Abstract. Previous seismic damage reports have shown that the damage and collapse of buildings is the leading cause of fatality and property loss. To enhance the estimation accuracy of economic loss and fatality in seismic risk assessment, a high-resolution building exposure model is important. Previous studies in developing global and regional building exposure models usually use coarse administrative level (e.g., county, or sub-country level) census data as model inputs, which cannot fully reflect the spatial heterogeneity of buildings in large countries like China. To develop a high-resolution residential building stock model for mainland China, this paper uses finer urbanity level population and building-related statistics extracted from the records in Tabulation of the 2010 Population Census of the People’s Republic of China (hereafter abbreviated as the “2010-census”). In the 2010-census records, for each province, the building-related statistics are categorized into three urbanity levels (urban, township, and rural). Statistics of each urbanity level are from areas with a similar development background but belong to different administrative prefectures and counties. Due to privacy protection-related issues, these urbanity level statistics are not geo-coded. Therefore, before disaggregating these statistics into high-resolution grid level, we need to determine the urbanity attributes of grids within each province. For this purpose, the geo-coded population density profile (with 1 km × 1 km resolution) developed in the 2015 Global Human Settlement Layer (GSHL) project is selected to divide the 31 provinces of mainland China into 1 km × 1 km grids. Then for each province, the grids are assigned with urban/township/rural attributes according to the population density in the 2015 GHSL profile. Next for each urbanity of each province, the urbanity level building-related statistics extracted from the 2010-census records can be disaggregated into the 2015 GHSL geo-coded grids, and the 2015 GHSL population in each grid is used as the disaggregation weight. Based on the four structure types (steel/reinforced-concrete, mixed, brick/wood, other) and five storey classes (1, 2–3, 4–6, 7–9, ≥ 10) of residential buildings classified in the 2010-census records, we reclassify the residential buildings into 17 building subtypes attached with both structure type and storey class and estimate their unit construction prices. Finally, we develop a geo-coded 1 km × 1 km resolution residential building exposure model for 31 provinces of mainland China. In each 1 km × 1 km grid, the floor areas of the 17 residential building subtypes and their replacement values are estimated. To evaluate the model performance, comparisons with the wealth capital stock values estimated in previous studies at the administrative prefecture-level and with the residential floor area statistics in the 2010-census at the administrative county/prefecture-level are conducted. The practicability of the modeled results in seismic risk assessment is also checked by estimating the seismic loss of residential buildings in Sichuan Province combined with the intensity map of the 2008 Wenchuan Ms8.0 earthquake and an empirical loss function developed from historical seismic damage information in China. Our estimated seismic loss range is close to that derived from field investigation reports. Limitations of this paper and future improvement directions are discussed. More importantly, the whole modeling process of this paper is fully reproducible, and all the modeled results are publicly accessible. Given that the building stock in China is changing rapidly, the results can be conveniently updated when new datasets are available.
Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment: an application case in Lima, Peru
