Storm Surge Model Research Articles

Impact of tidal dynamics and typhoon-induced inundation on saltwater intrusion in coastal farms

The increase in storm surge events caused by climate change exacerbates adverse effects on seawater inundation in coastal areas. An accurate description of the water level curve is crucial for understanding the process of saltwater intrusion (SWI) resulting from storm surge. Most studies involving empirical surges as inputs to groundwater models, often simplify spatial and temporal seawater inundation processes, which may increase the uncertainty in vertical seawater intrusion. To address this gap, we employed a comprehensive modeling approach using storm surge model ADCIRC and numerical simulator HydroGeoSphere to reveal SWI dynamics during a historical storm surge event in a coastal farm, considering varying tidal-surge phases and typhoon intensities. Our findings indicate pronounced SWI variations even with consistently highest water level during a storm surge, contingent on prior tidal processes. The timing of typhoon landfall on an hourly scale yielded diverse water level curves, altering the function of SWI. Intriguingly, SWI exacerbates following a high tide with 31.2 % average salinity higher, highlighting the profound modulation effect of tidal levels on SWI. Local topography significantly influenced SWI dynamics. Ponds, for instance, retained elevated salinity levels for over 15 h, indicating a more prolonged exposure to salinity than roads. These findings underscore the importance of considering both tidal influences and topographical factors in understanding and mitigating SWI in coastal agricultural management.

Hindcast Insights from Storm Surge Forecasting of Super Typhoon Saola (2309) in Hong Kong with the Sea, Lake and Overland Surges from Hurricanes Model

Super Typhoon Saola (2309) skirted past south-southeast of Hong Kong within 40 km on the night of 1 September 2023, posing a significant storm surge threat to Hong Kong. Given the close proximity of Saola with a peak intensity of about 210 km/h within 300 km of Hong Kong, a close call of the “super typhoon direct-hit” scenario, this case provides valuable insights from a hindcast review of storm surge forecasts and warning operation using the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, which is the operational storm surge model adopted by the Hong Kong Observatory (HKO). The performance of the HKO’s PRobabilistic Inundation Map Evaluation System (PRIMES) using both statistical and model ensemble approaches was also reviewed in this paper. Saola was a challenging case for operational forecasting of a compact TC structure with changes in storm size and intensity when it came close to Hong Kong. With major observations of storm structure using weather radar and dense automatic weather station, tide gauge and water level gauge networks, the high sensitivity of storm surge forecasts to the storm size parameter and the distance of closest approach was clearly revealed in the case of Saola. Even with a circularly symmetric TC parametric model like SLOSH, the hindcast review results illustrated that the model outputs were reasonably accurate during the closest approach of Saola given an accurate storm size and distance of closest approach were input, and using a highly computationally efficient storm surge model made it possible for the nowcasting of storm surges to handle compact and intense TC direct-hit cases in operational TC forecasting. Taking a nowcasting approach not only helps provide more reliable storm tide forecasts, but also facilitates the formulation of a better warning strategy when making final-call decisions in emergency response actions, based on the more frequent real-time analysis of TC position, intensity and storm size and the more accurate prediction of these parameters. A nowcasting workflow for storm surge operation was proposed in this paper.

Application of a subgrid-scale urban inundation model for a storm surge simulation: Case study of typhoon Haiyan

This study evaluated the individual drag force model (iDFM), a subgrid-scale model for simulating tsunami inundation in coastal urban areas. The iDFM was then applied to characterize Typhoon Haiyan-induced storm surge inundations in 2013. Using a post-typhoon survey dataset containing inundation depths at different survey points with inundation limits, the performance of iDFM was validated, and the results confirmed that iDFM accurately estimates the storm surge inundation depths but overestimates the limit of inundation. In addition, the results revealed that the iDFM flow velocity was a dominant factor, and the inclusion of buildings in the simulated affected the arrival of the leading edge of inundation. Furthermore, the reduction in flow velocity was sensitive to the subgrid-scale parameters, and the spatial distribution pattern of buildings located in the inundated area affected the relative difference in the maximum flow velocity with the regional roughness model. The inundation characteristics of the Typhoon Haiyan storm surge models were compared with a historical tsunami event. The inundation characteristics were similar and model differences were observed mainly in the fluid velocity and momentum flux rather than the inundation depth. However, the magnitudes of relative differences observed in Tacloban were larger for fluid velocity and momentum flux, respectively, than in the tsunami event. The effect of building drag on advection was also substantial in Tacloban due to the differences in the inflow mass and momentum fluxes.

Operational Storm Surge Forecasting at the National Hurricane Center: The Case for Probabilistic Guidance and the Evaluation of Improved Storm Size Forecasts Used to Define the Wind Forcing

Abstract The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.

Human displacements from Tropical Cyclone Idai attributable to climate change

Abstract. Extreme weather events, such as tropical cyclones, often trigger population displacement. The frequency and intensity of tropical cyclones are affected by anthropogenic climate change. However, the effect of historical climate change on displacement risk has so far not been quantified. Here, we show how displacement can be partially attributed to climate change using the example of the 2019 Tropical Cyclone Idai in Mozambique. We estimate the population exposed to high water levels following Idai's landfall using a combination of a 2D hydrodynamical storm surge model and a flood depth estimation algorithm to determine inland flood depths from remote sensing images, factual (climate change) and counterfactual (no climate change) mean sea level, and maximum wind speed conditions. Our main estimates indicate that climate change has increased displacement risk from this event by approximately 12 600–14 900 additional displaced persons, corresponding to about 2.7 % to 3.2 % of the observed displacements. The isolated effect of wind speed intensification is double that of sea level rise. These results are subject to important uncertainties related to both data and modeling assumptions, and we perform multiple sensitivity experiments to assess the range of uncertainty where possible. Besides highlighting the significant effects on humanitarian conditions already imparted by climate change, our study provides a blueprint for event-based displacement attribution.

Rapid simulation of storm surge inundation for hurricane evacuation in Florida by multi-scale nested modeling approach

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 h 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–150 m 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 min 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 h 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.

Comparative Analysis of BALSSA and Conventional NWP Methods: A Case Study in Extreme Storm Surge Prediction in Macao

In coastal regions, accurate storm surge prediction is crucial for effective disaster management and risk mitigation. This study presents a comparative analysis between BALSSA (Bidirectional Attention-based LSTM for Storm Surge Architecture) and the Japan Meteorological Agency (JMA) numerical storm surge model, focusing on the Saola-induced storm surge in Macao, September 2023. To train and assess the model, we leverage an extensive dataset comprising meteorological and tide level information from more than 80 typhoon occurrences in Macao spanning the period from 2017 to 2023. The results provide evidence of BALSSA’s effectiveness in capturing the complex spatio-temporal dynamics of storm surges, with a lead time of up to 72 h, as reflected by its MAE of 0.019 and RMSE of 0.024. It demonstrates reliable accuracy in predicting storm surge magnitude, timing, and spatial extent, potentially contributing to more precise and timely warnings for coastal communities. Furthermore, the real-time data assimilation feature of BALSSA ensures up-to-date information, aligned with the latest observations, which is essential for effective emergency preparedness and response. The high-resolution grids enhance risk assessment, highlighting BALSSA’s potential for early warnings, emergency planning, and coastal risk management. This study contributes valuable insights to the broader field of storm surge prediction, guiding decision-making processes and supporting the development of effective strategies to enhance coastal resilience.

SUITABLE BOUNDARY LOCATION IDENTIFCATION FOR RAINFALL-RUNOFF AND SURGE MODEL COUPLING TO EVALUATE COMPOUND FLOOD HAZARDS IN COASTAL REGIONS

Flooding in many coastal regions is exacerbated due to complex interactions of multiple flood drivers such as rainfall-runoff and coastal surge which can occur simultaneously or sequentially during storm events, leading to compound flooding hazards. The Texas Gulf Coasts (USA) are especially vulnerable from compound flooding due to frequent occurrence of tropical storms which bring strong winds to drive storm surge and heavy rainfall to generate inland flooding. Large scale studies {Coastal Texas Restoration and Protection Study (CTX, 2021), Texas General Land Office’s Texas River Basin Study, Base Level Engineering Study} are being conducted by state and federal agencies for flood hazard assessment and for development of mitigation and abatement strategies for reducing this risk and increasing community resilience. Traditionally, large scale storm surge models do not account for runoff contributions to water levels in the interior of their domain. Riverine models for their part use either normal depth or mean high water levels to assign downstream boundary conditions within tidally influenced area, thus ignoring effects of storm surge. In this Planning Assistance to States (PAS) study, located in the Lower Clear Creek and Dickinson Bayou watersheds (Texas, USA), we have evaluated rainfall-runoff and storm surge interactions to determine suitable locations to use as model boundary conditions for exchanging data between rainfall-runoff and surge models under compound flooding conditions.

The contribution of typhoon local and remote forcings to storm surge along the Makou-Dahengqin tidal reach of Pearl River Estuary

Due to the interaction between upstream discharge and astronomical tides in tidal reaches, the typhoon-induced storm surge processes are quite different from that in other coastal regions. Investigating the contributions of driving factors is essential to deepen the understanding of storm surges in tidal reaches. In this study, a coupled hydrological-hydrodynamic storm surge model is first developed to explore the main driving factors of storm surges in Makou-Dahengqin tidal reach during the three most influential typhoon events (Hagupit, Hato and Mangkhut). After that, the machine learning method is integrated to assess the water level in response to storm surges. The driving factors of storm surge are decomposed into remote forcing (upstream discharge, astronomical tide) and direct local forcing (wind stress, atmospheric pressure). The relative contributions of remote forcing are the highest near the estuary mouth. The relative contributions of local forcing to water levels are higher in the sections 40–80 km away from the estuary mouth. The most impacting period of the local forcing is about 48 h, while the relative contributions of remote forcing increase before and after the period. The local forcing-induced surges are highest at the upper reach during Hagupit, while it causes extreme surges at the estuary mouth during more powerful typhoons (Hato, Mangkhut). The maximum water levels and remote forcing-induced maximum surges invariably appear at the upper reach. However, when local and remote forcings are in the same phase, the maximum storm surge appears in the lower reaches during Hato. If local and remote forcings are in the same phase, the peak water levels would be amplified by up to 15.04 %, 36.23 % and 40.68 % during Hagupit, Hato and Mangkhut, respectively. Moreover, Remote forcing contributes more to the amplification of peak water levels than local forcing does, accounting for 68.5 % to 100 %.

Extreme Sea Levels and Their Attribution for the Canadian Pacific Coast From a Baroclinic Regional Ocean Model and Tide‐Gauge Data

AbstractThe existing operational storm surge model for the Canadian Pacific coast is barotropic without coupling with tides. Here a baroclinic ocean model, which includes tides, storm surges, intra‐seasonal, seasonal, and interannual variations, is developed to reproduce coastal sea levels in the northeast Pacific from 1993 to 2020. The model results are validated against observations at tide‐gauge sites, demonstrating overall good skills for tides and non‐tidal sea levels. The Gumbel distribution is applied to derive extreme sea levels from hourly sea level anomalies. The model‐based extreme sea levels agree approximately with the observation‐based ones. For the 100‐year return period, modeled total extreme sea levels range from 2.14 m at Victoria Harbour to 4.43 m at Queen Charlotte City, and modeled non‐tidal extreme sea levels from 1.02 m at Bella Bella to 1.78 m at Queen Charlotte City. On average, the model‐based values underestimate the 100‐year return level by 0.18 m for total extreme sea levels and by 0.23 m for non‐tidal extreme sea levels. Both the model‐ and observation‐based results reveal that the storm surge at periods less than 10 d accounts for 60% of the 100‐year non‐tidal extreme sea level, and the intra‐seasonal, seasonal, and interannual variations account for 40% of it. The present study improves coastal sea level modeling and model‐based extreme level estimates. Moreover, this study also demonstrates for the first time that the intra‐seasonal to interannual changes contribute substantially to extreme sea levels and that El Niño can enhance this contribution on the Canadian Pacific coast.

Spatial Accessibility Analysis of Emergency Shelters with a Consideration of Sea Level Rise in Northwest Florida

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.

A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields

Modeling typhoon-induced storm surges requires 10-m wind and sea level pressure fields as forcings, commonly obtained using parametric models or a fully dynamical simulation by numerical weather prediction (NWP) models. The parametric models are generally less accurate than the full-physics models of the NWP, but they are often preferred owing to their computational efficiency facilitating rapid uncertainty quantification. Here, we propose using a deep learning method based on generative adversarial networks (GAN) to translate the parametric model outputs into a more realistic atmospheric forcings structure resembling the NWP model results. Additionally, we introduce lead-lag parameters to incorporate a forecasting feature in our model. Thirty-four historical typhoon events from 1981 to 2012 are selected to train the GAN, followed by storm surge simulations for the four most recent events. The proposed method efficiently transforms the parametric model into realistic forcing fields by a standard desktop computer within a few seconds. The results show that the storm surge model accuracy with forcings generated by GAN is comparable to that of the NWP model and outperforms the parametric model. Our novel GAN model offers an alternative for rapid storm forecasting and can potentially combine varied data, such as those from satellite images, to improve the forecasts further.

Member Formation Methods Evaluation for a Storm Surge Ensemble Forecast System in Taiwan

The forecast of typhoon tracks remains uncertain and is positively related to the accuracy of the storm surge forecast. The storm surge prediction error increases dramatically when the forecast track error is larger than 100 km. This study aims to develop an ensemble storm surge prediction system using parametric weather models to account for the uncertainty in typhoon track prediction. The storm surge model adopted in this study is COMCOT-SS storm surge forecast system. Two methods are introduced and analyzed to generate the ensemble members in this study. One is from the weather ensemble prediction system (WEPS), and the other is from the error distribution of the deterministic forecasts (EDF). The ensemble prediction results show that the ensemble mean of WEPS performs similarly to the deterministic forecast. However, the maximum surge height of WEPS is often lower than one from EDF. The verification results suggest that, for disaster prevention, EDF provides stronger warnings to the coastal region than WEPS. However, it may provide overestimated forecasts in some cases.

An investigation on the effects of increasing maximum wind speed of tropical cyclones on the return periods of water levels in the sea area of the Yangtze River Delta

This paper investigates the impact of increasing maximum wind speed of tropical cyclones on the return periods of water levels in the sea area of the Yangtze River Delta. To conduct this study, a series of numerical experiments are performed using historical tropical cyclones that impacted the Yangtze River Delta from 1949 to 2019. The aim is to analyze the effects of global climate change on extreme water levels and the corresponding return periods. To obtain the historical water levels in the sea areas of the Yangtze River Delta, a storm surge model is driven by the selected tropical cyclones. The simulated astronomical tidal levels during the same period are also used. The extreme water levels of different return periods are then calculated. The maximum wind speeds of the selected tropical cyclones are increased by 11% according to the expected amount of increase under global climate change. The extreme water levels of different return periods under this scenario are calculated with the same procedure. The results of the study show that the impact of increasing maximum wind speed of tropical cyclones on the increases of extreme water levels and the decrease of return periods is more significant in the inner area of the estuaries than in the outer areas. Moreover, the responses of the extreme water levels and the corresponding return periods in the Yangtze River Estuary and the Hangzhou Bay show different characteristics. The results of this study provide significant reference value for the management of future coastal disaster prevention and mitigation in the Yangtze River Delta. Furthermore, the methodology used in this study can be applied in other estuaries to investigate the potential impacts of changes in climate and hydrology factors on extreme water levels and the corresponding return periods.

North Atlantic Tropical Cyclone Size and Storm Surge Reconstructions From 1950‐Present

AbstractTropical cyclones (TCs) are one of the greatest threats to coastal communities along the US Atlantic and Gulf coasts due to their extreme wind, rainfall and storm surge. Analyzing historical TC climatology and modeling TC hazards can provide valuable insight to planners and decision makers. However, detailed TC size information is typically only available from 1988 onward, preventing accurate wind, rainfall, and storm surge modeling for TCs occurring earlier in the historical record. To overcome temporally limited TC size data, we develop a database of size estimates that are based on reanalysis data and a physics‐based model. Specifically, we utilize ERA5 reanalysis data to estimate the TC outer size, and a physics‐based TC wind model to estimate the radius of maximum wind. We evaluate our TC size estimates using two high‐resolution wind data sets as well as Best Track information for a wide variety of TCs. Using the estimated size information plus the TC track and intensity, we reconstruct historical storm tides from 1950 to 2020 using a basin‐scale hydrodynamic model and show that our reconstructions agree well with observed peak storm tide and storm surge. Finally, we demonstrate that incorporating an expanded set of historical modeled storm tides beginning in 1950 can enhance our understanding of US coastal hazard. Our newly developed database of TC sizes and associated storm tides/surges can aid in understanding North Atlantic TC climatology and modeling TC wind, storm surge, and rainfall hazard along the US Atlantic and Gulf coasts.

Wave and storm surge evolutions in the Pearl River Estuary with large-scale land reclamation impacts

Globally, many estuarine areas are suffering from large-scale land reclamations to fulfill the land demands of city expansion. Understanding the wave and storm surge evolutions in response to human reclamations in estuaries is of vital importance regarding estuarine management (e.g., reclamation plan formulation, flood mitigation). The wave and storm characteristics, and their responses to 1990–2020 land reclamations in the Pearl River Estuary (PRE) were investigated by a storm surge model. Besides, the impacts of increasing typhoon intensity (TI) on the changes of waves and storm surges caused by reclamations were also illustrated under the background of global warming. Simulated results show that reclamations cause apparent variations of wave height and storm surge fields in the PRE. The wave height and surge height also increase with the enhancement of TI, consequently influencing the wave height and surge height change rates caused by reclamations. Furthermore, the combined actions of land reclamation and increased TI conduct the most remarkable effects and bring the highest surge height compared with other scenarios. Particularly, the maximum wave height and surge height can reach 0.88–2.31 m and 2.71–3.77 m along the seawalls of Zhuhai, respectively, indicating more enormous seawall submersion and inland flooding.

Comparing emulation methods for a high‐resolution storm surge model

AbstractRealistic simulations of complex systems are fundamental for climate and environmental studies. Large computer systems are often not sufficient to run sophisticated computational models for large numbers of different input settings. Statistical surrogate models, or emulators, are key tools enabling fast exploration of the simulator input space. Gaussian processes have become standard for computer simulator emulation. However, they require careful implementation to scale appropriately, motivating alternative methods more recently introduced. We present a comparison study of surrogates of the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) simulator—the simulator of choice for government agencies—using four emulation approaches: BASS; BART; SEPIA; and RobustGaSP. SEPIA and RobustGaSP use Gaussian processes, BASS implements adaptive splines, and BART is based on ensembles of regression trees. We describe the four models and compare them in terms of computation time and predictive metrics. These surrogates use proven and distinct methodologies, are available through accessible software, and quantify prediction uncertainty. Our data cover millions of response values. We find that SEPIA and RobustGaSP provide exceptional predictive power, but cannot scale to emulate experiments as large as the one considered in this paper as effectively as BASS and BART.

Storm Surge Modeling as an Application of Local Time‐Stepping in MPAS‐Ocean

AbstractThis paper presents the first practical application of local time‐stepping (LTS) schemes in the Model for Prediction Across Scales‐Ocean (MPAS‐O). We use LTS schemes in a single‐layer, global ocean model that predicts the storm surge around the eastern coast of the United States during Hurricane Sandy. The variable‐resolution meshes used are of unprecedentedly high resolution in MPAS‐O, containing cells as small as 125 m wide in Delaware Bay. It is shown that a particular, third‐order LTS scheme (LTS3) produces sea‐surface height solutions that are of comparable quality to solutions produced by the classical four‐stage, fourth‐order Runge‐Kutta method (RK4) with a uniform time step on the same meshes. Furthermore, LTS3 is up to 35% faster in the best cases considered, where the number of cells using the coarse time‐step relative to those using the fine time‐step is as low as 1:1. This shows that LTS schemes are viable for use in MPAS‐O with the added benefit of substantially less computational cost. The results of these performance experiments inform us of the requirements for efficient mesh design and configuration of LTS regions for LTS schemes. In particular, we see that for LTS to be efficient on a given mesh, it is important to have enough cells using the coarse time‐step relative to those using the fine time‐step, typically at least 1:5 to see an increase in performance.

Development and validation of a coupled storm surge and wave model for the coast of Shanghai

Development and validation of a coupled storm surge and wave model for the coast of Shanghai

Integrating storm surge modeling and accessibility analysis for planning of special-needs hurricane shelters in Panama City, Florida

ABSTRACT We investigated the transportation accessibility of special needs populations to Special Needs Shelters (SpNS) by incorporating storm surge modeling into hurricane shelter planning in Panama City, a medium-sized city located close to the landfall location of Hurricane Michael. The storm surge model validated for Hurricane Michael was used to predict the coastal inundation. Using this model, A Geographical Information Systems (GIS)-based optimization methodology was developed for evaluating the accessibility to special needs shelters and repurposing existing regular hurricane shelters for special needs populations. With the proposed optimization approach, the average travel time per person-trip decreased from 28.5 minutes to 7.4 minutes after repurposing one regular shelter and to 4.3 minutes when three regular shelters converted to SpNS. Emergency plans can be improved by the proposed methodology, which can estimate the inundation zones by storm surge modeling and allocate the emerging shelter demand by accessibility analysis and location modeling.