The identification of societal vulnerable counties and regions and the factors contributing to social vulnerability are crucial for effective disaster risk management. Significant advances have been made in the study of social vulnerability over the past two decades, but we still know little regarding China's societal vulnerability profiles, especially at the county level. This study investigates the county-level spatial and temporal patterns in social vulnerability in China from 1980 to 2010. Based on China's four most recent population censuses of 2,361 counties and their corresponding socioeconomic data, a social vulnerability index for each county was created using factor analysis. Exploratory spatial data analysis, including global and local autocorrelations, was applied to reveal the spatial patterns of county-level social vulnerability. The results demonstrate that the dynamic characteristics of China's county-level social vulnerability are notably distinct, and the dominant contributors to societal vulnerability for all of the years studied were rural character, development (urbanization), and economic status. The spatial clustering patterns of social vulnerability to natural disasters in China exhibited a gathering-scattering-gathering pattern over time. Further investigations indicate that many counties in the eastern coastal area of China are experiencing a detectable increase in social vulnerability, whereas the societal vulnerability of many counties in the western and northern areas of China has significantly decreased over the past three decades. These findings will provide policymakers with a sound scientific basis for disaster prevention and mitigation decisions.

International audienceDisaster loss estimates are helpful for managing post-disaster reconstruction and for designing disaster-risk mitigation strategies. However, most of these estimates in China merely consider direct losses, and only a few include indirect economic losses. As the most destructive earthquake since the founding of the People's Republic of China in 1949, the Wenchuan Earthquake that occurred in 2008 resulted in direct economic damages reached Chinese Yuan (CNY) 845 billion (US $124 billion). The aim of the study was to estimate indirect economic losses caused by the Wenchuan Earthquake in Sichuan Province through the Adaptive Regional Input-Output (ARIO) model, which can reflect disaster-related changes in production capacity, ripple effects within the economic system, and adaptive behaviors of economic actors. The results showed that indirect economic losses in the production and housing sectors were estimated at 40% of the direct economic losses, i.e., approximately CNY 300 billion; moreover, the model predicted an 8-year reconstruction period. Several factors contributed to these losses, including significant damages to key sectors, financial constraints on reconstruction, post-earthquake investment instability, and limits in reconstruction capacity. Active government support policies post-earthquake are a useful strategy to mitigate the adverse economic impact of an earthquake in developing countries

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The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top-down (or downscaling) approach to disaggregate administrative-unit level asset value to grid-cell level. To do so, finding the highly correlated "surrogate" indicators is the key. A combination of three data sets-nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc-second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time.