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Building level flood exposure analysis using a hydrodynamic model

Lookup NU author(s): Robert Bertsch, Dr Vassilis Glenis, Professor Chris Kilsby



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022 The Authors. The advent of detailed hydrodynamic model simulations of 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 flood exposure analysis tool applying high resolution flood data and building geometries derived from hydrodynamic simulations performed with the 2D hydrodynamic flood modelling software CityCAT. Validation data were obtained from a survey of affected residents following a large pluvial flood event in Newcastle upon Tyne, UK. Sensitivity testing was carried out for different hydrodynamic model and exposure tool settings and between 68% and 75% of the surveyed buildings were correctly modelled as either flooded or not flooded. The tool tends to underrepresent flooding with a better performance in identifying true negatives (i.e. no flooding observed with no flooding modelled) compared to true positives. As higher true positive rates were accompanied by higher false positive rates, no single scenario could be identified as the optimal solution. However, the results suggest a greater sensitivity of the results to the classification scheme than to the buffer distance applied. Overall, if applied to high resolution flood depth maps, the method is efficient and suitable for application to large urban areas for flood risk management and insurance analysis purposes.

Publication metadata

Author(s): Bertsch R, Glenis V, Kilsby C

Publication type: Article

Publication status: Published

Journal: Environmental Modelling and Software

Year: 2022

Volume: 156

Print publication date: 01/10/2022

Online publication date: 13/08/2022

Acceptance date: 10/08/2022

Date deposited: 12/10/2022

ISSN (print): 1364-8152

Publisher: Elsevier Ltd


DOI: 10.1016/j.envsoft.2022.105490


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Funder referenceFunder name
Willis Towers Watson Research Network