Representing buildings and urban features in hydrodynamic flood models

Abstract

Flood risk in cities and built‐up areas is a major threat which is likely to grow due to increased urbanisation and climate change. It is a priority for urban planning, civil defence and insurance to accurately represent buildings and urban features in hydrodynamic models to assess flood risk to people, properties, assets and infrastructure in an uncertain future. The correct representation of urban features in models is currently blocked by the lack of detailed and accurate techniques and has become a priority for the improvement of urban flood modelling now that better data and computational resources are available. This study has reviewed the available approaches for the representation of buildings and urban features and implemented the widely used ‘stubby building’ approximation as well as a more realistic and innovative ‘building hole’ approach using the hydrodynamic model CityCAT. The city centre of Newcastle upon Tyne, UK, was used as a case study, allowing independent validation of the methods and direct, systematic comparison of performance. Shortcomings of the approximate method are described, and guidance given on limits to its reliable application and scope for improvement.

Cloud Modelling of Property-Level Flood Exposure in Megacities

“…The model represents built-up areas with explicit representation of buildings by using the "Building Hole" approach [26], bridges [52], and different types of blue-green adaptation solutions [25]. The produced outputs of CityCAT are time series of water depth, velocity flow, flood maps and volume in and out of manholes, gully drains, buildings, etc.…”

“…The key consideration for selection of DEM resolution is the trade-off between accuracy of flow path representation, affected by buildings as well as slopes, and speed of simulation, as a doubling in grid resolution (e.g., from 2 to 1 m) may increase run times by a factor of eight due to the reduction in time step and increase in the number of calculations and memory requirements. Validation against historic storms in the past shows that 1 m and 2 m grid squares satisfactorily resolve streets and other flow paths between buildings while grid squares of size > 5 m may close flow paths between buildings, resulting in unrealistic flood depths [20,26].…”

“…The buildings and the permeable areas were extracted from OS Mastermap Topography [65]. The "Building Hole" technique was used in all models for the representation of the urban features, where the buildings' footprints are removed from the computational grid and the rainfall on every roof is redistributed to the nearest surface grid square [26]. The total number of buildings in the study area is 95,976.…”

“…The industry standard until now for large areas has typically used a DTM at 5 m resolution and the "stubby" platform for the representation of buildings where this approach, according to This is the first time that such a large urban area has been modelled with a hydrodynamic model at such a high spatial resolution and for a range of storm events. The industry standard until now for large areas has typically used a DTM at 5 m resolution and the "stubby" platform for the representation of buildings where this approach, according to Iliadis et al [26], causes unrealistic water flow paths in the domain and systematically underestimates flood risk. This study is a clear demonstration that modern, efficient codes like CityCAT, coupled with cloud-based computing, obviate the need for simulating large domains at either inadequately low resolutions or with inefficient subdivisions of the domain.…”

“…Among the many hydrodynamic models developed to solve full 2D SWEs, the City Catchment Analysis Tool (CityCAT) was employed in this study. CityCAT has undergone testing in various real flood events in the past [19][20][21][22][23][24][25][26], encompassing different cities within the UK. Additionally, it has been applied in studies conducted in the USA, with particular focus on urban flooding [27] (source: https://storymaps.arcgis.com/stories/ 3d982b40189c42aa9af56d52548caaf0, accessed on 14 September 2023).…”