Random index (RI) values.

<p>Random index (RI) values.</p

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

<div><p>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.</p></div

Resilience components in first level, extracted factors in second level, and primary variables of each factor.

<p>Resilience components in first level, extracted factors in second level, and primary variables of each factor.</p

Random index (RI) values.

<p>Random index (RI) values.</p

Diagnostics from the fitted generalized Pareto distribution function (modeled dataset) to the WRF-simulated maximum wind speeds (empirical dataset).

<p>(a) quantile plot, (b) density plot, and (c) return level plot.</p

Maps of resilience component score.

<p>a) social component; b) economic component; c) infrastructural component; d) natural component; classified as low (< −1.0 SD), medium-low (−1.0 to 0.0 SD), medium-high (0.0 to 1.0 SD), and high (>1.0 SD). The map was generated using the free and open source software QGIS version 2.18 (<a href="http://www.qgis.org/en/site/about/index.html" target="_blank">http://www.qgis.org/en/site/about/index.html</a>).</p

Results of extreme value analysis.

<p>(a) wind speed distribution (unit: m/s); (b) precipitation map (unit: mm/h); both are at 100-year return level. The map was generated using the free and open source software QGIS version 2.18 (<a href="http://www.qgis.org/en/site/about/index.html" target="_blank">http://www.qgis.org/en/site/about/index.html</a>).</p

Composite index of resilience to typhoon disaster in Nansha district.

<p>a) sub-district level; b) 1×1-km grid level (empty grids represent grids with zero population). The map was generated using the free and open source software QGIS version 2.18 (<a href="http://www.qgis.org/en/site/about/index.html" target="_blank">http://www.qgis.org/en/site/about/index.html</a>).</p

Sample results of typhoon Utor.

<p>(a) maximum grid wind distribution (unit: m/s); (b) maximum grid hourly precipitation (unit: mm/h); resolutions of both are 1 × 1 km. Wind speed modeled in this study is at 850 hpa height because wind speed at 850 hpa height is considered surface wind in meteorology and has the greatest effect on surface features, such as buildings and infrastructure. The map was generated using the free and open source software NCAR Command Language version 6.4.0 (2017) (<a href="http://dx.doi.org/10.5065/D6WD3XH5" target="_blank">http://dx.doi.org/10.5065/D6WD3XH5</a>).</p

Threshold value and parameter estimation of typhoon maximum wind speed in sample grid.

<p>(a) Mean residual life plots; (b) Re-parameterized scale parameter; (c) Shape parameter. Approximate straight line in (a) from 9 to 12 implies that suitable threshold value should be around 9, and similar trends of two parameters are also presented in (b) and (c).</p