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

Roland Löwe,Karsten Arnbjerg-Nielsen

doi:10.5194/nhess-20-981-2020

Roland Löwe, Karsten Arnbjerg-Nielsen

Open Access

https://doi.org/10.5194/nhess-20-981-2020

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Abstract

Abstract. Urban development models typically provide simulated building areas in an aggregated form. When using such outputs to parametrize pluvial flood risk simulations in an urban setting, we need to identify ways to characterize imperviousness and flood exposure. We develop data-driven approaches for establishing this link, and we focus on the data resolutions and spatial scales that should be considered. We use regression models linking aggregated building areas to total imperviousness and models that link aggregated building areas and simulated flood areas to flood damage. The data resolutions used for training regression models are demonstrated to have a strong impact on identifiability, with too fine data resolutions preventing the identification of the link between building areas and hydrology and too coarse resolutions leading to uncertain parameter estimates. The optimal data resolution for modeling imperviousness was identified to be 400 m in our case study, while an aggregation of the data to at least 1000 m resolution is required when modeling flood damage. In addition, regression models for flood damage are more robust when considering building data with coarser resolutions of 200 m than with finer resolutions. The results suggest that aggregated building data can be used to derive realistic estimations of flood risk in screening simulations.

Highlights

The development of pluvial flood risk adaptation measures in urban areas typically requires that a variety of combinations of different measures are tested (Radhakrishnan et al, 2018; van Berchum et al, 2018)
Use mix inside a pixel, which, through an assumed building density, can be translated into building area. These models operate with raster resolutions on the order of 100 to 200 m (Bach et al, 2018; Fuglsang et al, 2013; Mustafa et al, 2018). Such coarse input data will affect rainfall–runoff simulations, i.e., the location where flood hazards occur, and are likely to be incompatible with flood damage assessments derived from polygon data
Performance scores related to the simulation of flood hazards and the assessment of flood damage were collected in Tables 2 and 3, distinguishing between results for building data with varying resolutions xb

Summary

Introduction

The development of pluvial flood risk adaptation measures in urban areas typically requires that a variety of combinations of different measures are tested (Radhakrishnan et al, 2018; van Berchum et al, 2018). Flood risk is strongly affected by climate change, urbanization and socioeconomic changes (Di Baldassarre et al, 2015; Hinkel et al, 2014; Muis et al, 2015; Muller, 2007; Semadeni-Davies et al, 2008). Projections of these parameters are subject to substantial uncertainties over infrastructure lifetimes between 30 and 100 years (Cohen, 2004; Granger and Jeon, 2007; Hall et al, 2014; Madsen et al, 2014). Other studies have applied cellular automata to study the effect of urbanization on extreme rainfall and resulting flood risk (Huong and Pathirana, 2013) and to quantify changes in coastal flood areas as a result of urbanization (Sekovski et al, 2015)

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