Influence of urban pattern on inundation flow in floodplains of lowland rivers

Abstract

The objective of this paper is to investigate the respective influence of various urban pattern characteristics on inundation flow. A set of 2000 synthetic urban patterns were generated using an urban procedural model providing locations and shapes of streets and buildings over a square domain of 1×1km. Steady two-dimensional hydraulic computations were performed over the 2000 urban patterns with identical hydraulic boundary conditions. To run such a large amount of simulations, the computational efficiency of the hydraulic model was improved by using an anisotropic porosity model. This model computes on relatively coarse computational cells, but preserves information from the detailed topographic data through porosity parameters. Relationships between urban characteristics and the computed inundation water depths have been based on multiple linear regressions. Finally, a simple mechanistic model based on two district-scale porosity parameters, combining several urban characteristics, is shown to capture satisfactorily the influence of urban characteristics on inundation water depths. The findings of this study give guidelines for more flood-resilient urban planning.

Modelling urban floods using a finite element staggered scheme with an anisotropic dual porosity model

“…The SP model is isotropic by definition; yet, many urban layouts are characterized by markedly anisotropic patterns (Bruwier et al, 2018;Mignot et al, 2006;Sanders et al, 2008;Velickovic et al, 2017). Moreover, a fraction of the domain that is free of buildings does not contribute to transport due to the sheltering effect of buildings that results in dead zones.…”

“…While the SP approach is isotropic in nature, urban layouts are often characterized by a marked anisotropy, with preferential flow directions owing to asymmetric building shapes and and spacing, and the alignment of buildings along streets (Sanders et al, 2008;Bruwier et al, 2018). This means that obstacles, as well as the terrain elevation at a subgrid scale, affect the direction of flow (McMillan and Brasington, 2007;Yu and Lane, 2006).…”

Urban pluvial flood risk assessment – data resolution and spatial scale when developing screening approaches on the microscale

“…Our aim was to predict impervious area in simulated urban developments when the assumed output of an urban development is building footprint areas for different building types. Linear regression approaches for modeling such relationships were previously documented by Butler and Davies (2011) for detached housing only and by Chabaeva et al (2009) for a variety of land cover classes derived from satellite observations. To identify a regression relationship, we rasterized the high-resolution polygon data.…”

Effects of digital terrain model uncertainties on high‐resolution urban flood damage assessment

“…In the computational mesh, it is usually a compromise between resolution of spatial data and computational effort (Bates, 2012;Schubert & Sanders, 2012). There are three main ways to characterise buildings in hydrodynamic models, which with increasing complexity are: (a) buildings are parameterised as an area of increased roughness (Allitt, Adams, & Searby, 2008), (b) buildings are considered as waterproof blocks (Shaad, Ninsalam, Padawangi, & Burlando, 2016), (c) buildings are modelled using porosity (Bellos & Tsakiris, 2015;Bruwier et al, 2018;Sanders, Schubert, & Gallegos, 2008;Soares-Frazão, Lhomme, Guinot, & Zech, 2008). In the first hypothesis, the DTM represents the bare ground surface without any object and is usually adopted for large areas where the detail of a single building would be computationally expensive, but requires the calibration of the roughness parameter.…”