Articles | Volume 24, issue 8
https://doi.org/10.5194/nhess-24-2667-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-24-2667-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Aug 2024
Research article |
| 08 Aug 2024
| 08 Aug 2024
Risk-informed representative earthquake scenarios for Valparaíso and Viña del Mar, Chile
Engineering Risk Analysis Group, Technical University of Munich, Arcisstr. 21, 80333 Munich, Germany
Mauricio Monsalve
School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
Research Center for Integrated Disaster Risk Management (CIGIDEN), Santiago, Chile
Juan Camilo Gómez Zapata
Seismic Hazard and Risk Dynamics, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Elisa Ferrario
School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
Research Center for Integrated Disaster Risk Management (CIGIDEN), Santiago, Chile
Ricerca sul Sistema Energetico – RSE S.p.A., Milan, Italy
Alan Poulos
Department of Civil and Environmental Engineering, Stanford University, Stanford, California, USA
Juan Carlos de la Llera
Research Center for Integrated Disaster Risk Management (CIGIDEN), Santiago, Chile
Department of Structural and Geotechnical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
Daniel Straub
Engineering Risk Analysis Group, Technical University of Munich, Arcisstr. 21, 80333 Munich, Germany
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Cited articles
Abrahamson, N., Gregor, N., and Addo, K.: BC hydro ground motion prediction equations for subduction earthquakes, Earthq. Spectra, 32, 23–44, https://doi.org/10.1193/051712EQS188MR, 2016. a
Aguirre, P., Vásquez, J., de la Llera, J. C., González, J., and González, G.: Earthquake damage assessment for deterministic scenarios in Iquique, Chile, Nat. Hazards, 92, 1433–1461, https://doi.org/10.1007/s11069-018-3258-3, 2018. a, b
Allen, E., Chamorro, A., Poulos, A., Castro, S., de la Llera, J. C., and Echaveguren, T.: Sensitivity analysis and uncertainty quantification of a seismic risk model for road networks, Comput.-Aided Civ. Inf., 37, 516–530, https://doi.org/10.1111/mice.12748, 2022. a, b
Baker, J., Bradley, B., and Stafford, P.: Seismic Hazard and Risk Analysis, Cambridge University Press, Cambridge, UK, https://doi.org/10.1017/9781108425056, 2021. a, b
Bazzurro, P. and Cornell, C. A.: Disaggregation of seismic hazard, B. Seismol. Soc. Am., 89, 501–520, https://doi.org/10.1785/BSSA0890020501, 1999. a
Bodenmann, L., Baker, J. W., and Stojadinović, B.: Accounting for path and site effects in spatial ground-motion correlation models using Bayesian inference, Nat. Hazards Earth Syst. Sci., 23, 2387–2402, https://doi.org/10.5194/nhess-23-2387-2023, 2023. a
Borzoo, S., Bastami, M., and Fallah, A.: Extreme scenarios selection for seismic assessment of expanded lifeline networks, Struct. Infrastruct. E., 17, 1386–1403, https://doi.org/10.1080/15732479.2020.1811989, 2021. a
Candia, G., Poulos, A., de la Llera, J. C., Crempien, J. G., and Macedo, J.: Correlations of spectral accelerations in the Chilean subduction zone, Earthq. Spectra, 36, 788–805, https://doi.org/10.1177/8755293019891723, 2020. a
Carvajal, M., Cisternas, M., and Catalán, P. A.: Source of the 1730 Chilean earthquake from historical records: Implications for the future tsunami hazard on the coast of Metropolitan Chile, J. Geophys. Res.-Sol. Ea., 122, 3648–3660, https://doi.org/10.1002/2017JB014063, 2017. a, b
Chatelain, J.-L., Yepes, H., Bustamante, G., Fernández, J., Valverde, J., Kaneko, F., Villacis, C., Yamada, T., and Tucker, B.: Proyecto para Manejo del Riesgo sísmico de Quito, Escuela Politécnica Nacional, Quito, Ecuador, 1995. a
Cornell, C. A.: Engineering seismic risk analysis, B. Seismol. Soc. Am., 58, 1583–1606, 1968. a
de la Llera, J. C., Rivera, F., Mitrani-Reiser, J., Jünemann, R., Fortuño, C., Ríos, M., Hube, M., Santa María, H., and Cienfuegos, R.: Data collection after the 2010 Maule earthquake in Chile, B. Earthquake E., 15, 555–588, https://doi.org/10.1007/s10518-016-9918-3, 2017. a
Efron, B. and Tibshirani, R. J.: An Introduction to the Bootstrap, Chapman & Hall/CRC, Boca Raton, FL, USA, https://doi.org/10.1201/9780429246593, 1993. a
Esteva, L.: Regionalización sísmica de México para fines de ingeniería, Instituto de Ingeniería, UNAM, Mexico, https://aplicaciones.iingen.unam.mx/ConsultasSPII/DetallePublicacion.aspx?id=152 (last access: 18 July 2024), 1970. a
Feliciano, D., Arroyo, O., Cabrera, T., Contreras, D., Valcárcel Torres, J. A., and Gómez Zapata, J. C.: Seismic risk scenarios for the residential buildings in the Sabana Centro province in Colombia, Nat. Hazards Earth Syst. Sci., 23, 1863–1890, https://doi.org/10.5194/nhess-23-1863-2023, 2023. a
Fox, M.: Considerations on seismic hazard disaggregation in terms of occurrence or exceedance in New Zealand, Bull. N. Z. Soc. Earthq. Eng., 56, 1–10, https://doi.org/10.5459/bnzsee.56.1.1-10, 2023. a, b
Fox, M. J., Stafford, P. J., and Sullivan, T. J.: Seismic hazard disaggregation in performance-based earthquake engineering: occurrence or exceedance?, Earthq. Eng. Struct. D., 45, 835–842, https://doi.org/10.1002/eqe.2675, 2016. a, b
Frank, S. and Rebennack, S.: An introduction to optimal power flow: Theory, formulation, and examples, IIE Trans., 48, 1172–1197, https://doi.org/10.1080/0740817X.2016.1189626, 2016. a
Gisbert, F. J. G.: Weighted samples, kernel density estimators and convergence, Empir. Econ., 28, 335–351, https://doi.org/10.1007/s001810200134, 2003. a
Goda, K. and Atkinson, G. M.: Intraevent spatial correlation of ground-motion parameters using SK-net data, B. Seismol. Soc. Am., 100, 3055–3067, https://doi.org/10.1785/0120100031, 2010. a
Gómez-Zapata, J. C., Zafrir, R., Pittore, M., and Merino, Y.: Towards a sensitivity analysis in seismic risk with probabilistic building exposure models: An application in Valparaíso, Chile, using ancillary open-source data and parametric ground motions, ISPRS Int. J. Geo-Inf., 11, 113, https://doi.org/10.3390/ijgi11020113, 2022a. a, b, c, d, e
Gómez-Zapata, J. C., Pittore, M., Cotton, F., Lilienkamp, H., Shinde, S., Aguirre, P., and Santa María, H.: Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models, B. Earthquake E., 20, 2401–2438, https://doi.org/10.1007/s10518-021-01312-9, 2022b. a
Hayes, G. P., Wald, D. J., and Johnson, R. L.: Slab1.0: A three-dimensional model of global subduction zone geometries, J. Geophys. Res.-Sol. Ea., 117, B01302, https://doi.org/10.1029/2011JB008524, 2012. a, b
Hussain, E., Elliott, J. R., Silva, V., Vilar-Vega, M., and Kane, D.: Contrasting seismic risk for Santiago, Chile, from near-field and distant earthquake sources, Nat. Hazards Earth Syst. Sci., 20, 1533–1555, https://doi.org/10.5194/nhess-20-1533-2020, 2020. a, b
Jayaram, N. and Baker, J. W.: Correlation model for spatially distributed ground-motion intensities, Earthq. Eng. Struct. D., 38, 1687–1708, https://doi.org/10.1002/eqe.922, 2009a. a
Jayaram, N. and Baker, J. W.: Deaggregation of lifeline risk: Insights for choosing deterministic scenario earthquakes, in: Proceedings of the Technical Council on Lifeline Earthquake Engineering Conference, Oakland, California, 28 June–1 July 2009, https://doi.org/10.1061/41050(357)100, 2009b. a, b, c, d, e
Jiménez, B., Pelá, L., and Hurtado, M.: Building survey forms for heterogeneous urban areas in seismically hazardous zones: Application to the historical center of Valparaíso, Chile, Int. J. Archit. Herit., 12, 1076–1111, https://doi.org/10.1080/15583058.2018.1503370, 2018. a, b
Jiménez Martínez, M., Jiménez Martínez, M., and Romero-Jarén, R.: How resilient is the labour market against natural disaster? Evaluating the effects from the 2010 earthquake in Chile, Nat. Hazards, 104, 1481–1533, https://doi.org/10.1007/s11069-020-04229-9, 2020. a
Jünemann, R., de la Llera, J., Hube, M., Cifuentes, L., and Kausel, E.: A statistical analysis of reinforced concrete wall buildings damaged during the 2010, Chile earthquake, Eng. Struct., 82, 168–185, https://doi.org/10.1016/j.engstruct.2014.10.014, 2015. a, b
McGuire, R. K.: Probabilistic seismic hazard analysis and design earthquakes: Closing the loop, B. Seismol. Soc. Am., 85, 1275–1284, https://doi.org/10.1785/BSSA0850051275, 1995. a
Miller, M. and Baker, J.: Ground-motion intensity and damage map selection for probabilistic infrastructure network risk assessment using optimization, Earthq. Eng. Struct. D., 44, 1139–1156, https://doi.org/10.1002/eqe.2506, 2015. a
Montalva, G. A., Bastías, N., and Rodriguez-Marek, A.: Ground motion prediction equation for the Chilean subduction zone, B. Seismol. Soc. Am., 107, 901–911, https://doi.org/10.1785/0120160221, 2017. a, b
ODEPLAN: Plan de reconstrucción: Sismo marzo 1985, Of. de Planificación Nal., Santiago de Chile, https://www.desarrollosocialyfamilia.gob.cl/btca/txtcompleto/DIGITALIZADOS/ODEPLAN/O32Ppr-1985-pdf (last access: 18 July 2024), 1985. a
ONEMI: Informe sobre el terremoto de marzo 1985 en Chile, Of. Nal. de Emergencias del Min. del Interior, Santiago de Chile, https://bibliogrd.senapred.gob.cl/handle/2012/125 (last access: 18 July 2024), 1985. a
Pagani, M., Johnson, K., and Garcia Pelaez, J.: Modelling subduction sources for probabilistic seismic hazard analysis, Geol. Soc. Spec. Publ., 501, 225–244, https://doi.org/10.1144/SP501-2019-120, 2021. a
Pittore, M., Gómez-Zapata, J., Brinckmann, N., and Rüster, M.: Assetmaster and Modelprop: web services to serve building exposure models and fragility functions for physical vulnerability to natural-hazards. V.1.0., GFZ Data Services [code], https://doi.org/10.5880/riesgos.2021.005, 2021. a, b, c, d
Rasmussen, C. and Williams, C.: Gaussian Processes for Machine Learning, The MIT Press, Cambridge, MA, USA, https://doi.org/10.7551/mitpress/3206.001.0001, 2005. a, b
Rosero-Velásquez, H. and Straub, D.: Selecting Representative Scenarios for Contingency Analysis of Infrastructure Systems with Dependent Component Failures, in: Proceedings of the 13th International Conference on Applications of Statistics and Probability in Civil Engineering, 26–30 May 2019, Seoul, South Korea, 1–8, https://doi.org/10.22725/ICASP13.335, 2019. a
Salgado-Gálvez, M. A., Zuloaga, D., Henao, S., Bernal, G. A., and Cardona, O. D.: Probabilistic assessment of annual repair rates in pipelines and of direct economic losses in water and sewage networks: application to Manizales, Colombia, Nat. Hazards, 93, 5–24, https://doi.org/10.1007/s11069-017-2987-z, 2018. a, b
Silva, V.: Critical issues in earthquake scenario loss modeling, J. Earthq. Eng., 20, 1322–1341, https://doi.org/10.1080/13632469.2016.1138172, 2016. a
Tomar, A. and Burton, H. V.: Active learning method for risk assessment of distributed infrastructure systems, Comput.-Aided Civ. Inf., 36, 438–452, https://doi.org/10.1111/mice.12665, 2021. a
USGS – US Geological Survey: Earthquake Hazards Program, Advanced National Seismic System (ANSS) Comprehensive Catalog of Earthquake Events and Products: Various, https://doi.org/10.5066/F7MS3QZH, 2017. a
Villar-Vega, M., Silva, M., Crowley, H., Yepes, C., Tarque, N., Acevedo, A., Hube, M., Gustavo, C., and Santa María, H.: Development of a fragility model for the residential building stock in South America, Earthq. Spectra, 33, 581–604, https://doi.org/10.1193/010716EQS005M, 2017. a
Wood, A., Wollenberg, B., and Sheblé, G.: Power Generation, Operation, and Control, John Wiley & Sons Ltd, ISBN 9780471790556, 2013. a
Yepes-Estrada, C., Silva, V., Valcárcel, J., Acevedo, A. B., Tarque, N., Hube, M. A., Coronel, G., and María, H. S.: Modeling the residential building inventory in South America for seismic risk assessment, Earthq. Spectra, 33, 299–322, https://doi.org/10.1193/101915eqs155dp, 2017. a, b, c
Short summary
Seismic risk management uses reference earthquake scenarios, but the criteria for selecting them do not always consider consequences for exposed assets. Hence, we adopt a definition of representative scenarios associated with a return period and loss level to select such scenarios among a large set of possible earthquakes. We identify the scenarios for the residential-building stock and power supply in Valparaíso and Viña del Mar, Chile. The selected scenarios depend on the exposed assets.
Seismic risk management uses reference earthquake scenarios, but the criteria for selecting them...
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