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AbstractThe 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.

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.

New features of natural disasters have been observed over the last several years. The factors that influence the disasters' formation mechanisms, regularity of occurrence and main characteristics have been revealed to be more complicated and diverse in nature than previously thought. As the uncertainty involved increases, the variables need to be examined further. This article discusses the importance and the shortage of multivariate analysis of natural disasters and presents a method to estimate the joint probability of the return periods and perform a risk analysis. Severe dust storms from 1990 to 2008 in Inner Mongolia were used as a case study to test this new methodology, as they are normal and recurring climatic phenomena on Earth. Based on the 79 investigated events and according to the dust storm definition with bivariate, the joint probability distribution of severe dust storms was established using the observed data of maximum wind speed and duration. The joint return periods of severe dust storms were calculated, and the relevant risk was analyzed according to the joint probability. The copula function is able to simulate severe dust storm disasters accurately. The joint return periods generated are closer to those observed in reality than the univariate return periods and thus have more value in severe dust storm disaster mitigation, strategy making, program design, and improvement of risk management. This research may prove useful in risk‐based decision making. The exploration of multivariate analysis methods can also lay the foundation for further applications in natural disaster risk analysis.

Abstract. Understanding risk using quantitative risk assessment
offers critical information for risk-informed reduction actions, investing
in building resilience, and planning for adaptation. This study develops an
event-based probabilistic risk assessment (PRA) model for livestock snow
disasters in the Qinghai–Tibetan Plateau (QTP) region and derives risk
assessment results based on historical climate conditions (1980–2015) and
present-day prevention capacity. In the model, a hazard module was developed
to identify and simulate individual snow disaster events based on boosted
regression trees. By combining a fitted quantitative vulnerability function and
exposure derived from vegetation type and grassland carrying capacity, we
estimated risk metrics based on livestock mortality and mortality rate. In
our results, high-risk regions include the Nyainqêntanglha Range,
Tanggula Range, Bayankhar Mountains and the region between the Kailas Range
and the neighbouring Himalayas. In these regions, annual livestock mortality
rates were estimated as >2 % and mortality was estimated as
>2 sheep unit km−1 at a return period of 20 years.
Prefectures identified with extremely high risk include Guoluo in Qinghai
Province and Naqu, and Shigatse in the Tibet Autonomous Region. In these
prefectures, a snow disaster event with a return period of 20 years or higher
can easily claim total losses of more than 500 000 sheep units. Our
event-based PRA results provide a quantitative reference for preparedness
and insurance solutions in reducing mortality risk. The methodology
developed here can be further adapted to future climate change risk analyses
and provide important information for planning climate change adaption in
the QTP region.