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Geophysical domains typically exhibit intricate, irregular boundaries characterized by fractal-like geometries, while underlying physical processes operate across a broad spectrum of spatial scales. The challenge lies in generating spatial discretization of these domains that conform to their geographical constraints, utilizing anisotropic, fully adaptive meshes. This problem is compounded by the vast range of scales and a notably heterogeneous parameter space. Current methodologies often rely on ad hoc, model-specific, or application-dependent approaches, which lack comprehensive descriptions. Consequently, the development of new spatial domains is labor-intensive, prone to errors, challenging to replicate, and difficult to maintain consistency due to substantial human involvement. This predicament poses obstacles to the reproducibility of simulations and the establishment of provenance in data handling and model initialization, and it hinders rigorous model intercomparisons. Furthermore, the likelihood of discrepancies in model initialization and forcing parameters increases when employing flexible adaptive meshes. This paper introduces a systematic approach to the automated generation of adaptive meshes for geophysical models. This method is efficient in its generation process and readily reproducible, offering robust and consistent adherence to the source data. The proposed approach facilitates research in complex multi-scale geophysical domains, which would be challenging using existing methods. A simulation of monthly mean currents was carried out as a case study in the Gulf of Thailand. Results revealed that the simulated current circulations agreed with the observation. Examples of its application in various ongoing geophysical modeling endeavors illustrate its effectiveness. [ABSTRACT FROM AUTHOR]