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The urban transport network is threatened by urbanisation and climate change-enhanced urban flood, leading to substantial impacts on economic activities, social well-being and the environment. By taking the flood propagation process into consideration, we developed a flood-impact-assessment method to comprehensively assessed the economic impacts of traffic disruption in terms of time delay, fuel consumption and pollutant emission. The flood is simulated with CADDIES-2D flood model and the traffic flow is simulated with a microscopic model (SUMO). We applied this method to Beijing and quantified the economic damage of various flood scenarios. Comparing the baseline traffic scenario with those of three flooded scenarios yields the impacts of floods on traffic. The study revealed three key findings: (a) a rain occurring at 7 a.m. induces four times more cost than the baseline scenario, while rain of the same intensity and duration occurring at 8 a.m. or 9 a.m. lead to a traffic cost increase for 37.33% and 13.21% respectively. (b) The central and southern parts of Beijing suffer more from flooding and should be given priority for adaptation planning. (c) There is no significant spatial correlation between flood depth and traffic cost increase on a census block level. The proposed framework has the potential to assist decision-makers in prioritizing flood mitigation investments and therefore increase the resilience of transport networks to flooding impacts.

The typhoon is one major threat to human societies and natural ecosystems, and its risk perception is crucial for contextualizing and managing disaster risks in different social settings. Social media data are a new data source for studying risk perception, because such data are timely, widely distributed, and sensitive to emergencies. However, few studies have focused on crowd sensitivity variation in social media data-based typhoon risk perception. Based on the regional disaster system theory, a framework of analysis for crowd risk perception was established to explore the feasibility of using social media data for typhoon risk perception analysis and crowd sensitivity variation. The goal was to quantitatively analyze the impact of hazard intensity and social and geographical environments on risk perception and its variation among population groups. Taking the Sina Weibo data during Typhoon Lekima of 2019 as an example, we found that: (1) Typhoon Lekima-related Weibo public attention changed in accordance with the evolution of the typhoon track and the number of Weibo posts shows a significantly positive correlation with disaster losses, while socioeconomic factors, including population, gross domestic product, and land area, are not explanatory factors of the spatial distribution of disaster-related Weibo posts; (2) Females, nonlocals with travel plans, and people living in areas with high hazard intensity, low elevation, or near waterbodies affected by Lekima paid more attention to the typhoon disaster; and (3) Descriptions of rainfall intensity by females are closer to the meteorological observation data.

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

With worsening global climate change, we still do not fully understand how to cope with possible extreme precipitation events or secondary disasters on highway networks. Correctly estimating the impact on the highway network from extreme precipitation plays a vital role in decision making regarding future highway investment. This study uses datasets from 21 NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) under the RCP (Representative Concentration Pathway) 4.5 and RCP8.5 scenarios. We use the percentile method to select the extreme precipitation threshold. A set of system performance measures for an impact analysis of Chinese highways under different scenarios is developed from the perspectives of physical exposure, network function and sensitivity analyses for high-impact areas in China. The results show that the intensity of extreme precipitation will increase in the future. More than 10,000 km and at least 4,000 intersections will be affected by extreme precipitation in 2030 and 2050. Based on a functional analysis of the highway network in Guangdong and Guangxi, more than 80% of the mileage of highways in Guangdong and Guangxi will be exposed to extreme precipitation. The network function of Chinese highways will dramatically decrease when precipitation reaches a critical value, which will shed light on highway fortification standards and planning.

Recurrent outbreaks of the coronavirus disease 2019 (COVID-19) have occurred in many countries around the world. We developed a twofold framework in this study, which is composed by one novel descriptive model to depict the recurrent global outbreaks of COVID-19 and one dynamic model to understand the intrinsic mechanisms of recurrent outbreaks. We used publicly available data of cumulative infected cases from 1 January 2020 to 2 January 2021 in 30 provinces in China and 43 other countries around the world for model validation and further analyses. These time series data could be well fitted by the new descriptive model. Through this quantitative approach, we discovered two main mechanisms that strongly correlate with the extent of the recurrent outbreak: the sudden increase in cases imported from overseas and the relaxation of local government epidemic prevention policies. The compartmental dynamical model (Susceptible, Exposed, Infectious, Dead and Recovered (SEIDR) Model) could reproduce the obvious recurrent outbreak of the epidemics and showed that both imported infected cases and the relaxation of government policies have a causal effect on the emergence of a new wave of outbreak, along with variations in the temperature index. Meanwhile, recurrent outbreaks affect consumer confidence and have a significant influence on GDP. These results support the necessity of policies such as travel bans, testing of people upon entry, and consistency of government prevention and control policies in avoiding future waves of epidemics and protecting economy.

This article focuses on decision making for retrofit investment of road networks in order to alleviate severe consequences of roadside tree blowdown during tropical cyclones. The consequences include both the physical damage associated with roadside trees and the functional degradation associated with road networks. A trilevel, two-stage, and multiobjective stochastic mathematical model was developed to dispatch limited resources to retrofit the roadside trees of a road network. In the model, a new metric was designed to evaluate the performance of a road network; resilience was considered from robustness and recovery efficiency of a road network. The proposed model is at least a nondeterministic polynomial-time hardness (NP-hard) problem, which is unlikely to be solved by a polynomial time algorithm. Pareto-optimal solutions for this problem can be obtained by a proposed trilevel algorithm. The random forest method was employed to transform the trilevel algorithm into a single-level algorithm in order to decrease the computation burden. Roadside tree retrofit of a provincial highway network on Hainan Island, China was selected as a case area because it suffers severely from tropical cyclones every year, where there is an urgency to upgrade roadside trees against tropical cyclones. We found that roadside tree retrofit investment significantly alleviates the expected economic losses of roadside tree blowdown, at the same time that it promotes robustness and expected recovery efficiency of the road network.

Rapid urbanization and natural hazards are posing threats to local ecological processes and ecosystem services worldwide. Using land use, socioeconomic, and natural hazards data, we conducted an assessment of the ecological vulnerability of prefectures in Sichuan Province for the years 2005, 2010, and 2015 to capture variations in its capacity to modulate in response to disturbances and to explore potential factors driving these variations. We selected five landscape metrics and two topological indicators for the proposed ecological vulnerability index (EVI), and constructed the EVI using a principal component analysis-based entropy method. A series of correlation analyses were subsequently performed to identify the factors driving variations in ecological vulnerability. The results show that: (1) for each of the study years, prefectures with high ecological vulnerability were located mainly in southern and eastern Sichuan, whereas prefectures in central and western Sichuan were of relatively low ecological vulnerability; (2) Sichuan’s ecological vulnerability increased significantly (p = 0.011) during 2005–2010; (3) anthropogenic activities were the main factors driving variations in ecological vulnerability. These findings provide a scientific basis for implementing ecological protection and restoration in Sichuan as well as guidelines for achieving integrated disaster risk reduction.

The first phase of the novel coronavirus disease (COVID-19) that emerged at the end of 2019 has been brought under control in the mainland of China in March, while it is still spreading globally. When the pandemic will end is a question of great concern. A logistic model that depicts the growth rules of infected and recovered cases in China’s mainland may shed some light on this question. This model well explained the data by 13 April from 31 countries that have been experiencing serious COVID-2019 outbreaks (R2 ≥ 0.95). Based on this model, the semi-saturation period (SSP) of infected cases in those countries ranges from 3 March to 18 June. According to the linear relationship between the growth rules for infected and for recovered cases identified from the Chinese data, we predicted that the SSP of the recovered cases outside China ranges from 22 March to 8 July. More importantly, we found a strong positive correlation between the SSP of infected cases and the timing of a government’s response. Finally, this model was also applied to four regions that went through other coronavirus or Ebola virus epidemics (R2 ≥ 0.95). There is a negative correlation between the death rate and the logistic growth rate. These findings provide strong evidence for the effectiveness of rapid epidemic control measures in various countries.

Strategies of community-based disaster risk reduction have been advocated for more than 2 decades. However, we still lack in-depth quantitative assessments of the effectiveness of such strategies. Our research is based on a national experiment in this domain: the "Comprehensive Disaster Reduction Demonstration Community" project, a governmental program running in China since 2007. Information on more than 11,000 demonstration communities was collected. Combined with the local disaster information and socioeconomic conditions, the spatiotemporal characteristics of these communities over 12 years and their differences in performance by region and income group were analyzed. We performed an attribution analysis for disaster risk reduction effectiveness. This is the first time a series of quantitative evaluation methods have been applied to verify the effectiveness of a large-scale community-based disaster risk reduction project, both from the perspective of demonstrative effects and loss reduction benefits. Here, we find that the project is obviously effective from these two perspectives, and the disaster loss reduction effectiveness illustrates clear regional differences, where the regional economic level and hazard severity act as important drivers. Significant differences of urban-rural and income call for matching fortification measures, and the dynamic management of demonstration community size is required, since the loss reduction benefit converges when the penetration rate of the demonstration community reaches approximately 4% in a province. These and further results provide diverse implications for community-based disaster risk reduction policies and practices.

Tipping elements of the Earth system may shift abruptly and irreversibly from one state to another at tipping points, resulting in a growing threat to our society. Yet, it is not fully clear how to assess and quantify the influence of a tipping element and how to explore the teleconnections between different tipping elements. To fill this knowledge gap, we propose a climate network approach to quantitatively analyze the global impacts of a prominent tipping element, the Amazon Rainforest Area (ARA). We find that regions, such as, the Tibetan Plateau (TP) and West Antarctic ice sheet, are characterized by higher network weighted links and exhibit strong correlations with the ARA. We then identify a teleconnection propagation path between the ARA and the TP. This path is robust under climate change as simulated by various climate models of CMIP5 and CMIP6. In addition, we detect early warning signals for critical transition in the snow cover extent on the Tibetan Plateau by applying critical slowing down indicators, lag-1 autocorrelation and detrended fluctuation analysis. We find that the snow cover of the TP has been losing stability since 2008, revealing that the TP is operating like a tipping element and approaching a potential tipping point. We further uncover that various climate extremes between the ARA and the TP are significantly synchronized under climate change. Our framework provides new insights into how tipping elements are linked to each other and into the potential predictability of cascading tipping dynamics.

The COVID-19 pandemic has brought huge challenges to sustainable urban and community development. Although some recovery signals and patterns have been uncovered, the intra-city recovery process remains underexploited. This study proposes a comprehensive approach to quantify COVID-19 recovery leveraging fine-grained human mobility records. Taking Wuhan, a typical COVID-19 affected megacity in China, as the study area, we identify accurate recovery phases and select appropriate recovery functions in a data-driven manner. We observe that recovery characteristics regarding duration, amplitude, and velocity exhibit notable differences among urban blocks. We also notice that the recovery process under a one-wave outbreak lasts at least 84 days and has an S-shaped form best fitted with four-parameter Logistic functions. More than half of the recovery variance can be well explained and estimated by common variables from auxiliary data, including population, economic level, and built environments. Our study serves as a valuable reference that supports data-driven recovery quantification for COVID-19 and other crises.

Optimally deploying sensors into a complex highway network can significantly enhance the socio-economic benefits through accurately providing system users and operators with the latest traffic state information. This paper presents a linear optimization framework for designing a sensor network for travel time surveillance. The presented framework consists of a traditional static network model and a novel dynamic network model as well as an integer cut-based speed-up solution approach. The aims of the study are twofold. First, the proposed optimization model maximizes the network-level travel time surveillance benefits by taking into account the values of real-time measurements induced from both temporal and spatial traffic state patterns. Second, the mechanism of sensor relocation operations is designed and incorporated into the proposed model. This mechanism enables one to evaluate whether appropriately relocating sensors can optimize the utilization of the surveillance resources and further enhance the surveillance benefits over a given time horizon. We applied the proposed optimization framework to a mixed arterial-freeway commuting network located in Washington. D.C.-Baltimore metropolitan area. The study empirically revealed and illustrated two interesting findings: (1) relocation operations do bring additional surveillance benefits on travel time prediction, and the marginal benefit is highly correlated with the sensor fleet size; (2) spatial measurement is not a trivial part in travel time prediction, and one should fully recognize the effectiveness of temporal and spatial measurements when designing a traffic sensor network for real-time travel time surveillance.

The adverse effect of climate change continues to expand, and the risks of flooding are increasing. Despite advances in network science and risk analysis, we lack a systematic mathematical framework for road network percolation under the disturbance of flooding. The difficulty is rooted in the unique three-dimensional nature of a flood, where altitude plays a critical role as the third dimension, and the current network-based framework is unsuitable for it. Here we develop a failure model to study the effect of floods on road networks; the result covers 90.6% of road closures and 94.1% of flooded streets resulting from Hurricane Harvey. We study the effects of floods on road networks in China and the United States, showing a discontinuous phase transition, indicating that a small local disturbance may lead to a large-scale systematic malfunction of the entire road network at a critical point. Our integrated approach opens avenues for understanding the resilience of critical infrastructure networks against floods., The spread of flood-induced failures in critical infrastructure systems is understudied. Here the authors apply the CaMa-Flood global river flood simulation model to estimate the flood-induced failures and their spread in China and the US and find that the number of flood-induced total failures is in-between that of random and localized damage given the same intensity.

Air pollution causes widespread environmental and health problems and severely hinders the quality of life of urban residents. Traffic is critical for human life, but its emissions are a major source of pollution, aggravating urban air pollution. However, the complex interaction between traffic emissions and air pollution in cities and regions has not yet been revealed. In particular, the spread of COVID-19 has led various cities and regions to implement different traffic restriction policies according to the local epidemic situation, which provides the possibility to explore the relationship between urban traffic and air pollution. Here, we explore the influence of traffic on air pollution by reconstructing a multi-layer complex network base on the traffic index and air quality index. We uncover that air quality in the Beijing–Tianjin–Hebei (BTH), Chengdu–Chongqing Economic Circle (CCS), and Central China (CC) regions is significantly influenced by the surrounding traffic conditions after the outbreak. Under different stages of the fight against the epidemic, the influence of traffic in some regions on air pollution reaches the maximum in stage 2 (also called Initial Progress in Containing the Virus). For the BTH and CC regions, the impact of traffic on air quality becomes bigger in the first two stages and then decreases, while for CC, a significant impact occurs in phase 3 among the other regions. For other regions in the country, however, the changes are not evident. Our presented network-based framework provides a new perspective in the field of transportation and environment and may be helpful in guiding the government to formulate air pollution mitigation and traffic restriction policies.

The novel coronavirus disease (COVID-19) that emerged at the end of 2019 has been controlled in mainland China so far, while it is still spreading globally. When the pandemic will end is a question of great concern. A logistic model depicting the growth rules of infected and recovered cases in mainland China may shed some light on this question. We extended this model to 31 countries outside China experiencing serious COVID-2019 outbreaks. The model well explained the data in our study (R2≥ 0.95). For infected cases, the semi-saturation period (SSP) ranges from 63 to 170 days (March 3 to June 18). The logistic growth rate of infected cases is positively correlated with that of recovered cases, and the same holds for the SSP. According to the linear connection between the growth rules for infected and recovered cases identified from the Chinese data, we predicted that the SSP of the recovered cases outside China ranges from 82 to 196 days (March 22 to July 8). More importantly, we found a strong positive correlation between the SSP of infected cases and the timing of government’s response, providing strong evidence for the effectiveness of rapid epidemic control measures in various countries.

Increasing flood risk was caused by expanding climate change. The floods directly or indirectly disrupt the railway system and arise a significant negative impact on our social-economic system. This study developed an integrated approach to explore the impact of large-scale future floods on railway system. Firstly, A three layered traffic flow simulation model was constructed to study propagation and amplification effects of component failure after the event of flooding in the system. Secondly, future runoff scenarios were produced by using five global climate models and three different representative concentration pathways. The future floods was simulated by using CaMa-Flood model after inputting future runoff scenarios. Furthermore, we imposing simulated future floods into traffic simulation system and develop two measurements to evaluate the impact of floods on the railway system as the perspective of the entire system. Here we explore the impact of floods on the real-world highway network of China. The results illustrate that: (i) Unprecedented uncertainty. The damage of the flood to the railway system is not linearly and positively correlated with representative concentration pathway and the year within different global climate models; Floods in different years have different impacts in connections among regions; (ii) Unacceptable damage. 59.76 % of railway segments inundated and 98.61461% of large cities could not be reached by extreme floods. These results have critical policy implications for the transport sector in reference to railway location and design, railway network optimization and protection and can be also easily adapted to analyze other spatial damages for valuable protection suggestions.

In 2019, the typhoon Lekima hit China, bringing strong winds and heavy rainfall to the nine provinces and municipalities on the northeastern coast of China. According to the Ministry of Emergency Management of the People’s Republic of China, Lekima caused 66 direct fatalities, 14 million affected people and is responsible for a direct economic loss in excess of 50 billion yuan. The current observation technologies include remote sensing and meteorological observation. But they have a long time cycle of data collection and a low interaction with disaster victims. Social media big data is a new data source for natural disaster research, which can provide technical reference for natural hazard analysis, risk assessment and emergency rescue information management.We propose an assessment framework of social media data-based typhoon-induced flood assessment, which includes five parts: (1) Data acquisition. Obtain Sina Weibo text and some tag attributes based on keywords, time and location. (2) Spatiotemporal quantitative analysis. Collect the public concerns and trends from the perspective of words, time and space of different scales to judge the impact range of typhoon-induced flood. (3) Text classification and multi-source heterogeneous data fusion analysis. Build a hazard intensity and disaster text classification model by CNN (Convolutional Neural Networks), then integrate multi-source data including meteorological monitoring, population economy and disaster report for secondary evaluation and correction. (4) Text clustering and sub event mining. Extract subevents by BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) text clustering algorithms for automatic recognition of emergencies. (5) Emotional analysis and crisis management. Use time-space sequence model and four-quadrant analysis method to track the public negative emotions and find the potential crisis for emergency management.This framework is validated with the case study of typhoon Lekima. The results show that social media big data makes up for the gap of data efficiency and spatial coverage. Our framework can assess the influence coverage, hazard intensity, disaster information and emergency needs, and it can reverse the disaster propagation process based on the spatiotemporal sequence. The assessment results after the secondary correction of multi-source data can be used in the actual system.The proposed framework can be applied on a wide spatial scope and even full coverage; it is spatially efficient and can obtain feedback from affected areas and people almost immediately at the same time as a disaster occurs. Hence, it has a promising potential in large-scale and real-time disaster assessment.

Background An outbreak of a novel coronavirus (SARS-CoV-2)-infected pneumonia (COVID-19) was first occurred in Wuhan, China, in December 2019 and then spread rapidly to other regions. It has been declared that at least one confirmed case infected by SARS-CoV-2 was found in each province of China by late January 2020. Methods We collected the time series data of the cumulative number of confirmed infected, dead, and cured cases from the health commissions in 31 provinces in mainland China. A simple descriptive model in a logistic form was formulated to infer the intrinsic epidemic rules of COVID-19. Furthermore, we compared the intrinsic epidemic rules of COVID-19 in Hubei with that of the severe acute respiratory syndrome (SARS) in Beijing, which was obtained from the Ministry of Public Health of China in 2003. Results The feature of data of three population groups in all the provinces could be well captured (R-square>0.95) by the proposed descriptive model with slight variation among different provinces. By comparing fitted parameters, we found that the fitted curve of the infected case is the earliest to be saturated and has the lowest semi-saturation period in both COVID-19 and SARS. While all three population groups of SARS are later to saturate and have a longer semi-saturation period than that of COVID-19. Conclusions Our model is robust and stable to depict the intrinsic growth rule for the cumulative number of confirmed infected, dead, and cured cases in 30 provinces, which could depict the epidemic situation in mainland China very well. Despite the virus caused SARS (SARS-CoV) and the virus caused COVID-19 (SARS-CoV-2) are homologous, their epidemiologic characteristics are quite different. The duration of the outbreak would be shorter for COVID-19.

As socioeconomic systems continue to develop, their critical infrastructure systems become more intricate and the interdependencies among systems more intensive. This cascading effect on critical infrastructure systems significantly impacts system performance. We develop an approach to quantitatively assess the complex cascading effect on critical infrastructure systems under four different types of attack: (i) random, (ii) malicious, (iii) shell-based and local, and (iv) orientated and local. In the context of these four we study three types of cascading effect – non-cascading, inner-system cascading, and inter-system cascading – in both independent systems and interdependent systems. We model both logical and geographical interdependency. We apply this approach to the Chinese road and railway system and find that (i) the damage done by different types of attack on critical infrastructures systems varies significantly, (ii) under the same type of attack the damage caused by cascading effects on different critical infrastructure systems varies significantly, and (iii) different cascading effects contribute to damage in critical infrastructure systems. These findings indicate that more theoretical and practical research on cascading effects in infrastructure systems under different attacks is needed, especially when the attacks are local and oriented.

International journal of disaster risk science 9(3), 292-305 (2018). doi:10.1007/s13753-018-0185-6, Published by Springer, Heidelberg

The Sendai Framework for Disaster Risk Reduction 2015–2030 shifts the focus from managing disasters to reducing risks. Such a shift requires a better understanding of risk in all its dimensions of environment, hazards, exposure, and vulnerability; a disaster risk governance that ensures disaster risk is factored into planning and development at all levels across all sectors, as well as into disaster pre-paredness, rehabilitation, recovery, and reconstruction; and cost–benefit analyses to support the prioritization of investments in disaster risk reduction (DRR) for long-term resilience.

Understanding the environmental impacts of road networks and the success of policy initiatives is crucial to a country’s socioeconomic development. In this study, we propose a comprehensive approach to quantitatively assessing whether a given response is effective in mitigating the impacts of environmental shocks on roads. Our approach includes factor analysis, direct and indirect loss quantification, and cost-benefit analysis. Using nationwide data on road malfunctions and weather service performance in China, we found that the macro-level indirect economic losses from road malfunctions were more than the direct losses in multiples ranging from 11 to 21, and that information provided by the weather service could reduce losses, with benefits exceeding costs by a ratio of 51. The results of our study provide a quantitative tool as well as evidence of the effectiveness of sustainability investment, which should provide guidance for future disaster mitigation, infrastructure system resilience, and sustainability-building policy-making.

Shelter is one type of emergency facilities and plays a critical role in disaster response. In operation level, a shelter is the destination of post-disaster evacuation route, whose scientific service area demarcation can have a great positive influence on evacuation route planning. Effective evacuation route planning can also validate and support the service area demarcation of shelter. This study aims to optimize shelter service area demarcation and post-disaster evacuation route planning simultaneously in order to improve the efficiency of current shelter operation and further reduce disaster risk, which is seldom addressed before. We proposed an integrated optimization model for jointly solving shelter service area demarcation and evacuation route planning problems, which keeps contiguity of service areas, ensures an evacuation route within the same service district, shortens the evacuation route, satisfies capacity constraints and maintains integrity. To solve the problem, a ripple-spreading algorithm is adopted with combining contiguity and capacity operators. A real-world case study was conducted in the Chaoyang district of Beijing, China. A locally optimal scheme for service area demarcation and evacuation route planning was obtained with satisfying constraints as much as possible, which confirm the validity of ripple-spreading algorithm application in the joint problem.

In response to more frequent heatwaves, various regional or national heat-health warning systems (HHWSs) have been developed recently as adaptation measures. A wide range of methodologies have been utilized to issue warnings, as there is no universal definition of “heat event” or “heatwave”, nor are there quantified thresholds of human-health tolerance to extreme weather. The performance of these warning systems has rarely been evaluated with actual heat-health data, especially the morbidity data, in regions with severe impact. In this study, we assessed the performance of the Shanghai HHWS based on heat-related illness data collected by the Chinese Center for Disease Control and Prevention (China CDC) and then conducted a comparative analysis among the Shanghai HHWS, the China Meteorological Administration HHWS, the Chinese national standard for heatwave indexes, the heat index adopted by the USA's National Weather Service and the definition suggested by the World Meteorological Organization to understand their potential performance for application in Shanghai and to evaluate the temperature thresholds and different meteorological indices employed. The results show that: 1) during the research period, 50% of heat-related illnesses and 58.2% of heat-related deaths in Shanghai occurred on dates that had no heat warnings; 2) for the current threshold (35 °C), the single metric of temperature outperformed the temperature-duration two-parameter method; 3) different from existing studies, while infants and seniors are deemed as vulnerable population groups to heat, young and middle-aged males were found to suffer more heat-related illnesses in hot weather. More detailed analyses reveal that the performance of heat-health warning systems needs to be evaluated and revised periodically, and warning thresholds utilized must be localized to reflect public tolerance to heat and to address the vulnerability of target population groups. Temperature is the dominant threshold in heat-related morbidity and mortality analysis. While a decrease in the temperature threshold would definitely increase the warning frequency and socioeconomic costs, it might also cause warning fatigue. The trade-off between these two aspects is essential for decision-makers and other stakeholders in HHWS design and improvement.

The negative impact of climate change continues to escalate flood risk. Floods directly and indirectly damage highway systems and disturb the socioeconomic order. In this study, we propose an integrated approach to quantitatively assess how floods impact the functioning of a highway system. The approach has three parts: (1) a multi-agent simulation model to represent traffic, heterogeneous user demand, and route choice in a highway network; (2) a flood simulator using future runoff scenarios generated from five global climate models, three representative concentration pathways (RCPs), and the CaMa-Flood model; and (3) an impact analyzer, which superimposes the simulated floods on the highway traffic simulation system, and quantifies the flood impact on a highway system based on car following model. This approach is illustrated with a case study of the Chinese highway network. The results show that (i) for different global climate models, the associated flood damage to a highway system is not linearly correlated with the forcing levels of RCPs, or with future years; (ii) floods in different years have variable impacts on regional connectivity; and (iii) extreme flood impacts can cause huge damages in highway networks; that is, in 2030, the estimated 84.5% of routes between provinces cannot be completed when the highway system is disturbed by a future major flood. These results have critical implications for transport sector policies and can be used to guide highway design and infrastructure protection. The approach can be extended to analyze other networks with spatial vulnerability, and it is an effective quantitative tool for reducing systemic disaster risk.

This study investigated the impact of humidity and temperature on the spread of COVID-19 (SARS-CoV-2) by statistically comparing modelled pandemic dynamics (daily infection and recovery cases) with daily temperature and humidity of three climate zones (Mainland China, South America and Africa) from January to August 2020. We modelled the pandemic growth using a simple logistic function to derive information of the viral infection and describe the growth of infected and recovered cases. The results indicate that the infected and recovered cases of the first wave were controlled in China and managed in both South America and Africa. There is a negative correlation between both humidity (r = - 0.21; p = 0.27) and temperature (r = -0.22; p = 0.24) with spread of the virus. Though this study did not fully encompass socio-cultural factors, we recognise that local government responses, general health policies, population density and transportation could also affect the spread of the virus. The pandemic can be managed better in the second wave if stricter safety protocols are implemented. We urge various units to collaborate strongly and call on countries to adhere to stronger safety protocols in the second wave.

Disaster information acquisition and assessment in China primarily depends on disaster-related governmental departments at all levels. As new challenges faced by disaster emergency management arise, a gap still exists in the timeliness and completeness of rapid disaster assessment. The development and popularity of social media has opened up new channels. Social media data, containing massive amounts of disaster information, has the advantages of timeliness, efficiency, and multiple spatiotemporal scales and can supplement existing methods. Based on the dominant social media platform in China, Sina Weibo, this paper proposes a method of extracting intensity information from Weibo texts, including social perceptions such as shaking, emergency reaction, mood and visible damage, which can support the rapid assessment of seismic intensity. By taking the Changning earthquake in Sichuan Province, China, as a case study, we verify the feasibility of hazard information extraction from Weibo. The results show that the intensity information derived from Weibo in the short-term can respond to a sudden earthquake promptly, then effectively identify the main earthquake affected area, locate the possible orientation close to the meizoseismal area and depict the situation in 10 min. We also propose a grid-based correction method, which synthesizes the hotness-intensity matrix and a seismic attenuation-based model. Compared with taking the seismic attenuation model directly, the proposed correction method improves the accuracy of correct recognition rate of seismic intensity assessment to 82% on average and reduces the false recognition rate significantly. The proposed framework of rapid assessment and correction reveals that the combination of social perception and hazard intensity plays a valuable and promising role as a supplement of traditional disaster assessment approach.

Estimating flood impacts on daily activities is vital to disaster risk reduction and urban resilience. Human mobility on complex road networks has caused citizens, especially automobile commuters, to suffer from remarkable impacts on rainy days. However, quantitative studies of these harms have been limited. In this paper, we develop a new approach to estimating flood-affected populations, including their quantity, structure, and spatial distribution, by integrating commuter mobility patterns and road inundation records using multi-source data. Mobility patterns were built by the Gravity model with point-of-interest (POI) data and then downscaled to individual routes by Monte Carlo simulation. Inundation locations and impassable roads were retrieved by combining flood hazard maps with social media data. This approach was applied to Wuhan, a large city in Central China, to estimate the affected automobile commuters under a typical flood event (July 6, 2016) and three flood scenarios (10-year, 20-year, and 50-year). Our approach produces an estimate of the affected population that is more accurate and detailed than existing approaches. It also provides high-resolution impact information for urban studies and can be applied easily to other cities and disasters that include road closures. It will help governments to estimate affected populations rapidly and assess indirect economic losses caused by labor disruptions and work delays.

Social media, as a new data source, is a promising field in disaster research. Despite doubts about its validity, a growing number of research institutes and commercial companies are exploring the potential of social media in disaster risk management. To understand the development and trends in this domain, a bibliometric analysis was performed using 1573 related published articles in Web of Science between 1991 and 2019. We found that (1) the number of annual publications and new research institutes in this field grew rapidly but seems to have become saturated in recent years. (2) The main research force is independent universities with limited cooperation, and a knowledge network has not yet been formed in this arena. (3) Research hotspots evolve in the path of “conceptualization - refinement - application”. Due to the three features of social media data, namely, timeliness, subjectivity, and disequilibrium, obstacles still exist in applicable disaster types and population representativeness. We anticipate new scientific advances emerging to overcome some technical difficulties. Knowledge sharing, advanced computer science, and multiorganizational cooperation will benefit this arena. These findings indicate potential directions for the development of and innovation in social media-based disaster research.

Efficient evacuation routing and allocation of evacuees to safe shelters can effectively reduce casualties and property losses caused by storm surge disasters. A biobjective model is proposed to tackle shelter allocation and emergency routing problems while simultaneously considering network reliability. The proposed model maximizes the total reliability of routes connecting residential communities and shelters and those connecting shelters. A nondominated sorting genetic algorithm is developed to solve this biobjective model with a specially designed chromosome structure, initialization process, and genetic operators. The mathematical model is validated with a case study using storm surge scenario simulation data and real-world community and infrastructure data from Pingyang County, China. The case study illustrates the effectiveness of the proposed model and its potential application in real-world disaster risk reduction.