## 1. Introduction

[2] Formal observation (or monitoring) network design has a long history dating back to the pioneering work of *Drozdov and Sepelevskij* [1946] who developed a formalized framework for evaluating the spatial coverage of meteorological gauge networks. Early work in observation network design is strongly dominated by the meteorological community (for a review, see *Arnold and Dey* [1986]) beginning in the 1950s and continuing to the present, using the concept of observation system simulation events (OSSEs). In OSSEs, the values of observables are forecasted in terms of their ability to enhance system predictions. The meteorological OSSE formalizes the use of predictive models and statistical data assimilation to discover the need for and benefits of new observations. It can be viewed as a physics-informed experimental design. The classical OSSE combines predictive simulation and Bayesian data assimilation to forecast the value of observations. The literature reviewed by *Arnold and Dey* [1986] poses an important exemplar for the scientific value of coevolving observation and simulation systems, each of which benefit from the rigorous evaluation of predictive skill using forecasts of actual system events conditioned on proposed observation strategies.

[3] Although observation network design has also been a significant focus of the early water resources research literature as evidenced by the work included in the inaugural volume of this journal [see *Fiering*, 1965], hydrological observation network design frameworks have lagged behind the formality and innovations provided by the meteorological OSSEs. *Langbein*'s [1979] summary of one of the most comprehensive efforts in the water resources literature to focus on the science of observation network design provides cogent criticisms and challenges to the state of hydrological science at present. There are few examples of OSSE-type hydrological experiments where forecasts of system dynamics are used to inform subsequent laboratory- or field-based experimental design. Instead, the dominant approach is ad hoc observation and postevent analysis. *Moss* [1979a] highlights that our ability to understand the space and time tradeoffs implicit to hydrological observation network design requires the consideration of a third fundamental dimension for the problem, model errors. Systematic errors in our models of hydrological systems provide a barrier to using OSSE frameworks to advance our observation networks. Exacerbating this barrier, *Lettenmaier* [1979] highlights that as a problem class, observation network design poses a curse of dimensionality where there are large numbers of objectives and uses for data as well as exponentially scaled growth rates for the range of alternative space and time decisions that can be considered.

[4] Using a groundwater application context, this study contributes the Adaptive Strategies for Sampling in Space and Time (ASSIST) framework to advance our ability to manage the technical barriers posed by observation network design as a general class of problems. As demonstrated in this paper, the ASSIST framework is a highly adaptable methodology for improving long-term groundwater monitoring (LTGM) decisions across space and time while accounting for the influences of systematic model errors (or predictive bias). This paper demonstrates how bias-aware Ensemble Kalman Filtering (EnKF) [*Kollat et al.*, 2008a], many-objective (i.e., greater than three objectives) search using hierarchical Bayesian optimization [*Kollat et al.*, 2008b], and interactive high-dimensional visual analytics [*Kollat and Reed*, 2007a] can be combined to facilitate discovery and negotiation in the LTGM design process. Our use of the terms discovery and negotiation is motivated by the potential of many-objective solution sets to identify alternatives that capture a broad suite of system behaviors relevant to both modeled and unmodeled objectives [see *Brill et al.*, 1990]. This ultimately enables decision makers to discover system dependencies and/or tradeoffs and exploit this information in the adaptive long-term management of observation systems.