Multi-Scale Target-Specified Sub-Model Approach for Fast Large-Scale High-Resolution 2D Urban Flood Modelling

Research output: Contribution to journalJournal articleResearchpeer-review

The accuracy of two-dimensional hydrodynamic models (2D models) is improved when high-resolution Digital Elevation Models (DEMs) are used. However, the entailed high spatial discretisation results in excessive computational expenses, thus prohibiting their implementation in real-time forecasting especially at a large scale. This paper presents a sub-model approach that adapts 1D static models to tailor high-resolution 2D model grids relevant to specified targets, such that the tailor-made 2D hydrodynamic sub-models yield fast processing without significant loss of accuracy via a GIS-based multi-scale simulation framework. To validate the proposed approach, model experiments were first designed to separately test the impact of two outcomes (i.e., the reduced computational domains and the optimised boundary conditions) towards final 2D prediction results. Then, the robustness of the sub-model approach was evaluated by selecting four focus areas with distinct catchment terrain morphologies as well as distinct 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 goodness of fit regarding predicted flood extents was above 0.88 of F-2, flood depths yield Root Mean Square Errors (RMSE) below 1.5 cm and the discrepancies of u- and v-directional velocities at selected points were less than 0.015 ms(-1). As such, this approach reduces the 2D models' computing expenses significantly, thus paving the way for large-scale high-resolution 2D real-time forecasting.

Original languageEnglish
Article number259
Issue number3
Number of pages28
Publication statusPublished - 2021

    Research areas

  • GIS-based multi-scale simulation, targets-specified modelling, sub-model tailoring, large-scale high-resolution flood modelling, real-time forecasting

Number of downloads are based on statistics from Google Scholar and

No data available

ID: 261381241