Country model to assess potential financial losses to commercial, industrial and residential buildings due to earthquakes Project Name Products China Financial Loss Model Country model to assess potential financial losses to commercial, industrial and residential buildings due to earthquakes Share Facebook LinkedIn Description The China financial loss model has been developed by GEM using public sources of information, such as past seismicity, and geodetic and geologic data for the hazard component, combined with exposure and vulnerability data. The hazard component incorporates both faults and area sources. Earthquake occurrence rates on active faults are based on a new tectonic block model derived from the joint inversion of geodetic and geologic data. The model provides estimates of financial risk to residential, commercial and industrial buildings using GEM’s vulnerability models appropriate to Chinese construction practice. GEM has also developed an exposure model that can be used to estimate total losses to the building stock in addition to portfolio losses. Further technical information can be found in the documentation. GEM extensively collaborated with its private and public partners to test and validate the model against industry standards in order to produce a new model that represents GEM’s view of risk. The model is available in Oasis and Touchstone formats, as well as through the NASDAQ platform. How to cite this work N.A. Available Versions The latest version (v2022) created by GEM can be requested by clicking on the "License Request", where a specific license will be provided, depending on the use case. The model is also available through NASDAQ and Verisk by clicking on the links in the right panel. License information Currently, the model is only available under a restricted license that has to be tailored for each specific use case. Share License Custom license Available resources NASDAQ Access Verisk Access Documentation License Request Facebook LinkedIn text Map View Search Popup title Close Country/Region Available Resources Afghanistan Exposure Africa Exposure Alaska Exposure Albania Exposure Algeria Exposure American Samoa Exposure Andorra Exposure Angola Exposure Anguilla Exposure Antigua and Barbuda Exposure Arabia Exposure Argentina Exposure Armenia Exposure Aruba Exposure Australia Exposure Austria Exposure Azerbaijan Exposure Bahamas Exposure Bahrain Exposure Bangladesh Exposure Barbados Exposure Belarus Exposure Belgium Exposure Belize Exposure Benin Exposure Bhutan Exposure Bolivia Exposure Bosnia and Herzegovina Exposure Botswana Exposure Brazil Exposure British Virgin Islands Exposure Brunei Exposure Bulgaria Exposure Burkina Faso Exposure Burundi Exposure Cambodia Exposure Cameroon Exposure Canada Exposure Cape Verde Exposure Caribbean Central America Exposure Cayman Islands Exposure Central African Republic Exposure Central Asia Exposure Chad Exposure Chile Exposure China Exposure Colombia Exposure Comoros Exposure Congo Exposure Conterminous US Exposure Cook Islands Exposure Costa Rica Exposure Croatia Exposure Cuba Exposure Cyprus Exposure Czechia Exposure Democratic Republic of the Congo Exposure Denmark Exposure Djibouti Exposure Dominica Exposure Dominican Republic Exposure East Asia Exposure Ecuador Exposure Egypt Exposure El Salvador Exposure Equatorial Guinea Exposure Eritrea Exposure Estonia Exposure Eswatini Exposure Ethiopia Exposure Europe Exposure Fiji Exposure Finland Exposure France Exposure French Guiana Exposure Gabon Exposure Gambia Exposure Georgia Exposure Germany Exposure Ghana Exposure Gibraltar Exposure Greece Exposure Grenada Exposure Guadeloupe Exposure Guam Exposure Guatemala Exposure Guinea Exposure Guinea Bissau Exposure Guyana Exposure Haiti Exposure Hawaii Exposure Honduras Exposure Hong Kong Exposure Hungary Exposure Iceland Exposure India Exposure Indonesia Exposure Iran Exposure Iraq Exposure Ireland Exposure Isle of Man Exposure Israel Exposure Italy Exposure Ivory Coast Exposure Jamaica Exposure Japan Exposure Jordan Exposure Kazakhstan Exposure Kenya Exposure Kiribati Exposure Kosovo Exposure Kuwait Exposure Kyrgyzstan Exposure Laos Exposure Latvia Exposure Lebanon Exposure Lesotho Exposure Liberia Exposure Libya Exposure Liechtenstein Exposure Lithuania Exposure Luxembourg Exposure Macao Exposure Madagascar Exposure Malawi Exposure Malaysia Exposure Mali Exposure Malta Exposure Marshall Islands Exposure Martinique Exposure Mauritania Exposure Mauritius Exposure Mexico Exposure Micronesia Exposure Middle East Exposure Moldova Exposure Monaco Exposure Mongolia Exposure Montenegro Exposure Montserrat Exposure Morocco Exposure Mozambique Exposure Myanmar Exposure Namibia Exposure Nauru Exposure Nepal Exposure Netherlands Exposure New Caledonia Exposure New Zealand Exposure Nicaragua Exposure Niger Exposure Nigeria Exposure Niue Exposure North Africa Exposure North America Exposure North Asia Exposure North Korea Exposure North Macedonia Exposure North and South Korea Exposure Northeast Asia Exposure Northern Mariana Islands Exposure Northwest Asia Exposure Norway Exposure Oceania Exposure Oman Exposure Pacific Islands Exposure Pakistan Exposure Palau Exposure Palestine Exposure Panama Exposure Papua New Guinea Exposure Paraguay Exposure Peru Exposure Philippines Exposure Poland Exposure Portugal Exposure Puerto Rico Exposure Qatar Exposure Romania Exposure Russia Exposure Rwanda Exposure Saint Kitts and Nevis Exposure Saint Lucia Exposure Saint Vincent and the Grenadines Exposure Samoa Exposure Sao Tome and Principe Exposure Saudi Arabia Exposure Senegal Exposure Serbia Exposure Seychelles Exposure Sierra Leone Exposure Singapore Exposure Slovakia Exposure Slovenia Exposure Solomon Islands Exposure Somalia Exposure South Africa Exposure South America Exposure South Asia Exposure South Korea Exposure Country/Region Available Resources Afghanistan Vulnerability Africa Vulnerability Alaska Vulnerability Albania Vulnerability Algeria Vulnerability American Samoa Vulnerability Andorra Vulnerability Angola Vulnerability Anguilla Vulnerability Antigua and Barbuda Vulnerability Arabia Vulnerability Argentina Vulnerability Armenia Vulnerability Aruba Vulnerability Australia Vulnerability Austria Vulnerability Azerbaijan Vulnerability Bahamas Vulnerability Bahrain Vulnerability Bangladesh Vulnerability Barbados Vulnerability Belarus Vulnerability Belgium Vulnerability Belize Vulnerability Benin Vulnerability Bhutan Vulnerability Bolivia Vulnerability Bosnia and Herzegovina Vulnerability Botswana Vulnerability Brazil Vulnerability British Virgin Islands Vulnerability Brunei Vulnerability Bulgaria Vulnerability Burkina Faso Vulnerability Burundi Vulnerability Cambodia Vulnerability Cameroon Vulnerability Canada Vulnerability Cape Verde Vulnerability Caribbean Central America Vulnerability Cayman Islands Vulnerability Central African Republic Vulnerability Central Asia Vulnerability Chad Vulnerability Chile Vulnerability China Vulnerability Colombia Vulnerability Comoros Vulnerability Congo Vulnerability Conterminous US Vulnerability Cook Islands Vulnerability Costa Rica Vulnerability Croatia Vulnerability Cuba Vulnerability Cyprus Vulnerability Czechia Vulnerability Democratic Republic of the Congo Vulnerability Denmark Vulnerability Djibouti Vulnerability Dominica Vulnerability Dominican Republic Vulnerability East Asia Vulnerability Ecuador Vulnerability Egypt Vulnerability El Salvador Vulnerability Equatorial Guinea Vulnerability Eritrea Vulnerability Estonia Vulnerability Eswatini Vulnerability Ethiopia Vulnerability Europe Vulnerability Fiji Vulnerability Finland Vulnerability France Vulnerability French Guiana Vulnerability Gabon Vulnerability Gambia Vulnerability Georgia Vulnerability Germany Vulnerability Ghana Vulnerability Gibraltar Vulnerability Greece Vulnerability Grenada Vulnerability Guadeloupe Vulnerability Guam Vulnerability Guatemala Vulnerability Guinea Vulnerability Guinea Bissau Vulnerability Guyana Vulnerability Haiti Vulnerability Hawaii Vulnerability Honduras Vulnerability Hong Kong Vulnerability Hungary Vulnerability Iceland Vulnerability India Vulnerability Indonesia Vulnerability Iran Vulnerability Iraq Vulnerability Ireland Vulnerability Isle of Man Vulnerability Israel Vulnerability Italy Vulnerability Ivory Coast Vulnerability Jamaica Vulnerability Japan Vulnerability Jordan Vulnerability Kazakhstan Vulnerability Kenya Vulnerability Kiribati Vulnerability Kosovo Vulnerability Kuwait Vulnerability Kyrgyzstan Vulnerability Laos Vulnerability Latvia Vulnerability Lebanon Vulnerability Lesotho Vulnerability Liberia Vulnerability Libya Vulnerability Liechtenstein Vulnerability Lithuania Vulnerability Luxembourg Vulnerability Macao Vulnerability Madagascar Vulnerability Malawi Vulnerability Malaysia Vulnerability Mali Vulnerability Malta Vulnerability Marshall Islands Vulnerability Martinique Vulnerability Mauritania Vulnerability Mauritius Vulnerability Mexico Vulnerability Micronesia Vulnerability Middle East Vulnerability Moldova Vulnerability Monaco Vulnerability Mongolia Vulnerability Montenegro Vulnerability Montserrat Vulnerability Morocco Vulnerability Mozambique Vulnerability Myanmar Vulnerability Namibia Vulnerability Nauru Vulnerability Nepal Vulnerability Netherlands Vulnerability New Caledonia Vulnerability New Zealand Vulnerability Nicaragua Vulnerability Niger Vulnerability Nigeria Vulnerability Niue Vulnerability North Africa Vulnerability North America Vulnerability North Asia Vulnerability North Korea Vulnerability North Macedonia Vulnerability North and South Korea Vulnerability Northeast Asia Vulnerability Northern Mariana Islands Vulnerability Northwest Asia Vulnerability Norway Vulnerability Oceania Vulnerability Oman Vulnerability Pacific Islands Vulnerability Pakistan Vulnerability Palau Vulnerability Palestine Vulnerability Panama Vulnerability Papua New Guinea Vulnerability Paraguay Vulnerability Peru Vulnerability Philippines Vulnerability Poland Vulnerability Portugal Vulnerability Puerto Rico Vulnerability Qatar Vulnerability Romania Vulnerability Russia Vulnerability Rwanda Vulnerability Saint Kitts and Nevis Vulnerability Saint Lucia Vulnerability Saint Vincent and the Grenadines Vulnerability Samoa Vulnerability Sao Tome and Principe Vulnerability Saudi Arabia Vulnerability Senegal Vulnerability Serbia Vulnerability Seychelles Vulnerability Sierra Leone Vulnerability Singapore Vulnerability Slovakia Vulnerability Slovenia Vulnerability Solomon Islands Vulnerability Somalia Vulnerability South Africa Vulnerability South America Vulnerability South Asia Vulnerability South Korea Vulnerability Country/Region Resource Url Afghanistan Risk Profile Africa Risk Profile Alaska Risk Profile Albania Risk Profile Algeria Risk Profile American Samoa Risk Profile Andorra Risk Profile Angola Risk Profile Anguilla Risk Profile Antigua and Barbuda Risk Profile Arabia Risk Profile Argentina Risk Profile Armenia Risk Profile Aruba Risk Profile Australia Risk Profile Austria Risk Profile Azerbaijan Risk Profile Bahamas Risk Profile Bahrain Risk Profile Bangladesh Risk Profile Barbados Risk Profile Belarus Risk Profile Belgium Risk Profile Belize Risk Profile Benin Risk Profile Bhutan Risk Profile Bolivia Risk Profile Bosnia and Herzegovina Risk Profile Botswana Risk Profile Brazil Risk Profile British Virgin Islands Risk Profile Brunei Risk Profile Bulgaria Risk Profile Burkina Faso Risk Profile Burundi Risk Profile Cambodia Risk Profile Cameroon Risk Profile Canada Risk Profile Cape Verde Risk Profile Caribbean Central America Risk Profile Cayman Islands Risk Profile Central African Republic Risk Profile Central Asia Risk Profile Chad Risk Profile Chile Risk Profile China Risk Profile Colombia Risk Profile Comoros Risk Profile Congo Risk Profile Conterminous US Risk Profile Cook Islands Risk Profile Costa Rica Risk Profile Croatia Risk Profile Cuba Risk Profile Cyprus Risk Profile Czechia Risk Profile Democratic Republic of the Congo Risk Profile Denmark Risk Profile Djibouti Risk Profile Dominica Risk Profile Dominican Republic Risk Profile East Asia Risk Profile Ecuador Risk Profile Egypt Risk Profile El Salvador Risk Profile Equatorial Guinea Risk Profile Eritrea Risk Profile Estonia Risk Profile Eswatini Risk Profile Ethiopia Risk Profile Europe Risk Profile Fiji Risk Profile Finland Risk Profile France Risk Profile French Guiana Risk Profile Gabon Risk Profile Gambia Risk Profile Georgia Risk Profile Germany Risk Profile Ghana Risk Profile Gibraltar Risk Profile Greece Risk Profile Grenada Risk Profile Guadeloupe Risk Profile Guam Risk Profile Guatemala Risk Profile Guinea Risk Profile Guinea Bissau Risk Profile Guyana Risk Profile Haiti Risk Profile Hawaii Risk Profile Honduras Risk Profile Hong Kong Risk Profile Hungary Risk Profile Iceland Risk Profile India Risk Profile Indonesia Risk Profile Iran Risk Profile Iraq Risk Profile Ireland Risk Profile Isle of Man Risk Profile Israel Risk Profile Italy Risk Profile Ivory Coast Risk Profile Jamaica Risk Profile Japan Risk Profile Jordan Risk Profile Kazakhstan Risk Profile Kenya Risk Profile Kiribati Risk Profile Kosovo Risk Profile Kuwait Risk Profile Kyrgyzstan Risk Profile Laos Risk Profile Latvia Risk Profile Lebanon Risk Profile Lesotho Risk Profile Liberia Risk Profile Libya Risk Profile Liechtenstein Risk Profile Lithuania Risk Profile Luxembourg Risk Profile Macao Risk Profile Madagascar Risk Profile Malawi Risk Profile Malaysia Risk Profile Mali Risk Profile Malta Risk Profile Marshall Islands Risk Profile Martinique Risk Profile Mauritania Risk Profile Mauritius Risk Profile Mexico Risk Profile Micronesia Risk Profile Middle East Risk Profile Moldova Risk Profile Monaco Risk Profile Mongolia Risk Profile Montenegro Risk Profile Montserrat Risk Profile Morocco Risk Profile Mozambique Risk Profile Myanmar Risk Profile Namibia Risk Profile Nauru Risk Profile Nepal Risk Profile Netherlands Risk Profile New Caledonia Risk Profile New Zealand Risk Profile Nicaragua Risk Profile Niger Risk Profile Nigeria Risk Profile Niue Risk Profile North Africa Risk Profile North America Risk Profile North Asia Risk Profile North Korea Risk Profile North Macedonia Risk Profile North and South Korea Risk Profile Northeast Asia Risk Profile Northern Mariana Islands Risk Profile Northwest Asia Risk Profile Norway Risk Profile Oceania Risk Profile Oman Risk Profile Pacific Islands Risk Profile Pakistan Risk Profile Palau Risk Profile Palestine Risk Profile Panama Risk Profile Papua New Guinea Risk Profile Paraguay Risk Profile Peru Risk Profile Philippines Risk Profile Poland Risk Profile Portugal Risk Profile Puerto Rico Risk Profile Qatar Risk Profile Romania Risk Profile Russia Risk Profile Rwanda Risk Profile Saint Kitts and Nevis Risk Profile Saint Lucia Risk Profile Saint Vincent and the Grenadines Risk Profile Samoa Risk Profile Sao Tome and Principe Risk Profile Saudi Arabia Risk Profile Senegal Risk Profile Serbia Risk Profile Seychelles Risk Profile Sierra Leone Risk Profile Singapore Risk Profile Slovakia Risk Profile Slovenia Risk Profile Solomon Islands Risk Profile Somalia Risk Profile South Africa Risk Profile South America Risk Profile South Asia Risk Profile South Korea Risk Profile Search Found Country/Region Resource Url Afghanistan Exposure Africa Exposure Alaska Exposure Albania Exposure Algeria Exposure American Samoa Exposure Andorra Exposure Angola Exposure Anguilla Exposure Antigua and Barbuda Exposure Arabia Exposure Argentina Exposure Armenia Exposure Aruba Exposure Australia Exposure Austria Exposure Azerbaijan Exposure Bahamas Exposure Bahrain Exposure Bangladesh Exposure Barbados Exposure Belarus Exposure Belgium Exposure Belize Exposure Benin Exposure Bhutan Exposure Bolivia Exposure Bosnia and Herzegovina Exposure Botswana Exposure Brazil Exposure British Virgin Islands Exposure Brunei Exposure Bulgaria Exposure Burkina Faso Exposure Burundi Exposure Cambodia Exposure Cameroon Exposure Canada Exposure Cape Verde Exposure Caribbean Central America Exposure Cayman Islands Exposure Central African Republic Exposure Central Asia Exposure Chad Exposure Chile Exposure China Exposure Colombia Exposure Comoros Exposure Congo Exposure Conterminous US Exposure Cook Islands Exposure Costa Rica Exposure Croatia Exposure Cuba Exposure Cyprus Exposure Czechia Exposure Democratic Republic of the Congo Exposure Denmark Exposure Djibouti Exposure Dominica Exposure Dominican Republic Exposure East Asia Exposure Ecuador Exposure Egypt Exposure El Salvador Exposure Equatorial Guinea Exposure Eritrea Exposure Estonia Exposure Eswatini Exposure Ethiopia Exposure Europe Exposure Fiji Exposure Finland Exposure France Exposure French Guiana Exposure Gabon Exposure Gambia Exposure Georgia Exposure Germany Exposure Ghana Exposure Gibraltar Exposure Greece Exposure Grenada Exposure Guadeloupe Exposure Guam Exposure Guatemala Exposure Guinea Exposure Guinea Bissau Exposure Guyana Exposure Haiti Exposure Hawaii Exposure Honduras Exposure Hong Kong Exposure Hungary Exposure Iceland Exposure India Exposure Indonesia Exposure Iran Exposure Iraq Exposure Ireland Exposure Isle of Man Exposure Israel Exposure Italy Exposure Ivory Coast Exposure Jamaica Exposure Japan Exposure Jordan Exposure Kazakhstan Exposure Kenya Exposure Kiribati Exposure Kosovo Exposure Kuwait Exposure Kyrgyzstan Exposure Laos Exposure Latvia Exposure Lebanon Exposure Lesotho Exposure Liberia Exposure Libya Exposure Liechtenstein Exposure Lithuania Exposure Luxembourg Exposure Macao Exposure Madagascar Exposure Malawi Exposure Malaysia Exposure Mali Exposure Malta Exposure Marshall Islands Exposure Martinique Exposure Mauritania Exposure Mauritius Exposure Mexico Exposure Micronesia Exposure Middle East Exposure Moldova Exposure Monaco Exposure Mongolia Exposure Montenegro Exposure Montserrat Exposure Morocco Exposure Mozambique Exposure Myanmar Exposure Namibia Exposure Nauru Exposure Nepal Exposure Netherlands Exposure New Caledonia Exposure New Zealand Exposure Nicaragua Exposure Niger Exposure Nigeria Exposure Niue Exposure North Africa Exposure North America Exposure North Asia Exposure North Korea Exposure North Macedonia Exposure North and South Korea Exposure Northeast Asia Exposure Northern Mariana Islands Exposure Northwest Asia Exposure Norway Exposure Oceania Exposure Oman Exposure Pacific Islands Exposure Pakistan Exposure Palau Exposure Palestine Exposure Panama Exposure Papua New Guinea Exposure Paraguay Exposure Peru Exposure Philippines Exposure Poland Exposure Portugal Exposure Puerto Rico Exposure Qatar Exposure Romania Exposure Russia Exposure Rwanda Exposure Saint Kitts and Nevis Exposure Saint Lucia Exposure Saint Vincent and the Grenadines Exposure Samoa Exposure Sao Tome and Principe Exposure Saudi Arabia Exposure Senegal Exposure Serbia Exposure Seychelles Exposure Sierra Leone Exposure Singapore Exposure Slovakia Exposure Slovenia Exposure Solomon Islands Exposure Somalia Exposure South Africa Exposure South America Exposure South Asia Exposure South Korea Exposure Preview Preview is not available. Search Found Country/Region Resource Url Afghanistan Vulnerability Africa Vulnerability Alaska Vulnerability Albania Vulnerability Algeria Vulnerability American Samoa Vulnerability Andorra Vulnerability Angola Vulnerability Anguilla Vulnerability Antigua and Barbuda Vulnerability Arabia Vulnerability Argentina Vulnerability Armenia Vulnerability Aruba Vulnerability Australia Vulnerability Austria Vulnerability Azerbaijan Vulnerability Bahamas Vulnerability Bahrain Vulnerability Bangladesh Vulnerability Barbados Vulnerability Belarus Vulnerability Belgium Vulnerability Belize Vulnerability Benin Vulnerability Bhutan Vulnerability Bolivia Vulnerability Bosnia and Herzegovina Vulnerability Botswana Vulnerability Brazil Vulnerability British Virgin Islands Vulnerability Brunei Vulnerability Bulgaria Vulnerability Burkina Faso Vulnerability Burundi Vulnerability Cambodia Vulnerability Cameroon Vulnerability Canada Vulnerability Cape Verde Vulnerability Caribbean Central America Vulnerability Cayman Islands Vulnerability Central African Republic Vulnerability Central Asia Vulnerability Chad Vulnerability Chile Vulnerability China Vulnerability Colombia Vulnerability Comoros Vulnerability Congo Vulnerability Conterminous US Vulnerability Cook Islands Vulnerability Costa Rica Vulnerability Croatia Vulnerability Cuba Vulnerability Cyprus Vulnerability Czechia Vulnerability Democratic Republic of the Congo Vulnerability Denmark Vulnerability Djibouti Vulnerability Dominica Vulnerability Dominican Republic Vulnerability East Asia Vulnerability Ecuador Vulnerability Egypt Vulnerability El Salvador Vulnerability Equatorial Guinea Vulnerability Eritrea Vulnerability Estonia Vulnerability Eswatini Vulnerability Ethiopia Vulnerability Europe Vulnerability Fiji Vulnerability Finland Vulnerability France Vulnerability French Guiana Vulnerability Gabon Vulnerability Gambia Vulnerability Georgia Vulnerability Germany Vulnerability Ghana Vulnerability Gibraltar Vulnerability Greece Vulnerability Grenada Vulnerability Guadeloupe Vulnerability Guam Vulnerability Guatemala Vulnerability Guinea Vulnerability Guinea Bissau Vulnerability Guyana Vulnerability Haiti Vulnerability Hawaii Vulnerability Honduras Vulnerability Hong Kong Vulnerability Hungary Vulnerability Iceland Vulnerability India Vulnerability Indonesia Vulnerability Iran Vulnerability Iraq Vulnerability Ireland Vulnerability Isle of Man Vulnerability Israel Vulnerability Italy Vulnerability Ivory Coast Vulnerability Jamaica Vulnerability Japan Vulnerability Jordan Vulnerability Kazakhstan Vulnerability Kenya Vulnerability Kiribati Vulnerability Kosovo Vulnerability Kuwait Vulnerability Kyrgyzstan Vulnerability Laos Vulnerability Latvia Vulnerability Lebanon Vulnerability Lesotho Vulnerability Liberia Vulnerability Libya Vulnerability Liechtenstein Vulnerability Lithuania Vulnerability Luxembourg Vulnerability Macao Vulnerability Madagascar Vulnerability Malawi Vulnerability Malaysia Vulnerability Mali Vulnerability Malta Vulnerability Marshall Islands Vulnerability Martinique Vulnerability Mauritania Vulnerability Mauritius Vulnerability Mexico Vulnerability Micronesia Vulnerability Middle East Vulnerability Moldova Vulnerability Monaco Vulnerability Mongolia Vulnerability Montenegro Vulnerability Montserrat Vulnerability Morocco Vulnerability Mozambique Vulnerability Myanmar Vulnerability Namibia Vulnerability Nauru Vulnerability Nepal Vulnerability Netherlands Vulnerability New Caledonia Vulnerability New Zealand Vulnerability Nicaragua Vulnerability Niger Vulnerability Nigeria Vulnerability Niue Vulnerability North Africa Vulnerability North America Vulnerability North Asia Vulnerability North Korea Vulnerability North Macedonia Vulnerability North and South Korea Vulnerability Northeast Asia Vulnerability Northern Mariana Islands Vulnerability Northwest Asia Vulnerability Norway Vulnerability Oceania Vulnerability Oman Vulnerability Pacific Islands Vulnerability Pakistan Vulnerability Palau Vulnerability Palestine Vulnerability Panama Vulnerability Papua New Guinea Vulnerability Paraguay Vulnerability Peru Vulnerability Philippines Vulnerability Poland Vulnerability Portugal Vulnerability Puerto Rico Vulnerability Qatar Vulnerability Romania Vulnerability Russia Vulnerability Rwanda Vulnerability Saint Kitts and Nevis Vulnerability Saint Lucia Vulnerability Saint Vincent and the Grenadines Vulnerability Samoa Vulnerability Sao Tome and Principe Vulnerability Saudi Arabia Vulnerability Senegal Vulnerability Serbia Vulnerability Seychelles Vulnerability Sierra Leone Vulnerability Singapore Vulnerability Slovakia Vulnerability Slovenia Vulnerability Solomon Islands Vulnerability Somalia Vulnerability South Africa Vulnerability South America Vulnerability South Asia Vulnerability South Korea Vulnerability Preview Preview is not available. Search Found Country/Region Resource Url Afghanistan Risk Profile Africa Risk Profile Alaska Risk Profile Albania Risk Profile Algeria Risk Profile American Samoa Risk Profile Andorra Risk Profile Angola Risk Profile Anguilla Risk Profile Antigua and Barbuda Risk Profile Arabia Risk Profile Argentina Risk Profile Armenia Risk Profile Aruba Risk Profile Australia Risk Profile Austria Risk Profile Azerbaijan Risk Profile Bahamas Risk Profile Bahrain Risk Profile Bangladesh Risk Profile Barbados Risk Profile Belarus Risk Profile Belgium Risk Profile Belize Risk Profile Benin Risk Profile Bhutan Risk Profile Bolivia Risk Profile Bosnia and Herzegovina Risk Profile Botswana Risk Profile Brazil Risk Profile British Virgin Islands Risk Profile Brunei Risk Profile Bulgaria Risk Profile Burkina Faso Risk Profile Burundi Risk Profile Cambodia Risk Profile Cameroon Risk Profile Canada Risk Profile Cape Verde Risk Profile Caribbean Central America Risk Profile Cayman Islands Risk Profile Central African Republic Risk Profile Central Asia Risk Profile Chad Risk Profile Chile Risk Profile China Risk Profile Colombia Risk Profile Comoros Risk Profile Congo Risk Profile Conterminous US Risk Profile Cook Islands Risk Profile Costa Rica Risk Profile Croatia Risk Profile Cuba Risk Profile Cyprus Risk Profile Czechia Risk Profile Democratic Republic of the Congo Risk Profile Denmark Risk Profile Djibouti Risk Profile Dominica Risk Profile Dominican Republic Risk Profile East Asia Risk Profile Ecuador Risk Profile Egypt Risk Profile El Salvador Risk Profile Equatorial Guinea Risk Profile Eritrea Risk Profile Estonia Risk Profile Eswatini Risk Profile Ethiopia Risk Profile Europe Risk Profile Fiji Risk Profile Finland Risk Profile France Risk Profile French Guiana Risk Profile Gabon Risk Profile Gambia Risk Profile Georgia Risk Profile Germany Risk Profile Ghana Risk Profile Gibraltar Risk Profile Greece Risk Profile Grenada Risk Profile Guadeloupe Risk Profile Guam Risk Profile Guatemala Risk Profile Guinea Risk Profile Guinea Bissau Risk Profile Guyana Risk Profile Haiti Risk Profile Hawaii Risk Profile Honduras Risk Profile Hong Kong Risk Profile Hungary Risk Profile Iceland Risk Profile India Risk Profile Indonesia Risk Profile Iran Risk Profile Iraq Risk Profile Ireland Risk Profile Isle of Man Risk Profile Israel Risk Profile Italy Risk Profile Ivory Coast Risk Profile Jamaica Risk Profile Japan Risk Profile Jordan Risk Profile Kazakhstan Risk Profile Kenya Risk Profile Kiribati Risk Profile Kosovo Risk Profile Kuwait Risk Profile Kyrgyzstan Risk Profile Laos Risk Profile Latvia Risk Profile Lebanon Risk Profile Lesotho Risk Profile Liberia Risk Profile Libya Risk Profile Liechtenstein Risk Profile Lithuania Risk Profile Luxembourg Risk Profile Macao Risk Profile Madagascar Risk Profile Malawi Risk Profile Malaysia Risk Profile Mali Risk Profile Malta Risk Profile Marshall Islands Risk Profile Martinique Risk Profile Mauritania Risk Profile Mauritius Risk Profile Mexico Risk Profile Micronesia Risk Profile Middle East Risk Profile Moldova Risk Profile Monaco Risk Profile Mongolia Risk Profile Montenegro Risk Profile Montserrat Risk Profile Morocco Risk Profile Mozambique Risk Profile Myanmar Risk Profile Namibia Risk Profile Nauru Risk Profile Nepal Risk Profile Netherlands Risk Profile New Caledonia Risk Profile New Zealand Risk Profile Nicaragua Risk Profile Niger Risk Profile Nigeria Risk Profile Niue Risk Profile North Africa Risk Profile North America Risk Profile North Asia Risk Profile North Korea Risk Profile North Macedonia Risk Profile North and South Korea Risk Profile Northeast Asia Risk Profile Northern Mariana Islands Risk Profile Northwest Asia Risk Profile Norway Risk Profile Oceania Risk Profile Oman Risk Profile Pacific Islands Risk Profile Pakistan Risk Profile Palau Risk Profile Palestine Risk Profile Panama Risk Profile Papua New Guinea Risk Profile Paraguay Risk Profile Peru Risk Profile Philippines Risk Profile Poland Risk Profile Portugal Risk Profile Puerto Rico Risk Profile Qatar Risk Profile Romania Risk Profile Russia Risk Profile Rwanda Risk Profile Saint Kitts and Nevis Risk Profile Saint Lucia Risk Profile Saint Vincent and the Grenadines Risk Profile Samoa Risk Profile Sao Tome and Principe Risk Profile Saudi Arabia Risk Profile Senegal Risk Profile Serbia Risk Profile Seychelles Risk Profile Sierra Leone Risk Profile Singapore Risk Profile Slovakia Risk Profile Slovenia Risk Profile Solomon Islands Risk Profile Somalia Risk Profile South Africa Risk Profile South America Risk Profile South Asia Risk Profile South Korea Risk Profile Preview Preview is not available. 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Publications Probabilistic seismic hazard analysis for the Dominican Republic Share Facebook LinkedIn Download 2024 | Peer-reviewed The Dominican Republic experiences moderate to high seismic hazard mostly caused by oblique convergence at the Caribbean/North American plate boundary that manifests as subduction zones, less-pronounced subduction-like trenches with thrust faulting, long strike-slip faults parallel to the plate boundary, and onshore deformation. Historical earthquakes have damaged the Dominican Republic’s large cities and those in neighboring Haiti, once requiring relocation. Given this, the Dominican Republic joined the “Training and Communication for Earthquake Risk Assessment” (TREQ) project funded by the United States Agency for International Development, which aimed to increase earthquake risk assessment capacity in Latin American cities. The TREQ project was the basis for developing an openly available probabilistic seismic hazard model for the Dominican Republic. The input model was developed from two main datasets: a homogenized earthquake catalog and an active faults database that combines results of recent local projects with a global database. The seismic source characterization used these to constrain source geometries and occurrence rates for active shallow crustal earthquakes, subduction interfaces and subduction-like thrusts, and intraslab earthquakes. Shallow crustal earthquakes, including those on subduction-like thrusts, are modeled by smoothed seismicity and fault sources, the latter using pre-defined geometries that permit multi-fault ruptures. Seismicity on the Puerto Rico Trench subduction interface is modeled as a fault source, while intraslab sources use pre-defined gridded ruptures inside the intraslab volume. The source characterization applies epistemic uncertainties to modeling assumptions affecting occurrence rates and maximum magnitudes. The ground motion characterization used residual analyses from past regional projects as a basis, updating some components with more recent ground motion models. Computed hazard results reinforce those from recent studies in terms of geographical hazard patterns and levels. For 475-year return periods, peak ground acceleration (PGA) in Santiago de los Caballeros reaches 0.50 g, controlled by the Septentrional Fault, while all tectonic region types contribute to the PGA 0.31 g computed for Santo Domingo.

Seismic Regulations for Perú Overview This page describes the seismic regulations that have been introduced in Perú since 1970, along with the seismic zonation maps associated with each code, and our estimated fraction of the building stock designed according to the different code levels. If you find incorrect or missing information on this page, please provide your feedback using the form linked at the bottom. Current Seismic Design Regulation The current seismic design regulation in Peru is NTE E.030-2018. The latest modification, reported by the MVCS (Ministerio de Vivienda, Construcción y Saneamiento), dates back to 2018 and was approved by Ministerial Resolution No. 355 on October 22, 2018. The standard is available in Spanish and is freely accessible. Evolution of design regulations and seismic zonations Category Code Year of introduction Enforcement Seismic zonation Low code RNC-1970 1970 C Download Moderate code RNC-1977 1977 C Download High Code NTE e.030-1997 1997 B Download High Code NTE e.030-2003 2003 B Download High Code NTE e.030-2016 2016 B Download High Code NTE e.030-2018 2018 B Download Description of each regulation, including a link to access the document if available: RNC-1970: Reglamento Nacional de Construcciones de 1970 RNC-1977: Reglamento Nacional de Construcciones de 1977 NTE e.030-1997: Norma Técnica de Edificación E.030, Diseño Sismorresistente de 1997 NTE e.030-2003: Reglamento Nacional de Edificaciones, Norma E.030 – Diseño Sísmorresistente de 2003 NTE e.030-2016: Reglamento Nacional de Edificaciones, Norma E.030 – Diseño Sísmorresistente de 2016 NTE e.030-2018: Reglamento Nacional de Edificaciones, Norma E.030 – Diseño Sísmorresistente de 2018 Estimated fractions by code level for the country Estimated fractions by code level per region Send us your feedback or observations

Publications GEM1 Hazard: Description of Input Models, Calculation Engine and Main Results Share Facebook LinkedIn Download 2010 | Report This document provides an overview of the PSHA input models collected during GEM1, of the engine used to perform PSHA calculations, and the methods and criteria adopted for computing a proof-of-concept global hazard map. The GEM1 PSHA input repository contains seventeen national or regional models and one global model based on a smoothed seismicity approach. The oldest models were developed in the context of the GSHAP project, ended at the end of the 1990s; the most recent models are the ones prepared by the USGS-NSHM project for South America and a global smoothed seismicity model specifically produced for GEM1. In terms of geographical coverage the gathered models cover almost all the globe; the only missing regions are the Caribbean, the area around Papua-New Guinea and the Pacific Islands. These areas will hopefully be updated soon in the context of Regional Initiatives. In terms of information content, a relevant part of the PSHA input models is based on area sources while a minority uses fault sources. All the models but the Japanese and the model for the New Madrid Zone in the eastern US incorporate a time independent Poissonian model. Least but not last, epistemic uncertainties are taken into account by just some models, usually the most recent ones, and frequently treated as aleatory in the calculations.

News Social Vulnerability Index Construction: Accessing Open Data from National Censuses By: Jul 2, 2018 Share Facebook LinkedIn Miguel Toquica - GEM Social Vulnerability and Resilience Specialist shares his insights on GEM's experience in accessing socio-economic data from national censuses and public online databases. When it comes to accessing the demographic characteristics of the population of a country, researchers usually consider national population and household censuses as reliable sources of information. Ideally, most countries should update their national census data and procedures every 10 years. The need to keep track of socio-economic factors and statistical measures of societies is recognized globally to better understand the living conditions and characteristics of the population in a specific country. In this regard, national censuses are considered as the most reliable source of such type of information at specific level of territorial organization, i.e. regions, states, parishes, and local level. A national population and housing census has several uses for a country. It provides not just the total number of population and households but also the demographic information for population estimates and specific information for national agencies in the fields of education, health and economy. A national census also gives quantified information of socio-economic conditions of a specific subdivision and groups of people in a country.At GEM we are collecting and processing national census data for our research on what socio-economic conditions could contribute to the population’s vulnerability to natural hazards, i.e. earthquakes, volcanic eruptions, landslides, flooding, hurricanes, and droughts. In most cases, our research using census data have led us to information on the pre-existing characteristics i.e. average household size, unemployment rates, etc. that relate directly to why differential impacts from natural hazards occur across space. Social vulnerability helps to explain why some areas, such as a country’s sub-national parishes or city neighbourhoods, will experience the consequences of a natural hazard in different ways. Understanding the varying impacts of a natural hazard through social vulnerability assessments is a critical element for risk reduction, elaboration of mitigation plans, and the development of public policies to reduce the risk. To measure social vulnerability, the starting point is to capture the contextual conditions within the social structure of the study area. This social structure includes characteristics of the population and factors that increase or decrease the impact of natural hazards in the community. These factors include access to basic needs (potable water, electricity, and sanitary services), access to education and health, and characteristics of specific groups within the society that makes them vulnerable, e.g. the elderly population, children, population with disabilities, ethnic groups and so forth. As an example, indigenous people, like the women working in the crafts industry belonging to the Wayuu ethnic community in Colombia (Figure 2), typically live in isolated regions where access to financial means and basic public services like potable water, electricity, as well as public infrastructure is difficult or non-existent. These conditions may compromise their capacity for disaster preparedness and make it harder for government agencies to respond and conduct recovery efforts, thereby increasing their vulnerability in case of an emergency. In this context, information obtained from national censuses in Latin America has allowed the Social Vulnerability and Resilience (SVR) team at GEM (i) to develop databases for indicators of social vulnerability, and (ii) to construct social vulnerability indices for over 20 countries in Latin America and the Caribbean. This task has been possible thanks to the online access to national census databases made available by several countries in Latin America and the Caribbean such as the CELADE-Redatam. To start the development and construction of social vulnerability indices, GEM’s SVR team obtained the most recent socioeconomic data from available national population and housing censuses from countries in Latin America and the Caribbean. The collected raw values within the population, economy, infrastructure, health, and education dimensions were then processed to obtain standardised values using percentage, per capita, and density functions that can be used for country comparisons. In addition, a statistical multivariable analysis has been conducted to select a consistent set of indicators for all countries. The socio-economic variables obtained are then standardised and rescaled to create a set of indicators with the same measurement. The analysis also includes a correlation analysis, which is used to quantify the association between two continuous variables, hence narrowing the data to be selected for the regional set of variables that are acceptable to represent the social vulnerability, economic resilience and recovery capacity of the population in Latin America and the Caribbean. Figure 3 provides an example of the Social Vulnerability Index for Central America and the Caribbean region. Challenges Even though the process of collecting, processing and building SV databases and indices seem quite straight forward, accessing the data from censuses and other sources can prove to be challenging and sometimes frustrating. In some cases, the censuses are not fully available, or they are not provided in the desired working format. Some of the most common challenges we have encountered and possible solutions are outlined below:- There is lack of common data processing techniques that are compatible across all countries. Trying to keep the standards of data and indicators selected for all countries may not always work as most censuses are conducted on different basis and using different techniques. This may result in slightly different social vulnerability datasets per country, and therefore the final indicator selection and index composition may differ from country to country. This challenge has been minimized by performing multivariate and correlation analyses on the full set of socio-economic indicators. This technique allows the SVR team to carefully select a set of indicators that better represent the themes of social vulnerability, maintaining the robustness and composition of the index in all cases.- Not all statistical services in each country make the entire census available using a simple database or accessible format. This fact makes accessing and post-processing of data difficult. Some countries do not even make censuses open and available online. Nonetheless, new techniques of data extraction have been implemented so indexes are built with the most reliable and recent sets of data.- Accessing the most recent data from national censuses can be difficult. Some census data can be as old as early 2000’s and late 1990’s. The use of old data must be considered with caution as final results may be skewed. Keeping information of up-to-date country statistics may provide proxies of specific indicators, for example the total population and employment rate can be updated on a yearly basis for some countries. However, processing quantities using data from different time periods can drastically change the unit of measure of comparable values so special care is fundamental when doing so. The GEM social vulnerability team has been overcoming the challenges presented, and we keep improving data collection for index construction. We are also proud to produce and make available to the public the subnational social vulnerability databases and indices. The work is fundamental and a pivotal component for other risk information products developed at GEM. No images found. GALLERY 1/0 VIDEO RELATED CONTENTS

News UK Space Agency project METEOR quarterly meeting in Kathmandu By: Dec 18, 2019 Share Facebook LinkedIn METEOR meeting in Kathmandu, Nepal Anirudh Rao and Nicole Paul participated in the quarterly UK Space Agency #METEOR Project meeting and workshops in Kathmandu, Nepal. The weeklong event was hosted by the National Society for Earthquake Technology - Nepal (NSET) from 11-14 November. The event was attended by representatives from the British Geological Survey (BGS), ImageCat, National Society for Earthquake Technology (NSET)- Nepal, and Humanitarian OpenStreetMap Team (HOT). From November 11th-12th, project collaborators presented updates on their respective work packages, and reviewed project progress. Anirudh presented GEM updates on the compilation of a library of vulnerability functions for multiple perils for use in the project, and on the identification and propagation of uncertainties in risk assessment. The host organization, NSET provided an International Partner presentation, discussing the details of building code compliance programs in Nepal. On November 13th, METEOR stakeholders from the policy-making level, the acting UK Ambassador to Nepal, NSET personnel and various university professors met to discuss policies and gaps in risk information. The following day was devoted to technical discussions on exposure, landslide, seismic hazard and flood, which was well attended by various technical and scientific staff from government ministries, bureaus and academic institutions. The next METEOR project stakeholders meeting is scheduled in 2020. For more information on the METEOR project, please visit . No images found. GALLERY 1/12 Gallery VIDEO RELATED CONTENTS

Publications Development of a fragility and vulnerability model for global seismic risk analyses Share Facebook LinkedIn Download 2020 | Peer-reviewed Seismic fragility and vulnerability assessment is an essential step in the evaluation of probabilistic seismic risk. Ideally, models developed and calibrated for the building portfolio of interest would be readily available. However, the lack of damage data and insufficient analytical studies lead to a paucity of fragility and vulnerability models, in particular in the developing world. This study describes the development of an analytical fragility and vulnerability model covering the most common building classes at the global scale. Nearly five hundred functions were developed to cover the majority of combinations of construction material, height, lateral load resisting system and seismic design level. The fragility and vulnerability were derived using nonlinear time-history analyses on equivalent single-degree-of-freedom oscillators and a large set of ground motion records representing several tectonic environments. The resulting fragility and vulnerability functions were validated through a series of tests which include the calculation of the average annual loss ratio for a number of locations, the comparison of probabilities of collapse across all building classes, and the repetition of past seismic events. The set of vulnerability functions was used for the assessment of economic losses due to earthquakes as part of the global seismic risk model supported by the Global Earthquake Model Foundation.

Publications Material didáctico para sensibilizar a la comunidad sobre el riesgo sísmico. Aplicación para el Área Metropolitana del Valle de Aburrá (AMVA) Share Facebook LinkedIn Download 2022 | Report El presente documento es el resultado del esfuerzo colaborativo entre la Fundación GEM, la administración del Área Metropolitana del Valle de Aburrá (AMVA) y su proyecto Sistema de Alerta Temprana de Medellín y el Valle de Aburrá - SIATA y la Universidad EAFIT. El objetivo de este reporte es generar material didáctico para sensibilizar a la comunidad sobre el riesgo sísmico que pueda servir de apoyo a las personas encargadas de comunicar este tema a diferentes grupos de la sociedad. El público objetivo de la sesión va desde estudiantes en diferentes grados de escolaridad a la comunidad general, incluyendo al personal que participa en las actividades de planeación, reducción y mitigación del riesgo de desastres.

News Willis Research Network Conference 2021 By: Aug 30, 2021 Share Facebook LinkedIn On May 19th, GEM Secretary General, John Schneider, participated in the Willis Research Network conference - Celebrating 15 years of Science for Resilience - presenting GEM’s experience in harnessing knowledge from global and local research communities in Session 2: Standard for global modelling with local impact. Over three days, more than 450 delegates attended nine sessions showcasing WRN’s research partnerships, insights on how science has improved understanding of risk over the last 15 years, and discussions on how collaboration between science and business can further foster resilience for organizations and society as a whole. John also joined the Session 2 panel discussion moderated by Matt Foote, Director, Climate and Resilience Hub Willis Towers Watson, where panelists outlined several global public-private modelling initiatives and their application across different stakeholder communities,, and in particular how these can be applied to support global, regional, and local risk assessment and risk reduction strategies. John shared his thoughts on how the next generation of models should be provided and delivered saying, “Global models have focused typically on individual hazards so obviously integration into a cohesive or harmonized model is needed. At GEM, we're assessing the risk more holistically and in a more integrated fashion, which we believe is critical in understanding risk.” He also pointed out that openness of not only the platforms but the models themselves and all of the assumptions that go into them is important to make data - and in turn the models - more transparent and accessible. Although John expressed that there’s still a lot of room for improvements in sharing vulnerability and exposure information between and among private or commercial entities, he cited that GEM is starting to see this open up more and models are becoming more transparent. He attributes this in part to GEM’s efforts in bringing together public, private and academic institutions with substantially different motivations coming together to build models, tools and databases, and sharing them. The next challenges in modelling, John summarized, would be in organizations going beyond primarily sharing hazard data and models to sharing vulnerability and exposure data for risk models; and moving from disaggregated sets of mostly closed risk models to global collaborations with open models and tools for multi-hazard risk assessment. To download or watch all the plenary sessions, presentations and discussions, including on-demand videos for a more in-depth look into some of WRN partnerships and business applications and articles, visit https://www.willistowerswatson.com/en-GB/Insights/campaigns/15-years-of-science-for-resilience . No images found. GALLERY 1/0 Gallery VIDEO RELATED CONTENTS

News CRAVE project kicks off in Bogota, Colombia By: Jul 2, 2018 Share Facebook LinkedIn The USAID project – Collaborative Risk Assessment for Volcanoes and Earthquakes or CRAVE successfully kicked off with a workshop in Bogota, Colombia on February 22-23, 2018. Participants came from the British Geological Survey, the Volcano Disaster Assistance Program (VDAP) of the United States Geological Survey (USGS), the Colombian Geological Survey and the Rabaul Volcano Observatory of Papua New Guinea. The project also includes the participation of the Philippine Institute of Volcanology and Seismology (PHIVOLCS), the Earth Observatory of Singapore (EOS) and the University of Edinburgh. The kickoff meeting covered several topics, including the identification of relevant hazard and risk information for decision-makers, data needs for volcano and earthquake hazard assessment in the pilot areas (Colombia and Papua New Guinea), challenges in the harmonization of software, models and datasets, and available modeling tools for the assessment of the impact from these geo-hazards. Marta Cavalche of SGC highlighted the importance of expanding the assessment beyond hazard after a discussion that most of the available assessment software is only for hazard. The project anticipates that communicating earthquake and volcanic risk can be a challenging task due to other competing social issues, and the lack of resources. However, Vitor Silva of the GEM Foundation points out that “When resources are limited, and they are always limited, we cannot afford to tackle one problem at a time. A multi-hazard and multi-disciplinary approach to disaster risk reduction better utilizes available resources.” Despite several aspects that need to be addressed such deciding on a uniform assessment framework, data format, and data availability, participants identified points of common work, as well as strategies to achieve the objectives set by the project. CRAVE is funded by USAID and will run for 18 months to develop a common framework for the assessment of the impact from earthquakes and volcanoes, with an application to a few locations in the Philippines, Papua New Guinea and Colombia.The kickoff meeting also appeared on SGC’s website article: Reunión inicial del proyecto CRAVE. No images found. GALLERY 1/0 VIDEO RELATED CONTENTS