This dissertation contributes the ASSIST (Adaptive Strategies for Sampling in Space and Time) framework for improving long-term groundwater monitoring (LTGM) decisions across space and time while accounting for the influences of systematic model errors (or predictive bias). The new framework combines Monte Carlo based 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. The goal of the ASSIST framework is to provide decision makers with a fuller understanding of the information tradeoffs they must confront when performing long-term groundwater monitoring network design. Each chapter of this dissertation focuses on and addresses a specific challenge to LTGM network design. The scaling challenges of LTGM design are first explored in order to provide a basis for advancing the size and scope of LTGM design problems that can be effectively solved using multi-objective evolutionary algorithms (MOEAs). In addition, complex decision variable interdependencies that exist in large LTGM design problems cause traditional MOEAs to fail as problem sizes increase (defined in terms of increasing numbers of decisions and objectives). To address this, a new more robust MOEA termed the Epsilon-Dominance Hierarchical Bayesian Optimization Algorithm (ε-hBOA) was developed to learn and exploit the complex interdependencies that exist for large LTGM design problems. Building on the scalable many-objective optimization capabilities of ε-hBOA, the ASSIST framework contributes visual analytical tools, capable of providing decision makers with an improved understanding of the complex spatial and temporal tradeoffs that often exist between their LTGM design objectives. Finally, a biasaware EnKF framework was developed that dramatically enhances the accuracy of groundwater flow-and-transport forecasts in the presence of systematic modeling errors (or biases), while making computational innovations that again expand the size and scope of LTGM problems that can be addressed. This dissertation demonstrates that the forecasting, search, and visualization components of the ASSIST framework combine to represent a significant advance for LTGM network design that has a strong potential to innovate our future characterization, prediction, and management of groundwater systems.