Targets-specified grids-tailored sub-model approach for fast large-scale high-resolution 2D urban flood modelling

Abstract. The accuracy of two-dimensional urban flood models (2D models) is improved when high-resolution Digital Elevation Models (DEMs) is used, but the entailed high spatial discretisation results in excessive computational expenses, thus prohibiting the use of 2D models in real-time forecasting at a large scale. This paper presents a sub-model approach to tailoring high-resolution 2D model grids according to specified targets, and thus such tailor-made sub-model yields fast processing without significant loss of accuracy. Among the numerous sinks detected from full-basin high-resolution DEMs, the computationally important ones are determined using a proposed Volume Ratio Sink Screening method. Also, the drainage basin is discretised into a collection of sub-impact zones according to those sinks' spatial configuration. When adding full-basin distributed static rainfall, the drainage basin's flow conditions are modelled as a 1D static flow by using a fast-inundation spreading algorithm. Next, sub-impact zones relevant to the targets' local inundation process can be identified by tracing the 1D flow continuity, and thus suggest the critical computational cells from the high-resolution model grids on the basis of the spatial intersection. In MIKE FLOOD's 2D simulations, those screened cells configure the reduced computational domains as well as the optimised boundary conditions, which ultimately enables the fast 2D prediction in the tailor-made sub-model. To validate the method, model experiments were designed to test the impact of the reduced computational domains and the optimised boundary conditions separately. Further, the general applicability and the robustness of the sub-model approach were evaluated by targeting at four focus areas representing different catchment terrain morphologies as well as different rainfall return periods of 1–100 years. The sub-model approach resulted in a 45–553 times faster processing with a 99 % reduction in the number of computational cells for all four cases; the predicted flood extents, depths and flow velocities showed only marginal discrepancies with Root Mean Square Errors (RMSE) below 1.5 cm. As such, this approach reduces the 2D models' computing expenses significantly, thus paving the way for large-scale high-resolution 2D real-time forecasting.