Geology topics

Kishor Jaiswal

Publications and source records attributed to Kishor Jaiswal.

8 recordsLinked to original sources

USGS2013

Estimating economic losses from earthquakes using an empirical approach

We extended the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) empirical fatality estimation methodology proposed by Jaiswal et al. (2009) to rapidly estimate economic losses after significant earthquakes worldwide. The requisite model inputs are shaking intensity estimates made by the ShakeMap system, the spatial distribution of population available from the LandScan database, modern and historic country or sub-country population and Gross Domestic Product (GDP) data, and economic loss data from Munich Re's historical earthquakes catalog. We developed a strategy to approximately scale GDP-based economic exposure for historical and recent earthquakes in order to estimate economic losses. The process consists of using a country-specific multiplicative factor to accommodate the disparity between economic exposure and the annual per capita GDP, and it has proven successful in hindcast-ing past losses. Although loss, population, shaking estimates, and economic data used in the calibration process are uncertain, approximate ranges of losses can be estimated for the primary purpose of gauging the overall scope of the disaster and coordinating response. The proposed methodology is both indirect and approximate and is thus best suited as a rapid loss estimation model for applications like the PAGER system.

Earthquake Spectra↗

USGS2013

GEM Building Taxonomy (Version 2.0)

This report documents the development and applications of the Building Taxonomy for the Global Earthquake Model (GEM). The purpose of the GEM Building Taxonomy is to describe and classify buildings in a uniform manner as a key step towards assessing their seismic risk, Criteria for development of the GEM Building Taxonomy were that the Taxonomy be relevant to seismic performance of different construction types; be comprehensive yet simple; be collapsible; adhere to principles that are familiar to the range of users; and ultimately be extensible to non-buildings and other hazards. The taxonomy was developed in conjunction with other GEM researchers and builds on the knowledge base from other taxonomies, including the EERI and IAEE World Housing Encyclopedia, PAGER-STR, and HAZUS. The taxonomy is organized as a series of expandable tables, which contain information pertaining to various building attributes. Each attribute describes a specific characteristic of an individual building or a class of buildings that could potentially affect their seismic performance. The following 13 attributes have been included in the GEM Building Taxonomy Version 2.0 (v2.0): 1.) direction, 2.)material of the lateral load-resisting system, 3.) lateral load-resisting system, 4.) height, 5.) date of construction of retrofit, 6.) occupancy, 7.) building position within a block, 8.) shape of the building plan, 9.) structural irregularity, 10.) exterior walls, 11.) roof, 12.) floor, 13.) foundation system. The report illustrates the pratical use of the GEM Building Taxonomy by discussing example case studies, in which the building-specific characteristics are mapped directly using GEM taxonomic attributes and the corresponding taxonomic string is constructed for that building, with "/" slash marks separating attributes. For example, for the building shown to the right, the GEM Taxonomy string is: DX 1 /MUR+CLBRS+MOCL 2 /LWAL 3 / DY/MUR+CLBRS+MOCL/LWAL/YPRE:1939 4 /HEX:2 5 /RES 6 / 7 / 8 /IRRE 9 /10/RSH3+RWO2 11 /FW 12 / 13 / which can be read as (1) Direction = [DX or DY] (the building has the same lateral load-resisting system in both directions); (2) Material = [Unreinforced Masonry + solid fired clay bricks + cement: lime mortar]; (3) Lateral Load-Resisting System = [Wall]; (4) Date of construction = [pre-1939]; (5) Heaight = [exactly 2 storeys]; (6) Occupancy = [residential, unknown type]; (7) Building Position = [unknown = no entry]; (8) Shape of building plan = [unknown = no entry]; (9) Structural irregularity = [regular]; (10) Exterior walls = [unknown = no entry]; (11) Roof = [Shape: pitched and hipped, Roof covering: clay tiles, Roof system material: wood, Roof system type: wood trusses]; (12) Floor = [Floor system: Wood, unknown]; (13) Foundation = [unknown = no entry]. Mapping of GEM Building Taxonomy to selected taxonomies is included in the report -- for example, the above building would be referenced by previous structural taxonomies as: PAGER-STR as UFB or UFB4, by the World Housing Encyclopedia as 7 or 8 and by the European Macroseismic Scale (98) as M5. The Building Taxonomy data model is highly flexible and has been incorporated within a relational database architecture. Due to its ability to represent building typologies using a shorthand form, it is also possible to use the taxonomy for non-database applications, and we discuss possible application of adaptation for Building Information Modelling (BIM) systems, and for the insurance industry. The GEM Building Taxonomy was independently evaluated and tested by the Earthquake Engineering Research Institute (EERI), which received 217 TaxT reports from 49 countries, representing a wide range of building typologies, including single and multi-storey buildings, reinforced and unreinforced masonry, confined masonry, concrete, steel, wood, and earthern buildings used for residential, commercial, industrial, and educational occupancy. Based on these submissions and other feedback, the EERI team validated that the GEM Building Taxonomy is highly functional, robust and able to describe different buildings around the world. The GEM Building Taxonomy is accompanied by supplementary resources. All terms have been explained in a companion online Glossary, which provides both text and graphic descriptions. The Taxonomy is accompanied by TaxT, a computer application that enables a user record information about a building or a building typology using the attributes of the GEM Building Taxonomy v2.0. TaxT can generate a taxonomy string and enable a user to generate a report in PDF format which summarizes the attribute values (s)he has chosen as representative of the building typology under consideration. The report concludes with recommendations for future development of the GEM Building Taxonomy. Appendices provide the detailed GEM Building Taxonomy tables and additional resource, as well as mappings to other taxonomies.

GEM Technical Report↗

USGS2013

Metadata for selecting or submitting generic seismic vulnerability functions via GEM's vulnerability database

This memo lays out a procedure for the GEM software to offer an available vulnerability function for any acceptable set of attributes that the user specifies for a particular building category. The memo also provides general guidelines on how to submit the vulnerability or fragility functions to the GEM vulnerability repository, stipulating which attributes modelers must provide so that their vulnerability or fragility functions can be queried appropriately by the vulnerability database. An important objective is to provide users guidance on limitations and applicability by providing the associated modeling assumptions and applicability of each vulnerability or fragility function.

Report↗

USGS2011

Rapid estimation of the economic consequences of global earthquakes

The U.S. Geological Survey's (USGS) Prompt Assessment of Global Earthquakes for Response (PAGER) system, operational since mid 2007, rapidly estimates the most affected locations and the population exposure at different levels of shaking intensities. The PAGER system has significantly improved the way aid agencies determine the scale of response needed in the aftermath of an earthquake. For example, the PAGER exposure estimates provided reasonably accurate assessments of the scale and spatial extent of the damage and losses following the 2008 Wenchuan earthquake (Mw 7.9) in China, the 2009 L'Aquila earthquake (Mw 6.3) in Italy, the 2010 Haiti earthquake (Mw 7.0), and the 2010 Chile earthquake (Mw 8.8). Nevertheless, some engineering and seismological expertise is often required to digest PAGER's exposure estimate and turn it into estimated fatalities and economic losses. This has been the focus of PAGER's most recent development. With the new loss-estimation component of the PAGER system it is now possible to produce rapid estimation of expected fatalities for global earthquakes (Jaiswal and others, 2009). While an estimate of earthquake fatalities is a fundamental indicator of potential human consequences in developing countries (for example, Iran, Pakistan, Haiti, Peru, and many others), economic consequences often drive the responses in much of the developed world (for example, New Zealand, the United States, and Chile), where the improved structural behavior of seismically resistant buildings significantly reduces earthquake casualties. Rapid availability of estimates of both fatalities and economic losses can be a valuable resource. The total time needed to determine the actual scope of an earthquake disaster and to respond effectively varies from country to country. It can take days or sometimes weeks before the damage and consequences of a disaster can be understood both socially and economically. The objective of the U.S. Geological Survey's PAGER system is to reduce this time gap to more rapidly and effectively mobilize response. We present here a procedure to rapidly and approximately ascertain the economic impact immediately following a large earthquake anywhere in the world. In principle, the approach presented is similar to the empirical fatality estimation methodology proposed and implemented by Jaiswal and others (2009). In order to estimate economic losses, we need an assessment of the economic exposure at various levels of shaking intensity. The economic value of all the physical assets exposed at different locations in a given area is generally not known and extremely difficult to compile at a global scale. In the absence of such a dataset, we first estimate the total Gross Domestic Product (GDP) exposed at each shaking intensity by multiplying the per-capita GDP of the country by the total population exposed at that shaking intensity level. We then scale the total GDP estimated at each intensity by an exposure correction factor, which is a multiplying factor to account for the disparity between wealth and/or economic assets to the annual GDP. The economic exposure obtained using this procedure is thus a proxy estimate for the economic value of the actual inventory that is exposed to the earthquake. The economic loss ratio, defined in terms of a country-specific lognormal cumulative distribution function of shaking intensity, is derived and calibrated against the losses from past earthquakes. This report describes the development of a country or region-specific economic loss ratio model using economic loss data available for global earthquakes from 1980 to 2007. The proposed model is a potential candidate for directly estimating economic losses within the currently-operating PAGER system. PAGER's other loss models use indirect methods that require substantially more data (such as building/asset inventories, vulnerabilities, and the asset values exposed at the time of earthquake) to implement on a global basis and will thus take more time to develop and implement within the PAGER system.

Open-File Report↗

USGS2010

An empirical model for global earthquake fatality estimation

We analyzed mortality rates of earthquakes worldwide and developed a country/region-specific empirical model for earthquake fatality estimation within the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) system. The earthquake fatality rate is defined as total killed divided by total population exposed at specific shaking intensity level. The total fatalities for a given earthquake are estimated by multiplying the number of people exposed at each shaking intensity level by the fatality rates for that level and then summing them at all relevant shaking intensities. The fatality rate is expressed in terms of a two-parameter lognormal cumulative distribution function of shaking intensity. The parameters are obtained for each country or a region by minimizing the residual error in hindcasting the total shaking-related deaths from earthquakes recorded between 1973 and 2007. A new global regionalization scheme is used to combine the fatality data across different countries with similar vulnerability traits.

Earthquake Spectra↗

USGS2010

Global building inventory for earthquake loss estimation and risk management

We develop a global database of building inventories using taxonomy of global building types for use in near-real-time post-earthquake loss estimation and pre-earthquake risk analysis, for the U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) program. The database is available for public use, subject to peer review, scrutiny, and open enhancement. On a country-by-country level, it contains estimates of the distribution of building types categorized by material, lateral force resisting system, and occupancy type (residential or nonresidential, urban or rural). The database draws on and harmonizes numerous sources: (1) UN statistics, (2) UN Habitat’s demographic and health survey (DHS) database, (3) national housing censuses, (4) the World Housing Encyclopedia and (5) other literature.

Earthquake Spectra↗

USGS2009

Estimating Casualties for Large Earthquakes Worldwide Using an Empirical Approach

We developed an empirical country- and region-specific earthquake vulnerability model to be used as a candidate for post-earthquake fatality estimation by the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) system. The earthquake fatality rate is based on past fatal earthquakes (earthquakes causing one or more deaths) in individual countries where at least four fatal earthquakes occurred during the catalog period (since 1973). Because only a few dozen countries have experienced four or more fatal earthquakes since 1973, we propose a new global regionalization scheme based on idealization of countries that are expected to have similar susceptibility to future earthquake losses given the existing building stock, its vulnerability, and other socioeconomic characteristics. The fatality estimates obtained using an empirical country- or region-specific model will be used along with other selected engineering risk-based loss models for generation of automated earthquake alerts. These alerts could potentially benefit the rapid-earthquake-response agencies and governments for better response to reduce earthquake fatalities. Fatality estimates are also useful to stimulate earthquake preparedness planning and disaster mitigation. The proposed model has several advantages as compared with other candidate methods, and the country- or region-specific fatality rates can be readily updated when new data become available.

Open-File Report↗

USGS2008

Creating a Global Building Inventory for Earthquake Loss Assessment and Risk Management

Earthquakes have claimed approximately 8 million lives over the last 2,000 years (Dunbar, Lockridge and others, 1992) and fatality rates are likely to continue to rise with increased population and urbanizations of global settlements especially in developing countries. More than 75% of earthquake-related human casualties are caused by the collapse of buildings or structures (Coburn and Spence, 2002). It is disheartening to note that large fractions of the world's population still reside in informal, poorly-constructed & non-engineered dwellings which have high susceptibility to collapse during earthquakes. Moreover, with increasing urbanization half of world's population now lives in urban areas (United Nations, 2001), and half of these urban centers are located in earthquake-prone regions (Bilham, 2004). The poor performance of most building stocks during earthquakes remains a primary societal concern. However, despite this dark history and bleaker future trends, there are no comprehensive global building inventories of sufficient quality and coverage to adequately address and characterize future earthquake losses. Such an inventory is vital both for earthquake loss mitigation and for earthquake disaster response purposes. While the latter purpose is the motivation of this work, we hope that the global building inventory database described herein will find widespread use for other mitigation efforts as well. For a real-time earthquake impact alert system, such as U.S. Geological Survey's (USGS) Prompt Assessment of Global Earthquakes for Response (PAGER), (Wald, Earle and others, 2006), we seek to rapidly evaluate potential casualties associated with earthquake ground shaking for any region of the world. The casualty estimation is based primarily on (1) rapid estimation of the ground shaking hazard, (2) aggregating the population exposure within different building types, and (3) estimating the casualties from the collapse of vulnerable buildings. Thus, the contribution of building stock, its relative vulnerability, and distribution are vital components for determining the extent of casualties during an earthquake. It is evident from large deadly historical earthquakes that the distribution of vulnerable structures and their occupancy level during an earthquake control the severity of human losses. For example, though the number of strong earthquakes in California is comparable to that of Iran, the total earthquake-related casualties in California during the last 100 years are dramatically lower than the casualties from several individual Iranian earthquakes. The relatively low casualties count in California is attributed mainly to the fact that more than 90 percent of the building stock in California is made of wood and is designed to withstand moderate to large earthquakes (Kircher, Seligson and others, 2006). In contrast, the 80 percent adobe and or non-engineered masonry building stock with poor lateral load resisting systems in Iran succumbs even for moderate levels of ground shaking. Consequently, the heavy death toll for the 2003 Bam, Iran earthquake, which claimed 31,828 lives (Ghafory-Ashtiany and Mousavi, 2005), is directly attributable to such poorly resistant construction, and future events will produce comparable losses unless practices change. Similarly, multistory, precast-concrete framed buildings caused heavy casualties in the 1988 Spitak, Armenia earthquake (Bertero, 1989); weaker masonry and reinforced-concrete framed construction designed for gravity loads with soft first stories dominated losses in the Bhuj, India earthquake of 2001 (Madabhushi and Haigh, 2005); and adobe and weak masonry dwellings in Peru controlled the death toll in the Peru earthquake of 2007 (Taucer, J. and others, 2007). Spence (2007) after conducting a brief survey of most lethal earthquakes since 1960 found that building collapses remains a major cause of earthquake mortality and unreinforced masonry buildings are one of the mos

Open-File Report↗