Future Tropical Cyclone Research Articles

Review of tropical cyclones impacting the Western Arabian Sea and Oman

ABSTRACT Tropical cyclones (TCs) are major natural disasters that can cause significant damage and loss of life in coastal areas. Arabian Sea has experienced many such TCs, with varying degrees of severity. These TCs are associated with heavy rainfall, flash floods, and make significant damage to infrastructure, society, and the environment. A range of factors influence cyclones in the Arabian Sea including sea surface temperatures, ocean currents, eddy activity, the position of the monsoon jet axis, and the Indian Ocean Dipole (IOD). The frequency of these TCs has already increased due to rising sea surface temperatures and changes in wind patterns. Researchers have conducted extensive studies in the region to better understand tropical cyclones and their impacts, focusing on sea surface cooling, air–sea interactions, and the effects of climate change. By studying these TCs over several decades, trends and patterns can be identified, contributing to the development of more accurate forecasting models and early warning systems to better prepare for future events. This paper reviews the history, track patterns, biological and physical impacts, numerical modelling, and future trends of tropical cyclones in the Western Arabian Sea, with the aim of supporting policymakers in developing more effective strategies for mitigating the impacts of future TCs.

Using simulations of future extreme weather events to escape the resilience trap: Experimental evidence from Hong Kong

Hong Kong is a hyper-dense coastal city that has long learned to live with a potentially disastrous extreme weather event: tropical cyclones. This was largely a reactionary process, with investments in soft and hard infrastructure made in the aftermath of devastating tropical cyclones. While the experiences of devastating tropical cyclones remain strong in the collective memory of the city, Hong Kong's present-day resilience has led to complacency, especially among the general public. We suggest that Hong Kong may be caught in a resilience trap, where previous success in building resilience may be hindering the city's ability to adapt to the impacts of future tropical cyclones. We use downward counterfactual modelling and an experimental framework to test whether simulating and visualizing the impacts of a future tropical cyclone can substitute for first-hand experience and allow individuals to experientially process the expected future impacts of tropical cyclones. Using experimental data collected from a representative sample of the general population (n = 1240), we find that simulating the impacts of a future tropical cyclone can partially substitute for first-hand experience, increase risk perceptions, and help Hong Kong escape the resilience trap.

A Generative Super‐Resolution Model for Enhancing Tropical Cyclone Wind Field Intensity and Resolution

AbstractExtreme winds associated with tropical cyclones (TCs) can cause significant loss of life and economic damage globally, highlighting the need for accurate, high‐resolution modeling and forecasting for wind. However, due to their coarse horizontal resolution, most global climate and weather models suffer from chronic underprediction of TC wind speeds, limiting their use for impact analysis and energy modeling. In this study, we introduce a cascading deep learning framework designed to downscale high‐resolution TC wind fields given low‐resolution data. Our approach maps 85 TC events from ERA5 data (0.25° resolution) to high‐resolution (0.05° resolution) observations at 6‐hr intervals. The initial component is a debiasing neural network designed to model accurate wind speed observations using ERA5 data. The second component employs a generative super‐resolution strategy based on a conditional denoising diffusion probabilistic model (DDPM) to enhance the spatial resolution and to produce ensemble estimates. The model is able to accurately model intensity and produce realistic radial profiles and fine‐scale spatial structures of wind fields, with a percentage mean bias of −3.74% compared to the high‐resolution observations. Our downscaling framework enables the prediction of high‐resolution wind fields using widely available low‐resolution and intensity wind data, allowing for the modeling of past events and the assessment of future TC risks.

Impact of tropical cyclones and socioeconomic exposure on flood risk distribution in the Mekong Basin

Tropical cyclones have a big impact on flood risk, and understanding how their activity interacts with population exposure under climate change is critical. Here we investigate spatiotemporal changes in flood risk using numerical models together with historical observations and future projections of tropical cyclone tracks. We find that tropical cyclone-related flood risk shifts from the Mekong Delta to the eastern lower Mekong Basin, driven by the interaction between tropical cyclones and population exposure. Historically, extreme precipitation from tropical cyclones increased flood risk in about 14% and decreased in 7% of the basin. Future tropical cyclones may increase flood risk in about 7% and reduce in nearly 18% of the basin. Moreover, population exposure growth has historically increased flood risk in 3% of the basin and is projected to result in a 1% increase. These findings highlight the complex interactions of tropical cyclone hazards and socioeconomic factors influencing flood risk.

Projections of future tropical cyclone landfalling activity in the East Asia region

This study reveals the possible future changes in tropical cyclone (TC) landfalling activity along the East Asian coast under different climate change scenarios based on global circulation model (GCM) simulations. We first identify those GCMs that have the “best” performance in simulating the TC activity over the western North Pacific (WNP) during the current climate (1979–2014) by examining the simulated TCs in each of the GCMs and then compare these simulated TCs with the observed TC climatological features of annual frequency, track densities and genesis locations. Based on such comparisons, we have identified five (TaiESM1, EC-Earth3, ACCESS-CM2, ACCESS-ESM1-5 and HadGEM3-GC31-LL) models among all the available GCMs. A multi-model ensemble gives a further improvement when compared with observations.Future projections from some of these models are then used to identify the frequency of TC activity over the entire WNP as well as landfalling TCs in six East Asia coastal regions under two climate change scenarios (SSP2-4.5 and SSP5-8.5) for two periods, 2041-70 and 2071-2100. A bias-correction method is also applied to the projected intensity of these landfalling TCs to estimate the landfall intensity.In general, these GCMs project a possible decrease in TC genesis frequency over the entire WNP, consistent with the results of most of the other studies. At mid-century, decreases in TC genesis frequency are projected to be around 10% for both scenarios. Towards the end of the century, the decreases will be more significant, with the percentage changes of 14.9% (SSP2-4.5) and 22.4% (SSP5-8.5). For landfalling TCs, the northern part of the East Asian coast will likely have an increase in frequency, ranging from 17 to 60% but a decrease of 14–27% in the southern part. In general, the average intensity of landfalling TCs will likely increase although the percentages are not large, ranging from 2 to 14%.

Decreasing trend in destructive potential of tropical cyclones in the South Indian Ocean since the mid-1990s

Tropical cyclone activity often leads to many adverse impacts and assessing their destructiveness is a crucial scientific concern. Here we investigated changes in the destructiveness of tropical cyclones worldwide using the power dissipation index and found that there is no clear trend in most basins, but a significant decrease in power dissipation index has been detected in the South Indian Ocean basin since 1994, which is almost entirely due to a decrease in both tropical cyclone frequency and duration in this basin. The decrease in tropical cyclone frequency is influenced by increased atmospheric stability. The decrease in tropical cyclone duration can be attributed to the changes in tropical cyclone locations. In addition, the weakened subtropical high is only observed in the South Indian Ocean (i.e., the Mascarene High), which is also associated with the decrease in tropical cyclone destructiveness. These findings have implications for assessing the destructive potential of future tropical cyclones.

Dependence of tropical cyclone seeds and climate sensitivity on tropical cloud response.

Projections of future tropical cyclone frequency are uncertain, ranging from a slight increase to a considerable decrease according to climate models. Estimation of how much the Earth's surface temperature warms in response to greenhouse gas increase, quantified by effective climate sensitivity, is also uncertain. These two uncertainties have historically been studied independently as they concern different scales: One quantifies the extreme weather and the other the mean climate. Here, we show that these two uncertainties are not independent and are both influenced by the response of tropical clouds to warming. Across climate models, we show an anticorrelation between shortwave cloud radiative feedback and changes in the frequency of seed vortices, a prevalent type of tropical cyclone precursors. We further show an anticorrelation between effective climate sensitivity and tropical cyclone frequency changes, suggesting that global tropical cyclone frequency tends to decrease more substantially in models with larger temperature increase.

The atmospheric effect of aerosols on future tropical cyclone frequency and precipitation in the Energy Exascale Earth System Model

This study uses experiments from the Energy Exascale Earth System Model (E3SM) to compare the influence on tropical cyclone (TC) activity of: (i) the atmospheric effect of aerosols under specified sea-surface temperatures (SSTs); and (ii) the net effect of greenhouse gases (GhGs) (including changes in SSTs). The experiments were performed using the CMIP6 Shared Socioeconomic Pathway SSP5-8.5 emissions scenario with GhG-induced SST warming specified and atmospheric aerosol effects simulated but without explicit ocean coupling. Insignificant changes in global TC frequency are found in response to the atmospheric effect of future aerosols and GhGs, as significant regional responses in TC frequency counteract each other. Future GhGs contribute to more frequent TCs in the North Atlantic, and reductions over the Northwestern Pacific and Southern Indian Ocean. The atmospheric effect of future aerosols drives more frequent TCs over the Northwestern Pacific and reductions over the Northeast Pacific and North Atlantic. Along with increases in TC intensity, global TC precipitation (TCP) is projected to increase by 52.8% (14.1%/K) due to the combined effect of future aerosols and GhGs. Although both forcings contribute to TCP increases (14.7–19.3% from reduced aerosols alone and 28.1–33.3% from increased GhGs alone), they lead to different responses in the spatial structure of TCP. TCP increases preferentially in the inner-core due to increased GhGs, whereas TCP decreases in the inner-core and increases in the outer-bands in response to the atmospheric effects of decreased aerosols. These changes are distinct from those caused by aerosol-induced SST changes, which have been considered in other studies.

Projecting Future Tropical Cyclone Frequencies by Combining Uncertain Empirical Estimates of Baseline Frequencies with Climate Model Estimates of Change

Various studies have given projections for how frequencies of tropical cyclones (TCs) might change under climate change. In this study, we combine a set of such projections with uncertain estimates of frequencies of tropical cyclones in a baseline climate to produce probabilistic projections of tropical cyclone frequencies for the next 50 years. The novel aspect of our projections is the inclusion of baseline uncertainty. We consider frequencies of Saffir-Simpson Hurricane Wind Scale category 0-5 and category 4-5 storms for the six major tropical cyclone basins. We find that in several cases the means and medians of the frequency of category 0-5 storms are projected to decrease, but that increasing uncertainty nevertheless leads to increases in the likelihood of high rates of TC activity in the future. We then show how the variance of the distributions of uncertainty can be decomposed into terms due to baseline uncertainty and climate change uncertainty. We use this decomposition to determine the year in which climate change uncertainty overtakes baseline uncertainty. Over the next 20 years we find that in some basins baseline uncertainty dominates and in other basins climate change uncertainty dominates. We are able to relate these variations between basins to the coefficients of variation of the baseline and climate change inputs. Finally, we quantify how climate change affects estimates of near-term TC frequency, including the extent to which it increases the uncertainty. These results help us understand two of the major sources of uncertainty in estimates of tropical cyclone behaviour now and in the future, and the role climate change is playing in changing that behaviour. They also demonstrate how estimates of TC change can be combined with observations to create projections of future TC climate.

Anthropogenic effects on tropical cyclones near Western Europe

There is less consensus on whether human activities have significantly altered tropical cyclone (TC) statistics, given the relatively short duration of reliable observed records. Understanding and projecting TC frequency change is more challenging in certain coastal regions with lower TC activity yet high exposure, such as Western Europe. Here, we show, with large-ensemble simulations, that the observed increase in TC frequency near Western Europe from 1966 to 2020 is likely linked to the anthropogenic aerosol effect. Under a future scenario featuring regionally controlled aerosol emissions and substantially increased greenhouse gas concentrations (Shared Socioeconomic Pathway 5-85), our simulations show a potential decrease in TC frequency near Western Europe by the end of the 21st century. These contrasting trends in historical and future TC frequencies are primarily due to the rise for 1966–2020 and potentially subsequent fall for 2030–2100 in TC genesis frequency in the North Atlantic. The response of large-scale environmental conditions to anthropogenic forcing is found to be crucial in explaining the historical and future changes in TC frequency near Western Europe.

Sensitivity of Philippine historically damaging tropical cyclone events to surface and atmospheric temperature forcings

As the climate warms, sea surface temperature (SST) is projected to increase, along with atmospheric variables which may have an impact on tropical cyclone (TC) properties. Climate models have well-known errors in simulating current climate SSTs that will likely affect future TC projections. Therefore, a better understanding of the impact of SST changes will help us identify the largest uncertainty in projecting TC changes. This study employs three different and independent methodologies to investigate the impact of sea surface and atmospheric temperature changes and tropical cyclone (TC) characteristics, focusing on three historically damaging TCs in the Philippines: Typhoons Haiyan, Bopha, and Mangkhut. These methodologies include initially simulations with uniform SST anomalies between −4 to +4°C, then experiments using delta from CMIP6 CESM2 for SST and atmospheric temperature in the far future, and, finally, simulations imposing Radiative-Convective Equilibrium (RCE) conditions. The experiments reveal significant insights into TC dynamics under varying environmental conditions. Changes in SSTs resulted in changes in TC track, intensity, and rainfall. In the positive SST simulations, TCs tended to move northwards and resulted in substantial increases in maximum wind speeds reaching a difference of up to 10, 13, 23 ms−1 for Typhoons Haiyan, Bopha, and Mangkhut, respectively. Analysis of the accumulated rainfall also showed that increased SST results in increased rainfall. Inclusion of atmospheric warming offsets the intensification due to SST change. Moreover, warmer SSTs resulted in slower-moving TCs and increased TC size. Further analyses incorporating atmospheric temperature adjustments derived from CESM2 and RCE simulations offer better insights on TC response. Under near-RCE conditions, TCs exhibit reduced sensitivity to SST changes, with smaller intensity and size modifications simulated when stable relative humidity is imposed. The smaller changes in TC intensity and size observed in these experiments suggest that maintaining atmospheric stability through pre-storm atmospheric adjustments dampens the response of TCs to SST warming.

An environment-driven basin scale tropical cyclone model

This paper presents an environment-driven tropical cyclone (TC) model for the Western North Pacific basin, which comprises a revised Poisson regression genesis model, a tailored beta-advection track model, and a fast intensity model. The TC model reproduces the temporal and spatial distributions of genesis events, the motion pattern of tracks, as well as the intensity evolutions along tracks. Risk analyses for Hong Kong and along the southeast coastline of mainland China demonstrate that this model can simulate extreme TC events with high fidelity. And the Gaussian mixture model outperforms the Frank Copula in approximating the joint distributions of the annual maximum wind speeds and the corresponding wind directions. This model is driven by a set of environmental variables including relative vorticity, relative humidity, sea surface temperature, vertical wind shear, potential intensity, sub mixed layer depth stratification, mixture layer depth and so on. This enables the model to not only reproduce historical records, but also make predictions for future TC behaviors under climate change with combination of global climate models. Besides, the computational efficiency of the TC model is comparable to traditional purely statistical models. The proposed model can also be coupled with other natural hazard models to conduct multi-hazard analysis.

A longitudinal investigation of risk perceptions and adaptation behavior in the US Gulf Coast.

Climate change is occurring more rapidly than expected, requiring that people quickly and continually adapt to reduce human suffering. The reality is that climate change-related threats are unpredictable; thus, adaptive behavior must be continually performed even when threat saliency decreases (e.g. time has passed since climate-hazard exposure). Climate change-related threats are also intensifying; thus, new or more adaptive behaviors must be performed over time. Given the need to sustain climate change-related adaptation even when threat saliency decreases, it becomes essential to better understand how the relationship between risk perceptions and adaptation co-evolve over time. In this study, we present results from a probability-based representative sample of 2,774 Texas and Florida residents prospectively surveyed 5 times (2017-2022) in the presence and absence of exposure to tropical cyclones, a climate change-related threat. Distinct trajectories of personal risk perceptions emerged, with higher and more variable risk perceptions among the less educated and those living in Florida. Importantly, as tropical cyclone adaptation behaviors increased, personal risk perceptions decreased over time, particularly in the absence of storms, while future tropical cyclone risk perceptions remained constant. In sum, adapting occurs in response to current risk but may inhibit future action despite increasing future tropical cyclone risks. Our results suggest that programs and policies encouraging proactive adaptation investment may be warranted.

Improving our understanding of future tropical cyclone intensities in the Caribbean using a high-resolution regional climate model

The Caribbean region is prone to the strong winds and low air pressures of tropical cyclones and their corresponding storm surge that driving coastal flooding. To protect coastal communities from the impacts of tropical cyclones, it is important to understand how this impact of tropical cyclones might change towards the future. This study applies the storyline approach to show what tropical cyclones Maria (2017) and Dorian (2019) could look like in a 2 °C and 3.4 °C warmer future climate. These two possible future climates are simulated with a high-resolution regional climate model using the pseudo global warming approach. Using the climate response from these simulations we apply a Delta-quantile mapping technique to derive future changes in wind speed and mean sea level pressure. We apply this Delta technique to tropical cyclones Maria and Dorian’s observed wind and pressure fields to force a hydrodynamic model for simulating storm surge levels under historical and future climate conditions. Results show that the maximum storm surge heights of Maria and Dorian could increase by up to 0.31 m and 0.56 m, respectively. These results clearly show that future changes in storm surge heights are not negligible compared to end-of-the-century sea level rise projections, something that is sometimes overlooked in large-scale assessments of future coastal flood risk.

Increasing the resilience of the Texas power grid against extreme storms by hardening critical lines

The Texas power grid on the Gulf Coast of the United States is frequently hit by tropical cyclones (TCs) causing widespread power outages, a risk that is expected to substantially increase under global warming. Here we introduce a new approach that combines a probabilistic line failure model with a network model of the Texas grid to simulate the spatio-temporal co-evolution of wind-induced failures of high-voltage transmission lines and the resulting cascading power outages from seven major historical TCs. The approach allows reproducing observed supply failures. In addition, compared to existing static approaches, it provides a notable advantage in identifying critical lines whose failure can trigger large supply shortages. We show that hardening only 1% of total lines can reduce the likelihood of the most destructive type of outage by a factor of between 5 and 20. The proposed modelling approach could represent a so far missing tool for identifying effective options to strengthen power grids against future TC strikes, even under limited knowledge.

Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities

Abstract. There is evidence of an increased frequency of rapid intensification events of tropical cyclones (TCs) in global offshore regions. This will not only result in increased peak wind speeds but may lead to more intense heavy precipitation events, leading to flooding in coastal regions. Therefore, high impacts are expected for urban agglomerations in coastal regions such as the densely populated Pearl River Delta (PRD) in China. Regional climate models (RCMs) such as the Weather Research and Forecasting (WRF) model are state-of-the-art tools commonly applied to predict TCs. However, typhoon simulations are connected with high uncertainties due to the high number of parameterization schemes of relevant physical processes (including possible interactions between the parameterization schemes) such as cumulus (CU) and microphysics (MP), as well as other crucial model settings such as domain setup, initial times, and spectral nudging. Since previous studies mostly focus on either individual typhoon cases or individual parameterization schemes, in this study a more comprehensive analysis is provided by considering four different typhoons of different intensity categories with landfall near the PRD, i.e. Typhoon Neoguri (2008), Typhoon Hagupit (2008), Typhoon Hato (2017), and Typhoon Usagi (2013), as well as two different schemes for CU and MP, respectively. Moreover, the impact of the model initialization and the driving data is studied by using three different initial times and two spectral nudging settings. Compared with the best-track reference data, the results show that the four typhoons show some consistency. For track bias, nudging only horizontal wind has a positive effect on reducing the track distance bias; for intensity, compared with a model explicitly resolving cumulus convection, i.e. without cumulus parameterization (CuOFF; nudging potential temperature and horizontal wind; late initial time), using the Kain–Fritsch scheme (KF; nudging only horizontal wind; early initial time) configuration shows relatively lower minimum sea level pressures and higher maximum wind speeds, which means stronger typhoon intensity. Intensity shows less sensitivity to two MP schemes compared with the CuOFF, nudging, and initial time settings. Furthermore, we found that compared with the CuOFF, using the KF scheme shows a relatively larger latent heat flux and higher equivalent potential temperature, providing more energy to typhoon development and inducing stronger TCs. This study could be used as a reference to configure WRF with the model's different combinations of schemes for historical and future TC simulations and also contributes to a better understanding of the performance of principal TC structures.

Documenting the Progressions of Secondary Eyewall Formations

Abstract Intense tropical cyclones can form secondary eyewalls (SEs) that contract toward the storm center and eventually replace the inner eyewall, a process known as an eyewall replacement cycle (ERC). However, SE formation does not guarantee an eventual ERC, and often, SEs follow differing evolutionary pathways. This study documents SE evolution and progressions observed in numerous tropical cyclones, and results in two new datasets using passive microwave imagery: a global subjectively labeled dataset of SEs and eyes and their uncertainties from 72 storms between 2016 and 2019, and a dataset of 87 SE progressions that highlights the broad convective organization preceding and following an SE formation. The results show that two primary SE pathways exist: “No Replacement,” known as “Path 1,” and “Replacement,” known as the “Classic Path.” Most interestingly, 53% of the most certain SE formations result in an eyewall replacement. The Classic Path is associated with stronger column average meridional wind, a faster poleward component of storm motion, more intense storms, weaker vertical wind shear, greater relative humidity, a larger storm wind field, and stronger cold-air advection. This study highlights that a greater number of potential SE pathways exist than previously thought. The results of this study detail several observational features of SE evolution that raise questions about the physical processes that drive SE formations. Most important, environmental conditions and storm metrics identified here provide guidance for predictors in artificial intelligence applications for future tropical cyclone SE detection algorithms.

Value of Resilience for Self-Sustained Highway Transportation Systems

Nowadays, self-sustained highway transportation systems (HTSs) are calling for the inter-operation of road networks, distribution networks (DNs), and electric vehicles (EVs) towards the unexpected impacts inducted by extreme weather events (EWEs). A novel joint resilience assessment scheme is proposed to quantify the resilience of self-sustained HTSs under tropical cyclone (TC) events. The impacts of TCs on transmission lines and highways are formulated using a two-step ambiguity set. In the first step, a multitask learning (MTL) based model with a novel training strategy is proposed to simulate the future TC tracks; the impacts of TCs are then calculated in the second step based on the simulation results. Using the ambiguity set, the self-sustained HTSs resilience assessment problem is formulated based on the dynamic optimal traffic assignment problem, where EVs are integrated as third-party emergency resources before the advent of TCs. This scheme is then formulated as a two-stage distributionally robust optimization (DRO) problem and reformulated as a mixed-integer linear programming (MILP) problem. Case studies have been conducted on a self-sustained HTS consisting of a modified IEEE-14 bus test system with 2 wind farms (WFs) and a 14-node transportation network with 4 charging stations (CSs). Numerical results indicate that the proposed scheme can quantify the resilience of self-sustained highway transportation systems under the joint operation of EVs regarding the unmet travel demand and load-shedding costs. In comparison to the statistical TC forecasting models, the proposed MTL-based model is more accurate and can reduce the system cost in most cases.

Global population profile of tropical cyclone exposure from 2002 to 2019.

Tropical cyclones have far-reaching impacts on livelihoods and population health that often persist years after the event1-4. Characterizing the demographic and socioeconomic profile and the vulnerabilities of exposed populations is essential to assess health and other risks associated with future tropical cyclone events5. Estimates of exposure to tropical cyclones are often regional rather than global6 and do not consider population vulnerabilities7. Here we combine spatially resolved annual demographic estimates with tropical cyclone wind fields estimates to construct a global profile of the populations exposed to tropical cyclones between 2002 and 2019. We find that approximately 560 million people are exposed yearly and that the number of people exposed has increased across all cyclone intensities over the study period. The age distribution of those exposed has shifted away from children (less than 5 years old) and towards older people (more than 60 years old) in recent years compared with the early 2000s. Populations exposed to tropical cyclones are more socioeconomically deprived than those unexposed within the same country, and this relationship is more pronounced for people exposed to higher-intensity storms. By characterizing the patterns and vulnerabilities of exposed populations, our results can help identify mitigation strategies and assess the global burden and future risks of tropical cyclones.

Assessing the Impact of Climate Change on Characteristics of Tropical Cyclones in Houston, Texas

The objective of this research is to utilize a computational model for assessing the impact ofclimate change on tropical cyclones in the Houston, Texas area. In recent years, it has beenobserved that tropical cyclones have become more destructive, increasing in intensity andfrequency. The current body of knowledge on tropical cyclones has indicated the significant rolethat climate change plays in the worsening of tropical cyclones. This research focused onstudying the characteristics of tropical cyclones potentially affected by climate change in theHouston, Texas area, stemming from the researcher’s firsthand experience of Hurricane Harveywhich occurred in August of 2017. Nine global climate models were studied and incorporatedinto a MATLAB computational program in this research to generate a total of 9,000 synthetictropical cyclones with and without climate change for comparison purposes. The variouscharacteristics of tropical cyclones were analyzed based on the output of the MATLAB program.A comparison of these characteristics yielded the conclusion that climate change plays a key rolein increasing the likelihood of destructive characteristics of tropical cyclones. Therefore, it islikely that future tropical cyclones in the Houston, Texas area will demonstrate an increased riskseverity, causing more harm to people and more damage to local society. It is highlyrecommended that future research should focus on mitigation strategies for the Houston, Texasarea to reduce the consequence of tropical cyclones and prevent the situation of HurricaneHarvey from reoccurring.