Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics

This study contributes the Adaptive Strategies for Sampling in Space and Time (ASSIST) framework for improving long-term groundwater monitoring decisions across space and time while accounting for the influences of systematic model errors (or predictive bias). The new framework combines contaminant flow-and-transport modeling, bias-aware ensemble Kalman filtering (EnKF), many-objective evolutionary optimization, and visual analytics-based decision support. The ASSIST framework allows decision makers to forecast the value of investments in new observations for many objectives simultaneously. Information tradeoffs are evaluated using an EnKF to forecast plume transport in space and time in the presence of uncertain and biased model predictions that are conditioned on uncertain measurement data. This study demonstrates the ASSIST framework using a laboratory-based physical aquifer tracer experiment. In this initial demonstration, the position and frequency of tracer sampling was optimized to (1) minimize monitoring costs, (2) maximize the information provided to the EnKF, (3) minimize failures to detect the tracer, (4) maximize the detection of tracer fluxes, (5) minimize error in quantifying tracer mass, and (6) minimize error in quantifying the centroid of the tracer plume. Our results demonstrate that the forecasting, search, and visualization components of the ASSIST framework represent a significant advance for observation network design that has a strong potential to innovate our characterization, prediction, and management of groundwater systems.