Abstract

Tidal-flat changes during typhoon events are controlled by compound interactions among waves, tides, runoff, sediment transport, and vegetation resistance. However, rapid prediction remains challenging because high-resolution process-based morphodynamic models are computationally expensive. This study developed an observation-supported coastal morphodynamic emulator for rapid prediction of typhoon-induced tidal-flat erosion and deposition in the Jiuduansha Wetland, Yangtze Estuary. Multi-source field observations collected during Typhoons Bebinca and Pulasan in September 2024 were first used to validate a coupled MIKE21 FM model. The validated model was then applied to generate hydrodynamic and morphodynamic samples for emulator training and testing. Generalized Lagrangian mean velocity and bottom shear stress were selected as physically meaningful inputs. Current-timestep bed-level change was predicted using a UNet model enhanced with the Convolutional Block Attention Module (CBAM), hereafter referred to as CBAM-UNet. The numerical model reproduced the observed processes with acceptable accuracy, with Skill values of 0.98–0.99 for water level, 0.83–0.84 for wave height, and 0.82 for suspended sediment concentration. Compared with the conventional UNet, CBAM-UNet reduced the final cumulative RMSE from approximately 17.2 mm to 8.8 mm, corresponding to an error reduction of about 49%. Under prescribed wave, runoff, and tidal perturbations, the emulator reproduced the main erosion–deposition patterns, with final cumulative RMSE values of approximately 6.67 mm, 5.43 mm, and 10.68 mm, respectively. Across validation cases, replacing the morphodynamic module with the emulator reduced the average runtime by 42.50%. These results indicate that observation-supported morphodynamic emulation can support rapid tidal-flat assessment under compound typhoon forcing.

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