Received: 09 Jan 2017 – Accepted for review: 27 Jan 2017 – Discussion started: 30 Jan 2017
Abstract. Exposure is an integral part of any natural disaster risk assessment. As one of the consequences of natural disasters, damage to buildings is one of the most important concerns. As such, estimates of the building stock and the values at risk can assist natural disaster risk management, including determining the damage extent and severity. Unfortunately, only little information about building asset value is readily available in most countries (especially its spatial distributions) including in China, given that the statistical data on building floor area (BFA) is collected by administrative unities in China. In order to bridge the gap between aggregated census statistical buildings floor-area data to geo-coded building asset value data, this article introduces a methodology for a city-scale building asset value mapping using Shanghai as an example. It consists of a census BFA disaggregation (downscaling) by means of a building footprint map extracted from high-resolution remote sensing data and LandScan population density data, and a financial appraisal of building asset values. A validation with statistical data confirms the feasibility of the modelled building storey. The example of the use of the developed building asset value map in exposure assessment of a flood scenario of Shanghai demonstrated that the dataset offers immense analytical flexibility for flood risk assessment. The method used in this paper is transferable to be applied in other cities of China for building asset value mapping.
This preprint has been withdrawn.
How to cite. Wu, J., Wang, X., and Koks, E.: Building Asset Value Mapping in Support of Flood Risk Assessment:
A Case Study of Shanghai, China, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2017-17, 2017.
Building stock loss occupied a meaningful part of sudden-onset disasters, while raster-level building asset distribution map is scarce and not sufficient for disaster risk estimation. This paper introduces an efficient way for building asset value mapping by downscaling, given that the statistical building floor area and a building footprint map are available. It is expected that the method used in this paper is transferable to be applied in other cities if the two datasets are all available.
Building stock loss occupied a meaningful part of sudden-onset disasters, while raster-level...