Rainstorm Disaster Research Articles

Pre-rainy Season Rainstorms in South China—Risk Perception of the 11 April 2019 Rainstorm in Shenzhen City

With the acceleration of urbanization in South China, rainstorms and floods are threatening the safety of people in urban areas. The 11 April 2019 (4·11 hereafter) rainstorm in Shenzhen City was a typical pre-rainy season rainstorm that caused great damage, yet such pre-rainy season events have not attracted sufficient attention in research. Risk perception of the public may indirectly affect their disaster preparedness, which is important for disaster management. In this study, we conducted a questionnaire survey that considered demographic factors and the level of risk perception, knowledge of risk, impact of the 4·11 rainstorm event on public risk perception, and degree of trust in the government. We used a two-factor model of risk perception to evaluate the factors that influenced public risk perception of the 4·11 rainstorm in Shenzhen. The main conclusions are: The 4·11 rainstorm improved public awareness of both risk and impact through the medium term, but the public’s perceived low probability of disaster occurrence and lack of knowledge of the pre-rainy season rainstorm phenomenon led to serious losses during this event. Although the public has high trust in the Shenzhen government, the management of rainstorm disasters in the pre-rainy season needs to be further improved.

Identifying Evacuation Needs and Resources Based on Volunteered Geographic Information: A Case of the Rainstorm in July 2021, Zhengzhou, China

Recently, global climate change has led to a high incidence of extreme weather and natural disasters. How to reduce its impact has become an important topic. However, the studies that both consider the disaster's real-time geographic information and environmental factors in severe rainstorms are still not enough. Volunteered geographic information (VGI) data that was generated during disasters offered possibilities for improving the emergency management abilities of decision-makers and the disaster self-rescue abilities of citizens. Through the case study of the extreme rainstorm disaster in Zhengzhou, China, in July 2021, this paper used machine learning to study VGI issued by residents. The vulnerable people and their demands were identified based on the SOS messages. The importance of various indicators was analyzed by combining open data from socio-economic and built-up environment elements. Potential safe areas with shelter resources in five administrative districts in the disaster-prone central area of Zhengzhou were identified based on these data. This study found that VGI can be a reliable data source for future disaster research. The characteristics of rainstorm hazards were concluded from the perspective of affected people and environmental indicators. The policy recommendations for disaster prevention in the context of public participation were also proposed.

Assessment of shelter location-allocation for multi-hazard emergency evacuation

Intense rainstorms often trigger multiple disasters in mountain regions, such as floods and landslides. In disaster planning, the local administration allocates nearby schools or open fields as emergency evacuation shelters. However, access to these shelters is often cut off for certain population clusters during disaster impact on routes. We develop a framework for selecting emergency evacuation shelter locations for multi-disaster impact planning (floods and landslides). The framework consists of two parts. Firstly, we develop susceptibility maps of individual hazards using Random Forest algorithm and Google earth engine. Secondly, we assess the shelter's location-allocation by implementing two models in GIS: P-median and maximal covering location problem. Our framework treats existing schools as evacuation shelters and individual households as demand points in an emergency. The P-median method finds the shelter locations by minimizing maximum distances between the households. The maximal covering location problem method evaluates the coverage of households by the facilities of the evacuation shelters within an impedance cutoff. We tested our work in a mountainous village in the Western Ghat region, India, by recreating the 2005 rainstorm disaster that caused more than 190 fatalities and damaged 400 households. The result shows that existing shelters are insufficient to provide services to all households within 30 min and 60 min. This methodology helps develop simultaneous-hazard impact plans by local administration units in mountain regions to ensure emergency facilities' safe operation.

Power restoration model for distribution networks after extreme rainstorm disasters based on electric vehicle travel characteristics

Improving the resilience of the distribution grid to extreme rainstorms can effectively reduce the resulting outage losses. Based on the massive number of electric vehicles (EV) with objective capacity in the distribution network environment, it is essential to study the discharge of EVs to make up for the shortage load in the distribution network’s post-disaster recovery. In this paper, we first establish a coupling network integrating the distribution grid and road network, simultaneously coupling information from different levels. Then establish a model describing the characteristics of EV travel behavior based on the travel chain and probability distribution function to portray the spatial distribution of EVs in the time dimension. Then establish a model of EVs in this area and cross-area EV participating in discharge response based on the area after EV clustering. Also, establish the extreme storm weather affected EV discharge willingness model to describe the uncertainties of EVs. Finally, two faults set in the IEEE33 node are simulated and verified, and the model description and analysis of the influencing factors of EV participation in discharge are given.

Comprehensive risk assessment of non-typhoon rainstorms over the southeastern coastal region of China

The daily precipitation data (20-20 o’clock) of 66 meteorological stations from 1981 to 2020 were collected. According to the definition of rainstorm day and rainstorm process, 614 non-typhoon rainstorm processes data were obtained after removing the typhoon rainstorm processes data. Combined with the topographic data, the geological disaster points data and the social economic data, this study established an indices system from four aspects: disaster-causing factors, disaster-formative environment, disaster-affected bodies, and disaster prevention and mitigation abilities. Based on the analytic hierarchy process, the entropy method and the correlation coefficient analysis method, the combination weighting was assigned to carry out the non-typhoon rainstorm disaster risk assessment. The results show that the spatial pattern of the comprehensive risk of non-typhoon rainstorm in Fujian Province is completely different from that of typhoon rainstorm in this study area. It shows a gradually increasing trend from the eastern coastal area to the mountainous area in the northwest. The risk areas above middle were observed in the northwestern Fujian and the narrow coastal area of southeastern Fujian. Risk of most of the areas between the two mountain ranges in the middle and northwest of Fujian was relatively lower. The disaster-causing factors were the dominant risk factors of non-typhoon rainstorm. The spatial pattern of non-typhoon rainstorm hazard is obviously affected by the topography of Fujian Province. The comprehensive risk pattern of a specific historical non-typhoon rainstorm case is significantly different from that of the general comprehensive risk of non-typhoon rainstorm disasters. In terms of historical cases, most of the higher-risk and high-risk areas of non-typhoon rainstorm cases were generally small, and were concentrated in northwestern Fujian, but the comprehensive risk pattern of different cases was significantly different.

Towards rainstorm event identification: A transfer learning framework using citizen-report texts and multi-source spatial data

Urban rainstorms are often accompanied by various hazardous events (e.g., water inundation and small-scale debris flow) and subsequent consequential events (e.g., infrastructure failure and trapped citizens). It is imperative to achieve rapid identification of such events for timely and effective rainstorm disaster management. However, due to small-sample event classes, it becomes challenging to achieve the desired identification results. In this paper, a cross-domain transfer learning scheme is proposed to identify rainstorm events by combining citizen-report texts with multi-source spatial data. In the proposed framework, we recommend joint distribution adaptation (JDA) as the key to improving the identification of target events with inadequate samples by transferring knowledge from similar event domains. Meanwhile, multi-source spatial features are extracted and embedded in the text with linear discriminant analysis (LDA) to overcome the feature incompleteness that is caused by short text length. The proposed approach is verified based on the real data in Wuhan, China. Experimental results show that the knowledge transfer between different event classes helps cities to improve the performance of rainstorm event identification, with the accuracy increasing from 81% to 92%. Under data imbalance scenarios, the proposed scheme outperforms the state-of-the-art methods, such as sampling-based methods, ERNIE, and TCA. At the same time, the combination of textual and spatial features results in significant improvement in event identification, as evident by an 8% increase in accuracy. This framework can be followed to build a rainstorm event warning and assistance system to rapidly adapt management responses.

Dynamic risk assessment of compound hazards based on VFS–IEM–IDM: a case study of typhoon–rainstorm hazards in Shenzhen, China

Abstract. Global warming has led to increased compound hazards, and an accurate risk assessment of such hazards is of great importance to urban emergency management. Due to the interrelations between multiple hazards, the risk assessment of a compound hazard faces several challenges: (1) the evaluation of hazard level needs to consider the correlations between compound hazard drivers, (2) usually only a small number of data samples are available for estimating the joint probability distribution of the compound hazard drivers and the loss caused by the hazards, and (3) the risk assessment process often ignores the temporal dynamics of compound hazard occurrences. This paper aims to address the mentioned challenges and develop an integrated risk assessment model VFS–IEM–IDM to quantify the dynamic risk of compound hazards based on variable fuzzy set theory (VFS), information entropy method (IEM), and information diffusion method (IDM). For the first challenge, VFS–IEM–IDM measures the effect of the compound hazard drivers via the use of relative membership degree and analyses the correlation between drivers with the entropy weight method, which is combined to evaluate compound hazard level. To address the second challenge, VFS–IEM–IDM applies the normal diffusion function to estimate the probability distribution of the compound hazard and the corresponding loss vulnerability curve. To deal with the third challenge, VFS–IEM–IDM assesses the risk of a compound hazard in different months based on the definition of probabilistic risk. In the end, this paper takes the typhoon–rainstorm disaster in Shenzhen, China, as an example to evaluate the effectiveness of the proposed VFS–IEM–IDM model. The results show that VFS–IEM–IDM effectively estimates the typhoon–rainstorm compound hazard level and assesses the dynamic risk of the compound hazards.

The Trilogy of the Chinese Apple Futures Market: Price Discovery, Risk-Hedging and Cointegration

The agricultural futures market plays an extremely important role in price discovery, hedging risks, integrating agricultural markets and promoting agricultural economic growth. China is the largest apple producer and consumer in the world. In 2017, Chinese apple futures were listed on the Zhengzhou Commodity Exchange (CZCE) as the first fruit futures contract globally. This paper aims to study the efficiency of the apple futures market by using the Wild Bootstrapping Variance Ratio model to estimate the price discovery function, the ARIMA-GARCH model to estimate the risk-hedging function, and the ARDL-ECM model to estimate the cointegration relationship of the futures and spot market. Experimental results firstly demonstrate that the apple futures market conforms to the weak-form efficiency, which indicates that it is efficient in price discovery. Secondly, the apple futures market is not of semi-strong efficiency because it generated abnormal profit margins amid China–US trade friction, climate disaster, and COVID-19; in terms of the degree of impact, the COVID-19 pandemic had the greatest impact, followed by the rainstorm disaster and trade friction. Thirdly, the results of this study indicate that the cointegration relationships exist between the futures market and the spot markets of the main producing areas. This paper is not only conducive to sustainable development of the global fresh or fruit futures market, but also has potential and practical importance for China in developing the agricultural futures market, strengthening market risk management and promoting market circulation.

Research on Structural Toughness of Railway City Network in Yellow River Basin and Case Study of Zhengzhou 7–20 Rainstorm Disaster

With the gradual networking of inter-city relations and the increase in acute impact and chronic stress, the measurement of the resilience of urban network structures is particularly prominent. Based on the construction of the urban network by passenger train trips in the Yellow River Basin, this paper analyzes and assesses the characteristics of the structural resilience of the urban network, and probes into the network resilience and urban response under the circumstances of node failure and line failure in Zhengzhou. The main conclusions are as follows: (1) The urban network in the Yellow River Basin was clearly hierarchical, with a significant spatial distribution of “low in the north and high in the south”, and the overall characteristics of “robustness” in small areas and “fragility” in large areas. The network connection forms were diversified and open. The network transmission efficiency was high, and the edge cities depended on the core cities with prominent characteristics, and the risk load of regional core cities rose. (2) The network structure was “robust” as it maintained high operational efficiency and connectivity under random attacks. Under deliberate attacks, the city network operated efficiently with a small increase in connectivity before the 60% threshold, and after the threshold, the overall network started to split into many sub-networks, and the network fragmentation gradually increased until the network collapsed. (3) Zhengzhou node failure and line failure states in the Yellow River Basin urban network were resilient, in the sense that when suffering important nodes and lines going down it could still maintain good network operation efficiency, and the core nodes in the impact of natural disasters could adapt to the destructive nature of the network through the urban network structure self-regulation.

A Case Study of the “7-20” Extreme Rainfall and Flooding Event in Zhengzhou, Henan Province, China from the Perspective of Fragmentation

Disaster crisis management is the last defensive line in the face of extreme rainstorm disasters. However, fragmentation undermines the effectiveness of disaster crisis management, and the “7-20” extreme rainfall flooding disaster in Zhengzhou, Henan province, China in 2021 revealed a series of fragmentation problems. The effectiveness of China’s emergency storm flooding management must be seriously considered. We used the “7-20” extreme rainfall event in Zhengzhou, Henan province in China as a case study to perform an inductive, qualitative investigation to understand what fragmentation is and how fragmentation reduces efficacy. Most of the data used for this research were gathered from Chinese official records and online news articles. This study first highlights pertinent studies that have been performed and then presents a comprehensive theoretical framework of fragmentation in catastrophe crisis management, which consists of five aspects: fragmented emergency legislation, emergency organization, information, perception, and services. Second, we have deduced which human responses in the “7-20” event represent the fragmentation issues, and we have examined the detrimental effects of fragmentation in flood crisis management. Finally, suggestions are made for China to increase the effectiveness of disaster crisis management, including encouraging regulatory convergence, matching emergency responsibility and authority, establishing an information-sharing platform, bolstering emergency education and raising risk perception, and changing the dualistic system in disaster crisis management.

A CAST-Based Analysis of the Metro Accident That Was Triggered by the Zhengzhou Heavy Rainstorm Disaster.

Emergency management research is used to deal with the increasing number of extreme weather threats in urban areas. This paper uses causal analysis based on systems theory (CAST) to review the subway water ingress accident and the government’s emergency management actions in Zhengzhou, Henan Province, during the heavy rainstorm disaster on 20 July 2021. The aims of this article are to establish safety control structures at both the enterprise level and the government level, and to systematically analyze the problems in emergency management in Zhengzhou City. Our analysis found that the construction of disaster prevention facilities restricted emergency management. Therefore, we suggest that enterprises and governments not only pay attention to emergency management, but also to the construction of disaster prevention facilities. This article also points out that the system of chief executive responsibility that is implemented in China is becoming a double-edged sword in emergency management. Our study makes recommendations for enhancing the capacities of emergency management, points out the shortcomings of the existing emergency management structure, and provides knowledge gained for future emergency management research.

Rainstorm Disaster Risk Assessment and Influence Factors Analysis in the Yangtze River Delta, China.

Rainstorm disasters have had a serious impact on the sustainable development of society and the economy. However, due to the complexity of rainstorm disasters, it is difficult to measure the importance of each indicator. In this paper, the rainstorm disaster risk assessment framework was systematically proposed based on the disaster system theory and a system of corresponding indicators was established. Furthermore, the genetic algorithm optimized projection pursuit and XGBoost were coupled to assess the rainstorm disaster risk and to measure the relative importance of each indicator. Finally, the Yangtze River Delta was taken as the case study area. The results show that: the rainstorm disaster risk in the eastern and southeast is higher than those in the central and northwest of the Yangtze River Delta; the total precipitation from June to September and the top ten indicators contribute 9.34% and 74.20% to the rainstorm disaster risk assessment results, respectively. The results can provide references for decision makers and are helpful for the formulation of rainstorm adaptation strategies.

Focused crawling strategies based on ontologies and simulated annealing methods for rainstorm disaster domain knowledge

Focused crawling strategies based on ontologies and simulated annealing methods for rainstorm disaster domain knowledge

ON THE EXPRESSION AND GUIDANCE OF NETWORK EMOTION IN EMERGENCIES FROM THE CHANGE OF EMOTIONAL BEHAVIOR -- TAKING THE RAINSTORM IN ZHENGZHOU ON JULY 20 AS AN EXAMPLE

BackgroundWith the development of the Internet and the wide popularization of social media, the Internet has become an important channel for netizens to express their views and emotions. Due to the huge impact, emergencies often attract the high attention and extensive emotional response of Internet users. As the most active factor in network public opinion, network emotion affects the development of network public opinion. Therefore, it is of great significance to strengthen the research on the change law and guidance of network emotional expression under emergencies.Subjects and MethodsTaking the 7.20 rainstorm in Zhengzhou as an example, this study randomly selects some microblog comment data in the event as samples, uses the text emotion analysis method to analyze and study the microblog comment data samples, and discusses the generation and development rules and guidance strategies of network emotion expression, so as to provide reference for the in-depth study of the law of network emotion and the governance of network public opinion under emergencies.ResultsThe study found that in this emergency, the expression of network emotion mainly focused on positive emotions such as care, safety and encouragement, and negative emotions accounted for about 1 / 3, mainly referring to the emergency response of relevant departments and urban drainage. Negative emotions should be guided in time; The dedication of rescue workers and the spirit of mutual assistance of all parties in case of difficulties have stimulated the positive emotions of netizens; In terms of emotional guidance of Internet users, the government and media departments respond to the concerns of Internet users in a timely and accurate manner, which is helpful to dredge the Internet emotion; Effective information supply and topic focus transfer help to promote the rationalization of network emotion.Through the research, it is found that with the continuous development of the Internet, public emotional behavior will have an important impact on microblog public opinion. There are two directions: one is the top-down impact, the other is the bottom-up impact; It is mainly manifested in two typical ways: social mobilization and emotional social struggle in microblog public opinion. The main expressions of their emotional behavior are: weakness, anger, sadness and anger, etc. On the basis of combing the collective behavior and emotional struggle, the research finds that the communication framework of emotional behavior mainly includes the communication paths of discourse co meaning, identity co meaning and emotional co meaning; Functional analysis includes target function, attribution function and ideographic function. From the tendency of public sentiment, microblog public opinion shows criticism, populism, nationalism, pragmatism, patriotism and justice. The social expression of public sentiment in microblog public opinion includes the spiral phenomenon of silence, butterfly effect, herd effect, resentment and so on. From the perspective of psychology, the public emotions in microblog public opinion are expressed as fear, anxiety, anger and sadness, while in terms of expression, the public express their feelings through direct expression, folk language and other ways.ConclusionThrough the research, we found that the development and change of network emotion under emergencies has periodic characteristics. Government management departments should establish the prediction and disposal mechanism of network emotion according to the phased characteristics of network emotion, and scientifically deal with the development and change of network emotion. Network emotion is the realistic mapping and presentation of the development of emergencies. When disasters are effectively controlled, negative emotions will also be suppressed. Give full play to the social responsibility and guiding role of news media in network emotion guidance, provide real event information to Internet users in time and accurately, and dredge network emotion. In the guidance of network emotion, we should give full play to the hedging role of positive emotion and the communication role of positive emotion. Due to the limitations of the author's research technology and ability, the evolution of network emotion and the relationship between rainstorm disaster and people's emotional response need to be further studied.

Risk Assessment of Typhoon Disaster Chains in the Guangdong–Hong Kong–Macau Greater Bay Area, China

The typhoon disaster chain is one of the leading climate risks in constructing the Guangdong–Hong Kong–Macau Greater Bay Area (GBA). In this study, the risks of the typhoon disaster chains including typhoon-induced gales, rainstorms, and storm surges in the GBA, as well as the comprehensive risk of typhoon disaster, are investigated at county level by comprehensively analyzing the hazard, exposure, and vulnerability. The results show that the high- and very-high-risk areas of typhoon–gale disaster chain are located in Zhuhai, Zhongshan, Foshan, Dongguan, central-southern Jiangmen, southern Shenzhen, and parts of Huizhou. The high- and very high-risk areas of typhoon–rainstorm disaster chain include Zhuhai, Zhongshan, Shenzhen, central-southern Foshan, northern Dongguan, central Jiangmen, and central Huizhou. Regarding the typhoon–storm surge disaster chain, the areas at high and very high risk are located in Zhuhai, eastern Zhongshan, and the coastal areas of the Pearl River Estuary. In addition, the comprehensive risk of typhoon disaster is very high in Zhuhai and high in Zhongshan, Jiangmen, Dongguan, and Shenzhen. By verifying the spatial correlation between typhoon disaster risk indexes and actual losses, it is found that the comprehensive risk index of typhoon disaster constructed in this study can better reflect the actual losses. Overall, the findings of this study can provide a scientific basis for typhoon disaster prevention and mitigation in the GBA, and it can also serve as a reference for typhoon disaster risk research in other areas.

Applying ontology learning and multi-objective ant colony optimization method for focused crawling to meteorological disasters domain knowledge

The focused crawler based on semantic analysis is a research hotspot in the field of information retrieval. The domain ontology is generally applied to construct the topic model of the focused crawler. In order to overcome the limitations of builders' knowledge reserve and subjective consciousness in the process of constructing artificially ontology, a semi-automatic construction method of domain ontology based on ontology learning technology combining the latent Dirichlet allocation and the Apriori algorithm is proposed in this article. When evaluating the relevance between a hyperlink and a specific topic, the joint evaluation method considering both the web text and the link structure is usually used. However, the traditional weighted sum method is difficult to reasonably determine the optimal weights of these evaluating indicators. To solve this problem, a multi-objective optimization model for link evaluation and a subsequent multi-objective ant colony optimization algorithm (MOACO) are proposed. In the MOACO, a method of the nearest farthest candidate solution (NFCS) is combined with the fast non-dominated sorting to select a set of Pareto-optimal hyperlinks and guide the crawlers’ search directions. The experimental results of the focused crawling on the domain knowledge of typhoon disasters and rainstorm disasters prove that the ability of the proposed focused crawlers to retrieve topic-relevant webpages.

A novel focused crawler combining Web space evolution and domain ontology

In many fields, how to catch the related-topic Web resources is crucial. As a vertical search method, focused crawler has received great attention in recent years. Currently, most focused crawlers consider multiple evaluating factors of the hyperlinks and use the weighted sum approach to compute the priorities of unvisited hyperlinks. However, the proper weighted coefficients are hard to determine, and their unsuitable values may even cause the direction of crawlers to deviate seriously from the topic. To overcome this issue, this article builds a multi-objective optimization model based on Web text and link structure and designs a crawler framework called the Web space evolution (WSE), where a hyperlink bank whose radius is gradually increased is introduced to extend the search scape of crawlers in Web space. To improve the uniformity and diversity of hyperlinks, a nearest and farthest candidate solution method is combined with the fast non-dominated sorting to choose Pareto-optimal solutions (hyperlinks). A domain ontology based on the formal concept analysis is applied to establish the topic model. By incorporating the WSE and the domain ontology into the focused crawling, a novel focused crawler called FCWSEO is proposed to collect topic-relevant webpages. The experimental results on the rainstorm disaster domain show that the FCWSEO outperforms other focused crawler strategies in terms of the quantity and quality of retrieved relevant webpages.

Risk assessment of rainstorm disasters in the Guangdong–Hong Kong–Macao greater Bay area of China during 1990–2018

With climate change and urbanization, the rainstorm disaster has become one of the major climate risks in constructing the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Based on daily precipitation, socio-economic data and a comprehensive evaluation method, the characteristics and risks of rainstorm disaster in the GBA at county level are investigated in this paper. Considering the hazard, vulnerability and exposure, the risks of rainstorm disasters are high (Level 3) and very high (Level 4) in Zhuhai, Zhongshan, Dongguan, Jiangmen and Shenzhen. From 1990 to 2018, the frequency and intensity of rainstorms in the GBA have increased by 10 times and 2 mm per decade, respectively. Moreover, the population, gross domestic product, and built-up areas have increased significantly in the GBA, while the vegetation coverage has decreased. The combination of these changes results in an increase in the frequency of rainstorm disasters by two times from 2005 to 2018 compared with that from 1990 to 2004. The findings of this study can provide a positive reference for local governments to improve the pertinence of the strategies for rainstorm disaster prevention and mitigation.

Climate Risk and Enterprise Performance -Micro-evidence from Chinese A-share listed companies

Climate change has become the biggest challenge for human beings in the 21st century. Extreme climate risk not only causes a lot of direct economic losses but also affects the operation of a large number of micro-enterprises. In order to explore the impact of climate risk on business performance and its mechanism, based on the financial data of China's A-share listed companies from 2004 to 2018 and the meteorological disaster loss data of the province where the company is located, it is found that comprehensive meteorological disaster loss will significantly reduce the return on assets of enterprises, among which rainstorm (flood, landslide and debris flow) disasters, drought, gale, hail and lightning disasters are the most obvious. In addition, in the provinces affected by the typhoon, the return on corporate assets will also be greatly affected. Further analysis shows that the climate risks of enterprises with diversified businesses will be dispersed and enterprises with diversified businesses are less affected by meteorological disasters.

Rainstorm Disaster Prediction Based on Weather Research and Forecast (WRF): A Case of Nanjing, China

Rainstorm disaster causes great damage to human lives, environment and economies. Many environmental catastrophes happened every summer in southern China as result of the rainstorm. Therefore, heavy rain prediction remains the main focus of many scholars’ attention. However, the weather forecast is inaccurate and not prompt enough, causing casualties and financial losses. Weather Research Forecast (WRF) is an effective method and is utilized in this study to predict the precipitable water vapor (PWV) in meso-and small-scale in Nanjing. Rain is formed because of the PWV in the atmosphere, and therefore precipitation could be predicted according to the PWV. A method is proposed that the amount of rainstorm precipitation could be predicted based on the PWV, which can be simulated by WRF. The experimental results are consistent with the actual rainstorm situation. It demonstrates that promising measures based on the reliable WRF model could be taken to reduce the impending disasters.