Community resilience in China in the context of disaster management

In recent years, the frequent occurrence of natural disasters due to global climate change has brought devastating impacts on many countries’ economies, societies, resources, and environment. How decision makers and practitioners respond before, during, and after a disaster occurs is critical to reducing disaster impacts, restoring system functionality, and improving the adaptive capacity for future events, that is, enhancing CR. Currently, there is a lack of empirical data and process-based approaches to assess the dynamic characteristics of CR and to understand what contributes to CR evolution during the disaster management process (i.e., early warning, emergency response, recovery and adaptation). This research is based on the 7.20 heavy rainstorms that happened in Zhengzhou City. To quantify CR, a Dynamic Community Resilience Assessment Framework (DCRAF) is firstly developed. Then, the disaster management meta-network (DMMN), hazard evolution, and disaster management background are identified as the influencing factors of the dynamics of CR. In the next stage, an analytical model is built to explore the quantitative relationship between the dynamics of CR with its influencing factors, and based on this, propose CR enhancement strategies for different phases of future events. Furthermore, the analytical model is expected to have the ability for CR prediction, whereby decision-makers could adjust actions accordingly to mitigate disaster impacts and recover quickly. This presentation will (1) explain the indicators of the DCRAF in the infrastructure domain (including communication, transportation, water, power, municipal infrastructure, and protection works) and the approach to quantify and integrate them to get CR evolutions; (2) use the empirical data to explain how the DMMN, hazard evolution, and disaster management background influence CR at different stages of disaster management; (3) elaborate on the structure of the analytical model, the approach of extracting CR enhancement strategies, and the approach of CR prediction.