Vulnerability functions describe the expected loss for a given ground shaking intensity level and are an essential component in probabilistic seismic risk assessment. This manu-script presents a novel open-source platform for the derivation of analytical fragility and vulnerability models, covering state-of-the-art methodologies, and addressing critical issues in vulnerability modelling such as uncertainty propagation, validation/verification of results and sufficiency/efficiency of intensity measure types. This framework is divided into seven modules designed to guide users through the different stages of analytical vulnerability modelling from the selection of ground motion records to the validation and verification of the models. The platform was implemented in the Python programming language and it is freely accessible through a public GitHub repository. A graphical user interface is included with the toolkit and is intended to be a general-purpose method for modellers to interact with thevulnerability modellers toolkit (VMTK). Experienced users are encouraged to use Python’s scripting capabilities to explore all the features of the VMTK source code and to contribute to future releases of the toolkit. Download link: https://www.globalquakemodel.org/product/vmtk

A consortium comprised by the Global Earthquake Model Foundation, ImageCat Inc. and the Humanitarian OpenStreetMap Team has been chosen by the Global Facility for Disaster Risk Reduction and Recovery to develop an open exposure database for multi-hazard risk assessment, as part of the Challenge Funds supported by the Department for International Development of the United Kingdom. This database is capable of storing different assets, while considering relevant attributes for six natural hazards: earthquakes, floods, volcanoes, strong winds, tsunamis and drought. The development of an open database to characterize the built-up environment at a global scale requires a uniform methodology to classify the elements exposed to the natural hazards. This deliverable describes a comprehensive multi-hazard GED4ALL taxonomy capable of classifying the building stock, lifelines, crops, livestock, forestry and socio-economic data. This taxonomy will be applied to the country of Tanzania to develop an exposure model at the national scale, and to the city level of Dar Es Salaam to demonstrate how an exposure dataset at a building-by-building resolution can be created. Moreover, this process will be demonstrated for five countries around Tanzania (Ethiopia, Uganda, Kenya, Malawi and Mozambique).

This special issue of Earthquake Spectra documents the supporting research critical to the development of the Global Seismic Hazard and Risk models by the GEM (Global Earthquake Model) Foundation, representing a major step in understanding earthquake risk globally. Seismic hazard and risk models are needed for accurate assessment of risks in order to promote risk reduction and mitigating actions, such as the improvement of building codes and construction practices, sustainable land use, emergency response, and protection of critical infrastructures, as well as risk transfer through insurance. GEM’s Global Seismic Hazard Model comprises a mosaic of 30 probabilistic seismic hazard models. Using this collection of hazard models as input, GEM computed a Global Seismic Risk Model depicting the average Annual Economic Losses (AEL) caused by ground shaking on the residential, commercial, and industrial building stock. Additional results were successively compiled including global maps for different intensity measure types, soil conditions and return periods. This collection of papers is intended for scientists and researchers in the hazard and risk modeling sector, and risk professionals for application to disaster risk reduction around the globe. This issue is made possible with partial support by the Global Earthquake Model (GEM) Foundation. Founded in Pavia, Italy in 2009, GEM is a non-profit public-private partnership that drives global collaborative efforts to develop scientific and high-quality resources for transparent assessment of earthquake risk and to facilitate their application for risk management around the globe. Learn more at www.globalquakemodel.org.

The 2020 COVID-19 pandemic caused a human and economic impact of unprecedented magnitude in contemporary history. In an effort to reduce the rate of infection, most governments implemented measures to increase social distancing and to strengthen the capacity of the healthcare system. The occurrence of earthquakes coincident with the pandemic may prevent the effective practice of such measures, and consequently cause an increase in the virus spread. This study analyzes the potential impact that seismic events may have on the infection rate within regions afflicted by both epidemics and earthquakes and explores open software packages that can be employed to simulate the impact of future destructive earthquakes on the spread of an emerging virus. Recent data on the number of confirmed cases at the national or subnational level were combined with a global seismic hazard and risk map to produce a combined index. This index highlights regions where preparedness and contingency plans should be developed to account for the possibility of COVID-19 outbreaks due to the earthquake impact.

The GEM Global Active Faults Database (GAF-DB) is the first public, comprehensive database of active faults with worldwide coverage. The GAF-DB is a compilation of many regional datasets. The GAF-DB contains ∼13,500 faults, each with associated attributes that describe the geometry, kinematics, slip rate, references, and other characteristics, as the information is available. Spatial completeness is high, and about 77% of the faults have slip rate information. The GAF-DB is built from its constituent datasets algorithmically and is designed to fluidly incorporate changes to or addition of any of the underlying datasets. This process reflects a philosophy of easily incorporating a change to avoid obsolescence and to quickly provide the most up-to-date information possible to the users. The database is licensed under a free and open-source license (CC-BY-SA 4.0) and is available at https://github.com/GEMScienceTools/gem-global-active-faults.

In December 2018, at the conclusion of its second implementation phase, the Global Earthquake Model (GEM) Foundation released its first version of a map outlining the spatial distribution of seismic hazard at a global scale. The map is the result of an extensive, joint effort combining the results obtained from a collection of probabilistic seismic hazard models, called the GEM Mosaic. Together, the map and the underlying database of models provide an up-to-date view of the earthquake threat globally. In addition, using the Mosaic, a synopsis of the current state-of-practice in modeling probabilistic seismic hazard at national and regional scales is possible. The process adopted for the compilation of the Mosaic adhered to the maximum extent possible to GEM’s principles of collaboration, inclusiveness, transparency, and reproducibility. For each region, priority was given to seismic hazard models either developed by well-recognized national agencies or by large collaborative projects involving local scientists. The version of the GEM Mosaic presented herein contains 30 probabilistic seismic hazard models, 14 of which represent national or sub-national models; the remainder are regional-scale models. We discuss the general qualities of these models, the underlying framework of the database, and the outlook for the Mosaic’s utility and its future versions.

Resilience to natural hazards and disasters is often defined as “the capacity of individuals, communities, organisations, cities, and nations to respond, cope and recover from a disaster”(UNISDR, 2009). Following the axiom that “what gets measured gets managed,” the ability to measure resilience is increasingly being identified as a key step towards earthquake risk reduction. Measuring resilience is difficult, however, and existing quantitative metrics of resilience (often in the form of indicators or composite indicators) suffer from key limitations. For instance, the leading resilience metrics that are currently used in research and for practical applications are uncertain due to data limitations. Most indicator-based methods utilise a broad-brush approach using secondary source census data that may neglect the true underlying drivers (or lack thereof) of resilience within communities. Also, resilience indicators exhibit a large degree of uniformity in index construction approaches that ignore, because of ecological fallacy (Pacione, 2005), the context of the natural hazard or the communities at risk. Such uniformity may result in misleading conclusions if dimensions of resilience are ignored, or if weakly influential dimensions are overrepresented.

The constant growth of world population has led to growth in conurbations prone to disasters associated with natural hazards and - as a consequence - to an increase in the overall level of societal risk. Amongst natural catastrophes, earthquakes represent about one fifth of the economic losses, and are responsible for an average of 20 thousand fatalities per year. This increasing pressure requires the development and implementation of risk reduction measures, ideally supported by reliable and technically sound risk information, such as maps, with expected hazard intensities, annualised average losses, or losses for a particular return period (or probability of exceedance). Some of the challenges to the generation of this information are due to the lack of open models, datasets and tools, as well as insufficient local capacity to create or use such resources. The recognition of this shortage of models and need to improve institutional capacity to assess the impact of earthquakes propelled the Global Earthquake Model (GEM) and its partners to develop an open seismic hazard and risk model with global coverage. In this contribution we describe the hazard, exposure and vulnerability components of this model, and the open-source tools that have been created to allow experts to reproduce the hazard and risk results, or tailor parts of the model to specific needs. We also provide a discussion regarding how the results from the global earthquake model may be used to identify global risk trends, and support the monitoring of the Sendai Framework for Disaster Risk Reduction.

The data required for assessing disaster risk can generally be divided into three categories: hazard, exposure and vulnerability. To date there is no widely accepted approach for storing and sharing such risk-related data using a common data structure. As a result, using risk-related data often requires a significant amount of upfront work to collect, extract and transform data before it can be used for purposes such as a risk assessment. In addition, the lack of a consistent data structure hinders the development of tools that can be used for more than one set of data. In practice, this situation introduces a significant amount of friction in efforts to quantify and manage disaster risk. Here we report on an effort by three consortia to develop extensible, internally consistent schemas for hazard, exposure and vulnerability data. The consortia coordinated their efforts so the three schemas are compatible. For example, the intensity measure types used to define the hazard datasets are compatible with the intensity measures used by the vulnerability models. Similarly, the asset attributes used in the exposure data taxonomy are compatible with the asset attributes used for the vulnerability data. Hazard data can be provided as either event footprints or stochastic catalogs. Exposure classes include buildings, infrastructure, agriculture, livestock, forestry and socio-economic data. The vulnerability component includes fragility and vulnerability functions and indicators for physical and social vulnerability. The schemas also provide the ability to define uncertainties associated with the hazard data and allows the scoring of vulnerability data for relevance and quality. As a proof of concept, the schemas were populated with data covering the three components for Tanzania and with additional exposure data for several other countries.

Collection of damage data following major disaster events is a fundamental exercise for a multitude of purposes, such as emergency management, resource allocation, fund mobilization and reconstruction planning. The processes involved, and scales of damage assessments vary by country, peril and context. Numerous sector-specific data collection activities provide an estimation of damage, loss and post-disaster needs in order to provide relief and facilitate the commencement of reconstruction and recovery efforts.