GEM GLOBAL MOSAIC OF RISK MODELS

Caribbean and Central America (CCA)

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The Central America and the Caribbean Earthquake Hazard and Risk Model was developed within the scope of a regional programme supported by the United States Agency for International Development (USAID), in collaborations with over a dozen local institutions from the region. This project features the development of a probabilistic seismic hazard model, a uniform exposure dataset covering the residential, commercial and industrial building stock, and a set of vulnerability functions characterizing the likelihood of loss given a seismic hazard intensity. This initiative also covered a number of local events to improve the local capacity to assess earthquake hazard and risk in the region.

 

1. Exposure

Across Central America and the Caribbean, the most complete and updated databases containing exposure information were the national population and household census as well as data from the country Central Bank. With the exception of  Haiti, every country in the region provides information about household and population associated to a geographical variable that is either publicly available online or upon request. Therefore both, population and household census were taken from the respective statistical offices to determine the number and location of the residential dwellings. Commercial and industrial data are much less detailed, and usually only the number and size of the facilities are available (See Table below). This data is subjected to the following four-step process in order to create an exposure model:

  1. The most common building classes are identified using existing studies and the expert judgement. The World Household Encyclopedia has reports that include the building materials, architectural traits, construction process, socio-economic environment and even seismic performance of common dwelling configurations found in this region.

  2. Census variables are crossed to segregate the dwellings into subgroups, which are then subjected to a process of conditional selection. If a dwelling meets the criteria to belong to a certain building class, then it is assigned to that class. 

  3. Once the dwellings have been distributed among the identified classes with specific structural attributes (e.g height and expected level of ductility), they are converted into buildings and assigned a replacement cost.

  4. Models are calibrated based on expert judgement from over 80 professionals from the region providing feedback in key variables like code compliance, average dwelling area and average replacement cost per building class.

Country
Population
Dwellings
Commercial
Industrial
Guatemala (GTM)
16,176,133
2,574,908
226,352
49,595
Cuba (CUB)
11,167,328
3,644,001
4,806
3,410
Haiti (HTI)
10,291,060
2,061,875
27,879
138,295
Dom. Republic (DOM)
9,445,367
2,662,794
17,421
4,334
Honduras (HND)
8,249,574
1,837,855
134,658
14,689
Salvador (SLV)
6,377,195
1,372,831
140,872
21,079
Nicaragua (NIC)
6,167,237
983,928
142,982
32,247
Costa Rica (CRI)
4,301,006
1,360,625
37,829
10,190
Panama (PAN)
4,058,374
1,082,881
57,157
7,617
Jamaica (JAM)
2,697,053
881,021
34,547
16,221
Trinidad and Tobago (TTO)
1,114,777
313,032
9,532
20,042
Belize (BLZ)
468,310
79,235
7,456
1,372
Barbados (BRB)
277,819
78,934
4,727
1,111
regional building distribution.png
regional capital stock.png

Central America and the Caribbean Exposure Map

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 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:

 

  1. Identification of the most common building classes in the region, using peer-reviewed literature, web surveys (https://platform.openquake.org/building-class/), and World Housing Encyclopedia reports.
     

  2. 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.
     

  3. 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.
     

  4. 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.
     

  5. 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).
     

  6. 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.

CCA curve1.png
CCA curve3.png
CCA curve2.png
CCA curve4.png

3. Seismic Hazard

The main components concerning the probabilistic seismic hazard model for the region can be found in the associated technical documentation. 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.

Central America and the Caribbean Hazard Map

4. Seismic Risk Results

The risk results suggest that absolute annualized losses per country are not correlated with the relative level of risk. For example, Guatemala, Costa Rica and El Salvador hold most of the economic losses by earthquakes over time. However, in terms of relative levels of risk, Nicaragua and Haiti present higher loss ratios than Costa Rica. El Salvador seems to be the nation with the greatest level of earthquake risk in the region, while in the Caribbean loss ratios are the highest in the Dominican Republic, Haiti and Jamaica. The capital cities with the highest absolute losses are Guatemala City, Managua, San Salvador, San Jose and Santo Domingo. Panama City and Tegucigalpa show much lower levels of risk. In the case of Honduras San Pedro Sula presents higher risk than the country capital, Tegucigalpa. The countries with the lowest levels of seismic risk in the region, namely Belize, Barbados and Cuba, are located in the Caribbean. More detailed risk results by country have been detailed in specific country risk profiles [to be published soon]. In each profile it is possible to find seismic hazard, exposure and risk maps, the sets of EP curves and the list of the top regions at risk at a subnational level.

Central America and the Caribbean Risk Map

cca_reg_aal_new.png
cca_reg_lossratios_new.png

5. Partners and Contributors

The Central America and the Caribbean 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.

Title
Institution
Country
Jaime Hernandez De Paz
SISMICA S.A. de C.V
El Salvador
Rex Fernandez
SISMICA S.A de C.V
El Salvador
Rolando Castillo Barahona
Lanamme - Universidad de Costa Rica
Costa Rica
Antonio Cabrera
Lanamme - Universidad de Costa Rica
Guatemala
Luis Carlos Meseguer
Lanamme - Universidad de Costa Rica
Costa Rica
Andr̩es Abarca
Lanamme - Universidad de Costa Rica
Costa Rica
Luis Guillermo Vargas Alas
Lanamme - Universidad de Costa Rica
Costa Rica
Aisha Vargas Siles
Independent Consultant
Costa Rica
Juan Diego Miranda
FUPROVI
Costa Rica
Andres Arguedas
Acueductos y Alcantarillados - Ingeniero Estructural
Costa Rica
Alvaro Poveda Vargas
Universidad de Costa Rica
Costa Rica
Pablo Ruiz Rios
Universidad de Costa Rica
Costa Rica
Hugo Pallais
UVG
Guatemala
Javier Castro Gutierrez
Caja Costarricense de Seguro Social
Costa Rica
Luis Carlos Esquivel Salas
Universidad de Costa Rica
Costa Rica
Guillermo Gonzalez
Universidad de Costa Rica
Costa Rica
Rainer Parrales
Independent Consultant
Nicaragua
Luis Mendoza
MP Consultoria y Construccion
El Salvador
Ernesto Hernandez G.
Steelmax
Nicaragua
Gaspard Pierristal
Observatoire National de l'Environnement et de la Vulnerabilite (ONEV)
Haiti
Mauricio Araya
Lanamme - Universidad de Costa Rica
Costa Rica
Chris Burgess
Ceac solutions
Jamaica
Kenneth Otarola Madrigal
Ingenieria Sismo-Resistente
Costa Rica
Mario Yon
AGIES
Guatemala
Luis Gerardo Aguirre Calix
UNAH
Honduras
Jorge
Inypsa
Honduras
Leonardo Cruz
DICOMA
Honduras
Mellissa Bustillo
Postensa
Honduras
Dennis Aguilar
IHSS
Honduras
Julio Maltez Montiel
Alcaldia de Managua
Nicaragua
Marcel Toru̱o Mendez
Independent Consultant
Nicaragua
Maurilio Reyes
Universidad Nacional de Ingenieria
Nicaragua
Elmer Zelaya
Unitec
Honduras
F̩elix Nunez Lopez
Independent Consultant
Nicaragua
Carlos Alberto Delgado Aleman
Independent Consultant
Nicaragua
Oscar Pati̱o
Universidad Tecnologica de Panama
Panama
Juan Sampson Munguia
Independent Consultant
Nicaragua
Manuel Zeledon G
Independent Consultant
Nicaragua
Oscar Sequeira
AGIES
Guatemala
Adery Baltodano
DOP INGENIEROS
Nicaragua
Vladimir S. Jim̩nez Gonzalez
Ministerio de Obras Publicas y Comunicaciones (MOPC)
Republica Dominicana
Ramon Emilio Tavarez Bello
Universidad Nacional Pedro Henriquez Ure̱a
Republica Dominicana
Bernardo Rafael Cabrera Rosario.
INGPECASA
Republica Dominicana
Andr̩es Fulcar
Escuela Ingenieria Civil de Universidad Nacional Pedro Henriquez Ure̱a (UNPHU)
Republica Dominicana
Jose M. Lockhart
J.M. Lockhart Ingenieria Sismica
Republica Dominicana
Pedro Ivan Marquez Merceron
ONESVIE
Republica Dominicana
Santiago Rivera Acosta
Fuerza Aerea de La Republica Dominicana
Republica Dominicana
Zoraida Disla Morales
ONESVIE
Republica Dominicana
Cexnia Bueno Ortega
ONESVIE
Republica Dominicana
Galvy Nuñez
ONESVIE
Republica Dominicana
Bienvenido Hernandez
Yellow Ingenieros & Aquitectos SRL
Republica Dominicana
Edgardo A.
Novas
Republica Dominicana
Yzema Fritz Alemagne
Laboratoire Nationale du Batiment et des travaux Publics
Haiti
Marcial Rivera Rodriguez
Colegio Federado de Ingenieros y de Arquitectos de Costa Rica
Costa Rica
Manuel Alfredo Lopez Menjivar
Universidad de El Salvador
El Salvador
José Carlos Gil
Universidad Mariano Galvez de Guatemala
Guatemala