Rainstorm Disaster Research Articles

Study on Risk Assessment of Rainstorm Disaster in Shanxi Province based on GIS

This study focuses on Shanxi Province, establishing an index system for assessing rainstorm disaster risk based on four factors: risk, vulnerability, sensitivity, and disaster prevention capabilities. The evaluation employs the analytic hierarchy process and a weighted comprehensive assessment method. The findings indicate that certain areas in Taiyuan City and Hunyuan County are classified as high-risk zones, while Yangquan City and the western part of Luliang City are deemed medium-risk areas. Most regions in Shuozhou City, Xinzhou City, Changzhi City, Jincheng City, and Jinzhong City fall into the low-risk category.

Numerical simulation of the water flow field on subway stairs and evaluation of people safety evacuation

ABSTRACT Stairs in subway stations are vulnerable to floods when rainstorm disasters occur in cities. The stairs, as a critical way for human evacuation, can affect the safe evacuation of people on flood-prone stairs. To evaluate the risk of people evacuating through different slopes and forms of stairs when floods invade subway stations, a numerical model for the water flow on stairs based on the volume of fluid model and the realizable k-ε model was established. The water flow patterns on stairs at the subway station entrance under different slope conditions and with/without rest platforms were simulated. The real-time water flow process on stairs at different inlet depths was obtained, and the escape control index F was used to evaluate the risk of people evacuating through stairs at different slopes and water depths. The results indicate that the presence of a rest platform can cause an increase in water velocity and depth on pedestrian stairs, and people should choose stairs without a rest platform for evacuation during the evacuation process. The research results hope to provide a reference for the people evacuation on stairs, and further improve the theory of safe evacuation of personnel on flood-prone stairs.

Early warning response to rainstorm: Designing a model with incentive and supervision mechanisms based on the principal-agent theory

Owing to the increasing frequency of rainstorm disasters, strengthening the link between meteorological warning and emergency response that is early warning response has become crucial. However, early warning response usually fail, the underlying reasons behind this phenomenon and their improvement measures need to be urgently explored. This study uses the principal–agent theory to establish an incentive-constraining model based on hierarchical government to explore this problem. Considering the interest game between the superior and local governments, we mainly discuss how to design the optimal incentive mechanism under the condition of information asymmetry to enhance the local government's enthusiasm for providing an early warning response to rainstorm, thereby maximizing the benefits for both parties in the model. Furthermore, based on actual conditions, we use numerical simulation to present the model results more clearly. The results indicate that incentive and supervision mechanisms are mutually substitutable. Strong early warning capability and active public cooperation can improve the local government's enthusiasm for providing an early warning response. Excessive costs lead the local government to adopt a negative attitude toward warning, regardless of supervision is introduced. Only when the local government exhibits strong risk aversion and its investment is affected by numerous random factors, introducing supervision is effective. Meanwhile, too many external factors, excessive or insufficient supervision's intensity also affect the optimization effect of supervision on incentive contract. To maximize the effectiveness of supervision, the superior government should improve the supervision team's capability, and maintain the supervision's intensity within a reasonable range.

Evolution of regional rainstorm events in China's South-to-North Water Diversion Area, 1960–2022

The objective of this paper is to elucidate the patterns of evolution of regional rainstorm events (RREs) in a large-scale water resources allocation area. This is crucial for the safe operation of water transfer projects and water resources management. The Objective Identification Technique for Regional Extreme Events (OITREE) method is employed to identify all RREs in the South-to-North Water Diversion Area (SNWDA) between the years 1960 and 2022. The results indicate that among the 1397 RREs identified, the extreme RREs are significantly more severe than other classes of rainstorm events. While the extreme intensity (Im) of RREs exhibits a decreasing trend, their frequency, comprehensive index (Z), maximum area (Am), summed area (As), and summed intensity (Is) all demonstrate increasing trends. Some of these indices (Z, Am, As, and Is) exhibit a strong periodicity of approximately 20 years, while the shortening of the decline period will result in more frequent rising periods of RREs. In the second year following the water transfer operation (2015), all RRE indices exhibit abrupt changes. Furthermore, the rainy season lengthens, with more frequent and extreme RREs occurring in July, which may have implications for the Middle Route of year-round water transfers. Spatially, the occurrence frequency of regional rainstorm days (RRDs) increases by 35.9% since the 21st century, while regional rainstorm intensity (RRI) increases by 11.8%. The high-frequency zones (HFZs) of RRDs are primarily located in the southeast, with intensity increases of at least 30%. While their location remains stable, there is a clear trend of northward spread. Following the water transfer, the frequency of RREs is higher in the Eastern Route, and the high-intensity zones (HIZs) appear for the first time in the Beijing area of the Middle Route. These findings provide a scientific understanding of regional rainstorms and serve as a reference for rainstorm disaster response and optimal allocation of water resources in water transfer projects.

Establishing resilience-targeted prediction models of rainfall for transportation infrastructures for three demonstration regions in China

Rainstorm is one of the global meteorological disasters that threaten the safety of transportation infrastructure and the connectivity of transportation system. Aiming to support the resilience assessment of transportation infrastructure in three representative regions: Sichuan–Chongqing, Yangtze River Delta, and Beijing-Tianjin-Hebei-Shandong, rainfall data over 40 years in the three regions are collected, and the temporal distribution of rainfall are analyzed. Prediction equations of rainfall are established. For the purpose of this, the probabilistic density function (PDF) is assigned to the rainfall by fitting the frequency distribution histogram. Using the assigned PDF, the rainfall data are transformed into standard normal space where regression of prediction equations is performed and the prediction accuracy is tested. The results show that: (1) The frequency of rainfall in the three regions follows a lognormal distribution based on which the prediction equations of rainfall can be established in standard normal space. The error of regression shows no remarkable dependence on self-variables, and the significance analysis indicates that the equations proposed in this paper are plausible for predicting rainfalls for the three regions. (2) The Yangtze River Delta region has a higher risk of rainstorm disaster compared to the other two regions according to the frequency of rainfall and the return period of precipitation concentration. (3) Over the period of 1980–2021, the Sichuan–Chongqing region witnessed an increase in yearly rainfall but a decrease in rainstorm disasters, whereas the other two regions experienced a consistent rise in both metrics.

A Coordinated Emergency Response Scheme for Electricity and Watershed Networks Considering Spatio-Temporal Heterogeneity and Volatility of Rainstorm Disasters

A Coordinated Emergency Response Scheme for Electricity and Watershed Networks Considering Spatio-Temporal Heterogeneity and Volatility of Rainstorm Disasters

Assessing hotspot for tropical cyclone compound rainstorms from a disaster perspective: a case study in Guangdong, China

In Guangdong Province, China, known as a hotspot for tropical cyclone (TC) and rainstorm disasters, investigating the role of hazard factors for both TC and rainstorm disasters is of great scientific and operational importance. Daily observational data from meteorological stations along with rainstorm/TC disaster loss data at the county level in Guangdong during 2001–2020 were analyzed to compare the characteristics of rainstorm and TC disasters. During the study period, although non-TC rainstorm rainfall in Guangdong was generally greater than TC rainstorm rainfall, TC disaster losses were double those of rainstorm disasters. Non-TC rainstorms and their losses predominantly occurred during the first flood season, whereas TC disaster primarily occurred in the second flood season. Although regional-average TC rainstorm rainfall was similar to non-TC rainstorm rainfall during the second flood season in Guangdong, it was greater than non-TC rainstorm rainfall in western Guangdong. Additionally, the coastal areas were frequently affected by TC strong winds, resulting in greater TC disaster losses than rainstorm disaster losses. The maximum daily precipitation was significantly correlated with rainstorm disaster losses. In areas influenced by both TC strong winds and rainstorms, TC disaster losses exhibited a significant positive correlation with the daily maximum wind speed, and the combined effect of strong winds and rainstorms led to greater disaster losses than with TC rainstorms alone. These findings emphasize the effects of TC strong wind and storm surges, besides those of heavy rainfall, in coastal areas of China when assessing the risks related to TC disasters.

TISE-LSTM: A LSTM model for precipitation nowcasting with temporal interactions and spatial extract blocks

Precipitation nowcasting has a profound impact on humanity and society, especially in areas with heavy rainfall, playing a central role in alerting against rainstorm disasters. At present, numerous deep learning-based methods have been proposed and proven superior to traditional radar echo extrapolation techniques like the Recurrent Neural Networks (RNNs). Our study introduces a novel precipitation forecasting model named TISE-LSTM, which can use the real image of the past half hour to predict the radar echo image of the next hour. By integrating the TIB and SEB into TISL-LSTM, we can alleviate the inherent issue observed in existing models, which is the decrease in forecast accuracy for high radar echo regions with extended extrapolation time. On both the CIKM23017 and AHEM real radar echo datasets, the performance of TISL-LSTM (including POD, FAR, CSI and HSS) is improved compared to the second-ranked model by 7.53%, 2.32%, 8.93%, 2.6%, and 14.84%, 6.18%, 8.92%, 18.12%, respectively, when the precipitation threshold is set to 40. Moreover, our model obtained an optimal MAE, MSE, SSIM score. Predicted images and the graphs of each metric over extrapolation time both demonstrate that our model accurately forecasts regions with high radar echoes even with a one-hour extrapolation.

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Rainstorm disasters pose a significant threat to the sustainable development of urban areas, and effectively organizing diverse information sources about them is crucial for emergency management. In light of recent advances in knowledge graph theory and application technology, their notable knowledge integration and representation capabilities may offer support for dynamic monitoring and decision-making processes concerning urban rainstorm disaster events. However, conventional models do not adequately capture the spatiotemporal characteristics of these events. To fill this gap, we analyze the essence of urban rainstorm disaster events and divide their evolution into four stages, namely, pregnant, development, continuous, and decline stages. On this basis, a multilevel knowledge representation model is proposed from four layers, which are event, object–state, feature, and relationship layers, by analyzing the components and dynamic characteristics from the mechanism of urban rainstorm disaster events. The proposed model can not only express the comprehensive structure and relationships within urban rainstorm events, but also emphasize the evolution of disaster events through a series of ordered states. Moreover, we test the utility of the constructed knowledge graph through a case study of the Zhengzhou 720 rainstorm. The case study first validates that the selected machine learning models can extract the urban rainstorm disaster event information accurately by comparing them with some mainstream models. Then, it demonstrates that the knowledge graph is practical in the field of disaster knowledge representation, and disaster condition retrieval. Additionally, since the knowledge graph can show the evolution of a disaster event throughout its full life cycle, it can promote the understanding of the mechanisms of urban rainstorm disasters and pave the way for future applications of disaster prevention and reduction.

Exploring and Enhancing Community Disaster Resilience: Perspectives from Different Types of Communities

This study aimed to explore the differences in various aspects of community disaster resilience and how to enhance disaster resilience tailored to different community types. The evaluation results were validated using the flood event that occurred in Zhengzhou on 20 July 2021 (hereinafter referred to as the “7.20” rainstorm disaster). The main results of the analysis showed that the respondents’ overall evaluation of their community’s resilience to the “7.20” disaster was relatively high. Commercial housing communities performed the best, followed by urban village communities, and employee family housing communities performed the worst. Specifically, commercial housing communities scored highest in three dimensions: human capital, physical infrastructure, and adaptation. Urban village communities scored highest in the three dimensions of social capital, institutional capital, and community competence, while employee family housing communities consistently ranked the lowest in each dimension. The most significant disparities were found in human capital, followed by community competence and social capital, adaptation, and, lastly, institutional capital and physical infrastructure. Targeted improvement strategies and measures are suggested for each type of community, offering valuable recommendations for relevant government agencies aiming to enhance community disaster resilience and disaster risk reduction.

Analysis of the Collaborative Governance Mecharnism of public Emergency Events —— Take the Rainstorm in Henan and Beijing as an Example

In the context of climate change, the increase in extreme weather events and natural disasters has caused a certain degree of damage to economic development, social life, and the physical and mental health of the people. Therefore, further research, promotion, and optimization of the collaboration mechanism between various emergency departments and relevant departments have become particularly important. The research topic of this article is how to establish and optimize the collaboration mechanism of various emergency departments in emergency events, and propose some specific measures and suggestions. The research methods of this paper are as follows: using the case analysis method, through combing and comparing the emergency measures taken in the two cases of extremely heavy rainstorm in Henan Province in 2021 and rainstorm in Beijing in 2023, this paper focuses on the analysis of the shortcomings of the specific emergency cooperation measures taken in the two events, and points out the links that can be improved; Using the comparative analysis method, this paper conducts a comparative study on the collaborative governance mechanism of rainstorm disasters in Beijing and Henan from the three stages of preparation, response and recovery. Finally, this article proposes specific suggestions for the allocation of emergency coordination materials, the implementation of extensive and effective public publicity, the improvement of communication equipment establishment, the promotion of emergency management information sharing, the strengthening of the rights and responsibilities of the emergency management department, and the establishment of a unified governance center to establish emergency cooperation mechanisms.

The Value of Traditional Ecological Knowledge in Stormwater Management: A Case Study of a Traditional Village

In recent years, floods have occurred frequently in urban and rural areas around the world, causing heavy casualties and property damage. In contrast, some traditional Chinese villages have never flooded. It is hypothesized that these villages, because of their long-term adaptations to their environment, hold necessary ecological knowledge regarding stormwater management so that damaging flooding can be avoided. Previous studies on the traditional ecological knowledge regarding stormwater management in traditional Chinese villages are mostly qualitative studies, and these fail in their evaluation of the functional performance of stormwater management facilities and measures. Therefore, we use the Storm Water Management Model (SWMM) in our quantitative evaluation of stormwater management in Zhuge, a traditional Chinese village, so as to rationally analyze the traditional ecological knowledge regarding stormwater management in traditional Chinese villages. In order to analyze the functions and efficiency of stormwater management facilities such as ponds, canals, and permeable pavement in Zhuge Village, this study sets out four scenarios: the No Pond Scenario (NO-PO), the No Canal Scenario (NO-CO), the No Permeable Pavement Scenario (NO-PP), and the actual Current Scenario (CS). The SWMM is used to simulate and quantitatively analyze the stormwater hydrological processes of the four scenarios in different rainfall return periods. The following conclusions emerged from our evaluation of the approaches used in Zhuge Village: (1) The rainwater regulation system composed of ponds, canals, and permeable pavement can play a dual role in alleviating rainstorm disasters and fully storing rainwater, achieving the flexible allocation of rainwater resources. It can effectively alleviate the problem of uneven time and space of local rainfall in shallow, hilly areas, reflecting the traditional ecological wisdom of residents in adapting to the local natural environment. (2) As a rainwater regulation device, ponds are very effective in storing water and mitigating periods of intense runoff. (3) The main function of canals is to rapidly drain water and balance rainwater resources. (4) The main function of permeable pavement is to increase rainwater infiltration and reduce the peak runoff and runoff. (5) The use of the SWMM proved effective in both quantifying the results as well as elucidating stormwater management strategies.

Moisture sources and pathways during an extreme rainfall event over South Korea and the role of sea surface temperature anomalies in the Yellow Sea and East China Sea

AbstractOn August 8th, 2022, an extreme rainfall event (the 88ER) occurred over South Korea's metropolitan area and resulted in immense losses of human lives and properties. Previous study has attributed the rainfall event to the intersection of warm and cold air induced by a Northeast China Cold Vortex (NCCV) and the persistently northward displacement of the West Pacific Subtropical High (WPSH). However, in addition to dynamic drivers, understanding the moisture transport of the 88ER is likewise crucial for developing effective strategies to prevent rainstorm disasters. In this study, based on the output from a WRF model, the primary moisture sources and transport pathways of the 88ER are investigated in a Lagrangian view. The Yellow Sea and East China Sea (YSECS) are identified as the most significant moisture source region (84.42%), followed by South Korea (KR), the eastern China (EC) and Democratic People's Republic of Korea (DPRK), which contribute 12.52%, 1.52% and 1.43% of the released moisture, respectively. Furthermore, to assess the sensitivity of moisture fluxes and heavy rainfall to the sea surface temperature (SST) anomalies in the YSECS, an additional WRF model experiment is conducted in which the SST anomalies are replaced by the average SST over the past 30 years. It is found that the SST anomalies in the YSECS cause differences in atmospheric circulation, and therefore exert a strong influence on moisture transport. The SST anomalies finally enhance the moisture contribution of the YSECS by 1.72%, but decrease that over KR, EC and DPRK by 1.03%, 0.35% and 0.33%, respectively.

Risk perception and resilience assessment of flood disasters based on social media big data

Social media, as an emerging source of data, has become a valuable data source for disaster prevention and management with its vast database. This study takes the “7·20” extreme rainstorm disaster in Zhengzhou as an example, extracts social media information related to the flooding disaster on social media platforms, constructs a dataset of the rainstorm and flooding disaster, and analyzes its temporal evolution and public sentiment change trends. Secondly, the K-means text clustering method based on TF-IDF was used to extract geographic location information related to waterlogging in social media data, and the resilience matrix was used as a framework to construct a resilience assessment model for urban infrastructure systems in combination with the Pressure-State-Response model. The results show that there was a sharp increase in the discussion of microblog topics about flooding information during heavy rainfall, and there was a 4-h lag between the peak time of topic discussion and the peak time of rainfall intensity. The geographical locations affected by waterlogging extracted based on microblogs can cover 87.5 % of the officially announced waterlogging points. In the resilience assessment of infrastructure systems, the transportation system and drainage system of Zhengzhou performed poorly in response to this rainstorm disaster, whereas the power system and communication system had relatively stronger resilience. This study provides an effective solution to help identify disaster events and promote disaster risk management.

Public Demand Oriented Quality Evaluation Model for Rainstorm Disaster Information Disclosure

By investigating the current situation of information disclosure and public demand during rainstorm disaster in Henan Province, this paper builds a model of factors affecting the quality of information disclosure characterized by rainstorm disaster information based on TOE framework theory, metadata evaluation model, resource based theory and resource confirmation theory. With the help of the current situation and demand characteristics of the public’s actual information acquisition, it designs a model with information disclosure content quality, information disclosure accessibility, information disclosure process completeness 10 test dimensions of government information disclosure ability, government feedback ability, minimum information disclosure acceptance ability, information disclosure technology support ability, right to know protection ability, privacy protection ability, right to equality protection ability, and 35 test items of rainstorm disaster information disclosure quality impact factor evaluation scale. The research provides help for government information disclosure to more accurately identify public needs, provides targeted evaluation models and operable evaluation models for rainstorm disaster information quality evaluation in Henan, and provides ideas and strategies for improving rainstorm disaster information quality.

Research on cross-regional emergency materials intelligent dispatching model in major natural disasters.

The increasingly frequent occurrence of major natural disasters can pose a serious threat to national stability and the safety of people's lives, and cause serious economic losses. How to quickly and accurately dispatch emergency materials to all disaster areas across regions in post-disaster has attracted wide attention from the government and academia. In response to the characteristic of high uncertainty in emergency rescue for major natural disasters, and considering differentiated disaster severity levels in different disaster areas, the entropy weight method is used to determine the urgency coefficient of emergency material demand for disaster areas. This study aims to minimize the emergency materials dispatching time and cost, also maximize the dispatching fairness for disaster areas. The triangular fuzzy number method is used to represent the uncertain variables mentioned above, so that a cross-regional emergency materials intelligent dispatching model in major natural disasters (CREMIDM-MND) is constructed. The extremely heavy rainstorm disaster in Henan Province of China in 2021 is selected as a typical case. Based on objective disaster data obtained from official websites, this study applies the constructed model to real disaster case and calculates the results by MATLAB. The ant colony algorithm is further used to optimize the transportation route based on the calculation results of the emergency material dispatching for disaster areas, and finally forms the intelligent emergency materials dispatching scheme that meets the multiple objectives. The research results indicate that compared to the actual situation, CREMIDM-MND can help decision-maker to develop a cross-regional emergency materials intelligent dispatching scheme in time, thereby effectively improving the government's emergency rescue performance in major natural disasters. Moreover, some managerial insights related to cross-regional emergency materials dispatching practice problem in major natural disasters are presented.

Simulation Study on Rain-Flood Regulation in Urban “Gray-Green-Blue” Spaces Based on System Dynamics: A Case Study of the Guitang River Basin in Changsha

Urban rainstorms and flood disasters are the most common and severe environmental problems worldwide. Many factors influence rain-flood control simulation, forming a complex network system of interconnected and mutually constraining elements. In terms of spatial scale selection, existing research on rain-flood disaster risk largely relies on a single-scale infrastructure index system and has not yet focused on urban “gray-green-blue” spatial scale simulations for rain-flood storage. Regarding research methodology, applying system dynamics methods to the simulation of rain-flood storage and disaster prevention planning in watershed cities is still in its initial stages. System dynamics models can simulate the feedback interactions among various sub-elements in the coupled mega-system, fully addressing complex issues within the system structure that involve multiple variables, non-linear relationships, and numerous feedback loops, thereby compensating for the inadequacies of traditional linear models in the collaborative management of rain-flood risks. Taking the Changsha Guitang River Basin as an example, this paper constructs a system dynamics model covering four dimensions: natural environment, socio-economics, internal structure, and policy development. It aims to derive the optimal planning scheme for gray-green-blue spatial coordination in rain-flood storage by weighing four different development scenarios. The simulation results show: (1) Simply changing the surface substrates without considering rainwater discharge and the plan that emphasizes the construction of municipal drainage facilities will see the capacity gap for rain-flood storage-space construction continue to widen by 2035. This indicates that the plans mentioned above will struggle to bear the socio-economic losses cities face during rain-flood disasters. (2) The plan of combining gray and green infrastructures sees the rain-flood storage construction capacity turn from negative to positive from 2024, rising to 52.259 billion yuan by 2035. This reflects that the plan can significantly reduce the rainwater volume in the later stages of low-impact development infrastructure construction, mitigate rain-flood disaster risks, and reduce government investment in rain-flood disaster risk management, making it a relatively excellent long-term rain-flood storage space planning option. (3) The rain-flood regulation space planning scheme, under the combined effect of the urban “gray-green-blue” network system, sees the capacity for rain-flood storage construction turn positive a year earlier than the previous plan, reaching 54.232 billion yuan by 2035. This indicates that the scheme can not only effectively respond to extreme flood and rainstorm disasters but also maintain ecological environment benefits and mitigate the socio-economic losses caused by disasters, making it the optimal choice for future government disaster management planning. The research results provide a theoretical framework and practical insights for territorial spatial planning, rain-flood control management, and resilient city construction in watershed areas.

Reasoning Disaster Chains with Bayesian Network Estimated Under Expert Prior Knowledge

With the acceleration of global climate change and urbanization, disaster chains are always connected to artificial systems like critical infrastructure. The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers. The Bayesian network (BN) was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies. To capture the interacting relationships among different factors, a scenario representation model of disaster chains was developed, followed by the determination of the BN structure. In deriving the conditional probability tables of the BN model, we found that, due to the lack of data and the significant uncertainty of disaster chains, parameter learning methodologies based on data or expert knowledge alone are insufficient. By integrating both sample data and expert knowledge with the maximum entropy principle, we proposed a parameter estimation algorithm under expert prior knowledge (PEUK). Taking the rainstorm disaster chain as an example, we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior (MAP) algorithm and the direct expert opinion elicitation method. The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.

South Asian High Identification and Rainstorm Monitoring Using Fengyun-4-Derived Atmospheric Motion Vectors

Based on atmospheric motion vectors (AMVs) derived from the Fengyun-4 meteorological satellite (FY-4), in this paper, integrated multi-satellite retrievals for GPM precipitation and reanalysis datasets and the vertical distribution characteristics of FY-4 AMVs, their application in the identification of the South Asian high (SAH) anticyclone and their application in the real-time monitoring of rainstorm disasters are studied. The results show that the AMVs’ vertical distribution characteristics are different across regions and seasons. AMVs from 150 to 350 hPa can be chosen as the upper troposphere wind (the total number accounts for about 77.2% on average). The center and shape of the upper tropospheric anticyclone obtained from AMVs are close to or slightly southward compared with those of the SAH at 200 hPa obtained from the ERA5 geopotential height. The SAH ridge line identified using the upper troposphere AMV zonal wind (the zonal wind is equal to zero) is slightly southward by about 1–2 degrees of latitude from that identified using ERA5 at 200 hPa but with a similar seasonal advance. The upper troposphere AMV can be used to monitor the location of the SAH and the evolution of its ridge line. The abnormally strong precipitation in South China is related to the location of the SAH and its ridge line. When the precipitation is abnormally strong/weak, the upper troposphere AMV deviation airflow shows divergence/convergence. During the “Dragon Boat Water” period in South China in 2022, strong precipitation occurred in the strong westerly winds or divergent flow on the northeast side of the upper troposphere anticyclone obtained from AMVs, and the precipitation intensity was the strongest when the divergence reached its peak, but this is not shown clearly in the EAR5 dataset.

Harnessing ICT Resources to Enhance Community Disaster Resilience: A Case Study of Employing Social Media to Zhengzhou 7.20 Rainstorm, China

This study aimed to explore how community disaster resilience can be enhanced via the utilization of ICT resources. Three social media applications were selected. Taking the 2021 Zhengzhou 7.20 rainstorm as an example, questionnaire responses were collected and analyzed, and a linear regression model was constructed to explore the impact of the relationships between responses. The findings showed that the use of WeChat, TikTok, and Weibo had positive effects on community disaster resilience. Specifically, the use of social media (WeChat, TikTok, and Weibo) by the general public during this rainstorm disaster was positively related to convenience and trust, creation and dissemination, emotion and communication, cooperation and collective action, and relief and release. We also analyzed the differences in the use of the three social media platforms during the rainstorm disaster and found that the number of people who used TikTok was the highest, but the variable scores for TikTok were not the highest. WeChat had the highest variable scores, and both the number of users and variable scores for Weibo were in the middle.