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Real-time visualization of advective groundwater flow




Ferrie, Zach, author
Sale, Thomas, advisor
Blotevogel, Jens, advisor
Ham, Jay, committee member

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As the portfolio of sites with subsurface contamination matures, long-term monitoring is becoming the primary factor governing costs for managing historical releases of contaminants to soil and groundwater. Hydraulic gradients are the primary factor driving the velocity and direction in which subsurface contaminants move, making them an important parameter to resolve. Current best practices for tracking groundwater flow include either collecting head data by hand or deploying pressure transducers and periodically returning to manually download the data. Unfortunately, cost restraints and infrequent data collection and processing are not conducive to timely responses to adverse conditions. In this study, two low-cost cellular connected data acquisition systems are developed which allow for collection and analysis of head data in real-time. Using planar regressions of three head values, automated algorithms are used to estimate the direction and rate of groundwater flow on an hourly basis. Another novel addition is the integration of real-time alerts. By automating various alerts, site managers can be notified when conditions reach a pre-determined threshold. Automated alerts allow for swift action to be taken to adverse conditions and can lead to greater safety for the public while saving sites from costly mistakes. Following Devlin and McElwee (2007), uncertainty in groundwater flow direction is a function of measurement error, spacing between wells, and local hydraulic gradients. By using these sources of uncertainty to create synthetic datasets, algorithms are used to estimate the likely range of a groundwater flow path. The effects of pressure transducer drift (i.e. increasing measurement error over time) and their effect on uncertainty are also explored. Results from this study show that as long as the drift is similar in magnitude and direction for all pressure transducers, the effect on the uncertainty in the model is negligible. Additionally, the effects of uncertainty in anisotropy on deviation from the estimated flow path are considered by way of synthetic datasets, which is novel to this research. The results of this research reveal that the effects of anisotropy uncertainty on groundwater flow direction and seepage velocity are also tied to well spacing. Comparisons of the effects of measurement error vs anisotropy uncertainty are compared for four field sites. Results show that the magnitudes of each source of error are site specific and that the effects of measurement error are not always greater than the effects of anisotropy uncertainty and vice versa. Lastly, the seepage velocities are expressed by way of a color scheme common across sites. This novel addition allows for easy visualization of seepage velocities across time and space. Overall, the vision from this research is that real-time, continuous collection and analysis of head data can proceed as outlined in this Thesis. In the future manually collected and interpreted head data need to be compared to the automated analyses described in this Thesis to further support the validity of the methods proposed herein. Another future test is to investigate alternative technologies to pressure transducers for gaining head measurements that are more accurate and reliable.


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