A Bayesian Framework for Cost-Effective Groundwater Monitoring Design

Long-term monitoring of the quantity, quality, and susceptibility of groundwater to contamination has been and continues to be an issue of paramount importance within the state. Future groundwater management initiatives will require new tools for optimally balancing monitoring costs and data uncertainty.

The goal of this research is to aid monitoring stakeholders in determining costs, location, and monitoring frequency for wells in relation to the value of information gained by developing the Adaptive Strategies for Sampling In Space and Time (ASSIST) decision support framework. The ASSIST monitoring framework will provide the first adaptive observation system design paradigm that will enhance our abilities to balance multiple design objectives, while characterizing complex groundwater systems across space-and-time, merging physical model predictions with a broad range of data sources, considering a much broader range of model and data uncertainties, and adapting objectives and system design to account for advances in real-time sensing. ASSIST ‘s capabilities will be tested using data from Penn State’s Shavers Creek Experimental Watershed.

Research is being conducted by Dr. Patrick M. Reed, Assistant Professor in the Department of Civil and Environmental Engineering at Penn State University.