Coastal cities are highly vulnerable to compound flooding in which multiple flood drivers interact via complex nonlinear mechanisms under climate change. Although numerous studies have focused on individual flood drivers, integrated analyses of the spatiotemporal variations and compound effects remain limited. This study applied a high-resolution MRI-AGCM3-2-S climate model and the TempestExtremes tracking algorithm to construct a 6-hourly Tropical Cyclone (TC) track dataset affecting Haikou from 1960 to 2099. Storm tides during the TCs were simulated using the D-Flow FM model, whereas upstream river discharges were modeled with CaMa-Flood, incorporating climate-model-derived runoff data. Using rainfall data from the climate model, we applied the peak-over-threshold method and extreme value analysis to systematically assess changes in storm tides, rainfall, and upstream discharge under climate change. These analyses guided the construction of compound flood scenarios for simulating extreme events. Using a compound flood simulation model, we assessed the hazards under 10-year and 50-year Return Periods (RPs) for historical (1960–2014) and future (2015–2099) periods. Results indicate that significant differences exist in the compound flood characteristics between historical and future periods. In the 90th percentile scenario, all three flood drivers exhibited higher future thresholds, suggesting an increased risk of compound extreme flood events. The probability of concurrent heavy rainfall and high discharge events increased by 40.9%, whereas the probability of simultaneous high storm surge and high discharge events increased by 58.3%. Despite the potential reduction in extreme event intensity, the frequency of compounding events has increased significantly. Extreme value analysis revealed that extreme storm surges and upstream discharge events became more severe and extreme rainfall events showed a decreasing trend. For high RPs (e.g., 50-year events), the projected storm tides and upstream discharges significantly exceeded historical levels. Specifically, projected increases in storm surge levels (+0.24 m under 50-year RP) and upstream discharge (+1,271.13 m³/s) are offset by a 16.5% decline in 100-year accumulated rainfall for Haikou when compared to historical period. Third, compound flood simulations showed that under the 10-year RP scenario, the total inundation area slightly increased, but the flood volume and maximum depth decreased, indicating the stabilization of the flood hazard. However, under the 50-year RP scenario, both the inundation area and flood volume increased substantially, with the area experiencing flood depths greater than 3 m expanding by 56.5%. The most severe flooding occurred along the northern coastal areas and banks of the Nandu River, where the inundation extent and flood severity increased markedly. These findings provide valuable insights for flood risk assessments and adaptive planning in coastal cities facing intensifying climate-induced hazards.
The current risk assessment of single landslides and debris flow disasters ignores the increasing supply, accumulation, and superposition amplification effects of disasters from top to bottom, resulting in a serious underestimation of the risk of landslide-debris flow disaster chains. This study takes the "2010.9.21" mega-landslide debris flow disaster in the Magui River Basin in Gaozhou, western Guangdong as a case study. A landslide-debris flow disaster chain risk assessment index system, guided by the cumulative amplification effect, was established from the perspective of disaster chain initiation, transmission, and cumulative amplification. A comprehensive index model was used to scientifically evaluate the risk of the disaster chain, and actual investigation results were used for verification. The results are as follows: 1) The landslide-debris flow disaster chain in the Magui River Basin is characterized by multi-ditch collection, high impact force, and major terrain fluctuation. The landslide in the starting area is directly transformed into a debris flow during the instability process and flows into the debris flow branch ditch over a short distance. Several debris-flow branches received landslides along the path, converging into the main ditch. After potential energy accelerates through the circulation area, the flow rushes out of the ditch, leading to a large area of fan-shaped accumulations in the low- and slow-terrain areas, causing serious damage to residential houses and farmland. 2) A total of one small watershed unit carries an extremely high risk, accounting for 2.04% of the total number of small watersheds. The extremely high-risk area covers 3.64 km2, accounting for 2.24% of the total area. It is mainly distributed in a small watershed east of Liutang Village. There were eight small watersheds in high-risk areas, accounting for 16.33% of the total small watershed number. The dangerous area covers an area of 20.50 km2, accounting for 12.62% of the total area. Most watersheds are concentrated in Langlian Village, Shenshui Village, Makeng Village, and northern Longkeng Village in the Middle East region of Liutang Village. The number of small watersheds in the medium-risk area was 18, accounting for 36.73% of all the small watersheds, and the total area covered by dangerous area was 81.22 km2, accounting for approximately 44.90% of the total study area. The medium-risk areas were widely distributed within the scope of the study, especially in the southern part of Longkeng Village, most of the small watersheds of Liutang Village, the southern part of Langlian Village, Magui Village, Chengdong Village, Gancheng Village, the central area of Daxi Village, Houyuan Village, and Shanxin Village. There were 22 small watersheds in the low-risk area, accounting for 48.98% of the total number of small watersheds. The low-risk area covers 57.07 km2, accounting for 35.13% of the total study area. It is mainly distributed in the small watersheds of Shanxin Village, Houyuan Village South, Yadong Village South, and Zhoukeng Village in the northeast; Daxi Village in the west; Hemudong Village in the central region; and Longkeng Village in the south. 3) The evaluation results of this study were verified using actual investigation data, which showed high consistency with field survey results, thereby confirming the credibility of the method employed in this study. The index system and evaluation approach for the risk assessment of mass landslide-debris flow disaster chains proposed in this paper can serve as a reference for risk studies of landslide-debris flow disaster chains in South China and other similar areas.
Southeastern coastal China is sensitive to climate change and is characterized by an advanced economy and aging population. The region faces substantial exposure and vulnerability under climate change, making it a potential hotspot for Compound Heat-Drought Events (CHDEs). Therefore, in this study, we used multi-model integrated prediction data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate different climate change scenarios, along with the standardized precipitation evapotranspiration index (SPEI), sliding threshold method, and Copula joint probability distribution to define drought, heat, and compound events, respectively. Additionally, we aimed to analyze the temporal and spatial patterns of future CHDE hazards in southeastern coastal China under various climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and during different periods (2021-2040, 2041-2060, and 2081-2100). To further understand the lag effect of heat events on drought, we applied a lagged logistic regression model to quantify the attributable fraction (AF) for delays ranging from 1 to 7 days. In particular, we used the CN05.1 high-resolution gridded daily observation dataset to compare and analyze CMIP6 model prediction data, verifying their applicability to the study area and the accuracy of the prediction results. Our results indicate that CHDE hazards (number of occurrence days, intensity, and duration) in southeastern coastal China are expected to increase in the future, with a faster increase under the SSP5-8.5 scenario than under the SSP1-2.6, SSP2-4.5, and SSP3-7.0 scenarios. The intensity is projected to increase faster than the number of occurrence days and duration. Under the SSP5-8.5 scenario, the CHDE intensity at the end of the 21st century is projected to reach 3.41 times that during the baseline period (1995-2014), while the corresponding occurrence days and duration are projected to be 1.74 and 1.61 times those of the baseline period, respectively. This indicates that the probability of high-intensity CHDEs is expected to increase significantly considerably in the future. As for the spatial pattern, the spatial heterogeneity of the hazards (occurrence day, intensity, and duration) was more pronounced under the SSP5-8.5 scenario than under the other scenarios, especially during 2081-2100. Under the SSP5-8.5 scenario, the combined hazard indexes exceed 0.6 in southeastern Fujian, eastern Zhejiang, Jiangsu, and Shanghai, with some areas having indexes as high as 0.9. Spatial variability was shaped by factors such as distance from the coastline, availability of water resources, land use patterns, and human activities. Notably, the spatial heterogeneity in the CHDE duration was significantly greater than that in the occurrence days and intensity. Under the SSP5-8.5 scenario, the CHDE duration was approximately 2.23 times higher in the high-value areas than in the low-value regions, whereas the differences in occurrence days and intensity were smaller, at 1.13 and 1.11 times, respectively. This highlights the urgent need for regional adaptation strategies that focus on the persistence of CHDEs. The lagged effect of heat on drought events in southeastern coastal China exhibits an east-west sea-land gradient, with differences between the northern and southern areas. Specifically, the lag effect gradually intensifies from the inland to coastal regions. This may be attributed to the fact that coastal regions are influenced by the combined impact of heat on both the land and ocean and are more likely to experience delayed droughts. In the north-south divergence, northern Jiangsu experienced a stronger influence of heat on subsequent droughts than the other areas. This is primarily because of its predominantly dryland environment, which is highly vulnerable and in which heat events are more likely to trigger drought events. Under the SSP5-8.5 scenario, the AF value exceeded 5% in the northeastern Jiangsu coastline, eastern Zhejiang coastline, and Shanghai, with lag times of up to 7 days in some areas. This indicates that heat events will have a prolonged effect on subsequent droughts. These results provide a scientific foundation for policymakers to formulate effective disaster prevention and mitigation strategies tailored to their regional needs. Furthermore, they support decision making to promote a climate-adapted society and contribute to sustainable development.
In the context of global climate change and accelerated urbanization, coastal cities in China are facing increasing risks from compound disasters caused by the co-occurrence of extreme rainfall and high tide levels. These risks pose substantial threats to urban development and the safety of residents' lives and property. Therefore, it is essential to reasonably calculate the designed co-occurrence probability of rainfall and tide levels under different standards for the planning and design of flood control and drainage systems in coastal cities. In this study, we selected 105 drainage zones in Guangzhou, China with the aim of analyzing the spatial distribution characteristics and co-occurrence risk of extreme rainfall and high tide levels. Based on tide level and elevation data from Guangzhou, the 105 flood-prone zones were divided into 37 areas unaffected by high tide levels and 68 areas affected by high tide levels. Rainfall sequences and corresponding tide-level sequences for each zone were selected using the peak-over-threshold sampling method. On this basis, the designed combinations of rainfall and tide levels under different joint return periods were calculated using Copula functions and the co-frequency method, and their spatial distribution characteristics were analyzed. Our results show that, influenced by factors such as rainfall volume and elevation, the joint return periods of extreme rainfall and high tide levels for 50-year, 100-year, and 200-year events were approximately 30~35 years, 50~58 years, and 74~94 years, respectively. This indicates that the designed return periods for extreme rainfall and high tide levels individually were lower than their corresponding joint return periods, highlighting the obvious amplification effect of the co-occurrence of rainfall and tide levels. The designed storm intensity generally decreased from north to south, reflecting the spatial variability of rainfall patterns across the city. The probability of a 100-year daily rainfall event coinciding with a 100-year high tide level in Guangzhou showed an increasing trend from north to south, underscoring the heightened vulnerability of the southern regions to compound flooding. Additionally, areas in Guangzhou farther from the estuary were less affected by high tide levels than the central and southern regions, resulting in relatively lower risks of rainfall–tide level co-occurrence. This spatial heterogeneity emphasizes the need for region-specific flood control strategies. Our findings provide valuable insight into the spatial distribution and risk of compound flooding in Guangzhou, China. By quantifying the joint probabilities of extreme rainfall and tidal events, we offered a scientific basis for optimizing flood control and drainage infrastructure. The results of this study can guide policymakers and urban planners in developing targeted measures to mitigate the impacts of compound disasters, thereby enhancing the resilience of coastal cities to climate change and urbanization. This study not only contributes to the understanding of flood risks in Guangzhou but also provides a methodological framework that can be applied to other coastal cities facing similar challenges. The research outcomes serve as a critical reference for the planning and design of flood control and drainage systems in Guangzhou, offering practical solutions to reduce the risks posed by compound disasters and to safeguard urban development and public safety.
In recent years, the number of marathons in China has expanded rapidly, but the impact of high-temperature weather has become more apparent. Current high-temperature early warning systems lack tailored thresholds and unified standards for sports scenarios. This study aimed to develop an advanced early warning system for high-temperature exposure during marathons to enhance event safety and sustainability. The urgency of addressing high-temperature risks in sporting events is underscored by an increase in the frequency and severity of extreme heat events. These events not only threaten participants' health, but also challenge the organizational resilience of sporting events. Traditional early warning systems, designed primarily for general public health protection, fall short of providing the specificity required for sports settings. This study addresses this gap by proposing a refined early warning framework that is sensitive to the unique demands of marathons. Methodologically, this research moves beyond the limitations of a single air temperature index by employing the Wet-Bulb Globe Temperature (WBGT) as the primary indicator for thermal environment assessment. WBGT is recognized as a comprehensive metric that integrates temperature, humidity, and radiant heat, making it more suitable for evaluating heat stress during outdoor activities. By analyzing the relationship between human thermal comfort and meteorological factors, the study maps the Thermal Humidity Index (THI) sensory grading criteria to the WBGT system, creating a dynamic "red-orange-yellow" three-level early-warning system. The threshold setting considered the metabolic heat accumulation of marathoners during prolonged activity and was validated using six decades of national-scale meteorological data. Based on this, this study introduced the Marathon Exposure Index (MEI), which quantifies risks from three dimensions: exposure intensity (early warning level weight), exposure quantity (event frequency), and exposure value (event-grade coefficient). Results indicate a significant "long-south-short-north" pattern in China's marathon high-temperature exposure period. Southern regions, such as the Yunnan-Guizhou Plateau and the southeastern coast, have experienced extended high-temperature exposure periods compared with the historical baseline (1961-1990), with frequent red-alert zones coinciding with high-density Class A event areas (such as the Yangtze River Delta and Pearl River Delta). Further temporal analysis revealed that with accelerating global warming, extreme high-temperature red-alert events in China are becoming more frequent and prolonged. The innovative value of the study's findings is reflected in three key aspects. The early warning mechanism design established a graded-threshold dynamic-response-linked paradigm. By linking WBGT thresholds with event response measures, it enables a management paradigm shift from "passive response" to "proactive prevention and control," aligning general meteorological early-warnings with event safety management. In assessment technology, it breaks the traditional single-factor analysis framework, integrating an "intensity-quantity-value" three-dimensional model for comprehensive risk evaluation, integrating a refined early warning system with region-specific management measures, this approach ensures the safe operation and sustainable development of events. In practical applications, the proposed dynamic circuit-breaking response mechanism (e.g., event cancellation upon a red alert) was validated through situational simulations to significantly reduce heat-related injury rates and provide more forward-looking warning and response measures. Additionally, the research findings are broadly applicable to other outdoor sports and provide robust theoretical and practical tools for ensuring the safety of public sports activities amid climate change. The implications of this study extend beyond immediate applications to marathon event management. This study contributes to a broader discourse on climate change adaptation strategies in the sports and public health sectors. By offering a flexible and scalable framework, this study will enable stakeholders to tailor heat risk management strategies to diverse regional and event-specific contexts. Future research could explore the integration of real-time weather forecasting and participant physiological data to further enhance the precision and responsiveness of high-temperature early warning systems in sporting events.
Historical precipitation data are crucial for assessing the risks associated with natural disasters such as droughts and floods. However, some extreme precipitation scenarios may not have been included in historical records, particularly in China where the observed precipitation time series is relatively short compared to the return periods of rare extremes. This limitation poses a considerable challenge in disaster risk assessment, because the absence of data on certain extreme events can lead to risk underestimation. Therefore, the generation of spatially correlated stochastic precipitation events based on historical data is a key issue in disaster risk assessment. Current methodologies tend to focus on generating stochastic precipitation events for either a single site or a small number of sites. However, methods designed to generate stochastic precipitation events on large-scale grids have not yet been fully developed. To address this gap, we aimed to explore a method for generating daily stochastic precipitation events set at a 0.1° grid scale nationwide based on empirical orthogonal function (EOF) analysis and probabilistic fitting of principal component coefficients. We applied the EOF analysis method to decompose daily precipitation data for China from 1961 to 2022 (62 years). For each day of the year, 62 spatial modes and their corresponding mode coefficients were generated. Multiple probability distribution functions were used to fit the probability distributions of the mode coefficients for each day, with the optimal fitting function selected for each day. Based on these probability distributions, thresholds were set using twice the maximum and minimum values of the historical mode coefficients as the upper and lower boundaries, respectively. Monte Carlo sampling of daily precipitation scenarios was conducted using the 62-year historical data (1961-2022). Finally, using 62-year historical data (1961-2022), we performed Monte Carlo sampling to generate daily precipitation scenarios. To compare the consistency and differences between historical and stochastic precipitation characteristics, 5000 years of simulated daily precipitation events were generated. A comparative analysis was conducted using five statistical metrics: maximum value, mean, standard deviation, typical return period precipitation, and spatial correlation. The analysis results show that: (1) The stochastic precipitation adequately preserved the intensity-probability characteristics of historical precipitation, with the average difference between the two at the grid scale being less than 0.9 mm, which is considered negligible. The differences in the precipitation intensities for return periods of 10, 20, and 50 years were all less than 15%, and the differences in their standard deviations were all less than 8%. (2) The stochastic precipitation effectively extended the upper bound of the annual maximum values, with the maximum value in the grid with the greatest difference being 36% higher than the historical precipitation. (3) The stochastic precipitation maintained a good spatial correlation, with the daily Moran's index and Pearson correlation coefficient for all grids across the country having minimum values greater than 0.96 and 0.95, respectively. The national daily precipitation stochastic event set, based on empirical orthogonal decomposition, provides a robust data foundation for subsequent quantitative disaster risk assessments.
With the acceleration of climate change and urbanization in recent years, extreme rainstorms and urban flooding have increasingly threatened urban safety. Their impact on cultural, commercial, and tourism industries is widespread and significant, often leading to traffic paralysis, closure of tourist attractions, business shutdowns, and passenger stranding. In severe cases, this can endanger personal safety and result in significant economic losses. Shanghai, a representative coastal tourist city in China, is highly prone to rainstorm-induced flooding events from June to October each year due to the Meiyu front, extreme rainstorms, and typhoons. Conducting flood inundation simulations in Shanghai during the flood season is essential to identify high-impact urban flood areas and evaluate flood effects on densely populated cultural, commercial, and tourism hubs. This study used daily rainfall data from Shanghai between 1990 and 2020 to construct nine rainstorm scenarios based on three flood season periods (Meiyu, midsummer, and autumn) and three rainfall thresholds (maximum, 99th, and 95th percentiles). Using the SCS-CN and Mike21 hydrodynamic models for urban rainstorm flood simulations, a fuzzy comprehensive evaluation index system was developed based on a combination of Analytic Hierarchy Process(AHP) and Entropy Weighting Method (EWM) to assess the impact of flooding on Shanghai's cultural and tourism cluster areas. Results indicate the following: (1) Shanghai experiences the highest impact from rainstorm-induced flooding in the midsummer period. In the 95th percentile scenario, suburban areas experience minor flooding, whereas in the maximum value scenario, central urban areas experience a significant increase in flooding impact. (2) Control rules effectively improved the rationality and adaptability of the flood impact evaluation system. Resident and transient populations are key factors in evaluating flood impact. The flood impacts in Shanghai's cultural and tourism clusters showed significant spatial and temporal gradient characteristics, with medium-to-high- and high-impact areas primarily concentrated in the central urban cultural and tourism clusters. (3) Midsummer had the largest medium-to-high and high-impact zones, reaching 3.1 km² (8.79% of the total area), followed by the Meiyu period, whereas the autumn period has the smallest impact. (4) During midsummer, the largest proportion of high-impact areas was found in street- and road-type clusters, followed by waterfront leisure and comprehensive cultural tourism clusters, with areas accounting for 27.52%, 8.30%, and 6.44%, respectively. Cultural and tourism clusters should strengthen early warning, regulation, and preventive measures based on seasonal variations, especially during midsummer, when effective countermeasures must be implemented to reduce flooding impacts on visitor experience and regional safety. This study provides valuable insights for urban flood forecasting, early warning, and emergency response, as well as recommendations for sustainable development of the urban cultural, commercial, and tourism industries.
Under the influence of climate change, drought poses a novel and urgent challenge to sustainable development in the humid regions of southern China. Therefore, it is essential to estimate future drought changes and population exposure comprehensively. Using CMIP6 climate models and population forecast data, we estimated drought variations and population exposure in the Xijiang River Basin of Guangxi from 2021 to 2100 under three scenarios of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The findings are as follows: (1) By employing Taylor diagrams to evaluate the multi-model ensemble mean method (MME) of 18 CMIP6 climate models, we found that the method effectively simulated temperature and precipitation in the Xijiang River Basin, reducing the uncertainty associated with single-model simulations. Under all future scenarios, temperature and precipitation in the Xijiang River Basin are projected to increase, with effects of climate change becoming more pronounced in this region. (2) Using the Standardized Precipitation Evapotranspiration Index (SPEI), we observed a significant increasing trend in aridification in both historical and future periods. Significant differences and complex changes in the rate, occurrence time, frequency, intensity, and other drought characteristics were observed under various scenarios. Droughts are expected to be alleviated under low-emission scenarios but intensify comprehensively under high-emission scenarios. (3) The spatial variability of drought in the Xijiang River Basin will differ significantly under different scenarios. In low-emission scenarios, the intensity and extent of droughts nearly disappear in the long-term. Under medium-emission scenarios, the intensity and extent of droughts may increase. Drought events in this region are severe and worsen comprehensively, under high-emission scenarios, and the long-term impact will be extensive and serious. Drought events in this region are influenced by global climate change and are closely linked to the specific socioeconomic development path of the area. (4) Future, population exposure to drought will be highly correlated with different emission scenarios in the Xijiang River Basin. Under low-emission scenarios, the total population affected by droughts decreased. However, under medium-emission scenarios, the population exposed to each drought level will substantially increase in the medium- to long-term, and the spatial distribution will be more complex. In high-emission scenarios, although the exposure of populations may decrease in the short-term owing to extreme weather events, it will sharply increase in the medium- to long-term, especially with a sharp rise in exposure to severe droughts in the long-term. Climate change is the main factor affecting population exposure to drought; however, emission strategies are fundamental drivers, and population growth and structural changes cannot be ignored. Therefore, emission reduction measures play a key role in mitigating the risk of drought under the impact of global climate change. It is urgent to promote the transformation of low-carbon development models, strengthen regional coordination, and formulate adaptive strategies. This study provides scientific evidence for water resource management and drought response strategies in the Xijiang River Basin, and is of great significance for regional sustainable development.
Global climate change has increased the frequency and severity of urban flooding, posing significant risks to critical infrastructure. The resulting disruption of essential services has profoundly impacted the daily lives of urban residents and, in extreme cases, endangered their safety. A systematic framework has been developed to address this, integrating flood process simulation, critical infrastructure modeling, and social vulnerability analysis. This framework elucidates the complex interdependencies among urban infrastructure systems, evaluates the impacts of flood-induced service disruptions on urban populations in the context of climate change, and assesses the resilience of various infrastructure services. The Shenzhen-Maozhou River Basin, prone to flooding, was selected as the study area. Based on the Delft3D model and using the "Mangkhut" typhoon event as a benchmark, rainfall and sea-level rise were selected as uncertainty factors to simulate and identify three future extreme flood scenarios. A network-based approach was employed to construct an infrastructure system network model that included seven types of infrastructure: substations, communication base stations, hospitals, fire stations, police stations, shelters, and water supply plants. The flood simulation results were used as inputs to the infrastructure system network model to obtain the simulation results, which were then analyzed. The results revealed the following. 1) Within the system's topological structure, the degree value of substation nodes is significantly higher than that of other facilities, making them a critical node for cascading failures triggered by floods. The power-outage areas simulated by the constructed model demonstrate a 62% concordance rate with the historical validation data, indicating a relatively high credibility level. However, due to the sensitivity and confidentiality of the data, the validation work is not yet sufficient. The types of infrastructure involved in the validation are limited, affecting the model's reliability and parameters. Therefore, in future research, it is necessary to collaborate with relevant stakeholders to obtain the relevant data, which will be used for model validation and parameter calibration, to enhance the model's reliability. 2) During the floods triggered by Typhoon Mangkhut, the cascading effect significantly increased the number of fire stations and police stations affected, which rose from 1 to 11 and from 1 to 7, respectively. Meanwhile, the disruption of communication and medical services had a more pronounced impact on the urban population, with the ratio of the population affected by service disruptions exceeding 5 (a ratio of "1" represents 140,672 people). 3) Under the backdrop of climate change, the disturbance of future extreme flood disasters on the infrastructure system network is significantly intensified. After taking into account the cascading effects, the overall number of affected infrastructure facilities is, on average, 38% higher than the baseline scenario of "Mangkhut" 4) Thanks to the relatively rational spatial layout and the flood resistance of the facilities, the power system, emergency response (covering police and fire services), and shelter services in the Maozhou River Basin have demonstrated a certain degree of stability. This study helps clarify the complex interdependencies among urban infrastructures, assess the impact of floods on the stability of service systems, and identify potential cascading effects on residents ' lives. It provides decision-making support for urban disaster prevention, mitigation planning, and emergency response strategies.
Typhoons are among the most destructive natural disasters affecting China's coastal regions, often resulting in substantial economic loss and casualties. The annual average Direct Economic Loss (DEL) caused by typhoon disasters in China exceeds 60 billion yuan, accounting for 10%-30% of the DEL caused by all disasters each year. Consequently, the accurate assessment and prediction of typhoon-induced DEL are essential for improving disaster mitigation strategies and optimizing resource allocation. Rapid development of artificial intelligence and the growth of multi-source spatiotemporal big data have introduced data-driven methods for assessing disaster losses. These methods have the advantage of using large samples to improve adaptability and consider more risk factors. In this study, DELs of 30 typhoon events in Fujian Province at the county level and a total of 911 samples were collected from 2009 to 2021 to establish an assessment model. Owing to the large range of the DEL in different districts and counties during the same typhoon, the logarithm of the DEL was used as the model output. This study included three steps for constructing the model. First, 24 influencing factors of typhoons, including disaster-inducing factors, disaster-forming environmental factors, and disaster-bearing body exposure factors, were calculated using the Pearson correlation coefficient and variance inflation coefficient to analyze the multicollinearity effect, and 20 key factors were selected to assess the DEL. Second, a LightGBM-based model is developed using the selected indicator factors as model inputs. Of the 911 samples, 734 were used to train the model, and 177 were used for validation. Finally, Super Typhoon Meranti was used as a case study to evaluate the applicability of the model in the dynamic DEL assessment of a typhoon. This study evaluated predictive performance of the model using five indicators: the Pearson correlation coefficient (R), coefficient of determination (R 2), mean squared error, mean absolute error, and median absolute error. The importance of LightGBM factors shows that the maximum daily wind speed, river network density, maximum daily precipitation, cumulative precipitation, and GDP per unit area are the primary determinants of typhoon-induced economic losses in Fujian Province. In the training set, R between the predicted results of the model and the actual loss was 0.836, and R 2 was 0.66, indicating good fitting ability. In real-world applications, the proposed model effectively captured the spatial distribution of losses from Typhoon Meranti, demonstrating its potential for disaster loss prediction. This study provides valuable insights into typhoon risk assessment and emergency management in Fujian Province and other coastal areas. We sorted the relevant research literature and found that economic loss assessment is more difficult than population, housing, and other loss assessments because economic loss is a comprehensive statistical indicator in China. Therefore, we drew on the method of processing DEL as logarithms from the literature. By comparing with other studies, the results of this study can improve model performance in terms of data quality inspection and sample size.
In the context of global climate change, natural disasters pose increasingly serious threats to the Guangdong-Hong Kong-Macao Greater Bay Area. Therefore, in this study, we aimed to conduct integrated comprehensive zoning of natural disasters and to develop disaster prevention and mitigation countermeasures for the Greater Bay Area. To achieve this objective, we first carried out geomorphological division of the Greater Bay Area based on the land geomorphological classification system. Second, we performed comprehensive zoning of natural disasters according to the intensity of dominant natural disasters in various geomorphological units. Finally, we conducted risk zoning of natural disasters according to the main controlling factors of natural disasters in each zoning units. Based on the geomorphology of the Greater Bay Area and the risk of natural disasters, we proposed natural disaster prevention and mitigation countermeasures. The results show that: (1) The landform of the Greater Bay Area can be divided into four major first-class divisions: mountains, hills, platforms and plains. The landforms of the Greater Bay Area can be divided into 10 secondary subdivisions, including medium-altitude small undulating mountains, low-altitude small undulating mountains, low-altitude erosion and denudation hills, low-altitude erosion and denudation platforms, low-altitude alluvial platforms, low-altitude alluvial flood platforms, low-altitude alluvial plains, low-altitude marine plains, low-altitude marine alluvial plains, and low-altitude estuarine coasts. Among these subdivisions, low-altitude small undulating mountains represent the largest area (21,618.28 km2), while low-altitude erosion and denudation platforms represent the smallest area (849.77 km2). (2) The Greater Bay Area can be divided into three first-level major disaster subdivisions: mountain and hill disaster areas (52.77%), plain and platform disaster areas (40.43%), and estuary and coast disaster areas (6.80%). (3) The Greater Bay Area can be further divided into second-level disaster subdivisions, including the small undulating and low-altitude mountain disaster area, low-altitude alluvial plain land subsidence area, low-altitude plain and platform flood area, and 14 others. The largest second-level disaster subdivision area is the small undulating low-altitude mountain disaster area (20,892.18 km2), which is distributed in the east, north, and west of the Greater Bay Area, followed by the low-altitude plain and platform flood disaster area (13,320.98 km2), which is mainly distributed in Guangzhou, Jiangmen, Shenzhen, Huizhou, and Zhaoqing cities, among other areas. The karst collapse area of the low-altitude platform represents the smallest second-level disaster subdivision (163.62 km2) and is mainly distributed in some areas of Enping and Jiangmen cities. (4) The natural disaster risk in the Greater Bay Area can be divided into high-risk, medium-risk, and low-risk areas. The high-risk areas for mountain disasters are mainly in Deqing, Huaiji, and Guangning of Zhaoqing City; and Conghua District in the north of Guangzhou City, Longmen, Boluo, and other regions in Huizhou City. The high-risk areas for plain and platform disasters are mainly in Doumen District, Zhuhai City, Guanghua Basin, Huadu District, Nansha District, Guangzhou City, Foshan City, and other regions. The high-risk areas for estuary and coast disasters are mainly in Doumen District, Zhuhai City, and near the mouth of the Pearl River Delta. In this study, we proposed disaster prevention and mitigation countermeasures for natural disasters in the Greater Bay Area from four perspectives. Our results serve as a valuable reference for the Greater Bay Area urban agglomeration in regional development planning, comprehensive disaster reduction planning, and the improvement of disaster prevention and mitigation capabilities.
Rapid urbanization and geological disasters pose significant challenges to regional ecological security. Although Ecological Security Pattern (ESP) construction is important for ecosystem stability and sustainable development, traditional approaches rarely incorporate vertical geological factors, such as land subsidence. This study proposes a framework that integrates land subsidence into ESP construction through machine learning and multi-source data fusion methods. Using Zhuhai City as a case study, we analyzed 30 environmental variables, including historical land subsidence data, topography, soil distribution, land use, climatic factors, and human activity indicators. The methodology consisted of four main steps: (1) correlation and principal component analyses to identify key factors and reduce dimensionality; (2) development of a multilayer perceptron (MLP) deep learning model with three fully connected hidden layers using ReLU activation functions and dropout regularization to predict ecological pattern types; (3) comparison of four fusion methods (weighted average, nonlinear sigmoid transformation, information entropy, and principal component analysis) to integrate prediction results; and (4) spatial analysis of the relationship between land subsidence and ecological security patterns using chi-square tests and spatial overlay analysis. Results showed that the MLP model achieved an average prediction accuracy of 84.5% with an F1-score of 0.844, demonstrating the feasibility of deep learning approaches in ESP construction. The principal component analysis showed that the first four principal components cumulatively explained 71.4% of the total variance, with the first two components explaining 27.1% and 19.8%. The first principal component was dominated by climatic factors, whereas the second primarily reflected the topographic and geological vulnerability characteristics. Spatial analysis revealed significant spatial heterogeneity in the impact of land subsidence on the ESP, with moderate historical subsidence (8-41 mm/year) showing more notable effects (x²= 57.008, P<0.001). Land subsidence in the 8-16.5 mm/year range showed particularly significant differences in the corridor areas compared to the non-subsidence zones (P = 5.7e-05). Source and construction areas exhibited higher proportions of mild subsidence (7.14% and 9.84%, respectively), which should be prioritized for monitoring and management. Different fusion methods showed varying effectiveness. Principal component analysis and information entropy performed better in identifying construction and corridor areas, whereas nonlinear fusion showed advantages in source area identification. This study makes three key contributions: (1) it establishes a novel methodological framework for incorporating vertical geological factors into ESP construction, addressing a significant gap in traditional approaches; (2) it quantitatively reveals the spatial heterogeneity of land subsidence impacts on different functional ecological zones, providing evidence-based guidance for targeted management; and (3) it demonstrates the effectiveness of deep learning and multisource data fusion techniques in complex ecological-geological system modeling. These findings provide methodological support for developing an ecological security pattern centered on coastal wetlands and estuarine systems in Zhuhai City and suggest potential approaches for coordinating ecological protection, disaster prevention, and urban development under land subsidence conditions. Future research should focus on utilizing high-resolution spatiotemporal data, refining algorithms, and developing mechanisms to translate research findings into practical urban planning and ecological management policies.
Disaster prevention and mitigation policy texts serve as a guidance and basis for the government to respond to disasters. They contain rich information on disaster risk factors, records the degree of damage caused by disaster hazard factors to disaster-bearing bodies, and provide disaster prevention measures. Risk factors form the foundation of a disaster risk assessment index. This study considered storm surges as an example and deconstructs risk factors into three dimensions-hazard, vulnerability, and disaster resistance capacity–by integrating disaster prevention and mitigation policy texts. Text mining techniques were used to analyze the composition and evolution characteristics of risk factors in policy contexts, with a focus on emphasizing disaster prevention and reduction at different stages. This study constructed a policy text-driven theoretical framework for disaster risk assessment, overcoming the limitations of traditional indicator systems that rely on statistical data and expert experience, and revealed the key role of institutional factors in risk formation. The results are as follows: (1) Policy texts can be used to extract a large number of storm surge risk factors, with hazard factors linked to high-frequency terms such as "sea level rise" and "typhoons," vulnerability to "coastal areas", "coastal zones", "land use", etc., and disaster resilience to "engineering defense," "financial support," etc. (2) There are significant differences in the focus on risk factors in policy texts at different stages. Before 2010, the focus was on identifying and monitoring disaster risks. From 2010-2015, the focus was on further refining the vulnerabilities of disaster-bearing bodies. After 2015, there was greater emphasis on the role of technological development in disaster resistance. These changes reflect the gradual deepening of policymakers' understanding of storm surge disaster risks. (3) The elements extracted from the policy text, such as "astronomical tide," "land reclamation," and "disaster insurance," have compensated for the neglect of human intervention and institutional factors in traditional indicator systems. This study pioneered a new paradigm of policy text analysis in disaster risk assessment at the methodological level, breaking through the traditional reliance on structured data in storm surge disaster risk assessments. Policy evolution analysis revealed changes in risk concerns.
To enhance the scientific rigor and practical relevance of disaster resilience evaluation in mountainous rural communities, this study developed a multilevel assessment framework based on the Pressure-State-Response (PSR) model by integrating the entropy method and Analytic Hierarchy Process (AHP). The framework comprised three dimensions (pressure, state, and response), nine elements, and 32 indicators tailored to the unique environmental and socioeconomic contexts of mountainous regions. Focusing on four representative communities (Taoyuan, Caogu, Niulang, and Qunying) in the Anning River Basin of Liangshan Prefecture, Sichuan Province, China, a combination of field surveys, GIS spatial analysis, and multi-source datasets were used to empirically evaluate community resilience. The key findings revealed the following: (1) The comprehensive resilience scores ranked Taoyuan > Niulang > Qunying > Caogu. Taoyuan's top performance stemmed from its designation as a national disaster prevention demonstration community featuring robust infrastructure and frequent emergency drills, whereas Caogu's lowest resilience resulted from its high-altitude topography, aging population, and inadequate infrastructure. (2) State resilience contributed most significantly to overall resilience (51.43%), with the building quality (C9) being the pivotal driver. Pressure resilience was predominantly influenced by the proximity to active faults (C2) and population exposure to geological hazards (C6), whereas response resilience relied on disaster-monitoring equipment (C26) and early warning efficiency (C27). (3) A synergistic optimization strategy was proposed, emphasizing risk zoning and engineering controls (pressure layer), housing retrofitting and social capital cultivation (state layer), and intelligent early warning systems integrated with indigenous knowledge (response layer). The study validates the applicability of the PSR model in mountainous rural contexts, highlighting a "state resilience dominance with response capacity gaps" pattern. Notably, communities with higher state resilience demonstrate stronger recovery capabilities despite elevated hazard pressures, underscoring the importance of robust infrastructure and social cohesion. Conversely, insufficient investment in monitoring technologies and external rescue coordination hinders response effectiveness in remote villages such as Caogu. The framework provides methodological support for tailored disaster-prevention planning, particularly in ethnic regions where traditional ecological knowledge complements modern governance. However, limitations include a focus on earthquakes and geological hazards, excluding concurrent multi-hazard scenarios (e.g., wildfires and pandemics), and a static assessment that overlooks temporal resilience dynamics. Future research should incorporate longitudinal monitoring and cross-scale interactions to refine the generalizability of the model. This study advances the theoretical integration of socioecological systems into resilience assessments and offers actionable insights for sustainable rural development in hazard-prone mountainous areas.
Amidst the intensifying global climate change, coastal cities face multiple marine disaster threats due to sea level rise and frequent extreme weather events. Storm surge-induced flood disasters and their secondary effects (e.g., urban waterlogging) pose systemic risks to the lives of the residents, properties, and coastal system infrastructure. Compared with traditional disaster prevention models, the synergistic mechanism between resilience theory and community risk management not only provides a theoretical framework for urban complex risk prevention, but also demonstrates dynamic adaptive advantages in pre-disaster prevention, disaster response, and post-disaster recovery. Accordingly, this study integrated the resilience community theory with the sponge city concept, selecting 25 storm surge-prone bay communities in the Xiamen Wuyuan Bay Area as samples to establish a community resilience evaluation framework encompassing exposure, vulnerability, adaptability, and spatial connectivity. By integrating 16 subjective and objective indicators, including the rescue facility coverage rate and residents' disaster preparedness literacy, we employed the AHP-CRITIC combined weighting method to determine indicator weights and quantify community resilience levels using TOPSIS analysis. The key findings are categorized as follows: (1) an overview of the marine disaster context, the theoretical evolution of resilient communities, and existing research gaps. The literature review indicated that marine disaster threats to coastal urban safety showed significant upward trends, where communities, as direct disaster-bearing entities, needed urgent refined resilience assessments considering their spatial heterogeneity and component vulnerability. International practice comparisons revealed three critical deficiencies in China's resilient community development: overreliance on infrastructure hardware while neglecting the landscape spatial resilience layout, insufficient innovation in social organizational resilience and collaborative mechanisms, and superficial resident participation lacking substantive interactive mechanisms. (2) Development of multidimensional resilience evaluation system Through meta-analysis and expert consultation, we established a dual-dimensional ("vulnerability-adaptability") evaluation system comprising 7 primary and 16 secondary indicators. The AHP-CRITIC combined weighting results indicated that hazard level (0.221), disaster prevention capacity (0.169), and emergency response capacity (0.168) constituted the highest-weighted primary indicators. Secondary indicators, including coastal length, shoreline protection intensity, and volunteer rescue station accessibility, demonstrated significant spatial exposure and emergency response weights, suggesting for their prioritization in coastal community retrofitting. (3) Implementation of a resilience assessment system for coastal community in Wuyuan Bay Field surveys and questionnaire data enabled quantitative resilience analysis of 25 communities. TOPSIS results revealed geographical location and residents' disaster preparedness as core drivers of resilience differentiation. Inner bay communities (e.g., D25) achieved maximum resilience (0.872) through wetland regulation, natural terrain barriers, and emergency facility clusters, whereas outer bay communities (e.g., D1) showed minimal resilience (0.312), owing to high-risk exposure and medical resource scarcity. Wetland ecosystems notably reduced drainage system loads through hydrological regulation and flood detention mechanisms. (4) Optimization strategies for coastal community resilience. This study systematically identified the core elements for developing community resilience during flood-related disasters through the establishment of a coastal community resilience assessment system and empirical research. Through a comparative analysis of typical domestic and international scenarios, we proposed an actionable resilience enhancement strategy system. For public space optimization, dual-purpose strategies for both normal and emergency conditions were emphasized for road networks and green systems, integrating traffic management with ecological protection. For ecological water system development, the water conservation mechanisms of coastal wetland ecological barriers were systematically elucidated, and a synergistic optimization pathway for wetland protection and community water systems based on nature-based solutions was proposed. Regarding emergency shelter spatial planning, an innovative comprehensive evaluation framework was established, incorporating location accessibility, per capita shelter area thresholds, disaster prevention facility standards, and emergency transportation systems. For social governance, resident participation mechanisms and smart management platforms were suggested to amplify community resilience through flexible interaction and resource integration.
