A Study on the Association of Socioeconomic and Physical Cofactors Contributing to Power Restoration after Hurricane Maria

Shams Azad, Masoud Ghandehari

Research output: Contribution to journalArticlepeer-review

Abstract

The electric power infrastructure in Puerto Rico suffered substantial damage as Hurricane Maria crossed the island on September 20, 2017. Despite significant efforts made by authorities, it took almost a year to achieve near-complete power recovery. In this study, we used spaceborne daily nighttime lights (NTL) imagery as a surrogate measure of power loss and restoration. We quantified the spatial and temporal extent of loss of electric power and trends in gradual recovery at the 889 county subdivisions for over eight months and computed days without service at the above tabulation areas. We formulated a Quasi-Poisson regression model to identify the association of the features from physical and socioeconomic domains with the power recovery effort. According to the model, the recovery time extended for areas closer to the landfall location of the hurricane, with every 50-kilometer increase in distance from the landfall corresponding to 30% fewer days without power (95% CI = 26% - 33%). Road connectivity was a major issue for the restoration effort, areas having a direct connection with hi-speed roads recovered more quickly with 7% fewer outage days (95% CI = 1% - 13%). The areas which were affected by moderate landslide needed 5.5% (95% CI = 1% - 10%), and high landslide areas needed 11.4% (95% CI = 2% - 21%) more days to recover. Financially disadvantaged areas suffered more from the extended outage. For every 10% increase in population below the poverty line, there was a 2% increase in recovery time (95% CI = 0.3% - 2.8%).

Original languageEnglish (US)
Article number9468640
Pages (from-to)98654-98664
Number of pages11
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021

Keywords

  • Extreme weather
  • hurricane Maria
  • nighttime lights
  • power infrastructure
  • power recovery
  • quasi-Poisson regression
  • resiliency
  • VIIRS

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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