2016
DOI: 10.1007/s11069-016-2223-2
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From hazard to impact: flood damage assessment tools for mega cities

Abstract: In this paper, a set of GIS-based tools is presented that combines information from hydraulic modelling results, spatially varied object attributes and damage functions to assess flood damage. They can directly process the outputs of hydraulic modelling packages to calculate the direct tangible damage, the risk to life, and the health impact of individual flood events. The tools also combine information from multiple events to calculate the expected annual damage. The land cover classes from urban growth model… Show more

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Cited by 66 publications
(50 citation statements)
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“…In short, natural hazard modelling involves estimating potential hazard characteristics in terms of physical variables, such as potential flood extents and inundation depths in an area (e.g. Chen et al, 2016). As a next step, risk modelling aims to estimate the societal impacts, usually in terms of property damages, that are associated with specific hazard characteristics; for instance, the potential damage that a flood can cause in a certain geographical area (Grossi and Kunreuther, 2005).…”
Section: Introductionmentioning
confidence: 99%
“…In short, natural hazard modelling involves estimating potential hazard characteristics in terms of physical variables, such as potential flood extents and inundation depths in an area (e.g. Chen et al, 2016). As a next step, risk modelling aims to estimate the societal impacts, usually in terms of property damages, that are associated with specific hazard characteristics; for instance, the potential damage that a flood can cause in a certain geographical area (Grossi and Kunreuther, 2005).…”
Section: Introductionmentioning
confidence: 99%
“…The sum of these two raster maps thus creates a risk score map corresponding to both the flood class membership and perceived severity of the flood impact. Previous research has been done in translating water depth data into monetary damage via spatial analysis of water depth data and its proximity to buildings with different land use characteristic [6]; a modified version of this approach was used where the risk map data is substituted in place of water depth data. Spatially analysing the risk output data with respect to its proximity to critical infrastructures, allows for risk scores can to be applied to each infrastructure.…”
Section: Flood Simulationmentioning
confidence: 99%
“…These steps are combined in a single model developed by Chen et al . () that also compensates for potential misalignment of the spatial characteristics of the required flood maps (e.g. regular grids, irregular meshes) and feature maps (e.g.…”
Section: Urban Growth Model Flood Model and Damage Modelmentioning
confidence: 99%