Water quality impacts to surface water and groundwater receptors can be a major risk of mining operations. Managing that risk requires modelling, monitoring and management. One of the major uncertainties in developing a thorough water quality model is understanding the partitioning of seepage through a mine rock or tailings landform. Does the water report as surface water or as groundwater recharge?
A key control of this partitioning is the hydraulic conductivity (K) of geologic media underlying mine waste landforms. Similarly, hydraulic conductivity is an important control on mobilization and transport of contaminants through saturated mine rock and tailings. Hydraulic conductivity is a physical property which describes the ease with which a porous material can transmit fluid (usually water) through its pore spaces. It differs from permeability in that it is dependent on both the material and the fluid characteristics. Hydraulic conductivity is a parameter that is heavily relied on for use in numerical models to estimate travel times, velocity, and peak concentrations of contaminants of concern (COPC) reaching an environmental receptor. This article present considerations for one simple, yet effective, method to estimate in situ hydraulic conductivity and help conceptualization of water flowpaths at your site.
The reliability of hydraulic conductivity estimates is dependent on three primary factors:
1) How the tests were completed in the field
2) Estimation parameters selected during data processing
3) Appropriate interpretation by the solver
Error introduced at any of these stages may lead to under or over-estimation at the magnitude scale. Keep reading for tips to avoid these errors in your tests and improve your model results.
In situ horizontal hydraulic conductivity may be estimated in saturated deposits under confined or unconfined conditions. ‘Slug tests’ are a common and effective method to estimate in situ hydraulic conductivity in saturated zones. Other type of K tests could include pump tests, infiltration testing, or permeameter testing.
An in situ slug test begins by injecting or withdrawing a volume (typically a 1m3 solid slug) into the water column to generate an instantaneous change in pressure head. The change in displacement over time is monitored using a pressure transducer (logger). Appropriate test selection, range of logger, and reading interval of the logger is crucial to execute a reliable K test.
Some common areas where error can be introduced into the test include:
Parameter selection is dependent on which analytical solution is being used, under what conditions (confined vs. unconfined), and accuracy of physical measurements taken during execution of the test.
The most common mathematical solutions for overdamped tests include Hvorslev, Bouwer-Rice (B-R), and Kansas Geological Survey (KGS). Underdamped tests are typically solved using the Butler method. Each solution has been developed for specific aquifer properties, and care should be taken in selecting which solution to use.
To summarize some key consideration in the interpretation of K test, interpretation will be discussed in terms of B-R and KGS solutions. Assuming a test is completed in an unconfined aquifer in which the water table intersects the well screen, the B-R solution may be considered as a starting point. A filter pack correction is assumed for a porosity of 30%. Figure 2 illustrates the curve generated from the hypothetical test. Note the “double straight-line effect” which is an indication that B-R is an appropriate solution to use in this case.
Interpretation of this solution involves curve matching and feasibility assessment. When curve matching to the appropriate portion of the curve, an ideal head range between normalized head 0.2 and 0.3 m/m is typically a reasonable starting point. In the below example, the ideal head range captures later time recovery, and would not be considered the best part of the curve to fit. To assess if the estimated K value is feasible, consider what is known about the site, like lithology, groundwater flow direction, and confined/unconfined conditions.
Figure 2: B-R Example.
Created in AQTESOLV (demo version) 
Figure 3 shows an example of a reliable KGS solution in an unconfined aquifer with water level above the well screen. Curve matching in this case is typically computed by a program and involves less interpretation than the B-R solution above.
In this example, the Ss value may be used to assess the reliability of the estimate. An Ss < 1E-6 m/s may indicate an underdeveloped well or other potential issues. Adjustments can be made to initial displacement, anisotropy ratio, and/or well radius relative to borehole radius (to account for skin effects) until Ss provides a reasonable magnitude estimate. In most programs, Ss can also be forced set to a minimum value of 1E-6 m/s.
Figure 3: KGS Example
Created in AQTESOLV (demo version) 
Okane applies a comprehensive approach for developing hydrological and water quality models for greenfield and operating sites. The hydrological and water quality performance of a mine waste landform are interpreted within the context of the site-specific hydrological and hydrogeological settings, and with appropriate characterization of materials governing COPC fate and transport.
Okane takes great care in developing and interpretating numerical models that serve as powerful tools for understanding water flow and quality in mine-impacted watersheds. Hydraulic conductivity is a key input in developing quantitative estimates of water flow and quality. In the development of 2D and 3D numerical models, horizontal hydraulic conductivity is especially important as it dictates the lateral movement of water through the subsurface towards potential environmental receptors. Used in conjunction with other properties such as hydraulic gradients and COPC source and sink terms, accurate estimation of hydraulic conductivity is an important step towards developing a water quality model that informs the potential risk to water quality and effective strategies to mitigate this risk.
 Solinst. 2021. https://www.solinst.com/products/dataloggers-and-telemetry/3001-levelogger-series/ltc-levelogger/
 Sun, H., & Koch,M. 2014. Under -versus overestimation of Aquifer Hydraulic Conductivity from Slug Tests. Hamburg - Lehfeldt & Kopmann (eds)
 AQTESOLV. 2021. https://www.aqtesolv.com