Groundwater model diagnostic calibration and uncertainty analysis using information theory

This study aims, as a first attempt, to establish a framework for multi-objective optimization diagnostic model calibration and uncertainty analysis based on information theory, including mutual information (MI) and variation of information (VI). Moreover, a global sensitivity analysis was performed using the information coefficient of correlation to determine the incremental contribution of each model parameter. The applicability of the proposed framework was tested in an arid region of Oman with complex hydrogeological conditions and hardrock-alluvial aquifer systems. The findings highlight the capability of the information theory approach to simultaneously calibrate the model and assess uncertainty, while providing valuable diagnostics for identifying areas of the model that require further refinement. Furthermore, the results indicate that the proposed framework successfully reproduced 96% of the observed data, demonstrating its ability to reduce parameter uncertainty and ensure an accurate match between the simulated and observed data.