Building level flood exposure analysis using a hydrodynamic model

The advent of detailed hydrodynamic model simulations urban flooding has not been matched by improved capabilities in flood exposure analysis which rely on validation against observed data. This work introduces a generic, building-level tool applying high resolution data and building geometries derived from performed with the 2D modelling software CityCAT. Validation were obtained survey affected residents following large pluvial event Newcastle upon Tyne, UK. Sensitivity testing was carried out for different settings between 68% 75% surveyed buildings correctly modelled as either flooded or flooded. tends to underrepresent better performance identifying true negatives (i.e. no modelled) compared positives. As higher positive rates accompanied false rates, single scenario could be identified optimal solution. However, results suggest greater sensitivity classification scheme than buffer distance applied. Overall, if applied depth maps, method is efficient suitable application areas risk management insurance purposes. • A uses inundation grids cities avoiding aggregation validated collected after event. Developed Python-based Jupyter notebook code can easily adapted extended accommodate specific user needs. Decision making support stakeholders management.