Cyclone Risk Research Articles

A Tropical Cyclone Risk Prediction Framework using Flood Susceptibility and Tree-based Machine Learning Models: County-level Direct Economic Loss Prediction in Guangdong Province

Tropical cyclones (TCs), characterized by strong winds, heavy rainfall, storm surges, and flooding, have caused significant economic losses and fatalities in coastal regions globally. However, existing TC risk prediction frameworks often fail to adequately account for the direct impacts of flooding. In this study, we propose integrating flood susceptibility, a critical component of flood early warning systems, into TC risk prediction frameworks. Focusing on Guangdong Province, we employ four tree-based machine learning (ML) models (random forest, extreme gradient boosting, light gradient boosting machine, and categorical boosting) to predict county-level direct economic losses (DELs) based on flood susceptibility, oceanographic-meteorological data, and vulnerability data. These ML models are trained and tested on a dataset of 896 samples, achieving high prediction accuracies, with Pearson correlation coefficients exceeding 0.81 between the predicted and observed DEL values. Among the four models, the light gradient boosting machine demonstrates the best performance, achieving the highest values of R and R2, and the lowest values of MSE, MAE, and MedAE. The integration of flood susceptibility is validated by comparing it with traditional methods that directly incorporate environmental factors. Furthermore, the proposed TC risk prediction framework is applied to forecast the impacts of Super Typhoon Mangkhut in 2018, illustrating its potential ability for “real-time” TC risk assessments. These “real-time” DEL predictions not only estimate potential losses but also facilitate timely interventions, thereby enhancing the practical value of the model for disaster prevention and response.

Geo-spatial based cyclone shelter suitability assessment using analytical hierarchy process (AHP) in the coastal region of Bangladesh

The coastal district of Bangladesh is susceptible to cyclones, which cause significant damage to infrastructure, economy, and social structures every year. The importance of protecting lives and property in these vulnerable areas is a top priority, especially in times of cyclones and storm surges. Therefore, the identification of potentially suitable shelter locations is essential for disaster risk resilience planning and implementation in the coastal regions. In this context, our research focuses on Barguna, which has witnessed severe damage from previous cyclones over the last several decades. We aim to identify and map feasible cyclone shelter suitability by utilizing GIS, AHP, Hotspot Analysis, and Remote Sensing techniques. The use of advanced techniques enables a comprehensive assessment of multiple variables that influence shelter suitability, ensuring the selection of the most strategically significant locations. The goal is to enhance disaster preparedness and resilience in Barguna District, reducing the risk and vulnerability of its coastal communities to cyclones. Seven variables associated with cyclone hazards, such as elevation, slope, distance from roads and rivers, population density, land use, and cover, and proximity to healthcare facilities, are considered to identify the safest and most suitable cyclone shelter areas. The assessment of the appropriateness of shelter locations for the study area was facilitated by the collection of 181 GPS locations regarding existing cyclone shelters. The findings reveal that approximately 15.53 % (1956 ha) of the total land area is considered less suitable, 67.31 % (84763 ha) moderately suitable, 16.70 % (21024 ha) suitable, and 0.29 % (362 ha) highly suitable for optimal cyclone shelter establishment in the study area. This study has a major limitation due to the use of Landsat imagery, which may not always provide fully accurate image classification. To validate our methods, we collected existing cyclone shelter data and performed a pixel-based accuracy assessment, achieving high accuracy and confirming the reliability of our approach. This research addresses a critical gap in cyclone shelter planning, offering valuable insights for residents and decision-makers to mitigate cyclone risks not only in the Barguna district but also in similar coastal regions of Bangladesh. The framework developed in this study can aid non-government organizations and the government in determining the most appropriate locations to construct cyclone shelters to build a safer, more resilient future for the region, capable of withstanding the relentless threats posed by cyclones every year.

Climate defaults and financial adaptation

We analyze the relationship between climate-related disasters and sovereign debt crises using a model with capital accumulation, sovereign default, and disaster risk. We find that disaster risk and default risk together lead to slow post-disaster recovery and heightened borrowing costs. Calibrating the model to Mexico, we find that the increase in cyclone risk due to climate change leads to a welfare loss equivalent to a permanent 0.95% consumption drop. However, financial adaptation via catastrophe bonds and disaster insurance can reduce these losses by about 21%. Our study highlights the importance of financial frictions in analyzing climate change impacts.

China coasts facing more tropical cyclone risks during the second decaying summer of double-year La Niña events

Long-lasting La Niña events (including double-year and triple-year La Niña events) have become more frequent in recent years. How the multi-year La Niña events affect tropical cyclone (TC) activities in the western North Pacific (WNP) and whether they differ from single-year La Niña events are unknown. Here we show that TCs are more active over the far-WNP (FWNP, 110°–150°E), leading to marked high risks at China coasts during the second decaying summer of double-year La Niña events. The anomalous TC activities are directly related to the enhanced cyclonic anomaly over the FWNP, possibly a result of large-scale remote forcing initiated by the tropical North Atlantic (TNA) cooling. The persistent TNA cooling from the decaying winter to summer of double-year La Niña events drives westerlies over the Indo-western Pacific through Kelvin waves, which induce the cooling over the north Indian Ocean via the wind-evaporation-sea surface temperature effect, favoring the asymmetric heat distribution pattern and stimulating an anomalous vertical circulation over the eastern Indian Ocean to FWNP. The cooling over the north Indian Ocean also excites Gill responses, magnifying the TNA-induced westerlies and boosting the anomalous vertical circulation, and thus gives rise to the strong cyclonic circulation anomaly over the FWNP in summer. We suggest that the key point of the process is the strong TNA cooling related to the persistent negative Pacific-North American pattern (PNA) and positive North Atlantic Oscillation (NAO) while double-year La Niña events decay, distinct from the rapid decline of PNA and NAO during single-year La Niña events. The work provides a unique perspective on understanding TC activities over the WNP related to the El Niño-Southern Oscillation.

Are restoration plans missing the target? Land tenure and cyclone risks reshuffles priorities for mangrove restoration

Coastal wetlands, vital for fisheries habitats, have suffered extensive losses. Ecosystem restoration offers opportunities to improve fish catch and restore the valuable services these ecosystems provide. Successful restoration is dependent on choosing a site where restoration is feasible, which encompasses biophysical, social, governance, logistical, and resource factors. However, factors that influence feasibility such as land tenure (governance feasibility) and future climate risks (biophysical feasibility) are often overlooked in quantitative analyses of site selection. We ask how spatial priorities for restoration change when considering how feasibility is affected by land tenure, cyclone risk, and both factors together. Specifically, we analyzed a case‐study of mangrove restoration to improve fish catch in Queensland, where there is interest in restoring coastal habitats to support fish habitats. We found that the rank order of planning units by restoration feasibility was highly influenced by both land tenure and cyclones, with cyclones changing ranks with clustered regions along the coastline and land tenure variably changing ranks throughout. In planning units where fisheries benefit is expected to be high, but cyclone risk substantially reduces restoration feasibility, practitioners could consider strategically planting mangroves near established mangrove forest and selecting resilient species for restoration. Formalizing regulations for incentives to private land holders and amending legislation for easier permitting are additional suggestions for addressing land tenure challenges. Our study emphasizes the importance of systematic approaches to considering feasibility in spatial planning for restoration to minimize the risk of failure.

Risk of Tropical Cyclones and Floods to Power Grids in Southeast and East Asia

Power grids play a critical role in modern society, serving as the lifeline of a well-functioning economy. This article presents a first large-scale study on the risk estimation of tropical cyclone (TC)-induced winds and coastal floods, which can widely impact power grids in Southeast and East Asia. Our comprehensive risk model incorporates detailed infrastructure data from OpenStreetMap (OSM) and government power grid maps, along with global hazard maps and vulnerability curves. The results reveal that the estimated expected annual damages from TCs and coastal floods to OSM-mapped assets account for approximately 0.07% (0.00–0.38%) and 0.02% (0.00–0.02%) of the total GDP of the study area, respectively. We analyzed the main sources of uncertainty in the risk model and emphasized the importance of understanding asset vulnerability. These results highlight the urgent need to strengthen power infrastructure to withstand the impacts of natural hazards, and the significance of reliable risk information for improving power grid design and planning. Focusing on developing more region-specific infrastructure data and vulnerability curves will improve the accuracy of risk estimation and provide valuable insights not only for the electricity sector but also for customers of other infrastructure systems that heavily rely on a stable supply of electricity.

The risk of synoptic-scale Arctic cyclones to shipping

The risk of synoptic-scale Arctic cyclones to shipping

Typology and Design of Parametric Cat-in-a-Box and Cat-in-a-Grid Triggers for Tropical Cyclone Risk Transfer

The insurance industry has used parametric solutions to transfer catastrophe risks since the 1990s. Instead of relying on a lengthy process to assess a claim, these products pay the insured a pre-agreed amount if the physical characteristics of the event fulfill pre-defined conditions. Cat-in-a-box or cat-in-a-circle triggers, commonly used tools for tropical cyclone risk transfer, provide a payout to the insured if the track of a hurricane crosses the perimeter of a geographic area defined by a polygon or a circle with a certain intensity. Cat-in-a-grid solutions are novel and more sophisticated. They rely on a set of multiple cat-in-a-box triggers arranged on an orthogonal grid. The consideration of multiple geographic domains instead of a single box or circle is helpful to reduce basis risk, i.e., the difference between the parametric loss estimate and the target loss. In the case study for Miami presented here, for instance, a cat-in-a-grid solution showed 18.5% less basis risk than a typical cat-in-a-box alternative. To organize the different types of triggers within a common framework, we classify the existing alternatives based on whether they use a single geographic domain (like a box or a circle) or multiple domains (like a grid). We discuss their advantages and disadvantages and describe the process required to calibrate any one solution with the help of a catastrophe-risk model. We focus, in particular, on the analysis and construction of cat-in-a-grid triggers, the alternative that we believe offers the greatest potential for global standardization and adoption.

Alternative point‐wise metrics for tropical cyclone risks

AbstractTo minimize the risks from tropical cyclones (TCs), the quantification and regular monitoring of TC activities are strongly needed. While the accumulated cyclone energy (ACE) (Bell et al., 2000, Climate assessment for 1999. Bulletin of the American Meteorological Society, 81, S1–S50) has been widely used for examining the level of basin‐wide TC activities, the conventional ACE does not discriminate the regional characteristics of TC risks. By introducing a point‐wise version of ACE, this paper proposes a geographical approach to the risk map for TCs. Here, the alternative metric is named the localized ACE (LACE), which interprets the TC risk directly felt by the local residents. Annual LACE at a geographical point measures TC activity by merging the quantities of frequency, intensity and duration factors, which contribute to the local TC activity in a year. In conjunction with LACE, a concept of LACE partial contribution (LACEP) is also proposed. The LACEP enables quantitative comparison of the contribution by each factor to the LACE, and thereby identifies the characteristics of the regional TC risks. To demonstrate the efficacy of the indices, that is, LACE and LACEP, this paper provides the response of the local TC activities to El Niño–Southern Oscillation in the western North Pacific and confirms the value of these indices.

Making Our Hospitals a Safe Workplace: Hazard Identification and Risk Assessment at a Tertiary-Level Public Hospital in Eastern India.

Hospitals are complex places with a large number of employees, patients, furniture, equipment, etc. Healthcare workers (HCWs), patients, or the general public are vulnerable to injuries and illness due to unseen hazards at the workplace. This study aims to identify the hazards and assess the risks at a hospital to ensure safety for HCWs, patients, and the public and generate awareness about the same. It helps in reducing the financial obligation of the institution due to the treatment of illnesses of staff, absenteeism, and service disruption and slows down manpower turnover. Hazard Identification and Risk Assessment (HIRA) helps reduce human errors and promote safe behavior. This study aims to identify and study the hazards in a hospital, assess the risks associated with the hazards, and recommend methods to reduce or eliminate the hazards based on the outcomes of the study. An observational study was conducted at a 1000-bed tertiary-level teaching public sector hospital in eastern India. A checklist was used for direct observation, conducting staff interviews, and document reviews. A risk scoring tool was used, and hazards were ranked as per the risk score. Thirty-eight hazards were identified in the study and classified under the categories of natural, physical, chemical, biological, ergonomic, psychological, and safety. The fire risk and occurrence of cyclones had the highest risk scores. The study identified hazards through direct observations, record reviews, and staff interviews. These findings can guide the prioritization of areas requiring necessary action in risk reduction, ensuring a safe workplace for healthcare workers (HCWs), patients, and the public. They can also help the institution shift from a reactive approach to a proactive method for HCW safety.

The Imperial College Storm Model (IRIS) Dataset

Assessing tropical cyclone risk on a global scale given the infrequency of landfalling tropical cyclones (TC) and the short period of reliable observations remains a challenge. Synthetic tropical cyclone datasets can help overcome these problems. Here we present a new global dataset created by IRIS, the ImpeRIal college Storm model. IRIS is novel because, unlike other synthetic TC models, it only simulates the decay from the point of lifetime maximum intensity. This minimises the bias in the dataset. It takes input from 42 years of observed tropical cyclones and creates a 10,000 year synthetic dataset of wind speed which is then validated against the observations. IRIS captures important statistical characteristics of the observed data. The return periods of the landfall maximum wind speed are realistic globally.

Are Indian ports safe? Identifying, analysing and prioritizing the risks affecting India's major ports

Seaports are exposed to multiple risks that may affect security and operations. This research aims to develop a framework for identifying, ranking, and prioritizing the risks. Also, this study ranks the 12 major Indian ports based on the identified risk factors. The study follows an extensive literature review, prioritization by Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and sensitivity analysis for robustness. Thirty-two risks were identified and classified into five categories: natural disasters, geographical risks, security risks, operational and financial risks, and socio-political risks. The analysis suggests that the most important risk that the ports in India should consider is natural disasters. The risk of cyclones and tsunamis occupied the first two ranks among the global risks. Operational and financial risks, such as complex and lengthy approval procedures and infrastructure risks, are ranked third and fourth. Mumbai is ranked first as the riskiest Indian port, followed by Kolkata, JNPT (Jawaharlal Nehru Port Trust), and Vishakapatnam Port

Assessing tropical cyclone risk for improving mitigation strategies in Coastal Odisha, India.

Tropical cyclone causes large-scale devastation and destruction in the coastal plains of India, particularly in Odisha, which is the most cyclone-affected state in the country. Tropical cyclones are projected to be more powerful and widespread due to changing climate. Hence, the risk assessment of tropical cyclone is necessary to identify cyclone-risk areas in coastal Odisha which may help in the mitigation of the damages caused by cyclones. Therefore, this study utilizes geospatial techniques to produce a comprehensive risk map posed by tropical cyclones and to estimate the degree of risk for coastal districts of Odisha. For this, we evaluated the district-level cyclone risk for coastal Odisha using multi-criteria decision-making (MCDM) technique by considering 21 parameters for each of the four components of risk, i.e., exposure, hazard, vulnerability, as well as mitigation capacity. For each criterion, thematic raster map layers were created and weighted using a fuzzy analytical hierarchy process (FAHP). We prepared individual risk component maps using weighted overlay techniques and finally integrated all indices to create the risk map. The study shows that 13% area of the study area comes under a very high-risk zone whereas, 25% area comes under a high-risk zone. The central (Cuttack, northern parts of Khordha, and south-western parts of Jajpur district) and the eastern part (most of the parts of Jagatsinghpur, Kendrapara, and northern parts of Puri district) of the study area come under high to very high tropical cyclone impact zone. Almost 67% of the total area is highly vulnerable to tropical cyclones and mainly concentrated near the shoreline. The applied approach and results can assist the local authorities in identifying vulnerable and hazardous locations and developing workable solutions for the mitigation of revised cyclone threats in the coastal districts of Odisha.

Developing climate services for vulnerable islands in the Southwest Indian Ocean: A combined statistical and dynamical CMIP6 downscaling approach for climate change assessment

Climate change is a global challenge necessitating adaptation at the local level. Small island developing states (SIDS) in the southwest Indian Ocean (SWIO) basin are particularly vulnerable and already facing significant challenges due to climate variability and extreme weather events like tropical cyclones (TCs). Tailored climate services, catering to their specific needs and contexts, are crucial for formulating appropriate adaptation strategies. This study aims to fill the gap of localized and reliable information for climate services in the SWIO region. A 12-km resolution regional climate model is dynamically downscaled from a CMIP6 model to capture the climatology of tropical cyclones. Outputs are bias-corrected at kilometer-scale resolution over multiple islands. A subset of CMIP6 simulations is also statistically downscaled over La Réunion to quantify model uncertainties at the local scale and compare statistical and dynamical downscaling methods.CMIP6 models project an average increase in daily mean temperatures over small islands ranging from 1.2 °C (SSP1-2.6) to 3.7 °C (SSP5-8.5) by the end of the century compared to the reference period of 1981–2010, and up to 4.4 °C on Madagascar (SSP5-8.5). The dry season in the SWIO region is anticipated to become significantly drier, with precipitation deficits ranging from 10 % to 40 %, primarily due to a delayed onset of the rainy season. The cyclonic risk is expected to increase due to stronger cyclone intensities, a higher proportion of strong TCs, and the poleward migration of very intense TCs by one to two degrees latitude, further amplifying the risk faced by the Mascarene islands.

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.

Modeling Variability in Tropical Cyclone Maximum Wind Location and Intensity Using InCyc: A Global Database of High-Resolution Tropical Cyclone Simulations

Abstract While tropical cyclone (TC) risk is a global concern, high regional differences exist in the quality of available data. This paper introduces InCyc, a globally consistent database of high-resolution full-physics simulations of historical cyclones. InCyc is designed to facilitate analysis of TC wind risk across basins and is made available to research institutions. We illustrate the value of this database with a case study focused on key wind risk parameters, namely, the location and intensity of peak winds for the North Atlantic and western North Pacific basins. A novel approach based on random forest algorithms is introduced to predict the full distribution of these TC wind risk parameters. Based on a leave-one-storm-out evaluation, the analysis of the predictions shows how this innovative approach compares to other parametric models commonly used for wind risk assessment. We finally discuss why capturing the full distribution of variability is crucial as well as the broader use in the context of TC risk assessment systems (i.e., “catastrophe models”).

Assessment of cyclone risk and case study of Gaja cyclone using GIS techniques and machine learning algorithms in coastal zone of Tamil Nadu, India

Cyclones can cause devastating impacts, including strong winds, heavy rainfall, storm surges, and flooding. The aftermath includes infrastructure damage, loss of life, displacement of communities, and ecological disruptions. Timely response and recovery efforts are crucial to minimize the socio-economic and environmental consequences of cyclones. To accelerate the time-consuming risk assessment process, particularly in geographically diverse regions, a blend of multi-criteria decision-making and machine learning models was utilized. This novel approach swiftly assessed cyclone risk and the impact of the Gaja cyclone in Nagapattinam, India. The method involved assigning weights to distinct criteria, unveiling notable vulnerability aspects like elevation, slope, proximity to the coast, distance from cyclone tracts, Lu/Lc, population density, proximity to cyclone shelters, household density, accessibility to healthcare facilities, NDVI, and levels of awareness. Daddavari, Ettugudi, Kodikarai, Vedharanyam, Velankanni, and Thirupoondi face high/extreme cyclone risk. Nagore, Nagapattinam, Pillai, Enangudi, and Sannanllur have low/no threat. To further enhance the precision of the study, machine learning algorithms like SVM, SAM, and MLC were deployed. These models were instrumental in generating pre- and post-cyclone land use maps. The influence of Gaja cyclones effects shows decreasing of agriculture land from 34% to 30%, aquaculture increase 1%, barren land decrease from 8% to 6%, Built-up land decrease from 15% to 13%, land with scrub and salt pan also decrease from 21% to 17% and 10%–8%. Mostly effect of Gaja cyclone is dramatic increase of water body from 8% to 21%. Conducting cyclone risk zone analysis and pre/post-cyclone Land Use Land Cover (LULC) detection in Nagapattinam offers valuable insights for disaster preparedness, infrastructure planning, and climate resilience. This study can enhance understanding of vulnerability and aid in formulating strategies to mitigate cyclone impacts, ensuring sustainable development in the region.

Assessing Tropical Cyclone Risk in Australia Using Community Exposure–Vulnerability Indices

Tropical cyclones (TCs) are one of the most destructive natural hazards to impact on Australia’s population, infrastructure, and the environment. To examine potential TC impacts, it is important to understand which assets are exposed to the hazard and of these, which are vulnerable to damage. The aim of this study is to improve TC risk assessments through developing an exposure–vulnerability index, utilising a case study for the six Local Government Areas (LGAs) impacted by the landfall of TC Debbie in 2017: Burdekin Shire, Charters Towers Region, Isaac Region, Mackay Region, City of Townsville, and Whitsunday Region. This study utilised a natural hazard risk assessment methodology, linking exposure and vulnerability indicators related to social factors, infrastructure, and the environment. The two LGAs with the most extreme exposure–vulnerability values were the coastal regions of Mackay Region and the City of Townsville. This is consistent with urbanisation and city development trends, with these LGAs having more people (social) and infrastructure exposed, while the environmental domain was more exposed and vulnerable to TC impacts in rural LGAs. Therefore, further resilience protocols and mitigation strategies are required, particularly for Mackay Region and the City of Townsville, to reduce the damage and ultimate loss of lives and livelihoods from TC impacts. This study serves as a framework for developing a TC risk index based on hazard, exposure, and vulnerability indices, and insight into the improved mitigation strategies for communities to implement in order to build resilience to the impacts of future TCs.

Uncertainties and sensitivities in the quantification of future tropical cyclone risk

Tropical cyclone risks are expected to increase with climate change and socio-economic development and are subject to substantial uncertainties. We thus assess future global tropical cyclone risk drivers and perform a systematic uncertainty and sensitivity analysis. We combine synthetic tropical cyclones downscaled from CMIP6 global climate models for several emission scenarios with economic growth factors derived from the Shared Socioeconomic Pathways and a wide range of vulnerability functions. We highlight non-trivial effects between climate change and socio-economic development that drive future tropical cyclone risk. Furthermore, we show that the choice of climate model affects the output uncertainty most among all varied model input factors. Finally, we discover a positive correlation between climate sensitivity and tropical cyclone risk increase. We assert that quantitative estimates of uncertainty and sensitivity to model parameters greatly enhance the value of climate risk assessments, enabling more robust decision-making and offering a richer context for model improvement.

Warming-induced contraction of tropical convection delays and reduces tropical cyclone formation

The future risk of tropical cyclones (TCs) strongly depends on changes in TC frequency, but models have persistently produced contrasting projections. A satisfactory explanation of the projected changes also remains elusive. Here we show a warming-induced contraction of tropical convection delays and reduces TC formation. This contraction manifests as stronger equatorial convection and weaker off-equatorial convection. It has been robustly projected by climate models, particularly in the northern hemisphere. This contraction shortens TC seasons by delaying the poleward migration of the intertropical convergence zone. At seasonal peaks of TC activity, the equatorial and off-equatorial components of this contraction are associated with TC-hindering environmental changes. Finally, the convection contraction and associated warming patterns can partly explain the ensemble spread in projecting future TC frequency. This study highlights the role of convection contraction and provides motivation for coordinated research to solidify our confidence in future TC risk projections.