Abstract An integrated storm surge modeling and traffic analysis were conducted in this study to assess the effectiveness of hurricane evacuations through a case study of Hurricane Irma. The Category 5 hurricane in 2017 caused a record evacuation with an estimated 6.8 million people relocating statewide in Florida. The Advanced Circulation (ADCIRC) model was applied to simulate storm tides during the hurricane event. Model validations indicated that simulated pressures, winds, and storm surge compared well with observations. Model simulated storm tides and winds were used to estimate the area affected by Hurricane Irma. Results showed that the storm surge and strong wind mainly affected coastal counties in south-west Florida. Only moderate storm tides (maximum about 2.5 m) and maximum wind speed about 115 mph were shown in both model simulations and Federal Emergency Management Agency (FEMA) post-hurricane assessment near the area of hurricane landfall. Storm surges did not rise to the 100-year flood elevation level. The maximum wind was much below the design wind speed of 150–170 mph (Category 5) as defined in Florida Building Code (FBC) for south Florida coastal areas. Compared with the total population of about 2.25 million in the six coastal counties affected by storm surge and Category 1–3 wind, the statewide evacuation of approximately 6.8 million people was found to be an over-evacuation due mainly to the uncertainty of hurricane path, which shifted from south-east to south-west Florida. The uncertainty of hurricane tracks made it difficult to predict the appropriate storm surge inundation zone for evacuation. Traffic data were used to analyze the evacuation traffic patterns. In south-east Florida, evacuation traffic started 4 days before the hurricane’s arrival. However, the hurricane path shifted and eventually landed in south-west Florida, which caused a high level of evacuation traffic in south-west Florida. Over-evacuation caused Evacuation Traffic Index ( ETI ) to increase to 200% above normal conditions in some sections of highways, which reduced the effectiveness of evacuation. Results from this study show that evacuation efficiency can be improved in the future by more accurate hurricane forecasting, better public awareness of real-time storm surge and wind as well as integrated storm surge and evacuation modeling for quick response to the uncertainty of hurricane forecasting.
more »
« less
Integrating Evacuation and Storm Surge Modeling Considering Potential Hurricane Tracks: The Case of Hurricane Irma in Southeast Florida
Hurricane Irma, in 2017, made an unusual landfall in South Florida and the unpredictability of the hurricane’s path challenged the evacuation process seriously and left many evacuees clueless. It was likely to hit Southeast Florida but suddenly shifted its path to the west coast of the peninsula, where the evacuation process had to change immediately without any time for individual decision-making. As such, this study aimed to develop a methodology to integrate evacuation and storm surge modeling with a case study analysis of Irma hitting Southeast Florida. For this purpose, a coupled storm surge and wave finite element model (ADCIRC+SWAN) was used to determine the inundation zones and roadways with higher inundation risk in Broward, Miami-Dade, and Palm Beach counties in Southeast Florida. This was fed into the evacuation modeling to estimate the regional clearance times and shelter availability in the selected counties. Findings show that it takes approximately three days to safely evacuate the populations in the study area. Modeling such integrated simulations before the hurricane hit the state could provide the information people in hurricane-prone areas need to decide to evacuate or not before the mandatory evacuation order is given.
more »
« less
- Award ID(s):
- 1832068
- NSF-PAR ID:
- 10349915
- Date Published:
- Journal Name:
- ISPRS International Journal of Geo-Information
- Volume:
- 10
- Issue:
- 10
- ISSN:
- 2220-9964
- Page Range / eLocation ID:
- 661
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Hurricanes cause devastating amounts of damage to structures and infrastructure. It harms especially those coastal residents along its track. Over the last couple of years, evacuation planning for populated coastal regions has been challenging and time-consuming due to the uncertainty of the hurricane’s track. As such, with a focus on Northwest Florida, this research aims to focus on the development of evacuation scenarios for coastal communities that combines hurricane inundation and strong wind forecast and evacuation modeling. The proposed approach integrates storm surge simulation models (ADCIRC and SWAN modeling) and traffic evacuation models (Cube and TIME) by using hurricane forecasting datasets to explore the designation of evacuation zones and the calculation of evacuation clearance times in different counties. This approach was applied to three distinct scenarios with a focus on possible populated coastal cities that Hurricane Michael would have hit in 2018. Selected cities are Pensacola, Destin, and Panama City. This type of approach has the potential to help agencies make more informed decisions on evacuations using the accuracy and timeliness of forecasts and provide safer evacuations in coastal areas by avoiding the traffic jams on evacuation routes.more » « less
-
Storm surge and evacuation traffic under the observed track of Hurricane Michael (2018) showed clear accessibility and evacuation challenges for Panama City, Florida although the city was not hit directly. Since a possible Hurricane Michael track within National Hurricane Center (NHC)'s forecasted hurricane cone was Panama City, this paper tries to answer the following questions: What if Hurricane Michael hit Panama City directly? How would the special needs populations and their accessibility to Special Needs Shelters (SpNS) be impacted, and what could have been done to alleviate this impact? A previously validated storm surge model was used to predict storm surge inundations under this different hurricane track. Based on the impact of these coastal inundations, a GIS-based optimization methodology was developed to evaluate the accessibility and siting of special needs shelters. Results indicate that if Hurricane Michael had shifted to Panama City in 2018, most of the coastal region of Panama City would have been inundated, compelling residents to evacuate. The possible landfall of Michael in this simulation would also lead to a maximum storm surge of 5 to 6 m on the coast, which is above FEMA's 100-year flood elevation. In addition, the only evacuation route out of Panama City area, when the bridges with their access roads were flooded, was US 231. This would have been life-threatening since there is only one SpNS in the north of the city accessible by this roadway. The proposed analysis studies the accessibility of this SpNS shelter and provides a reasonable approach for SpNS shelter siting or repurposing regular shelters for this purpose based on the hypothesized travel time most likely to be experienced on roadway networks based on the impact of Hurricane Michael. Emergency plans can be updated by the results of this optimization model, which can locate additional sites or shelter locations while minimizing the travel costs and integrating the impact of storm surge modeling and transportation accessibility analysis.more » « less
-
Whether to evacuate a nursing home (NH) or shelter in place in response to the approaching hurricane is one of the most complex and difficult decisions encountered by nursing home administrators. A variety of factors may affect the evacuation decision, including storm and environmental conditions, nursing home characteristics, and the dwelling residents’ health conditions. Successful prediction of evacuation decision is essential to proactively prepare and manage resources to meet the surge in nursing home evacuation demands. In current nursing home emergency preparedness literature, there is a lack of analytical models and studies for nursing home evacuation demand prediction. In this paper, we propose a predictive analytics framework by applying machine learning techniques, integrated with domain knowledge in NH evacuation research, to extract, identify and quantify the effects of relevant factors on NH evacuation from heterogeneous data sources. In particular, storm features are extracted from Geographic Information System (GIS) data to strengthen the prediction accuracy. To further illustrate the proposed work and demonstrate its practical validity, a real-world case study is given to investigate nursing home evacuation in response to recent Hurricane Irma in Florida. The prediction performance among different predictive models are also compared comprehensively.more » « less
-
more » « less
Hurricane-induced storm surge and flooding often lead to the closures of evacuation routes, which can be disruptive for the victims trying to leave the impacted region. This problem becomes even more challenging when we consider the impact of sea level rise that happens due to global warming and other climate-related factors. As such, hurricane-induced storm surge elevations would increase nonlinearly when sea level rise lifts, flooding access to highways and bridge entrances, thereby reducing accessibility for affected census block groups to evacuate to hurricane shelters during hurricane landfall. This happened with the Category 5 Hurricane Michael which swept the east coast of Northwest Florida with long-lasting damage and impact on local communities and infrastructure. In this paper, we propose an integrated methodology that utilizes both sea level rise (SLR) scenario-informed storm surge simulations and floating catchment area models built in Geographical Information Systems (GIS). First, we set up sea level rise scenarios of 0, 0.5, 1, and 1.5 m with a focus on Hurricane Michael’s impact that led to the development of storm surge models. Second, these storm surge simulation outputs are fed into ArcGIS and floating catchment area-based scenarios are created to study the accessibility of shelters. Findings indicate that rural areas lost accessibility faster than urban areas due to a variety of factors including shelter distributions, and roadway closures as spatial accessibility to shelters for offshore populations was rapidly diminishing. We also observed that as inundation level increases, urban census block groups that are closer to the shelters get extremely high accessibility scores through FCA calculations compared to the other block groups. Results of this study could guide and help revise existing strategies for designing emergency response plans and update resilience action policies.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/ijgi10100661
