Tell us about your experience on SpringerLink and you could win a MacBook Pro. Take survey. Download PDF · Natural Hazards. May 1997 , Volume 15, Issue ...
Natural Hazards 15: 217–229, 1997. c 1997 Kluwer Academic Publishers. Printed in the Netherlands.
217
Quick and Approximate Estimation of Earthquake Loss Based on Macroscopic Index of Exposure and Population Distribution QI-FU CHEN1 , YONG CHEN1 , JIE LIU1 and LING CHEN2 Center for Analysis and Prediction, State Seismological Bureau, Beijing 100036, P.R. China Institute of Geophysics, State Seismological Bureau, Beijing 100081, P.R. China (Received: 22 May 1996; in final form: 2 October 1996) Abstract. In the traditional method of earthquake loss estimation, all exposed facilities are classified according to their structural type and/or occupancy. Inventory data is collected and the total loss is estimated as the aggregate of all facility losses from each facility class separately. For many regions of the world, however, the vast amount of data required for this method is difficult or impossible to obtain. The traditional method is also unable to estimate quickly the loss from an unexpected catastrophic earthquake. It is difficult to give the necessary risk information to help the government with rescue and relief after the earthquake disaster. In this paper, we propose a quick and approximate estimation method of earthquake loss based on a macroscopic index of exposure and population distribution from GIS. This method was applied to analyze several earthquake scenarios with World Bank and CIESIN data. The preliminary analysis and comparison results show that our method is effective and reasonable for quick assessment of earthquake risk. Key words: earthquake, loss estimation, scenario, GDP, population
1. Introduction Earthquake risk not only depends on the intensity caused by earthquakes, but also highly depends on exposure and the inventory vulnerability. Exposure refers to all man-made facilities that may be affected in an earthquake. It includes all residential, commercial, and industrial buildings, schools, hospitals, roads and railroads, bridges, pipelines, power plants, communication systems, and so on (Shah, 1995). There are two ways to address exposure. In the traditional method (Applied Technology Council, 1985; Panel on Earthquake Loss Estimation Methodology, 1989; Chen et al., 1992; GeoHazards International, 1994), all exposed facilities are classified according to their structural type or occupancy. Inventory data is collected and the losses are estimated for each facility class separately. The final loss estimate is the aggregate of all facility losses. The two main obstacles in this approach are the unavailability and the inconsistency of data. For many regions of the world, the vast amount of data required are difficult or impossible to obtain.
218
QI-FU CHEN ET AL.
Regional differences in classification schemes make even the information that is available, difficult to compile into a single, consistent, worldwide database. The second approach to exposure bypasses the data collection problems of the traditional method by employing a macroscopic indicator to represent the total exposure directly. This study expresses exposure according to the latter approach. It uses social wealth as the macroscopic indicator, and uses Gross Domestic Product (GDP) to represent social wealth and to estimate earthquake loss. The fundamental assumption of this holistic technique is that the number of man-made facilities is directly proportional to social wealth, specifically to GDP. 2. Global Exposure Distribution with GDP 2.1. INVENTORY AND MACROSCOPIC INDICATOR OF EXPOSURE (GDP) The macroscopic indicator GDP refers to newly created social wealth, counted by the National Geographic principle (World Bank, 1995). GDP refers to the gross domestic product which measures the total output of goods and services for final use from all resident units (enterprises and self-employed individuals) of a country (or region) during a certain period of time. In contrast, Gross National Product (GNP) measures the total domestic and foreign value added by residents. GNP includes GDP plus the net factor income from abroad as wages and property income. GDP is considered the better exposure indicator of the two. GDP is one of the many economic and social indicators compiled on a regular basis by various agencies and institutions (for example, World Bank, World Resources Institute, and United Nations). The data is represented at a national level, and increasingly is available in digital form, either as a database file or in a geographical information system (GIS) format. 2.2. EXPOSURE AND POPULATION Population density and economic exposure are closely related (World Bank, 1995). In many cities, exposure is currently increasing at an unprecedented rate to accommodate massive population increase and urbanization. Based on the high resolution population data available and on this relationship between population and exposure, the national GDP data can be distributed according to population distribution to estimate the GDP per unit area throughout the country. GDP per unit area = (GDP of region)
(population in unit area) : (regional population)
(1)
Note that a region may be a country, province, county, city, or any other region for which GDP data are available. Within the advance on global change research, a project of ‘Population Data and Global Environmental Change’ (Tobler, 1995) is supported by the Consortium for
219
ESTIMATION OF EARTHQUAKE LOSS WITH GDP
International Earth Science Information Network (CIESIN) and the Environmental Systems Research Institute, Inc. (ESRI) in the United States of America. The principal motivation for this project is that global change research largely neglects the human dimensions of environmental degradation processes. In the first phase of the project, a consistent GIS database of about 19 000 subnational administrative boundaries and associated population figures for the whole world has been compiled. As much as possible this project relied on existing data sets that have been produced by various agencies and universities. CIESIN kindly provided our working group with the latest population data in 1994 of 5 minute by 5 minute for the whole world from this project. Figure 1 illustrates the global population distribution in 1994 by 0.5 0.5 unit cells integrated from 50 50 CIESIN population data. 2.3. GEOGRAPHIC DISTRIBUTION OF GDP In this study, we divide the world map into a uniform grid of unit area 0.5 0.5 , then determine the population located in each cell. Finally, we can calculate the GDP located in each cell with Equation (1). The global GDP distribution (Figure 2) is obtained by using the population data and GDP or GNP per capita for each country or region. Table I and II list countries or regions with GDP/GNP data available from various sources. For those countries or regions without available GDP data, the GNP data is used as an approximation. There are 20 countries or regions where besides the list in Table I and II, we could not find out their GDP or GNP data. The areas of those countries or regions, however, are very small except for Western Sahara. In fact, the seismicity of those countries or regions is very low. 3. Earthquake Loss Estimation with GDP In traditional engineering loss estimation models, expected loss at a site is determined by the following equation:
28 X 4
