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  • Africa Risk Model | Global Earthquake Model Foundation

    Global Earthquake Maps GEM GLOBAL MOSAIC OF RISK MODELS North and Sub-Saharan Africa (NSSA) VIEW 1. Global Risk Modeling – Data and Results for Africa Within the framework of GEM’s Global Earthquake Risk Model, the components necessary for the estimation of economic and human losses were developed for 56 countries in Africa. This information represents the most comprehensive resource for seismic hazard and risk assessment in the region. It comprises the characterization of seismic hazard, the description of the physical building stock, and definition of vulnerability and fragility functions. The seismic hazard model for Africa was delivered in 4 separate projects covering Eastern Africa, Southern Africa, West and Central Africa and North Africa. Three occupancy classes were considered in modeling the physical building stock: residential, commercial and industrial. Population exposed to ground shaking is modeled taking into account occupancies at different times of the day (e.g. day, night and transient hours). Fragility and vulnerability functions are described according to the probability of loss ratio for all the predominant building typologies identified in Africa. 2. Exposure Modeling For most countries in Africa, the majority of the population live in rural areas in contrast with the rest of the world where the building stock, infrastructure and population are mainly concentrated in urban areas. For example, the most recent census conducted in Ethiopia in 2007 estimated about 81% of the Ethiopians live in rural areas. The settlement of a household is usually related to their economic condition and the type of house they live in. To assess and calibrate earthquake losses for each country in Africa, we have used data from population and housing censuses (at the smallest available administrative level) which capture demographic variations in a country. They are the most complete and updated databases containing information about population and households. 2.1 Identification of variables National Census Data describes various dimensions of demographic, economic and social data pertaining to persons within a country. To model the building stock exposed to ground shaking, we collected the variables relevant to describe the main material of construction, lateral load resisting system, number of storeys, and less frequently, also the date of construction. Type of foundation, roof type, floor type, vertical and horizontal irregularities were collected (when available) to support assigning vulnerability classes to assets. Secondary information regarding the built-up area, construction cost and household size was also collected from the existing literature, technical reports, national housing census and the judgement of local experts. 2.2 Mapping and Estimating Buildings A key step in developing a reliable exposure model is the mapping of building classes to attributes featured by the housing census and the socio-economic data. The three main steps involved in the mapping are: ​ Identification of the most common building classes using the GEM taxonomy. Association of each building class with the attributes used by the local dataset. Definition of the fraction of each building class. Based on information available for each country and the procedure described above, all dwellings were classified according to the predefined building classes. In total, over 30 unique building classes were identified for Africa. The number of dwellings were converted to the number of buildings using the number of storeys per building class, average number of households per storey, and the type of area (urban, rural or large city). 2.3 Average Area and Replacement Cost To understand and estimate the total economic losses due to direct damage, we assigned an average area and replacement cost using construction manuals and technical documentation available for the region. The average area and replacement cost were assigned according to building class, number of stories and area type (urban, rural or large city). The replacement costs were further disaggregated into costs for structural components, nonstructural components and contents. ​ The table below presents the basic statistics from the exposure model for each country or territory in Africa. Africa Exposure Map 3. Vulnerability The vulnerability component characterizes the likelihood to suffer damage or loss given a hazard intensity. The relation between probability of loss and hazard intensity is expressed by a vulnerability function, whilst the relation between probability of damage for each damage state and hazard intensity is represented by a fragility function. Despite the notable advances in regional seismic vulnerability modelling in the last three decades, a uniform set of vulnerability or fragility functions covering all of the building classes in Africa was not available. Moreover, with a few exceptions, most of the existing vulnerability functions have not been tested against damage data from previous events and have not been applied within a probabilistic framework for earthquake loss assessment. In general, this approach relies on the following steps: Identification of the most common building classes in the region, using peer-reviewed literature, web surveys ( ), and World Housing Encyclopedia reports. https://platform.openquake.org/building-class/ Development of simplified numerical models for each building class, using data from the literature and results from experimental campaigns (e.g. yield and ultimate global drift, elastic and yield period of the first mode of vibration, participation factor of the first mode of vibration, common failure mechanisms). Some of the building classes had to be explicitly modelled using complex 3D models due to the lack of information in the literature. Selection of ground motion records using local strong motion databases, and considering the local seismicity and tectonic environment. To this end, seismic hazard disaggregation at the location of the most urbanized centers supported the identification of the combinations of magnitude and distance, which contribute the most to the seismic hazard. The use of a large set of actual time histories aims at propagating the record-to-record variability to the vulnerability assessment. Performing nonlinear time history analysis to evaluate the structural response (i.e. engineering demand parameter (EDP) – maximum displacement and acceleration) of the simplified numerical model against the selected ground motion records. This step uses the open-source package for structural analysis OpenSees, and the Risk Modelers Toolkit developed and supported by GEM. Evaluation of the structural responses of the numerical models in order to evaluate the evolution of damage with increasing hazard intensities. In this process, the probability of exceeding each damage state for a set of intensity measure levels is defined (i.e. fragility functions). The fragility functions can be converted into vulnerability functions (i.e. probability of loss ratio conditional on ground shaking) using a damage-to-loss model. Such functions can be used directly in the assessment of economic and human losses due to earthquakes. This framework is supported by a set of tools that can be improved upon the release of new models and datasets. As an example, fragility models for the four most common building classes in Africa are illustrated below. 4. Seismic Hazard The seismic hazard for the African continent was developed considering four regions. The SSAHARA project encompasses countries in eastern Sub-Saharan Africa. The North Africa and West Africa models were developed internally at GEM with collaborations from local experts in the respective regions. For southern Africa, the Council of Geoscience of South Africa developed and shared a seismic hazard model for the region. Below are the links to the datasets and results of all four models and associated technical documentation. ​ Eastern Sub-Saharan Africa North Africa West Africa South Africa ​ ​ Africa Hazard Map 5. Seismic Risk Results The seismic hazard of the African continent is generally considered to be low to moderate. However, the physical vulnerability of the built environment is relatively high, which exacerbates the impact of ground shaking. In order to understand and develop pragmatic solutions towards seismic risk reduction, it is imperative to estimate accurately the possible losses. The OpenQuake-engine was used to estimate a number of risk metrics for each country. The loss parameters include annualized average loss, loss ratio and probable maximum loss for different return periods. Moreover, as part of GEM’s initiatives to increase risk awareness, a country risk profile was developed for each country in Africa ( ). Below are figures showing the top 20 countries in Africa in terms of average annual losses and their corresponding loss ratios. https://www.globalquakemodel.org/country-risk-profiles 6. Partners and Contributors The Africa seismic risk model extensively relies on the enthusiasm and commitment of various organizations that openly collaborated with GEM and its partners. The creation of this model would not have been possible without the support provided by many experts. A list of the individuals that contributed to the development of the Africa seismic risk model is provided below.

  • GEM | Events

    GEM EVENTS To further advance the science and application of earthquake risk mitigation and resilience, GEM organizes and participates in various international events. Below is a list of events in the succeeding months where GEM will be actively participating. Go

  • History | Global Earthquake Model Foundation | Italy

    HISTORY In 2004, during the 11th OECD-GFS Meeting, realizing the lack of venue to discuss earthquake science, the German delegation proposed to hold a workshop to discuss the issue. Over the next two years, a series of workshops and meetings culminated to the identification of a need to create a Global Earthquake Risk Mapping and Monitoring Programme or GEM in 2006. ​ GEM’s financial feasibility received a critical boost in 2007, after MunichRe agreed to become its first and main private participant through a 5M Euro contribution for a period of five years. In the succeeding year, the interim Governing Board selected EUCENTRE in Pavia, Italy as the host institution for the GEM Secretariat. Following two significant milestones, GEM Foundation was incorporated in Pavia, Italy as a non-profit organization in 2009, giving the Foundation a legal identity with a vision of a world that is resilient to earthquakes. Key Events GEM Meeting, Zurich 2008 Key Events Hover mouse on side arrow or mouse scroll up or down to view GEM's timeline Early Supporters Four scientists played a particularly relevant role in bringing GEM to fruition during the early years: Jochen Zschau (GFZ Potsdam, Retired) Ross Stein (USGS, Retired) Domenico Giardini (ETH Zurich) Anselm Smolka (Munich Re, Retired) ​ ​ The contribution of the following individuals in the early stages of GEM is also acknowledged: Frederic Sgard (OECD-GSF), Kate Stillwell (UC Berkeley) and Conrad Lindholm (NORSAR). Founding Members Public Italy - Department of Civil Protection (2009) Turkey - Bogazici University (2009) Belgium - Belgium Science Policy (2009) Singapore - Nanyang Technoligical University (2010) Switzerland - Swiss Federal Institute of Technology Zurich (2010) Germany - GFZ Helmholtz Centre Potsdam (2010) Private EUCENTRE (2009) MunichRe (2009) Zurich Insurance Group (2010) Air Worldwide (2010) Willis (2010) Associate OECD - Organization for Economic Cooperation and Development (2009) UNESCO - United Nations Educational, Scientific and Cultural Organization (2012) WB - The World Bank (2012) IASPEI - International Association of Seismology and Physics of the Earth’s Interior (2009) IAEE - International Association of Earthquake Engineering (2009)

  • Pacific Islands Risk Model | Global Earthquake Model Foundation

    Global Earthquake Maps The Earthquake Risk Model for the Pacific Islands has compiled by the GEM Foundation using publicly accessible sources of information. Even though the exposure model was developed for 19 countries, given the disparity in the hazard levels that can be found in the region, the risk metrics were calculated exclusively for the 9 countries that present non-negligible levels of seismic hazard. 1. Exposure Modelling In the framework of the Global Seismic Risk Model, an open exposure model describing the residential, commercial and industrial building stock for 19 countries has been developed, using only publicly available sources of information. The 19 countries were divided in three groups. For the first group the information used to build the exposure models was retrieved from the Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI), a joint initiative from different organizations to provide disaster risk modelling tools for the Pacific Islands. For the second group of countries, which includes the three unincorporated United States territories located in the Pacific, the information was collected from HAZUS. For the remaining countries, the primary source of information was the latest national and sub-national population and housing census databases of each country. The table below presents the population and number of residential, commercial and industrial buildings for the 19 countries, as well the primary source of information. Pacific Islands Exposure Map Although 15 countries are included in the PCRAFI initiative, three of them (Fiji, Papua New Guinea, and Timor-Leste) had insufficient information and therefore the data could not be considered. PCRAFI data describes the occupancy type, the number and type of buildings, the number of stories, the floor area and the economic value of each asset. A building class was then assigned to each occupancy and construction type, according to different publications that describe the building practices of the region. After analyzing the housing census data for most of the countries, it was noticed that the number of households reported was systematically lower than the total number of residential buildings described in PCRAFI. The difference in these two metrics can be related to the definition of household: while in some countries it is defined as “a group of persons who sleep in the same housing unit and have a common arrangement in the preparation and consumption of food”, in other cases it is stated that “the definition is based on eating together rather than on living or sleeping in the same building or dwelling”, indicating that a household may include more than one building. Moreover, the PCRAFI methodology uses satellite imagery to obtain the building footprints, which can lead to an overestimations of the number of buildings, since this automatic process may, in certain cases, identify other surfaces as buildings. In such cases, the total number of households in each administrative region was distributed according to the number of buildings in the same area and, consequently, the number of dwellings per building is below one. ​ Since the PCRAFI data was collected in 2010 and 2011, the economic values had to be updated, considering the inflation rates of each country. The structural, non-structural, and contents values were estimated as a fraction of the total value provided by PCRAFI, using different ratios according to the general occupancy of each asset (residential, commercial and industrial). ​ For the unincorporated United States territories located in the Pacific (American Samoa, Guam and Northern Mariana Islands), the HAZUS building stock inventory was used. This methodology uses a dasymetric mapping to distribute buildings inside each census block. Then, a general and specific occupancy is assigned to each location, based on land use maps and on more specific information, when available. To assign building classes to each building, the Earthquake Model State Occupancy to Building Type Mapping Scheme for Hawaii is used, since these do not exist for the Pacific territories. Replacement costs are estimated based on RSMeans values, which are then adjusted considering local costs of materials and services. ​ For the remaining countries in which the census databases were used (Fiji, New Caledonia, Papua New Guinea and Timor Leste), the development of the exposure model followed these main steps: ​ 1 - Mapping census data to building classes: The housing census databases typically provide information about the number of households and specific attributes (e.g. exterior walls material, roof material, or number of rooms) but do not describe the number of households in specific building classes. For example, the census data for Timor-Leste provide different tables that detail the number of households by construction material of the external walls. However, it is not possible to know the percentage of concrete/brick buildings that have corrugated iron/zinc or concrete roofs. Therefore, it was necessary to establish a relationship between the attributes used in the census data and a list of building classes, which is herein called a mapping scheme. The number of buildings per building class was calculated by multiplying the quantity defined in the census by the associated building fraction, at each geographical scale. Since the building classes are expected to have distinct distributions depending on the settlement type (for example metropolitan, urban and rural areas), when the geographical regions of the country were categorized according to these classes, different mapping schemes were developed. 2 - Converting the number of dwellings/households to number of buildings: In most of the countries analyzed, the census data reports the total number of households by geographical region. Whilst this information is useful to estimate the total area or replacement cost of a given building class, it does not give information about the number of buildings in each location. Given the construction practices of these countries, the vast majority of the population live in detached buildings. Therefore, in rural areas the number of households was considered equivalent to the number of buildings, as well as for the single storey buildings in urban areas. In urban or metropolitan areas, the number of buildings with 2 or more floors was obtained by dividing the total number of households by the average number of households per building, for each building class. 3 - Estimation of areas, replacement costs and occupants per building class: The final step to complete the exposure model is the estimation of the replacement cost. In this context, the replacement cost includes the cost of structural and non-structural components and contents, but not the cost of the land. Since construction costs are commonly found per square meter of dwelling, the average floor area per dwelling was used. Given the difficulty in finding reliable estimation for the contents cost, this value was estimated as a percentage of the structural value of the building. Depending on the information available for each country, the cost was differentiated across metropolitan, urban, and rural areas. Finally, the population in each region was distributed across the different building classes proportionally to the number of dwellings. Industrial and commercial exposure model For the development of the industrial and commercial exposure models the data available in the census was also the primary source of information used. In most cases, the number of workers in each region or administrative division is available, disaggregated by type of industry or sector of work. An average number of workers per business was defined to estimate the number of buildings in each sector, based on the available literature or reports for each country. The total number of buildings was also adjusted to match the ratio of population per building (industrial and commercial) verified in the neighboring countries with similar economic characteristics. Subsequently, mapping schemes were defined for each sector to define the number of buildings per building class. Based on the building class and on the average number of workers in each sector, the average building area and cost per square meter were defined for each building class. The figures below present the distribution of buildings and the aggregated replacement cost for the 9 countries with significant seismic hazard (American Samoa, Tonga, Vanuatu, Solomon Islands, Timor-Leste, Samoa, Fiji, New Caledonia and Papua New Guinea). The exposure (in terms of the number of buildings) is also presented on an evenly spaced grid in the interactive map below. 2. Vulnerability The vulnerability component characterizes the likelihood to suffer damage or loss given a hazard intensity. The relation between probability of loss and hazard intensity is expressed by a vulnerability function, whilst the relation between probability of damage and hazard intensity is represented by fragility functions. For the Pacific Islands region, the general set of global vulnerability functions from GEM were employed. The methodology followed for the development of these functions relied on the following steps: Identification of the most common building classes in the region, using peer-reviewed literature, web surveys ( ), and World Housing Encyclopedia reports. https://platform.openquake.org/building-class/ Development of simplified numerical models for each building class, using data from the literature and results from experimental campaigns (e.g. yield and ultimate global drift, elastic and yield period of the first mode of vibration, participation factor of the first mode of vibration, common failure mechanisms). Some of the building classes had to be explicitly modelled using complex 3D models due to the lack of information in the literature. Selection of ground motion records using local strong motion databases, and considering the local seismicity and tectonic environment. To this end, seismic hazard disaggregation at the location of the most urbanized centers supported the identification of the combinations of magnitude and distance, which contribute the most to the seismic hazard. The use of a large set of actual time histories aims at propagating the record-to-record variability to the vulnerability assessment. Performing nonlinear time history analysis to evaluate the structural response (i.e. engineering demand parameter (EDP) – maximum displacement and acceleration) of the simplified numerical model against the selected ground motion records. This step uses the open-source package for structural analysis OpenSees, and the Risk Modelers Toolkit supported by GEM. Evaluation of the structural responses of the numerical models in order to evaluate the evolution of damage with increasing hazard intensities. In this process, the probability of exceeding a number of damage states for a set of intensity measure levels is defined (i.e. fragility functions). The fragility functions can be converted into vulnerability functions (i.e. probability of loss ratio conditional on ground shaking) using a damage-to-loss model. Such functions can be used directly in the assessment of economic and human losses due to earthquakes. A similar approach has been followed for the development of fragility and vulnerability functions within the scope of regional programs in Central America and the Caribbean, South America, Sub-Saharan Africa and South-East Asia. This framework aims at being a dynamic tool which can be improved upon the release of new models and datasets. As an example, fragility models for four building classes in the Pacific Islands are illustrated below. 3. Seismic Hazard The main components concerning the probabilistic seismic hazard model for the region can be found in the associated . The seismic hazard in terms of the peak ground acceleration (PGA) for a probability of exceedance of 10% in 50 years (equivalent to approximately 475 years return period) is presented in the interactive map below. technical documentation Pacific Islands Hazard Map 4. Seismic Risk Results As previously explained, the risk metrics were calculated for the 9 countries in the region that have non-negligible levels of seismic hazard. The risk results suggest that the countries with greatest earthquake risk in the Pacific are Vanuatu, Tonga, Solomon Islands and Papua New Guinea. The region is part of the circum-Pacific belt (also called “Ring of Fire”), one of the most seismically active regions of the world. Throughout the history, high magnitude earthquakes have caused human and economic losses in these countries, due to the convergence between the Pacific and Indo-Australian plate. In addition to being in one of the most seismically active regions in the world, Papua New Guinea shows also a high concentration of population which increases the risk of economic and human losses. In fact, according to the Global Seismic Risk Model estimations, this country presents not only a high loss ratio but also high absolute average annual losses. The capital stock of the other countries in the region with high seismic hazard (Tonga, Vanuatu and Solomon Islands) are relatively low, leading to low absolute average annual losses, but high loss ratios. More detailed risk results by country are presented in specific country risk profiles [link]. In each profile it is possible to find seismic hazard, exposure and risk maps, sets of exceedance probability curves and the list of the top regions at risk at a subnational level. The seismic risk (in terms of normalized average economic losses) is also presented in the interactive map below. Pacific Islands Risk Map 5. Partners and Contributors The Pacific Islands seismic risk model extensively relies on the enthusiasm and commitment of various organizations that openly collaborated with GEM and its partners. The creation of this model would not have been possible without the support provided by many experts. A list of the individuals that contributed to the development of the Pacific Islands seismic risk model is provided below. GEM GLOBAL MOSAIC OF RISK MODELS Pacific Islands (PAC) VIEW

  • GEM | Who We Are

    WHO WE ARE The GEM Foundation is a non-profit, public-private partnership that drives a global collaborative effort to develop scientific and high-quality resources for transparent assessment of earthquake risk and to facilitate their application for risk management around the globe. ​ Assisted by an initiative of the OECD's Global Science Forum, GEM was formed in 2009 as a non-profit foundation in Pavia, Italy, funded through a public-private sponsorship with the vision to create a world that is resilient to earthquakes . GEM’s mission is to become one of the world’s most complete sources of risk resources and a globally accepted standard for seismic risk assessment; and to ensure that its products are applied in earthquake risk management worldwide. Openness Open data, open software, transparent processes, freely accessible to the public Collaboration Public-private partnership, inclusiveness, working together across geographies and disciplines Credibility Commitment to scientific credibility, trusted by local and global partners and peers Public Good Motivated by the welfare of the public, works to serve the public good CORE PRINCIPLES GEM builds capacity to assess and manage risk through open, transparent and collaborative seismic risk assessment at local, national, regional and global scales. Using state-of-the-art tools, GEM is committed to share and advocate open, reliable earthquake risk information to support sound disaster risk-reduction planning at various levels.

  • Platform | Global Earthquake Model Foundation

    Openquake OPENQUAKE Platform The OpenQuake Platform is a website that allows the community to explore, manipulate and visualize the datasets and models and to use tools that GEM produces. The platform also allows users to contribute, share and discuss new findings and results with the GEM community. Share your outputs - datasets, maps, models - to the GEM OpenQuake community through the Platform. The OpenQuake Platform hosts a number of national, regional and global models. Follow the instructions below to access data from GEM and the OQ community. For users who only need outputs such as datasets, layers or maps, you can simply register for free and browse the Platform for the data that you need. Sign in Register To start sharing your data, follow the instructions below. or register . Sign in here Click Layers > Upload Layers Create maps based on GEM’s existing datasets or create one based on your uploaded Layer. Click Maps > Create Maps Save and Publish your map to share with the OpenQuake community. To start browsing and downloading data, follow the instructions below. or register Sign in here . In the Search box, type the name of the map or dataset you’re looking for. Look for your item from the search results, click to Download. To customize or create your own maps, click Maps > Create Maps Click the Add Layer icon and select from the available layers from the dropdown list. Save and Publish your map. Download your map.

  • Global Earthquake Maps | Global Earthquake Model Foundation | Italy

    Global earthquake maps The development of the Global Earthquake Hazard and Risk Model was a key priority for GEM under its 2014-2018 Work Program. The objective is to collaboratively develop a complete set of earthquake data and models, and to deliver a comprehensive global assessment of earthquake risk. Hazard PNG Map Hazard PDF Map Hazard Viewer Hazard Global Earthquake Hazard Map The Global Earthquake Model (GEM) Global Seismic Hazard Map (version 2018.1) depicts the geographic distribution of the Peak Ground Acceleration (PGA) with a 10% probability of being exceeded in 50 years, computed for reference rock conditions (shear wave velocity, V , of 760-800 m/s). Documentation Contributors Technical Description Risk Global Earthquake Risk Map The Global Seismic Risk Map (v2018.1) comprises four global maps. The main map presents the geographic distribution of average annual loss (USD) normalised by the average construction costs of the respective country (USD/m2) due to ground shaking in the residential, commercial and industrial building stock, considering contents, structural and non-structural components. Documentation Technical Description Country Profiles Contributors Risk PNG Map Risk PDF Map Exposure Viewer Risk Viewer MAJOR SPONSORS AIR ARUP GEOSCIENCE AUSTRALIA CSSC NRCan EAFIT ETH ZURICH EUCENTRE FM GLOBAL GFZ GIROJ GNS SCIENCE HANNOVER RE MUNICH RE NTU ICRM NEPHILA NERC NIED NSET OYO PARTNER RE DPC SGC SWISS SER SWISS RE FOUNDATION SURAMERICANA TEM RCN USGS USAID WTW ZURICH INSURANCE Show More

  • GEM | What We Do

    WHAT WE DO PROJECTS IMPACT FUTURE WORK WHAT WE DO GEM engages in several interrelated activities that focus on reducing earthquake risk and improving earthquake risk management especially in areas that are underserved, exposed and vulnerable to seismic risk. To serve the needs of our stakeholders, our activities include: OpenQuake software development and maintenance Standardization of hazard and risk datasets Public outreach and risk information dissemination Capacity development Hazard and risk assessment Technical assistance CONTACT US

  • Hazard Technical Description | Global Earthquake Model | Italy

    Global Earthquake Maps GEM GLOBAL MOSAIC OF HAZARD MODELS Technical description READ MORE The Global Earthquake Model (GEM) Global Seismic Hazard Map (version 2018.1) depicts the geographic distribution of the Peak Ground Acceleration (PGA) with a 10% probability of being exceeded in 50 years, computed for reference rock conditions (shear wave velocity, V , of 760-800 m/s). The map was created by collating maps computed using national and regional probabilistic seismic hazard models developed by various institutions and projects, and by GEM Foundation scientists. The OpenQuake engine, an open-source seismic hazard and risk calculation software developed principally by the GEM Foundation, was used to calculate the hazard values. A smoothing methodology was applied to homogenise hazard values along the model borders. The map is based on a database of hazard models described using the OpenQuake engine data format (NRML); those models originally implemented in other software formats were converted into NRML. While translating these models, various checks were performed to test the compatibility between the original results and the new results computed using the OpenQuake engine. Overall the differences between the original and translated model results are small, notwithstanding some diversity in modelling methodologies implemented in different hazard modelling software. The hashed areas in the map (e.g. Greenland) are currently not covered by a hazard model. The map and the underlying database of models are a dynamic framework, capable to incorporate newly released open models. Due to possible model limitations, regions portrayed with low hazard may still experience potentially damaging earthquakes. The GEM Foundation plans to release future updates of this map on a regular basis as new information becomes available. Technical details on the compilation of the hazard and risk maps and the underlying models are available at http://www.globalquakemodel.org/gem. ​ How to use and cite this work Please cite this work as: M. Pagani, J. Garcia-Pelaez, R. Gee, K. Johnson, V. Poggi, R. Styron, G. Weatherill, M. Simionato, D. Viganò, L. Danciu, D. Monelli (2018). Global Earthquake Model (GEM) Seismic Hazard Map (version 2018.1 - December 2018), DOI: 10.13117/GEM-GLOBAL-SEISMIC-HAZARD-MAP-2018.1 This work is licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA): https://creativecommons.org/licenses/by-nc-sa/4.0/. ​ Acknowledgements This map is the result of a collaborative effort and extensively relies on the enthusiasm and commitment of various organisations and projects to openly share and collaborate. The creation of this map would not have been possible without the support provided by many public and private organisations during GEM’s second implementation phase (2014-2018). These key contributions are profoundly acknowledged. None of this would have been possible without the extensive support of all GEM Secretariat staff. The map was plotted using the Generic Mapping Tools software (Wessel et al., 2013). ​ Legal statements ​ This map was created for dissemination purposes. The information included in this map must not be used for the design of earthquake-resistant structures or to support any important decision involving human life, capital and movable and immovable properties. The values of seismic hazard in this map do not constitute an alternative nor do they replace building actions defined in national building codes. Readers seeking this information should consult national databases. This hazard map is the combination of results computed using 30 hazard input models covering the vast majority of landmass. These models represent the best information publicly accessible, and the GEM Foundation recognises their credibility and authoritativeness. This hazard map results from an integration process that is solely the responsibility of the GEM Foundation.

  • Middle East Risk Model | Global Earthquake Model Foundation

    Global Earthquake Maps 1. Exposure The Middle East exposure database covers the building stock for 18 countries, divided into three occupancy classes:, residential, commercial and industrial. The database provides number of buildings, number of dwellings, human population, and economic value, each expressed in terms of urban and rural areas. The development of the exposure model relied mostly on national housing census surveys, which were collected at the lowest available administrative division. The following steps illustrate the development process: ​ Identification of the predominant building typologies and construction techniques. The identified building typologies are classified according to the GEM Taxonomy for each country, where each building type is defined by the lateral load resisting system material, construction technique, type of lateral load resisting system, ductility level and number of floors. This step builds on the available literature and the feedback from local experts in the region. Mapping identified building types to census information. In this process census variables (i.e building type, wall material, construction age, etc) are related to the common building types in each country. Given, the generic nature of the census variables, building classes are assigned by giving weights to the probable types. For example, buildings with stone facade in Jordan could be stone masonry or reinforced concrete frame cladded with stone. In order to distinguish them, building age has been assigned different weights depending on the construction epochs. Estimation of floor area per building or dwelling. Most of the statistical information from the Middle East provides number of buildings for residential occupancy. In this step, building floor area is estimated based on the average dwelling size and floors per building type. Estimation of replacement costs. The economic values of structural, non-structural and contents components are estimated based on occupancy type and construction quality. The quality is allocated to each building type depending on the construction material and settlement type (e.g. urban, rural). Middle East Exposure Map Additional details about the exposure datasets for the residential building stock of Jordan, Syria, Palestine, Saudi Arabia, Lebanon, United Arab Emirates, Yemen, Oman, Kuwait, Qatar, Bahrain and Iraq can be found in Dabbeek and Silva (2019) “Modelling the residential building stock in the Middle-East for multi-hazard risk assessment”. Natural Hazards, in Review. The exposure datasets can be downloaded . here 2. Vulnerability The vulnerability component characterizes the likelihood to suffer damage or loss given a hazard intensity. The relation between probability of loss and hazard intensity is expressed by a vulnerability function, whilst the relation between probability of damage and hazard intensity is represented by fragility functions. Despite the notable advances in regional seismic vulnerability modelling in the last three decades, a uniform set of vulnerability or fragility functions covering all of the building classes in the Middle East was not available. Moreover, with a few exceptions, most of the existing vulnerability functions have not been tested against damage data from previous events and have not been applied within a probabilistic framework for earthquake loss assessment. In general, this approach relies on the following steps: Identification of the most common building classes in the region, using peer-reviewed literature, web surveys ( ), and World Housing Encyclopedia reports. https://platform.openquake.org/building-class/ Development of simplified numerical models for each building class, using data from the literature and results from experimental campaigns (e.g. yield and ultimate global drift, elastic and yield period of the first mode of vibration, participation factor of the first mode of vibration, common failure mechanisms). Some of the building classes had to be explicitly modelled using complex 3D models due to the lack of information in the literature. Selection of ground motion records using local strong motion databases, and considering the local seismicity and tectonic environment. To this end, seismic hazard disaggregation at the location of the most urbanized centers supported the identification of the combinations of magnitude and distance, which contribute the most to the seismic hazard. The use of a large set of actual time histories aims at propagating the record-to-record variability to the vulnerability assessment. Performing nonlinear time history analysis to evaluate the structural response (i.e. engineering demand parameter (EDP) – maximum displacement and acceleration) of the simplified numerical model against the selected ground motion records. This step uses the open-source package for structural analysis OpenSees, and the Risk Modelers Toolkit supported by GEM. Evaluation of the structural responses of the numerical models in order to evaluate the evolution of damage with increasing hazard intensities. In this process, the probability of exceeding a number of damage states for a set of intensity measure levels is defined (i.e. fragility functions). The fragility functions can be converted into vulnerability functions (i.e. probability of loss ratio conditional on ground shaking) using a damage-to-loss model. Such functions can be used directly in the assessment of economic and human losses due to earthquakes. This framework is supported by a set of tools that can be improved be improved upon the release of new models and datasets. As an example, fragility models for the four most common building classes in the Middle East are illustrated below. 3. Seismic Hazard The main components concerning the probabilistic seismic hazard model for the region can be found in the associated technical documentation at: and and the seismic hazard in terms of peak ground acceleration (PGA) for a probability of exceedance of 10% in 50 years (equivalent to approximately 475 years return period) is presented in the figure below. https://hazard.openquake.org/gem/models/ARB/ https://hazard.openquake.org/gem/models/MIE/ Middle East Hazard Map 4. Seismic Risk Results The results show that the risk is significant in the majority of Middle East countries, specifically Iran, Pakistan and Syria have the highest absolute economic losses. While in relative terms, Iran, Afghanistan and Armenia have the highest risk. In addition, the results show that Bahrain and Qatar have the lowest losses. Generally, the countries with larger building stock (i.e Pakistan, Syria and Afghanistan) have higher absolute losses, while countries with smaller building stock located in less populous areas have higher relative losses (i.e Lebanon, Jordan, Armenia and Georgia). Iran is the exception, in which both absolute and relative losses are high although buildings stock is large and distributed over large area. Middle East Risk Map 5. Partners and Contributors The Africa seismic risk model extensively relies on the enthusiasm and commitment of various organizations that openly collaborated with GEM and its partners. The creation of this model would not have been possible without the support provided by many experts. A list of the individuals that contributed to the development of the Africa seismic risk model is provided below. GEM GLOBAL MOSAIC OF RISK MODELS Middle East (MIE) VIEW

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