This study presents the results of the seismic risk assessment for the city of Santiago de Cali (Colombia), a collaborative effort between the Mayor's Office of Santiago de Cali (including the Municipal Planning Office and the Disaster Risk Management Secretariat), the Colombian Geological Survey (SGC), the United States Geological Survey (USGS), EAFIT University and the GEM Foundation. The primary objective was to provide actionable insights for Disaster Risk Management (DRM) across its stages: awareness, risk reduction, and disaster response, and to enable the creation of a risk assessment specifically tailored to the city's needs, ensuring that its results can be effectively integrated into local risk management processes. Early engagement with stakeholders ensured that the models aligned with local needs, facilitating their integration into DRM policies. The seismic risk analysis utilizes the latest hazard model developed for Colombia, advanced non-linear site effects characterization, and a detailed building inventory with local expertise. For the first time, data-driven deterministic and probabilistic seismic risk estimates are presented for the city. The results indicate that destructive events could heavily impact the city, particularly events coming from the Dagua-Calima Fault or Nazca Plate subduction zone. Moreover, probabilistic outcomes show that human casualties and building damage disproportionally affect low-income areas, while almost 50 % of economic losses occur to middle-to high-income areas due to higher asset values. These findings highlight the importance of risk reduction strategies that address simultaneously the physical vulnerability and socio-economic disparities.

The Sendai Framework proposes recording disaster losses from hazard events between 2015 and 2030 to monitor the progress towards reduction targets to curtail risk worldwide. In the case of earthquakes, relying on losses over 15 years is unlikely to yield sufficient evidence to support risk mitigation strategies. In this study we propose a general methodology to monitor and forecast Sendai indicators. We apply our approach to explore trajectories of probabilistic indicators of mortality (A1) and economic loss (C1) in the form of custom Sendai Indicators for the Dominican Republic. Risk reduction targets, at national and subnational level, are established and tested along with two mitigation strategies: nationwide retrofitting campaign and stronger code-enforcement. The baseline projection indicates that earthquake risk is expected to increase at a national level, with indicators A1 and C1 increasing over 17% and 27%, respectively, in a period of 35 years. At that scale, code enforcement is more effective in meeting the targets of reduced mortality by 2030 and 2050. At the sub-national level, results depend on the risk drivers in each municipality. We provide two cases: in Santiago de los Caballeros, the baseline risk projection suggests that indicators A1 and C1 will increase by more than 18% and 26% by 2050, respectively, while in Distrito Nacional they decrease by almost 5% and 3%, respectively. In the former region, code-enforcement is the most effective measure to meet the risk reduction targets, while for the latter it was found to be the retrofit campaign.

This document contains frequently asked questions about public partnership opportunities with the GEM Foundation. It covers eligibility requirements for applicant organizations, guidelines for the application form, and the multi-stage selection process.

Occupancy patterns are known to strongly affect the number of people killed by earthquakes. Existing exposure models for Europe based on housing census do not account for the daily movement of the population between the place of residence (residential occupancy) and places of economic activity (non-residential occupancy), or the seasonal patterns due to tourism. This study presents a framework to upgrade exposure models from static to 'dynamic', i.e., allowing the input population to change in time and space based on daily and monthly population movement patterns. Open-source population data is used to disaggregate and rescale occupants inside residential, commercial and industrial buildings of 28 European countries, resulting in 24 occupancy categories: two times (i.e., day and night) x 12 months at 30 arc-seconds resolution. The static vs dynamic exposure models are compared using the number and distribution of fatalities resulting from loss calculations for a stochastic set of earthquakes generated from the European Seismic Hazard model (ESHM20). The results demonstrate that the spatiotemporal patterns of population can significantly impact earthquake mortality rates and should not be neglected in scenario loss assessment. The results also demonstrate that the worst occurrence time depends on both the distribution of indoor population between building occupancies and the earthquake rupture characteristics. The ability to capture population distribution during the day and night or seasonal changes (e.g., winter vs summer) is a feature that can advance the ongoing rapid damage/loss assessment services in Europe and consequently support emergency response planning.

This study introduces a unified framework for evaluating the physical impacts of earthquakes and volcanic eruptions on buildings, leveraging upon the existing capabilities of the OpenQuake engine for earthquake risk assessment and various existing packages for computing volcanic hazard footprints. We illustrate the capabilities of the new OpenQuake volcanic scenario module using two case studies: a VEI (Volcanic Explosivity Index) 3-4 eruption of Nevado del Ruiz volcano in Colombia and a VEI 6 eruption of Mount Pinatubo in the Philippines, employing various methods for simulating hazard footprints for tephra fall, lava flows, pyroclastic density currents, and lahars. The findings demonstrate the versatility of OpenQuake in managing diverse geohazards and its potential for further extension to other hazards, while this integration advances our ability to assess and manage disaster risk.

In recent decades, hundreds of studies have covered seismic vulnerability assessment and the derivation of fragility models. However, these studies primarily focus on structural damage and the associated repair costs. Assessing fatalities requires an evaluation of the proportion of damaged buildings that suffer partial or total collapse, as well as the expected fatality rates for occupants inside those collapsed buildings. To support these modeling needs, we reviewed past studies on casualties, existing proposals for collapse and fatality rates, and detailed damage databases that characterize different collapse mechanisms. Based on this review, collapse and fatality rates are proposed relative to a baseline building class. These rates are further calibrated considering reported fatalities since 1950 and the average annual fatalities estimated by the Global Seismic Risk Model of the Global Earthquake Model (GEM) Foundation. Results show that the probability of collapse tends to decrease with the number of stories, while fatality rates have the opposite trend. Furthermore, an open-access database of calibrated collapse and fatality rates is provided and can be used to assess fatalities due to earthquake scenarios or in probabilistic seismic risk analysis.

The estimation of road disruptions due to building debris in urban contexts requires the availability of exposure data at the building level, which is often not available. In this study, we explore how open global datasets at different scales can be integrated with machine learning algorithms to estimate road disruptions following seismic events, overcoming the need for detailed datasets. Using simulated impact data for the municipality of Lisbon, we train a Random Forest model to predict road disruptions due to building collapses. Then, we apply this model to another urban environment (the municipality of Amadora) to evaluate the performance of the model using input data unseen during the training process. Finally, we employ the surrogate model using information extracted from globally available datasets characterizing the built environment and the road network. The proposed approach allows identifying areas within urban centers where road disruptions are likely to occur, and where risk reduction measures should be prioritized to minimize the impact of destructive earthquakes.

This study has been sponsored by the Asian Development Bank (ADB) and is a contribution to the project ADB TA 9307-MYA: Strengthening Climate and Disaster Resilience of Myanmar Communities. This report has been produced as part of a collaboration between the GEM Foundation and the Myanmar Earthquake Committee.

Please prepare your proposals with this Application Template and convert to a PDF file. Please ensure that your responses are clear and concise. If your organisation can provide additional supporting documents (e.g., reports, case studies, project summaries), you may attach them to your application.

The Global Earthquake Model (GEM) Foundation is committed to advancing earthquake risk assessment, modelling, and mitigation efforts worldwide. As part of our mission to strengthen global collaboration, we are offering a unique opportunity for public organisations to join GEM’s Governing Board as Public Governors. While GEM’s Public Governors typically contribute annual sponsorship fees, selected public sector partners under this opportunity will instead be granted Public Governor status (for a period of 3 years) based purely on merit, strategic alignment, and contributions to seismic risk reduction. Through this partnership, Public Governors will gain access to GEM’s global network, research, models and tools, thus contributing to and benefiting from the latest advances in earthquake hazard and risk knowledge. We invite interested public organisations to submit a proposal outlining their commitment and potential contributions to GEM’s mission using the template that can be downloaded from the GEM website.