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China is one of the countries that are most severely affected by floods worldwide. Due to the geographical and climatic environment, floods frequently occur in China. Rapid socioeconomic growth and urban sprawl in the past decades have significantly changed both exposure and vulnerability dimensions of flood risk in China. In response to high risks of flood, the Chinese government has adopted a series of effective measures, such as the “Spongy City” Program and building many large dams and reservoirs, and some measures have achieved significant results. However, there is still a lack of studies with an integrated view on analyzing the causes, socioeconomic impact trends, and disaster risk reduction (DRR) measures of flood risk in China in the past decades. Accordingly, this paper aims to fill in the gap and provides some new insights into China’s contributions in DRR over the period of 1950–2019. Our results show that annual flood-induced fatalities and socioeconomic vulnerability to floods have significantly decreased in China, owing to a range of structural and non-structural measures. Nevertheless, China still faces the complex coupling effects of climate change and urbanization, and thus threats from extreme floods. In addition, China needs to further improve its flood risk management system.

© 2019 American Meteorological Society.Tropical cyclones (TCs) can wreak havoc on the landscape and overwhelm communities. Since economic exposure is an important factor in damage function, an evaluation of economic exposure is essential because the characteristics of TC-related hazards are changing under accelerating economic development patterns. Here, we first reconstructed the wind and rainfall fields of historical TCs through an extensive database to extract the economic exposure to TC-prone areas on the mainland of China. We found that rainfall is an important factor in determining the affected extent of a TC event and that economic exposure will be misestimated when considering only the wind field. The results reveal that economic exposure to TCs has increased considerably from 1990 to 2015 and will continue to increase until the year 2100 under shared socioeconomic pathways (SSPs). We found that 66.7% of China’s gross domestic product [GDP; CNY 48.6 trillion (7.8 trillion U.S. dollars)] and 63.9% of China’s asset value [CNY 139.5 trillion (22.4 trillion U.S. dollars)] were concentrated in TC-prone areas in 2015 and increased at an average annual rate of 10.6% and 13.9%, respectively. Projections of GDP scenarios under SSPs revealed continued growth in the early twenty-first century, and the range of GDP and asset value in TC-prone areas by 2100 varied. Further detailed studies are needed to provide a detailed damage function for TC loss assessments under climate change and to consider how TC hazards will interact under changes in exposure and vulnerability related to economic development and social change.

Diagnosing all components of risk is essential in earthquake loss attribution science, but quantitative estimates on how sensitive the earthquake-induced direct economic losses (DELs) are to changes in hazard, exposure and vulnerability is rarely known. Here the relationship between earthquake DELs and earthquake magnitude (Ms), asset value exposure (K), proportion of non-steel-concrete residential buildings (H) and physical environment instability (E) is quantified using the concept of economic elasticity. Earthquake disaster event-based DEL records over the period from 1990 to 2016 for the mainland of China are fitted to a regression model. Elasticity values for Ms, K, H and E are 7.63, 0.75, 4.92 and 0.91, respectively, indicating that on average, DEL changes are more sensitive to changes in Ms and H-a 13% increase in Ms or a 20% increase in H would double earthquake DELs, while it may take a 133% increase in K or a 110% increase in E to cause the same economic losses. In turn, this suggests that human factors-decreasing H and K-could be efficient ways to reduce earthquake risk, while these two factors will become increasingly relevant for risk assessment in the future with continued economic growth. The elasticity estimate results could be used for studying future change in earthquake risks and for supporting disaster risk reduction strategies.

Exploring precipitation threshold from an economic loss perspective is critical for rainstorm and flood disaster risk assessment under climate change. Based on the daily gridded precipitation dataset and direct economic losses (DELs) of rainstorm and flood disasters in the mainland of China, this paper first filtered a relatively reasonable disaster-triggering daily precipitation threshold (DDPT) combination according to the relationship between extreme precipitation days and direct economic loss (DEL) rates at province level and then comprehensively analyzed the spatial landscape of DDPT across China. The results show that (1) the daily precipitation determined by the combination of a 10 mm fixed threshold and 99.3th percentile is recognized as the optimal DDPT of rainstorm and flood disasters, and the correlation coefficient between annual extreme precipitation days and DEL rates reached 0.45 (p < 0.01). (2) The optimal DDPT decreases from southeast (up to 87 mm) to northwest (10 mm) across China, and the DDPTs of 7 out of 31 provinces are lower than 25 mm, while 5 provinces are higher than 50 mm on average. These results suggest that DDPTs exist with large spatial heterogeneity across China, and adopting regional differentiated DDPT is helpful for conducting effective disaster risk analysis.

The existence of multiple disaster-hit regions with spatially heterogeneous economic structures is vital to the investigation of the spatial ripple effects of disaster disturbances in the inter-regional supply-demand chain. We employed the inter-regional input-output (IRIO) model to modify the adaptive regional input-output model (ARIO) and reproduce indirect economic losses (IELs) after the Bohai Sea ice disaster event in the mainland of China. The results showed that the total IEL in China comprised 38% of the direct economic loss. The disaster area (the Bohai Rim region) lost 1460.55 million CNY, and other provinces were burdened with 49% of the total IEL. Agricultural services absorbed 44% of the total IEL in China. Furthermore, lingering effects of the disaster were detected in non-disaster areas ∼5–10 months after the removal of the spatial ripple effect compared with the disaster areas. Finally, the difference in total IEL in China caused by spatial heterogeneity in the supply-demand chain was ∼46.8%. Our findings suggest that the spatial ripple effect and the spatial heterogeneity of economic structures play an important role in the extent of disaster impacts on inter-regional economic systems.

As an emerging big data source, social media data has been attracted more and more attention in the field of disaster emergency management. This study took the case of the Super Typhoon Lekima, which landed in China in 2019, to explore how the public's disaster risk perception changed during disaster response based on microblog data from Sina Weibo, commonly regarded as “Chinese Twitter”. We first analyzed characteristics of microblogs and found that microblog activities were closely related to Super Typhoon Lekima's landing process, the public could sense typhoon landfall about 72 h in advance. Second, we found that there exists a significant linear correlation between microblog counts and precipitation on a daily and provincial scale. Finally, we constructed Chinese disaster-relevant keyword sets and used Naive Bayesian classification to calculate population-adjusted disaster score of affected-people, collapsed-house and affected-agriculture, and we found that the extracted disaster information from microblogs was corelated to real loss at provincial scale, and Weibo provide further detailed spatial distribution characteristics of Lekima's destruction. This research demonstrates that disaster information extracted form Sina Weibo could reflect the public's disaster risk perception well and have potential to serve as a data source for disaster management in China.

Inter-regional economic development is threatened by high direct economic damage (DED) from multiple natural disaster events. However, estimations of the indirect economic impacts (IEIs) of a single disaster event cannot accurately reflect the superimposed impacts of sequential disasters on the inter-regional social-economic system. Thus, we employed the modified adaptive inter-regional input-output (MAIIO) model to reproduce the superimposed impacts of three typhoons (Utor, Usagi, and Fitow) on the economy under three scenarios. The results showed that the superimposed disturbance of sequential disasters amplified the IEIs of the DED and increased the amplitude of the negative fluctuations of the value added; the spatial ripple effects induced by the superimposed disturbance of multiple typhoon disasters were also different due to the spatial heterogeneity of the economic structure; the dynamic adaptive process of economic system with sequential disasters was that it tried to recover from early inter-regional IEIs after an initial disaster event, and then the occurrence of a new disaster before completion of the initial recovery brought new (amplified) IEIs. This study suggests that the sequential disasters within a short time interval impose a complex superimposed disturbance on the inter-regional economic system, and modelling the economic trajectory with sequential disasters is valuable for designing strategies for the synergistic development of inter-regional economic sustainability and disaster mitigation.

Understanding the spatiotemporal pattern of historical earthquake disasters and resultant socioeconomic consequences is essential for designing effective disaster risk reduction measures. Based on historical earthquake disaster records, this study compiles a Chinese earthquake disaster catalog (CH-CAT) that includes records of 722 earthquake disasters that occurred during 1950&ndash, 2017 in the mainland of China. This catalog includes more complete data records than other existing global earthquake databases for China as a whole. Statistical results demonstrate that the number of earthquake disasters and the resultant direct economic losses (DELs) exhibit significant increasing trends (p &lt, 0.01) over the studied 68-year period. Earthquake-induced deaths vary greatly between individual years and exhibit no significant trend. The Qinghai-Tibet seismic zone is the area with the highest frequency of earthquake disasters and the largest accumulated DELs, whereas the North China seismic zone is associated with the highest number of deaths. Among the 722 earthquake disasters, nearly 99.0% of deaths and 95.0% of DELs are attributable to 1.8% and 3.9% of the earthquake disasters, respectively. Approximately 54.2% of recorded earthquake disasters have earthquake magnitude (Ms) values between 5.0 and 5.9, while earthquake disasters with Ms greater than or equal to 7.0 account for 88.5% of DELs and 98.8% of deaths. On average, earthquake-induced DELs and deaths increase nonlinearly with increasing Ms per earthquake. DELs have a positive correlation with deaths and casualties on a logarithmic scale. This study further discusses that during different stages of socioeconomic development, changes in both exposure and vulnerability may be the major factors leading to change differences in earthquake-induced socioeconomic consequences. This study is a beneficial supplement to the global earthquake database and is useful for calibrating global or regional empirical loss models.

On the tenth anniversary of the 2008 Wenchuan Earthquake, investigating the evolution of disaster science is worthwhile and can be used to improve the future execution of disaster risk management. Based on more than 55,786 articles on the relative topic of “Disaster” derived from the Web of Science Core Collection from 1999–2017, this study employs CiteSpace and Google Earth to identify and visualize the spatial distribution of publications, bursts of keywords and categories, highly cited references, and interdisciplinary levels and then identify the emerging trends of disaster research over the past 20 years. The results show that the earthquake indeed jumpstarted a massive wave of disaster research around the world and increased international cooperation over the last decade. However, in terms of both the quantity and quality of publications in disaster research fields, China is lagging behind the U.S. and European countries. Moreover, although designing disaster prevention and mitigation strategies is a new popular field of disaster science, geological environment changes and geologic hazards triggered by earthquakes are more popular research topics than disaster emergency and recovery. In addition, the transdisciplinary level of disaster science increased after the earthquake. This interdisciplinary characteristic of disaster science gradually increased in popularity, which demonstrates that people can learn from catastrophes. These emerging trends could serve as a scientific basis to clearly understand disaster science progress over the last 20 years and provide a reference for rapidly identifying frontier issues in disaster science.

This paper implements a temporal–spatial recovery measurement of the catastrophic 1976 Tangshan earthquake using available statistical data. The results show that the gross regional product (GRP) level of the Tangshan region achieved a new normality after seven years. During this recovery process, net indirect losses totaled RMB3.7 billion and net indirect gains totaled RMB3.9 billion at the 2007 price level. The area surrounding the Tangshan region benefited from the disaster, both in terms of GRP level and per capita GRP level, at least in the short term. The sector-level economic recovery process seems longer. The production level of the construction sector was 0.9 to 2.5 times that of the pre-disaster level during its 11-year recovery period. The per capita GRP level of the Tangshan region was 1.7 times that of pre-earthquake 30 years later. This quantitative disaster recovery analysis is critical for validating or initializing economic loss estimation models.

Significant advancements have been made in examining the relationship between economic development and disaster losses at the global and national scales, but very little research has been done at the sub-national level, especially in China. Based on socioeconomic and disaster impact data from 31 provinces (municipalities, and autonomous regions) in China from 1990 to 2010, ordinary least squares regression was used to determine the relationship between socioeconomic development and effects of natural disasters. Results showed that economic development played a distinct role in mitigating disaster damages in the whole China and its eastern, central and western regions. There existed a U-shaped relationship between economic growth and disaster losses in the whole China and its eastern region, and an inverted-U nonlinearity linkage in its central and western areas. These findings further confirmed the existence of a nonlinear relationship between economic development and disaster losses. Economic growth had played a more important role in mitigating disaster losses in the central region of China than that in the western one. Further investigations demonstrated that as economic develops, there were fewer deaths caused by natural hazards in whole China and all its three regions. The combination of the lower level of education, higher unemployment rate and greater gross dependence ratio has contributed to the increase in death toll caused by natural disasters, but this trend could be partly offset by wealth growth.

The exploration of the characteristics of natural disasters (NDs) and their impacts have important implications for guiding disaster prevention and mitigation. This paper discussed the characteristics of the major NDs that occurred in mainland China between 1980 and 2011 based on the number of disasters and fatalities, the affected population, and the resulting economic damages. We further examined the relationships between the level of social/economic development and the disaster losses. At last, we also explored the trend variations in disaster losses and losses expressed as a percentage of gross domestic product (losses/GDP) for 31 provinces in China over the period from 1990 to 2011. The results show that over the past three decades, the number of major NDs and the number of affected population appear to exhibit a detectable increasing trend, while the direct economic losses and the deaths show a slight increase trend. The correlations between the number of disasters and the economic losses as well as deaths and between the economic losses and socioeconomic status were not significant (p > 0.05), which indicates that the growth in the number of disasters do not result in a significant increase in the disaster losses. Further investigations demonstrate that as economies develop, there are fewer losses/GDP in China over the past two decades. The per capita accumulative losses and losses/GDP in the western and central regions were higher than that in the eastern parts of China. There are significant regional differences in the trends in disaster losses and losses/GDP in China. Statistics also indicate that more than 70 % provinces in underdeveloped regions show an increasing trend in disaster losses, whereas exceeding 90 % provinces exhibit a decreasing trend in their losses/GDP. These results suggest that the economic development may have played an important role in improving the capacity of disaster prevention and reduction.

This study evaluates and compares the indirect economic loss (IEL) resulting from two hypothetical catastrophes occurring in China – in developed Shanghai and in less-developed Sichuan – to provide new measures of disaster reduction. IEL was divided into indirect economic loss due to the disruption of production process (IEL I) and indirect economic loss induced by the disturbance of industrial lines (IEL II). An input-output model was used to assess these two types of IEL. The study showed that (1) developed regions may be more vulnerable with respect to IEL; (2) IEL II is the primary factor contributing to total IEL; (3) decision makers need to focus on IEL II beside IEL I which is usually the main disaster-reduction target after a disaster; and (4) tradeoff between economic growth and disaster prevention is needed to achieve regional sustainable development.

This study presents a methodology for return period analysis and risk assessment of severe dust storm disaster. Meteorological observation data, soil moisture data, and remote sensing data from 30 meteorological stations in Inner Mongolia (western China) from 1985 to 2006 were used for the study. A composite index of severe dust storm disaster (Index I (SDS)) based on the influence mechanisms of the main contributing factors was developed by using the analytic hierarchy process and the weighted comprehensive method, and the hazard risk curves (i.e., the transcendental probability curves of I (SDS)) for the 30 stations were established using the parameter estimation method. We then analyzed the risk of the occurrence of severe dust storm under different scenarios of 5-, 10-, 20-, and 50-year return periods. The results show that the risk decreased from west to east across Inner Mongolia, and there are four severe dust storm occurrence peak value centers, including Guaizihu, Jilantai, Hailisu, and Zhurihe-Erenhot. The severity of dust storms in seven places will be intolerable in the 50-year return period scenario and in three places in the 20-year return period scenario. These results indicate that these locations should concentrate forces on disaster prevention, monitoring, and early warning. The I (SDS) was developed as an easily understandable tool useful for the assessment and comparison of the relative risk of severe dust storm disasters in different areas. The risk assessment was specifically intended to support local and national government agencies in their management of severe dust storm disasters in their efforts to (1) make resource allocation decisions, (2) make high-level planning decisions, and (3) raise public awareness of severe dust storm risk.

Exposure and vulnerability are the main contributing factors of growing impact from climate-related disasters globally. Understanding the spatiotemporal dynamic patterns of vulnerability is important for designing effective disaster risk mitigation and adaptation measures. At national scale, most cross-country studies have suggested that economic vulnerability to disasters decreases as income increases, especially for developing countries. Research covering sub-national climate-related natural disasters is indispensable to obtaining a comprehensive understanding of the effect of regional economic growth on vulnerability reduction. Taking China as a case, this subnational scale study shows that economic development is correlated with the significant reduction in human fatalities but increase in direct economic losses (DELs) from climate-related disasters since 1949. The long-term trend in climate-related disaster vulnerability, reflected by mortality (1978–2015) and DELs (1990–2015) as a share of the total population and Gross Domestic Product, has seen significant decline among all economic regions in China. While notable differences remain among its West, Central and East economic regions, the temporal vulnerability change has been converging. The study further demonstrated that economic development level is correlated with human and economic vulnerability to climate-related disasters, and this vulnerability decreased with the increase of per-capita income. This study suggested that economic development can have nuanced effects on overall human and economic vulnerability to climate-related disasters. We argue that climate change science needs to acknowledge and examine the different pathways of vulnerability effects related to economic development.

Accurate exposure estimation is essential for seismic risk assessment. Recent rapid urbanization and economic growth in China have led to massive spatiotemporal changes in both the asset value and GDP exposed to seismic hazards. Using available GDP data, the asset value dataset produced by Wu et al (2014a) and spatial disaggregation technology, gridded maps of GDP and asset value are overlaid with the latest seismic map to investigate spatiotemporal changes in economic exposure in the most seismically hazardous areas (MSHAs) in China in 1990, 2000 and 2010. We found that 15.4% of China's asset value and 14.1% of China's GDP were located in MSHAs in 2010, and the asset value and GDP exposed to MSHAs reached 15.9 trillion CNY and 6.2 trillion CNY, respectively, with average annual rates of increase of 14.4% and 11.3% over the two decades. The evidence of increased exposure provides valuable information regarding whom or what risk managers should give the most attention based on the economic exposure changes in earthquake-prone areas of China. Notably, the North China seismic belt, which is associated with the largest economic exposure to earthquakes and a rapidly increasing rate of economic exposure compared to those in other seismic belts, and the Qinghai-Tibet seismic belt, which has the highest earthquake occurrence, are two seismic belts of interest. A more detailed study is required to determine the relationship between increased economic exposure and earthquake disaster losses combined with hazard level and vulnerability.

To compare the coverage rate of the hypoglycemic agents base on 2009 and 2012 versions of the national essential medicine list among patients with type 2 diabetes mellitus (T2DM) in Beijing communities.A total of 900 patients with T2DM were enrolled in the study from four community health service centers in Beijing and followed up for one year. The following data were collected and analyzed, including patients characteristics, the proportion of patients with glycosylated hemoglobin A1c (HbA1c) less than 7% and the coverage rate of the hypoglycemic agents.(1) The coverage rate of using the hypoglycemic agents in 2012 version of the national essential medicine list was significantly higher than that in 2009 version (91.4% vs 42.9%, χ(2) = 481.09, P0.05). The coverage rates of the seven hypoglycemic agents in national essential medicine list were significantly different (χ(2) = 1519.65, P0.05) . The coverage rates of acarbose (48.9%), metformin(40.7%) and human insulin (31.1%) were higher than those of glimepiride (9.4%), glipizide (3.0%), glibenclamide (0.6%) and animal insulin (0.2%). (2) After one year follow-up, the proportion of the patients with HbA1c less than 7% was higher than that at the baseline(53.6% vs 32.3%, χ(2) = 77.26, P0.05). The coverage rate of using the hypoglycemic agents in 2012 version of the national essential medicine list was significantly higher than that in 2009 version (85.5% vs 37.4%, χ(2) = 376.367, P0.05). The coverage rates of acarbose (49.7%), metformin (36.3%) and insulin (30.4%) were still higher than those of glimepiride (6.3%), glipizide (2.2%) , glibenclamide (0.4%) and animal insulin(0.0%).The hypoglycemic agents in 2012 version of the national essential medicine list could meet the current need of the type 2 diabetes patients in Beijing communities better than those in 2009 version.

The identification of societal vulnerable counties and regions and the factors contributing to social vulnerability are crucial for effective disaster risk management. Significant advances have been made in the study of social vulnerability over the past two decades, but we still know little regarding China's societal vulnerability profiles, especially at the county level. This study investigates the county-level spatial and temporal patterns in social vulnerability in China from 1980 to 2010. Based on China's four most recent population censuses of 2,361 counties and their corresponding socioeconomic data, a social vulnerability index for each county was created using factor analysis. Exploratory spatial data analysis, including global and local autocorrelations, was applied to reveal the spatial patterns of county-level social vulnerability. The results demonstrate that the dynamic characteristics of China's county-level social vulnerability are notably distinct, and the dominant contributors to societal vulnerability for all of the years studied were rural character, development (urbanization), and economic status. The spatial clustering patterns of social vulnerability to natural disasters in China exhibited a gathering-scattering-gathering pattern over time. Further investigations indicate that many counties in the eastern coastal area of China are experiencing a detectable increase in social vulnerability, whereas the societal vulnerability of many counties in the western and northern areas of China has significantly decreased over the past three decades. These findings will provide policymakers with a sound scientific basis for disaster prevention and mitigation decisions.

New features of natural disasters have been observed over the last several years. The factors that influence the disasters' formation mechanisms, regularity of occurrence and main characteristics have been revealed to be more complicated and diverse in nature than previously thought. As the uncertainty involved increases, the variables need to be examined further. This article discusses the importance and the shortage of multivariate analysis of natural disasters and presents a method to estimate the joint probability of the return periods and perform a risk analysis. Severe dust storms from 1990 to 2008 in Inner Mongolia were used as a case study to test this new methodology, as they are normal and recurring climatic phenomena on Earth. Based on the 79 investigated events and according to the dust storm definition with bivariate, the joint probability distribution of severe dust storms was established using the observed data of maximum wind speed and duration. The joint return periods of severe dust storms were calculated, and the relevant risk was analyzed according to the joint probability. The copula function is able to simulate severe dust storm disasters accurately. The joint return periods generated are closer to those observed in reality than the univariate return periods and thus have more value in severe dust storm disaster mitigation, strategy making, program design, and improvement of risk management. This research may prove useful in risk-based decision making. The exploration of multivariate analysis methods can also lay the foundation for further applications in natural disaster risk analysis.

International audience; Disaster loss estimates are helpful for managing post-disaster reconstruction and for designing disaster-risk mitigation strategies. However, most of these estimates in China merely consider direct losses, and only a few include indirect economic losses. As the most destructive earthquake since the founding of the People's Republic of China in 1949, the Wenchuan Earthquake that occurred in 2008 resulted in direct economic damages reached Chinese Yuan (CNY) 845 billion (US $124 billion). The aim of the study was to estimate indirect economic losses caused by the Wenchuan Earthquake in Sichuan Province through the Adaptive Regional Input-Output (ARIO) model, which can reflect disaster-related changes in production capacity, ripple effects within the economic system, and adaptive behaviors of economic actors. The results showed that indirect economic losses in the production and housing sectors were estimated at 40% of the direct economic losses, i.e., approximately CNY 300 billion; moreover, the model predicted an 8-year reconstruction period. Several factors contributed to these losses, including significant damages to key sectors, financial constraints on reconstruction, post-earthquake investment instability, and limits in reconstruction capacity. Active government support policies post-earthquake are a useful strategy to mitigate the adverse economic impact of an earthquake in developing countries.