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CHEN Yong , CHEN QifuHUANG Jing and XU Wenli

China Seismological Bureau, Beijing 100036, China


The urbanization is a worldwide trend. According to UN figures (UN, 1990), the rate of urbanization throughout the world is continually rising. Whereas in 1950, a little under 30% of the world population (2.5 billion) lived in cities. It is now about 50% (of 6 billion), which will increase to over 60% (of 8.3 billion) by the year 2025. At the same time, the number of cities with over millions of inhabitants has risen from 83 in 1950 to 325 nowadays, increased almost four times. This trend is especially obvious in the Third World countries, where the number of cities over a million inhabitants has increased six times. The urbanization is an obvious trend in China, according to the data from State Statistics Bureau, and the number of cities increases rapidly in the past half-century (Fig.1, State Statistics Bureau of China, 1999).


Fig.1  Number of China's Cities is changing with time.

City people are more dependent on the infrastructure, i.e. the supply of water, electricity, gas, heating, telecommunications and transport connections than the more self-reliant rural population, which is more accustomed to helping one another in emergencies. Numerous natural catastrophes of recent years have shown quite emphatically how vulnerable the infrastructures of major cities are to minor breakdowns and how acute shortages in supply can develop within a short time. Chen et al. (Chen et al., 2001a) showed that there is an empirical relation between the economic loss caused by natural hazards (in million USD) and social wealth expressed by GDP (Gross Domestic Product, in million USD):

Loss caused by natural hazards = a + b×GDP + c×GDP2

where a, b and c are constants:

                        a = 4×107
                        b = -5000

c = 2.5×10-1

The losses increase remarkably when population concentrates in urban area and social wealth increases. Table 1 gives the economic losses caused by earthquakes in China for every decade since 1950, which shows the remarkable increase of losses.

Table 1.  Damage Caused by Earthquake Events during 1950-1999


Number of Events

Economic Loss(hundred million)
















Data sources: Lou Baotang(1996); Monitoring and prediction division of China Seismological Bureau (1996, 2001)

The historical records of maximum losses caused by earthquakes brushed up recently in many nations. For example, the 1994 Northridge earthquake occurred along a fault that even did not breach the surface, yet the natural disaster created by the movement along it is the most expensive one in US nation's history. The Mw 7.7 Gujarat Earthquake of 2001 in India is one of the largest recorded events, which occurred within an intra-plate setting as well as the most fatal earthquake over the history of India.


A key question that must be addressed in earthquake disaster reduction is: how much loss might a city or a region suffers in future earthquakes? The quantification of earthquake disasters is the basis for the reduction of natural disasters and risk management (Fig.2).

Fig.2  Quantification of earthquake disaster (Chen et al, 2002a).

1. Seismic Hazard and Risk Analysis: A Simplified Approach

The fault model, as the linear form of potential earthquake sources, has been widely used as the essential basis for both deterministic and probabilistic hazard analyses due to its clear geological background (McGuire, 1993). However, there are some noticeable problems existing in the application of fault model to hazard assessment, which include the difficulties in data collections, problem about the unclear earthquake-fault relation, and uncertainties from the determination of maximum length of rupture. Moreover, recent studies also found that neither the uniform seismicity within a zone nor the Euclidean geometry of a zone accords with the fractal spatial distribution of seismicity, therefore, the construction of zone geometry in fault model may become contentiously subjective.

Fig.3  Global seismic hazard map (by 0.5´°0.5° with exceeding probability of intensity 10% in 50 years).

Considering above problems in seismic hazard assessment, Chen et al. ( Chen et al., 2002a,b; Chen et al., 1998; Chan et al., 1998; Chen Qifu et al., 1997; Chen, 2001b; Li et al. 2000) proposed a new methodology of hazard analysis by using area model for defining potential earthquake sources, taking recent instrumental earthquake catalogs as basic data to determine the source parameters which described seismicity characteristics, and employing kernel method to estimate the upper magnitude of earthquakes in each source based on historical data. Applying this methodology to the available global earthquake catalog, we reconstructed the global seismic hazard map (Fig.3), which is also roughly compared with those previously produced as a practical test of the new methodology.

There are two kinds of problems existing in seismic hazard analysis: scientific and other technological problems.

2. A Key Scientific Problem – Where the Earthquake Will Occur?

For mitigating natural hazards, scientists have to face many scientific challenges. One of them is the active tectonics research. Seismic events at Tangshan (China), New Madrid (United States), the Latur and Jabalpur (India) event, and many other earthquakes suggest that the likely stable continental regions are much more vulnerable to earthquakes than was once thought (Chen et al., 1986). The integrated study of active tectonics (including active faults and those hidden faults) for the purpose of understanding their seismic potential is a new topic of continental dynamics (Chen Yong, Chen Qifu and Li Juan, 2001).

Active tectonics should be integrative, innovative and focus on natural laboratories in tectonically active regions with the purpose of understanding the operation of tectonic processes that have been responsible for shaping the Earth's continental crust, and should take advantage of new and emerging technologies. The area of research should be an actively deforming region or sub-region of the Earth's continental crust, where dynamic properties that are directly relevant to understanding tectonic processes can be observed or measured. These areas commonly have the following attributes:

topographic, stress and fluid profiles or distributions that interact with the active deformation;

heat flow and geothermal gradients that accompany fluid and mineral chemical reactions;

seismic and seismic slip that relates to the present tectonism;

strain and metamorphism not overprinted by younger deformation.

3 Technological Problem-Improved Collection and Management of Data, Information, and Knowledge

Modern natural disaster reduction principles and practices depend on a solid information base, best-practice information management, and robust methodologies and tools for analyzing hazard and risk.

A more integrated approach to database development is needed at the national level to support risk-assessment and natural disaster reduction. The GIS database scaled 1250000 now is available for the whole nation of China, and more detailed database for some regions and cities (Chen et al., 1998)

Collection and collation of data and information on the impact of individual natural disasters need to be encouraged, coordinated, and systematized.  Data must include observations of hazard, physical damage, and cost of repair or replacement of buildings and other infrastructure. This type of information is crucial for obtaining improved estimates of the direct costs of disasters. Much of this information is obtained readily by field teams visiting disaster areas soon after the events (such as with pre-formatted, palm-top computers) and cooperating with insurers, local officials, and engineers regarding damage and repair costs (Li Li and Chen Yong, 2002; Chen Yong, 1998).  


In most countries, including China, the Geo-science Agencies-such as China Seismological Bureau and many universitiesoperate the national network for earthquake detection and reporting. Research tends to be directed at understanding the geophysical processes of seismic hazards. Most of this work can be referred to ashazards science.

Important changes in approach have taken place during the last two decades, in large part through the inter-agency collaboration engendered by the successful International Decade for Natural Disaster Reduction (IDNDR). There is now increased emphasis on understanding the consequences of hazard impacts on communities rather than simplifying the geophysics of the hazards themselves. Thus, work in both the physical and social sciences needs to be brought together to give a more holistic understanding of these shocks to communities.  

An important driver in the shift from hazard science to community and risk-based science has been the introduction of national Risk Management Standards.  The first of these was the Australian Standard 4360 (1995, 1999). In China, the law of the People's Republic of China on protecting against and mitigating earthquake disasters was promulgated in 1997 and came into force since March 1, 1998 (China Seismological Bureau, 1998). These standards and law provide generic framework for the identification, assessment and treatment of risks. They also offer the possibility for greater cooperation between science agencies, research bodies and natural disaster management agencies by providing a common approach to risks. Most of the work can be referred to asrisk science.

The approaches, methodologies, and tool sets used in the scientific arena to assess hazards, vulnerability, risk, and uncertainty are not restricted to any one group, or groups of hazards. Assessment and model of risk and uncertainty are generic processes that can be applied equally to, for example, tropical cyclones (sudden-impact natural hazard), dry-land salinity (slow-onset environmental hazard), or terrorist attacks (societal hazard), as long as there is an information base on which can undertake the analysis and define the uncertainty.  Science directed towards the safety, security, sustainability, and stability of communities therefore provided the potential for communities and governments to take a truly all-hazards and holistic approach to risk.


Fig.4  (a) Seismic hazard map in China with exceeding probability of intensity 10% in next 50 years; (b) The expected physical losses caused by earthquakes in future 50 years in the mainland of China.



Loss-estimation models generally capture the risk in a rather limited context (in most cases, some types of buildings and facilities ATC, 1985), commonly in terms of the direct damage or cost of a disaster.  Most of this work can be referred to as engineering disaster. However, research is needed to extend these estimates to include indirect effects (e.g. loss of income, quality of life) as well as social, political, and other economic factors that invariably play an important role in decisions about risk treatment. Several recent earthquake disaster cases showed that the economic losses from estimate of engineering disasters are often remarkably less than the total economic loss. For example, the economic losses of buildings and facilities caused by Kobe 7.2 earthquake (1995.1.17) is 48 billion USD, which is much less than the total economic losses of 100 billion USD. In 1995, Risk Management Solution Inc. (RMS, 1995) published a book titled “What if the 1923 earthquake strikes again? A five- prefecture Tokyo Region Scenario”, in which the economic losses by the buildings and contents ($1,000 billion USD) were much less than the total economic losses (2100 billion USD).  With the development of urbanization and the increase of economies, in many places, particularly in the megacities, the indirect loss in the past meaning is now becoming the more fundamental component of the economic losses than the losses caused by damage of buildings\facilities and contents. We call the total losses of earthquake disasters as the social disaster.

Most earthquake loss studies use an inventory approach, which predicted damages in various categories of structures and facilities in a concerned region. They are estimated and added together to obtain the total estimated loss for particular intensity ranges. Such an approach requires a detailed inventory database of the structures and facilities in the region, which is not always readily available in many regions of the world. Therefore, we use an alternative means of estimating earthquake losses based on several macroeconomic indices such as the Gross Domestic Product (GDP) and population. Based on the published earthquake loss data during 1980-1995, the relations between GDP and earthquake loss have been formulated empirically for several intensity ranges. The world's land surface was divided into unit cells with 0.5° ´ 0.5° in size, the GDP


Fig.5  The expected physical losses caused by earthquakes in future 50 years all over the world.

of each cell was apportioned based on its population and the GDP and population of the region to which it belongs. The predicted seismic loss of the cell was then estimated from the seismic hazard probability function, its GDP, and the empirical relation between GDP and seismic loss. Accordingly a global seismic loss map is compiled for intensity VI and above. Employing readily available social economic data as the basis for the vulnerability analysis, the method enables us to obtain seismic loss estimates for regions without a detailed inventory of exposed structures or the required collateral geological information. Seismic loss estimates can also be upgraded easily with social economic data collection for the fast developing areas of the world.

Table 2  Expected Annual Losses for Different Countries in the Future 50 years (Exceeding Probability of 10 %) (Chen et al, 2002a)

Country and Zones


(hundred million)

GDP (hundred

million USD in 1999)

Average losses/year

(hundred million USD in 1999)



285 840




46 000


China (mainland)


8 050




74 320




2 260




102 080



Our method first estimated the economic loss of countries in the world, caused by future earthquakes, and first showed that there is an economic loss of 230 hundred million USD every year caused by earthquake damage all over the world (Table 2). This result is accordant with the statistic result made by global re-insurance agent, such as Swiss Re and Munich Re, during the year of 1995 to 1999.

We are familiar to the engineering vulnerability, but what is the vulnerability of social wealth? Adobes in Costa Rica have almost the same vulnerability as old civil houses in China, which represent the vulnerability in worst cases. On the other hand, high quality buildings in the Middle East have the same vulnerability as reinforced concrete buildings in China, due to their employing the state-of art-design and construction techniques, which represent the vulnerability of the best cases. The macroeconomic vulnerability is defined as the ratio of physical economic loss caused by earthquake to the Gross Domestic Product (GDP) within a given area. Since the total macroscopic loss is the sum of losses of different types of buildings and facilities, the macroeconomic vulnerability must be greater than that of the best case while less than that of the worst case.

Fig.6  Vulnerability relation based on the loss data for the intensity range VI – IX. MDF is the Mean Damage Factor (represented in % of the total wealth). The upper curve and the lower curve were obtained from the inventory data (Middle East: Akkas and Erdik, 1984; Central America: Sauter and Shah, 1978; China: Yin, 1995), which represent the best and worst building situations respectively.  The LIE, MIE and HIE represent the Low Income Economy, Middle Income Economy and High Income Economy respectively (Chen Yong, Chen Qifu and Chen Ling, 2001c).

It can be seen from Fig.6 that the MDFs vary with the increasing intensity I for all types of buildings, but the forms of the variation are almost the same. Through statistical analysis, the variation form could be approximated as:

log MDFI, I = log MDFi, VI - 0.065 I2 + 1.44 I - 6.3                  (1)

where log MDFi, I is the logarithms of Mean Damage Factor of i-th type buildings at intensity I, the first term on the right side is the logarithms of MDF of i-th buildings at intensity I = VI, which is building type dependent, that is, the more poor resistant quality, the higher the log MDFi, VI. The remaining three terms on the right side of (1) are intensity dependent only, not dependent on the type of buildings. Formula (1) shows that the impacts on MDF arising from building types and from intensity can be separated.

For clarity, we rewrite formula (1) as:

MDFi, I = MDFi,VI F(I)                                 (2)

F(I) =                                         (3)



A community-centred approach to natural disaster reduction should produce better management of risks. Thus, effective risk reduction and mitigation require the active contribution of community members. Natural disaster management agencies cannot achieve these results by themselves because the causes of risks are commonly in areas beyond the scope of practice of agencies. In many cases, community members can be far more effective in addressing the causes of risks through their everyday practices.

The shift to a community-centred approach requires a better understanding of community profiles, cultures, expectations, decision-making processes and needs. This is a difficult issue because communities are complex. There are no two communities that are totally the same.  Social science research can assist here. Importantly, there are also great differences within communities in terms of the susceptibility of community members to different hazards and their ability to cope with extreme events.  Community engagement with risk management therefore becomes an important issue. Risk communication research identifies that provision of information is not enough: people need to have a vested interest in the issue to take it on board, and agencies therefore must develop processes that engage and maintain people's interest in issues about risk.

Strategies for understanding and engaging the community have become central to many areas of public policy.  Research and practice from other disciplines and policy areas will contribute to successful community-centred natural disaster reduction.  The wide range of ways being used to understand and engage communities in other areas need to be identified, tested, and reviewed to ensure that they are appropriate for effectively managing risk.

The challenges facing the natural disaster reduction sector will benefit from input from research in both the physical and social sciences. Building, enhancing, and maintaining an appropriate research capability are essential in order to achieve the required outcome of safer sustainable communities. In 2001, European Seismological Society suggests to support research that will assist in the implementation of a “community centered” approach to natural disaster reduction in vulnerable areas. The same trend exists in China. Scientific research and information management in the natural disaster reduction arena have developed significantly in recent years in China and are poised to have an even greater impact on risk-management, and give the current international focus on community vulnerability and needs.


Akkas, N. and M. Erdik, 1984. Consideration on assessment of earthquake resistance of existing buildings. Int. J. Housing Science and application, 8, 49-66.
ATC (Applied Technology Council), 1985. Earthquake damage estimation data for California, Applied Technology Council, Redwood city, California.
Chan, L. S., Y. Chen, Q. Chen, J. Liu, W. Dong, and H. Shah, 1998. Assessment of global seismic loss based on macroeconomic indicators, Natural Hazards, 17, 269-283.
Chen, Q. F., Y. Chen, J. Liu, and L. Chen, 1997. Quick and approximate estimation of earthquake loss based on macroscopic index of exposure and population distribution, Natural Hazards, 15, 217-229.
Chen, Y., K. L. Tsoi, F. B. Chen, Z. H. Gao, Q. J. Zhou, and Z. L. Chen, 1986. The Great Tangshan Earthquake of 1976 – An Anatomy of Disaster, Oxford: Pergamon Press.
Chen, Y., G. P. Li, Q. F. Chen, L. Chen, and M. F. Li, 1998. Earthquake damage and loss estimation with Geographic Information System, Acta Seimologica Sinaca, 11, 6, 751-758 (in Chinese).
Chen, Y., 1998. Zhangbei earthquake of northern China, January 1998.INCEDE Newsletter –International Center for Disaster Mitigation Engineering, 6, 4, 1-3.
Chen, Y., J. Liu, Q. Chen, and L. S. Chan, 1998. Global seismic hazard assessment based on area source model and seismicity data, Natural Hazards, 17, 151-267.
Chen, Y., Q. F. Chen, and J. Li, 2001. Advances on active tectonics research, Earthquake Research in China, 15, 4, 346-353.
Chen, Y., L. Li, and B. S. Wang, 2001a. Human activity, natural disasters and active tectonics, Quaternary Science, 4313-4320 (in Chinese).
Chen, Y., Seismic Risk Assessment, 2001b. Encyclopedia of Global Environmental Change, GA365, Toronto: John Wiley and Sons.
Chen, Y., Q. F. Chen, and L. Chen, 2001c. Vulnerability analysis in earthquake loss estimation, Natural Hazards, 23, 349-364.
Chen, Y., Q. F. Chen, J. Liu, L. Chen, and J. Li, 2002a. Seismic Hazard and Risk Analysis: A Simplified Approach. Beijing: Science Press.
Chen, Y., L. Chen, F. Guendel, O. Kulhanek, and J. Juan, 2002b. Seismic Hazard and loss estimation for Central America, Natural Hazards, 35, 16-175.
China Seismological Bureau, 1998. Law of the People's Republic Of China on protection and mitigating earthquake disasters. Official document (in Chinese).
Division of Monitoring and Prediction, China Seismological Bureau, 1996. Compilation of Earthquake Disasters Data (1990 -1995) in China, Beijing: Seismological Press (in Chinese).
Division of Monitoring and Prediction, China Seismological Bureau, 2001. Compilation of Earthquake Disasters Data (1996-1999) in China, Beijing: Seismological Press (in Chinese).
Li, J., Y. Chen, and Q. F. Chen, 2000. Earthquake loss assessment in Central Asia, Inland Earthquake, 14, 1, 1-8 (in Chinese).
Li, L. and Y. Chen 2002. Preliminary report on the Ms 8.1 Kokoxili (Qinghai, China) earthquake of 14 November 2001, Episodes – Journal of International Geosciences, 25, 2, 95-99.
Lou, B. Y., 1996. Review of Chinese Earthquake Disasters (historic and recent), Beijing: Seismological Press (in Chinese).
McGuire, R. K., 1993. The practice of earthquake hazard assessment, International Association of Seismology and the Physics of Earth's Interior, IASPEI Report, Washington D. C: AGU Press.
Sauter, F. and H. C. Shah, 1978. Studies on earthquake insurance, Proceedings of the Central American Conference on earthquake Engineering, V2, San Salvador.
State Statistical Bureau of China, 1999. China Daily, October 1, 1999.
United Nations, 1990. World Demographic Estimates and Projections (1950-2025)Press of United Nations, New York.

Yin, Z., 1995. The Earthquake Disaster and the Estimate of Earthquake Losses, Beijing: Seismological Press (in Chinese).

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