Cloud Modelling of Property-Level Flood Exposure in Megacities

Abstract

Surface water flood risk is projected to increase worldwide due to the growth of cities as well as the frequency of extreme rainfall events. Flood risk modelling at high resolution in megacities is now feasible due to the advent of high spatial resolution terrain data, fast and accurate hydrodynamic models, and the power of cloud computing platforms. Analysing the flood exposure of urban features in these cities during multiple storm events is essential to understanding flood risk for insurance and planning and ultimately for designing resilient solutions. This study focuses on London, UK, a sprawling megacity that has experienced damaging floods in the last few years. The analysis highlights the key role of accurate digital terrain models (DTMs) in hydrodynamic models. Flood exposure at individual building level is evaluated using the outputs from the CityCAT model driven by a range of design storms of different magnitudes, including validation with observations of a real storm event that hit London on the 12 July 2021. Overall, a novel demonstration is presented of how cloud-based flood modelling can be used to inform exposure insurance and flood resilience in cities of any size worldwide, and a specification is presented of what datasets are needed to achieve this aim.

Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection

“…A flood is a type of complex meteorological disaster caused by heavy rainfall and typically characterized as sudden strong, wide coverage, and great damage [1,2], which can lead to emergencies, such as dams bursting [3], submerged farmland [4], and damaged houses [5]. Floods have always been the most frequent and severe natural disaster around the world.…”