Quantitative Risk Assessment of Population Affected by Tropical Cyclones Through Joint Consideration of Extreme Precipitation and Strong Wind—A Case Study of Hainan Province

Meng, Chenna; Xü, Wei; Qiao, Yongping; Liao, Xinli; Qin, Lianjie

doi:10.1029/2021ef002365

Cited by 16 publications

(6 citation statements)

References 65 publications

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“…Precipitation‐driven risk regions should be attuned to precipitation‐induced floods, dam breaks, debris flows or landslides (Cao, 2015; Lei et al., 2015). Finally, fully considering the impact of complex driving forces such as strong winds, extreme rainfall, and storm surges, as well as their combined effects (Meng et al., 2021), is crucial for effective management and adaptation to tropical cyclones in Hainan and other wind‐precipitation‐driven regions.…”

Section: Discussionmentioning

confidence: 99%

Spatial Scale Dependence of Tropical Cyclone Damage Function: Evidence From the Mainland of China

Tang,

Wu,

Ding

et al. 2023

Earth's Future

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Tropical cyclone (TC) damage function (DF) is widely used to model TC‐event level damage and thus assess the TC risk for a country or region. The scalability of these DFs at more localized scales, such as the province scale, has not been systematically explored. We use a unique Chinese data set to examine the damage at the TC‐event scale and province scale. Our results show that the parameters and performance of TC DF are spatially dependent. For a sigmoidal DF, the parameter dependence is manifested by a flatter curve calibrated on the TC‐event scale compared to the province scale. In the case of a power‐law DF, the dependence of its parameters is evident in the statistically more significant coefficients of the explanatory variables that are aggregated to the TC‐event scale, compared to the province scale. Performance comparison results further reveal that the scale dependence of performance is related to the type of DF. Integrating hazard, exposure, and vulnerability, the power‐law DF complements the typical sigmoidal DF, producing more accurate estimates of direct economic loss and annual average damage at both the TC‐event and province scales. However, its performance, compared to that of the sigmoidal DF, is more influenced by the scale at which it is calibrated. Our findings elucidate scale‐related research questions in TC risk assessment, offer insights into the selection of DFs, and inspire the future prospect of using multiple DFs to reduce the functional uncertainty.

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Section: Discussionmentioning

confidence: 99%

Spatial Scale Dependence of Tropical Cyclone Damage Function: Evidence From the Mainland of China

Tang,

Wu,

Ding

et al. 2023

Earth's Future

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“…Compared with the comprehensive assessment approaches that combine various environmental factors (Meng et al 2021), our method only takes the MWS as a convenient hazard factor for rapid damage prediction. The damage data were integrated statistics that included all losses caused by extreme wind, heavy rainfall, and even storm surges during a TC.…”

Section: Vulnerability and Disaster Loss Curvesmentioning

confidence: 99%

“…For example, Lu et al (2018) developed an intensity index combined with wind speed and population vulnerability to evaluate the TC wind disaster risk in Zhejiang Province. Meng et al (2021) developed a comprehensive assessment approach that used maximum wind speed (MWS), extreme precipitation, and environmental factors to evaluate the rate of affected population in different return periods in Hainan Province. These methods merely classify the relative risk or risk severity categories, however, and they cannot reflect the potential damage to all the elements at risk.…”

Section: Introductionmentioning

confidence: 99%

Rapid Damage Prediction and Risk Assessment for Tropical Cyclones at a Fine Grid in Guangdong Province, South China

Ning

Wang

et al. 2023

Int J Disaster Risk Sci

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Rapid damage prediction for wind disasters is significant in emergency response and disaster mitigation, although it faces many challenges. In this study, a 1-km grid of wind speeds was simulated by the Holland model using the 6-h interval records of maximum wind speed (MWS) for tropical cyclones (TC) from 1949 to 2020 in South China. The MWS during a TC transit was used to build damage rate curves for affected population and direct economic losses. The results show that the Holland model can efficiently simulate the grid-level MWS, which is comparable to the ground observations with R2 of 0.71 to 0.93 and mean absolute errors (MAEs) of 3.3 to 7.5 m/s. The estimated damage rates were in good agreement with the reported values with R2 = 0.69–0.87 for affected population and R2 = 0.65–0.84 for GDP loss. The coastal areas and the Guangdong-Hong Kong-Macao Greater Bay Area have the greatest risk of wind disasters, mainly due to the region’s high density of population and developed economy. Our proposed method is suitable for rapid damage prediction and supporting emergency response and risk assessment at the community level for TCs in the coastal areas of China.

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“…However, the data are mainly the maximum or accumulated value of annual statistics [33] or TC events [6,34] from a single meteorological station. Recent studies have used meteorological data from multiple stations to depict the regional difference of hazard intensity [35,36]. These studies have indicated that Copula theory can be an effective tool for TC hazard assessment.…”

Section: Tc Hazard Assessmentmentioning

confidence: 99%

High-Resolution Hazard Assessment for Tropical Cyclone-Induced Wind and Precipitation: An Analytical Framework and Application

Tang

Liu

et al. 2022

Sustainability

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Intensified tropical cyclones (TCs) threaten the socioeconomic development of coastal cities. The coupling of strong wind and precipitation with the TC process usually amplifies the destructive effects of storms. Currently, an integrated analytical framework for TC hazard assessment at the city level that combines the joint statistical characteristics of multiple TC-induced hazards and local environmental features does not exist. In this study, we developed a novel hazard assessment framework with a high spatiotemporal resolution that includes a fine-tuned K-means algorithm for clustering TC tracks and a Copula model to depict the wind–precipitation joint probability distribution of different TC categories. High-resolution wind and precipitation data were used to conduct an empirical study in Shenzhen, a coastal megacity in Guangdong Province, China. The results show that the probabilities of TC-induced wind speed and precipitation exhibit significant spatial heterogeneity in Shenzhen, which can be explained by the characteristics of TC tracks and terrain environment factors. In general, the hazard intensity of TCs landing from the west side is higher than that from the east side, and the greatest TC intensity appears on the southeast coast of Shenzhen, implying that more disaster prevention efforts are needed. The proposed TC hazard assessment method provides a solid base for highly precise risk assessment at the city level.

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Quantitative Risk Assessment of Population Affected by Tropical Cyclones Through Joint Consideration of Extreme Precipitation and Strong Wind—A Case Study of Hainan Province

Cited by 16 publications

References 65 publications

Spatial Scale Dependence of Tropical Cyclone Damage Function: Evidence From the Mainland of China

Spatial Scale Dependence of Tropical Cyclone Damage Function: Evidence From the Mainland of China

Rapid Damage Prediction and Risk Assessment for Tropical Cyclones at a Fine Grid in Guangdong Province, South China

High-Resolution Hazard Assessment for Tropical Cyclone-Induced Wind and Precipitation: An Analytical Framework and Application

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