Evacuation planning for plausible worst case inundation scenarios in Honolulu, Hawaii


  • Karl Kim, PhD
  • Pradip Pant, PhD
  • Eric Yamashita, MURP




sea level rise, hurricane storm surge, river flooding, travel demand modeling, evacuation, risk reduction, Honolulu


Honolulu is susceptible to coastal flooding hazards. Like other coastal cities, Honolulu's long-term economic viability and sustainability depends on how well it can adapt to changes in the natural and built environment. While there is a disagreement over the magnitude and extent of localized impacts associated with climate change, it is widely accepted that by 2100 there will be at least a meter in sea level rise (SLR) and an increase in extreme weather events. Increased exposure and vulnerabilities associated with urbanization and location of human activities in coastal areas warrants serious consideration by planners and policy makers.

This article has three objectives. First, flooding due to the combined effects of SLR and episodic hydrometeorological and geophysical events in Honolulu are investigated and the risks to the community are quantified. Second, the risks and vulnerabilities of critical infrastructure and the surface transportation system are described. Third, using the travel demand software, travel distances and travel times for evacuation from inundated areas are modeled.

Data from three inundation models were used. The first model simulated storm surge from a category 4 hurricane similar to Hurricane Iniki which devastated the island of Kauai in 1992. The second model estimates inundation based on five tsunamis that struck Hawaii. A 1-m increase in sea level was included in both the hurricane storm surge and tsunami flooding models. The third model used in this article generated a 500-year flood event due to riverine flooding. Using a uniform grid cell structure, the three inundation maps were used to assess the worst case flooding scenario. Based on the flood depths, the ruling hazard (hurricane, tsunami, or riverine flooding) for each grid cell was determined. The hazard layer was analyzed with socioeconomic data layers to determine the impact on vulnerable populations, economic activity, and critical infrastructure. The analysis focused both on evacuation needs and the critical elements of the infrastructure system that are needed to ensure effective response and recovery in the advent of flooding.

This study shows that the coastal flooding will seriously affect the economy and employment. Extreme flooding events could affect 38 percent of the freeways, 44 percent of the highways, 69 percent of the arterial roads, and 40 percent of the local streets in the area examined. Approximately 80 percent of the economy and 76 percent of the total employment in the urban core of Honolulu is exposed to flooding. Evacuation modeling, shelter accessibility, and travel time to shelter analyses revealed that there is a significant shortage in sheltering options, as well as increases in travel times and distances as inundation depth increases. The findings are useful for evacuation and shelter planning for extreme coastal events, as well as for climate change adaptation planning in Honolulu. Recommendations for emergency responders as well as those interested in the integration of long-term SLR and low probability, high consequence coastal hazards are included. The study shows how to integrate travel demand modeling across multiple hazards and threats related to evacuating, sheltering, and disaster risk reduction.

Author Biographies

Karl Kim, PhD

National Disaster Preparedness Training Center, University of Hawaii at Manoa, Honolulu, Hawaii.

Pradip Pant, PhD

National Disaster Preparedness Training Center, University of Hawaii at Manoa, Honolulu, Hawaii.


Eric Yamashita, MURP

National Disaster Preparedness Training Center, University of Hawaii at Manoa, Honolulu, Hawaii


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How to Cite

Kim, PhD, K., P. Pant, PhD, and E. Yamashita, MURP. “Evacuation Planning for Plausible Worst Case Inundation Scenarios in Honolulu, Hawaii”. Journal of Emergency Management, vol. 13, no. 2, Mar. 2015, pp. 93-108, doi:10.5055/jem.2015.0223.