An unsteady drag force model for simulating dam break flows through rigid emergent vegetation

• An unsteady drag force model was proposed based on a previous drag force model for non-uniform open channel flows. • The existing drag force models' limitations were clarified through numerical simulations for DBF through the vegetation. • Numerical simulations with the new unsteady drag force model succeeded to predict propagations of DBF through vegetation. • Experimental data for DBF though different vegetation configurations were provided. Accurately predicting Dam break flows (DBF) is essential for developing effective flood mitigation strategies to protect downstream communities. However, few numerical models adequately address the impact of vegetation on DBF, as an appropriate evaluation method of the vegetation drag forces under rapidly varying unsteady flow conditions remains a significant challenge. This study fills this research gap by developing a drag force model to incorporate unsteady effects into the non-uniform drag force equation proposed by Uchida et al. (2022) for simulating DBF through vegetation. Experimental investigations were conducted in a horizontal flume, using circular wooden cylinders to simulate various configurations of rigid, emergent vegetation. These experiments were conducted to validate the drag force model under multiple DBF scenarios. The model’s performance was compared against existing drag force models, which either captured wave height or wave celerity effectively but failed to predict both simultaneously. In contrast, the newly developed drag force model for the unsteady flows demonstrated superior accuracy, predicting both wave height and celerity successfully for different vegetation configurations. This advancement provides a critical tool for improving flood risk assessments and refining flood mitigation strategies, especially in areas where vegetation plays a significant role in modifying flow dynamics. The model’s ability to provide reliable predictions in complex flow environments makes it a valuable contribution in DBF modeling.