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Abstract Despite the growing use of Aquatic Ecosystem Models for lake modeling, there is currently no widely applicable framework for their configuration, calibration, and evaluation. Calibration is generally based on direct data comparison of observed versus modeled state variables using standard statistical techniques, however, this approach may not give a complete picture of the model's ability to capture system‐scale behavior that is not easily perceivable in observations, but which may be important for resource management. The aim of this study is to compare the performance of “naïve” calibration and a “system‐inspired” calibration, an approach that augments the standard state‐based calibration with a range of system‐inspired metrics (e.g., thermocline depth, metalimnetic oxygen minima), to increase the coherence between the simulated and natural ecosystems. A coupled physical‐biogeochemical model was applied to a focal site to simulate two key state‐variables: water temperature and dissolved oxygen. The model was calibrated according to the new system‐inspired modeling convention, using formal calibration techniques. There was an improvement in the simulation using parameters optimized on the additional metrics, which helped to reduce uncertainty predicting aspects of the system relevant to reservoir management, such as the occurrence of the metalimnetic oxygen minima. Extending the use of system‐inspired metrics when calibrating models has the potential to improve model fidelity for capturing more complex ecosystem dynamics. 

Shallow groundwater resources overlaying deep saline formations used in carbon storage applications are subjected to a potential contamination threat by CO2/brine leakage via natural or anthropogenically-induced conductive pathways in the confining caprock. Identifying the leakage source location and rate is critical for developing remediation plans and designing corrective actions. Owing to limited information about the flow and transport characteristics of deep regimes and high cost of obtaining data on their response to CO2 injection operation, estimating accurate source settings (i.e., location and rate) can be extremely challenging. Under such conditions, Bayesian inverse frameworks become useful tools to help identify potential leakage patterns. This study tests and validates an ensemble-based data-assimilation approach that reduces the uncertainty in the prior knowledge about source settings through conditioning forward transport models using relatively inexpensive easy-to-acquire shallow zone data. The approach incorporates the newly developed ensemble smoother tool in the inversion code “PEST++” with the transport code “FEFLOW” to perform history matching and uncertainty analysis. A novel parameterization method that allows the disposition of potential source was used to search the leakage location during calibration process. In the absence of field data, the approach was validated using experimental data generated in ~8 m long soil tank simulating leakage from storage zone migrating to the shallow aquifer. The results show that source location uncertainty can be reasonably reduced using shallow zone data collected from near-surface aquifers. However, more prior information about the system and deeper data are essential to estimate a practical probability range for the leakage rate.