Quantitative Assessment of Typhoon Disaster Risk at County Level

Abstract

Using the historical disaster records of 28 typhoons in Cangnan County since 2000, combining typhoon paths and hazard-bearing bodies data and based on the theoretical framework of climate change risk, the social and economic risks of typhoon disasters in Cangnan County with four intensity levels—severe tropical storm, typhoon, severe typhoon, and super typhoon—were quantitatively assessed. The results show that with the increase in typhoon disaster intensity, the spatial pattern of typhoon disaster hazard in Cangnan County changes from high in the west and low in the east to high in the south and low in the north. Super typhoons mainly affected Mazhan town and Dailing town in the south. The vulnerability shows an obvious upward trend. Super typhoons cause more than 40% of the population to be affected, more than 20% of direct economic losses and house collapse, and nearly 30% of crops to be affected in Cangnan County. The spatial pattern of risks that typhoon disasters have on populations, economies, crops, and houses change from low in south and high in north to high in north and south, and these risks increase gradually. The comprehensive risk of typhoon disasters is higher in the north and lower in the south, with the risk level being higher in the central and northern regions.

1. Introduction

Since the 21st century, climate-related disasters worldwide have affected 6.2 billion people and resulted in 510,000 deaths, with an average annual economic loss exceeding USD 143 billion [1]. Among these, tropical cyclones are one of the most devastating extreme events, affecting an average of 20.4 million people annually and causing USD 51.5 billion of direct economic losses over the past decade [2]. China is one of the countries most impacted by tropical cyclones, with an average of 7.6 landfalls per year over the past 20 years, resulting in over 30 million people affected, nearly 190 deaths or missing persons, 4.5 million emergency relocations, the collapse of 100,000 houses, 1.9 million hectares of affected crops, and direct economic losses amounting to 46 billion yuan [3]. The track of cyclones is influenced by factors such as latitude, Coriolis force, convection, and the topography of the landfall area [4,5,6]. In the Northwest Pacific, the occurrence of cyclones has shown a lag, often forming at more northern latitudes, with landfall locations gradually shifting northward [7]. The Coriolis force determines the direction of cyclone rotation, causing it to rotate toward the poles. Under certain conditions, topography and convection can alter the local structure of tropical cyclones, leading to changes in their intensity [8]. Accurately understanding the trends and influencing factors of cyclone changes is of significant practical importance for enhancing the understanding of regional cyclones impacts and conducting impact assessments. Tropical cyclones, which occur over tropical or subtropical oceans, are essentially synonymous with the broader term “typhoons”. This paper specifically refers to tropical depressions and stronger tropical cyclones occurring over the Northwest Pacific Ocean. Quantitative assessment of the socioeconomic risks of typhoon disasters provides a scientific basis for disaster prevention and mitigation efforts to reduce the impact of typhoon disasters.

The risk of typhoon disasters has garnered widespread attention from both policymakers and the scientific community. Related research has concentrated on the observational facts and future projections of the spatiotemporal characteristics of typhoon disasters, the socioeconomic impact assessment, risk evaluation, and regional delineation [9,10,11,12]. The hazardous factors of typhoon disasters are assessed using indicators such as maximum wind speed, daily and process maximum precipitation, and typhoon paths and frequency [13,14,15]. These studies primarily focus on the spatiotemporal distribution characteristics and environmental conditions of typhoon landfalls at different spatial scales, the evolution process of typical typhoons, and influencing factors [16,17,18]. The vulnerability of typhoon-affected entities is evaluated based on an index system that includes elements such as loss conditions, exposure, sensitivity, and adaptability [19,20,21]. However, there is a paucity of vulnerability assessments reflecting the quantitative relationship between typhoon intensity and socioeconomic losses [22,23]. Consequently, typhoon disaster risk assessments often involve semi-quantitative grade evaluations that integrate the hazard levels of disaster-causing factors, the vulnerability and exposure of hazard-affected bodies [24,25,26,27,28,29,30,31,32,33,34,35], indicating a lack of quantitative risk research on potential losses from typhoon disasters [9]. Additionally, research on the zoning of typhoon disaster risks has also been conducted [26].

Current research focuses on analyzing the spatiotemporal patterns of typhoon disasters [27,28]. The impact assessment of typhoon disasters is primarily derived from an evaluation system based on socioeconomic indicators, with risk levels originating from qualitative combinations of various elements [29,30]. This approach indicates the severity of typhoon impacts but fails to quantitatively express the actual losses caused by typhoons. Moreover, quantitative research on typhoon disaster risk based on historical disaster data is often conducted at regional and provincial scales, leading to high degrees of data dispersion and insufficient spatial representativeness. There is a need to focus on typical counties that are severely affected, employing precise loss and vulnerability data to estimate more accurate socioeconomic losses [31,32]. This study selects typical counties impacted by typhoons, utilizing landfall typhoon data and historical disaster records to develop a vulnerability assessment method that reflects the relationship between typhoon intensity and disaster losses. Additionally, this study establishes a quantitative risk assessment method that integrates hazard, vulnerability, and exposure. This approach aims to provide scientific support for county-level typhoon disaster risk assessment and disaster prevention and mitigation efforts.

2. Materials and Methods

2.1. Study Area

Cangnan County, located at the southernmost tip of Zhejiang Province, falls under the jurisdiction of Wenzhou and is one of its five counties. It administers 17 towns and 2 ethnic townships (including Longgang town). The county’s name, Cangnan, is derived from its location south of Yucang Mountain. To the east and southeast, it borders the East China Sea, while to the southwest, it adjoins Fuding City in Fujian Province. To the west, it neighbors Taishun County, and to the north, it borders Pingyang and Wencheng counties (Figure 1). Geographically, Cangnan is situated at 27°30′ N latitude and 120°23′ E longitude. The county experiences a subtropical maritime monsoon climate, characterized by mild winters and cool summers, with an average annual temperature of 17.9 °C and an average annual precipitation of 1670.1 mm. Covering a total area of 1261.08 km², it also includes a maritime area of 37,200 km² and boasts a coastline of 155 km. Most of Cangnan falls within the Aojiang River system, with a terrain that is higher in the southwest and lower in the northeast.

Cangnan County is situated in a high-risk area for typhoon disasters [26]. From 2000 to 2018, 28 typhoon disasters were recorded in the county, including 6 severe tropical storms, 11 typhoons, 7 severe typhoons, and 4 super typhoons. Notably, the Saomai Typhoon struck Cangnan head-on in August 2006, resulting in 1,050,000 people affected, 153 deaths, direct economic losses amounting to CNY 9.124 billion, the collapse of 73,350 houses, and damage to 28,000 hectares of crops.

Cangnan County’s typhoon defense strategy is shifting from a focus on full-scale resistance and primarily defensive measures to scientific prevention. In light of the increasing intensity of typhoon disasters due to climate change and the new circumstances brought by socioeconomic development, there is a pressing need for a clear understanding of the socioeconomic impacts of typhoons to enhance disaster prevention and mitigation [33].

2.2. Data Sources

This study utilizes historical typhoon disaster data, typhoon path data, and exposure data from the following sources:

(1) Historical Typhoon Disaster Data: Collected by the Cangnan County Emergency Management Bureau, this dataset comprises records of 28 typhoon disasters. The data include typhoon names, start and end times, affected towns, affected population, death populations, missing populations, emergency relocations, the numbers of collapsed houses, the numbers of severely damaged houses, the numbers of generally damaged houses, affected crop areas, damaged crop areas, completely destroyed crop areas, direct economic losses, and agricultural direct economic losses. This dataset is primarily used to determine the socioeconomic loss rate.

(2) Typhoon Track Data: Sourced from the China Meteorological Administration’s Tropical Cyclone Data Center’s best track dataset for the Northwest Pacific tropical cyclones (https://tcdata.typhoon.org.cn/zjljsjj.html, accessed on 25 January 2024), these data include the positions and intensities of the 28 typhoons that affected Cangnan County (Figure 2). It is used to assess typhoon intensity and hazard levels.

(3) Exposure Data: Population, GDP, housing counts, and crop planting areas at the town and community (village) scale in Cangnan County from 2000 to 2018 were obtained from the Cangnan County Statistics Bureau. These data are used to determine the exposure levels during typhoon occurrences and, in conjunction with historical disaster data, to evaluate the vulnerability of the affected communities to typhoon disasters.

2.3. Typhoon Risk Quantitative Assessment Methods

Based on the risk framework of Intergovernmental Panel on Climate Change [34], this study refers to the quantitative assessment method of climate change risk by Wu et al. [35] and defines typhoon risk R as deriving from the dynamic interaction between the hazard H caused by typhoon disasters, the exposure E of the affected socioeconomic systems, and the vulnerability V:

R_ij = H_i × E_j × V_ij

(1)

where R_ij is the risk of typhoon disaster for typhoon of intensity level i affecting hazard-bearing body j in Cangnan County; H_i is the hazard of typhoon disaster of intensity level i; E_j is the exposure of hazard-bearing body j; and V_ij is the vulnerability of hazard-bearing body j to a typhoon of intensity level i.

The classification standards for typhoon intensity levels are derived from the “Grade of Tropical Cyclones” (GB/T19201-2006) [36]. Tropical cyclones are categorized into six levels based on the maximum average wind speed near their central surface: tropical depression, tropical storm, severe tropical storm, typhoon, severe typhoon, and super typhoon. Considering that tropical depressions and tropical storms have limited impact on Cangnan County, this study focuses on the typhoon disaster risk associated with four intensity levels: severe tropical storms, typhoons, severe typhoons, and super typhoons. Vulnerability is heterogeneous and varies with the intensity of the disaster. This study uses a vulnerability curve fitting method to determine the loss rates of population, economy, housing, and crops for the four intensity levels of typhoons as vulnerability indicators.

Typhoon hazard levels are closely related to the disaster environment, such as wind speed, pressure, temperature, precipitation, atmospheric circulation, and geostrophic deflection force [37], but these factors are uncertain at the county scale, and it is difficult to quantify the degree of impact caused by each typhoon. Therefore, typhoon hazard levels are assessed using the method described by Yin et al. [9], which normalizes the frequency and path length of typhoons to represent the likelihood of occurrence and the spatial extent of impact (Figure 2). A higher frequency of typhoons in a region means that the probability of typhoons in the region is greater. The influence of a typhoon on a specific area is related to the length of its path, which can represent the spatial influence mode of a typhoon. First, the frequency of typhoon impact and path length for each community (village) are analyzed. Both indicators are normalized, and the hazard for different intensity levels of typhoons is averaged to obtain the equivalent average. The formulas are as follows:

$$p_i=0.1+{\displaystyle \frac{P_i-P_{imin}}{P_{imax}-P_{imin}}}\times (0.9-0.1)$$

(2)

$$l_i=0.1+{\displaystyle \frac{L_i-L_{imin}}{L_{imax}-L_{imin}}}\times (0.9-0.1)$$

(3)

$$H={\displaystyle \frac{p_i+p_i}{2}}$$

(4)

where $p_i$ and $l_i$ represent the normalized indices for the frequency and path length of typhoons of intensity level i, respectively, while $P_i$ and $L_i$ represent the actual frequency and path length of typhoons of intensity level i.

Typhoon disaster vulnerability refers to the degree of damage or destruction that a typhoon disaster can cause to the hazard-bearing bodies, representing the probability of casualties or damage to socioeconomic aspects within the affected area. We reasonably hypothesize that “the higher the hazard of typhoon disasters, the greater the rate of socioeconomic losses”. By statistically analyzing the intensity of typhoon occurrences and their impact on population, economy, houses, and croplands, we express the vulnerability of the hazard-bearing bodies through the loss rate caused by the typhoon disaster. Then, we use a nonlinear regression method to establish the quantitative relationship between typhoon disasters and their corresponding loss rates, forming a vulnerability curve. This curve helps in developing loss standards for typhoon disasters of different intensities. The nonlinear regression method is chosen primarily to account for the nonlinear trend in the economic loss rate as the intensity of typhoon disasters increases. The specific process is as follows. First, the loss rates for population, GDP, houses, and crops are calculated:

$$v_j={\displaystyle \frac{D_j}{E_j}}$$

(5)

where $v_j$ represents the loss rate of the hazard-bearing body j, $D_j$ represents the loss amount of the hazard-bearing body j, and $E_j$ represents the exposure of the hazard-bearing body j.

Using the four typhoon intensity levels in Cangnan County as the independent variables and the affected population rate, direct economic loss rate, affected crop rate, and house collapse rate under each intensity level as the dependent variables, various regression methods including linear model, logarithmic model, exponential model, power-law model, and hyperbolic model are employed to establish the quantitative relationship between typhoon intensity and the corresponding element loss rates, i.e., the vulnerability curves. The best model is selected based on the R² to represent the vulnerability of Cangnan County to typhoon hazards.

$$R^2=1-{\displaystyle \frac{{\sum }_{i=1}^n{\left(y_i-{\widehat{y}}_i\right)}^2}{{{\sum }_{i=1}^n\left(y_i-\overline{y}\right)}^2}}$$

(6)

At the same time, the direction of the trend is considered in order to avoid the phenomenon of overfitting, to ensure the accuracy of the predictions and the representativeness of the average impact.

Finally, the quantitative classification of typhoon risk levels—integrating hazard, vulnerability, and exposure of equal weights—employs the standard deviation method [38].

3. Results

3.1. Typhoon Hazard

The total frequency and path length of typhoons affecting Cangnan County exhibit a pattern of higher values in the west and lower values in the central and eastern regions. The typhoon hazard levels for different intensity categories, derived from these two indicators, are illustrated in Figure 3.

The hazard level for strong tropical storms decreases gradually from west to east (Figure 3a). The highest hazard level of 0.6 is found in the western part of Juxi town. The central part of Cangnan County, comprising most towns, has a hazard level of 0.5. The southeastern towns of Chixi, Mazhan, and Xiaguan exhibit moderate hazard levels. The lowest hazard levels are observed in the eastern towns of Longgang, Yanting, Jinxiang, and Dayu.

The hazard level for typhoons shows a pattern of lower levels in the central region and higher levels in the surrounding areas (Figure 3b). The highest hazard levels, approximately 0.5, are found in most areas of Longgang town in the northeast as well as in Yanting, Jinxiang, and Qianku. The western part of Juxi town and the southern towns of Yanpu and Xiaguan have moderate hazard levels. The central region of Cangnan County, encompassing most towns, has the lowest hazard level of 0.1.

The hazard level for severe typhoons is generally consistent across Cangnan County, with a hazard index of 0.5, except for the southern part of Xiaguan town, where the hazard level is lower, around 0.2 (Figure 3c).

The hazard level for super typhoons shows a pattern of higher levels in the southern part and lower levels in the northern part of Cangnan County (Figure 3d). The highest hazard levels, exceeding 0.7, are found in the eastern part of Mazhan town and the northern part of Dailing She Ethnic Township. The coastal towns in the eastern part of Cangnan also exhibit high hazard levels. The lowest hazard levels are found in Lingxi, Zaoxi, Wangli, and Yishan towns.

3.2. Typhoon Vulnerability

The analysis of socioeconomic losses caused by typhoons of different intensities, based on historical disaster data, shows that the socioeconomic losses caused by typhoons increase significantly with the intensification of typhoon strength. This trend is particularly pronounced for economic loss rates and house collapse rates, which exhibit clear nonlinear characteristics. The economic loss rate and house collapse rate for super typhoons are approximately five times and ten times higher, respectively, compared to those caused by severe typhoons. In contrast, the changes in the rates of the affected population and crop damage are more gradual.

The typhoon disaster vulnerability curves, constructed by correlating typhoon intensity with loss rates, are shown in Figure 4. Due to differing trends, the vulnerability curves for different hazard-bearing bodies vary slightly. Direct economic losses and house collapses are closely related, both exhibiting an exponential increase trend. The impact of typhoons up to severe intensity levels is relatively small, while super typhoons have a significantly greater impact. The affected population and crop damage rates show a consistent linear increase trend. The goodness of fit of the vulnerability curve for crops is relatively low, at around 0.78, while the goodness of fit for the vulnerability curves of the other hazard-bearing bodies is above 0.9.

3.3. Typhoon Risk

Figure 5 illustrates the socioeconomic risks associated with typhoons of different intensity levels, while Figure 5 shows the comprehensive socioeconomic risk levels of typhoon disasters by different intensities, combining the risks across various hazard-bearing bodies.

The impacts of severe tropical storms include approximately 51 thousand affected population, with direct economic losses estimated at approximately CNY 44 million. The affected regions are primarily concentrated in the northern regions, including Lingxi, Longgang, Yishan, and Qianku towns. Affected crop areas encompass about 2 thousand hectares, predominantly affecting Lingxi, Zaoxi, Qiaodun, and Mazhan towns in the north, while approximately 10 houses collapsed mainly in Lingxi town.

In contrast, typhoon-related impacts involve approximately 70 thousand affected population, with direct economic losses amounting to CNY 200 million. The affected crop areas span approximately 1.7 thousand hectares, with 209 houses collapsing, primarily concentrated in the northeast around Longgang town and its surrounding areas. This region exhibits a higher level of typhoon hazard, followed by Lingxi town, which has a greater exposure of hazard-bearing bodies. Overall, Cangnan County shows lower typhoon hazard values, resulting in relatively smaller differences in damage rates compared to other hazard-bearing bodies, leading to a smaller affected crop area compared to severe tropical storms.

Severe typhoons affect approximately 208 thousand population, with direct economic losses reaching CNY 922 million. Affected crop areas extend over 5.5 thousand hectares, with 729 houses collapsing. The spatial distribution pattern aligns closely with that of severe tropical storms. However, due to the significant differences in vulnerability between the two intensity levels, the losses incurred by hazard-bearing bodies are substantially higher.

Super typhoons affect around 1.3 million population, with direct economic losses totaling CNY 2.3 billion. The most affected areas include Jinxiang, Qianku, and Yanting towns in the east and Mazhan, Dailing She Ethnic Township, and Yanpu towns in the south. Affected crop areas cover approximately 3.6 thousand hectares, primarily impacting Qiaodun, Mazhan, and Jinxiang towns, while approximately 4 thousand houses collapsed, concentrated mainly in Mazhan, Jinxiang, and Yanpu towns. Super typhoons result in the highest direct economic losses and house collapses, largely influenced by vulnerabilities. However, the affected population and crop damage are not as large as those of severe typhoons, primarily due to higher exposure and hazard values in areas such as Lingxi and Zaoxi towns.

The comprehensive risk patterns of severe tropical storms, typhoons, and severe typhoons exhibit a north–high, south–low distribution, primarily influenced by the exposure of hazard-bearing bodies (Figure 6a–c). In contrast, the comprehensive risk pattern of super typhoons displays a south–high, north–low distribution, mainly influenced by the southward shift of high-hazard areas of causative factors (Figure 6d). The overall risk of typhoon disasters shows a pattern of high distribution in the north and south and low distribution in the middle, reflecting the dual impact of the spatial distribution of causative factor hazards and the exposure of hazard-bearing bodies (Figure 6e).

4. Discussion

Based on previous research, this study aims to develop a quantitative typhoon disaster risk assessment model suitable for county-level scales. The model integrates three key elements: hazard, vulnerability, and exposure, considering different disaster risk subsystems such as typhoon events, disaster-prone environments, and hazard-bearing bodies. The intention behind this model is to provide targeted recommendations for pre-disaster prevention, disaster resistance during typhoons, and post-disaster emergency decision-making in various county-level regions. An illustrative case study was conducted in Cangnan County, Zhejiang Province, which is severely affected by typhoons. This study collected comprehensive data on typhoon disasters and socioeconomic factors from 2000 to 2018 at the county level. It identified high-hazard areas for different typhoon intensities, high-sensitivity areas of disaster-prone environments, and high-risk areas of hazard-bearing bodies in Cangnan County, yielding scientifically reasonable analysis results. Vulnerability quantified in this study can be linked to disaster-causing factors and disaster-bearing bodies, and the results of the risk assessment can provide guidance specific to each risk element for disaster prevention. This model can fill the gap of quantitative risk assessment at the county scale and provide reference for the change in natural disaster risk assessment from qualitative to quantitative [39,40].

Compared to the qualitative methods used for assessing typhoon risk, the quantitative assessment method used in Cangnan County similarly concludes that the risk level is a high level. However, its advantage lies in its ability to clearly quantify the socioeconomic losses caused by typhoons of different intensity levels [41]. Compared with standards for typhoon intensity and potential disaster losses in China, Cangnan County has higher rates of population, economic, and housing damage than the national average for the same typhoon intensity levels [22,42,43]. In the typhoon disaster risk assessment for the Yangtze River Delta, the Zhejiang Province has a high frequency of typhoon impacts and a high typhoon disaster risk level [24,44]. Within Zhejiang Province, Cangnan County is identified as being in the highest risk category in county-level assessments [26]. In grid-scale risk assessments, Cangnan County also ranks high in risk level, with a risk pattern consistent with that observed in this study [45]. From the above studies, it can be seen that typhoon disasters occur frequently in Cangnan County, the exposure is relatively concentrated, and the disaster prevention and reduction measures are relatively underdeveloped, resulting in a high-risk level. This reflects the relatively concentrated exposure and the relatively underdeveloped disaster prevention and mitigation measures in the area. At the national and regional scales, Cangnan County is in a high-risk region, necessitating the development of targeted disaster mitigation plans.

By integrating hazard, exposure, and vulnerability, this method provides a comprehensive risk assessment of typhoons in Cangnan County over the past 20 years. The analysis highlights the differential impacts of typhoons of varying intensities on the socioeconomic structure of Cangnan County, primarily reflected in the changes in risk values and spatial distribution. Understanding the evolution trends of these risk patterns and the varying contributions of hazard, exposure, and vulnerability allows for the development of targeted strategies to mitigate the socioeconomic impacts of typhoon disasters [46]. The results emphasize the importance of focusing on high-risk areas and the need for precise assessment of vulnerability and exposure to improve disaster preparedness and response [47,48,49]. At the same time, the risk assessment model developed in this study can be applied to individual typhoon events or a specific disaster-bearing body or even the disaster chain caused by the typhoon disaster, and the dynamic risk assessment can be carried out according to the real-time path of a typhoon if the data are sufficient to support it [41,50,51,52].

By analyzing the spatial distribution of typhoon disasters, this study provides important references for monitoring, early warnings, and disaster prevention of typhoon disasters in Cangnan County. Understanding the areas with the highest hazards, particularly for higher-intensity typhoon disasters, can guide management departments in the rational allocation of disaster prevention and mitigation resources and efforts, effectively protecting communities in high-hazard areas [53]. From the typhoon paths shown in Figure 2, it can be observed that the paths of severe tropical storms, typhoons, and severe typhoons exhibit a high degree of spatial consistency, resulting in relatively small differences in hazard levels within the county. However, the paths of super typhoons tend to be more southern and western, leading to a pronounced spatial heterogeneity with higher hazard levels in the south and lower levels in the north [54,55]. Meanwhile, it is necessary to pay attention to the changes in the temporal and spatial characteristics of typhoon landfall and the possible scenarios of the increase in the number of typhoons, especially typhoons of stronger grades, under the background of future climate change, which will lead to higher socioeconomic risks [56,57].

The vulnerability curves for direct economic losses and house collapses in Cangnan County show exponential growth, with super typhoons causing disproportionately higher losses. The vulnerability curves for affected populations and crop areas follow linear trends, but the fit for crop vulnerability is slightly lower compared to other factors. These vulnerability curves help quantify the relationship between typhoon intensity and the resulting damage, providing a foundation for risk assessment and the formulation of mitigation strategies under different scenarios, which is crucial for enhancing Cangnan County’s resilience to future typhoon impacts. It should be noted that the vulnerability curve represents the average impact of typhoons on Cangnan County as a whole. Towns and communities (villages) within the county can further collect data to analyze the corresponding relationship between vulnerability and the amount of disaster prevention and reduction material reserves and reduce vulnerability levels by increasing disaster prevention and reduction measures [58,59].

Socioeconomic development and the continuous implementation of disaster prevention and mitigation measures, the region’s disaster mitigation capacity has improved to some extent. However, there are significant shortcomings in the region’s capacity to prevent major catastrophes, as evidenced by the vulnerability curves for Cangnan County’s economy and houses. To address this, it is necessary to improve precise early predictions and quantitative assessments for major disasters and to establish an integrated theoretical and technical system for major-disaster risk management and a resilient society, combining natural sciences, engineering technology, and the social sciences [60].

Current typhoon disaster risk assessments at different temporal and spatial scales are still evolving and improving, and this study has some limitations. On one hand, obtaining natural and socioeconomic data remains challenging. Affected by the distribution of meteorological stations, the typhoon track method is adopted to carry out hazard assessments. This method is feasible, but the result may be different from that of precipitation and wind speed. Data are missing on factors that have a significant impact on typhoon risk, such as infrastructure quality, governance, and community preparedness. Future research needs to address the issue of using multi-source data and machine learning methods for quantitative risk assessments, while exploring long-term trends in typhoon risk in the context of climate change. Due to limited access to historical disaster data, this study has only analyzed the characteristics of typhoon disasters in the past 20 years, and further research is needed on the impact of long-term trends in the context of climate change. On the other hand, typhoon disaster risk is closely related to the comprehensive disaster reduction capacity at the regional scale, which is mainly determined by various social statistical indicators and defensive engineering. Future research should further quantify the impact of comprehensive disaster reduction capacity on typhoon disaster risk, establish a quantitative relationship model between vulnerability and disaster reduction capability based on vulnerability curve and index evaluation, clarify disaster prevention and reduction objectives and construction paths, and provide more targeted guidance for typhoon disaster prevention.

5. Conclusions

Based on data from 28 typhoon disaster events in Cangnan County since 2000, this study utilized regression analysis to quantify the socioeconomic vulnerabilities to severe tropical storms, typhoons, severe typhoons, and super typhoons. A county-level quantitative assessment model for typhoon disaster risk was constructed, integrating analyses risks of hazard-bearing bodies, i.e., population, economy, crops, houses, and socioeconomic comprehensive risk. This study concludes with the following findings.

(1) The hazard levels of typhoon disasters in Cangnan County exhibits a transition from a pattern of higher levels in the west and lower levels in the east with increasing intensity to a pattern of higher levels in the south and lower levels in the north. Western areas face higher hazard levels from severe tropical storms, northeastern regions from typhoons, county-wide from severe typhoons, and southern areas from super typhoons.

(2) Socioeconomic losses caused by typhoon disasters of different intensity levels show an increasing trend. Direct economic losses and housing collapses increase exponentially, while affected populations and crop areas increase linearly. A super typhoon could potentially affect over 40% of the population, cause over 20% of direct economic losses and housing collapses, and affect nearly 30% of crop areas of Cangnan County.

(3) From the perspective of the township scale, risks gradually escalate as the intensity of typhoon disasters increases. Super typhoons result in the highest direct economic losses and housing collapses due to vulnerabilities. Severe typhoons lead to the highest numbers of the affected population and crop areas influenced by both hazard and exposure levels.

(4) Under the same intensity of typhoon disasters, the exposure of hazard-bearing bodies determines the level of risk. Townships like Lingxi, Zaoxi, and Longgang, with dense populations and relatively developed economies, face comparatively higher risks.