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Hurricane evacuations require fast updates of coastal inundation predictions based on the update of hurricane forecasting track. NOAA usually updates the hurricane track at about 6 hr interval. This paper presents a multi-scale nested modeling method for faster simulations of storm surge and coastal inundations. A medium-resolution model with minimum mesh size of 1200 m for the Gulf of Mexico is used to simulate the storm surge in the Gulf of Mexico. A high-resolution model with 120m-150m mesh sizes is used to predict coastal inundations in the area of potential hurricane landfall. A nested modeling method has been developed to transfer boundary conditions from the large-scale storm surge model to the nested local-scale high-resolution model. The nested models have been satisfactorily validated and applied in the case study of Hurricane Michael. Results indicate that, by applying the nested models, it takes about 85 minutes for the simulation of one hurricane track for a 5-day forecasting, which will provide sufficient time before the next NOAA forecast update of hurricane’s track in 6 hour interval. The nested model application to the case study of Hurricane Michael demonstrates the coastal inundation patterns in the city of Mexico Beach with the root-mean-square error of 0.12 m from all measurement stations. Results of the nested model inundations on coastal critical infrastructure and roadways are further used with models that investigate risk assessments to support hurricane mitigation planning and evacuation operations sufficiently in advance. 

The world has experienced an unprecedented global health crisis since 2020, the COVID-19 pandemic, which inflicted massive burdens on countries' healthcare systems. During the peaks of the pandemic, the shortages of intensive care unit (ICU) beds illustrated a critical vulnerability in the fight. Many individuals suffering the effects of COVID-19 had difficulty accessing ICU beds due to insufficient capacity. Unfortunately, it has been observed that many hospitals do not have enough ICU beds, and the ones with ICU capacity might not be accessible to all population strata. To remedy this going forward, field hospitals could be established to provide additional capacity in helping emergency health situations such as pandemics; however, location selection is a crucial decision ultimately for this purpose. As such, we consider finding new field hospital locations to serve the demand within certain travel-time thresholds, while accounting for the presence of vulnerable populations. A multi-objective mathematical model is proposed in this paper that maximizes the minimum accessibility and minimizes the travel time by integrating the Enhanced 2-Step Floating Catchment Area (E2SFCA) method and travel-time-constrained capacitated p-median model. This is performed to decide on the locations of field hospitals, while a sensitivity analysis addresses hospital capacity, demand level, and the number of field hospital locations. Four counties in Florida are selected to implement the proposed approach. Findings can be used to identify the ideal location(s) of capacity expansions concerning the fair distribution of field hospitals in terms of accessibility with a specific focus on vulnerable strata of the population 

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 

Dynamically-coupled SWAN and ADCIRC models have been applied to enhance the predictions of extreme waves and storm surges induced by hurricanes and sea level rise (SLR) in the Gulf of Mexico. The model performance was evaluated using Hurricane Michael, a Category-5 hurricane, as a case study. Modeled wave heights were compared to the observations. Results indicate that the dynamically-coupled SWAN-ADCIRC models substantially enhance the modeling accuracy. By comparing to the maximum observed 2.69 m of wave height near the hurricane landing site, the error is 0.04 m by the SWAN-ADCIRC models in comparison to the 0.39 m by the SWAN stand-alone simulation. Effects of sea level rise on hurricane wave heights were investigated under four SLR scenarios of 0.2m, 0.5m, 1m, and 1.5m. Results indicate that, as sea level rises, wave heights increase non-linearly in shallow waters near the hurricane landing site. At the wave observation station near the hurricane landing site, the ratio of the wave-height change to SLR increases to 117% and the ratio of the combined wave-surge change to SLR increases to 265%. Analysis indicates that this is due to the substantial percentage changes in water depth occurring in shallow water compared to deep water caused by SLR. 

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. 

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. 

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.