Conference PaperPDF Available

Integrated Risk Modeling Within The Global Earthquake Model (Gem): Test Case Application For Portugal

  • GEM, EUCENTRE, University of Aveiro

Abstract and Figures

At the forefront of the Global Earthquake Model (GEM) is the development of uniform standards, datasets, and state-of-the-art modeling tools for the communication of earthquake risk. For a more holistic assessment of the scale and consequences of earthquake impacts, spatially enabled and open databases, methods, and Open Source software tools are being incorporated into the GEM modeling framework to assess earthquake risk beyond the estimation of direct physical earthquake impacts and loss of life. The latter is accomplished via the integration of estimates of physical risk (i.e. estimates of human or economic loss) with quantified metrics that represent social and economic characteristics of populations. This paper describes a test case/proof of concept for Portugal that was developed to assess the total (or integrated) risk of the country. Integrated risk is described here as the convolution of physical earthquake risk estimations with the social characteristics at a particular place. The test case/proof of concept was constructed in order to inform the development of GEM's Integrated Risk Modelling Toolkit that is an Open Source Software tool that will allow users to meaningfully integrate quantitative assessments of social and economic conditions with physical risk estimates for earthquakes using GEM's OpenQuake modeling suite. The results indicate that the impacts from a damaging earthquake event in Portugal will not be random, but manifested from a set of interacting conditions, some the result of geography and location, some the result of building exposure, and some having to do with the social characteristics of populations.
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Christopher BURTON1 and Vitor SILVA2
At the forefront of the Global Earthquake Model (GEM) is the development of uniform
standards, datasets, and state-of-the-art modeling tools for the communication of earthquake risk. For a
more holistic assessment of the scale and consequences of earthquake impacts, spatially enabled and
open databases, methods, and Open Source software tools are being incorporated into the GEM
modeling framework to assess earthquake risk beyond the estimation of direct physical earthquake
impacts and loss of life. The latter is accomplished via the integration of estimates of physical risk (i.e.
estimates of human or economic loss) with quantified metrics that represent social and economic
characteristics of populations. This paper describes a test case/proof of concept for Portugal that was
developed to assess the total (or integrated) risk of the country. Integrated risk is described here as the
convolution of physical earthquake risk estimations with the social characteristics at a particular place.
The test case/proof of concept was constructed in order to inform the development of GEM’s
Integrated Risk Modelling Toolkit that is an Open Source Software tool that will allow users to
meaningfully integrate quantitative assessments of social and economic conditions with physical risk
estimates for earthquakes using GEM’s OpenQuake modeling suite. The results indicate that the
impacts from a damaging earthquake event in Portugal will not be random, but manifested from a set
of interacting conditions, some the result of geography and location, some the result of building
exposure, and some having to do with the social characteristics of populations.
Earthquakes are a complex spatial phenomenon that vary greatly in magnitude and frequency, and
often result in the loss of lives, livelihoods, and property. There has been an exponential growth in the
losses from earthquakes throughout the world, and seismic disasters such as the Haitian Earthquake in
2010 and the Great East Japan Earthquake in 2011 provide reminders of the susceptibility of
communities to the loss of lives and property from damaging events. These disasters illustrated how
earthquakes impact people and communities, and despite sustained efforts to reduce earthquake risk, a
long history of development in seismically active areas has increased the susceptibility of populations
to earthquake impacts. This has stimulated great interest in understanding how to manage the
associated seismic risk.
While losses from earthquakes are the outcome most commonly associated with damaging
earthquakes, it is increasingly becoming clear that some populations are impacted differentially.
Additionally, the ability to prepare for, respond to, and recover from damaging events varies spatially.
1 Senior Scientist, Social Vulnerability and Integrated Risk Coordinator, GEM Foundation, Pavia, Italy,
2 Senior Scientist, Physical Risk Coordinator, GEM Foundation, Pavia, Italy,
This is partially because the impacts from earthquakes are not the product of a singular source. Rather,
the impacts suffered are the result of interactions between the earth’s biophysical systems, the
engineered environment, and the social context at particular places. It is when damaging earthquakes
intersect with concentrations of populations and development that they become disasters.
To assess and communicate earthquake risk and the potential for disasters, the Global
Earthquake Model (GEM) addresses earthquake hazard, physical risk, and the differential
susceptibility of populations to adverse impacts. GEM is a foundation that was created to develop
state-of-the-art models, data, and open-source tools and software for understanding and
communicating earthquake risk (see Silva et al. 2013; Pagani et al. 2014). For a holistic evaluation of
the scale and consequences of earthquake impacts, GEM is developing a set of open-source software
tools, methods, and metrics to assess seismic risk and impact potential beyond the estimation of direct
physical impacts and loss of life. This is accomplished via the incorporation (or convolution) of
estimates of physical impacts from earthquakes with social characteristics at particular places, what we
refer to here as an integrated risk assessment. To calculate integrated risk, users will be able to draw
from estimates of physical earthquake risk (i.e. estimates of human or economic loss) and combine
those estimates with socio-economic indicators or computed measures of social vulnerability (i.e.
characteristics within social systems that create the potential for loss or harm). Within the GEM suite
of tools, the integrated evaluation of seismic risk will be accomplished using the Integrated Risk
Modelling Toolkit (Burton et al., in press) that will be publically available November 2014.
The purpose of this paper is to describe a test case/proof of concept for Portugal that was
developed to assess the integrated risk of that country using OpenQuake. The test case/proof of
concept for developing an integrated risk assessment was carried out in order to inform the
development of GEM’s Integrated Risk Modeling Toolkit. The test case was also carried out to
demonstrate the context in which the methods, metrics, and software under development for the
integrated assessment of risk within OpenQuake may be used for decision-making.
Perhaps the first attempts to combine assessments of risk with social characteristics were the product
of multihazard analysis where in a seminal work Hewitt and Burton (1971) coupled event magnitude
and frequency with a measure of human impact potential to better understand natural hazard impacts.
Building upon this work, Cutter (1996) and Cutter et al. (2000) formulated the hazards-of-place
approach to vulnerability analysis. The hazards-of-place approach constitutes a detailed delineation of
exposure for a particular study area and an investigation of population sensitivities at levels of analysis
appropriate for both regional and local investigations. The approach is prevalent within the literature
in the United States and has been applied to case studies in South Carolina (Cutter et al. 2000),
California (Burton and Cutter 2008), and Oregon (Wood et al. 2010). International applications of the
hazards-of-place approach include New Zealand (Montz 2000), Europe (Kumpulainen 2006), and
Barbados and St. Vincent (Boruff and Cutter 2007).
In earthquake engineering, the Earthquake Disaster Risk Index (EDRI) (Davidson 1997)
provides an early example of an integrated risk assessment framework. The EDRI includes measures
of hazard, exposure, vulnerability, external contexts, and emergency response and recovery capacities.
Building upon this work, the Urban Disaster Risk Index (UDRI) (Carreño et al. 2007; Carreño et al.
2012) describes risk using a weighted combination of indices. The UDRI was initially applied to
Barcelona (Spain) and Bogota (Colombia) using a wieghted combination of indices aimed at
measuring physical risk and social fragilities within communities. Similar approaches include the
application of physical risk and social indicators and indices in Metro Manila (Fernandez et al. 2007),
Istanbul (Khazai et al. 2008) and Mumbai (Khazai and Bendimerad, 2011). It is within this context
that the term index (or the plural form indices) designates the manipulation of individual variables to
produce an aggregate measure of a phenomenon (e.g. social vulnerability). An indicator is a
quantitative or qualitative measure derived from observed facts that simplify and communicate the
reality of a complex situation (Freudenberg 2003). Indicators are pieces of information that summarize
the characteristics of a system or highlight what is happening in a system. The mathematical
combination of a set of indicators is a composite index.
C. Burton and V. Silva 3
Although a multitude of individual assessment frameworks are applicable, Figure 1 represents the
overarching framework for an integrated risk assessment in OpenQuake. The framework was inspired
by theoretical constructs designed to guide the convolution of assessments of a natural hazard threat,
potential economic losses, and social vulnerability (see Cutter 1996; Cardona 2005). The starting point
of the integrated risk-modeling framework is the modeling of seismic hazard that may be
accomplished for a particular study area in GEM’s OpenQuake-Hazard Engine. The OpenQuake-
Hazard Engine is a Python-based module that uses OpenSHA-lite for modeling earthquake ruptures
and calculating hazard results such as stochastic event sets and ground motion fields. Here, the seismic
hazard is combined with exposure and physical vulnerability from which estimates of physical
earthquake risk are derived using a number of possible calculation workflows. These modules include
1) a deterministic scenario (i.e. a single event) calculator which estimates loss and damage for a
collection of exposed assets such as buildings; 2) a probabilistic event-based risk calculator to estimate
the probability of exceedance of certain levels of loss in a given time span; and 3), a classical PSHA-
based risk calculator to compute the probability of losses for single assets (Silva et al. 2013).
With respect to the distribution of potential losses in an area, both exposure and physical
vulnerability interact with the underlying social fabric of a particular area of analysis. The social fabric
includes socioeconomic characteristics and measures of the overall capacity of a population to respond
to an event (Cutter et al. 2000). It is the inherent characteristics of populations or communities that
help to redistribute risk before an event and after an event in the distribution of losses, and it is the
underlying social fabric of a place that creates a community’s social vulnerability, which when
measured can be viewed as a factor that aggravates or attenuates risk.
Seismic Hazard
Earthquake Scenarios;
Probabilistic Hazard
Physical Vulnerability
Physical Risk
Estimates: Human &
Economic Loss Potential
Social Fabric
Place Specific;
Context Specific
Social & Economic
(Place-Based) Risk
Figure 1. Framework for integrated risk assessment in OpenQuake
The evaluation of the physical seismic risk model arises from the convolution of three main
components: seismic hazard, physical vulnerability and exposure data. The probabilistic seismic
hazard for Portugal was derived based on an existing source model proposed by Vilanova and Fonseca
(2007) and a set of ground motion prediction equations compatible with the tectonic environment of
the region (Vilanova et al. 2012). For what concerns the physical vulnerability of the exposed
elements, a recently proposed set of vulnerability functions was employed for the reinforced concrete
building typologies (Silva et al. 2014a), whilst for the masonry building stock an existing model
developed by Carvalho et al. (2002) was adopted. Finally, with respect to the location and economic
value of the residential building portfolio, information from the Portuguese Building Census Survey of
2011 was utilized to derive an exposure model with a spatial resolution at the level of the counties.
The seismic risk calculations were carried out using the Classical PSHA-based Risk calculator from
the OpenQuake-engine (Silva et al. 2013). The results included loss exceedance curves for each
county, as well as average annual losses, as illustrated in Figure 3 (Silva et al. 2014b).
Models serve an important role in helping to understand earthquake risk. The social analog to a
quantitative physical risk model for earthquakes is a social vulnerability index. Building an index
involves a number of steps that are articulated in the literature, including indicator selection, variable
transformation, scaling, weighting, and aggregation. Typically, social vulnerability indices include
deductive, hierarchical, and inductive modelling arrangements where: 1) deductive models contain
typically fewer than 10 indicators which are normalized and aggregated into an index; 2) hierarchical
models employ roughly 10 to 20 indicators that are separated into groups (or sub-indices) that share
the same dimension in which individual indicators are aggregated into sub-indices, and sub-indices are
aggregated; and 3) inductive approaches begin with a large set of indicators which are reduced to a
smaller set of uncorrelated factors using principle components analysis (PCA) (Tate 2012). All model
types were constructed to help inform the development of the workflow of the Integrated Risk
Modelling Toolkit. Only the hierarchical model is reported here due to space constraints.
Because there is no definitive set of indicators for measuring social vulnerability, the selection
of variables was subjective. The index was developed using data for 278 counties in Portugal that were
culled from the country’s census in order to characterize the broader dimensions of social vulnerability
in which there is a consensus in the literature. Initially data to compute 95 variables were collected and
categorized into five basic themes (population, economy, education, infrastructure, and governance
and institutional capacity). Each theme was treated separately for variable selection and aggregation (a
procedure discussed in more detail below), yet when all subcomponents are combined it is intended
that the coupling of constituent parts represent the social vulnerability concept as a whole.
The quality of composite indices and the soundness of the messages they convey depend not
only on the methods used in the construction process, but also on the internal consistency of the
variables selected (i.e. how well the variables may measure the underlying concept). A series of
multivariate analysis were conducted to select an internally consistent and parsimonious set of metrics.
As a first step, the raw data was transformed into comparable scales using percentage, per capita, and
density functions. The data was then standardized using a Min-Max rescaling scheme to create a set of
indicators on the same measurement scale. Min-Max rescaling rescales each variable into an identical
range between 0 and 1 (a score of 0 being the worst rank for an indicator score and 1 being the best
rank). Min-Max rescaling was chosen due the simplicity of the scaling algorithm and interpreting the
resulting indicator ranks for the proof of concept. Further work is being conducted using alternate data
transformation methods to better understand the extent to which data transformation contributes to
sensitivities and uncertainties within the final model output.
A correlation analysis comprised the second step. Preliminary testing of the data revealed a
number of non-parametric and non-linear relationships that were non-transformable. Thus, a non-
linear/non-parametric correlation analyses was applied to assess associations between the data. Highly
correlated variables (Spearman’s R>0.700) were eliminated from further consideration to avoid
subjectively choosing one variable over another for inclusion in subsequent analyses.
In addition to correlation, multidimensional scaling was used to gauge the internal consistency
of the variables to discriminate relevant data from potentially irrelevant data. Multidimensional scaling
is a technique that is often considered a non-parametric alternative to Factor Analysis (FA). Given a
matrix of variables, the procedure represents the data as points in a Euclidian plane in a manner in
C. Burton and V. Silva 5
which two points are closer together when the respective variables are similar in terms of their
distances. The Euclidean plane of points was evaluated under the premise that points spaced closer
together may be internally consistent, e.g. appropriate for measuring the same underlying phenomenon
which is the 5 data themes referred to above. Using this procedure, variables mapped at great distances
from clusters of similar points were scrutinized to understand their source for being an outlier and all
were removed from subsequent analyses.
The multivariate analysis procedures were useful in reducing the data from n=95 to n=27
variables. The remaining 27 variables (Table 1) were considered internally consistent and appropriate
for social vulnerability modeling. The method of aggregation that we employed represents the
summation of equally weighted sub-index scores. In other words, variable scores in each sub-index
(e.g. population, economy, etc.) were averaged to reduce the influence of the number of variables in
each sub-index. Each sub-component was then summed to derive a final composite score. Since there
are five sub-components, the summed score of the composite index ranges between 0 and 5 (0 being
the least socially vulnerable and 5 being the most). As a subsequent step, the composite social
vulnerability scores were rescaled using Min-Max rescaling to produce a final composite score
between zero and one (0 being the least socially vulnerable and 1 being the most vulnerable). An
aggregation method using equal weights was applied due to the lack of theoretical justification for
weighting one variable over another for use in this proof of concept.
Table 1: Variable selection for social vulnerability index
Indicator description
Percent of the population that is female
Cutter et al. 2003
Percent of the population living in statistical cities
Cutter et al. 2003
Number of recent foreign in-migrations per 1000 population
Cutter et al. 2003
Number of recent in-migrations from another municipality per
1000 Population
Fekete 2009
Percent of the population of foreign nationality
Cutter et al. 2003
Percent of the population under 5 years of age and over 65 years of
Cutter et al. 2003
Population density
Mendes 2009
Number of persons per housing unit
Mendes 2009
Percent of female headed households
Mendes 2009
Percent renter occupied housing units
Mendes 2009
Percent of the population with a disability
Mendes 2009
Percent population receiving social integration income of social
Fekete 2009
Percent of the working aged population that is unemployed
Cutter et al. 2003
Percent female labor force participation
Cutter et al. 2003
Percent of the labor force working in secondary sector employment
Mendes 2009
Percent of the labor force that is employed in service industries
Mendes 2009
Percent of the labor force employed in non-skilled elementary
Mendes 2009
Per capita purchasing power
Khazai et al. 2013
Percent of buildings in need of large and very large repairs
Mendes 2009
Completed buildings in new constructions per 1000 population
Mendes 2009
Percentage of the population not served by public water supply
Schneiderbauer and Ehrlich
Percentage of the population not served by wastewater treatment
Mendes 2009
Percentage of the population without a complete level of education
Mendes 2009
Percent of the population with tertiary education completed
Mendes 2009
Abstention rate in election for presidency
Cutter et al. 2003
Abstention rate in municipal elections
Cutter et al. 2003
Crime rate
Khazai et al. 2013
The evaluation of integrated risk (i.e. the combination of estimated losses with the social vulnerability
index outlined above) required the modeling of expected economic losses for each county (or first
order impacts) and the modeling of conditions within social systems that create the potential for harm
and loss. The modeled losses in the form of estimates of average annual loss for each county were also
rescaled using the Min-Max rescaling method to render them commensurate to the social vulnerability
index. To derive an estimate of integrated risk, a total risk index was constructed via the convolution
of the social vulnerability index with the estimates of physical earthquake risk. Carreño et al. (2007;
2012) provide the aggregation method that was adopted for this work due to its mathematical
simplicity. In this method, the direct potential impact of an earthquake (in a general sense) is denoted
( )
FRR FT += 1
is a total risk index,
is a physical earthquake risk index which is an
average annual loss estimate for Portugal derived utilizing the physical risk model outlined above, and
is the composite social vulnerability index which may be described as an aggrevating coefficient of
the estimated loss.
The probabilistic seismic hazard for mainland Portugal was calculated using the Classical PSHA-
based hazard calculator (Pagani et al. 2014). A large number of seismic hazard curves were derived
following a 0.01x0.01 decimal degrees spatial resolution considering a wide spectrum of epistemic
uncertainties (e.g. various ground motion prediction models, seismic zonations, magnitude-frequency
distributions), through the employment of a logic tree structure. Using the set of hazard curves at each
location, a mean seismic hazard map for a probability of exceedance of 10% in 50 years was
calculated, as depicted in Figure 2.
Figure 2. Hazard map PGA (g) for Portugal counties
The appraisal of the spatial distribution of hazard in Portugal indicates an expected higher peak
ground acceleration on rock for the Lower Tagus Valley (region around Lisbon) and the south of
Portugal, thus highlighting regions where a combination of seismically vulnerable structures and high
population concentration could lead to significant earthquake losses. The hazard results were
combined with the exposure and vulnerability models to derive loss exceedance curves for each
county. These curves were converted into annual rate of exceedance, and numerically integrated in
order to obtain the average annual economic loss, as illustrated in Figure 3.
C. Burton and V. Silva 7
Figure 3. Map of predicted average annual loss for Portugal counties
In addition to hazard and physical risk, understanding the distribution of the social vulnerability
of populations is an integral part of disaster management, planning, and mitigation. Figure 4 depicts
the spatial variation in social vulnerability for Portugal’s counties. The classification scheme was
simplified (i.e. high, moderate, low) for presentation purposes. The counties delineated in the darker
shades of red along the classification continuum exhibit higher levels of social vulnerability. While the
spatial pattern is not uniform throughout, there are significant pockets of social vulnerability that could
warrant management concern given the seismic threat. Of special interest is the clustering of moderate
to high and high levels of social vulnerability in the southwestern portion of the country. Here,
counties that host cities such as Santarem, Setubal, and Faro have populations with high levels of
social vulnerability. These areas are also in zones of high seismicity (see Figure 2) and zones of high
physical earthquake risk (see Figure 3). In addition to these clusters of counties with high social
vulnerability and physical risk, there is another large section in Portugal’s northern portion with high
levels of social vulnerability. These are in largely rural areas outside of the high-risk zones. However,
exceptions in these areas exist where counties are exposed to considerable risk of loss and that also
contain highly socially vulnerable populations. Populations within these counties may not only suffer
greater impacts due to ground shaking and building damage, they may also lack the ability to
adequately mitigate, prepare for, and recover from a damaging earthquake event. The spatial
distribution of risk within these counties is compounded when viewed as an integrated risk map
(Figure 5).
Figure 4. Map of social vulnerability for Portugal counties
Figure 5. Map of integrated risk for Portugal counties
The impacts from an earthquake will be expressed differentially across communities. To be effective,
governments, disaster planners, and managers must not only understand the physical agents of
earthquake risk, but also the social characteristics that give rise to vulnerabilities within the
communities they protect. This paper presented a method, workflow, and analysis conducted within
GEM’s OpenQuake that consists of a spatial delineation of physical earthquake risk combined with an
index of social vulnerability. The overall approach leads to an encompassing perspective on risk
C. Burton and V. Silva 9
assessment that considers loss and damage as part of a dynamic system, and our findings suggest that
there are spatial differences in physical earthquake risk, social vulnerability, and integrated risk within
Portugal. Disaster mitigation and planning under such circumstances may require special attention
where different aspects of social vulnerability affect the way in which communities may prepare for
and respond to the seismic threat. In sum, the approach mainstreams risk and social vulnerability into
policy discussions on earthquake loss and damage reduction, makes it possible to use risk estimates in
benchmarking exercises to monitor changes in loss potential over time, and recognizes that both the
causes and solutions for earthquake loss are found in human, environmental, and built-environment
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... Interpreted as the direct danger to the presence of societies, environmental system and properties, and the extent of the geographical context that could be adversely affected by disaster risk occurrence [4,11]. Exposure is measured through an integrated understanding of how relevant factors can be combined to determine a community's level of resilience [14,15]. These techniques include methods for assessing the vulnerability of local societies, which are generally based on statistical data obtained from national censuses and supplemented data sources, in order to determine how they are likely to respond in the face of natural disasters. ...
Full-text available
Due to the complex nature of seismic vulnerability assessment, different approaches and data are required, based on the country. Alternatively, seismic vulnerability assessment can be categorized into two common techniques, the conventional and holistic methods, the use of which depends on the region's conditions. Generally, conventional methods concern the consequences of an earthquake by estimating the potential loss caused by the structural inventory damage and the number of casualties. Meanwhile, holistic methods focus on the different primary factors that contribute to seismic vulnerability, which are represented by the social, economic, physical, and environmental elements of a community or structure in a region. However, less attention has been given to the quantitative evaluation of holistic seismic vulnerability in Malaysia compared to hazard-related research. Therefore, the aim of this study was to identify the holistic seismic vulnerability indicators in the context of an earthquake in Malaysia. Analysis is critical for understanding the numerous indicators of causes of earthquakes to define their relative relationships and the disaster risk probability. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting method, a comprehensive review of the Scopus and Web of Science databases was undertaken to search for indicators with a substantial impact on the aforementioned dimensions of earthquake vulnerability. This article concludes that there are three major elements of vulnerability (exposure, resilience, and coping capacity), comprising eighteen indicators of seismic vulnerability, in the context of earthquakes in Malaysia.
... i. Exposure, is a crucial element of vulnerability risk that defines the extent of societies and properties under assessment in the geographical context of risk occurrence UNISDR 2016). Measuring exposure demands an integrated understanding of components and how these factors can be amalgamated to contribute to the resilience of communities (Burton and Silva 2014;Carreno et al. 2016). These approaches include methods that use predominantly integral statistical data gathered from national census to assess the vulnerability of local populations. ...
Full-text available
Various techniques and frameworks for an evaluation of seismic vulnerability have been developed and established in previous studies. However, some techniques demand a significant amount of empirical data currently not readily available in developing countries. Therefore, this study proposes a new seismic risk evaluation method at the local district level. A holistic model was constructed for the purpose of assessing potential seismic vulnerability based on appropriate indices and their relative contribution towards vulnerability and coping capacity. It allowed the estimation of vulnerability in terms of exposure, resilience, and capacity factors. Then, utilization of Geographical Information System (GIS) tools resulted in the generation of a total vulnerability map via integration of the study variables to highlight the socio-economic and physical characteristics of vulnerability for the districts in Pahang, Malaysia. Subsequently, a seismic risk map of the study area was derived by overlying the derived map with the seismic hazard map. Consequently, the study revealed the highest levels of seismic risk were concentrated in the central-west of the Pahang region, namely the Bentong district. In contrast, the least vulnerable areas encompassed the Pekan and Jerantut areas, which were located in the eastern region. In brief, the study findings would serve as the foundation towards reducing the country’s vulnerability to disasters.
... Map of seismic hazard in the Iberian Peninsula. Source: Designed by the authors based on data fromBurton and Silva (2014) and from the Instituto Geogr afico Nacional(2015). ...
This study investigated the importance given by two groups of pre-service teachers of primary education from Spain and Portugal to seismic risk in a framework of different natural risks, both in personal terms and as future teachers. A questionnaire was used for data collection. Some questions about the seismic phenomenon were also included. The sample groups consisted of 110 students from an institution in Spain and 121 from one in Portugal. Both institutions are in cities affected by the historic Lisbon earthquake of 1755. The results showed that the risk of forest fire was the first choice for classroom study in both cases. The Spanish group was also more focused on the importance of other risks like flood and drought. The Portuguese group showed a greater concern with seismic risk, frequently referring to their own historic earthquake of 1755. A few gaps in knowledge concerning earthquake prediction and comparing seismic risk in different regions of their own countries were also found. In accordance with the results, it is suggested that training courses for primary school teachers should include Disaster Risk Education in Science Education for a better understanding of the impact of various hazards and a greater concern with seismic risk due to its particular features, especially in regions where the seismic pattern is characterized by long seismic cycles with major earthquake episodes.
... En esta evaluación, los daños o pérdidas físicas potenciales son agravadas por un conjunto de condiciones socioeconómicas Figura A2-1-. Enfoque holístico a la evaluación probabilista del riesgo (Cardona, 2001;Carreño et al., 2007;Marulanda et al., 2009) • 250 Metodología Esta metodología para dar cuenta del riesgo en forma integral o comprensiva ha sido aplicada con diferentes niveles de resolución ( Daniell et al. 2010; Burton y Silva 2014) y ha sido incluido en manuales y bases de datos para evaluación del riesgo sísmico ( Khazai et al. 2014;Burton et al. 2014). Dado que no siempre se cuenta con la misma información en términos de indicadores útiles disponibles para el área de estudio, cada evaluación representa un reto en cuanto a la selección y en algunos casos al cálculo de los factores o descriptores que dan cuenta del contexto social e institucional. ...
Technical Report
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El Atlas tiene como objetivo dar a conocer diversos estudios y avances en relación con la evaluación de las diferentes amenazas de origen natural y tecnológico, desarrollados por entidades públicas y privadas en el país; así como también dar a conocer resultados de la evaluación probabilista del riesgo para diferentes amenazas, basados en métricas del riesgo apropiadas para la toma de decisiones. Se presentan mapas de amenaza sísmica, inundación, tsunami, ciclones tropicales, incendios forestales, sequía y movimientos en masa a nivel nacional. A nivel departamental se presentan perfiles de riesgo multi-amenaza con mapas de la pérdida anual esperada, para representar el riesgo físico, y los resultados del índice integral del riesgo de desastres para dar cuenta del impacto potencial, teniendo en cuenta factores de agravamiento asociados con la fragilidad socioeconómica y la falta de resiliencia a nivel municipal. En el formato original se puede obtener en:
Technical Report
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This background paper has been made in the framework of the project "Development of a Global Infrastructure Risk Model and Resilience Index (GIRI) for the Coalition for Disaster Resilient Infrastructure (CDRI), Biennial Report on Disaster and Climate Resilient Infrastructure, 2023", supported by the United Nations Development Program (UNDP), and developed by the consortium INGENIAR CAD/CAE LTDA., UNIGE, NGI, and CIMA. The GIRI, or the Global Infrastructure Risk and Resilience Model and Index of CDRI, is a comprehensive system of indicators of risk and resilience that encompasses all countries and territories worldwide. Currently, GIRI addresses six natural hazards: earthquakes, tsunamis, landslides, floods, tropical cyclones, and droughts. The last four include the alterations induced by climate change, thus offering hydrometeorological risk metrics related to various greenhouse gas emission scenarios in the future, in addition to stationary risk metrics for geological hazards. GIRI, presently, encompasses nine infrastructure sectors: power, highways and railways, transportation, water and wastewater, communications, oil and gas, education, health, and housing. The composite indicator for measuring infrastructure resilience combines the financial risk metrics, obtained by the probabilistic risk model, with three sets of indicators representing absorptive, adaptive, and transformative capacities to withstand, respond to, and recover from hazard events. This index presents an operational view of resilience grounded in multi-hazard physical risk in infrastructure systems, influenced by various social, economic, and environmental factors. Vulnerability is considered both in its physical dimension, representing susceptibility to damage, and its contextual dimension, expressed through various attributes and variables.
The objective of the holistic risk assessment is to evaluate risk from a comprehensive perspective, integrating physical risk, or potential physical damage, linked to the happening of hazard events and socio-economic and environmental factors, non-hazard-dependent. This approach seeks to capture how these latter have an incidence on physical risk, exacerbating the negative impacts of a dangerous event, as well as affecting the capacity of the society to anticipate or resist or to respond and recover from adverse impacts. This article presents the results of the holistic evaluation obtained at the subnational level in Colombia in the framework of the Risk Atlas of Colombia of the National Unit for Disaster Risk Management (UNGRD, its Spanish acronym). The evaluation was performed using the probabilistic physical risk results derived from a multi-hazard risk assessment, with 16 socio-economic indicators available for the 1123 municipalities of Colombia. These results are relevant for comparison purposes and are also useful to identify the risk drivers associated not only with the current risk conditions but also those shaping future risk.
From a macroeconomic perspective, the occurrence of disasters, especially high-impact events, can lead to financial stress in a country due to the sudden high demand for resources to restore affected exposed assets. Disaster risk is a sovereign risk and implies a non-explicit contingent liability that, in many cases, has a major impact on fiscal sustainability. Two risk composite indicators have been used to measure the impact that potential disasters can mean for a country: (i) The Disaster Deficit Index (DDI), which measures a country's financial capacity to cover the economic losses generated after the occurrence of high-impact events; and (ii) A complementary index (DDI′), which indicates the fraction that the expected annual loss would represent to the annual surplus of a country. This paper describes the overall macroeconomic impact of disasters and presents DDI results for Chile, which allows national-level decision-makers to understand the economic implications of disasters for the country and the need to consider this kind of information in the long-term policies. Results of the DDI for Chile illustrate that extreme disasters would imply the need for a significant amount of budgetary resources from the government. Estimated losses would be double the available budget resources and the financing of the recovery could mean restrictions to invest in other ongoing social and development needs of the country. This macroeconomic risk in Chile may be hedged by strategically setting up a risk financial structure based on adequate loss estimation criteria, using different available alternatives such as public and private assets insurance, disasters' reserves, contingency credits contracts, and investing in prevention and mitigation to reduce potential economic losses.
Risk identification is the first step on a comprehensive disaster risk management strategy, and nowadays, when new open-source tools to conduct those analyses are becoming widely available, the interest and need to increase their transparency has increased. Catastrophic risk due to natural hazards should be considered in a prospective way quantifying the damages and losses before the real event occurs, and for that task, it is necessary to consider events that have not yet occurred. Since there are uncertainties related to when and where the next hazardous event will happen, how severe will it be, and how can it affect the exposed elements, it is important to adopt a probabilistic approach that considers those uncertainties and propagates them through the damage and loss calculation process following a rigorous methodology. This chapter develops the theoretical catastrophe risk model considering both retrospective and prospective analyses. In addition, it summarizes the methodology for the inclusion of second-order effects (nonphysical risk drivers), the approach to rationally incorporate background trends (e.g., climate change), an extension of the model to incorporate non-probabilistic uncertainty, and a methodology to define management actions that fit resilience targets. The work presented herein serves to provide a ground base for the minimum requirements of probabilistic risk assessment models.
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Disaster risk is not only associated with the occurrence of intense hazard events but also with the vulnerability conditions that favour or facilitate disasters when such events occur. Vulnerability is closely linked to social processes and governance weaknesses in disaster-prone areas and is usually related to a set of factors of fragility, susceptibility, and lack of resilience of the exposed human settlements. The holistic risk assessment aims to reflect risk from a comprehensive perspective by using, in one hand, the physical risk or potential physical damage directly linked to the occurrence of hazard events and, on the other hand by capturing how underlying risk drivers or amplifiers –social, economic, environmental factors, non-hazard dependent elements, may worsen the current existing physical risk conditions in terms of lack of capacity to anticipate or resist, or to respond and recover from adverse impacts. This article presents the results of the holistic evaluation obtained at subnational level in Colombia in the framework of the Risk Atlas of Colombia of the National Unit for Disaster Risk Management, UNGRD. The evaluation was performed using the probabilistic physical risk results obtained in the multi-hazard risk assessment and 16 socio-economic indicators available for 1,123 municipalities of Colombia. These results are useful to identify risk drivers that are associated not only to the physical vulnerability of the buildings and infrastructure but also to social issues that should be examined and tackled in a comprehensive way.
Disaster risk is not only associated with the occurrence of intense hazard events but also with the vulnerability conditions that facilitate disasters when such events occur. Vulnerability is closely linked to social processes and governance weaknesses in disaster-prone areas, and is usually related to a set of fragilities, susceptibilities, and issues regarding the lack of resilience of the exposed human settlements. The holistic risk assessment aims to reflect risk from a comprehensive perspective by using, in one hand, the physical risk or potential physical damage directly linked to the occurrence of hazard events and, on the other hand by capturing how underlying risk drivers or amplifiers –social, economic, environmental issues, non-hazard dependent elements, worsen the current existing physical risk conditions in terms of lack of capacity to anticipate or resist, or to respond and recover from adverse impacts. This article presents the results of the holistic evaluation of risk as well as of the implications of risk for development obtained at global level in the framework of the UN Atlas-GAR: Unveiling Disaster Risk, using the probabilistic physical risk results, obtained from the multi-hazard Global Risk Assessment 2015, socioeconomic indicators and macroeconomic flow variables available in worldwide databases for more than 200 countries and territories. These results are useful to identify risk drivers that are associated not only to the physical vulnerability of countries’ assets but also to social, economic and financial issues that should be examined and tackled in a comprehensive way.
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Composite indicators are synthetic indices of individual indicators and are increasingly being used to rank countries in various performance and policy areas. Using composites, countries have been compared with regard to their competitiveness, innovative abilities, degree of globalisation and environmental sustainability. Composite indicators are useful in their ability to integrate large amounts of information into easily understood formats and are valued as a communication and political tool. However, the construction of composites suffers from many methodological difficulties, with the result that they can be misleading and easily manipulated. This paper reviews the steps in constructing composite indicators and their inherent weaknesses. A detailed statistical example is given in a case study. The paper also offers suggestions on how to improve the transparency and use of composite indicators for analytical and policy purposes ...
Conference Paper
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At the core of the Global Earthquake Model (GEM) is the development of state-of-the-art modeling capabilities that can be used worldwide for the assessment and communication of seismic risk. While many approaches for understanding seismic risk exist, it is the dynamic interrelationships between hazard potential, physical risk, and the social conditions of populations that are becoming the focal point for policy-makers, emergency managers, stakeholders, and the general public. The purpose of this paper is to introduce GEM's Integrated Risk Modeling Toolkit, an open-source software tool that will allow risk analysts to draw from results on exposure, predicted mortality, and property loss, and combine these results with socio-economic data and/or computed models of social and economic vulnerability in a robust and meaningful way. A proof of concept using Portugal is demonstrated to assess the total (seismic) risk of that country.
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Since its inception in the 1960s, probabilistic seismic‐hazard analysis (PSHA) (Cornell, 1968; McGuire, 2004, 2008) has emerged as the principal methodology for assessing the potential hazard posed by earthquake ground motion in a broad range of contexts. Seismic‐hazard analysis serves different needs coming from a wide spectrum of users and applications. These may encompass engineering design, assessment of earthquake risk to portfolios of assets within the insurance and reinsurance sectors, engineering seismological research, and effective mitigation via public policy in the form of urban zoning and building design code formulation. End users of seismic‐hazard analyses from different sectors of industry may often have specific requirements in terms of the types of results and, as a consequence, in terms of the methodologies preferred for calculation. A large majority of studies for the analysis of structural and geotechnical systems require the calculation of a target response spectrum derived from PSHA results (e.g., Lin et al. , 2013). Often the calculation of uniform hazard spectra is performed in conjunction with a disaggregation analysis, which in the simplest cases highlights the combinations of magnitude and distances, providing the largest contributions to a specific level of hazard for a particular intensity measure type, such as the spectral acceleration for a period close to the fundamental elastic period of a structure (Bazzurro and Cornell, 1999; Pagani and Marcellini, 2007). In contrast, in the insurance sector it is more common to use stochastic methodologies (e.g., Weatherill and Burton, 2010; Musson, 2012) to produce multiple realizations of the likely earthquake activity that may be pertinent to a portfolio of assets. Monte Carlo–based methods can provide results in a form that offers a practical comparison with past events and can better account for the temporal and spatial variability of earthquake shaking occurring on a distributed set …
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A vulnerability model capable of providing the probabilistic distribution of loss ratio for a set of intensity measure levels is a fundamental tool to perform earthquake loss estimation and seismic risk assessment. The aim of the study presented herein is to develop a set of vulnerability functions for 48 reinforced concrete building typologies, categorized based on the date of construction (which has a direct relation with the design code level), number of storeys (height of the building) and seismic zonation (which affects the design of the buildings). An analytical methodology was adopted, in which thousands of nonlinear dynamic analyses were performed on 2D moment resisting frames with masonry infills, using one hundred ground motion records that are compatible, to the extent possible, with the Portuguese tectonic environment. The generation of the structural models was carried out using the probabilistic distribution of a set of geometric and material properties, compiled based on information gathered from a large sample of drawings and technical specifications of typical Portuguese reinforced concrete buildings, located in various regions in the country. Various key aspects in the development of the vulnerability model are investigated herein, such as the selection of the ground motion records, the modelling of the infilled frames, the definition of the damage criterion and the evaluation of dynamic (i.e. period of vibration) and structural (i.e. displacement and base shear capacity) parameters of the frames. A statistical bootstrap method is demonstrated to estimate the variability of the loss ratio at each intensity measure level, allowing the estimation of the mean, as well as 10 and 90 % percentile vulnerability curves.
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The assessment of the seismic risk at a national scale represents an important resource in order to introduce measures that may reduce potential losses due to future earthquakes. This evaluation results from the combination of three components: seismic hazard, structural vulnerability and exposure data. In this study, a review of existing studies focusing on each one of these areas is carried out, and used together with data from the 2011 Building Census in Portugal to compile the required input models for the evaluation of seismic hazard and risk. In order to better characterize the epistemic uncertainty in the calculations, several approaches are considered within a logic tree structure, such as the consideration of different seismic source zonations, the employment of vulnerability functions derived based on various damage criteria and the employment of distinct spatial resolutions in the exposure model. The aim of this paper is thus to provide an overview of the recent developments regarding the different aspects that influence the seismic hazard and risk in Portugal, as well as an up-to-date identification of the regions that are more vulnerable to earthquakes, together with the expected losses for a probability of exceedance of 10 % in 50 years. The results from the present study were obtained through the OpenQuake engine, the open-source software for seismic risk and hazard assessment developed within the global earthquake model (GEM) initiative.
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The Global Earthquake Model aims to combine the main features of state-of-the-art science, global collaboration and buy-in, transparency and openness in an initiative to calculate and communicate earthquake risk worldwide. One of the first steps towards this objective has been the open-source development and release of software for seismic hazard and risk assessment called the OpenQuake engine. This software comprises a set of calculators capable of computing human or economic losses for a collection of assets, caused by a given scenario event, or by considering the probability of all possible events that might happen within a region within a certain time span. This paper provides an insight into the current status of the development of this tool and presents a comprehensive description of each calculator, with example results.
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The focus of this study is on multi-dimensional vulnerability of regions to indirect disaster losses. An integrated indicator framework has been developed which captures the multi-layered vulnerability drivers in industrial production systems and also accounts for the social fragilities and coping capacities in communities. By combining industrial vulnerability and social vulnerability spatially, and proposing a methodology to account between their interactions, the total vulnerability to indirect risks of regions is revealed. The outcome of the framework is a ranking of industrial sectors and geographic areas according to their vulnerability against indirect losses. It answers the question which of the two affected regions is in a better position to cope with indirect consequences in a disaster. Indicators provide a flexible framework for the comparison and integration of different data types and allow the combination of social as well as economic aspects. Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology was applied to analyze direct and indirect dependencies within the selected social and industrial vulnerability indicators. The hierarchical indicator system has been implemented in a software system based on multi-criteria decision theory (MCDA) with an interactive interface to take into account a broader range of expert judgement. The methodology was applied in a case study in the state of Baden-Wuerttemberg in Germany for 16 different industrial sectors. The approach helps to identify particular vulnerable processes and points out where mitigation measures could be implemented most effectively.
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Social vulnerability indices have emerged over the past decade as quantitative measures of the social dimensions of natural hazards vulnerability. But how reliable are the index rankings? Validation of indices with external reference data has posed a persistent challenge in large part because social vulnerability is multidimensional and not directly observable. This article applies global sensitivity analyses to internally validate the methods used in the most common social vulnerability index designs: deductive, hierarchical, and inductive. Uncertainty analysis is performed to assess the robustness of index ranks when reasonable alternative index configurations are modeled. The hierarchical design was found to be the most accurate, while the inductive model was the most precise. Sensitivity analysis is employed to understand which decisions in the vulnerability index construction process have the greatest influence on the stability of output rankings. The deductive index ranks are found to be the most sensitive to the choice of transformation method, hierarchical models to the selection of weighting scheme, and inductive indices to the indicator set and scale of analysis. Specific recommendations for each stage of index construction are provided so that the next generation of social vulnerability indices can be developed with a greater degree of transparency, robustness, and reliability.