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Abstract Monitoring brine leakage from CO₂geological storages (CGS) is necessary to protect shallow aquifers against contamination. A framework for designing CGS monitoring systems that optimally use both easily available shallow zone data and hard‐to‐obtain deep zone observations is developed and validated. This framework is based on calibrating a transport model using monitoring data to determine leakage source conditions and then predict the subsequent brine plume that potentially contaminates shallow aquifers. As cost considerations are expected to limit monitoring deep formations, the framework is developed to minimize the number of deep observation points (e.g., deep sensors). The best monitoring locations that yield the most worthful data for reducing predictive uncertainty is selected by integrating linear uncertainty analysis with Genetic Algorithm under this framework. Due to practical challenges, testing such a framework in the field is not feasible. Thus, the framework was tested in an intermediate‐scale soil tank, where monitoring data on brine leakage plume development from the storage zone to the shallow aquifer were collected. Predictions made by a transport model calibrated on these data were then compared with experimental measurements to evaluate data informativity and thus validate the framework's applicability. The results demonstrate the framework ability to select the optimum monitoring locations for leakage detection and model calibration. It was also found that not only deep observations, but also shallow zone data are worthful to determine source conditions. Moreover, the results showed the possibility of identifying the likely areas to be impacted in the shallow aquifer using early stage monitoring data.