Coastal erosion and sedimentation processes in macro-tidal environments, such as the west coast of Korea, are exceptionally complex. Driven by highly dynamic tidal currents, wave interactions, and rapid morphological feedbacks, these coastal zones remain challenging to accurately quantify and predict using traditional numerical models. To overcome these long-standing bottlenecks, next-generation coastal governance increasingly relies on Artificial Intelligence (AI) and Digital Twin frameworks. However, the success of these predictive, data-driven systems depends heavily on continuous, high-fidelity spatiotemporal data for model calibration and data fusion—information that is notoriously difficult, hazardous, and cost-prohibitive to acquire through conventional marine fields.
To address this critical bottleneck, this study introduces the Natural Wave Tank Experiment (NWTE) as a highly efficient, low-risk framework for validating numerical models and proving digital twin architectures. Utilizing the macro-tidal environment (4–8 m range) of the Korean west coast, the intertidal zone serves as a natural, full-scale 1:1 laboratory that cyclically replicates the "survey-fill-wave generation-drain-survey" process. By enabling safe equipment installation and direct, high-precision bathymetric surveys on exposed land during low tide, NWTE provides reliable "ground truth" datasets to validate remote sensing observations and AI-driven predictive models operating during high tide.
As a practical case study, we present the application of NWTE at Chunjangdae Beach, Korea, focusing on wave dynamics and intertidal topography. High-tide wave imagery was analyzed using the cBathy algorithm to estimate nearshore bathymetry and wave parameters. These remote sensing outputs were then rigorously validated against direct topographic measurements and pressure-type wave gauges deployed during the low-tide window. This study demonstrates that the NWTE framework not only enhances observational efficiency but also serves as a critical validation bridge, enabling more realistic, data-integrated digital twins for sustainable coastal monitoring and governance.

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