A subgrid method for the linear inertial equations of a compound flood model

Abstract. Accurate flood risk assessments and early warning systems are needed to protect and prepare people in coastal areas from storms.  In order to provide this information efficiently and on time, computational costs need to be kept as low as possible. Reduced-complexity models using linear inertial equations and subgrid approaches have been used previously to achieve this goal. In this paper, for the first time, we developed a subgrid approach for the Linear Inertial Equations (LIE) that account for bed level and friction variations. We implemented this method in the SFINCS model. Pre-processed lookup tables that correlate water levels with hydrodynamic quantities make more precise simulations with lower computational costs possible. These subgrid corrections have undergone validation through a variety of conceptual and real-world application scenarios, including analyses of hurricane hazards and tidal fluctuations. We demonstrate that the subgrid corrections for Linear Inertial Equations significantly improve model accuracy while utilizing the same resolution without subgrid corrections.  Moreover, coarser model resolutions with subgrid corrections can provide the same accuracy as finer resolutions without subgrid corrections. Limitations are discussed, for example, when grids do not adequately resolve river meanders, fluxes can be overestimated. Our findings show that subgrid corrections are an invaluable asset for hydrodynamic modelers striving to achieve a balance between accuracy and efficiency.