Extending Resilience Assessment to Dynamic System Modeling: Perspectives on Human Dynamics and Climate Change Research - Lam, Nina S.-N.; Qiang, Yi; Li, Kenan; Cai, Heng; Zou, Lei; Mihunov#, Volodymyr (Journal of Coastal Research, 2018)
Nina S.-N. Lam.; Qiang, Y.; Li K.; Cai, H.; Zou, L., and Mihunov, V. 2018. Extending Resilience Assessment to Dynamic System Modeling: Perspectives on Human Dynamics and Climate Change Research. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 1401–1405. Coconut Creek (Florida), ISSN 0749-0208. It is widely known that the same type and strength of hazard could lead to very uneven impacts on different communities due to their varying vulnerability and resilience capacity. Hence, identifying the factors that make a community more resilient to hazards is critical to its sustainability and is central to climate change research and planning. This paper addresses three questions: what is the best way to measure community resilience to disasters and how to identify the key indicators? How do the resilience indicators dynamically interact in a quantitative manner that would lead to long-term resilience? And how can we translate the scientific results into practical tools for decision making? Using the population change pattern in the Mississippi River Delta as a case study, this paper demonstrates the use of a relatively new resilience assessment method called the Resilience Inference Measurement (RIM) method to measure resilience. Then, a newly developed spatial dynamic model is used to simulate population changes in the study area. The results show that without any changes in the current condition, the coastal portion of the study area will continue to suffer population loss and the region is unlikely to sustain in the future.