Formatted Title
Who’s behind the Wheel: Driving Data Analysis into the Future of Groundwater Hydrology
Background/Objectives
As recently discussed in an editorial published in the industry journal Groundwater (Shapiro and Day-Lewis, 2022), the science of groundwater hydrology is now at a point where we should not just be considering but actually applying data-driven approaches where possible. Historically, our sites could generously be considered data-poor environments where we may have a limited monitoring well network with several years of sampling and gauging data that provided a snapshot of conditions at a moment in time. This might be supplemented with some limited aquifer testing and some remedial pilot testing. This necessitated the use of physics-based models to fill in the gaps. However, over the last couple of decades we have seen significant technological advances specific to our industry (e.g., high resolution site characterization tools). This has coincided with a technology boom in general computing power and automation, significant advancements in data generation/acquisition/processing/analysis, and decreasing costs associated with these improvements. We now have access to more data than ever before, but how do we, as groundwater hydrologists, leverage this new abundance of data in the most effective and meaningful way that provides improved understanding of our sites?
Approach/Activities
Hydraulic head measurements underpin pretty much everything we do as groundwater hydrologists (e.g., developing groundwater contours, calculating hydraulic gradients, interpreting aquifer tests to estimate aquifer properties, assessing groundwater-surface water interaction, and so on). While we were often limited in the past to discrete snapshots in time to assess groundwater flow dynamics, we can now inexpensively deploy data-logging pressure transducers for months at a time collecting frequent, real-time measurements (i.e., high resolution water-level data) allowing us to capture and assess the dynamic nature of the subsurface in ways that were not previously possible. Several examples culled from our own experience are presented to demonstrate and highlight the utility and insights gained from applying readily-available and easily-implemented analytical methods to high resolution water-level datasets.
Results/Lessons Learned
Lack of groundwater data (primarily water levels) is no longer a limitation to site characterization, conceptual site model development, or remedy design/performance evaluation. Advances in technology have made high resolution water-level data readily “available to the masses”. However, the valuable insights that can be uncovered through proper examination of this data remain buried unless we are willing to dig a little by learning and applying new methods. Given the current state of groundwater conditions globally (severe lack of both quantity and quality), it is imperative that we, as groundwater hydrologist, recognize and utilize the best available tools to better manage this valuable resource.