Assessing Community Resilience to Coastal Hazards in the Lower Mississippi River Basin

Abstract

This paper presents an assessment of community resilience to coastal hazards in the Lower Mississippi River Basin (LMRB) region in southeastern Louisiana. The assessment was conducted at the census block group scale. The specific purpose of this study was to provide a quantitative method to assess and validate the community resilience to coastal hazards, and to identify the relationships between a set of socio-environmental indicators and community resilience. The Resilience Inference Measurement (RIM) model was applied to assess the resilience of the block groups. The resilience index derived was empirically validated through two statistical procedures: K-means cluster analysis of exposure, damage, and recovery variables to derive the resilience groups, and discriminant analysis to identify the key indicators of resilience. The discriminant analysis yielded a classification accuracy of 73.1%. The results show that block groups with higher resilience were concentrated generally in the northern part of the study area, including those located north of Lake Pontchartrain and in East Baton Rouge, West Baton Rouge, and Lafayette parishes. The lower-resilience communities were located mostly along the coastline and lower elevation area including block groups in southern Plaquemines Parish and Terrebonne Parish. Regression analysis between the resilience scores and the indicators extracted from the discriminant analysis suggests that community resilience was significantly linked to multicomponent capacities. The findings could help develop adaptation strategies to reduce vulnerability, increase resilience, and improve long-term sustainability for the coastal region.

Integrating Typhoon Destructive Potential and Social‐Ecological Systems Toward Resilient Coastal Communities

“…Earth's Future the social dimension (percent population with mobile phone, code-E5), which shows that our finding is in line with the results of Cai et al (2016). The results show that an increment in the population with mobile phones helps increase the community resilience.…”

“…The present study compiles a set of indicators on the basis of resilience analysis in a typhoon-specific setting (Burby et al, 2000;Cai et al, 2016;Cutter et al, 2008;Norris et al, 2008;Sajjad & Chan, 2019). Therefore, the assessment of resilience in these communities is desirable for disaster risk reduction and preparedness for the future.…”

“…Therefore, the assessment of resilience in these communities is desirable for disaster risk reduction and preparedness for the future. These five dimensions are the acknowledged elements in grasping the multidimensional notion of the resilience of the SES against natural hazards (Cai et al, 2016). These indicators from the SES are categorized under five potential dimensions including ecological, economic, social, institutional, and safety (supporting information Table S1).…”

“…A negative relationship between native population (code E1) and community resilience is found as the larger native population provides fewer opportunities for nonnative population to migrate in these areas (Cai et al, 2016; see supporting information Figure S4). Tourist flows can contribute to economic growth of communities that can lead to widened opportunities to invest more in public safety through local fiscal expenditure (indicator C3 of institutional dimension).…”

Measuring Recovery to Build up Metrics of Flood Resilience Based on Pollutant Discharge Data: A Case Study in East China

“…The results show that health service, sex ratio, ratio of urban to rural, access to open space, road density and slope condition are statistically significant in characterizing disaster resilience. The six variables validated in this study are consistent with the set of variables mentioned as proxy for community resilience to other natural hazards [8,13,16,61]. This implies that, in general, investment in promoting adaptation activities and policies for one kind of natural hazard should have a synergistically positive effect on community resilience to other natural hazards; and to flash flood hazards, especially in regions within China, the six variables play more dominant roles in determining the recovery capability that was represented by the return speed of people's living and industrial productivity.…”