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This paper explores static non-uniform grid solvers that adapt threeraster-based flood models on an optimised non-uniform grid: the second-orderdiscontinuous Galerkin (DG2) model representing the modelled data aspiecewise-planar fields, the first-order finite volume (FV1) model usingpiecewise-constant fields, and the local inertial (ACC) model only evolvingpiecewise-constant water depth fields. The optimised grid is generated byapplying the multiresolution analysis (MRA) of multiwavelets (MWs) topiecewise-planar representation of raster-formatted topography data, for moresensible grid coarsening based on one user-specified parameter. Two adaptivesolvers are also explored that apply the MRA of MWs and of Haar wavelets (HWs)to, respectively, scale and adapt the DG2 (MWDG2) and FV1 (HWFV1) modelled datadynamically in time. The performance of the non-uniform grid and adaptivesolvers is assessed in terms of flood depth and extent, velocities, and CPUruntimes, with reference to the raster-based DG2 model predictions on theirfinest resolution grid. The assessments considered three large-scale floodingscenarios, involving rapid and slow-to-gradual flows. MWDG2 is found to be themost favourable choice when modelling rapid flows, where it excels in capturingsmall velocity variations. For slow-to-gradual flows, the adaptive solversdeliver less accurate outcomes, and their efficiency can be hampered byoverhead costs of the dynamic MRA. Instead, non-uniform DG2 is recommended tocapture urban flow interactions more accurately. Non-uniform ACC is 5 timesfaster to run than non-uniform DG2 but delivers close flooding depth and extentpredictions, thus is more attractive for fluvial/pluvial flood simulation overlarge areas.

Benchmarking (multi)wavelet-based dynamic and static non-uniform grid solvers for flood inundation modelling