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A method for the stochastic design of groundwater quality observation well network is presented. The method uses calibration‐constrained Null‐space Monte Carlo analysis for the stochastic simulation of the reduction ratio of peak concentration and the time corresponding to this in an injection well field. The numerical groundwater model simulations are constrained with a limited amount of field measurements. The objective of the monitoring network design is to identify optimal monitoring locations that allow for prediction of spatial fields from the data collected at limited number of points in the spatial domain. These locations need to be robust to different possible outcomes simulated using the stochastic model runs, and result in good spatial predictions, regardless of which one of the manypossibilities turned out to be the true representation of nature. Multiple simulated fields of concentration and time are used to identify a small set of empirical orthogonal functions (spatial basis functions) for reduced‐rank prediction of the spatial patterns in these two fields. The Differential Evolution algorithm was used to find the monitoring locations that allowed for optimal reconstruction of all the simulated fields (potential future states of reality) from the set of empirical orthogonal functions. The applicability is demonstrated for designing a monitoring network for an injection well field. Optimal locations of 10 monitoring wells were identified. The method has the capability to simultaneously identify the optimal locations and inform optimal times for monitoring reduction ratio of peak concentration. The method is flexible to iteratively combine stochastic modeling and monitoring for optimal groundwater management. Groundwater data collection from monitoring wells is important for evaluation of impacts from resource development. When and where to place a monitoring well to collect these data is an important decision to make for groundwater managers, primarily because groundwater monitoring is expensive. Mathematical modeling can provide predictive information about potential water quality changes arising from resource development. However, often there is large uncertainty in these predictions which makes it difficult to make the basis for investment decisions. In this study, we propose a novel method for optimizing monitoring network design in which many plausible future outcomes of water quality are considered and synthesized to help the design of monitoring network. Novel method for stochastic design of groundwater monitoring networkNull‐space Monte Carlo simulation combined with reduced‐rank spatial prediction and Differential Evolution for optimizing well locationsReduced‐rank spatial prediction helps to drastically reduce the computational burden of the monitoring network design problem

This study develops a methodology based on simulation-optimization to derive optimal combined operation plans for the management of production and barrier wells in a coastal aquifer in order to ensure the sustainable use of the fresh water resource. A numerical model for the simulation of saltwater intrusion process, a neural network based meta-model and a multi-objective genetic algorithm are used in a linked simulation-optimization approach to develop the management model. The pumping strategies derived using the management model was found to be effective in finding optimal and sustainable operation strategies for coastal well fields by controlling saltwater intrusion.

This study develops a methodology based on simulation-optimization to derive optimal combined operation plans for the management of production and barrier wells in a coastal aquifer in order to ensure the sustainable use of the fresh water resource. A numerical model for the simulation of saltwater intrusion process, a neural network based meta-model and a multi-objective genetic algorithm are used in a linked simulation-optimization approach to develop the management model. The pumping strategies derived using the management model was found to be effective in finding optimal and sustainable operation strategies for coastal well fields by controlling saltwater intrusion.