In recent years, the frequent catastrophic disasters have caused great losses of human lives and properties in the world. This indicates that disaster losses may be accumulated and amplified through disaster chains, in which one disaster triggers another, and so forth. The losses are much heavier in disaster chains than in a single disaster. Disaster chain is a typical complex form of regional disaster system. Understanding the amplification mechanisms of disaster chains is very important in catastrophe risk governance. This review first focuses on the concepts and understandings of disaster chain and summarizes a comprehensive definition from the geographic perspective through literature research. It is found that disaster chains have two common features, including the casualty relationship between different disasters and the spatial and temporal expansion of disaster losses. Only under a geographical framework, a sound and complete understanding of the disaster chain concept may be possible. A regional disaster system includes the environment, hazards, and exposure units and the complex spatial and temporal interactions of these elements. Second, considering the different research philosophies, five method categories in recently disaster chain research are summarized and analyzed. The five categories include empirical statistics methods, probabilistic models, complex network models, disaster system simulations, and multidisciplinary theories. The appropriateness and disadvantages of each category of methods are discussed with respect to their utility in describing disaster chain loss accumulation and amplification. Empirical statistics methods are a classical one that often use weighted average of a series of indicators. They have great advantage in taking into consideration various geographic factors and the modeling process is simple. But these methods cannot reveal the disaster chain evolution and processes. Probabilistic models can generate an overview of all possible events after a disaster have happened, as well as calculate the conditional probability. But they have the same problem as the statistics models. Disaster chain is a typical complex network. So the complex network theory may be used to describe the evolution of disaster chain networks. Such method should consider the spatial and temporal features of the disaster chain components in order to make the result more precise. Simulation methods are a promising one that can support the understanding of disaster chains dynamics, as well as the mechanism of the accumulation and amplification of disaster chain losses. However, simulation precision should be improved by including the spatial and temporal features of disaster chains in the future. Two types of important disaster chains, the seismic and typhoon disaster chains were used as examples to show the practical application of these methods. Finally, this review shows that the main trend of disaster chain research is to build and improve the dynamical model of disaster chain loss accumulation and amplification processes. The key is to connect all the factors spatially and temporally in a disaster chain system. It is necessary to transform the research approaches from "static-descriptive-explanatory" to "dynamic-process oriented-simulation" in order to understand the complexity of a disaster system.

Vulnerability is a concept that evolved out of the social sciences and was introduced as a response to the purely hazard-oriented perception of disaster risk in the 1970s. Recently, the study about social vulnerability of human system and coupled human-environmental system has become increasingly more popular in the field of vulnerability research and sustainability science. The concept of social vulnerability aims at identifying and understanding which groups of people may be more sensitive and susceptible to the impacts of natural disasters and why. In this article, we discuss the progress of social vulnerability research and review the concept, characteristics, analytical frameworks, and assessment methods of social vulnerability. This article first reviews definitions of social vulnerability from different types of stresses including risk and hazard, climate change, demographic characteristics and inequality, resource exploitation, land use change, and environmental pollution. Social vulnerability is one dimension of vulnerability. Social vulnerability refers to negative impacts of social system exposed to natural or human factors due to its own sensitivity characteristics and lack of ability to cope with adverse disturbance. Second, this article introduces several analytical frameworks of social vulnerability from political economy, social-ecological system, and comprehensive perspectives. These frameworks include the Pressure and Release Model, Sustainable Livelihood Framework, Hazards-of-Place Model, Coupled Human–Environmental System Framework, BBC Conceptual Framework, and MOVE Framework. Strictly speaking, these frameworks are not specialized social vulnerability framework, and most of them were derived from vulnerability frameworks. Some researchers improved and applied them to explain elements, process, and mechanism of social vulnerability. The article compares critical rationale of different frameworks and reviews their merits and defects from three perspectives. At last, the article summarizes assessment methods, computational formulas, and evaluation index systems of social vulnerability. The methods include comprehensive index, function model, back propagation artificial neural network, decision tree, object- oriented analysis, spatial multi criteria evaluation, and GIS methods. Every method has different features and advantages and disadvantages. The choice of method should be based on research purposes. There are also many kinds of evaluation index systems and social vulnerability indexes such as the SoVI, CVI, CCSVI, SV, and so on. Most studies use population and relevant social and economic indicators to establish evaluation index systems. However, there are many questions about the validity and reliability of index systems. Generally, current studies on social vulnerability have the following problems: a unified conceptual and analytical framework has not been formed; assessment methods of social vulnerability are too simplistic; a comprehensive evaluation index system is absent; studies on mitigation and countermeasures are insufficient. In the future, social vulnerability study needs to establish a unified concept and analytical framework, expand the research contents, improve theoretical system, and promote multi-disciplinary integration. At the same time, it is necessary to improve evaluation index system and method of social vulnerability, strengthen research on social vulnerability mitigation and countermeasures, and integrate social vulnerability and social adaptation. We hope social vulnerability research can provide the scientific basis for social adaptability and sustainable development.