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Regional groundwater flow models play an important role in decision making regarding water resources; however, the uncertainty embedded in model parameters and model assumptions can significantly hinder the reliability of model predictions. One way to reduce this uncertainty is to collect new observation data from the field. However, determining where and when to obtain such data is not straightforward. There exist a number of data-worth and experimental design strategies developed for this purpose. However, these studies often ignore issues related to real-world groundwater models such as computational expense, existing observation data, high-parameter dimension, etc. In this study, we propose a methodology, based on existing methods and software, to efficiently conduct such analyses for large-scale, complex regional groundwater flow systems for which there is a wealth of available observation data. The method utilizes the well-established d-optimality criterion, and the minimax criterion for robust sampling strategies. The so-called Null-Space Monte Carlo method is used to reduce the computational burden associated with uncertainty quantification. And, a heuristic methodology, based on the concept of the greedy algorithm, is proposed for developing robust designs with subsets of the posterior parameter samples. The proposed methodology is tested on a synthetic regional groundwater model, and subsequently applied to an existing, complex, regional groundwater system in the Perth region of Western Australia. The results indicate that robust designs can be obtained efficiently, within reasonable computational resources, for making regional decisions regarding groundwater level sampling.

Plain Language Summary Water supply for the public, industry, and the environment can be heavily reliant on groundwater resources. Therefore, decision makers must be able to make predictions about how a groundwater system will respond to management options. These predictions often contain a significant degree of uncertainty. This uncertainty must be reduced in order for decision makers to make optimal use of groundwater resources with minimal risk to the environment. One way to reduce this uncertainty is to obtain more information about the nature of the groundwater system by collecting new measurement data from the study site. However, it is often not clear where and when to collect this data. This study proposes a new methodology for collecting data in an optimal fashion so that the information acquired is maximized. The method incorporates any existing information, examines the characteristics of uncertainty, and alleviates the high computing costs associated with conducting the necessary calculations. The procedure is applied to a regional groundwater system in the Perth area of Western Australia. The results are consistent with the hydrogeologic conceptualization of the Perth system, and provide important insight into where new observation wells could be constructed to obtain information about the hydraulic nature of faults.