983.8 815,983.8 1,815,983.8 1,000,000.0 22,903.2 838,887.0 18,246,414.9 0.0 17,754,164.9 1,000,000.0 OCT 31.0 354.8 0.0 508,658.3 816,132.0 1,324,790.3 21,735.0 197,109.3 14,904.0 233,748.3 508,658.3 508,658.3 1,091,042.0 1,091,042.0 2,091,042.0 1,000,000.0 31,935.6 1,122,977.6 18,877,142.5 0.0 18,368,484.2 1,000,000.0 NOV 30.0 337.9 0.0 492,250.0 777,239.0 1,269,489.0 0.0 190,750.9 14,904.0 205,654.9 492,250.0 492,250.0 1,063,834.1 1,063,834.1 2,063,834.1 1,000,000.0 30,413.7 1,094,247.8 19,462,732.0 0.0 18,970,482.0 1,000,000.0 DEC 31.0 313.0 0.0 508,658.3 719,900.0 1,228,558.3 0.0 197,109.3 14,904.0 212,013.3 508,658.3 508,658.3 1,016,545.0 1,016,545.0 2,016,545.0 1,000,000.0 28,170.0 1,044,715.0 20,015,197.0 0.0 19,506,538.7 1,000,000.0     89  Figure 4.7 Schematics of water balance for different solids contents in the wet condition for an unlined impoundment 35% solids 35% solids1038.84 m3/h1038.84 m3/h45% solids 60% solids683.68 m3/h 372.92 m3/h438.04 m3/h 543.3 m3/h35% solids 35% solids1038.84 m3/h1038.84 m3/h70% solids 80% solids239.73 m3/h 139.84 m3/h607.63 m3/h 620.83 m3/hSeepage loss20.42 m3/hSeepage loss20.42 m3/h0 m3Evaporation Entrained in solids67.6 m3/h 264.93 m3/hSolids Feed Solids Feed22.38 m3/hPrecipitation30.65 m3/h816.71 m3/h57.94 m3/h 127.83 m3/h 57.94 m3/h 109.55 m3/h1038.84 m3/hEntrained in solids30.65 m3/hSeepage loss20.42 m3/h60% Solids70% Solids 80% SolidsSolids FeedRemaining in TSF45% SolidsPrecipitation783.18 m3/h1091.08 m3/hRemaining in TSFRemaining in reservoir22.83 m3/hSolids Feed22.38 m3/h1038.84 m3/hOnly for the first month 320328.2 m3355.16 m3/h 683.68 m3/hSeepage loss665.92 m3/h 372.92 m3/h783.18 m3/h22.38 m3/h22.38 m3/h1038.84 m3/hOnly for the first month 242015.7 m3Precipitation30.65 m3/h885.57 m3/h Remaining in reservoir20.42 m3/hEvaporationRemaining in TSFRemaining in reservoir22.83 m3/h0 m3/h730.22 m3/h67.6 m3/h 159.67 m3/h0 m31038.84 m3/h139.84 m3/hEvaporation Entrained in solids Evaporation Entrained in solids0 m3/hRemaining in TSFRemaining in reservoirPrecipitation783.18 m3/h 778.28 m3/h799.11 m3/h 239.73 m3/h 899 m3/h30.45 m3/hProcessing DewateringTMF ReservoirTank Fresh waterMineProcessing ConditionerTSF ReservoirTank Fresh waterMineProcessing PlantConditionerTSF ReservoirTank Fresh waterMineProcessing PlantConditionerTSF ReservoirTank Fresh waterMineDewa eringTMFDewa eringTMFDewa eringTMF 90 The cumulative water surplus and cumulative water surplus per tonne of mill throughput for solids contents of 45, 60, 70 and 80% are compared in Figures 4.8 and 4.9. The water surplus per tonne of mill throughput using the average values of parameters in an unlined impoundment is 4% less than the corresponding values in a lined impoundment due to the seepage losses.  Figure 4.8 Cumulative water surplus for different solids contents in the wet condition for an unlined impoundment 0.05,000,000.010,000,000.015,000,000.020,000,000.025,000,000.030,000,000.0JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3)TimeCumulative water surplus45% solids60% solids70% solids80% solids 91  Figure 4.9 Cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for an unlined impoundment 4.2.2 Dry climate The monthly seepage loss of 4,968 m3/month has been considered for the unlined impoundment in dry conditions. The model results for solids content of 45% for dry climate is shown in  Table 4.4. The water balance models for solids contents of 60, 70, and 80 percent are provided in Appendix C, Tables C.4 to C.6. Figure 4.10 illustrates the schematic flowchart of the water circulations for four solids contents. The volume of water flowing between each unit is calculated based on the average of water flow over 60 months. The make up water required does not differ from the the make up water for the 0.000.100.200.300.400.500.600.700.800.901.001.101.201.301.401.501.601.701.801.902.002.10JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3/tonne)TimeCumulative water surplus45% solids60% solids70% solids80% solids 92 lined impoundment because there is almost no water left in the TMF to be reclaimed in both the lined and unlined cases.   93 Table 4.4 Deterministic water balance for solids content of 45% in the dry condition for an unlined impoundment Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation from pond Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   Year 1 JAN 31.0 17.6 0.0 508,658.3 40,480.0 549,138.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 347,061.1 508,658.3 -161,597.3 -0.4 FEB 28.0 13.8 0.0 459,433.3 31,740.0 491,173.3 0.0 178,034.2 4,968.0 183,002.2 459,433.3 459,433.3 308,171.2 459,433.3 -312,859.4 -0.4 MAR 31.0 17.4 0.0 508,658.3 40,020.0 548,678.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 346,601.1 508,658.3 -474,916.7 -0.4 APR 30.0 16.8 0.0 492,250.0 38,640.0 530,890.0 157,095.8 190,750.9 4,968.0 352,814.6 492,250.0 492,250.0 178,075.4 492,250.0 -789,091.3 -0.5 MAY 31.0 15.9 0.0 508,658.3 36,570.0 545,228.3 320,953.5 197,109.3 4,968.0 523,030.8 508,658.3 508,658.3 22,197.6 508,658.3 -1,275,552.1 -0.6 JUN 30.0 7.8 0.0 492,250.0 17,940.0 510,190.0 416,587.5 190,750.9 4,968.0 612,306.4 492,250.0 492,250.0 0.0 492,250.0 -1,767,802.1 -0.7 JUL 31.0 8.9 0.0 508,658.3 20,470.0 529,128.3 411,999.0 197,109.3 4,968.0 614,076.3 508,658.3 508,658.3 0.0 508,658.3 -2,276,460.4 -0.8 AUG 31.0 8.0 0.0 508,658.3 18,400.0 527,058.3 355,246.5 197,109.3 4,968.0 557,323.8 508,658.3 508,658.3 0.0 508,658.3 -2,785,118.8 -0.9 SEP 30.0 12.5 0.0 492,250.0 28,750.0 521,000.0 180,642.0 190,750.9 4,968.0 376,360.9 492,250.0 492,250.0 144,639.1 492,250.0 -3,132,729.7 -0.9 OCT 31.0 18.3 0.0 508,658.3 42,090.0 550,748.3 70,155.8 197,109.3 4,968.0 272,233.0 508,658.3 508,658.3 278,515.3 508,658.3 -3,362,872.7 -0.8 NOV 30.0 30.4 0.0 492,250.0 69,920.0 562,170.0 0.0 190,750.9 4,968.0 195,718.9 492,250.0 492,250.0 366,451.1 492,250.0 -3,488,671.6 -0.8 DEC 31.0 30.8 0.0 508,658.3 70,840.0 579,498.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 377,421.1 508,658.3 -3,619,908.9 -0.7 Year 2 JAN 31.0 17.6 0.0 508,658.3 40,480.0 549,138.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 347,061.1 508,658.3 -3,781,506.1 -0.7 FEB 28.0 13.8 0.0 459,433.3 31,740.0 491,173.3 0.0 178,034.2 4,968.0 183,002.2 459,433.3 459,433.3 308,171.2 459,433.3 -3,932,768.3 -0.7 MAR 31.0 17.4 0.0 508,658.3 40,020.0 548,678.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 346,601.1 508,658.3 -4,094,825.5 -0.7 APR 30.0 16.8 0.0 492,250.0 38,640.0 530,890.0 157,095.8 190,750.9 4,968.0 352,814.6 492,250.0 492,250.0 178,075.4 492,250.0 -4,409,000.2 -0.7 MAY 31.0 15.9 0.0 508,658.3 36,570.0 545,228.3 320,953.5 197,109.3 4,968.0 523,030.8 508,658.3 508,658.3 22,197.6 508,658.3 -4,895,461.0 -0.7 JUN 30.0 7.8 0.0 492,250.0 17,940.0 510,190.0 416,587.5 190,750.9 4,968.0 612,306.4 492,250.0 492,250.0 0.0 492,250.0 -5,387,711.0 -0.7 JUL 31.0 8.9 0.0 508,658.3 20,470.0 529,128.3 411,999.0 197,109.3 4,968.0 614,076.3 508,658.3 508,658.3 0.0 508,658.3 -5,896,369.3 -0.8 AUG 31.0 8.0 0.0 508,658.3 18,400.0 527,058.3 355,246.5 197,109.3 4,968.0 557,323.8 508,658.3 508,658.3 0.0 508,658.3 -6,405,027.6 -0.8 SEP 30.0 12.5 0.0 492,250.0 28,750.0 521,000.0 180,642.0 190,750.9 4,968.0 376,360.9 492,250.0 492,250.0 144,639.1 492,250.0 -6,752,638.5 -0.8 OCT 31.0 18.3 0.0 508,658.3 42,090.0 550,748.3 70,155.8 197,109.3 4,968.0 272,233.0 508,658.3 508,658.3 278,515.3 508,658.3 -6,982,781.5 -0.8 NOV 30.0 30.4 0.0 492,250.0 69,920.0 562,170.0 0.0 190,750.9 4,968.0 195,718.9 492,250.0 492,250.0 366,451.1 492,250.0 -7,108,580.4 -0.8 DEC 31.0 30.8 0.0 508,658.3 70,840.0 579,498.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 377,421.1 508,658.3 -7,239,817.7 -0.7 Year 3 JAN 31.0 17.6 0.0 508,658.3 40,480.0 549,138.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 347,061.1 508,658.3 -7,401,415.0 -0.7 FEB 28.0 13.8 0.0 459,433.3 31,740.0 491,173.3 0.0 178,034.2 4,968.0 183,002.2 459,433.3 459,433.3 308,171.2 459,433.3 -7,552,677.1 -0.7 MAR 31.0 17.4 0.0 508,658.3 40,020.0 548,678.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 346,601.1 508,658.3 -7,714,734.4 -0.7 APR 30.0 16.8 0.0 492,250.0 38,640.0 530,890.0 157,095.8 190,750.9 4,968.0 352,814.6 492,250.0 492,250.0 178,075.4 492,250.0 -8,028,909.0 -0.7 MAY 31.0 15.9 0.0 508,658.3 36,570.0 545,228.3 320,953.5 197,109.3 4,968.0 523,030.8 508,658.3 508,658.3 22,197.6 508,658.3 -8,515,369.8 -0.7 JUN 30.0 7.8 0.0 492,250.0 17,940.0 510,190.0 416,587.5 190,750.9 4,968.0 612,306.4 492,250.0 492,250.0 0.0 492,250.0 -9,007,619.8 -0.7 JUL 31.0 8.9 0.0 508,658.3 20,470.0 529,128.3 411,999.0 197,109.3 4,968.0 614,076.3 508,658.3 508,658.3 0.0 508,658.3 -9,516,278.1 -0.8 AUG 31.0 8.0 0.0 508,658.3 18,400.0 527,058.3 355,246.5 197,109.3 4,968.0 557,323.8 508,658.3 508,658.3 0.0 508,658.3 -10,024,936.5 -0.8 SEP 30.0 12.5 0.0 492,250.0 28,750.0 521,000.0 180,642.0 190,750.9 4,968.0 376,360.9 492,250.0 492,250.0 144,639.1 492,250.0 -10,372,547.4 -0.8 OCT 31.0 18.3 0.0 508,658.3 42,090.0 550,748.3 70,155.8 197,109.3 4,968.0 272,233.0 508,658.3 508,658.3 278,515.3 508,658.3 -10,602,690.4 -0.8  94 Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation from pond Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   NOV 30.0 30.4 0.0 492,250.0 69,920.0 562,170.0 0.0 190,750.9 4,968.0 195,718.9 492,250.0 492,250.0 366,451.1 492,250.0 -10,728,489.3 -0.8 DEC 31.0 30.8 0.0 508,658.3 70,840.0 579,498.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 377,421.1 508,658.3 -10,859,726.6 -0.7 Year 4 JAN 31.0 17.6 0.0 508,658.3 40,480.0 549,138.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 347,061.1 508,658.3 -11,021,323.8 -0.7 FEB 28.0 13.8 0.0 459,433.3 31,740.0 491,173.3 0.0 178,034.2 4,968.0 183,002.2 459,433.3 459,433.3 308,171.2 459,433.3 -11,172,586.0 -0.7 MAR 31.0 17.4 0.0 508,658.3 40,020.0 548,678.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 346,601.1 508,658.3 -11,334,643.2 -0.7 APR 30.0 16.8 0.0 492,250.0 38,640.0 530,890.0 157,095.8 190,750.9 4,968.0 352,814.6 492,250.0 492,250.0 178,075.4 492,250.0 -11,648,817.9 -0.7 MAY 31.0 15.9 0.0 508,658.3 36,570.0 545,228.3 320,953.5 197,109.3 4,968.0 523,030.8 508,658.3 508,658.3 22,197.6 508,658.3 -12,135,278.7 -0.7 JUN 30.0 7.8 0.0 492,250.0 17,940.0 510,190.0 416,587.5 190,750.9 4,968.0 612,306.4 492,250.0 492,250.0 0.0 492,250.0 -12,627,528.7 -0.7 JUL 31.0 8.9 0.0 508,658.3 20,470.0 529,128.3 411,999.0 197,109.3 4,968.0 614,076.3 508,658.3 508,658.3 0.0 508,658.3 -13,136,187.0 -0.7 AUG 31.0 8.0 0.0 508,658.3 18,400.0 527,058.3 355,246.5 197,109.3 4,968.0 557,323.8 508,658.3 508,658.3 0.0 508,658.3 -13,644,845.3 -0.8 SEP 30.0 12.5 0.0 492,250.0 28,750.0 521,000.0 180,642.0 190,750.9 4,968.0 376,360.9 492,250.0 492,250.0 144,639.1 492,250.0 -13,992,456.2 -0.8 OCT 31.0 18.3 0.0 508,658.3 42,090.0 550,748.3 70,155.8 197,109.3 4,968.0 272,233.0 508,658.3 508,658.3 278,515.3 508,658.3 -14,222,599.2 -0.8 NOV 30.0 30.4 0.0 492,250.0 69,920.0 562,170.0 0.0 190,750.9 4,968.0 195,718.9 492,250.0 492,250.0 366,451.1 492,250.0 -14,348,398.1 -0.7 DEC 31.0 30.8 0.0 508,658.3 70,840.0 579,498.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 377,421.1 508,658.3 -14,479,635.4 -0.7 Year 5 JAN 31.0 17.6 0.0 508,658.3 40,480.0 549,138.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 347,061.1 508,658.3 -14,641,232.7 -0.7 FEB 28.0 13.8 0.0 459,433.3 31,740.0 491,173.3 0.0 178,034.2 4,968.0 183,002.2 459,433.3 459,433.3 308,171.2 459,433.3 -14,792,494.8 -0.7 MAR 31.0 17.4 0.0 508,658.3 40,020.0 548,678.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 346,601.1 508,658.3 -14,954,552.1 -0.7 APR 30.0 16.8 0.0 492,250.0 38,640.0 530,890.0 157,095.8 190,750.9 4,968.0 352,814.6 492,250.0 492,250.0 178,075.4 492,250.0 -15,268,726.7 -0.7 MAY 31.0 15.9 0.0 508,658.3 36,570.0 545,228.3 320,953.5 197,109.3 4,968.0 523,030.8 508,658.3 508,658.3 22,197.6 508,658.3 -15,755,187.5 -0.7 JUN 30.0 7.8 0.0 492,250.0 17,940.0 510,190.0 416,587.5 190,750.9 4,968.0 612,306.4 492,250.0 492,250.0 0.0 492,250.0 -16,247,437.5 -0.7 JUL 31.0 8.9 0.0 508,658.3 20,470.0 529,128.3 411,999.0 197,109.3 4,968.0 614,076.3 508,658.3 508,658.3 0.0 508,658.3 -16,756,095.8 -0.7 AUG 31.0 8.0 0.0 508,658.3 18,400.0 527,058.3 355,246.5 197,109.3 4,968.0 557,323.8 508,658.3 508,658.3 0.0 508,658.3 -17,264,754.2 -0.8 SEP 30.0 12.5 0.0 492,250.0 28,750.0 521,000.0 180,642.0 190,750.9 4,968.0 376,360.9 492,250.0 492,250.0 144,639.1 492,250.0 -17,612,365.1 -0.8 OCT 31.0 18.3 0.0 508,658.3 42,090.0 550,748.3 70,155.8 197,109.3 4,968.0 272,233.0 508,658.3 508,658.3 278,515.3 508,658.3 -17,842,508.1 -0.8 NOV 30.0 30.4 0.0 492,250.0 69,920.0 562,170.0 0.0 190,750.9 4,968.0 195,718.9 492,250.0 492,250.0 366,451.1 492,250.0 -17,968,307.0 -0.7 DEC 31.0 30.8 0.0 508,658.3 70,840.0 579,498.3 0.0 197,109.3 4,968.0 202,077.3 508,658.3 508,658.3 377,421.1 508,658.3 -18,099,544.3 -0.7     95  Figure 4.10 Schematics of water balance for different solids contents in the dry condition for an unlined impoundment 35% solids 35% solids1038.84 m3/h 1038.84 m3/h45% solids 60% solids683.68 m3/h 372.92 m3/h35% solids 35% solids1038.84 m3/h 1038.84 m3/h70% solids 70% solids239.73 m3/h 239.73 m3/hSeepage loss6.81 m3/hSeepage loss6.81 m3/hSeepage loss6.81 m3/hSeepage loss6.81 m3/h0 m3/h0 m3/h80% SolidsSolids Feed22.38 m3/h1038.84 m3/h 239.73 m3/h799.11 m3/h 0 m3/hPrecipitation52.04 m3/hRemaining in TSFEvaporation Entrained in solids158.04 m3/h 127.83 m3/hEvaporation Entrained in solids158.04 m3/h 127.83 m3/hPrecipitation52.04 m3/hRemaining in TSF70% SolidsSolids Feed22.38 m3/h1038.84 m3/h 239.73 m3/hEvaporation Entrained in solids195.47 m3/h 159.67 m3/hEvaporation Entrained in solids218.34 m3/h 264.93 m3/h0 m3/h0 m3/h799.11 m3/h665.92 m3/h 0 m3/hPrecipitation52.04 m3/hRemaining in TSF60% SolidsSolids Feed22.38 m3/h1038.84 m3/h 372.92 m3/h45% SolidsPrecipitation52.04 m3/hRemaining in TSF0 m3/hSolids Feed22.38 m3/h1038.84 m3/h 683.68 m3/h355.16 m3/h 0 m3/hProcessing PlantDewateringTMFTank Fresh water Processing PlantDewateringTSFTank Fresh waterProcessing PlantDewateringTSFTank Fresh water Processing PlantDewateringTSFTank Fresh waterTMFTMF TMF 96  The cumulative water deficit and cumulative water deficit per tonne of mill throughput for solids contents of 45, 60, 70 and 80% are compared in Figures 4.11 and 4.12.   Figure 4.11 Cumulative water deficit for different solids contents in the dry condition for an unlined impoundment ‐20,000,000.0‐18,000,000.0‐16,000,000.0‐14,000,000.0‐12,000,000.0‐10,000,000.0‐8,000,000.0‐6,000,000.0‐4,000,000.0‐2,000,000.00.0JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEPYear 1 Year 2 Year 3 Year 4 Year 5Water deficit(m3)TimeCumulative water deficit45% solids60% solids70% solids80% solids 97   Figure 4.12 Cumulative water deficit per tonne of mill throughput for different solids contents in the dry condition for an unlined impoundment ‐0.90‐0.85‐0.80‐0.75‐0.70‐0.65‐0.60‐0.55‐0.50‐0.45‐0.40‐0.35‐0.30‐0.25‐0.20‐0.15‐0.10‐0.050.00JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEPYear 1 Year 2 Year 3 Year 4 Year 5Water deficit(m3/tonne)TimeCumulative water deficit45% solids60% solids70% solids80% solids 98  Chapter 5: Probabilistic water balance results This chapter summarizes the results of probabilistic water balance for lined and unlined impoundments. The Monte Carlo algorithm in Oracle Crystal Ball ran the models for 10000 trials for each month. The graphs that are presented here are based on data extracted from the histograms of monthly cumulative surplus and deficit. 5.1 Lined impoundment 5.1.1 Wet climate Figures 5.1 to 5.3 respectively show the mean cumulative water surplus as well as the 5th and 95th percentiles of cumulative water surplus per tonne of mill throughput in the wet condition for a lined impoundment.   99   Figure 5.1 Mean cumulative water surplus for different solids contents in the wet climate condition for a lined impoundment 0.05,000,000.010,000,000.015,000,000.020,000,000.025,000,000.030,000,000.0JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3)TimeMean cumulative water surplus45% solids60% solids70% solids80% solids 100   Figure 5.2 The 5th percentile of cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for a lined impoundment Figure 5.2 shows that for 5% of the results, cumulative water surpluses for dry stack tailings in any given August are less than corresponding values for filtered tailings. This is from the random sample selections from the ranges of dry densities. As noted in Chapter 3, the dry density for paste tailings ranges from 1.7 to 1.9 tonne/m3. For filtered tailings, dry density varies between 1.8 and 2.0 tonne/m3. It means the samples for the solids content of 70% has been selected randomly from the range of [>1.8 tonne/m3] for any given August. The graphs of 95th percentile show that the water surplus for 95% of the results, ranges from 1.2 to 1.5 m3/tonne. 0.00.10.20.30.40.50.60.70.80.91.0JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3/tonne)Time5th percentile cumulative water surplus45% solids60% solids70% solids80% solids 101   Figure 5.3 The 95th percentile of cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for a lined impoundment 5.1.2 Dry climate Figures 5.4 to 5.6 respectively show the mean cumulative water surplus as well as the 5th and 95th percentiles of cumulative water surplus per tonne of mill throughput in the dry condition for a lined impoundment. Figure 5.5 shows that the water deficit for 5% of the results, ranges from 0.16 to 0.76m3/tonne (because the values are negative, the 5th percentile represents the 95th percentiles of water deficits absolute values).  0.00.20.40.60.81.01.21.41.61.82.02.22.42.62.83.03.23.43.63.84.0JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3/tonne)Time95th percentile cumulative water surplus45% solids60% solids70% solids80% solids 102    Figure 5.4 Mean cumulative water deficit for different solids contents in the dry climate condition for a lined impoundment ‐18,000,000.0‐16,000,000.0‐14,000,000.0‐12,000,000.0‐10,000,000.0‐8,000,000.0‐6,000,000.0‐4,000,000.0‐2,000,000.00.0JANAPRJULOCTJANAPRJULOCTJANAPRJULOCTJANAPRJULOCTJANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3)TimeCumulative water deficit45% solids60% solids70% solids80% solids 103   Figure 5.5 The 5th percentile of cumulative water deficit per tonne of mill throughput for different solids contents in the dry condition for a lined impoundment ‐0.95‐0.90‐0.85‐0.80‐0.75‐0.70‐0.65‐0.60‐0.55‐0.50‐0.45‐0.40‐0.35‐0.30‐0.25‐0.20‐0.15‐0.10‐0.050.00JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEPYear 1 Year 2 Year 3 Year 4 Year 5Water deficit(m3/tonne)Time5th Percentile cumulative water deficit45% solids60% solids70% solids80% solids 104   Figure 5.6 The 95th percentile of cumulative water surplus/deficit per tonne of mill throughput for different solids contents in the dry condition for a lined impoundment 5.2 Unlined impoundment 5.2.1 Wet climate Figures 5.7 to 5.9 respectively show the mean cumulative water surplus, the 5th, and 95th  percentiles of cumulative water surplus per tonne of mill throughput in the wet condition for an unlined impoundment.  ‐0.7‐0.6‐0.5‐0.4‐0.3‐0.2‐0.10.00.10.20.3JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEPYear 1 Year 2 Year 3 Year 4 Year 5Water deficit(m3/tonne)Time95th Percentile cumulative water deficit45% solids60% solids70% solids80% solids 105   Figure 5.7 Mean cumulative water surplus for different solids contents in the wet climate condition for an unlined impoundment 0.05,000,000.010,000,000.015,000,000.020,000,000.025,000,000.030,000,000.035,000,000.0JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3)TimeMean cumulative water surplus45% solids60% solids70% solids80% solids 106   Figure 5.8 The 5th percentile of cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for an unlined impoundment Similar to the results for the lined impoundment, Figure 5.2 shows that for 5% of the results in an unlined impoundment, cumulative water surpluses for 80% solids content tailings in any given August are less than corresponding values for 70% solids content tailings.  0.000.050.100.150.200.250.300.350.400.450.500.550.600.650.700.750.800.850.900.95JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3/tonne)Time5th percentile cumulative water surplus45% solids60% solids70% solids80% solids 107   Figure 5.9 The 95th percentile of cumulative water surplus per tonne of mill throughput for different solids contents in the wet condition for an unlined impoundment Figures 5.3 and 5.9 show that for both lined and unlined impoundments for 95 % of the results, the cumulative water surplus for paste tailings and filtered tailings are very close and based on this water balance model and the assumption made, there is no reason to use the filtered tailings method over the paste tailings method in the wet condition. 5.2.2 Dry climate Mean cumulative water deficit, the 5th, and 95th  percentiles of cumulative water deficit per tonne of mill throughput in the dry condition are plotted in Figures 5.10 to 5.12 respectively.  0.00.20.40.60.81.01.21.41.61.82.02.22.42.62.83.03.23.43.63.84.0JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCT JANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3/tonne)Time95th percentile cumulative water surplus45% solids60% solids70% solids80% solids 108   Figure 5.10 Mean cumulative water deficit for different solids contents in the dry climate condition for an unlined impoundment ‐18,000,000.0‐16,000,000.0‐14,000,000.0‐12,000,000.0‐10,000,000.0‐8,000,000.0‐6,000,000.0‐4,000,000.0‐2,000,000.00.0JANAPRJULOCTJANAPRJULOCTJANAPRJULOCTJANAPRJULOCTJANAPRJULOCTYear 1 Year 2 Year 3 Year 4 Year 5Water surplus(m3)TimeCumulative water deficit45% solids60% solids70% solids80% solids 109   Figure 5.11 The 5th percentile of cumulative water deficit per tonne of mill throughput for different solids contents in the dry condition for an unlined impoundment ‐0.95‐0.90‐0.85‐0.80‐0.75‐0.70‐0.65‐0.60‐0.55‐0.50‐0.45‐0.40‐0.35‐0.30‐0.25‐0.20‐0.15‐0.10‐0.050.00JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEPYear 1 Year 2 Year 3 Year 4 Year 5Water deficit(m3/tonne)Time5th Percentile cumulative water deficit45% solids60% solids70% solids80% solids 110   Figure 5.12 The 95th percentile of cumulative water deficit per tonne of mill throughput for different solids contents in the dry condition for an unlined impoundment  ‐0.75‐0.70‐0.65‐0.60‐0.55‐0.50‐0.45‐0.40‐0.35‐0.30‐0.25‐0.20‐0.15‐0.10‐0.050.000.050.100.150.200.25JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEP JAN MAY SEPYear 1 Year 2 Year 3 Year 4 Year 5Water deficit(m3/tonne)Time95th Percentile cumulative water deficit45% solids60% solids70% solids80% solids 111  Chapter 6: Discussion Results from a deterministic water balance model were presented in Chapter 4. Uncertainities and variabilities of water balance parameters were added to the model using Oracle Crystal Ball to obtain the results presented in Chapter 5. Figures 6.1 and 6.2 show the water quantities of each water balance parameter in arid and wet climate conditions respectively for solids content of 45%. Figure 6.3 shows similar values for a hydraulic fill TMF water balance developed by Blight (2010).   Figure 6.1 Cumulative water balance parameters in the arid condition for a lined impoundment 0.05.010.015.020.025.030.035.0JANMARMAY JULSEPNOVJANMARMAY JULSEPNOVJANMARMAY JULSEPNOVJANMARMAY JULSEPNOVJANMARMAY JULSEPNOVYear 1 Year 2 Year 3 Year 4 Year 5Water  (m3 )MillionsTimeCumulative water balancePreciptation Water with slurryEvaporation Interstitial waterMill required water Difference between water in and water out 112   Figure 6.2 Cumulative water balance parameters in wet condition for a lined impoundment 0.010.020.030.040.050.060.0JANMARMAY JULSEPNOVJANMARMAY JULSEPNOVJANMARMAY JULSEPNOVJANMARMAY JULSEPNOVJANMARMAY JULSEPNOVYear 1 Year 2 Year 3 Year 4 Year 5Water (m3)MillionsTimeCumulative water balancePreciptation Water with slurryEvaporation Interstitial waterMill required water Difference between water in and water outMine impacted water runoff 113   Figure 6.3 Water balance parameters for hydraulic fill TMF (Blight, 2010) As seen in Figure 6.2, in the arid climate, the difference between “water inflow” and “water outflow” is less than the water that was reported to TMF with tailings. This means that fresh make up water is required to be added to the processing plant. However, in the wet condition, the difference between “water inflow” and “water outflow” is greater than the water required to be returned to the processing plant and there is a water surplus that can be stored in the TMF or a separate reservoir.   114  In this research, it is assumed that for the lined impoundment, the water loss is only due to the water trapped in the tailings pores after initial settlement and evaporation. Seepage losses are considered to be negligible. Table 6.1 shows the water losses in different scenarios.  Table 6.1 Water loss proportion in different scenarios     Water loss (m3/tonne)    Water with tailings Evaporation from pond Entrainment Seepage loss Total   Solids content (%) m3 per tonne throughput m3 per tonne throughput Ratio to water with tailings m3 per tonne throughput Ratio to water with tailings m3 per tonne throughput Ratio to water with tailings m3 per tonne throughput Ratio to water with tailings Wet Climate Lined Imppoundment 45 1.22 0.12 0.10 0.47 0.39 0.00 0.00 0.59 0.48 60 0.67 0.12 0.18 0.29 0.43 0.00 0.00 0.41 0.61 70 0.43 0.10 0.23 0.23 0.53 0.00 0.00 0.33 0.77 80 0.25 0.10 0.40 0.20 0.80 0.00 0.00 0.30 1.20 Unlined Imppoundment 45 1.22 0.12 0.10 0.47 0.39 0.04 0.03 0.63 0.52 60 0.67 0.12 0.18 0.29 0.43 0.04 0.06 0.45 0.67 70 0.43 0.10 0.23 0.23 0.53 0.04 0.09 0.37 0.86 80 0.25 0.10 0.40 0.20 0.80 0.04 0.16 0.34 1.36 Dry Climate Lined Imppoundment 45 1.22 0.39 0.32 0.47 0.39 0.00 0.00 0.86 0.70 60 0.67 0.35 0.52 0.29 0.43 0.00 0.00 0.64 0.96 70 0.43 0.28 0.65 0.23 0.53 0.00 0.00 0.51 1.19 80 0.25 0.16 0.64 0.21 0.84 0.00 0.00 0.37 1.48 unlined Imppoundment 45 1.22 0.39 0.32 0.47 0.39 0.01 0.01 0.87 0.71 60 0.67 0.35 0.52 0.29 0.43 0.01 0.01 0.65 0.97 70 0.43 0.28 0.65 0.23 0.53 0.01 0.02 0.52 1.21 80 0.25 0.16 0.64 0.21 0.84 0.01 0.04 0.38 1.52 The water deficit/ water suplus for dry and wet climate in the month with highest precipitation and for the month with highest evaporation is showed in Table 6.2.   115  Table 6.2 Water deficit/water surplus for the month with highest precipitation and the month with highest evaporation   Solids content (%) Water with tailings (m3/month) Highest precipitation Highest evaporation Entrainment Water surplus/deficit for the month with highest precipitation in year 4 Water surplus/deficit for the month with the highest evaporation in year 4    (m3/ month) Ratio to water with tailings (m3/ month) Ratio to water with tailings (m3/ month) Ratio to water with tailings (m3/ month) Ratio to water with tailings (m3/ month) Ratio to water with tailings (m3/ month) Ratio to water with tailings Wet Lined 45 508,658.3 1.00 858,107.0 1.69 128,977.1 0.25 190,750.9 0.38 694,575.8 1.37 358,123.8 0.70 80 100,687.5 1.00 858,107.0 8.52 110,551.8 1.10 81,505.1 0.81 810,180.0 8.05 488,424.1 4.85 Unlined 45 508,658.3 1.00 858,107.0 1.69 128,977.1 0.25 190,750.9 0.38 679,671.8 1.34 343,219.8 0.67 80 100,687.5 1.00 858,107.0 8.52 110,551.8 1.10 81,505.1 0.81 795,276.0 7.90 473,520.1 4.70 Dry Lined 45 508,658.3 1.00 70,840.0 0.14 416,587.5 0.82 190,750.9 0.38 - 126,269.3 - 0.25 - 492,250.0 - 0.97 80 100,687.5 1.00 70,840.0 0.70 174,570.0 1.73 81,505.1 0.81 - 17,271.0 - 0.17 - 100,687.5 - 1.00 Unlined 45 508,658.3 1.00 70,840.0 0.14 416,587.5 0.82 190,750.9 0.38 - 131,237.3 - 0.26 - 492,250.0 - 0.97 80 100,687.5 1.00 70,840.0 0.70 174,570.0 1.73 81,505.1 0.81 - 22,239.0 - 0.22 - 100,687.5 - 1.00    116  The comparison of losses from Table 6.1 and the total evaporation loss from TMF and entrainment loss (including rewetting) from Gunson’s study (2012) is showed in Table 6.3. The difference in results can be due to the difference in: pan evaporation, dry densities, and other assumptions (pond and beaches areas, and related pan evaporation factors). Table 6.3 Comparison of Gunson’s study (2012) with this study: Evaporation and entrainment losses in different scenarios  Gunson’s study (based on Table 2.5) This research (based on Table 6.1)  Evaporation (m3/tonne) Entrainment and rewetting (m3/tonne) Evaporation (m3/tonne throughput) Entrainment (m3/tonne throughput) 70% solids content 0.11 0.41 0.28 0.23 80% solids content 0.18 0.24 0.16 0.21 The most significant loss from the TMF in both the dry and the wet climate conditions is related to entrainment, which over the 5 year perating life of the lined TMF reached up to 84 % of the total water loss for wet climate and 55% of the total water loss for the dry climate when the solids content was 45%. The entrainment loss in the water balance model of Wels et al (2004) reached almost 75% water loss for Pampa Pabellon tailings impoundment. The entrained water loss to the total loss ratio depends on the tailings dry densities after the initial settlement, evaporation and seepage losses.  The initial and final dry densities of the tailings which present the void ratio of the tailings before deposition and after settlement respectively are important assumptions in the model, particularly at higher solids contents where the pores are not fully saturated. Typically, in dry seasons settled solids have higher dry density, because an increase in evaporation rate increases the density due to dessication.   117  Evaporation in arid climate is significant and can reach up to 65% of the total loss in a lined impoundment. Evaporation is a function of pan evaporation and the evaporation pan factor.  Table 6.4 shows the average water deficit/surplus over the last 3 years of the mine life (after the 2nd year, the water deficit/surplus graphs reach to steady states). The 95th percentile of lined impoundment water surplus in the wet condition reached 1.6 m3/tonne of mill throughput. The 5th percentile of the lined impoundment water deficit results in the dry condition ranged between 0.16 and 0.74 m3/tonne of mill throughput.  Table 6.4 Cumulative water deficit/surplus averaged over the last 3 years of mine life   Solids contents (%) Deterministic model Probabilistic model   Mean  (m3/tonne) Mean (m3/tonne) 5th percentile (m3/tonne) 95th percentile (m3/tonne) Wet Climate Lined Impoundment 45 0.86 0.93 0.56 1.31 60 1.05 1.13 0.77 1.49 70 1.12 1.20 0.84 1.56 80 1.16 1.23 0.86 1.60 Unlined Impoundment 45 0.83 0.90 0.53 1.27 60 1.01 1.09 0.73 1.46 70 1.09 1.16 0.80 1.53 80 1.12 1.19 0.83 1.56 Dry Climate Lined Impoundment 45 -0.70 -0.63 -0.74 -0.50 60 -0.40 -0.33 -0.40 -0.25 70 -0.27 -0.21 -0.27 -0.14 80 -0.18 -0.11 -0.16 -0.05 unlined Impoundment 45 -0.70 -0.64 -0.75 -0.51 60 -0.40 -0.34 -0.41 -0.26 70 -0.27 -0.22 -0.27 -0.15 80 -0.18 -0.12 -0.17 -0.05   118  The mean deterministic water deficit for the slurry tailings (45% solids content) in the lined impoundment is 0.70 m3/tonne. The reported average required make-up water in mines are between 0.4 and 0.7 m3/tonne (Obermeyer et al., 2013). Gunson (2013) in his study estimated the water consumption for paste tailings and filtered tailings to be respectively 0.59 and 0.31 m3/tonne. The difference of his results with the results of this research (water deficits of 0.27 m3/tonne for paste tailings and 0.17 m3/tonne for filtered tailings) is that Gunson (2013) used the Water Recovery (RGC) Model. This model only considers the water with tailings as the water inflow and do not consider the water inflows from precipitation and run offs.  For another comparison, results of the study on “platinum project on the eastern limb of the Bushveld Complex” (Moolman and Vietti, 2012), located in a semi arid climate, are presented here. The case study included three tailings concentrations of low, medium and high density respectiely of 50%, 60%, and 75% solids contents. The study was conducted to find the optimum scenario with lowest cost per ton and the lowest water consumption. Water consumptions of these three cases are listed in Table 6.5.  Table 6.5 Water consumption (m3/t solids) for a platinum project on the eastern limb of the Bushveld Complex (Moolman and Vietti, 2012)  Water consumption (m3/t solids) Winter Annual average Low-density tailings 0.57  0.39  Medium-density tailings 0.46  0.28  High-density tailings 0.29  0.11    119  It was assumed that the seepage loss in wet climate condition was 25% of the total seepage (calculated from Darcy’s equation). If the seepage loss varies to 50% and 75% of the total seepage, water surplus for different solids contents changes according to Table 6.6. Table 6.6 Effect of changing seepage losses on water surplus for different solids contents in wet condition  Solids content (%) Seepage loss= 25% of total seepage Seepage loss= 50% of total seepage Seepage loss= 75% of total seepage Water surplus (m3 / tonne mill throughput) 45 0.83 0.79 0.75 60 1.01 0.98 0.94 70 1.09 1.05 1.01 80 1.12 1.08 1.05  Figure 6.4 shows the certainty levels of the results of the lined TMF water surpluses in wet condition for the 45% solids content. The percentiles of the water surpluses were derived from the monthly cumulative water surpluse histograms. In this example, the required reservoir capacity according to the mean of cumulative water surpluses is  22 million m3. Whereas, the required capacity based on the 95th percentile is 30% greater (29 million m3). As it is seen depending on how risk averse one can be. They might choose the values with the higher degree of certanity (95th percentile).   120   Figure 6.4 Cumulative water surplus percentiles for 45% solids content in wet climate for a lined impoundment Figure 6.5 shows the water entrainment percentiles relative to the water with tailings. Beta distributioins were the best fits to the entrainment histograms. For slurry tailings case, 32% to 48% of the water with slurry is lost due to the entrainment. For filtered tailings case, the entrainment loss accounts for 63% to 97% of the water with tailings. 0.01.02.03.04.05.06.07.08.09.010.011.012.013.014.015.016.017.018.019.020.021.022.023.024.025.026.027.028.029.030.0JANMARMAY JULSEPNOVJANMARMAY JULSEPNOVJANMARMAY JULSEPNOVJANMARMAY JULSEPNOVJANMARMAY JULSEPNOVYear 1 Year 2 Year 3 Year 4 Year 5Water (m3)MillionsTimeCumulative water surplus for 45% solids contentMean deterministic Mean probabilistic 5th percentile 50th percentile60th percentile 75th percentile 95th percentileRisk averseness  121   Figure 6.5 The entrainment loss realative to the water with tailings at the end of year 4 Figure 6.6 compares the 5th percentile, mean and 95th percentile of water deficits and water surplus for lined and unlined impoundments in wet and dry conditions for different solids contents. 0.000.050.100.150.200.250.300.350.400.450.500.550.600.650.700.750.800.850.900.951.001.055th Percentile Mean 95th PercentileWater entrainment / water with tailingsWater entrainment at the end of year 445% Solids content 60% Solids content 70% Solids content 80% Solids content 122   Figure 6.6 Range of water deficits and water surplus for different conditions at the end of year 4  123  Chapter 7: Conclusion and recommendations The question to be answered by this research was: How do different tailings management options in differentt climates affect mine water requirements and surpluses? To answer to this question, the research objectives were set to: - Review and compare available methods for tailings water balance. - Extract and analyze climate data for wet and dry conditions. Statistical analyses of climate data are used to reflect the uncertainty of the input data. - Develop both a deterministic and probabilistic water balances. - Conduct analyses on different tailings management options. Water balance models were developed in this research. The study for two scenarios of lined and unlined impoundments included: - a deterministic water balance for two different climate conditions - analyses of the models for four different management options - a probabilistic water balance for these same climate conditions and management options For the dry climate condition, due to the high rate of evaporation, free water did not exist in the TMF at the end of some months to be reclaimed and returned to the processing plant. In contrast, in the wet climate condition, because of the high intensity of precipitation and relatively low evaporation rates, water was available to be returned to the plant.  The results of modelling indicated that a hypothetical unlined TMF located in an arid region could reduce water withdrawals from 0.70 m3/tonne to 0.18 m3/tonne, when the solids content was elevated from 45% to 80%. The same lined TMF in a wet condition could increase the excess water from 0.86 m3/tonne to 1.16 m3/tonne with the tailings dewatered from 45% solids  124  content to 80%. Dewatering techniques can be the best techniques for dry climates if the rate of mill production is within the range appropriate for these techniques. The difference between cumulative water surplus per tonne mill throughput in the wet climate condition for paste tailings and filtered tailings were minor. Overall, if the water balance models with the assumptions in this research are considered, it is not recommended to use paste and filtered tailings in a wet climate condition due to the large surpluses that must be stored and/or treated. In a dry condition, it is better to use the water recovery model developed by Wels & Robertson (2003) because precipitation is very low. The amount of water due to direct precipitation into the pond, and runoff from beaches and catchment areas surrounding the TMF (even with a high coefficient of runoff), would constitute a very small portion of total water input even during the wettest months. It is recommended the water balance be developed by adding the refinements to the developed model in this research. RGC Water Recovery Model and Consolidation/ Seepage Model can be used. Future Research For future research, the influence of different input assumptions distributions such as Beta distribution on the water balance could be studied. For specific case studies, a mass balance could be added to the model to calculate the required capacity of the impoundments for design purposes. A chemical balance could be done to make decisions about how to best recycle, treat or repurpose the excess water. Mine water management does not only include the TMF water balance, therfore the water circulations in other units of the mine and processing plant could be added to the model.  125  An analysis could be done to study the effect of the size of the areas of active and inactive beaches on evaporation, seepage, rewetting and entrainment losses. In a wet climate, an economical analysis could be done for a specific case study to evaluate the financial benefits of implementing a reservoir.  Consideration of extreme storm events in the models is crucial because these events are  important in calculating the embankment capacity and the water levels required to comply with the regulations. 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New York: SME.    130  Appendices Appendix A  Data input and methodology Table A.1 Average monthly rainfall obtained through ClimateBC from 1970 to 2013 (mm) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1970 240 116 116 79 119 63 111 121 282 328 171 175 1971 590 167 179 134 47 92 20 148 184 439 486 249 1972 371 315 196 133 70 91 80 114 242 464 400 233 1973 530 138 167 50 93 77 73 97 327 294 194 229 1974 439 239 170 96 95 116 89 68 226 734 297 376 1975 398 135 112 54 72 108 97 125 168 242 385 286 1976 557 174 168 82 130 116 94 126 243 333 268 432 1977 342 168 219 91 63 122 91 102 134 310 381 186 1978 138 108 126 77 51 40 35 200 221 426 596 377 1979 260 246 143 36 129 133 67 56 289 246 232 560 1980 290 163 207 93 84 46 155 166 265 348 375 569 1981 273 207 160 188 125 118 34 121 267 291 497 206 1982 903 149 183 77 95 18 74 133 147 377 280 158 1983 340 162 48 67 97 104 139 162 335 289 173 94 1984 557 215 144 71 73 98 96 194 175 364 239 287 1985 297 257 183 80 117 65 58 88 252 364 324 277 1986 417 95 183 79 103 60 118 93 178 614 464 194 1987 385 210 129 104 159 156 51 119 341 290 559 309 1988 368 184 195 83 117 91 150 133 219 287 412 264 1989 632 10 116 36 59 34 55 94 186 372 601 477 1990 378 237 142 87 93 121 65 134 139 430 332 514 1991 270 162 115 65 80 116 131 289 311 506 694 671 1992 341 130 137 314 111 53 63 95 434 285 322 406 1993 254 250 103 62 140 128 101 91 122 342 388 277 1994 718 197 188 115 140 86 122 120 400 337 337 230 1995 105 204 156 101 65 57 104 181 139 345 462 282 1996 257 379 199 138 46 124 83 218 215 353 221 251 1997 328 333 295 171 64 148 153 189 175 413 226 423 1998 258 190 133 221 70 101 88 235 185 377 194 349 1999 400 248 147 167 186 139 78 224 249 324 337 367 2000 316 125 221 243 82 81 179 271 340 279 286 272 2001 479 134 197 112 181 139 131 284 328 329 280 225 2002 255 280 135 143 113 139 137 179 299 206 279 278 2003 299 192 212 116 123 142 92 287 328 391 276 273  131  Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2004 362 144 176 150 63 81 88 139 259 333 330 317 2005 415 303 258 142 49 91 191 195 183 346 395 297 2006 319 153 108 198 101 90 121 161 279 272 266 423 2007 345 237 235 204 82 88 141 175 190 507 230 267 2008 247 311 194 124 129 89 161 312 178 336 360 262 2009 370 207 164 144 87 77 91 216 317 295 293 145 2010 324 187 261 123 73 149 83 161 317 445 261 259 2011 357 128 135 103 93 76 157 252 271 315 232 296 2012 378 174 183 78 154 172 177 132 276 199 202 294 2013 314 334 117 152 110 89 100 196 217 236 332 456   Figure A.1 Distribution of average monthly rainfall obtained through ClimateBC over the period of 1970-2013 (mm)   02004006008001000Rainfall (mm)Average of monthly rainfall in wet climateJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 132  Table A.2 Average monthly snowfall obtained through ClimateBC over the period of 1970-2013 (mm) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1970 223 69 94 59 41 3 5 6 22 37 386 112 1971 561 150 172 106 20 7 0 8 54 114 131 165 1972 366 307 186 121 28 11 2 4 26 285 322 242 1973 480 129 149 38 29 7 3 5 41 287 234 220 1974 429 209 162 58 31 12 4 2 52 182 185 188 1975 351 132 109 45 28 10 3 6 19 310 189 211 1976 430 166 163 64 66 13 5 5 21 89 298 227 1977 265 76 190 46 15 7 3 2 31 151 137 254 1978 125 87 114 37 14 1 1 7 17 119 308 181 1979 252 244 129 25 44 12 1 1 35 110 492 340 1980 275 121 191 51 13 1 6 6 31 56 137 409 1981 115 160 113 137 13 8 1 3 37 67 144 514 1982 886 144 169 67 38 0 2 5 30 85 254 164 1983 203 99 39 22 11 3 4 8 15 154 232 126 1984 361 123 90 37 22 7 4 9 59 120 103 91 1985 167 236 166 65 38 7 1 5 31 202 201 277 1986 244 91 132 62 37 3 3 4 36 226 319 215 1987 259 145 120 75 52 10 1 3 23 76 342 111 1988 316 154 159 45 34 7 8 5 41 90 246 162 1989 540 9 111 20 13 1 1 2 33 60 208 229 1990 273 230 134 46 14 3 1 2 10 105 354 167 1991 249 105 114 38 13 3 3 6 8 191 314 470 1992 127 112 125 193 36 1 1 2 21 280 526 219 1993 231 229 100 25 8 3 2 2 70 122 225 372 1994 467 196 168 53 29 2 2 1 7 38 282 100 1995 81 189 154 48 6 1 2 4 35 70 302 188 1996 254 341 196 87 15 4 2 6 11 94 397 179 1997 291 246 292 111 9 3 3 3 23 163 205 241 1998 223 119 124 111 5 1 1 6 11 145 157 103 1999 336 225 142 119 82 5 2 5 16 74 158 273 2000 280 112 204 184 30 2 3 8 24 63 259 133 2001 227 127 194 79 89 8 4 6 30 83 187 205 2002 170 262 135 115 37 3 4 5 28 116 232 163 2003 143 176 210 70 39 4 1 8 28 36 159 112 2004 324 104 166 65 8 0 1 2 27 60 245 143 2005 361 261 233 57 4 1 5 4 30 125 245 148 2006 195 146 107 135 26 1 2 4 16 96 271 120 2007 166 224 233 152 21 2 2 3 18 78 262 134 2008 203 294 187 101 21 5 5 7 16 141 190 237  133  Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2009 302 201 163 99 24 1 1 4 13 79 259 252 2010 208 115 225 65 11 4 1 2 20 99 230 132 2011 237 125 134 79 16 2 3 7 24 102 214 236 2012 300 137 179 38 57 6 3 3 21 76 211 124 2013 149 205 116 127 20 1 1 3 22 114 186 258   Figure A.2 Distribution of average monthly snowfall obtained through ClimateBC over the period of 1970-2013 (mm)   020040060080010001970197219741976197819801982198419861988199019921994199619982000200220042006200820102012Snowfall (mm)Average of monthly snowfall in the wet conditionJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 134  Table A.3 Average monthly pan evaporation obtained through ClimateBC over the period of 1970-2013 (mm) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1970 0 0 0 33 50 77 68 62 30 14 0 0 1971 0 0 0 0 52 69 81 50 35 0 0 0 1972 0 0 0 0 63 69 87 67 33 14 0 0 1973 0 0 0 34 59 71 77 65 35 12 0 0 1974 0 0 0 30 55 63 76 78 40 14 0 0 1975 0 0 0 0 59 65 70 59 34 12 0 0 1976 0 0 0 31 45 67 68 60 33 14 0 0 1977 0 0 0 32 61 69 71 84 35 13 0 0 1978 0 0 0 36 56 87 85 67 34 11 0 0 1979 0 0 0 38 47 63 80 81 33 14 0 0 1980 0 0 0 32 64 88 64 65 31 14 0 0 1981 0 0 0 26 68 68 85 74 32 14 0 0 1982 0 0 0 0 55 97 79 63 37 12 0 0 1983 0 0 0 43 61 70 72 57 31 12 0 0 1984 0 0 0 36 56 67 68 60 35 12 0 0 1985 0 0 0 0 62 74 89 63 35 11 0 0 1986 0 0 0 0 57 83 74 72 41 14 0 0 1987 0 0 0 30 52 76 89 78 31 14 0 0 1988 0 0 0 38 54 66 62 63 33 14 0 0 1989 0 0 0 42 60 91 81 67 37 12 0 0 1990 0 0 0 42 64 79 95 71 36 13 0 0 1991 0 0 0 41 68 85 72 66 31 13 0 0 1992 0 0 0 37 59 92 87 74 28 14 0 0 1993 0 0 0 43 78 78 84 75 42 17 0 0 1994 0 0 0 42 64 77 83 79 32 14 0 0 1995 0 0 0 45 75 91 84 71 36 14 0 0 1996 0 0 0 39 66 82 82 58 33 13 0 0 1997 0 0 0 42 74 82 73 72 36 13 0 0 1998 0 0 0 41 78 98 87 65 37 14 0 0 1999 0 0 0 38 53 78 83 69 33 14 0 0 2000 0 0 0 35 61 82 74 63 35 14 0 0 2001 0 0 0 39 53 77 69 68 33 14 0 0 2002 0 0 0 0 59 87 69 63 34 15 0 0 2003 0 0 0 41 61 79 82 67 32 17 0 0 2004 0 0 0 43 70 101 87 79 34 14 0 0 2005 0 0 0 43 75 87 70 68 36 14 0 0 2006 0 0 0 40 64 93 87 68 37 16 0 0 2007 0 0 0 35 64 82 79 72 34 13 0 0 2008 0 0 0 0 64 81 74 67 37 15 0 0  135  Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2009 0 0 0 39 65 93 97 71 35 15 0 0 2010 0 0 0 42 71 84 89 77 38 16 0 0 2011 0 0 0 38 69 84 78 66 35 15 0 0 2012 0 0 0 43 57 80 87 70 39 14 0 0 2013 0 0 0 0 68 93 88 72 40 16 0 0   Figure A.3 Distribution of average monthly evaporation obtained through ClimateBC over the period of 1970-2013 (mm)   0204060801001201970197219741976197819801982198419861988199019921994199619982000200220042006200820102012Evaporation (mm)Average of monthly evaporation in the wet conditionJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 136  Table A.4 Rainfall (mm/month) over the period of 1978-2012 recorded in Project Site Weather Station  (EMA BN) (after Wade, 2014) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1979 0 0 0 15.2 84.9 65.7 47.3 30.1 38.5 0 0 0 1980 0 0 0 82.2 39.3 14.4 43.3 17.6 11.2 0 0 0 1981 19.2 0 12.8 24 33.7 56.1 6.4 0 27.2 0 0 0 1982 0 0 0 16 33.7 17.6 0 20.8 32.1 27.2 0 0 1983 0 0 27.2 0 32.1 0 0 4.8 0 14.4 0 0 1984 0 14.4 0 0 0 0 0 0 0 0 0 0 1985 0 0 0 0 0 138.5 0 0 4 64.9 0 2.4 1986 0 0 0 46.2 14.4 27.2 6.4 0 28.8 51.3 62.5 0 1987 0 0 0 0 24 109 91.4 0 0 0 0 0.8 1988 0 0 0 0 4.8 4.8 0 0 0 0 0 0 1989 0 0 0 0 0 0 7.2 42.5 3.2 0 0 1.6 1990 0 0 0 30.1 47.6 20.8 0 0 6.4 48.1 0 0 1991 46 0 2.4 6.4 25.6 46.5 4.8 0 51.3 8 0 17.6 1992 0 14.4 22.4 54.5 122.6 68.9 23.9 8 33.7 16 222.8 0 1993 0 0 44.9 24 57.7 62.5 41.7 35.3 9.6 24 17.6 25.6 1994 97.8 0 34.5 3.2 34.5 36.9 83.3 0 17.6 4.8 4 13.6 1995 5.6 52.7 72.4 16.8 25.6 34.5 12.8 13.6 0 31.7 6.4 5.6 1996 8 23.7 0.8 8.5 15.7 22.4 6.6 9.3 2.9 23.2 1.6 0 1997 23.4 5.8 6.4 43.9 26.3 53.7 38.5 14.4 58.7 17.6 7.7 4.8 1998 0 0 0 77.4 80.8 34.1 14.6 2.1 1.1 0 30.6 4.2 1999 9.3 4.8 18.3 2.4 9.1 6.3 0.8 59.6 30.6 25.3 1.6 2.2 2000 2.9 6.4 21.6 0 27.2 20.4 27.1 0 48.1 23.7 28.4 0 2001 0 0 45.5 4.5 11.9 10.9 21.2 39.1 20.5 7.4 0 0 2002 17.3 9.3 26.9 4.2 51.4 0 11.9 46.5 8.3 5.8 7.2 4 2003 22.1 0 21.2 3.2 46.8 32.4 2.2 30.1 44.9 54.8 34.6 3.8 2004 4.2 0.6 3.5 34 29.8 45.8 20.2 24 22.4 5.6 36.7 6.6 2005 11.9 9 5.1 20.8 29.5 47.4 7.4 19.9 8.7 4.2 0 3.8 2006 2.2 21.5 1.3 29.2 28.2 22.8 46.2 12.7 9.6 3.8 0.6 0.2 2007 0.2 2.4 1.3 0.5 10.4 1 3.2 1 18 1.6 4.2 0 2008 0 2.7 5.8 19.2 3.5 6.3 11.4 2.54 4.6 12.4 4.8 14 2009 4.3 9.7 3.3 10.9 26.2 2 4.8 13.7 6.1 15.2 5.8 21.3 2010 9.9 51.8 12.2 7.4 1.5 18 0.5 8.6 16.3 38.1 19.1 78.7 2011 7.4 11.4 15 31.8 22.4 13.5 3 10.7 16.8 30.2 27.4 21.3 2012 10.9 32.8 18.8 5.8 32.5 6.1 9.4 1 10.7 10.2 16 32.5    137   Figure A.4 Distribution of average monthly rainfall over the period of 1978-2012 recorded in Project Site Weather Station (EMA BN) (mm) 05010015020025019781979198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200720082009201020112012Rainfall (mm)Average of monthly rainfal in dry climateJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 138  i=1Temp 1=0Temp 2=0Calculate monthly TMF storageCumulative TMF storage =Monthly TMF storage + Temp 1Is  Cumulative TMF storage > Minimum storage required in TMF? Water remained in TMF= Minimum storage required in TMF Water moved to reservoir from TMF= Cumulative TMF storage – Minimum storage required in TMF Monthly reservoir storage = Mine impacted water + Water moved to reservoir from TMF Cumulative reservoir storage = Monthly reservoir storage + Temp 2 Water return to TMF= 0 Water remained in TMF= Cumulative TMF storage Water moved to reservoir from TMF= 0 Monthly reservoir storage = Mine impacted water Cumulative reservoir storage = Monthly reservoir storage + Temp 2 Water return to TMF= 0Is Cumulative reservoir storage > Minimum storage required in TMF– Water remained in TMF? Water returned to TMF  from reservoir = Minimum storage required in TMF– Water remained in TMF Water returned to TMF  from reservoir = Cumulative reservoir storage Water remained in reservoir = Cumulative reservoir storage – Water returned to TMF  from reservoir‐ Mill required water Temp 1 = Water remained in TMF+ Water returned to TMF  from reservoir Temp 2 = Water remained in reservoiri = i + 1NoYesYesNo Figure A.5 Flow chart of water balance simulation in the wet condition  139 Appendix B  Results of wet climate simulation B.1 Lined impoundment   140 Table B.1 Deterministic model for solids content of 60% in the wet condition for a lined impoundment       Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3)   Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water moved  to reservoir Cumulative change in storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Water returned to TMF from reservoir Water remained in reservoir Water remained in TMF+ Water returned to TMF from reservoir     Year 1 JAN 31.0 373.1 0.0 277,450.0 858,107.0 1,135,557.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 1,016,760.2 16,760.2 1,016,760.2 1,000,000.0 33,578.1 50,338.3 50,338.3 0.0 0.0 1,000,000.0 FEB 28.0 197.7 0.0 250,600.0 454,618.0 705,218.0 0.0 107,300.3 107,300.3 250,600.0 250,600.0 597,917.7 597,917.7 1,597,917.7 1,000,000.0 17,789.4 615,707.1 615,707.1 0.0 365,107.1 1,000,000.0 MAR 31.0 167.2 0.0 277,450.0 384,468.0 661,918.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 543,121.2 543,121.2 1,543,121.2 1,000,000.0 15,044.4 558,165.6 923,272.7 0.0 645,822.7 1,000,000.0 APR 30.0 117.8 60.6 268,500.0 410,297.0 678,797.0 48,622.0 114,964.6 163,586.6 268,500.0 268,500.0 515,210.4 515,210.4 1,515,210.4 1,000,000.0 16,055.1 531,265.5 1,177,088.2 0.0 908,588.2 1,000,000.0 MAY 31.0 98.5 118.9 277,450.0 499,905.0 777,355.0 99,385.3 118,796.8 218,182.1 277,450.0 277,450.0 559,172.9 559,172.9 1,559,172.9 1,000,000.0 19,561.5 578,734.4 1,487,322.6 0.0 1,209,872.6 1,000,000.0 JUN 30.0 98.3 185.4 268,500.0 652,326.0 920,826.0 128,977.1 114,964.6 243,941.7 268,500.0 268,500.0 676,884.3 676,884.3 1,676,884.3 1,000,000.0 25,525.8 702,410.1 1,912,282.7 0.0 1,643,782.7 1,000,000.0 JUL 31.0 102.8 37.6 277,450.0 322,943.0 600,393.0 127,560.3 118,796.8 246,357.1 277,450.0 277,450.0 354,035.9 354,035.9 1,354,035.9 1,000,000.0 12,636.9 366,672.8 2,010,455.5 0.0 1,733,005.5 1,000,000.0 AUG 31.0 163.6 1.4 277,450.0 379,385.0 656,835.0 109,995.2 118,796.8 228,792.0 277,450.0 277,450.0 428,043.0 428,043.0 1,428,043.0 1,000,000.0 14,845.5 442,888.5 2,175,894.0 0.0 1,898,444.0 1,000,000.0 SEP 30.0 246.2 8.3 268,500.0 585,304.0 853,804.0 55,915.3 114,964.6 170,879.9 268,500.0 268,500.0 682,924.1 682,924.1 1,682,924.1 1,000,000.0 22,903.2 705,827.3 2,604,271.3 0.0 2,335,771.3 1,000,000.0 OCT 31.0 354.8 0.0 277,450.0 816,132.0 1,093,582.0 21,735.0 118,796.8 140,531.8 277,450.0 277,450.0 953,050.2 953,050.2 1,953,050.2 1,000,000.0 31,935.6 984,985.8 3,320,757.1 0.0 3,043,307.1 1,000,000.0 NOV 30.0 337.9 0.0 268,500.0 777,239.0 1,045,739.0 0.0 114,964.6 114,964.6 268,500.0 268,500.0 930,774.4 930,774.4 1,930,774.4 1,000,000.0 30,413.7 961,188.1 4,004,495.2 0.0 3,735,995.2 1,000,000.0 DEC 31.0 313.0 0.0 277,450.0 719,900.0 997,350.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 878,553.2 878,553.2 1,878,553.2 1,000,000.0 28,170.0 906,723.2 4,642,718.4 0.0 4,365,268.4 1,000,000.0 Year 2 JAN 31.0 373.1 0.0 277,450.0 858,107.0 1,135,557.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 1,016,760.2 1,016,760.2 2,016,760.2 1,000,000.0 33,578.1 1,050,338.3 5,415,606.7 0.0 5,138,156.7 1,000,000.0 FEB 28.0 197.7 0.0 250,600.0 454,618.0 705,218.0 0.0 107,300.3 107,300.3 250,600.0 250,600.0 597,917.7 597,917.7 1,597,917.7 1,000,000.0 17,789.4 615,707.1 5,753,863.8 0.0 5,503,263.8 1,000,000.0 MAR 31.0 167.2 0.0 277,450.0 384,468.0 661,918.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 543,121.2 543,121.2 1,543,121.2 1,000,000.0 15,044.4 558,165.6 6,061,429.4 0.0 5,783,979.4 1,000,000.0 APR 30.0 117.8 60.6 268,500.0 410,297.0 678,797.0 48,622.0 114,964.6 163,586.6 268,500.0 268,500.0 515,210.4 515,210.4 1,515,210.4 1,000,000.0 16,055.1 531,265.5 6,315,244.9 0.0 6,046,744.9 1,000,000.0 MAY 31.0 98.5 118.9 277,450.0 499,905.0 777,355.0 99,385.3 118,796.8 218,182.1 277,450.0 277,450.0 559,172.9 559,172.9 1,559,172.9 1,000,000.0 19,561.5 578,734.4 6,625,479.3 0.0 6,348,029.3 1,000,000.0 JUN 30.0 98.3 185.4 268,500.0 652,326.0 920,826.0 128,977.1 114,964.6 243,941.7 268,500.0 268,500.0 676,884.3 676,884.3 1,676,884.3 1,000,000.0 25,525.8 702,410.1 7,050,439.4 0.0 6,781,939.4 1,000,000.0 JUL 31.0 102.8 37.6 277,450.0 322,943.0 600,393.0 127,560.3 118,796.8 246,357.1 277,450.0 277,450.0 354,035.9 354,035.9 1,354,035.9 1,000,000.0 12,636.9 366,672.8 7,148,612.2 0.0 6,871,162.2 1,000,000.0 AUG 31.0 163.6 1.4 277,450.0 379,385.0 656,835.0 109,995.2 118,796.8 228,792.0 277,450.0 277,450.0 428,043.0 428,043.0 1,428,043.0 1,000,000.0 14,845.5 442,888.5 7,314,050.7 0.0 7,036,600.7 1,000,000.0 SEP 30.0 246.2 8.3 268,500.0 585,304.0 853,804.0 55,915.3 114,964.6 170,879.9 268,500.0 268,500.0 682,924.1 682,924.1 1,682,924.1 1,000,000.0 22,903.2 705,827.3 7,742,428.0 0.0 7,473,928.0 1,000,000.0 OCT 31.0 354.8 0.0 277,450.0 816,132.0 1,093,582.0 21,735.0 118,796.8 140,531.8 277,450.0 277,450.0 953,050.2 953,050.2 1,953,050.2 1,000,000.0 31,935.6 984,985.8 8,458,913.8 0.0 8,181,463.8 1,000,000.0 NOV 30.0 337.9 0.0 268,500.0 777,239.0 1,045,739.0 0.0 114,964.6 114,964.6 268,500.0 268,500.0 930,774.4 930,774.4 1,930,774.4 1,000,000.0 30,413.7 961,188.1 9,142,651.9 0.0 8,874,151.9 1,000,000.0 DEC 31.0 313.0 0.0 277,450.0 719,900.0 997,350.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 878,553.2 878,553.2 1,878,553.2 1,000,000.0 28,170.0 906,723.2 9,780,875.1 0.0 9,503,425.1 1,000,000.0 Year 3 JAN 31.0 373.1 0.0 277,450.0 858,107.0 1,135,557.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 1,016,760.2 1,016,760.2 2,016,760.2 1,000,000.0 33,578.1 1,050,338.3 10,553,763.4 0.0 10,276,313.4 1,000,000.0 FEB 28.0 197.7 0.0 250,600.0 454,618.0 705,218.0 0.0 107,300.3 107,300.3 250,600.0 250,600.0 597,917.7 597,917.7 1,597,917.7 1,000,000.0 17,789.4 615,707.1 10,892,020.5 0.0 10,641,420.5 1,000,000.0 MAR 31.0 167.2 0.0 277,450.0 384,468.0 661,918.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 543,121.2 543,121.2 1,543,121.2 1,000,000.0 15,044.4 558,165.6 11,199,586.1 0.0 10,922,136.1 1,000,000.0 APR 30.0 117.8 60.6 268,500.0 410,297.0 678,797.0 48,622.0 114,964.6 163,586.6 268,500.0 268,500.0 515,210.4 515,210.4 1,515,210.4 1,000,000.0 16,055.1 531,265.5 11,453,401.6 0.0 11,184,901.6 1,000,000.0 MAY 31.0 98.5 118.9 277,450.0 499,905.0 777,355.0 99,385.3 118,796.8 218,182.1 277,450.0 277,450.0 559,172.9 559,172.9 1,559,172.9 1,000,000.0 19,561.5 578,734.4 11,763,636.0 0.0 11,486,186.0 1,000,000.0 JUN 30.0 98.3 185.4 268,500.0 652,326.0 920,826.0 128,977.1 114,964.6 243,941.7 268,500.0 268,500.0 676,884.3 676,884.3 1,676,884.3 1,000,000.0 25,525.8 702,410.1 12,188,596.1 0.0 11,920,096.1 1,000,000.0 JUL 31.0 102.8 37.6 277,450.0 322,943.0 600,393.0 127,560.3 118,796.8 246,357.1 277,450.0 277,450.0 354,035.9 354,035.9 1,354,035.9 1,000,000.0 12,636.9 366,672.8 12,286,768.9 0.0 12,009,318.9 1,000,000.0 AUG 31.0 163.6 1.4 277,450.0 379,385.0 656,835.0 109,995.2 118,796.8 228,792.0 277,450.0 277,450.0 428,043.0 428,043.0 1,428,043.0 1,000,000.0 14,845.5 442,888.5 12,452,207.4 0.0 12,174,757.4 1,000,000.0 SEP 30.0 246.2 8.3 268,500.0 585,304.0 853,804.0 55,915.3 114,964.6 170,879.9 268,500.0 268,500.0 682,924.1 682,924.1 1,682,924.1 1,000,000.0 22,903.2 705,827.3 12,880,584.7 0.0 12,612,084.7 1,000,000.0 OCT 31.0 354.8 0.0 277,450.0 816,132.0 1,093,582.0 21,735.0 118,796.8 140,531.8 277,450.0 277,450.0 953,050.2 953,050.2 1,953,050.2 1,000,000.0 31,935.6 984,985.8 13,597,070.5 0.0 13,319,620.5 1,000,000.0  141       Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3)   Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water moved  to reservoir Cumulative change in storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Water returned to TMF from reservoir Water remained in reservoir Water remained in TMF+ Water returned to TMF from reservoir     NOV 30.0 337.9 0.0 268,500.0 777,239.0 1,045,739.0 0.0 114,964.6 114,964.6 268,500.0 268,500.0 930,774.4 930,774.4 1,930,774.4 1,000,000.0 30,413.7 961,188.1 14,280,808.6 0.0 14,012,308.6 1,000,000.0 DEC 31.0 313.0 0.0 277,450.0 719,900.0 997,350.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 878,553.2 878,553.2 1,878,553.2 1,000,000.0 28,170.0 906,723.2 14,919,031.8 0.0 14,641,581.8 1,000,000.0 Year 4 JAN 31.0 373.1 0.0 277,450.0 858,107.0 1,135,557.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 1,016,760.2 1,016,760.2 2,016,760.2 1,000,000.0 33,578.1 1,050,338.3 15,691,920.1 0.0 15,414,470.1 1,000,000.0 FEB 28.0 197.7 0.0 250,600.0 454,618.0 705,218.0 0.0 107,300.3 107,300.3 250,600.0 250,600.0 597,917.7 597,917.7 1,597,917.7 1,000,000.0 17,789.4 615,707.1 16,030,177.2 0.0 15,779,577.2 1,000,000.0 MAR 31.0 167.2 0.0 277,450.0 384,468.0 661,918.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 543,121.2 543,121.2 1,543,121.2 1,000,000.0 15,044.4 558,165.6 16,337,742.8 0.0 16,060,292.8 1,000,000.0 APR 30.0 117.8 60.6 268,500.0 410,297.0 678,797.0 48,622.0 114,964.6 163,586.6 268,500.0 268,500.0 515,210.4 515,210.4 1,515,210.4 1,000,000.0 16,055.1 531,265.5 16,591,558.3 0.0 16,323,058.3 1,000,000.0 MAY 31.0 98.5 118.9 277,450.0 499,905.0 777,355.0 99,385.3 118,796.8 218,182.1 277,450.0 277,450.0 559,172.9 559,172.9 1,559,172.9 1,000,000.0 19,561.5 578,734.4 16,901,792.7 0.0 16,624,342.7 1,000,000.0 JUN 30.0 98.3 185.4 268,500.0 652,326.0 920,826.0 128,977.1 114,964.6 243,941.7 268,500.0 268,500.0 676,884.3 676,884.3 1,676,884.3 1,000,000.0 25,525.8 702,410.1 17,326,752.8 0.0 17,058,252.8 1,000,000.0 JUL 31.0 102.8 37.6 277,450.0 322,943.0 600,393.0 127,560.3 118,796.8 246,357.1 277,450.0 277,450.0 354,035.9 354,035.9 1,354,035.9 1,000,000.0 12,636.9 366,672.8 17,424,925.6 0.0 17,147,475.6 1,000,000.0 AUG 31.0 163.6 1.4 277,450.0 379,385.0 656,835.0 109,995.2 118,796.8 228,792.0 277,450.0 277,450.0 428,043.0 428,043.0 1,428,043.0 1,000,000.0 14,845.5 442,888.5 17,590,364.1 0.0 17,312,914.1 1,000,000.0 SEP 30.0 246.2 8.3 268,500.0 585,304.0 853,804.0 55,915.3 114,964.6 170,879.9 268,500.0 268,500.0 682,924.1 682,924.1 1,682,924.1 1,000,000.0 22,903.2 705,827.3 18,018,741.4 0.0 17,750,241.4 1,000,000.0 OCT 31.0 354.8 0.0 277,450.0 816,132.0 1,093,582.0 21,735.0 118,796.8 140,531.8 277,450.0 277,450.0 953,050.2 953,050.2 1,953,050.2 1,000,000.0 31,935.6 984,985.8 18,735,227.2 0.0 18,457,777.2 1,000,000.0 NOV 30.0 337.9 0.0 268,500.0 777,239.0 1,045,739.0 0.0 114,964.6 114,964.6 268,500.0 268,500.0 930,774.4 930,774.4 1,930,774.4 1,000,000.0 30,413.7 961,188.1 19,418,965.3 0.0 19,150,465.3 1,000,000.0 DEC 31.0 313.0 0.0 277,450.0 719,900.0 997,350.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 878,553.2 878,553.2 1,878,553.2 1,000,000.0 28,170.0 906,723.2 20,057,188.5 0.0 19,779,738.5 1,000,000.0 Year 5 JAN 31.0 373.1 0.0 277,450.0 858,107.0 1,135,557.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 1,016,760.2 1,016,760.2 2,016,760.2 1,000,000.0 33,578.1 1,050,338.3 20,830,076.8 0.0 20,552,626.8 1,000,000.0 FEB 28.0 197.7 0.0 250,600.0 454,618.0 705,218.0 0.0 107,300.3 107,300.3 250,600.0 250,600.0 597,917.7 597,917.7 1,597,917.7 1,000,000.0 17,789.4 615,707.1 21,168,333.9 0.0 20,917,733.9 1,000,000.0 MAR 31.0 167.2 0.0 277,450.0 384,468.0 661,918.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 543,121.2 543,121.2 1,543,121.2 1,000,000.0 15,044.4 558,165.6 21,475,899.5 0.0 21,198,449.5 1,000,000.0 APR 30.0 117.8 60.6 268,500.0 410,297.0 678,797.0 48,622.0 114,964.6 163,586.6 268,500.0 268,500.0 515,210.4 515,210.4 1,515,210.4 1,000,000.0 16,055.1 531,265.5 21,729,715.0 0.0 21,461,215.0 1,000,000.0 MAY 31.0 98.5 118.9 277,450.0 499,905.0 777,355.0 99,385.3 118,796.8 218,182.1 277,450.0 277,450.0 559,172.9 559,172.9 1,559,172.9 1,000,000.0 19,561.5 578,734.4 22,039,949.4 0.0 21,762,499.4 1,000,000.0 JUN 30.0 98.3 185.4 268,500.0 652,326.0 920,826.0 128,977.1 114,964.6 243,941.7 268,500.0 268,500.0 676,884.3 676,884.3 1,676,884.3 1,000,000.0 25,525.8 702,410.1 22,464,909.5 0.0 22,196,409.5 1,000,000.0 JUL 31.0 102.8 37.6 277,450.0 322,943.0 600,393.0 127,560.3 118,796.8 246,357.1 277,450.0 277,450.0 354,035.9 354,035.9 1,354,035.9 1,000,000.0 12,636.9 366,672.8 22,563,082.3 0.0 22,285,632.3 1,000,000.0 AUG 31.0 163.6 1.4 277,450.0 379,385.0 656,835.0 109,995.2 118,796.8 228,792.0 277,450.0 277,450.0 428,043.0 428,043.0 1,428,043.0 1,000,000.0 14,845.5 442,888.5 22,728,520.8 0.0 22,451,070.8 1,000,000.0 SEP 30.0 246.2 8.3 268,500.0 585,304.0 853,804.0 55,915.3 114,964.6 170,879.9 268,500.0 268,500.0 682,924.1 682,924.1 1,682,924.1 1,000,000.0 22,903.2 705,827.3 23,156,898.1 0.0 22,888,398.1 1,000,000.0 OCT 31.0 354.8 0.0 277,450.0 816,132.0 1,093,582.0 21,735.0 118,796.8 140,531.8 277,450.0 277,450.0 953,050.2 953,050.2 1,953,050.2 1,000,000.0 31,935.6 984,985.8 23,873,383.9 0.0 23,595,933.9 1,000,000.0 NOV 30.0 337.9 0.0 268,500.0 777,239.0 1,045,739.0 0.0 114,964.6 114,964.6 268,500.0 268,500.0 930,774.4 930,774.4 1,930,774.4 1,000,000.0 30,413.7 961,188.1 24,557,122.0 0.0 24,288,622.0 1,000,000.0 DEC 31.0 313.0 0.0 277,450.0 719,900.0 997,350.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 878,553.2 878,553.2 1,878,553.2 1,000,000.0 28,170.0 906,723.2 25,195,345.2 0.0 24,917,895.2 1,000,000.0     142 Table B.2 Deterministic model for solids content of 70% in the wet condition for a lined impoundment       Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3)   Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water moved  to reservoir Cumulative change in storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Water returned to TMF from reservoir Water remained in reservoir Water remained in TMF+ Water returned to TMF from reservoir     Year 1 JAN 31.0 373.1 0.0 178,360.7 858,107.0 1,036,467.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 941,362.1 941,362.1 941,362.1 0.0 33,578.1 974,940.2 974,940.2 0.0 796,579.5 0.0 FEB 28.0 197.7 0.0 161,100.0 454,618.0 615,718.0 0.0 85,901.8 85,901.8 161,100.0 161,100.0 529,816.2 529,816.2 529,816.2 0.0 17,789.4 547,605.6 1,344,185.1 0.0 1,183,085.1 0.0 MAR 31.0 167.2 0.0 178,360.7 384,468.0 562,828.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 467,723.1 467,723.1 467,723.1 0.0 15,044.4 482,767.5 1,665,852.6 0.0 1,487,491.9 0.0 APR 30.0 117.8 60.6 172,607.1 410,297.0 582,904.1 41,676.0 92,037.7 133,713.7 172,607.1 172,607.1 449,190.4 449,190.4 449,190.4 0.0 16,055.1 465,245.5 1,952,737.4 0.0 1,780,130.3 0.0 MAY 31.0 98.5 118.9 178,360.7 499,905.0 678,265.7 85,187.4 95,105.6 180,293.0 178,360.7 178,360.7 497,972.7 497,972.7 497,972.7 0.0 19,561.5 517,534.2 2,297,664.5 0.0 2,119,303.8 0.0 JUN 30.0 98.3 185.4 172,607.1 652,326.0 824,933.1 110,551.8 92,037.7 202,589.5 172,607.1 172,607.1 622,343.6 622,343.6 622,343.6 0.0 25,525.8 647,869.4 2,767,173.2 0.0 2,594,566.1 0.0 JUL 31.0 102.8 37.6 178,360.7 322,943.0 501,303.7 109,337.4 95,105.6 204,443.0 178,360.7 178,360.7 296,860.7 296,860.7 296,860.7 0.0 12,636.9 309,497.6 2,904,063.7 0.0 2,725,703.0 0.0 AUG 31.0 163.6 1.4 178,360.7 379,385.0 557,745.7 94,281.6 95,105.6 189,387.2 178,360.7 178,360.7 368,358.5 368,358.5 368,358.5 0.0 14,845.5 383,204.0 3,108,907.0 0.0 2,930,546.3 0.0 SEP 30.0 246.2 8.3 172,607.1 585,304.0 757,911.1 47,927.4 92,037.7 139,965.1 172,607.1 172,607.1 617,946.0 617,946.0 617,946.0 0.0 22,903.2 640,849.2 3,571,395.5 0.0 3,398,788.4 0.0 OCT 31.0 354.8 0.0 178,360.7 816,132.0 994,492.7 18,630.0 95,105.6 113,735.6 178,360.7 178,360.7 880,757.1 880,757.1 880,757.1 0.0 31,935.6 912,692.7 4,311,481.1 0.0 4,133,120.4 0.0 NOV 30.0 337.9 0.0 172,607.1 777,239.0 949,846.1 0.0 92,037.7 92,037.7 172,607.1 172,607.1 857,808.4 857,808.4 857,808.4 0.0 30,413.7 888,222.1 5,021,342.5 0.0 4,848,735.4 0.0 DEC 31.0 313.0 0.0 178,360.7 719,900.0 898,260.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 803,155.1 803,155.1 803,155.1 0.0 28,170.0 831,325.1 5,680,060.5 0.0 5,501,699.8 0.0 Year 2 JAN 31.0 373.1 0.0 178,360.7 858,107.0 1,036,467.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 941,362.1 941,362.1 941,362.1 0.0 33,578.1 974,940.2 6,476,640.0 0.0 6,298,279.3 0.0 FEB 28.0 197.7 0.0 161,100.0 454,618.0 615,718.0 0.0 85,901.8 85,901.8 161,100.0 161,100.0 529,816.2 529,816.2 529,816.2 0.0 17,789.4 547,605.6 6,845,884.9 0.0 6,684,784.9 0.0 MAR 31.0 167.2 0.0 178,360.7 384,468.0 562,828.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 467,723.1 467,723.1 467,723.1 0.0 15,044.4 482,767.5 7,167,552.4 0.0 6,989,191.7 0.0 APR 30.0 117.8 60.6 172,607.1 410,297.0 582,904.1 41,676.0 92,037.7 133,713.7 172,607.1 172,607.1 449,190.4 449,190.4 449,190.4 0.0 16,055.1 465,245.5 7,454,437.2 0.0 7,281,830.1 0.0 MAY 31.0 98.5 118.9 178,360.7 499,905.0 678,265.7 85,187.4 95,105.6 180,293.0 178,360.7 178,360.7 497,972.7 497,972.7 497,972.7 0.0 19,561.5 517,534.2 7,799,364.3 0.0 7,621,003.6 0.0 JUN 30.0 98.3 185.4 172,607.1 652,326.0 824,933.1 110,551.8 92,037.7 202,589.5 172,607.1 172,607.1 622,343.6 622,343.6 622,343.6 0.0 25,525.8 647,869.4 8,268,873.0 0.0 8,096,265.9 0.0 JUL 31.0 102.8 37.6 178,360.7 322,943.0 501,303.7 109,337.4 95,105.6 204,443.0 178,360.7 178,360.7 296,860.7 296,860.7 296,860.7 0.0 12,636.9 309,497.6 8,405,763.5 0.0 8,227,402.8 0.0 AUG 31.0 163.6 1.4 178,360.7 379,385.0 557,745.7 94,281.6 95,105.6 189,387.2 178,360.7 178,360.7 368,358.5 368,358.5 368,358.5 0.0 14,845.5 383,204.0 8,610,606.8 0.0 8,432,246.1 0.0 SEP 30.0 246.2 8.3 172,607.1 585,304.0 757,911.1 47,927.4 92,037.7 139,965.1 172,607.1 172,607.1 617,946.0 617,946.0 617,946.0 0.0 22,903.2 640,849.2 9,073,095.3 0.0 8,900,488.2 0.0 OCT 31.0 354.8 0.0 178,360.7 816,132.0 994,492.7 18,630.0 95,105.6 113,735.6 178,360.7 178,360.7 880,757.1 880,757.1 880,757.1 0.0 31,935.6 912,692.7 9,813,180.9 0.0 9,634,820.2 0.0 NOV 30.0 337.9 0.0 172,607.1 777,239.0 949,846.1 0.0 92,037.7 92,037.7 172,607.1 172,607.1 857,808.4 857,808.4 857,808.4 0.0 30,413.7 888,222.1 10,523,042.3 0.0 10,350,435.2 0.0 DEC 31.0 313.0 0.0 178,360.7 719,900.0 898,260.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 803,155.1 803,155.1 803,155.1 0.0 28,170.0 831,325.1 11,181,760.3 0.0 11,003,399.6 0.0 Year 3 JAN 31.0 373.1 0.0 178,360.7 858,107.0 1,036,467.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 941,362.1 941,362.1 941,362.1 0.0 33,578.1 974,940.2 11,978,339.8 0.0 11,799,979.1 0.0 FEB 28.0 197.7 0.0 161,100.0 454,618.0 615,718.0 0.0 85,901.8 85,901.8 161,100.0 161,100.0 529,816.2 529,816.2 529,816.2 0.0 17,789.4 547,605.6 12,347,584.7 0.0 12,186,484.7 0.0 MAR 31.0 167.2 0.0 178,360.7 384,468.0 562,828.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 467,723.1 467,723.1 467,723.1 0.0 15,044.4 482,767.5 12,669,252.2 0.0 12,490,891.5 0.0 APR 30.0 117.8 60.6 172,607.1 410,297.0 582,904.1 41,676.0 92,037.7 133,713.7 172,607.1 172,607.1 449,190.4 449,190.4 449,190.4 0.0 16,055.1 465,245.5 12,956,137.0 0.0 12,783,529.9 0.0 MAY 31.0 98.5 118.9 178,360.7 499,905.0 678,265.7 85,187.4 95,105.6 180,293.0 178,360.7 178,360.7 497,972.7 497,972.7 497,972.7 0.0 19,561.5 517,534.2 13,301,064.1 0.0 13,122,703.4 0.0 JUN 30.0 98.3 185.4 172,607.1 652,326.0 824,933.1 110,551.8 92,037.7 202,589.5 172,607.1 172,607.1 622,343.6 622,343.6 622,343.6 0.0 25,525.8 647,869.4 13,770,572.8 0.0 13,597,965.7 0.0 JUL 31.0 102.8 37.6 178,360.7 322,943.0 501,303.7 109,337.4 95,105.6 204,443.0 178,360.7 178,360.7 296,860.7 296,860.7 296,860.7 0.0 12,636.9 309,497.6 13,907,463.3 0.0 13,729,102.6 0.0 AUG 31.0 163.6 1.4 178,360.7 379,385.0 557,745.7 94,281.6 95,105.6 189,387.2 178,360.7 178,360.7 368,358.5 368,358.5 368,358.5 0.0 14,845.5 383,204.0 14,112,306.6 0.0 13,933,945.9 0.0 SEP 30.0 246.2 8.3 172,607.1 585,304.0 757,911.1 47,927.4 92,037.7 139,965.1 172,607.1 172,607.1 617,946.0 617,946.0 617,946.0 0.0 22,903.2 640,849.2 14,574,795.1 0.0 14,402,188.0 0.0 OCT 31.0 354.8 0.0 178,360.7 816,132.0 994,492.7 18,630.0 95,105.6 113,735.6 178,360.7 178,360.7 880,757.1 880,757.1 880,757.1 0.0 31,935.6 912,692.7 15,314,880.7 0.0 15,136,520.0 0.0 NOV 30.0 337.9 0.0 172,607.1 777,239.0 949,846.1 0.0 92,037.7 92,037.7 172,607.1 172,607.1 857,808.4 857,808.4 857,808.4 0.0 30,413.7 888,222.1 16,024,742.1 0.0 15,852,135.0 0.0  143       Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3)   Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water moved  to reservoir Cumulative change in storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Water returned to TMF from reservoir Water remained in reservoir Water remained in TMF+ Water returned to TMF from reservoir     DEC 31.0 313.0 0.0 178,360.7 719,900.0 898,260.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 803,155.1 803,155.1 803,155.1 0.0 28,170.0 831,325.1 16,683,460.1 0.0 16,505,099.4 0.0 Year 4 JAN 31.0 373.1 0.0 178,360.7 858,107.0 1,036,467.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 941,362.1 941,362.1 941,362.1 0.0 33,578.1 974,940.2 17,480,039.6 0.0 17,301,678.9 0.0 FEB 28.0 197.7 0.0 161,100.0 454,618.0 615,718.0 0.0 85,901.8 85,901.8 161,100.0 161,100.0 529,816.2 529,816.2 529,816.2 0.0 17,789.4 547,605.6 17,849,284.5 0.0 17,688,184.5 0.0 MAR 31.0 167.2 0.0 178,360.7 384,468.0 562,828.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 467,723.1 467,723.1 467,723.1 0.0 15,044.4 482,767.5 18,170,952.0 0.0 17,992,591.3 0.0 APR 30.0 117.8 60.6 172,607.1 410,297.0 582,904.1 41,676.0 92,037.7 133,713.7 172,607.1 172,607.1 449,190.4 449,190.4 449,190.4 0.0 16,055.1 465,245.5 18,457,836.8 0.0 18,285,229.7 0.0 MAY 31.0 98.5 118.9 178,360.7 499,905.0 678,265.7 85,187.4 95,105.6 180,293.0 178,360.7 178,360.7 497,972.7 497,972.7 497,972.7 0.0 19,561.5 517,534.2 18,802,763.9 0.0 18,624,403.2 0.0 JUN 30.0 98.3 185.4 172,607.1 652,326.0 824,933.1 110,551.8 92,037.7 202,589.5 172,607.1 172,607.1 622,343.6 622,343.6 622,343.6 0.0 25,525.8 647,869.4 19,272,272.6 0.0 19,099,665.5 0.0 JUL 31.0 102.8 37.6 178,360.7 322,943.0 501,303.7 109,337.4 95,105.6 204,443.0 178,360.7 178,360.7 296,860.7 296,860.7 296,860.7 0.0 12,636.9 309,497.6 19,409,163.1 0.0 19,230,802.4 0.0 AUG 31.0 163.6 1.4 178,360.7 379,385.0 557,745.7 94,281.6 95,105.6 189,387.2 178,360.7 178,360.7 368,358.5 368,358.5 368,358.5 0.0 14,845.5 383,204.0 19,614,006.4 0.0 19,435,645.7 0.0 SEP 30.0 246.2 8.3 172,607.1 585,304.0 757,911.1 47,927.4 92,037.7 139,965.1 172,607.1 172,607.1 617,946.0 617,946.0 617,946.0 0.0 22,903.2 640,849.2 20,076,494.9 0.0 19,903,887.8 0.0 OCT 31.0 354.8 0.0 178,360.7 816,132.0 994,492.7 18,630.0 95,105.6 113,735.6 178,360.7 178,360.7 880,757.1 880,757.1 880,757.1 0.0 31,935.6 912,692.7 20,816,580.5 0.0 20,638,219.8 0.0 NOV 30.0 337.9 0.0 172,607.1 777,239.0 949,846.1 0.0 92,037.7 92,037.7 172,607.1 172,607.1 857,808.4 857,808.4 857,808.4 0.0 30,413.7 888,222.1 21,526,441.9 0.0 21,353,834.8 0.0 DEC 31.0 313.0 0.0 178,360.7 719,900.0 898,260.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 803,155.1 803,155.1 803,155.1 0.0 28,170.0 831,325.1 22,185,159.9 0.0 22,006,799.2 0.0 Year 5 JAN 31.0 373.1 0.0 178,360.7 858,107.0 1,036,467.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 941,362.1 941,362.1 941,362.1 0.0 33,578.1 974,940.2 22,981,739.4 0.0 22,803,378.7 0.0 FEB 28.0 197.7 0.0 161,100.0 454,618.0 615,718.0 0.0 85,901.8 85,901.8 161,100.0 161,100.0 529,816.2 529,816.2 529,816.2 0.0 17,789.4 547,605.6 23,350,984.3 0.0 23,189,884.3 0.0 MAR 31.0 167.2 0.0 178,360.7 384,468.0 562,828.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 467,723.1 467,723.1 467,723.1 0.0 15,044.4 482,767.5 23,672,651.8 0.0 23,494,291.1 0.0 APR 30.0 117.8 60.6 172,607.1 410,297.0 582,904.1 41,676.0 92,037.7 133,713.7 172,607.1 172,607.1 449,190.4 449,190.4 449,190.4 0.0 16,055.1 465,245.5 23,959,536.6 0.0 23,786,929.5 0.0 MAY 31.0 98.5 118.9 178,360.7 499,905.0 678,265.7 85,187.4 95,105.6 180,293.0 178,360.7 178,360.7 497,972.7 497,972.7 497,972.7 0.0 19,561.5 517,534.2 24,304,463.7 0.0 24,126,103.0 0.0 JUN 30.0 98.3 185.4 172,607.1 652,326.0 824,933.1 110,551.8 92,037.7 202,589.5 172,607.1 172,607.1 622,343.6 622,343.6 622,343.6 0.0 25,525.8 647,869.4 24,773,972.4 0.0 24,601,365.3 0.0 JUL 31.0 102.8 37.6 178,360.7 322,943.0 501,303.7 109,337.4 95,105.6 204,443.0 178,360.7 178,360.7 296,860.7 296,860.7 296,860.7 0.0 12,636.9 309,497.6 24,910,862.9 0.0 24,732,502.2 0.0 AUG 31.0 163.6 1.4 178,360.7 379,385.0 557,745.7 94,281.6 95,105.6 189,387.2 178,360.7 178,360.7 368,358.5 368,358.5 368,358.5 0.0 14,845.5 383,204.0 25,115,706.2 0.0 24,937,345.5 0.0 SEP 30.0 246.2 8.3 172,607.1 585,304.0 757,911.1 47,927.4 92,037.7 139,965.1 172,607.1 172,607.1 617,946.0 617,946.0 617,946.0 0.0 22,903.2 640,849.2 25,578,194.7 0.0 25,405,587.6 0.0 OCT 31.0 354.8 0.0 178,360.7 816,132.0 994,492.7 18,630.0 95,105.6 113,735.6 178,360.7 178,360.7 880,757.1 880,757.1 880,757.1 0.0 31,935.6 912,692.7 26,318,280.3 0.0 26,139,919.6 0.0 NOV 30.0 337.9 0.0 172,607.1 777,239.0 949,846.1 0.0 92,037.7 92,037.7 172,607.1 172,607.1 857,808.4 857,808.4 857,808.4 0.0 30,413.7 888,222.1 27,028,141.7 0.0 26,855,534.6 0.0 DEC 31.0 313.0 0.0 178,360.7 719,900.0 898,260.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 803,155.1 803,155.1 803,155.1 0.0 28,170.0 831,325.1 27,686,859.7 0.0 27,508,499.0 0.0   144 Table B.3 Deterministic model for solids content of 80% in the wet condition for a lined impoundment       Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3)   Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water moved  to reservoir Cumulative change in storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Water returned to TMF from reservoir Water remained in reservoir Water remained in TMF+ Water returned to TMF from reservoir     Year 1 JAN 31.0 373.1 0.0 104,043.8 858,107.0 962,150.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 880,645.7 880,645.7 880,645.7 0.0 33,578.1 914,223.8 914,223.8 0.0 810,180.0 0.0 FEB 28.0 197.7 0.0 93,975.0 454,618.0 548,593.0 0.0 73,617.5 73,617.5 93,975.0 93,975.0 474,975.5 474,975.5 474,975.5 0.0 17,789.4 492,764.9 1,302,944.9 0.0 1,208,969.9 0.0 MAR 31.0 167.2 0.0 104,043.8 384,468.0 488,511.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 407,006.7 407,006.7 407,006.7 0.0 15,044.4 422,051.1 1,631,021.0 0.0 1,526,977.2 0.0 APR 30.0 117.8 60.6 100,687.5 410,297.0 510,984.5 41,676.0 78,875.9 120,551.9 100,687.5 100,687.5 390,432.6 390,432.6 390,432.6 0.0 16,055.1 406,487.7 1,933,464.9 0.0 1,832,777.4 0.0 MAY 31.0 98.5 118.9 104,043.8 499,905.0 603,948.8 85,187.4 81,505.1 166,692.5 104,043.8 104,043.8 437,256.3 437,256.3 437,256.3 0.0 19,561.5 456,817.8 2,289,595.2 0.0 2,185,551.4 0.0 JUN 30.0 5.0 185.4 100,687.5 437,805.0 538,492.5 110,551.8 78,875.9 189,427.7 100,687.5 100,687.5 349,064.8 349,064.8 349,064.8 0.0 17,131.5 366,196.3 2,551,747.7 0.0 2,451,060.2 0.0 JUL 31.0 102.8 37.6 104,043.8 322,943.0 426,986.8 109,337.4 81,505.1 190,842.5 104,043.8 104,043.8 236,144.3 236,144.3 236,144.3 0.0 12,636.9 248,781.2 2,699,841.4 0.0 2,595,797.6 0.0 AUG 31.0 163.6 1.4 104,043.8 379,385.0 483,428.8 94,281.6 81,505.1 175,786.7 104,043.8 104,043.8 307,642.1 307,642.1 307,642.1 0.0 14,845.5 322,487.6 2,918,285.2 0.0 2,814,241.4 0.0 SEP 30.0 246.2 8.3 100,687.5 585,304.0 685,991.5 47,927.4 78,875.9 126,803.3 100,687.5 100,687.5 559,188.2 559,188.2 559,188.2 0.0 22,903.2 582,091.4 3,396,332.8 0.0 3,295,645.3 0.0 OCT 31.0 354.8 0.0 104,043.8 816,132.0 920,175.8 18,630.0 81,505.1 100,135.1 104,043.8 104,043.8 820,040.7 820,040.7 820,040.7 0.0 31,935.6 851,976.3 4,147,621.6 0.0 4,043,577.8 0.0 NOV 30.0 337.9 0.0 100,687.5 777,239.0 877,926.5 0.0 78,875.9 78,875.9 100,687.5 100,687.5 799,050.6 799,050.6 799,050.6 0.0 30,413.7 829,464.3 4,873,042.1 0.0 4,772,354.6 0.0 DEC 31.0 313.0 0.0 104,043.8 719,900.0 823,943.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 742,438.7 742,438.7 742,438.7 0.0 28,170.0 770,608.7 5,542,963.3 0.0 5,438,919.5 0.0 Year 2 JAN 31.0 373.1 0.0 104,043.8 858,107.0 962,150.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 880,645.7 880,645.7 880,645.7 0.0 33,578.1 914,223.8 6,353,143.3 0.0 6,249,099.5 0.0 FEB 28.0 197.7 0.0 93,975.0 454,618.0 548,593.0 0.0 73,617.5 73,617.5 93,975.0 93,975.0 474,975.5 474,975.5 474,975.5 0.0 17,789.4 492,764.9 6,741,864.4 0.0 6,647,889.4 0.0 MAR 31.0 167.2 0.0 104,043.8 384,468.0 488,511.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 407,006.7 407,006.7 407,006.7 0.0 15,044.4 422,051.1 7,069,940.5 0.0 6,965,896.7 0.0 APR 30.0 117.8 60.6 100,687.5 410,297.0 510,984.5 41,676.0 78,875.9 120,551.9 100,687.5 100,687.5 390,432.6 390,432.6 390,432.6 0.0 16,055.1 406,487.7 7,372,384.4 0.0 7,271,696.9 0.0 MAY 31.0 98.5 118.9 104,043.8 499,905.0 603,948.8 85,187.4 81,505.1 166,692.5 104,043.8 104,043.8 437,256.3 437,256.3 437,256.3 0.0 19,561.5 456,817.8 7,728,514.7 0.0 7,624,470.9 0.0 JUN 30.0 98.3 185.4 100,687.5 652,326.0 753,013.5 110,551.8 78,875.9 189,427.7 100,687.5 100,687.5 563,585.8 563,585.8 563,585.8 0.0 25,525.8 589,111.6 8,213,582.5 0.0 8,112,895.0 0.0 JUL 31.0 102.8 37.6 104,043.8 322,943.0 426,986.8 109,337.4 81,505.1 190,842.5 104,043.8 104,043.8 236,144.3 236,144.3 236,144.3 0.0 12,636.9 248,781.2 8,361,676.2 0.0 8,257,632.4 0.0 AUG 31.0 163.6 1.4 104,043.8 379,385.0 483,428.8 94,281.6 81,505.1 175,786.7 104,043.8 104,043.8 307,642.1 307,642.1 307,642.1 0.0 14,845.5 322,487.6 8,580,120.0 0.0 8,476,076.2 0.0 SEP 30.0 246.2 8.3 100,687.5 585,304.0 685,991.5 47,927.4 78,875.9 126,803.3 100,687.5 100,687.5 559,188.2 559,188.2 559,188.2 0.0 22,903.2 582,091.4 9,058,167.6 0.0 8,957,480.1 0.0 OCT 31.0 354.8 0.0 104,043.8 816,132.0 920,175.8 18,630.0 81,505.1 100,135.1 104,043.8 104,043.8 820,040.7 820,040.7 820,040.7 0.0 31,935.6 851,976.3 9,809,456.4 0.0 9,705,412.6 0.0 NOV 30.0 337.9 0.0 100,687.5 777,239.0 877,926.5 0.0 78,875.9 78,875.9 100,687.5 100,687.5 799,050.6 799,050.6 799,050.6 0.0 30,413.7 829,464.3 10,534,876.9 0.0 10,434,189.4 0.0 DEC 31.0 313.0 0.0 104,043.8 719,900.0 823,943.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 742,438.7 742,438.7 742,438.7 0.0 28,170.0 770,608.7 11,204,798.1 0.0 11,100,754.3 0.0 Year 3 JAN 31.0 373.1 0.0 104,043.8 858,107.0 962,150.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 880,645.7 880,645.7 880,645.7 0.0 33,578.1 914,223.8 12,014,978.1 0.0 11,910,934.3 0.0 FEB 28.0 197.7 0.0 93,975.0 454,618.0 548,593.0 0.0 73,617.5 73,617.5 93,975.0 93,975.0 474,975.5 474,975.5 474,975.5 0.0 17,789.4 492,764.9 12,403,699.2 0.0 12,309,724.2 0.0 MAR 31.0 167.2 0.0 104,043.8 384,468.0 488,511.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 407,006.7 407,006.7 407,006.7 0.0 15,044.4 422,051.1 12,731,775.3 0.0 12,627,731.5 0.0 APR 30.0 117.8 60.6 100,687.5 410,297.0 510,984.5 41,676.0 78,875.9 120,551.9 100,687.5 100,687.5 390,432.6 390,432.6 390,432.6 0.0 16,055.1 406,487.7 13,034,219.2 0.0 12,933,531.7 0.0 MAY 31.0 98.5 118.9 104,043.8 499,905.0 603,948.8 85,187.4 81,505.1 166,692.5 104,043.8 104,043.8 437,256.3 437,256.3 437,256.3 0.0 19,561.5 456,817.8 13,390,349.5 0.0 13,286,305.7 0.0 JUN 30.0 98.3 185.4 100,687.5 652,326.0 753,013.5 110,551.8 78,875.9 189,427.7 100,687.5 100,687.5 563,585.8 563,585.8 563,585.8 0.0 25,525.8 589,111.6 13,875,417.3 0.0 13,774,729.8 0.0 JUL 31.0 102.8 37.6 104,043.8 322,943.0 426,986.8 109,337.4 81,505.1 190,842.5 104,043.8 104,043.8 236,144.3 236,144.3 236,144.3 0.0 12,636.9 248,781.2 14,023,511.0 0.0 13,919,467.2 0.0 AUG 31.0 163.6 1.4 104,043.8 379,385.0 483,428.8 94,281.6 81,505.1 175,786.7 104,043.8 104,043.8 307,642.1 307,642.1 307,642.1 0.0 14,845.5 322,487.6 14,241,954.8 0.0 14,137,911.0 0.0 SEP 30.0 246.2 8.3 100,687.5 585,304.0 685,991.5 47,927.4 78,875.9 126,803.3 100,687.5 100,687.5 559,188.2 559,188.2 559,188.2 0.0 22,903.2 582,091.4 14,720,002.4 0.0 14,619,314.9 0.0 OCT 31.0 354.8 0.0 104,043.8 816,132.0 920,175.8 18,630.0 81,505.1 100,135.1 104,043.8 104,043.8 820,040.7 820,040.7 820,040.7 0.0 31,935.6 851,976.3 15,471,291.2 0.0 15,367,247.4 0.0  145       Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3)   Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water moved  to reservoir Cumulative change in storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Water returned to TMF from reservoir Water remained in reservoir Water remained in TMF+ Water returned to TMF from reservoir     NOV 30.0 337.9 0.0 100,687.5 777,239.0 877,926.5 0.0 78,875.9 78,875.9 100,687.5 100,687.5 799,050.6 799,050.6 799,050.6 0.0 30,413.7 829,464.3 16,196,711.7 0.0 16,096,024.2 0.0 DEC 31.0 313.0 0.0 104,043.8 719,900.0 823,943.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 742,438.7 742,438.7 742,438.7 0.0 28,170.0 770,608.7 16,866,632.9 0.0 16,762,589.1 0.0 Year 4 JAN 31.0 373.1 0.0 104,043.8 858,107.0 962,150.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 880,645.7 880,645.7 880,645.7 0.0 33,578.1 914,223.8 17,676,812.9 0.0 17,572,769.1 0.0 FEB 28.0 197.7 0.0 93,975.0 454,618.0 548,593.0 0.0 73,617.5 73,617.5 93,975.0 93,975.0 474,975.5 474,975.5 474,975.5 0.0 17,789.4 492,764.9 18,065,534.0 0.0 17,971,559.0 0.0 MAR 31.0 167.2 0.0 104,043.8 384,468.0 488,511.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 407,006.7 407,006.7 407,006.7 0.0 15,044.4 422,051.1 18,393,610.1 0.0 18,289,566.3 0.0 APR 30.0 117.8 60.6 100,687.5 410,297.0 510,984.5 41,676.0 78,875.9 120,551.9 100,687.5 100,687.5 390,432.6 390,432.6 390,432.6 0.0 16,055.1 406,487.7 18,696,054.0 0.0 18,595,366.5 0.0 MAY 31.0 98.5 118.9 104,043.8 499,905.0 603,948.8 85,187.4 81,505.1 166,692.5 104,043.8 104,043.8 437,256.3 437,256.3 437,256.3 0.0 19,561.5 456,817.8 19,052,184.3 0.0 18,948,140.5 0.0 JUN 30.0 98.3 185.4 100,687.5 652,326.0 753,013.5 110,551.8 78,875.9 189,427.7 100,687.5 100,687.5 563,585.8 563,585.8 563,585.8 0.0 25,525.8 589,111.6 19,537,252.1 0.0 19,436,564.6 0.0 JUL 31.0 102.8 37.6 104,043.8 322,943.0 426,986.8 109,337.4 81,505.1 190,842.5 104,043.8 104,043.8 236,144.3 236,144.3 236,144.3 0.0 12,636.9 248,781.2 19,685,345.8 0.0 19,581,302.0 0.0 AUG 31.0 163.6 1.4 104,043.8 379,385.0 483,428.8 94,281.6 81,505.1 175,786.7 104,043.8 104,043.8 307,642.1 307,642.1 307,642.1 0.0 14,845.5 322,487.6 19,903,789.6 0.0 19,799,745.8 0.0 SEP 30.0 246.2 8.3 100,687.5 585,304.0 685,991.5 47,927.4 78,875.9 126,803.3 100,687.5 100,687.5 559,188.2 559,188.2 559,188.2 0.0 22,903.2 582,091.4 20,381,837.2 0.0 20,281,149.7 0.0 OCT 31.0 354.8 0.0 104,043.8 816,132.0 920,175.8 18,630.0 81,505.1 100,135.1 104,043.8 104,043.8 820,040.7 820,040.7 820,040.7 0.0 31,935.6 851,976.3 21,133,126.0 0.0 21,029,082.2 0.0 NOV 30.0 337.9 0.0 100,687.5 777,239.0 877,926.5 0.0 78,875.9 78,875.9 100,687.5 100,687.5 799,050.6 799,050.6 799,050.6 0.0 30,413.7 829,464.3 21,858,546.5 0.0 21,757,859.0 0.0 DEC 31.0 313.0 0.0 104,043.8 719,900.0 823,943.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 742,438.7 742,438.7 742,438.7 0.0 28,170.0 770,608.7 22,528,467.7 0.0 22,424,423.9 0.0 Year 5 JAN 31.0 373.1 0.0 104,043.8 858,107.0 962,150.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 880,645.7 880,645.7 880,645.7 0.0 33,578.1 914,223.8 23,338,647.7 0.0 23,234,603.9 0.0 FEB 28.0 197.7 0.0 93,975.0 454,618.0 548,593.0 0.0 73,617.5 73,617.5 93,975.0 93,975.0 474,975.5 474,975.5 474,975.5 0.0 17,789.4 492,764.9 23,727,368.8 0.0 23,633,393.8 0.0 MAR 31.0 167.2 0.0 104,043.8 384,468.0 488,511.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 407,006.7 407,006.7 407,006.7 0.0 15,044.4 422,051.1 24,055,444.9 0.0 23,951,401.1 0.0 APR 30.0 117.8 60.6 100,687.5 410,297.0 510,984.5 41,676.0 78,875.9 120,551.9 100,687.5 100,687.5 390,432.6 390,432.6 390,432.6 0.0 16,055.1 406,487.7 24,357,888.8 0.0 24,257,201.3 0.0 MAY 31.0 98.5 118.9 104,043.8 499,905.0 603,948.8 85,187.4 81,505.1 166,692.5 104,043.8 104,043.8 437,256.3 437,256.3 437,256.3 0.0 19,561.5 456,817.8 24,714,019.1 0.0 24,609,975.3 0.0 JUN 30.0 98.3 185.4 100,687.5 652,326.0 753,013.5 110,551.8 78,875.9 189,427.7 100,687.5 100,687.5 563,585.8 563,585.8 563,585.8 0.0 25,525.8 589,111.6 25,199,086.9 0.0 25,098,399.4 0.0 JUL 31.0 102.8 37.6 104,043.8 322,943.0 426,986.8 109,337.4 81,505.1 190,842.5 104,043.8 104,043.8 236,144.3 236,144.3 236,144.3 0.0 12,636.9 248,781.2 25,347,180.6 0.0 25,243,136.8 0.0 AUG 31.0 163.6 1.4 104,043.8 379,385.0 483,428.8 94,281.6 81,505.1 175,786.7 104,043.8 104,043.8 307,642.1 307,642.1 307,642.1 0.0 14,845.5 322,487.6 25,565,624.4 0.0 25,461,580.6 0.0 SEP 30.0 246.2 8.3 100,687.5 585,304.0 685,991.5 47,927.4 78,875.9 126,803.3 100,687.5 100,687.5 559,188.2 559,188.2 559,188.2 0.0 22,903.2 582,091.4 26,043,672.0 0.0 25,942,984.5 0.0 OCT 31.0 354.8 0.0 104,043.8 816,132.0 920,175.8 18,630.0 81,505.1 100,135.1 104,043.8 104,043.8 820,040.7 820,040.7 820,040.7 0.0 31,935.6 851,976.3 26,794,960.8 0.0 26,690,917.0 0.0 NOV 30.0 337.9 0.0 100,687.5 777,239.0 877,926.5 0.0 78,875.9 78,875.9 100,687.5 100,687.5 799,050.6 799,050.6 799,050.6 0.0 30,413.7 829,464.3 27,520,381.3 0.0 27,419,693.8 0.0 DEC 31.0 313.0 0.0 104,043.8 719,900.0 823,943.8 0.0 81,505.1 81,505.1 104,043.8 104,043.8 742,438.7 742,438.7 742,438.7 0.0 28,170.0 770,608.7 28,190,302.5 0.0 28,086,258.7 0.0    146 B.2 Unlined impoundment  147 Table B.4 Deterministic model for solids content of 60% in the wet condition for an unlined impoundment Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3) Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly storage Water move  to reservoir Cumulative storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Returned to pond Water remained in reservoir Water remained on TMF+ return from reservoir   Year 1 JAN 31.0 373.1 0.0 277,450.0 858,107.0 1,135,557.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 1,001,856.2 1,856.2 1,001,856.2 1,000,000.0 33,578.1 35,434.3 35,434.3 0.0 0.0 1,000,000.0 FEB 28.0 197.7 0.0 250,600.0 454,618.0 705,218.0 0.0 107,300.3 14,904.0 122,204.3 250,600.0 250,600.0 583,013.7 583,013.7 1,583,013.7 1,000,000.0 17,789.4 600,803.1 600,803.1 0.0 350,203.1 1,000,000.0 MAR 31.0 167.2 0.0 277,450.0 384,468.0 661,918.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 528,217.2 528,217.2 1,528,217.2 1,000,000.0 15,044.4 543,261.6 893,464.7 0.0 616,014.7 1,000,000.0 APR 30.0 117.8 60.6 268,500.0 410,297.0 678,797.0 48,622.0 114,964.6 14,904.0 178,490.6 268,500.0 268,500.0 500,306.4 500,306.4 1,500,306.4 1,000,000.0 16,055.1 516,361.5 1,132,376.2 0.0 863,876.2 1,000,000.0 MAY 31.0 98.5 118.9 277,450.0 499,905.0 777,355.0 99,385.3 118,796.8 14,904.0 233,086.1 277,450.0 277,450.0 544,268.9 544,268.9 1,544,268.9 1,000,000.0 19,561.5 563,830.4 1,427,706.6 0.0 1,150,256.6 1,000,000.0 JUN 30.0 98.3 185.4 268,500.0 652,326.0 920,826.0 128,977.1 114,964.6 14,904.0 258,845.7 268,500.0 268,500.0 661,980.3 661,980.3 1,661,980.3 1,000,000.0 25,525.8 687,506.1 1,837,762.7 0.0 1,569,262.7 1,000,000.0 JUL 31.0 102.8 37.6 277,450.0 322,943.0 600,393.0 127,560.3 118,796.8 14,904.0 261,261.1 277,450.0 277,450.0 339,131.9 339,131.9 1,339,131.9 1,000,000.0 12,636.9 351,768.8 1,921,031.5 0.0 1,643,581.5 1,000,000.0 AUG 31.0 163.6 1.4 277,450.0 379,385.0 656,835.0 109,995.2 118,796.8 14,904.0 243,696.0 277,450.0 277,450.0 413,139.0 413,139.0 1,413,139.0 1,000,000.0 14,845.5 427,984.5 2,071,566.0 0.0 1,794,116.0 1,000,000.0 SEP 30.0 246.2 8.3 268,500.0 585,304.0 853,804.0 55,915.3 114,964.6 14,904.0 185,783.9 268,500.0 268,500.0 668,020.1 668,020.1 1,668,020.1 1,000,000.0 22,903.2 690,923.3 2,485,039.3 0.0 2,216,539.3 1,000,000.0 OCT 31.0 354.8 0.0 277,450.0 816,132.0 1,093,582.0 21,735.0 118,796.8 14,904.0 155,435.8 277,450.0 277,450.0 938,146.2 938,146.2 1,938,146.2 1,000,000.0 31,935.6 970,081.8 3,186,621.1 0.0 2,909,171.1 1,000,000.0 NOV 30.0 337.9 0.0 268,500.0 777,239.0 1,045,739.0 0.0 114,964.6 14,904.0 129,868.6 268,500.0 268,500.0 915,870.4 915,870.4 1,915,870.4 1,000,000.0 30,413.7 946,284.1 3,855,455.2 0.0 3,586,955.2 1,000,000.0 DEC 31.0 313.0 0.0 277,450.0 719,900.0 997,350.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 863,649.2 863,649.2 1,863,649.2 1,000,000.0 28,170.0 891,819.2 4,478,774.4 0.0 4,201,324.4 1,000,000.0 Year 2 JAN 31.0 373.1 0.0 277,450.0 858,107.0 1,135,557.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 1,001,856.2 1,001,856.2 2,001,856.2 1,000,000.0 33,578.1 1,035,434.3 5,236,758.7 0.0 4,959,308.7 1,000,000.0 FEB 28.0 197.7 0.0 250,600.0 454,618.0 705,218.0 0.0 107,300.3 14,904.0 122,204.3 250,600.0 250,600.0 583,013.7 583,013.7 1,583,013.7 1,000,000.0 17,789.4 600,803.1 5,560,111.8 0.0 5,309,511.8 1,000,000.0 MAR 31.0 167.2 0.0 277,450.0 384,468.0 661,918.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 528,217.2 528,217.2 1,528,217.2 1,000,000.0 15,044.4 543,261.6 5,852,773.4 0.0 5,575,323.4 1,000,000.0 APR 30.0 117.8 60.6 268,500.0 410,297.0 678,797.0 48,622.0 114,964.6 14,904.0 178,490.6 268,500.0 268,500.0 500,306.4 500,306.4 1,500,306.4 1,000,000.0 16,055.1 516,361.5 6,091,684.9 0.0 5,823,184.9 1,000,000.0 MAY 31.0 98.5 118.9 277,450.0 499,905.0 777,355.0 99,385.3 118,796.8 14,904.0 233,086.1 277,450.0 277,450.0 544,268.9 544,268.9 1,544,268.9 1,000,000.0 19,561.5 563,830.4 6,387,015.3 0.0 6,109,565.3 1,000,000.0 JUN 30.0 98.3 185.4 268,500.0 652,326.0 920,826.0 128,977.1 114,964.6 14,904.0 258,845.7 268,500.0 268,500.0 661,980.3 661,980.3 1,661,980.3 1,000,000.0 25,525.8 687,506.1 6,797,071.4 0.0 6,528,571.4 1,000,000.0 JUL 31.0 102.8 37.6 277,450.0 322,943.0 600,393.0 127,560.3 118,796.8 14,904.0 261,261.1 277,450.0 277,450.0 339,131.9 339,131.9 1,339,131.9 1,000,000.0 12,636.9 351,768.8 6,880,340.2 0.0 6,602,890.2 1,000,000.0 AUG 31.0 163.6 1.4 277,450.0 379,385.0 656,835.0 109,995.2 118,796.8 14,904.0 243,696.0 277,450.0 277,450.0 413,139.0 413,139.0 1,413,139.0 1,000,000.0 14,845.5 427,984.5 7,030,874.7 0.0 6,753,424.7 1,000,000.0 SEP 30.0 246.2 8.3 268,500.0 585,304.0 853,804.0 55,915.3 114,964.6 14,904.0 185,783.9 268,500.0 268,500.0 668,020.1 668,020.1 1,668,020.1 1,000,000.0 22,903.2 690,923.3 7,444,348.0 0.0 7,175,848.0 1,000,000.0 OCT 31.0 354.8 0.0 277,450.0 816,132.0 1,093,582.0 21,735.0 118,796.8 14,904.0 155,435.8 277,450.0 277,450.0 938,146.2 938,146.2 1,938,146.2 1,000,000.0 31,935.6 970,081.8 8,145,929.8 0.0 7,868,479.8 1,000,000.0 NOV 30.0 337.9 0.0 268,500.0 777,239.0 1,045,739.0 0.0 114,964.6 14,904.0 129,868.6 268,500.0 268,500.0 915,870.4 915,870.4 1,915,870.4 1,000,000.0 30,413.7 946,284.1 8,814,763.9 0.0 8,546,263.9 1,000,000.0 DEC 31.0 313.0 0.0 277,450.0 719,900.0 997,350.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 863,649.2 863,649.2 1,863,649.2 1,000,000.0 28,170.0 891,819.2 9,438,083.1 0.0 9,160,633.1 1,000,000.0 Year 3 JAN 31.0 373.1 0.0 277,450.0 858,107.0 1,135,557.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 1,001,856.2 1,001,856.2 2,001,856.2 1,000,000.0 33,578.1 1,035,434.3 10,196,067.4 0.0 9,918,617.4 1,000,000.0 FEB 28.0 197.7 0.0 250,600.0 454,618.0 705,218.0 0.0 107,300.3 14,904.0 122,204.3 250,600.0 250,600.0 583,013.7 583,013.7 1,583,013.7 1,000,000.0 17,789.4 600,803.1 10,519,420.5 0.0 10,268,820.5 1,000,000.0 MAR 31.0 167.2 0.0 277,450.0 384,468.0 661,918.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 528,217.2 528,217.2 1,528,217.2 1,000,000.0 15,044.4 543,261.6 10,812,082.1 0.0 10,534,632.1 1,000,000.0 APR 30.0 117.8 60.6 268,500.0 410,297.0 678,797.0 48,622.0 114,964.6 14,904.0 178,490.6 268,500.0 268,500.0 500,306.4 500,306.4 1,500,306.4 1,000,000.0 16,055.1 516,361.5 11,050,993.6 0.0 10,782,493.6 1,000,000.0 MAY 31.0 98.5 118.9 277,450.0 499,905.0 777,355.0 99,385.3 118,796.8 14,904.0 233,086.1 277,450.0 277,450.0 544,268.9 544,268.9 1,544,268.9 1,000,000.0 19,561.5 563,830.4 11,346,324.0 0.0 11,068,874.0 1,000,000.0 JUN 30.0 98.3 185.4 268,500.0 652,326.0 920,826.0 128,977.1 114,964.6 14,904.0 258,845.7 268,500.0 268,500.0 661,980.3 661,980.3 1,661,980.3 1,000,000.0 25,525.8 687,506.1 11,756,380.1 0.0 11,487,880.1 1,000,000.0 JUL 31.0 102.8 37.6 277,450.0 322,943.0 600,393.0 127,560.3 118,796.8 14,904.0 261,261.1 277,450.0 277,450.0 339,131.9 339,131.9 1,339,131.9 1,000,000.0 12,636.9 351,768.8 11,839,648.9 0.0 11,562,198.9 1,000,000.0 AUG 31.0 163.6 1.4 277,450.0 379,385.0 656,835.0 109,995.2 118,796.8 14,904.0 243,696.0 277,450.0 277,450.0 413,139.0 413,139.0 1,413,139.0 1,000,000.0 14,845.5 427,984.5 11,990,183.4 0.0 11,712,733.4 1,000,000.0 SEP 30.0 246.2 8.3 268,500.0 585,304.0 853,804.0 55,915.3 114,964.6 14,904.0 185,783.9 268,500.0 268,500.0 668,020.1 668,020.1 1,668,020.1 1,000,000.0 22,903.2 690,923.3 12,403,656.7 0.0 12,135,156.7 1,000,000.0 OCT 31.0 354.8 0.0 277,450.0 816,132.0 1,093,582.0 21,735.0 118,796.8 14,904.0 155,435.8 277,450.0 277,450.0 938,146.2 938,146.2 1,938,146.2 1,000,000.0 31,935.6 970,081.8 13,105,238.5 0.0 12,827,788.5 1,000,000.0 NOV 30.0 337.9 0.0 268,500.0 777,239.0 1,045,739.0 0.0 114,964.6 14,904.0 129,868.6 268,500.0 268,500.0 915,870.4 915,870.4 1,915,870.4 1,000,000.0 30,413.7 946,284.1 13,774,072.6 0.0 13,505,572.6 1,000,000.0  148 Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3) Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly storage Water move  to reservoir Cumulative storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Returned to pond Water remained in reservoir Water remained on TMF+ return from reservoir   DEC 31.0 313.0 0.0 277,450.0 719,900.0 997,350.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 863,649.2 863,649.2 1,863,649.2 1,000,000.0 28,170.0 891,819.2 14,397,391.8 0.0 14,119,941.8 1,000,000.0 Year 4 JAN 31.0 373.1 0.0 277,450.0 858,107.0 1,135,557.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 1,001,856.2 1,001,856.2 2,001,856.2 1,000,000.0 33,578.1 1,035,434.3 15,155,376.1 0.0 14,877,926.1 1,000,000.0 FEB 28.0 197.7 0.0 250,600.0 454,618.0 705,218.0 0.0 107,300.3 14,904.0 122,204.3 250,600.0 250,600.0 583,013.7 583,013.7 1,583,013.7 1,000,000.0 17,789.4 600,803.1 15,478,729.2 0.0 15,228,129.2 1,000,000.0 MAR 31.0 167.2 0.0 277,450.0 384,468.0 661,918.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 528,217.2 528,217.2 1,528,217.2 1,000,000.0 15,044.4 543,261.6 15,771,390.8 0.0 15,493,940.8 1,000,000.0 APR 30.0 117.8 60.6 268,500.0 410,297.0 678,797.0 48,622.0 114,964.6 14,904.0 178,490.6 268,500.0 268,500.0 500,306.4 500,306.4 1,500,306.4 1,000,000.0 16,055.1 516,361.5 16,010,302.3 0.0 15,741,802.3 1,000,000.0 MAY 31.0 98.5 118.9 277,450.0 499,905.0 777,355.0 99,385.3 118,796.8 14,904.0 233,086.1 277,450.0 277,450.0 544,268.9 544,268.9 1,544,268.9 1,000,000.0 19,561.5 563,830.4 16,305,632.7 0.0 16,028,182.7 1,000,000.0 JUN 30.0 98.3 185.4 268,500.0 652,326.0 920,826.0 128,977.1 114,964.6 14,904.0 258,845.7 268,500.0 268,500.0 661,980.3 661,980.3 1,661,980.3 1,000,000.0 25,525.8 687,506.1 16,715,688.8 0.0 16,447,188.8 1,000,000.0 JUL 31.0 102.8 37.6 277,450.0 322,943.0 600,393.0 127,560.3 118,796.8 14,904.0 261,261.1 277,450.0 277,450.0 339,131.9 339,131.9 1,339,131.9 1,000,000.0 12,636.9 351,768.8 16,798,957.6 0.0 16,521,507.6 1,000,000.0 AUG 31.0 163.6 1.4 277,450.0 379,385.0 656,835.0 109,995.2 118,796.8 14,904.0 243,696.0 277,450.0 277,450.0 413,139.0 413,139.0 1,413,139.0 1,000,000.0 14,845.5 427,984.5 16,949,492.1 0.0 16,672,042.1 1,000,000.0 SEP 30.0 246.2 8.3 268,500.0 585,304.0 853,804.0 55,915.3 114,964.6 14,904.0 185,783.9 268,500.0 268,500.0 668,020.1 668,020.1 1,668,020.1 1,000,000.0 22,903.2 690,923.3 17,362,965.4 0.0 17,094,465.4 1,000,000.0 OCT 31.0 354.8 0.0 277,450.0 816,132.0 1,093,582.0 21,735.0 118,796.8 14,904.0 155,435.8 277,450.0 277,450.0 938,146.2 938,146.2 1,938,146.2 1,000,000.0 31,935.6 970,081.8 18,064,547.2 0.0 17,787,097.2 1,000,000.0 NOV 30.0 337.9 0.0 268,500.0 777,239.0 1,045,739.0 0.0 114,964.6 14,904.0 129,868.6 268,500.0 268,500.0 915,870.4 915,870.4 1,915,870.4 1,000,000.0 30,413.7 946,284.1 18,733,381.3 0.0 18,464,881.3 1,000,000.0 DEC 31.0 313.0 0.0 277,450.0 719,900.0 997,350.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 863,649.2 863,649.2 1,863,649.2 1,000,000.0 28,170.0 891,819.2 19,356,700.5 0.0 19,079,250.5 1,000,000.0 Year 5 JAN 31.0 373.1 0.0 277,450.0 858,107.0 1,135,557.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 1,001,856.2 1,001,856.2 2,001,856.2 1,000,000.0 33,578.1 1,035,434.3 20,114,684.8 0.0 19,837,234.8 1,000,000.0 FEB 28.0 197.7 0.0 250,600.0 454,618.0 705,218.0 0.0 107,300.3 14,904.0 122,204.3 250,600.0 250,600.0 583,013.7 583,013.7 1,583,013.7 1,000,000.0 17,789.4 600,803.1 20,438,037.9 0.0 20,187,437.9 1,000,000.0 MAR 31.0 167.2 0.0 277,450.0 384,468.0 661,918.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 528,217.2 528,217.2 1,528,217.2 1,000,000.0 15,044.4 543,261.6 20,730,699.5 0.0 20,453,249.5 1,000,000.0 APR 30.0 117.8 60.6 268,500.0 410,297.0 678,797.0 48,622.0 114,964.6 14,904.0 178,490.6 268,500.0 268,500.0 500,306.4 500,306.4 1,500,306.4 1,000,000.0 16,055.1 516,361.5 20,969,611.0 0.0 20,701,111.0 1,000,000.0 MAY 31.0 98.5 118.9 277,450.0 499,905.0 777,355.0 99,385.3 118,796.8 14,904.0 233,086.1 277,450.0 277,450.0 544,268.9 544,268.9 1,544,268.9 1,000,000.0 19,561.5 563,830.4 21,264,941.4 0.0 20,987,491.4 1,000,000.0 JUN 30.0 98.3 185.4 268,500.0 652,326.0 920,826.0 128,977.1 114,964.6 14,904.0 258,845.7 268,500.0 268,500.0 661,980.3 661,980.3 1,661,980.3 1,000,000.0 25,525.8 687,506.1 21,674,997.5 0.0 21,406,497.5 1,000,000.0 JUL 31.0 102.8 37.6 277,450.0 322,943.0 600,393.0 127,560.3 118,796.8 14,904.0 261,261.1 277,450.0 277,450.0 339,131.9 339,131.9 1,339,131.9 1,000,000.0 12,636.9 351,768.8 21,758,266.3 0.0 21,480,816.3 1,000,000.0 AUG 31.0 163.6 1.4 277,450.0 379,385.0 656,835.0 109,995.2 118,796.8 14,904.0 243,696.0 277,450.0 277,450.0 413,139.0 413,139.0 1,413,139.0 1,000,000.0 14,845.5 427,984.5 21,908,800.8 0.0 21,631,350.8 1,000,000.0 SEP 30.0 246.2 8.3 268,500.0 585,304.0 853,804.0 55,915.3 114,964.6 14,904.0 185,783.9 268,500.0 268,500.0 668,020.1 668,020.1 1,668,020.1 1,000,000.0 22,903.2 690,923.3 22,322,274.1 0.0 22,053,774.1 1,000,000.0 OCT 31.0 354.8 0.0 277,450.0 816,132.0 1,093,582.0 21,735.0 118,796.8 14,904.0 155,435.8 277,450.0 277,450.0 938,146.2 938,146.2 1,938,146.2 1,000,000.0 31,935.6 970,081.8 23,023,855.9 0.0 22,746,405.9 1,000,000.0 NOV 30.0 337.9 0.0 268,500.0 777,239.0 1,045,739.0 0.0 114,964.6 14,904.0 129,868.6 268,500.0 268,500.0 915,870.4 915,870.4 1,915,870.4 1,000,000.0 30,413.7 946,284.1 23,692,690.0 0.0 23,424,190.0 1,000,000.0 DEC 31.0 313.0 0.0 277,450.0 719,900.0 997,350.0 0.0 118,796.8 14,904.0 133,700.8 277,450.0 277,450.0 863,649.2 863,649.2 1,863,649.2 1,000,000.0 28,170.0 891,819.2 24,316,009.2 0.0 24,038,559.2 1,000,000.0    149 Table B.5 Deterministic model for solids content of 70% in the wet condition for an unlined impoundment Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3) Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly storage Water move  to reservoir Cumulative storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Returned to pond Water remained in reservoir Water remained on TMF+ return from reservoir   Year 1 JAN 31.0 373.1 0.0 178,360.7 858,107.0 1,036,467.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 926,458.1 926,458.1 926,458.1 0.0 33,578.1 960,036.2 960,036.2 0.0 781,675.5 0.0 FEB 28.0 197.7 0.0 161,100.0 454,618.0 615,718.0 0.0 85,901.8 14,904.0 100,805.8 161,100.0 161,100.0 514,912.2 514,912.2 514,912.2 0.0 17,789.4 532,701.6 1,314,377.1 0.0 1,153,277.1 0.0 MAR 31.0 167.2 0.0 178,360.7 384,468.0 562,828.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 452,819.1 452,819.1 452,819.1 0.0 15,044.4 467,863.5 1,621,140.6 0.0 1,442,779.9 0.0 APR 30.0 117.8 60.6 172,607.1 410,297.0 582,904.1 41,676.0 92,037.7 14,904.0 148,617.7 172,607.1 172,607.1 434,286.4 434,286.4 434,286.4 0.0 16,055.1 450,341.5 1,893,121.4 0.0 1,720,514.3 0.0 MAY 31.0 98.5 118.9 178,360.7 499,905.0 678,265.7 85,187.4 95,105.6 14,904.0 195,197.0 178,360.7 178,360.7 483,068.7 483,068.7 483,068.7 0.0 19,561.5 502,630.2 2,223,144.5 0.0 2,044,783.8 0.0 JUN 30.0 98.3 185.4 172,607.1 652,326.0 824,933.1 110,551.8 92,037.7 14,904.0 217,493.5 172,607.1 172,607.1 607,439.6 607,439.6 607,439.6 0.0 25,525.8 632,965.4 2,677,749.2 0.0 2,505,142.1 0.0 JUL 31.0 102.8 37.6 178,360.7 322,943.0 501,303.7 109,337.4 95,105.6 14,904.0 219,347.0 178,360.7 178,360.7 281,956.7 281,956.7 281,956.7 0.0 12,636.9 294,593.6 2,799,735.7 0.0 2,621,375.0 0.0 AUG 31.0 163.6 1.4 178,360.7 379,385.0 557,745.7 94,281.6 95,105.6 14,904.0 204,291.2 178,360.7 178,360.7 353,454.5 353,454.5 353,454.5 0.0 14,845.5 368,300.0 2,989,675.0 0.0 2,811,314.3 0.0 SEP 30.0 246.2 8.3 172,607.1 585,304.0 757,911.1 47,927.4 92,037.7 14,904.0 154,869.1 172,607.1 172,607.1 603,042.0 603,042.0 603,042.0 0.0 22,903.2 625,945.2 3,437,259.5 0.0 3,264,652.4 0.0 OCT 31.0 354.8 0.0 178,360.7 816,132.0 994,492.7 18,630.0 95,105.6 14,904.0 128,639.6 178,360.7 178,360.7 865,853.1 865,853.1 865,853.1 0.0 31,935.6 897,788.7 4,162,441.1 0.0 3,984,080.4 0.0 NOV 30.0 337.9 0.0 172,607.1 777,239.0 949,846.1 0.0 92,037.7 14,904.0 106,941.7 172,607.1 172,607.1 842,904.4 842,904.4 842,904.4 0.0 30,413.7 873,318.1 4,857,398.5 0.0 4,684,791.4 0.0 DEC 31.0 313.0 0.0 178,360.7 719,900.0 898,260.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 788,251.1 788,251.1 788,251.1 0.0 28,170.0 816,421.1 5,501,212.5 0.0 5,322,851.8 0.0 Year 2 JAN 31.0 373.1 0.0 178,360.7 858,107.0 1,036,467.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 926,458.1 926,458.1 926,458.1 0.0 33,578.1 960,036.2 6,282,888.0 0.0 6,104,527.3 0.0 FEB 28.0 197.7 0.0 161,100.0 454,618.0 615,718.0 0.0 85,901.8 14,904.0 100,805.8 161,100.0 161,100.0 514,912.2 514,912.2 514,912.2 0.0 17,789.4 532,701.6 6,637,228.9 0.0 6,476,128.9 0.0 MAR 31.0 167.2 0.0 178,360.7 384,468.0 562,828.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 452,819.1 452,819.1 452,819.1 0.0 15,044.4 467,863.5 6,943,992.4 0.0 6,765,631.7 0.0 APR 30.0 117.8 60.6 172,607.1 410,297.0 582,904.1 41,676.0 92,037.7 14,904.0 148,617.7 172,607.1 172,607.1 434,286.4 434,286.4 434,286.4 0.0 16,055.1 450,341.5 7,215,973.2 0.0 7,043,366.1 0.0 MAY 31.0 98.5 118.9 178,360.7 499,905.0 678,265.7 85,187.4 95,105.6 14,904.0 195,197.0 178,360.7 178,360.7 483,068.7 483,068.7 483,068.7 0.0 19,561.5 502,630.2 7,545,996.3 0.0 7,367,635.6 0.0 JUN 30.0 98.3 185.4 172,607.1 652,326.0 824,933.1 110,551.8 92,037.7 14,904.0 217,493.5 172,607.1 172,607.1 607,439.6 607,439.6 607,439.6 0.0 25,525.8 632,965.4 8,000,601.0 0.0 7,827,993.9 0.0 JUL 31.0 102.8 37.6 178,360.7 322,943.0 501,303.7 109,337.4 95,105.6 14,904.0 219,347.0 178,360.7 178,360.7 281,956.7 281,956.7 281,956.7 0.0 12,636.9 294,593.6 8,122,587.5 0.0 7,944,226.8 0.0 AUG 31.0 163.6 1.4 178,360.7 379,385.0 557,745.7 94,281.6 95,105.6 14,904.0 204,291.2 178,360.7 178,360.7 353,454.5 353,454.5 353,454.5 0.0 14,845.5 368,300.0 8,312,526.8 0.0 8,134,166.1 0.0 SEP 30.0 246.2 8.3 172,607.1 585,304.0 757,911.1 47,927.4 92,037.7 14,904.0 154,869.1 172,607.1 172,607.1 603,042.0 603,042.0 603,042.0 0.0 22,903.2 625,945.2 8,760,111.3 0.0 8,587,504.2 0.0 OCT 31.0 354.8 0.0 178,360.7 816,132.0 994,492.7 18,630.0 95,105.6 14,904.0 128,639.6 178,360.7 178,360.7 865,853.1 865,853.1 865,853.1 0.0 31,935.6 897,788.7 9,485,292.9 0.0 9,306,932.2 0.0 NOV 30.0 337.9 0.0 172,607.1 777,239.0 949,846.1 0.0 92,037.7 14,904.0 106,941.7 172,607.1 172,607.1 842,904.4 842,904.4 842,904.4 0.0 30,413.7 873,318.1 10,180,250.3 0.0 10,007,643.2 0.0 DEC 31.0 313.0 0.0 178,360.7 719,900.0 898,260.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 788,251.1 788,251.1 788,251.1 0.0 28,170.0 816,421.1 10,824,064.3 0.0 10,645,703.6 0.0 Year 3 JAN 31.0 373.1 0.0 178,360.7 858,107.0 1,036,467.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 926,458.1 926,458.1 926,458.1 0.0 33,578.1 960,036.2 11,605,739.8 0.0 11,427,379.1 0.0 FEB 28.0 197.7 0.0 161,100.0 454,618.0 615,718.0 0.0 85,901.8 14,904.0 100,805.8 161,100.0 161,100.0 514,912.2 514,912.2 514,912.2 0.0 17,789.4 532,701.6 11,960,080.7 0.0 11,798,980.7 0.0 MAR 31.0 167.2 0.0 178,360.7 384,468.0 562,828.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 452,819.1 452,819.1 452,819.1 0.0 15,044.4 467,863.5 12,266,844.2 0.0 12,088,483.5 0.0 APR 30.0 117.8 60.6 172,607.1 410,297.0 582,904.1 41,676.0 92,037.7 14,904.0 148,617.7 172,607.1 172,607.1 434,286.4 434,286.4 434,286.4 0.0 16,055.1 450,341.5 12,538,825.0 0.0 12,366,217.9 0.0 MAY 31.0 98.5 118.9 178,360.7 499,905.0 678,265.7 85,187.4 95,105.6 14,904.0 195,197.0 178,360.7 178,360.7 483,068.7 483,068.7 483,068.7 0.0 19,561.5 502,630.2 12,868,848.1 0.0 12,690,487.4 0.0 JUN 30.0 98.3 185.4 172,607.1 652,326.0 824,933.1 110,551.8 92,037.7 14,904.0 217,493.5 172,607.1 172,607.1 607,439.6 607,439.6 607,439.6 0.0 25,525.8 632,965.4 13,323,452.8 0.0 13,150,845.7 0.0 JUL 31.0 102.8 37.6 178,360.7 322,943.0 501,303.7 109,337.4 95,105.6 14,904.0 219,347.0 178,360.7 178,360.7 281,956.7 281,956.7 281,956.7 0.0 12,636.9 294,593.6 13,445,439.3 0.0 13,267,078.6 0.0 AUG 31.0 163.6 1.4 178,360.7 379,385.0 557,745.7 94,281.6 95,105.6 14,904.0 204,291.2 178,360.7 178,360.7 353,454.5 353,454.5 353,454.5 0.0 14,845.5 368,300.0 13,635,378.6 0.0 13,457,017.9 0.0 SEP 30.0 246.2 8.3 172,607.1 585,304.0 757,911.1 47,927.4 92,037.7 14,904.0 154,869.1 172,607.1 172,607.1 603,042.0 603,042.0 603,042.0 0.0 22,903.2 625,945.2 14,082,963.1 0.0 13,910,356.0 0.0 OCT 31.0 354.8 0.0 178,360.7 816,132.0 994,492.7 18,630.0 95,105.6 14,904.0 128,639.6 178,360.7 178,360.7 865,853.1 865,853.1 865,853.1 0.0 31,935.6 897,788.7 14,808,144.7 0.0 14,629,784.0 0.0 NOV 30.0 337.9 0.0 172,607.1 777,239.0 949,846.1 0.0 92,037.7 14,904.0 106,941.7 172,607.1 172,607.1 842,904.4 842,904.4 842,904.4 0.0 30,413.7 873,318.1 15,503,102.1 0.0 15,330,495.0 0.0  150 Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3) Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly storage Water move  to reservoir Cumulative storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Returned to pond Water remained in reservoir Water remained on TMF+ return from reservoir   DEC 31.0 313.0 0.0 178,360.7 719,900.0 898,260.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 788,251.1 788,251.1 788,251.1 0.0 28,170.0 816,421.1 16,146,916.1 0.0 15,968,555.4 0.0 Year 4 JAN 31.0 373.1 0.0 178,360.7 858,107.0 1,036,467.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 926,458.1 926,458.1 926,458.1 0.0 33,578.1 960,036.2 16,928,591.6 0.0 16,750,230.9 0.0 FEB 28.0 197.7 0.0 161,100.0 454,618.0 615,718.0 0.0 85,901.8 14,904.0 100,805.8 161,100.0 161,100.0 514,912.2 514,912.2 514,912.2 0.0 17,789.4 532,701.6 17,282,932.5 0.0 17,121,832.5 0.0 MAR 31.0 167.2 0.0 178,360.7 384,468.0 562,828.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 452,819.1 452,819.1 452,819.1 0.0 15,044.4 467,863.5 17,589,696.0 0.0 17,411,335.3 0.0 APR 30.0 117.8 60.6 172,607.1 410,297.0 582,904.1 41,676.0 92,037.7 14,904.0 148,617.7 172,607.1 172,607.1 434,286.4 434,286.4 434,286.4 0.0 16,055.1 450,341.5 17,861,676.8 0.0 17,689,069.7 0.0 MAY 31.0 98.5 118.9 178,360.7 499,905.0 678,265.7 85,187.4 95,105.6 14,904.0 195,197.0 178,360.7 178,360.7 483,068.7 483,068.7 483,068.7 0.0 19,561.5 502,630.2 18,191,699.9 0.0 18,013,339.2 0.0 JUN 30.0 98.3 185.4 172,607.1 652,326.0 824,933.1 110,551.8 92,037.7 14,904.0 217,493.5 172,607.1 172,607.1 607,439.6 607,439.6 607,439.6 0.0 25,525.8 632,965.4 18,646,304.6 0.0 18,473,697.5 0.0 JUL 31.0 102.8 37.6 178,360.7 322,943.0 501,303.7 109,337.4 95,105.6 14,904.0 219,347.0 178,360.7 178,360.7 281,956.7 281,956.7 281,956.7 0.0 12,636.9 294,593.6 18,768,291.1 0.0 18,589,930.4 0.0 AUG 31.0 163.6 1.4 178,360.7 379,385.0 557,745.7 94,281.6 95,105.6 14,904.0 204,291.2 178,360.7 178,360.7 353,454.5 353,454.5 353,454.5 0.0 14,845.5 368,300.0 18,958,230.4 0.0 18,779,869.7 0.0 SEP 30.0 246.2 8.3 172,607.1 585,304.0 757,911.1 47,927.4 92,037.7 14,904.0 154,869.1 172,607.1 172,607.1 603,042.0 603,042.0 603,042.0 0.0 22,903.2 625,945.2 19,405,814.9 0.0 19,233,207.8 0.0 OCT 31.0 354.8 0.0 178,360.7 816,132.0 994,492.7 18,630.0 95,105.6 14,904.0 128,639.6 178,360.7 178,360.7 865,853.1 865,853.1 865,853.1 0.0 31,935.6 897,788.7 20,130,996.5 0.0 19,952,635.8 0.0 NOV 30.0 337.9 0.0 172,607.1 777,239.0 949,846.1 0.0 92,037.7 14,904.0 106,941.7 172,607.1 172,607.1 842,904.4 842,904.4 842,904.4 0.0 30,413.7 873,318.1 20,825,953.9 0.0 20,653,346.8 0.0 DEC 31.0 313.0 0.0 178,360.7 719,900.0 898,260.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 788,251.1 788,251.1 788,251.1 0.0 28,170.0 816,421.1 21,469,767.9 0.0 21,291,407.2 0.0 Year 5 JAN 31.0 373.1 0.0 178,360.7 858,107.0 1,036,467.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 926,458.1 926,458.1 926,458.1 0.0 33,578.1 960,036.2 22,251,443.4 0.0 22,073,082.7 0.0 FEB 28.0 197.7 0.0 161,100.0 454,618.0 615,718.0 0.0 85,901.8 14,904.0 100,805.8 161,100.0 161,100.0 514,912.2 514,912.2 514,912.2 0.0 17,789.4 532,701.6 22,605,784.3 0.0 22,444,684.3 0.0 MAR 31.0 167.2 0.0 178,360.7 384,468.0 562,828.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 452,819.1 452,819.1 452,819.1 0.0 15,044.4 467,863.5 22,912,547.8 0.0 22,734,187.1 0.0 APR 30.0 117.8 60.6 172,607.1 410,297.0 582,904.1 41,676.0 92,037.7 14,904.0 148,617.7 172,607.1 172,607.1 434,286.4 434,286.4 434,286.4 0.0 16,055.1 450,341.5 23,184,528.6 0.0 23,011,921.5 0.0 MAY 31.0 98.5 118.9 178,360.7 499,905.0 678,265.7 85,187.4 95,105.6 14,904.0 195,197.0 178,360.7 178,360.7 483,068.7 483,068.7 483,068.7 0.0 19,561.5 502,630.2 23,514,551.7 0.0 23,336,191.0 0.0 JUN 30.0 98.3 185.4 172,607.1 652,326.0 824,933.1 110,551.8 92,037.7 14,904.0 217,493.5 172,607.1 172,607.1 607,439.6 607,439.6 607,439.6 0.0 25,525.8 632,965.4 23,969,156.4 0.0 23,796,549.3 0.0 JUL 31.0 102.8 37.6 178,360.7 322,943.0 501,303.7 109,337.4 95,105.6 14,904.0 219,347.0 178,360.7 178,360.7 281,956.7 281,956.7 281,956.7 0.0 12,636.9 294,593.6 24,091,142.9 0.0 23,912,782.2 0.0 AUG 31.0 163.6 1.4 178,360.7 379,385.0 557,745.7 94,281.6 95,105.6 14,904.0 204,291.2 178,360.7 178,360.7 353,454.5 353,454.5 353,454.5 0.0 14,845.5 368,300.0 24,281,082.2 0.0 24,102,721.5 0.0 SEP 30.0 246.2 8.3 172,607.1 585,304.0 757,911.1 47,927.4 92,037.7 14,904.0 154,869.1 172,607.1 172,607.1 603,042.0 603,042.0 603,042.0 0.0 22,903.2 625,945.2 24,728,666.7 0.0 24,556,059.6 0.0 OCT 31.0 354.8 0.0 178,360.7 816,132.0 994,492.7 18,630.0 95,105.6 14,904.0 128,639.6 178,360.7 178,360.7 865,853.1 865,853.1 865,853.1 0.0 31,935.6 897,788.7 25,453,848.3 0.0 25,275,487.6 0.0 NOV 30.0 337.9 0.0 172,607.1 777,239.0 949,846.1 0.0 92,037.7 14,904.0 106,941.7 172,607.1 172,607.1 842,904.4 842,904.4 842,904.4 0.0 30,413.7 873,318.1 26,148,805.7 0.0 25,976,198.6 0.0 DEC 31.0 313.0 0.0 178,360.7 719,900.0 898,260.7 0.0 95,105.6 14,904.0 110,009.6 178,360.7 178,360.7 788,251.1 788,251.1 788,251.1 0.0 28,170.0 816,421.1 26,792,619.7 0.0 26,614,259.0 0.0    151 Table B.6 Deterministic model for solids content of 80% in the wet condition for an unlined impoundment  Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3) Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly storage Water move  to reservoir Cumulative storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Returned to pond Water remained in reservoir Water remained on TMF+ return from reservoir   Year 1 JAN 31.0 373.1 0.0 104,043.8 858,107.0 962,150.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 865,741.7 865,741.7 865,741.7 0.0 33,578.1 899,319.8 899,319.8 0.0 795,276.0 0.0 FEB 28.0 197.7 0.0 93,975.0 454,618.0 548,593.0 0.0 73,617.5 14,904.0 88,521.5 93,975.0 93,975.0 460,071.5 460,071.5 460,071.5 0.0 17,789.4 477,860.9 1,273,136.9 0.0 1,179,161.9 0.0 MAR 31.0 167.2 0.0 104,043.8 384,468.0 488,511.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 392,102.7 392,102.7 392,102.7 0.0 15,044.4 407,147.1 1,586,309.0 0.0 1,482,265.2 0.0 APR 30.0 117.8 60.6 100,687.5 410,297.0 510,984.5 41,676.0 78,875.9 14,904.0 135,455.9 100,687.5 100,687.5 375,528.6 375,528.6 375,528.6 0.0 16,055.1 391,583.7 1,873,848.9 0.0 1,773,161.4 0.0 MAY 31.0 98.5 118.9 104,043.8 499,905.0 603,948.8 85,187.4 81,505.1 14,904.0 181,596.5 104,043.8 104,043.8 422,352.3 422,352.3 422,352.3 0.0 19,561.5 441,913.8 2,215,075.2 0.0 2,111,031.4 0.0 JUN 30.0 5.0 185.4 100,687.5 437,805.0 538,492.5 110,551.8 78,875.9 14,904.0 204,331.7 100,687.5 100,687.5 334,160.8 334,160.8 334,160.8 0.0 17,131.5 351,292.3 2,462,323.7 0.0 2,361,636.2 0.0 JUL 31.0 102.8 37.6 104,043.8 322,943.0 426,986.8 109,337.4 81,505.1 14,904.0 205,746.5 104,043.8 104,043.8 221,240.3 221,240.3 221,240.3 0.0 12,636.9 233,877.2 2,595,513.4 0.0 2,491,469.6 0.0 AUG 31.0 163.6 1.4 104,043.8 379,385.0 483,428.8 94,281.6 81,505.1 14,904.0 190,690.7 104,043.8 104,043.8 292,738.1 292,738.1 292,738.1 0.0 14,845.5 307,583.6 2,799,053.2 0.0 2,695,009.4 0.0 SEP 30.0 246.2 8.3 100,687.5 585,304.0 685,991.5 47,927.4 78,875.9 14,904.0 141,707.3 100,687.5 100,687.5 544,284.2 544,284.2 544,284.2 0.0 22,903.2 567,187.4 3,262,196.8 0.0 3,161,509.3 0.0 OCT 31.0 354.8 0.0 104,043.8 816,132.0 920,175.8 18,630.0 81,505.1 14,904.0 115,039.1 104,043.8 104,043.8 805,136.7 805,136.7 805,136.7 0.0 31,935.6 837,072.3 3,998,581.6 0.0 3,894,537.8 0.0 NOV 30.0 337.9 0.0 100,687.5 777,239.0 877,926.5 0.0 78,875.9 14,904.0 93,779.9 100,687.5 100,687.5 784,146.6 784,146.6 784,146.6 0.0 30,413.7 814,560.3 4,709,098.1 0.0 4,608,410.6 0.0 DEC 31.0 313.0 0.0 104,043.8 719,900.0 823,943.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 727,534.7 727,534.7 727,534.7 0.0 28,170.0 755,704.7 5,364,115.3 0.0 5,260,071.5 0.0 Year 2 JAN 31.0 373.1 0.0 104,043.8 858,107.0 962,150.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 865,741.7 865,741.7 865,741.7 0.0 33,578.1 899,319.8 6,159,391.3 0.0 6,055,347.5 0.0 FEB 28.0 197.7 0.0 93,975.0 454,618.0 548,593.0 0.0 73,617.5 14,904.0 88,521.5 93,975.0 93,975.0 460,071.5 460,071.5 460,071.5 0.0 17,789.4 477,860.9 6,533,208.4 0.0 6,439,233.4 0.0 MAR 31.0 167.2 0.0 104,043.8 384,468.0 488,511.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 392,102.7 392,102.7 392,102.7 0.0 15,044.4 407,147.1 6,846,380.5 0.0 6,742,336.7 0.0 APR 30.0 117.8 60.6 100,687.5 410,297.0 510,984.5 41,676.0 78,875.9 14,904.0 135,455.9 100,687.5 100,687.5 375,528.6 375,528.6 375,528.6 0.0 16,055.1 391,583.7 7,133,920.4 0.0 7,033,232.9 0.0 MAY 31.0 98.5 118.9 104,043.8 499,905.0 603,948.8 85,187.4 81,505.1 14,904.0 181,596.5 104,043.8 104,043.8 422,352.3 422,352.3 422,352.3 0.0 19,561.5 441,913.8 7,475,146.7 0.0 7,371,102.9 0.0 JUN 30.0 98.3 185.4 100,687.5 652,326.0 753,013.5 110,551.8 78,875.9 14,904.0 204,331.7 100,687.5 100,687.5 548,681.8 548,681.8 548,681.8 0.0 25,525.8 574,207.6 7,945,310.5 0.0 7,844,623.0 0.0 JUL 31.0 102.8 37.6 104,043.8 322,943.0 426,986.8 109,337.4 81,505.1 14,904.0 205,746.5 104,043.8 104,043.8 221,240.3 221,240.3 221,240.3 0.0 12,636.9 233,877.2 8,078,500.2 0.0 7,974,456.4 0.0 AUG 31.0 163.6 1.4 104,043.8 379,385.0 483,428.8 94,281.6 81,505.1 14,904.0 190,690.7 104,043.8 104,043.8 292,738.1 292,738.1 292,738.1 0.0 14,845.5 307,583.6 8,282,040.0 0.0 8,177,996.2 0.0 SEP 30.0 246.2 8.3 100,687.5 585,304.0 685,991.5 47,927.4 78,875.9 14,904.0 141,707.3 100,687.5 100,687.5 544,284.2 544,284.2 544,284.2 0.0 22,903.2 567,187.4 8,745,183.6 0.0 8,644,496.1 0.0 OCT 31.0 354.8 0.0 104,043.8 816,132.0 920,175.8 18,630.0 81,505.1 14,904.0 115,039.1 104,043.8 104,043.8 805,136.7 805,136.7 805,136.7 0.0 31,935.6 837,072.3 9,481,568.4 0.0 9,377,524.6 0.0 NOV 30.0 337.9 0.0 100,687.5 777,239.0 877,926.5 0.0 78,875.9 14,904.0 93,779.9 100,687.5 100,687.5 784,146.6 784,146.6 784,146.6 0.0 30,413.7 814,560.3 10,192,084.9 0.0 10,091,397.4 0.0 DEC 31.0 313.0 0.0 104,043.8 719,900.0 823,943.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 727,534.7 727,534.7 727,534.7 0.0 28,170.0 755,704.7 10,847,102.1 0.0 10,743,058.3 0.0 Year 3 JAN 31.0 373.1 0.0 104,043.8 858,107.0 962,150.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 865,741.7 865,741.7 865,741.7 0.0 33,578.1 899,319.8 11,642,378.1 0.0 11,538,334.3 0.0 FEB 28.0 197.7 0.0 93,975.0 454,618.0 548,593.0 0.0 73,617.5 14,904.0 88,521.5 93,975.0 93,975.0 460,071.5 460,071.5 460,071.5 0.0 17,789.4 477,860.9 12,016,195.2 0.0 11,922,220.2 0.0 MAR 31.0 167.2 0.0 104,043.8 384,468.0 488,511.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 392,102.7 392,102.7 392,102.7 0.0 15,044.4 407,147.1 12,329,367.3 0.0 12,225,323.5 0.0 APR 30.0 117.8 60.6 100,687.5 410,297.0 510,984.5 41,676.0 78,875.9 14,904.0 135,455.9 100,687.5 100,687.5 375,528.6 375,528.6 375,528.6 0.0 16,055.1 391,583.7 12,616,907.2 0.0 12,516,219.7 0.0 MAY 31.0 98.5 118.9 104,043.8 499,905.0 603,948.8 85,187.4 81,505.1 14,904.0 181,596.5 104,043.8 104,043.8 422,352.3 422,352.3 422,352.3 0.0 19,561.5 441,913.8 12,958,133.5 0.0 12,854,089.7 0.0 JUN 30.0 98.3 185.4 100,687.5 652,326.0 753,013.5 110,551.8 78,875.9 14,904.0 204,331.7 100,687.5 100,687.5 548,681.8 548,681.8 548,681.8 0.0 25,525.8 574,207.6 13,428,297.3 0.0 13,327,609.8 0.0 JUL 31.0 102.8 37.6 104,043.8 322,943.0 426,986.8 109,337.4 81,505.1 14,904.0 205,746.5 104,043.8 104,043.8 221,240.3 221,240.3 221,240.3 0.0 12,636.9 233,877.2 13,561,487.0 0.0 13,457,443.2 0.0 AUG 31.0 163.6 1.4 104,043.8 379,385.0 483,428.8 94,281.6 81,505.1 14,904.0 190,690.7 104,043.8 104,043.8 292,738.1 292,738.1 292,738.1 0.0 14,845.5 307,583.6 13,765,026.8 0.0 13,660,983.0 0.0 SEP 30.0 246.2 8.3 100,687.5 585,304.0 685,991.5 47,927.4 78,875.9 14,904.0 141,707.3 100,687.5 100,687.5 544,284.2 544,284.2 544,284.2 0.0 22,903.2 567,187.4 14,228,170.4 0.0 14,127,482.9 0.0  152 Precipitation water (mm/month) Water in (m3) Water out (m3) Mill required water (m3) Water in the TMF (m3)   Reservoir storage (m3) Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Total Water Input Evaporation Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly storage Water move  to reservoir Cumulative storage Water remained in TMF Mine impacted water runoff Monthly reservoir storage Cumulative reservoir storage Returned to pond Water remained in reservoir Water remained on TMF+ return from reservoir   OCT 31.0 354.8 0.0 104,043.8 816,132.0 920,175.8 18,630.0 81,505.1 14,904.0 115,039.1 104,043.8 104,043.8 805,136.7 805,136.7 805,136.7 0.0 31,935.6 837,072.3 14,964,555.2 0.0 14,860,511.4 0.0 NOV 30.0 337.9 0.0 100,687.5 777,239.0 877,926.5 0.0 78,875.9 14,904.0 93,779.9 100,687.5 100,687.5 784,146.6 784,146.6 784,146.6 0.0 30,413.7 814,560.3 15,675,071.7 0.0 15,574,384.2 0.0 DEC 31.0 313.0 0.0 104,043.8 719,900.0 823,943.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 727,534.7 727,534.7 727,534.7 0.0 28,170.0 755,704.7 16,330,088.9 0.0 16,226,045.1 0.0 Year 4 JAN 31.0 373.1 0.0 104,043.8 858,107.0 962,150.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 865,741.7 865,741.7 865,741.7 0.0 33,578.1 899,319.8 17,125,364.9 0.0 17,021,321.1 0.0 FEB 28.0 197.7 0.0 93,975.0 454,618.0 548,593.0 0.0 73,617.5 14,904.0 88,521.5 93,975.0 93,975.0 460,071.5 460,071.5 460,071.5 0.0 17,789.4 477,860.9 17,499,182.0 0.0 17,405,207.0 0.0 MAR 31.0 167.2 0.0 104,043.8 384,468.0 488,511.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 392,102.7 392,102.7 392,102.7 0.0 15,044.4 407,147.1 17,812,354.1 0.0 17,708,310.3 0.0 APR 30.0 117.8 60.6 100,687.5 410,297.0 510,984.5 41,676.0 78,875.9 14,904.0 135,455.9 100,687.5 100,687.5 375,528.6 375,528.6 375,528.6 0.0 16,055.1 391,583.7 18,099,894.0 0.0 17,999,206.5 0.0 MAY 31.0 98.5 118.9 104,043.8 499,905.0 603,948.8 85,187.4 81,505.1 14,904.0 181,596.5 104,043.8 104,043.8 422,352.3 422,352.3 422,352.3 0.0 19,561.5 441,913.8 18,441,120.3 0.0 18,337,076.5 0.0 JUN 30.0 98.3 185.4 100,687.5 652,326.0 753,013.5 110,551.8 78,875.9 14,904.0 204,331.7 100,687.5 100,687.5 548,681.8 548,681.8 548,681.8 0.0 25,525.8 574,207.6 18,911,284.1 0.0 18,810,596.6 0.0 JUL 31.0 102.8 37.6 104,043.8 322,943.0 426,986.8 109,337.4 81,505.1 14,904.0 205,746.5 104,043.8 104,043.8 221,240.3 221,240.3 221,240.3 0.0 12,636.9 233,877.2 19,044,473.8 0.0 18,940,430.0 0.0 AUG 31.0 163.6 1.4 104,043.8 379,385.0 483,428.8 94,281.6 81,505.1 14,904.0 190,690.7 104,043.8 104,043.8 292,738.1 292,738.1 292,738.1 0.0 14,845.5 307,583.6 19,248,013.6 0.0 19,143,969.8 0.0 SEP 30.0 246.2 8.3 100,687.5 585,304.0 685,991.5 47,927.4 78,875.9 14,904.0 141,707.3 100,687.5 100,687.5 544,284.2 544,284.2 544,284.2 0.0 22,903.2 567,187.4 19,711,157.2 0.0 19,610,469.7 0.0 OCT 31.0 354.8 0.0 104,043.8 816,132.0 920,175.8 18,630.0 81,505.1 14,904.0 115,039.1 104,043.8 104,043.8 805,136.7 805,136.7 805,136.7 0.0 31,935.6 837,072.3 20,447,542.0 0.0 20,343,498.2 0.0 NOV 30.0 337.9 0.0 100,687.5 777,239.0 877,926.5 0.0 78,875.9 14,904.0 93,779.9 100,687.5 100,687.5 784,146.6 784,146.6 784,146.6 0.0 30,413.7 814,560.3 21,158,058.5 0.0 21,057,371.0 0.0 DEC 31.0 313.0 0.0 104,043.8 719,900.0 823,943.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 727,534.7 727,534.7 727,534.7 0.0 28,170.0 755,704.7 21,813,075.7 0.0 21,709,031.9 0.0 Year 5 JAN 31.0 373.1 0.0 104,043.8 858,107.0 962,150.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 865,741.7 865,741.7 865,741.7 0.0 33,578.1 899,319.8 22,608,351.7 0.0 22,504,307.9 0.0 FEB 28.0 197.7 0.0 93,975.0 454,618.0 548,593.0 0.0 73,617.5 14,904.0 88,521.5 93,975.0 93,975.0 460,071.5 460,071.5 460,071.5 0.0 17,789.4 477,860.9 22,982,168.8 0.0 22,888,193.8 0.0 MAR 31.0 167.2 0.0 104,043.8 384,468.0 488,511.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 392,102.7 392,102.7 392,102.7 0.0 15,044.4 407,147.1 23,295,340.9 0.0 23,191,297.1 0.0 APR 30.0 117.8 60.6 100,687.5 410,297.0 510,984.5 41,676.0 78,875.9 14,904.0 135,455.9 100,687.5 100,687.5 375,528.6 375,528.6 375,528.6 0.0 16,055.1 391,583.7 23,582,880.8 0.0 23,482,193.3 0.0 MAY 31.0 98.5 118.9 104,043.8 499,905.0 603,948.8 85,187.4 81,505.1 14,904.0 181,596.5 104,043.8 104,043.8 422,352.3 422,352.3 422,352.3 0.0 19,561.5 441,913.8 23,924,107.1 0.0 23,820,063.3 0.0 JUN 30.0 98.3 185.4 100,687.5 652,326.0 753,013.5 110,551.8 78,875.9 14,904.0 204,331.7 100,687.5 100,687.5 548,681.8 548,681.8 548,681.8 0.0 25,525.8 574,207.6 24,394,270.9 0.0 24,293,583.4 0.0 JUL 31.0 102.8 37.6 104,043.8 322,943.0 426,986.8 109,337.4 81,505.1 14,904.0 205,746.5 104,043.8 104,043.8 221,240.3 221,240.3 221,240.3 0.0 12,636.9 233,877.2 24,527,460.6 0.0 24,423,416.8 0.0 AUG 31.0 163.6 1.4 104,043.8 379,385.0 483,428.8 94,281.6 81,505.1 14,904.0 190,690.7 104,043.8 104,043.8 292,738.1 292,738.1 292,738.1 0.0 14,845.5 307,583.6 24,731,000.4 0.0 24,626,956.6 0.0 SEP 30.0 246.2 8.3 100,687.5 585,304.0 685,991.5 47,927.4 78,875.9 14,904.0 141,707.3 100,687.5 100,687.5 544,284.2 544,284.2 544,284.2 0.0 22,903.2 567,187.4 25,194,144.0 0.0 25,093,456.5 0.0 OCT 31.0 354.8 0.0 104,043.8 816,132.0 920,175.8 18,630.0 81,505.1 14,904.0 115,039.1 104,043.8 104,043.8 805,136.7 805,136.7 805,136.7 0.0 31,935.6 837,072.3 25,930,528.8 0.0 25,826,485.0 0.0 NOV 30.0 337.9 0.0 100,687.5 777,239.0 877,926.5 0.0 78,875.9 14,904.0 93,779.9 100,687.5 100,687.5 784,146.6 784,146.6 784,146.6 0.0 30,413.7 814,560.3 26,641,045.3 0.0 26,540,357.8 0.0 DEC 31.0 313.0 0.0 104,043.8 719,900.0 823,943.8 0.0 81,505.1 14,904.0 96,409.1 104,043.8 104,043.8 727,534.7 727,534.7 727,534.7 0.0 28,170.0 755,704.7 27,296,062.5 0.0 27,192,018.7 0.0  153 Appendix C  Results of dry climate simulation C.1 Lined impoundment  154 Table C.1 Deterministic model for solids content of 60% in the dry condition for a lined impoundment Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from TMF Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   Year 1 JAN 31.0 17.6 0.0 277,450.0 40,480.0 0.0 317,930.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 199,133.2 277,450.0 -78,316.8 -0.2 FEB 28.0 13.8 0.0 250,600.0 31,740.0 0.0 282,340.0 0.0 107,300.3 107,300.3 250,600.0 250,600.0 175,039.7 250,600.0 -153,877.1 -0.2 MAR 31.0 17.4 0.0 277,450.0 40,020.0 0.0 317,470.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 198,673.2 277,450.0 -232,653.8 -0.2 APR 30.0 16.8 0.0 268,500.0 38,640.0 0.0 307,140.0 140,638.1 114,964.6 255,602.7 268,500.0 268,500.0 51,537.3 268,500.0 -449,616.5 -0.3 MAY 31.0 15.9 0.0 277,450.0 36,570.0 0.0 314,020.0 287,329.8 118,796.8 406,126.6 277,450.0 277,450.0 0.0 277,450.0 -727,066.5 -0.4 JUN 30.0 7.8 0.0 268,500.0 17,940.0 0.0 286,440.0 372,945.0 114,964.6 487,909.6 268,500.0 268,500.0 0.0 268,500.0 -995,566.5 -0.4 JUL 31.0 8.9 0.0 277,450.0 20,470.0 0.0 297,920.0 368,837.2 118,796.8 487,634.0 277,450.0 277,450.0 0.0 277,450.0 -1,273,016.5 -0.4 AUG 31.0 8.0 0.0 277,450.0 18,400.0 0.0 295,850.0 318,030.2 118,796.8 436,827.0 277,450.0 277,450.0 0.0 277,450.0 -1,550,466.5 -0.5 SEP 30.0 12.5 0.0 268,500.0 28,750.0 0.0 297,250.0 161,717.6 114,964.6 276,682.2 268,500.0 268,500.0 20,567.8 268,500.0 -1,798,398.7 -0.5 OCT 31.0 18.3 0.0 277,450.0 42,090.0 0.0 319,540.0 62,806.1 118,796.8 181,602.9 277,450.0 277,450.0 137,937.1 277,450.0 -1,937,911.6 -0.5 NOV 30.0 30.4 0.0 268,500.0 69,920.0 0.0 338,420.0 0.0 114,964.6 114,964.6 268,500.0 268,500.0 223,455.4 268,500.0 -1,982,956.2 -0.4 DEC 31.0 30.8 0.0 277,450.0 70,840.0 0.0 348,290.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 229,493.2 277,450.0 -2,030,913.0 -0.4 Year 2 JAN 31.0 17.6 0.0 277,450.0 40,480.0 0.0 317,930.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 199,133.2 277,450.0 -2,109,229.7 -0.4 FEB 28.0 13.8 0.0 250,600.0 31,740.0 0.0 282,340.0 0.0 107,300.3 107,300.3 250,600.0 250,600.0 175,039.7 250,600.0 -2,184,790.0 -0.4 MAR 31.0 17.4 0.0 277,450.0 40,020.0 0.0 317,470.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 198,673.2 277,450.0 -2,263,566.8 -0.4 APR 30.0 16.8 0.0 268,500.0 38,640.0 0.0 307,140.0 140,638.1 114,964.6 255,602.7 268,500.0 268,500.0 51,537.3 268,500.0 -2,480,529.5 -0.4 MAY 31.0 15.9 0.0 277,450.0 36,570.0 0.0 314,020.0 287,329.8 118,796.8 406,126.6 277,450.0 277,450.0 0.0 277,450.0 -2,757,979.5 -0.4 JUN 30.0 7.8 0.0 268,500.0 17,940.0 0.0 286,440.0 372,945.0 114,964.6 487,909.6 268,500.0 268,500.0 0.0 268,500.0 -3,026,479.5 -0.4 JUL 31.0 8.9 0.0 277,450.0 20,470.0 0.0 297,920.0 368,837.2 118,796.8 487,634.0 277,450.0 277,450.0 0.0 277,450.0 -3,303,929.5 -0.4 AUG 31.0 8.0 0.0 277,450.0 18,400.0 0.0 295,850.0 318,030.2 118,796.8 436,827.0 277,450.0 277,450.0 0.0 277,450.0 -3,581,379.5 -0.4 SEP 30.0 12.5 0.0 268,500.0 28,750.0 0.0 297,250.0 161,717.6 114,964.6 276,682.2 268,500.0 268,500.0 20,567.8 268,500.0 -3,829,311.7 -0.4 OCT 31.0 18.3 0.0 277,450.0 42,090.0 0.0 319,540.0 62,806.1 118,796.8 181,602.9 277,450.0 277,450.0 137,937.1 277,450.0 -3,968,824.6 -0.4 NOV 30.0 30.4 0.0 268,500.0 69,920.0 0.0 338,420.0 0.0 114,964.6 114,964.6 268,500.0 268,500.0 223,455.4 268,500.0 -4,013,869.2 -0.4 DEC 31.0 30.8 0.0 277,450.0 70,840.0 0.0 348,290.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 229,493.2 277,450.0 -4,061,826.0 -0.4 Year 3 JAN 31.0 17.6 0.0 277,450.0 40,480.0 0.0 317,930.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 199,133.2 277,450.0 -4,140,142.7 -0.4 FEB 28.0 13.8 0.0 250,600.0 31,740.0 0.0 282,340.0 0.0 107,300.3 107,300.3 250,600.0 250,600.0 175,039.7 250,600.0 -4,215,703.0 -0.4 MAR 31.0 17.4 0.0 277,450.0 40,020.0 0.0 317,470.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 198,673.2 277,450.0 -4,294,479.8 -0.4 APR 30.0 16.8 0.0 268,500.0 38,640.0 0.0 307,140.0 140,638.1 114,964.6 255,602.7 268,500.0 268,500.0 51,537.3 268,500.0 -4,511,442.5 -0.4 MAY 31.0 15.9 0.0 277,450.0 36,570.0 0.0 314,020.0 287,329.8 118,796.8 406,126.6 277,450.0 277,450.0 0.0 277,450.0 -4,788,892.5 -0.4 JUN 30.0 7.8 0.0 268,500.0 17,940.0 0.0 286,440.0 372,945.0 114,964.6 487,909.6 268,500.0 268,500.0 0.0 268,500.0 -5,057,392.5 -0.4 JUL 31.0 8.9 0.0 277,450.0 20,470.0 0.0 297,920.0 368,837.2 118,796.8 487,634.0 277,450.0 277,450.0 0.0 277,450.0 -5,334,842.5 -0.4 AUG 31.0 8.0 0.0 277,450.0 18,400.0 0.0 295,850.0 318,030.2 118,796.8 436,827.0 277,450.0 277,450.0 0.0 277,450.0 -5,612,292.5 -0.4 SEP 30.0 12.5 0.0 268,500.0 28,750.0 0.0 297,250.0 161,717.6 114,964.6 276,682.2 268,500.0 268,500.0 20,567.8 268,500.0 -5,860,224.7 -0.4 OCT 31.0 18.3 0.0 277,450.0 42,090.0 0.0 319,540.0 62,806.1 118,796.8 181,602.9 277,450.0 277,450.0 137,937.1 277,450.0 -5,999,737.6 -0.4 NOV 30.0 30.4 0.0 268,500.0 69,920.0 0.0 338,420.0 0.0 114,964.6 114,964.6 268,500.0 268,500.0 223,455.4 268,500.0 -6,044,782.2 -0.4  155 Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from TMF Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   DEC 31.0 30.8 0.0 277,450.0 70,840.0 0.0 348,290.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 229,493.2 277,450.0 -6,092,738.9 -0.4 Year 4 JAN 31.0 17.6 0.0 277,450.0 40,480.0 0.0 317,930.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 199,133.2 277,450.0 -6,171,055.7 -0.4 FEB 28.0 13.8 0.0 250,600.0 31,740.0 0.0 282,340.0 0.0 107,300.3 107,300.3 250,600.0 250,600.0 175,039.7 250,600.0 -6,246,616.0 -0.4 MAR 31.0 17.4 0.0 277,450.0 40,020.0 0.0 317,470.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 198,673.2 277,450.0 -6,325,392.8 -0.4 APR 30.0 16.8 0.0 268,500.0 38,640.0 0.0 307,140.0 140,638.1 114,964.6 255,602.7 268,500.0 268,500.0 51,537.3 268,500.0 -6,542,355.5 -0.4 MAY 31.0 15.9 0.0 277,450.0 36,570.0 0.0 314,020.0 287,329.8 118,796.8 406,126.6 277,450.0 277,450.0 0.0 277,450.0 -6,819,805.5 -0.4 JUN 30.0 7.8 0.0 268,500.0 17,940.0 0.0 286,440.0 372,945.0 114,964.6 487,909.6 268,500.0 268,500.0 0.0 268,500.0 -7,088,305.5 -0.4 JUL 31.0 8.9 0.0 277,450.0 20,470.0 0.0 297,920.0 368,837.2 118,796.8 487,634.0 277,450.0 277,450.0 0.0 277,450.0 -7,365,755.5 -0.4 AUG 31.0 8.0 0.0 277,450.0 18,400.0 0.0 295,850.0 318,030.2 118,796.8 436,827.0 277,450.0 277,450.0 0.0 277,450.0 -7,643,205.5 -0.4 SEP 30.0 12.5 0.0 268,500.0 28,750.0 0.0 297,250.0 161,717.6 114,964.6 276,682.2 268,500.0 268,500.0 20,567.8 268,500.0 -7,891,137.7 -0.4 OCT 31.0 18.3 0.0 277,450.0 42,090.0 0.0 319,540.0 62,806.1 118,796.8 181,602.9 277,450.0 277,450.0 137,937.1 277,450.0 -8,030,650.5 -0.4 NOV 30.0 30.4 0.0 268,500.0 69,920.0 0.0 338,420.0 0.0 114,964.6 114,964.6 268,500.0 268,500.0 223,455.4 268,500.0 -8,075,695.2 -0.4 DEC 31.0 30.8 0.0 277,450.0 70,840.0 0.0 348,290.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 229,493.2 277,450.0 -8,123,651.9 -0.4 Year 5 JAN 31.0 17.6 0.0 277,450.0 40,480.0 0.0 317,930.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 199,133.2 277,450.0 -8,201,968.7 -0.4 FEB 28.0 13.8 0.0 250,600.0 31,740.0 0.0 282,340.0 0.0 107,300.3 107,300.3 250,600.0 250,600.0 175,039.7 250,600.0 -8,277,529.0 -0.4 MAR 31.0 17.4 0.0 277,450.0 40,020.0 0.0 317,470.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 198,673.2 277,450.0 -8,356,305.7 -0.4 APR 30.0 16.8 0.0 268,500.0 38,640.0 0.0 307,140.0 140,638.1 114,964.6 255,602.7 268,500.0 268,500.0 51,537.3 268,500.0 -8,573,268.5 -0.4 MAY 31.0 15.9 0.0 277,450.0 36,570.0 0.0 314,020.0 287,329.8 118,796.8 406,126.6 277,450.0 277,450.0 0.0 277,450.0 -8,850,718.5 -0.4 JUN 30.0 7.8 0.0 268,500.0 17,940.0 0.0 286,440.0 372,945.0 114,964.6 487,909.6 268,500.0 268,500.0 0.0 268,500.0 -9,119,218.5 -0.4 JUL 31.0 8.9 0.0 277,450.0 20,470.0 0.0 297,920.0 368,837.2 118,796.8 487,634.0 277,450.0 277,450.0 0.0 277,450.0 -9,396,668.5 -0.4 AUG 31.0 8.0 0.0 277,450.0 18,400.0 0.0 295,850.0 318,030.2 118,796.8 436,827.0 277,450.0 277,450.0 0.0 277,450.0 -9,674,118.5 -0.4 SEP 30.0 12.5 0.0 268,500.0 28,750.0 0.0 297,250.0 161,717.6 114,964.6 276,682.2 268,500.0 268,500.0 20,567.8 268,500.0 -9,922,050.7 -0.4 OCT 31.0 18.3 0.0 277,450.0 42,090.0 0.0 319,540.0 62,806.1 118,796.8 181,602.9 277,450.0 277,450.0 137,937.1 277,450.0 -10,061,563.5 -0.4 NOV 30.0 30.4 0.0 268,500.0 69,920.0 0.0 338,420.0 0.0 114,964.6 114,964.6 268,500.0 268,500.0 223,455.4 268,500.0 -10,106,608.1 -0.4 DEC 31.0 30.8 0.0 277,450.0 70,840.0 0.0 348,290.0 0.0 118,796.8 118,796.8 277,450.0 277,450.0 229,493.2 277,450.0 -10,154,564.9 -0.4    156 Table C.2 Deterministic model for solids content of 70% in the dry condition for a lined impoundment Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from TMF Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   Year 1 JAN 31.0 17.6 0.0 178,360.7 40,480.0 0.0 218,840.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 123,735.1 178,360.7 -54,625.6 -0.1 FEB 28.0 13.8 0.0 161,100.0 31,740.0 0.0 192,840.0 0.0 85,901.8 85,901.8 161,100.0 161,100.0 106,938.2 161,100.0 -108,787.4 -0.1 MAR 31.0 17.4 0.0 178,360.7 40,020.0 0.0 218,380.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 123,275.1 178,360.7 -163,873.0 -0.1 APR 30.0 16.8 0.0 172,607.1 38,640.0 0.0 211,247.1 113,707.4 92,037.7 205,745.1 172,607.1 172,607.1 5,502.1 172,607.1 -330,978.1 -0.2 MAY 31.0 15.9 0.0 178,360.7 36,570.0 0.0 214,930.7 232,309.2 95,105.6 327,414.8 178,360.7 178,360.7 0.0 178,360.7 -509,338.8 -0.3 JUN 30.0 7.8 0.0 172,607.1 17,940.0 0.0 190,547.1 301,530.0 92,037.7 393,567.7 172,607.1 172,607.1 0.0 172,607.1 -681,945.9 -0.3 JUL 31.0 8.9 0.0 178,360.7 20,470.0 0.0 198,830.7 298,208.8 95,105.6 393,314.4 178,360.7 178,360.7 0.0 178,360.7 -860,306.6 -0.3 AUG 31.0 8.0 0.0 178,360.7 18,400.0 0.0 196,760.7 257,130.8 95,105.6 352,236.4 178,360.7 178,360.7 0.0 178,360.7 -1,038,667.3 -0.3 SEP 30.0 12.5 0.0 172,607.1 28,750.0 0.0 201,357.1 130,750.4 92,037.7 222,788.1 172,607.1 172,607.1 0.0 172,607.1 -1,211,274.5 -0.3 OCT 31.0 18.3 0.0 178,360.7 42,090.0 0.0 220,450.7 50,779.4 95,105.6 145,885.0 178,360.7 178,360.7 74,565.7 178,360.7 -1,315,069.5 -0.3 NOV 30.0 30.4 0.0 172,607.1 69,920.0 0.0 242,527.1 0.0 92,037.7 92,037.7 172,607.1 172,607.1 150,489.5 172,607.1 -1,337,187.1 -0.3 DEC 31.0 30.8 0.0 178,360.7 70,840.0 0.0 249,200.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 154,095.1 178,360.7 -1,361,452.7 -0.3 Year 2 JAN 31.0 17.6 0.0 178,360.7 40,480.0 0.0 218,840.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 123,735.1 178,360.7 -1,416,078.3 -0.3 FEB 28.0 13.8 0.0 161,100.0 31,740.0 0.0 192,840.0 0.0 85,901.8 85,901.8 161,100.0 161,100.0 106,938.2 161,100.0 -1,470,240.1 -0.3 MAR 31.0 17.4 0.0 178,360.7 40,020.0 0.0 218,380.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 123,275.1 178,360.7 -1,525,325.7 -0.2 APR 30.0 16.8 0.0 172,607.1 38,640.0 0.0 211,247.1 113,707.4 92,037.7 205,745.1 172,607.1 172,607.1 5,502.1 172,607.1 -1,692,430.8 -0.3 MAY 31.0 15.9 0.0 178,360.7 36,570.0 0.0 214,930.7 232,309.2 95,105.6 327,414.8 178,360.7 178,360.7 0.0 178,360.7 -1,870,791.5 -0.3 JUN 30.0 7.8 0.0 172,607.1 17,940.0 0.0 190,547.1 301,530.0 92,037.7 393,567.7 172,607.1 172,607.1 0.0 172,607.1 -2,043,398.6 -0.3 JUL 31.0 8.9 0.0 178,360.7 20,470.0 0.0 198,830.7 298,208.8 95,105.6 393,314.4 178,360.7 178,360.7 0.0 178,360.7 -2,221,759.3 -0.3 AUG 31.0 8.0 0.0 178,360.7 18,400.0 0.0 196,760.7 257,130.8 95,105.6 352,236.4 178,360.7 178,360.7 0.0 178,360.7 -2,400,120.1 -0.3 SEP 30.0 12.5 0.0 172,607.1 28,750.0 0.0 201,357.1 130,750.4 92,037.7 222,788.1 172,607.1 172,607.1 0.0 172,607.1 -2,572,727.2 -0.3 OCT 31.0 18.3 0.0 178,360.7 42,090.0 0.0 220,450.7 50,779.4 95,105.6 145,885.0 178,360.7 178,360.7 74,565.7 178,360.7 -2,676,522.2 -0.3 NOV 30.0 30.4 0.0 172,607.1 69,920.0 0.0 242,527.1 0.0 92,037.7 92,037.7 172,607.1 172,607.1 150,489.5 172,607.1 -2,698,639.9 -0.3 DEC 31.0 30.8 0.0 178,360.7 70,840.0 0.0 249,200.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 154,095.1 178,360.7 -2,722,905.4 -0.3 Year 3 JAN 31.0 17.6 0.0 178,360.7 40,480.0 0.0 218,840.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 123,735.1 178,360.7 -2,777,531.0 -0.3 FEB 28.0 13.8 0.0 161,100.0 31,740.0 0.0 192,840.0 0.0 85,901.8 85,901.8 161,100.0 161,100.0 106,938.2 161,100.0 -2,831,692.8 -0.3 MAR 31.0 17.4 0.0 178,360.7 40,020.0 0.0 218,380.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 123,275.1 178,360.7 -2,886,778.4 -0.3 APR 30.0 16.8 0.0 172,607.1 38,640.0 0.0 211,247.1 113,707.4 92,037.7 205,745.1 172,607.1 172,607.1 5,502.1 172,607.1 -3,053,883.5 -0.3 MAY 31.0 15.9 0.0 178,360.7 36,570.0 0.0 214,930.7 232,309.2 95,105.6 327,414.8 178,360.7 178,360.7 0.0 178,360.7 -3,232,244.2 -0.3 JUN 30.0 7.8 0.0 172,607.1 17,940.0 0.0 190,547.1 301,530.0 92,037.7 393,567.7 172,607.1 172,607.1 0.0 172,607.1 -3,404,851.4 -0.3 JUL 31.0 8.9 0.0 178,360.7 20,470.0 0.0 198,830.7 298,208.8 95,105.6 393,314.4 178,360.7 178,360.7 0.0 178,360.7 -3,583,212.1 -0.3 AUG 31.0 8.0 0.0 178,360.7 18,400.0 0.0 196,760.7 257,130.8 95,105.6 352,236.4 178,360.7 178,360.7 0.0 178,360.7 -3,761,572.8 -0.3 SEP 30.0 12.5 0.0 172,607.1 28,750.0 0.0 201,357.1 130,750.4 92,037.7 222,788.1 172,607.1 172,607.1 0.0 172,607.1 -3,934,179.9 -0.3 OCT 31.0 18.3 0.0 178,360.7 42,090.0 0.0 220,450.7 50,779.4 95,105.6 145,885.0 178,360.7 178,360.7 74,565.7 178,360.7 -4,037,974.9 -0.3 NOV 30.0 30.4 0.0 172,607.1 69,920.0 0.0 242,527.1 0.0 92,037.7 92,037.7 172,607.1 172,607.1 150,489.5 172,607.1 -4,060,092.6 -0.3  157 Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from TMF Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   DEC 31.0 30.8 0.0 178,360.7 70,840.0 0.0 249,200.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 154,095.1 178,360.7 -4,084,358.2 -0.3 Year 4 JAN 31.0 17.6 0.0 178,360.7 40,480.0 0.0 218,840.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 123,735.1 178,360.7 -4,138,983.7 -0.3 FEB 28.0 13.8 0.0 161,100.0 31,740.0 0.0 192,840.0 0.0 85,901.8 85,901.8 161,100.0 161,100.0 106,938.2 161,100.0 -4,193,145.6 -0.3 MAR 31.0 17.4 0.0 178,360.7 40,020.0 0.0 218,380.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 123,275.1 178,360.7 -4,248,231.2 -0.3 APR 30.0 16.8 0.0 172,607.1 38,640.0 0.0 211,247.1 113,707.4 92,037.7 205,745.1 172,607.1 172,607.1 5,502.1 172,607.1 -4,415,336.2 -0.3 MAY 31.0 15.9 0.0 178,360.7 36,570.0 0.0 214,930.7 232,309.2 95,105.6 327,414.8 178,360.7 178,360.7 0.0 178,360.7 -4,593,696.9 -0.3 JUN 30.0 7.8 0.0 172,607.1 17,940.0 0.0 190,547.1 301,530.0 92,037.7 393,567.7 172,607.1 172,607.1 0.0 172,607.1 -4,766,304.1 -0.3 JUL 31.0 8.9 0.0 178,360.7 20,470.0 0.0 198,830.7 298,208.8 95,105.6 393,314.4 178,360.7 178,360.7 0.0 178,360.7 -4,944,664.8 -0.3 AUG 31.0 8.0 0.0 178,360.7 18,400.0 0.0 196,760.7 257,130.8 95,105.6 352,236.4 178,360.7 178,360.7 0.0 178,360.7 -5,123,025.5 -0.3 SEP 30.0 12.5 0.0 172,607.1 28,750.0 0.0 201,357.1 130,750.4 92,037.7 222,788.1 172,607.1 172,607.1 0.0 172,607.1 -5,295,632.6 -0.3 OCT 31.0 18.3 0.0 178,360.7 42,090.0 0.0 220,450.7 50,779.4 95,105.6 145,885.0 178,360.7 178,360.7 74,565.7 178,360.7 -5,399,427.6 -0.3 NOV 30.0 30.4 0.0 172,607.1 69,920.0 0.0 242,527.1 0.0 92,037.7 92,037.7 172,607.1 172,607.1 150,489.5 172,607.1 -5,421,545.3 -0.3 DEC 31.0 30.8 0.0 178,360.7 70,840.0 0.0 249,200.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 154,095.1 178,360.7 -5,445,810.9 -0.3 Year 5 JAN 31.0 17.6 0.0 178,360.7 40,480.0 0.0 218,840.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 123,735.1 178,360.7 -5,500,436.5 -0.3 FEB 28.0 13.8 0.0 161,100.0 31,740.0 0.0 192,840.0 0.0 85,901.8 85,901.8 161,100.0 161,100.0 106,938.2 161,100.0 -5,554,598.3 -0.3 MAR 31.0 17.4 0.0 178,360.7 40,020.0 0.0 218,380.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 123,275.1 178,360.7 -5,609,683.9 -0.3 APR 30.0 16.8 0.0 172,607.1 38,640.0 0.0 211,247.1 113,707.4 92,037.7 205,745.1 172,607.1 172,607.1 5,502.1 172,607.1 -5,776,788.9 -0.3 MAY 31.0 15.9 0.0 178,360.7 36,570.0 0.0 214,930.7 232,309.2 95,105.6 327,414.8 178,360.7 178,360.7 0.0 178,360.7 -5,955,149.7 -0.3 JUN 30.0 7.8 0.0 172,607.1 17,940.0 0.0 190,547.1 301,530.0 92,037.7 393,567.7 172,607.1 172,607.1 0.0 172,607.1 -6,127,756.8 -0.3 JUL 31.0 8.9 0.0 178,360.7 20,470.0 0.0 198,830.7 298,208.8 95,105.6 393,314.4 178,360.7 178,360.7 0.0 178,360.7 -6,306,117.5 -0.3 AUG 31.0 8.0 0.0 178,360.7 18,400.0 0.0 196,760.7 257,130.8 95,105.6 352,236.4 178,360.7 178,360.7 0.0 178,360.7 -6,484,478.2 -0.3 SEP 30.0 12.5 0.0 172,607.1 28,750.0 0.0 201,357.1 130,750.4 92,037.7 222,788.1 172,607.1 172,607.1 0.0 172,607.1 -6,657,085.4 -0.3 OCT 31.0 18.3 0.0 178,360.7 42,090.0 0.0 220,450.7 50,779.4 95,105.6 145,885.0 178,360.7 178,360.7 74,565.7 178,360.7 -6,760,880.4 -0.3 NOV 30.0 30.4 0.0 172,607.1 69,920.0 0.0 242,527.1 0.0 92,037.7 92,037.7 172,607.1 172,607.1 150,489.5 172,607.1 -6,782,998.0 -0.3 DEC 31.0 30.8 0.0 178,360.7 70,840.0 0.0 249,200.7 0.0 95,105.6 95,105.6 178,360.7 178,360.7 154,095.1 178,360.7 -6,807,263.6 -0.3    158 Table C.3 Deterministic model for solids content of 80% in the dry condition for a lined impoundment Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from TMF Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   Year 1 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 56,412.7 104,043.8 -47,631.0 -0.1 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 79,584.2 93,975.0 93,975.0 46,130.8 93,975.0 -95,475.2 -0.1 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 55,952.7 104,043.8 -143,566.3 -0.1 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 151,099.4 100,687.5 100,687.5 0.0 100,687.5 -244,253.8 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 222,605.8 104,043.8 104,043.8 0.0 104,043.8 -348,297.5 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 259,838.8 100,687.5 100,687.5 0.0 100,687.5 -448,985.0 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 260,758.2 104,043.8 104,043.8 0.0 104,043.8 -553,028.8 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 236,976.2 104,043.8 104,043.8 0.0 104,043.8 -657,072.5 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 160,966.4 100,687.5 100,687.5 0.0 100,687.5 -757,760.0 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 117,509.6 104,043.8 104,043.8 28,624.1 104,043.8 -833,179.7 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 85,268.8 100,687.5 100,687.5 85,338.7 100,687.5 -848,528.4 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 86,772.7 104,043.8 -865,799.5 -0.2 Year 2 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 56,412.7 104,043.8 -913,430.5 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 79,584.2 93,975.0 93,975.0 46,130.8 93,975.0 -961,274.7 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 55,952.7 104,043.8 -1,009,365.7 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 151,099.4 100,687.5 100,687.5 0.0 100,687.5 -1,110,053.2 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 222,605.8 104,043.8 104,043.8 0.0 104,043.8 -1,214,097.0 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 259,838.8 100,687.5 100,687.5 0.0 100,687.5 -1,314,784.5 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 260,758.2 104,043.8 104,043.8 0.0 104,043.8 -1,418,828.2 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 236,976.2 104,043.8 104,043.8 0.0 104,043.8 -1,522,872.0 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 160,966.4 100,687.5 100,687.5 0.0 100,687.5 -1,623,559.5 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 117,509.6 104,043.8 104,043.8 28,624.1 104,043.8 -1,698,979.1 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 85,268.8 100,687.5 100,687.5 85,338.7 100,687.5 -1,714,327.9 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 86,772.7 104,043.8 -1,731,598.9 -0.2 Year 3 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 56,412.7 104,043.8 -1,779,230.0 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 79,584.2 93,975.0 93,975.0 46,130.8 93,975.0 -1,827,074.2 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 55,952.7 104,043.8 -1,875,165.2 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 151,099.4 100,687.5 100,687.5 0.0 100,687.5 -1,975,852.7 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 222,605.8 104,043.8 104,043.8 0.0 104,043.8 -2,079,896.5 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 259,838.8 100,687.5 100,687.5 0.0 100,687.5 -2,180,584.0 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 260,758.2 104,043.8 104,043.8 0.0 104,043.8 -2,284,627.7 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 236,976.2 104,043.8 104,043.8 0.0 104,043.8 -2,388,671.5 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 160,966.4 100,687.5 100,687.5 0.0 100,687.5 -2,489,359.0 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 117,509.6 104,043.8 104,043.8 28,624.1 104,043.8 -2,564,778.6 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 85,268.8 100,687.5 100,687.5 85,338.7 100,687.5 -2,580,127.4 -0.2  159 Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from TMF Entrainment Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 86,772.7 104,043.8 -2,597,398.4 -0.2 Year 4 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 56,412.7 104,043.8 -2,645,029.5 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 79,584.2 93,975.0 93,975.0 46,130.8 93,975.0 -2,692,873.6 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 55,952.7 104,043.8 -2,740,964.7 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 151,099.4 100,687.5 100,687.5 0.0 100,687.5 -2,841,652.2 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 222,605.8 104,043.8 104,043.8 0.0 104,043.8 -2,945,695.9 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 259,838.8 100,687.5 100,687.5 0.0 100,687.5 -3,046,383.4 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 260,758.2 104,043.8 104,043.8 0.0 104,043.8 -3,150,427.2 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 236,976.2 104,043.8 104,043.8 0.0 104,043.8 -3,254,470.9 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 160,966.4 100,687.5 100,687.5 0.0 100,687.5 -3,355,158.4 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 117,509.6 104,043.8 104,043.8 28,624.1 104,043.8 -3,430,578.1 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 85,268.8 100,687.5 100,687.5 85,338.7 100,687.5 -3,445,926.8 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 86,772.7 104,043.8 -3,463,197.9 -0.2 Year 5 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 56,412.7 104,043.8 -3,510,828.9 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 79,584.2 93,975.0 93,975.0 46,130.8 93,975.0 -3,558,673.1 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 55,952.7 104,043.8 -3,606,764.2 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 151,099.4 100,687.5 100,687.5 0.0 100,687.5 -3,707,451.7 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 222,605.8 104,043.8 104,043.8 0.0 104,043.8 -3,811,495.4 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 259,838.8 100,687.5 100,687.5 0.0 100,687.5 -3,912,182.9 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 260,758.2 104,043.8 104,043.8 0.0 104,043.8 -4,016,226.7 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 236,976.2 104,043.8 104,043.8 0.0 104,043.8 -4,120,270.4 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 160,966.4 100,687.5 100,687.5 0.0 100,687.5 -4,220,957.9 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 117,509.6 104,043.8 104,043.8 28,624.1 104,043.8 -4,296,377.6 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 85,268.8 100,687.5 100,687.5 85,338.7 100,687.5 -4,311,726.3 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 88,111.0 104,043.8 104,043.8 86,772.7 104,043.8 -4,328,997.4 -0.2  160 C.2 Unlined impoundment  161 Table C.4 Deterministic model for solids content of 60% in the dry condition for an unlined impoundment Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from pond Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   Year 1 JAN 31.0 17.6 0.0 277,450.0 40,480.0 0.0 317,930.0 0.0 118,796.8 4,968.0 123,764.8 277,450.0 277,450.0 194,165.2 277,450.0 -83,284.8 -0.2 FEB 28.0 13.8 0.0 250,600.0 31,740.0 0.0 282,340.0 0.0 107,300.3 4,968.0 107,300.3 250,600.0 250,600.0 175,039.7 250,600.0 -158,845.1 -0.2 MAR 31.0 17.4 0.0 277,450.0 40,020.0 0.0 317,470.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 198,673.2 277,450.0 -237,621.8 -0.2 APR 30.0 16.8 0.0 268,500.0 38,640.0 0.0 307,140.0 140,638.1 114,964.6 4,968.0 255,602.7 268,500.0 268,500.0 51,537.3 268,500.0 -454,584.5 -0.3 MAY 31.0 15.9 0.0 277,450.0 36,570.0 0.0 314,020.0 287,329.8 118,796.8 4,968.0 406,126.6 277,450.0 277,450.0 0.0 277,450.0 -732,034.5 -0.4 JUN 30.0 7.8 0.0 268,500.0 17,940.0 0.0 286,440.0 372,945.0 114,964.6 4,968.0 487,909.6 268,500.0 268,500.0 0.0 268,500.0 -1,000,534.5 -0.4 JUL 31.0 8.9 0.0 277,450.0 20,470.0 0.0 297,920.0 368,837.2 118,796.8 4,968.0 487,634.0 277,450.0 277,450.0 0.0 277,450.0 -1,277,984.5 -0.4 AUG 31.0 8.0 0.0 277,450.0 18,400.0 0.0 295,850.0 318,030.2 118,796.8 4,968.0 436,827.0 277,450.0 277,450.0 0.0 277,450.0 -1,555,434.5 -0.5 SEP 30.0 12.5 0.0 268,500.0 28,750.0 0.0 297,250.0 161,717.6 114,964.6 4,968.0 276,682.2 268,500.0 268,500.0 20,567.8 268,500.0 -1,803,366.7 -0.5 OCT 31.0 18.3 0.0 277,450.0 42,090.0 0.0 319,540.0 62,806.1 118,796.8 4,968.0 181,602.9 277,450.0 277,450.0 137,937.1 277,450.0 -1,942,879.6 -0.5 NOV 30.0 30.4 0.0 268,500.0 69,920.0 0.0 338,420.0 0.0 114,964.6 4,968.0 114,964.6 268,500.0 268,500.0 223,455.4 268,500.0 -1,987,924.2 -0.4 DEC 31.0 30.8 0.0 277,450.0 70,840.0 0.0 348,290.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 229,493.2 277,450.0 -2,035,881.0 -0.4 Year 2 JAN 31.0 17.6 0.0 277,450.0 40,480.0 0.0 317,930.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 199,133.2 277,450.0 -2,114,197.7 -0.4 FEB 28.0 13.8 0.0 250,600.0 31,740.0 0.0 282,340.0 0.0 107,300.3 4,968.0 107,300.3 250,600.0 250,600.0 175,039.7 250,600.0 -2,189,758.0 -0.4 MAR 31.0 17.4 0.0 277,450.0 40,020.0 0.0 317,470.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 198,673.2 277,450.0 -2,268,534.8 -0.4 APR 30.0 16.8 0.0 268,500.0 38,640.0 0.0 307,140.0 140,638.1 114,964.6 4,968.0 255,602.7 268,500.0 268,500.0 51,537.3 268,500.0 -2,485,497.5 -0.4 MAY 31.0 15.9 0.0 277,450.0 36,570.0 0.0 314,020.0 287,329.8 118,796.8 4,968.0 406,126.6 277,450.0 277,450.0 0.0 277,450.0 -2,762,947.5 -0.4 JUN 30.0 7.8 0.0 268,500.0 17,940.0 0.0 286,440.0 372,945.0 114,964.6 4,968.0 487,909.6 268,500.0 268,500.0 0.0 268,500.0 -3,031,447.5 -0.4 JUL 31.0 8.9 0.0 277,450.0 20,470.0 0.0 297,920.0 368,837.2 118,796.8 4,968.0 487,634.0 277,450.0 277,450.0 0.0 277,450.0 -3,308,897.5 -0.4 AUG 31.0 8.0 0.0 277,450.0 18,400.0 0.0 295,850.0 318,030.2 118,796.8 4,968.0 436,827.0 277,450.0 277,450.0 0.0 277,450.0 -3,586,347.5 -0.4 SEP 30.0 12.5 0.0 268,500.0 28,750.0 0.0 297,250.0 161,717.6 114,964.6 4,968.0 276,682.2 268,500.0 268,500.0 20,567.8 268,500.0 -3,834,279.7 -0.4 OCT 31.0 18.3 0.0 277,450.0 42,090.0 0.0 319,540.0 62,806.1 118,796.8 4,968.0 181,602.9 277,450.0 277,450.0 137,937.1 277,450.0 -3,973,792.6 -0.4 NOV 30.0 30.4 0.0 268,500.0 69,920.0 0.0 338,420.0 0.0 114,964.6 4,968.0 114,964.6 268,500.0 268,500.0 223,455.4 268,500.0 -4,018,837.2 -0.4 DEC 31.0 30.8 0.0 277,450.0 70,840.0 0.0 348,290.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 229,493.2 277,450.0 -4,066,794.0 -0.4 Year 3 JAN 31.0 17.6 0.0 277,450.0 40,480.0 0.0 317,930.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 199,133.2 277,450.0 -4,145,110.7 -0.4 FEB 28.0 13.8 0.0 250,600.0 31,740.0 0.0 282,340.0 0.0 107,300.3 4,968.0 107,300.3 250,600.0 250,600.0 175,039.7 250,600.0 -4,220,671.0 -0.4 MAR 31.0 17.4 0.0 277,450.0 40,020.0 0.0 317,470.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 198,673.2 277,450.0 -4,299,447.8 -0.4 APR 30.0 16.8 0.0 268,500.0 38,640.0 0.0 307,140.0 140,638.1 114,964.6 4,968.0 255,602.7 268,500.0 268,500.0 51,537.3 268,500.0 -4,516,410.5 -0.4 MAY 31.0 15.9 0.0 277,450.0 36,570.0 0.0 314,020.0 287,329.8 118,796.8 4,968.0 406,126.6 277,450.0 277,450.0 0.0 277,450.0 -4,793,860.5 -0.4 JUN 30.0 7.8 0.0 268,500.0 17,940.0 0.0 286,440.0 372,945.0 114,964.6 4,968.0 487,909.6 268,500.0 268,500.0 0.0 268,500.0 -5,062,360.5 -0.4 JUL 31.0 8.9 0.0 277,450.0 20,470.0 0.0 297,920.0 368,837.2 118,796.8 4,968.0 487,634.0 277,450.0 277,450.0 0.0 277,450.0 -5,339,810.5 -0.4 AUG 31.0 8.0 0.0 277,450.0 18,400.0 0.0 295,850.0 318,030.2 118,796.8 4,968.0 436,827.0 277,450.0 277,450.0 0.0 277,450.0 -5,617,260.5 -0.4 SEP 30.0 12.5 0.0 268,500.0 28,750.0 0.0 297,250.0 161,717.6 114,964.6 4,968.0 276,682.2 268,500.0 268,500.0 20,567.8 268,500.0 -5,865,192.7 -0.4 OCT 31.0 18.3 0.0 277,450.0 42,090.0 0.0 319,540.0 62,806.1 118,796.8 4,968.0 181,602.9 277,450.0 277,450.0 137,937.1 277,450.0 -6,004,705.6 -0.4 NOV 30.0 30.4 0.0 268,500.0 69,920.0 0.0 338,420.0 0.0 114,964.6 4,968.0 114,964.6 268,500.0 268,500.0 223,455.4 268,500.0 -6,049,750.2 -0.4  162 Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from pond Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   DEC 31.0 30.8 0.0 277,450.0 70,840.0 0.0 348,290.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 229,493.2 277,450.0 -6,097,706.9 -0.4 Year 4 JAN 31.0 17.6 0.0 277,450.0 40,480.0 0.0 317,930.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 199,133.2 277,450.0 -6,176,023.7 -0.4 FEB 28.0 13.8 0.0 250,600.0 31,740.0 0.0 282,340.0 0.0 107,300.3 4,968.0 107,300.3 250,600.0 250,600.0 175,039.7 250,600.0 -6,251,584.0 -0.4 MAR 31.0 17.4 0.0 277,450.0 40,020.0 0.0 317,470.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 198,673.2 277,450.0 -6,330,360.8 -0.4 APR 30.0 16.8 0.0 268,500.0 38,640.0 0.0 307,140.0 140,638.1 114,964.6 4,968.0 255,602.7 268,500.0 268,500.0 51,537.3 268,500.0 -6,547,323.5 -0.4 MAY 31.0 15.9 0.0 277,450.0 36,570.0 0.0 314,020.0 287,329.8 118,796.8 4,968.0 406,126.6 277,450.0 277,450.0 0.0 277,450.0 -6,824,773.5 -0.4 JUN 30.0 7.8 0.0 268,500.0 17,940.0 0.0 286,440.0 372,945.0 114,964.6 4,968.0 487,909.6 268,500.0 268,500.0 0.0 268,500.0 -7,093,273.5 -0.4 JUL 31.0 8.9 0.0 277,450.0 20,470.0 0.0 297,920.0 368,837.2 118,796.8 4,968.0 487,634.0 277,450.0 277,450.0 0.0 277,450.0 -7,370,723.5 -0.4 AUG 31.0 8.0 0.0 277,450.0 18,400.0 0.0 295,850.0 318,030.2 118,796.8 4,968.0 436,827.0 277,450.0 277,450.0 0.0 277,450.0 -7,648,173.5 -0.4 SEP 30.0 12.5 0.0 268,500.0 28,750.0 0.0 297,250.0 161,717.6 114,964.6 4,968.0 276,682.2 268,500.0 268,500.0 20,567.8 268,500.0 -7,896,105.7 -0.4 OCT 31.0 18.3 0.0 277,450.0 42,090.0 0.0 319,540.0 62,806.1 118,796.8 4,968.0 181,602.9 277,450.0 277,450.0 137,937.1 277,450.0 -8,035,618.5 -0.4 NOV 30.0 30.4 0.0 268,500.0 69,920.0 0.0 338,420.0 0.0 114,964.6 4,968.0 114,964.6 268,500.0 268,500.0 223,455.4 268,500.0 -8,080,663.2 -0.4 DEC 31.0 30.8 0.0 277,450.0 70,840.0 0.0 348,290.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 229,493.2 277,450.0 -8,128,619.9 -0.4 Year 5 JAN 31.0 17.6 0.0 277,450.0 40,480.0 0.0 317,930.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 199,133.2 277,450.0 -8,206,936.7 -0.4 FEB 28.0 13.8 0.0 250,600.0 31,740.0 0.0 282,340.0 0.0 107,300.3 4,968.0 107,300.3 250,600.0 250,600.0 175,039.7 250,600.0 -8,282,497.0 -0.4 MAR 31.0 17.4 0.0 277,450.0 40,020.0 0.0 317,470.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 198,673.2 277,450.0 -8,361,273.7 -0.4 APR 30.0 16.8 0.0 268,500.0 38,640.0 0.0 307,140.0 140,638.1 114,964.6 4,968.0 255,602.7 268,500.0 268,500.0 51,537.3 268,500.0 -8,578,236.5 -0.4 MAY 31.0 15.9 0.0 277,450.0 36,570.0 0.0 314,020.0 287,329.8 118,796.8 4,968.0 406,126.6 277,450.0 277,450.0 0.0 277,450.0 -8,855,686.5 -0.4 JUN 30.0 7.8 0.0 268,500.0 17,940.0 0.0 286,440.0 372,945.0 114,964.6 4,968.0 487,909.6 268,500.0 268,500.0 0.0 268,500.0 -9,124,186.5 -0.4 JUL 31.0 8.9 0.0 277,450.0 20,470.0 0.0 297,920.0 368,837.2 118,796.8 4,968.0 487,634.0 277,450.0 277,450.0 0.0 277,450.0 -9,401,636.5 -0.4 AUG 31.0 8.0 0.0 277,450.0 18,400.0 0.0 295,850.0 318,030.2 118,796.8 4,968.0 436,827.0 277,450.0 277,450.0 0.0 277,450.0 -9,679,086.5 -0.4 SEP 30.0 12.5 0.0 268,500.0 28,750.0 0.0 297,250.0 161,717.6 114,964.6 4,968.0 276,682.2 268,500.0 268,500.0 20,567.8 268,500.0 -9,927,018.7 -0.4 OCT 31.0 18.3 0.0 277,450.0 42,090.0 0.0 319,540.0 62,806.1 118,796.8 4,968.0 181,602.9 277,450.0 277,450.0 137,937.1 277,450.0 -10,066,531.5 -0.4 NOV 30.0 30.4 0.0 268,500.0 69,920.0 0.0 338,420.0 0.0 114,964.6 4,968.0 114,964.6 268,500.0 268,500.0 223,455.4 268,500.0 -10,111,576.1 -0.4 DEC 31.0 30.8 0.0 277,450.0 70,840.0 0.0 348,290.0 0.0 118,796.8 4,968.0 118,796.8 277,450.0 277,450.0 229,493.2 277,450.0 -10,159,532.9 -0.4    163 Table C.5 Deterministic model for solids content of 70% in the dry condition for an unlined impoundment Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from pond Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   Year 1 JAN 31.0 17.6 0.0 178,360.7 40,480.0 0.0 218,840.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 118,767.1 178,360.7 -59,593.6 -0.1 FEB 28.0 13.8 0.0 161,100.0 31,740.0 0.0 192,840.0 0.0 85,901.8 4,968.0 90,869.8 161,100.0 161,100.0 101,970.2 161,100.0 -118,723.4 -0.1 MAR 31.0 17.4 0.0 178,360.7 40,020.0 0.0 218,380.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 118,307.1 178,360.7 -178,777.0 -0.1 APR 30.0 16.8 0.0 172,607.1 38,640.0 0.0 211,247.1 113,707.4 92,037.7 4,968.0 210,713.1 172,607.1 172,607.1 534.1 172,607.1 -350,850.1 -0.2 MAY 31.0 15.9 0.0 178,360.7 36,570.0 0.0 214,930.7 232,309.2 95,105.6 4,968.0 332,382.8 178,360.7 178,360.7 0.0 178,360.7 -529,210.8 -0.3 JUN 30.0 7.8 0.0 172,607.1 17,940.0 0.0 190,547.1 301,530.0 92,037.7 4,968.0 398,535.7 172,607.1 172,607.1 0.0 172,607.1 -701,817.9 -0.3 JUL 31.0 8.9 0.0 178,360.7 20,470.0 0.0 198,830.7 298,208.8 95,105.6 4,968.0 398,282.4 178,360.7 178,360.7 0.0 178,360.7 -880,178.6 -0.3 AUG 31.0 8.0 0.0 178,360.7 18,400.0 0.0 196,760.7 257,130.8 95,105.6 4,968.0 357,204.4 178,360.7 178,360.7 0.0 178,360.7 -1,058,539.3 -0.3 SEP 30.0 12.5 0.0 172,607.1 28,750.0 0.0 201,357.1 130,750.4 92,037.7 4,968.0 227,756.1 172,607.1 172,607.1 0.0 172,607.1 -1,231,146.5 -0.3 OCT 31.0 18.3 0.0 178,360.7 42,090.0 0.0 220,450.7 50,779.4 95,105.6 4,968.0 150,853.0 178,360.7 178,360.7 69,597.7 178,360.7 -1,339,909.5 -0.3 NOV 30.0 30.4 0.0 172,607.1 69,920.0 0.0 242,527.1 0.0 92,037.7 4,968.0 97,005.7 172,607.1 172,607.1 145,521.5 172,607.1 -1,366,995.1 -0.3 DEC 31.0 30.8 0.0 178,360.7 70,840.0 0.0 249,200.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 149,127.1 178,360.7 -1,396,228.7 -0.3 Year 2 JAN 31.0 17.6 0.0 178,360.7 40,480.0 0.0 218,840.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 118,767.1 178,360.7 -1,455,822.3 -0.3 FEB 28.0 13.8 0.0 161,100.0 31,740.0 0.0 192,840.0 0.0 85,901.8 4,968.0 90,869.8 161,100.0 161,100.0 101,970.2 161,100.0 -1,514,952.1 -0.3 MAR 31.0 17.4 0.0 178,360.7 40,020.0 0.0 218,380.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 118,307.1 178,360.7 -1,575,005.7 -0.3 APR 30.0 16.8 0.0 172,607.1 38,640.0 0.0 211,247.1 113,707.4 92,037.7 4,968.0 210,713.1 172,607.1 172,607.1 534.1 172,607.1 -1,747,078.8 -0.3 MAY 31.0 15.9 0.0 178,360.7 36,570.0 0.0 214,930.7 232,309.2 95,105.6 4,968.0 332,382.8 178,360.7 178,360.7 0.0 178,360.7 -1,925,439.5 -0.3 JUN 30.0 7.8 0.0 172,607.1 17,940.0 0.0 190,547.1 301,530.0 92,037.7 4,968.0 398,535.7 172,607.1 172,607.1 0.0 172,607.1 -2,098,046.6 -0.3 JUL 31.0 8.9 0.0 178,360.7 20,470.0 0.0 198,830.7 298,208.8 95,105.6 4,968.0 398,282.4 178,360.7 178,360.7 0.0 178,360.7 -2,276,407.3 -0.3 AUG 31.0 8.0 0.0 178,360.7 18,400.0 0.0 196,760.7 257,130.8 95,105.6 4,968.0 357,204.4 178,360.7 178,360.7 0.0 178,360.7 -2,454,768.1 -0.3 SEP 30.0 12.5 0.0 172,607.1 28,750.0 0.0 201,357.1 130,750.4 92,037.7 4,968.0 227,756.1 172,607.1 172,607.1 0.0 172,607.1 -2,627,375.2 -0.3 OCT 31.0 18.3 0.0 178,360.7 42,090.0 0.0 220,450.7 50,779.4 95,105.6 4,968.0 150,853.0 178,360.7 178,360.7 69,597.7 178,360.7 -2,736,138.2 -0.3 NOV 30.0 30.4 0.0 172,607.1 69,920.0 0.0 242,527.1 0.0 92,037.7 4,968.0 97,005.7 172,607.1 172,607.1 145,521.5 172,607.1 -2,763,223.9 -0.3 DEC 31.0 30.8 0.0 178,360.7 70,840.0 0.0 249,200.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 149,127.1 178,360.7 -2,792,457.4 -0.3 Year 3 JAN 31.0 17.6 0.0 178,360.7 40,480.0 0.0 218,840.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 118,767.1 178,360.7 -2,852,051.0 -0.3 FEB 28.0 13.8 0.0 161,100.0 31,740.0 0.0 192,840.0 0.0 85,901.8 4,968.0 90,869.8 161,100.0 161,100.0 101,970.2 161,100.0 -2,911,180.8 -0.3 MAR 31.0 17.4 0.0 178,360.7 40,020.0 0.0 218,380.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 118,307.1 178,360.7 -2,971,234.4 -0.3 APR 30.0 16.8 0.0 172,607.1 38,640.0 0.0 211,247.1 113,707.4 92,037.7 4,968.0 210,713.1 172,607.1 172,607.1 534.1 172,607.1 -3,143,307.5 -0.3 MAY 31.0 15.9 0.0 178,360.7 36,570.0 0.0 214,930.7 232,309.2 95,105.6 4,968.0 332,382.8 178,360.7 178,360.7 0.0 178,360.7 -3,321,668.2 -0.3 JUN 30.0 7.8 0.0 172,607.1 17,940.0 0.0 190,547.1 301,530.0 92,037.7 4,968.0 398,535.7 172,607.1 172,607.1 0.0 172,607.1 -3,494,275.4 -0.3 JUL 31.0 8.9 0.0 178,360.7 20,470.0 0.0 198,830.7 298,208.8 95,105.6 4,968.0 398,282.4 178,360.7 178,360.7 0.0 178,360.7 -3,672,636.1 -0.3 AUG 31.0 8.0 0.0 178,360.7 18,400.0 0.0 196,760.7 257,130.8 95,105.6 4,968.0 357,204.4 178,360.7 178,360.7 0.0 178,360.7 -3,850,996.8 -0.3 SEP 30.0 12.5 0.0 172,607.1 28,750.0 0.0 201,357.1 130,750.4 92,037.7 4,968.0 227,756.1 172,607.1 172,607.1 0.0 172,607.1 -4,023,603.9 -0.3 OCT 31.0 18.3 0.0 178,360.7 42,090.0 0.0 220,450.7 50,779.4 95,105.6 4,968.0 150,853.0 178,360.7 178,360.7 69,597.7 178,360.7 -4,132,366.9 -0.3 NOV 30.0 30.4 0.0 172,607.1 69,920.0 0.0 242,527.1 0.0 92,037.7 4,968.0 97,005.7 172,607.1 172,607.1 145,521.5 172,607.1 -4,159,452.6 -0.3  164 Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from pond Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   DEC 31.0 30.8 0.0 178,360.7 70,840.0 0.0 249,200.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 149,127.1 178,360.7 -4,188,686.2 -0.3 Year 4 JAN 31.0 17.6 0.0 178,360.7 40,480.0 0.0 218,840.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 118,767.1 178,360.7 -4,248,279.7 -0.3 FEB 28.0 13.8 0.0 161,100.0 31,740.0 0.0 192,840.0 0.0 85,901.8 4,968.0 90,869.8 161,100.0 161,100.0 101,970.2 161,100.0 -4,307,409.6 -0.3 MAR 31.0 17.4 0.0 178,360.7 40,020.0 0.0 218,380.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 118,307.1 178,360.7 -4,367,463.2 -0.3 APR 30.0 16.8 0.0 172,607.1 38,640.0 0.0 211,247.1 113,707.4 92,037.7 4,968.0 210,713.1 172,607.1 172,607.1 534.1 172,607.1 -4,539,536.2 -0.3 MAY 31.0 15.9 0.0 178,360.7 36,570.0 0.0 214,930.7 232,309.2 95,105.6 4,968.0 332,382.8 178,360.7 178,360.7 0.0 178,360.7 -4,717,896.9 -0.3 JUN 30.0 7.8 0.0 172,607.1 17,940.0 0.0 190,547.1 301,530.0 92,037.7 4,968.0 398,535.7 172,607.1 172,607.1 0.0 172,607.1 -4,890,504.1 -0.3 JUL 31.0 8.9 0.0 178,360.7 20,470.0 0.0 198,830.7 298,208.8 95,105.6 4,968.0 398,282.4 178,360.7 178,360.7 0.0 178,360.7 -5,068,864.8 -0.3 AUG 31.0 8.0 0.0 178,360.7 18,400.0 0.0 196,760.7 257,130.8 95,105.6 4,968.0 357,204.4 178,360.7 178,360.7 0.0 178,360.7 -5,247,225.5 -0.3 SEP 30.0 12.5 0.0 172,607.1 28,750.0 0.0 201,357.1 130,750.4 92,037.7 4,968.0 227,756.1 172,607.1 172,607.1 0.0 172,607.1 -5,419,832.6 -0.3 OCT 31.0 18.3 0.0 178,360.7 42,090.0 0.0 220,450.7 50,779.4 95,105.6 4,968.0 150,853.0 178,360.7 178,360.7 69,597.7 178,360.7 -5,528,595.6 -0.3 NOV 30.0 30.4 0.0 172,607.1 69,920.0 0.0 242,527.1 0.0 92,037.7 4,968.0 97,005.7 172,607.1 172,607.1 145,521.5 172,607.1 -5,555,681.3 -0.3 DEC 31.0 30.8 0.0 178,360.7 70,840.0 0.0 249,200.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 149,127.1 178,360.7 -5,584,914.9 -0.3 Year 5 JAN 31.0 17.6 0.0 178,360.7 40,480.0 0.0 218,840.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 118,767.1 178,360.7 -5,644,508.5 -0.3 FEB 28.0 13.8 0.0 161,100.0 31,740.0 0.0 192,840.0 0.0 85,901.8 4,968.0 90,869.8 161,100.0 161,100.0 101,970.2 161,100.0 -5,703,638.3 -0.3 MAR 31.0 17.4 0.0 178,360.7 40,020.0 0.0 218,380.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 118,307.1 178,360.7 -5,763,691.9 -0.3 APR 30.0 16.8 0.0 172,607.1 38,640.0 0.0 211,247.1 113,707.4 92,037.7 4,968.0 210,713.1 172,607.1 172,607.1 534.1 172,607.1 -5,935,764.9 -0.3 MAY 31.0 15.9 0.0 178,360.7 36,570.0 0.0 214,930.7 232,309.2 95,105.6 4,968.0 332,382.8 178,360.7 178,360.7 0.0 178,360.7 -6,114,125.7 -0.3 JUN 30.0 7.8 0.0 172,607.1 17,940.0 0.0 190,547.1 301,530.0 92,037.7 4,968.0 398,535.7 172,607.1 172,607.1 0.0 172,607.1 -6,286,732.8 -0.3 JUL 31.0 8.9 0.0 178,360.7 20,470.0 0.0 198,830.7 298,208.8 95,105.6 4,968.0 398,282.4 178,360.7 178,360.7 0.0 178,360.7 -6,465,093.5 -0.3 AUG 31.0 8.0 0.0 178,360.7 18,400.0 0.0 196,760.7 257,130.8 95,105.6 4,968.0 357,204.4 178,360.7 178,360.7 0.0 178,360.7 -6,643,454.2 -0.3 SEP 30.0 12.5 0.0 172,607.1 28,750.0 0.0 201,357.1 130,750.4 92,037.7 4,968.0 227,756.1 172,607.1 172,607.1 0.0 172,607.1 -6,816,061.4 -0.3 OCT 31.0 18.3 0.0 178,360.7 42,090.0 0.0 220,450.7 50,779.4 95,105.6 4,968.0 150,853.0 178,360.7 178,360.7 69,597.7 178,360.7 -6,924,824.4 -0.3 NOV 30.0 30.4 0.0 172,607.1 69,920.0 0.0 242,527.1 0.0 92,037.7 4,968.0 97,005.7 172,607.1 172,607.1 145,521.5 172,607.1 -6,951,910.0 -0.3 DEC 31.0 30.8 0.0 178,360.7 70,840.0 0.0 249,200.7 0.0 95,105.6 4,968.0 100,073.6 178,360.7 178,360.7 149,127.1 178,360.7 -6,981,143.6 -0.3    165 Table C.6 Deterministic model for solids content of 80% in the dry condition for an unlined impoundment Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from pond Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   Year 1 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 51,444.7 104,043.8 -52,599.0 -0.1 FEB 28.0 13 .8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 4,968.0 84,552.2 93,975.0 93,975.0 41,162.8 93,975.0 -105,411.2 -0.1 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 50,984.7 104,043.8 -158,470.3 -0.1 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 4,968.0 156,067.4 100,687.5 100,687.5 0.0 100,687.5 -259,157.8 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 4,968.0 227,573.8 104,043.8 104,043.8 0.0 104,043.8 -363,201.5 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 4,968.0 264,806.8 100,687.5 100,687.5 0.0 100,687.5 -463,889.0 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 4,968.0 265,726.2 104,043.8 104,043.8 0.0 104,043.8 -567,932.8 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 4,968.0 241,944.2 104,043.8 104,043.8 0.0 104,043.8 -671,976.5 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 4,968.0 165,934.4 100,687.5 100,687.5 0.0 100,687.5 -772,664.0 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 4,968.0 122,477.6 104,043.8 104,043.8 23,656.1 104,043.8 -853,051.7 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 4,968.0 90,236.8 100,687.5 100,687.5 80,370.7 100,687.5 -873,368.4 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 81,804.7 104,043.8 -895,607.5 -0.2 Year 2 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 51,444.7 104,043.8 -948,206.5 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 4,968.0 84,552.2 93,975.0 93,975.0 41,162.8 93,975.0 -1,001,018.7 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 50,984.7 104,043.8 -1,054,077.7 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 4,968.0 156,067.4 100,687.5 100,687.5 0.0 100,687.5 -1,154,765.2 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 4,968.0 227,573.8 104,043.8 104,043.8 0.0 104,043.8 -1,258,809.0 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 4,968.0 264,806.8 100,687.5 100,687.5 0.0 100,687.5 -1,359,496.5 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 4,968.0 265,726.2 104,043.8 104,043.8 0.0 104,043.8 -1,463,540.2 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 4,968.0 241,944.2 104,043.8 104,043.8 0.0 104,043.8 -1,567,584.0 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 4,968.0 165,934.4 100,687.5 100,687.5 0.0 100,687.5 -1,668,271.5 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 4,968.0 122,477.6 104,043.8 104,043.8 23,656.1 104,043.8 -1,748,659.1 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 4,968.0 90,236.8 100,687.5 100,687.5 80,370.7 100,687.5 -1,768,975.9 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 81,804.7 104,043.8 -1,791,214.9 -0.2 Year 3 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 51,444.7 104,043.8 -1,843,814.0 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 4,968.0 84,552.2 93,975.0 93,975.0 41,162.8 93,975.0 -1,896,626.2 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 50,984.7 104,043.8 -1,949,685.2 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 4,968.0 156,067.4 100,687.5 100,687.5 0.0 100,687.5 -2,050,372.7 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 4,968.0 227,573.8 104,043.8 104,043.8 0.0 104,043.8 -2,154,416.5 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 4,968.0 264,806.8 100,687.5 100,687.5 0.0 100,687.5 -2,255,104.0 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 4,968.0 265,726.2 104,043.8 104,043.8 0.0 104,043.8 -2,359,147.7 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 4,968.0 241,944.2 104,043.8 104,043.8 0.0 104,043.8 -2,463,191.5 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 4,968.0 165,934.4 100,687.5 100,687.5 0.0 100,687.5 -2,563,879.0 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 4,968.0 122,477.6 104,043.8 104,043.8 23,656.1 104,043.8 -2,644,266.6 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 4,968.0 90,236.8 100,687.5 100,687.5 80,370.7 100,687.5 -2,664,583.4 -0.2  166 Precipitation water (mm/month) Water in (m3) Water out (m3) Water required in the mill (m3) Water in the TMF (m3) Water deficit Month Number of days in month Rainfall Snow water equivalent from snow melt Water with tailings Precipitation onto impoundment Recovery from open pit Total Water Input Evaporation from pond Entrainment Seepage loss Total Water Output Water with tailings Total mill required water Monthly change in storage Water Return to the mill Cumulative water deficit (m3) Water deficit (m3/tonne)   DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 81,804.7 104,043.8 -2,686,822.4 -0.2 Year 4 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 51,444.7 104,043.8 -2,739,421.5 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 4,968.0 84,552.2 93,975.0 93,975.0 41,162.8 93,975.0 -2,792,233.6 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 50,984.7 104,043.8 -2,845,292.7 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 4,968.0 156,067.4 100,687.5 100,687.5 0.0 100,687.5 -2,945,980.2 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 4,968.0 227,573.8 104,043.8 104,043.8 0.0 104,043.8 -3,050,023.9 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 4,968.0 264,806.8 100,687.5 100,687.5 0.0 100,687.5 -3,150,711.4 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 4,968.0 265,726.2 104,043.8 104,043.8 0.0 104,043.8 -3,254,755.2 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 4,968.0 241,944.2 104,043.8 104,043.8 0.0 104,043.8 -3,358,798.9 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 4,968.0 165,934.4 100,687.5 100,687.5 0.0 100,687.5 -3,459,486.4 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 4,968.0 122,477.6 104,043.8 104,043.8 23,656.1 104,043.8 -3,539,874.1 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 4,968.0 90,236.8 100,687.5 100,687.5 80,370.7 100,687.5 -3,560,190.8 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 81,804.7 104,043.8 -3,582,429.9 -0.2 Year 5 JAN 31.0 17.6 0.0 104,043.8 40,480.0 0.0 144,523.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 51,444.7 104,043.8 -3,635,028.9 -0.2 FEB 28.0 13.8 0.0 93,975.0 31,740.0 0.0 125,715.0 0.0 79,584.2 4,968.0 84,552.2 93,975.0 93,975.0 41,162.8 93,975.0 -3,687,841.1 -0.2 MAR 31.0 17.4 0.0 104,043.8 40,020.0 0.0 144,063.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 50,984.7 104,043.8 -3,740,900.2 -0.2 APR 30.0 16.8 0.0 100,687.5 38,640.0 0.0 139,327.5 65,830.6 85,268.8 4,968.0 156,067.4 100,687.5 100,687.5 0.0 100,687.5 -3,841,587.7 -0.2 MAY 31.0 15.9 0.0 104,043.8 36,570.0 0.0 140,613.8 134,494.8 88,111.0 4,968.0 227,573.8 104,043.8 104,043.8 0.0 104,043.8 -3,945,631.4 -0.2 JUN 30.0 7.8 0.0 100,687.5 17,940.0 0.0 118,627.5 174,570.0 85,268.8 4,968.0 264,806.8 100,687.5 100,687.5 0.0 100,687.5 -4,046,318.9 -0.2 JUL 31.0 8.9 0.0 104,043.8 20,470.0 0.0 124,513.8 172,647.2 88,111.0 4,968.0 265,726.2 104,043.8 104,043.8 0.0 104,043.8 -4,150,362.7 -0.2 AUG 31.0 8.0 0.0 104,043.8 18,400.0 0.0 122,443.8 148,865.2 88,111.0 4,968.0 241,944.2 104,043.8 104,043.8 0.0 104,043.8 -4,254,406.4 -0.2 SEP 30.0 12.5 0.0 100,687.5 28,750.0 0.0 129,437.5 75,697.6 85,268.8 4,968.0 165,934.4 100,687.5 100,687.5 0.0 100,687.5 -4,355,093.9 -0.2 OCT 31.0 18.3 0.0 104,043.8 42,090.0 0.0 146,133.8 29,398.6 88,111.0 4,968.0 122,477.6 104,043.8 104,043.8 23,656.1 104,043.8 -4,435,481.6 -0.2 NOV 30.0 30.4 0.0 100,687.5 69,920.0 0.0 170,607.5 0.0 85,268.8 4,968.0 90,236.8 100,687.5 100,687.5 80,370.7 100,687.5 -4,455,798.3 -0.2 DEC 31.0 30.8 0.0 104,043.8 70,840.0 0.0 174,883.8 0.0 88,111.0 4,968.0 93,079.0 104,043.8 104,043.8 81,804.7 104,043.8 -4,478,037.4 -0.2  

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