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PLoS One. 2018 Mar 9;13(3):e0190701. doi: 10.1371/journal.pone.0190701. eCollection 2018.

Assessing local resilience to typhoon disasters: A case study in Nansha, Guangzhou.

Song J1, Huang B1,2,3, Li R2.

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Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.


Building communities' resilience to natural weather hazards requires the appropriate assessment of such capabilities. The resilience of a community is affected not only by social, economic, and infrastructural factors but also by natural factors (including both site characteristics and the intensity and frequency of events). To date, studies of natural factors have tended to draw on annual censuses and to use aggregated data, thus allowing only a limited understanding of site-specific hot or cold spots of resilience. To improve this situation, we carried out a comprehensive assessment of resilience to typhoon disasters in Nansha district, Guangzhou, China. We measured disaster resilience on 1×1-km grid units with respect to socioeconomic and infrastructural dimensions using a set of variables and also estimated natural factors in a detailed manner with a meteorological modeling tool, the Weather Research and Forecast model. We selected typhoon samples over the past 10 years, simulated the maximum typhoon-borne strong winds and precipitation of each sample, and predicted the wind speed and precipitation volume at the 100-year return-level on the basis of extreme value analysis. As a result, a composite resilience index was devised by combining factors in different domains using factor analysis coupled with the analytic hierarchy process. Resilience mapping using this composite resilience index allows local governments and planners to identify potential hot or cold spots of resilience and the dominant factors in particular locations, thereby assisting them in making more rational site-specific measures to improve local resilience to future typhoon disasters.

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