Abstract

Studies on inequalities in exposure to flood risk have explored whether population of a lower socio-economic status are more exposed to flood hazard. While evidence exist for coastal flooding, little is known on inequalities for riverine floods. This paper addresses two issues: (1) is the weakest population, in socio-economic terms, more exposed to flood hazard, considering different levels of exposure to hazard? (2) Is the exposure to flood risk homogeneous across the territory, considering different scales of analysis? An analysis of the exposure of inhabitants of Liège province to flood risk was conducted at different scales (province, districts, and municipalities), considering three levels of exposure to flood hazard (level 1- low hazard, level 3- high hazard), and five socio-economic classes (class 1-poorest, class 5-wealthiest households). Our analysis confirms that weaker populations (classes 2 and 3) are usually more exposed to flood hazards than the wealthiest (classes 4 and 5). Still it should be stressed that the most precarious households (class 1) are less exposed than low to medium-range ones (classes 2 and 3). Further on the relation between socio-economic status and exposure to flood hazard varies along the spatial scale considered. At the district level, it appears that classes 4 and 5 are most exposed to flood risk in some peripheral areas. In municipalities located around the center of the city, differences of exposure to risk are not significant.

Highlights

  • Worldwide, floods represent the most frequent natural disaster (UNISDR, 2015) and, over the period 1995–2015, they have affected more than 2 billion people (UNDRR, 2015)

  • Distribution of Households in the Province vs. in Flood Hazard Zones The distribution of households across the five index of social disparity (ISD) classes was first compared between the whole province of Liege and in the flood hazard zones

  • The portion of households in class 4 is slightly higher in flood hazard zones compared to the whole province (18 vs. 17%), whereas it is considerably lower in class 1 (19 vs. 30%)

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Summary

Introduction

Floods represent the most frequent natural disaster (UNISDR, 2015) and, over the period 1995–2015, they have affected more than 2 billion people (UNDRR, 2015). Improving flood management is of critical importance (e.g., Klijn et al, 2004; IPCC, 2012; Aerts et al, 2014). The design, optimal sizing, and prioritization of measures for reducing the flood impacts must be based on a reliable appraisal of the distribution of flood risk in space and in time under various scenarios. This requires accurate tools for modeling both flood hazard and flood vulnerability (Wright, 2014; Vorogushyn et al, 2018).

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