Groundwater Modelling For Impact Assessment of Natural Resource Projects

3.1 Introduction

This section describes the basic concepts of groundwater modelling as applicable to mining, aggregate, and groundwater extraction projects. Although the concepts are generally applicable, the discussions focus on modelling related to the EA (and sub-EA) process.

3.2 Definition of Groundwater Model

The term "Groundwater Model" can be found within the literature to mean different things to different people. Groundwater model is often used interchangeably for conceptual groundwater model or mathematical representation of a groundwater flow system (either analytical or numerical). In the broad sense, groundwater models can be considered as a sum of multiple components, both physically and mathematically based, each of which contributes to the general understanding of the processes that are operative in a groundwater system or the response to a specific question.

In these guidelines, the following definitions are used:

• "Groundwater Model": A generic term describing the sum of the components used to describe a groundwater system, including a conceptual and mathematical model.
• "Conceptual Model": The general description or representation of groundwater flow and transport at a site based on site-specific data or factors that influence hydrogeological processes. The ASTM definition for a conceptual model is "an interpretation or working description of the characteristics and dynamics of the physical system" (ASTM D5447-04, 2010).
• "Mathematical Model": A mathematical description of the groundwater flow system. The ASTM definition of a mathematical model is the "mathematical equations expressing the physical system and including simplifying assumptions. The representation of a physical system by mathematical expressions from which the behavior of the system can be deduced with known accuracy." (ASTM D5447-04, 2010).

Mathematical models can be of two types:

• "Analytical Model": A closed form solution (e.g. Darcy Law, Theis solution, advection-dispersion equation) of representative flow and transport equations. Analytical models are typically used to represent simple systems or to illustrate broad, generalized effects of different parameter assumptions.
• "Numerical Model": A computer model, using finite difference, finite element or other method, with an approximate solution of the governing flow and/or transport equations. The ASTM defines a numerical model as the "application of a mathematical model to represent a site-specific groundwater flow system" (ASTM D5447-04, 2010). Numerical models are typically used to represent more complex systems.

3.3 Use of Groundwater Models

Groundwater models are used to answer specific questions or to achieve a specific objective. Modelling objectives and methods vary depending on the nature of the question being asked and the characteristics of the site or system. The necessary level of detail or accuracy of results can vary, depending on the objective.

Within the natural resource industry, groundwater models are used for many purposes. In terms of environmental effects, models are used for environmental assessments or other permitting requirements. Typical uses of groundwater models include:

• Conceptualize and quantify current conditions (synthesize existing information)
• Understand system dynamics to identify and quantify controlling and significant processes (e.g., surface water - groundwater interactions, recharge areas, seepage rates, transport dynamics, etc.)
• Predict a future change or impact in response to a planned or potential stress, such as water table drawdown related to a planned extraction well or construction of a given mine plan component (e.g. inflow to an open pits, seepage from a tailings facilities, etc.)
• Evaluate sensitivity of the system to model uncertainty and/or magnitude of stresses
• Identify capture zones or source protection areas (for groundwater resource projects)
• Assess mitigation options (e.g. seepage interception, pump & treat etc.)
• Guide future data collection
• Improve the design of monitoring networks (e.g. determine aquifer units and/or specific areas requiring additional monitoring)
• Act as a management tool (e.g., assess different proposed management scenarios in managing a multiple use aquifer), and/or
• Evaluate engineering designs (e.g. phreatic surface in a tailings dam, mine dewatering systems, detailed mitigation designs).

The groundwater "models" that can be used to address any of the uses mentioned above encompass a wide range of model types, from conceptual models that describe how the groundwater system is envisioned to operate, to many varieties of a mathematical model. Mathematical models can include:

• Analytical or numerical models
• Different model dimensions (D): 1D, 2D or 3D models
• Groundwater flow model or groundwater flow & transport models
• Steady-state or transient models
• Equivalent porous media or discrete fracture models, etc.

Further description of model types and the selection process is provided in Section 5.

Selection of model type or, for that matter, the need for modelling, is a function of the perceived risk or potential impact to a VEC, the nature of the groundwater system, and the objectives of a given assessment.

From a simple perspective, if there is no perceived risk to a VEC (or if mitigation measures are used that do not require input from a groundwater model, or are not relevant to groundwater) groundwater modelling is unlikely to be required. Likewise, even if there is a perceived risk, if the results of simple, conservative assumptions suggest impacts are highly unlikely, more complex modelling may not be required. Ultimately, the need for groundwater modelling is a judgment call based on the risk, groundwater conceptual model (and complexity of that model), and the objectives of a given impact assessment.

Assuming that groundwater modelling is determined to be required, regardless of the specific purpose, the following general guidelines should be followed when developing a groundwater model:

• The modelling objective should be clearly defined and stated (as specifically as possible) to determine the necessary data requirements and modelling approach and methods.
• In the context of environmental assessments for proposed resource projects, the modelling objectives should be defined based on the specific issue(s), or VEC of concern.
• The methods and complexity of the modelling exercise should be consistent with the modelling objectives and the required accuracy in model predictions.
• The scope of the modelling study should take into account data availability, available budget, and time constraints while ensuring that the modelling objective(s) are met.

3.4 Model Complexity

Groundwater models (conceptual and mathematical) vary in complexity based on the potential impacts, modelling objectives, hydrogeological framework, and data availability. In these guidelines, "complexity" can be considered to be "the degree to which a model application resembles, or is designed to resemble, the physical hydrogeological system" (MDBC, 2001). Model complexity may relate to, but does not necessarily have to relate to the spatial extent of a model. Model complexity may apply to either or both the conceptual model or the mathematical model, but a simple conceptual model may necessitate a simple mathematical model.

For the purpose of these guidelines, three levels of model complexity are defined:

• Basic - These are Scoping Level Models based on sparse or limited data (e.g., minimal hydraulic conductivity data; few or no hydraulic head data, etc.). These models have conceptual models with broad assumptions and, mathematically, can be analytical, or "basic" numerical models and often include the type of model termed "parametric". These models are often used to gain an understanding of the parameters to which a given system is most sensitive and which could be the focus for future data collection, or the basis for broad monitoring networks. Regional or catchment scale models based on limited data that are used to provide a general sense of potential groundwater flow directions are of this type.
• Moderate - These are conceptual and numerical models based on a reasonable, though often limited, dataset and having limited calibration. These models may be used to determine the potential range of change or to "bracket" potential effects that may occur due to a given stress. Such models are often used to guide specific data collection programs or more detailed monitoring plan designs, and are often combined with mitigation plans. Moderate complexity models are often used to support EA applications (e.g., predictions of seepage and resulting water quality impacts on VECs) and require detailed sensitivity/uncertainty analysis.
• Complex - These are conceptual and numerical models based on extensive long-term data and/or specific environmental impacts assessment requirements. Data availability is typically high, calibration is rigorous based on induced stresses, and the models are verified. For mining projects, these models are often calibrated to observations collected as the mine is developed. For example, inflows to an open pit or seepage data are used for calibration and integrated with detailed structural geology reviews or mapping.

Model complexity is determined primarily by the modelling objective(s), which may be influenced by data availability, time, budget, and regulatory context. During the initial stage of model planning, the model objectives, approach, and complexity, as well as the limitations or uncertainty related to that approach, should be discussed amongst the proponent and the modeler to be clear on what the model will or will not be capable of. A key aspect to keep in mind is that a numerical model cannot be better than the conceptual model used to formulate it.

For any given objective, model complexity can be reduced but, possibly, resulting in greater uncertainty. The model complexity should be determined in conjunction with the method in which the results will be used. For all types, the model complexity should be consistent with the data available from which to derive model assumptions. Complex models built with limited data are no better than basic models with an appropriate formulation, but are often times more difficult to calibrate and justify. A numerical model cannot be better than the conceptual model used to formulate it.

3.5 Modelling Process

This section presents an overview of the general groundwater modelling process. Subsequent portions of Section 3, as well as other sections of these guidelines, present further detail on each of the process "steps" that are introduced.

3.5.1 General

Modelling is a multi-phase process progressing through the broad stages of objective definition (which is typically in reference to a VEC), conceptual model development, mathematical model development and analyses, to predictions and uncertainty analysis (see Figure 3-1). Data collection may occur at many points during this progression, but is often required following initial model conceptualization, when a preliminary understanding of the system is being developed. Further data collection efforts may happen at other points in the process, including the calibration and sensitivity step as well as later, following uncertainty analysis. Uncertainty analysis on predictive models may indicate areas or data types that are required to decrease uncertainty.

While the process implies a start and finish, any individual model should not be considered final, but representing a point in time within an overall process of improved conceptualization, review, and refinement.

Figure 3-1: Modelling as a multi-phase, iterative process.

3.5.2 Modelling Objective

Determining "What are the model objectives?" is an important first step in the modelling process. Modelling objectives need to be appropriately defined to meet the overall project objectives and need to take into consideration the data availability, budget, and time constraints. Models developed with the objective of assessing environmental effects must have the ecological or environmental receptors defined as well as the nature and scale of effect significance. Models developed for engineering purposes may have objectives defined differently than those for effects assessment. For example, engineering models may be constructed to estimate maximum expected pit wall pore pressures or order of magnitude inflows from groundwater seepage.

Model objectives for natural resource projects may be defined in the context of risk assessments. Framing model uncertainty in relation to risk can often provide guidance to the model objective and the required complexity/accuracy in model predictions. Mitigation plans may be required (or implemented) if the risk of incorrect modelling predictions leads to unacceptable consequence(s). In these cases, model objectives may be defined to support a mitigation concept.

Whatever the objective, it must be clearly defined at the start of the process and should be as specific as possible. The definition of very general modelling objectives such as "determination of the groundwater flow field" or "the assessment of seepage mitigation measures" should be avoided. Instead, the modelling objectives should provide specific targets. For example, the modelling objectives for an EA study may be as follows:

• Predict the future (transient) volumetric flow of seepage from Tailings Impoundment "X" to VEC "Y" during active operation.
• Predict the future contaminant transport from Tailings Impoundment "X" to valued ecosystem component VEC "Y" during active operation and post-closure.
• Predict the reduction in impacted groundwater (and associated contaminant load on VEC "Y")) in response to alternative seepage mitigation strategies (including drains, interceptor wells).

The definition of specific modelling objectives greatly assist the modeller to select the appropriate modelling approach and model complexity. Examples of model objectives for natural resource extraction projects are presented in Sections 3.6 to 3.8.

3.5.3 Review and Interpretation of Available Data

Compilation and review of available data is the first step in model conceptualization. The data available at the initiation of model development, in light of the model objectives, constitute the basis for model conceptualization and preliminary identification of significant gaps that may require additional field data collection programs.

The initial data review has two broad steps: compilation of existing data, and analysis to improve understanding of fundamental system dynamics.

Compilation of existing data may include:

• Identification and review of pertinent literature, such as reports on the site or regional geology, hydrogeology, hydrology, water management, water use, etc.
• Municipal/local, provincial or federal databases (e.g., Provincial observation well network, water quality, streamflow, climate, pumping wells, etc.).
• Results of previous investigations related to geology, hydrogeology, engineering, etc.
• Baseline data already available for the site (water levels, hydraulic testing, streamflows, climate, etc.).

The existing dataset should be reviewed in comparison to baseline monitoring requirements outlined in the Interim Water and Air Baseline Monitoring Guidance Document for Mine Proponents and Operators (MoE, 2011). While baseline monitoring requirements may not perfectly match the given objective, this comparison will provide an opportunity to identify gaps in baseline monitoring or different ways in which baseline data may be utilized.

The dataset should be analyzed to provide initial assumptions on system dynamics and parameter distributions. Analyses should include all components that may be pertinent to the system of interest, such as:

• Spatial distributions and temporal variations in groundwater levels, flow directions, flow rates
• Spatial distribution of hydraulic properties such as hydraulic conductivity or transmissivity and values for aquifers or aquitards of interest; correlation to lithology, geologic structure (i.e., faults) or geotechnical parameters (i.e., fractures)
• Groundwater recharge rates
• Recharge/discharge zonation
• Stream baseflow
• Transport parameters, if appropriate.

This initial data review provides the following two outputs:

• Development of a database and understanding of the database that will form the basis for the conceptual model
• A preliminary identification of significant data gaps requiring field data collection programs.

At this stage, it may be appropriate to review and/or adjust model objectives to better reflect model limitations.

3.5.4 Model Conceptualization

Development of a hydrogeological conceptual model ("conceptual model") is a critical early step in the overall modelling process. A conceptual model is a simplified representation of the essential features of the physical hydrogeological system, and its hydraulic behavior, to an adequate degree of detail (MDBC, 2001) to answer the question or issue at hand.

Conceptualization of the groundwater flow and transport system is an ongoing activity throughout the modelling process. There are two key phases in the modelling process when model conceptualization is critically important (see Figure 3-1):

• Initial model conceptualization (usually based on a desktop review of available data)
• Development of a detailed conceptual model (usually after completion of a site-specific field program that was planned with the modelling objectives in mind).

The initial model conceptualization tends to be more general and has the objective of identifying critical data gaps and the design of a suitable data collection program. The second phase of model conceptualization is more detailed (and quantitative) and is aimed at defining and simplifying the flow and transport problem in such a way that it can be expressed in a mathematical model.

The scope and complexity of a hydrogeological conceptual model should reflect the modelling objectives. The conceptual model must have the necessary detail to achieve model objectives, but model objectives also should not be defined that are beyond the limits or level of detail capable in the conceptual model.

The conceptual model is the basis for the design of a mathematical model; the mathematical model provides a solution to the flow system for the given conceptual model. As such, the conceptual model includes a description of the components that are incorporated into the mathematical model.

The potential for updates to the conceptual model over time as further understanding of the system is gained should be recognized. For example, problems in model calibration may trigger a review and possibly a revision (or update) of the conceptual model (see Figure 3-1). Additional data collection may also result in a revision of the conceptual model (Figure3-1).

Further guidance on model conceptualization is provided in Section 4.

3.5.5 Field Data Collection

3.5.5.1 Purpose

Field data collection in the modelling process is intended to fill gaps or uncertainties specific to the site or project identified during development of the conceptual model and which are considered necessary to achieving model objectives. These data requirements may differ from or expand upon earlier baseline monitoring requirements as outlined in the Interim Water and Air Baseline Monitoring Guidance Document for Mine Proponents and Operators (MoE, 2011), in that the modelling being undertaken may be occurring after submission of an Application for an Environmental Assessment Certificate. Additional information for mining projects may be available, such as updated mine plans or mitigation measures, or potential impacts to VEC's in which groundwater pathways are believed to play a significant role, may be better recognized.

3.5.5.2 Scope of Data Collection

Identification of the requirement for additional data collection should be a fundamental component of the initial data review, conceptual model development, and mathematical model planning. Additional requirements may vary depending on project type, scope and permitting stage but, for this guideline, are specifically considered in terms of mathematical model requirements.

Data collection program requirements identified at this stage in the modelling or permitting process could involve physical groundwater parameters or requirements for transport modelling. In both cases, the additional data requirements may relate more to a specific component of the model objectives or information specifically needed for mathematical modelling itself than have previous data collection programs (by area, potential effect/pathway or regional groundwater question).

3.5.5.3 Types of Field Data

General data inputs specific to completion of a mathematical model as outlined in these guidelines can be categorized into three types:

1. Site characterization (parameters for model input such as stratigraphy, aquifer properties, topography, climate)
2. Groundwater monitoring (parameters to be simulated such as water levels, groundwater discharge)
3. Performance monitoring (parameters predicted by the model such as drawdown, contaminant breakthrough).

At a minimum, type 1 and 2 data are required for mathematical models of any significant complexity, but particularly if numerical modelling is envisioned. Data requirements specific to types 1 and 2 inputs should include:

• Coverage of all areas of potential concern (e.g. WRDs, open pit, tailings storage facility [TSF])
• Coverage of all hydrogeological units of interest
• Good spatial distribution (not all clustered in one small area of the domain)
• Coverage of the entire depth range of interest
• Transient aquifer hydrogeological unit properties (storage) and transient monitoring (if problem is transient).

For mining projects, Type 3 data may be required, for example, for assessments of specific mitigation methods (i.e., groundwater interception systems) or calibration of pit or underground inflows. In these cases, data requirements may include:

• Transient pumping rates and pumping water levels
• Transient water levels from observation networks, and/or
• Streamflow and/or seepage flow monitoring over time.

3.5.5.4 Additional Requirements for Groundwater Extraction

For large (>75 L/s) groundwater extraction projects, pumping tests can be considered a mandatory requirement, but additional data may be required to address specific issues, such as groundwater - surface water interaction or interference with other pumping wells, that may not be sufficiently understood to constrain model uncertainty.

The need for additional data may be identified early enough in this process to justify further work, but also may be a result of the mathematical modelling itself. For groundwater extraction (and mining), the potential for mathematical modelling results to identify additional data needs cannot be ruled out.

3.5.5.5 Data Requirements for Transport Modelling

If transport modelling is to be completed, additional data requirements will exist. Specific data requirements for transport modelling could include:

• Groundwater quality measurements over time
• Location, history and mass loading rate of chemical sources and sinks
• Average groundwater velocities (horizontal and/or vertical)
• Effective porosity
• Soil bulk density
• Soil organic content
• Longitudinal and Transverse dispersivity
• Reactive transport parameters (may require laboratory studies).

In certain cases, data requirements for transport modelling can be directly determined from field data. In other cases, sensitivity analyses based on a plausible range of values or calibration to observations may be the only way to constrain a given parameter. Therefore, the practical benefit of data collection should always be considered in designing a field program. From a modelling point-of-view, any additional data collected should lead to a measurable improvement in model conceptualization and/or the ability to calibrate the model (i.e. reduce model uncertainty).

3.5.6 Selection & Construction of Mathematical Model

Selection and construction of a mathematical model needs to be completed in a manner that is able to meet the modelling objectives, includes relevant aspects of the conceptual model, and is consistent with data available for model calibration.

Selection of a modelling code is required at this point. The modelling code is the computer software that solves the groundwater flow equations. Selection of a modelling code will depend, for example, on the level of assessment required (i.e., simple or complex; analytical or numerical), dimensionality (i.e., 2D plan, 2D cross-section, axisymmetric or 3D) and the required outputs (see Section 5).

Once a code has been selected, model construction can commence. If analytical models are to be used, model construction is relatively simple, involving spreadsheets or a simple model code. If numerical models are used, model construction is more complicated (see Section 6).

Model construction involves converting the conceptual model into a mathematical model. This process entails definition of a model domain or grid and assignment of parameters to each node or grid cell (if numerical). Numerical model construction may require adjustments to the conceptual model, particularly in areas of particular interest, such as a specific VEC (e.g., stream).

Further guidance on model selection and model construction are provided in Sections 5 and 6, respectively.

3.5.7 Model Calibration & Verification

Model calibration and verification is necessary for any predictive model. The ability of a model to simulate observed conditions provides confidence in the conceptual and mathematical model.

Calibration is the process of adjusting parameters or fluxes, such as hydraulic conductivity and recharge, within reasonable limits to match observations. Verification is a process of testing the calibrated model by demonstrating that it can successfully predict a set of observations not used previously for model calibration (see Section 7).

The calibration and verification process can include several iterations:

• Initial review of model calibration and verification: existing data is compared to model calibration and results.
• Data or performance gaps are identified.

Assuming that data or performance gaps cannot be addressed through reasonable modifications to the conceptual model or plausible (preferably constrained) variations in parameters, make a decision to:

• Collect more data, or
• Confirm model verification (at this modelling level) to be able to accomplish the modelling objectives and proceed to predictive scenario simulations

To the extent possible, the model should be calibrated for stresses that the model was developed to predict. For example, a model that is aimed at predicting the influence of a new groundwater extraction project, pumping should be calibrated to a transient data set (preferably a pumping test).

The use of a model calibrated to different hydraulic stresses than those predicted (e.g. use of a steady-state baseline model to predict pit inflow) results in greater uncertainty in model predictions and requires additional sensitivity analyses and/or more conservative assumptions (see below).

A model that is not calibrated should not be used for prediction of environmental impacts. Such a model may be used to illustrate possible outcomes for "what-if" scenarios using sensitivity analyses.

The uncertainty analysis which follows the predictive scenarios should focus, at a minimum, on model sensitivity to model input parameters, but may also focus on model sensitivity to conceptual model changes or alternative conceptual models.

Further guidance on the topic of model calibration and verification is provided in Section 7.

3.5.8 Model Predictions and Uncertainty Analysis

The calibrated baseline model may be used as the basis of a predictive model that is run in order to predict and assess how the groundwater system might change in response to postulated changes in hydrological stresses, model boundaries, or parameters (e.g. inflow in response to pit excavation) (Section 8).

Uncertainty analysis may be undertaken for these reasons:

• Illustrate (and quantify) how conceptual model limitations affect predictive model results
• Quantify the impact of variation in parameter estimates and assumptions
• Provide insight into how model results may be used.

Further guidance on the topic of model predictions and uncertainty analysis is provided in Section 8.

3.5.9 Model Documentation

Documentation should include all aspects of the modelling study from definition of modelling objectives, data review and conceptual modelling, to model setup (including disclosure of key modelling assumptions) and calibration through to model predictions and uncertainty analysis.

Proper documentation of the steps in the modelling process is essential to facilitate a review of the model study and the modelling results. In order for a reviewer to assess the validity (or reasonableness) of the modelling results (often times predictions) the reviewer has to have a good understanding of the whole process that led to these predictions, including the conceptual model, the field data the model is based on and the simplifying assumptions used to construct the mathematical model.

Model documentation is also important for the user (say modeller or proponent) as it facilitates subsequent use of the model, for example when the model is verified and/or recalibrated to new data or used for a different modelling objective.

Further guidance on model documentation is provided in Section 10.

3.6 Hardrock Mining

This section describes aspects of hardrock mining that may be included in groundwater modelling undertaken at one or more of the phases of mining described in Section 2.

3.6.1 Evaluate Mining Impacts

Predictive groundwater modelling may be undertaken to establish the impact of one or more of the following typical mining activities---for each, a discussion of data needs, modelling accuracy, and use is included:

• Inflow to open pit/underground workings inflow. Estimates may be required to address issues related to the management of discharge from mine dewatering. Level of accuracy (e.g., flow estimates within +/- 50%, 100%, 500%, etc.) can vary depending on available data and what the results are going to be used for; the mathematical modelling approach can be analytical or numerical, depending on the model objectives and required complexity or accuracy Unless large scale pumping tests have been completed, observations of inflow are available from an existing development on site, or from a nearby site, predictions will often focus on uncertainty analyses and a range of possible inflows.
• Aquifer drawdown & loss of groundwater discharge (to surface water). Often combined with inflow predictions, groundwater models may be used to assess potential effects on other nearby groundwater users, or to estimate changes in groundwater baseflow contributions to surface waters. The latter objective is common in BC for mining projects that may affect aquatic habitats. Accuracy of drawdown predictions is typically a function of how well hydraulic conductivity distribution and recharge are understood for the area of interest. In terms of loss of groundwater discharge to surface waters, predictions are often as sensitive to characteristics of surface water-groundwater connections as deeper groundwater conditions (i.e., gaining or losing stream reaches, streambed conductance, magnitude of surface flow vs. change in leakage, etc.). Uncertainty in these predictions is often high because these surface water-groundwater connections are difficult to determine at a detailed level. As a result, effects are often based on conservative assumptions. Catchment-scale water balance approaches with conservative groundwater assumptions may be used in place of groundwater models.
• Groundwater mounding & decrease (or increase) in groundwater discharge (to surface water). Model objectives including predictions of these effects are common for assessment of mine components such as tailings storage facilities or waste rock piles. Accuracy of predictions for proposed facilities is typically related as much to parameters of the tailings or waste rock themselves, and calibration and verification is not possible. As such, focus should be on uncertainty or sensitivity analyses. Mathematical models can be analytical or numerical, and 2D or 3D, depending on characteristics of the facility.
• Seepage & associated contaminant transport from mine waste units. The objective of this type of predictive model is usually to assess the role of groundwater pathways in impacting downstream water quality impacts, from such mine components as waste rock piles, tailings, backfilled and/or flooded pits/underground workings. The required accuracy of predictions will be a function of the magnitude of source terms themselves and sensitivity of the downstream environment to contaminants of concern. The accuracy of predictions will be affected by the understanding of how much load may reach groundwater, the groundwater pathways themselves and engineering design parameters for water management facilities and/or mitigation measures. Mathematical models may be analytical or numerical, but available detail of the conceptual model may dictate what approach is reasonable. Water and load balance approaches using conservative assumptions for groundwater may be used. Numerical models will typically focus on uncertainty analyses and be used to direct mitigation designs. If seepage contaminants are of high concern or VECs highly sensitive, transport models may be required. In such cases, data requirements will be greater and models subject to more extensive uncertainty analyses.
• Effects of post closure slope failures, climate variability (e.g. thawing of permafrost), or other long term factors. Objectives of such predictive models are typically based on potential future scenarios that could affect post-closure site management requirements. Modelling is typically numerical, based on predictive models designed to assess effects or water management requirements during earlier mine phases. As such, uncertainty is high and predictions commonly labeled "what-if" scenarios. Such analyses may result from risk trade-off studies related to factors such as long-term or closure design of tailings or water impoundment facilities and final pit slope angles, bulk-heading of portals for underground developments. Such predictions are often combined with engineering assessments.

3.6.2 Evaluate Engineering Designs or Mitigation Options

Groundwater models may be used to assess the design of water management or mitigation options. In terms of engineering designs, models may be used to assess:

• Dewatering designs (Open Pit, Underground)) - assess scope, requirements and/or effectiveness of a proposed design or design concept.
• Dam seepage - assess or provide input to a proposed design or design concept in terms of potential seepage rate or seepage rate minimization.
• Backfill strategies (underground/open pit) - assess effectiveness or effect of backfilling strategies. Objectives may be to predict how well backfilling minimizes groundwater inflow, the influence of groundwater on flooding of backfilled materials for ML/ARD control, or how groundwater may transport contaminants out of a backfilled area.
• Effects related to surface water management structures - groundwater models may be used to assess effectiveness (or ineffectiveness) of proposed structures. Examples include assessment of effectiveness of groundwater collection using collection ditches, potential leakage from ditches or leakage from constructed ponds.

In terms of evaluation of mitigation options, groundwater modelling can be used to:

• Design requirements for or assess effectiveness of grouting or sealing of fractured bedrock for seepage control.
• Design or assess effectiveness of seepage recovery systems (e.g., wells, drains).
• Design or assess effectiveness of cutoff walls; funnel & gate systems, etc.
• Assess the influence of groundwater flow on bioremediation system effectiveness.
• Provide design input or assessment of passive remediation options (e.g., reactive barriers, natural attenuation, etc.).

As suggested by the wide variety of modelling applications presented in this section, definition of project-specific modelling objectives and the necessary (or appropriate) level of model complexity may be influenced by a large number of factors. The scope of any modelling effort for a hardrock mining project site is likely to be affected by additional complexities. Examples of the potential complexity that may need to be recognized when designing a groundwater model study for a hardrock mining project include:

• Large project scale (often several watersheds and potentially many decades of operation)
• Remote locations (lack of background data and high budget/level of effort for data collection)
• Steep topography
• Complex geology
• Fractured rock hydraulics (fracture flow vs. equivalent porous media - often higher budgets necessary to appropriately characterize heterogeneous, anisotropic fractured systems).
• Hydraulic significance of faults or other significant geologic structures.
• High to extreme aquifer heterogeneity (overburden, bedrock; structures)
• Difficult to quantify groundwater - surface water interaction.
• Additional data requirements necessary to conduct contaminant transport modelling, if necessary.
• Potential for multiple sensitive receiving waters and environments.
• Changing mine designs or mine site conditions during modelling process or over life of mine.

3.7 Aggregate Mining

3.7.1 General

This section describes predictive groundwater modelling that may be undertaken for aggregate mines in terms of these guidelines. Predictive modelling is typically focused on assessing potential effects related to proposed operations. As with modelling for hardrock mines, predictive modelling can occur at any time during mine life: the mine planning and approval phase or the active mining or post-closure periods. Predictive models completed as part of the permitting process are often used for initial EA effects assessments. Predictive modelling at later stages in the project is less frequently used, but possible.

3.7.2 Evaluate Mining Impacts

The following are examples of groundwater model studies that may be undertaken for aggregate mines.

• Estimating Inflow to open pit - If the development is expected to intersect the water table, modelling may be required to assess inflow quantities for discharge permits or water management facilities.
• Groundwater - surface water interaction - In a related sense, if the development intersects the water table, modelling may be required to estimate aquifer drawdown and the potential for, or magnitude of, loss of groundwater discharge (to surface water).
• Well Interference (ZOI Analysis) - Again, if the development intersects the water table, modelling may be required to assess the zone of influence (ZOI) related to aquifer drawdown and subsequent effects on other groundwater users.
• Recharge effects: Groundwater recharge may be affected whether or not the development intersects the water table or involves extensive land clearing. Modelling may be required to estimate this effect.
• Assess effects on groundwater quality: Modelling may be used to assess mine-related effects on groundwater quality or offsite migration via groundwater pathways.

3.7.3 Evaluate Engineering Designs

Groundwater models may be used to assess such engineering components as dewatering or water management. Examples include:

• Assess dewatering designs - Groundwater models may be used to design or assess effectiveness of pit dewatering (pumping) schemes.
• Assess mitigation options - In some instances, mitigation may be required to offset potential effects on groundwater. Modelling may be used to assist design of mitigation systems, such as surface water flow augmentation requirements, or to assess mitigation option effectiveness.

As with hardrock mining, groundwater modelling in support of engineering designs is typically completed in conjunction with mine planners or mine engineers. Specific objectives may be quite different than those required for permitting but, if designed appropriately, can be used to address both requirements simultaneously.

3.8 Groundwater Extraction

3.8.1 General

Groundwater extraction projects differ from the previously discussed project types in that extraction of groundwater itself is the objective, rather than something that must be managed or addressed to extract a precious mineral or aggregate commodity.

The hydrogeologic setting for groundwater projects is often significantly different from that for hardrock or aggregate mining projects and is often relatively less complex due to more uniform aquifer type and lack of contaminant transport issues; however, groundwater - surface water interaction(s) can be challenging in the use of groundwater extraction from large aquifers. Also, the presence of multiple groundwater users within a given aquifer may require specific attention.

3.8.2 Baseline Modelling

The baseline conceptual model is important for assessing such factors as the basin water balance, recharge sources and seasonal variability, discharge areas, as well as potential areas of groundwater - surface water interaction. Conceptual model development is a focus at project planning and EA application preparation stages (or equivalent stage for other permits). As with other projects, the conceptual model may be updated at any time, as additional information becomes available. Simple, often analytical or water balance type mathematical models, may be used at the basic to moderate complexity level, to confirm the conceptual model, to assess effects of uncertainty, or better define data collection programs.

3.8.3 Predictive Modelling

Predictions made for groundwater extraction projects typically address a broader range of objectives than other types of natural resource projects. Predictions may not only be necessary to assess potential impacts, but to assess the capacity of an aquifer to support the proposed extraction. Predictions are typically completed to:

• Assess aquifer drawdown - Models may be used to estimate the cone of depression related to an extraction system and assess how it may be affected by varying climate or recharge conditions.
• Assess the potential for well Interference (ZOI Analysis) or effects on other groundwater users.
• Determine the capacity of the aquifer to sustain the proposed extraction. - Basin scale groundwater models or water balance models will be used to assess the sustainability of the proposed extraction.
• Assess loss of groundwater discharge (to surface water). - Models may be used to quantify the effect of groundwater extraction on baseflow contributions to surface water. These effects may have specific implications for aquatic or lentic habitats.
• Assess the potential for saltwater intrusion (coastal areas) - In coastal areas, models may be used to assess the potential for saltwater intrusion (or up-coning) and determine safe extraction rates.
• Determine wellhead protection area (WHPA) - Commonly, large groundwater extractions involve municipalities as the proponent and delineation of well protection areas is a requirement of infrastructure funding grants.

3.8.4 Evaluate Engineering Designs

From the engineering perspective, groundwater models for extraction projects are typically used to:

• Assist in design of extraction wells or well fields.
• Evaluate potential mitigation measures, such as artificial recharge.

3.9 Case Studies used Throughout These Guidelines

To illustrate the use of groundwater modelling for assessment of natural resource projects, three case studies are referenced throughout the guidelines: two hardrock mining projects and one groundwater extraction project. Detailed descriptions of the case studies are presented in Appendix C, while specific components are used as examples throughout these guidelines to illustrate good practice or unique approaches.

These case studies are:

Case Study 1: Open Pit Mine

Conceptual and numerical groundwater models were used to assess a proposed open pit mine in north central B.C. The mine site occupies a valued ecosystem comprising fish-bearing creeks and small lakes hosted in geologic materials indicative of a glacial valley. Key project components will include a two-stage open pit development and a prominent tailings storage facility (TSF).

The conceptual model is presented as well as numerical models used to assess baseline conditions, groundwater flow patterns and groundwater - surface water interaction, make estimates of pit inflow and seepage from the proposed TSF.

This case study illustrates a reasonable approach at this phase in mine planning and impact assessment to understand potential effects of the proposed project on groundwater and make initial decisions in regards to mitigation requirements.

Case Study 2: Underground Mine

Conceptual and numerical groundwater models were used to assess a proposed underground mine in northwestern B.C. Seepage from the Site is considered to have the potential to effect down gradient fish-bearing streams, as well as surface water and groundwater used for drinking water supply. The key project component of interest is the underground workings.

The conceptual model is presented as well as numerical models used to assess groundwater flow directions and quantities, estimates of mine discharge and to assess potential impacts on surface water discharge processes. Reactive and non-reactive transport modelling were used to illustrate potential timing of contaminant breakthrough.

This case study illustrates another reasonable approach at this phase in mine planning and impact assessment to understand potential effects, and presents a thorough uncertainty analysis incorporating multiple conceptual models.

Case Study 3: Groundwater Extraction

Conceptual and numerical groundwater models were used to assess a proposed groundwater extraction project in southwest B.C. Extraction was considered to have the potential to effect residential and municipal groundwater users, surface water flows and lake levels. The key project component is the well field itself.

The conceptual model is presented as well as numerical models used to assess potential effects on the aquifer, other groundwater users and surface water features, and to define the zone of influence.

This case study illustrates a reasonable approach to EA-level assessment of a groundwater extraction project and uncertainties.

Review Questions

1. What is the definition of a groundwater model used in these guidelines?
   1. An interpretation or working description of the characteristics and dynamics of the physical system.
   2. The representation of a physical groundwater system by mathematical expressions.
   3. A computer model with an approximate solution of the governing flow and/or transport equations.
   4. A sum of the components used to describe a groundwater system, including a conceptual and mathematical model.
   5. None of the above.
2. Which of the following summarizes uses of groundwater modelling for assessing impacts related to resource extraction?
   1. Estimating mine inflows, aquifer drawdown, changes to groundwater-surface water interaction, seepage or contaminant transport.
   2. Estimating well interference, salt water intrusion or defining wellhead protection areas.
   3. Assessing engineering designs, such as dewatering systems or mitigation measures.
   4. To quantify exact changes to a groundwater system and provide a single tool for impact assessment.
   5. A, B and C.
3. Which of the following statements about model objective is false:
   1. Modelling objectives can vary depending on the question being asked.
   2. Model objectives should be clearly defined and stated at the beginning of a modelling study.
   3. The first objective of modelling is to achieve a calibration target.
   4. Model complexity and accuracy should be consistent with model objectives.
   5. Modelling objectives can be met using only a conceptual model.
4. Model complexity, which can range from basic to complex, is a function of the level of available data, hydrogeologic understanding and model objectives.
   1. True
   2. False
5. The general process of groundwater modelling may include iterations at intermediate steps. When might these iterations typically occur?
   1. Conceptual model development
   2. Code selection
   3. Calibration and sensitivity analysis
   4. Documentation
   5. A and C
   6. All of the above

---

Proceed to Section 4: Conceptual Model Development